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Sand fly saliva contains molecules that modify the host's hemostasis and immune responses . Nevertheless , the role played by this saliva in the induction of key elements of inflammatory responses , such as lipid bodies ( LB , also known as lipid droplets ) and eicosanoids , has been poorly investigated . LBs are cytoplasmic organelles involved in arachidonic acid metabolism that form eicosanoids in response to inflammatory stimuli . In this study , we assessed the role of salivary gland sonicate ( SGS ) from Lutzomyia ( L . ) longipalpis , a Leishmania infantum chagasi vector , in the induction of LBs and eicosanoid production by macrophages in vitro and ex vivo . Different doses of L . longipalpis SGS were injected into peritoneal cavities of C57BL/6 mice . SGS induced increased macrophage and neutrophil recruitment into the peritoneal cavity at different time points . Sand fly saliva enhanced PGE2 and LTB4 production by harvested peritoneal leukocytes after ex vivo stimulation with a calcium ionophore . At three and six hours post-injection , L . longipalpis SGS induced more intense LB staining in macrophages , but not in neutrophils , compared with mice injected with saline . Moreover , macrophages harvested by peritoneal lavage and stimulated with SGS in vitro presented a dose- and time-dependent increase in LB numbers , which was correlated with increased PGE2 production . Furthermore , COX-2 and PGE-synthase co-localized within the LBs induced by L . longipalpis saliva . PGE2 production by macrophages induced by SGS was abrogated by treatment with NS-398 , a COX-2 inhibitor . Strikingly , SGS triggered ERK-1/2 and PKC-α phosphorylation , and blockage of the ERK-1/2 and PKC-α pathways inhibited the SGS effect on PGE2 production by macrophages . In sum , our results show that L . longipalpis saliva induces lipid body formation and PGE2 production by macrophages ex vivo and in vitro via the ERK-1/2 and PKC-α signaling pathways . This study provides new insights regarding the pharmacological mechanisms whereby L . longipalpis saliva influences the early steps of the host's inflammatory response .
To obtain a blood meal , sand flies locate blood by introducing their mouthparts into the vertebrate host's skin , tearing tissues , lacerating capillaries and creating hemorrhagic pools upon which they feed . During this process , sand flies need to circumvent a number of the host's homeostatic responses , such as activation of blood coagulation cascades , vasoconstriction , platelet aggregation and immune responses [1] , [2] . In this environment , sand flies evolved an array of potent pharmacologic components with redundant and synergistic activities that subvert the host's physiological responses and favor the blood meal . Intense research using high-throughput analyses has been conducted to identify salivary factors and their biological activities . Lutzomyia ( L . ) longipalpis , the main vector of visceral leishmaniasis in South America , has been extensively studied . During the inflammatory response , L . longipalpis saliva induces cellular recruitment , modulates both antibody production and the formation of immunocomplexes [3] , [4] , regulates T cell activities and inhibits dendritic cells and macrophages , the latter being preferential host cells for Leishmania [5] , [6] . There is also evidence that maxadilan , a L . longipalpis salivary protein with vasodilator properties , down-regulates LPS-induced TNF-α and NO release through a mechanism dependent on PGE2 and IL-10 [7] . PGE2 is an eicosanoid derived from arachidonic acid ( AA ) metabolism by the enzyme cyclooxygenase ( COX ) . Prostanoids and leukotrienes can be intensely produced by macrophages during inflammatory responses [8] , and these mediators are implicated in cellular recruitment and activation . Among the eicosanoids , LTB4 induces neutrophil recruitment [9] , whereas PGE2 and PGD2 attract mainly macrophages [10] . Previous studies used different experimental models to show that L . longipalpis saliva induces an influx of neutrophils [11] and macrophages [12] , but neither the role of saliva in LTB4 and PGE2 release nor the involvement of these mediators in this process has been fully addressed . Under inflammatory and infectious conditions , prostaglandins and others lipid mediators are mainly produced by cytoplasmic organelles called lipid bodies ( LB ) [13] . Intense research over the past few years has defined lipid bodies as dynamic cytoplasmic organelles . It has been demonstrated that lipid bodies compartmentalize enzymes involved in the biosynthesis , transport and catabolism of lipids , proteins involved in membrane and vesicular transport and proteins involved in cell signaling and inflammatory mediator production , including eicosanoid-forming enzymes , phospholipases and protein kinases . All of these molecules can be localized into lipid bodies in various cells under a range of activation conditions , suggesting a wide role for lipid bodies in the regulation of cellular lipid metabolism and signaling [13] . Herein , we evaluated the effect of L . longipalpis salivary gland sonicate ( SGS ) on the induction of LB formation as well as PGE2 and LTB4 production in vitro and ex vivo . Moreover , we explored the role of peritoneal macrophages in the production of these lipid mediators in response to L . longipalpis SGS in vitro . Finally , we found that the PGE2 production induced by L . longipalpis saliva is dependent on intracellular mechanisms involving the phosphorylation of signaling proteins such as PKC-α and ERK-1/2 and subsequent activation of COX-2 .
Dimethylsulfoxide ( DMSO ) was purchased from ACROS Organics ( New Jersey , NJ ) . RPMI 1640 medium and L-glutamine , penicillin , and streptomycin were from Invitrogen ( Carlsbad , CA ) . Nutridoma-SP was from Roche ( Indianapolis , IN ) . A23187 calcium ionophore , was from Calbiochem/Novabiochem Corp . ( La Jolla , CA ) . NS-398 , PGE2 and LTB4 enzyme-linked immunoassay ( EIA ) Kits , anti-murine COX-2 and PGE-synthase antibodies were all from Cayman Chemical ( Ann Arbor , MI ) . 4 , 4-difluoro-1 , 3 , 5 , 7 , 8-pentamethyl-4-bora-3a , 4a-diaza-s-indacene ( BODIPY 493/503 ) was obtained from Molecular Probes ( Eugene , OR ) . Osmium tetroxide ( OsO4 ) was obtained from Electron Microscopy Science ( Fort Washington , PA ) . Aqua Polymount was from Polysciences ( Warrington , PA ) . Thiocarbohydrazide , Ca2+-Mg2+-free HBSS ( −/− ) , HBSS ( +/+ ) with Ca2+-Mg2+ , LPS from Escherichia coli ( serotype 0127:b8 ) , and N-ethyl-N'- ( 3-dimethylaminopropyl ) carbodiimide hydrochloride ( EDAC ) were purchased from Sigma-Aldrich ( St . Louis , MO ) . Rabbit anti-mouse kinase proteins were from Santa Cruz Biotechnology ( Santa Cruz , CA ) . PD 98059 , 2′-Amino-3′-methoxyflavone and Bisindolylmaleimide-I , 2-[1- ( 3-Dimethylaminopropyl ) -1H-indol-3-yl]-3- ( 1H-indol-3-yl ) -maleimide were obtained from Merck-Calbiochem ( Darmstadt , Hessen ) . Inbred male C57BL/6 mice , age 6–8 weeks , were obtained from the animal facility of Centro de Pesquisas Gonçalo Moniz , Fundação Oswaldo Cruz ( CPqGM-FIOCRUZ , Bahia , Brazil ) . All experimental procedures were approved and conducted according to the Animal Care and Using Committee of the FIOCRUZ . Adult Lutzomyia longipalpis captured in Cavunge ( Bahia , Brazil ) were reared at the Laboratório de Imunoparasitologia/CPqGM/FIOCRUZ ( Bahia , Brazil ) as described previously [3] . Salivary glands were dissected from 5- to 7-day-old L . longipalpis females under a Stemi 2000 Carl Zeiss stereoscopic microscope ( Göttingen , Germany ) and stored in groups of ten pairs in 10 µL of endotoxin-free PBS at −70°C . Immediately before use , the glands were sonicated with a Branson Sonifier 450 ( Danbury , CT ) and centrifuged at 10 , 000× g for four minutes . The supernatant from salivary gland sonicate ( SGS ) was used for experiments . The level of LPS contamination of L . longipalpis SGS preparations was determined using a commercially available LAL Chromogenic Kit ( Lonza Bioscience , Walkersville , MD ) ; negligible levels of endotoxin were found in the salivary gland supernatant ( 0 . 1 ηg/mL ) . We measured 0 . 7 micrograms of protein in an amount equivalent to 0 . 5 pair of salivary glands and used SGS dilutions ( 2 . 0–0 . 2 pairs ) in our experiments [14] . To assess the leukocyte recruitment induced by L . longipalpis SGS , we used the well-established peritoneal model of inflammation because the peritoneal cavity is a self-contained and delineated compartment and thus provides a large number of post-stimulus leukocytes . As previously established in the air pouch murine model [12] and peritoneal cavity ( unpublished data ) , a 0 . 5-pair dose of SGS was used for the leukocyte recruitment assay . C57BL/6 mice were inoculated i . p . with 0 . 1 mL of L . longipalpis SGS ( 0 . 5 pair/cavity ) , endotoxin-free saline ( negative control ) or 0 . 1 mL of LPS ( 20 µg/mL , positive control ) . At 1 , 3 and 6 h post-stimulus , leukocytes inside the peritoneal cavity were harvested by injection and recovery of 10 mL of endotoxin-free saline . Total counts were performed on a Neubauer hemocytometer after staining with Turk's solution . Differential cell counts ( 200 cells total ) were carried out microscopically on cytospin preparations stained with Diff-Quick . Cells harvested by peritoneal lavage 1 , 3 , 6 or 24 h after i . p . injection of 0 . 1 mL of L . longipalpis SGS ( 0 . 5 pair/cavity ) , endotoxin-free saline or LPS ( 20 µg/mL ) were centrifuged at 400× g and the lipid bodies within the leukocytes were stained with BODIPY 493/503 ( 5 ug/mL ) according to Plotkowisk et al . [15] . Samples were analyzed using a FACSort flow cytometer from Becton Dickinson Immunocytometry Systems ( San Jose , CA ) and by fluorescence microscopy . Macrophages adhered to coverslips within 24-well plates were fixed with 3 . 7% formaldehyde and stained with osmium tetroxide as described previously [16] . The morphology of the fixed cells was observed , and lipid bodies were counted by light microscopy with a 100x objective lens in 50 consecutively scanned macrophages . For in vitro assays , macrophages were obtained by peritoneal lavage with cold RPMI 1640 . Then , cells were centrifuged at 400× g for 10 minutes . Macrophages ( 3×105/well ) were cultured in 1 mL of RPMI 1640 medium supplemented with 1% Nutridoma-SP , 2 mM L-glutamine , 100 U/mL penicillin and 100 µg/mL streptomycin in 24-well plates for 24 hours . Next , the macrophages were stimulated with different doses of L . longipalpis SGS ( 0 . 2 , 0 . 5 , 1 . 0 , 1 . 5 , 2 . 0 pairs/well ) . In some experiments , LPS ( 500 ng/well ) was used as a positive control . One , 6 , 24 , 48 and 72 hours after stimuli , supernatants were collected and cells were fixed with 3 . 7% formaldehyde . For inhibitory assays , macrophages were pretreated for one hour with 1 μM NS-398 , a COX-2 inhibitor; 20 ηM BIS , a PKC inhibitor; or 50 μM PD98059 , an ERK-1/2 inhibitor . Then , the cells were stimulated with SGS ( 1 . 5 pairs/well ) or medium containing vehicle ( DMSO ) for 24 hours , and the supernatants were collected for eicosanoid measurement . Cell viability as assessed by trypan blue exclusion was always greater than 95% after the end of treatment . Resident peritoneal macrophages were cultured on coverslips in the presence of L . longipalpis SGS ( 1 . 5 pair/well ) as described above . After 24 h , the cells were washed twice with 500 µl of HBSS−/− and immediately fixed with 500 µL of water-soluble EDAC ( 1% in HBSS−/− ) , used to cross-link eicosanoid carboxyl groups to amines in adjacent proteins . After 15 min of incubation at room temperature ( RT ) with EDAC to promote both cell fixation and permeabilization , macrophages were then washed with HBSS−/− and incubated with 1 µM BODIPY 493/503 for 30 min . Then , the cover slips were washed with HBSS−/− and incubated with mouse anti-COX-2 ( 1∶150 ) or anti-PGE-synthase ( 1∶150 ) for 1 h at RT . MOPC 21 ( IgG1 ) was used as a control . After further washes , cells were incubated with biotinylated goat anti-rabbit IgG secondary Ab , washed twice and incubated with avidin conjugated with PE for 30 min . The cover slips were then washed three times and mounted in Vectashield medium containing DAPI ( Vector Laboratories , Burlingame , CA ) . The samples were observed by fluorescence microscopy and images were acquired using the software Image-Pro Plus ( Media Cybernetics , Silver Spring , MD ) . Macrophages were treated or not with SGS ( 1 . 0 pair/well ) for 40 min . Next , the cells were washed once with phosphate-buffered saline , homogenized in lysis buffer containing phosphatase inhibitors ( 10 mM TRIS-HCl , pH 8 . 0 , 150 mM NaCl , 0 . 5% v/v Nonindet-P40 , 10% v/v glycerol , 1 mM DTT , 0 . 1 mM EDTA , 1 mM sodium orthovanadate , 25 mM NaF and 1 mM PMSF ) and a protease inhibitor cocktail ( Roche , Indianapolis , IN ) . Protein concentrations were determined using the method of Lowry et al . [17] with BSA as the standard . Total proteins ( 20 µg ) were then separated by 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis ( SDS–PAGE ) as described previously [18] and transferred onto nitrocellulose membranes . The membranes were blocked in Tris-buffered saline ( TBS ) supplemented with 0 . 1% Tween 20 ( TT ) plus 5% BSA for 1 h before incubation overnight in the primary rabbit anti-mouse PKC-α and anti-ERK-1/2 ( 1∶1 , 000 ) antibodies . After removal of the primary antibody and washing five times in TT , the membranes were incubated in the secondary antibody conjugated to peroxidase ( 1∶10 , 000 ) for 1 h . Washed blots were then incubated with an ECL chemiluminescence kit ( Amersham , UK ) . The membranes were discharged and immunoblotted again using primary rabbit anti-mouse phosphorylated-PKC-α and ERK-1/2 ( 1∶1 , 000 ) antibodies according to the manufacturer's instructions ( Amersham , UK ) . Quantification of the level of proteins in the western blotting membranes was determined by densitometry . Briefly , bands were scanned and processed using Adobe Photoshop 5 . 0 software ( Adobe Systems Inc . ) , and arbitrary values for protein density were estimated . Ratios between phosphorylated and unphosphorylated proteins were obtained to calculate the difference between groups . C57BL/6 mice were inoculated i . p . with 0 . 1 mL of L . longipalpis SGS ( 0 . 5 pair/cavity ) , endotoxin-free saline or 0 . 1 mL of LPS ( 500 ηg/mL ) . At 1 , 3 and 6 h post-stimulus , leukocytes were harvested by peritoneal washing with HBSS−/− and 1×106 cells/mL were resuspended in HBSS+/+ and stimulated with A23187 ( 0 . 5 µM ) for 15 min [16] . The reactions were stopped on ice , and the samples were centrifuged at 500× g for 10 min at 4°C . Supernatants from leukocytes re-stimulated ex vivo or those of in vitro assays were collected for measurement of PGE2 and LTB4 by enzyme-linked immunoassay ( EIA ) according to the manufacturer's instructions ( Cayman Chemical , Ann Arbor , MI ) . The in vivo assays were performed using at least five mice per group . Each experiment was repeated at least three times . Data are reported as the mean and standard error of representative experiments and were analyzed using GraphPad Prism 5 . 0 software . Disparities in leukocyte recruitment , lipid bodies and lipid mediator quantification were explored using Student's t test . Means from different groups from the in vitro assays were compared by ANOVA followed by Bonferroni's test or a post-test for linear trends . Differences were considered statistically significant when p≤0 . 05 .
To measure the leukocyte recruitment induced by SGS , we injected 100 μL of saline or SGS ( 0 . 5 pair/cavity ) , and 1 , 3 and 6 hours after injection , we enumerated total leukocytes recruited to the peritoneal cavity . Most of the cells recruited were mononuclear cells and neutrophils ( Figure 1 ) . In this context , SGS induced mononuclear cell recruitment for 3 hours ( Figure 1 A and B ) and neutrophil recruitment for over 6 hours ( Figure 1A–C ) of stimulation when compared with the saline group . Other cell populations ( eosinophils and mast cells ) were not altered after SGS stimulation , and there was no variation in these numbers over time ( Figure 1 ) . The peritoneal cell population in unstimulated animals ( time zero ) was composed of mononuclear cells ( 2 . 985×104 ±0 . 027 ) and negligible amounts of neutrophils ( 0 . 018×104 ±0 . 027 ) . At this time , macrophages are the major cells within the mononuclear population in the peritoneal cavity besides lymphocytes , which represent ∼10% of mononuclear cells ( data not shown ) . As shown in Figure 2 , SGS administration led to enhanced PGE2 ( Figure 2A ) and LTB4 ( Figure 2B ) release within those cells recruited to the peritoneal cavity . Because LBs are sites of eicosanoid production [19] , we evaluated LB formation in leukocytes recruited to the peritoneal cavity by FACs using the neutral lipid probe BODIPY 493/503 . The kinetics of LB formation was evaluated at 1 , 3 , 6 and 24 hours after SGS stimulation by measuring mean fluorescence intensity ( MFI ) . SGS increased MFI in mononuclear but not in polymorphonuclear cells after 3 and 6 hours , ( Figure 3A and B ) compared with the saline group . Histograms ( Figure 3C and D ) and fluorescence microscopic images ( Figures 3E and F ) at the 3-hour time point confirmed these effects of SGS on macrophages . To assess the role of SGS in lipid body formation in resident macrophages , we stimulated these cells with different doses of SGS ( 0 . 2–2 . 0 pairs/well ) for different time periods ( 1 , 6 , 24 , 48 and 72 hours ) . At 24 hours post-stimulus , SGS strongly induced LB formation compared with the untreated group ( Figure 4A–D ) . LB formation was induced in a dose-dependent manner , and the maximum of LBs per macrophage was observed at a dose of 2 . 0 pairs/well ( Figure 4C ) . Because LB formation induced by SGS ( 1 . 5 pairs/well ) was more evident at 24 hours ( Figure 4D ) , we selected this time point to perform further experiments . Prostaglandins are produced by cyclooxygenases , which occur in constitutive ( COX-1 ) and inducible ( COX-2 ) forms [20] . We investigated the expression and subcellular localization of COX-2 within SGS-stimulated macrophages . Immunofluorescence microscopy revealed the presence of COX-2 ( Figure 5A–C ) and PGE-synthase ( Figure 5D–F ) within LBs in macrophages stimulated with SGS . Next , we measured PGE2 and LTB4 production in the supernatant of macrophage cultures . SGS induced PGE2 production starting at 1 . 0 pair/well ( Figure 6A ) , whereas LTB4 was not detectable under any conditions ( data not shown ) . As expected , PGE2 production by macrophages stimulated with SGS was reduced to basal levels when the cells were pre-incubated with NS-398 , a COX-2 inhibitor ( Figure 6B ) . Thus , the PGE2 production in peritoneal macrophages induced by SGS occurs in newly formed lipid bodies and is dependent on COX-2 . Multiple pathways are involved in the signaling for PGE2 production [13] . Recently , ERK and PKC-α were shown to be involved in COX-2 activity [21] . We observed that SGS activated both ERK ( Figure 7A and C ) and PKC-α phosphorylation ( Figure 7B and D ) , but it did not alter the levels of the unphosphorylated proteins . To investigate whether these kinases are involved in the induction of PGE2 production by SGS , we pretreated macrophages with bisindolylmaleimide I ( BIS I ) and PD98059 , PKC-α and ERK-1/2 inhibitors , respectively ( Figure 8A–B ) . Inhibition of both enzymes completely abrogated PGE2 production induced by SGS ( Figure 8A–B ) . In sum , these results suggest that PKC-α and ERK-1/2 are involved in the PGE2 production induced by SGS .
Sand fly saliva triggers an inflammatory response characterized by cellular influx followed by hemostatic and immune mechanism suppression . Nevertheless , the role of sand fly saliva in eicosanoid production during the early steps of the innate immune response is poorly understood . In inflammatory conditions , eicosanoids are mostly produced in cytoplasmic organelles called lipid bodies ( LBs ) , which are formed in leukocytes and other cells involved in the inflammatory and infectious responses to several stimuli [13] . Herein , we showed that L . longipalpis saliva induces lipid body formation and PGE2 production in peritoneal macrophages ex vivo and in vitro via kinase phosphorylation and COX-2 activation . Previous investigations have demonstrated that sand fly saliva plays an important role in cellular recruitment in multiple experimental models [3] , [9] , [11] , [12] , including in vivo sand fly bites [22] . Herein , we confirmed previous reports that L . longipalpis SGS induces an inflammatory infiltration composed mainly of macrophages and neutrophils . Moreover , we showed that the cellular recruitment induced by L . longipalpis saliva is concomitant with PGE2 and LTB4 production . In this scenario , lipid mediators could be triggering cellular recruitment . Secretion of LTB4 by resident macrophages plays an important role in neutrophil migration [23] . In addition , lipopolysaccharides induce macrophage migration via prostaglandin D2 and prostaglandin E2 [10] . Prostaglandin E2 is an abundant eicosanoid produced by inflammatory cells , and it is known to exert anti-inflammatory and vasodilator effects . PGE2 is found in Ixodes scapularis saliva and is also implicated in the immunomodulatory activity of tick saliva on dendritic cell and macrophage activation [24] . Furthermore , previous studies using saliva from several Phlebotomus species have suggested that the anti-inflammatory properties of sand fly saliva could be attributed to PGE2 and IL-10 released by dendritic cells [9] , [25] . In these studies , the cellular recruitment induced by OVA stimulation was abrogated by saliva from various sand fly species [9] , [25] , which was associated with an anti-inflammatory profile dependent on the production of IL-10 , IL-4 [25] and PGE2 [9] . Intriguingly , maxadilan , a vasodilator peptide with immunomodulatory activities present in L . longipalpis saliva , is able to induce LPS-activated macrophages to release PGE2 via COX-1 , an enzyme that is constitutively active [7] . In the present study , we showed that L . longipalpis SGS triggers PGE2 production in resident macrophages by an inducible pathway , since this effect was completely abrogated when the cells were incubated in the presence of NS-398 , a COX-2 inhibitor . Nevertheless , whether sand fly saliva contains other molecules involved in PGE2 production or pharmacological amounts of this mediator similarly to tick saliva remains unknown . Our study is the first to establish a direct link between L . longipalpis saliva , eicosanoid production and lipid body formation . Under inflammatory and infectious conditions , lipid mediators are mainly produced within LBs , which compartmentalize both the substrate and the enzymatic machinery required for eicosanoid production [13] . In this regard , the enzymes COX and 5-LO have been localized to lipid bodies in various inflammatory cells by the use of multiple techniques including fluorescence microscopy [13] . Previous studies have shown that various inflammatory and infectious stimuli are able to trigger LB formation in macrophages [13] , [19] . Our findings demonstrate that SGS induces LB formation in macrophages in vivo and in vitro , suggesting that L . longipalpis saliva acts directly on these cells , but not on neutrophils . Indeed , L . longipalpis SGS triggered LB formation in macrophages committed to PGE2 production via COX-2 and PGE-synthase . Data regarding the direct effects of sand fly salivary compounds on host signaling pathways cells are scarce . The extracellular signal-regulated kinases ( ERKs ) and protein kinase C ( PKC ) are among the key enzymes implicated in signaling pathways of diverse cellular responses , including eicosanoid production . The MAP kinases ERK1 and ERK2 induce activation of cPLA2 , an enzyme that hydrolyzes arachidonic acid , which is metabolized to prostaglandin H2 by COX [13] . Previous studies have demonstrated the compartmentalization of MAP kinases and cPLA2 at arachidonate-enriched lipid bodies [26] , [27] , as well as COX-2 and PGE-synthase [16] , [28] , [29] . Herein , it is shown for the first time that L . longipalpis SGS triggers ERK-1/2 and PKC-α phosphorylation in macrophages . Other studies have shown that COX-2 activation and PGE2 production in LPS stimulated-macrophages is dependent on the phosphorylation of protein kinases such as PKC-α [21] and ERK-1/2 [30] . We showed that the PGE2 production induced by SGS is dependent on both ERK-1/2 and PKC . This association between the activation of kinases and the metabolism of eicosanoids within lipid bodies may serve to enhance rapid eicosanoid production in response to extracellular stimuli such as sand fly saliva . Of note , in addition to their role in regulating the host response to infection by modulating inflammatory mediator production , lipid bodies may also serve as rich sources of nutrients for intracellular pathogens , thus favoring intracellular pathogen replication [31] , [32] . In brief , the present work provides new insights into the mechanisms involved in macrophage responses to L . longipalpis saliva , including LB formation and the signaling pathways that trigger PGE2 release . Although the roles of the newly formed LBs and PGE2 induced by sand fly saliva in the pathogenesis of leishmaniasis have not yet been addressed , several studies have shown that PGE2 is essential to the infection of macrophages [33] , [34] and parasite dissemination after infection [35] . The induction of PGE2 production by sand fly saliva demonstrated herein can influence the initial steps of host infection by favoring less intense macrophage activation . Our group and others have been providing strong evidence that saliva components are immunogenic and have potential as markers of exposure to sand fly vectors [36]–[39] . Further studies are required to determinate if the immunization based on components of vector saliva interferes in eicosanoid production with consequences for the host's immune response and the transmissibility of the parasite . | After the injection of saliva into the host's skin by sand flies , a transient erythematous reaction is observed , which is related to an influx of inflammatory cells and the release of various molecules that actively facilitate the blood meal . It is important to understand the specific mechanisms by which sand fly saliva manipulates the host's inflammatory responses . Herein , we report that saliva from Lutzomyia ( L . ) longipalpis , a widespread Leishmania vector , induces early production of eicosanoids . Intense formation of intracellular organelles called lipid bodies ( LBs ) was noted within those cells that migrated to the site of saliva injection . In vitro and ex vivo , sand fly saliva was able to induce LB formation and PGE2 release by macrophages . Interestingly , PGE2 production induced by L . longipalpis saliva was dependent on intracellular mechanisms involving phosphorylation of signaling proteins such as PKC-α and ERK-1/2 and subsequent activation of cyclooxygenase-2 . Thus , this study provides new insights into the pharmacological properties of sand fly saliva and opens new opportunities for intervening with the induction of the host's inflammatory pathways by L . longipalpis bites . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"immunology/leukocyte",
"signaling",
"and",
"gene",
"expression",
"immunology/immune",
"response",
"immunology/leukocyte",
"activation"
] | 2010 | Lutzomyia longipalpis Saliva Triggers Lipid Body Formation and Prostaglandin E2 Production in Murine Macrophages |
The maintenance and reformation of gene expression domains are the basis for the morphogenic processes of multicellular systems . In a leaf primordium of Arabidopsis thaliana , the expression of FILAMENTOUS FLOWER ( FIL ) and the activity of the microRNA miR165/166 are specific to the abaxial side . This miR165/166 activity restricts the target gene expression to the adaxial side . The adaxial and abaxial specific gene expressions are crucial for the wide expansion of leaf lamina . The FIL-expression and the miR165/166-free domains are almost mutually exclusive , and they have been considered to be maintained during leaf development . However , we found here that the position of the boundary between the two domains gradually shifts from the adaxial side to the abaxial side . The cell lineage analysis revealed that this boundary shifting was associated with a sequential gene expression switch from the FIL-expressing ( miR165/166 active ) to the miR165/166-free ( non-FIL-expressing ) states . Our genetic analyses using the enlarged fil expression domain2 ( enf2 ) mutant and chemical treatment experiments revealed that impairment in the plastid ( chloroplast ) gene expression machinery retards this boundary shifting and inhibits the lamina expansion . Furthermore , these developmental effects caused by the abnormal plastids were not observed in the genomes uncoupled1 ( gun1 ) mutant background . This study characterizes the dynamic nature of the adaxial-abaxial specification process in leaf primordia and reveals that the dynamic process is affected by the GUN1-dependent retrograde signal in response to the failure of plastid gene expression . These findings advance our understanding on the molecular mechanism linking the plastid function to the leaf morphogenic processes .
The expansion of a flat organ from an undifferentiated organ primordium provides an excellent model for studying the dynamics of formation and maintenance of gene expression domains . In the case of wing development in Drosophila , the wing primordium ( wing disc ) is subdivided into dorsal and ventral compartments . Each compartment specifically expresses key genes determining the future pattern of tissue growth and cell differentiation . Cells in each compartment are related by lineage and they are prevented from crossing the dorso-ventral boundary [1]–[4] . In the case of leaf development in the model plant Arabidopsis thaliana , previous studies have revealed that several genes are expressed in an adaxial- or abaxial- specific manner . Their specific expression patterns are required for lamina expansion with adaxial-abaxial asymmetric cell differentiation ( see [5]–[8] for review ) . It has been considered that their expression patterns are established during leaf initiation , that is , stages P0 to P1 , and maintained during later stages [5]–[7] . However , previous studies have not focused on whether the gene expression states are maintained in each cell lineage as in the case of the fly wing . The adaxial-specific genes are three Class III Homeodomain-Leucine Zipper genes , PHABULOSA ( PHB ) , PHAVOLUTA and REVOLUTA ( REV ) ( PHB-like genes hereafter ) [9]–[11] , and a LOB-domain family gene , ASYMMETRIC LEAVES2 [12] . The abaxial-specific genes are four YABBY family genes , including FILAMENTOUS FLOWER ( FIL ) [13]–[16] , three KANADI genes ( KANs ) [17] , [18] and two AUXIN RESPONSE FACTOR genes ( ARFs ) [19] , [20] . In addition to such transcription factor genes , small regulatory RNAs are also distributed in an adaxial- or abaxial-specific manner . The microRNA miR165/166 represses the expression of PHB-like genes through mRNA cleavage in the abaxial region [21] . The trans-acting small interfering RNAs targeting the two ARFs ( tasiR-ARFs ) also repress their targets through mRNA cleavage in the adaxial region [20] , [22] . Especially , the intercellular mobility of these small RNAs has been recently emphasized as the key feature to formation of spatial gene expression patterns [20] , [23] , [24] . The expression patterns of these transcription factors and small RNAs are considered to be the results from complex regulatory networks among themselves though many parts of the networks are yet to be elucidated [25] . Nonetheless , it has been discussed that the adaxial- and abaxial-specific expression domains of such genes are separated and maintained owing to the mutual repression between these adaxial- and abaxial-specific genes via direct transcriptional repression , mRNA degradation and other negative regulations [5]–[8] , [11] . It is highly likely that intracellular mutual repression between two ( groups of ) genes allows each cell to express only one ( group of ) gene ( s ) and maintain the gene expression state . On the other hand , it is not necessarily likely that the intercellular mutual repression between the genes contributes to the maintenance of the gene expression domains . While such intercellular effects prevent a cell from misexpressing the other genes within one gene expression domain , both the two domains might not be maintained for a long time because such intercellular effects might change the gene expression state of the cells on the domain boundary . This speculation is consistent with many studies showing that when two mobile factors decrease each other's quantity , the boundary between their distribution domains shifts in theory and real observations [26]–[28] . In this study , we first performed computer simulations of a simple mathematical model assuming mutual repression between two factors representing the adaxial- and abaxial-specific genes . These simulations showed that the boundary position between their expression domains is not necessarily maintained , but might shift toward one end when the repression is mediated by mobile factors . It has been described previously that the FIL-expression domain is separated from the expression domain of PHB-like genes , or the miR165/166-free domain , with no or a little overlap in primordia of leaves [29] , flowers [30] , cotyledons [31] and sepals [25] . Orthologous genes of FIL and PHB also show similar complementary expression patterns in Antirrhinum [32] and Cabomba [33] . Though the previous studies have not clearly shown whether or not the boundary position between these domains changes during leaf development , it has been reported that FIL-expression domains differ at different developmental stages of leaves [13] , [14] , [34] . While the Arabidopsis leaves basically consist of six cell layers: the adaxial epidermis , four layers of mesophyll and the abaxial epidermis , FIL is initially expressed in the whole leaf at approximately the P0 stage , and then restricted to the abaxial four cell layers at later stages [13] , [14] . This expression domain is further restricted to the three abaxial cell layers at approximately the P6 ( sixth youngest leaf ) stage [34] . The similar gradual restriction of the FIL expression to the abaxial cells has been reported in tomatoes [35] . Here we characterized in detail how FIL-expression and miR165/166-free ( presumptively PHB-like genes expression ) domains change during leaf development by careful observations of the gene expression markers and by lineage analysis of FIL-expressing cells . The results showed that all leaf founder cells express FIL and have miR165/166 activity , but the FIL-expressing cells become miR165/166-free cells sequentially from the adaxial to the abaxial side after leaf initiation . In other words , the boundary between FIL-expression and miR165/166-free domains shifts from the adaxial to the abaxial side similarly to the above computer simulation . Our genetic analyses demonstrated that excessively fast shifting of the boundary is associated with narrow lamina formation and excess adaxialization in cell differentiation , whereas excessively slow shifting of the boundary is associated with narrow lamina formation and excess abaxialization . Furthermore , detailed analyses of the mutant enlarged fil expression domain2 ( enf2 ) revealed that the boundary shifting is retarded by the GENOMES UNCOUPLED1- ( GUN1- ) dependent mechanism when plastid ( chloroplast ) gene expression machinery is compromised by inhibitor treatments and genetic mutations . GUN1 is an indispensable factor for the plastid-nucleus communication system known as retrograde signaling ( see [36]–[40] for review ) . Therefore , our results strongly suggest that the GUN1-dependent plastid retrograde signal regulates leaf morphogenesis by affecting the dynamic change in the FIL-expression and miR165/166-free domains in leaf primordia . From physiological point of view , the main advantage of lamina expansion is the efficiency in light reception for photosynthesis , thus depends on the functional integrity of plastids . On the other hand , from developmental viewpoint , the lamina expansion depends on the adaxial- and abaxial-specific genes in leaf primordia . We discuss how the link between adaxial-abaxial gene expression and plastid condition contributes to plant growth and development .
To know the theoretical stability of neighboring two gene expression domains when the two ( groups of ) genes repress each other's expression , we examined the domain stability by numerical analysis . Here we suppose a situation where Thus , the dynamics of AD and AB in cell “i” are formulated as ( 1 ) ( 2 ) where DAD and DAB are constant diffusion coefficients of AD and AB , respectively . For simplicity , the functions f and g are described as basal independent constants ( the first terms ) plus the Hill equations ( the second terms ) . ( 3 ) ( 4 ) ( j = 1 , 2 ) where pj is the basal production rate , rj is the inhibitory effect of the production rate ( e . g . , the effect of transcriptional repression ) , dj is the basal degradation rate and cj is the promotion strength of the degradation rate ( e . g . , the effect of mRNA cleavage ) ( Figure S1A , S1B ) . Such a mutual repression dynamics has the AD-expressing and the AB-expressing states as stable steady states with a certain range of parameter values ( e . g . , p1 = p2 = 0 . 1 , r1 = r2 = 2 . 0 , d1 = d2 = 0 . 1 and c1 = c2 = 2 . 0 , Figure 1B ) when only a single cell is considered ( Figure 1B ) . After such a bistable mathematical model was developed , we lay AD-expressing cells and AB-expressing cells side by side and simulate the time evolution of AD and AB expression . Here we consider only three AD-expressing cells and three AB-expressing cells as the initial states . The results are shown in Figure 1C–E . In the cases where parameters are symmetric between AD and AB ( i . e . , p1 = p2 , r1 = r2 , d1 = d2 , c1 = c2 and DAD = DAB ) , the expression domains is maintained , in other wards , the initial domain boundary is fixed ( Figure 1C ) . On the other hand , when a parameter is changed from such symmetric parameter set to asymmetric one , one gene expression domain expands and the other shrinks , namely , the domain boundary shifts toward the either end ( e . g . , p1 = p2 , r1>r2 , d1 = d2 , c1 = c2 and DAD = DAB for Figure 1D , p1 = p2 , r1<r2 , d1 = d2 , c1 = c2 and DAD = DAB for Figure 1E ) ( see also Figure S1C–F ) . These simulations give examples of the known mathematical rule that negatively interacting mobile factors easily cause boundary shifting between their distribution domains [26] , and suggest that this rule can be applied to molecular-biological systems including the regulatory network among the adaxial- and abaxial-specific genes in leaf primordia . The almost exclusive relationship between the abaxial FIL-expression domain and the adaxial miR165/166-free domain has been characterized well [25] , [29]–[31] . However , whether or not these domains are simply maintained during leaf development had not yet been explicitly demonstrated . If the domain separation is due to mutual repression between miR165/166 and PHB-like genes , FIL and PHB-like genes and between upstream regulators for these genes , the domain boundary might not be necessarily maintained . To characterize in detail the spatio-temporal patterns of the FIL-expression and miR165/166-free domains during leaf development , we observed these domains at a series of leaf developmental stages . The FIL-expression and miR165/166-free domains were visualized by two fluorescent markers , FILpro:GFP ( green fluorescent protein driven by the FIL promoter ) and 35Spro:miYFP-W ( miR165/166-sensor yellow fluorescent protein driven by the Cauliflower Mosaic Virus 35S promoter ) , respectively , as previously described [29] . Before leaf initiation ( around P0 stages ) , FIL was likely expressed in all the leaf founder cells ( Figure S2C , S2D ) , as previously reported [13] , [14] , and miR165/166 activity was higher in these cells than in the surrounding cells ( Figure S2C , S2D ) . However , it was difficult to determine whether the FIL-expressing and miR165/166-active cells are all leaf founder cells because of the absence of a marker to distinguish those cells from the remaining meristem cells in which FIL expression and miR165/166-activity were not detected . Just after a leaf primordium initiated but before the six cell layers become obvious ( P1 stage , approximately 20-µm-long ) , most leaf cells appeared to express FIL and have miR165/166 activity with the exception of only a few adaxial epidermal cells ( Figure S2E , S2F ) . At all of the developmental stages after the leaf initiation , the two domains were separated with not more than one-cell-width overlaps ( Figure 2A–C , S2A , S2B ) . We hereafter refer to the boundary between FIL-expression and miR165/166-free domains as the FMB . In 50-µm-long primordia ( P1–P2 stages ) , the FIL-expression and miR165/166-free domains occupied four to five abaxial cell layers and one to two adaxial cell layers , respectively , within the six cell layers ( Figure 2A ) . It should be noted that FIL expression was detected in the distal tip cell ( Figure 2A arrowhead ) and the neighboring adaxial cell which presumably corresponds to future distal adaxial epidermis . Thus , FMB position was relatively adaxial rather than exactly the middle of the primordium at such early stages . When the leaf reached 300 µm in length ( P6–P7 stages ) , the FIL-expression domain was restricted to one to two cell layers in the distal part and approximately three cell layers in the proximal part of the leaf ( Figure 2C–F ) . On the other hand , the miR165/166-free domain in this stage was expanded compared to those in the early stages keeping the slight overlap with the FIL-expression domain . ( Figure 2C–F ) . Thus , FMB position was more abaxial at these stages than at the early stages . In regard to the marginal epidermis , the elongating marginal tip cells but not the neighboring adaxial cells expressed FIL in the distal part ( arrowheads in Figure 2D , S2G–J ) . In contrast , the marginal tip cells and the neighboring cells still expressed FIL in the proximal part where marginal cell elongation is not evident yet ( arrowheads in Figure 2F , S2G ) . During the later stages , though the expansion of the miR165/166-free domain and restriction of the FIL-expression domain continued to some extent , FIL expression was kept in the elongating margin cells , whole abaxial epidermis and most of the abaxial-most mesophyll even after the leaves exceed 1 mm in length and the round lamina morphology is developed ( Figure S2K–P ) . However , the GFP signal intensity of FILpro:GFP gradually reduced to a level not enough to distinguish FIL-expressing cells clearly from the other cells at further later stages ( data not shown ) . The FMB position changes suggest that leaf cells switch from the FIL-expressing state to the miR165/166-free state , and the switch occurs sequentially from the adaxial to the abaxial cell layers and also from the central to the marginal cells within a layer . Despite the clear abaxial shifting in FMB position , there remained a possibility that the switch in gene expression does not occur but gene expression states are just maintained in all cell lineages . If this were true , the FMB shifting should be attributed to rapid proliferation of the adaxial cells . To address this point by tracing the lineages of FIL-expressing cells , we used the dexamethasone- ( DEX- ) inducible CRE/loxP recombination system [41] , which has recently been used in planta for cell lineage tracing [42] , [43] . Our system consists of two constructs: FILpro:CRE-GR and 35Spro:loxP-Ter-loxP-VENUS . 35Spro:loxP-Ter-loxP-VENUS does not express VENUS gene without FILpro:CRE-GR because of the transcriptional termination sequence ( Ter ) placed between Cauliflower Mosaic Virus 35S promoter ( 35Spro ) and VENUS regions . However , this construct can conditionally generate a marker gene , 35Spro:VENUS ( 35Spro:loxP-VENUS in a precise description ) , only in the FIL-expressing cells by DEX-dependent and CRE-mediated DNA recombination at the two loxP sites . Thus , the FIL-expressing cells and their progenies are permanently marked by the fluorescence of VENUS retained in the endoplasmic reticulum after DEX application ( see Text S1 and Figure S3 for the efficiency and specificity of this CRE/loxP system ) . If the FMB shifting is just due to the rapid proliferation of adaxial cells , DEX application at any developmental stage results in the same VENUS expression pattern . In contrast , if FMB is shifting depending on the gene expression switch , DEX applications at different developmental stages generate various expression patterns of VENUS corresponding to those of the FILpro:GFP at the stages of DEX application . To reveal the FIL-expression dynamics during leaf development , DEX was applied to the FILpro:CRE-GR 35Spro:loxP-Ter-loxP-VENUS plants from a series of developmental stages and the VENUS expression patterns were observed in mature leaves . For this analysis , the third true leaves were analyzed because their growth rate is well characterized under our laboratory conditions . Fluorescent images of the adaxial-side view were captured to elucidate the quasi-three-dimensional expression patterns of VENUS in the leaves ( Figure 2H–O ) because the areas of strong and intermediate fluorescence intensities correspond to those of VENUS expression in the adaxial epidermis and the adaxial-most mesophyll , respectively ( Figure S4 ) . When the plants had been treated with DEX since seed germination ( day 0 ) , VENUS expression was detected either in all leaf cells or in almost all leaf cells except for a small region in the adaxial central part of the epidermis ( Figure 2G–I , 2Q ) . DEX treatments from just before third leaf initiation ( day 3 ) resulted in the same VENUS expression patterns as the treatments since day 0 did ( Figure 2J ) . Given that FIL-expression patterns does not vary among the plants , such two patterns of VENUS expression likely reflect that FIL expression is initially induced in all leaf founder cells at the P0 stage and immediately repressed in the adaxial central epidermal cell ( s ) that does not accomplish the CRE/loxP recombination to generate the 35Spro:VENUS gene in some cases . DEX treatment from day 4 , when the leaf reaches approximately 20- to 50-µm-long ( P1–P2 stage ) , generated slightly more VENUS-negative cells in the adaxial central part than DEX treatment beginning on day 0 or day 3 ( Figure 2K , S5E ) . However , the marginal epidermis still expressed VENUS in the distal and proximal parts ( Figure S4A , S4D ) showing a good agreement with the aforementioned FILpro:GFP expression pattern at the comparable stage ( Figure 2A , S2E , S2F ) . In a series of DEX treatments beginning from day 4 to 8 , the later the treatment was performed , the more the VENUS expression area was restricted to the abaxial cell layers and to lateral and proximal parts of the two adaxial cell layers ( Figure 2K–O , S4 , S5E ) . In regard to the marginal epidermis , VENUS expression is detected in the proximal part , but not in the distal part when DEX was applied from day 6 ( Figure 2M , S4B , S4E , S4F ) . Because the third leaf reaches to 200 to 300 µm in length ( approximate P6 stage ) by day 6 , this VENUS expression pattern is in good agreement with the FILpro:GFP expression pattern in such leaves ( Figure 2D–F ) . When DEX was applied from day 8 ( approximately 1 , 000-µm-long , P9–P10 stage ) , VENUS expression in the two adaxial cell layers almost disappeared ( Figure 2O , S4C , S5E ) , but two or three abaxial cell layers still expressed VENUS ( Figure 2P , 2R , S4G , S4H ) , showing a good agreement again with the FILpro:GFP expression pattern ( Figure S2K–P ) . In summary , the series of DEX treatments generated a variety of the VENUS expression patterns each of which highly correlates with each FILpro:GFP expression pattern at the corresponding stage . Therefore , it is indicated that FMB shifting is mainly caused by the gene expression switch from the FIL-expressing to the non-FIL-expressing state in each cell . To gain insights into the regulatory mechanisms for FMB shifting , we sought mutants defective in the shifting process . A candidate of such mutants , enlarged fil expression domain2 ( enf2 ) was isolated from a genetic screen for altered FILpro:GFP expression patterns [29] . Whereas the FIL-expression domain in the wild type did not surround the center provascular cells ( Figure 3A ) , the domain in this mutant was abnormally large , frequently surrounding the provascular cells in around the P5 stage ( Figure 3B ) . To characterize the dynamics of FIL-expression and miR165/166-free domains , the markers FILpro:GFP and 35Spro:miYFP-W were observed in various developmental stages of enf2 leaf primordia . Whereas the two domains were almost separated at all stages in this mutant as in the wild type , the domain sizes were different between enf2 and the wild type especially in later developmental stages ( Figure 3A–E , S5A–D ) . In small leaf primordia , the size of the FIL-expression domain in enf2 was comparable to that in the wild type ( Figure 3F , S5A , S5C ) . By contrast , in relatively large leaf primordia , the FIL-expression domain in enf2 was significantly larger than that in the wild type ( Figure 3F , S5B , S5D ) . For example , in 300-µm-long primordia of enf2 , the FIL-expression domain was restricted to two to four abaxial cell layers ( Figure 3C–E ) . Such a domain size was larger than that in the wild type at the same developmental stage ( Figure 2D–F , 3C–F ) suggesting the slower FMB shifting than in the wild type . Nonetheless , the FIL-expression domain in enf2 at this stage was smaller than that in the early stages ( Figure S5C , S5D ) , suggesting that the FMB shifting did occur even in this mutant . Furthermore , the FILpro:CRE-GR and 35Spro:loxP-Ter-loxP-VENUS system revealed that FIL expression is initially induced in all of the leaf cells and gradually restricted during later stages in enf2 ( Figure S5F–N ) . This analysis also showed that the sizes of the VENUS-negative area in leaves treated with DEX from day 3 did not significantly differ between the wild type and enf2 ( the left data points in Figure S5E ) . This suggests that the earliest repression of FIL expression in the adaxial central epidermis at the P0 stage is not affected in enf2 . The results from marker observation and lineage tracing can be interpreted as showing that the FMB shifting occurs in enf2 , but more slowly than in the wild type . Mature leaves in enf2 are pale green , more serrated and narrower than those in the wild type ( Figure 3G , 3H ) . In some cases , enf2 formed needle-like leaves lacking trichomes at a frequency of less than one percent ( Figure S5O ) . These narrow and needle-like morphologies are similar to those of abaxialized leaves , including the leaves of 35Spro:FIL plants [13] , [14] , rev recessive mutants harboring another enhancer mutations [10] , [44] , [45] and 35Spro:MIR165 plants [46]–[48] . Further observations of leaf anatomy by scanning electron microscopy revealed that adaxial mesophyll cells in enf2 were not as densely packed and columnar shape as those of the wild type but had air space and a bumpy cell surface looking more like that of the abaxial spongy mesophyll of the wild type ( Figure 3I , 3J ) . In summary , enf2 leaves are partially abaxialized with respect to the lamina morphology and mesophyll differentiation , suggesting that slow FMB shifting results in narrow lamina formation and abaxialized mesophyll . To know the relationship between the leaf morphological features and the FMB shifting speed , we analyzed FMB shifting in phb-1d heterozygous ( phb-1d/+ ) plant , which is well known for the partially adaxialized leaf phenotype of narrow and cup-shaped leaves [49] ( Figure 4H , 4I ) with excessive densely packed and smooth mesophyll cells [50] ( Figure 4J , 4K ) . When FILpro:GFP 35Spro:miYFP-W markers were introduced into phb-1d/+ , the FIL-expression and miR165/166-free domains were separated as in the wild type ( Figure 4A–E ) . The FIL-expression domain occupied three to four abaxial cell layers in the early primordium ( approximately 50-µm-long ) ( Figure 4A , 4B; see also Figure 2A , S5A ) and two to zero cell layers in the later primordium ( approximately 300-µm-long ) ( Figure 4C–E; see also Figure 2D–F ) . The FILpro:CRE-GR and 35Spro:loxP-Ter-loxP-VENUS system revealed that FIL is expressed throughout the leaf at the initiating stages , even in phb-1d/+ plants ( Figure 4F , 4G ) . Therefore , the speed of FMB shifting is greater in phb-1d/+ than in the wild type , suggesting that fast FMB shifting results in narrow and abnormal lamina formation and excess adaxialization of mesophyll . A positional cloning approach found a single base substitution at the second exon terminus ( enf2-1 allele ) of the gene AT1G31410 in the enf2 genome ( Figure 5A ) . Because the mutant phenotype was rescued by introducing the wild-type genomic fragment of this gene ( Figure S6A–C ) , we concluded that ENF2 is AT1G31410 . The enf2-1 mutation results in no amino acid substitution when the mRNA is spliced as in the wild type . However , RT-PCR analysis revealed that the mutation leads to unusual splicing events ( Figure 5B ) that generate premature stop codons in the majority of the mRNA ( data not shown ) . Thus , the amount of functional ENF2 mRNA is reduced in this mutant . However , the mutant of another enf2 allele ( enf2-2 ) found in the SALK T-DNA insertion lines ( Figure 5A ) showed whiter and narrower leaves than enf2-1 and was seedling lethal ( Figure S7B ) . In addition , ENF2 mRNA with the normal exon junctions was detected from the enf2-1 mutant as a small peak in an electropherogram of the RT-PCR product ( Figure S7A ) . Therefore , viable enf2-1 mutant retains some ENF2 function and is a weak allele that is useful for analyses of ENF2 function without side effects of lethality . We found an enf2 enhancer mutation ( ene hereafter ) in the process of cloning ENF2 . The existence of this enhancer locus was indicated by the fact that some F2 plants from the cross between the wild type and enf2 showed a milder mutant phenotype than the parental enf2 in terms of the leaf morphology , color ( Figure S7D , S7F ) and FIL-expression pattern ( Figure S7G ) . The positional cloning approach and rescue experiments identified a missense mutation in AT1G80070/SUS2 as the ene mutation ( Figure S7H–L ) . This gene product is a homolog of yeast Prp8 , which plays an important role in recognizing exon-intron junctions during mRNA splicing [51] . To examine the possibility that the ene mutation weakens ENF2 function by affecting the splicing efficiency of ENF2 mRNA in the enf2-1 mutant , we compared the amounts of normally spliced ENF2 mRNA in the enf2-1 single mutant and the enf2-1 ene double mutant . The electropherogram of the ENF2 RT-PCR products revealed that enf2-1 ene contained less wild-type mRNA than enf2-1 ( Figure S7A ) . Moreover , the ene single mutant was indistinguishable from the wild type in leaf morphology , color ( Figure S7C , S7E ) and FIL-expression pattern ( Figure S7G ) . These data suggest that the ene mutation enhances the enf2 phenotype by decreasing the splicing efficiency of the enf2-1 allele , and we continued to use the enf2-1 ene double mutant ( described as enf2 again below ) for further analyses as a plant defective in ENF2 function . To analyze expression and subcellular localization of ENF2 protein , we created transgenic plants expressing VENUS-ENF2 fusion gene under the control of the ENF2 promoter . This transgene , ENF2pro:VENUS-ENF2 , was able to rescue enf2 ( Figure S6A , S6B , S6D ) , indicating that ENF2pro:VENUS-ENF2 can confer authentic ENF2 expression and that VENUS-ENF2 is functional . VENUS-ENF2 was localized in chloroplasts ( Figure 5C–E ) , suggesting that ENF2 is localized in chloroplasts . This result is consistent with recent reports of proteomic analyses that the Arabidopsis ENF2 protein and its maize homolog were detected in the plastid ( chloroplast ) fraction [52] , [53] . To clarify ENF2 function , the ENF2pro:VENUS-ENF2 expression pattern was analyzed . VENUS-ENF2 was expressed throughout the shoot apical meristem and leaf primordium ( Figure 5F , 5G ) . Such a broad expression pattern of ENF2 is consistent with a previous transcriptomic study in which the ENF2 mRNA was detected from the FIL-expressing part and the central-zone of the shoot apical meristem [54] ( http://bar . utoronto . ca/efp/cgi-bin/efpWeb . cgi ) . While there is no previous report showing the protein function of ENF2 and the homologs in plant , BLAST search ( protein blast in NCBI: http://blast . ncbi . nlm . nih . gov/Blast . cgi ) found that ENF2 amino acid sequence shows low similarity to those of two bacterial proteins , PotD ( 44% similarity ) and PotF ( 46% similarity ) , which specifically bind to polyamines and transport them into cells [55] . The amino acid residues indispensable for the polyamine binding of PotD and PotF are widely conserved among the bacterial homologs [55] ( Figure S8 red ) . However , the corresponding regions are substituted in ENF2 by dissimilar residues that are conserved only among green plant lineages and cyanobacteria ( Figure S8 green ) . The similarity and difference among the homologous proteins might imply that ENF2 in plastids has some functions similar to but distinct from those of PotD and PotF in bacteria . The mutant phenotypes of pale green ( enf2 ) ( Figure 3H ) and white ( enf2-2 ) ( Figure S7B ) color and the localization of ENF2 protein to plastid imply a role of ENF2 in chloroplast development . To characterize the effect of the enf2 mutation on chloroplast development , we observed the plastid inner structures by transmission electron microscopy ( Figure S9A–F ) . Mature enf2 leaves had normal-looking chloroplasts because they showed highly stacked thylakoid membranes and large starch granules , as did the wild-type leaves ( Figure S9C , S9F ) . In contrast , plastids in the enf2 leaf primordia had less-developed inner structures than those in the wild type in comparable stages ( Figure S9B , S9E ) . Taken together with the similarity of the proplastid structure in the meristems of wild type and enf2 ( Figure S9A , S9D ) , the chloroplast development is likely to be delayed in the mutant . The enf2 phenotypes of pale green appearance , narrow laminae and defective differentiation of palisade mesophyll are common defects to plants harboring dysfunctional plastidial ribosome or plastidial RNA polymerase [56]–[63] . To examine whether plastid gene expression is impaired in enf2 , the expression levels of all of the 80 protein-coding and two rRNA genes encoded by the plastid genome were analyzed by Quantitative Reverse Transcription PCR ( qRT-PCR ) . On average , the expression levels were reduced to 49 . 0% of the wild type , with a range of 93 . 2% to 23 . 7% ( Figure S9G ) . Because the amounts of rRNA were also reduced ( the right-most two bars in Figure S9G ) , plastid gene expression might be globally down-regulated , not only at the RNA level but also at the level of translation in enf2 . It has been known that the plastid gene expression profile is affected when chloroplast development is inhibited by external stresses [64] and that inhibition of plastid gene expression by chemical treatments or genetic mutations leads to defective chloroplast development [59] , [61] , [65] , [66] . ENF2 is also important for chloroplast development and plastid gene expression , though it is unclear which of the chloroplast development and plastid gene expression is primarily affected in enf2 . The abnormalities in plastid condition suggest the involvement of plastid function in regulating the FMB shifting . To test whether the defective chloroplast development is sufficient to retards FMB shifting and lead to narrow lamina development , chloroplast development was inhibited in the wild type by norflurazon treatment and dark growth condition . Norflurazon is an inhibitor of carotenoid biosynthesis [67] that bleaches seedlings by causing oxidative damaging of plastids in the light . Although application of 0 . 25 µM norflurazon caused leaf bleaching , the leaves had roundly expanded lamina even at 25 µM concentration ( Figure S10A ) . Dark-grown seedlings were etiolated and lacking in chloroplast but formed round lamina ( Figure S10B ) . The narrow appearance of the leaves was due to their elongated petioles ( Figure S10C ) . Neither norflurazon nor dark condition led to as clear alterations in the FILpro:GFP 35Spro:miYFP-W expression patterns ( Figure S10D , S10E ) as enf2 showed . These results indicate that the FMB shifting and lamina morphology are not necessarily affected by the inhibition of chloroplast development . To know whether the plastid gene expression activity affects the FMB shifting and lamina development , we inhibited the plastid translation in the wild type by lincomycin and erythromycin treatments . Both lincomycin and erythromycin specifically inhibit plastidial ribosome without clear effects on cytosolic and mitochondrial ribosomes [68] . At concentrations of 100 µM , these chemicals bleached seedlings , and narrow and filamentous leaf formation was observed at concentrations greater than 200 µM ( Figure 6A , 6B ) . In the leaf primordia of the lincomycin- and erythromycin-treated plants , FILpro:GFP 35Spro:miYFP-W marker expression patterns showed that the FMB positions were close to the adaxial side , similarly to that in enf2 ( Figure 6E , 6F ) . The effects of lincomycin and erythromycin treatments on the lamina morphology and FMB position indicate that enf2-like phenotype is regenerated by inhibition of protein synthesis in plastids . Though the lincomycin- and erythromycin-treated plants showed enf2-like phenotype in morphology , they were albino and seedling lethal suggesting that plastid function is more extensively affected in these plants than in enf2 which is pale green and viable . To examine whether the impaired plastid gene expression is responsible for defective FMB shifting independently of abnormal photosynthesis and developmental arrest , we sought a mutant impaired in plastid gene expression machinery that exhibits narrow lamina with a mild phenotype in color and viability . For this purpose , the mutant flavodentata ( flv ) was selected because the FLV gene has been shown to encode a plastid-localized PPR protein required for RNA editing of the rpoC1 mRNA , which encodes a subunit of plastid-encoded RNA polymerase ( I . Small , personal communication ) , and because flv , which is allelic to defectively organized tributaries4 [69] ( I . Small , personal communication ) , is viable and known for its narrow serrated leaves that are pale green in color [69] , [70] ( Figure 6C ) . Observations of FILpro:GFP 35Spro:miYFP-W marker expression in flv revealed that the FMB position was close to the adaxial side , similarly to that of enf2 ( Figure 6G ) . This result supports the role of plastid gene expression in FMB shifting and lamina expansion , and shows the separable nature of this role from the development of the photosynthetic apparatus and lethality . From the result that enf2-like phenotype was regenerated by lincomycin and erythromycin treatments and flv mutation but not by norflurazon treatment and dark growth condition , it is suggested that the impaired plastid gene expression is the key to the defective FMB shifting and lamina morphology in enf2 . However , it has been known that the plastid gene expression profile of the whole seedling RNA is also globally fluctuated by norflurazon treatment [71] , . To examine whether or not the alterations in plastid gene expression in the shoot apex is less severe in the norflurazon-treated plants than in enf2 , we also quantified the expression levels of several plastid genes in these plants . The result showed that not only the photosynthesis-related genes ( rbcL , psaA , psaB , psbA , psbE ) but also the genes for transcription ( rpoA , rpoB , rpoC1 , rpoC2 ) and for translation ( rrn16S , rrn23S ) are down-regulated as severely as or more than in enf2 ( Figure S10F ) . This means that the expression levels of plastid genes in the whole shoot apex tissue are not always linked to the enf2-like phenotype . Nonetheless , simultaneous treatment of norflurazon and lincomycin resulted in narrow lamina formation ( Figure S10G ) . This result indicates that the norflurazon-treated plants are not insensitive to , but show the developmental response to , the inhibition of plastid gene expression . Therefore , it is likely that the enf2-like phenotype is caused by direct inhibition of plastid gene expression machinery rather than by the reduced levels of plastid gene expression . To further characterize the similarity between enf2 mutation and the lincomycin treatment , enf2 was also treated with lincomycin . When enf2 was treated with 150 µM of lincomycin , filamentous leaves were formed in more than 90 percent of seedlings ( Figure S10H ) . In contrast to enf2 , wild type required 450 µM of lincomycin to form such leaves at a similar frequency ( Figure S10I ) . This result supports that enf2 mutation leads partial impairment in plastid gene expression as a certain concentration of lincomycin does , thus they both retard FMB shifting and inhibit lamina expansion . The mechanism with which nuclear gene expression levels respond to the plastid condition is called the plastid retrograde signal . It has been known that changes in plastid gene expression and other plastid conditions affect the expression levels of nuclear genes , including photosynthetic genes , through the GENOMES UNCOUPLED1- ( GUN1- ) dependent retrograde signal ( see [36]–[40] for review ) . In addition , another specific retrograde signal is also known to couple the nuclear gene expression with tetrapyrrole biosynthetic activity but not with plastid gene expression . Other GUN genes ( GUN2 , 3 , 4 and 5 ) are involved in this specific pathway [73] , [74] . To examine the involvement of known retrograde signals in the plastid effect on the lamina expansion and FMB shifting , we analyzed the responses of gun mutants to the inhibition of plastid gene expression . While gun2 , 3 , 4 and 5 mutants formed narrow or filamentous leaves as the wild type did ( Figure S11A and data not shown ) , the gun1 mutant formed relatively round lamina ( Figure 7A ) when they were treated with lincomycin . The lincomycin-treated gun1 showed a similar FMB position to that of the untreated wild type ( Figure 7D ) , suggesting that the unaffected FMB position in leaf primordia is the basis for the lamina expansion . In contrast to such distinct phenotype of gun1 under the lincomycin-treated condition , untreated gun1 mutant showed the leaf morphology indistinguishable from that of the wild type ( Figure 7C ) , as previously reported [75] , [76] . The FMB position in gun1 leaf primordia did not differ significantly from that of the wild type at any stage of leaf development ( Figure 7F , S11C ) . These gun1 phenotypes indicate that the GUN1 is involved in the retardation of FMB shifting and defective lamina expansion only in response to the inhibition of plastid gene expression , but not under normal plastid conditions . If the retarded FMB shifting in enf2 is due to the failure of plastid gene expression , additional gun1 mutation may suppress this phenotype . As expected , the enf2 gun1 double mutant , but not enf2 gun5 , showed round lamina ( Figure 7B , S11B ) . The FMB position in enf2 gun1 was similar to that of the wild type grown normally ( Figure 7E , 6H ) . However , enf2 gun1 was different from enf2 not only in the FMB position and the lamina morphology , but also in the color and viability . The double mutant showed an almost albino phenotype and was seedling lethal ( Figure 7B ) , suggesting that the suppressed developmental phenotypes were not due to rescued plastid condition . To confirm this point , we checked plastid gene expression levels in enf2 gun1 by qRT-PCR . Though the expression levels of the transcription-related genes ( rpoA , rpoB , rpoC1 , rpoC2 ) were similar to those in the wild type , the amount of ribosomal RNA ( rrn16S , rrn23S ) and other genes' mRNA were much more reduced in enf2 gun1 than in enf2 ( Figure S11D ) . Therefore , it is likely that that gun1 mutation suppresses the enf2 phenotype not by rescuing the failure of plastid gene expression , but by diminishing the response to the abnormal plastid condition . The albino and lethal phenotype of enf2 gun1 and the pale green and viable phenotype of enf2 suggest that the wild type allele of GUN1 is important for the viability and autotrophic growth of the plant when the plastid gene expression is accidentally impaired . This hypothesis was also supported by the observation that the wild type and gun1 plants differed in the viability after transient lincomycin treatment . After transfer from the lincomycin-containing medium to the standard medium , wild type plants produced green shoots and continued growth , whereas gun1 did not grow any longer ( Figure S11E , S11F ) . These results indicate that GUN1 has a role in retarding FMB shifting and inhibiting lamina expansion when plastid gene expression is inhibited by internal and external damages , but the plant can adapt to such a severe situation thanks to the GUN1 function .
The mathematical model shows that a boundary between two gene expression domains easily shifts when the genes repress each other's expression via mobile factors . Though the whole regulatory network for the adaxial- and abaxial- specific genes is still largely unclear [25] , this theory of boundary shifting gives a good working hypothesis for the mechanism of FMB shifting because of the following reasons . The mathematical model assumes only three points; two ( groups of ) genes ( AD and AB ) are repressing each other's expression; a cell can express only one ( group ) of the genes due to the mutual repression when there is no external cues fluctuating the gene expression; some of the gene products move between neighboring cells depending on the concentration gradient . Accordingly , a mutual repressive relationship is known at least between PHB-like genes and miR165/166 whereas the regulatory relationship between PHB-like genes and FIL is yet to be elucidated . PHB-like genes repress miR165/166 activity by positively regulating AGO10/PINHEAD [77] , [78] , by decreasing miR165/166 expression level via cytokinin signal [79] and possibly by activating tasiR-ARFs [25] . The activity of miR165/166 in turn represses the expression of PHB-like genes through mRNA cleavage [80] and DNA methylation [81] . In this context , the candidates of the mobile factors are miR165/166 , cytokinin and possibly tasiR-ARFs . Therefore , an important suggestion from the mathematical model is that any unknown factors are not necessarily required to explain the shifting nature of FMB . Our mathematical model is also compatible with the phb-1d/+ phenotype because the shifting speed of the domain boundary toward the abaxial side is increased in the computer simulation when the AD degradation by AB is weakened ( Figure S12 ) . Such situation roughly corresponds to phb-1d/+ mutant in which the cleavage efficiency for PHB mRNA by miR165/166 is reduced [80] and the speed of FMB shifting is faster than in the wild type . Further comparisons between the model and real observations will be an important approach to evaluate the model and elucidate the molecular basis for the FMB shifting . Our data suggest that the speed of the FMB shifting is important for round and wide lamina expansion ( Figure 8A ) because fast and slow FMB shifting were associated with narrow or abnormal lamina formation in phb-1d/+ and enf2 mutants . It has been characterized well that the functions of FIL , PHB-like genes and miR165/166 are required for the lamina growth because their loss-of-function mutations and overexpression lead to narrow lamina or needle-like leaf formation [10] , [13] , [14] , [44]–[48] . However , a clear explanation of how FMB shifting is linked to the lamina expansion is one of the future challenges . Lamina expansion in the lateral direction requires WUSCHEL-RELATED HOMEOBOX1 ( WOX1 ) and PRESSED FLOWER/WOX3 ( PRS ) expression [34] and local auxin biosynthesis by YUCCA genes [82] at the adaxial-abaxial juxtaposition domain . Therefore , the expression domains and durations of such genes for lamina expansion might be regulated in response to the stage-specific positions of FMB . To further characterize the relationship between FMB shifting and lamina expansion , an important challenges is the three-dimensional live-imaging of FMB shifting with simultaneous monitoring of cell proliferation and other genes' expression . Our data show that chemical and genetic inhibition of plastid gene expression machinery retards FMB shifting via the GUN1-dependent mechanism ( Figure 8B ) . It is known that the GUN1-dependent retrograde signal down-regulates photosynthetic genes in the nucleus by changing the expression levels of the transcription factor genes ABI4 and GLK1 , which subsequently changes the expression levels of the downstream photosynthetic genes [73] , [83] , [84] . One possible scenario is that ABI4 and GLK1 also affect the expression levels of FIL , miR165/166 and other adaxial- and abaxial-specific genes through the transcriptional regulation . Among the adaxial- and abaxial-specific genes , KAN1 and ETTIN/ARF3 are known to be up-regulated in response to the impaired plastid gene expression though the involvement of GUN1 in the up-regulation is unclear [62] . On the other hand , it is also possible that the slow FMB shifting is a more indirect effect than such direct transcriptional regulations . For example , there are some reports pointing out that lincomycin-treated plant differently express the genes encoding cytosolic ribosomal proteins [85] and that some mutants of the genes for cytosolic ribosomal proteins form the partially abaxialized leaves as plastid-defective mutants do [62] , [86]–[89] . Another study shows the importance of abscisic acid metabolism for the leaf morphological phenotype of a plastid-defective mutant [63] . These reports suggest a possibility that changes in the cytosolic ribosomal proteins and abscisic acid metabolism mediate the regulation of the FMB position by the GUN1-dependent retrograde signal . Previous genetic studies have reported that the mutant plants harboring dysfunctional plastidial ribosomes or plastidial RNA polymerase show narrow laminae and/or defective palisade mesophyll differentiation as enf2 does [56]–[63] . However , the question of whether such developmental effects reflect only the inability to run the normal developmental program or a significant response to the plastid dysfunction has been unanswered . This question is partially answered by the phenotype of enf2 gun1 and lincomycin-treated gun1 because their normal FMB positions and round laminae indicate that the leaf primordia retain the ability to run the developmental program for FMB shifting and lamina expansion even when the plastids are dysfunctional ( Figure 8C ) . This finding raises a question of whether the inhibition of such normal lamina development by the GUN1-dependent mechanism is beneficial for plant life in any respect when the plastid gene expression is impaired . The GUN1-dependent retrograde signal is considered to be the plastid-nucleus communication system to coordinate the nuclear gene expression with the changing plastid condition during chloroplast development [37] , [39] , [40] . Because plastid gene expression is affected differently by various biotic and abiotic stresses [64] and genetic mutations in nuclei and plastids , the coordinated regulation of nuclear genes is required for the successful development of photosynthetic apparatus from non-chloroplast plastids . However , when the plastid gene expression machinery is heavily impaired at the primordial stages , it is difficult to develop a fully functional photosynthetic apparatus . In such a case , full lamina expansion is risky because the cost of the lamina formation is not compensated for by little photosynthetic product . Therefore , the inhibition of lamina expansion by the GUN1-dependent mechanism can be interpreted as the avoidance of such wasteful development . From this viewpoint , it is suggestive that the gun1 mutant becomes seedling lethal when transiently treated with lincomycin and in the enf2 mutant background . This less viable gun1 phenotype shows that the GUN1-dependent mechanism enables robust and sustainable plant development by optimizing the total photosynthetic efficiency even when plastid gene expression is impaired by internal and external fluctuations . It is considered that the planar lamina morphology of seed plants had been evolved depending on the adaxial-abaxial asymmetry of gene expression in leaf primordia [90] . Meanwhile , the evolutionary advantage of the planar morphology is the efficient light reception in chloroplasts for photosynthesis , thus depends on ensuring the functional integrity of plastids . From this evolutionary point of view , the problem of how the regulatory system for adaxial-abaxial gene expression pattern and lamina expansion by plastid has been evolved is as important as that of how planar lamina morphology with adaxial-abaxial asymmetry has been evolved .
Arabidopsis thaliana plants of Columbia ( Col ) accession with or without FILpro:GFP 35Spro:miYFP-W markers were used as the wild type . The phb-1d/+ mutant ( L . er accession background ) was used after crossing to Col more than four times . The enf2 mutants ( enf2-1 ene and each single mutant ) were isolated from the EMS-treated FILpro:GFP plants as described previously [29] and used for all analyses after backcrossing more than 4 times to parental line . enf2-2 ( SALK_063761 ) was obtained from the Arabidopsis Biological Resources Center ( ABRC ) at Ohio State University . The flv ( CS3254 from ABRC ) , gun1-1 , gun2-1 , gun3-1 , gun4-1 and gun5-1 mutants [75] , [76] were previously described . Plant seeds were sterilized and kept at 4°C in the dark for two days before sowing on 0 . 9% agar plates containing 1% sucrose and 0 . 5× Murashige-Skoog salt medium . For chemical treatments , each plate contained DEX ( 10 µM final concentration ) , lincomycin , erythromycin or norflurazon ( see Results and Figures for the concentrations ) . For DEX treatment , growing seedlings were dipped in a DEX solution ( 50 µM ) before being transferred to DEX plates . The plants were grown at 22°C and under continuous white fluorescent light of approximately 60 µmol photon m−2 s−1 except for the dark treatment , in which the plates were covered with aluminum foil . The phb-1d/+ mutant was grown at 16°C to moderate its phenotypic severity . Some plants were transferred to or sown in soil when seeds were needed . To observe GFP , YFP and VENUS marker expression patterns by confocal microscopy , plant samples were embedded into agarose gel , sectioned and observed as previously described [29] . The height of the observation plane from the leaf base was estimated from the section thickness , the section number from the meristem-containing section and the focal plane position within the observed section . The FILpro:GFP-positive areas in leaf primordia were measured using ImageJ v1 . 45s ( National Institutes of Health , MD ) as previously described [29] . In most of the observations , the plants were observed when the first leaf grew as big as the cotyledon . To observe the VENUS expression area by stereoscopy , an SZX16 fluorescence stereoscope equipped with a GFP filter and a CCD camera DP72 ( OLYMPUS , Japan ) was used . From the acquired RGB color images , the green channel image was extracted , and the VENUS-positive areas in the adaxial epidermis and the adaxial-most mesophyll were measured using ImageJ . Normal stereoscopy images were acquired with the same stereoscope under white light illumination . Scanning electron microscopy was performed as previously described [34] . The frozen leaves were cracked to observe the mesophyll structure . For plastid ultrastructure observation , 8- or 16-day-old seedlings of the wild type and enf2 were fixed by two steps in 0 . 05 M cacodylate buffer at pH 7 . 4 . The first buffer contained 4% ( w/v ) paraformaldehyde and 1% ( v/v ) glutaraldehyde , and the second buffer contained 0 . 5% osmium tetraoxide . These fixation steps took overnight and two hours , respectively . Fixed samples were dehydrated with a series of ethanol solutions and transferred into propylene oxide . These processed samples were embedded into EPON 812 resin ( TAAB Laboratories , UK ) . Ultrathin sections made with an Ultramicrotome ( Leica , Austria ) were stained by 4% uranyl acetate and 0 . 4% lead citrate . The ultrathin sections were examined with a transmission electron microscope H-7600 ( Hitachi , Japan ) at 80 kV . All T-DNA transformation was performed by vacuum infiltration using the Agrobacterium tumefaciens strain ASE . Transgenic plants were screened for BASTA or Kanamycin resistance . The marker genes FILpro:GFP [15] , 35Spro:miYFP-W [29] , FILpro:CRE-GR ( see below ) and 35Spro:loxP-Ter-loxP-VENUS [42] were introduced into each mutant by genetic cross after T-DNA transformation into the wild type ( Col ) . Double transgenic FILpro:CRE-GR 35Spro:loxP-Ter-loxP-VENUS plants were obtained by crossing the single transgenic lines and analyzed in the F1 generation . For the enf2 mutant complementation , a 6 . 1-kb AT1G31410/ENF2 genomic fragment , including 2647 bp upstream from the start codon and 1199 bp downstream from the stop codon , was amplified from BAC#T8E3 ( from ABRC ) by PCR using the primers 5′-CGGGGTACCTGATTGAGAATGTGATGAAGG-5′ and 5′-GGCTCTAGAGACCTCGGGTAAAACCC-3′ . This fragment was cloned into a modified pBluescript II SK+ ( Agilent Technologies , CA ) , transferred to a binary vector , pGWB-NB1 [29] , by the GATEWAY system ( Life Technologies , CA ) , and then introduced into enf2 plants . To express the VENUS-ENF2 fusion gene from the ENF2 promoter , this 6 . 1-kb fragment was modified by insertion of VENUS CDS between the region of the putative plastid-transit peptide ( predicted by TargetP , http://www . cbs . dtu . dk/services/TargetP/ ) and the PotD/F homology domain . For the ene complementation experiment , a 10 . 5-kb AT1G80070/SUS2 genomic fragment , including 527 bp upstream from the start codon and 498 bp downstream from the stop codon , was amplified from the wild-type genome by PCR using the primers 5′-CGGGGTACCTGCCGATTCTCCCGGATTTTCA-5′ and 5′-ATGAGCTGCGGCCGCAGGAGGGATGATAAAACTGCTGT-3′ . This 10 . 5-kb fragment was finally transferred into the vector pGWB-NB1 as the 6 . 1-kb ENF2 fragment above . For the construction of FILpro:CRE-GR gene , the 6 , 011-bp FIL promoter [15] and the CRE-GR coding sequence in pML518 [42] were cloned in tandem into the multicloning site of a modified pBluescript II SK+ and finally transferred into the vector pGWB-NB1 . The binary vector containing the 35Spro:loxP-Ter-loxP-VENUS gene was previously described as pML988 [42] . For mapping of the ENF2 and ENE loci , the F2 population from the F1 hybrid between enf2 ( enf2-1 ene , Col accession background ) and an L . er accession plant was used . The ENF2 locus was mapped into a 54 kb region of chromosome I using polymorphism markers . Sequencing of all of the annotated genes ( AT1G31370 to AT1G31540 ) in this region found a mutation in the AT1G31410 gene . The ENE locus was linked to the lower arm terminus of chromosome I , and the mutation in the AT1G80070/SUS2 gene was found by sequencing the linking region . New CAPS and SSLP markers were designed using information from the Monsanto Arabidopsis Polymorphism and L . er Sequence Collection ( http://www . arabidopsis . org/Cereon/index . jsp ) . Total RNA was extracted from whole aerial parts of 8-day-old seedlings with the Plant RNeasy Mini Kit ( QIAGEN , Germany ) . cDNA synthesis was performed using the SuperScriptIII First Strand Synthesis System for RT-PCR ( Life Technologies ) with a mixed primer of random hexanucleotide and Oligo ( dT ) , and part of the ENF2 cDNA was amplified by PCR of 40 thermal cycles to saturate the amplification . The primer sequences were 5′-CCGATTGTCGTTACAGAGAATG-3′ , 5′-AGGAGCTTTTTCTCCCGCATA-3′ and 5′-ACTCGTCCTCCTCTTTGTTC-3′ ( blue , pink and orange arrows in Figure 5A , respectively ) . The PCR products were analyzed by conventional agarose gel-electrophoresis , and each fragment of a distinct size was sequenced to identify the abnormal exon junctions . The same PCR products were also applied to microfluidics-based electrophoresis , using a 2100 Bioanalyzer ( Agilent Technologies , CA ) , to detect the normally spliced mRNAs . Total RNA was extracted from a shoot apex sample containing the apical meristem and only approximately the seven youngest leaves of not more than 500 µm in height . The cotyledons and hypocotyl were eliminated as much as possible . To compare the wild type , enf2 , norflurazone-treated plant and enf2 gun1 at comparable stages with similar leaf numbers and sizes , their shoot apices were collected at 5 . 5 , 7 , 10 and 13 days old , respectively . cDNA synthesis was performed with QuantiTect Reverse Transcription Kit ( QIAGEN ) accordingly to the manufacturer's instructions . For quantitative PCR , QuantiTect SYBR Green PCR Kit ( QIAGEN ) and the primer sets shown in Table S1 were used . The data collection and analysis were performed with Rotor-Gene Q ( QIAGEN ) and the Rotor-Gene 6000 series software 1 . 7 ( QIAGEN ) . Some primer sequences were based on the previous study [66] . The average expression levels and the standard errors were calculated from biological quadruplicate data . All calculations and related graphical representation were performed with Mathematica 7 . 0 ( Wolfram Research , IL ) . The Hill coefficients for the functions fi and gi were set to n = 2 and n = 1 , respectively , because the transcriptional repression is generally implemented by dimerized or larger complexes of transcription factors and mRNA cleavage by small RNA is a one-to-one reaction , but other higher values of the Hill coefficients gave similar results ( data not shown ) . For numerical simulations , equations ( 1–2 ) were discretized in time with the time step Δt = 0 . 2 by the fourth-order Runge–Kutta method , and the reflective boundary condition was imposed . Sequence data from this article can be found in GenBank/EMBL databases under the following accession numbers: AtENF2 , NP_174426 . 2 ( Arabidopsis thaliana ) ; NtENF2 , XP_003628983 . 1 ( Medicago truncatula ) ; OsENF2 , CAE01823 . 2 ( Oryza sativa ) ; ZmENF2 , NP_001146059 . 1 ( Zea mays ) ; SmENF2 , XP_002991704 . 1 ( Selaginella moellendorffii ) ; PpENF2 , XP_001760514 . 1 ( Physcomitrella patens ) ; CsENF2 , EIE23974 . 1 ( Coccomyxa subellipsoidea ) ; VcENF2 , XP_002946447 . 1 ( Volvox carteri ) ; AvENF2 , YP_323566 . 1 ( Anabaena variabilis ) ; NsENF2 , ZP_01631537 . 1 ( Nodularia spumigena ) ; NaENF2 , YP_003722186 . 1 ( Nostoc azollae ) ; BbPotD , AAB91528 . 1 ( Borrelia burgdorferi ) ; MhLpp38 , AAA84748 . 1 ( Mannheimia haemolytica ) ; PfPotD , AAC15511 . 1 ( Pseudomonas fluorescens ) ; AaPotD , AAC27498 . 1 ( Aggregatibacter actinomycetemcomitans ) ; TpPotD , AAC65630 . 1 ( Treponema pallidum ) ; HiPot2 , P44731 . 2 ( Haemophilus influenzae ) ; HiPot1 , P45168 . 1 ( Haemophilus influenzae ) ; EcPotF , AAC73941 . 1 ( Escherichia coli ) and EcPotD , NP_415641 . 1 ( Escherichia coli ) . | The efficient photosynthesis in plant leaves depends on the wide planar morphology of lamina with a lot of chloroplasts ( plastids ) . Development of the planar lamina requires the specific expression of several key genes in the foreside ( adaxial side ) and backside ( abaxial side ) of the tiny leaf primordium . Such abaxial-specific genes include FIL and miR165/166 . In this study , we characterized that the expression of FIL and the activity of miR165/166 are induced in all cells of initiating leaf primordia , and then repressed sequentially from the adaxial cells , thus gradually restricted to the abaxial cells . Furthermore , we found that this restriction of FIL expression and miR165/166 activity is retarded and lamina becomes narrow when the plastid function is inhibited in leaf primordia . Interestingly , such plastid effects on leaf development were not observed when the communication between plastid and nucleus was inhibited by gun1 mutation . Our study suggests that plastids modulate the gene expression dynamics in leaf primordia leading to narrow lamina formation when the plastid function is severely inhibited . Such developmental regulation by plastid presumably contributes to preventing the wasteful expansion of lamina with low photosynthetic activity . | [
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] | 2013 | Pattern Dynamics in Adaxial-Abaxial Specific Gene Expression Are Modulated by a Plastid Retrograde Signal during Arabidopsis thaliana Leaf Development |
Mycobacterium leprae ( M . leprae ) is a human pathogen and the causative agent for leprosy , a chronic disease characterized by lesions of the skin and peripheral nerve damage . Zoonotic transmission of M . leprae to humans by nine-banded armadillos ( Dasypus novemcinctus ) has been shown to occur in the southern United States , mainly in Texas , Louisiana , and Florida . Nine-banded armadillos are also common in South America , and residents living in some areas in Brazil hunt and kill armadillos as a dietary source of protein . This study examines the extent of M . leprae infection in wild armadillos and whether these New World mammals may be a natural reservoir for leprosy transmission in Brazil , similar to the situation in the southern states of the U . S . The presence of the M . leprae-specific repetitive sequence RLEP was detected by PCR amplification in purified DNA extracted from armadillo spleen and liver tissue samples . A positive RLEP signal was confirmed in 62% of the armadillos ( 10/16 ) , indicating high rates of infection with M . leprae . Immunohistochemistry of sections of infected armadillo spleens revealed mycobacterial DNA and cell wall constituents in situ detected by SYBR Gold and auramine/rhodamine staining techniques , respectively . The M . leprae-specific antigen , phenolic glycolipid I ( PGL-I ) was detected in spleen sections using a rabbit polyclonal antibody specific for PGL-I . Anti-PGL-I titers were assessed by ELISA in sera from 146 inhabitants of Belterra , a hyperendemic city located in western Pará state in Brazil . A positive anti-PGL-I titer is a known biomarker for M . leprae infection in both humans and armadillos . Individuals who consumed armadillo meat most frequently ( more than once per month ) showed a significantly higher anti-PGL-I titer than those who did not eat or ate less frequently than once per month . Armadillos infected with M . leprae represent a potential environmental reservoir . Consequently , people who hunt , kill , or process or eat armadillo meat are at a higher risk for infection with M . leprae from these animals .
The human pathogen , M . leprae , causes leprosy , a slowly developing chronic granulomatous disease mainly affecting the skin and peripheral nerves , resulting in disfiguring lesions and progressive nerve damage that can lead to muscle weakness or atrophy , bone loss , amputations and blindness [1] . The discovery of the bacillus was credited by work by the Norwegian physician Gerhardt Henrik Armauer Hansen in 1873 . The name Hansen’s disease ( hanseníase in Portuguese ) is used in Brazil to lessen the stigma associated with the common name . Multidrug therapy ( MDT ) was introduced by the World Health Organization ( WHO ) in the mid-1980’s , and has been provided free of charge upon diagnosis worldwide for over 30 years . The worldwide prevalence of the disease has decreased from >5 million cases in the 1980’s to <200 , 000 by 2016 . Nevertheless , the WHO recorded 214 , 783 new cases in 2016 [2] , slightly higher than the previous year , with around 80% of all cases being found in only three countries: India , Brazil and Indonesia . The Americas recorded 27 , 356 cases in 2016 , with Brazil having 25 , 218 , or 92 . 2% of the total . Brazil is still the only country in the world that has not reached the WHO goal of <1 new case per 10 , 000 population and is currently at around 1 . 2/10 , 000 nationally ( SINAN , Brazil’s Notifiable Diseases Information System ) [3] . However , there is a wide variation in new case detection in regional areas in Brazil , ranging from as low as <0 . 2/10 , 000 in the southern state of Rio Grande do Sul to >4/10 , 000 , considered hyperendemic , in the central ( Mato Grosso , Rondônia ) , north ( Pará , Tocantins ) , and northeastern states ( Maranhão ) [4] . Historically , Pará and the Amazon region have recorded some of the highest new case detection rates in the country , despite having one of the lowest population densities [5] . Reasons behind this have been well-documented , and include living in a hyperendemic area , low human development index ( HDI , which combines life expectancy at birth , per capita income , and education level ) , living with an untreated index case or within 200 meters of a case , high household density ( >2 people per bedroom ) , poor nutritional status , and lack of healthcare availability [6 , 7] . The disease causes a broad array of skin lesions , nerve damage , peripheral neuropathy and anesthesia . The variability in clinical manifestations of leprosy is aligned with the hosts’ abilities to mount effective immune responses to M . leprae , dependent on the interplay of both cell mediated and humoral responses [8] . There is an overall genetic resistance towards developing leprosy , with over 90% of people having a natural immunity [9] . For those individuals who do progress to disease , the interplay of cell mediated and humoral immunity to M . leprae becomes clear from the well-known immunological and clinical leprosy spectrum , ranging from tuberculoid ( TT/BT ) or paucibacillary ( PB ) leprosy to lepromatous ( BB/BL/LL ) or multibacillary ( MB ) leprosy , defined by Ridley-Jopling classification based on histopathology and bacillary load [10] . PB patients generally show high cellular responses to M . leprae antigens in vitro as measured by the production of Th1 cytokines , particularly IFN-γ , and have low antibody titers to M . leprae-specific antigens . MB patients have lost some or all capacity to mount a cell mediated response due to T cell anergy but have high antibody titers to M . leprae antigens , particularly to the M . leprae-specific glycolipid , PGL-I [11 , 12] . The humoral response to PGL-I in leprosy patients , mainly IgM , correlates very well with the BI ( bacillary index ) and is highly specific , and the anti-PGL-I titer can be easily assessed using either the standard ELISA assay or in a lateral flow device [13–16] using the synthetic di- or trisaccharide of PGL-I linked to a protein carrier , bovine or human serum albumin ( BSA or HSA ) derivatives . Anti-PGL-I IgM seropositivity has been used to reliably assess the prevalence of leprosy in endemic areas , since a positive titer is a definitive biomarker of prior M . leprae infection [17–21] . Since M . leprae cannot be grown in vitro on axenic medium , the mechanism of human transmission has long been debated . The most widely accepted theory is that untreated index cases , particularly MB individuals who are capable of discharging an estimated 107 bacilli per day from nasal secretions [22] , are the main source of transmission via the aerosol route . A number of studies have shown that consanguineous individuals of index cases living for extended periods , months or years , in the same household ( household contacts , HC ) have the highest risk for developing disease [23–26] . Besides human contact , the only other known transmission route is from human contact with armadillos that have been naturally infected with M . leprae . Armadillos have an immune system that responds similarly to M . leprae infection , and essentially recapitulates the spectrum of human disease [27] . This includes developing progressive nerve damage and characteristic ulcers and skin lesions due to loss of sensation in the feet and face and even develop high antibody titers to PGL-I and other M . leprae proteins [28] . As early as 1975 , wild armadillos were found to be naturally infected with M . leprae , but it was later shown that sylvan leprosy had existed in this species for decades before they were artificially infected [29 , 30] . Surveys in Texas and Louisiana showed that disease prevalence rates among nine-banded armadillos were >20% in some areas [31] . In this study we investigated armadillos from an area in Brazil that is hyperendemic for leprosy in the town of Belterra in western Pará state to explore whether sylvan leprosy exists in wild armadillos in this area . We also performed a survey of the relationship of the people in this town with armadillos to determine if any activities related to hunting , killing , preparing or handling the armadillo meat for consumption , as well as the frequency of armadillo in the diet had any effect on the anti-PGL-I titer .
The research protocol was approved by the institutional review boards at the Universidade Federal do Oeste do Pará ( UFOPA ) and the Universidade Federal do Pará ( UFPA ) ( IRB protocol #517 . 394 ICS/UFPA ) and Colorado State University ( IRB protocol 15-6340H ) and conducted in accordance with the guidelines of the Declaration of Helsinki . All individuals who agreed to participate read and signed a written informed consent document . Environmental approval of wild armadillo tissue sampling was obtained with ICMBio authorization for research activities ( SISBIO 44831–1 ) . Environmental approval of wild armadillo tissue sampling was obtained with ICMBio authorization for research activities ( SISBIO 44831–1 ) , Ministério do Meio Ambiente , Instituto Chico Mendes de Conservação da Biodiversidade—ICMBio , Sistema de Autorização e Informação em Biodiversidade—SISBIO , Brazil . The survey site chosen was the city of Belterra in western Pará state where two rural communities exist at 92 Km ( São Jorge ) and 135 Km ( Corpus Christi ) on the Santarém-Cuiabá highway , located roughly by coordinates at 2° 38'S and 54° 56'W . The city has a total area of 4 , 398 km2 with 17 , 036 inhabitants ( IBGE , Instituto Brasileiro de Geografia e Estatistica , 2015 ) and is roughly 1 , 300 Km southwest from the capital city of Belém . The city was chosen because of its proximity to Santarém where one of the affiliated universities is located ( UFOPA ) and because it was determined that there was a high density of armadillos living in the surrounding forest with a high percentage of people living in this rural area that hunted or consumed armadillos as food . A total of 146 individuals living in the town of Belterra were asked to participate in a research protocol approved by the institutional review boards at the Universidade Federal do Oeste do Pará ( UFOPA ) and the Universidade Federal do Pará ( UFPA ) ( IRB protocol #517 . 394 ICS/UFPA ) and Colorado State University ( IRB protocol 15-6340H ) and conducted in accordance with the guidelines of the Declaration of Helsinki . All individuals who agreed to participate read and signed a written informed consent document . In the case of minors , consent was obtained from a parent or guardian of the child . All individuals received a free dermatologic exam performed by experienced leprosy clinicians , and a sample of blood was drawn from each person by a trained phlebotomist for anti-PGL-I titer assessment . Besides demographic information for each individual , a survey included questions about the extent of contact with armadillos ( hunting armadillos in the forests; killing and/or handling the armadillo meat for consumption; and the frequency of eating armadillo meat ) . The diagnosis of leprosy was performed using internationally accepted clinical criteria based on the presence of skin lesions with sensory loss and/or nerve damage associated with nerve swelling and pain , muscle weakness or disability . Individuals diagnosed with leprosy received free MDT treatment from their local basic health unit . An indirect ELISA was used to measure the anti-PGL-I IgM titer of all of the serum samples tested at a 1:300 dilution using a protocol previously reported [32] . The cut-off for positivity was established at an optical density ( O . D . ) of 0 . 295 based on the average plus three times the standard deviation of healthy subjects from a hyperendemic area as reported . The O . D . for each well was read at 490 nm using an ELISA plate reader . By collaborating with local residents who hunted armadillos in the surrounding forest in the area , we obtained samples of armadillo liver and spleen from freshly killed animals from different households from residents in both villages in Belterra ( “A” armadillos from Corpus Christi , n = 3 , and “B” armadillos from São Jorge , n = 13 ) . A sterile scalpel blade was used to excise several pieces of tissue , at least 1cm3 each , and placed in individual sterile 5 ml plastic tubes containing 70% ethanol to fix the specimen before DNA extraction . A fresh scalpel blade was used for each tissue for each animal . Environmental approval of wild armadillo tissue sampling was obtained with ICMBio authorization for research activities ( SISBIO 44831–1 ) . DNA was extracted in the laboratory from approximately 1 gm of fixed tissue using the Qiagen DNeasy Blood and Tissue Kit ( Qiagen , Germantown , MD ) following the protocol supplied by the manufacturer . The amount of DNA in each sample was quantified using nanodrop . The M . leprae-specific repetitive sequence , RLEP , was amplified by PCR using a Qiagen Multiplex PCR Kit ( Qiagen ) and primers ( LP1 forward primer: 5'-TGCATGTCATGGCCTTGAGG-3' and LP2 reverse primer: 5'-CACCGATACCAGCGGCAGAA-3' ) that amplifies a 129-base pair fragment found in the M . leprae genome . The primer sequences and the protocol used were adapted from Donoghue [33] using the following PCR protocol: denaturation at 95°C for 15 min , 40 cycles of denaturation at 94°C for 30 s , primer annealing at 58°C for 40 s , extension at 72°C for 30 s , and final extension at 72°C for 10 min . Each reaction set included a positive control tube using purified M . leprae DNA ( 2 ng ) and a negative control tube without template DNA . At the time of necropsies , samples of spleen were aseptically removed and prepared for histological examination and specific staining procedures for visualization of mycobacteria . Tissue samples were fixed in 4% paraformaldehyde in phosphate buffered saline ( PBS ) , embedded in paraffin and sectioned to 5 μm thickness . Subsequent tissue sections were mounted on glass slides , deparaffinized and stained either with auramine-rhodamine ( AR ) or SYBR Gold fluorescent stain [34] . For AR staining , each section was stained using the TB Fluorescent Stain Kit T ( Becton-Dickinson , Sparks , Maryland ) per manufacturer's instructions . The AR stain was added to the deparaffinized section on the slide and incubated in the dark at room temperature for 25 min , washed in acid-alcohol ( 0 . 5% HCl in 70% isopropanol ) for no more than 3 min , followed by washing with water and counterstaining with potassium permanganate ( 0 . 5% ) for 4 min . The slides were washed again in water and then mounted with Prolong Gold anti-fade mounting medium ( Invitrogen , Carlsbad , California ) . SYBR Gold staining was performed using a dilution of SYBR Gold fluorescence dye at 1:1 , 000 in a stain solution of 0 . 85 M phenol in a 60% glycerol/14% isopropanol solution in distilled water . The slides were heated on a block at 65°C for 5 min and then cooled at room temperature for an additional 5 min . The tissue sections were washed with acid alcohol ( 0 . 5% HCl in 70% isopropanol ) for 3 min , then washed with water and counterstained with hematoxylin QS ( Vector Laboratories , Inc . , Burlingame , CA ) , for 5–10 s . The excess hematoxylin was washed away with distilled H2O and slides were subsequently stained with 4 , 6-diamidino-2-phenylindole ( DAPI; Sigma Chemical , St . Louis , MO ) at 200 ng/ml final concentration for 10 min and washed again with water . Slides were mounted with Prolong Gold antifade mounting media . All stained sections were visualized using Zeiss 510 confocal microscopy and Zen software . For PGL-I antigen localization , deparaffinized spleen sections were covered with peroxidazed 1 solution ( Biocare Medical , Concord , CA ) for 5 min to block endogenous peroxidase , followed by antigen retrieval procedure ( Dako , Agilent Technologies , Carpinteria , CA ) . Background sniper ( Biocare Medical ) was used for 10 min to block nonspecific binding sites , followed by addition of a 1:500 dilution of rabbit polyclonal antisera specific for PGL-I ( produced at Colorado State University ) and incubated for 2 h in a humidified chamber at room temperature . Thereafter , the sections were washed 3 times for 5 min in PBS and incubated with a secondary goat anti-rabbit IgG-F ( abʹ ) 2 coupled to horseradish peroxidase ( Santa Cruz Biotechnology , Santa Cruz , CA ) for 1 h . After washing the sections with PBS , the substrate ( ImmPACT from AEC , Vector Laboratories ) was added for 10 min or until brownish-red color was developed . Slides were washed with PBS and counterstained with hematoxylin , followed by visualization using light microscopy . Control M . leprae infected and non-infected armadillo spleens that were formalin fixed and paraffin embedded were generously provided by Dr . Maria Pena from the National Hansen’s Disease Program ( NHDP ) , Baton Rouge , LA . Sections of control tissues were stained for PGL-I antigen as above . Sections were also stained by the Fite Faraco modification of the Ziehl-Neelsen staining technique to identify acid fast bacilli in control and wild armadillo tissues . The anti-PGL-I titer expressed as the O . D . was compared with the epidemiologic data and individual’s contact with armadillos by the Mann-Whitney test . The frequency of anti-PGL-I positives in the total population and among diagnosed leprosy patients were estimated between variables by the ratio of crossed products-Odds Ratio ( OR ) and their 95% confidence intervals using the χ2 test or Fisher’s exact test to verify the significance of associations . The GraphPad Prism ( GraphPad Software Inc . , La Jolla , CA ) statistical software program was used . The threshold for statistical significance was set at 5% .
Sixteen armadillos with an average weight of 3 . 7 Kg and 54 . 3 cm in length were captured by local residents by hunting in the surrounding tropical forest located around the communities . When extracted DNA from spleen tissues was tested for the presence of the RLEP repetitive sequence by PCR , 10/16 armadillos were positive ( 62% ) , indicating that a high percentage of these animals were infected with M . leprae ( Fig 1A ) . Since M . leprae infects internal organs in armadillos , particularly spleen and liver , we examined both tissues to determine if infection was consistently found in these organs . Of five armadillos examined from the same group , armadillos that were RLEP positive in the spleen were also RLEP positive in the liver while animals that were RLEP negative were negative in both ( Fig 1B ) . In addition , the signal strength in positive animals was much stronger in spleen than in liver . This finding is consistent with yields of M . leprae from experimentally infected armadillos from NHDP , with the yields in the spleen averaging 4 to 10 fold higher than in liver . The sequence of the RLEP product produced by PCR was confirmed to be identical to the published sequence found in M . leprae for all positive samples by submitting the PCR product for sequencing . Whole genome sequence results confirmed M . leprae sequence in three animals ( A7 , B21 and B22 ) with ~2X coverage . Thin sections of paraffin embedded armadillo spleens were stained to identify M . leprae in situ using SYBR Gold ( Fig 2A and 2B ) and auramine/rhodamine ( Fig 2C ) , staining techniques which are highly specific for DNA or mycobacterial cell wall components , respectively . A rabbit polyclonal serum raised against the M . leprae-specific PGL-I antigen was used to stain spleen sections by immunohistochemistry . Wild or control infected armadillo sections stained with the pre-immune serum and control uninfected armadillo spleens from NHDP stained with the anti-PGL-I serum were negative ( Fig 3A and 3B , respectively ) . Diffusely localized PGL-I antigen was seen in spleen sections from control infected NHDP armadillos ( Fig 3C and 3D ) and wild infected armadillos ( Fig 3E and 3F ) . In all cases , positive staining was identified using these techniques only in infected armadillo tissues . Hematoxylin and Eosin ( H&E ) and Fite Faraco staining ( acid fast staining ) of control NHDP infected and noninfected armadillo and infected wild armadillo tissues sections were included to show the architecture of the spleen and acid fast bacilli ( Fig 4A–4F ) . Of the 146 people surveyed in the town of Belterra , the subjects were divided equally ( n = 73 ) between two villages located within the town 44 Km apart , namely the village of São Jorge , where residents of 32 households participated , and the village of Corpus Christi , where 31 households were surveyed ( Fig 5 ) . Four new cases were diagnosed based on clinical signs and symptoms during our visit ( 2 . 7% ) , and we identified 3 patients who had been previously diagnosed and received treatment , bringing the total to 7 patients identified . Testing for anti-PGL-I titer by ELISA showed that 92/146 ( 63% ) were positive . These numbers are consistent with previously published data from our group on new case detection and anti-PGL-I positivity rates for individuals living in hyperendemic areas in the state of Pará . The four newly diagnosed cases and three other individuals who had completed treatment displayed a wide range of characteristics ( Table 1 ) . The age range was from 13 to 72 , with two residing in the São Jorge community , while five lived in Corpus Christi . Four of these individuals hunted armadillos resulting in a relatively high risk ( OR 6 . 73 , 95% CI 1 . 41–32 . 09 , p = 0 . 02 ) . Although hunting armadillos in the forest was exclusively a male activity and handling or cleaning the armadillo meat to prepare it for cooking was primarily a responsibility for women , 6 of these male patients indicated that they also participated in cleaning or preparing armadillo meat for cooking and also ate armadillo regularly , although these factors were not significant , probably due to the small sample size . This suggests that the majority of the leprosy patients had patterns of exposure ( hunting , handling , and eating ) to armadillos that were all at the high end relative to other groups in the village . We collected information from all of the individuals surveyed ( n = 146 ) regarding the level of exposure they had with armadillos based on different behavioral aspects and dietary preferences . This included detailed questions about various activities , including whether or not individuals hunted armadillos in the surrounding forest; whether they were involved in killing the animals and preparing the meat for cooking or consumption; and the frequency with which they consumed armadillo meat , ranging from not at all to those who ate armadillo meat more than once per month . Of all of the individuals surveyed , 27/146 ( 18 . 5% ) hunted armadillos in the forest , 96/146 ( 65 . 8% ) either handled or prepared the meat for consumption , and 91/146 ( 62 . 3% ) ate armadillo meat at least once during the past year , with 27/146 ( 18 . 5% ) individuals eating them more than once per month . The percentage of individuals that participated in at least one of these activities ( hunting , preparing the meat for consumption , or eating the meat ) was 96/146 ( 65 . 8% ) . We then examined the PGL-I titers for each group of individuals based on whether or not they participated in these activities or preferences ( Table 2 ) . Overall , there was no statistical difference in the anti-PGL-I titers of those individuals who hunted or did not hunt armadillos ( p = 0 . 99 ) ; those who did or did not handle armadillo meat in preparation for consumption ( p = 0 . 90 ) ; or between individuals who did not eat or ate armadillos ( p = 0 . 50 ) ( Fig 6A–6C ) . However , when we subdivided individuals who ate armadillo meat in low to moderate frequencies ( less than once per month ) versus those who ate it frequently ( more than once per month ) , there was a significantly higher median anti-PGL-I titer in those individuals who consumed armadillo meat most frequently ( p = 0 . 01 ) with a higher risk ( OR = 1 . 77 , 95% CI [0 . 64–4 . 89] ) ( Fig 6D ) compared to other groups .
The mammalian order Xenarthra includes sloths , anteaters and armadillos . Armadillos are observed only in the Americas , having ten known genera composed of 21 different species in the wild , and are known reservoirs for a number of bacterial and parasitic pathogens , including Mycobacteria , Trypanosoma , Toxoplasma , Sarcocystis , Leptospirosis , Sporothrix , Chagas and Paracoccidioides [35 , 36] . D . novemcinctus , commonly known as the nine-banded armadillo in the U . S . or chicken-armadillo in Brazil , is the only species whose range includes North , Central and South America [37] , and are ground burrowers and opportunistic feeders ( almost 500 separate food items , mostly insects , make up their diet ) . Nine-banded armadillos extended their range from Mexico into Texas some time during the 1800s , and then eventually increased their range north and east into the gulf states of the southern U . S . [38] . In the early 1970s , it was determined that armadillos were capable of sustaining the growth of M . leprae to extremely high bacillary loads ( up to 1011 bacilli per gram of tissue ) , with around 80% of the animals showing some level of susceptibility to experimental infection [39] . Shortly after this , in 1975 it was discovered that wild armadillos living in Texas and Louisiana were naturally infected with M . leprae , and serological studies of archived armadillo serum samples for anti-PGL-I antibodies indicated that they had likely shown this biomarker of infection as early as the 1960s . Other investigators , using different approaches to identify acid-fast bacilli by histopathology , M . leprae DNA ( such as RLEP sequence ) by PCR or serological studies to show anti-PGL-I antibody positivity revealed evidence of infection in armadillo species in a number of countries in Central and South America , including Mexico [40] , Colombia [41] , and Argentina [42] . In Brazil , several groups have reported the possibility of M . leprae infection in armadillos in different regions , including in the southeastern state of Espírito Santo [43] and more recently in the northeast in Ceará state [44 , 45] . Another case control study indicated that human contact with armadillos increased the risk of leprosy in Espírito Santo , Brazil [46] . There was one previous study that examined whether the consumption of armadillo meat had an effect on the incidence of leprosy , but no association was found [47] . However , this study examined individuals living in the southern state of Paraná , considered a medium endemic area ( <1 new case per 10 , 000 population ) , which is around fifty times lower than in the state of Pará , and there was no evidence presented that armadillos in this area were infected with M . leprae . In a recent study of 98 marmosets captured from different regions of Brazil to look for Ag85B and RLEP DNA by PCR , none were found to be positive by this method [48] , although 14 were found to be positive for rpoB , another mycobacterial genetic marker . Nevertheless , the likelihood that environmental reservoirs , including armadillos , amoeba [49 , 50] and most recently , red squirrels in Scotland and the U . K . [51 , 52] , could play a role in M . leprae persistence and transmission to humans has been increasingly cited as a real possibility [53] , which would necessitate a different approach to leprosy control and prevention . Although the link between zoonotic M . leprae infection in armadillos and transmission of these particular unique strain types to humans has been firmly established in the southern U . S . , the actual mechanism of transmission of M . leprae between armadillos and humans would be difficult to determine because of the lack of being able to grow this species in vitro in axenic medium . Possible routes of infection that have been proposed include inhalation of particles of soil contaminated by infected armadillos during the process of gardening activity [54] or from contact with contaminated soil or water samples in leprosy endemic areas [55–57] . The evidence for transmission of M . leprae to humans from these environmental sources is somewhat circumstantial , and has relied mainly on the identification of certain genetic markers , including SNP type and variable nucleotide tandem repeats ( VNTR ) found in M . leprae DNA isolated from these sources and matching these with strain types existing in the human population living in the same area . A more definitive transmission link between distinct SNP subtypes circulating in armadillos and those found in human leprosy patients in the southern and southeastern U . S . has been more rigorously confirmed by whole genome sequencing of M . leprae from tissue or biopsy specimens [58 , 59] , which is currently being pursued with samples from Pará , Brazil . Notably , in this study we used four different techniques to demonstrate the presence of M . leprae in the armadillo tissue samples . First , PCR was used show RLEP positivity in 62% of the tissues sampled , with confirmation of a match of the RLEP sequence to that already published; second , auramine/rhodamine and SYBR Gold staining showed positively stained bacilli within histological sections , techniques specifically used to identify mycobacteria in situ; third , the M . leprae-specific antigen , PGL-I , was localized in situ with a polyclonal rabbit antibody; and finally by showing acid fast staining of bacilli in wild armadillo spleen sections by the Fite Faraco technique . The total number of new cases of leprosy in the U . S . has remained relatively constant at ~200 per year , while the new case detection rate in 2016 in Brazil of around 25 , 000 cases in 207 million people translates to 1 . 2 per 10 , 000 population . Since armadillos occur at very high numbers in many rural areas in Brazil and the new case detection rate in humans has been considered hyperendemic in the Amazon region for a long time , it is extremely likely that the introduction of M . leprae in armadillos due to interactions with infected humans is not a recent event , particularly as the percentage of infection in these animals exceeds that found in the southern U . S . Due to the already high percentage of anti-PGL-I positivity in the general population , we initially wondered whether exposure to armadillos would have a measurable effect on increasing the rates of positivity of this biomarker of infection . However , recording the habits of some of the families , we discovered certain practices that could dramatically influence exposure to M . leprae from armadillos . Hunters in the villages who capture wild armadillos in the surrounding forest sometimes bring the animals to their home alive where they are kept in an enclosure inside the house for up to six months while being well-fed to increase their body weight and sometimes even bathed like a pet . If an animal was infected and shedding bacilli , this would greatly facilitate infection of individuals living in this dwelling via the aerosol route [60] . Another risk of exposure to viable M . leprae would likely occur by killing the animal and handling the meat for consumption , as blood or other tissue fluids could gain entry through any cuts in the skin . Cooking the meat would effectively kill M . leprae bacilli and render infection by eating cooked meat a very low probability . However , another practice in certain areas was for individuals to prepare a kind of raw liver and onion ceviche . Consumption of raw meat , particularly the liver of armadillos , which is one of the main organs where M . leprae growth is highest , would be considered a very high risk behavior and among the practices most likely leading to successful infection . Although we did not see a significant increase in the anti-PGL-I titer comparing groups of individuals who did or did not eat armadillos , the finding that individuals who consumed armadillo meat frequently ( more than once per month and even up to twice per week ) had a significantly higher titer indicates that this behavior has an effect on the frequency of infection . Although capturing of wild armadillos for consumption is prohibited in Brazil , it is obvious that a good percentage of people in poor rural areas enjoy and utilize this source of dietary protein , and this preference would be hard to change even by educating them about this potential source of M . leprae infection . In addition , the WHO has only considered strategies for intervention and treatment of cases involving human-to-human transmission . Interruption of infection by zoonotic transmission ( armadillo-to-human ) has not been addressed , and would be a difficult challenge for the WHO [61] . Nevertheless , determining the extent of infection in these wild animals and applying whole genome sequencing to identify strain types circulating in armadillos and human populations interacting with them is important to clarify the relative risk that nonhuman reservoirs have in the transmission of this ( and perhaps other ) tropical diseases and may help to improve strategies to combat leprosy . | Armadillos have been shown to be a natural reservoir of Mycobacterium leprae infection in the southern states of the U . S . and have been implicated in the zoonotic transmission of leprosy to humans . To investigate this in Brazil , we conducted surveys of armadillos in western Pará state in the Brazilian Amazon region where leprosy is hyperendemic in humans . Individuals living in the small town of Belterra were surveyed for the extent and frequency of interaction with armadillos ( hunting , preparing the meat for cooking , or eating the meat for food ) . We also took samples of liver and spleen from armadillos to look for M . leprae infection in the tissues . We found that a majority of residents had some contact with armadillos ( ~65% ) and that infection by M . leprae in armadillos in this area was also very high ( 62% ) . Those individuals who ate armadillo meat more than once a month had a significantly higher antibody titer to the M . leprae-specific antigen , PGL-I . Understanding the dynamics of leprosy transmission in different geographic regions and knowing the behavioral risks of humans interacting with potentially infected animals will help clarify the relative risk of zoonotic transmission of leprosy in this region . | [
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"prod... | 2018 | Evidence of zoonotic leprosy in Pará, Brazilian Amazon, and risks associated with human contact or consumption of armadillos |
Nonalcoholic fatty liver disease ( NAFLD ) is characterized by abnormal accumulation of triglycerides ( TG ) in the liver and other metabolic syndrome symptoms , but its molecular genetic causes are not completely understood . Here , we show that mice deficient for ubiquitin ligase ( E3 ) Smad ubiquitin regulatory factor 1 ( Smurf1 ) spontaneously develop hepatic steatosis as they age and exhibit the exacerbated phenotype under a high-fat diet ( HFD ) . Our data indicate that loss of Smurf1 up-regulates the expression of peroxisome proliferator-activated receptor γ ( PPARγ ) and its target genes involved in lipid synthesis and fatty acid uptake . We further show that PPARγ is a direct substrate of Smurf1-mediated non-proteolytic lysine 63 ( K63 ) -linked ubiquitin modification that suppresses its transcriptional activity , and treatment of Smurf1-deficient mice with a PPARγ antagonist , GW9662 , completely reversed the lipid accumulation in the liver . Finally , we demonstrate an inverse correlation of low SMURF1 expression to high body mass index ( BMI ) values in human patients , thus revealing a new role of SMURF1 in NAFLD pathogenesis .
Nonalcoholic fatty liver disease ( NAFLD ) is a chronic liver condition associated with obesity , non–insulin-dependent diabetes , and hyperglyceridemia [1] . Although presenting few clinical symptoms at early stages , a subset of patients with NAFLD will progress to nonalcoholic steatohepatitis ( NASH ) consisting of hepatic steatosis and inflammation , which can ultimately lead to cirrhosis and even liver cancer [2] . Myriad social–behavioral and genetic causes of NAFLD are now known , but the roles of peroxisome proliferator-activated receptors ( PPARs ) have emerged as crucial molecular underpinnings of these metabolic imbalances and targets of several investigational drugs [3–5] . A thorough understanding of regulatory mechanisms governing PPAR activities will undoubtedly aid in the development of much-needed treatments . PPARs are nuclear hormone receptors that heterodimerize with retinoid X receptors to modulate metabolic transcriptional programs in response to nutritional inputs [6] . Of three PPARs encoded by distinct mammalian genes , PPARα , which is highly expressed in the liver , kidney , and muscle , directs expression of a network of genes that promote utilization of fat as an energy source . PPARγ , on the other hand , is normally expressed in adipose tissues , where it activates target genes involved in fatty acid uptake , transport , and lipogenesis to promote lipid storage . In the liver , PPARγ expression is normally low but becomes drastically induced as hepatic steatosis develops [7] . Reports in the literature have shown that overexpression of PPARγ promotes the accumulation of lipid droplets in the liver , whereas hepatic disruption of PPARγ improves the fatty liver condition in leptin-deficient obese mice or mice that were fed on a high-fat diet ( HFD ) [8 , 9] . In adipose tissues , ligand binding was reported to induce degradation of PPARγ via the ubiquitin-proteasome system , whereas small ubiquitin-like modifier ( SUMO ) ylation of PPARγ was shown to repress its transcriptional activity [10] . However , how steatogenic activities of PPARγ are regulated in the liver remains to be determined . Smad ubiquitin regulatory factor 1 ( Smurf1 ) and its close relative , Smurf2 , are members of homologous to E6-AP carboxyl terminus ( HECT ) domain–containing ubiquitin ligases ( E3s ) , which were initially identified as negative regulators of transforming growth factor-β ( TGF-β ) and bone morphogenetic protein ( BMP ) signaling pathways [11–14] . Subsequent studies broadened the repertoire of Smurf substrates and extended their function to cell differentiation , polarity , and DNA repair [15–18] . During our ongoing quest for physiological functions of Smurfs , we found abnormal accumulation of lipid droplets in the livers of 9–12-month-old Smurf1 knockout ( KO ) mice and other signs that phenocopy NAFLD in human patients . Here , we report that Smurf1 induces non-proteolytic ubiquitination of PPARγ and inhibits PPARγ transcriptional activity in hepatocytes , thereby acting as a critical safeguard against the development of hepatic steatosis .
We previously reported an increased bone density phenotype in aged Smurf1KO mice that were commonly observed under mixed black Swiss × 129/SvEv ( BL ) and C57BL/6N ( B6 ) genetic background [18] . Further analysis revealed a conspicuous accumulation of lipid droplets in the livers of aged Smurf1KO mice that was unique to the BL background ( S1 Table ) . The liver sections of these mice were characterized by large , clear , sharp-bordered cytoplasmic vacuoles upon hematoxylin–eosin ( HE ) staining ( Fig 1A ) . The bright red staining of frozen sections by Oil Red-O confirmed the high fat and neutral lipid content therein ( Fig 1A ) . This phenotype was observed in 12 out of 15 male and female mice examined beyond 9 months of age , implying a 75% penetrance . Microscopic quantification of HE-stained sections reaffirmed the statistically significant increase of steatosis in the livers of Smurf1KO mice compared with that of the wild-type ( WT ) controls ( Fig 1B ) . Surprisingly , this steatosis phenotype was not observed in the livers of Smurf2KO mice ( Fig 1A and 1B ) , suggesting that it is specifically associated with disruption of the Smurf1 function . To determine which lipid fractions were increased , we carried out colorimetric assays in liver lipid extracts prepared from Smurf1KO mice at 9–12 months of age , and the results showed that the level of triglycerides ( TG ) increased more than 3-fold compared with that of WT or Smurf2KO mice ( Fig 1C ) . Moreover , the levels of total cholesterol ( CHO ) and free fatty acids ( FFAs ) were also increased significantly in Smurf1KO livers ( Fig 1C ) . Compared with WT mice , Smurf1KO mice were approximately 30% heavier in body weight , bore more white adipose tissue ( WAT ) , and had a higher liver to body weight ratio ( Fig 1D ) . Nevertheless , despite exhibiting ostensible steatosis , the mutant livers appeared to function normally , as indicated by aspartate transaminase ( AST ) and alanine transaminase ( ALT ) activity measurements ( Fig 1E ) . Because the manifestation of hepatic steatosis is usually accompanied by a constellation of adverse alterations in glucose metabolism , we conducted glucose tolerance and insulin resistance tests . At the fasting state , there was not much difference in plasma glucose levels between aged ( 9–12 months old ) WT and Smurf1KO mice that had developed steatosis; however , following intraperitoneal injection of glucose , the blood glucose level of the mutant mice showed a more dramatic flash increase of the blood glucose level within 30 minutes of injection and more than 100% accumulative gain in the area under the curve ( AUC ) ( Fig 1F ) . On the other hand , after an initial dip following the insulin injection , the blood glucose level in aged mutant mice recovered more rapidly and to a higher extent than that in WT controls ( Fig 1G ) . The AUC of the insulin resistance test of aged Smurf1KO mice was 13 . 5% more compared with that of WT mice . Because young Smurf1KO mice ( at 4–5 months of age ) that had yet to develop steatosis scored no difference from their WT counterparts in both the tests ( S1 Fig ) , the systemic change in glucose metabolism observed in aged mutant mice was most likely associated with the steatosis . Taken together , the phenotypes of hepatic steatosis , obesesity , glucose intolerance , and insulin resistance make these aged Smurf1KO mice a good mouse model of NAFLD . In rodents , difference in genetic background has a well-known influence on the susceptibility to obesity and hepatic steatosis [19–21] . Although the spontaneous steatosis hereto described was only observed at old age , young Smurf1KO mice of the BL background were grossly normal except for a higher body fat content compared with their age- and background-matched WT counterparts and showed no sign of steatosis ( Fig 2A and 2B ) when fed on normal diet ( ND ) . Mice of this strain background are notoriously known for their resistance to HFD-induced obesity , as evident by the lack of apparent gain in body weight and ratio of fat-to-lean mass in young WT mice that were put on a HFD feeding regimen beginning at 10–12 weeks of age and continuing for 8 consecutive weeks ( Fig 2A , S2 Table ) . In contrast , HFD feeding significantly increased fat content in the Smurf1KO mice ( Fig 2A , S2 Table ) . Despite a lack of significant weight gain , HFD feeding did cause mild steatosis ( Fig 2A and 2B ) , as well as an increase in liver TG content in WT mice ( Fig 2C ) ; however , these changes were all dramatically exacerbated in BL-Smurf1KO mice ( Fig 2A–2C ) . As alluded earlier , Smurf1KO mice of the B6 background did not show accumulation of lipid droplets in the liver ( S1 Table ) , and they were not overweight or overtly obese either ( S2A Fig ) . To ascertain that the steatosis pheneotype was not a mere coincidence unique to the BL background , we carried out the HFD feeding study on WT and Smurf1KO mice of the B6 background with the same regimen as for the young BL mice . In contrast to BL mice , B6 mice gained body weight and fat content on HFD as expected , regardless of the presence of Smurf1 gene ( S2A Fig , S2 Table ) . However , the B6-Smurf1KO mice on HFD became ostensibly more obese ( S2A Fig ) and showed more severe lipid droplet accumulation in the liver compared to WT mice of the same background ( S2A and S2B Fig ) . In addition , the increase in the liver TG content was also more pronounced ( S2C Fig ) . Thus , the steatosis associated with Smurf1 loss is likely the result of an overall gain in body fat content in both strain backgrounds , suggesting that Smurf1 may have a systemic role in regulating lipid metabolism . To address if what we learned from the Smurf1KO mice is applicable to human populations , we took the advantage of the non-tumor liver tissue data sets compiled from a cohort of 247 Chinese liver cancer patients from the Liver Cancer Institute ( LCI ) [22] . According to the SMURF1 mRNA expression levels retrieved from the gene expression profile ( GEO: GSE14520 ) , we separated non-tumor liver tissue samples into the high SMURF1 expression ( top 25% ) group ( n = 61 ) and the low SMURF1 expression ( bottom 25% ) group ( n = 59 ) ( Fig 2D , left panel ) . We then graphed the body mass index ( BMI ) of these 120 patients against these two groups of SMURF1 expression , and found that patients with the low SMURF1 expression have a statistically significant higher BMI ( Fig 2D , right panel ) . It is worth noting that the average BMI of the Asian population is lower than that in the United States and European countries , and an Asian with BMI > 27 . 5 is considered obese [23 , 24] . This inverse correlation was further corroborated with non-tumor liver tissue data sets from the cancer genome atlas-liver hepatocellular carcinoma ( TCGA-LIHC ) ( Fig 2E ) . Because there are only 37 cases of non-tumor liver samples that have linked BMI values in the TCGA data set , the median SMURF1 expression level was used as the cutoff to plot BMI values ( Fig 2E ) . Because the BMI is widely used in clinics as a surrogate prognostic indicator for fatty liver [25 , 26] , these results suggest that low Smurf1 expression appears to be associated with high fat accumulation in humans , as well . To investigate the underlying causes of steatosis associated with Smurf1 loss , we compared hepatic gene expression profiles of 11-month-old Smurf1KO , Smurf2KO , and their respective matching WT mice from the BL background , and selected genes that showed either increased or decreased expression by a cutoff of 1 . 5-fold ( false discover rate [FDR] <0 . 1 ) . The results showed that 987 genes in the Smurf1KO livers were differentially expressed over their WT controls , whereas only 13 genes were differentially expressed in the Smurf2KO livers ( Fig 3A , left panel , and S3 Table ) . This result is in line with the notion that Smurf1 plays a more prominent role in the liver than Smurf2 . Many genes that are involved in the lipid metabolism were up-regulated in Smurf1KO livers , and the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway enrichment analysis of differentially expressed genes between Smurf1KO and WT livers revealed a number of metabolically relevant pathways ( Fig 3B ) . We were intrigued by the enrichment of the PPAR signaling pathway that has known strong effects on steatosis [4] . Of the three PPAR genes , Pparγ encodes two protein isoforms , PPARγ1 and PPARγ2 , whose mRNAs are transcribed from two separate promoters [27 , 28] . Quantitative real-time PCR ( qRT-PCR ) analyses showed severalfold increases of both Pparγ isoforms in the livers of aged Smurf1KO but not Smurf2KO mice ( Fig 3C ) . Interestingly , the expression of Pparα was not altered in the liver of any mouse examined ( Fig 3C ) . In young BL mice ( 10–12 weeks of age ) that had yet to develop steatosis , loss of Smurf1 increased the expression of total Pparγ ( about 1 . 57-fold ) when the mice were fed on ND ( S3A Fig ) , suggesting that Smurf1 has a direct causal effect on Pparγ expression . HFD feeding further exacerbated the difference of Pparγ expression to 3 . 42-fold between WT and Smurf1KO livers ( S3A Fig ) . On the other hand , no difference was observed in TNFα and F4/80 expression ( S3B Fig ) , two genes involved in inflammatory response , which is consistent with the absence of any inflammation in Smurf1KO mice ( S1 Table and S1 Data ) . Western blot analyses confirmed the corresponding up-regulation of the PPARγ protein in the livers of aged Smurf1KO mice ( Fig 3D ) . According to data from The Human Protein Atlas ( https://www . proteinatlas . org/ENSG00000198742-SMURF1/tissue ) , Smurf1 protein is highly expressed in visceral organs , but its expression levels in muscle and adipose tissues are extremely low or moderate , respectively . This likely accounts for the dramatic increase of PPARγ in the Smurf1KO livers , where Smurf1 function is expected to be robust . Consistent with tissue distribution of Smurf1 expression , qRT-PCR revealed that total Pparγ expression increased dramatically in the liver and WAT but did not change in the muscle of Smurf1KO mice ( Fig 3E ) . Finally , loss of Smurf1 cast a profound impact on the hepatic expression of PPARγ transcriptional target genes that are involved in fatty acid synthesis , uptake , and transport ( Fig 3F ) , thus lending further support to the activation of PPARγ and its signaling pathway in aged Smurf1KO mice . PPARγ is a strong lipogenic factor essential for steatosis [7] . Although our qRT-PCR analysis alluded that loss of Smurf1 has a direct causal effect on PPARγ1 up-regulation , further evidence is needed to confirm this finding . Toward this end , we silenced Smurf1 using short interfering RNA ( siRNA ) s in human hepatocarcinoma Hep3B cells and mouse normal hepatocyte AML12 cells . Relative to the effect by non-silencing control siRNA ( siNS ) , knockdown by siSmurf1 significantly increased the level of PPARγ but not PPARα or PPARδ in both cell lines ( Fig 4A ) . As expected , siSmurf2 had little effect in either of these two cell lines ( Fig 4A ) . Because Pparγ is a direct transcriptional target of itself in a positive feedback loop [29] , siRNA-mediated silencing of Smurf1 drastically increased the expression level of Pparγ , but not other paralogous Ppars or their transcriptional regulatory partners retinoid x receptor ( Rxr ) α and Rxrβ ( Fig 4B ) . In adipose tissues , transcription of Pparγ genes is under the control of CCAAT enhancer binding protein ( CEBP ) α/β [30 , 31]; however , we were unable to detect any increase of either Cebpα or Cebpβ mRNA by qRT-PCR ( S4A Fig ) , suggesting that the regulation of PPARγ by Smurf1 is by way of a C/EBPα/β-independent mechanism . In line with the low expression of PPARγ in AML12 cells , introducing siPPARγ showed little effect on the expression of PPARγ transcriptional target genes , Fabp1 , Cd36 , Acacb , and Apoc3 , but siSmurf1 significantly increased the expression of these genes ( Fig 4C , and S4B Fig ) . Furthermore , introducing siPPARγ completely blocked the enhancing effect of siSmurf1 ( Fig 4C ) , thus confirming the direct causal relationship between Smurf1 and PPARγ . The fact that up-regulation caused by siSmurf1was particularly pronounced in Fabp1 and Cd36 , two genes that are essential for fatty acid uptake [32 , 33] , suggested a strong connection between Smurf1 and fatty acid uptake . Indeed , using 3H-labelled palmitic acid as a tracer , we observed a 20% increase in fatty acid uptake by AML12 cells upon Smurf1 depletion ( Fig 4D ) . We also measured lipid synthesis in AML12 cells by measuring the incorporation of 3H-labelled acetate into lipids and found it was increased by siSmurf1 as well ( Fig 4E ) . Once again , these two effects of Smurf1 loss were specifically mediated through PPARγ as they were reversed by siPPARγ ( Fig 4D and 4E ) . To further show if Smurf1 actually regulates lipid metablism in vivo , we injected fluorescent 4 , 4-Difluoro-5 , 7-Dimethyl-4-Bora-3a , 4a-Diaza-s-Indacene-3-Hexadecanoic Acid ( BODIPY-FL-C16 ) into the peritoneal cavities of WT and Smurf1KO mice and found that the fatty acid uptake was greatly enhanced in the liver and WAT tissues but not the muscles of Smurf1KO mice compared with that of WT mice ( Fig 4F ) . We also repeated the 3H-labelled acetate incorporation experiment in primary hepatocytes isolated from WT and Smurf1KO mice and confirmed the enhancement effect of Smurf1 ablation on lipid synthesis ( Fig 4G ) . The increased body fat content in aged BL-Smurf1KO mice and HFD-fed young Smurf1KO mice from both background suggests that Smurf1 may also regulate adipogenesis . To determine if this was the case , we took advantage of an in vitro adipogenic differentiation system using 3T3-L1 pre-adipocytes . Following a 6-day differentiation protocol , both PPARγ1 and PPARγ2 as well as their target Cd36 were all induced , as shown by western blot analysis , and the induction was greatly enhanced by siSmurf1 but reversed by the double transfection of siSmurf1 and siPPARγ ( S5A Fig ) . In keeping with the western blot analysis results , Oil Red-O staining of these differentiated 3T3-L1 cells was also enhanced by siSmurf1 and reversed by siSmurf1 and siPPARγ double transfection ( S5B Fig ) . Finally , expression of a cohort of adipogenic target genes of PPARγ also followed the same pattern as influenced by siSmurf1 and siPPARγ ( S5C Fig ) . Taken together , these data indicate that Smurf1 has an intrinsic role in controlling adipogenesis and lipid metabolism through PPARγ . The WW domains of HECT E3 ligases recognize a PPxY ( PY ) motif that is present in the primary sequence of many of their targets [34] . There is one such sequence motif in both human and mouse PPARγ but not in PPARα , which might potentially account for the lack of an effect on this closely related protein by the loss of Smurf1 ( Fig 3C ) . By co-immunoprecipitation experiments , we found that endogenous Smurf1 interacted specifically with PPARγ in the AML12 cells ( Fig 5A ) , and the PY motif of PPARγ contributed to the interaction , because removing it considerably weakened the interaction between Myc-tagged Smurf1 and FLAG-tagged PPARγ , as assayed in transiently transfected AML12 cells ( Fig 5B ) . Also in AML12 cells , Smurf1 but not Smurf2 showed the propensity to ubiquitinate both PPARγ1 and PPARγ2 isoforms ( Fig 5C ) . The substrate and enzyme relationship was further demonstrated in Smurf1KO mouse embryonic fibroblasts ( MEFs ) , in which exogenous Smurf1 but not the catalytically inactive Smurf1 C699A ( CA ) mutant ubiquitinated PPARγ ( Fig 5D ) , as well as in a reconstituted in vitro reaction with recombinant Smurf1 and PPARγ ( Fig 5E ) . Finally , the ubiquitin chain of the modified PPARγ is likely of the K63 linkage , as only the ubiquitin mutant with a single lysine residue at the amino acid residue position 63 supported the polyubiquitination of PPARγ in the reconstituted in vitro reaction , whereas other single-lysine ubiquitin mutants with lysine at other positions did not ( Fig 5F ) . In light of this result and the fact that co-expressing Smurf1 with PPARγ did not alter the stability of the latter ( Fig 5B and 5C ) , we concluded that Smurf1 mediates a non-proteolytic ubiquitin modification of PPARγ . PPARs recognize a consensus sequence of PPAR response element ( PPRE ) that consists of two AGGTCA-like sequences arranged in tandem with a single nucleotide spacer and is present in all PPAR target gene promoters [35 , 36] . In AML12 cells , where PPARγ expression is very low , overexpressing Smurf1 had little effect on a luciferase reporter driven by PPRE , whereas both PPARγ1 and PPARγ2 significantly activated it; however , co-expressing Smurf1 with either PPARγ1 or PPARγ2 severely curtailed their transcriptional activity ( Fig 6A ) . Because Smurf1 has no effect on PPARγ protein levels per se ( Fig 6A , right panel ) , these results suggested that Smurf1 inhibits the transcriptional activity of PPARγ . The regulation by Smurf1 depends on its E3 ligase activity because a ligase-deficient mutant , Smurf1CA , could not reverse the activation of PPRE-luc by PPARγ ( Fig 6B ) . Chromatin immunoprecipitation ( ChIP ) experiments on Pparγ1 , Pparγ2 , and Fabp1 promoters indicated that the binding of PPARγ to these promoters was blocked when it was co-expressed with Smurf1 ( Fig 6C ) . Once again , the E3 ligase activity of Smurf1 is required for its ability to block DNA binding of PPARγ ( Fig 6C ) . ChIP experiments performed in liver extracts isolated from WT and Smurf1KO mice also revealed a much stronger binding of PPARγ to its own Pparγ1 and Pparγ2 promoters , as well as its target Fabp1 promoter in the absence of Smurf1 ( Fig 6D ) , thus lending further support to Smurf1 regulating transcriptional activity of PPARγ . To directly test if the increased PPARγ activity and expression are responsible for steatosis associated with Smurf1 loss , we treated a group of WT and Smurf1KO mice from the BL background with the PPARγ antagonist GW9662 [37] . The compound was administered by intraperitoneal injection starting at 7–9 months of age , and the treatment lasted for 2 months; in this time period , the steatosis was expected to fully develop in Smurf1KO mice . The GW9662 treatment decreased body weight of both WT and Smurf1KO mice ( Fig 7A ) , but because the average beginning weight of Smurf1KO mice was higher , the reduction thereof was more dramatic than that of the WT controls ( about 10% reduction versus about 5% ) . The body fat mass content in Smurf1KO mice was also significantly lowered , to an extent that was comparable to that of the untreated WT mice ( Fig 7B ) . Commensurate to the systemic reduction in obesity , the lipid droplets were essentially cleared from Smurf1KO livers by GW9662 ( Fig 7B and 7C ) . Although the GW9662 treatment caused no significant change in the serum TG and CHO levels ( Fig 7D ) , hepatic contents of TG , CHO , and FFA were all reduced to normal levels ( Fig 7E ) and so was hepatic expression of Pparγ2 , as well as several PPARγ target genes ( Fig 7F ) . These results unequivocally demonstrated that the elevated PPARγ activity and expression account for the NAFLD phenotypes observed in Smurf1KO mice .
PPARγ is a nuclear hormone receptor with principle functions of increasing insulin sensitivity and promoting lipid storage in adipose tissues [6] . In the liver , the physiological function of PPARγ is less clear , although its expression is associated with injury-induced activation of hepatostellate cells and provides an anti-fibrogenic protection [3 , 5] . PPARγ up-regulation is also a known property of steatotic livers , and liver-specific disruption of PPARγ was reported to protect leptin-deficient mice or HFD-fed mice from developing fatty liver [7–9] . Here , we show that mice deficient for HECT-domain E3 ligase Smurf1 in the mixed BL genetic background develop hepatosteatosis spontaneously as they age or are more susceptible to HFD-induced hepatosteatosis . These mutant mice are overweight and obese , as well as glucose intolerant and insulin resistant . These NAFLD phenotypes can be attributed to the heightened transcriptional activity of PPARγ , which in turn increases the expression of itself and genes involved in lipogenesis and fatty acid transport via a positive feedback loop . We further demonstrate that Smurf1 catalyzes the K63-linked non-proteolytic ubiquitination that normally attenuates PPARγ transcriptional activity , and show an inverse correlation between low SMURF1 expression and high BMI values in human patients . This investigation thus reveals a previously unknown mechanism that regulates the lipogenic activity of PPARγ and sheds light on a new role of Smurf1 in NAFLD pathogenesis . Different HECT E3 ligases are known to catalyze ubiquitination with different ubiquitin chains that mark modified protein substrates for different fates [38] . Members of the neural precursor cell expressed developmentally down-regulated protein 4 ( NEDD4 ) family E3 ligases preferentially support monoubiquitin modification or K63-linked chains associated with non-proteolytic functions , but can also assemble lysine 48 ( K48 ) -linked chains that target proteins for proteasome-mediated degradation [39] . As members of this E3 ligase family , Smurf1 and Smurf2 have been shown to target many proteins for K48-linked ubiquitination and degradation [16] . Smurf2 was also shown to induce multi-monoubiquitin modification of Smad3 , thereby inhibiting Smad3 activity [40] , but the K63-linked ubiquitination by Smurfs has not been reported in mammalian species . Recently , NEDD4 itself was shown to induce both K48- and K63-linked ubiquitination of PPARγ in adipocytes , with different functional outcomes [41] . In our study , Smurf1 inhibits PPARγ activity , and deletion of Smurf1 enhances PPARγ activity and up-regulates PPARγ levels through a positive feedback mechanism . In contrast , NEDD4 was shown to stabilize PPARγ , and knockdown of NEDD4 reduced PPARγ expression [41] . Moreover , the PY motif in PPARγ played a role in mediating interaction with Smurf1 , but it was not demonstrated for NEDD4 as such . Perhaps these apparent discrepancies reflect the differences in experimental conditions conducted in different cell types , or the mixed linkages in ubiquitin chains formed by NEDD4 could have compounded the functions of modified PPARγ . In any event , the steatosis observed in Smurf1KO mice is consistent with the heightened PPARγ activity in the liver . Despite the conspicuous steatosis , overweight , and obesity that were present in 75% aged BL-Smurf1KO mice , their liver functions were nevertheless normal . Because these animals were well shielded from inflammatory insults by their accommodative housing facility , it is likely that the elevated PPARγ activity unleashed by the loss of Smurf1 was only sufficient to manifest a restricted impact in bringing about the early-stage NAFLD phenotypes . Future studies are necessary to ascertain the tissue origin of the steatogenic effect of Smurf1 ablation using conditional knockout approaches and to determine if and how BL-Smurf1 mice could be enticed to progress through NASH or even liver cancer to model the entire NAFLD disease spectrum . Regulation of PPARγ by the Smurf1-mediated K63-linked ubiquitin modification centers on its transcriptional activity . Because PPARγ is also a transcriptional target of its own , a disturbance of Smurf1 would create an “all or none” effect: a rise or fall of Smurf1 across a threshold level would either maximize or minimize PPARγ activity . This scenario may normally operate to keep the lipogenic activity of PPARγ to a minimum in the liver but maximized in the adipose tissues . Epidemiology studies indicate that an estimated 27%–34% of the general population within North America have NAFLD [42] , for which there is no approved treatment available at present . Current NAFLD drug developmental effort centers on repurposing fibric acid derivatives , which are lipid-lowering PPARα agonists and insulin sensitivity–improving PPARγ agonists , thiazolidinediones , but the clinical trials yielded mixed results [3 , 4] . Because of the opposite actions of PPARα and PPARγ on hepatic steatosis , the “spillover” effects of these PPAR agonists might prevent a net gain in their ability to reduce TG accumulation in the liver . As to PPARγ agonists , although clinical trials for rosiglitazone in patients with type 2 diabetes reported improvement of steatosis by a median of 20% during the first year , no further improvement was found after 2 additional years of treatment , and the trials exposed severe cardiovascular risks and weight gain [43] . Intuitively , it is possible that the benefit is derived from the systemic lipid clearance by increased fat storage in adipose tissue , because PPARγ is normally expressed in adipose tissues , and its activation in the liver was clearly linked to fatty liver formation . Given our current finding of Smurf1 in protecting the liver from steatosis , a viable strategy to treat NAFLD may be to curtail the transcriptional activity of PPARγ by turning on Smurf1-mediated non-proteolytic ubiquitin modification .
All mice were maintained and handled under protocols ( LCMB-014 , ASP 10–214 , 13–214 , 16–214 ) approved by the Animal Care and Use Committee of the National Cancer Institute , National Institutes of Health ( NIH ) , according to NIH guidelines . Generation of Smurf1KO and Smurf2KO mice in the mixed BL and pure C57BL/6N ( B6 ) background was described previously [18 , 40] . For spontaneous hepatosteosis development , animals were maintained on a ND , monitored weekly , and euthanized and necropsied at 9–12 months of age . For the HFD treatment , male mice were maintained on a ND until 10–12 weeks of age before they were given HFD ( Research Diets , Cat# D12266B ) containing 16 . 8% kcal protein , 31 . 8% kcal fat , and 51 . 4% kcal carbohydrate for 8 weeks . For the GW9662 treatment , a dose of 1 mg/kg of GW9662 dissolved in DMSO was injected intraperitoneally ( i . p . ) into 7–9-month-old BL-WT and BL-Smurf1KO mice for 5 days per week for 2 months . Age and sex of mice used in these studies are listed in S1 Table . Body composition was determined using an EchoMRI mouse scanner ( EchoMRI , Houston , TX ) . Mouse liver and epididymal fat pad were dissected , weighed , then either snap-frozen in liquid N2 or fixed in 10% neutral buffered formalin prior to paraffin embedding . Frozen liver tissues were used for Oil Red-O staining . Liver and fat tissue histology were read by board-certified veterinary pathologists in the Pathology and Histotechnology Laboratory of the Frederick National Laboratory for Cancer Research . Serum TG , CHO , and albumin concentrations , as well as ALT and AST activities were measured by standard methods with a Vitro 250 dry slide analyzer ( Ortho Clinical Diagnostics ) in the Pathology and Histotechnology Laboratory of the Frederick National Laboratory for Cancer Research . Liver TG , CHO , and FFA concentrations were determined using the EnzyChrom TG , CHO , and FFA assay kits ( Bioassay Systems ) after extracting total lipids from 50-mg liver tissues as described [44] . To perform the gluclose tolerance test ( GTT ) or insulin tolerance test ( ITT ) , mice were fasted overnight before receiving an i . p . injection of 20% glucose ( 2 g/kg body weight ) or recombinant insulin ( Humulin R , 0 . 75 U/kg; Lily ) , respectively . Blood samples were collected from the tail 0 , 0 . 5 , 1 , 2 , and 4 hours later , after injection for analysis using the Accu-Chek Compact Plus blood glucose meter ( Roche Diagnostics ) . AML12 cells ( ATCC CRL-2254 ) were cultured in DMEM/F12 supplemented with 10% fetal bovine serum ( FBS ) , 0 . 005 mg/mL insulin , 0 . 005 mg/mL transferrin , 5 ng/mL selenium , and 40 ng/mL dexamethasone . Hep3B cells were cultured in MEM supplemented with 1% Non-Essential Amino Acids ( NEAA ) and 10% FBS . Smurf1KO MEFs were cultured in DMEM supplemented with 10% FBS . Primary hepatocytes were isolated by a two-step collagenase perfusion of the liver and cultured as described [45] . Flag-tagged PPARγ1 , PPARγ2 plasmids , and PPRE-Luc reporter plasmids were obtained from Addgene . Flag-tagged PPARγ2ΔPY plasmid was generated using Site Directed Mutagenesis Kit ( Agilent Technologies ) . Myc-tagged Smurf1 , Smurf2 , and Smurf1CA mutant , HA-tagged Ubiquitin plasmids were described before [13 , 18 , 46] . Anti-Smurf1 ( Novus , 1D7 ) ; anti-Smurf2 ( Abcam , EP629Y3 ) ; anti-PPARγ ( Santa Cruz , sc-7273 ) ; anti-PPARα ( Rockland , 600-401-4215 ) ; anti-PPARδ ( ThermoFisher , PA1-823A ) ; anti-HSC70 ( Santa Cruz , B-6 ) ; Anti-Flag-Peroxidase ( A8592 , Sigma ) ; anti-HA ( Covance , HA11 ) ; and anti-Myc ( Santa Cruz , 9E10 ) were used for western blotting and immunoprecipitation . Knockdown experiments were performed using the following siRNAs: siPPARγ ( J-040712-05 and J-040712-07 , Dharmacon ) . Validated siSmurf1 , siSmurf2 and siNS were previously described [47 , 48] . Lipogenesis assay in AML12 cells and primary hepatocytes were performed using 3H-acetate as described [45] . Fatty acid uptake assay in AML12 cells was performed in 12-well plates . Briefly , AML12 cells were incubated with assay buffer ( Hanks’ balanced buffer containing 1% BSA and 5 μCi/mL 3H-palmitic acid ) for 60 minutes at 37°C . The cells were then washed twice with ice-cold PBS and lysed with 0 . 3 M NaOH . The radioactivity of the cell lysates was measured by liquid scintillation counting . In vivo fatty acid uptake assays were performed as described [49] . Briefly , mice were i . p . injected with BODIPY-FL-C16 ( Life Technologies ) after being fasted for 4 hours , then were euthanized at 5 hours after injection; liver , epididymal fat pad , and skeletal muscle were collected . Fluorescence was analyzed from cleared tissue homogenate using a plate reader and normalized to tissue weight . Preadipocytes 3T3-L1 ( ATCC , CL-173 ) were cultured in basal medium ( DMEM supplemented with 10% FBS ) . Two days after transfection with siRNA , basal media were changed to differentiation media ( day 0 ) , which is DMEM supplemented with 10% FBS , 0 . 5 mM IBMX , 1μM dexamethasone , and 4 μg/mL insulin , for 2 days , then replaced with basal media with 2 μg/mL insulin for another 4 days . After 6 days of differentiation , cells were harvested for protein and mRNA analysis or subjected to Oil Red staining . The purified recombinant PPARγ ( 0 . 25 μg ) ( Abcam , ab81807 ) and His6-Smurf1 ( 1 . 5 μg ) were used in in vitro ubiquitination assay , which was carried out for 1 hour at 37°C in 30 μl reaction buffer supplemented with 2 mM Mg-ATP , 1 μg E1 , 1 μg of recombinant UbcH5c , and 20 μg HA-ubiquitin or HA-ubiquitin variants ( all from Boston Biochem ) . Total RNA from AML12 cells or liver tissues was extracted by RNeasy Mini Kit ( Qiagen ) according to the manufacturer’s instructions . High Capacity Reverse Transcription Kit ( ABI , Life Tech ) was used to generate cDNA from RNA ( 500–2 , 000 ng ) . qRT-PCR was performed with Power SYBR Green PCR Master Mix ( Life Technologies ) using specific oligonucleotide primers as specified ( S4 Table ) . ChIP assays were carried out with an EZ-ChIP Chromatin Immunoprecipitation Kit ( Millipore ) according to the manufacturer’s instructions . Immunoprecipitations were carried out using anti-PPARγ antibody ( Abcam , A3409A ) and an isotype-matched IgG as the control . Reporter assays were performed in 12-well plates using PPRE-Luc ( 0 . 5 μg ) and pRL-TK ( 0 . 2 μg ) reporter plasmids , and the luciferase activities were determined using Dual Luciferase Reporter Assay System ( Promega ) . Microarray experiments for mouse liver tissues were performed on Affymetrix GeneChip Mouse Gene 1 . 0 ST arrays according to the standard Affymetrix GeneChip protocol at the Affymetrix service core in the Frederick National Laboratory for Cancer Research . The raw array data were then analyzed with packages oligo and lima under R platform , as described before [50 , 51] , to identify differentially expressed genes among groups ( fold > 1 . 5 , FDR cutoff = 0 . 1 ) , and results were visualized using VennDiagram ( https://cran . r-project . org/web/packages/VennDiagram ) and gplots ( https://cran . r-project . org/web/packages/gplots ) under R platform . Data were submitted to GEO ( accession number GSE113995 ) . KEGG pathway analysis was performed by gage package , as described [52] , to identify significantly enriched pathway ( FDR q-value cutoff = 0 . 1 ) between Smurf1KO and WT liver samples . The microarray analysis for human liver tissues from the LCI cohort of 247 Chinese patients was previously published [22] and data are accessible through GEO ( accession number GSE14520 ) . TCGA non-tumor liver tissue gene expression data were downloaded from TCGA-LIHC ( https://portal . gdc . cancer . gov ) . Unless indicated in the figure legends , two-tailed Student t test was used for statistical analysis . | Nonalcoholic fatty liver disease ( NAFLD ) is a disease associated with abnormal fat accumulation in the liver and other metabolic symptoms . Among its many social–behavioral and genetic causes , dysregulation of peroxisome proliferator-activated receptor γ ( PPARγ ) is an investigative focal point for therapeutic intervention . This lipid-sensing nuclear receptor plays a major role in promoting lipogenesis in adipose tissues , whereas its expression is low in the liver . We show here that in the absence of ubiquitin ligase ( E3 ) Smurf1 , PPARγ expression increases dramatically in the liver , causing fatty acid uptake and fat accumulation in hepatocytes . We also found that the low SMURF1 expression in human populations correlates with high body mass index ( BMI ) values . We demonstrate that Smurf1 catalyzes the lysine 63 ( K63 ) -linked non-proteolytic modification of PPARγ that suppresses the transcriptional activity of PPARγ and breaks the positive feedback loop governing its own expression . Our data further indicate that treating this mouse model with a PPARγ antagonist , GW9662 , completely reverses the fat accumulation in the liver . | [
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"organis... | 2018 | Non-proteolytic ubiquitin modification of PPARγ by Smurf1 protects the liver from steatosis |
Neural tube defects ( NTDs ) is a general term for central nervous system malformations secondary to a failure of closure or development of the neural tube . The resulting pathologies may involve the brain , spinal cord and/or vertebral column , in addition to associated structures such as soft tissue or skin . The condition is reported among the more common birth defects in humans , leading to significant infant morbidity and mortality . The etiology remains poorly understood but genetic , nutritional , environmental factors , or a combination of these , are known to play a role in the development of NTDs . The variable conditions associated with NTDs occur naturally in dogs , and have been previously reported in the Weimaraner breed . Taking advantage of the strong linkage-disequilibrium within dog breeds we performed genome-wide association analysis and mapped a genomic region for spinal dysraphism , a presumed NTD , using 4 affected and 96 unaffected Weimaraners . The associated region on canine chromosome 8 ( pgenome = 3 . 0×10−5 ) , after 100 , 000 permutations , encodes 18 genes , including NKX2-8 , a homeobox gene which is expressed in the developing neural tube . Sequencing NKX2-8 in affected Weimaraners revealed a G to AA frameshift mutation within exon 2 of the gene , resulting in a premature stop codon that is predicted to produce a truncated protein . The exons of NKX2-8 were sequenced in human patients with spina bifida and rare variants ( rs61755040 and rs10135525 ) were found to be significantly over-represented ( p = 0 . 036 ) . This is the first documentation of a potential role for NKX2-8 in the etiology of NTDs , made possible by investigating the molecular basis of naturally occurring mutations in dogs .
Neural tube defects ( NTDs ) is a general term used to describe developmental defects resulting from abnormal closure or development of the neural tube during embryogenesis . The resulting pathologies may involve the brain , spinal cord , and associated structures such as vertebrae , soft tissue or skin [1] . The condition is reported among the more common birth defects in humans ( incidence of ∼1 per 1 , 000 pregnancies worldwide ) , leading to significant infant morbidity and mortality [2] , [3] . Clinically , human NTDs are defined as a severe , “open” form , in which tissues of the nervous system are exposed to the environment or a “closed , ” form , characterized by skin-covered lesions [1] . NTDs are the outcome of aberrant primary or secondary neurulation during embryogenesis . During primary neurulation , the neural plate folds on itself and fuses on the midline into the neural tube [4] . The brain and most of the spinal cord are formed by primary neurulation . Flawed primary neurulation typically leads to open forms of NTDs which include anencephaly and spina bifida . Spina bifida ( failure of vertebral fusion ) usually occurs secondary to failure of closure of the neural tube , and causes a spectrum of physical and developmental disabilities , depending on the magnitude and position of the spinal defect [3] . Spinal dysraphism is an alternative terminology , describing conditions with malformations of structures relating to the midline raphe of the developing spine; generally implying neural tube defects [5] . Secondary neurulation is defined as the formation of the caudal portion of the neural tube from the pluripotent cells of the tail bud and does not require folding as the solid cell mass undergoes cavitation . Secondary neurulation creates most of the sacral and all of the coccygeal tissues of the spinal cord [4] . Pathologic secondary neurulation may result in closed forms of spina bifida , where the nervous tissue fails to separate from the other tissues of the tail bud [6] . The etiology of NTDs remains poorly understood and genetic , nutritional ( folate , inositol ) , and environmental factors , or a combination of these , are known to play a role in the development of NTDs [1] , [2] . Genes involved in the complex multistep process of neurulation such as those in the the planar cell polarity ( PCP ) pathway [4] , [7] , and genes involved in folate metabolism [8] , [9] , have been found to contribute to NTDs . Mouse models have led to the identification of over 200 genes with roles in NTDs [10] , [11] . In addition to null mutants , many knock-down or compound mutants have been identified , and some mutations , like the kinky tail mouse [12]–[14] , cause NTDs in oligogenic combinations , or like the curly tail mouse , display complex inheritance [11] , [15] , [16] . Similarly , it is presumed that most human NTDs have a multifactorial etiology and arise in a similar fashion to some of the mouse phenotypes [6] . Although multiple genetic variants that increase the risk of developing NTDs have also been recognized for humans [17] , the identified genetic variation does not explain the total genetic contribution to the incidence of NTDs observed in human populations [3] . Utilizing conventional approaches such as positional cloning and genetic linkage to identify additional associated variants is hampered by the rarity of families with multiple affected individuals , and because of undersized cohorts leading to suboptimal power for association studies [3] , [17] . In contrast to association studies in human populations , the dog is a large animal model that is particularly useful for whole genome association studies due to its unique population structure [18] , [19] . Haplotype blocks in LD ( linkage disequilibrium , non-random association of alleles at two loci or more ) extend across 0 . 4 to 3 . 2 megabases [20] , [21] , and are similar to the extent of LD in inbred strains of mice; simplifying genetic analyses [22] , [23] . In addition , the variable conditions associated with NTDs occur naturally in dogs [24] , and spina bifida has been reported to occur sporadically in breeds such as the English Bulldog [25] , Toy Poodle [26] , [27] Collie , Chihuahua , mixed-bred dogs [27] and Samoyed [28] . Furthermore , Weimaraner dogs with a NTD , commonly referred to as “spinal dysraphism” were studied previously [29] , [30] . Research colonies of affected Weimaraners have been maintained [31] , [32] , and breeding experiments included mating of severely affected Weimaraner dogs which resulted in 10/10 affected fetuses [32] , [33] , suggesting a recessive mode of inheritance . However , other breeding experiments did not support a conclusive mode of transmission [30] and the disorder was speculated to have a complex mode of inheritance in the Weimaraner [29] . Extensive studies by McGrath [30] , demonstrated that the heterogenic spinal pathology in Weimaraners includes duplicated , stenotic , or absent central canal , hydromyelia or syringomyelia , chromatolysis and loss of nerve cell bodies in gray matter , disrupted dorsal median septum and ventral median fissure , and gray matter ectopias [30] . In any affected Weimaraner , these histopathological changes may be present in varying degrees within different spinal cord segments , but occur most frequently in the lumbosacral region . Engel and Draper [33] reported that abnormalities in affected Weimaraner prenates were evident in embryos ( 24 days of gestation ) and consisted of failure of the dura mater to separate from the periosteum , absence of the ventral median fissure and fusion of ventral white matter , and disruption of gray matter structure . Central canal diverticula were common and the diameter ratio of gray matter/spinal cord was significantly greater in affected fetuses [33] . In the live Weimaraner , McGrath [30] observed abnormal hair streams along the back , similar to those observed in some of the human patients [34] , kinked tails resembling the curly and kinked tail mouse phenotypes [15] , [12] , and scoliosis of the vertebral column in the lumbar spinal region [30] . Clinical signs include paraparesis and a symmetric “bunny-hopping” or simultaneous use of the pelvic limbs , a bilateral withdrawal reflex; pinching one paw elicits flexion of both hindlimbs , a crouched stance , and deficient proprioception in the pelvic limbs [30] . Although spina bifida was not observed in Weimaraner cases , they share many of the human and mouse NTDs phenotypes which suggests that an evolutionary conserved molecular pathway may be contributing to the pathoetiology of NTDs across these species . Whereas previously maintained colonies of affected Weimaraners allowed for a thorough description of the phenotype and experimental breeding has confirmed that “spinal dysraphism” is an inherited condition in the Weimaraner breed , no mutation has been identified to date . We used four cases of presumed spinal dysraphism to map a genomic location for the disorder in the breed . A regional homeobox candidate gene with functions in neuronal development , NKX2-8 , was sequenced and a frameshift mutation was identified in affected Weimaraners . Human patients with spina bifida had a significant increase in the rate of rare missense mutations within evolutionary conserved residues of NKX2-8 .
Four unrelated Weimaraners showing the clinical signs typical of “spinal dysraphism” ( Video S1 , Figure S1 ) and 96 unaffected Weimaraners were genotyped using ∼173 k SNP markers . Following quality control , 114 , 775 SNPs were retained in the genome-wide association study ( GWAS ) analysis using PLINK , and an associated region on canine chromosome 8 was observed ( Figure 1A and 1B ) . To confirm that this region was not falsely associated due to population substructure , we reviewed the quantile–quantile plots with the associated SNPs on chromosome 8 ( λ = 1 . 03 , Figure S2A ) and without the SNPs on chromosome 8 ( λ = 1 . 01 , Figure S2B ) . The associated region extended over ∼1 . 5 Mb . Within this region , a distinct homozygous haplotype was present within the affected dogs ( Figure 1C ) , and was absent in all 96 unaffected dogs . Of the 18 regional genes ( Table S1 ) NKX2-8 ( chr8: 18 , 156 , 525–18 , 157 , 928 ) was shown to regulate key steps in spinal accessory motor neuron development in the mouse , and adult mice with targeted disruptive NKX2-8 mutations exhibit abnormal locomotion , including a permanent or intermittent hopping gait . The two exons of NKX2-8 were sequenced in genomic DNA and an alteration of a G to AA was identified within exon 2 in an affected Weimaraner when compared to unaffected Weimaraners and to the Boxer reference genome [21] . Two obligate carriers ( parents of affected dogs ) and two littermates of affected dogs were heterozygous for the mutation . The three genotypic variants observed within exon 2 of NKX2-8 are shown in Figure 2 . In order to determine the NKX2-8 protein sequence , we acquired the complete cDNA sequence , including the 5′ and 3′ untranslated regions from brain tissue of an unaffected Beagle . Subsequent translation of the exonic sequence of an affected Weimaraner revealed that the identified alteration functions as a frameshift mutation which introduces an amino acid change ( A150VfsX1 ) and a downstream stop codon ( Figure 3 ) . No additional mutations were identified within the promoter region or within the exon-intron boundary of NKX2-8 in genomic DNA of affected dogs . 109 additional unrelated unaffected Weimaraners were tested for the presence of the A150fs mutation by direct sequencing and three carrier dogs were identified . The mutation frequency was therefore calculated to be ∼1 . 4% within the Weimaraner breed . One additional case of clinically affected Weimaraner had no copies of the mutation . Additionally , 496 unaffected dogs , from six breeds reported to be clinically affected by NTDs , and a Chesapeake Bay Retriever diagnosed with myelodysplasia , absent ventral median fissure , hydromyelia , and syringomyelia by histopathology , were tested to determine whether or not this is an allelic mutation . No copies of the mutation were found within non-Weimaraner dogs . To investigate a potential role for NKX2-8 in cases of NTDs in human patients , 149 unrelated samples from patients with lumbosacral myelomeningocele , ( spina bifida ) , were sequenced . Six missense variants were identified in exon 2 of NKX2-8 within the spina bifida cohort . Five patients ( 3 females , 2 males ) , all European Americans were heterozygous for variant rs61755040 , which has a reported minor allele frequency ( MAF ) of 0 . 0073 in dbSNP . This missense variant , results in an amino acid change of serine to threonine at position 62 of human NKX2-8 , within an area of complete evolutionary conservation ( Figure 3 ) . The S62T alteration is predicted to be “probably damaging” by PolyPhen . Of the 149 samples , only 19 belonged to African American spina bifida patients . While we did not have adequate sample size to examine this ethnic group statistically , we did find that one female African American was heterozygous for variant rs10135525 , which has a reported MAF of 0 . 0014 in dbSNP . This missense mutation results in an amino acid change of alanine to threonine at position 94 , within the evolutionary conserved homeobox functional domain of human NKX2-8 ( Figure 3 ) and is also predicted to be “probably damaging” by PolyPhen . As shown in Figure 3 , both variants reside within domains of 100% identity between human ( Homo sapiens ) , dog ( Canis lupus familiaris ) , cat ( Felis catus ) , cow ( Bos taurus ) , bat ( Pteropus alecto ) , wild boar ( Sus scrofa ) , mouse ( Mus musculus ) , tree-shrew ( Tupaia chinensis ) , chicken ( Gallus gallus ) and zebra fish ( Danio rerio ) . Using the Exome Variant Server ( EVS ) data as a control population for spina bifida , we compared missense variants in the European American spina bifida population versus the EVS population . The EVS European American database contains 6 variants in NKX2-8 ( nonsense or missense ) in a total of 72 variant alleles out of an average of 8 , 500 alleles sequenced ( Table S2 ) . The difference between the frequency of missense variants in spina bifida cases versus controls was significant by one tailed Chi-squared analysis with Yate's correction ( p = 0 . 036 ) .
Using LD mapping in dogs , we identified an A150fs frameshift mutation which segregates within the Weimaraner breed in spinal dysraphism affected dogs and their relatives . The frameshift mutation was absent in 496 dogs from six breeds that were previously reported in the literature as presenting with cases of spina bifida , and in a case of spinal dysraphism in a Chesapeake Bay Retriever dog . Our results suggest that this is a private mutation in Weimaraners which is not shared between breeds . The mutation does not segregate as a benign polymorphism in canine populations , supportive of a causative role for the mutation . The mutation was found in a homozygous state within spinal dysraphism cases and recessive Mendelian transmission was verified by genotyping two parents ( obligate carriers ) and two littermates whose samples were available . Additionally , the ∼1 . 5 Mb genome-wide associated region is comprised of a homozygous haplotype present in the affected dogs which best fit a recessive model of inheritance [35] . Previously , Karlsson et al . demonstrated that a Mendelian recessive trait could be successfully mapped within a single dog breed with fewer than 15 cases and 15 control dogs [18] . We used a genome-wide case-control association study to map spinal dysraphism with merely four cases; providing further evidence for the effectiveness of the dog as a model organism for inherited diseases . The ∼1 . 5 Mb associated haplotype contained a tight cluster of associated SNPs and 18 regional candidate genes . Among these genes , NKX2-8 was an appealing candidate since it belongs to a family of vertebrate developmental regulators ( homeodomain transcription factors ) that are homologues of the Drosophila homeodomain transcription factor , NK2 [36]–[38] . NKX2-8 is expressed in the developing neural tube [39] , connecting it with the sub-group of the Nk2 genes which is expressed in the central nervous system [36] , [37] . This group includes the Drosophila vnd gene and the vertebrate Nkx2 . 1 and Nkx2 . 2 genes [39] . This suggests an early role for the Nk2 gene family in the development of the nervous system before the divergence of deuterostomes . The NKX2-8 protein has an N-terminus conserved homeobox DNA binding domain; involved in the transcriptional regulation of key developmental processes , and a C-terminus conserved NK specific domain with transcriptional activity [40] . The A150fs frameshift mutation identified in Weimaraners introduces an early stop codon and the truncated protein lacks the NK specific domain . This mutation may lead to impaired NKX2-8 function during embryonic development and thus the observed neurospinal pathologies in homozygous Weimaraners . Support for this proposed etiology come from extensive experiments in mice [41] . The function of the murine NKX2-8 homolog , Nkx2-9 , was evaluated by targeted disruptive mutations in the related homeodomain transcription factor Nkx2-2 [42] , and in Nkx2-9 [43] . The experiments suggested that both proteins play essential and partially redundant roles in the development of distinct neuronal populations in hindbrain and ventral spinal cord [41] . The authors observed impaired floor plates that led to defects in axonal pathfinding of commissural neurons in Nkx2-9 mutants . Intriguingly , adult mice with disruptive mutations in Nkx2-9 exhibit varying degrees of abnormal locomotion , observed predominantly for hindlimbs as continuous hopping with no alternating activity of left and right legs , similar to the Weimaraner phenotype ( supplementary video ) [41] . In vitro recordings in spinal cord preparations from newborn mutant mice , showed markedly reduced coordination of locomotor-like activity with increased variability in both left-right and flexor-extensor coordination [41] . The authors concluded that disruption of the Nkx2-9 gene results in a strong walking impairment and substantial locomotor deficits , both in vitro and in vivo [41] . Interestingly , only 75–90% of homozygous Nkx2-9 mutants exhibit the hopping gait phenotype , suggesting that there is reduced penetrance in the mouse , or possibly a “leakage” of the null phenotype [44] . While spinal dysraphism in Weimaraners was previously reported as a disorder for which the penetrance is reduced [30] , we have not seen evidence for reduced penetrance within the tested group of Weimaraners ( n = 210 ) . A single case of a Weimaraner with clinical signs of ataxia and paraparesis did not share the NKX2-8 A150fs mutation . It is possible that a second mutation exists in Weimaraners , which may account for the previously reported inconsistent transmission [30] . It also cannot be ruled out that in this case the clinical signs are the result of environmental factors such as maternal hyperthermia , nutritional imbalances , medication or abnormal glucose metabolism; factors mentioned in epidemiological studies as leading to congenital spinal defects [1] . In absence of diagnostic imaging or histopathological evidence , only a suggestive diagnosis could be made for spinal dysraphism based on case history , signalment and findings on a neurological examination . The identification of a mutation which segregates in the breed may aid in reaching a diagnosis in live pet dogs . In the future , Weimaraner breeders will be able to select against this mutation through DNA screening of prospective breeding animals . Identification of a mutation leading to a NTD in the dog could be utilized to improve our understanding of NTDs in human patients . When a cohort of 149 spina bifida patients was tested , we identified 6 cases that had one of two heterozygous missense mutations ( rs61755040 or rs10135525 ) within exon 2 of the NKX2-8 gene . Interestingly , the dog frameshift mutation is also located in exon 2 of the NKX2-8 gene , suggesting that exon 2 might be more susceptible to damaging DNA mutations than exon 1 of NKX2-8 . The missense mutations identified in spina bifida patients alter evolutionary conserved amino acid residues and functional consequences are predicted . While these variants are rare ( MAF<0 . 007 ) in controls , they may explain 4% of the cases within this cohort . Nonetheless , future functional studies are needed in order to confirm that these are harmful mutations which may cause a mutant phenotype under certain conditions . Previous studies that present existing evidence to support a causative role for the variants identified within NKX2-8 include the work performed on VANGL1 , one of the genes of the well-studied PCP pathway [45] , [46] . Similar to our results; Loop-tail ( Lp ) mice with NTDs had recessively inherited mutations , but when human patients were sequenced , heterozygous missense mutations were identified within the VANGL1 gene . This suggests that heterozygous missense mutations within genes with critical functions during development play a role in the etiology of NTDs in human patients . Another discovery of heterozygous variants in human patients was made while investigating the FZD6 gene of the PCP pathway [47] . While the inheritance in the mouse model was recessive , the variants discovered within human patients were heterozygous [48] , resembling our results . It is possible that the variants identified in spina bifida patients , have a dominant negative effect . NKX2-8 has a DNA-binding homeobox domain which is shared by a large variety of transcriptional regulators involved in controlling development [49] . The mutations in spina bifida patients were identified in domains of absolute evolutionary conservation; both adjacent to , and within the homeobox domain . Missense mutations within homeobox domains of various genes were studied previously to reveal that in patients with congenital diseases , most missense mutations have dominant effects [49] . Amino acids of homeobox domains play the critical roles of determining the correct structural fold of proteins , regulating DNA-protein interactions , regulating protein-protein interactions and signaling nuclear localization . It was previously proposed that heterozygous mutations within homeobox genes may have a detrimental effect during development due to haploinsufficiency [50] , [51] . While it is possible that the mutations identified in NKX2-8 have a dominant negative effect , it was previously hypothesized that NTDs inheritance is multifactorial [1] . A threshold model is used to explain multifactorial contribution to the NTD dichotomous phenotype , where multiple genetic variants interact with environmental factors to cause NTDs [52] . According to the threshold model , the variants contributing to the elevated risk would be present in controls , but a significant higher frequency of these variants is expected in cases [52] , [53] . Our results are consistent with this theory; however , further studies are required in order to determine the inheritance pattern of NKX2-8 mutations in NTDs patients . Using the tractable genome of the dog for association mapping of naturally occurring NTDs , we identified a frameshift mutation in NKX2-8 . Additionally , rare missense variants in NKX2-8 were identified in 4% of the cases in a cohort of spina bifida patients . To the best of our knowledge , this is the first documentation of a potential role for NKX2-8 in the development of NTDs . Future functional studies are required in order to provide insights into the mechanisms and etiologies which constitute NTDs in both species .
All research involving human participants was approved by Northwestern University ( Chicago ) and the University of Iowa institutional review boards ( IRBs ) . Informed consent was obtained and all clinical investigation must have been conducted according to the principles expressed in the Declaration of Helsinki . DNA samples of domestic dogs ( Canis familiaris ) owned by private individuals were used in this study . We accepted samples from dogs of all ages and of both sexes . The sample collection protocol was approved by the University of California , Davis Animal Care and Use Committee ( protocol #16892 ) . Weimaraner samples were solicited using advertisements posted in the Weimaraner Club of America ( WCA ) magazine , on the WCA website , by direct communication with Weimaraner owners and treating veterinarians , and via the Veterinary Information Network ( VIN ) . All of the samples used in this study came from shorthaired American dogs . Presumed ( no histopathology ) spinal dysraphism cases were brought to our attention by treating clinicians , Weimaraner breeders and owners . Veterinary evaluation of congenital non-progressive neurological abnormalities which consisted of pelvic limb ataxia , paraparesis , and delayed proprioceptive positioning in the pelvic limbs; together with patient signalment and history served to make a suggestive diagnosis . Samples from additional breeds of dogs were obtained from patients of the Veterinary Medical Teaching Hospital at UC Davis . DNA was extracted from blood samples in EDTA using a commercially available kit ( Puregene , Gentra Systems , Minneapolis , MN ) . Additional DNA isolation from buccal swabs was performed as previously described [54] . DNA samples were genotyped using the Illumina 170K CanineHD BeadChip ( Illumina , San Diego , CA ) . Quality control checks on the canine dataset were performed for individuals and SNPs using GenABEL in the R statistical package [55] . SNPs were excluded if they had a minor allele frequency ( MAF ) <5% , a genotype call rate <95% , or if they deviated from the Hardy-Weinberg Equilibrium ( HWE ) . A total of 114 , 775 SNPs passed the quality control check and were available for analysis . The retained SNPs were then used for case-control chi-square statistical analysis by PLINK [56] , and Manhattan and quantile-quantile ( QQ ) plots were generated using GenABEL [55] . We assessed the effect of population stratification by examining the QQ plots for deviation of the p-values from the null hypothesis . We considered a significant genome-wide association if the SNPs p-value was below the 5% Bonferroni-corrected threshold ( p≤0 . 05; −log 10≥1 . 3 ) . To derive the genome-wide significance thresholds we repeated the GWAS with 100K Max ( T ) permutations . Adult beagle total RNA was obtained from Zyagen ( San Diego , CA , USA ) . cDNA was synthesized with the SuperScript III First-Strand Synthesis System for RT-PCR ( life technologies , Grand Island , NY 14072 , USA ) . Primers for the complete cDNA of NKX2-8 were designed using the Primer3 program [57] . cDNA PCR products were cloned using the TOPO TA Cloning kit ( pCR2 . 1-TOPO vector ) with One Shot TOP10 Chemically Competent E . coli ( life technologies , Grand Island , NY 14072 , USA ) . Products were isolated with the Qiaprep Spin Miniprep kit ( QIAGEN , Valencia , CA 91355 , USA ) and sequenced as described below . Nucleotide sequences were translated into amino-acid sequences with Vector NTI software ( Applied Biosystems , CA 92008 ) . Primers for the two exons and for the 3 and 5′ UTRs of NKX2-8 were designed using the Primer3 program [57] ( Table S3 ) . The primers , E2F2: 5′ CTGGTAGGCGGGGAAGAG; and E2R2: 5′ GGTTCCAGAACCATCGCTAC , were used to generate PCR products which flank the frameshift mutation within exon 2 of the NKX2-8 gene . PCR was performed using 40 ng of DNA , 1 unit of AccuPrime GC-Rich DNA Polymerase , 5 ul of buffer A ( AccuPrime high GC DNA polymerase kit; life technologies , Grand Island , NY 14072 , USA ) , 50 ng forward and reverse primers , in 25 ul reaction volume . Cycle conditions of 3 min at 95°C followed by 35 cycles of 30 s at 95°C , 30 s at 62°C , and 1 min at 72° , with a final extension of 20 min at 72°C were used . Primers ( Table S1 ) were used to generate overlapping sequences to complete the genomic sequence of NKX2-8 . The sequence upstream of the gene was missing on the May 2005 CanFAm2 . 0 genome assembly ( viewed using the UCSC genome browser ) , and was captured using the LongAmp Taq PCR Kit ( New England BioLabs Ipswich , MA 01938 , USA ) . The PCR products were electrophoresed on 1–2% agarose , and cleaned using ExoSAP-IT . Purified PCR products were sequenced using the Big Dye terminator mix on ABI 3500 Genetic Analyzer ( Applied Biosystems , CA 92008 ) . Sequences were visualized using Chromas2 ( Technelysium , Tewantin , QLD , Australia ) and analyzed with Vector NTI software ( Applied Biosystems , CA 92008 ) . Cases comprised a total of 149 patients . Unrelated European American ( n = 130 ) , and unrelated African American ( n = 19 ) samples from patients with lumbosacral myelomeningocele ( spina bifida ) . Collected at Children's Memorial Hosptial in Chicago , IL , USA . All cases had open spina bifida ( myelomeningocele ) . Genomic DNA fragments spanning the two exons of NKX2-8 were amplified by PCR . Purified PCR products were sequenced using Big Dye terminator chemistry ( Applied Biosystems ) and analyzed on a MegaBACE 1000 ( Amersham ) . Sequence reads derived from both strands were assembled , aligned and analyzed for nucleotide differences using Sequencher ( GeneCodes ) . We assessed the presence of variants in the Exome Variant Server , NHLBI GO Exome Sequencing Project ( ESP ) , Seattle , WA ( URL: http://evs . gs . washington . edu/EVS/ ) ; data release ESP6500 , November 2012 . PCR primers and conditions used are shown in Table S4 . NCBI BLASTP [58] was used to compare protein sequence conservation across species . The biological sequence alignment tool , Bio Edit , ( Ibis Biosciences , Carlsbad , CA ) , was used to align protein sequences . The PolyPhen online tool was used to predict the possible impact of missesne mutations [59] . | Neural tube defects ( NTDs ) are birth defects resulting from errors in the closure of the neural tube , an embryonic structure which develops into tissues of the central nervous system during pregnancy . NTDs commonly lead to costly lifelong disabilities . They are considered to be caused by a combination of nutritional , inherited and environmental factors , and their interactions . However , an obvious mechanism is currently unknown . Genetic studies in human populations are made difficult by the multifactorial nature of NTDs and because multiple cases within a single family are rare . Animal models are helpful in dissecting the genetics of such complex traits; however existing rodent models do not explain all of the NTD cases in humans . Dogs are excellent biomedical models for humans since they receive comparable medical care , share our home environment , and develop naturally occurring diseases comparable to those in humans . We used a naturally occurring NTD in Weimaraner dogs , termed spinal dysraphism , to identify a mutation in an associated regulatory gene , NKX2-8 . Mutations in NKX2-8 were subsequently documented in human patients with a generally similar NTD termed spina bifida . This is the first documented evidence that NKX2-8 has a role in NTDs . It is expected that this discovery will contribute to our understanding of the mechanisms leading to NTDs . | [
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"science"... | 2013 | Genome-Wide Association Mapping in Dogs Enables Identification of the Homeobox Gene, NKX2-8, as a Genetic Component of Neural Tube Defects in Humans |
Aedes aegypti is the primary vector of several medically relevant arboviruses including dengue virus ( DENV ) types 1–4 . Ae . aegypti transmits DENV by inoculating virus-infected saliva into host skin during probing and feeding . Ae . aegypti saliva contains over one hundred unique proteins and these proteins have diverse functions , including facilitating blood feeding . Previously , we showed that Ae . aegypti salivary gland extracts ( SGEs ) enhanced dissemination of DENV to draining lymph nodes . In contrast , HPLC-fractionation revealed that some SGE components inhibited infection . Here , we show that D7 proteins are enriched in HPLC fractions that are inhibitory to DENV infection , and that recombinant D7 protein can inhibit DENV infection in vitro and in vivo . Further , binding assays indicate that D7 protein can directly interact with DENV virions and recombinant DENV envelope protein . These data reveal a novel role for D7 proteins , which inhibits arbovirus transmission to vertebrates through a direct interaction with virions .
Dengue virus ( DENV ) is a mosquito-borne arbovirus that is transmitted primarily by the species Aedes aegypti . The global burden of DENV has grown dramatically in the last few decades [1 , 2] . We now expect approximately one hundred million clinically recognized cases of disease each year . Targeted therapeutics do not exist . Fortunately , conventional vaccines are in development and regulatory approval has been granted in a few countries . Development of a conventional DENV vaccine has been difficult due to the co-circulation of four serotypes [3 , 4] . It is critical that conventional vaccines elicit robust antibody titers to avoid antibody-dependent enhancement , which occurs during a sub-neutralizing response . It is theoretically possible to target mosquito saliva or midgut proteins to block either transmission or acquisition of DENV [3 , 5 , 6] . This strategy would not be subject to antibody-dependent enhancement or viral genetic drift . Ae . aegypti saliva contains over one hundred unique proteins that have been classified as D7 proteins , phosphatidylethanolamine binding proteins , odorant and juvenile hormone binding proteins , serpins and other protease inhibitors , a sialokinin vasodilator , nucleotidases , serine proteases , sugar digestion related proteins and other enzymes , lectins , angiopoietins , anti-microbial proteins and peptides , mucins and peritrophins , antigen 5 proteins , and many more proteins of unknown function [7–11] . Functional data is not available for the majority of these proteins , although it is expected that the saliva of all hematophagous arthropods have anti-coagulant , anti-platelet , and vasodilatory activities . It is also likely that saliva proteins serve to reduce host inflammation and prevent infection . In addition to the normal physiological roles of hematophagous arthropod saliva , many vector-borne microorganisms have enhanced fitness in the presence of arthropod saliva . Arthropod saliva can enhance infectivity of West Nile virus , DENV , Rift Valley fever virus , and Powassan virus , among others [5 , 12–18] . The exact mechanism of saliva-mediated infectivity enhancement is not known , although prior literature suggests that saliva proteins may locally modify the immune system in favor of arbovirus replication and/or stimulate dissemination by enhancing migration of target cells to draining lymph nodes [3] . Interestingly , individual saliva components can have inhibitory activities against arbovirus infection . For instance , the collagen-binding protein aegyptin decreased DENV infection in vivo [19] . Additionally , previous literature showed that vaccination of mice with a recombinant D7 protein from Culex spp . enhanced mortality in a West Nile virus mouse model , suggesting that D7 protein may be inhibitory in vivo [20] . Structural studies suggest that D7 proteins can simultaneously bind biogenic amines and cysteinyl leukotrienes , which is likely involved in preventing the host inflammatory response [21 , 22] . Prevention of the host inflammatory response may reduce influx or activation of target cells . Our previous work relied on high performance liquid chromatography ( HPLC ) to fractionate Ae . aegypti salivary gland extracts ( SGEs ) [5] . HPLC fractions were tested to see if they had virus enhancing or blocking activities in vitro . Here , we identified a number of fractions that inhibited DENV cell binding and then pooled these fractions for downstream tandem liquid chromatography tandem mass spectrometry ( LC+MS/MS ) analysis . D7 proteins were the most abundant proteins in the inhibitory fractions . We synthesized a recombinant D7 protein and found that it inhibited DENV infection in vitro and in vivo . Additionally , the D7 protein interacted directly with DENV virions and recombinant envelope protein . These data support a model whereby D7 proteins inhibit DENV infection through two independent mechanisms: ( i ) direct binding and neutralization of DENV virions , and ( ii ) inhibition of immune cell infiltration or activation , which reduces the number of permissive target cells . Characterization of virus-vector-host interactions at the transmission interface will further development of arthropod-based therapeutics and transmission-blocking vaccines .
Aedes aegypti were provided by staff at the Connecticut Agricultural Experiment Station . Mosquitoes were maintained in a sugar solution at 27°C and 80% humidity according to standard rearing procedures . Salivary glands and saliva were isolated as described previously [5] . Salivary gland extracts were prepared by placing 100 salivary glands in 100 μl sterile phosphate-buffered saline ( PBS ) , freeze-thawing by placing on dry ice three times , and then removing insoluble debris by centrifugation at 5 , 000 × g for 10 min . Saliva was isolated using the immersion oil technique . Mouse embryonic fibroblasts ( MEFs ) and a human monocyte-like ( U937 ) cell line from the American Type Culture Collection were maintained in Dulbecco's modified Eagle medium ( DMEM ) containing 10% fetal bovine serum and antibiotics at 37°C with 5% CO2 ( Gibco ) . C6/36 cells were maintained in DMEM containing 10% fetal bovine serum , tryptose phosphate , and antibiotics at 30°C . DENV2 was passaged in C6/36 cells . DENV2 New Guinea C strain was obtained from the Connecticut Agricultural Experiment Station and C6/36 cells were a kind gift from Erol Fikrig . Approximately 1 × 105 genome equivalents ( GE ) were used for in vitro infections of MEFs and U937 cells . One hundred salivary glands were dissected from female Ae . aegypti and placed in 100 μl PBS . The sample was freeze-thawed three times at −80°C , and insoluble debris was pelleted by centrifugation at 5 , 000 × g for 10 min . The supernatant was reserved . SGE was either processed directly for LC+MS/MS analysis or fractionated by high-performance liquid chromatography ( HPLC ) on a nonporous reverse-phase column with a TFA buffer system into 80 100-μl fractions . Ten μl of each fraction was diluted into 90 μl PBS and used as SGE treatments for in vitro SGE-mediated cell binding assays as stated below . The remaining 90 μl from inhibitory fractions 31–49 were pooled and submitted for liquid chromatography tandem mass spectrometry ( LC+MS/MS ) analysis . Proteins were digested with trypsin and analyzed using LC+MS/MS on a Thermo Scientific LTQ-Orbitrap XL mass spectrometer using Waters nanoACQUITY ultra-high-pressure liquid chromatographs ( UPLC ) for peptide separation . MS/MS spectra were searched in-house using the Mascot algorithm for uninterpreted MS/MS spectra after using the Mascot Distiller program to generate Mascot-compatible files . An A . aegypti database was used for searching . The Keck Biotechnology Resource at Yale University performed both HPLC and LC+MS/MS . MEFs were seeded at 25 , 000 cells/well in 48-well plates and grown to approximately 70% confluence overnight . Medium was aspirated , and cells were washed with PBS . Ten μl of HPLC fractions were diluted in a total volume of 100 μl PBS and inoculated into cells at room temperature for 10 min . Saliva material was removed , and then Approximately 1 × 105 GE of DENV2 was inoculated into cells in a total volume of 500 μl for 1 h at 37°C . Unbound virus was then removed , and fresh medium was added . Infections progressed for up to 18 h . Total RNA was extracted using RNeasy kits ( Qiagen ) . For analysis of relative DENV vRNA , total RNA was harvested using RNeasy kits ( Qiagen ) . Amplification of both the viral target and reference gene target was performed using a duplex format in 0 . 2-ml , 96-well PCR plates ( Bio-Rad ) with a total reaction volume of 25 μl . Reverse transcription and quantitative PCR ( RT-qPCR ) were performed in the same closed tube with 250 ng of total RNA per reaction using the Quantitect RT-PCR kit ( Qiagen ) . All primers were used at a final concentration of 4 μM and were synthesized by the Keck Facility at Yale University . DENV2 vRNA was amplified using F 5’ CCACTGCCTCTGGAAAACTC 3’ and R 5’ GTACCAGCACCCATCCTCAC 3’ primers . Primers were developed using Gene Link Software ( OligoAnalyzer 1 . 2 and OligoExplorer 1 . 2 ) . All RT-qPCRs were performed using an iQ5 machine ( Bio-Rad ) . Cycling conditions were 50°C for 30 min ( reverse transcription ) and 95°C for 15 min , followed by 42 cycles of 94°C for 15 s and 54 . 5°C for 1 min . Relative quantities of viral target cDNA were determined using REST software . Ae . aegypti total cellular RNA was converted to cDNA using random primers and the Superscript III First-Strand Synthesis System ( ThermoFisher ) . Long form D7 protein ( AAEL006424 ) was amplified using F 5’ GGAGGTACCGATGAAGCTGCCTCTATTACTCGCAATAGTTAC 3’ and R 5’ GGAGCGGCCGCAATTGTGGACACTGTTTACCGTCG 3’ primers and cloned into the pMT/BiP/V5-His A plasmid via BamHI and NotI restriction sites . pMT/BiP/D7/V5-His A and pCoHygro plasmids were transfected into S2 cells using the calcium phosphate method according to manufacturers’ instructions ( ThermoFischer ) and a stable cell line was generated through hygromycin selection . D7 synthesis and secretion was induced by treating cells with copper sulfate according to manufacturer’s instructions ( ThermoFisher ) . D7 expression was confirmed by Coomassie Blue gel staining and Western blot using an anti-6 His antibody . Supernatants were also harvested from uninduced S2 cell supernatants and used as negative controls . D7 protein was purified using HisPur Cobalt Spin Columns ( Thermo Scientific/Pierce , MA ) according to manufacturer instructions . There were a total of 3 washes and 3 elutions . Samples were stored at -80°C until use . The U937 cell line ( ATCC , VA ) was used for the in vitro infection studies . The cells were grown at 37°C and 5% CO2 in DMEM supplemented with 10% fetal bovine serum ( Gemini , CA ) , and 1% penicillin-streptomycin . D7 was used in 1/10 , 1/100 , and 1/1000 dilutions in complete media for final concentrations of 8 , 0 . 8 , and 0 . 08 ng/mL , respectively . Dilutions were added for pretreatment of cells in a total volume of 250 μL . Cells were then incubated for 1 hour at 37°C and then DENV was added to cells at a multiplicity of infection ( MOI ) of 1 . 0 . For simultaneous treatments , ten-fold dilutions of D7 were mixed with DENV2 and these mixtures were pre-incubated for 1 hour at 37°C . D7-DENV2 mixtures were then inoculated onto cells . Unbound virions were removed by washing after 1 hour and then cells were incubated for 24 hours at 37°C . RNA was isolated from cells and qRT-PCR performed as described above . DENV2 vRNA was amplified using Forward: 5’ CAG ATC TCT GAT GAA TAA CCA ACG 3’ and Reverse: 5’ CAT TCC AAG TGA GAA TCT CTT TGT CA 3’ primers . Human B2M RNA was amplified using Forward: 5’ CTC CGT GGC CTT AGC TGT G 3’ and Reverse: 5’ TTT GGA GTA CGC TGG ATA GCC T 3’ primers . Animals were maintained and procedures were performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Research Council . Protocol 14051879 was approved by the University of Pittsburgh's IACUC committee . Approved euthanasia criteria were based on weight loss and morbidity . Mice deficient in receptors for type I and type II interferons , ( IFNAGR-/- , AGB6 ) were bred under specific pathogen-free conditions . Groups of 4-5-week-old , age-matched , mixed-sex AGB6 mice were inoculated subcutaneously into both rear footpads with 20 μl containing either 107 genome equivalents ( GE ) of either DENV2 alone or DENV2 plus recombinant D7 protein . DENV2 alone samples contained purified S2 cell supernatant without D7 protein as a vehicle control . Forty-eight hours post-infection , mice were euthanized and left and right foot pads and left and right popliteal draining lymph nodes ( DLN ) were collected independently . Total RNA was extracted using an RNeasy kit ( Qiagen ) . qRT-PCR was performed by adding equal amounts of RNA into each reaction and data were normalized to the actin reference gene . Mouse actin RNA was amplified using Forward: 5’ GGC TGT ATT CCC CTC CAT CG 3’ and Reverse: 5’ CCA GTT GGT AAC AAT GCC ATG T 3’ primers . Amplification of targets were performed using a duplex format in 0 . 2 ml , 96-well PCR plates ( BIO-RAD ) with a total reaction volume of 25 μl . Reverse transcription and quantitative PCR were performed in the same closed tube with 100 ng of total RNA per reaction using the Quantitect RT-PCR Kit ( Qiagen ) . Seventy μg of anti-dengue virus type II antibody , clone 3H5-1 ( Millipore ) was covalently coupled to 25 μL amine-active resin according to the Pierce Co-Immunoprecipitation Kit manual ( ThermoFisher Scientific ) . One hundred μg purified , formaldehyde-inactivated dengue virus type 2 virions ( Microbix ) was then mixed with 100 Ae . aegypti salivary gland equivalents and added to the antibody-containing resin with gentle end-over-end mixing for 2 hr at 4°C . Unbound proteins were washed away and bound proteins were eluted in a final volume of 50 μL . The experimental details are described in the Pierce Co-Immunoprecipitation Kit manual . Eluted proteins were identified by LC+MS/MS analysis as stated above . High binding 96-well plates were coated overnight at 4°C with a 1:1 dilution of D7 or matrix metalloproteinase protein ( MMP; AAEL003012 ) in coating buffer ( R&D systems ) for a 0 . 01mg/mL final concentration . The next day , plates were rinsed twice with PBS and blocked with blocking buffer ( 1% BSA in 1XPBST ) for 1 h at 37 . Plates were incubated with 80μL of DENV 2 strain 186881 ( 2x105 p . f . u ) or recombinant DENV Envelope protein ( 1μg/mL ) ( L2 Diagnostics , CT ) overnight at 4°C . Plates were washed three times with buffer ( 1X PBST+ 0 . 01% Tween 20 ) and incubated with a 1:250 dilution of primary antibody for 1h at 37°C: mouse anti-DENV2 antibody ( MAB10226 , Millipore ) to detect virus and mouse anti-E antibody ( MA1-71251 , Pierce ) to detect envelope protein . After washing plates three times , wells were incubated with 100 μL of a 1:1000 dilution of anti-mouse IgG , HRP-linked antibody ( Cell Signaling Technologies ) and washed after 1h of incubation at 37°C . Reaction was visualized after incubating with 80 μL of TMB . After 3min the reaction was stopped with stop solution ( 2M Sulfuric acid ) and plates were read at 450nm in a Synergy HT plate reader ( Biotek Instruments Inc . , VT ) .
We previously showed that pre-treatment of mouse embryonic fibroblasts ( MEFs ) with salivary gland extract ( SGE ) increased DENV cell binding [5] . Reverse phase HPLC fractionation and LC+MS/MS analysis was then used to identify fractions of Ae . aegypti salivary gland extract ( SGE ) that increased DENV vRNA levels associated with mouse embryonic fibroblasts ( MEFs ) [5] . During this analysis , we also noted that a cluster of HPLC fractions ( i . e . , 31–49 ) inhibited DENV infection ( Fig 1 ) . These fractions were pooled and submitted for LC+MS/MS analysis and searched against the NCBI Ae . aegypti database to identify the most abundant proteins . Multiple proteins were identified with high score values , although long forms of the D7 protein family were the most prevalent ( Table 1 ) . A number of these proteins were not predicted as secreted proteins that would be present in saliva . To validate the proteins that are present in saliva and may play a role at the virus-vector-host interface , saliva was harvested from one hundred Ae . aegypti using the immersion oil technique [23] . Soluble proteins were extracted from immersion oil using phosphate buffered saline and the sample was submitted for LC+MS/MS analysis . We confirmed that 7 proteins in the inhibitory fraction are also present in saliva ( Tables 1 and 2 ) . Three of these proteins were either long or short form D7 proteins . In a separate experiment , we sought to elucidate proteins that are upregulated in salivary glands during DENV2 infection . Ae . aegypti were either mock or infected with DENV2 via blood feeding using an artificial membrane . Twenty salivary glands were isolated from each group of mosquitoes 14 dpi and equal amounts of protein were loaded on to an SDS-PAGE gel for one-dimensional gel electrophoresis . Gels were stained with Coomassie Blue . Multiple bands were different between mock and DENV2 lanes , but one band appeared significantly darker in the DENV2-infected lane ( Fig 2A ) . The band from the DENV2-infected lane was excised and submitted for mass spectrometry . Only four unique Ae . aegypti proteins were detected , which included tropomyosin , two long form D7 proteins , and a 34 kDa salivary protein ( Fig 2B ) . We confirmed that both long and short form D7 proteins and the 34 kDa salivary protein are upregulated in Ae . aegypti salivary glands at the transcriptional level during DENV2 infection by assessing previously published RNA Seq data ( Table 3 ) [11] . Ae . aegypti D7 proteins are multifunctional and have been shown to bind to cysteinyl leukotrienes ( cysLT ) and biogenic amines with high affinity . Based on the discovery of D7 proteins in inhibitory HPLC fractions , and of increased D7 protein expression in DENV2-infected salivary glands , we hypothesized that D7 proteins may bind to additional substrates with varying affinities , and that these interactions inhibit DENV cell binding and infectivity . To test this hypothesis , a recombinant D7 long form protein ( AAEL006424 ) was produced in S2 cells and harvested from the supernatant at a concentration of 80 ng/mL ( Fig 3A and 3B ) . We determined if D7 protein was toxic to cells by incubating ten-fold dilutions of D7 protein with monocyte-like U937 cells for 24 hours followed by analysis using Promega’s CellTox Green Cytotoxicity Assay . This assay measures membrane integrity that occurs as a result of cell death . Both positive and negative controls were included . None of the dilutions tested were toxic to U937 cells at the time point tested ( Fig 4A ) . We then tested if D7 protein had antiviral activity in vitro by either pre-incubating U937 cells with ten-fold dilutions of recombinant D7 protein for 1 hour at 37°C prior to inoculation with DENV2 , or incubating ten-fold dilutions of D7 protein with DENV2 for 1 hour at 37°C prior to co-treatment with D7-DENV2 mixtures . The different treatment strategies were employed to determine if D7 needed to directly interact with virions for activity . Total RNA was extracted 24 hpi and the relative amount of DENV2 vRNA was normalized to a housekeeping gene . Both pre-treatment and co-treatment of recombinant D7 protein and DENV2 significantly reduced DENV2 vRNA levels in U937 cells and there wasn’t a statistical difference between these two treatment groups ( Fig 4B ) . A post-treatment experiment was also performed to determine if D7 inhibited DENV at an early time point during infection . In this experiment , U937 cells were infected with DENV for 24 hours , followed by an 8 hour treatment with ten-fold dilutions of D7 protein . Total RNA was extracted 48 hpi and the relative amount of DENV2 vRNA was normalized to a housekeeping gene . This treatment strategy did not significantly inhibit DENV infection , suggesting that D7 inhibits DENV at an early time point ( Fig 4C ) . We also tested if an irrelevant His-tagged protein could inhibit DENV infection . Recombinant His-tagged GFP was unable to inhibit DENV infection at the highest concentration tested for D7 ( Fig 4D ) . To determine if recombinant D7 can inhibit DENV2 infection in vivo , we challenged AGB6 mice with DENV2 with and without D7 protein by subcutaneous footpad inoculation . AGB6 mice were chosen because they are highly permissive to DENV2 infection and were a suitable model for early DENV2 replication and dissemination to draining lymph nodes . Briefly , 2 groups of 4-5-week-old , age-matched , sex-matched mice were inoculated into a single rear footpad with 20 μl containing 1 x 107 genome equivalents , DENV2 alone or in combination with approximately 1 ng recombinant D7 protein . In order to test the impact of D7 treatment on early replication and dissemination , RNA was harvested from footpads and draining lymph nodes 48 hpi and qRT-PCR was used to measure the relative levels of DENV2 normalized to an actin reference gene [5] . D7 protein significantly reduced DENV2 vRNA levels in both the footpads and draining lymph nodes 24 hpi ( Fig 5A and 5B ) . DENV2 vRNA amplified at Ct values of 21–24 in footpad samples and 17–19 in draining lymph node samples . Our results suggest that D7 protein can inhibit DENV2 infection in vitro and in vivo , although it is unclear how D7 protein mediates its antiviral effect . Pre-treatment of U937 cells with recombinant D7 protein led to a significant decrease in DENV2 infection , suggesting that D7 protein may modulate the host cell . Although , co-treatment of D7 protein with DENV2 appeared to be more effective at preventing cell binding and/or infection of U937 cells . We hypothesized that D7 protein can interact with multiple substrates including proteins at the cell surface and viral proteins . To test this hypothesis , we performed binding assays to determine if D7 protein can bind to DENV virions and envelope protein . First , anti-DENV2 antibody , clone 3H5-1 was covalently coupled to 25 μL amine-active resin in two separate columns . One hundred μg of purified , formaldehyde-inactivated dengue virus type 2 virions was then mixed with 100 Ae . aegypti salivary gland equivalents and added to the antibody-containing resin and incubated for 2 hr at 4°C . A negative control column containing anti-DENV2 antibody was also prepared with only 100 salivary gland equivalents . Unbound proteins were washed away and bound proteins were eluted . Eluted proteins were identified by LC+MS/MS analysis . Although Mascot scores were low , 8 proteins were identified that were unique and not in the negative control and three of these were present in saliva ( Tables 2 and 4 ) . This included the long form D7 protein ( AAEL006424 ) . To determine if the proteins we detected were simply correlated with their abundance , we performed LC+MS/MS analysis on Ae . aegypti salivary gland extracts ( Table 5 ) . Although actin and serine protease inhibitor ( serpin ) had Mascot scores in the top 3% , Mascot data suggested that the remaining proteins were less abundant , and that our co-immunoprecipitation data did not simply represent the most abundant proteins in salivary gland extracts . To confirm if D7 protein can directly interact with DENV2 , we performed an enzyme-linked immunosorbent assay ( ELISA ) by binding a control MMP protein or recombinant D7 protein to a 96 well plate [24] . Plates were then incubated with DENV2 16881 virions . Unbound proteins were then removed by washing each well with buffer . The degree of association between immobilized proteins and DENV2 virions was assessed using an antibody that recognized DENV followed by a secondary antibody conjugated to horse radish peroxidase . DENV2 16881 virions bound to D7 protein more readily than MMP protein . ( Fig 6A ) . Plates coated with D7 protein were then incubated with bovine serum albumin , DENV2 16881 virions , or recombinant DENV2 envelope protein . Unbound proteins were then removed by washing each well with buffer . The degree of association between D7 and the above proteins was assessed using an antibody recognizing DENV followed by a secondary antibody conjugated with horse radish peroxidase . We found that recombinant D7 interacted with both DENV2 16881 virions and recombinant DENV2 envelope protein ( Fig 6B ) .
Hematophagous arthropod saliva contains a complex mixture of proteins with anti-hemostatic , anti-inflammatory , and immunomodulatory properties . Hematophagous arthropod saliva can also enhance transmission of arboviruses , although the exact mechanism and saliva proteins involved in this process are not known [3] . Interestingly , individual saliva components can have inhibitory activities that prevent arbovirus infection [19] . Here , we identified D7 protein in biochemical fractions of salivary gland extracts that inhibited DENV2 cell binding and/or infection in mouse embryonic fibroblasts and then determined if this activity could be recapitulated with recombinant D7 protein . We found that recombinant D7 protein prevented DENV2 cell binding and/or infection in two permissive cell types and prevented infection and dissemination in a mouse model . Additionally , two separate binding assays suggest that D7 protein can physically interact with DENV virions and envelope protein . These data support the previous observation that D7 protein vaccination enhanced mortality in a West Nile virus mouse model and suggest that D7 protein inhibits virus transmission [20] . D7 proteins are some of the most abundant proteins expressed in the salivary glands of blood feeding Diptera . Long and short forms of D7 proteins exist in mosquitoes . Each of these proteins appear capable of binding either cysteinyl leukotrienes , and/or biogenic amines such as serotonin , histamine , and norepinephrine . These functions are predicted to antagonize the host’s inflammatory response , and ability to vasoconstrict , induce platelet-aggregation , and induce a sense of pain–each critical to efficiently obtaining a blood meal [21 , 22] . It is theoretically possible that the functions of D7 proteins are counter to the needs of arboviruses during transmission to a vertebrate host . D7 proteins bind multiple substrates and it is possible that they bind other proteins with lower affinity , which may limit virus-host interactions . Additionally , limiting the host inflammatory response may reduce influx or activation of target cells , such as Langerhans cells , monocytes , or macrophage . Our data support that D7 protein mediates its antiviral effect through direct protein-protein interaction in vitro , although it is possible that modulation of the inflammatory response also occurs in vivo . D7 proteins are some of the most abundant and immunogenic proteins present in mosquito saliva [25] . The presence of anti-D7 antibodies has been used as a marker of exposure to certain mosquito species [26–29] . Considering that individuals who are exposed to mosquitoes have high levels of anti-D7 antibodies , it is likely that these antibodies inhibit D7 protein function . In fact , the presence of anti-D7 antibodies has been linked to disease severity [29] . Although anti-D7 antibodies may prevent efficient blood feeding by a mosquito , it may also enhance disease transmission and disease severity . Characterizing the complex interplay of virus-vector-host interactions will lead to the development of better models of pathogenesis , strategies to limit disease transmission and promote the development of therapeutics , and transmission-blocking vaccines . | Dengue virus ( DENV ) is transmitted to humans by Aedes aegypti during the blood feeding process . During blood feeding , DENV and saliva proteins are inoculated into human skin . D7 proteins are prevalent and immunogenic proteins present in Ae . aegypti saliva , and assist the blood feeding process by scavenging biogenic amines . Previous data suggests that antibodies against D7 protein from Culex spp . can increase West Nile virus infection . We hypothesized that D7 proteins may also have antiviral activity . Here , we show that recombinant Ae . aegypti D7 protein can inhibit DENV infection in vitro and in vivo , and that D7 can bind to DENV virions . | [
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"infec... | 2016 | Aedes aegypti D7 Saliva Protein Inhibits Dengue Virus Infection |
Influenza A NS1 and NS2 proteins are encoded by the RNA segment 8 of the viral genome . NS1 is a multifunctional protein and a virulence factor while NS2 is involved in nuclear export of viral ribonucleoprotein complexes . A yeast two-hybrid screening strategy was used to identify host factors supporting NS1 and NS2 functions . More than 560 interactions between 79 cellular proteins and NS1 and NS2 proteins from 9 different influenza virus strains have been identified . These interacting proteins are potentially involved in each step of the infectious process and their contribution to viral replication was tested by RNA interference . Validation of the relevance of these host cell proteins for the viral replication cycle revealed that 7 of the 79 NS1 and/or NS2-interacting proteins positively or negatively controlled virus replication . One of the main factors targeted by NS1 of all virus strains was double-stranded RNA binding domain protein family . In particular , adenosine deaminase acting on RNA 1 ( ADAR1 ) appeared as a pro-viral host factor whose expression is necessary for optimal viral protein synthesis and replication . Surprisingly , ADAR1 also appeared as a pro-viral host factor for dengue virus replication and directly interacted with the viral NS3 protein . ADAR1 editing activity was enhanced by both viruses through dengue virus NS3 and influenza virus NS1 proteins , suggesting a similar virus-host co-evolution .
Influenza A viruses are the causative agents of seasonal and pandemic infections and are responsible for the death of at least half a million people worldwide each year . The genome of influenza A viruses is composed of eight negative-sense single-stranded RNAs encoding 13 proteins . NS1 and NS2 are derived from alternatively spliced RNAs that are transcribed from the eighth RNA segment . The segments are encapsidated by binding to nucleoproteins ( NP ) and the polymerase complex ( PA , PB1 and PB2 ) forming the viral ribonucleoproteins ( vRNPs ) . The viral particle contains eight vRNPs , the surface glycoproteins haemagglutinin ( HA ) and neuraminidase ( NA ) , the matrix proteins ( M1 and M2 ) and the NS2 protein . Some strains express the pro-apoptotic PB1-F2 protein and two additional virulence factors , PB1-N40 and PA-X , have been recently identified [1]–[3] . The NS1 protein is not incorporated in the virus . It exerts a large spectrum of functions through interactions with a variety of cellular components residing either in the cytoplasm or in the nucleus . NS1 is a pleiotropic virulence factor repressing innate antiviral mechanisms e . g . by interfering with the type I interferon system through direct interaction with PKR and TRIM25 , or through the sequestration of double-stranded RNA [4]–[8] . NS1 is also known to perturb the mRNA processing by interacting with CPSF4 and PABPN1 to inhibit nuclear export of cellular mRNA [9] and is suspected to hijack the RNA translation machinery in favor of translation of viral protein e . g . by interacting with STAU1 [10]–[11] . In contrast to NS1 , NS2 protein is a structural component of the viral particle and it associates with the viral matrix M1 protein [12] . NS2 mediates the export of vRNPs from the nucleus to the cytoplasm through export signal [13] via its interaction with XPO1 [14] . In addition , NS2 interacts with nucleoporins and was suggested to serve as an adaptor between vRNPs and the nuclear pore complex [13] . A role of NS2 in the regulation of influenza virus transcription and replication has also been proposed [15] . However , many functions of NS2 , in particular its transit through the cytoplasm and its incorporation into the viral particle , are not understood . Several screens have been performed to identify host factors involved in the influenza virus replication cycle , mainly focusing on interactors of vRNPs or of the polymerase by using affinity purification or yeast two-hybrid techniques [16]–[18] . A proteome-wide screen of virus-host protein-protein interactions has provided an important resource of 135 interactions [19] . However , the weak overlap of the public datasets suggests that they are far from being complete . The impact of cellular proteins on the influenza virus replication has been extensively studied using RNAi screens [19]–[24] . Although poorly overlapping at the gene level , these screens better converge at the level of biological processes [25]–[27] . Hence , more than the identification of host factors , these studies highlighted major cellular functions that are essential for the virus replication . However , for the majority of identified host factors , the mode of action remains to be determined . Furthermore , comparisons of strain-specific virus-host interactomes are clearly missing , which is required to reveal general principles governing infection mechanisms and to identify common therapeutic targets as well as broad-spectrum antivirals . In the present study we conducted stringent yeast two-hybrid screens to identify human proteins interacting with NS1 and NS2 from 9 influenza A virus strains representative of the variability in nature . The functional impact of all NS1 and NS2 interactors on viral replication was systematically addressed by RNA interference . In combination with published datasets , our new results offer a comprehensive view of NS1 and NS2 interactomes and corresponding targeted cellular functions . The global analysis of the NS1 and NS2 host cell targets reveals an enrichment of double-stranded RNA binding domain ( DRBD ) containing proteins for the 9 tested influenza virus strains . A focus was put on ADAR1 since this protein is critical for the replication of other viruses [28] , is highly expressed in human lung cells [29] , is induced by type I interferon [30] , is interfering with interferon signalling production [31] and is interacting with all tested NS1 proteins . In addition , we also observed in another screen that ADAR1 interacts with the dengue virus NS3 protein which is a bifunctional enzyme containing protease and helicase activity [32] . We show that ADAR1 is a pro-viral host factor favoring replication of influenza virus and dengue virus and that these viral proteins can control ADAR1 editing activity .
To identify all cellular proteins interacting with influenza virus NS1 and/or NS2 proteins , yeast two-hybrid screens ( Y2H ) were carried out using NS1 and NS2 proteins from 9 different virus strains as baits ( Table S1 ) and three cDNA libraries ( from human spleen , fetal brain and respiratory epithelium ) . Key features of the virus strains are provided in Text S1 . NS1 and NS2 proteins selected for this study are representative of the natural diversity since they are distributed all along the phylogenetic trees of known NS1 and NS2 sequences ( Text S1 , Figures S1 and S2 in Text S1 , Alignments of NS1 and NS2 protein sequences are presented in Figures S3 and S4 in Text S1 ) . Seventy nine non-redundant cellular proteins were identified to interact with NS1 , NS2 or both and were individually retested in a pairwise array ( Figure 1A ) . From a total of 1422 possible interactions tested ( 79 cellular proteins tested against 9 NS1 and 9 NS2 proteins ) , 562 tested positive . In this way , we identified 33 cellular proteins interacting exclusively with NS1 , 28 exclusively with NS2 , and 18 with both NS1 and NS2 . The vast majority ( 97 . 5% ) of the NS1 and NS2 interactors are known to be expressed in the respiratory epithelium ( Table S2 ) . Twelve out of the 79 host interactors have already been reported ( AIMP2 , SCRIB , CPSF4 , the kinases PIK3R1 , PIK3R2 , MAPK9 , CRK and proteins with a double-stranded RNA-binding domain STAU1 , PRKRA , ADAR1 , TARBP2 , ILF3 ) [9] , [11] , [19] , [33]–[38] . 21 . 5% of host interactors are targeted by all virus strains ( Figure 1B ) and 5% appear to be strain specific . 80% of the cellular interactors bind to more than 50% of the tested NS1 and NS2 proteins indicating that the dataset is more appropriate to the identification of common rather than differential interaction profiles . Together with previously published data available in the VirHostNet database [39] , we now provide a list of 111 non-redundant cellular proteins interacting exclusively with NS1 , 32 exclusively with NS2 and 18 with both proteins ( a complete list of influenza virus interactors is given in Table S3 ) . Consistent with observations from previous virus-host interactome studies , NS1 and NS2 proteins tend to interact with highly central proteins in the human interactome [40]–[43] . Indeed , the degree distribution of targeted human proteins was significantly higher than the degree distribution in the human interactome ( U-test , p-value<2 . 2×10−16 ) ( Figure 1C ) . Similarly , the betweenness distribution of targeted human proteins was significantly higher than the betweenness distribution in the human interactome ( U-test , p-value<2 . 2×10−16 ) ( Figure 1D ) . This suggests that influenza NS1 and NS2 proteins preferentially target pleiotropic cellular proteins [44] . Finally , an assessment of Gene Ontology categories revealed a significant enrichment ( p-value = 3 . 3×10−14 ) for DRBD-containing proteins ( DRBPs ) in the interaction dataset . Strikingly , DRBPs were exclusively targeted by NS1 proteins . All virus strains interacted with most of the DRBPs suggesting that the direct targeting of DRBDs is of special importance for influenza A viruses . Among the 79 NS1 and NS2 interactors identified here , 12 have been previously identified in recent genome-wide siRNA screens as modulators of viral replication - ATP6V1G1 , RPL13A [21] , [23] , GMEB1 , PIK3R2 [19] , SON , EEF1A1 [23] , CHCHD5 , RPL23A [24] and NUP214 [22] . However , since these genome-wide siRNA screens are weakly overlapping , it is very likely that numerous modulators of viral replication have been missed or remain to be confirmed [25]–[27] . We have therefore performed a systematic siRNA-based screen in A549 human lung epithelial cells to explore the functional contribution of the 79 cellular NS1 and NS2 interactors to virus replication . The silencing phenotype was first tested by measuring replication of the A/H1N1/Puerto Rico/8/34 virus strain which was used in the yeast two- hybrid screen . The complete replication cycle was first probed by measuring the neuraminidase activity in the supernatant 48 h post-infection . The assay was calibrated by using siRNAs against ATP6V1G1 and CSNK2B that have been previously described as pro-viral host factor and anti-viral host factor respectively . ATP6V1G1 is a subunit of the vacuolar ATPase proton pump required for influenza A virus replication [21] while CSNK2B gene silencing increases virus replication in A549 infected cells [45] . As expected , siRNAs targeting ATP6V1G1 and CSNK2B respectively reduced and increased the neuraminidase activity in the supernatant , thus validating the assay ( Figure 2A and 2B ) . By comparison with these controls , virus replication should be altered by at least 35% according to the threshold defined by König et al . in their genome-wide siRNA screen [22] . This threshold together with a silencing efficiency greater than 60% for each siRNAs without detectable cytotoxicity were used for a stringent selection of the pro-viral and anti-viral host factors ( Table S4 ) . These criteria are in the range applied in earlier siRNA-based screens [23] , [46] , [47] ( information on individual silencing efficiency is also provided in Table S4 ) . In this way , we identified the two pro-viral host factors , ADAR1 and RPSA , and confirmed ATP6V1G1 , RPL13A , EEF1A1 and SON ( Figure 2A ) . In addition , one new anti-viral host factor , N-PAC , was identified ( Figure 2A ) . These results were confirmed by using plaque assays , revealing a broader range of inhibition or activation of virus replication ( Table S4 ) . In conclusion , out of the 79 cellular interactors of NS1 and NS2 identified in this study , 7 were identified as possible direct modulators of A/H1N1/Puerto Rico/8/34 virus replication . Importantly , these results were confirmed with the A/H1N1/New Caledonia/2006 influenza virus strain that was not used in the yeast two-hybrid screen ( Figure 2B ) , although RPL13A only scored positive 72 h post infection ( Figure S5 in Text S1 ) . As it is a critical component of virus-host interaction , production of type I interferon was quantified in the supernatant of infected cells transfected with siRNAs ( Table S4 ) . Silencing of ADAR1 , ATP6V1G1 , BCLAF1 , RPL13A and SON increased interferon production in infected cells . These proteins interact with NS1 and , except for BCLAF1 , are pro-viral host factors for the virus . Therefore , 4 of the 6 pro-viral host factors are implicated in interferon production . This is consistent with the role of NS1 in interfering with the type I interferon system . The function and subcellular localization of cellular interactors identified in the present and published studies indicate that both NS1 and NS2 are pleiotropic proteins required for several essential steps of the viral life cycle ( Figure 3 ) . Although NS2 function in the cytoplasm remains elusive , it is shown here that NS2 mostly targets proteins of the cytoskeleton and involved in intracellular transport ( Figure 3 ) . Given that NS2 also interacts with the vRNPs , it might also mediate their transport to the plasma membrane or to the nucleus . In case of the latter , NS2 is implicated in the export of vRNPs , consistent with the observed interaction of NS2 with the NPC [13] . The targeting of transcription-regulating proteins by NS2 is much less documented . A role of NS2 in regulating influenza virus RNA genome transcription via its interaction with vRNPs has been previously proposed [15] , [48] . Although such a direct interaction with the components of the vRNPs is not ruled out , our data indicate a direct targeting of the cellular transcriptional machinery by NS2 ( Figure 3 , box regulation of transcription ) . Interestingly , since NS1 also targets this process , a potential cooperation between NS1 and NS2 for the control of the cellular transcription machinery can be speculated . NS1 proteins target several DRBPs either localized in the nucleus or in the cytoplasm [49] . These proteins are critical transcriptional or translational checkpoints . NS1 is known to inhibit the activation of PKR , one of the major interferon-inducible antiviral effectors , through direct interaction [6] . More recently , SON has been described to be important for the trafficking of influenza virions [23] . Here , we confirmed that SON is essential for viral replication and suggest that this activity could be related to the NS1 protein . ADAR1 and PKR have an opposite effect on virus replication although they are both induced by type I interferon . ADAR1 is a type I interferon-induced protein that is expressed in human lung [29] , [30] and interacts with all tested NS1 proteins . The 150 kDa interferon-inducible ADAR1 isoform is expressed in A549 cells upon influenza A virus infection and by type I interferon . The constitutive 110 kDa ADAR1 isoform was only induced upon infection indicating that ADAR1 expression can also be controlled by an interferon-independent mechanism , at least in the setting of an influenza A virus infection ( Figure 4A ) . ADAR1-specific siRNAs efficiently reduced the expression of ADAR1 isoforms and blocked their induction upon infection ( Figure 4B ) . The silencing of ADAR1 inhibited virus release from 15% at 8 h post-infection to 90% at 48 h post infection ( Figure 4C ) . Expression of viral proteins ( here HA , NP , M1 and NS1 ) was also significantly reduced as early as 8 h post infection . NS1 , NP and M1 expression was delayed while HA expression remained very low until 24 h post infection ( Figure 4B ) . Thus , ADAR1 is a pro-viral host factor for virus protein expression and virus production . Immunofluorescence revealed that ADAR1 is diffusely distributed in the nucleus and relocalized in nuclear structures in influenza virus-infected cells ( Figure 5A ) . In these structures ADAR1 colocalized with NS1 but not with HA for which no interaction with ADAR1 could be detected . As NS1 interacts with several DRBD-containing proteins , the NS1 binding site in ADAR1 could be a DRBD . Amino acid sequence alignment of DRBDs revealed a conserved region of 47 amino acid residues within the two firsts DRBD of ADAR1 ( Figure S6 in Text S1 ) . A set of 4 ADAR1 deletion mutants , differing in their number of DRBDs , and a plasmid encoding the 47 amino acid residues of the first DRBD were constructed ( Figure 5B ) . In a yeast two-hybrid array , ADAR1 interacted with NS1 even in the absence of its first DRBD while interaction was completely abrogated when the first two DRBDs were deleted . The peptide of 47 amino acid residues also interacted with NS1 ( Figure 5C ) in the array and in GST pull-down assays ( Figure 5D ) . Thus , ADAR1 displays two potential NS1 interaction sites located on the first two double-stranded RNA-binding domains . To validate these results the NS1 RNA-binding domain ( RBD ) and effector domain fused to GST were used in pull-down experiments for the mapping of NS1 interaction with 3×Flag tagged ADAR1 after co-expression in HEK293T cells ( Figure 5E ) . Full-length NS1 and NS1 RBD domain efficiently co-precipitated ADAR1 but not the effector domain ( Figure 5F ) indicating that NS1 interacts with ADAR1 through its RBD . GST pull-down and RNAse A treatment showed that RNA is marginally involved in the NS1-ADAR-1 interaction ( Figure S7 in Text S1 ) . A mutant of NS1 that lacks double-stranded RNA-binding activity still interacts with ADAR1 , albeit with reduced efficiency ( Figure S8 in Text S1 ) confirming that RNA is not strictly required for NS1 interaction with ADAR1 . To evaluate the functional impact of ADAR1-NS1 interaction on the catalytic activity of the enzyme , an original editing reporter system was constructed . This reporter system consists of a 24 nucleotide-long minimal ADAR1 substrate derived from the sequence of the antigenome of the hepatitis delta virus that is edited by this enzyme [50] . In this sequence , ADAR1 editing activity changes a stop codon into a tryptophane codon ( Figure 6A ) [51] . The reporter plasmid contains the ADAR1 substrate sequence inserted in frame in-between the Renilla and the Firefly luciferase genes ( Figure 6A ) . In this configuration , the Firefly luciferase activity reflects the extend of editing and thus ADAR1 activity , leading to the conversion of the stop codon into the tryptophane codon . ADAR1 was co-expressed in HEK293T cells with NS1 or its RBD and with the editing reporter construct . The NS1 effector domain or DLG4 , which does not bind to ADAR1 ( data not shown ) , was used as negative control in analogous co-transfection experiments . NS1 RBD and full-length NS1 increased the Firefly luciferase signal by 30% and 60% respectively ( Figure 6B ) suggesting that NS1 can cooperatively interact with ADAR1 via its RNA-binding domain to promote ADAR1 editing activity ( Figure 6B ) . Editing activity was also analyzed in the context of influenza virus infection after expression of the editing reporter construct ( Figure 6C ) . H1N1 influenza virus infection increased the editing activity of ADAR1 by 70% and this was completely reversed when ADAR1 expression was silenced by RNA interference . To validate these observations , a catalytically inactive ADAR1 ( E912A ) mutant was constructed [52] . Unfortunately , A549 cells became refractory to plasmid DNA transfection after siRNA transfection , thus precluding functional tests of the mutant in this cell line ( not shown ) . As an alternative , we tested a potential transdominant negative effect of the ADAR1 mutant on influenza virus growth . The catalytically inactive ADAR1 ( E912A ) mutant construct was therefore transfected into A549 cells and virus growth in these cells was compared to the one achieved with mock-transfected cells or in wild type ADAR1-transfected cells . Viral protein expression was reduced in A549 cells expressing the ADAR1 mutant compared to control cells ( Figure 6D ) . Consistent with this result , neuraminidase activity in the supernatant was also significantly reduced ( Figure 6E ) . Importantly , since influenza A virus infection induces endogenous ADAR1 expression , the impact of the ADAR1 mutant is most likely underestimated in this experimental system . We therefore concluded that the RNA editing function is required for the pro-viral activity of ADAR1 . During the course of a systematic screening for virus-host protein-protein interactions with a yeast two-hybrid system , we also identified ADAR1 as an interactant of the NS3 protein of dengue virus type 2 . This interaction was confirmed in a yeast two-hybrid pairwise array ( Figure 7A ) . As for NS1 of influenza A virus , interaction between NS3 and ADAR1 was validated by GST pull-down experiments ( Figure 7B ) and also in this case , RNA contributed to this interaction only to a very minor extent ( Figure S9 in Text S1 ) . Both ADAR1 isoforms were induced upon dengue virus infection as well as upon type I interferon treatment of Huh-7 cells ( Figure 7C ) . Silencing of ADAR1 expression by RNA interference ( Figure S10 in Text S1 ) resulted in a strong decrease of dengue virus replication ( Figure 7D ) . This result was confirmed with a subgenomic dengue virus replicon stably replicating in Huh-7 cells , indicating that ADAR1 acts at a post-entry step in the dengue virus life cycle ( Figure S11 in Text S1 ) . Likewise , as observed for influenza virus , dengue virus infection strongly increased the editing activity of ADAR1 ( Figure 7E ) . In fact , full-length NS3 and the helicase domain increased the Firefly signal by 24% and 44% respectively , suggesting that NS3 cooperatively interacts with ADAR1 to enhance its editing activity ( Figure 7F ) . In conclusion , both influenza virus and dengue virus ( i ) induce over-expression of ADAR1 , ( ii ) interact with ADAR1 through the RNA-binding domain of influenza virus NS1 and the helicase domain of dengue virus NS3 , ( iii ) enhance the editing activity of ADAR1 and ( iv ) are dependent on ADAR1 expression for efficient virus replication .
This study describes an exhaustive interaction profile for NS1 and NS2 proteins of 9 influenza virus strains . More than 560 interactions between 79 cellular proteins and NS1 and NS2 were identified . Thirty-three cellular proteins interacted exclusively with NS1 , 28 exclusively with NS2 , and 18 with both NS1 and NS2 . Since NS1 and NS2 are the products of alternatively spliced RNAs , shared interactions may reflect binding to the common N-terminal 10 amino-acid residues long sequence . This result suggests that influenza viruses have evolved two proteins to interact with cellular proteins that are potentially essential for them . Twelve out of the 79 NS1 and NS2 cellular interactors have already been reported in the literature , demonstrating the reliability and robustness of our screening approach . For NS1 , there is a strong overlap with hits published by others ( 11 of the 51 interactors identified in the present study , which is well above the average overlap ) [41] , [53] , suggesting that the NS1 interactome dataset is now close to completion . In case of NS2 , only 4 cellular interactors have been published and one of them , AIMP2 , has been confirmed in our screens . Although 46 new NS2 interactors have been identified , it is difficult at this stage to estimate the completion level of the NS2 interactome due to the lack of published interaction data . Overall , most of the cellular targets interacted with the majority of NS1 or NS2 proteins of the different influenza viruses arguing that we have identified highly relevant and evolutionary conserved interactions . Interestingly , a significant proportion of these proteins is also targeted by other viruses ( 44 . 7% , exact Fisher test , p-value<2 . 2×10−16 ) indicating that these cellular proteins are likely to be involved in a generic process of viral infection [39] . Our interaction dataset indicates that NS1 and NS2 proteins are likely to be involved in multiple steps of the viral replication cycle , paving the way for challenging functional explorations . This was largely unexpected for NS2 , which is known to be involved in the nuclear export of the vRNPs . Its interaction with the cytoskeleton appears particularly interesting for further studies . Although the pleiotropic nature of NS1 is well established [54] , our study provides new insights into the breadth of interactions and activities of this regulatory protein . In addition to the 67 new interactors , the current dataset also provides additional information on previously known interactors and related targeted functions . For instance , the CPSF4 interaction with NS1 has been described as a potential therapeutic target [55] and is confirmed in our study . Three NS1 proteins also interacted with CPSF3L , a protein participating in the endonuclease activity of CPSF , suggesting that the corresponding viruses evolved alternative strategies to interfere with the cellular 3′end mRNA processing [56] . The phenotypic analysis of the cellular targets of NS1 and NS2 by RNA interference revealed an enrichment in modulators of influenza virus replication , further validating the interaction dataset . Indeed , out of the 79 cellular interactors of NS1 and NS2 identified in this study , 7 revealed to control positively or negatively the replication of two influenza virus strains . Interaction profiles suggest that the data could be extrapolated to other strains with the noticeable exception of RPL13A , an exclusive target of A/H1N1/Puerto Rico/8/34 NS1 . The validation rate of cellular interactors by RNAi reached about 9% ( 15 . 2% when data from the literature are included ) and is similar to that of Shapira et al . [19] while the validation rate of virus replication modulators identified from genome-wide siRNA screens ranges from 0 . 75 to 1 . 5% . Therefore , combining interactomic screens with genetic screens drastically enhances the rate of functional validation , providing lists of cellular proteins strongly enriched in pro- and anti-viral host factors ( exact Fisher test , p-value = <2 . 1 10−4 , Text S1 ) . Interaction of NS1 with some members of the DRBD protein family have been sporadically documented [6] , [10] , [19] . Here we observed a massive enrichment of the DRBD protein family in our NS1 interactome for which we used 9 different influenza virus strains . One hundred and sixty five independent screens have been performed with other viral baits using the same cDNA libraries ( 45 with the fetal brain cDNA library , 31 with the respiratory epithelium library and 89 with the spleen library ) . The GO term “Double-stranded RNA-binding domain ( DRBD ) containing proteins” has never been enriched in any of these screens while it was enriched for the 9 tested influenza virus strains . Reciprocally , a large diversity of other GO terms was enriched in these different screens and in screens performed by other laboratories using the same libraries . Therefore , we could be confident that the DRBD containing proteins enrichment reflects a real propensity of NS1 to interact with this protein family . This is most likely reflecting the ability of NS1 to interact with the double-stranded RNA-binding domain of cellular partners through its own RNA-binding domain . Two DRBD-containing proteins , SON and ADAR1 , were found to be essential for virus replication . Conflicting results on the role of ADAR1 for virus replication have been published . Initially suspected to have an antiviral activity because of its induction by interferon , ADAR1 appears to promote the replication of several viruses ( measles virus , vesicular stomatitis virus , hepatitis delta virus , human immunodeficiency virus type 1 and Kaposi's sarcoma-associated virus ) . In contrast ADAR1 was reported to display an antiviral activity against hepatitis C virus and lymphocytic choriomeningitis virus [28] , [57] . Concerning influenza A virus , two studies provided evidence for an antiviral role of ADAR1 . Mice lacking IKKε become highly susceptible to influenza virus infection , express ADAR1 only to low amounts and show a reduced editing of matrix M1 mRNA isolated from infected lung . However , since IKKε knock-out also strongly affects the expression of other type I interferon-stimulated genes , the susceptibility of these mice to infection could not be attributed to a unique defect in ADAR1 activity [58] . An increased cytopathic effect of influenza A virus has been observed in mouse cells derived from non-viable embryos unable to express the p150 isoform of ADAR1 . However , this effect was not correlated to an increased virus replication [59] . In the present study , we show that inhibiting ADAR1 expression by RNA interference reduced viral protein expression and drastically impaired virus replication . Thus , ADAR1 appeared as an important host dependency factor for influenza viruses . Several studies have demonstrated a role of ADAR1 in modulating interferon signaling . Inducible ADAR1 disruption in mice causes a global interferon response [31] . Mutations in ADAR1 responsible for Aicardi-Goutières syndrome in humans are associated with upregulation of interferon-stimulated genes [60] . ADAR1 also suppresses measles virus-induced production of interferon-β mRNA [61] . Here , we show that interferon-β is enhanced in ADAR1-deficient cells after infection with influenza A virus . NS1 is a well-known antagonist of the antiviral response . Its mode of action is pleiotropic including interference with signaling induced by RIG-I like receptors ( RLRs ) [62] . A combined action of ADAR1 and NS1 protein is suggested by our results . The double-strand RNA editing activity of ADAR1 produces double-strand RNA with I:U pairs instead of A:U pairs . Interestingly , I:U-containing double-strand RNA can suppress the induction of interferon-stimulated genes [63] . Conceivably NS1 might potentiate the hyperediting of an as yet unknown double-strand RNA substrate and thus interfere with interferon induction . A similar mechanism can be expected for dengue virus NS3 protein . Both NS1 and ADAR1 also interfere with PKR activity [6] , [64] . ADAR1 and PKR are recognized by non overlapping domains of NS1 ( respectively the RNA binding domain and the effector domain [7] ) . Thus , both NS1 and ADAR1 could sequester double-strand RNA or could form inactive complexes , suppressing PKR-mediated proapoptotic and interferon-mediated amplification activities . Influenza A NS1 protein is considered as a valid target for the development of antiviral drugs . The druggability of NS1 has been demonstrated in a proof-of-concept study with an inhibitory peptide derived from CPSF30 , a cellular protein that interacts and interferes with the effector domain of NS1 [55] . Such a strategy can be extended to other NS1 interactors once the interacting sequences have been mapped and the 3D structure is solved . The interacting sequences , e . g . the ADAR1-derived 47 amino acid peptide , could then be used for the design of low molecular weight compounds . In this respect , the systematic screening for protein-protein interactions between a virus and its host cell identifies cellular proteins promoting or restricting virus replication . Interference with these interactions may offer new alternatives to enlarge the diversity of potential therapeutic targets and prevent the emergence of resistance caused by rapid viral adaptation . Small molecules targeting these host interaction surfaces and developed for other therapeutic purposes could now be tested for their ability to control virus replication . Concerning ADAR1 , new inhibitors of the RNA editing activity are being screened and could be tested for their capacity to block the replication of influenza A virus or anti-dengue virus [65]–[67] . The dual luciferase editing reporter described in this study is well suited for screening RNA editing inhibitors at a high throughput level .
Human HEK293T and human lung adenocarcinoma A549 cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , 50 IU/ml penicillin G , 50 µg/ml streptomycin , at 37°C under 5% CO2 . Huh-7 cells were grown in DMEM supplemented with 2 mM L-glutamine , non-essential amino acids , 100 IU/ml of penicillin , 100 µg/ml of streptomycin and 10% fetal calf serum . Influenza ORFs ( Text S1 ) were transferred from pDONR207 into bait vector ( pPC97 , Lifetechnologies ) to be expressed as Gal4-DB fusions in yeast . Bait vectors were transformed into AH109 ( bait strain , Clontech [68] ) , and human spleen , fetal brain and respiratory epithelium Gal4-AD-cDNA libraries ( each containing more than 106 primary clones ) were transformed into Y187 ( prey strain , Clontech ) . Single bait strains were mated with prey strains and diploids were plated on SD-W-L-H+ 10 mM 3-AT medium . Each screen has covered more than one time the libraries . Positive clones were maintained onto this selective medium for 15 days to eliminate any contaminant AD-cDNA plasmid . AD-cDNAs were PCR-amplified , sequenced and analyzed using pISTil [69] . Cellular ORFs ( interacting domains found in Y2H screens ) were amplified from a pool of human cDNA libraries or from a plasmid encoding the corresponding cDNA from the MGC collection ( IMAGE consortium ) using KOD polymerase ( Toyobo ) and cloned by recombinational cloning into pDONR207 ( Invitrogen ) . Primers contained the attB1 . 1 and attB2 . 1 gateway recombination sites . All entry clones were sequence-verified and individually transferred by recombinational cloning into a prey vector ( pPC86 , Invitrogen ) to be expressed as Gal4-AD ( activating domain ) fusion in yeast . Pairwise yeast two-hybrid interaction analyses were also performed by yeast mating using Y187 and AH109 yeasts strains ( Clontech [68] ) , as described in [70] . Bait and prey strains were mated in an all-against-all array ( together with negative controls , either empty bait vector or empty prey vector ) and plated on a selective medium lacking histidine and supplemented with increasing concentrations of 3-AT ( 0 , 5 , 10 , 15 mM ) to test the interaction-dependent transactivation of HIS3 reporter gene . Interactions were scored as positive if observed in at least 2 out of 3 independent arrays . When yeasts containing an empty bait vector and a prey vector were still able to grow , the corresponding proteins were rejected as being auto-activators and thus false positives . The R statistical environment was used to perform statistical analysis and the igraph R package to compute network topology measures [71] . Protein-protein interaction networks are formed by a set of N nodes ( or vertices ) representing proteins connected by E edges representing physical interactions between these proteins . The topology of protein-protein interaction networks can be described by a set of measures: The degree or connectivity ( k ) of a node v in a graph is a local centrality measure which summarizes the number of edges that are incident to this node v . The betweenness ( b ) of a node v in a graph is a global centrality measure which can be defined by the number of shortest paths going through this node v and is normalized by twice the total number of protein pairs in the graph . The equation used to compute betweenness centrality , b ( v ) , for a node v is:where gij is the number of shortest paths going from node i to j , i and j ∈ V and gij ( v ) the number of shortest paths from i to j that pass through the node v . DAVID database was used for functional annotation [72] . DAVID functional annotation chart tool was used to perform Gene Ontology categories analysis . Gene Ontology terms with a Benjamini-Hochberg corrected p-value smaller than 5 . 102 were considered as significantly overrepresented . 5 pmoles of each siRNA ( stealth select RNAi , Invitrogen ) were arrayed in 96 plates in 10 µl of OptiMEM ( 2 siRNAs per gene ) . After 20 minutes of room temperature incubation with a transfection agent ( 0 . 2 µl of lipofectamine RNAiMAX in 10 µl of OptiMEM ) , siRNA-transfection agent mix was added to 3 . 104 A549 suspension cells . Cells were incubated for 48 hours at 37°C and 5% CO2 before influenza A virus infection at MOI 0 . 5 . At indicated time post-infection , supernatants were titered . siRNA-transfected cells were washed twice with DMEM and infected with the A/H1N1/Puerto Rico/8/34 strain or the A/H1N1/New Caledonia/2006 strain at indicated MOI in infection medium ( DMEM supplemented with 0 . 2 µg . ml−1 TPCK-trypsin ( Sigma ) ) . After 1 h at 37°C , the inoculum was discarded and cells were washed again and incubated in infection medium at 37°C and 5% CO2 . Standard fluorimetric assay was used to measure influenza virus neuraminidase activity [73] . Influenza virus neuraminidase is able to cleave the methyl-umbelliferyl-N-acetylneuraminic acid ( 4-MUNANA , Sigma ) yielding a fluorescent product that can be quantified . In 96-black plate , 25 µl infection supernatants were diluted in 25 µl D-PBS containing calcium and magnesium and the reaction was started with 50 µl of 20 µM 4-MUNANA . After 1 h incubation at 37°C , the reaction was terminated by adding 100 µl of glycine 0 . 1 M , 25% ethanol pH 10 . 7 . Fluorescence was recorded with TECAN infinite M1000 instrument at 365 nm excitation and 450 nm emission wavelengths . ADAR1 was transferred from pDONR207 to pCIneo3×Flag ( kind gift of Dr Y . Jacob , Pasteur Institute , Paris , France ) . NS1 and NS3 constructs were transferred in pDEST27 ( Invitrogen ) . Plasmids coding for mutant NS1 ( pCAGGS-NS1-R38AK41A ) and control NS1 ( pCAGGS-NS1 ) are kind gifts from A . Garcia-Sastre ( Mount Sinai School of Medicine , New York ) . HEK293T cells were transfected in 6-well plates using JetPEI transfection reagent ( Polyplus Transfection ) . 48 h post-transfection , cells were lysed in a cold extract buffer ( 20 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , 0 . 5% Igepal and a protease inhibitor cocktail ( Roche ) ) . Protein extracts ( 300 µg ) were incubated overnight with Glutathione Sepharose 4B beads ( GE Healthcare ) at 4°C . Beads were then extensively washed with the cold extract buffer , proteins were separated by SDS-PAGE and transferred to a nitrocellulose membrane . GST-tagged viral proteins and 3×FLAG-tagged cellular proteins were detected using standard immunoblotting techniques with a mouse peroxidase-conjugated anti-GST monoclonal antibody ( Sigma ) or a mouse peroxidase-conjugated anti-FLAG M2 monoclonal antibody ( Sigma ) . When indicated , pull-downs were treated with 2 µg of RNAse A ( Invitrogen ) in a buffer containing 100 mM NaCl for 30 min at 4°C . Proteins bound and released in the supernatants were then detected by immunoblotting using anti-ADAR ( Sigma ) , anti-influenza A virus ( Chemicon ) and anti-NS1 antibodies ( Abcam ) . Anti-actin antibody was purchased from Sigma . HEK293T were transfected in 24-well plates with a total of 1 µg plasmid DNAs ( editing reporter plasmid , 3XF-ADAR1 and plasmids coding for indicated viral proteins ) using the JetPEI . 24 h post-transfection , cells were seeded in 96-well plates in DMEM and incubated for 24 h . The Dual-Glo Luciferase Assay System ( Promega ) was then added to measure both Firefly and Renilla luminescence activities using the TECAN infinite M1000 instrument . Relative Light Unit ( RLU ) is the ratio of luminescence from FLUC to luminescence from RLUC . For influenza virus , HEK293T cells were seeded at 20 , 000 cells/well in 96-well plate and were transfected or not with anti-ADAR1 or control siRNAs , 24 h prior transfection with the editing reporter plasmid . 24 h post transfection , cells were infected influenza A virus at MOI 10 in DMEM supplemented with 10% FCS . 24 h post-infection cells were subjected to the procedure of editing assay described above . For dengue virus , Huh-7 cells were seeded at 3 . 105 cell/well in 12-well plates and were transfected or not with anti-ADAR1 or control siRNAs , 24 h day prior transfection with the editing reporter . 24 h later , cells were infected with dengue virus type 2 with an MOI of 20 . Luciferase values were measured as described above . A549 cells were infected with influenza A virus at MOI 3 in DMEM supplemented with 50 IU/ml penicillin , 50 µg/ml streptomycin and 0 . 25 µg/ml TPCK-trypsin . Eight hours post-infection cells were fixed with 4% formaldehyde for 30 min and permeabilized with 0 . 5% Triton X100 . Double staining were performed by incubation with mouse monoclonal antibodies anti-NS1 ( clone 1A7 , kindly provided by Robert G . Webster ) or anti-HA ( Abcam ) and rabbit anti-ADAR ( Sigma ) in combination with Alexa 488-labeled anti-rabbit F ( ab ) ′2 fragment and Alexa 546-labeled anti-mouse F ( ab ) ′2 fragment ( Molecular Probes ) . Analyzes were performed with a laser-scanning confocal microscope ( Axioplan LSM510 v3 . 2 ( Zeiss ) ) and images were processed using LSM Image Browser ( Zeiss ) . | Viruses are obligate intracellular parasites that rely on cellular functions for efficient replication . As most biological processes are sustained by protein-protein interactions , the identification of interactions between viral and host proteins can provide a global overview about the cellular functions engaged during viral replication . Influenza viruses express 13 viral proteins , including NS1 and NS2 , which are translated from an alternatively spliced RNA derived from the same genome segment . We present here a comprehensive overview of possible interactions of cellular proteins with NS1 and NS2 from 9 viral strains . Seventy nine cellular proteins were identified to interact with NS1 , NS2 or both NS1 and NS2 . These interacting host cell proteins are potentially involved in many steps of the virus life cycle and 7 can directly control the viral replication . Most of the cellular targets are shared by the majority of the virus strains , especially the double-stranded RNA binding domain protein family that is strikingly targeted by NS1 . One of its members , ADAR1 , is essential for influenza virus replication . ADAR1 colocalizes with NS1 in nuclear structures and its editing activity is enhanced by NS1 expressed on its own and during virus infection . A similar phenomenon is observed for dengue virus whose NS3 protein also interacts with ADAR1 , suggesting a parallel virus-host co-evolution . | [
"Abstract",
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"Methods"
] | [
"systems",
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"virology",
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] | 2013 | The Interactomes of Influenza Virus NS1 and NS2 Proteins Identify New Host Factors and Provide Insights for ADAR1 Playing a Supportive Role in Virus Replication |
Major Histocompatibility Complex ( MHC ) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells , thereby preventing disease progression . MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface . Competing fragments ( peptides ) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I , by a process known as peptide optimization . A better understanding of the optimization process is important for our understanding of immunodominance , the predominance of some T lymphocyte specificities over others , which can determine the efficacy of an immune response , the danger of immune evasion , and the success of vaccination strategies . In this paper we present a dynamical systems model of peptide optimization by MHC class I . We incorporate the chaperone molecule tapasin , which has been shown to enhance peptide optimization to different extents for different MHC class I alleles . Using a combination of published and novel experimental data to parameterize the model , we arrive at a relation of peptide filtering , which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates . From this relation , we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface , and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding . Importantly , our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles , providing a quantitative basis to investigate viral infection or disease at the cellular level . We exemplify this by simulating optimization of the distribution of peptides derived from Human Immunodeficiency Virus Gag-Pol polyprotein .
MHC class I molecules are encoded within the genetic region known as the Major Histocompatibility Complex and are present in all nucleated human cells . MHC class I molecules direct cytotoxic T lymphocytes ( CTL ) to destroy virus-infected or cancerous cells , thereby preventing disease progression [1] . They provide a snapshot of the internal contents of a cell by binding to peptides arising from intracellular protein turnover and presenting these peptides at the cell surface , where the peptide-MHC complex can be recognized by CTL ( Fig . 1 ) . Most cells will present an array of tens of thousands of different peptides at their surface , some of which will be unique to virus-infected or cancerous cells . The efficacy of a CTL response to these peptides depends to a large extent on the ability of MHC class I molecules to select only a limited number of the potentially billions of different peptides that are generated by the hydrolysis of all intracellular proteins [2] . Peptides are selected for presentation on the basis of their ability to form a stable complex with MHC class I , by a process known as peptide optimization . A better understanding of the optimization of peptides is important for our understanding of immunodominance [1] , the predominance of some CTL specificities over others , which can determine the efficacy of an immune response , the danger of immune evasion , and the success of vaccination and immunotherapeutic strategies . Peptide binding to MHC class I occurs in the endoplasmic reticulum ( ER ) and is assisted by multiple cofactors that are thought to enable the optimization process [3] . The transporter associated with antigen processing ( TAP ) supplies the lumen of the ER with peptides generated in the cytosol and forms the backbone of the peptide loading complex ( PLC ) . A number of chaperone molecules also comprise the PLC , namely calreticulin , calnexin , ERp57 and importantly tapasin , which bridges the gap between MHC class I and TAP [4] ( Fig . 1 ) . Of these chaperones , only tapasin is known to influence the extent of peptide optimization , in such a way as to skew the cell surface cargo towards peptides with low off-rates [5] , and this influence is known to vary across different MHC class I alleles [6] . While a range of interactions between tapasin and MHC class I have previously been identified [7] , [8] , the effects of these interactions on peptide optimization are still not well-understood . A recent study used computational modeling to distinguish between different hypotheses of tapasin function within the ER [9] , but the model assumed that only peptides with low off-rates could egress to the cell-surface , and was therefore unable to predict the optimization of peptides with different off-rates . As a result , the model was unable to account for observed effects of tapasin on peptide optimization , both over time [6] and at steady state [5] . In this paper we present a dynamical systems model for predicting MHC class I peptide optimization . We include interactions with the chaperone molecule tapasin , and propose a relation of peptide filtering to quantify peptide optimization as a function of peptide supply and peptide off-rates . Using a combination of published and novel experimental data , together with a combination of Bayesian inference and kinetic analysis , we show that tapasin can improve both the rate and extent of peptide optimization by accelerating peptide off-rate , and that differences in optimization across MHC class I alleles can be explained by an allele-specific peptide on-rate . Our filtering relation provides a mechanistic interpretation for recent experimental observations of peptide optimization both over time [6] and at steady state [5] . Finally , we demonstrate how the filtering relation can be used to quantify optimization of a large set of competing peptides in the context of an immune response , by simulating the cell surface abundance of Human Immunodeficiency Virus ( HIV ) peptides in complex with different MHC class I alleles .
We formulated a dynamical systems model of MHC class I peptide optimization using a biological modeling language ( SPiM [10] , Fig . S1 in Text S1 ) and exported the model to an equivalent set of biochemical reactions for further analysis ( Fig . 2 ) . The model characterizes the interactions between MHC , peptides and tapasin within the endoplasmic reticulum , together with the dynamics of egressed peptide-MHC complexes at the cell surface . A number of simplifying assumptions were made when constructing the model: ( i ) Peptides are supplied to the ER at rate and then degraded or removed from the ER at rate . Since different peptides can have different levels of abundance within the cytoplasm and different rates of TAP transport , each peptide is associated with its own generation rate . ( ii ) MHC class I heavy chain and m are assumed to represent a single unit , where m dissociation from empty class I heavy chain is interpreted as a form of MHC degradation . ( iii ) All peptides are assumed to have a similar rate of binding to MHC , such that peptide affinity is determined by a peptide-specific rate of unbinding , and is defined as . This is motivated by the measurements in [11] . ( iv ) Since MHC , tapasin and peptide continually cycle between the ER and Golgi apparatus [12] , [13] , we do not explicitly represent the Golgi as a separate compartment . Instead , we consider our ER compartment to include both the ER and Golgi , where the rate of egress represents the rate of transit from the Golgi to the cell surface . By representing this process as a first-order reaction , we are making the simplifying assumption that the quantity of peptide-MHC complexes which egress is related to the quantity of complexes in the ER . ( v ) MHC can load peptides in the presence of tapasin at a higher rate , which implicitly models the stabilization of TAP molecules by tapasin , but we neglect egress of tapasin-bound MHC , since tapasin retains MHC by bridging it to the TAP transporter [14] . ( vi ) Tapasin can increase the rate of peptide unbinding from MHC by a factor [8] , while peptide can increase the rate of tapasin unbinding from MHC by a factor [15] . Tapasin has been shown to increase the peptide off-rate to a similar extent for peptides with a range of off-rates , though some variation has been shown for certain classes of peptide [8] . ( vii ) We neglect egress of empty MHC , which is retained and recycled in the ER by the chaperone calreticulin [16] , [17] . ( viii ) Furthermore , we assume that m dissociation from peptide-loaded or tapasin-bound class I heavy chain is negligible compared to m dissociation from empty class I heavy chain . ( ix ) Once at the cell-surface , peptide unbinds from MHC irreversibly at rate , and empty MHC is degraded at rate . These assumptions can be refined in future iterations of the model . MHC class I HLA-B alleles were previously shown to differ in their ability to optimize their peptide cargo over time , both in the presence and absence of tapasin [6] . Specifically , the HLA-B4402 ( B4402 ) allele was shown to be highly dependent on tapasin for peptide optimization , while the HLA-B2705 ( B2705 ) and HLA-B4405 ( B4405 ) alleles were shown to be less tapasin-dependent . B2705 is of particular interest because it is a susceptibility factor for certain autoimmune diseases and is associated with long-term non-progression of HIV [18] . Therefore , we sought to use our peptide optimization model to explain the variation in tapasin-dependence between HLA-B alleles , through a combination of model simulation and Information Theory . We simulated pulse-chase experiments [6] using the peptide optimization model of Fig . 2 , with representative peptides of low , medium and high affinity ( Text S1 ) . The experiments followed the thermostability of fixed cohorts of MHC class I complexes over time , making use of the known correlation between the thermostability of complexes and the affinity of their peptide cargo . Specifically , complexes stable at were shown to contain only high affinity peptides , complexes stable at were shown to contain a combination of medium and high affinity peptides , while all complexes were shown to be stable at , including empty MHC . Since the measurements correspond to both ER-localized and egressed peptide-MHC complexes , our assessment of the model was performed by comparing total peptide-MHC complexes with the measurement , total medium and high affinity complexes with the measurements , and total high affinity complexes with the measurements . Since many of the kinetic parameters of the model have not previously been measured directly , due to the technical difficulties involved in obtaining such measurements , we used heuristic search methods to infer the parameter values from the experimental data [6] ( see Methods ) . Essentially , this involved finding values for the parameters which minimized the deviation between the experimental data and the corresponding model simulation . Using this approach , we investigated how allelic variation in HLA-B might affect peptide optimization , by distinguishing between allele parameters , which were allowed to vary between alleles , and fixed parameters , which were assumed to be invariant between alleles . Each hypothesized set of allele parameters defined a variant of the model , which possessed a different intrinsic ability to reproduce the observed dynamics . The Bayesian Information Criterion ( BIC ) [19] was used to quantify the performance of each set of allele parameters ( equation ( 18 ) in Methods ) . BIC incorporates a term which penalizes the deviation of the simulation from the data , and a second term which penalizes increasing numbers of allele parameters . Therefore , BIC can be used to assess a range of models by taking into account the added cost of additional unconstrained variables . Since the dynamics of peptide optimization varied considerably between HLA–B alleles in the absence of tapasin [6] , we reasoned that at least one allele parameter must be tapasin-independent . To incorporate this insight whilst focusing on the principal contributors to allelic variation , we examined combinations of up to two allele parameters , with at least one tapasin-independent parameter selected from , , , , and . The best BIC scores were obtained when the peptide on-rate was the only allele parameter ( 470 . 38 ) , and when both and the rate of egress were the allele parameters ( 469 . 39; Fig . 3 ) , suggesting that at least peptide on-rate is allele-specific . However , having both and as allele parameters required unrealistically fast egress of B2705 and B4405 complexes to obtain a closer fit to the data ( Fig . S2 in Text S1 ) . Therefore a single allele parameter ( Fig . 4 ) was used , which was able to effectively account for the experimental data [6] . Specifically , in the absence of tapasin B4402 exhibited worse time-dependent optimization than both B2705 and B4405 ( Fig . 4 A ) , while in the presence of tapasin B4402 exhibited better time-dependent optimization than both of these alleles ( Fig . 4 B ) . To ensure that the MCMC search algorithm was robust to random variations , and could reproducibly generate consistent parameter estimates , we produced 10 different chains for each model hypothesis . For the allele-specific model , 8 out of 10 chains converged to BIC values between 470 . 38 and 470 . 69 , while the other two chains performed poorly . We next plotted the mean and standard deviation of the posterior distributions of the model parameters for each of the 10 chains , which revealed that the 8 high performing chains had overlapping posterior distributions ( Fig . S3 in Text S1 ) , and were therefore producing consistent parameter estimates . To understand the effects of an allele-specific peptide on-rate on peptide optimization , we plotted MHC complexes with high , medium , and low affinity peptide separately , and distinguished free and tapasin-bound MHC complexes within the ER and at the cell surface ( Fig . S4 in Text S1 ) . For B4402 without tapasin , an intrinsically low peptide on-rate meant that the majority of B4402 complexes remained in the ER without peptide , resulting in very low optimization . For B2705 and B4405 without tapasin , an intrinsically high peptide on-rate meant that these alleles rapidly bound their peptide cargo and exhibited good time-dependent optimization . In contrast , for B4402 with tapasin , most complexes first bound to tapasin and were subsequently able to rapidly bind peptides and optimize their peptide cargo , presenting almost exclusively high affinity peptides at the cell surface . For B2705 and B4405 with tapasin , the intrinsically high peptide on-rate meant that peptide out-competed tapasin for binding to MHC , such that a higher proportion of peptides followed the non-tapasin pathway , resulting in reduced optimization . Thus , variation in the intrinsic ability of free HLA–B alleles to bind peptide in the absence of tapasin was shown to be the most likely explanation for allelic variation in peptide optimization , both in the presence and absence of tapasin . For all three alleles , cell surface optimization could not be improved by modifying most other parameters in the model ( , , , and ) ( Fig . S5 in Text S1 ) . This indicates that the balance between peptide binding and tapasin binding is a major determinant of peptide optimization , achieved by controlling the effectiveness of the tapasin-mediated pathway . The prediction that allele-specific tapasin dependency results from variations in peptide binding to MHC class I molecules is consistent with analysis from molecular dynamics simulations , which suggest that tapasin stabilizes peptide-receptive conformations [20] . This stabilization in the presence of tapasin is represented in our model by setting the binding rate to be allele-independent and greater than or equal to the binding rate . In the absence of tapasin , MHC class I molecules of different alleles may have varying levels of peptide receptiveness , which is represented in our model by allowing to vary between alleles . Having established a hypothesis which explains how MHC alleles experience differential tapasin-dependence , we sought to identify the mechanisms that determine the extent and rate of peptide optimization , both in the presence and absence of tapasin . Peptide optimization is the process by which high affinity peptides are selected for presentation at the cell surface [6] . Peptide-MHC complexes generally need to be stable for many hours or days at the cell surface in order to effectively elicit an immune response [21] , yet peptide optimization in the ER is typically limited to tens of minutes [3] , [6] , [22] . This requires optimization beyond the limit that would be obtained in equilibrium . How such high optimization is achieved in so little time is still not well-understood [3] . One way to increase the extent of peptide optimization is for peptide-MHC complexes to be retained in the ER for an extended period prior to egress , so that unstable peptides have an opportunity to unbind [22] . However , delaying egress also increases the time for complexes to reach the cell surface . Therefore , a trade-off exists between the extent of optimization and the rate at which this optimization can be achieved . We quantify this trade-off by calculating the relative probabilities of MHC egress and peptide unbinding . Consider an MHC complex containing a peptide with off-rate ( Fig . 5 A ) . The complex can either egress to the cell surface at rate , or the peptide can unbind at rate . The probability of each event is proportional to its rate , such that the probability of egress is given by . The competition between unbinding and egress defines a peptide filtering step , where the basic filtering mechanism is comparable to principles of kinetic proof-reading [23] . Let denote the expected number of peptide-MHC complexes that egress to the cell surface before the peptide can escape ( Fig . 5 A ) . If there are MHC complexes containing peptides with off-rate in the ER , we expect to egress and the remainder to release their peptide cargo . For very high , all complexes will egress irrespective of their peptide cargo . For very low , the number of egressed complexes will tend to . Let denote the proportion of egressed MHC complexes containing peptides with off-rate ( Fig . 5 A ) . This defines a measure of peptide optimization . For very high egress we observe no optimization , where the proportion of peptides at the cell surface is equal to the proportion of peptides in the ER . For very low egress we observe maximum optimization , where the proportion of peptides at the cell surface varies inversely with the peptide off-rate . The introduction of tapasin provides an additional filtering step ( Fig . 5 B ) , involving a competition between peptide unbinding and tapasin unbinding . Let denote the expected number of MHC complexes that unbind from tapasin and egress to the cell surface before the peptide can escape ( Fig . 5 B ) . For very high and , all complexes will egress irrespective of their peptide cargo . For very low and , the number of egressed peptide-MHC complexes will tend to . The number of egressed complexes therefore varies with in the presence of tapasin ( Fig . 5 B ) , compared with in the absence of tapasin ( Fig . 5 A ) . This implies that tapasin enhances presentation according to peptide affinity , in agreement with experimental results [5] . Since the proportion of egressed complexes now varies inversely with the square of peptide off-rate , low affinity peptides are much more likely to escape than high affinity peptides , resulting in improved peptide optimization . The peptide filtering relation presented above also holds for the full dynamical systems model of Fig . 2 , in which peptides can bind and unbind multiple times to MHC . By translating the reactions of Fig . 2 to a set of ordinary differential equations , we obtained the following expression for the steady-state concentration of peptide-MHC complexes at the cell surface ( see Methods ) : ( 1 ) where . The equation includes the ER peptide filtering steps described in Fig . 5 , together with peptide optimization at the cell surface given by , where peptides with a lower off-rate are more likely to remain bound to MHC . The equation also quantifies the ratio of egressed peptide-MHC complexes that are loaded in the presence and absence of tapasin , given by the ratio of to . Assuming peptide loading takes place via the tapasin pathway ( ) and that peptides have a high turnover in the ER [24] , characterized by high generation and degradation rates ( ) , we can simplify equation ( 1 ) as ( 2 ) where . This corresponds to an upper bound on peptide optimization in the presence of tapasin . In the absence of tapasin , the equation for is the same as ( 2 ) but without the tapasin optimization step . This implies that tapasin enhances peptide presentation according to peptide affinity , in agreement with the analysis of Fig . 5 and experimental findings [5] . To further place our insights in a biological context , we used the dynamical systems model to identify the mechanisms that determine the rate of peptide optimization . Consider the filtering step between peptide unbinding and tapasin unbinding . Tapasin can enhance peptide optimization to the same extent either by increasing the peptide off-rate by a given factor , or by decreasing the tapasin unbinding rate by the same factor . However , decreasing the tapasin unbinding rate essentially delays the transit of MHC to the cell surface , resulting in slower optimization . In contrast , increasing the peptide off-rate allows tapasin to increase the extent of peptide optimization while still maintaining a rapid flux of peptide-MHC complexes to the cell surface . To further probe the applicability of our model , we investigated whether it could be used to predict peptide optimization at steady state . Previously , the effects of tapasin on steady-state peptide optimization were measured for peptides in the MHC class I allele H2− ( ) [5] . The experiments were conducted using four target peptides , obtained by performing substitutions at positions 5 and 8 of the amino acid sequence SIINFEKL . Peptide off-rates were measured in RMA-S cells and each of the target peptides were introduced as minigenes into a tapasin-deficient cell line ( . 220 ) and into the same cell line transfected with tapasin ( . 220 . Tpn ) . Steady-state levels of cell surface peptide-MHC complexes were measured by flow cytometry using mAb 25 . D1 ( Fig . 6 C , Table S3 in Text S1 ) , which specifically recognizes the SIINFEKL peptide variants bound to [25] . Total cell-surface MHC was also measured with mAb Y3 , which recognizes empty and peptide-occupied ( Fig . 6 D , Table S3 in Text S1 ) . We complemented previous experimentation [5] by measuring the off-rates of the target peptides in . 220 cells directly . Cells were treated with Brefeldin A ( BFA ) to prevent further MHC egress , allowing direct characterization of the dissociation of cell-surface MHC complexes carrying SIINFEKL peptide variants with 25 . D1 . The off-rates of the target peptides were estimated by fitting single exponential decays ( SIINFEKL: , SIINFEKV: , SIINFEKM: , SIINYEKL: ; Fig . 6 A ) . We also used Y3 to measure total MHC during BFA incubation of cells with no target peptide , in the presence and absence of tapasin , to provide an indication of the off-rates of endogenous peptides presented by . 220 and . 220 . Tpn cells ( Fig . 6 B ) . We simulated the above experiments using the peptide optimization model of Fig . 2 with a parameter set specific to H2− ( Table S2 and Fig . S6 in Text S1 ) . The full range of endogenous peptides was characterized by two representative peptides with off-rates and , and supply rates and ( Text S1 ) . Each experiment was simulated using one of the target peptides with off-rate , together with the representative endogenous peptides . Since target peptides were expressed at approximately equal levels inside cells [5] , we assumed that they were generated at the same rate . The model simulations agreed with the trends observed experimentally , accurately recapitulating the enhancement of steady state optimization conferred by tapasin ( Fig . 6 C , D , Table S3 in Text S1 ) . However , we observed that the model did not fit the experimental data for SIINFEKM as well as for the other target peptides . We hypothesized that the poor fit could be caused by increased TAP transport of the SIINFEKM peptide , due to a change in the terminal residue at position 8 [26] . To explore this idea , we increased the generation rate of SIINFEKM by a factor of 2 . 5 , which gave a better fit to the experimental results ( Fig . 6 D ) . This hypothesis further highlights the potential importance of peptide supply in predicting relative presentation levels [27] , as can be seen from the peptide filtering relation ( 2 ) . Although experimental measurements were only obtained for four distinct peptides , the model predicts the presentation levels for a continuum of peptide off-rates over a broad range , which can be checked in future experiments . To distinguish between optimization resulting from delayed tapasin unbinding versus enhanced peptide off-rate , we measured the time taken for a fixed cohort of pulse-labeled MHC complexes to reach the cell surface by measuring endoglycosidase-H ( EndoH ) resistance ( Fig . 6 E–G ) . By taking into account the temporal constraints of the EndoH data , we found that enhanced peptide off-rates were required to allow increased peptide optimization in the presence of tapasin without significantly delaying the transit of peptide-MHC complexes to the cell surface ( see Fig . S7 in Text S1 ) . Further parameter variation analysis indicated that cell surface optimization is nearly maximal in the H2− model with respect to , , and , but could be improved by reducing ( Fig . S8 in Text S1 ) . However , reducing decreases the export of peptide-MHC complexes , suggesting a possible trade-off between optimization and the efflux of new information concerning cellular protein content . To illustrate how the peptide optimization model may be used in more realistic scenarios , we simulated the presentation of HIV-derived peptides using our models for the HIV-associated allele HLA–B2705 ( B2705 ) , and HLA–B4402 ( B4402 ) for comparison . Peptides between 8 and 10 amino acids in length were identified from the Gag-Pol polyprotein ( UniProt; accession P03367 ) and assessed for their off-rates using the BIMAS prediction algorithm [28] . For B2705 , the slowest off-rate identified was for the immunodominant KRWIILGLNK ( positions 262–272 ) epitope [29] ( off-rate: ) . For B4402 , the allele parameters of the BIMAS algorithm were not available , so we quantified off-rates based on the BIMAS algorithm parameters for the closely related allele B4403 . The off-rates identified were generally higher than for B2705 ( Fig . 7 A ) . When comparing simulations of B4402 in the presence and absence of tapasin , the highest affinity peptide AETGQETAY ( positions 1250–1258; off-rate: ) was enhanced by a factor of 445 by tapasin ( Fig . 7 B ) , though cell surface presentation was over 25 times less than the presentation of KRWIILGLNK by B2705 ( Fig . 7 B ) . Despite B2705 being a relatively tapasin-independent allele [6] ( Fig . 4 ) , tapasin significantly enhanced presentation of peptide KRWIILGLNK by a factor of 120 ( Fig . 7 C ) .
The optimization of peptide-MHC class I complexes at the surface of antigen presenting cells is one of the key factors that determines the hierarchy of the T-cell response to a complex antigen [1] . Peptide optimization is also important for vaccine design , where vaccine peptides compete with endogenous peptides for presentation [1] , [30] . In this paper we propose a dynamical systems model of MHC class I peptide optimization , which takes into account the supply of peptides in the cytosol , the affinity of peptides to MHC and the interactions between peptide and MHC at the different stages of the optimization process , both within the ER and at the cell surface . The model also incorporates the effects of tapasin , which is known to increase peptide optimization [5] and to affect different MHC class I alleles to different extents [6] . This variation in tapasin dependence may protect from viral immune evasion strategies such as tapasin inhibition by an adenovirus [31] . The dynamical systems model is firmly grounded in experimental data , and techniques already exist to measure many of the model parameters [5]–[8] . The model therefore allows a multitude of experimental results to be unified within a common framework , so that a range of mechanistic hypotheses can be formulated and tested . We derive a peptide filtering relation which , for the first time , provides a mechanistic explanation for experimental data on MHC class I peptide optimization , both over time [6] and at steady state [5] . Specifically , it suggests that tapasin enhances peptide off-rate in order to improve peptide optimization without significantly delaying the transit of MHC to the cell surface . We have also shown that an allele-specific peptide on-rate is the most likely mechanistic explanation for differences in peptide optimization across HLA–B alleles . A possible interpretation is that differences in peptide on-rate are due to allelic differences in molecular conformation . For example , alleles such as B4402 could adopt a closed conformation , reducing the ability of peptides to bind MHC , while alleles such as B2705 could adopt a more open conformation , allowing peptides to readily bind MHC , as suggested in [32] . When tapasin binds to MHC the peptide binding groove may then adopt a peptide receptive conformation , allowing MHC to bind peptides more readily , as suggested in [20] . Although allelic differences in the conformation of MHC class I are largely peptide-independent , variations in the on-rates of different peptides have nevertheless been observed . These variations can be incorporated in future versions of the model by allowing a separate on-rate for each peptide . However , published estimates indicate that variations in the affinity of peptide-MHC interactions are mostly governed by variations in peptide off-rate [11] , supporting our assumption that the on-rate is allele-specific and largely peptide-independent . Although the current model makes a number of simplifying assumptions on the antigen presentation process , the model can be readily extended to incorporate additional details as more data are acquired . These details could include the explicit contribution of TAP transport , proteasomal cleavage and cytosolic protein abundance to ER peptide supply [26] , [33] , [34] . At present these mechanisms are only implicitly represented in the model via peptide-specific supply rates . Further extensions could also include conformational changes in MHC [7] , and chaperones such as ERp57 and calreticulin which are known to influence total cell-surface presentation [15] , [17] . Since the mechanisms by which additional chaperones interact with MHC class I are only partially known , we can investigate a variety of hypotheses by using our Information Theoretic framework to assess allele-specific chaperone-dependency . In the future , coupling model analysis with additional experimental measurements will enable quantitative predictions of peptide optimization for a wide range of MHC class I genotypes . Having a robust model , known to make accurate predictions , will improve our ability to assess the efficacy of vaccines involving multiple peptides , and will provide a quantitative means to prioritize different vaccination strategies . The current work is part of a broader research programme to use experimental data to build credible mathematical models of immunological processes , ranging from relatively simple examples to complex systems such as organ-specific autoimmunity . The resulting models can then be used to make specific and testable predictions that relate directly to immunological function . Subsequent iterations offer an opportunity to refine or develop the models from the simple to the complex , or from the static to the time-resolved , at the molecular , cellular or organ level .
The chemical reaction representation of the dynamical systems model of Fig . 2 is as follows: By assuming mass-action kinetics , we converted the system of reactions to a set of ordinary differential equations ( ODEs ) , where denotes the concentration of a given species and denotes rate of change in concentration . ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) ( 10 ) The dependent variables consist of MHC , peptide , tapasin , egressed MHC and the complexes formed between these elements . The equations denote a two-compartment system , comprising an ER model ( 3 ) – ( 8 ) and a cell surface model ( 9 ) – ( 10 ) . Equation ( 1 ) was derived by considering the steady state ( equilibrium ) solutions of the ODE representation . By equating , and with zero , we obtained the following expressions for and : ( 11 ) ( 12 ) By substituting ( 12 ) in ( 11 ) we obtained the expression for the steady-state concentration of egressed MHC complexes with a given peptide in equation ( 1 ) where . The equation incorporates the peptide filtering steps described in the main text , where denotes the optimization of free MHC in the ER , while denotes the optimization of MHC bound to tapasin in the ER . For a given peptide with off-rate , the optimization of free and tapasin-bound MHC is therefore fully determined by and , respectively . Moreover , MHC complexes optimized in the presence of tapasin will always be subject to an additional optimization step after tapasin unbinding . The equation also incorporates optimization of MHC at the cell surface given by , where peptides with a lower off-rate are more likely to remain bound to MHC . Heuristic search methods were used to fine-tune model parameters , based on the available experimental data . Our approach was to minimize a cost function , defined as the sum of the squared differences between experimental data ( ) and corresponding model simulated output , subject to an arbitrary proportionality constant . i . e . ( 13 ) ( 14 ) where is the space of search parameters , which may be the full parameter set or a subset thereof . For the inner minimization problem ( 14 ) , it is possible to assign an optimal , by equating the partial derivative of ( with respect to ) to 0 . ( 15 ) ( 16 ) Approximate minimizers of the multi-dimensional objective function ( 13 ) were found using a Metropolis-Hastings ( MH ) algorithm . During execution of the MH algorithm , a Markov chain of proposal parameter sets is formed . Starting from an initial parameter set with associated objective function value , the algorithm iteratively searches neighboring parameter sets by accepting or rejecting new proposal parameter sets at each step . Neighboring points are proposed with probability , according to a jump rule ( 17 ) The chain moves to the new point according to an acceptance criterion , which makes a probabilistic choice about whether to accept . Given an observation drawn from , the proposal point is accepted providing where is the probability that the parameter set matches the data and is the proposal density ( which we fix to be symmetric ) . Assuming the deviations from the experimental data are Gaussian distributed and that makes only small jumps , the acceptance ratio is approximately given by Note that when we always move to , because the exponential function of a positive number is greater than one . The algorithm iterates until some termination condition is reached , such as a maximum number of iterations or a convergence condition . All parameter searches were performed using ‘Filzbach’ , a software library for carrying out Metropolis-Hastings Markov chain Monte Carlo parameter estimation in C++ or C# . Filzbach is under development in the computational science lab at Microsoft Research Cambridge and is available for download , complete with a suite of example uses , via http://research . microsoft . com/science . To assess which model parameter ( s ) should be allele-specific , different hypotheses were compared using the Bayesian Information Criterion ( BIC ) [19] . The BIC is defined as ( 18 ) where is the parameter set associated with model hypothesis that maximizes the likelihood function , and are the experimental observations . This is equivalent to minimizing the residual sum of squares , as in ( 14 ) . It can be seen from this equation that the BIC penalizes the introduction of additional parameters . The model equations ( 3 ) – ( 10 ) were solved throughout using the CVODE routine [35] , as part of SUNDIALS suite of numerical integrators [36] . During optimization with Filzbach , simulation code was written in C and compiled using Microsoft Visual Studio 2010 . For plotting the simulations results , we used MATLAB and the SUNDIALS Toolbox for MATLAB . We have also provided an implementation of the model in SBML format for HLA–B ( Protocol S1 ) and H2− ( Protocol S2 ) . The equilibrium concentrations were computed from the model equations by equating the right hand sides to zero . This amounts to solving a system of nonlinear equations , where is the vector of concentrations and describes the fluxes resulting from production , degradation , binding , unbinding and ER egression . When using MATLAB , we used an implementation of the Levenberg-Marquhardt ( L–M ) search algorithm [37] to find solutions . When using the C implementation or in the case where the L–M algorithm did not converge to a non-negative solution , the ODEs were simulated using CVODE until the solution had not changed by more than 0 . 1% over a time interval of 1000 minutes . The latter method guarantees a non-negative solution providing the initial condition is also non-negative . Brefeldin A ( BFA ) blocks anterograde traffic from the ER and thus the Golgi fuses with the ER . This prevents export of any newly synthesized class I from the ER to the cell-surface [38] , [39] . BFA ( Sigma , UK ) was dissolved in methanol at 4 mg/ml for storage at and used at 5 . suspension cells were plated in 1 ml cell medium in a 24-well plate . 5 BFA was added for the indicated times and all the cells were harvested at the same time for flow cytometry . Cells were harvested for flow cytometry as previously described [5] . | Major Histocompatibility Complex ( MHC ) class I molecules bind to protein fragments ( peptides ) within the cell and present these fragments at the cell surface , thus providing a snapshot of the cell contents that can subsequently be used to trigger an immune response . Only a fraction of the potentially billions of peptides inside a cell are selected for presentation , and the process is optimized to select for peptides that form a stable complex with MHC class I . The mechanisms of the optimization process are important for predicting the efficacy of an immune response and for designing effective vaccines , yet are still not well-understood . In this article we present a dynamical systems model of peptide optimization by MHC class I . We show that peptide optimization can be quantified and mechanistically explained by a peptide filtering relation , which relates cell surface abundance to peptide supply , peptide unbinding and interactions with the chaperone molecule tapasin . The filtering relation also accounts for differences in optimization across MHC class I alleles . Finally , we show how the filtering relation can be used to quantify the cell-surface presentation of virus-derived peptides for immune system surveillance . | [
"Abstract",
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] | 2011 | A Peptide Filtering Relation Quantifies MHC Class I Peptide Optimization |
Necator americanus , a haematophagous hookworm parasite , infects ~10% of the world’s population and is considered to be a significant public health risk . Its lifecycle has distinct stages , permitting its successful transit from the skin via the lungs ( L3 ) to the intestinal tract ( L4 maturing to adult ) . It has been hypothesised that the L3 larval sheath , which is shed during percutaneous infection ( exsheathment ) , diverts the immune system to allow successful infection and reinfection in endemic areas . However , the physicochemical properties of the L3 larval cuticle and sheath , which are in direct contact with the skin and its immune defences , are unknown . In the present study , we controlled exsheathment , to characterise the sheath and underlying cuticle surfaces in situ , using atomic force microscopy ( AFM ) and time-of-flight secondary ion mass spectrometry ( ToF-SIMS ) . AFM revealed previously unseen surface area enhancing nano-annuli exclusive to the sheath surface and confirmed greater adhesion forces exist between cationic surfaces and the sheath , when compared to the emergent L3 cuticle . Furthermore , ToF-SIMS elucidated different chemistries between the surfaces of the cuticle and sheath which could be of biological significance . For example , the phosphatidylglycerol rich cuticle surface may support the onward migration of a lubricated infective stage , while the anionic and potentially immunologically active heparan sulphate rich deposited sheath could result in the diversion of immune defences to an inanimate antigenic nidus . We propose that our initial studies into the surface analysis of this hookworm provides a timely insight into the physicochemical properties of a globally important human pathogen at its infective stage and anticipate that the development and application of this analytical methodology will support translation of these findings into a biological context .
Necator americanus , the “American Murderer” [1] , is a hookworm parasite of humans [2] . Infection occurs in areas of poor hygiene and sanitation when individuals come into contact with soil containing the faecal matter of infected hosts [3] . Fertilised eggs hatch in the faecal mass and the emerging larvae develop and moult from a larval L1 stage to an infective L3 stage ( ~ 10 days ) . The L3 stage is protected from desiccation in tropical climates by a sheath , which is formed from the shed cuticle of the L2 developmental stage [4] . This permits the infective L3 stage to survive long enough to support an encounter with the next human host in the transmission cycle . When the ensheathed L3 encounter human skin surfaces stimulatory cues heighten nictitating behaviour , which are thought to promote exsheathment [5] and transdermal invasion [6] . Exsheathed L3 resume development in the skin before they transit to the lungs , where they migrate from the airways and are swallowed into the gastrointestinal tract [7] . During this migratory period the larvae mature to the L4 stage . On arrival to the small intestine , N . americanus attach to the intestinal mucosa and feed on host mucosa and blood [8] , which supports their development into separately sexed adults . Mated females produce progeny in the form of fertilised eggs , which are transferred with the faeces on to the soil . This completes and facilitates the continuation of the hookworm life-cycle [9] . The hookworm life-cycle is very successful with an estimated 576–700 million people infected worldwide [10] . Individuals with a light hookworm burden are usually asymptomatic , while hosts with a heavy hookworm burden present with symptoms of ‘hookworm disease , ’ which include anaemia , fever , diarrhoea , nausea , vomiting , rashes , abdominal pain and intestinal cramps [11 , 12] . Untreated chronic infections can cause long term discomfort and disability , with patients exhibiting lassitude and peripheral oedema [13 , 14] . These symptoms are caused by the haematophagous capacity of the hookworm [15] supported by the secretion of defined anti-haemostatic molecules [16] . Hookworm infections can be adequately controlled in the short term through use of anthelmintic drugs such as mebendazole , albendazole , and pyrantel pamoate [17] . However , for individuals inhabiting endemic areas , successful drug treatment is ephemeral , as infections quickly return post-treatment and can persist throughout a lifetime [18] . Drug resistance can be problematic [19] and reinfection may occur due to an absence of effective immunity [20] . Nevertheless , the allergic phenotype , characterised by elevated IgE , IL5 and eosinophilia , appear to have a controlling influence over infecting hookworms , which could effect on their ability to feed and contribute to the reduction in adult hookworm size and fecundity [21] . At this point , it is important to note that the absence of effective immunity is not due to the failure of the immune system to recognise the parasite , as immunological responses are detectable at all parasitic stages [22 , 23] . Therefore , it has been postulated that N . americanus utilises a number of complementary immune evasion strategies to invade , survive and maintain its successful colonisation of human hosts [24] . Research into the host evasion strategies are diverse [25–27] , but have concentrated on the later life cycle stages of helminth infections , in particular the role of adult hookworm secretions in manipulating the immune response [28] . More recently , N . americanus have been shown to effectively control autoimmune enteropathy at the site of infection [29] . Despite these advances in understanding of hookworm infection , little is known of the percutaneous infection process in humans , and its contribution to immune evasion and reinfection . In the present work we develop a hypothesis that focuses on the primary stages of N . americanus infection and reinfection . In animal models , L3 larvae remain in the skin for approximately 48 hours [30] . During this period , they receive environmental cues to resume feeding , prior to their migration to the lungs . Should this occur in humans , this period of residence in the skin would provide the immune system with ample opportunity to arrest larval development , at what is considered to be a highly immunologically active anatomical site . However , as reinfection regularly occurs in endemic areas , this strongly suggests that the immune system fails to effectively target the infective L3 larva . We propose that during infection the deposited larval sheath functions to divert the immune system away from the emerging and migrating L3 larva , a phenomenon achieved as a result of differences in both physical and chemical surface properties of the emergent cuticle and the sheath . This hypothesis is dependent on evidence that the sheath is deposited in the skin during infection . Although the exact sequence of events during infection in humans is unknown , previously published ex vivo work shows; I ) surface fluoresceinated ensheathed L3 present in the dermal layers and subcutaneous adipose tissue following infection [6] , only 30% of the larval sheaths were recoverable from the skin surface in these experiments , suggesting that 70% had been carried into the skin , II ) an enduring pruritic erythematous papular rash at the site of infection suggesting the presence of an antigenic nidus , III ) the age-related presence of circulating antibodies in 203 infected humans to cetyltrimethylammonium bromide stripped sheath proteins and to exsheathing fluid [6] , and IV ) the age-related appearance of antibodies to collagen , a major component of L3 larval sheath [31 , 32] . These age-related profiles could be indicative of an acquired immune response in humans to repeated sheath deposition , although antigenic cross reactivity cannot be discounted with later moulting life cycle stages . These observations cannot be totally supportive of the hypothesis given our current knowledge of infection in humans , yet they are highly suggestive of antigenic sheath deposition at an immunologically active site . Conclusive evidence will only come from human volunteer studies , where skin biopsies will be required to fully investigate this phase of the infection process . We postulate that the physicochemical properties of the cuticle and sheath will be fundamentally different , to the extent that any observed differences could contribute to immune evasion as well as the biological process of larval migration . These chemical differences were alluded to in early electrophoretic experiments [33] , where iodination of the sheath and cuticle produced significantly different profiles on subsequent SDS-PAGE analysis . In these experiments the sheath was diffusely labelled , which indicates of a high degree of glycosylation . In contrast , the cuticle demonstrates a defined labelling profile , indicating the presence of discrete proteins . To understand the physicochemical properties of nematode surfaces further , studies have also used surface labels and complementary optical techniques to investigate topography [34] and produced nematode homogenates , which have been examined with liquid chromatography mass spectrometry ( LC-MS ) to determine chemical characteristics [35] . However , surface labels can generate optical artefacts when interpreting the data and homogenates can often produce uncertainty as to whether the chemical entities are present on the surface , as the body would observe their presence , or distributed throughout the homogenate . To investigate the physical and chemical properties of the emerging L3 cuticle and sheath we initially studied the behaviour of ensheathed larvae to enhance our understanding of the exsheathment process . The surface topography of partially exsheathed N . americanus was primarily investigated using optical microscopy and environmental scanning electron microscopy ( ESEM ) . This was followed by an in depth analysis of sheath and cuticle height using atomic force microscopy ( AFM ) [36] and quantitative nanomechanical mapping ( QNM ) , which was employed to determine surface adhesive forces . The surface chemistries of the partially exsheathed L3 larvae were directly ascertained using time-of-flight secondary ion mass spectrometry ( ToF-SIMS ) [37] . Chemical similarities and dissimilarities between the L3 cuticle and sheath were identified using a combination of statistical analyses , which included principal component analysis ( PCA ) and multivariate curve resolution ( MCR ) analysis .
In order to examine the surface properties of the L3 cuticle and sheath the larvae are required to be exsheathed in a controlled manner rendering them amenable to analysis . Therefore , the exsheathment process was investigated through deposition of axenic larvae , produced according to cGMP standards and deemed to be free of bacterial contamination , on either clean glass slides or glass slides freshly coated with poly-L-lysine , whilst being subjected to relatively high ( 37°C ) and low ( 20°C ) temperatures . Poly-L-lysine is a polycationic molecule that can be used to coat solid surfaces , such as glass , providing sites of adhesion for biological systems through interaction with anionic crypts present on biological surfaces . In addition , by controlling ambient temperature , L3 larval activity was observed at room temperature ( 20°C ) to simulate the quiescent pre-infective stage and at 37°C to mimic an encounter with the human host . In the absence of poly-L-lysine larvae move freely on glass surfaces . Their activity , as indicated by the rate of sinusoidal movement , is greater at 37°C than 20°C ( S1 and S2 Movies , respectively ) . Even though the ensheathed larvae appear to have enough activity to propel them across the surface of the glass they do not exsheath . In contrast , ensheathed larvae deposited on poly-L-lysine coated slides experience restricted motility . The poly-L-lysine anchors the sheath to the glass surface , such that at 20°C the ensheathed nematode is able only to move within the realms of the static sheath ( S3 Movie ) . At 37°C the poly-L-lysine still provides an anchoring mechanism to attach the sheath to the glass slide; however , the ensheathed larvae are stimulated to lift sections of their anatomy off the poly-L-lysine coated surfaces . The anchoring of the sheath to the glass surface in combination with the increased activity demonstrated at 37°C permits the larvae to exsheath , as the time-lapse images show in Fig 1A ( S4 Movie ) . The exsheathment efficiency was found to be 80 . 41 ± 1 . 75% and 1 . 75 ± 3 . 04% on poly-L-lysine coated and uncoated surfaces , respectively ( see S1 File ) . Exsheathed larvae are not anchored to poly-L-lysine and are able to travel across coated surfaces . This movement is irregular , when compared to the controlled sinusoidal movement in the absence of poly-L-lysine , as the exsheathed larvae appear to ‘jolt’ from location-to-location ( S5 Movie ) . These observations provided an early indication that the physical and chemical properties of the N . americanus L3 cuticle and sheath are different . The interaction between the sheath and poly-L-lysine restricts the movement of ensheathed larvae on coated surfaces . Conversely , the interaction between the exsheathed L3 cuticle and the poly-L-lysine is substantially reduced . Therefore , as anionic sites on biological surfaces predominantly bind to cationic poly-L-lysine , these findings suggest the larval sheath could possess a greater abundance of anionic functional groups , with respect to the L3 cuticle . Importantly , the use of poly-L-lysine coated glass slides provides a new method for the controlled exsheathment of N . americanus , such that the sheath and cuticle properties can be studied independently . Partially exsheathed larval ultrastructure can be captured using chemical or low temperature fixation . Fig 1Bi shows an optical image of a fully hydrated chemically fixed partially exsheathed nematode , with what would appear to be a refractile ring [38–41] present at the interface between the sheath and the L3 cuticle . Under environmental scanning electron microscopy ( ESEM ) the sheath appears to compress the anatomy of the nematode , as indicated in Fig 1Bii and 1Biii . Compression may occur due to differences between the size of the initial sheath puncture , produced by the force generated by the narrow head and the relatively larger width of the emerging anatomy . By analysing the optical images , the relative size differences between the L3 larva and the deposited sheath can be determined . The length and the apparent width ( in the middle of the larval anatomy ) of the exsheathed L3 larva is approximately 542 ± 25 μm and 25 ± 3 μm , respectively . In contrast , the sheath length and apparent width ( at the centre of the sheath measured after the exsheathment process ) is approximately 610 ± 28 μm and 38 ± 2 μm , respectively . These findings indicate the length and the apparent width of the sheath is greater than the emergent L3 larva . It is important to note that the apparent width does not account for the three-dimensional properties of the emergent L3 larva or sheath . Therefore , the circumference , which can be calculated by determining the height of the three-dimensional structures , provides an improved description of the L3 larva and sheath physical properties . Accurate physical characterisation of the emergent larva and sheath height , as well as QNM information such as adhesion forces , can be determined using AFM [42] . The height of exsheathed N . americanus and double sheath layer were 4 . 26 ±0 . 20 μm and 0 . 71 ± 0 . 04 μm respectively , such that their circumferences can be estimated to be ~56 μm and ~76 μm , respectively . Therefore , the thickness of the sheath , which acts as a protective hydrating shield , is extremely thin at 0 . 35 μm . Analysis of the surface topography revealed both cuticle and sheath possess primary structure , in the form of repeating surface annuli , Fig 2Ai & 2Aii . Annuli on the cuticle are closer together and shallower , occurring every 1 . 72 ± 0 . 13 μm with depths between annuli of 35 ± 12 nm , when compared to the sheath , which occur every 1 . 80 ± 0 . 25 μm with depths between annuli of 42 ± 15 nm , Fig 2Aiii . These differences between the spacing of the annuli can be attributed to the relative maturity of the sheath to the growing L3 larva , and the exsheathment process , which is known to expand the sheath in all directions . Additionally , the sheath exhibits a secondary structure , nano-annuli , which are found in-between and perpendicular to the annuli , Fig 2Bii . The periodicity and depth of the nano-annuli found on the sheath surface is 323 ± 18 nm and 29 ± 5 nm . This additional surface topography enhances the sheaths surface area by ~ 4% , when compared the L3 cuticle ( see S1 File ) . This surface area enhancement may contribute to the differential adherence observed for ensheathed L3 larva as well its exsheathment on solid supports and during infection . However , due to the marginal degree of surface area enhancement we postulate the surface topography of the sheath may play a minor , although important , role in its overall infection mechanism . Determination of the adhesion forces between coated surfaces and the larval cuticle and sheath permit quantification of our observations that show ensheathed larva exhibit restricted motility when in contact with poly-L-Lysine . Images for the adhesion forces measured between poly-L-lysine functionalised tip and cuticle and poly-L-lysine functionalised tip and sheath are shown in Fig 2Ci and 2Cii , respectively . Fig 2Ciii shows the distribution of the measured adhesion forces between the functionalised tip and sheath were 33 ± 6% greater than the adhesion forces between a functionalised tip and the cuticle . These measurements are in agreement with our observations of limited motility of ensheathed N . americanus on poly-L-lysine coated glass surfaces in comparison with exsheathed L3 larvae . These results suggest there are differences in the surface chemistries of the sheath and cuticle , which were explored further with ToF-SIMS . From the outset it is important to note L3 larva and their sheaths are complex biochemical structures and are challenging to analyse and interpret . ToF-SIMS was used to directly probe the cuticle and sheath surface chemistry . Partially exsheathed N . americanus samples were prepared ( n = 12 ) to obtain surface chemical data from the cuticle and sheath . Data analysis of these surfaces was approached in a systematic manner using objective processes , specifically PCA analysis , MCR analysis and statistical scoring of the full data series ( as shown in the S1 File ) . PCA analysis was conducted on the full data series of 12 partially exsheathed axenic N . americanus using regions of interest to distinguish the cuticle from sheath . Preliminary analysis of the normalised eigenvalues on the first ten principal components shows PC 1 ( 59 . 6% ) , 2 ( 18 . 9% ) and 3 ( 12 . 0% ) account for 90 . 5% of the variability within the data set ( see S1 File ) . When a three-dimensional scores plot is generated for the first three principal components the data unambiguously separates into two distinct populations , Fig 3 . However , although the PCA analysis appears to separate the sheath and cuticle , due to the complexity of the dataset under consideration 95% confidence limits on individual PC loadings alone were not able to identify mass ions that were significantly expressed on cuticle or sheath surface . This observation can be attributed to the high degree of biological variability within the data series . Therefore , in order to reduce the variability , surface chemical differences within a data set ( single partially exsheathed larva ) were investigated using MCR image analysis . On first inspection the MCR scores heat maps , Fig 4A , were able to differentiate the different chemical surfaces on display , such that MCR components 1 , 2 and 3 are specific for L3 cuticle , sheath and glass substrate , respectively . MCR component 4 demonstrates non-specificity and may indicate the presence of poly-L-lysine residues . Through analysis of the highly loaded mass ions for MCR components 1–4 ( Fig 4B ) , chemical identities were assigned ( Fig 4C ) . A detailed table of mass assignment interpretations of MCR 3 , 4 and MCR residuals , and reconstructed images of highly loaded ions for MCR 1–4 are provided in the S1 File . An investigation of the physical and chemical properties of the emerging infective L3 N . americanus larvae has been performed in unprecedented detail . Differences have been demonstrated between larval sheath and cuticle that may provide an insight into the biology of their percutaneous infections . A method to control the exsheathment process has been established , which enables simple separation of the N . americanus sheath from cuticle . AFM and ToF-SIMS were used to analyse of the surface properties of resulting cuticles and sheaths . AFM revealed the sheath possesses surface area enhancing secondary structure ( nano-annuli ) , which are found in-between and perpendicular to the annuli . Additionally , QNM demonstrated greater adhesion forces between a cationic functionalised tip and sheath than tip and cuticle confirming the observations of limited N . americanus motility on poly-L-lysine coated surfaces . The application of ToF-SIMS and multivariate analysis revealed significantly different chemistries on the cuticle and the sheath . Specifically , the cuticle expressed phosphatidylglycerol head groups , whereas the sheath exhibited the presence of heparan sulphate-like monosaccharides . We postulate the anionic heparan sulphate-like monosaccharides facilitated sheath adherence to the cationic poly-L-lysine coated solid supports , a phenomenon which may be replicated in vivo . Furthermore , the differential chemistries discovered may have an influence on immune evasion as well as successfully avoiding host immunological barriers via deposition of an immunologically active sheath . We believe the development and application of this analytical approach enables the direct probing of innate parasite surfaces , which will enhance understanding of infection processes . | Necator americanus is an intestinal hookworm parasite of humans that is commonly found in tropical and sub-tropical climates . N . americanus infections can be treated effectively with anthelmintic drug therapy; however , in endemic areas re-infection quickly returns . Chronic hookworm infection can lead to intestinal blood loss , iron deficiency anaemia , malnutrition and physical and intellectual impairment . N . americanus surfaces may possess key physicochemical properties that permit successful host infection . Therefore , we harnessed controlled exsheathment of infective axenic L3 larva to investigate the physicochemical properties of the emergent cuticle and deposited sheath , using atomic force microscopy ( AFM ) and time-of-flight secondary ion mass spectrometry ( ToF-SIMS ) . Our results provide an early insight into the differential physicochemical properties of these bio-surfaces , allowing the development of a hypothesis as to how these chemistries may be involved in infection and immune evasion . This new analytical platform will allow us to test this hypothesis and translate our findings into an immuno-biological context . | [
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"mu... | 2017 | The physicochemical fingerprint of Necator americanus |
Deltaretroviruses such as human T-lymphotropic virus type 1 ( HTLV-1 ) and bovine leukemia virus ( BLV ) induce a persistent infection that remains generally asymptomatic but can also lead to leukemia or lymphoma . These viruses replicate by infecting new lymphocytes ( i . e . the infectious cycle ) or via clonal expansion of the infected cells ( mitotic cycle ) . The relative importance of these two cycles in viral replication varies during infection . The majority of infected clones are created early before the onset of an efficient immune response . Later on , the main replication route is mitotic expansion of pre-existing infected clones . Due to the paucity of available samples and for ethical reasons , only scarce data is available on early infection by HTLV-1 . Therefore , we addressed this question in a comparative BLV model . We used high-throughput sequencing to map and quantify the insertion sites of the provirus in order to monitor the clonality of the BLV-infected cells population ( i . e . the number of distinct clones and abundance of each clone ) . We found that BLV propagation shifts from cell neoinfection to clonal proliferation in about 2 months from inoculation . Initially , BLV proviral integration significantly favors transcribed regions of the genome . Negative selection then eliminates 97% of the clones detected at seroconversion and disfavors BLV-infected cells carrying a provirus located close to a promoter or a gene . Nevertheless , among the surviving proviruses , clone abundance positively correlates with proximity of the provirus to a transcribed region . Two opposite forces thus operate during primary infection and dictate the fate of long term clonal composition: ( 1 ) initial integration inside genes or promoters and ( 2 ) host negative selection disfavoring proviruses located next to transcribed regions . The result of this initial response will contribute to the proviral load set point value as clonal abundance will benefit from carrying a provirus in transcribed regions .
The deltaretrovirus genus includes human T-lymphotropic viruses ( HTLVs ) , simian T-lymphotropic viruses ( STLVs ) and the bovine leukemia virus ( BLV ) . These viruses induce a life-long persistent infection that remains generally asymptomatic ( reviewed by [1]–[3] ) . Nevertheless , HTLV-1 and BLV cause leukemia or lymphoma in a minority of infected hosts after a long period of latency [3] , [4] . Viral spread within the host uses two distinct processes . First , the infectious cycle results from virion attachment to target lymphocytes , entry of viral single-stranded RNA , reverse-transcription and integration as provirus into the host genome ( also known as the infectious cycle ) [5]–[7] . The second strategy of replication relies on driving cell proliferation using viral regulatory proteins such as Tax ( i . e . the mitotic cycle ) [8] , [9] . These two viral replication routes thus generate a series of infected cell populations that are composed of numerous distinct clones ( i . e . a population of cells carrying the provirus at a given site of the host genome ) . Animal models using experimental inoculation of squirrel monkey with HTLV-1 or sheep with BLV demonstrated that the infectious cycle dominates early infection and finishes 1 to 8 months later [10] , [11] . Thereafter , the proviral load ( PVL ) is mainly maintained by mitotic replication of infected cells [12]–[15] . In HTLV-1 infected individuals , the majority of the infected clones are indeed relatively stable during many years [16] . Remarkably , using BLV-sheep experimental infection , it has been shown that the leukemic clone can be detected as early as one month after inoculation [17] . Thus , efficient virus replication via production of virions and infection of new target cells occurs mostly during a very short period following viral inoculation ( so-called primary infection ) . As a consequence , the vast majority of the infected clones were created during this crucial period of primary infection . Unfortunately , these important early times of HTLV-1 infection cannot be studied due to the paucity of available samples . Therefore , very little is known about the modes of viral replication during this primary phase of infection . For instance , what proportion of clones generated during primary infection will establish in the long term ? Is there a negative selection against particular clones ? And if so , is there a role of genomic integration sites in clonal selection ? This is particularly important because clones are not equal regarding their proliferative potential . In HTLV-1 infected patients , the abundance of a given clone is enhanced by the integration of its provirus in an actively transcribed area of the genome [16] , [18] . Furthermore , it is well described that increased proviral load ( PVL ) and clonal expansion of infected cells are key events during the process of leukemogenesis in HTLV-1 carriers . Indeed , HTLV-1 PVL correlates with the risk of developing adult T-cell leukemia or lymphoma ( ATLL ) [19] and HTLV-1 clonality of ATLL patients is characterized by the presence of massively expanded clones [12] , [13] , [16] . In this context , the aim of this study was to evaluate clonal evolution of the infected cells with longitudinal samples during primary infection with respect to clonal diversity and host-dependent negative selection . Due to the difficulty at accessing samples from newly HTLV-1 infected patients , we addressed this question in a closely related animal model by inoculating cows with BLV . We selected animals whose BLV PVL set point values encompassed the whole range of natural variability . We then used a newly developed method to map and quantify the insertion sites of the provirus in order to monitor the clonality of the BLV-infected cells population ( i . e . the number of distinct clones and abundance of each clone ) during primary infection . We demonstrated that ( 1 ) BLV initially targets transcribed regions of the genome for integration; ( 2 ) later , a massive clone selection occurs during primary infection disfavoring proviruses located nearby genes; ( 3 ) nevertheless , the abundance of the long term maintained clones benefits from the transcriptional activity of the genomic region surrounding the provirus .
Five cows were inoculated with a cloned BLV provirus ( strain 344 ) as described in the Materials and Methods section . Figure 1A shows that all animals developed a humoral response directed against BLV SU , the viral surface envelope protein . Anti-SU antibodies were detected at 16 and 30 days post-inoculation of animals #21 and #23/31/492/535 , respectively . Since then , all animals remained sero-positive for SU antibodies . Figure 1B shows the PVL ( number of proviral copies per 100 peripheral blood mononuclear cells , PBMCs ) following BLV inoculation of cows at day 0 . The PVLs sharply increased with a maximal value reached between 30 to 68 days post-inoculation ( dpi ) ( 30 days for #21 , 43 days for #23 , #31 and #535 and 68 days for #492 , arrows on figure 1B ) . PVL peak values differed widely among animals ranging from 1 . 6 proviral copies per 100 PBMCs for animal #21 up to 236 proviral copies per 100 PBMCs for animal #535 . Then , the PVLs subsequently decreased and reached a level that differed widely between animals . Cows #21 , #23 and #31 ( green lines ) presented a low PVL set point ( respectively 0 . 2 , 0 . 5 and 0 . 1 copies per 100 PBMCs at day 252 ) as observed in the majority of infected animals kept in herds [20] . Animals #492 and #535 ( red lines ) developed a very high BLV burden ( respectively 43 . 3 and 117 . 6 copies per 100 PBMCs at day 252 ) as measured in a small proportion of infected animals ( [21] and INTA experimental facilities , data not shown ) . None of the five animals progressed to disease within two years post inoculation . BLV inoculation into 5 cows thus resulted in the onset of an antibody response at days 16–30 which slightly preceded the maximal proviral burden ( at days 30–68 ) . This experimental setting is thus representative of BLV infection in herd conditions . To trace BLV clonality during primary infection , proviral insertion sites were selectively amplified , identified by high-throughput sequencing and quantified as schematized in Supporting Figure S1 in Text S1 . Briefly , genomic DNA was extracted from PBMCs and sonicated . The end of the BLV 3′LTR and a fragment of bovine genomic DNA were amplified by linker-mediated PCR and the products subjected to high-throughput sequencing . Proviral insertion sites were determined by alignments of sequences downstream of the 3′ LTR ( read 1; Figure S1 in Text S1 ) . A clone was defined as a population of cells carrying the BLV provirus at a given insertion site in the bovine genome . The abundance of a clone was quantified by counting the number of different shear sites for that particular clone using read 2 alignments . A complete description of the procedure is given in the Material and Methods section . During primary infection , a broad range of clones were generated within each animal: from 264 in #21 to 12906 in #535 ( Table S1 in Text S1 ) . The distributions of clone abundance for each animal and time point are depicted in Figure 2A by pie charts where each slice represents the relative abundance of a given clone . Based on this data , we calculated an oligoclonality index which is a measure of the non-uniformity of the clone abundance distribution ( i . e . an index close to 1 corresponds to an unevenly-distributed population composed almost of a single clone , see Supplemental Materials and Methods in Text S1 ) . The oligoclonality index was extremely low at 16 dpi , showing that all infected clones had approximately the same abundance ( Figure 2B ) . The oligoclonality index then rose and reached a peak value after 43 days for animals #21 , #23 , #31 and #535 and after 57 days for animal #492 . These data thus demonstrate that early infection is characterized by a very polyclonal propagation . At later times post-infection , a massive depletion of BLV-infected clones occured ( Figure 3A ) . The left-hand dot plot shows that , on average , 97% of the clones identified at seroconversion were no longer detected at 267 dpi . Similarly , the other dot plot shows that , on average , 92% of the proviral load at seroconversion resulted from clones that disappeared at 267 dpi . The pie chart on the right of Figure 3A illustrates this massive clonal depletion , the red slices representing the clones of #492 at dpi = 30 that were not detected in the longer term . We conclude that primary infection is thus characterized by a massive depletion of proviral clones . To quantify the clonal overlap between two successive time points , a similarity index was calculated ( see Supplemental Materials and Methods in Text S1 ) . Figure 3B shows the evolution of the similarity index calculated between two successive populations separated by a period of two weeks . Soon after seroconversion , the similarity indexes were close to zero showing that , between 2 successive time points , the infected cell populations shared few clones . Rapidly , the similarity indexes shown on Figure 3B rose to reach values close to 1 in all animals except in #21 . Infected cell populations thus stabilized in terms of clonal composition and abundance . In the long term ( at dpi = 252 ) , the similarity indexes remained close to 1 in all animals except #21 . Figure 3C illustrates the kinetics of appearance of clones that were detected in the long term . It appeared that a small percentage of the long term proviral load originated from clones detected at the seroconversion period . Nevertheless , the cumulative curves rose sharply from day 30 and then reached a plateau at day 68 in all animals except #21 ( Figure 3C ) . This shows that most clones detected in the long term originated from the PVL peak period . The pie chart on the right illustrates that a large proportion of clones established in the long term originated from the beginning of the infection . The red slices symbolize the clones present in the long term and already detected at day 68 or before . In contrast , all clones detected in the long term in animal #21 were novel , resulting in a similarity index of 0 at day 252 ( Figure 3B ) and a flat cumulative curve ( no clone at day 267 was detected at earlier time points , Figure 3C ) . So , long term proviral load in animal #21 might have been maintained either by newly infected cells or by pre-existing clones not detected beforehand . It should be mentioned that the numbers of clones detected in this particular animal were low at every time point compared to the 4 other cows ( see Table S1 in Text S1 ) due to a constantly low proviral load . The kinetics of the appearance of long term clones and of the similarity indexes thus demonstrate that BLV propagation shifts from cell neoinfection to proliferation of preexisting clones ( i . e . clonal expansion ) generally soon after seroconversion . The upper panel of Figure 4A shows the favored genomic consensus sequence surrounding the BLV provirus insertion site . As comparison , Figure 4B depicts the preferred insertion site of HTLV-1 in the human genome [22] . It thus appears that BLV and HTLV-1 favor highly similar genomic sequences for insertion . BLV provirus integration did not occur randomly as illustrated on Figure 4C . Indeed , sequence analyzes demonstrate that BLV proviruses initially inserted more frequently than expected by chance inside Refseq genes ( Figure 4C , second row and first column: 0 bp ) . Insertion was also favored nearby CpG islands , Refseq genes , tRNA genes and tRNA pseudogenes ( Figure 4C , first 4 rows of the heat map ) . These different DNA sequences are associated with transcribed regions of the genome . Indeed , CpG islands are genomic regions with relatively high CG content generally associated with promoters [23] . Genes , whose promoters are rich in CpG sequences , tend to be expressed in most tissues ( housekeeping genes ) . On the other hand , tRNA ( transfer RNA ) genes are constitutively transcribed by RNA polymerase III ( pol III ) unlike mRNA coding genes ( which are like Refseq genes ) transcribed by RNA polymerase II ( pol II ) . Finally , tRNA pseudogenes are tRNA-derived repeats that no longer produce functional tRNA but that are still associated with polIII activity [24] . We conclude that BLV initial integration favors pol II and pol III transcribed regions of the genome . In contrast , BLV disfavors interspersed repeats such as LINE , SINE and ERVs ( Figure 4C ) . These elements are the largest class of sequences in mammalian genomes , making for about 50% of their total length . Most common repeats are retrotransposons that can replicate and reinsert in another site of the genome . Nevertheless , transposition does not occur in somatic cells where retrotransposons are silenced , thereby preventing insertional mutagenesis . Retrotransposons can be divided in several classes with notably SINE ( Short Interspersed Nuclear Element ) , LINE ( Long Interspersed Nuclear Element ) and LTR-ERVs ( Endogenous RetroVirus that are also retrotransposons with Long Terminal Repeats , LTR ) . Figure 4C demonstrates that insertion occurred less frequently than expected nearby retrotransposons LINE BovB , SINE BOV-A2 , SINE ART2A and LTR ERVs . We conclude that BLV initial integration disfavors transcriptionally silenced regions of the genome . Figure 5 illustrates the characteristics of the genomic integration site during clonal evolution from seroconversion up to day 68 . The proviral environment at seroconversion serves as reference point ( Figure 5 , first column of the heat map ) . The proportion of clones carrying a provirus located next to CpG islands , Refseq genes , tRNA genes and pseudogenes significantly decreased from the seroconversion date up to 68 days after inoculation ( Figure 5 , first 4 lanes ) . In contrast , there was a trend to increase the proportion of clones containing a provirus next to genomic repeats ( Figure 5 , six last lanes: LINE , SINE and ERVs ) . The proviral environment of clones detected at days 252 or 267 was not statistically different from that observed at day 68 ( data not shown ) . We conclude that massive depletion of clones during primary infection is characterized by a preferential selection against proviruses inserted in transcribed regions . Finally , we characterized the genomic environment of the proviruses that thrived in the long term . Therefore , we analyzed clonality in a series of cows infected for more than 2 years and harboring a wide range of PVL ( Figure 6A ) . These animals presented a polyclonal distribution of BLV-infected cells ( Figure 6B ) . The numbers of detected clones per animal are shown in Table S2 in Text S1 . The distributions of clone abundance for each animal are depicted in Figure 6C . Figure 6D compares the genomic environment of proviruses from clones of increasing abundance . The first column of the heat map , which shows the less represented clones ( below 1 cell per 103 PBMCs ) and was used as reference . The proportion of clones carrying a provirus located next to CpG islands or Refseq genes significantly increased with clone abundance ( Chi-squared test for trend , respectively p = 0 . 0008 and p = 0 . 001 ) . Although not significant , there was a similar trend for proximity with tRNA genes and tRNA pseudogenes . We conclude that the abundance of a long term established BLV-infected clone is enhanced by the integration of its provirus nearby a transcribed unit .
HTLV-1 induces a persistent infection that is fortunately generally asymptomatic . Nevertheless , in a small proportion of individual and after a long latency , the infection leads to leukemia or lymphoma . These clinical manifestations are correlated with a persistently elevated proviral load and an oligo- or mono-clonal distribution of the infected cells . The same mechanism applies to BLV that induces leukemia or lymphoma in a minor fraction of infected animals . BLV also induces a persistent lymphocytosis in about 40% of the infected cows whereas this is far less common in HTLV-1 infected individuals . In fact , BLV and HTLV-1 are related deltaretroviruses sharing a similar genomic organization and infecting cells of the hematopoietic system ( respectively mainly B cells and T cells ) . In this respect , BLV infection might be useful to address important questions unanswered in the HTLV-1 system . In particular , early infection appears to be critical at determining the different populations of cells ( i . e . clones ) that will subsequently thrive and expand during pathogenesis [10] , [11] , [17] . The mechanisms undergoing during this initial period of HTLV-1 infection cannot be addressed because of lack of samples ( e . g . when transmission occurs via breast feeding ) or due to the absence of systematic screening of populations at risk . In this report , we aimed at identifying the clones created during primary infection ( i . e . position of the proviral insertion site in the host genome ) and quantifying their abundance ( i . e . number of cells per clone ) . With this objective , we performed high throughput sequencing of proviral insertion sites in BLV-inoculated cows during the early days of infection . We first show that thousands of clones are generated during primary infection indicating that initial replication occurs through the infectious cycle . None of the newly generated clones massively expanded during this early period revealing a polyclonal pattern of viral expansion and confirming observations made in the BLV-infected sheep model [11] and in squirrel monkey experimentally inoculated with HTLV-1 [10] . A second contribution of this report concerns the genomic sequence of the insertion sites that are strikingly similar in the BLV and HTLV-1 systems . Thirdly , BLV provirus insertion occurs nearby transcribed genomic regions , as reported for HTLV-1 [16] , [18] , [25] . In fact , most ( if not all ) retroviruses target a characteristic weak palindromic consensus nucleotide sequence at the site of integration [26] , [27] and favor insertion within a particular genomic environment [28]–[30] . These preferences originate from the structure of the pre-integration complex given by the viral integrase [31] , [32] , its possible cellular partners [33] and from the relative accessibility of the genome [30] . Taken together , our observations thus reinforce the similarities between BLV and HTLV-1 . Therefore , the BLV model can be informative to understand mechanisms of early infection by deltaretroviruses , a period that cannot be easily addressed in the HTLV-1 system as discussed previously due to the logistic and ethical problems of collecting sequential neonatal samples . In BLV-inoculated sheep , the time period delimiting the infectious cycle was estimated to last the first 8 months [11] . Sheep are not natural hosts for BLV and develop leukemia/lymphoma at higher frequencies after shorter latency periods compared to cattle . In our report , we now show that this period generally lasts about 2 months in the bovine species . It is possible that a shorter infectious cycle period also limits the probability of leukemia/lymphoma in the natural host . Indeed , oncogenesis , which is a rare event that occurs in a single or in a limited number of cells , correlates with the number of infected cells . Even though we showed that BLV propagation rapidly shifts to clonal expansion to maintain the bulk of the infected cells , we cannot exclude that infection of new cells by virions has totally been stopped . Because analysis is based on the detection of proviral insertion sites in PBMCs , intermittent burst of virus replication with clearance of the newly infected cells and/or replication in tissues other than the peripheral blood may occur . Alternatively , it is also possible that the infectious cycle is ongoing permanently but that the immune response is sufficiently efficient to destroy all newly infected cells . Also , our method is limited by the number of infected cells present in the collected blood sample and so , the clonality analysis is constrained by the representative sample , making difficult the interpretation of clonal succession data from low proviral load animals such as #21 . Currently , there are no available data pertaining to the early viral replication in HTLV-1 infected patients . Based on this study and previous publications using animal models [10] , [11] , it is likely that the HTLV-1 infectious cycle is also mainly restricted to the first months post-infection . This prediction has important clinical applications in terms of viral transmission . Since the infectious cycle requires production of virions and reverse transcription , anti-retroviral treatments could be instrumental to avoid mother-to-child transmission during delivery or breast feeding . In several endemic regions , breastfeeding cannot be prohibited because of societal and sanitary reasons . In particular , formula milk requires access to clean water and does not provide passive immunity to local pathogens . Based on this report , we propose that an early and short anti-retroviral treatment soon after birth might reduce HTLV-1 transmission and even possibly limit the long term HTLV-1 load . This option has recently been tested in another retrovirus , HIV that propagates preferentially via neoinfection of lymphocytes . Indeed , HIV-infected patients that initiated cART ( combination antiretroviral therapy ) during primary infection remained post-treatment controllers with very low viremia for many years [34] . A major contribution of our report is to demonstrate the massive depletion of the early generated clones . In Figure 3 , we indeed show that the primary infection period is characterized by the disappearance of the vast majority of clones . Considering the current knowledge in the BLV/HTLV-1 systems [35] , [36] , it is likely that this clonal selection is due to counter selection by the host immune response . Importantly , the clones carrying a provirus in a transcribed genomic environment were even more susceptible to be destroyed . We speculate that the stronger selection against proviruses located nearby genes might be the result of an increased viral expression and a higher exposure to the host immune system [37] . In this context , HDAC inhibitors have been shown to induce BLV [38] , [39] and HTLV-1 expression [40]–[42] and consequently increase the exposure of the infected cells to the host immune system . Remarkably , treatment of STLV-1 infected monkeys with a combination of the HDAC inhibitor valproate and the reverse transcriptase inhibitor AZT induced a persistent decrease of the PVL [43] . Thus , one might consider testing such combination of an antiretroviral drug and an HDAC inhibitor in the early days of the infection . It has been previously shown that HTLV-1 infected clones are not equal regarding their proliferative potential . Indeed , the abundance of a given clone is enhanced by the integration of its provirus in an actively transcribed area of the genome [16] , [18] . We demonstrated here that this mechanism is also present in BLV-infected cows where clones that thrive and proliferate in the long term carry a provirus in a transcribed environment . We should recall that transcriptional activity of the genomic environment of the provirus is inferred from genomic features like CpG islands and genes and so , it is not a direct experimental measurement of the transcription . It thus appears that two opposite forces will act during primary infection and dictate the fate of the long term clonal composition: ( 1 ) BLV initially favors integration into genes or promoters and ( 2 ) host negative selection disfavors proviruses located next to transcribed regions . The outcome of these two forces will determine the PVL set point value as clonal abundance will benefit from carrying a provirus in transcribed regions but will concomitantly also be reduced by the immune response . Infected hosts able to more efficiently eliminate clones carrying a provirus integrated nearby transcribed units will thus have lower PVLs . Differences in immune response efficiencies have been identified in BLV-infected animals as well as in HTLV-1 infected individuals [44]–[47] . These differences translate into disease susceptibilities and were related at least partially to the genotype of the host [48]–[53] . Our preliminary data show that negative selection against clones carrying a provirus nearby transcribed regions is more efficient in animals that will subsequently present low proviral load set point ( data not shown ) . At a first glance , the observation that clone abundance positively correlates with provirus proximity with transcribed units appears to conflict with the extremely low levels of viral expression measured in vivo [54] , [55] . Indeed , structural viral transcripts from peripheral blood lymphocytes or tumors can only be amplified by the means of very sensitive techniques such as in situ hybridization or RT-PCR . With progression from persistent lymphocytosis to leukemia , BLV expression levels even tend to decrease [56] . In tumors , BLV proviruses can be completely silent regarding viral messenger RNAs albeit carrying an intact genomic sequence [57] . Similarly , 5′-LTR directed transcription of HTLV-1 can be completely abrogated in leukemic clones by methylation and by deletion or mutation of viral genes [58]–[60] . Nevertheless , proviral silencing of BLV and HTLV-1 appeared to be selective , leaving some specific transcript untouched . This is the case for HBZ ( HTLV-1 b-ZIP factor ) that remains strongly expressed in ATLL cells [61] . Recently , BLV has been shown to produce microRNAs that are also actively transcribed in BLV malignant cells [62] . Mechanistically , the silencing of BLV structural genes can be achieved by repressive histone marks deposited onto the promoter [63] without interfering with pol III dependent transcription of the BLV microRNAs [62] , [64] . Consistently , we detected constitutive expression of BLV-miR-B4-3p despite very low levels of structural viral transcripts ( data not shown ) . In this context , it is important to note that BLV favors pol III transcribed regions ( tRNA genes and pseudogenes ) upon insertion ( Figure 4C ) . Furthermore , abundance of these established clones benefits from a provirus inserted in pol III transcribed regions ( Figure 6 ) . We might speculate that this genomic environment could in turn favor BLV microRNA expression . We propose that the abundance of successful clones benefit from the transcriptional activity of the genomic region surrounding the provirus to maintain robust expression of BLV microRNAs . Alternatively , BLV insertion nearby pol III regulated genes such as tRNAs , 5S rRNA and other small RNAs may affect their activity and modify the cell metabolism . In particular , pol III directed transcription of housekeeping genes is a key parameter of cell growth and replication . In summary , we have characterized BLV clonality during primary infection using high throughput sequencing of the proviral insertion sites in 7 sequential samples of 5 BLV-inoculated cows and as well as in long term infected animals . We demonstrate that BLV proviruses initially integrate into transcribed regions of the genome but are massively depleted later on . This mechanism may have important outcomes for HTLV-1 prevention and treatment .
Cows were experimentally infected with the wild-type BLV strain 344 . Two 15 cm-diameter dishes containing subconfluent Hela cells were transfected with 80 µg of plasmid pBLV344 containing the BLV provirus , recovered in 5 ml PBS at day 3 and injected subcutaneously . The presence of anti-BLV antibodies was determined using a competitive ELISA test ( IDEXX Leukosis Blocking Ab Test ) . Peripheral blood mononuclear cells ( PBMCs ) were isolated by Ficoll Hypaque density gradient centrifugation ( Sigma-Aldrich ) , washed and cryopreserved in fetal calf serum with 10% DMSO ( Sigma-Aldrich ) . Animal experimentations were conducted in accordance with national and international guidelines for animal care and use described in the Manual for use and care of experimental animals emitted by INTA . Handling of cows and experimental procedures were reviewed and approved by INTA's Institutional Committee for Care and Use of Experimental Animals ( CICUAE-INTA ) under protocol number 35/2010 . DNA was extracted from PBMCs using DNeasy Blood and Tissue kit ( Qiagen ) . BLV DNA was PCR amplified using pol gene sequence-specific primers 5′-GAAACTCCAGAGCAATGGCATAA-3′ and 5′-GGTTCGGCCATCGAGACA-3′ . As reference for genomic DNA , β-actin was amplified with oligonucleotides 5′-TCCCTGGAGAAGAGCTACGA-3′ and 5′-GGCAGACTTAGCCTCCAGTG-3′ . Three dilutions of DNA ( 100 ng , 33 ng and 11 ng ) were amplified by real-time quantitative PCR in a Roche light cycler using MESA green master mix ( Eurogentec ) . The thermal protocol used started with a 95°C 5 min denaturation step; then 45 cycles as follows ( 95°C 15 sec , 60°C 20 sec , 72°C 40 sec ) and terminated with a melting curve . PCR efficacies were calculated for each sample using the three dilutions . Standard curves were generated using PCR4topo vectors ( Life Technologies ) containing the corresponding pol or actin amplicon . Proviral load was calculated , as an average of the three dilutions , from the number of proviral copies divided by half of the number of actin copies and expressed as number of proviral copies per 100 of PBMCs . Ten micrograms of genomic DNA extracted from PBMCs were sheared by sonication with the Diagenode Bioruptor instrument using the following protocol ( 15 sec ON , 90 sec OFF , 4 cycles in a 4°C water bath ) . DNA ends were then end-repaired using 15 units of T4 DNA polymerase ( New England Biolabs ) , 5 units of DNA polymerase I Klenow fragment ( NEB ) , 50 units of T4 polynucleotide kinase ( NEB ) and 0 . 8 mM of dNTP ( Sigma ) in T4 DNA ligase buffer ( NEB ) at 20°C during 30 min . DNA was then cleaned using a Qiaquick PCR purification kit ( Qiagen ) and eluted in 64 µl of EB buffer . Addition of an adenosine at the 3′ ends of the DNA was performed by adding 0 . 2 mM of dATP ( Sigma ) and 15 units of Klenow Fragment 3′ to 5′ exo- ( NEB ) in NEB2 buffer ( NEB ) at 37°C for 30 min . DNA was then cleaned using a Qiaquick PCR purification kit and eluted in 40 µl of EB . One hundred pmol of a partially double stranded DNA linker was ligated to the DNA ends using a Quick ligation kit ( NEB ) . Twenty four different linkers were designed , each one with a specific 8 bp tag ( see Primer list in Text S1 ) to allow multiplexing of DNA samples during the sequencing . DNA was cleaned using a Qiaquick PCR purification kit and eluted in 60 µl of EB . The 60 µl of ligated product was then split into 3 aliquots of 20 µl and each aliquot was used in a separate PCR1 reaction ( see Figure S1 ) . For each PCR reaction , 20 µl of ligated product was mixed with 0 . 2 mM of dNTP ( Sigma ) , 50 pmol of BLV_LMPCR1 primer ( binds BLV LTR ) , 10 pmol of VU primer ( which anneals to the strand of the Vectorette Unit linker generated by the amplification from BLV_LMPCR1 ) , 1 unit of Phusion DNA polymerase in High Fidelity buffer ( Finnzyme , NEB ) . The following thermal protocol was used: denaturation for 30 sec at 98°C; then 30 cycles ( 5 sec at 98°C , 10 sec at 62°C , 30 sec at 72°C ) ; followed by 10 min at 72°C; and finally cooled at 4°C . The 3 PCR1 products , derived from the same sample were then pooled . The DNA was cleaned using a Qiaquick PCR purification kit and eluted in 150 µl of EB . To perform PCR2 , 1 ul of the cleaned PCR1 product was mixed with 0 . 2 mM of dNTP ( Sigma ) , 25 pmol of P5_BLV_LMPCR2 primer binding the BLV LTR ) , 25 pmol of P7 primer binding the linker , 1 unit of Phusion DNA polymerase in High Fidelity buffer ( NEB ) . The following thermal protocol was used: 98°C for 30 sec; 30 cycles ( 5 sec at 98°C , 10 sec at 62°C , 30 sec at 72°C ) ; 10 min at 72°C; 4°C until user stops . DNA was then cleaned using a Qiaquick PCR purification kit and eluted in 50 µl of EB . A library was constructed by pooling the different PCR2 products ( each one possessing a specific tag ) . Quantification of the libraries was made by qPCR using primers P5 and P7 ( see Primer list in Text S1 ) and a MESA green master mix ( Eurogentec ) in a Roche light cycler 480 instrument . Three dilutions of the library ( 200 pg , 66 pg and 22 pg ) were amplified . Standard curves were generated using a library quantified on a titration flow cell previously run on a Genome Analyzer II ( Illumina ) . Stock libraries were diluted down to 8 pM and clustered on the flow cell . Paired-end reads ( read1 and read2 each 50 bp ) plus a 8 bp tag read ( read 3 ) were acquired on a GA II or a Myseq Illumina instrument . Read 1 and read 2 were mapped against the bovine genome ( build Bos_taurus_UMD_3 . 1/bosTau6 ) and the proviral insertion site and the shear site were deduced . “Sister cells” are cells where the BLV provirus is inserted at the same site in the cellular genome and a “clone” is a population of sister cells . For each unique insertion site , the number of amplicons of different length ( i . e . different shear sites ) give an estimate of the number of sister cells of that infected clone [65] . The absolute abundance of a given clone i ( number of cells per 100 PBMCs ) was calculated from the number of sister cells and the measurement of the proviral load as follows:where Xi is the number of sister cells of the ith clone , D the number of observed clones and PVL the proviral load . The relative abundance of a given clone i ( in percent of the proviral load ) was expressed as follows: To measure the clonality of the infected cell population , i . e . the non-uniformity of the clone abundance distribution , we used the oligoclonality index based on the Gini coefficient [66] . Oligoclonality index ranges from 0 ( all the BLV-infected clones having the same abundance , i . e . perfect polyclonality ) to 1 ( only one BLV-infected clone constitutes the total proviral load , i . e . perfect monoclonality ) . Details of the calculations are given in supplemental file Text S1 ( Supplemental Materials and Methods ) . To measure the likeliness or overlap between two populations of BLV-infected cells , we calculated a similarity index . This number ranges from 0 to 1 , with 0 indicating that no clones are shared between the two populations and 1 corresponding to a complete identity ( all the clones present in population 1 were also present in population 2 and vice versa ) . Because this index takes clone abundance into account , populations that contain the same clones but have different clone abundance will have an index value of less than 1 . Similarity indexes were calculated between 2 successive time points separated by two weeks . Details of the calculations are given in supplemental file Text S1 ( Supplemental Materials and Methods ) . Favored genomic DNA sequences for BLV or HTLV-1 integration were represented using WebLogo 3 [67] . Genomic annotations flanking the proviral insertion sites were retrieved using Galaxy [68] which is a web-based genome analysis tool . DNA sequences ( read 1 and read 2 like sequences ) from 100 , 000 random sites in the bovine genome were generated using Galaxy and back-aligned to the bovine genome using the same pipeline to eliminate any potential bias due to alignment limitations . Statistical tests were performed using GraphPad Prism , Microsoft Excel and R softwares . The symbol *** was used when p<0 . 001 , ** when p<0 . 01 , * when p<0 . 05 , NS ( Non Significant ) when p>0 . 05 and NA for Non Applicable . | Human T-lymphotropic Virus 1 ( HTLV-1 ) induces a persistent infection that remains generally asymptomatic . Nevertheless , in a small proportion of individuals and after a long latency , HTLV-1 infection leads to leukemia or lymphoma . Onset of clinical manifestations correlates with a persistently elevated number of infected cells . Because the vast majority of cells are infected at early stages , primary infection is a crucial period for HTLV-1 persistence and pathogenesis . Since HTLV-1 is transmitted through breast feeding and because systematic population screenings are rare , there is a lack of available samples at early infection . Therefore , we addressed this question in a closely related animal model by inoculating cows with Bovine Leukemia Virus ( BLV ) . We show that the vast majority of cells becoming infected during the first weeks of infection and do not survive later on . We also demonstrate that the initial host selection occurring during primary infection will specifically target cells that carry a provirus inserted in genomic transcribed regions . This conclusion thus highlights a key role exerted by the host immune system during primary infection and indicates that antiviral treatments would be optimal when introduced straight after infection . | [
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] | [] | 2013 | Massive Depletion of Bovine Leukemia Virus Proviral Clones Located in Genomic Transcriptionally Active Sites during Primary Infection |
Dengue diagnosis is complex and until recently only specialized laboratories were able to definitively confirm dengue infection . Rapid tests are now available commercially making biological diagnosis possible in the field . The aim of this study was to evaluate a combined dengue rapid test for the detection of NS1 and IgM/IgG antibodies . The evaluation was made prospectively in the field conditions and included the study of the impact of its use as a point-of-care test for case management as well as retrospectively against a panel of well-characterized samples in a reference laboratory . During the prospective study , 157 patients hospitalized for a suspicion of dengue were enrolled . In the hospital laboratories , the overall sensitivity , specificity , PPV and NPV of the NS1/IgM/IgG combination tests were 85 . 7% , 83 . 9% , 95 . 6% and 59 . 1% respectively , whereas they were 94 , 4% , 90 . 0% , 97 . 5% and 77 . 1% respectively in the national reference laboratory at Institut Pasteur in Cambodia . These results demonstrate that optimal performances require adequate training and quality assurance . The retrospective study showed that the sensitivity of the combined kit did not vary significantly between the serotypes and was not affected by the immune status or by the interval of time between onset of fever and sample collection . The analysis of the medical records indicates that the physicians did not take into consideration the results obtained with the rapid test including for care management and use of antibiotic therapy . In the context of our prospective field study , we demonstrated that if the SD Bioline Dengue Duo kit is correctly used , a positive result highly suggests a dengue case but a negative result doesn't rule out a dengue infection . Nevertheless , Cambodian pediatricians in their daily practice relied on their clinical diagnosis and thus the false negative results obtained did not directly impact on the clinical management .
The World Health Organization estimates that 50 million dengue infections occur annually and approximately 2 . 5 billion people live in area at high risk of infection . These areas are located in tropical and sub-tropical regions in South East Asia , Africa , Eastern Mediterranean , Western Pacific , Central and South America . The number of reported cases increased approximately 30 times over the last 50 years [1] and this could be in relation to many factors including population growth , urbanization , failure to control mosquito vectors , etc . [2] . Dengue is a viral disease transmitted by Aedes mosquitoes , principally Ae . aegypti . Dengue virus ( DENV ) belongs to the family Flaviviridae , genus Flavivirus . There are 4 antigenically and genetically distinct serotypes ( DENV-1 , -2 , -3 and -4 ) . In human , the virus can cause a spectrum of illness ranging from asymptomatic infection or self-limiting influenza-like illness ( dengue fever or DF ) to life-threatening disease associated with vascular leakage , hemorrhage ( dengue hemorrhagic fever or DHF ) , potentially leading to vascular shock ( dengue shock syndrome or DSS ) . There is currently no specific treatment available for dengue . An early diagnosis is nevertheless very important for efficient clinical management in order to cure or prevent life-threatening complications . In addition , accurate and early diagnosis directs clinical attention to warning signs of an evolution to severe forms and avoids unnecessary use of antibiotics . A range of serological and virological diagnostic methods are available but most of them require specialized laboratory equipment , experienced personnel and are time consuming which is not adapted for a field and point-of-care use . Serological diagnosis by ELISA or rapid diagnostic tests ( RDTs ) is technically easy to perform and provides fast results but requires most of the time paired sera to definitively confirm the diagnosis [1] , [3] . Detection of the NS1 antigen in the blood is a recent and very popular diagnostic method . This viral protein is secreted in the blood and can be detected by ELISA or immunochromatographic tests from the first day of fever and up to 14 days after infection [4]–[7] . The purpose of this study was to evaluate a commercial rapid dengue diagnostic kit , the SD Bioline Dengue Duo device ( Standard Diagnostic Inc . , Korea ) , in particular in point-of-care applications , and to evaluate the impact of the results of this combined test on the clinical management decision . The SD Bioline Dengue Duo kit is composed of 2 tests designed to detect DENV NS1 antigen ( first test ) and anti-DENV IgM/IgG ( second test ) in serum , plasma or whole blood . The kit evaluation was double . Firstly , the use of the test in the field was for the first time evaluated during a prospective study in 2 Cambodian provincial hospitals . The results obtained in the hospital's laboratories were then compared with those reported with the same samples by a national reference laboratory at Institut Pasteur in Cambodia ( IPC ) . We also investigated how the results of this point-of-care test designed to assist clinical management were perceived and subsequently incorporated into the clinical management decision of physicians from 2 hospitals during a dengue epidemic . Secondly , a more usual retrospective case-control evaluation against reference methods was performed at IPC in order to assess the kit performances in the context of a dengue-endemic South-East Asian country .
Patients were enrolled in the pediatric wards of Kampong Cham and Takeo provincial hospitals during the 2011 dengue epidemic in Cambodia i . e . between June and October 2011 . Patients presenting spontaneously to these hospitals or referred by health centers with a history of fever during the previous 7 days and at least one of the following symptoms: rash or severe headache or retro-orbital pain or myalgia or joint pain or bleeding , were examined by physicians who decided whether or not the child should be hospitalized . When the number of beds available was limited , priority was obviously given to the most severe cases . In each hospital , a maximum of 10 hospitalized patients , randomly selected , were enrolled weekly . Patient's information and clinical data were collected by physicians using a specific case report form and blood samples were taken at the time of hospital admission ( early/acute specimen ) and discharge ( convalescent/late specimen ) . Patients with incomplete test kit results , missing blood samples and incomplete clinical records were excluded . The panel used for the retrospective laboratory evaluation of the kit performances consisted of 157 samples collected in 2011 during the field prospective evaluation and tested negative or positive by the reference methods available at IPC completed with an additional 167 samples selected from IPC's dengue laboratory's biobank ( samples collected between 2008 and 2010 ) . Positive samples were selected in order to obtain an evaluation panel as balanced as possible in terms of DENV serotypes , day of collection after onset of fever ( DAOF ) , anti-DENV antibodies titer and immune status ( primary/secondary infections ) . Negative samples were selected from patients presenting with a non-dengue febrile illness and also from pregnant women . For the field prospective evaluation , a written consent was signed by the children's legal representatives before enrolment . This study was approved by the Cambodian National Ethics Committee . The use of stored samples from IPC's biobank was also approved by the Cambodian National Ethics Committee . The SD Bioline Dengue Duo kits were provided by Standard Diagnostics ( Kyonggi-do , Korea ) and tests were performed according to the manufacturer's instructions . For the prospective study , only acute blood samples were tested with the kit in hospitals as well as at IPC . At IPC , laboratory diagnosis was based on RT-PCR , isolation of DENV after inoculation into mosquito cell lines , detection of anti-DENV IgM and measure of an increase of anti-DENV antibodies titer measured by hemagglutination inhibition assay ( HIA ) between acute and convalescent sera . RT-PCR was performed after viral RNA extraction from acute serum samples using QIAmp Viral RNA Mini kit ( Qiagen , Hilden , Germany ) . Either a conventional nested RT-PCR according to Lanciotti et al . [8] protocol and modified by Reynes et al . [9] or a real-time multiplex RT-PCR based on the technique developed by Hue et al . [10] was performed . DENV was isolated on C6/36 cells and the virus serotype identified by immunofluorescence assay using monoclonal antibodies as described previously [11] . An in-house IgM capture Enzyme-Linked Immuno-Sorbent Assay ( MAC-ELISA ) was used to detect anti-DENV and anti-Japanese Encephalitis virus ( JEV ) IgM as describe previously [11] . A result was considered positive for dengue when the optical density ( OD ) was higher than 0 . 1 for the DENV IgM and when the OD of the anti-DENV ELISA was higher than the OD of the anti-JEV ELISA . HIA followed the method described by Clark and Casals [12] adapted to 96-well microtiter plate . Primary or secondary acute dengue infection was determined by HI titer according to criteria established by WHO [13] . In brief , the patient was defined as having a primary infection when the convalescent serum had a HI titer ≤2560 associated with a fourfold rise of the titer between the acute and convalescent sera ( collected with a time interval of at least 7 days ) . When the convalescent serum had an HI titer >2560 , the patient was defined as having a secondary dengue infection . All early samples were tested by PCR , viral isolation , IHA and MAC-ELISA whereas late samples were only tested by HIA and MAC-ELISA . Confirmed and suspected dengue cases were defined according to WHO guidelines [1] . A confirmed case was defined by a RT-PCR and/or a culture positive result and/or an IgM seroconversion in paired sera and/or a fourfold antibodies titer increase measured by HIA in paired sera . A probable dengue infection was defined by an HI antibody titer >2560 in paired sera without a fourfold increase or IgM positive result in the acute serum [1] . At IPC , technicians were blinded for the results of the kit evaluated as well as for the results of gold standard tests . In hospitals , the staff performing rapid diagnostic tests was blinded for the results obtained with these tests as well as for the results of the gold standard assays . Each clinical record contained the complete medical data recorded at the time of admission and the complete follow-up of the patient during the hospitalization ( temperature , blood pressure , pulse , diuresis , medical prescriptions , etc . ) until discharge . These data were anonymized by the physicians for the purpose of the analysis . Statistical analysis was performed using STATA version 11 . 0 ( StataCorp , College Station Texas , USA ) . Significance was assigned at P<0 . 05 for all parameters and were two-sided unless otherwise indicated . Uncertainty was expressed by 95% confidence intervals ( CI95 ) . For the prospective study , agreement between hospital's laboratories and IPC laboratory's data was measured by agreement percentage and Kappa ( κ ) coefficient . For the prospective study , sensitivity and specificity obtained when tests were performed at hospitals were compared with those obtained at IPC with McNemar test . Positive and negative predictive values ( PPV and NPV ) were compared with Fisher exact test . For the retrospective laboratory study and for the analysis of medical records Fisher exact test was used . During the retrospective laboratory evaluation , sensitivity was calculated according to infecting serotype , DAOF , immune status and antibodies profiles . Four different antibodies profiles were arbitrarily defined according to HIA and MAC-ELISA results: profile 1 , low HI titer ( <640 ) and negative MAC-ELISA; profile 2 , low HI titer and positive MAC-ELISA; profile 3 , high HI titer ( ≥640 ) and negative MAC-ELISA; profile 4 , high HI titer and positive MAC-ELISA .
The medical records of 129 patients ( 82 . 2% of all patients enrolled ) were provided by the two hospitals and subsequently analyzed . All the 66 patients who tested positive for acute dengue infection using the IPC gold standard test were also clinically diagnosed by the physicians as dengue cases , with or without co-infection ( 63 and 3 patients , respectively ) . One patient with a laboratory-suspected DENV infection as well as two children who tested negative were clinically diagnosed as non-dengue febrile illness ( Table 1 ) . All patients received a treatment based on WHO 2009 recommendations , i . e . , intravenous fluid therapy with 0 . 9% saline , Ringer's lactate or Ringer's acetate with or without dextrose , paracetamol if fever and oral rehydration solution or other fluids containing electrolytes and sugar when possible . Patients in circulatory shock received dextran , O2 and blood transfusion when necessary . Twenty-nine patients ( 27 . 7% ) also received antibiotics . The prescription of antibiotics was justified by the phisicians in the medical records of 11 patients because the following diagnoses: 4 dysenteric syndromes with suspicion of typhoid fever , 3 meningitis or meningo-encephalitis , 1 suspicion of nosocomial infection , 2 pharyngitis and 1 bronchiolitis . Among the 90 patients with a positive NS1 and/or IgM and/or IgG test , 17 . 8% ( 16/90 ) were treated with antibiotics . Out of 39 patients who tested negative by the RDT , 13 ( 33 . 3% ) also received antibiotics . The comparison of antibiotic prescription between both groups was at the limit of significance ( p-value = 0 . 067 ) . There was no difference in the duration of antibiotic therapy between patients with a positive test and those with a negative test ( p-value = 0 . 216 ) . Among the 16 positive patients who received antibiotics , only 7 ( 43 . 7% ) had their antibiotic therapy stopped once the point-of-care kit tested positive for dengue . Among the 13 patients with a negative result who received antibiotics , 8 ( 61 . 5% ) had their antibiotic therapy stopped once the test was performed . The decision to maintain or discontinue the antibiotic therapy was not affected by the result of the RDT ( p-value = 0 . 338 ) .
Early management of patients with dengue infection is essential to ensure a favorable evolution of the disease and prevent the occurrence of severe forms . Until recently an early confirmed diagnosis was only achievable in specialized laboratories . The discovery of the NS1 protein as an early marker for DENV infection , especially in RDT format , now allows dengue diagnosis during the early phase of the disease , even in laboratories with limited equipments and human resources . Evaluations are required to ensure that these tests are suitable for diagnosis and clinical management or epidemiological surveillance and outbreak investigations . Different methodologies can be used: laboratory-based evaluations ( or retrospective evaluations ) and field evaluations ( or clinical-based/prospective evaluations ) [14] . Retrospective evaluations are easy to perform but tend to overestimate tests accuracy . Prospective evaluations allow determination of PPV and NPV with tests performed on patients in the real clinical settings . However , accuracy of diagnostic tests estimated by prospective evaluations could be biased due to imperfect gold standard in the prospective clinical setting . In our study we combined both prospective and retrospective evaluations . The retrospective part was added in order to better understand the results obtained in the field during the prospective study . Since the two test kits of the SD Bioline Dengue Duo combo test do not give exactly the same information , the NS1 assay was initially assessed alone in the prospective as well as in the retrospective study . If a positive NS1 test can confirm a dengue diagnosis , this is not the case for IgM and IgG tests as the antibodies remain detectable for months and thus a positive result obtained on a single blood specimen is only suggestive of a dengue infection . Indeed , to confirm an acute dengue infection by serology , an IgM seroconversion or a four-fold increase of IgG antibody titers in paired sera must be demonstrated ( which cannot be done with the RDT kit as result is only qualitative ) [1] . By evaluating separately , but in parallel , the NS1 test and the serological kit , we estimated the ability of the test to both suggest and confirm a dengue infection . During the prospective study , the sensitivity of the SD Bioline Dengue Duo NS1 when performed at the hospitals was only 44 . 5% to confirm dengue infections in children hospitalized for dengue-like illness during the epidemic season . The tests were carried out in laboratories equipped for routine medical biology . Out of the 127 patients included in the prospective evaluation , 70 ( 54 . 7% ) had an HI titer ≥640 which could probably explains such a poor sensitivity . The retrospective study helps to understand why the sensitivity was limited . It suggested that the presence of high level of anti-DENV HI antibodies in the sample was a major factor for sensitivity decrease . Indeed , while a sensitivity >80% was obtained with samples containing no or low HI antibodies titer ( <640 ) , the sensitivity dropped to 37% when the HI titer was ≥640 . Almost 86% of the samples with a high HI titer issued from patients with a secondary infection . Since HI titer reflects mainly IgG response , the poor sensitivity observed during secondary infections is probably directly linked to the high IgG titer . Similar observations were already made by other authors . In Vietnam , the same NS1 test demonstrated a sensitivity of 24 . 6% for samples positive for IgG by GAC-ELISA and a sensitivity of 77 . 3% in sera negative for IgG [15] . In Colombia , Osario et al . reported an even lower sensitivity ( IgG negative: 65 . 6% , IgG positive: 15 . 6% ) [16] . Of note , the methods used for IgG detection in these evaluations were all different and rather than giving the real performance of the kit , the data indicate a global trend to a lower sensitivity when IgG titers increase . As others [16] , [17] , we observed that the sensitivity of this test decreased when the window of time between onset of fever and sampling increased . This was expected since the IgG titer also increased with the time . Finally , a higher IgG titer also characterizes the secondary dengue infections and the better sensitivity of the NS1 in primary infections was also already reported [15]–[17] . The performances of the NS1 test reported here as well as by other retrospectives studies are close to those observed with other commercial NS1 RDTs [15] , [18] . A major value of the kit marketed by SD is the combination of the NS1 test with an anti-DENV antibodies detection kit . Indeed , the serological results improved the sensitivity by compensating for the loss of sensitivity usually observed with the NS1 test when used alone in the presence of specific anti-DENV antibodies . During the prospective evaluation , we demonstrated that the addition of IgM and IgG results to the NS1 data was only associated with a slight non-significant decrease of the specificity . However , this result should be interpreted with caution as the number of negative patients included was relatively small . In addition , the relatively low overall performance of the IgM/IgG test could well be partially due to imperfect gold standard tests . In the retrospective study , we did not observed any cross-reactivity with Chikungunya virus , Orientia tsutsugamushi or Plasmodium sp . . However when evaluating the SD Bioline Dengue Duo kit , Blacksell et al . [18] reported 12 . 2% ( 10/82 ) of cross-reactivity with Chikungunya virus , 12 . 5% ( 1/8 ) with Orientia tsutsugamuhi and 100% ( 1/1 ) with Plasmodium sp . When evaluating only the IgM part of the kit , Hunsperger et al . [19] reported around 35% of IgM cross-reactivity with malaria as well as some false positive results with leptospirosis , tuberculosis and West-Nile infections . During the prospective evaluation , the PPV value of the NS1 test was 98 . 2% , suggesting that the probability to correctly confirm a dengue infection was very high when the test was positive . When the test was used in combination , the PPV decreased only very slightly ( NS1/IgM: 96 . 9%; NS1/IgM/IgG: 95 . 6% ) . Consequently , the NPV observed when the tests were performed in the hospitals was only 29% for the NS1 test alone and 56 . 8% for the combination test . In other words , the probability of truly exclude a dengue infection when the tests were negatives was low . These PPV and NPV results should be regarded with caution as they depend on the dengue disease prevalence that can be extremely different in other contexts and epidemiological situations . In this prospective study , the prevalence of dengue infection was very high ( 80 . 3% , 126/157 ) because the evaluation was performed during the peak epidemic season and only involved dengue suspect patients . Observing high prevalence of dengue infections in suspect patients hospitalized is common in Cambodia ( 87 . 8% of average between 2000 and 2008 ) and in neighboring countries like Vietnam ( 86 . 2% during a DENV-4 epidemic in 2002 ) [20] , [21] . On the samples collected during the prospective study , the comparison of the results of the tests performed by technicians in hospital laboratories or by health workers who did not receive any specific training for the use of the kits with the results reported by the staff of the national reference laboratory at IPC demonstrated a moderate agreement with the serological tests and an excellent agreement with the NS1 test . Indeed , 49 discordant results between the hospitals and IPC were observed with the IgM/IgG test out of which 34 ( 69 . 3% ) were positive at IPC but negative at the hospitals while 13 ( 26 . 5% ) were negative at IPC but positive at the hospitals . These discrepancies could be explained if the reading was made before the recommended 15 minutes ( leading to false negative results ) or after the correct time ( leading to apparition of unspecific bands ) or because of problems with the interpretation of weak signals ( faint bands ) . To evaluate if the issue was the interpretation of the faint bands , these data were removed from the analysis and a better agreement percentage and Kappa coefficient were obtained ( 82 . 0% vs 68 . 8% and 0 . 73 vs 0 . 55 ) . A problem of reproducibility could also have accounted for some of the discrepancies observed . Nevertheless , in the case of bad reproducibility an equal number of discrepancies should have been observed in each laboratory which was not the case in our study . Moreover all tests were from the same manufacturing lot . During a malaria RDTs evaluation , misinterpretation of weak signal in the field had already been reported [22] . It was also reported that health workers in the field tend to read the results before the time recommended by the manufacturer [22] , [23] . Despite its relative ease to use , the performances of the IgM/IgG RDT are obviously partially person-dependent , hence the importance of providing specific training or at least very clear pamphlets which could guide the health worker in its interpretations and expose the risks of false results when the recommendations are not strictly followed . On the contrary a very good agreement was observed with the NS1 test since the bands in this immunochromatographic device almost always appear very clearly . As the RDTs have a significant cost , promoting the use of these kit does only make sense if the health workers can perform the tests in good conditions , which seems to be sometimes challenging in intensive care units and pediatric wards that are often unable to cope during peak epidemics . Knowing these constraints and limitations , the manufacturer should be encouraged to correct , if possible , the reading issues of the serological test . The outcomes of the patients who were wrongly tested negative by the kit was a matter of concern as RDTs are designed for rapid diagnostic and to assist physicians in their decisions . Dengue is a life-threatening disease that requires specific clinical care . The analysis of the medical records demonstrated that physicians ignored the negative results and followed their clinical instinct as all patients who tested negative by RDT received an intravenous fluid therapy which is recommended in patients with warning signs [1] but which is also often administrated in mild cases to prevent complications . Similar observations were also made in the context of malaria RDTs use . Between 54% and 85% of the patients with negative malaria RDT results were treated with anti-malaria drugs in Nigeria , Tanzania , Burkina Faso , Philippines and Laos [22]–[25] . There are probably several reasons that could explain that physicians did not consider the negative results obtained with the RDT: the habit to rely mostly on clinical intuition explained by a frequent limited access to laboratory tests , some mistrust against a new test , the difficulties to understand the kinetic of the immune response during dengue infection and the significance of NS1 , IgM and IgG test results , a high confidence in clinical diagnosis when children present to pediatric wards with dengue-like symptoms during the epidemic season ( especially since the national virological surveillance confirms usually more than 80% of the dengue clinical diagnosis ) [20] , the fear that a misdiagnosed dengue infection evolves towards a DHF or a DSS while these complications are pretty easy to prevent with simple clinical management , etc . The SD Bioline dengue Duo test could have a better utility in smaller medical care structures , like health care centers and dispensary where the proportion of dengue among all febrile diseases is lower ( e . g . , 12% of all the febrile episodes in Kampong Cham province , 2006–2008 ) [11] and where routine hematology ( e . g . , hematocrit , platelet count ) that could help to orientate the diagnosis are not often available . One of the advantages to perform a rapid confirmatory diagnostic of dengue in the context of febrile illness is to avoid the unnecessary use of antibiotics . In the context of Cambodia , it seems the RDT results did not have a significant impact on the decision to start or discontinue an antibiotic therapy . In an endemic country , especially in the context of an epidemic , it seems that the sensitivity of the NS1 RDT alone is too low and that only positive results should be taken into consideration . Nevertheless , the performances of the combined kits are good and these kits appear to be a useful tool for the clinicians as they can quickly confirm the diagnosis of dengue and therefore contribute to the an optimal clinical management of the cases and avoid an unnecessary use of antibiotics or other drugs which is important in the context of a developing country with limited resources . In conclusion , we observed that for a patient presenting with dengue-like symptoms in a dengue-endemic/epidemic region , a NS1 positive result obtained with the SD Bioline Dengue Duo kit confirms a dengue diagnosis , an IgM and/or IgG positive result highly suggests dengue infection but a negative result doesn't rule out a dengue infection . We have also demonstrated that the performances of the test in the field were lower than the ones obtained in the more experienced hands of technicians working in a national reference laboratory . This suggest that even for a point of care test theoretically designed to be used by untrained staff , there is still a significant improvement of the performance of the test to expect if a proper training and a quality assurance program can be implemented . With the time , the trust of the physician will probably increase if the accuracy of the test improves . In general , manufacturers should always bear in mind that the ultimate goal of the RDTs is essentially to be used as a point-of-care test or in support of epidemiological investigation and as such should be easy to use , stable at room temperature but also not posing reading difficulties unless they can provide proper training and organize quality programs . More prospective field evaluations are still necessary now to better assess the interest to use such point-of-care tests in the real conditions that justified their development and to address some of the questions and concerns raised by this study . | Dengue is a potentially life-threatening viral disease . Symptoms are often not specific hence the importance to confirm the diagnosis during the early stage of the disease . Nevertheless , until recently only specialized laboratories were able to confirm dengue diagnosis . The discovery of the NS1 protein as a marker of infection has allowed the development of point-of-care tests for a rapid diagnosis confirmation . These tests have previously been evaluated by laboratories , but their performances have never been assessed in field conditions . In this study we evaluated the performance of SD Bioline Dengue Duo kit when tests were performed by hospital laboratories staff in a dengue hyper-endemic country . We also assessed the impact of the test results on the clinical management decision . The combination of NS1 test with antibodies detection improved the performance , though discordances on IgM and IgG results were observed between the hospitals and the national reference laboratories . Physicians treated patients according to their clinical diagnosis and did not take negative results into consideration . | [
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] | 2012 | Field Evaluation and Impact on Clinical Management of a Rapid Diagnostic Kit That Detects Dengue NS1, IgM and IgG |
Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies . In combination with genotyping , such studies offer the prospect to 1 ) identify mechanisms involved with regulation of protein expression in plasma , and 2 ) determine whether the plasma proteins are likely to be causally implicated in disease . We report here the results of genome-wide association ( GWA ) studies of 83 proteins considered relevant to cardiovascular disease ( CVD ) , measured in 3 , 394 individuals with multiple CVD risk factors . We identified 79 genome-wide significant ( p<5e-8 ) association signals , 55 of which replicated at P<0 . 0007 in separate validation studies ( n = 2 , 639 individuals ) . Using automated text mining , manual curation , and network-based methods incorporating information on expression quantitative trait loci ( eQTL ) , we propose plausible causal mechanisms for 25 trans-acting loci , including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand . Using public GWA study data , we further evaluate all 79 loci for their causal effect on coronary artery disease , and highlight several potentially causal associations . Overall , a majority of the plasma proteins studied showed evidence of regulation at the genetic level . Our results enable future studies of the causal architecture of human disease , which in turn should aid discovery of new drug targets .
Cardiovascular disease ( CVD ) , especially coronary artery disease ( CAD ) is a leading cause of human morbidity and mortality . Data from the World Health Organization ( WHO ) showed that CVD caused approximately 17 . 5 million deaths in 2012 , corresponding to 31% of all deaths globally . Of these 7 . 4 million were estimated to be due to coronary heart disease and 6 . 7 million to stroke [1] . Specific and mechanistically relevant biomarkers are important tools in risk prediction , disease diagnosis and successful development of new therapies [2] . Proteins in the circulation have been extensively explored as biomarkers across numerous disease conditions , not least because of the relative ease with which blood plasma and serum can be accessed , stored and analysed in observational studies and randomized controlled trials . The usefulness of a plasma biomarker in disease prediction , or as surrogate endpoint in a clinical trial , depends on its specificity and sensitivity . These metrics reflect the relationship of the biomarker with a pre-specified disease endpoint , but are inherently influenced by biological factors such as the tissue expression , stability , regulation and variability of the biomarker . The genetic contribution to the variability of plasma biomarkers can be explored in genome-wide association ( GWA ) studies using single nucleotide polymorphisms ( SNPs ) , and this approach has been applied to uncover numerous such relationships [3–5] . For distinct plasma biomarkers such as circulating proteins , the associations are also known as protein quantitative trait loci ( pQTLs ) [6–9] . Genetic loci for biomarkers and pQTLs have wide applicability in research . Firstly , pQTLs in trans can identify previously unknown regulatory pathways . Using trans-pQTLs to discover regulatory pathways is beneficial because it is based on in-vivo human observations that have well-established direction of causality , flowing from SNP to protein [7] . This approach has been extensively used in-vitro , for example in yeast studies [8] , and the overall goal of such analysis is a deeper understanding of the regulatory check-points giving rise to a particular biomarker concentration . For a biomarker that is causally involved in disease , e . g . low-density lipoprotein cholesterol ( LDL-C ) , this is crucial knowledge as it allows targeting of upstream factors , e . g . HMG-CoA reductase . Secondly , GWA study loci associated with circulating levels of plasma biomarkers that are predictive of disease risk enable evaluation of whether the biomarker association with disease is likely to be a causal relationship , using Mendelian randomization ( MR ) . For example , although both C-reactive protein ( CRP ) and LDL-C predict risk of CVD and are lowered by treatment with statins , MR studies have concluded that plasma LDL-C is an aetiologically important factor , while plasma CRP is a biomarker that is not causally related to CVD [10 , 11] . Similarly , all efforts towards HDL-cholesterol lowering drugs have failed , consistent with MR results showing that SNPs affecting HDL-levels are unrelated to risk of CVD [12] . Based on these experiences of pharmacological treatment lowering the LDL-C concentration , one may suggest that a biomarker which is both predictive and causal provides a more attractive target for novel therapeutics . Numerous associations between biomarkers and disease have been described in the literature , but the potential causal involvement of these biomarkers has only been addressed for a limited number , partly due to a lack of robust genetic predictors for many plasma proteins . In the present study , we analyzed 83 plasma proteins using the Olink ProSeek CVD array in 3 , 394 European subjects with at least 3 established CVD risk factors . The majority of these proteins are strong candidates for involvement in atherosclerosis , plaque rupture or thrombosis and many are upregulated in CVD patients compared to controls or predict future risk of CVD events , such as CAD . The proteins analysed included well-known candidates such as interleukin-6 , interleukin-18 , CD40 ligand , and NTproBNP: a full list is available as supplementary S3 Table . The aims of the study were to i ) identify genetic loci for circulating plasma proteins that have previously been connected with CVD , ii ) explore the mechanisms underpinning novel loci by integrating genetics with other biological information and iii ) apply the tools to test causality in CAD .
For each of the reported trans associations , we evaluated the most likely cis-gene intermediary , and investigated pathways in the direction of the plasma protein ( Table 2 ) . Cis-gene intermediary we define as a gene within 500 kb of the index SNP that is likely to be the first step in conveying the effect on plasma protein levels , according to the hypothesis that an effect on a proximal gene is a likely first step . Analysis of coding proxies revealed that 10 trans loci had missense mutations in linkage disequilibrium ( LD ) with the index-SNPs , providing an obvious explanatory model for a cis-gene intermediary mechanism of action . The analysis of cis-eQTLs in 11 large cardiovascular eQTL data sets provided evidence for an additional 13 mediator cis-genes . The basic eQTL analysis investigates if the expression of a gene is associated with the genotype of a proximal index SNP , and is motivated by common cases of cis-genes not being the gene closest to the index SNP [15 , 16] . Some of the findings were remarkably independent of tissue and cell-type , and showed concordant results in several of the 11 eQTL datasets under analysis , as indicated in Table 2 . At each locus with significant cis-eQTL association , we additionally investigated neighbouring eQTL and pQTL effects as LocusZoom plots ( supplementary S2 Fig ) . In some cases , like rs4810479/KITLG , the index-SNP shows both the strongest association with KITLG and the strongest cis-gene association ( PLTP in liver ) . However , cases also exist , like rs200373/CTSL1 , where stronger eQTL effects for the candidate cis-gene intermediary exists from other SNPs , with low LD between the SNPs precluding straightforward interpretation . Further studies would be required to address this issue . In pathway analysis using the String-database of protein interactions , an additional 6 trans-genes were highlighted as possible mediator genes through functional protein connections . The criterion in this analysis was that less than 5% of randomly re-wired networks had shorter distance , dictating simply that connections of length 1 from a cis-gene to the trait gene should be selected . Additionally , a more sophisticated weighted network analysis was performed where each path through the network was weighted by the strength of the ( trans ) eQTL of the index-SNP . The eQTL values were calculated using a large collection of eQTL databases with tissues and cells relevant to cardiovascular disease . Like in the unweighted network analysis permutation was used to determine significance threshold . Through this weighted network analysis approach we discovered 11 additional mediator candidates , examples being the rs61598054 -> FOXO3 -> AKT1 -> NGF and the rs693918 -> XDH -> TLR4 -> IL18 that are illustrated in Fig 2A and 2B . Systematic literature mining suggested an additional 5 possible mediators . Co-occurrence in scientific abstracts can indicate real biological relationships that may be missing from the String network . Interestingly , across all trans-pQTL loci , the largest number of abstract co-occurrences was 626 for the receptor-ligand pair encoded by TNFSF11 and TNFRSF11B , a protein-protein interaction also reported in String-db . The results of these five cis-gene mediator approaches are summarised in Table 2 . While examples given above provide relatively clear indications of trans mechanism , more challenging cases do exist: several strong SNP-protein associations gave no evidence of pathway or cis-gene intermediary , including the disease-relevant rs16873402 -> -> -> PDGFB association . Clearly alternative non-obvious mechanisms must be responsible for these . Other findings gave vague and discrepant results , such as the rs10947260 -> -> -> IL6 association , which pointed to several candidate cis-mediator genes: BTNL2 , NOTCH4 , AGER , and ATF6B , each with different types of evidence and in the context of non-significant replication for this SNP-protein association ( Fig 2D ) . We conclude that in all these cases further experimentation is required to establish the main mechanism in this case . Inspection of potential pleiotropic effects of index SNPs on measured protein traits as described in Methods revealed 6 distinct candidate loci ( supplemental S1 Fig ) . The ABO locus affecting THBD , TEK , F3 , PECAM1 , and SELE in our dataset and the FUT2 locus affecting MMP10 , F3 , and LGALS3 are well known for their pleiotropic effects [17] . Furthermore , all SNPs affecting BNP levels seem to also impact NPPB levels . This likely indicates an effect on steps before cleavage of the precursor protein . NTproBNP is a prohormone with an inactive N-terminal part that is cleaved to produce the active BNP . However , because of its half-life NTproBNP is typically used as a prognostic biomarker . A locus within the ZFPM2 gene seems to have a strong effect on PDGFB , DDK1 , and , to a lesser extent , on VEGFA . Finally , the cluster of cis-acting variants in the MMP1 , MMP3 , and MMP12 loci are not specific to only one of the proteins but seem to impact all three of the metalloproteinases in this genomic region . Additionally , we investigated the known associations of the index-SNPs with a broad range of other phenotypes , as previously reported in literature ( supplemental S2 Table ) . To assess a potential causal involvement of each protein in CAD , we calculated genetic risk scores from the publically available CARDIoGRAMplusC4D GWAS data with the aim to construct a more powerful genetic instrument for those markers for which there were multiple SNPs . First , a systematic look-up of all reported pQTL-SNPs was performed to test for association with CAD ( Table 3 ) . Then , we further explored proteins with multiple independent loci by calculating pooled SNP scores per protein , thus creating more powerful instruments to analyze the causality for proteins with multiple SNPs . Results show that of the SNPs contributing to the concentrations of proteins ( Table 1 ) , eight were also significantly associated with risk of CAD at FDR corrected significance levels ( Table 3 ) . These findings suggest a causal role for these proteins , and whilst the cis IL6R finding confirms previous observations [18] , the other observations extend our knowledge of important factors in CVD . Results from pooled-scores include highlights such as the multi-SNP support of LGALS3 and the contradiction of CHI3L1 having a CAD-associated trans-effect but no CAD-association in the cis-loci ( Table 3 and data from [19] ) .
A proteomics GWA study provides an interesting opportunity for the study of trans-regulatory effects , because the trait is a well-defined biological entity . In some cases , the trans-pQTL investigating methods in Table 2 converged on a very plausible candidate gene . For example , at the CCL4-rs62625034 locus the effector transcript is probably the CCR5 gene , while at the TNFSF11-rs7813952 locus , the effector transcript is likely the TNFRSF11B gene , two examples of known ligand-receptor pairs . Another example is the IL27-rs4905 variant , which sits within the EBI3 gene . The IL27 and EBI3 genes encode the two subunits of the IL27 cytokine complex . The effector transcript at the KITLG-rs4810479 locus may be MMP9 , which encodes a metalloproteinase that cleaves the KITLG gene product , a membrane-bound stem cell factor [26] . Thus this trans pQTL may represent an example of genetic regulation via post-translational modification . At a few loci , we found either nothing or multiple lines of evidence suggesting different mediator genes at the same locus . This is not biologically impossible , nor is it uncommon in the literature [27] , but it does require more careful analysis . The challenge is illustrated by the IL6-SNP rs10947260 , for which separate lines of evidence pointed to three candidate cis-mediator genes . As shown in Fig 2D , a criticism against concluding on the importance of a pathway to IL6 through the CCND1 gene is that NOTCH4 has many neighbours in the String-network , thereby increasing the risk of a spurious discovery . While these examples seem specific , they illustrate challenges that have major consequences for the general interpretation of any genetic association result . Analyses such as these have driven the development of popular risk-gene assignment tools ( e . g . [28] ) . Our findings illustrate the increased power of knowing a certain pathway destination through the use of pQTL . The study provided an important opportunity to systematically test each of the plasma proteins for a potential causal role in CVD by investigating whether identified pQTLs also were associated with CAD risk . If an instrumental variable , e . g . a SNP or a set of SNPs , exclusively affects one factor , and also affects an overall phenotype , such as disease risk–then it may be deduced that the protein is causally involved in the development of this disease . According to this principle , eight proteins ( PECAM1 , SELE , F3 , IL6R , CHI3L1 , LGALS3 , MMP12 , and PDGFB ) showed evidence of potentially causal involvement in CAD . The connection between IL6R and CAD has already been described [18] , and several drug trials are underway to test whether an ILR6-inhibitor ( tocilizumab ) is effective in treatment of CAD ( clinicaltrials . org ) . In light of this , the remaining proteins could be of interest as therapeutic targets . However , there are some important limitations to the approach , as compared to a formal MR . A formal MR study requires that the genetic instrument is specific , is not in LD with other functional variants , and that there are no hidden population strata [29] . There is no reason to suspect that the second and third requirements were violated; the study was based on high-resolution imputation of cohorts that were ethnically homogeneous . Importantly , the specificity requirement was not always satisfied , weakening the findings for some proteins . This includes all the trans associations , as well as proteins for which pleiotropy was detected ( supplemental S1 Fig and supplemental S2 Table ) . In addition , association between plasma protein concentrations per se and future CVD risk has not been carefully investigated for the majority of proteins included in the present study . These limitations leave LGALS3 , MMP12 and PDGFB as candidates for having a causal effect on CAD . Of the three SNPs affecting levels of LGALS3 , rs1169306 , rs7928577 and rs33988101 in trans , only the first two also contribute to CAD risk , resulting in a pooled CAD association P-value of P = 1 . 46e-4 . For MMP12 and PDGFB , the results are based on single SNPs showing associations with protein levels . Of the three , only MMP12 is a cis effect thereby strengthening the case for it being a specific MR instrument . These limitations notwithstanding , the map of pQTLs presented here , and in particular those acting in cis , should provide the means to systematically assess potential causal roles of these biomarkers in other common complex diseases . Additionally , we highlight the online resource found at www . olink-improve . com where the data pQTL can be browsed in greater detail . This may in turn help to prioritise drug targets for development of disease-modifying therapies . In conclusion , the main contributions of this paper are: i ) identification of 79 pQTLs regulating important circulating cardiovascular plasma proteins , ii ) novel evidence of the regulatory mechanisms underpinning at least half of these novel loci and iii ) evidence of potential causal roles in CAD development for several plasma proteins . We believe that these three principal findings provide a strong contribution to the field of cardiovascular biomarkers and beyond .
The IMPROVE study is a multicentre , observational study , which recruited 3 , 711 men and women aged between 55 to 79 years with at least three cardiovascular risk factors but without symptoms of CVD ( previously described [30] ) . Serum and plasma from the study participants were collected at baseline , dispensed in polypropylene tubes and frozen at –80°C prior to shipment for centralized biochemical analyses and biobanking at the Karolinska Institutet in Stockholm , Sweden . The study was conducted in accordance with the declaration of Helsinki and all participants gave written informed consent . The individuals in the discovery cohort , IMPROVE , were recruited in 7 different centres in Finland , France , Italy , the Netherlands , and Sweden . The relevant permits were given by ethical committees for each the 7 different centers as follows: Kuopio Research Institute of Exercise Medicine , Finland . Kuopio University Hospital , Finland . Karolinska Institute , Stockholm . University Medical Center Groningen , Groningen , the Netherlands . Groupe Hospitalier Pitié-Salpétrière , Unités de Prévention Cardiovasculaire , Paris , France . Dipartimento di Scienze Farmacologiche e Biomolecolari , Milan . University of Perugia , Italy . The ethics and sampling of this cohort have been further documented in prior publications , e . g . [33] . The individuals in the replication cohorts , NSPHS , PIVUS and ULSAM were likewise recruited following informed written consent . The relevant permits were all given by the regional ethics committee at Uppsala University , Sweden . The ethics and sampling of these cohorts have been further documented in prior publications [31 , 32] . DNA genotyping in the IMPROVE study was performed using the Illumina CardioMetabochip and Immunochip arrays . The combined SNP genotyping data from both platforms were merged and subjected to the following quality control ( QC ) using PLINK 1 . 7: SNPs were excluded for probe to genome mismatch , incorrect assignment of allelic variants in the array design , failed Hardy-Weinberg Equilibrium test at 1x10-6 , call rate <95% or failed Illumina genotype calling QC . Samples were excluded if they showed evidence of gender mismatch , abnormal inbreeding coefficient , failed cryptic relatedness test or had an overall sample call rate <95% . After quality control , a total number of 3 , 394 subjects remained for analysis . Imputation was performed with MACH 1 . 0 algorithm with 1000 genomes CEU v3 as reference panel . The pre-imputation data set contained 244 , 814 SNPs and the post-imputation data set contained 5 , 270 , 624 SNPs . In total , there were 3 , 394 IMPROVE participants for whom quality controlled genotype information and plasma samples were available . Plasma concentrations were measured in baseline EDTA plasma samples using the ProSeek CVD array I ( Olink Biosciences , Uppsala , Sweden ) , according to the standard protocol . The ProSeek method is based on the highly sensitive and specific proximity extension assay ( PEA ) , which involves the binding of distinct polyclonal oligonucleotide-labelled antibodies to the target protein followed by quantification by real-time quantitative PCR [13] . In addition to the controls provided by Olink Biosciences , a pooled plasma control was included in all plates to enable further quality control ( QC ) such as calculation of variation coefficients . Prior to statistical analyses , we excluded individual assays with more than 20% of samples below the lower detection limit and those with final inter-plate coefficients of variation above 25% . After QC , a total number of 83 proteins out of the 92 remained for analysis ( full overview in supplementary S3 Table ) . The native scale of Olink protein measurements is log ( 2 ) but additional log ( 10 ) transformations were performed to ensure normally distributed variables . Overview of standard curves for all proteins are given in supplemental S1 Dataset . Validation of the OLINK method has been conducted [13] , and the method has been used to validate previous findings obtained with established protein quantification methods [31 , 34] . Plasma protein readings were log10 transformed prior to analyses . Standardized residuals for each of the 83 plasma proteins were calculated using a linear model adjusting for age , sex , recruitment centre , protein analysis batch , smoking , diabetes and hypertension at baseline . To merge loci in Table 1 and supplementary S1 Table , signals with R2 higher than 0 . 1 and distance within 250 KB were omitted , retaining only the strongest signal in each block , referred to as the index SNP . The standardized residuals were used in a Wald-test in PLINK 1 . 9 to test association between genetic data and each plasma protein , using a significance threshold of P < 5e-8 . All summary statistics can be downloaded at www . olink-improve . com , or from the Zenodo data-repository ( DOI 10 . 5281/zenodo . 264128 ) . Narrow-sense heritability for all proteins was calculated using Genome-Wide Complex Trait Analysis [14] . A genetic relationship matrix was calculated using all measured autosomal SNPs with , less than 1% missingness and allele frequency above 5% , using the restricted maximum likelihood analysis ( REML ) . Attempts at quantifying heritability using imputed data failed for 37 of 83 measured proteins . Replication studies of all pQTLs were performed in three community-based cohorts in which Olink array protein data and genotypes were available . These cohorts were the NSPHS [32] , the Prospective Investigation of the Vasculature in Uppsala Seniors ( PIVUS ) and the Uppsala Longitudinal Study of Adult Men ( ULSAM ) [31] , consisting of samples from 976 , 933 and 730 participants , respectively . Statistics were calculated according to additive association models , and findings were matched either directly on imputed SNP-id ( 96% of cases ) or using a proxy with R2 > 0 . 8 linkage disequilibrium . Replication P-values were calculated using the METAL meta-analysis software ( version 2011-03-25 ) . For each index-SNP , cis- and trans-eQTL data were calculated from the following sources: aorta intima-media , aorta adventitia , liver , mammary artery , and heart from the ASAP study [35] , monocytes and B-cells from the Fairfax et al study [36] , and monocytes stimulated with LPS-2h , LPS-24h and interferon-2h from another Fairfax et al study [37] . Each of these 11 data sets had information from gene expression microarrays and genotyping microarrays as described in the respective references . The mean sample size was 223 with a range of 89–367 . Data from genotyping microarrays were imputed using the MACH 1 . 0 algorithm with 1000 genomes CEU v3 data as reference ( mean rsq quality score 0 . 89 ) [38] . The strength of eQTL association was calculated using a linear additive model between log2-transformed expression value and numerically encoded genotype data . For cis-eQTL associations , un-corrected p-values from cis-eQTL were reported if the association was stronger than P < 0 . 0005 ( corresponding to a false discovery rate ( FDR ) <5% ) . For all significant cis-eQTL associations , locusZoom plots were generated showing regional effect differences between eQTL and pQTL studies [39] . The network analysis was performed based on the String database network ( version 10 ) [40] , using all edges with a confidence score above 400 . For all genes within 0 . 5 MB of an effect-SNP ( “cis-genes” ) , the shortest path length was calculated between the cis-gene and the gene encoding the measured protein biomarker ( “trait-gene” ) using the igraph package in R ( version 1 . 0 . 1 ) . This was done both with an unweighted version of the Stringdb-network as well as with a weighted version , wherein each gene along the path was weighted by the trans-eQTL strength calculated from the effect-SNP ( scored as 1 , except if PeQTL < 0 . 05 which gave score 0 . 8 , and if PeQTL < 0 . 005 , which gave score 0 . 6 ) . For both weighted and unweighted networks , significance of a path was calculated as the fraction of 1000 randomly permuted networks that obtained a shorter path length than the one tested . Random networks were generated using permutation of the original scores and random rewiring of the network using the igraph rewire function , as detailed in code repository http://github . com/lassefolkersen/olink-improve . Given our data , only paths of length 1 , i . e . direct links in String-db , were significant at a 0 . 05 level in the unweighted case . For the weighted case , only paths of length 2 with an intermediate trans-eQTL gene reached significance . Paths were subsequently checked for biological plausibility . To support the assignment of potential causal genes in pQTLs , we mined the literature for topical co-occurrences of each gene in a pQTL ( defined by a window extending 500kb in both directions ) with its associated protein . The Pfizer-internal LitMS tool can provide such matches based on all PubMed abstracts , a large synonym dictionary and manually curated rules that limit findings to more relevant articles , e . g . those in which gene and protein occur in the abstract’s title . The system outputs the number of co-occurrences and underlying article references for each gene-protein input pair . We then reviewed the literature findings to assign the most plausible causal genes where possible . To understand the specificity of all reported index-SNPs we inspected all index SNPs that had at least 2 associations with distinct proteins at P<0 . 05 / ( 83* 79 ) = 7 . 7e‐6 . This cutoff reflects a conservative approach to the multiple testing burden for all identified index SNPs ( 79 ) with all tested protein traits ( 83 ) . The resulting association matrix was then clustered and visualized based on the negative log10 of the p-values of association . For the clustering , we used a complete-linkage hierarchical clustering approach based on the negative log10 of the p-values with Pearson correlation coefficients as a metric . In addition , index-SNPs were investigated for other associations in publically available GWAS databases . To assess the effect on disease , the publicly available CARDIoGRAMplusC4D 1000G imputed data was interrogated [19] . The goal was to perform in silico analysis for every SNP that showed significant associations with any of the measured traits . For traits that had multiple associated SNPs , pooled scores per affected protein were calculated using the R-package gtx version 0 . 0 . 8 . Specifically for the pooled risk scores , the alleles of each protein were encoded so that the coded allele was increasing CAD risk regardless of its protein concentration effect . This ensured that pooled effect sizes reflected uniform directionality on CAD risk . | Several proteins that circulate in blood have been linked to cardiovascular disease through the use of classic epidemiology and correlation studies . If individuals with higher risk of disease have higher levels of a protein , the protein may be associated with disease . However , this does not necessarily mean that the protein causes disease; it may merely be an innocent bystander or a consequence of the disease process . To establish whether a protein causes disease , a genetic approach , insensitive to reverse causation , can be used . Instead of correlating the levels of the protein itself , gene variants that regulate the protein levels are used in the analysis . This approach requires prior knowledge of which genetic variants are linked to individual proteins . Therefore we completed a map of how common genetic variants affect the blood concentration levels of 83 proteins that have been implicated in cardiovascular disease . By using this map of cause-to-effect findings , we gained insights into the regulation of a majority of the proteins under study and how they relate to risk of coronary artery disease . This study provides a map of genetic regulation of important cardiovascular plasma proteins , insights into their upstream regulatory environment , as well as novel leads for cardiovascular drug development . | [
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"... | 2017 | Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease |
Molecular genetic approaches typically detect recombination in microbes regardless of assumed asexuality . However , genetic data have shown the AIDS-associated pathogen Penicillium marneffei to have extensive spatial genetic structure at local and regional scales , and although there has been some genetic evidence that a sexual cycle is possible , this haploid fungus is thought to be genetically , as well as morphologically , asexual in nature because of its highly clonal population structure . Here we use comparative genomics , experimental mixed-genotype infections , and population genetic data to elucidate the role of recombination in natural populations of P . marneffei . Genome wide comparisons reveal that all the genes required for meiosis are present in P . marneffei , mating type genes are arranged in a similar manner to that found in other heterothallic fungi , and there is evidence of a putatively meiosis-specific mutational process . Experiments suggest that recombination between isolates of compatible mating types may occur during mammal infection . Population genetic data from 34 isolates from bamboo rats in India , Thailand and Vietnam , and 273 isolates from humans in China , India , Thailand , and Vietnam show that recombination is most likely to occur across spatially and genetically limited distances in natural populations resulting in highly clonal population structure yet sexually reproducing populations . Predicted distributions of three different spatial genetic clusters within P . marneffei overlap with three different bamboo rat host distributions suggesting that recombination within hosts may act to maintain population barriers within P . marneffei .
Hypotheses of globally continuous populations and strict clonality in putatively asexual microbial pathogens are rarely supported [1] , [2] , [3] , [4] , [5] . Instead , genetic approaches detect recombination in microbes regardless of assumed asexuality , and pathogens are surprisingly promiscuous despite strong population genetic structure [6] , [7] , [8] . In some eukaryotic pathogens spatial structuring is readily attributable to dispersal limitations [9] , [10] , but many fungal pathogens of humans display extensive spatial population genetic structure despite their ability to disperse via aerosolized spores [11] , [12] , [13] , [14] , [15] . Examples that cause extensive morbidity include Cryptococcus neoformans , Coccidioides sp . , Histoplasma capsulatum and Penicillium marneffei . These fungi are maintained in natural environmental reservoirs that might contribute to structured populations via local adaptation , and they are thought to be largely clonal . However , fungi have many different mating systems that encompass asexual propagation t7hrough multiple forms of sexual and parasexual recombination , and clonal structure may be arrived at via very different mechanisms [16] , [17] . Evidence suggests that population structure in fungal pathogens is strongly influenced by host distributions and extrinsic geographic boundaries [12] , [18] , [19] , [20] , [21] . Therefore , the interplay between mating systems , population structure , and host adaptation is a central question underpinning the evolutionary epidemiology of fungal pathogens . Heterothallic mating systems in fungi require physical contact between two isolates containing opposite mating types at the mating-type locus ( MAT ) in order to undergo sexual reproduction . If no mating partners are present , then sexual reproduction does not occur and the fungus reproduces asexually ( but see Lin et al . [22] for evidence of same-sex mating in the otherwise heterothallic fungus Cryptococcus neoformans ) . In this case , the relative capacities of fungal lineages to disperse and co-occupy environmental niches can drive population-level recombination rates . If strains of opposite mating type do not equally penetrate environments then species recombination rates may be reduced to levels nearing complete asexuality [23] . Previously , it has been shown that the HIV-associated emerging pathogen Penicillium marneffei shows extensive spatial genetic structure at local and regional scales across Thailand [XPATH ERROR: unknown variable "start2" . ] , [24] . Although there has been some genetic evidence that a sexual cycle is possible in P . marneffei , this haploid fungus is thought to be genetically , as well as morphologically , asexual within these populations [25] . In this study , we use comparative genomics , experimental approaches , and population genetic data to identify the role of sexual recombination in maintaining spatial and genetic structure in this infection . We attempt to answer 4 specific questions: 1 ) Does the P . marneffei genome show evidence of sex ? 2 ) How are populations of P . marneffei genetically structured ? 3 ) Can population structure be reconciled with sex ? 4 ) Do spatial or host factors correlate with population structure and sex ? We use comparative genomics to identify genes linked to mating and genomic signatures of mutation bias associated with meiosis , and we experimentally detect recombination in vivo . We expand our collection of population genetic data across southeast Asia to include mating type data and 34 isolates from bamboo rats in India , Thailand and Vietnam , and 273 isolates from humans in China , India , Thailand , and Vietnam . Together these data form a mosaic that reveals some physical and genetic underpinnings of mating in P . marneffei that are linked to patterns of genetic diversity across its known endemic range .
We used 84 sexual cycle genes ( Table S1 ) to blast against the NCBI genome sequences NZ_AAHF00000000 ( A . fumigatus ) , NZ_ABAR00000000 ( P . marneffei ) , and NZ_ABAS00000000 ( T . stipitatus ) . We screened transposon families for substitution bias by making BLAST based alignments to determine the dominant form of a functional integrase gene in each family . We counted the type of substitution based on differences of alleles as low as 70% identical to the dominant intact type . We compared gene sequences of the genomic region between slaB and apnB ( the genes that flank the MAT idiomorph in related fungi ) of strains FRR2161 and FRR3842 . We acquired 307 isolates of P . marneffei from humans and vertebrate hosts ( bamboo rats ) , covering the known global range of the fungus . Our study obtained 273 epidemiologically unlinked human isolates of P . marneffei from HIV-AIDS patients covering the time-period 1959 to 2005 . Of these isolates , 258 were georeferenced to either the broad geographical region of collection or the patients home address . The remaining 15 isolates were recovered from patients whose infections were diagnosed in non-endemic regions , and no accurate geographical origin could be assigned . In addition to human isolates of P . marneffei , we obtained 34 isolates from the bamboo rats species Rhizomys pruinosis ( n = 3 ) , R . sumatrensis ( n = 13 ) , R . sinensis ( n = 1 ) and Cannomys badius ( n = 17 ) . We also include the type isolate for P . marneffei Segretain et al . ATCC 18224 , CBS 388 . 87 , isolated from R . sinensis in 1959 [26] . All isolates were cultured on Sabouraud's agar and DNA extracted as previously described [27] . Subsequently , isolates were genotyped at 21 microsatellite loci using the methods described in Fisher et al . [11] , [27] . The presence within each of isolate of the MAT1-1 α box and MAT1-2 high mobility group idiomorphs was determined using the PCR protocol detailed by Woo et al . [25] . Genotypes were analysed using GenAlex 6 . 0 [28] to determine allelic diversity , genotypic diversity and spatial correlation across regions and the global distribution of P . marneffei . We used the package adegenet and its dependencies in R to conduct spatial PCA and DAPC analyses [29] . To compare our inferred results against a model of a single continuous population structured by a dingle dispersal kernel and mutation rate we used the coalescent based program IBDsim [30] . Additional distribution data for bamboo rats were collected from specimen databases AMNH , FMNH , NMNH , and the GBIF . We used the bioclim layers 1–21 at 30 sec from the world clim database in MAXENT to generate predicted distributions for bamboo rats and P . marneffei genetic clusters . We measured distributional overlap using Schoener's D and a resampling approach [31] . We compared the relative overlap of genetic clusters to host distributions by generating null distributions of D based on resampling of R . sumatrensis and Cannomys ( Text S1 ) . Possible parental distances were compared to null distributions generated by choosing isolates randomly that met the genetic distance criteria from the population that met the parental criteria . Five co-housed outbred CD-1 male mice ( 16–18 g ) were inoculated intranasally with 107 spores suspended in 40 µl of PBS . Conidia from two isolates , PM9 , a MAT 1–2 isolate from Thailand , and the type strain ATCC18824 ( FR2161 ) were mixed in a 1∶1 ratio to form the inoculum ( S9 ) . Serial dilutions of homogenized saline samples were plated ( no later than 6 hours after they were removed from the mice ) on Sabouraud agar . Colonies were counted after 4 days in 27°C . Individual colonies used for DNA extraction and subsequent genotyping as before [11] . Isolate genotypes were compared to the initial genotypes of the inoculum and genotypes differing from inoculum were confirmed via DNA sequencing . All the clinical studies from which isolates are available were approved by the Wellcome Trust ethics committees at the study sites , in the UK and by the regulatory authorities of the countries involved . All patients or their next of kin gave written informed consent and all patient data are anonymised . This work strictly complied with the animal regulations and guidelines under UK law and was approved by Imperial College's Ethical Review Process ( ERP ) Committee and the British Home Office . All murine work was carried out in a Biosafety level 3 secure animal facility under licensed approval from the British Home Office .
Sexual reproduction leaves an imprint on fungal genomes by maintaining genes required for mating and by generating patterns of mutation and recombination restricted to meiotic processes [32] , [33] , [34] , [35] , [36] . Successful mating in fungi requires that a genome contains a functioning series of interconnected genetic pathways [37] . Using a comparative genomic approach we assessed the presence and functionality in P . marneffei of genes known to be involved in sexual development in fungi . First , comparing between strains FRR2161 and FRR3842 revealed that the region between genes slaB and apnB resembled other fungal mating type idiomorphs . A region of complete dissimilarity was flanked by regions that were nearly identical between the strains ( Fig . 1A and B ) . We found homologs for nearly all of the genes needed for a complete sexual cycle in yeast to be present and putatively functional ( Table S1 ) . Those genes not detected in P . marneffei were also not detected in Talaromyces stipitatus , a fungus with a complete sexual cycle , and most genes that were absent in those two fungi were also missing in the recently demonstrated heterothallic fungus Aspergillus fumigatus . Although these sex-related genes may be conserved to function in processes other than mating , their presence suggests that P . marneffei has preserved the ability to complete a sexual cycle . We detected another genomic signature of a functional sexual cycle , a type of mutation bias associated with meiosis . Repeat induced point mutation ( RIP ) , a process by which some fungi silence genes involved in mobile genetic element function by preferentially mutating repeated sequences within their genomes , is associated with meiosis [38] . This process results in skewed base pair distributions due to the induced mutations . Using an approach similar to that of Clutterbuck [39] , we found evidence of an excess of sliding windows with zero AG and CT dinucleotides and mutation bias in P . marneffei transposon family Ty-1 with a skew towards G to A and C to T transitions ( Fig . 2 ) . We also detected a putatively functional RID gene ( Locus ID PMAA079888 ) , the only conserved gene so far implicated in RIP [40] , [41] , [42] . Although the RID gene and the observed mutation bias can be explained by several factors including those acting during mitosis , they point towards a RIP or RIP-like process that is generally considered a feature of sexually reproducing fungi and an overall genomic pattern consistent with sex . Although these genomic signatures could represent relics from a sexual past rather than ongoing sexual recombination within P . marneffei , in the related human pathogenic fungus Aspergillus fumigatus , the discovery of mating type genes and evidence of RIP heralded the eventual description of a full sexual life cycle [35] , [43] , [44] . Microsatellite allelic diversity was high overall and within localities ( Table 1 ) . With the exception of Thai Central and Thai South , populations assigned a priori by locality were significantly differentiated from one another by Wright's FST [45]; this metric ascertains the proportion of genetic variance among geographical regions relative to the total variance . FST values near zero mean that populations are not distinct and variation is shared equally within and between them , while higher FST values mean that more genetic differences occur between populations compared to those within populations . The China and Taiwan populations were most different from the other a priori populations ( Table 2 ) . Phylogenetic analysis revealed associations between sampling area and the occurrence of phylogenetic clustering ( Fig . 3 ) . Using discriminant analysis of principal components ( DAPC ) to identify genetic clusters [46] , [47] , we assigned individuals to 3 clusters based on the Bayesian information criterion . The clusters show some spatial association . Cluster 1 is composed mostly of isolates from central and southern Thailand , Cluster 2 of isolates from China , and Cluster 3 of isolates from northern Thailand ( Fig . 3 , Figure S1 ) . As expected , we observed a strong pattern of spatial genetic correlation ( r2 = 0 . 41 , p<0 . 01 ) . We also detected significant ‘global’ genetic structure ( positive correlation between spatial and genetic distance ) but no ‘local’ genetic structure ( negative correlation ) using spatial principal coordinate analysis [46] , [47] . To test for a homogenous neutral process of genetic differentiation we used a spatially explicit coalescent-based simulation of isolation by distance generated with IBDsim [30] to simulate a uniform dispersal/mutation process across our exact sampling scheme . This uniform genetic structure was then compare against our recovered spatial genetic pattern . By controlling for the spatial distribution of our sample sites we are able to determine if the apparent genetic clustering is simply an artifactual product of clustered sampling and a single uniform process of genetic clustering . Because the hypothesised parameter space is nearly infinite , we concentrated on dispersal scenarios that most closely resembled the spatial genetic correlation present in our data , namely , the strength of spatial genetic correlation at the smallest spatial scale and the decay rate of the correlation . Although the simulated datasets largely overlapped with our recovered data we observed important departures between the two . The simulated datasets had a single peak in spatial genetic correlation at the smallest spatial scale and a decay in correlation dependent on the dispersal kernel , a feature of all single population isolation-by-distance models , but the observed pattern had additional peaks in certain distance classes that disrupted the uniform decay ( Fig S2 and S3 ) . One peak was composed of distances between individuals belonging to the outer edges of clusters 1 and 3 . Another major peak comes at the spatial scale where the outer edges of Clusters 1 and 3 contact with Cluster 2 . These results differ from previous results that observed different rates of decay for spatial genetic correlation [24] , a feature that probably owes to the limited geographic scope and power of the earlier study . Our data now suggest that the observed genetic clusters are not the result of a process of uniform decay with geographic distance , and that other factors are also driving the heterogeneity observed in our dataset . Linkage disequilibrium was high throughout the sample with an overall of 0 . 113 ( Table S2 ) . We determined the relative frequency of mutation to recombination using the single locus variant approach applied by Fisher et al . [24] and found a mutation to recombination frequency of 0 . 083 suggesting that mutation is up to 12 times less frequent than recombination across the whole population . When restricted to only bamboo rat isolates , all single locus variants would be due to recombination , while for human-only isolates the ratios are unchanged in comparison to the entire dataset . Average fungal microsatellite mutation rates have been inferred from between 2 . 80×10−6 and 2 . 50×10−5 mutations per generation [48] , making the inferred recombination rate in P . marneffei between 3×10−5 and 2 . 50×10−4 , a rate about half that observed in wild yeast [49] . This approach only detects single locus recombination events , which may be a minority in eukaryotic populations , while it should detect virtually all mutations that have not otherwise been masked by recombination . However , the method could be strongly biased towards inferring recombination due to convergent mutations in microsatellite length . The measure of minimal recombination ( RM ) , which represents the minimum number of recombination events necessary to explain alleles failing the four gamete test [50] given the arrangement of the alleles in a contig , showed that recombination did occur within contigs ( Figure S4 ) . Complete clonality and complete panmixia are rejected for P . marneffei , but similar to previous results the inferred levels of clonality remain among the highest observed for fungi [24] . To explain the high level of clonal structure either recombination must be rare or it must occur largely between closely related individuals . The entire sample population of P . marneffei showed a distribution of mating types that was significantly skewed ( p = 0 . 02 or p = 0 . 04 when clone corrected ) from a ratio of 1∶1 in favour of an overabundance of MAT1-1 alleles , but some local populations were skewed towards MAT1-2 alleles ( Table 1 ) . Two of the genetic clusters inferred by DAPC were skewed towards more MAT1-1 alleles , but MAT genes within the central cluster did not differ from a 1∶1 ratio ( Table 3 ) . As predicted in work prior to the discovery of MAT loci in P . marneffei , highly skewed MAT ratios would be expected in a predominately asexual population [24] . On one hand , in the absence of sex and selection , MAT genes at an initial frequency of 0 . 5 are expected to be fixed in a population on average by ln 2 ( Ne ) generations . Alternatively , in a completely sexual population without selection associated with a mating type , MAT alleles would be maintained at frequencies near 0 . 5 with very limited variance because all individuals in each generation will possess MAT alleles according to a binomial distribution , and there is no opportunity for drift beyond a single generation . MAT allele counts can be used to represent the reduction in effective population size caused by drift in MAT ratios [51] , [52] , but this assumes a fully sexual population . When sex is limited , the average allele frequencies for populations that do not lose sex and become fixed remain 0 . 5 , but the variance in MAT allele frequency depends on population size and the frequency of sex . Based only on the differences in MAT allele frequencies between clusters and an intrinsic restriction on sex , P . marneffei would have an intrinsic upper bound of sexual recombination frequency at less than 4 . 5% given a modest population size of 1000 . This small level of sex could explain the highly skewed ratio of MAT alleles in Cluster 3 and still accommodate the 1∶1 ratio in Cluster 1 while avoiding any fixation of MAT alleles . However , if the intrinsic sexual recombination rate explained the distribution of MAT alleles it would predict equal frequencies of clone detection across populations . Instead , percent clonality tracks the MAT allele skew , suggesting that sexual recombination in Cluster 3 is reduced relative to Cluster 1 ( Table 3 ) . We do note , however , that MAT allele frequencies would not be informative about where sex occurs if unisexual mating occurs in P . marneffei as is known in C . neoformans [22] . Given that recombination occurs in P . marneffei , we wanted to determine the geographic and genetic scope of sex . Out of 43 clonal groups inferred with EBURST [53] , six contained both MAT alleles , and four otherwise genetically identical multi-locus microsatellite types contained both MAT alleles ( Fig . 4 ) . Otherwise genetically identical isolates that differ only at mating type have also been detected in Cryptococcus gattii populations [54] , [55] , [56] . Clones with both MAT alleles represent the smallest possible genetic scale of sex , and are unequivocal evidence for recombination . To detect the more divergent recombination events we defined putative recombinants as any genotype that had no unique alleles , yet differed from the most similar genotype for at least three loci . Putative parents or ancestral parents were defined as all isolates that together could complete the multi-locus microsatellite type ( MLMT ) of the recombinant genotype ( Figure S5 ) . This allows us to compare between observed distances of maximal observed recombination against a null hypothesis that any two isolates could recombine . We identified 11 potential recombinants with 54 possible parent genotype combinations . Geographic distance between putative parents was shorter , 382 km , and genetic similarity higher , 60 . 06% identical , than random potential parents drawn from the entire population , 675 km ( p = 0 . 005 ) and 49 . 29% identical ( p = 0 . 025 ) respectively . The scale of recombination determines the efficacy of adaptation and the adaptive potential of populations . Although recombination across large distances allows generation of greater genetic diversity and more rapid spread of advantageous alleles , it disrupts locally advantageous combinations reducing local ecological genetic correlation . When sex is limited to small geographic distances it can reinforce local adaptation , and when limited to smaller genetic distances can reinforce genomic coadaptation . Together these effects can promote ecological speciation [57] , [58] , [59] . When ecological adaptation acts to reinforce genetic differentiation , strong correlations between key ecological factors and population distributions will exist [60] , [61] , [62] , [63] , [64] . To assess the possibility that ecological adaptation drives population differentiation in P . marneffei we used MAXENT [64] to predict overlap between the ecological niches of the genetic clusters ( Fig . 5 ) . Cluster 1 , the cluster with the ratio of MAT alleles nearest 1∶1 , had the widest predicted range and overlapped with the entire predicted range of Cluster 3 , including the predicted range that was not sampled in Myanmar . Cluster 1 , Cluster 2 , and Cluster 3 isolates are all found in bamboo rats , but 16 of 17 samples from Cannomys badius and 13 of 14 unique genotypes were from cluster 1 and distributed among India , Thai Central , Thai North , and Thai South sampling localities . None of the Cluster 1 isolates were among the 13 recovered from Rhizomys sumatrensis , which were all in Cluster 3 . Cannomys badius is relatively more abundant in the western portion of the range of P . marneffei . The predicted distributions of bamboo rats were similar to the IUCN species ranges and had overlap with P . marneffei distribution . Although our spatial sample of Cluster 2 was geographically restricted it was entirely within the distribution of Rhizomys sinensis , a species that has been shown to consistently harbour clinically relevant P . marneffei [65] . The distributions for R . sumatrensis overlapped with Cannomys and Clusters 1 and 3 ( Fig . 5 ) . However , using ENMTools [66] to account for sampling error we found that Cluster 1 predicted distributions overlapped more with the Cannomys distribution than R . sumatrensis distribution , and Cluster 3 similarly overlapped more with R . sumatrensis than Cannomys distribution ( S8 ) . This observed range overlap supports a host specific effect on P . marneffei population structure . Hosts may structure populations of pathogenic fungi in many ways , including by providing an environment in which recombination can occur and by acting as a selective filter on population genetic diversity [67] , [68] . We used a murine inhalation model of co-infection with genetically distinct strains to investigate the effect of host infection on P . marneffei ( Text S2 ) . Isolates of different mating types were used for experimental co-infection of 5 mice . Subsequent culture after 15 days from the livers showed a strong bias towards recovery of the MAT1-1 genotype for each of the mice . However , in two mice , genotypes of 4 isolates recovered from co-infections also revealed infrequent transfer of alleles between isolates of different mating type and genetic cluster ( Table 4 ) , suggesting that recombination may be possible across genetic barriers if multiple strains are within a host . In a smaller but similar in vitro experiment we did not observe significant bias towards MAT1-1 , and from our scan of partial genotypes we did not recover any recombinants ( S9 , Table 4 ) . We do not rule out regular recombination outside of hosts , but in the context of our spatial genetic evidence , the result of experimental infections indicate that hosts may play an important role in the development of sexual neighborhoods in populations of P . marneffei . However , the evolution of that role may involve restricted mating with or without host adaptation and remains to be explored . Asexual spores are common in vitro and likely a feature of natural P . marneffei populations , but sexual recombination may be an unexpectedly common occurrence in natural populations . The evidence supports the occurrence of recombination and perhaps even frequent sex , yet the natural populations remain strongly clonal and spatially structured . Although many mycologists might perceive this as a paradox because clonality is usually used as a proxy for asexuality , many fungi , including key pathogens , also employ same clone mating or sibling mating [7] , [16] , [69] . Three key hypotheses could explain the perceived clonality in P . marneffei; 1 ) Spatially restricted dispersal keeps individuals in contact with only closely related individuals; 2 ) Genetic incompatibility between dissimilar individuals restricts sex to genetically similar individuals; 3 ) Local adaptation restricts the ability of dissimilar genotypes to penetrate habitats ensuring mating between genetically similar individuals . All three are likely to be partially correct . Although the genetic evidence shows spatial limitations to effective dispersal , the physical dispersal of airborne conidia is not likely to be a limiting factor , and four genetically identical clones are dispersed across distances over 800 km . We have little information about the effect of genetic similarity on mating success in P . marneffei , but genetic restrictions on successful recombination are present in some plant pathogens [63] , [70] , [71] and should not be completely discounted . Local adaptation is not fully supported by ecological niche models that show overlap between distinct genetic clusters , but there is limited evidence of host specialisation . A key question unanswered in all of these hypotheses is why have sex at all ? Previous work has focused on the consequences of selectively neutral loss of sex in P . marneffei [72] , but the persistence of a sexual cycle in P . marneffei despite abundant asexual reproduction in the lab suggests that there is a selective advantage for sex not associated with the advantages of greater adaptive potential provided by outcrossing . One major consequence of sexual clonality is release from Muller's Ratchet compared to asexuality [51] . Large numbers of haploid offspring and wide dispersal maximize environmental exposure of genets and increase the efficiency of purging deleterious alleles [73] , [74] . However , the sexual process itself may also reduce the accumulation of deleterious mutations independent of recombinational effects [75] . Among the close relatives of P . marneffei in the subgenus Biverticillium , outcrossing has not been shown to occur , but self-fertility is common [76] , [77] , and the distribution of mating systems in the subgenus suggests that inbreeding may not reduce the evolutionary longevity of this group [78] . Another compelling scenario favouring sex recognises the opportunity presented by mating itself for dramatic shifts in morphology and physiology . A predominant view in the fungal literature is that sex occurs in otherwise mostly clonal fungi in response to stressful conditions [16] , [79] . Sexually produced spores are often viable for long periods of time and are resistant to extreme environmental conditions [78] , [80] , [81] , [82] . Regardless of the costs and benefits of recombination , P . marneffei might withstand stress by mating when it would otherwise not survive . If that were the case , recombination in P . marneffei might be clustered in space or time where or when stress occurs . Unfortunately , little is known about the natural ecology of P . marneffei , and any conditions that might allow mating to occur are unknown . Isolates are commonly recovered from bamboo rats , yet the epizoology of the fungus is poorly known including unknown routes of infection and unknown course and outcomes of the zoonosis . In other dimorphic fungi including Histoplasma and Blastomyces , mating in natural populations is also poorly known , but in these species it mating has long been studied outside of the host and at lower temperatures in vitro [83] , [84] . Nevertheless , the association with small mammals may be the best starting point for a search for the natural sexual niche of P . marneffei . Cryptic mating and inbreeding in P . marneffei has some parallels with other fungal and non-fungal eukaryotic pathogens [7] . There is growing support for high inbreeding in addition to asexual reproduction in Leishmania brasiliensis [85] . Experimental data support a role for within-vector recombination and it is thought that this restricted recombination in Leishmania results in sexual neighbourhoods of pathogen genotypes with high differentiation at multiple spatial scales [86] . In the malaria parasite Plasmodium falciparum , high inbreeding has been linked to faster emergence of drug resistance in some low infection intensity regions , but inbreeding has also emerged as a general property of P . falciparum populations regardless of infection intensity [87] , [88] . In Toxoplasma gondii and Sarcocystis neurona , ‘clonal’ emergence is enabled by high rates of selfing and results in spatially structured populations [89] , [90] . The most common fungal infection of humans , Candida albicans , also undergoes same-sex mating that can facilitate inbreeding and the ability to accelerate evolution via sex within clonal populations [91] . In C . neoformans , multiple ecological niches where recombination between strains with opposite mating type occurs have been found [92] , [93] . However , most mating in C . neoformans globally is likely to occur between strains of the same mating type , and although there is as yet no indication of what factors alter the probability of this kind of inbreeding across natural populations , it may have facilitated the global emergence of a single mating type and highly clonal populations [15] , [22] , [94] , [95] . Although some of the mechanisms underlying these inbreeding eukaryotic pathogens remain mysterious and likely differ between organisms , there is an emerging consistent pattern of clonality resulting from inbreeding rather than strictly asexual propagation even in the absence of a recognized sexual stage . | Fungal pathogen populations show patterns ranging from globally recombining to endemic and clonal . Among the most genetically and spatially restricted fungi is the highly clonal pathogen Penicillium marneffei , an endemic AIDS-associated pathogen in Southeast Asia . Previous studies have shown that P . marneffei has a pattern of extreme clonality despite the ability to disperse across wide distances and the presence of mating type genes that are required for sexual recombination . In this study we used genetic markers , comparative genomics , experimental data , and spatial models to determine the influence of sex on P . marneffei populations , and we found that although there was substantial evidence of sexual recombination , most of the recombination in natural populations was limited to sexual neighborhoods , amongst genetically similar and spatially close individuals . Based on the results of experiments and spatial models we found support for sex occurring in bamboo rats that are known to harbor P . marneffei and the pathogens sexual neighborhoods . Our study suggests that the high levels of effective clonality and endemicity found in P . marneffei may have more to do with specific host interactions than with an innate inability to generate population genetic diversity through sexual recombination . | [
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"mycosis",... | 2012 | Clonality Despite Sex: The Evolution of Host-Associated Sexual Neighborhoods in the Pathogenic Fungus Penicillium marneffei |
Phylogenetic networks generalize phylogenetic trees by allowing the modelization of events of reticulate evolution . Among the different kinds of phylogenetic networks that have been proposed in the literature , the subclass of binary tree-child networks is one of the most studied ones . However , very little is known about the combinatorial structure of these networks . In this paper we address the problem of generating all possible binary tree-child ( BTC ) networks with a given number of leaves in an efficient way via reduction/augmentation operations that extend and generalize analogous operations for phylogenetic trees , and are biologically relevant . Since our solution is recursive , this also provides us with a recurrence relation giving an upper bound on the number of such networks . We also show how the operations introduced in this paper can be employed to extend the evolutive history of a set of sequences , represented by a BTC network , to include a new sequence . An implementation in python of the algorithms described in this paper , along with some computational experiments , can be downloaded from https://github . com/bielcardona/TCGenerators .
Phylogenetic networks are , mathematically , a generalization of phylogenetic trees that , containing nodes with more than one ancestor , permit to model reticulated evolutionary events such as recombinations , lateral gene transfers and hybridizations . We note here that other representations , for example gene tree-species tree reconciliations [1] , permit to model scenarios including other classes of evolutionary events such as duplications , losses and transfers of genes . In this paper , we shall focus on directed phylogenetic networks ( see [2] for a short survey on the phylogenetic network paradigm also covering undirected phylogenetic networks ) . Mathematically , such networks are , in the broadest sense , directed acyclic graphs with a single node with no incoming arcs –the root– representing the common ancestor of all the Operational Taxonomic Units ( OTUs for short ) under study , which are represented by the nodes with no outgoing arcs –the leaves– of the graph; internal nodes represent either ( hypothetical ) speciations or ( hypothetical ) reticulated events . Nodes with a single incoming arc –tree nodes– model extant or non-extant OTUs , and arcs between tree nodes model direct descent through mutation; nodes with two incoming arcs –hybrid nodes– model reticulated events involving the OTUs corresponding to the two parents of the node under consideration , and whose resulting OTU is modeled as its single child . Unfortunately , this definition is too broad , both for representing biologically-meaningful evolutionary scenarios , and for giving objects that can be efficiently handled . So far , several restrictions on this general definition have been introduced in the literature . A few of them are based on biological considerations , while the majority have been introduced to artificially narrow the space of networks under study . This led to the introduction of a panoply of different classes of phylogenetic networks , such as time-consistent networks [3] , regular networks [4] , orchard networks [5] , galled trees [6] and galled networks [7] , level-k networks [8] , tree-sibling networks [9] , tree-based networks [10] and LGT networks [11] , just to name a few . In this paper , we shall focus on binary tree-child networks ( BTC networks , for short ) , which were introduced by [9] and are one of the most studied classes of phylogenetic networks [12–15] . Mathematically , being tree-child means that every internal node is compelled to have at least a child node that is a tree node . BTC networks have been introduced in order to adjust a complex biological reality in a computationally tractable way . Although the original motivation for these networks is not biological , and hence they present some limitations , the mathematical constraint on BTC networks translates biologically as follows: every non-extant OTU is required to have at least an offspring species that evolved only through mutation . This means that not all biologically-meaningful evolutionary scenarios can be modeled with BTC networks . For example , the scenarios depicted in Fig 1 ( a ) and 1 ( e ) are not allowed since , in these cases , the node labeled with u has no child with a single incoming arc . Still , BTC networks are one of the most permissive classes of phylogenetic networks and they permit to model quite a lot of meaningful scenarios , and those that cannot be modeled can be approximated pretty well , see Fig 1 . The combinatorial study of phylogenetic networks is nowadays a challenging and active field of research . Nevertheless , the problem of counting how many phylogenetic networks are in a given subclass of networks is still open even for long-established classes . More precisely , this problem has been only recently solved for galled networks [18]; for other classes , including tree-child networks , we only have asymptotic results [19 , 20] . Associated to the problem of counting networks , we find the problem of their “injective” generation , i . e . without having to check for isomorphism between pairs of constructed networks . The main result of this paper is a systematic way of recursively generating , with unicity , all BTC networks with a given number of leaves . This generation relies on a pair of reduction/augmentation operations –both producing BTC networks– where reductions decrease by one the number of leaves in a network , and augmentations increase it . The idea of using pairs of operations has already been used to deal either with other classes of phylogenetic networks [21 , 22] , or for BTC networks but without the unicity feature [5] . In order to give a biological meaning to these augmentation operations , assume that the evolutive history of a given group of species is known and modeled by a BTC network , and a new species has to be taken into account . The augmentation operation determines exactly how the phylogenetic network has to be modified , and what is the minimum information needed to establish this modification , in order to model the evolution of the group of species with the newly incorporated one . As an interesting side product , this procedure gives a recursive formula providing an upper bound on the number of BTC networks . Note also that being able to generate all BTC networks with a given number of leaves may also be interesting as part of a divide-and-conquer framework to reconstruct phylogenetic networks , where we start by computing BTC networks on 3/5 leaves that are then combined together , as done for example in [23 , 24] . The paper is organized as follows . In Section Methods , we review the basic definitions that will be used throughout the paper . The main part of the paper is in Section Results , which is split between different subsections . Subsection Reduction of networks is devoted to the reduction procedure , while in Subsection Generation of networks we introduce the augmentation operation and prove that any BTC network can be obtained , in a unique way , via a sequence of augmentation operations applied to the trivial network with one leaf . In Subsection Bounding the number of networks , we show how to relax the conditions for the applicability of the augmentation operation to obtain a recursive formula providing an upper bound on the number of BTC networks . In Subsection An application to phylogenetic reconstruction , we give a concrete biological application of the methods we have developed . In Subsection Computational experiments , we introduce the implementation of the algorithms presented in the paper , and some experimental results , including the exhaustive generation of all BTC networks with up to six leaves and an upper bound of their number up to ten leaves . Finally , in Section Discussion we discuss how our reduction/augmentation operations extend and generalize analogous operations for phylogenetic trees .
In this section we introduce the mathematical notations that are used in the rest of the paper . Throughout this paper , a tree node in a directed graph is a node u whose pair of degrees d ( u ) = ( indegree u , outdegree u ) is ( 1 , 0 ) for the leaves , ( 0 , 2 ) for the roots , or ( 1 , 2 ) for internal tree nodes; a hybrid node is a node u with d ( u ) = ( 2 , 1 ) . If two nodes u and v are linked by an arc ( u , v ) we say that u is a parent of v , or that v is a child of u . Also , two nodes are siblings if they have a common parent . A binary phylogenetic network over a set X of taxa is a directed acyclic graph with a single root such that all its nodes are either tree nodes or hybrid nodes , and whose leaf set is bijectively labeled by the set X . In the following , we will implicitly identify every leaf with its label . A binary phylogenetic network is tree-child if every node either is a leaf or has at least one child that is a tree node [9]; in particular , the single child of a hybrid node must be a tree node . We will denote by BTC n the set of binary tree-child phylogenetic networks over the set [n] = {1 , … , n} . An elementary node in a directed graph is a node u with d ( u ) = ( 1 , 1 ) or d ( u ) = ( 0 , 1 ) . An elementary path p is a path u1 , … , uk composed of elementary nodes such that neither the single parent of u1 ( if it exists ) nor the single child of uk are elementary . We call these last two nodes respectively the grantor ( if this node is well-defined ) and heir of the nodes in the elementary path . In case of an elementary node , its grantor and heir are those of the nodes in the single elementary path that contains the given node . The elimination of an elementary path p consists in deleting all nodes in p , together with their incident arcs , and adding an arc between the grantor and the heir of p ( provided that the grantor exists; otherwise , no arc is added ) . The elimination of an elementary node is defined as the elimination of the elementary path that contains the given node . Given a node u , we can split it by adding a new node u ˜ , an arc ( u ˜ , u ) , and replacing every arc ( v , u ) with ( v , u ˜ ) . If u is a tree node , then u ˜ is an elementary node whose heir is u , and the elimination of u ˜ recovers the original network . The successive splitting ( say k times ) of a tree node u generates an elementary path formed by k nodes , whose heir is u , and whose elimination recovers the original network . Fig 2 illustrates the definitions given in this section .
The goal of this subsection is to define a reduction procedure on BTC networks that can be applied to any such network , and producing a BTC network with one leaf less . By successive application of this procedure , any BTC network can thus be reduced to the trivial network with a single leaf . We start by associating to each leaf ℓ a path whose removal will produce the desired reduction ( up to elementary paths ) . Let ℓ be a leaf of a BTC network N . A pre-TH-path for ℓ is a path u1 , … , ur = ℓ such that ( see Fig 3 ) : A TH-path is a maximal pre-TH-path , i . e . a pre-TH-path that cannot be further extended . Note that , since all nodes in a pre-TH-path p are tree nodes , if p can be extended by prepending one node , then this extension is unique . Hence , starting with the trivial pre-TH-path formed by the leaf ℓ alone , and extending it by prepending the parent of the first node in the path as many times as possible , we obtain a TH-path that is unique by construction . Let u1 , … , ur = ℓ be a TH-path; different possibilities may arise that make it maximal: ( 1 ) u1 is the root of N; ( 2 ) the parent of u1 , call it x , is a hybrid node; ( 3 ) x is a tree node whose both children are tree nodes; ( 4 ) x is a parent of vi for some i ∈ [r − 1] . We shall see in Lemma 1 that the first case cannot hold; the other three possibilities are depicted in Fig 4 . For each leaf ℓ , we denote by TH ( ℓ ) its single TH-path and by TH ( ℓ ) 1 the first node of this path . Note that we allow the case r = 1 . In this case , if we are not in a trivial BTC network ( i . e . a network consisting of a single node ) , the parent of ℓ is either a hybrid node , or a tree node whose two children are tree nodes . Lemma 1 . Let N be a non-trivial BTC network and let ℓ be any of its leaves . Then , TH ( ℓ ) 1 cannot be the root of N . Proof . Let u1 , … , ur = ℓ be the path TH ( ℓ ) and assume for the sake of contradiction that u1 is the root of N . For each i = 1 , … , r − 1 , let vi be the hybrid node that is a child of ui and xi the parent of vi different from ui ( see Fig 5 ) ; recall that xi does not belong to TH ( ℓ ) by the definition of a pre-TH-path . Since u1 is the root of N , every node of N either belongs to the path TH ( ℓ ) or is descendant of a node in {vi ∣ i ∈ [r − 1]} . In particular , for each i ∈ [r − 1] , there exists some σ ( i ) ∈ [r − 1] such that xi is descendant of vσ ( i ) , and since this node is descendant of xσ ( i ) , xi is descendant of xσ ( i ) . Hence , starting with x1 we get a sequence x1 , xσ ( 1 ) , xσ ( σ ( 1 ) ) , … where each node in the sequence is a descendant of the following one . Since there is a finite number of nodes , at some point we find a repeated node , which means that N contains a cycle and hence we have a contradiction . ■ We say that a leaf ℓ is of type T ( resp . of type H ) if the parent of TH ( ℓ ) 1 is a tree node ( resp . a hybrid node ) . If ℓ is of type H , we indicate by TH ¯ ( ℓ ) the path obtained by prepending to TH ( ℓ ) the parent of TH ( ℓ ) 1 . For convenience , we let TH ¯ ( ℓ ) = TH ( ℓ ) if ℓ is of type T . Definition 1 . Let ℓ be a leaf in a BTC network N . We define the reduction of N with respect to ℓ as the result of the following procedure ( see Figs 6 and 7 ) : We indicate this reduction by R ( N , ℓ ) . If we want to emphasize the type of the deleted leaf , we indicate the reduction by T ( N , ℓ ) and say it is a T-reduction if ℓ is of type T , or by H ( N , ℓ ) and say that it is a H-reduction if ℓ is of type H . To ease of reading , we shall introduce some notations that will be used hereafter and are also illustrated in Figs 6 and 7: Definition 2 . Let u1 , … , ur = ℓ be the path TH ( ℓ ) and let u0 be the first node in TH ¯ ( ℓ ) . For each i ∈ [r − 1] , vi is the hybrid child of ui , xi the parent of vi different from ui , and yi the single child of vi . The parent ( s ) of u0 is w1 ( are w1 , w2 ) ; the node wj is always a tree node , zj is its parent ( if it exists , since wj could be the root of N ) , and tj its child different from u0 , where j = 1 for T-reductions and j ∈ [2] for H-reductions . Remark 1 . Since N is tree-child , the nodes yi are always tree nodes , and so are t1 and t2 in case of an H-reduction . In case of a T-reduction , by definition of a TH-path , t1 is either a tree node or coincides with one of the hybrid nodes vi . Also , the removal of the arcs of the form ( ui , vi ) and ( wj , u0 ) makes nodes vi and wj elementary in N \ TH ¯ ( ℓ ) , where i ∈ [r − 1] , and j = 1 for T-reductions and j ∈ [2] for H-reductions . Since no other arc is removed , no other node can be elementary . In order to find the heirs of nodes vi and wj , we must analyse under which circumstances two of these elementary nodes are adjacent in N \ TH ¯ ( ℓ ) . In all other cases , the elementary nodes vi and wj are isolated , and their respective heirs are yi and tj . We study now what we call the recovering data of a reduction . This information will be used in the next subsection to recover the original network from its reduction . Definition 3 . The recovering data of the reduction N′ = R ( N , ℓ ) is the pair ( S1 , S2 ) , where: We introduce now a set of conditions on multisets and tuples of nodes , and prove that the recovering data associated to any of the defined reductions satisfies them . Definition 4 . Given a BTC network N′ and a pair ( S1 , S2 ) with consider the following set of conditions: We say that ( S1 , S2 ) is T-feasible if it satisfies conditions 1 , 2 , 3 , and 4T , and H-feasible if it satisfies conditions 1 , 2 , 3 , and 4H . Finally , we say that ( S1 , S2 ) is feasible if it is either T-feasible or H-feasible . Proposition 2 . Let N′ = T ( N , ℓ ) be a T-reduction of a BTC network N . Then , its recovering data ( {τ1} , ( y1 , … , yr−1 ) ) is T-feasible . Proof . First , note that , by Remark 1 , all nodes in ( {τ1} , ( y1 , … , yr−1 ) ) are tree nodes and that Condition 4T holds trivially . Note also that τ1 is equal to yi if t1 = vi , or to t1 if this node is different from all the nodes vi . We now prove that Conditions 1 , 2 and 3 hold: Proposition 3 . Let N′ = H ( N , ℓ ) be an H-reduction of a BTC network N . Then , its recovering data ( {τ1 , τ2} , ( y1 , … , yr−1 ) ) is H-feasible . Proof . Again we have , by Remark 1 , that all nodes in the recovering data are tree nodes . Additionally , by the same remark , we have that |S1| = 2 –and hence the first part of Condition 4H holds– and if ( w1 , w2 ) is an arc of N , then S1 = {t2 , t2} , otherwise S1 = {t1 , t2} with t1 ≠ t2 . Note that Condition 3 implies that Conditions 1 and 2 can be simplified as follows: for all i , j ∈ [r − 1] with i ≠ j , yi and yj are neither equal nor siblings , and for all i ∈ [r − 1] , yi is neither the child nor the sibling of a hybrid node . Conditions 1 and Conditions 2 and 3 in their simplified form follow using the same arguments as in the previous proposition . As for the condition 4H , the nodes τ1 and τ2 are different from the nodes yi since the parents of τ1 and τ2 in N are tree nodes , while the parent of each of the nodes yi is hybrid . ■ The following proposition is the main result of this subsection , since it shows that the reduction that we have defined , when applied to a BTC network , gives another BTC network with one leaf less . Hence , successive applications of these reductions reduce any BTC network to the trivial BTC network . Proposition 4 . Let N be a BTC network over X and ℓ one of its leaves . Then , R ( N , ℓ ) is a BTC network over X \ {ℓ} . Proof . First , it is easy to see that , since no new path is added , the resulting directed graph is still acyclic . Then , we need to check that R ( N , ℓ ) is binary . To do so , we start noting that every node in N \ TH ¯ ( ℓ ) is either a tree node , a hybrid node , or an elementary node . Indeed , the removal of TH ¯ ( ℓ ) ( Phase 1 of Definition 1 ) only affects the nodes adjacent to this path , that is the nodes vi and wi , which , as shown in Remark 1 , become elementary . The elimination of all elementary nodes ( Phase 2 of Definition 1 ) does not affect the indegree and outdegree of any other node , apart when the root ρ of N \ TH ¯ ( ℓ ) is elementary . In such a case , the heir of ρ becomes the new root . Hence , R ( N , ℓ ) is binary and rooted . Note also that the set of leaves of R ( N , ℓ ) is X \ {ℓ} , since in N \ TH ¯ ( ℓ ) no node becomes a leaf and the only leaf that is removed is ℓ . Finally , we need to prove that R ( N , ℓ ) is tree-child . Note that , from what we have just said about how the reduction affects indegrees and outdegrees of the nodes that persist in the network , it follows that each hybrid node of R ( N , ℓ ) is also a hybrid node of N , and that its parents in R ( N , ℓ ) are the same as in N . It follows that no node in R ( N , ℓ ) can have that all its children are hybrid , since this would imply that N is not tree-child , a contradiction . ■ Corollary 5 . Let N ∈ BTC n be a BTC network over [n] . Let Nn = N and define recursively Ni = R ( Ni+1 , i + 1 ) for each i = n − 1 , n − 2 , … , 1 . Then , Ni is a BTC network over [i] . In particular , N1 is the trivial BTC network with its single node labeled by 1 . We finish this subsection with the computation of the number of tree nodes and hybrid nodes that the reduced network has , both in terms of the original network and of the reduction operation that has been applied . But before , we give an absolute bound on the number of these nodes in terms of the number of leaves . Lemma 6 . Let N be BTC network over [n] with t tree nodes and h hybrid nodes . Then t − h = 2n − 1 , h ≤ n − 1 and t ≤ 3n − 2 . Proof . The equality t − h = 2n − 1 follows easily from the handshake lemma taking into account the number of roots , internal tree nodes , leaves and hybrid nodes in N , and their respective indegrees and outdegrees . The inequality h ≤ n − 1 is shown in Proposition 1 in [9] , and the last inequality is a simple consequence of the equality and the inequality already proved . ■ Proposition 7 . Let N be a BTC network and ℓ one of its leaves , and N′ = R ( N , ℓ ) . Let t , h ( resp . t′ , h′ ) be the number of tree nodes and hybrid nodes of N ( resp . of N′ ) . Then t ′ = t - | TH ¯ ( ℓ ) | - 1 , h ′ = h - | TH ¯ ( ℓ ) | + 1 , where | TH ¯ ( ℓ ) | is the number of nodes in TH ¯ ( ℓ ) . Proof . Since the number of tree nodes and hybrid nodes are linked by the equality in Lemma 6 , it is enough to prove that h ′ = h - | TH ¯ ( ℓ ) | + 1 . From the discussion in Remark 1 , it is straightforward to see that the number of hybrid nodes in N that are not in N′ is r − 1 if ℓ is of kind T , and r otherwise . Hence , in both cases we have h ′ = h - ( | TH ¯ ( ℓ ) | - 1 ) and the result follows . ■ In this subsection , we consider the problem of how to revert the reductions defined in the previous subsection , taking as input the reduced network and its recovering data . This will allow us to define a procedure that , starting with the trivial BTC network with one leaf , generates all the BTC networks with any number of leaves in a unique way . We start by defining two augmentation procedures that take as input a BTC network and a feasible pair , and produce a BTC network with one leaf more . Definition 5 . Let N be a BTC network over X , ℓ a label not in X , and ( {τ1} , ( y1 , … , yr−1 ) ) a T-feasible pair . We apply the following operations to N ( see Fig 10 ) : We denote by T−1 ( N , ℓ; {τ1} , ( y1 , … , yr−1 ) ) the resulting network and say that it has been obtained by an augmentation operation of type T . Note that the order in which steps 2 and 3 are done is relevant in the case that τ1 = yi for some i ∈ [r − 1] . In such a case , two nodes w1 and vi are created , linked by an arc ( w1 , vi ) ( see Fig 11 ) . Proposition 8 . Using the notations of Definition 5 , the network N ˜ = T - 1 ( N , ℓ ; { τ 1 } , ( y 1 , … , y r - 1 ) ) is a BTC network over X ∪ {ℓ} . Moreover , if N has h hybrid nodes , then N ˜ has h + r − 1 hybrid nodes . Proof . We first check that the resulting directed graph is acyclic . Let us assume that N ˜ contains a cycle . If we define U1 = {u1 , … , ur} and U 2 = V ( N ˜ ) \ U 1 , we have that the only arcs connecting U1 with U2 are ( ui , vi ) ( with i = 1 , … , r − 1 ) , and ( w1 , u1 ) is the only arc connecting U2 with U1 . The cycle can be contained neither inside U1 , since these nodes are linked by a single path , nor inside U2 , since otherwise N would contain a cycle . Hence , the cycle must contain at least the arc ( w1 , u1 ) and an arc ( ui , vi ) . This implies the existence of a path from vi to w1 visiting only nodes in U2 , which in turn means that N contains a path from yi to τ1 , against Condition 3 of Definition 4 . Note that the nodes in U1 are tree nodes by construction . Also by construction , the node w1 is a tree node , the nodes vi are hybrid nodes and ur is a leaf which is labelled with ℓ . Finally , the other nodes keep the same degrees they had in N and hence N ˜ is a binary phylogenetic network over X ∪ {ℓ} with h + r − 1 hybrid nodes . Since N is tree-child , in order to check that N ˜ is also tree-child , we only need to check the newly added hybrid nodes , which are the parents of the nodes vi . Let us first consider the case that τ1 ≠ yi for all i ∈ [r − 1] . For each node vi , its parents are ui and the parent xi of yi in N . The node ui is by construction a tree node whose other child is ui+1 , which , in turn , is a tree node . Since τ1 ≠ yi , by Condition 2 of Definition 4 , yi can have neither a hybrid parent nor a hybrid sibling , and it cannot be a sibling of any other node yj with j ∈ [r − 1] . This latter restriction implies that yi has the same sibling x ˜ i in N and N ˜ . Thus both xi and x ˜ i are not hybrid nodes , and the network is tree-child . Let us now consider the case that τ1 = yi for a single choice of i ∈ [r − 1] . The hybrid node vi in N ˜ has as parents the nodes w1 and ui , and these two nodes have as respective children u1 and ui+1 , which are tree nodes . For each other node vj with j ≠ i and such that yj is a not sibling of yi , the same argument as in the previous case proves that both parents of vj have a tree child . If yj is a sibling of yi , it is easy to see that the parent of vj is still tree-child since it has w1 as child . ■ Definition 6 . Let N be a BTC network over X , ℓ a label not in X , and ( {τ1 , τ2} , ( y1 , … , yr−1 ) a H-feasible pair . We apply the following operations to N ( see Fig 12 ) : We denote by H−1 ( N , ℓ;{τ1 , τ2} , ( y1 , … , yr−1 ) ) the resulting network and say that it has been obtained by an augmentation operation of type H . Proposition 9 . Using the notations of Definition 6 , the network N ˜ = H - 1 ( N , ℓ ; { τ 1 , τ 2 } , ( y 1 , … , y r - 1 ) ) is a BTC network over X ∪ {ℓ} . If N has h hybrid nodes , then N ˜ has h + r hybrid nodes . Proof . The proof is completely analogous to that of Proposition 8 , taking into account that one extra hybrid node is created . ■ Given a BTC network over X , a label ℓ ∉ X and a feasible pair ( S1 , S2 ) , in order to unify notations we define the augmented network R−1 ( N , ℓ; S1 , S2 ) as T−1 ( N , ℓ; S1 , S2 ) , if |S1| = 1 , and as H−1 ( N , ℓ; S1 , S2 ) , if |S1| = 2 . Also , we shall generically say that the offspring of a BTC network is the set of networks that can be obtained from it by means of augmentation operations . Our next goal is to prove that different augmentation operations applied to a same BTC network or different BTC networks over the same set of taxa provide different networks . We start with the case of different networks . Proposition 10 . Let N ˜ 1 and N ˜ 2 be two BTC networks , both obtained by one augmentation operation applied to two non-isomorphic BTC networks N1 and N2 over the same set of taxa X . Then N ˜ 1 and N ˜ 2 are not isomorphic . Proof . If N ˜ 1 and N ˜ 2 have different sets of labels , then it is clear that they are not isomorphic . We can therefore assume that both augmentation operations introduced the same new leaf ℓ . Suppose that N ˜ 1 ≃ N ˜ 2 . Then R ( N ˜ 1 , ℓ ) ≃ R ( N 2 , ℓ ) . Now , from the definitions of the reductions and augmentations it is straightforward to check that R ( N ˜ i , ℓ ) = N i and we get that N1 ≃ N2 , a contradiction . ■ We treat now the case of applying different augmentation operations to the same BTC network . But first , we give a technical lemma that will be useful in the proof of the proposition . Lemma 11 . Let N be a BTC network . Then , the identity is the only automorphism ( as a leaf-labeled directed graph ) of N . Proof . Let ϕ be any automorphism of N . Since ϕ is an automorphism of directed graphs and sends each leaf to itself , it follows that μ ( u ) = μ ( ϕ ( u ) ) for each node u of N , where μ ( u ) is the μ-vector of u as defined in [9] . Then , by [9 , Lemma 5c] , it follows that u and ϕ ( u ) are either equal , or one of them is the single child of the other one; according to our definition of BTC networks , this last possibility implies that one of them is a hybrid node and the other one is a tree node , which is impossible if ϕ is an automorphism . Hence ϕ ( u ) = u for every node u . ■ Proposition 12 . Let N ˜ 1 and N ˜ 2 be two BTC networks , both obtained by one augmentation operation applied to the same BTC network N . If either the kinds of operation or the feasible pairs used to construct N ˜ 1 and N ˜ 2 are different , then N ˜ 1 and N ˜ 2 are not isomorphic . Proof . Let us assume that N ˜ 1 and N ˜ 2 are isomorphic . Then , it is clear that they have the same set of labels , and exactly one of them , say ℓ , is not a label of N . Since N ˜ 1 and N ˜ 2 are isomorphic , the kind of ℓ is the same in both networks , which implies that the kind of augmentation operations used to construct N ˜ 1 and N ˜ 2 are the same . Also , since N ˜ 1 and N ˜ 2 are isomorphic , the nodes in the respective recovering data of the reductions R ( N ˜ i , ℓ ) must be linked by an isomorphism of phylogenetic networks . Therefore , and since by Lemma 11 BTC networks do not have a nontrivial automorphism , the respective recovering data must be equal . ■ The following proposition shows that the reduction procedure defined in the previous subsection can be reverted using the augmentation operations presented in this subsection . Proposition 13 . Let N be a BTC network and ℓ a leaf of N . Let N′ = R ( N , ℓ ) , ( S1 , S2 ) its recovering data , and N ˜ = R - 1 ( N ′ , ℓ ; S 1 , S 2 ) . Then , N and N ˜ are isomorphic . Proof . It is straightforward to see that the operations T−1 and H−1 reverse the effects of T and H , respectively . The only points worthy of attention correspond to the cases where the single node in S1 appears in S2 ( for reductions/augmentations of type T ) or where there is a single node in S1 with multiplicity two ( for reductions/augmentations of type H ) . In the first case , the augmentation process creates two elementary nodes , w1 and vi , connected by an arc ( w1 , vi ) , which is the same situation as in N after the removal of the nodes in TH ¯ ( ℓ ) . In the second case , two elementary nodes τ1 and τ2 are created , connected by an arc , once again the same situation as in N after the removal of the nodes in TH ¯ ( ℓ ) . ■ A direct consequence of the results in this subsection is the following theorem , which can be used to generate in an effective way all BTC networks over a set of taxa . See Fig 14 for an example . Theorem 14 . Let N ∈ BTC n be a BTC network over [n] . Then , N can be constructed from the trivial network in BTC 1 ( with one node labeled by 1 ) by application of n − 1 augmentation operations , where at each step i , the leaf i + 1 is added . Moreover , these augmentation operations are unique . Proof . The existence is a direct consequence of Corollary 5 and Proposition 13 . Unicity comes from Propositions 10 and 12 . ■ It should be noted that very recently , other methods to generate all BTC networks over a set of taxa have been proposed [5] , but , to our knowledge , this is the first time that the networks are generated with unicity . In previous attempts , an isomorphism check was needed after the generation phase . In this subsection , we shall first give bounds for the number of BTC networks that can be obtained from a given one by means of augmentation operations . This will be done by bounding the number of feasible pairs in such a network . Then , we shall find bounds for the number of BTC networks with a fixed number n of leaves . Let N be a BTC network over [n] with h hybrid nodes . From Lemma 6 we know that it has t = 2n + h − 1 tree nodes , and that h ≤ n − 1 and t ≤ 3n − 2 . In the following , we shall show how to compute the number of pairs ( S1 , S2 ) satisfying all conditions of Definition 4 , except for Condition 3 , via an auxiliary problem . Note that this will only give an upper bound for the number of networks , since the pairs we find can produce networks with cycles . Several models of reticulate evolution on biological sequences have been proposed in the last decades , for example the displayed trees model [25] , an extension of the multispecies coalescent ( MSC ) to phylogenetic networks [26] and the ancestral recombination graph model –ARG for short [27]– to only name a few . The associated problems are difficult to solve and big efforts have been done by the community to provide practitioners with fast algorithms . Suppose we are given a BTC network N over a set of OTUs X , where each tree node is associated with a word in an alphabet ( for instance a DNA sequence ) s ( u ) ∈ Σ* . The pair ( N , s ) can , for example , be the outcome of an ML search in the space of BTC networks given an alignment over X . Now , suppose we are given a new sequence and we want to update N to include it , ensuring that the resulting network is still BTC . We may want to do this , for instance , to update the network without redoing the whole ML search , or in a phylogenetic placement perspective ( for example , we want to know where to place a given strain of a virus in N ) , or even because we use a heuristic algorithm that reconstructs a network by adding one sequence at the time . We assume that a model of evolution is given , and we assume that we can compute the following probabilities: For each tree node t of N , we let ϕt: Σ* → [0 , 1] be the function defined as follows . If t is the root of N , then ϕt is the constant function equal to 1 . Otherwise , if the single parent p of t is a tree node , then ϕt ( s ) = PS ( s ( p ) , s ) . If p is a hybrid node with parents g1 , g2 , then ϕt ( s ) = PH ( s ( g1 ) , s ( g2 ) , s ) . That is , ϕt ( s ) is the probability that a given sequence s is the result of the evolution of the sequences at the parent node ( or grandparents , in case of hybrid parent ) of t . Now , we want to extend N to another BTC network in order to include an extant OTU ℓ ∉ X identified by its sequence sℓ ∈ Σ* , while keeping the sequences associated to all tree nodes of N . According to the results presented in this paper , we need to identify the augmentation operation R−1 ( N , ℓ; S1 , S2 ) that has to be applied , and determine the sequences at the newly created tree nodes . If the operation to be applied is of type T , that is , N ˜ = T - 1 ( N , ℓ ; S 1 , S 2 ) , we need to find certain nodes τ1 , y1 , … , yr−1 , with the additional condition that S1 = {τ1} and S2 = ( y1 , … , yr−1 ) form a T-feasible pair . Analogously , if it is of type H , N ˜ = H - 1 ( N , ℓ ; S 1 , S 2 ) , then S1 = {τ1 , τ2} and S2 = ( y1 , … , yr−1 ) must form a H-feasible pair . Intuitively , the node τ1 in case of an augmentation of type T , or the nodes τ1 and τ2 in case of type H , have to be chosen in order to maximize the probability of appearance of the new OTU , while the other nodes appear in order to give a better explanation of the corresponding sequences by means of hybridization with the lineage leading to ℓ . We present here an heuristic to find the augmentation operation , together with the assignment of sequences to new tree nodes , that deploys this intuitive idea: We emphasize that we do not claim that the heuristic we present here gives a global optimum . In fact , usually a sequence of optimal choices does not lead to a global optimum . The analysis , and eventually improvement , of this method of reconstruction is left as future work . Example 1 . We consider a simple model of evolution where: We consider three species , with sequences s ( α ) = AAAC , s ( β ) = BBCC , s ( γ ) = BBBB . The network N ( which is in fact a tree ) that fits these extant OTUs best , together with an optimal assignment of sequences to all nodes is shown in Fig 17 ( left ) . Now , we wish to extend N in order to add a new OTU with sequence s ( δ ) = AACC . We thus proceed as discussed in the previous pages: The algorithms in this paper have been implemented in python using the python library PhyloNetworks [28] . This implementation , together with the sources for the experiments that we comment in this subsection can be downloaded from https://github . com/bielcardona/TCGenerators .
The main result of this paper is a systematic way of recursively generating , with unicity , all BTC networks with a given number of leaves . This procedure relies on a pair of reduction/augmentation operations that generalize analogous operations for phylogenetic trees . Indeed , given a ( rooted , binary ) phylogenetic tree over [n] , we can obtain a phylogenetic tree over [n − 1] by deleting the leaf labeled by n and removing the elementary node that this deletion generates . Conversely , given a tree T over [n − 1] and one of its nodes u , we can construct a tree over [n] by simply hanging a pendant leaf labeled by n to the single incoming arc of u . Since different choices for T and u give different trees over [n] , this gives a recursive procedure to generate , with unicity , all binary rooted phylogenetic trees over a given set of taxa: we start with the leaf labeled by 1 , then we add the leaf labeled by 2 , then the leaf labeled by 3 in all possible ways , and so on . Biologically , we can think of this procedure as follows: Once the evolutionary history of a given set of OTUs is correctly established ( notice that , in practice , we can never be sure that we got the correct tree , but here we suppose we do ) and modeled by a phylogenetic tree , extending this evolutionary history to consider a “new” OTU n consists in finding where to place n in the tree , i . e . finding the speciation event that leads to the diversification of n . Unfortunately , when working with classes of phylogenetic networks , the removal of a single leaf ( and of all elementary nodes created by this removal ) does not necessarily give a phylogenetic network within the same class . In the case of BTC networks , we were able to find the minimal set of nodes that one must remove so that , after their deletion and that of all elementary nodes created by this removal , one gets a BTC network with one leaf less . As in the case of trees , given a BTC network over [n − 1] and some set of nodes with certain restrictions ( i . e . the feasible pairs S1 and S2 ) we can construct a BTC network over [n] leaves , in such a way that different choices for the BTC network or for the feasible pair give different BTC networks over [n] . Hence , we find a procedure to recursively generate all BTC networks over a given set of taxa . Biologically , we can think of this procedure as an extension of what can happen when adding a new OTU n to a phylogenetic tree: here the diversification of n can involve a reticulated event ( when n is added as hybrid node ) and the ancestors of n participate to |S2| reticulated events , which were impossible to detect before the introduction of n . | Phylogenetic networks are widely used to represent evolutionary scenarios with reticulated events , and among them , the class of binary tree-child ( BTC for short ) networks is one of the most studied ones . Despite its importance , BTC networks , as mathematical objects , are not yet fully understood . In this paper we introduce two operations ( reduction and augmentation ) on the set of BTC networks that generalize well known operations on phylogenetic trees , and show how they can be used to analyze and synthesize any BTC network . Apart from the mathematical formulation of the problem , we exhibit how these operations can be used in biological applications to add a new sequence to a given BTC network . This can be useful , for instance , to update the network without redoing the whole search , or in a phylogenetic placement perspective . We also obtain a recursive formula for a bound on the number of such networks . We have implemented the algorithms in this paper , made them available on a public repository , and used this implementation to perform some computational simulations . | [
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"resea... | 2019 | Generation of Binary Tree-Child phylogenetic networks |
Distinct firing properties among touch receptors are influenced by multiple , interworking anatomical structures . Our understanding of the functions and crosstalk of Merkel cells and their associated neurites—the end organs of slowly adapting type I ( SAI ) afferents—remains incomplete . Piezo2 mechanically activated channels are required both in Merkel cells and in sensory neurons for canonical SAI responses in rodents; however , a central unanswered question is how rapidly inactivating currents give rise to sustained action potential volleys in SAI afferents . The computational model herein synthesizes mechanotransduction currents originating from Merkel cells and neurites , in context of skin mechanics and neural dynamics . Its goal is to mimic distinct spike firing patterns from wildtype animals , as well as Atoh1 knockout animals that completely lack Merkel cells . The developed generator function includes a Merkel cell mechanism that represents its mechanotransduction currents and downstream voltage-activated conductances ( slower decay of current ) and a neurite mechanism that represents its mechanotransduction currents ( faster decay of current ) . To mimic sustained firing in wildtype animals , a longer time constant was needed than the 200 ms observed for mechanically activated membrane depolarizations in rodent Merkel cells . One mechanism that suffices is to introduce an ultra-slowly inactivating current , with a time constant on the order of 1 . 7 s . This mechanism may drive the slow adaptation of the sustained response , for which the skin’s viscoelastic relaxation cannot account . Positioned within the sensory neuron , this source of current reconciles the physiology and anatomical characteristics of Atoh1 knockout animals .
A diverse array of touch receptors signal information from the periphery to the central nervous system , enabling the detection of objects we encounter at our skin surface [1 , 2] . In mammals , at least four classes of afferents serve to signal mechanical interactions , each tuned to extract specific features of a tactile stimulus . These classes of mechanosensory afferents encode tactile stimuli as trains of action potentials , or spikes , each with distinctive firing properties . One class of mechanosensitive neurons , myelinated Aβ slowly-adapting type I ( SAI ) afferents , are gentle touch receptors that encode edges and curvature . These mechanoreceptors localize to skin regions specialized for high tactile acuity , including fingertips , whisker follicles and touch domes . Several physiological characteristics distinguish SAI afferents from other mechanosensitive classes of neurons: 1 ) association with epidermal Merkel cells , 2 ) high frequency responses to moving stimuli , 3 ) slowly adapting responses to held stimuli , 4 ) irregular firing patterns with large variability in inter-spike intervals , and 5 ) sensitivities to a wide range of stimulus forces . A common feature of mechanosensory neurons is specialized anatomical structures , termed end organs , which shape their neuronal outputs . Each myelinated SAI afferent branches and extends unmyelinated projections ( neurites ) that form synaptic-like contacts with Merkel cells ( Merkel cell-neurite complexes ) . The SAI afferent’s end organ is a cluster of multiple Merkel cell-neurite complexes , which are required to produce canonical SAI firing patterns [3 , 4] . In response to mechanical stimulation , individual Merkel cell-neurite complexes produce a generator current in unmyelinated neurites , which are summed at a spike initiation sites located at myelinated branch points , termed heminodes , in the SAI afferent . At spike initiation sites , generator currents from clusters of Merkel cell-neurite complexes are converted to action potentials , which propagate towards the spinal cord . Despite recent computational modeling to determine how the architecture of Merkel cell-neurite complexes governs SAI firing properties [5] , it remains unknown how Merkel cells and neurites individually contribute to mechanically evoked responses in SAI afferents . It is experimentally difficult to directly measure the generator currents that Merkel cells and neurites individually create in response to mechanical stimulation , however recent physiological data suggests at least a two-component model . Ex vivo extracellular recordings of SAI afferents from a skin-specific Atoh1 conditional knockout ( Atoh1CKO ) [3 , 4] , which lack Merkel cells , exhibit truncated firing patterns during sustained mechanical stimulation . Additionally , similar recordings from mice whose Merkel cells lack Piezo2 ( Piezo2CKO ) [3] , a mechanically activated ion channel that is required for the intrinsic mechanosensitivity of Merkel cells , also showed truncated sustained firing . Finally , knockdown of Piezo2 in rat whisker follicles attenuate whisker-stimulated firing [6] . From these data emerge a two-receptor-site model of mechanotransduction at the Merkel cell-neurite complex: 1 ) Merkel cells are required for sustained firing in SAI afferents , while 2 ) neurites generate rapidly adapting firing to mechanical stimulation [3 , 7 , 8] . Here , we sought out to evaluate this conceptual framework by computationally modeling how Merkel cells and neurites individually contribute to SAI action potential volleys . Although previous groups have attempted to model SAI responses , the fundamental mechanics of these models are directly tied and fitted to a presented stimulus [9–15] . This critical confound obscures the individual biophysical interactions of Merkel cells and neurites , and reduces the physiological relevance of these models . The generator function developed herein is based on physiological data and is composed of a Merkel-cell mechanism ( slower decay of current ) and a neurite mechanism ( faster decay of current ) . Numerical experiments , both at the level of the interaction of a single Merkel cell and neurite , and at the level of an entire end organ , demonstrate the impact of parameter changes , contributions , and interactions . Such computational experiments may help elucidate mechanisms of sensory encoding at the Merkel cell-neurite complex that govern tactile encoding and function , and in particular , reveal biological mechanisms , in silico , that are technically difficult to observe in vivo . Thus , the models generate specific predictions for future experimental studies .
Our computational model of mechanotransduction at the Merkel cell-neurite complex has three primary structural components: 1 ) a finite element model of skin mechanics , 2 ) a generator function for Merkel-cell and neurite based currents , and 3 ) a leaky integrate-and-fire ( LIF ) model to fire spikes at heminodes , which propagate to the afferent ( Fig 1 ) . Though the generator function is the focus of this work , its physiology is inseparably intertwined with the skin and arbor . To comprehensively model mechanical forces in the skin , we utilized a finite element model of the skin’s layers that converts a displacement stimulus , with a linearly decelerating ramp-up ( ramp phase ) , and a static hold ( hold phase ) , into compressive stress within the skin’s layers ( Fig 1 , top ) . Each layer of the multi-layered model was represented by axisymmetric hybrid elements , comprising about 14 , 000 in number , of quasi-linear viscoelastic material using a two-term Prony series with Ogden and Neo-Hookean hyperelastic properties . This finite element model was used in a prior effort [16] and was directly informed by hyperelastic and viscoelastic properties based directly upon measurements of mouse skin [17 , 18] . In particular , the model includes mouse skin ( all intact layers including epidermis , dermis and hypodermis , and excluding muscle ) as well as a thin nylon mesh and elastomeric backing material . The skin specimen modeled was 380 μm thick , with the following mechanical properties ( μ = 1 . 3 kPa , α = 7 . 9 , τ1 = 0 . 08 s , τ2 = 1 . 2 s , G1 = 0 . 59 , G2 = 0 . 10 , G∞ = 0 . 31; note no unit indicates dimensionless quantity ) . The model has been rigorously validated [16] . In particular , force over time at the stimulus tip , in response to displacement clamped stimuli , have proven to be quite similar in magnitude and form to measurements made during physiological experiments . In response to indentation by ramp-and-hold stimuli , compressive stress over time from the finite element model is passed to the generator function , which calculates generator current for one Merkel cell-neurite complex ( Fig 1 , middle ) . The generator function is composed of three individual currents , an SI current arising from Merkel-cell stimulation , and an RI and USI arising in neurites . We propose that the SI current is generated in the neurite downstream of activation of mechanosensitive ion-channels , such as Piezo2 , in Merkel cells . The kinetics of this current was constrained by in vitro electrophysiological recordings in Merkel cells , as both Piezo-dependent mechanically activated currents and downstream voltage-activated calcium and potassium currents are expected to contribute to prolonged signaling between Merkel cells and sensory neurites . The SI current is sustained during the stimulus hold phase , with a gradual increase and large peak amplitude during the ramp phase of mechanical stimulation , and slow decay during the hold phase . The RI current is generated directly through mechanical activation of Piezo2 in neurites . Compared with the SI current , the RI current is more sensitive to changes in stimulation over the ramp phase , but has a low peak amplitude , and quickly decays to zero during the hold phase . The origin of the USI current is not tied to a specific molecular mechanism . This current has lowest sensitivity to the ramp phase , is of moderate peak amplitude , and has the slowest rate of decay during the hold phase . The sum of all three currents is highly sensitive and has a large peak amplitude during the ramp phase . During the hold phase , this summed current decays with an initial slow phase ( from peak value of stimulation to about 0 . 5 s afterwards ) and a secondary ultra-slow phase ( in response to the stress relaxation of the skin ) , maintaining a steady level through the late-hold phase ( from 2 to 5 s of the stimulation ) . It is this generator current that underlies the model’s prediction of spike trains in SAI afferents . In particular , the summed current from each Merkel cell-neurite complex is multiplied by the number of complexes in a cluster , which predicts the total current entering an SAI afferent’s heminode . We then employed a LIF model to predict the required accumulated current to elicit action potential trains in SAI afferents ( Fig 1 , bottom ) . For the purposes of this simulation , the irregular inter-spike intervals were unimportant so noise was removed from the model , though present in a prior work [5] . Therefore , the output spike times are quite regular relative to actual SAI firing . The key conceptual contribution of the generator function is the linear convolution of internal skin stress over time with each of three physiologically-based sub-functions ( SI , RI , and USI ) . This computational strategy enables the recent history of skin stress to be captured at any instantaneous time point . Each sub-function consists of a unique time constant and ratio of peak to steady state current . These parameters are directly derived from in vitro recordings of Merkel cells and SAI neurons in current clamp and voltage clamp mode , respectively ( S1 Fig ) . Therefore , the modeled responses to mechanical step stimulation exhibit an instantaneous increase or decrease proportional to stress magnitude followed by exponential decay , as do the recorded responses . Electrophysiological recordings suggest that current in a neuron rapidly decays with a step stimulation , and we assume that a neurite behaves similarly . Therefore , when the three sub-functions sum together , the recent time history of stimulus magnitude and rate , as generator current , is carried to the present . The mechanics of the generator function are demonstrated by magnifying the view of compressive stress interior to the skin ( Fig 2A ) , generated by a finite element model of skin mechanics in response to a ramp-and-hold stimulus , to show the impact of small , discretized step stresses ( σ1 , σ2 – σn , etc . ) in creating receptor current . In reality , stress output by the skin mechanics model is continuous but a discrete representation demonstrates the following concepts more readily . In Fig 2B , top , the generator function representing a single Merkel cell-neurite complex is input with one instantaneous step stress with value σ1 at time t1 , where σ1 is a very small . In response , in Fig 2B , middle-top , the generator function produces an instantaneous current response ( I1 ) linear to the stress value σ1 at time t1 . Its value decreases over time to a stimulus held at that level of stress . I1 is composed of a fast-decaying current IRI ( Fig 2B , bottom ) from the neurite mechanism , and a slow-decaying current ISI ( Fig 2B , middle-bottom ) from the Merkel cell mechanism . Note that the USI current is omitted from Fig 2 to simplify the explanation of the concept . In Fig 2C , increasing stress over time and the generated current response is demonstrated . A second step stress at time t2 is added , making the total stress σ2 at time t2 . In response , the current response increases to I2 from the I1 value which formed at σ1 and then began to decay over time . A third step stress at time t3 is then added , making the total stress σ3 at time t2 . In response , the current response increases to I3 and will decay back to the baseline if there is no further stress input . Skin relaxation , which occurs at the end of the ramp phase of mechanical indentation , results in a small decrease in skin stress . In Fig 2D , we mimic the case of a decay in stress beginning at peak force . Assuming the stress decays from σn to σn+1 at time tn+1 , the current response drops immediately to In+1 , which is of a magnitude linearly related to the absolute change in stress , before continuing to decay in the fashion described in Fig 2B . Note that this overview of the generator function is detailed mathematically in Methods . The generator function relies upon three free and five biologically derived parameters . Three of the biologically derived parameters , τSI , KSI_Peak , and KSI_Steady , were directly fitted to data obtained from in vitro electrophysiological recordings of Merkel cells ( S1A Fig ) where the latter two represent the peak to steady-state ratio of decaying current . In particular , we assumed that Merkel cells and neurites communicate via synaptic transmission , where changes in membrane potential in Merkel cells are linearly related to post-synaptic current changes in neurites . Thus , current clamped recordings of Merkel cells most accurately reflect Merkel-cell dependent generator currents in neurites . In contrast , τRI was derived from data obtained from in vitro electrophysiological recordings of Piezo2-dependent currents in DRG neurons recorded under voltage clamp ( S1B Fig; [19] ) . The τUSI parameter was set to the time constant of 1 . 7 ms , found for putative LTMRs [20] [21] ( see Discussion ) , and slightly model fit around that value . In contrast to the five biologically derived parameters , the three free parameters were fitted in the context of simulating an entire end organ in silico . These parameters set the relative magnitudes of the SI , RI , and USI currents . Their values were fitted in the context of the end-organ model so that firing rates predicted over the ramp-up , early-hold , and late-hold phases of the stimulus accurately recapitulated electrophysiological recordings from wildtype animals . Note that this overview of the physiological data is detailed further in Methods . The model as described involves three generator currents . A previously published two-receptor-site model proposed that Merkel cells contribute SI currents and neurites contribute RI current to generate SAI firing patterns in afferents [8] . To test that hypothesis in silico , we used our model to predict SAI firing with only SI and RI current components in the generator function ( Fig 3 , Table 1 ) . We find that a model incorporating these two currents is able to replicate the SAI firing for the ramp and very early hold of the stimulus from 0–1 s , ( Fig 3C , “No USI , 200 ms SI” ) . It also can replicate the slow adaptation of the sustained response to a certain degree . That said , in order to fit the slow adapting firing such that it does not plateau from about 1 . 5 to 5 sec , a larger SI current time constant is needed than the 200 ms recorded from isolated Merkel cells . Therefore , in order to correct the discrepancy , we tested two solutions: 1 ) extending the SI function’s time constant and 2 ) introducing a third USI generator current . In the former solution , extending the SI function’s time constant to 570 ms ( Fig 3C , “No USI , 570 ms SI” ) generated a predicted SAI firing pattern that reasonably well recapitulated the decay . However , as noted in the section below considering SAI firing in Atoh1CKO mice , relying only on a RI time constant will be quite problematic . In the latter solution , introducing a third USI current , with a time constant of 1 . 7 s ( Fig 3C , “USI , 200 ms SI” ) likewise fit the recording data . The current that underlies the IFF responses of Fig 3C is shown in Fig 3G . Note that in another attempt to fit the time course of the decay in IFF response—without the USI current—the skin’s viscoelasticity was varied in a set of computational experiments , but could not achieve the time course of the decay ( S4 Fig ) . Several additional computational experiments were performed to vary combinations of parameters , though none impacted the issue of the plateau in the sustained phase . For example , small increases in the decay time constant of the RI current , τRI , resulted in increased peak current amplitude during the ramp phase of stimulation ( Fig 3D ) . Increasing the decay time constant of the SI current , τSI , resulted in increased peak current amplitude during the early-hold phase ( Fig 3E ) . Lastly , increasing the peak/steady-state ratio of the SI current , KSI_Peak , led to increased peak current amplitude during early-hold and late-hold phases ( Fig 3F ) . Together , these experiments demonstrated that the model was sensitive to small changes in biological parameters , with a high degree of sensitivity . Note that each of the computational experiments in Fig 3D–3F ( with corresponding IFFs in S2 Fig ) was done with the USI current enabled . Of the aforementioned model parameter solutions that afford slow adaptation in firing in the sustained phase for wildtype animals , that one which extends the time constant on the SI current is highly problematic for Atoh1CKO animals for which there is only RI current . This situation substantially contributes to the argument for the inclusion of the USI term . In particular , Merkel cells are required for canonical SAI responses in mice [3 , 4] . Mice that lack either Merkel cells entirely ( Atoh1CKO ) , or Piezo2 , the principal mechanosensitive ion channel in Merkel cells ( Piezo2CKO ) , exhibit truncated SAI firing in response to mechanical stimulation . In order to predict SAI firing in Atoh1CKO mice , which lack Merkel cells but maintain touch-dome branching arbors , the model’s SI current was set to zero . With the USI current enabled , in the neurite along with the RI current , we are able to recapitulate the observed , truncated firing patterns in Atoh1CKO mice ( Fig 4 ) . In particular , in alignment with the recorded data , the peak IFF in the simulated Atoh1CKO mice was attenuated in magnitude , as compared to the wildtype animals . As well , the ramp and early decay of the IFF was attenuated in time , as compared to the wildtype animals , though the recording data does not elicit spikes at less than about 15 Hz . Note that the model’s free parameters were kept at the same values as when fitted to recording data from wildtype mice . Neither these nor the biologically derived parameters were modified in extending to the predictions for the Atoh1CKO mice . The only change was in setting the SI current to zero , and noting the positioning of the USI current within the neurite due the anatomy of Atoh1CKO mice . Finally , the relative relationships of the current values underlying the wildtype and Atoh1CKO mice cases are shown in Fig 5 . It is notable the magnitude of USI current is greater than RI current , in both wildtype and Atoh1CKO mouse simulations . As well , the SI current is of larger magnitude than the RI current for the wildtype case , even during the ramp of the stimulus .
Similar to the multi-level stratification in Johnson’s model [10] , we included three input-output factors in modeling the SAI afferent: 1 ) surface stimuli propagates towards its interior layers ( skin mechanics ) , 2 ) local tissue deformation is converted into current at neurites ( generator function ) , and 3 ) generator current is converted into potential and the generation of spikes ( neural dynamics ) . Previous attempts to model the Merkel cell-neurite complex have taken a simplifying approach , where the fundamental parameters of spike generation are directly tied and fitted to a presented stimulus . Additionally , these models do not rely on biologically derived parameters [9–15] . Such assumptions obscure the individual biophysical interactions of Merkel cells and neurites , and reduce the physiological relevance of these models . Specifically , the direct conversion of time derivatives of stimulus position into receptor current , used in these models , makes them non-physiologically based and heavily dependent upon parameter fitting to particular surface stimuli . For example , in a previous study that predicted the timing of individual spikes evoked by mechanical vibrations in three types of mechanoreceptive afferents [12] , stimulus displacement and its derivatives ( position , velocity , acceleration , and jerk ) were separately filtered using different temporal linear filters and summed with different weights to form current input to a neural dynamics model . Similarly , in a focus on ramp-and-hold stimuli [5] , stresses and strains within the skin were converted into receptor current . While perhaps a stress term represents a static response similar to the Merkel cell mechanism and its first derivative a dynamic response similar to the neurite mechanism , the mapping of such derivatives to either physiological mechanism is rudimentary and not clearly differentiated from what could also be framed as direct ties to stimulus position and movement . In contrast , the model herein combines internal skin stress with fixed , experimentally observed time constants , to predict mechanically evoked Merkel-cell afferent firing . Furthermore , in transforming skin stress to current , the model incorporates a linear convolution over time , enabling a recent “biomechanical history” of the stimulus to be carried forward . While our model affords a means of storing the prior biomechanical history of the stimulus , via the present value of decaying current , there are alternative strategies to accomplishing this goal . For example , one could simply sum the three most recent timestamps of modeled current . However , such means of time-dependent storage is not a biologically relevant modeling strategy . Thus , the instantaneous linear convolution of skin stress more adequately reflects putative neurobiological mechanisms due to a carry-forward characteristic . This strategy provides a biologically relevant way of temporally generating and preserving generator current . In order to recapitulate the experimentally observed touch-evoked firing patterns of Merkel-cell afferents , in particular the slow adaptation in firing in the sustained hold of the stimulus , our in silico analysis predicts that sensory neurons produce a USI generator current in addition to RI generator current . The USI current was essential in this model for replicating the truncated adaptation in firing for Atoh1CKO animals , when the SI currents contributed by the Merkel cell are not present . Without the USI current , the existence of only RI current drives spike firing to zero too rapidly ( about 0 . 25 s , Fig 4C ) as compared to neural recording data ( ~1–2 s , Fig 4A ) . We have yet to observe a mechanosensitive USI current in Merkel-cell afferents , but this may be due to under sampling this rare neuronal population , or in vitro recording conditions . Although mechanically activated USI currents have primarily been observed in small-diameter , presumably nociceptive DRG somata , some groups have reported USI currents in putative LTMRs [20] [21] . Thus , we speculate that Merkel-cell afferents might express mechanosensitive USI generator currents . Alternatively , keratinocyte-derived signals might drive the USI current in the sensory neurons [22–24] . A third possibility is that voltage- or calcium-dependent currents that activate downstream of neuronal Piezo2 might account for the USI current . Although future experimental studies are needed to identify the origin of the USI current , a mechanism along these lines is playing a significant role , as Fig 5 denotes , the RI current plays a relatively smaller role . Our findings raise the possibility that a complex interaction between Piezo2 ion channels and previously unsuspected conductances in sensory neurons govern firing at the Merkel cell-neurite complex . This work sets the stage to identify downstream molecular mechanisms , as well as enhances our understanding of the fundamental mechanosensory principles that govern tactile function and encoding in the nervous system . In sensory systems , adaptation mechanisms work on multiple time scales to maintain sensitivity to dynamic stimuli . For example , light adaptation in vertebrate rod photoreceptors can be fit with a double exponential function whose fast time constant is in the range of the USI currents our models predict [25] . Thus , our models suggest that Merkel cell-neurite complexes , like other sensory receptors , employ multiple adaptive mechanisms that operate on different time scales . The relative contributions of these mechanisms to sensory signaling remain to be tested experimentally . Our model depends on several underlying assumptions . First , we assumed that the overall generator current is simply the superposition of three individual generator current sources ( SI , RI and USI ) . Second , as direct recordings from Merkel-cell neurites have not been reported in any species , we made a simplifying assumption that the current level and decay of the Merkel-cell dependent SI current in neurites is linearly related to the membrane potential recorded for Merkel cells under current-clamped conditions . A linear relationship was chosen because more complex transformations are not warranted based on the available biological data . Third , we assumed that the current level and decay rate of the neurite based RI current are similar to those recorded from rapidly inactivated DRG neurons in vitro , which correspond to low-threshold mechanoreceptors . Fourth , we have only modeled a single Merkel cell to single neurite interaction . However , Merkel cell-neurite complexes can connect in both chains and clusters [5 , 26] , and during skin renewal Merkel cells and neurites undergo dynamic architectural remodeling [27] . It will be critical to integrate these additional complexities in future modeling studies to better understand how tactile information is coded . A final note regards the relationship between the skin’s time-dependent viscoelasticity and the ultra-slowly inactivating current . Our computational effort includes a finite element model to account for the skin’s mechanics , which exhibit non-linear behaviors of hyperelasticity and viscoelasticity . The material properties in the finite element model were directly obtained from skin measurements made across a range of animals spanning stages of the mouse hair cycle [17 , 18] . The model utilizes clamped displacement stimuli , identical to those of the electrophysiological experiments [3] . Upon indentation into the skin , force at and stress around the stimulus tip is observed . As our prior validation indicates , model output well matches observations of tip force as it relaxes over time [16] . One might wonder if the issue of slow adaptation to a sustained stimulus ( Fig 3C ) might be accounted for by greater viscoelastic relaxation of the skin . To address this topic , simulations where the relaxation is varied over a biologically observed range are presented in S4 Fig . The results indicated that the time course of the decay in the spike firing could not be achieved by varying skin viscoelasticity alone . Even in the most extreme case , the model begins to yield an intermediately adapting response . Furthermore , even if the stress trace was inaccurate , considerably more change in stress ( over a duration of about 1–2 s ) would be required to influence RI current ( given its 8 ms time constant ) in order to recapitulate the Atoh1CKO response ( Fig 4 ) . As well , given the constitution of the generator function , when stress decreases , current decreases . In fact , a paradoxical slow increase in stress following the stimulus ramp would be required to make-up for the absence of USI current . Skin mechanics are simulated here by bulk tissue layers and do not include the micro-level mechanics of the touch dome . The micromechanics of touch domes have not been investigated , to our knowledge; however , several anatomical features suggest that the touch domes mechanical properties might differ from other skin areas . For example , touch domes contain a highly vascularized dermis , a thickened epidermis and a thin stratum corneum compared with adjacent skin regions [28] . Moreover , touch domes are marked by columnar keratinocytes and Merkel cells , whose intermediate filament cytoskeletons are molecularly distinct from that of surrounding epidermal keratinocytes [29 , 30] . Little is known about how specific cytokeratin isoforms contribute to skin mechanics [31]; however , a recent study has shown that , along the human hair follicle , mechanical stiffness changes with the organization of keratin networks [32] . Thus , future work is needed to determine whether touch domes have specialized tissue mechanics and , if so , how they might contribute to neuronal firing patterns .
The generator function is a convolution of compressive stress interior to the simulated skin and three exponential functions that describe how a single Merkel cell-neurite complex responds to a step stimulation input with an instantaneous increase or decrease proportional to stress magnitude followed by an exponential decay . Electrophysiological recordings suggest that current in a neuron rapidly decays with a step stimulation , and we assume that a neurite behaves similarly . Therefore , the rapidly inactivating ( RI ) current in Eq 1 corresponds to decay time constant τRI and linear transformation coefficient a . The slowly inactivating ( SI ) current in Eq 2 corresponds to decay time constant τSI , linear transformation coefficient b , and two ratio parameters KSI_Peak and KSI_Steady , representing the peak and steady portions of a decaying trace ( where KSI_Peak + KSI_Steady = 1 ) . The ultra-slowly inactivating ( USI ) current in Eq 3 corresponds to decay time constant τUSI and linear transformation coefficient c . Note that linear transformation coefficients a , b , and c serve to convert instantaneous stress to instantaneous current , linearly . Bringing Eqs 1–3 into the bracket of Eq 4 , the complete form of the generator function is a convolution of these terms and the first derivative of stress input σ over time: I ( t ) =∫x=0t[a*exp ( -t-xτRI ) +b* ( KSI_Peak*exp ( -t-xτSI ) +KSI_Steady ) +c*exp ( -t-xτUSI ) ]*dσdxdx ( 4 ) where I is the output generator current , t is time , and x is a variable of the integral . We use 0 as the mathematical baseline of I , and set it to 0 when it becomes negative . The terms a and b are instantaneous values while dσdx along with the integral represents their decay over time , which is the means of storing the prior history of the stimulus , via the present value of receptor current , in a decaying fashion . Since we cannot directly measure the receptor currents in a neurite that would emerge from the contribution of Merkel cell and neurite mechanisms , the generator function was validated in the context of an end-organ model for the SAI afferent [5] . In this model , one Merkel cell and its connecting neurite form a Merkel cell-neurite complex , where multiple complexes are clustered per heminode . For example , the end-organ structure includes 4 heminodes and therefore 4 clusters , with 3 , 1 , 5 , and 8 Merkel cell-neurite complexes in each , noted as a {8 , 5 , 3 , 1} structure ( sequence does not matter ) . In each Merkel cell-neurite complex , a finite element model of the skin’s layers outputs compressive stress in to the skin given a stimulus input of displacement with a linear decelerating ramp-up . Different from prior work [11] , a refined finite element model was used that was both hyper and viscoelastic as based directly upon measurements of the mouse , and using the output of maximum compressive stress instead of strain energy density components [16] . Each layer of the multi-layered model was represented by axisymmetric hybrid elements of quasi-linear viscoelastic material with Ogden and Neo-Hookean hyperelastic . In response to indentation by ramp-and-hold stimuli , its output of compressive stress over time is passed to the generator function , which calculates receptor current for one Merkel cell-neurite complex . Then , receptor current is multiplied by the number of Merkel cell-neurite complexes in a cluster as the total current entering the heminode , which is taken in a leaky integrate-and-fire model to accumulate enough potential to elicit a spike . There is therefore one LIF model at each heminode . Once the potential at a heminode reaches the firing threshold and elicits a spike , the potentials at other heminodes are immediately reset to baseline , and a refractory period of 1 ms is set . The parameters R , C , and V ( resistance , capacitance , and firing voltage threshold ) of the LIF model are set to 5 GΩ , 30 pF , and 30 mV , and are the same for all 4 LIF models in the model . The generator function utilizes eight parameters . Five biologically-derived parameters ( τRI , τSI , τUSI , KSI_Peak and KSI_Steady ) were obtained from experimental data . Three free parameters ( a , b , and c ) were model fitted in the context of simulating an entire end organ . Regarding the biologically derived parameters , time constant τRI in the neurite mechanism was fitted to the decay time constant obtained from the current recorded in the neuron of a whole cell over time under a voltage clamped prep , with step mechanical stimulation [19] . We assume similarity of current decay between such neurons and simulated neurites herein . A characteristic recording and its fitted trace are shown in S1B Fig . As shown in Table 2 , a total of 44 measurements from nine preps were fitted using a single exponential decay functions of the form y=a*exp ( -xτ ) . The mean value of all fitted time constants , 0 . 008 s , was used for τRI , and the mean value ± standard deviation of all fitted time constants , 0 . 001 and 0 . 015 s , were used in numerical experiments with parameter changes . In contrast , the time constant τSI , as well as the peak to steady state ratio parameters KSI_Peak and KSI_Steady of the Merkel cell mechanism were generated directly from single isolated Merkel cells . In this case , however , membrane potential over time was recorded in the current clamped prep [3] . We assume that the Merkel cell’s transmission mechanism most likely behaves like a synapse where changes in the cell membrane’s potential are linearly related to post-synaptic current under a step stimulation . A characteristic recording and its fitted trace shown in S1A Fig . A total of 12 voltage measurements from three Merkel cells were fitted using a similar single exponential decay plus a constant function , of the form y=a*exp ( -xτ ) +b . The mean value of all fitted time constants ( Table 3 ) , 0 . 2 s , was used for τSI , and the mean value ± standard deviation of all fitted time constants , 0 . 05 and 0 . 35 s , were used in numerical experiments with parameter changes . KSI_Peak and KSI_Steady ( Table 4 ) are dependent of each other , and have a sum of 1 . Their values were obtained through numerical optimization , and falls within the range of the data from Table 1 . Furthermore , the τUSI parameter was tied to the time constant of 1 . 7 ms , found for putative LTMRs [20] [21] , though it was slightly model fit around that value , using the procedure as noted below . Free parameters a , b , and c represent the linear transformation from instantaneous stress to instantaneous current for RI , SI , and USI components , respectively , and their true values are not presently measurable . In particular , the magnitude of one mechanism relative to another , as well as the way in which the Merkel cell current is transferred to the neurite and mixes with it are unknown . Therefore , they are fitted in the context of simulating an entire end organ . Ultimately they are set at 0 . 74 , 0 . 24 , and 0 . 07 , respectively , such that the RI current in the neurite is more sensitive than the SI current in the Merkel cell , and both are far more sensitive than the USI current . Before delving into further details of parameter and model fitting , we note several relationships among the parameters of the generator function . First , increasing the magnitudes of parameters τRI , τSI and KSI_Peak ( decaying time constants and peak/steady ratio ) independently increase receptor currents in ramp-up , early-hold , and late-hold phases , respectively , as noted ( Fig 3D , 3E and 3F ) . The ratio of these parameters can also change the ratio of the firing rates in different phases of the stimulus . For example , decreasing τRI will decrease the overall ramp-up firing rate magnitudes only , and therefore can result a decrease of the ramp-up:late-hold firing rate ratio . As well , decreasing a:b from 3 . 08 to , say , 1:1 , will decrease the firing rate ratio of ramp-up:late-hold . Finally , the ratio of a:b , though at present not constrainable by biopotential measurement , could potentially be constrained in the future by either spike or current recordings at the neuron by comparing Piezo2 deficient and wildtype mice . The model fitting procedure and its justification is as follows . As typical values of currents in whole afferent recordings can reach up to 250 pA , and that our model contains 17 Merkel cell-neurite complexes to achieve this value , we estimated the receptor currents from a single Merkel cell-neurite complex should be evenly divided by 17 , with a peak value of about 10–20 pA . With this as a starting point , their values were fitted in the whole end-organ model so that firing rates predicted over the ramp-up , early-hold , and late-hold phases of the stimulus mimic the electrophysiological recordings in Table 2 , for an afferent described elsewhere [16] with the indenter tip size adjusted from 1 to 1 . 5 mm in diameter . Specifically , the experimentally recorded spike timings were first converted to instantaneous firing rates , and then smoothed with a moving average with window width of 5 . Then , all smoothed firing rates were logarithmically sampled ( a total of 50 data points ) to put higher weight of the early-hold phase during the fitting . Finally , the Levenberg-Marquardt method was used to numerically maximize the coefficient of determination , calculated by interpolating the modeled firing for comparison to the smoothed firing from recordings [33] . Two stimulus displacements were fitted and we averaged the parameters from these two fits to obtain the final values , which are 0 . 74 , 0 . 24 , and 0 . 07 pA/Pa , for a , b , and c respectively . | Slowly-adapting type I ( SAI ) cutaneous afferents help us discriminate fine spatial details . Their physiology and anatomy are distinguished by their slow adaptation in firing to held stimuli and innervation of Merkel cells , respectively . How mechanotransduction currents in Merkel cells and sensory neurons combine to give rise to neural spike firing is unknown . In considering wildtype animals , as well as Atoh1 conditional knockout animals that lack Merkel cells , this effort employs a computational modeling approach constrained by biological measurements . For the developed generator function to recapitulate firing responses across genotype , a previously unsuspected current source is required . Thus , the model makes specific predictions for future experimental studies . | [
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"sk... | 2018 | Computation predicts rapidly adapting mechanotransduction currents cannot account for tactile encoding in Merkel cell-neurite complexes |
Clostridium difficile is a Gram-positive spore-forming pathogen and a leading cause of nosocomial diarrhea . C . difficile infections are transmitted when ingested spores germinate in the gastrointestinal tract and transform into vegetative cells . Germination begins when the germinant receptor CspC detects bile salts in the gut . CspC is a subtilisin-like serine pseudoprotease that activates the related CspB serine protease through an unknown mechanism . Activated CspB cleaves the pro-SleC zymogen , which allows the activated SleC cortex hydrolase to degrade the protective cortex layer . While these regulators are essential for C . difficile spores to outgrow and form toxin-secreting vegetative cells , the mechanisms controlling their function have only been partially characterized . In this study , we identify the lipoprotein GerS as a novel regulator of C . difficile spore germination using targeted mutagenesis . A gerS mutant has a severe germination defect and fails to degrade cortex even though it processes SleC at wildtype levels . Using complementation analyses , we demonstrate that GerS secretion , but not lipidation , is necessary for GerS to activate SleC . Importantly , loss of GerS attenuates the virulence of C . difficile in a hamster model of infection . Since GerS appears to be conserved exclusively in related Peptostreptococcaeace family members , our results contribute to a growing body of work indicating that C . difficile has evolved distinct mechanisms for controlling the exit from dormancy relative to B . subtilis and other spore-forming organisms .
Clostridium difficile is a Gram-positive spore-former capable of causing diarrheal disease that can lead to fatal colitis . Disease symptoms are caused by the production of two toxins , TcdA and TcdB , which are secreted when C . difficile establishes infection in the gastrointestinal tract of mammals [1–3] . C . difficile infections have primarily been associated with individuals undergoing antibiotic therapy , but long hospitalizations , underlying comorbidities , community-acquired infections , and age-related risk factors have also been documented [4–6] . These complications lead to C . difficile disease treatment costs between $1–5 billion per year in the United States [7 , 8] . Of the 0 . 5 million C . difficile infections in the United States each year , approximately 30 , 000 lead to death [9] . These deaths are primarily due to recurrent C . difficile infections , which occur in ~20–30% of people that clear the first infection [9 , 10] . Since C . difficile is an obligate anaerobe , its endospore , or spore form , is responsible for initiating infection and mediating disease recurrence [11] . Spores are highly resistant , oxygen-tolerant , multi-layered structures composed of a tightly packed , dehydrated inner core surrounded by the inner forespore membrane , a germ cell wall , a thick modified peptidoglycan layer known as cortex , an outer forespore membrane , a series of proteinaceous layers known as the coat , and , in some spore formers , an outermost exosporium layer [12 , 13] . The specialized packaging of spores confers resistance to many chemical and physical insults and allows them to persist in the environment , and potentially an infected human , for long periods of time [1 , 14] . The dehydrated core renders spores metabolically dormant and is achieved by the displacement of water by calcium dipicolinic acid ( Ca-DPA ) in late stages of spore formation [15 , 16] . The thick cortex layer surrounding the core physically constrains its expansion and prevents hydration [17] . C . difficile infections begin when spores are ingested by a susceptible host and transit to the gastrointestinal ( GI ) tract [18–20] . In the GI tract , C . difficile spores sense specific bile salts , which induce them to transform into vegetative cells in a process known as germination [18 , 21] . While germination has been primarily characterized in the model organism Bacillus subtilis and in C . perfringens [13 , 22] , recent studies in C . difficile have revealed that C . difficile uses a unique mechanism to regulate the initiation of spore germination [21 , 23–26] . While B . subtilis and C . perfringens employ highly conserved inner membrane germinant receptors to sense small molecule nutrients ( germinants ) , which can be amino acids , sugars , and potassium ions [13] , C . difficile and related Peptostreptococcaceae family members do not encode inner membrane germinant receptors [22 , 27] . Instead , C . difficile uses the subtilisin-like serine protease CspC as a germinant receptor [21] to sense bile salt germinants such as taurocholate [18 , 20 , 28–30] . Although C . perfringens encodes a CspC homolog and the related Csp family serine proteases , CspA and CspB [25 , 26] , CspC is dispensable for germination in C . perfringens [25] in contrast with C . difficile [21] . Furthermore , C . perfringens CspC is catalytically competent and undergoes autoprocessing similar to other subtilisin-like serine proteases [26] , whereas C . difficile CspC carries two mutations in its catalytic triad and lacks autoprocessing activity [21 , 23] . Unlike the catalytically competent C . perfringens CspA , C . difficile CspA is produced as a pseudoprotease that is fused to a catalytically competent CspB protease [23] . During spore formation , the C . difficile CspBA fusion protein undergoes interdomain processing , and the CspB domain is incorporated into mature spores [23] . Despite these differences , CspB in both C . perfringens and C . difficile functions to process the cortex lytic enzyme ( CLE ) SleC , which is found in dormant spores as the pro-SleC zymogen [21 , 23–26 , 31] . SleC degrades the cortex layer , which is essential for spore germination to proceed [32] . In the Clostridia , SleC targets the cortex-specific modification muramic-δ-lactam ( MAL ) , which allows SleC to avoid degrading the germ cell wall of the outgrowing cell [33 , 34] . In B . subtilis , the cortex lytic enzymes CwlJ and SleB target MAL [16 , 35] , although these enzymes exhibit little primary sequence homology to clostridial SleC . Cortex hydrolysis in C . difficile was recently shown to be required for Ca-DPA to be released from the core [36 , 37] , whereas in B . subtilis , Ca-DPA is released before the cortex is hydrolyzed and actually activates CwlJ [38 , 39] . These observations indicate that different regulatory factors and mechanisms control germination in C . difficile relative to B . subtilis and even C . perfringens . In this report , we describe the identification of a novel regulator of C . difficile spore germination , CD3464 in strain 630 , herein referred to as GerS , which is conserved among sequenced Peptostreptococcaceae family members . Using a series of biochemical , genetic , and cell biological assays , we characterize the gerS− phenotype and identify the stage at which spore germination is arrested . We also demonstrate that GerS is essential for virulence in hamsters .
We previously conducted RNA-Seq analyses of C . difficile sporulation-specific sigma factor mutants to identify gene products that might be required for spore formation and/or germination [40 , 41] . We hypothesized that highly expressed genes induced during sporulation would likely encode proteins that regulate spore formation and/or germination . gerS ( CD3464 ) and alr2 are the second and sixth most highly expressed , sporulation-induced genes [40 , 41] , respectively , and their gene products have not been previously characterized . Interestingly , alr2 is encoded downstream of gerS ( Fig 1A ) , and these genes are part of a σE-activated operon ( S1 Fig , [42] ) . alr2 encodes a putative alanine racemase that in Bacillus . spp . converts L-alanine to D-alanine and reduces the sensitivity of spores to L-alanine germinant [43–45] . gerS is predicted to encode a lipoprotein that appears to be unique to the Peptostreptococcaceae family ( Fig 1B ) . To test whether Alr2 or GerS regulate C . difficile sporulation and/or spore germination , we constructed TargeTron gene disruption mutants in alr2 and gerS ( S2 Fig ) . Analysis of the alr2 and gerS mutants by phase contrast microscopy revealed that both strains produced phase-bright spores ( Fig 1C ) . Fluorescence microscopy analyses indicated that alr2− and gerS− forespores appeared to develop similar to wild type ( S3 Fig ) . However , when the alr2− and gerS− strains were tested for functional spore formation , the gerS mutant failed to produce detectable heat-resistant spores , while the alr2 mutant produced wildtype levels of heat-resistant spores ( Fig 1C ) . Western blot analysis confirmed that the gerS mutant was defective in producing GerS , while the alr2 mutant produced wildtype levels of GerS ( Fig 1D ) . The inability of the gerS mutant to produce heat-resistant spores could be due to heat sensitivity [17 , 46] or a general defect in spore germination . To distinguish between these possibilities , we isolated spores from wild type and the gerS and alr2 mutants and tested their ability to germinate following heat-treatment using a plate-based assay . No obvious defect in spore morphology was apparent when gerS− and alr2− spores were visualized by phase contrast microscopy ( Fig 2A ) . However , alr2− spores germinated at wildtype levels , whereas gerS− spores exhibited an ~5-log defect in spore germination relative to wild type ( Fig 2B ) . Heating wildtype and alr2− spores to 60°C for 30 min had no impact on spore germination , whereas heat treatment reduced the germination efficiency of gerS− spores by three-fold ( p < 0 . 05 , Fig 2B ) . Although a similar heat treatment potentiates Bacillus sp . spore germination [47–49] , this effect has not been observed in C . difficile [37 , 50] . Western blot analyses verified that GerS is packaged into wildtype and alr2− mutant spores but not gerS− spores ( Fig 2C ) . Taken together , these results strongly suggest that gerS− spores have a significant germination defect that is slightly heat sensitive . Furthermore , the germination defect of gerS mutant spores is unlikely to be caused by polar effects on alr2 expression , since Alr2 itself is dispensable for heat-resistant spore formation . To validate that the gerS mutant phenotype was due to absence of GerS , we complemented the mutant in trans by ectopically expressing gerS from its native promoter ( s ) . Since gerS transcription originates from the proximal promoter ( P1 ) directly upstream of gerS [42] and possibly the distal promoter upstream of acpS ( P2 , S4 Fig ) , we constructed gerS complementation constructs in which gerS transcription originates from the proximal promoter ( P1 , single ) or from both P1 and P2 promoters ( dual , including the two genes upstream of gerS ) . Heat resistance analyses revealed that the single and dual promoter complementation constructs both restored heat-resistant spore formation to wildtype levels ( S4 Fig ) . Western blot analyses indicated that the dual and single promoter gerS complementation constructs restored GerS production to wildtype levels in the gerS− background ( S4 Fig ) . We chose to use the dual promoter complementation construct , since it produced GerS levels that were most similar to wildtype carrying empty vector . We next sought to determine why gerS mutant spores exhibit such a strong germination defect . We first considered that GerS could affect the rate of spore germination , since a lipoprotein , GerD , controls the speed of germination in B . subtilis [51 , 52] . Loss of B . subtilis GerD results in an ~20-fold germination defect after a 15 hr incubation with germinant; however , after 48 hr , it resembles wild type [52] . Although B . subtilis GerD exhibits no homology to C . difficile GerS , we assessed whether gerS mutants germinated after prolonged incubation . After 48 hrs of germination on BHIS plates containing taurocholate , the change in number of colonies formed following gerS− spore germination was minimal and appeared to arise from spontaneous germination [53] . We next wondered whether GerS regulates germinant accessibility in C . difficile spores , since GerP in B . subtilis and B . anthracis facilitates the interaction of germinants to inner membrane germination receptors , potentially by altering coat permeability [54–56] . Bacillus spp . gerP mutants exhibit slower germination and require higher levels of germinant in order to achieve equivalent levels of germination as wild type . To test whether C . difficile gerS mutant spores are differentially sensitive to germinant , we compared the effect of increasing concentrations of taurocholate germinant on gerS− spores carrying empty vector ( gerS−/EV ) relative to wildtype carrying empty vector ( WT/EV ) and gerS− spores carrying the wildtype complementation construct ( gerS−/gerS ) . gerS− spores exhibited a similar dose-dependent germination response to taurocholate as wild type and the gerS complementation spores , although gerS− spores still had an ~5-log defect in spore germination in the presence of 1% taurocholate ( Fig 3A ) , which leads to germination levels equivalent to those obtained by plating on BHIS plates containing 0 . 1% taurocholate . This result suggested that gerS− spores can sense germinant similar to wildtype spores . Consistent with this finding , no difference in the levels of CspC germinant receptor and CspB germination protease were observed between the strains by Western blotting ( Fig 3B ) , and no difference in CspB-mediated processing of SleC in response to increasing amounts of germinant was observed . Since CspB-mediated processing of C . perfringens SleC activates its cortex hydrolase function [26] , and CspB-mediated processing of C . difficile SleC is required for optimal spore germination [23] , these results suggested that GerS acts after SleC-mediated cortex hydrolysis . In order to test this hypothesis , we developed a transmission electron microscopy ( TEM ) assay to visualize and quantify cortex hydrolysis . Although cortex hydrolysis can be measured biochemically [24 , 36 , 57] , it is difficult to obtain the amount of spores required for these analyses using the JIR8094 strain background . For the TEM assay , wildtype , gerS− , and sleC− spores were exposed to germinant for 45 min , and cortex thickness was measured over time for a minimum of 50 spores per time point ( Fig 4 ) . Within 15 min of exposure to germinant , cortex thinning was visible in wildtype spores ( Fig 4A ) , and the average thickness decreased by 3-fold ( p < 0 . 0001 , Fig 4B ) . Cortex thickness decreased even further at 45 min . In contrast with wild type , no change in cortex thickness was observed in either sleC− or gerS− spores even after 45 min of incubation with germinant ( Fig 4B ) . Thus , even though taurocholate induces CspB-mediated pro-peptide removal from the pro-SleC zymogen in gerS− spores ( Fig 3 ) , SleC does not appear to be active ( Fig 4 ) . These results suggest that GerS may regulate SleC activity through an unknown post-translational mechanism or by altering the availability of the SleC substrate , MAL , in the cortex layer . If SleC activity is indeed dependent on the presence of GerS , it should be possible to bypass the need for SleC-mediated cortex hydrolysis by artificially germinating gerS− spores . During artificial germination , a reducing agent , thioglycollate , is added to permeabilize the coat layers followed by lysozyme addition to degrade the cortex layer [13 , 58] . Treatment of wildtype , gerS− , and sleC− spores with thioglycollate and lysozyme restored outgrowth to gerS− and sleC− spores ( Fig 5 ) ; no statistically significant difference in artificial germination between wildtype , gerS− , sleC− spores was observed . In contrast , wildtype spores germinated much more efficiently than the mutants upon “natural” exposure to taurocholate . The small amount of germination observed in sleC− spores is likely due to spontaneous germination [37 , 46] , which can occur even in the absence of the germinant receptor [21] . Since recent studies have shown that Ca-DPA release immediately follows cortex hydrolysis [36 , 37] , we next tested whether the gerS mutant releases Ca-DPA in response to germinant ( S5 Fig ) . Whereas wildtype spores released ~80% of their Ca-DPA stores in response to germinant , gerS− spores released <5% of their Ca-DPA stores . Since wildtype and gerS− spores contained similar amounts of Ca-DPA ( 88% , S5 Fig ) , Ca-DPA storage does not appear to be affected by the gerS mutation , consistent with the recent observation that Ca-DPA release depends on cortex hydrolysis in C . difficile [36 , 37] in contrast with B . subtilis [13 , 38] . Having shown that GerS is important for SleC activity , we next wanted to understand how GerS carries out its function . We first tested whether GerS and SleC were present in the “coat-extractable” ( CE ) fraction . To this end , we subjected wildtype and gerS− mutant spores to a mild boric acid decoating treatment [57] to generate a CE supernatant fraction and a pellet fraction . Western blotting of these fractions revealed that both SleC and GerS co-localized to the CE fraction in wildtype spores but not to the pellet fraction , which consists of decoated spore lysate ( Fig 6 ) . Analysis of germination regulators CspC and CspB revealed that they also are concentrated in the CE fraction of wildtype and gerS− spores . These results indicate that the germination regulators GerS , SleC , CspB , and CspC are located in a similar cellular fraction . Since C . perfringens SleC has been shown to localize to the cortex using immunoelectron microscopy [59] , and CspB and SleC were recently reported to localize to a CE fraction in C . perfringens [31] , these results imply that the CE fraction includes cortex and outer forespore membrane proteins in C . perfringens and likely in C . difficile ( although it remains formally possible that SleC does not localize to the cortex region in C . difficile ) . Importantly , the coat morphogenetic protein SpoIVA [60] localized exclusively to the CE fraction of wildtype and gerS− mutant spores , whereas the forespore-localized germination protease ( GPR ) [40 , 61 , 62] was found exclusively in the pellet fraction of these spores . Since GerS is predicted to be a lipoprotein based on the presence of a putative N-terminal signal peptide containing a lipobox [63–65] , we tested whether GerS undergoes lipidation and whether its function depends on lipidation and/or secretion . The signal peptide of lipoproteins directs their transport across membranes after which Lgt , a prolipoprotein diacylglyerol transferase , adds a diacylglycerol group to the lipobox cysteine via a thioether bond . Following lipidation , the lipoprotein signal peptidase ( Lsp ) cleaves off the signal peptide , and the lipoprotein inserts into the plasma membrane in Gram-positive bacteria [63] . Since mutation of the conserved cysteine residue in the lipobox to serine is sufficient to prevent lipidation [63–65] , we complemented the gerS mutant with a construct that produces GerS carrying a cysteine 22 to serine ( C22S ) mutation . We also complemented the gerS mutant with a construct that deletes the GerS signal peptide sequence to prevent secretion ( ΔSP , Figs 1B and 7A ) . The C22S complementation strain produced heat-resistant spores at levels comparable to wild type , whereas the ΔSP complementation strain exhibited a >4-log decrease in functional ( heat-resistant ) spore formation relative to wildtype ( H . R . , Fig 7B ) . These results suggest that secretion but not lipidation is required for GerS to activate cortex hydrolysis . Western blot analyses of the complementation strains revealed that only full-length GerS was detectable in the C22S strain , whereas both full-length and cleaved GerS were observed in wild type and the wildtype gerS complementation strain . These observations strongly suggest that Cysteine 22 is important for cleavage of the signal peptide , similar to other lipoproteins [63–65] . Neither full-length nor cleaved GerS could be detected in ΔSP sporulating cells , implying that loss of the signal peptide leads to destabilization of GerS ( Fig 7B ) . To test this hypothesis , we measured gerS transcript levels in the complementation strains by qRT-PCR relative to wildtype carrying empty vector . The gerS− complementation strains all produced an excess of gerS transcripts relative to wildtype carrying empty vector; this over-expression is likely due to the multi-copy nature of the pMTL83151 plasmid used for complementation ( S6 Fig ) . Thus , GerS lacking its signal peptide appears to be unstable in the mother cell cytosol of sporulating cells . Consistent with our analyses of sporulating cells , purified spores from the C22S strain germinated at wildtype levels , while ΔSP spores exhibited an ~4-log defect in germination relative to wild type ( Fig 7B ) . Only full-length GerS was detected in C22S spores , whereas only cleaved GerS was detected in wildtype spores carrying empty vector and gerS− spores carrying the wildtype complementation construct . GerS was undetectable in ΔSP spores . Taken together , these analyses suggest that GerS secretion across the mother cell-derived membrane is necessary for GerS function , while lipidation and signal peptide removal are dispensable for GerS to activate cortex hydrolysis . To test whether alterations to the signal peptide affected the heat sensitivity of gerS− mutant spores , we heated spores for 30 min at 60°C prior to plating on media containing taurocholate germinant . As expected , heat treatment had no impact on the germination of wildtype spores carrying empty vector or wildtype complementation spores ( S7 Fig ) . No difference in spore germination between untreated and heat-treated C22S or ΔSP spores was observed . In contrast , gerS− mutant spores carrying empty vector showed a statistically significant decrease in the number of germinating spores following heat treatment ( p < 0 . 01 ) , similar to results with gerS− spores ( Fig 2B ) . Since bile acid-mediated germination has previously been shown to be important for C . difficile pathogenesis [21] , we tested whether gerS− could cause disease in the hamster model of C . difficile infection ( CDI ) . Hamsters inoculated with gerS− spores carrying empty vector had a 100% survival 7 days post inoculation , whereas wildtype spores carrying empty vector resulted in 50% survival at the same time point ( Fig 8 ) . Inoculation with the gerS−/gerS construct resulted in 100% of the hamsters being euthanized by day 5 after inoculation . These results indicate that the gerS mutant’s in vitro germination defect correlates with an inability to cause disease in a hamster model of CDI . Since a possible explanation for the greater lethality of the gerS−/gerS strain might be a faster rate of spore germination relative to wildtype spores carrying empty vector , we analyzed the rate of germination initiation by measuring the decrease in optical density at 600 nm when spores form the complementation strains were exposed to taurocholate germinant ( S8 Fig ) . This assay revealed that the C22S and gerS−/gerS strains germinated with similar kinetics as wild type , albeit slightly less efficiently , whereas no major change in OD600 was observed for ΔSP and gerS−/EV spores , as expected .
Recent studies of C . difficile spore germination have uncovered a unique signaling pathway for sensing bile salt germinants and initiating spore outgrowth relative to previously studied organisms [12] . Although the germination regulators SleC and the Csp family proteases are conserved between C . difficile and C . perfringens [22] , they can have different functions and/or activities in these organisms [21 , 23–26] . In this study , we identified a novel protein specific to C . difficile and related Peptostreptococcaceae family members that functions as a critical regulator of SleC cortex hydrolase activity and is essential for germination in vivo in a hamster model of infection under the conditions tested . While C . difficile strain JIR8094 contains mutations in the flagellar operon that impacts motility and toxin gene expression , a gerS mutant nevertheless causes significantly less disease than wild type JIR8094 . In particular , we showed that GerS regulates SleC activity downstream of CspB-mediated processing of SleC . This processing event had previously been thought to be sufficient to activate SleC’s cortex hydrolase activity , since studies in C . perfringens showed that CspB-mediated cleavage of the pro-SleC zymogen was necessary for SleC to degrade cortex fragments in vitro [26 , 57] , and loss of C . difficile CspB protease activity markedly reduced SleC processing and spore germination [23] . However , unlike C . perfringens SleC , full-length C . difficile SleC can degrade cortex fragments in vitro [33] , calling into question why SleC does not automatically degrade cortex in dormant spores . It will be important in future studies to precisely determine the impact of pro-peptide removal in activating SleC function in vitro and in C . difficile . How then does GerS regulate SleC activity ? Our results indicate that gerS is under the control of the mother cell-specific sigma factor σE ( Fig 1A ) and thus should be produced in the mother cell cytosol [40 , 41 , 61] . Deletion of the signal peptide from GerS destabilizes GerS in sporulating cells ( Fig 7 and S6 Fig ) . This observation is consistent with the notion that GerS is transported across the outer forespore membrane into the cortex region during sporulation ( Fig 9 ) ; more evidence is nevertheless needed to test this hypothesis . Since mutation of the invariant cysteine in the GerS lipobox prevents signal peptide removal but does not affect GerS function ( Fig 7 ) , the signal peptide of GerS C22S may insert into the outer forespore membrane where it can apparently function like lipidated wildtype GerS ( Fig 7 ) . Although mutation of the invariant lipobox cysteine frequently disrupts lipoprotein function [51 , 66 , 67] , lipidation of some bacterial lipoproteins can be dispensable for their activity because they remain embedded in the plasma membrane through their signal peptide [63 , 68] . These observations suggest that GerS may exert its function on the surface of the outer forespore membrane facing the cortex ( Fig 9 ) . Notably , SleC activity also appears to be localized to this region , since TEM analyses of germinating wildtype spores revealed that cortex thinning initiates at the outer forespore membrane and radiates inward in C . difficile ( Fig 4 ) . While more studies are clearly needed to determine the exact locations of SleC and GerS in mature spores , our results suggest that these germination regulators may be localized to the outer forespore membrane , which likely fractionates with the coat ( Fig 9 ) , raising the intriguing possibility that GerS retains SleC at this site . It will be interesting to determine in future work whether GerS acts as a direct or indirect activator of SleC and/or whether GerS is necessary for SleC to recognize its cortex substrate , for example by controlling the predicted modification of NAM residues to muramic acid δ-lactam in the cortex [34 , 35] , particularly since GerS lacks homology to other proteins aside from its lipobox . Although GerS carries a signal peptide that directs its secretion across mother cell-derived membranes ( Fig 9 ) , SleC lacks a canonical N-terminal signal sequence . Thus , it is unclear how SleC is transported across the outer forespore membrane so that it can bind its cortex substrate . Similarly , how CspB is transported across this mother cell-derived membrane to cleave pro-SleC , and how CspC is presumably translocated across this membrane to activate CspB , remains unknown , since both CspB and CspC lack a canonical signal sequence . Intriguingly , all the known germinant regulators in C . difficile , CspC , CspBA , SleC , and GerS , are produced under the control of mother cell-specific sigma factors [40 , 42 , 61] . In contrast , the germination regulators of B . subtilis , the GerAA-AC complex and GerD , are all under the control of the forespore-specific sigma factor σG [69] . These observations suggest that the topology of germination signaling differs significantly between C . difficile and B . subtilis . In B . subtilis , the germinant receptors are located in the inner forespore membrane [70–72] , since decoated spores germinate efficiently [73] . Germinant sensing stimulates release of Ca-DPA from the core through the inner forespore membrane-localized channel SpoVAC [13]; Ca-DPA then activates the CwlJ cortex lytic enzyme [46] . In C . difficile , the germinant receptor CspC , the germination protease CspB , the cortex hydrolase SleC , and the lipoprotein GerS , all localize to the CE fraction ( Fig 5 ) . Thus , these regulators are unlikely to be associated with the inner forespore membrane in contrast with B . subtilis . Since SleC cortex hydrolase activation precedes Ca-DPA release in C . difficile ( S5 Fig , [21 , 37] ) , the germinant signal appears to travel from the outside-in , whereas in B . subtilis the signal appears to travel from the inside-out . While our genetic analyses demonstrated that GerS is a key germination regulator in C . difficile , they also showed that Alr2 , a putative alanine racemase , is dispensable for germination ( Fig 2 ) . It should be noted that this observation does not exclude the possibility that Alr2 could alter the sensitivity of C . difficile spores to L-alanine , which has been shown to function as a co-germinant for C . difficle in vitro [74] . In B . anthracis and B . cereus , the Alr2 homolog alanine racemase converts L-alanine , a known germinant , to D-alanine to reduce the sensitivity of spores to L-alanine germinant [43 , 44 , 75] . Whether Alr2 modulates C . difficile spore germination remains to be determined , in particular whether it functions in suppressing germination . However , Howerton and Abel-Santos have shown that D-alanine is not an inhibitor of C . difficile spore germination [74] , suggesting that Alr2 plays little role in C . difficile spore germination or has an as-yet-unknown function . In summary , in identifying a novel germination regulator conserved in C . difficile and other Peptostreptococcaceae family members , our study reveals yet another difference between the regulation of spore germination in C . difficile relative to B . subtilis and C . perfringens . While many unanswered questions remain , cortex hydrolysis in C . difficile appears to be subject to an additional level of regulation during germination by GerS . Thus , GerS could be a potential target for inhibiting C . difficile disease transmission , especially given its limited conservation in spore-forming organisms .
C . difficile strains are listed in Table 1 and derive from the parent strain JIR8094 , an erythromycin-sensitive derivative of the sequenced clinical isolate 630 . C . difficile strains were grown on solid BHIS media , which consists of brain heart infusion media supplemented with yeast extract and 0 . 1% ( w/v ) L-cysteine [76] . BHIS media was supplemented with taurocholate ( TA; 0 . 1% w/v ) , thiamphenicol ( 5–10 μg/mL ) , kanamycin ( 50 μg/mL ) , cefoxitin ( 16 μg/mL ) , FeSO4 ( 50 μM ) , and/or erythromycin ( 10 μg/mL ) as indicated . Cultures were grown at 37°C , under anaerobic conditions using a gas mixture containing 85% N2 , 5% CO2 , and 10% H2 . Sporulation was induced on solid media containing 70% BHIS and 30% SMC ( 90 g BactoPeptone , 5 g protease peptone , 1 g NH4SO4 , 1 . 5 g Tris base , 15 g agar per liter ) [77] , as previously described . For strains carrying pMTL83151 derivatives , sporulation was induced on 70:30 media containing 5 μg/mL thiamphenicol . HB101/pRK24 strains were used for conjugations and BL21 ( DE3 ) strains were used for protein production . E . coli strains ( Table 1 ) were routinely grown at 37°C , shaking at 225 rpm in Luria-Bertani broth ( LB ) . Media was supplemented with chloramphenicol ( 20 μg/mL ) , ampicillin ( 50 μg/mL ) , or kanamycin ( 30 μg/mL ) as indicated . E . coli strains are listed in Table 1; all primers are listed in S1 Table . For disruption of gerS and alr2 , a modified plasmid containing the retargeting group II intron , pCE245 ( a gift from C . Ellermeier , University of Iowa ) , was used as the template . Primers for amplifying the targeting sequence from the template carried flanking regions specific for each gene target and are listed as follows: gerS ( #1122 , 1123 , 1124 and 532 , the EBS Universal primer ( Sigma Aldrich ) and alr2 ( #1385 , 1386 , 1385 and 532 ) . The resulting retargeting sequences were digested with BsrGI and HindIII and cloned into pJS107 [21] , which is a derivative of pJIR750ai ( Sigma Aldrich ) . The ligations were transformed into DH5α and confirmed by sequencing . The resulting plasmids were used to transform HB101/pRK24 . To construct the dual promoter complementation construct ( S4 Fig ) , primers #1464 and 1466 were used to amplify an ~1 . 8 kB construct containing acpS , CD3465 , gerS , and 360 bp upstream of acpS using 630 genomic DNA as the template . To construct the single promoter complementation construct , primers #1667 and 1466 were used to amplify gerS containing 367 bp upstream of gerS using 630 genomic DNA as the template . The gerS C22S and ΔSP complementation constructs were made using PCR splicing by overlap extension ( SOE ) . For C22S , primer pair #1464 and 1734 was used to amplify the 5’ SOE product ( containing the C22S mutation ) , while primer pair #1733 and 1466 was used to amplify the 3’ SOE product ( containing the C22S mutation ) . The resulting fragments were mixed together , and flanking primers #1464 and 1466 were used to generate the dual promoter complementation construct that encodes the C22S mutation . To construct the ΔSP complementation construct , SOE primers #1464 and 1727 were used to generate a 5’ fragment; primers #1726 and 1466 were used for the 3’ SOE product . The flanking primers #1464 and 1466 were used to amplify the ΔSP complementation construct , which deletes the region encoding residues 2–22 . All complementation constructs were digested with NotI and XhoI and ligated into pMTL83151 digested with the same enzymes . To construct a strain producing GerS for antibody production , primer pairs #1278 and 1173 were used to amplify gerS lacking the signal peptide sequence using genomic DNA as the template . The resulting PCR products were digested with NdeI and XhoI , ligated to pET28a , and transformed into DH5α . The resulting pET28a-gerS plasmid was used to transform BL21 ( DE3 ) for protein production . To construct a strain for generating mouse anti-CspC antibodies , primer pairs #1128 and 1166 were used to amplify codon-optimized cspC using pJS148 as the template . The resulting PCR products were digested with NdeI and SacI , ligated to pET22b-CPDSacI [78] , and transformed into DH5a . The resulting pET22b-cspC_opt-CPD was transformed into BL21 ( DE3 ) for protein production . Homologs of C . difficile 630 GerS ( CD3464 ) were identified using NCBI psi-blast . Homologs identified in Peptostreptococcaceae family members gave an e-value < e-52 , whereas the next closest homolog in a Clostridium spp . gave an e-value > e-27 . When GerS lacking its N-terminal signal peptide was used in the psi-blast search , the difference in e-value cut-offs was < e-52 for Peptostreptococcaceae family members and the next closest homolog in a Clostridium spp . gave an e-value > e-23 . C . difficile strains were constructed using TargeTron-based gene disruption as described previously ( S2 Fig , [40 , 79] ) . TargeTron constructs in pJS107 were conjugated into C . difficile using E . coli HB101/pRK24 as the donor strain . HB101/pRK24 strains containing the appropriate pJS107 construct were grown aerobically to exponential phase in 2 . 5 mL of LB supplemented with ampicillin ( 50 μg/mL ) and chloramphenicol ( 10 μg/mL ) . Cultures were pelleted , transferred into the anaerobic chamber , and resuspended with 1 . 5 mL of late-exponential phase C . difficile JIR8094 cultures ( grown anaerobically in BHIS broth ) . The resulting cell mixture was plated as seven 100 μL spots onto pre-dried , pre-reduced BHIS agar plates . After overnight incubation , all growth was harvested from the BHIS plates , resuspended in 2 . 5 mL pre-reduced BHIS , and twenty-one 100 μL spots per strain were plated onto three BHIS agar plates supplemented with thiamphenicol ( 10 μg/mL ) , kanamycin ( 50 μg/mL ) , and cefoxitin ( 16 μg/mL ) to select for C . difficile containing the pJS107 plasmid . After 24–48 hrs of anaerobic growth , single colonies were patched onto BHIS agar supplemented with thiamphenicol ( 10 μg/mL ) , kanamycin ( 50 μg/mL ) , and FeSO4 ( 50 μM ) to induce the ferredoxin promoter of the group II intron system . After overnight growth , patches were transferred to BHIS agar plates supplemented with erythromycin ( 10 μg/mL ) for 24–72 hrs to select for cells with activated group II intron systems . Erythromycin-resistant patches were struck out for isolation onto the same media and individual colonies were screened by colony PCR for a 2 kb increase in the size of gerS ( primer pair #1212 and 1173 ) and alr2 ( primer pair #1352 and 1359 ) ( S2 Fig ) . HB101/pRK24 donor strains carrying the appropriate complementation construct were grown in LB containing ampicillin ( 50 μg/mL ) and chloramphenicol ( 20 μg/mL ) at 37°C , 225 rpm , under aerobic conditions , for 6 hrs . C . difficile recipient strains gerS− and alr2− containing group II intron disruptions , were grown anaerobically in BHIS broth at 37°C with gentle shaking for 6 hrs . HB101/pRK24 cultures were pelleted at 2500 rpm for 5 min and the supernatant was removed . Pellets were transferred to the anaerobic chamber and gently resuspended in 1 . 5 mL of the appropriate C . difficile culture . The resulting mixture was inoculated onto pre-dried , pre-reduced BHIS agar plates , as seven 100 μL spots for 12 hrs . All spots were collected anaerobically and resuspended in 1 mL PBS . One hundred microliters of the resulting suspension was spread onto pre-dried , pre-reduced BHIS agar plates supplemented with thiamphenicol ( 10 μg/mL ) , kanamycin ( 50 μg/mL ) , and cefoxitin ( 10 μg/mL ) , five plates per conjugation . Plates were monitored for colony growth for 24–72 hrs . Individual colonies were struck out for isolation and analyzed for complementation using the heat resistance assay to test for functional spore formation and Western blot analysis . A minimum of two independent clones from each complementation strain was phenotypically characterized . C . difficile strains were grown from glycerol stocks on BHIS plates supplemented with TA ( 0 . 1% w/v ) , or with TA and thiamphenicol ( 5 μg/mL ) for strains carrying pMTL83151-derived vectors . Colonies that arose on BHIS agar plates were then used to inoculate 70:30 agar plates containing 5 μg/mL thiamphenicol for 17–24 hrs depending on the assay . Sporulating cells were harvested into PBS , pelleted , and resuspended in PBS for visualization by phase contrast microscopy and further processing as needed . C . difficile strains were induced to sporulate as described above and functional ( heat-resistant ) spore formation was measured as previously described [41] with the following exceptions . After 24 hrs of growth , cells were harvested into 600 μL of pre-reduced PBS . The sample was divided into two tubes . One tube was exposed to 60°C for 25–30 minutes . Heat-treated and untreated cells were serially diluted , and dilutions were plated on pre-reduced BHIS-TA plates . After ~20 hrs colonies were counted , and cell counts were determined . The percent of heat-resistant spores was determined based on the ratio of heat-resistant cells to total cells , and heat-resistance efficiencies were determined based on the ratio of heat-resistant cells for a strain compared to wildtype . Results are based on a minimum of three biological replicates . The raw data for the heat resistance assay is provided in S2 Table . Sporulation was induced by growing C . difficile strains on 70:30 plates ( with 5 μg/mL thiamphenicol when appropriate for 2–3 days , and spores were harvested in ice-cold water as previously described [23 , 76] with the following modifications . Spores were incubated on ice overnight following multiple water washes . The following day , they were pelleted and treated with DNase ( New England Biolabs ) at 37°C for 30 minutes . Following DNAse treatment , the spores were purified on a HistoDenz ( Sigma Aldrich ) gradient , evaluated for purity by phase contrast microscopy , and the optical density of the suspension was measured at OD600 . Spores were stored in water at 4°C . Approximately 1 x 107 spores ( equivalent of 0 . 35 OD600 units ) were re-suspended in 100 μL of water . Ten microliters of the suspension was serially diluted in PBS , and dilutions were plated onto pre-reduced BHIS-TA . After ~22 hrs , colonies arising from germinated spores were counted . Germination efficiency represents the number of CFUs produced by germinating spores of a given strain relative to wild type . Results are based on a minimum of three biological replicates . The remaining 90 μL of the spores were pelleted and resuspended in EBB buffer for Western blot analyses . To assess the effect of heat treatment on spore viability , the procedure above was followed , with the exception that 2 x 107 spores were re-suspended in 200 μL of water ( equivalent of 0 . 7 OD600 units ) and the sample was divided into two . One half was incubated at 60°C for 30 min , while the other half was left untreated . The effect of taurocholate concentration on spore germination efficiency was determined by re-suspending ~4 x 107 spores ( ~1 . 4 OD600 units ) in 160 μL of water in triplicate . Two hundred microliters of BHIS was added to each spore suspension . Ninety microliters of this suspension was added to either 10 μL of water , 0 . 1% TA , 1% TA , or 10% TA ( to give a final concentration of 0 . 01% TA , 0 . 1% TA , or 1% TA ) . The samples were incubated for 20 min at 37°C , and a 10 μL aliquot was removed for 10-fold serial dilutions into PBS . Ten microliters of the serial dilutions were plated on BHIS to determine the number of spores that had initiated germination . The serial dilutions for untreated and 1% TA-treated spores were also plated on BHIS-TA plates to determine the maximum level of spore germination . Spore germination was maximal following exposure to 1% TA . The remaining samples were pelleted for Western blot analysis . The anti-GerS used in this study was raised against GerS-His6 lacking its signal peptide in rabbits by Cocalico Biologicals ( Reamstown , PA ) . The anti-CspC mouse antibodies were raised against recombinant untagged CspC in mice by Cocalico Biologicals ( Reamstown , PA ) . GerS-His6 was purified from E . coli strains #1112 using Ni2+-affinity resin as previously described [23] . Recombinant , untagged CspC was purified using the autoprocessing CPD tag as previously described [78] followed by gel filtration [23] . C . difficile cell pellets were processed as previously described [40 , 60] . Briefly , pellets were freeze-thawed three times , diluted in EBB buffer ( 8 M urea , 2 M thiourea , 4% ( w/v ) SDS , 2% ( v/v ) β-mercaptoethanol ) , and incubated at 95°C for 20 min with vortexing every 5 min . C . difficile spores ( ~1 x 106 ) were resuspended in EBB buffer , which can extract proteins in all layers of the spore including the core . Samples were centrifuged for 5 min at 15 , 000 rpm and 4X sample buffer ( 40% ( v/v ) glycerol , 1 M Tris pH 6 . 8 , 20% ( v/v ) β-mercaptoethanol , 8% ( w/v ) SDS , and 0 . 04% ( w/v ) bromophenol blue ) , was added . Samples were incubated again at 95°C for 5–15 minutes with vortexing followed by centrifugation for 5 min at 15 , 000 rpm . The samples were resolved by SDS-PAGE and transferred to Millipore Immobilon-FL membrane . The membranes were blocked in Odyssey Blocking Buffer . Rabbit polyclonal rabbit anti-GerS or anti-GPR [40] antibodies were used at a 1:1 , 000 dilution; anti-CspB [23] antibodies were used at a 1:2 , 500 dilution , and the anti-SleC [23] antibody was used at a 1:5 , 000 dilution . Polyclonal mouse anti-SpoIVA [80] and anti-CspC antibodies were used at 1:2 , 500 dilutions . IRDye 680CW and 800CW infrared dye-conjugated secondary antibodies were used at 1:20 , 000 dilutions . The Odyssey LiCor CLx was used to detect secondary antibody infrared fluorescence emissions . RNA from WT/EV , gerS−/EV , gerS−/gerS , gerS−/C22S , and gerS−/ΔSP strains grown for 24 hrs on 70:30 sporulation media containing thiamphenicol ( 5 μg/mL ) was extracted for qRT-PCR analyses of the gerS transcript . RNA was extracted using a FastRNA Pro Blue Kit ( MP Biomedical ) and a FastPrep-24 automated homogenizer ( MP Biomedical ) . Contaminating genomic DNA was depleted using three successive DNase treatments , and mRNA enrichment was done using an Ambion MICROBExpress Bacterial mRNA Enrichment Kit ( Invitrogen ) . Samples were tested for genomic DNA contamination using quantitative PCR for rpoB . Enriched RNA was reverse transcribed using Super Script First Strand cDNA Synthesis Kit ( Invitrogen ) with random hexamer primers . Transcript levels of gerS and rpoB ( housekeeping gene ) were determined from cDNA templates prepared from 3 biological replicates of WT/EV , spo0A−/EV , gerS−/EV , gerS−/gerS , gerS−/C22S , and gerS−/ΔSP strains . Gene-specific primer pairs for gerS ( #1278 and #1173 ) , alr2 ( #1668 and #1356 ) , and rpoB [40] were used . Quantitative real-time PCR was performed ( as described by [41] . Briefly , using MaximaTM SYBRTM Green qPCR Master Mix ( Thermo Scientific ) , 50 nM of gene specific primers , and an ABI PRISM 7900HT Sequence Detection System ( Applied Biosystems ) . Transcript levels were normalized to the housekeeping gene rpoB using the standard curve method . For live cell fluorescence microscopy studies , C . difficile strains were harvested in PBS , pelleted , and resuspended in PBS . For characterization of mutant phenotypes , cells were resuspended in PBS containing 1 μg/mL FM4-64 ( Molecular Probes ) and 15 μg/mL Hoechst 33342 ( Molecular Probes ) . All live bacterial suspensions ( 4 μL ) were added to a freshly prepared 1% agarose pad on a microscope slide , covered with a 22 x 22 mm #1 coverslip and sealed with VALAB ( 1:1:1 of vaseline , lanolin , and beeswax ) as previously described [41 , 81] . DIC and fluorescence microscopy was performed using a Nikon PlanApo Vc 100x oil immersion objective ( 1 . 4 NA ) or a Nikon PlanApo Vc 60x oil immersion objective ( 1 . 4 NA ) on a Nikon Eclipse Ti2000 epifluorescence microscope . Multiple fields for each sample were acquired with an EXi Blue Mono camera ( QImaging ) with a hardware gain setting of 1 . 0 and driven by NIS-Elements software ( Nikon ) . Images were subsequently imported into Adobe Photoshop CS6 for minimal adjustments in brightness/contrast levels and pseudocoloring . Artificial germination was determined using thioglycollate and lysozyme treatment . About 1 x 106 spores were pelleted at 8 , 000 RPM for 3 min , resuspended in 250mM thioglycollate , and incubated at 50°C for 30 min based on previously methods developed [21 , 58] . Spores were washed with 150 μL of PBS , pelleted , resuspended in 150 μL ( 2 mg/mL ) lysozyme and incubated at 37°C for 15 min . Equivalent numbers of spores for each strain were incubated at the indicated temperatures without thioglycollate or lysozyme treatment for the untreated sample . The spore samples were plated on either BHIS or BHIS-TA . Natural germination represents the number of spores in the untreated sample that outgrew to form colonies on BHIS-TA media . Artificial Germination represents the number of thioglycollate/lysozyme-treated spores that germinated and outgrew to form colonies on BHIS media . About 1 x 107 spores ( ~0 . 35 OD600 ) were pelleted and resuspended in 30 μL of decoat buffer ( 0 . 1 M H3BO3 pH 10 . 0 , 1% SDS , 2% β-ME ) [57] . The sample was incubated for 30 min at 37°C and then pelleted . The supernatant , representing the “coat-extractable” ( CE ) fraction , was removed , and the pellet was washed in 20 μL decoat buffer and incubated for 10 min . The sample was re-pelleted , and the supernatant was added to the CE; 40 μL of EBB was added to the pooled fractions . The decoated spores were re-suspended in 90 μL EBB to produce the cell lysate ( pellet ) fraction . For the input fraction , representing whole spore lysate , equal numbers of spores were pelleted and resuspended in 90 μL EBB . All samples were boiled for a minimum of 15 min , followed by centrifugation and sample resuspension . Fractions were pelleted one more time before the samples were resolved by SDS-PAGE and analyzed by Western blotting as described above . Cortex hydrolysis was analyzed by transmission electron microscopy for untreated spores ( 0’ ) and 15’ and 45’ after germinant addition . About 4 x 107 spores ( 1 . 4 OD600 units ) were resuspended in 160 μL of water in triplicate . Two hundred microliters of BHIS was added to each spore suspension . Forty microliters of water was added to one sample , while 40 μL of 10% taurocholate ( w/v ) was added to the remaining samples . The spores were incubated under anaerobic conditions for the indicated time point after which a small sample was removed for visualizing by phase-contrast microscopy and plating on BHIS and BHIS-TA . The remainder of the sample was pelleted and re-suspended in osmium tetroxide fixative for TEM analysis as previously described [60] . TEM grids for each sample analyzed were prepared as previously described [41] . A minimum of 50 spore pictures chosen at random were analyzed for each strain observed . To account for asymmetrical spore shapes , two orthologous cortex lengths were measured such that a minimum and maximum cortex thickness was obtained for every spore . Cortex length was defined as the distance between the outer most germ cell wall and the cortex outer edge . The minimum and maximum measurements were averaged for each spore and the upper and lower values were discarded . The cortex length reported represents the average of these measurements . To evaluate the amount of Ca-DPA released in response to germinant relative to the total amount of Ca-DPA observed in the spore core , a modified Ca-DPA release assay was adopted from [21] . About 2x107 spores from each strain were re-suspended in ( i ) 1 mL of germination buffer ( 0 . 3 m M ( NH4 ) 2SO4 , 6 . 6 mM KH2PO4 , 15 mM NaCl , 59 . 5 mM NaHCO3 , and 35 . 2 mM Na2HPO4 ) and incubated at 37°C for 30 min ( background ) ; ( ii ) 1 mL of germination buffer containing 10% freshly prepared taurocholate and 10 mM glycine and incubated at 37°C for 30 min ( DPA release ) ; ( iii ) 1 mL of germination buffer and incubated at 100°C for 1 hr ( total DPA ) . After incubation , samples were spun down at 15 , 000 RPM for 2 min . 700 μL was transferred to UV clear cuvettes , and the A270 was determined . The % Ca-DPA release was determined by subtracting the background DPA release value from the germinant containing DPA release value and dividing by total DPA . Total DPA measured in wild type was set as 100% total DPA . Approximately 1 x 107 spores ( 0 . 48 OD600 units ) were resuspended in BHIS to a total volume of 1100 . The sample was divided into two: 540 μL was added to a cuvette containing 60 μL of water , while the other sample was added to a cuvette containing 60 μL of 10% taurocholate . The samples were mixed , and the OD600 was measured every 3–6 mins for 45 min . All animal studies were performed with prior approval from the Texas A&M University Institutional Animal Care and Use Committee . Female Syrian golden hamsters ( 80–120 g ) were housed and tested for C . difficile susceptibility as previously described [21 , 82] . To induce C . difficile infection , hamsters were gavaged with 30 mg/kg clindamycin prior to C . difficile spore inoculum . After 5 days , 10 hamsters per C . difficile strain tested were gavaged with 1 , 000 spores and monitored for signs of disease . Hamsters showing disease symptoms ( wet tail , poor fur coat and lethargy ) were euthanized by CO2 asphyxia followed by thoracotomy as a secondary means of death . All animal procedures were performed with prior approval from the Texas A&M Institutional Animal Care and Use Committee under the approved Animal Use Protocol number 2014–0085 . Animals showing signs of disease were euthanized by CO2 asphyxia followed by thoracotomy as a secondary means of death , in accordance with Panel on Euthanasia of the American Veterinary Medical Association . Texas A&M University’s approval of Animal Use Protocols is based upon the United States Government’s Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research and Training and complies with all applicable portions of the Animal Welfare Act , the Public Health Service Policy for the Humane Care and Use of Laboratory Animals , and all other federal , state , and local laws which impact the care and use of animals . | Clostridium difficile is a spore-forming bacterium capable of causing severe diarrhea . The dormant spore-form of C . difficile is necessary to cause infection , since vegetative cells of this organism cannot survive in the presence of oxygen . Spores are difficult to eradicate because they can withstand extreme environmental conditions and chemical insults including antibiotics . However , since spores cannot grow , they must transform back into actively replicating cells once the appropriate environmental conditions are sensed through a process called germination . A key step during germination is the break-down of a specialized cell wall layer in the spore known as cortex by the SleC hydrolase . In this paper , we identify GerS as a novel lipid-modified protein that is important for C . difficile germination to occur . GerS is made at high levels during spore formation and gets packaged into mature spores . We show that GerS is required for the cortex hydrolase SleC to degrade the protective cortex layer , since a strain lacking GerS does not lose its cortex layer . Loss of GerS prevents C . difficile from causing infection in a hamster model of infection , suggesting that GerS is a novel target for drug development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Identification of a Novel Lipoprotein Regulator of Clostridium difficile Spore Germination |
Erythrocyte polymorphisms associated with a survival advantage to Plasmodium falciparum infection have undergone positive selection . There is a predominance of blood group O in malaria-endemic regions , and several lines of evidence suggest that ABO blood groups may influence the outcome of P . falciparum infection . Based on the hypothesis that enhanced innate clearance of infected polymorphic erythrocytes is associated with protection from severe malaria , we investigated whether P . falciparum-infected O erythrocytes are more efficiently cleared by macrophages than infected A and B erythrocytes . We show that human macrophages in vitro and mouse monocytes in vivo phagocytose P . falciparum-infected O erythrocytes more avidly than infected A and B erythrocytes and that uptake is associated with increased hemichrome deposition and high molecular weight band 3 aggregates in infected O erythrocytes . Using infected A1 , A2 , and O erythrocytes , we demonstrate an inverse association of phagocytic capacity with the amount of A antigen on the surface of infected erythrocytes . Finally , we report that enzymatic conversion of B erythrocytes to type as O before infection significantly enhances their uptake by macrophages to observed level comparable to that with infected O wild-type erythrocytes . These data provide the first evidence that ABO blood group antigens influence macrophage clearance of P . falciparum-infected erythrocytes and suggest an additional mechanism by which blood group O may confer resistance to severe malaria .
Plasmodium falciparum malaria is responsible for an estimated 1 . 24 million deaths annually , with the majority of deaths occurring in individuals before reproductive age [1] . P . falciparum malaria predated the development of modern Homo sapiens and has co-evolved with human populations [2] , [3] . It is considered to be one of the strongest forces for evolutionary selection of the human genome [2] , [3] . In populations where P . falciparum infection is highly prevalent , common erythrocyte polymorphisms , such as deficiencies in globin synthesis , membrane proteins and erythrocyte enzymes , are associated with protection against severe and fatal disease [4]–[6] . Recent evidence suggests that the ABO blood group system has also been subject to malaria-related selection pressure [2] , [3] . The ABO phenotype is determined by a polymorphic gene that encodes an enzyme , ABO glycosyltransferase that conjugates A- or B-specific sugar residues onto the precursor molecule known as the H antigen . If functionally active ABO glycosyltransferase is inherited via the co-dominant A or B alleles , transfer of either α-1 , 3-linked N-acetylgalactosamine or α-1 , 3-linked galactose produces A and B antigens , respectively , resulting in the non-O blood groups ( A , B and AB ) . Molecular evidence indicates that the predominant O allele arose as the result of a loss-of-function mutation at nucleotide position 261 [7] , [8] . Consequently , in O erythrocytes , the H antigen is left unaltered and ends with an α-1 , 2-linked fucose moiety that lacks the terminal α-1 , 3-linked monosaccharides [9] . Host pathogen interactions have been proposed as an important evolutionary force shaping the global distribution of ABO blood groups [10] . There is strong epidemiological evidence that the ABO phenotype may modulate disease severity and outcome of P . falciparum malaria , with blood groups A and B associated with increased disease severity compared to blood group O [11]–[15] . This association is consistent with the higher prevalence of group O observed in malaria-endemic sub-Saharan Africa compared to many parts of the world where malaria is not endemic , suggesting that blood group O may be a selected , protective adaptation against severe and fatal infection [2] , [16] , [17] . While several studies have reported that individuals with blood groups A and B are more likely to develop severe malaria , the mechanisms underlying the putative protection afforded by blood group O remain unclear [11] , [12] , [15] . Proposed mechanisms of protection parallel those implicated in other erythrocyte polymorphisms and include decreased erythrocyte invasion and reduced erythrocyte rosetting [12] , [18] . Several studies have examined the association of ABO blood groups with rosetting and have reported that infected O erythrocytes exhibit fewer or smaller rosettes in vitro [12] , [19]–[23] and in vivo [24] than infected A and B erythrocytes . Decreased rosetting may reduce microvascular obstruction that is believed to contribute to the pathogenesis of severe disease [12] , [13] , [16] . However , alternative mechanisms may also exist that contribute to the protective effect to P . falciparum malaria observed in individuals with blood group O . P . falciparum infection and intracellular growth induce profound changes to the erythrocyte membrane resembling red cell senescence [25] . These changes , including hemichrome formation and band 3 aggregation resulting in erythrophagocytosis , may be accelerated in the presence of underlying erythrocyte disorders [26] , [27] . Based on these observations , we hypothesized that enhanced senescence and phagocytosis of infected O erythrocytes , resulting in improved innate clearance and lower parasite densities , may provide an alternative explanation for protection observed in blood group O individuals . Studies of other erythrocyte polymorphisms associated with malaria-endemic areas , including sickle cell trait , beta-thalassemia , G6PD trait , and pyruvate kinase deficiency [26]–[28] that have reported increased phagocytosis of P . falciparum-infected variant erythrocytes , are also consistent with this hypothesis . Here , we show that P . falciparum parasites invade and mature similarly in group A , B and O erythrocytes . However , compared to P . falciparum-infected A and B erythrocytes , infected O erythrocytes display enhanced hemichrome deposition , band 3 aggregation and increased macrophage phagocytosis in vitro and in vivo . These data suggest that enhanced clearance of infected O erythrocytes may represent a novel mechanism by which blood group O contributes to protection against severe malaria .
To determine if ABO polymorphism influences malaria parasite invasion and maturation , we examined P . falciparum ( ITG clone ) parasite invasion and development in A , B , and O erythrocytes in vitro . No statistically significant differences in parasite invasion of group A , B and O erythrocytes were observed during two growth cycles ( Figure 1 ) . In addition , there were no significant differences noted in intracellular maturation ( from ring stage to trophozoite stage parasites ) within A , B and O erythrocytes ( Figure 1 ) . Similar results were obtained using two other clones ( 3D7 , E8B ) of P . falciparum malaria ( data not shown ) . To test the hypothesis that infected O erythrocytes may be preferentially phagocytosed compared to infected A or B erythrocytes , infected A , B and O erythrocytes at ring- or mature-stage were co-incubated with human monocyte-derived macrophages for 2 hours and the phagocytic index was determined blinded to the erythrocyte blood group . These data were then normalized to the average phagocytic index of infected A erythrocytes . The phagocytic uptake of ring-stage infected O erythrocytes ( Figure 2A; 1 . 43±0 . 16 , mean±SEM ) was significantly higher than ring-stage infected A ( 1 . 09±0 . 08 , p = 0 . 022 ) and B erythrocytes ( 0 . 75±0 . 076 , p = 0 . 007 ) . Similarly , the mean uptake of mature-stage infected O erythrocytes ( Figure 2B; 2 . 3±0 . 29 , mean±SEM ) was significantly greater than the uptake of infected A ( 1 . 0±0 . 07 , p = 0 . 001 ) and infected B ( 1 . 02±0 . 16 , p = 0 . 026 ) erythrocytes . By contrast there were no significant differences observed in the uptake of control uninfected A , B , or O erythrocytes ( Figure 2A , B ) . Similar results were observed with parasite clone 3D7 ( not shown ) suggesting that these were not a P . falciparum clone-specific phenomenon . We next examined the influence of different blood group macrophage donors on infected erythrocyte uptake at mature-stage . No significant differences were observed in the preferential uptake of infected O erythrocytes by macrophage donors of A versus O blood group ( Figure 2C , p = 0 . 568 , two-way ANOVA , main effect: macrophage donor ) . Macrophages obtained from either group O or A donors displayed enhanced phagocytosis of P . falciparum-infected O erythrocytes compared to infected A erythrocytes ( Figure 2C , p = 0 . 002 , two-way ANOVA , main effect: blood group ) . These data indicate that infected O erythrocytes are preferentially cleared by macrophages independent of the macrophage donor blood group . Previous studies have established that murine macrophages express CD36 and bind and mediate the update of P . falciparum infected erythrocytes . This model has proven to be useful to investigate the molecular basis for the interaction of CD36 with malaria-infected erythrocytes as well as the interactions of other pattern recognition receptors such as TLRs with their respective cognate ligands in vitro [29] , [30] and in vivo [31] . We used a previously established murine model system [31] to investigate phagocytosis of P . falciparum-infected A , B and O erythrocytes in vivo by macrophages in the peritoneal cavity of C57BL/6 mice . Three hours after intraperitoneal injection with infected or uninfected ( as controls ) A , B and O erythrocytes , peritoneal lavage was performed , and phagocytosis of infected and uninfected erythrocytes by peritoneal macrophages was quantified . In agreement with our in vitro observations , the phagocytic uptake of infected O erythrocytes ( 4 . 97±1 . 04 , mean±SEM ) in vivo was 3- to 4-fold greater than the phagocytic uptake of infected A ( 1 . 00±0 . 08 , p = 0 . 01 ) or infected B ( 1 . 49±0 . 29 , p = 0 . 04 ) erythrocytes ( Figure 3A , B ) . There were no significant differences observed in the uptake of uninfected A , B and O erythrocytes ( Figure 3A ) . There are various subgroups of each ABO blood group , with the two most common subgroups of A being A1 and A2 . The differences between them are both qualitative and quantitative: A antigens are more complex on erythrocytes of the A1 phenotype and have an approximate 5× greater antigen site density than on A2 cells , thus leaving the latter with a greater number of unmodified H antigens [32] , [33] . The quantitative spectrum of H antigen expression is therefore O>A2>A1>Bombay ( where the latter is a genetically H-deficient phenotype ) ( Figure 4A ) [8] , [9] , [34] . Based on our above observations , we postulated that an increase in H antigen expression , and a corresponding decrease in A antigen levels , would be associated with increased macrophage phagocytosis of infected erythrocytes . Within the spectrum of H antigen expression , we also predicted that phagocytosis of infected A2 erythrocytes would be greater than that of infected A1 erythrocytes , but less than infected O erythrocytes . In agreement with this hypothesis , we observed a dose-dependent effect with presumed H antigen expression on the phagocytosis of P . falciparum infected cells , with phenotypically less A antigen expression ( or increasing H antigen expression ) associated with increased phagocytosis ( Figure 4B; Spearman's correlation , r = 0 . 80 , p<0 . 0001 ) . To confirm that increased relative H antigen expression and decreased A/B antigen expression was associated with enhanced phagocytosis of infected O erythrocytes , we enzymatically converted B erythrocytes to type as O erythrocytes using a specific glycosidase , B-zyme ( Bacteroides fragilis , α-galactosidase ) . This enzyme has been shown to efficiently convert virtually all B antigen on B erythrocytes to H antigen at neutral pH , without changing the sialic acid content on the cell membrane [35] . By treating B erythrocytes with this α-galactosidase , we cleaved the terminal α1–3-linked galactose residues responsible for blood group B specificity , converting these cells serologically to type as blood group O [35] . As controls , untreated group B erythrocytes were treated with the conversion buffer alone and control O erythrocytes were treated with the conversion buffer plus B-zyme . Efficient enzyme conversion of blood group B to O was achieved as demonstrated by flow cytometric analysis ( Figure 5A ) . Positive reactions with anti-B were also demonstrated serologically with B erythrocytes while negative reactions were obtained with O erythrocytes and B erythrocytes converted with B-zyme . Consistent with our previous findings , there was increased uptake of infected untreated O erythrocytes when compared to infected untreated B erythrocytes ( Figure 5B , p = 0 . 042 , Mann-Whitney with Bonferroni correction for multiple comparisons ) . Moreover , the phagocytosis of infected B erythrocytes was significantly enhanced following cleavage of the B antigen ( p = 0 . 008 , Figure 5B ) and comparable to mock-treated and infected O erythrocytes . The phagocytic index of infected O erythrocytes was not significantly altered by B-zyme treatment ( p>0 . 05 ) . Additionally , there was no significant difference observed in the uptake of control uninfected , treated or untreated , B and O erythrocytes ( Figure 5B ) . Phagocytic recognition and clearance of senescent erythrocytes has been reported to depend , at least partly , on increased expression of erythrocyte senescence antigens such as phosphatidylserine ( PS ) and aggregated band 3 [36] . Increased outer leaflet exposure of PS has been associated with enhanced macrophage erythrophagocytosis [36] . To examine potential mechanism ( s ) underlying enhanced uptake of infected O erythrocytes , we initially investigated P . falciparum-induced PS exposure by comparing Annexin-V staining on infected O , A , and B erythrocytes ( Figure 6 ) . Although infected erythrocytes had increased PS expression compared to uninfected erythrocytes , there were no significant differences observed in PS levels on infected O compared to infected A and B erythrocytes ( Figure 6 ) . In contrast , ABO blood groups influenced P . falciparum-induced hemichrome deposition and band 3 aggregation . Figures 7A and B shows the presence of membrane-bound hemichromes in uninfected and infected A , B , and O erythrocytes . No differences in hemichrome levels were observed in uninfected erythrocytes maintained in the same culture conditions as infected erythrocytes . However , increased hemichrome deposition was detected in infected ring-stage ( p = 0 . 005 and p = 0 . 038 , Figure 7A ) and mature-stage ( p = 0 . 013 and p = 0 . 024 , Figure 7B ) O erythrocytes , compared to infected A and B erythrocytes , respectively . Since hemichromes are thought to bind to and oxidize the band 3 cytoplasmic domain inducing band 3 clustering [25] , [36] , we compared band 3 aggregation in infected O , A and B erythrocytes using gel filtration chromatography ( Figure 7C ) and eosin-5-maleimide fluorescence ( Figure 7C insert ) , a specific label for band 3 . We observed that extracts from infected O erythrocytes fractionated by gel filtration , display an earlier and higher protein peak in membrane extracts compared to infected A or B erythrocytes . The absorption spectrum of the heme-containing fractions corresponded to that of hemichromes [37] . The same fractions contained aggregated band 3 , which was localized by labeling infected ABO erythrocyte membranes with the specific fluorescent band 3 label eosin-5-maleimide as described [38] . The observed chromatographic co-elution of hemichromes and aggregated band 3 is indicative of hemichrome-induced clustering of band 3 [25] .
This study provides the first evidence that the phagocytic uptake of P . falciparum-infected erythrocytes is influenced by ABO blood group antigens . These data provide a new putative mechanism by which blood group O may contribute to protection against severe malaria . In order to define potential mechanisms of protection associated with blood group O , we investigated P . falciparum invasion and growth in ABO erythrocytes , as well as examined a role for differential clearance of infected ABO erythrocytes in vitro and in vivo . We found no difference in the invasion or maturation of P . falciparum parasites in A , B or O erythrocytes . However , we did observe enhanced phagocytosis of infected O erythrocytes by human macrophages ( Figure 2 ) that was attributable to increased hemichrome deposition and band 3 aggregation ( Figure 7 ) . This observation was dependent on P . falciparum infection as no differences were observed in the baseline uptake of uninfected A , B or O erythrocytes . Preferential phagocytosis of infected O erythrocytes was independent of the donor ABO blood group ( Figure 2C ) . We extended these observations to an in vivo model , and demonstrated increased macrophage uptake of infected O erythrocytes in vivo compared to infected A or B erythrocytes ( Figure 3A , B ) [29] . Taken together , our data suggest that there are differences in phagocytic clearance of infected O versus infected A and B erythrocytes which may contribute to reduced parasite burdens and improved malaria outcomes in blood group O individuals . In order to investigate whether blood group antigens might directly affect phagocytic uptake , we performed phagocytosis assays on infected erythrocytes that varied in their relative expression of H and A antigens . We observed a relationship between phagocytic index and lower levels of immunodominant A or B expression ( or higher reciprocal levels of erythrocyte H antigen expression ) on infected erythrocytes ( Figure 4B ) . Differences in blood group terminal monosaccharides may influence phagocytosis either directly on the basis of H antigen density , or indirectly by the absence of the A or B antigens . Given that no differences in the baseline uptake of uninfected A , B or O erythrocytes were observed , the preferential phagocytosis of infected O erythrocytes is therefore dependent on P . falciparum infection and may be attributable to group-specific differences in parasite-encoded erythrocyte membrane proteins or other P . falciparum-induced structural modifications to the erythrocyte membrane . Given the possibility that other inter-individual ABO-associated differences might have accounted for the observed ABO effect on phagocytosis , we examined the uptake of infected erythrocytes that had and had not been enzymatically modified to resemble O erythrocytes ( Figure 5 ) . B erythrocytes were chosen since enzymatic conversion of A erythrocytes to O results in not only the common H antigens of types 1 and 2 regularly found on wild type O erythrocytes but also a qualitatively different H antigen of type 3 found on A erythrocytes [39] . In these experiments B- zyme α-glycosidase treatment of B erythrocytes removed the terminal α-1 , 3-galactose from blood group B antigens , resulting in loss of anti-B recognition and conversion to erythrocytes which type as group O ( Figure 5A ) . Subsequent infection of B-zyme-converted erythrocytes resulted in enhanced macrophage uptake to levels observed with infected wild type O erythrocytes , and different from the levels seen with unmodified B erythrocytes ( Figure 5B ) . B-zyme treatment per se was not responsible for this effect , as treatment of uninfected O or B erythrocytes , or infected O erythrocytes , had no affect on their uptake by macrophages . There are a number of potential explanations for how A/B/H antigens could modify macrophage recognition and uptake . ABO may influence the differential expression of parasite ligands such as PfEMP-1 , or the steric accessibility of other parasite-dependent pattern recognition motifs . The H antigen found at high levels on O erythrocytes may alternatively act as a co-receptor to a parasite ligand , or influence other parasite-induced erythrocyte modifications ( for example , increased senescence antigen expression by infected O erythrocytes ) . Recent evidence suggests that ABH antigens can stabilize sialylated glycan clusters on the erythrocyte membrane in a manner that is unique for each blood group [40] . In this way ABH antigens can differentially modulate cellular interactions without being a direct ligand themselves by modifying other cell surface glycans and making them more or less accessible for binding . Cohen et al . have shown that by stabilizing such structures , ABH antigens can also modulate interaction with pathogens such as P . falciparum [40] . Therefore , it is possible that ABH antigens may non-covalently alter the expression or presentation of other cell surface glycans including parasite encoded proteins such as P . falciparum erythrocyte membrane protein-1 ( PfEMP-1 ) . PfEMP-1 , an important parasite virulence factor [41] , has also been shown to demonstrate differential expression on the erythrocyte membrane in erythrocyte disorders , including hemoglobin C , associated with protection to severe malaria [42] . It is therefore plausible that modified expression of PfEMP-1 or other parasite ligands on O erythrocytes , results in increased interaction with macrophage pattern recognition and phagocytic receptors and enhances uptake . Our data are consistent with a model whereby infected O erythrocytes bind more avidly to phagocytic cell receptors resulting in enhanced uptake ( Figure S1 ) . In addition to a putative role for ABH antigens in modifying parasite-erythrocyte interactions , phagocytic recognition and clearance of erythrocytes have also been associated with increased expression of erythrocyte senescence antigens such as aggregated band 3 and phosphatidylserine ( PS ) [43] , [44] . Although PS exposure has been reported to be elevated in variant erythrocytes , ( e . g . , sickle cell trait ) where it may serve as a senescence signal for accelerated clearance [45] , we found no significant difference in P . falciparum-induced PS expression on infected red cells to account for the observed preferential uptake of infected O erythrocytes . With respect to band 3 , there are approximately 1 million ABH glycan antigen sites on each erythrocyte , and many are presented on this protein [43] . Increased band 3 aggregation has been reported in sickle cell and β-thalassemic erythrocytes , contributing to erythrocyte membrane modification and enhanced phagocytic uptake by macrophages . Whether the absence of immunodominant sugars is more permissive to malaria-induced band 3 aggregation is unknown , as ABO effects have not previously been specified in such studies . In the present study we observed increased hemichrome formation and band 3 aggregation in infected O erythrocytes compared to infected A and B erythrocytes . The mechanism by which O erythrocytes might be more susceptible to malaria-induced oxidant stress is not known . Erythrocytes under increased oxidative stress , such as that induced by malaria parasite invasion and growth , may show preferential oligomerization/phosphorylation of less-glycosylated band 3 fractions [46] . This possibility is consistent with reports that band 3 displays an increased tendency to cluster in congenital dyserythropoietic anemia type 2 which is characterized by band 3 under-glycosylation , and with the irreversible cross linking observed in poorly glycosylated band 3 fractions in G6PD-deficient erythrocytes [46]–[48] . Glycosylation of band 3 appears to be a restraint to its oxidative cross-linking , clustering and subsequent phagocytic uptake . Collectively these observations provide a putative molecular mechanism for the observed enhanced uptake of infected O erythrocytes . In summary , we have demonstrated a novel mechanism by which blood group O may contribute to protection against severe disease . The present model is complementary to , and not incompatible or inconsistent with , the decreased rosetting of infected O erythrocytes reported by others [12] . Both increased phagocytosis and decreased rosetting of blood group O may contribute functionally to reduced parasite burden , decreased infected erythrocyte adhesion to the endothelium and decreased microvascular obstruction , all of which are believed to play important mechanistic roles in the pathophysiology of severe falciparum malaria .
Whole blood was donated from healthy malaria-naïve individuals after informed consent using a protocol approved by the University Health Network Research Ethics Board . Animal use protocols were reviewed and approved by the Faculty of Medicine Advisory Committee on Animal Services at the University of Toronto according to the Guide to the Care and Use of Experimental Animals ( Canadian Council on Animal Care , 1993 ) . Endotoxin-free RPMI 1640 and gentamicin were purchased from Invitrogen Life Technologies ( Burlington , ON , Canada ) . Human AB serum was purchased from Wisent Inc ( St-Bruno , Quebec , Canada ) . Diff-Quik staining kit and fetal bovine serum ( FBS ) were purchased from Fisher Scientific ( Ottawa , ON , Canada ) . FBS and human AB serum were heat-inactivated for 30 minutes at 55°C before use . Alanine was purchased from Sigma Aldrich ( Oakville , Ontario , Canada ) . Mycoplasma removal agent was purchased from MP Biochemical ( Solon , Ohio , USA ) . Ficoll-Paque and Percoll were purchased from GE Healthcare ( Baie D'Urfé , Québec , Canada ) . NOVACLONE blood grouping reagent was purchased from Dominion Biologicals Ltd ( Dartmouth , Nova Scotia , Canada ) . All other reagents were purchased from common commercial sources . C57BL/6 mice used in this study were 6–10 weeks old and were purchased from Charles River Laboratories ( Hollister , CA ) and maintained under pathogen-free conditions with a 12-h light cycle . P . falciparum ( clones ITG , 3D7 and E8B ) was cultured as previously described [47] . Cultures were treated with Mycoplasma-Removal Agent , confirmed to be Mycoplasma-free ( MycoAlert Mycoplasma Detection Kit , Lonza ) and synchronized by alanine treatment . Whole blood was obtained from hematologically healthy laboratory staff members ( 11 group A , 4 group B and 6 group O ) . Individuals with underlying red cell traits or disorders , or previous malaria exposure were excluded . Erythrocytes were separated from whole blood as previously described [27] . Briefly , whole blood was layered on an 80% Percoll gradient [80% ( vol/vol ) Percoll , 6% ( w/v ) mannitol , 10 mM glucose and 10% ( vol/vol ) PBS 10×] and spun for 15 minutes at 3000 RPM at 24°C . The erythrocyte pellet was collected and washed in R-0G media ( RPMI 1640 medium supplemented with 10 mM glucose and 10 g/L gentamicin ) and resuspended in equal volumes of parasite growth medium R-10G ( RPMI-1640 containing 20 mM glucose , 2 mM glutamine , 6 g/L HEPES , 2 g NaHCO3 , 10 g/L gentamicin , 10% human AB serum and 1 . 35 mg/L hypoxanthine , pH 7 . 3 ) . Serum was isolated by centrifugation at 1500 RPM for 10 minutes at 24°C , and 200 µl aliquots were stored at −20°C for future use . Each aliquot was thawed only once and discarded after use . Blood samples were tested by standard hemagglutination techniques with commercially available anti-A and anti-B reagents approved for diagnostic use [39] . Donors expressing A antigens were further typed using Dolichos biflorus lectin to differentiate between the A1 and A2 subgroups . Human monocytes were purified from the peripheral blood of healthy donors and cultured on glass cover slips in 24-well polystyrene plates as previously described [49] . Briefly , whole blood was diluted 1∶1 with warm PBS , layered onto Ficoll ( 25 mL/15 mL ) and centrifuged at 1800 RPM for 30 minutes at 20°C . The peripheral blood mononuclear cell ( PBMC ) layer was washed 3 times with cold RPMI-1640 and resuspended in R-10G FBS media ( RPMI-1640 medium containing L-glutamine and HEPES supplemented with 10% heat-inactivated FBS and 25 mg/L gentamicin ) . 1 . 25×106 PBMCs were pipetted onto 24-well plate containing coverslip and incubated at 37°C for 1 hour . Wells were washed twice to remove non-adherent cells and the monocytes were cultured in R-10G FBS for 5 days at 37°C to allow differentiation into macrophages . To assess parasite invasion and maturation , schizont stage P . falciparum-infected erythrocytes from synchronized cultures were purified on a Percoll-mannitol gradient [27] , [50] and mixed with erythrocytes of different blood groups ( A , B and O ) in R-10G as described [27] . Invasion of erythrocytes was assessed at 24 hours , and 72 hours , and maturation was assessed at 48 hours , and 96 hours . Slides were stained with Diff-Quik , and 1000 erythrocytes were examined microscopically . Percent parasitemia was determined as follows: [number of parasites÷number of total erythrocytes counted]×100 . Uninfected and infected ring-stage or mature-stage parasitized erythrocytes were incubated with 50% fresh autologous serum for 30 minutes at 37°C . Erythrocytes were then washed twice , resuspended at 10% hematocrit , and incubated with macrophages adherent to glass coverslips at a target-to-effector ratio of 40∶1 ( ring-stage ) or 1∶20 ( mature-stage ) . Phagocytosis assays were performed as described previously [27] and were counted and analyzed blinded to the erythrocyte blood group . To assess phagocytosis of infected A , B and O erythrocytes in vivo , 50×106 infected erythrocytes , or uninfected erythrocytes as a control , were injected into the peritoneal cavity of C57BL/6 mice as previously described [29] . Three hours after injection , peritoneal cells were collected , and washed with R-0G media . The cells were then suspended in cold water to lyse and remove non-internalized erythrocytes . Cells were then resuspended in 500 µl of R-10G media . 150 µl aliquots of peritoneal cells from each mouse were placed on a glass coverslip in a 24 well plate , allowed to adhere for 30 minutes at 37°C and stained with Diff-quick . In addition , 200 µl of the suspension were cytospun at 800 RPM for 10 minutes and stained with Diff-quick . Images from these slides were acquired with an Olympus BX41 microscope and an Infinity2 camera at 1000× magnification . Flow cytometric detection of H antigens was performed as previously described [34] , using a FITC-conjugated monoclonal anti-H ( BRIC231 ) antibody . Blood groups A1 , A2 , O and H negative control ( Bombay ) , were tested simultaneously and ten thousand events were collected . In the histogram FITC-derived fluorescence is displayed on the x axis on a logarithmic scale and the number of cells is on the y axis . Removal of B antigens was achieved as previously described [34] . Briefly , erythrocytes were prewashed 1∶1 and 1∶4 vol/vol in glycine buffer ( 200 mM glycine and 3 mM NaCl , pH 6 . 8 ) . The conversion reaction consisting of a 30% suspension of erythrocytes in glycine buffer with addition of bacterially-derived GH110 family α-galactosidase ( B-zyme from Bacteroides fragilis ) [34] , [51] . The bacterial glycosidase was incubated for 60 min during gentle mixing at 26°C , followed by 4 repeated washing cycles with 1∶4 vol/vol of saline by centrifugation at 1000 RPM . To verify the removal of B antigen after enzyme conversion flow cytometric detection of ABO antigens was performed as recently outlined [51] , using the IgM anti-B clone 9621A8 ( Diagast , Loos , France ) as primary antibody and phycoerythrin ( PE ) -conjugated rat-anti-mouse Ig kappa light chain ( Becton Dickinson , San Jose , CA , USA ) as secondary antibody . Treated and untreated erythrocytes were tested simultaneously and control cells of known phenotype ( B , Bweak subgroup and O cells were included in each run to confirm sensitivity and specificity of the assay , as previously shown ) [51] . Ten thousand events were collected and log fluorescence data was gated on a linear forward scatter versus linear side scatter dot plot . A , B and O uninfected and P . falciparum ring-stage and mature-stage infected erythrocytes were washed in PBS supplemented with 0 . 5% BSA and 0 . 1% azide and lysed with Tris-HCl/EDTA ( pH 8 . 0 ) in the presence of protease inhibitors cocktail ( Roche Diagnostics GmbH ) . Where indicated , erythrocytes were incubated for 30 minutes at room temperature in the dark in PBS-glucose containing 10 µmol/L eosin-5-maleimide in order to label band 3 in situ [52] . Membrane pellets were extracted as described [38] . Hemichromes were quantified using the Winterbourne equation [37] . Tween-20 detergent-extracted membrane proteins ( 500 µL ) were then loaded onto Sepharose CL-6B column equilibrated with 10 mmol/L Tris buffer and separated at a flow rate of 0 . 760 mL/min . The effluent was collected in 1 . 2 mL fractions . Total proteins in the fractions were assayed using Bedford reagent at 595 nm and labeled band 3 was assayed using fluorometry ( Ex-522 nm and Em-550 nm ) . Aggregated band 3 was assayed in the Tween-20 fractions of uninfected and mature stage-separated infected erythrocytes previously labeled by the band 3-specific fluorescent label eosin-5-maleimide as described [53] . In order to quantify the percentage of aggregated band 3 , eosin-5-maleimide-labeled band 3 in Tween-20 fractions , the fluorescence value measured in the high-molecular-weight fractions was normalized to the total fluorescence measured in all fractions . Statistical analysis was performed using GraphPad Prism 4 software ( San Diego , CA , USA ) . To confirm the normal distribution of data , all continuous data sets were assessed using the Kolmogorov-Smirnov test . Data sets that displayed normal distribution were analyzed by Student's t-test ( two-tailed ) or a one-way ANOVA as appropriate . Data sets that did not display normal distribution were analyzed by the Mann-Whitney rank sum test . Multiple comparisons were corrected using the Bonferroni method . A general linear model was used to analyze experiments with multiple independent variables ( e . g . , macrophage and erythrocyte group ) . To test the dose-dependent effect of the A and H antigen , we used a Spearman's rank correlation on the individual data points ( phagocytic index ) correlate with decreasing level of A antigen . Data are presented as box plots representing the median , inter-quartile range and range or as bar graphs representing the mean±SEM . | Plasmodium falciparum malaria is considered to be one of the strongest forces for evolutionary selection pressure on the human genome . Different red blood cell variants associated with a survival advantage to P . falciparum infection have undergone positive selection . Blood group O is found more frequently in malaria-endemic regions and has been associated with protection against severe malaria and death . However the biological basis of protection remains unclear . In this study , we investigated innate immune clearance of P . falciparum-infected erythrocytes by macrophages as a possible mode of protection . We show that macrophages clear P . falciparum-infected O erythrocytes more avidly than infected A and B erythrocytes . We also report that enzymatic conversion of infected blood group B red cells to type as “O” like erythrocytes significantly enhances their uptake by macrophages to a level comparable to that observed with infected O wild type erythrocytes . These data provide the first evidence that clearance of P . falciparum-infected erythrocytes is influenced by human ABO blood groups and suggest a new mechanism by which blood group O may contribute to protection against severe malaria . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"biology"
] | 2012 | ABO Blood Groups Influence Macrophage-mediated Phagocytosis of Plasmodium falciparum-infected Erythrocytes |
Human African trypanosomiasis , endemic to sub-Saharan Africa , is invariably fatal if untreated . Its causative agent is the protozoan parasite Trypanosoma brucei . Eflornithine is used as a first line treatment for human African trypanosomiasis , but there is a risk that resistance could thwart its use , even when used in combination therapy with nifurtimox . Eflornithine resistant trypanosomes were selected in vitro and subjected to biochemical and genetic analysis . The resistance phenotype was verified in vivo . Here we report the molecular basis of resistance . While the drug's target , ornithine decarboxylase , was unaltered in resistant cells and changes to levels of metabolites in the targeted polyamine pathway were not apparent , the accumulation of eflornithine was shown to be diminished in resistant lines . An amino acid transporter gene , TbAAT6 ( Tb927 . 8 . 5450 ) , was found to be deleted in two lines independently selected for resistance . Ablating expression of this gene in wildtype cells using RNA interference led to acquisition of resistance while expression of an ectopic copy of the gene introduced into the resistant deletion lines restored sensitivity , confirming the role of TbAAT6 in eflornithine action . Eflornithine resistance is easy to select through loss of a putative amino acid transporter , TbAAT6 . The loss of this transporter will be easily identified in the field using a simple PCR test , enabling more appropriate chemotherapy to be administered .
Human African trypanosomiasis ( HAT ) is a neglected tropical infectious disease transmitted by biting tsetse flies and is prevalent in sub-Saharan Africa [1] , [2] . In humans , the disease is caused by two sub-species of the protozoan Trypanosoma brucei – T . b . gambiense and T . b . rhodesiense . T . b . gambiense is responsible for around 95% of all cases of the disease . An alarming resurgence of the disease in the latter part of the twentieth century stimulated a renewed interest in HAT control [2] . There are two stages of HAT . The first stage is characterised by parasite proliferation in the blood and lymph , while the second stage occurs when parasites enter the CSF ( cerebrospinal fluid ) and brain , resulting in symptoms that include confusion , depression , personality changes and the altered sleep-wake patterns that give the disease its common name of sleeping sickness . Death follows , inevitably , without treatment . Chemotherapy in stage two HAT requires melarsoprol , a melaminophenyl arsenical , or eflornithine , an amino acid analogue which inhibits the polyamine biosynthetic enzyme ornithine decarboxylase ( ODC ) . Melarsoprol is exceedingly toxic , killing 5% of recipient HAT patients [2] . Furthermore , treatment failure with melarsoprol has led to its being superseded by eflornithine . Recently , nifurtimox use with eflornithine has been recommended [3] , [4] and the combination added to the WHO list of essential medicines . Eflornithine targets ornithine decarboxylase in trypanosomes [5] , [6] , and this causes diminished polyamine biosynthesis [5] and reduced production of the trypanosome specific redox active metabolite trypanothione [7] . Accumulation of S-adenosyl methionine has been reported in eflornithine treated cells , which might perturb cellular methylation reactions [8] although recent data identified increased levels of decarboxylated S-adenosyl methionine , but not its precursor [9] . How eflornithine enters trypanosomes is a subject of debate . An early report that eflornithine uptake by trypanosomes was not saturable established the idea that eflornithine enters trypanosomes by passive diffusion [10] . However , studies on eflornithine resistant procyclic trypanosomes showed reduced accumulation of eflornithine [11] and uptake of eflornithine was by a saturable process typical of a transporter . Bellofatto et al [12] also found uptake of eflornithine to be temperature dependent and thus likely to be transporter mediated . Indeed as a zwitterionic , charged amino acid , eflornithine would not be expected to diffuse across membranes and transport mediated uptake would be a pre-requisite for uptake . In T . brucei loss of transport has been shown to be a key determinant in resistance to melaminophenylarsenicals [13] and diamidine drugs [14]–[16] . Given the increased use of eflornithine , alone or in combination with nifurtimox , a better understanding of the risk of resistance is critical . Such an understanding may help limit its spread and allow the development of diagnostic tools such as those described for melarsoprol resistance [15] , [16] . We have investigated the mechanism of resistance to eflornithine and show that acquisition of selected resistance is accompanied by loss of a specific transporter . We further show , using genetic manipulation , that this transporter mediates uptake of eflornithine and that its loss confers resistance , whilst its expression in resistant lines restores sensitivity .
Eflornithine resistant parasites were derived in vitro from a wildtype bloodstream form T . brucei brucei strain 427 by growth in increasing concentrations of drug . It took two months ( 24 passages ) to attain a line expressing forty fold less sensitivity to drug , based on the IC50 value of eflornithine in the drug sensitive parent strain ( Fig . 1A ) and no growth phenotype was observed . Two independent cell lines were generated in this way . There was no cross-resistance with other currently used trypanocides ( Table 1 ) , although there was a significant increase in sensitivity to pentamidine , which we cannot explain at this juncture . The resistant lines also grew in female ICR ( Institute for Cancer Research ) mice and exhibited resistance to both the minimum curative dose of 2% w/v and a higher 5% w/v eflornithine whilst mice infected with wildtype cells were cured with the lower 2% w/v dose . Resistant cells remained susceptible to pentamidine ( 4 mg kg−1 , four daily doses ) ( Fig . 1B ) . This demonstrates that the in vitro selected mechanism for resistance is also operative in vivo . Interestingly , isobologram analyses ( Fig . 2 ) revealed that nifurtimox and eflornithine are not synergistic to one another's activity in vitro . The average fractional inhibitory concentration ( FIC ) is used as a measure of interaction between two drugs and is a sum of the IC50 of the drug acting in combination divided by the IC50 of the drug acting alone . An FIC of 1 . 5 was recorded for eflornithine and nifurtimox , where a value ≥1 . 4 is taken as antagonistic [17] ) . This was a surprise given the theory that eflornithine would deplete cellular trypanothione thus rendering the cells more susceptible to oxidative stress induced by nifurtimox . Eflornithine's target is the enzyme ornithine decarboxylase . Alterations to the amino acid composition of proteins is often responsible for drug resistance as variants with diminished ability to bind drug are selected [18] . We therefore amplified the ODC gene from the wildtype and the resistant cell line ( DFMOR1 and R2 ) and found no differences in the sequence or copy number . Earlier work [19] had pointed to possible changes in S-adenosyl methionine and polyamine metabolism relating to refractoriness to eflornithine . We therefore subjected wildtype and resistant cells to untargeted metabolomic analysis to determine whether changes in relative levels of key metabolites could be determined ( Figure S1 and Table S1 ) . Significant differences between the untargeted metabolite profiles of wildtype and resistant cells were not apparent using multivariate statistical analysis , nor were changes seen in any of the identified polyamine pathway metabolites including S-adenosyl methionine ( Fig . 3A ) . However , in a targeted analysis of eflornithine ( m/z = 183 . 0940 ) accumulation , it was evident that eflornithine levels were greatly reduced in resistant cells compared to wildtype ( Fig . 3B ) . This result indicated that exclusion of drug from the resistant line ( DFMOR1 ) rather than changes to metabolism were responsible for loss of sensitivity . To determine quantitatively the relative transport rates of the drug in wildtype and resistant cells , 3H-eflornithine was used to measure accumulation in each cell type . A greater rate of eflornithine uptake was observed in the wildtype cell line compared to the resistant line ( DFMOR1 ) , with around five fold more drug taken into wildtype cells after 30 minutes ( Fig . 3C ) . These data indicated a transporter phenotype , as seen previously in selection of resistance to melamine based arsenicals [13] and diamidines [14] , [16] , [20] , [21] . As eflornithine is an amino acid analogue ( Fig . 4 ) , we hypothesised loss of an amino acid transporter . To test this , members of the amino acid permease gene family ( Fig . 5 ) in the T . brucei genome [22] were systematically amplified from both wildtype and each of the two independently selected resistant lines . In each of the independently selected lines only one single copy amino acid transporter gene , TbAAT6 ( Tb927 . 8 . 5450 ) , was shown to be absent ( Fig . 5 ) . PCR analysis indicated a deletion of this , and surrounding genes , from both resistant lines ( DFMOR1 , Fig . 6 , R2 not shown ) . This result indicated the possibility that the TbAAT6 gene could play a role in eflornithine's entry into T . brucei and that its loss was responsible for drug resistance . The gene was amplifiable at day 34 ( Fig . 1A ) , but by day 50 ( Fig . 1A ) was no longer amplifiable . To confirm a role for TbAAT6 in eflornithine resistance we used RNA interference [23] to ablate its expression in Trypanosoma brucei . A cloned line was selected and this TbAAT6RNAi mutant became resistant to eflornithine to an extent similar to the lines selected for resistance to the drug ( 40 . 1×resistance factor ) ( Fig . 7A ) when expression was ablated by addition of tetracycline . Next , we expressed the TbAAT6 gene in the eflornithine selected trypanosomes using vector pHD676 [24] . Cloned cells in which the gene was re-expressed regained levels of eflornithine sensitivity similar to wildtype ( Fig . 7B ) . Loss of expression of TbAAT6 is therefore both necessary and sufficient to confer resistance to eflornithine and its re-expression in defective lines capable of restoring sensitivity , regardless of other changes to the cell .
Human African trypanosomiasis , also known as sleeping sickness in its second stage when parasites have invaded the brain , is a neglected tropical disease [1] . Major epidemics at the end of the twentieth century were brought under control largely through increased efforts in distribution and treatment with the few drugs available to treat the disease [2] . An alarming increase in the incidence of treatment failure with melarsoprol has led to its being replaced with eflornithine as first line treatment for stage 2 HAT [2] . Combination therapy using eflornithine with the nitrofuran , nifurtimox , licensed for use in Chagas' disease has been added to the World Health Organisation's list of essential medicines as part of the nifurtimox-eflornithine combination therapy for HAT [3] . Although several initiatives are underway to develop new drugs for human African trypanosomiasis , none are currently in human trials and a minimum of five years will elapse before a new drug could complete trials and reach the market place . The loss of eflornithine , alone or in the nifurtimox combination , would represent a calamity in terms of sustaining control of HAT . The data presented here show that resistance to eflornithine is easily selected in the laboratory . Selection of resistance in two independently derived lines led to deletion of the TbAAT6 gene . Eflornithine uptake was lost indicating that this gene encodes a transporter capable of carrying the drug into trypanosomes . The loss of TbAAT6 either by gene deletion as observed in the selected drug resistance lines , or by RNAi is sufficient to render trypanosomes over 40 fold less sensitive to eflornithine than wildtype cells . Furthermore , ectopic expression of TbAAT6 in trypanosomes that have deleted the gene is sufficient to restore wildtype levels of eflornithine sensitivity confirming that loss of TbAAT6 alone is necessary and sufficient to generate resistance . We have , as yet , been unable to assign a physiological function to TbAAT6 in African trypanosomes , and this is a topic of ongoing research . However , it is one of a large family of related genes described in the kinetoplastida belong to the amino acid transporter 1 superfamily . Only a few other members of the family have been functionally characterised . These include an arginine transporter in Leishmania donovani [25] , an arginine transporter in T . cruzi [26] and polyamine transporters in L . major [27] and T . cruzi [28] . The AAT6 gene is not syntenic with genes in Leishmania spp . or T . cruzi . Furthermore , the evolution of the AAT family [22] makes it impossible , currently , to define specific functionality to any of these transporters based on homology alone . Previous work with bloodstream and procyclic form trypanosomes also revealed a relative simplicity in selecting eflornithine resistance [11] , [12] , [29] . In procyclic forms reduced rates of eflornithine uptake were identified [11] , [12] with possible changes to other transporters for ornithine and putrescine also suggested . In bloodstream forms reduction in eflornithine uptake was noted in two of six eflornithine refractory T . b . rhodesiense lines [29] , but in the majority of cases no difference in eflornithine uptake was noted leading the authors to dismiss altered drug uptake as an underlying mechanism for the natural refractoriness of many strains of T . b . rhodesiense in the field [30] . Possible changes to S-adenosylmethionine metabolism instead were inferred as being significant in that study [29] . Our metabolomics experiments showed that none of the measured polyamine pathway metabolites differed significantly between wildtype and resistant lines in our study . Furthermore , as noted above , the reduced uptake of eflornithine by trypanosomes lacking TbAAT6 , without further requirement of changes in metabolism , is both necessary and sufficient to yield a resistance phenotype without any requirement for changes to metabolic pathways which will be essentially unchanged as drug no longer accumulates to inhibitory levels in trypanosomes . Recently , two groups have employed high throughput RNAi screening to determine whether knockdown of any genes correlate with to resistance to various trypanocides including eflornithine . In both instances , TbAAT6 was implicated in loss of sensitivity to eflornithine ( David Horn , London School of Hygiene and Tropical Medicine , personal communication ) and Isabel Roditi [31] . Since eflornithine has only recently been implemented as first line treatment for stage two HAT , formal published reports of clinical resistance have not yet appeared , although unpublished data ( Enock Matovu ( Makerere University ) , personal communication ) points to a substantial increase in eflornithine treatment failures in Northern Uganda . Furthermore , given that the actions of nifurtimox and eflornithine are not synergistic , trypanosomes already bearing resistance , through loss of transport , to eflornithine would effectively be subject to nifurtimox monotherapy even in combination chemotherapy . Nifurtimox resistance has been selected in vitro and has been shown to be cross resistant with another emerging trypanocide , fexinidazole , currently in clinical trials [32] . Given nifurtimox's lack of efficiency [33] , eflornithine resistance alone is likely to lead to large numbers of treatment failures from the combination . If the loss of TbAAT6 is involved in resistance in the field , then it will be possible to implement a simple PCR-based test for resistance , allowing for more suitable treatments to be administered .
This study was undertaken in adherence to experimental guidelines and procedures approved by the UK Home Office under Project Licence No . 60/3760 as complying with the Animals ( Scientific Procedures ) Act 2006 entitled Biochemistry , genetics and immunology of parasitic protozoa . Wildtype 427 bloodstream form trypanosomes were cultured in HMI-9 ( Biosera ) [34] supplemented with 10% foetal calf serum ( Biosera ) at 37°C , 5% CO2 . Eflornithine resistant parasites were selected in increasing concentrations of drug starting at 15 µM . When cells were growing at a rate comparable to wildtype they were cloned by limiting dilution and subcultured into double the drug concentration . The Alamar blue assay developed by Raz et al . [35] for bloodstream form trypanosomes was used . Bloodstream form parasites were seeded 4×104 cells per ml into a serial dilution of eflornithine ( a gift from Pere Simarro , WHO ) starting at 20 mM . Plates were incubated for 48 hours at 37°C , 5% CO2 then 20 µL Resazurin dye ( Sigma ) at 0 . 49 µM was added to each well . Plates were incubated for a further 24 hours then read on a fluorimeter ( emission 530 , excitation 595 ) ( FLUOstar Optima , BMG Labtech ) . IC50 values were calculated using Graphpad Prism5 Software and defined as the concentration of drug required to diminish fluorescence output by 50% . Significance was determined using an unpaired t-test with a Dunnett's post hoc test . For the isobologram analysis Alamar blue assays were conducted using nifurtimox in serial dilution under eflornithine concentrations of 2 . 5 µM , 15 µM and 25 µM . Four groups of mice ( three mice per group ) were inoculated with T . brucei 427 wildtype and another four groups with one of the selected eflornithine resistant lines ( termed DFMOR2 ) . Each inoculum consisted of 1×106 parasites per animal ( i . e . 200 µL of 5×106 cells mL−1 ) which was administered via intraperitoneal injection . The groups of mice infected with T . brucei 427 wildtype and T . brucei eflornithine resistant clones were treated in parallel to each other 24 hours post-infection with the different treatment groups as described below following earlier protocols [30] . ( a ) Eflornithine 2% w/v for six days in drinking water with the eflornithine solution being refreshed every three days; ( b ) Eflornithine , 5% w/v for six days in drinking water , with the eflornithine solution being refreshed every three days; ( c ) Pentamidine 4 mg kg−1 injected daily via intraperitoneal route for four days ( 200 µL per injection ) ; and ( d ) Untreated ( i . e . no treatment administered ) . The exact dosing of eflornithine was determined by daily water consumption measurements . Parasitaemia levels of each animal were monitored daily via venepunctures and microscopic observations of subsequent blood smears . In instances where infection reaches ∼108 cells mL−1 or at the end of the experiment , mice were euthanised using a Schedule 1 method . Genomic DNA was denatured at 94°C for two minutes , followed by 30 cycles of 94°C for 15 seconds , annealing ( 50–55°C depending on specific oligonucleotide ) for 15 seconds and extension at 72°C for 30 seconds/500 bases . A final elongation of 7 minutes was used . See Text S1 for primer sequences used . 2T1 bloodstream form cells were used to create the RNAi cell line with the pRPaSLi stem loop construct [23] . Eflornithine resistant cells ( derived from wildtype 427 ) were used with the pHD676 [24] construct to create the re-expressor line . Linearised plasmid was transfected into the cells using programme X-001 on an Amaxa Nucleofector II . For the RNAi construct , selection was with hygromycin ( 15 µg/ml ) ( Sigma ) . Cells positive for the re-expression construct were selected with hygromycin ( 15 µg/ml ) ( Roche ) were added after 24 hours and clones were obtained . Uptake was analysed using tritiated substrate and eflornithine accumulation using a mass spectrometry approach . In the mass spectrometry approach cells were harvested in mid-log growth phase and resuspended at 1×109 in HMI-9 with added eflornithine at 0 . 1 mM . These were incubated for 30 minutes , washed in HMI-9 and quenched in hot ethanol . The cell lysate was then run on the Orbitrap mass spectrometer as detailed below . Tritiated eflornithine was obtained from Moravek Biochemicals with a specific activity of 1 . 6 Ci/mmol , 1mCi/ml . Mid-logarithmic growth phase cells were grown up to attain sufficient cell densities to permit use of 2×107 cells per reaction . Cells were washed in CBSS buffer ( 25 mM HEPES , 120 mM NaCl , 5 . 4 mM KCl , 0 . 55 mM CaCl . 2H2O , 0 . 5 mM MgSO4 . 7H2O , 5 . 6 mM Na2HPO4 , 11 . 1 mM D-glucose ) and resuspended to a density of 1×108/ml . A rapid oil/stop spin protocol , previously described by Carter & Fairlamb [13] , was used . 100 µl of oil ( 1-Bromodo-decane , density: 1 . 066 gcm-3 ) ( Aldrich ) and 100 µl radiolabelled eflornithine in CBSS buffer was added to 0 . 5 ml Eppendorf tubes . These were centrifuged briefly to remove bubbles . Cells were added to the tubes at room temperature and centrifuged through the oil at 16 , 000 RCF for one minute to stop the uptake after various time points . The resulting cell pellet was flash frozen in liquid nitrogen and the base of the tube containing the pellet was cut into 200 µl of 2% SDS in scintillation vials and left for 30 minutes . Three ml of scintillation fluid was added to each vial and these were left overnight at room temperature . Samples were read on a 1450 microbeta liquid scintillation counter ( Perkin Elmer ) . Southern blots performed according to standard procedures [36] . DNA was digested with Eco RI ( Promega ) , blotted using a hybond-N membrane ( Amersham ) and probed with Easytides 32P-ATP ( Perkin Elmer ) incorporated into TbAAT6 using the Stratagene Prime-it kit . 2Ti bloodstream form cells were used to create the RNAi cell line with the pRPaSLi construct [23] . Cells were induced with 1 µg/ml tetracycline for 8 days before calculation of the IC50 value . Cultures were kept in log phase growth ( below 1×106/ml ) . Metabolites were extracted from cell cultures simultaneously by two methods . In method A , cells were centrifuged at 1 , 250 RCF for 10 minutes and re-suspended in HEPES-free HMI-9 to a density of 1×109 cells/ml . These cells were left to recover in an incubator for 30 minutes before quenching by addition of 80°C ethanol to the cell suspension at a 4∶1 ratio ethanol∶cell suspension . These were left at 80°C for two minutes to allow the cells to lyse and denature any proteins . Extracts were then transferred to ice and left for 5 minutes and vortexed briefly . In method B , 4×107 cells were rapidly cooled to 4°C by submersion of the flask in a dry ice/ethanol bath , and kept at 4°C for all subsequent steps . The cold cell culture was centrifuged at 1 , 000 RCF for 10 minutes , supernatant removed , and the pellet washed in 30 mL HEPES-free HMI-9 . The washed cells were then centrifuged and the supernatant completely removed . Cell lysis and protein denaturation was achieved by addition of 200 µL of cold chloroform/methanol/water ( ratio 1∶3∶1 ) , followed by vigorous mixing for 1 hour at 4°C . For both methods , extract mixtures were centrifuged for two minutes at 16 , 000 RCF , 4°C . The supernatant was collected , frozen and stored at −80°C until further analysis . Samples were analysed on an LTQ Orbitrap mass spectrometer ( Thermo Fisher ) in positive mode , coupled to HPLC separation using a ZIC-HILIC column ( Sequant ) according to the method published by Kamleh et al . [37] . Each sample was also analysed on an Exactive orbitrap mass spectrometer ( Thermo Fisher ) in both positive and negative modes ( rapid switching ) , coupled to HPLC with a ZIC-HILIC column . Exactive data was acquired at 25 , 000 resolution , with spray voltages +4 . 5kV and −2 . 6kV , capillary temperature 275°C , sheath gas 20 , aux gas 15 and sweep gas 1 unit . Minor adjustments were made to the published HPLC mobile phase gradient as follows: Solvent A is 0 . 1% formic acid in water , and solvent B is 0 . 1% formic acid in acetonitrile , 80% B ( 0 min ) , 50% B ( 12 min ) , 50% B ( 26 min ) , 20% B ( 28 min ) , 20% B ( 36 min ) , 80% B ( 37 min ) , 80% B ( 47 min ) . Metabolite identification and relative quantitation was undertaken using ToxID software ( Thermo Fisher ) , by searching for peaks that correspond to the accurate mass of metabolite ions within a 3 ppm window ( or 5 ppm window for Exactive data ) . The metabolite lists were obtained from trypanosome-specific databases in Trypanocyc ( metacyc . org ) and KEGG ( www . genome . jp/kegg/ ) , lipids were excluded from the data analysis . Metabolite levels are expressed as mean peak height from 3 biological replicates . Multivariate statistical analysis comprised a principal component analysis based on putatively identified metabolites , and significance for individual metabolites was calculated by t-test ( α = 0 . 05 ) . Cladograms were constructed using the CLC genomics workbench software alignment and tree building tools . A neighbour joining algorithm was used and the tree was bootstrapped 1000 times . | We have found that the loss of a single gene , TbAAT6 , is sufficient to render African trypanosomes resistant to the only safe drug , eflornithine , in use against them . The fact that parasites lacking TbAAT6 are viable in animals and retain the resistance phenotype indicates a simple means by which parasite populations could develop resistance . The loss of this gene can be detected by PCR apparatus , offering the potential for a simple , cheap test in the field , meaning that the drug will not be prescribed when it would be inefficient . It will be critical to monitor parasite populations in endemic regions for the status of this gene as eflornithine is used increasingly in trypanosomiasis therapy . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"pharmacology/drug",
"resistance",
"microbiology/parasitology"
] | 2010 | A Molecular Mechanism for Eflornithine Resistance in African Trypanosomes |
Antibodies targeting receptor-mediated entry of HCV into hepatocytes confer limited therapeutic benefits . Evidence suggests that exosomes can transfer genetic materials between cells; however , their role in HCV infection remains obscure . Here , we show that exosomes isolated from sera of chronic HCV infected patients or supernatants of J6/JFH1-HCV-infected Huh7 . 5 cells contained HCV RNA . These exosomes could mediate viral receptor-independent transmission of HCV to hepatocytes . Negative sense HCV RNA , indicative of replication competent viral RNA , was present in exosomes of all HCV infected treatment non-responders and some treatment-naïve individuals . Remarkably , HCV RNA was associated with Ago2 , HSP90 and miR-122 in exosomes isolated from HCV-infected individuals or HCV-infected Huh7 . 5 cell supernatants . Exosome-loading with a miR-122 inhibitor , or inhibition of HSP90 , vacuolar H+-ATPases , and proton pumps , significantly suppressed exosome-mediated HCV transmission to naïve cells . Our findings provide mechanistic evidence for HCV transmission by blood-derived exosomes and highlight potential therapeutic strategies .
Hepatitis C virus ( HCV ) infection is one of the leading causes of liver disease with over 170 million individuals chronically infected worldwide [1] , [2] . Severe complications including fibrosis , cirrhosis , and hepatocellular carcinoma are among the long-term effects of HCV infection , making liver transplantation the ultimate choice of treatment for advanced liver disease [3] . Even with successful liver transplantation , patients face eminent HCV re-infection of the newly transplanted liver . Recent therapies with anti-HCV E1-E2 or other neutralizing antibodies that attempted to block HCV transmission achieved only limited success [4]–[7] . HCV is a positive-sense single-stranded RNA enveloped virus of the Flaviviridae family . The 9 . 6 kb HCV genomic RNA encodes a single polypeptide that is proteolytically cleaved to structural ( core , E1 , and E2 ) and non-structural ( p7 , NS2 , NS3 , NS4A , NS4B , NS5A and NS5B ) HCV viral proteins [8] . The HCV viral envelope E1 and E2 proteins engage numerous host cell proteins for viral entry including CD81 [9]–[11] . CD81 interaction with HCV E1/E2 is critical in HCV entry and anti-CD81 or anti-E1/E2 antibodies have been shown to block HCV virus entry [7] , [12] . Given the importance of these viral envelope proteins in regulating HCV infection , numerous immune therapies have been developed to specifically target and/or neutralize HCV envelope proteins [7] , [13]–[15] . Targeted antibody therapies have offered limited success in preventing liver allograft infection by HCV . Recently , a potent human-derived monoclonal antibody was demonstrated to effectively prevent and treat HCV1 infection in chimpanzees [7] . However , the same antibody was not completely effective in humans [7] , raising the possibility of other mechanisms of virus entry into hepatocytes . Previous reports have suggested receptor independent transmission of HCV [6] , [16] , though the precise mechanisms or possible therapeutic strategies remain to be explored . Exosomes are a subpopulation of extracellular vesicles that originate from multivesicular bodies ( MVBs ) , ranging from 40–150 nm in size and are produced by most cell types . These vesicles can be detected in blood , urine , and other body fluids [17] . Exosomes can modulate signal transduction , antigen presentation to T-cells , and transmission of genetic material between cells [18] . Over the past decade , a great body of evidence shows that exosomes can be secreted into the extracellular space and can mediate indirect cell-to-cell communication by transferring bio macromolecules , functional proteins , and RNAs between cells [19] , [20] . HCV infection occurs via cell free virus and direct cell-to-cell transmission [6] . Indirect cell-to-cell transmission is another pathway to consider . Previously , HCV viral RNA has been identified in supernatant of HCV-SGR cells [21] and exosome-like structures have been detected in the supernatant of HCV infected cells [22] and in the plasma of HCV-infected patients [23] . Recently , Dreux et al ( 2013 ) showed that HCV-RNA-containing exosomes can mediate transfer of RNA to co-cultured plasmacytoid dendritic cells ( pDCs ) and trigger the production of type I interferon ( IFN ) in vitro [24] . Here we tested the hypothesis that exosomes derived from HCV infected hepatocytes or from the sera of HCV infected patients carry viral RNA and exploit the cellular exosomal delivery system to mediate receptor-independent HCV transmission to hepatocytes . We found that exosomes derived from HCV infected Huh7 . 5 cells and HCV infected patients contained HCV RNA that induced active infection in primary human hepatocytes ( PHH ) . These exosomes were rich in replication-competent HCV-RNA in complex with miR-122 , Ago2 , and HSP90; and mediated HCV transmission independent of CD81 , SB-RI and APOE . Mechanistically , functional inhibition of miR-122 , HSP90 or modification of cellular micro-environmental pH using a vacuolar-type H+-ATPase ( V-ATPase ) and proton pump inhibitor significantly suppressed the capacity of exosomes to mediate HCV transmission .
Given our interest to investigate the capacity of exosomes derived from HCV J6/JFH-1-infected Huh7 . 5 cells and serum of HCV infected patients to mediate active transmission , we had to efficiently separate exosomes from the free HCV virus . Because HCV virions and exosomes have very similar sizes and densities , the traditional ultracentrifugation and sucrose gradient isolation method is insufficient for isolating pure exosomes free of virus contamination . To overcome this limitation , we optimized a CD63 immuno-magnetic isolation method to purify exosomes from cell culture supernatants of HCV J6/JFH-1 infected Huh7 . 5 cells and sera of HCV infected patients . Briefly , after serial filtration ( 1 µm , 0 . 44 µm and 0 . 22 µm ) of supernatants , exosomes were initially isolated using Exoquick . To further purify exosomes and exclude other microparticles or free HCV contamination , Exoquick-isolated exosomes were subjected to immuno-magnetic selection with CD63 , a selection marker of exosomes . This protocol was verified by analysis for other exosomal markers by western blotting ( CD9 and CD81 ) , electron microscopy , and Nanoparticle tracking analysis ( NTA ) ( Figures S1A , S1B & S1C ) . The Exoquick-CD63 immuno-magnetic selection procedure recovered more exosomes compared to , ultracentrifugation-CD63 immuno-selection of exosomes ( Fig . 1A & 1B ) ; based on this observation we used the Exoquick followed by CD63 immuno-magnetic selection for subsequent experiments . We observed that in a fixed total volume there were significantly more free HCV viral particles compared to the number of exosome particles in HCV J6/JFH-1 infected Huh7 . 5 cell supernatants [approximately 7∶1 ratio] and in HCV infected patient serum samples [approximately 4∶1 ratio] ( Fig . 1C ) . We also found higher HCV viral copy numbers in the free virus fraction compared to exosomes in J6/JFH-1 infected Huh7 . 5 cell supernatants ( Fig . 1D ) and HCV patients' serum ( Fig . 1E ) . Purified exosomes were analyzed by transmission electron microscopy , demonstrating their vesicular shape and size range between 50 and 100 nm ( Figure S1A ) . Further analysis with NanoSight demonstrated comparable histogram size plots of exosomes from culture supernantants of HCV J6/JFH-1 infected Huh7 . 5 cells and exosomes from serum of HCV infected patients ( Figure S1B & S1C ) . To rule out exosome contamination with free HCV virus , we carried out a simulation experiment mixing cell free HCV virus with uninfected exosomes from Huh7 . 5 cell culture supernatants for 24 h and re-isolated exosomes with Exoquick followed by CD63 immuno-selection or ultracentrifugation followed by CD63 immuno-selection . The uninfected exosomes exposed to free HCV virus showed no detectable HCV viral RNA while HCV RNA was present in the flow through following immuno-magnetic CD63 selection of exosomes ( Fig . 2A & 2B ) . Further characterization of exosomes and free virus showed that isolated exosomes contained Apolipoprotein B ( APOB ) which was not present in cell free HCV viruses ( Fig . 2C ) . Apolipoprotein E ( APOE ) was found to be associated to a large extent with HCV virus fraction and significantly lower in exosomes compared to cell free virus fractions ( Fig . 2D ) . These observations suggest that our purified exosomes were to a large extent devoid of lipo-viral contamination . RNase H treatment to destroy free RNA in the cell free virus concentrate and isolated exosomes from HCV infected Huh 7 . 5 cells failed to prevent transfer of HCV infection to naïve cells , thus ruling out the possibility of envelope free viral RNA mediating HCV infection . These data indicate that both HCV derived exosomes and HCV virus are resistant to RNase treatment similar to the previous report [25] and still cause productive infection even after RNase treatment ( Figure S2 ) . First , we established efficient methods for exosome purification using Exoquick followed by CD63-based isolation as described above . These exosomes were devoid of free HCV virus contamination as detailed . Exosomes isolated from sera of some HCV-infected patients or supernatants of HCV J6/JFH1 infected Huh7 . 5 cells contained comparable HCV RNA content for the same number of free HCV viral particles compared to the same number of HCV exosome particles ( Fig . 3A ) . These observations allowed us to use the same number of infectious HCV viral particles and HCV exosomes for subsequent experiments . Treatment-naïve and non-responder ( interferon plus ribavirin ) patients with active HCV infection had detectable HCV RNA in serum-derived exosomes ( Fig . 3B ) . In contrast , treatment responders , who cleared HCV infection , showed no detectable HCV in exosomes ( Fig . 3B ) . Additionally , interferon alpha treatment of Huh7 . 5 cells had no effect on the number of exosomes released from hepatocytes ( Figure S3 ) compared to untreated cells . Recent studies have consistently demonstrated that miR-122 , Ago2 , and HSP90 enhance HCV replication [26]–[30] . We found that HCV J6/JFH-1 infected Huh7 . 5 cells produced exosomes that are enriched in Ago2 and contain barely detectable HSP90 protein compared to control exosomes from Huh7 . 5 cells ( Fig . 3C ) . Interestingly , exosomes from HCV infected treatment-naïve and treatment non-responder individuals , but not treatment responders , were rich in Ago2 and HSP90 ( Fig . 3D ) compared to control healthy uninfected individuals . GW182 a RISC complex protein which we recently identified as an enhancer of HCV replication associated with alcohol use [31] , was not detected in exosomes in our experimental conditions . Micro RNA-122 , a host factor utilized by HCV for replication , was present in exosomes isolated from both HCV J6/JFH-1 infected Huh7 . 5 cells and HCV-infected individuals ( Fig . 3E & 3F ) . We observed that exosomes from HCV J6/JFH-1 infected Huh 7 . 5 cells showed higher levels of miR-122 compared to exosomes from non-infected cells ( Fig . 3E ) , while exosomes from HCV-infected patients contained lower miR-122 levels compared to those from healthy controls ( Fig . 3F ) . Exosomes were recently shown to mediate retroviral infection independent of envelope protein-receptor interaction [32] . More recently , exosomes from Huh7 . 5 infected cells were found to induce type I interferon production in dendritic cells [24] . Observation of the presence of HCV RNA in exosomes prompted us to evaluate if exosomes from J6/JFH-1-infected hepatocytes or from HCV infected individuals could transmit infection to uninfected cells . We found that exosomes derived from supernatants of HCV J6/JFH-1 infected Huh7 . 5 cells mediated HCV infection after co-culture with uninfected Huh 7 . 5 cells ( Fig . 4A and Figure S4 ) which could be inhibited by Telaprevir ( VX-950 ) an NS3 . 4A serine protease inhibitor ( Fig . 4B ) . Further , exposure of primary human hepatocytes ( PHH ) to exosomes isolated from treatment-naïve or treatment non-responder HCV infected patients resulted in effective virus infection and replication as indicated by detectable HCV RNA in the culture supernatants ( Fig . 4C ) . Active virus replication after infection of PHH with HCV exosomes was indicated by a 2–3 log increase in HCV copy numbers in PHH at 48 hours after infection compared to the initial HCV copy numbers introduced by the HCV exosomes used for induction of infection ( Fig . 4D ) . Additionally , the use of Telaprevir ( VX-950 ) , an NS3 . 4A serine protease inhibitor , could inhibit HCV replication caused by free virus and HCV exosomes in infected PHH ( Fig . 4E ) . CD81 , SB-RI , APOE and HCV E1/E2 proteins are important host and viral molecules for HCV infection [33] . We and others have shown that anti-CD81 and anti-HCV E1/E2 antibodies can block HCV infection [7] , [12] , [34]; however , in some instances antibody therapy in patients could not fully prevent HCV infection [16] . Based on these observations , we tested whether the presence of anti-CD81 antibodies would block exosome and cell free virus transmission of HCV . We found that anti-CD81 pre-treatment effectively blocked free HCV virus infection of target Huh7 . 5 cells , indicated by significantly low HCV RNA expression ( Fig . 5A ) and by lack of expression of HCV NS3 protein ( Fig . 5B ) . However , exosomes containing HCV RNA could still transmit HCV infection despite anti-CD81 antibody pre-treatment ( 1∶50 dilution ) ( Fig . 5A & 5B ) . These findings were validated in primary human hepatocytes where anti-CD81 pre-treatment significantly inhibited free HCV virus infection but failed to prevent patient exosome-mediated HCV transmission ( Fig . 5C & 5D ) . Additionally , SB-RI ( Fig . 5E ) or APOE ( Fig . 5F ) antibody pre-treatment could block HCV J6/JFH-1 free virus transmission but not HCV-exosome transmission of HCV to naïve Huh7 . 5 cells ( Fig . 5E & 5F ) . We next tested CD81-deficient Huh7 . 25-CD81 cells [35] and found that HCV exosomes could still mediate HCV transmission but infection rate with the free virus entry was significantly diminished ( Fig . 6A ) . In the parental Huh7 . 0 cells , both exosomes and free HCV virus resulted in comparable extent of HCV infection ( Fig . 6B ) . HCV E1 and E2 envelope glycoproteins which can modulate HCV infection [36] have been shown to associate with exosomes [23] , thus we tested if anti-HCV E2 antibody treatment could block HCV transmission by exosomes . We found that anti-HCV E2 antibody treatment of HCV J6/JFH-1 virus could significantly block HCV transmission by free HCV particles but not by exosomes ( Fig . 6C ) . Recent reports have demonstrated the role of Ago2 and miR-122 in enhancing HCV replication when bound to the 5′-UTR of HCV dsRNA [26] . We observed that the same MOI of free HCV viruses or HCV-exosomes resulted in a trend ( but not statistically significant ) of greater HCV transmission by exosomes compared to the cell free virus ( Fig . 5 ) . Based on this observation , we surmised that exosomes might contain replication-competent RNA in association with RISC complex proteins that could enhance HCV RNA stability and enhance viral replication [26] , [37] , [38] . Using RNA-chromatin immunoprecipitation ( RNA-ChIP ) analysis of exosomes isolated from HCV J6/JFH-1 infected Huh 7 . 5 cells or HCV infected patients after Ago2 pull-down , we found that Ago2 was associated with miR-122 ( Fig . 7A ) , positive sense HCV RNA ( Fig . 7B upper panel ) and , in some cases , negative sense HCV RNA ( Fig . 7B lower panel ) . Using free HCV virus RNA and RNA from HCV infected cells we confirmed primer specificity for detection of positive and negative sense HCV RNA ( Figures S5A & S5B ) . Additionally , using co-immuno precipitation , we confirmed that HSP90 and Ago2 formed complexes within the HCV containing exosomes likely providing further stabilization of the HCV RNA-replication complex ( Fig . 7C ) [39] . These striking observations indicate that serum exosomes from some HCV infected treatment-naïve patients contain positive sense RNA of HCV virus and are able to transmit active HCV infection . We found that , even in the few patients where we could not detect viral RNA in the exosomes due to the limitation of the sensitivity of the Real Time PCR method ( Table 1 & 2 ) , HCV infection of PHH was still evident ( Fig . 4C ) . Furthermore , replication competent , negative sense HCV RNA was also present in some treatment-naïve and in all non-responder patients ( Table 1 & 2 ) . Given that exosomes from HCV-infected treatment-naïve and treatment non-responders contained Ago2 in complex with miR-122 , and HSP90 , we tested the effect of miR-122 or HSP90 inhibitors which have been suggested for HCV treatment [29] , [37] , [40] . Delivery of a miR-122 inhibitor resulted in about 50% reduction in miR-122 levels in Huh7 . 5 cells that is significant considering the high abundance of miR-122 in hepatocytes ( Fig . 8A ) . However , inhibition of miR-122 in Huh7 . 5 cells prior to infection with HCV exosome failed to significantly suppress HCV transmission ( Fig . 8A ) . Given that exosomes harbored HCV in complex with miR-122/HSP90 , we hypothesized that miR-122 in the exosomes provides advantages for HCV transmission . To test this hypothesis , we transfected HCV-exosomes with a miR-122 inhibitor or control , washed and re-purified the miR-122 inhibitor- or control inhibitor-loaded HCV-exosomes and used them for infection of naïve Huh7 . 5 cells . The miR-122 inhibitor-loaded HCV-exosomes resulted in a significant reduction in intracellular miR-122 levels in Huh7 . 5 cells ( Fig . 8B ) . Importantly , we found reduced virus transmission by HCV-exosomes loaded with the miR-122 inhibitor as indicated by decreased HCV NS3 protein compared to the controls ( Fig . 8C ) . We also assessed the potential of the HSP90 activity inhibitor , 17-DMAG , or HSP90 siRNA treatment to modulate HCV infection transmitted by exosomes ( Fig . 8D ) . We found that DMAG treatment but not HSP90 siRNA treatment could significantly block exosome-mediated HCV transmission ( Fig . 8D ) . Previous data showed that viral HCV entry and subsequent infection can be prevented by administering vacuolar-type H+-ATPase inhibitor [41] . Moreover , Meertens et al [42] reported that entry of HCV pseudoparticles ( HCVpp ) was efficiently blocked by bafilomycin A1 , a specific vacuolar-type H+-ATPase inhibitor , which neutralizes the pH in early endosomes and injures progression of endocytosis beyond this level . Exosome entry through endocytosis is reported to be pH dependent in the traffic of tumor exosomes in regulating both their release and uptake by tumor cells [43] . Based on these reports , we set up an in vitro model utilizing a vacuolar-type H+-ATPase inhibitor ( bafilomycin A1 ) or a proton pump inhibitor ( Lansoprazole ) to study the role of low pH in favoring HCV infected exosome uptake in Huh 7 . 5 cells . We found that low pH plays a role in the entry of infected exosome into the Huh 7 . 5 cells and infection by HCV infected exosomes can be blocked using vacuolar-type H+-ATPase or a proton pump inhibitor . Our data show that both Lansoprazole ( Fig . 9A & 9B ) and bafilomycin A1 ( Fig . 9C & 9D ) could significantly inhibit HCV transmission by exosomes and cell free HCV viruses to Huh7 . 5 hepatoma cells in a dose-dependent manner without causing significant cellular cytotoxicity ( Figures S6A & S6B ) .
Exosomes are found in different biofluids and represent a small ( 40–150 nm ) subpopulation of extracellular vesicles of endocytic origin released by almost all cell types . They act as natural carriers of genetic materials , namely miRNA , mRNA and proteins [44] , [45] . Notably , exosomes have been shown to mediate disease transmission caused by bacteria , infectious prion protein , and viruses [46] , [47] . In the context of HCV , recent studies showed that hepatocyte-derived exosomes containing viral RNA induced production of IFN-α in plasmacytoid dendritic cells ( pDCs ) in vitro [24] . In this study , we demonstrate that circulating exosomes derived from sera of treatment-naïve HCV infected individuals or HCV treatment non-responder individuals contain HCV virus that can transmit active HCV infection to primary human hepatocytes , confirmed with the observation of a 2–3 log increase in HCV copy numbers in PHH compared to the initial HCV copy numbers in exosomes used for infection indicated virus replication . A recent study also reported exosome-mediated transmission of HCV in Huh7 . 5 cells [48] . Our observations confirmed and extended a recent report that also found that exosomes derived from HCV J6/JFH-1 infected Huh7 . 5 cells can shuttle virus to normal Huh7 . 5 cells and establish a productive infection . Using a stringent isolation methodology of serial filtration followed by density separation and immune magnetic CD63-positive exosome isolation , we optimized a method of HCV exosome isolation without carryover of free virus thereby further underscoring the capacity of exosomes to transmit HCV infection . Our findings showed for the first time that exosomes from sera of HCV infected patients or culture supernatants of HCV J6/JFH-1 infected Huh7 . 5 cells can mediate effective CD81 , SB-RI , HCV E2 and APOE -independent HCV transmission to hepatocytes . A recent report by Ramakrishnaiah et al [48] indicated that exosomes can mediate partial CD81-independent HCV transmission in Huh7 . 5 cells , however in that study cell free HCV transmission could not be fully excluded . Our results indicate that exosomes that are devoid of free virus contamination are capable of HCV transmission even in the presence of a potent anti-CD81 , anti-SB-RI , anti-HCV E2 and anti-APOE antibody treatment and in CD81-deficient cells . These observations could explain in part why neutralizing antibodies or therapies that target host/viral protein interactions at the level of cell entry can be compromised and likely occur via cell-to-cell transmission by exosomes . Given that infections with HCV-exosomes compared to the same MOI of free HCV virus particles , showed a tendency for higher levels of HCV transmission to hepatocytes , it was unclear if these exosomes contained replication competent HCV RNA , factors that enhanced virus replication or facilitated mechanisms of exosomes entry to target cells . Recently , reports have consistently demonstrated that RISC-like complexes involving Ago2 and miR-122 can protect the HCV 5′ internal ribosome entry site ( 5′ IRES ) and enhance HCV replication [26] , [38] . We found higher miR-122 expression in HCV J6/JFH-1 infected Huh7 . 5 cells derived exosomes compared to HCV infected patient exosomes and their respective controls , possibly as a result of suppressed interferon production in Huh7 . 5 cells since Huh7 . 5 cells harbor a mutation in the dsRNA sensor retinoic acid-inducible gene-I ( RIG-I ) [49]–[51] . However , using RNA ChIP analyses we found that exosomes from HCV J6/JFH-1 infected Huh 7 . 5 cells and exosomes from the two patient groups that have active infection , treatment-naïve and treatment non-responders , showed increased proportion of miRNA-122 in complex with Ago2 . Additionally , it was remarkable that Ago2 and miR-122 bound to the HCV 5′-UTR was also in association with HSP90 which has been shown to stabilize RISC complexes [52] and potentially increase HCV replication . Our observations support a hypothesis whereby exosomes mediate higher HCV transmission because they contain replication-competent viral RNA , as well as , known HCV replication enhancers- Ago2 [26] , miR-122 [26] , [29] , [37] , and HSP90 [37] , [53] , [54] . Additionally , HCV RNA in exosomes might mediate higher levels of infection possible due to the higher stability of HCV RNA when associated with Ago2 and miR-122 as suggested by Shimakami et al [39] . The presence of host proteins within HCV-exosomes is a clever strategy by the virus to ensure effective replication once in the endoplasmic reticulum ( ER ) given that the ER does not contain these exosomal proteins [55] . Our novel findings may translate and offer possible clinical implications to HCV treatment resistance with interferon/ribavirin given that exosomes from HCV-infected treatment resistant patients contained HCV negative sense RNA , which is mostly associated with replication-competent HCV RNA . Strikingly , only some of the treatment-naïve patients with HCV positive-sense RNA detected in their exosomes contained negative-sense HCV RNA ( Table 2 ) . Importantly , none of HCV treatment responder patients harbored detectable HCV RNA in their serum-derived exosomes consistent with their status of HCV viral clearance . The implication of our findings needs additional clinical follow-up to determine whether treatment and/or disease outcome using anti-HCV immune therapies would be influenced by the composition of serum-derived exosomes in HCV infected patients . Based on our novel findings that exosomes can mediate virus transmission via CD81 , SB-RI and APOE -independent mechanisms potentially compromising the efficacy of HCV immunotherapies , we next aimed to test therapeutic alternatives . We analyzed the potential use of miR-122 inhibition and DMAG treatment both of which have been successfully explored for HCV treatment but not yet assessed in the context of exosome-mediated HCV transmission . We found that using an exosome targeted miR-122 inhibitor system or the HSP90 inhibitor , DMAG , which could inhibit the effective function of these host factors which modulate HCV infection/replication , could significantly suppress HCV transmission by exosomes . Strikingly , attenuation of HSP90 or miR-122 levels by siRNA knockdown and miR-122 inhibitor in target cells was not sufficient to inhibit HCV transmission via exosomes . This could be due to the fact that HCV exosomes contain all the necessary viral and host protein factors that are otherwise not present in the endoplasmic reticulum [18] , and can thus mediate effective replication once cellular entry is accomplished by exosome uptake . Since exosomes originate from lumen of multivesicular bodies ( MVBs ) , their release and uptake are associated with the endocytic pathway [56] . Acidification of intracellular organelles is reported to be fundamental to the function of the endocytic pathways and exosomes uptake [57] . The vacuolar H+-ATPases ( V-ATPases ) and proton pumps are responsible for generating and maintaining intra-cellular pH gradients across cell membranes . Disruption to their functions were reported to be accompanied by lysosomal dysfunction and impaired endocytosis [58] , [59] . From another perspective , several reports show a crucial role of low pH and endosome acidification for triggering virus entry , not addressing the distinction between exosomes and cell free viruses [41] , [60] . Here we show that the use of bafilomycin A1 , a specific vacuolar H+-ATPase proton pump inhibitor , and Lansoprazole , a proton pump inhibitor , prevented the capacity of exosomes and cell free virus to transmit infection , suggesting their use in the treatment regimens for HCV infection . This usage seems to be more influential as it is reported that the intracellular pH was not noticeably changed by dosages less than 100 nM of bafilomycin A1 and for a short period of time ( 4 h ) , which is reported to be the critical time point for effect of bafilomycin A1 to prevent viral entry [61] . In summary , our novel findings , illustrated in Figure 10 provide mechanistic insights into how exosomes can mediate indirect cell-to-cell viral receptor independent transmission of HCV . Furthermore , we provide evidence that circulating exosomes of HCV infected patients can infect primary human hepatocytes . Additionally , our findings further support the rationale for using miR-122 inhibitors , HSP90 inhibitor , and potentially proton-pump and Vacuolar-type H+-ATPase inhibitors to prevent exosome-mediated HCV transmission .
Huh7 . 5 , Huh7 . 0 ( a gift from Dr . Charlie Rice , Rockefeller University , New York ) and CD81-deficient Huh7 . 25 [a gift from Dr . Takaji Wakita ( National Institute of Infectious Disease , Tokyo , Japan ) and Dr . T . Jake Liang ( NIDDK , National Institutes of Health , USA ) ] cells were cultured as previously described [35] , [49] with slight modification , using exosome depleted FBS ( System Bioscience cat . #EXO-FBS-50A-1 ) . Primary human hepatocytes were obtained from the National Institutes of Health ( NIH ) liver tissue cell distribution system ( LTCDS; Minneapolis , MN , USA; Pittsburgh , PA; Richmond , VA , USA ) , which was funded by NIH contract #N01-DK-7-004/HHSN2670070004C and from BD Bioscience . Highly infectious and replication competent HCV J6/JFH-1 virus ( genotype 2a ) were generated as previously described [62] . The pFL-J6/JFH-1 plasmid used for virus generation was provided by Dr . Charlie Rice and Dr . Takaji Wakita ( National Institute of Infectious Disease , Tokyo , Japan ) . HCV J6/JFH-1 virus concentration in culture supernatants was determined using NanoSight LM10 ( MOI of infectious viral particles or infectious exosomes ) and by quantitative real-time PCR as previously described [37] . Subjects were recruited from the Hepatology clinic at the University of Massachusetts Medical School . This research protocol was reviewed by the Committee for the Protection of Human Subjects in Research at the University of Massachusetts Medical School ( IRB #2284 ) . All subjects who donated samples for this project provided signed written informed consent . Subjects were assessed for baseline demographics , Hepatitis C viral serology and liver function parameters ( Table 1 ) . Healthy control subjects had no evidence of systemic disease , HCV infection , or other liver diseases . Informed consent was obtained from all subjects . Blood samples were drawn and serum samples were analyzed for HCV RNA using RT-PCR and processed as subsequently indicated . Huh7 . 5 cells and HCV J6/JFH-1 infected Huh7 . 5 cells were maintained in DMEM low glucose medium supplemented with 10% exosome depleted FBS and 1% penicillin/streptomycin ( Gibco , cat . #15140-163 ) . Cell culture supernatants following cell infection or not , or patient serum samples were collected , centrifuged at 2500 rpm for 10 mins at 4°C to remove cell debris , then filtered through a 0 . 2 µm filter . The 40 mL of filtered culture supernatant for exosome isolation was concentrated to a final 1 mL volume using the Amicon Ultra-15 Centrifugal Filter Unit with Ultracel-100 membrane ( Millipore , cat . #UFC910024 ) . Concentrated culture supernatants or filtered patient serum ( 500 uL ) were mixed with the appropriate volume of Exoquick-TC reagent ( System Biosciences cat . #EXOTC10A-1 ) or Exoquick ( System Bioscience cat . #EXOQ5A-1 ) respectively , for exosome isolation according to the manufacturers' specification . Samples were gently mixed and incubated for 1 h at 4°C . Following incubation , exosomes were precipitated by centrifugation at 1400 rpm for 10 mins at 4°C . The recovered exosomes were re-suspended in 1× phosphate buffered saline ( PBS ) . Positive selection of exosomes was done using anti-CD63 immuno-magnetic capturing with primary anti-CD63 antibody ( Abcam cat . #ab8219 and Santa Cruz cat . #15363 ) followed by corresponding secondary antibody coupled to magnetic beads ( Miltenyi Biotec cat . #130-048-602 ) . The Miltenyi Biotec MidiMACS separator was used with LD columns ( cat . # 130-042-901 ) for exosome isolation . Exosomes isolated by positive anti-CD63 immuno-magnetic bead selection were re-suspended in PBS and transferred to a formvar-coated copper grid then allowed to settle/attach for 30 minutes . The grid was washed by sequentially positioning droplets of PBS on top and using absorbing paper in between . The samples were then fixed by drop-wise addition of 2% paraformaldehyde onto parafilm and placing the grid on top of the paraformaldehyde drop for 10 min . Fixation was followed by five washes with deionized water and samples contrasted by adding 2% uranyl acetate for 15 minutes . Afterward , the samples were embedded by adding a drop of 0 . 13% methyl cellulose and 0 . 4% uranyl acetate for 10 minutes . The grid was visualized using a Philips CM10 transmission electron microscope and images were captured using a Gatan CCD digital camera . Quantification of immuno-magnetic CD63 bead captured infectious HCV J6/JFH-1-exosomes and HCV J6/JFH-1 virus preparations was determined using NanoSight LM10 system ( NanoSight , Amesbury , UK ) equipped with a fast video capture and Nanoparticle Tracking Analysis ( NTA ) system , according to the manufacturer's instructions . Quantification of HCV RNA copy numbers was done as previously described [31] . The following siRNA and miRNA inhibitors were used: human HSP90 siRNA ( Santa Cruz cat . #sc-35608 ) ; control siRNA ( Santa Cruz cat . #sc-44236 ) , hsa-miR-122 anti-miR miRNA Inhibitor ( Ambion , Austin , Tx cat . #AM11012 ) and anti-miR Negative Control ( Ambion , Austin , Tx cat . #AM17010 ) . miRNA or control inhibitors were complexed with the liver specific in vivo Altogen delivery reagent ( Altogen Biosystems cat . #5060 ) which was loaded into control exosomes or HCV exosomes then co-cultured with target cells as indicated . Specific SiRNA or control siRNA was complexed with FugeneHD ( Roche cat . # 04709705001 ) and transfected into target cells according to the manufacturer's specifications as indicated . Exosomes isolated from cell culture supernatants or patient serum samples were fixed at room temperature with 4% formaldehyde buffered saline . Afterward , exosomes were lysed in SDS ChIP lysis buffer ( Millipore cat . # 20-163 ) supplemented with protease inhibitor and RNase inhibitor . Total exosome proteins were pre-cleared with protein G beads . 50 µg of total protein was incubated with Ago2 antibody . Immunoprecipitation was performed for 90 minutes at 4°C using 10 µg/ml primary Ago2 antibody and normal rabbit IgG ( Santa Cruz cat # sc-2027 ) non-specific antibody used as IP control . A mixture of Protein A/G PLUS-Agarose beads ( Santa Cruz cat . #sc-2003 ) was added , and the incubation was continued for an additional 60 minutes . The samples were washed with SDS ChIP lysis buffer supplemented with protease inhibitor and RNase inhibitor . The immunoprecipitated protein-RNA complex was either used for Western blot analysis or RNA purification after Ago2 pull down using the Zymo research Direct-zol RNA MiniPrep kit ( cat . #R2050 ) , according to the manufacturer's specification . TaqMan MicroRNA assay was used for quantification of miRNA , using a CFX Connect Real-Time PCR Detection System ( Philadelphia , USA ) . The exosome miRNA data was normalized to Cel39 and fold change was calculated using delta-delta ct method as previously described [63] . Western blots were performed using the following established protocols . Briefly , proteins were resolved on 10% SDS-PAGE gels . After electrophoresis resolved proteins were transferred onto nitrocellulose membranes . Following protein transfer , membranes were blocked for 1 hour in PBS containing 5% non-fat dry milk and 0 . 1% Tween-20 . Blots were then incubated overnight with primary antibody at 4°C . The following primary antibodies were used: anti-HCV NS3 ( Abcam cat . #ab13830 ) ; anti-HSP90 ( Cell Signaling cat . #4874 ) ; anti-CD63 ( Abcam cat . #ab8219 used for western blotting and Santa Cruz Biotechnology cat . #sc-15363 used for exosomes purification ) ; anti-Ago2 ( Sigma cat . #SAB4200274 ) ; anti-CD81 ( Santa Cruz Biotechnology cat . #sc-23962 ) , normal rabbit IgG-AC antibody ( Santa Cruz Biotechnology cat . # sc-2345 ) ; anti-beta actin [Ac-15] ( Abcam , cat . #ab6276 ) . The membranes were then incubated for 1 hour with horseradish peroxidase-conjugated secondary antibodies ( dilution 1∶10 , 000 ) that included: goat anti-mouse IgG-HRP ( Santa Cruz Biotechnology cat . #sc-2005 ) ; goat anti-rabbit IgG-HRP ( Santa Cruz Biotechnology cat . #sc-2004 ) . Finally , the proteins were visualized with the Clarity Western ECL substrate ( BioRad , cat . #170-5061 ) chemiluminescence system according to the manufacturer's protocol using the Fujifilm LAS-4000 luminescent image analyzer . Prior to total RNA isolation , equal volume of plasma ( 500 µL ) or 500 uL of 10 mL concentrated culture supernatant samples were thawed on ice , mixed with QIAzole ( Qiagen ) , vortexed and incubated at RT for 5 mins . Synthetic C . elegans ( cel ) -miR-39 was spiked and after this step total RNA was extracted using Zymo research Direct-zol RNA MiniPrepKit as per instructions . TaqMan miRNA Assay ( Applied Biosystems ) was used to analyze the miRNA from serum or plasma samples . Cel-miR-39 was used to normalize the technical variation between the exosomes samples and when comparing miRNA or HCV RNA content in cell lines compared to exosomes . Quantification of miR-122 was performed using Taqman microRNA assays ( Applied Biosystems ) . RNU48 was used as an endogenous control for miR-122 expression in cells and Cel-miR-39 was used as an exogenous control to normalize for technical variation in RNA isolation for determining miR-122 levels in exosomes . After RNA isolation as indicated , reverse transcription was performed by two different methods both of which were designed to amplify the 5′-UTR of HCV as previously described [37] . Briefly , positive sense RNA was amplified involving a first cDNA synthesis reaction using 500 ng of total RNA using the Bio-Rad cDNA synthesis kit according to the manufacturer's specification . The positive sense HCV 5′ UTR was then amplified using the following primer sequence: HCV Forward Primer: 5′-TCTGCGGAACCGGTGAGTAC-3′; HCV Reverse primer: 5′-TCAGGCAGTACCACAAGGCC-3′ . HCV negative sense RNA was detected using primers and PCR conditions as previously described [64] . Bafilomycin A1 was purchased from Sigma Aldrich and the proton pump inhibitor; Lansoprazole ( Prevacid 24 hr OTC , Novartis ) , was purchased over the counter . Lansoprazole was dissolved in DMSO and applied to Huh7 . 5 cells at concentrations of 2 . 5 µg/ml , 5 µg/ml , and 10 µg/ml . Telaprevir ( VX-950 ) was purchased from Selleckchem and used as previously described [65] . One hour later , HCV virus suspension and HCV infected exosomes ( captured with CD63 ) were added to the cells . Twenty-four hour later , the cells were washed 3 times and assessed for viral structural protein , NS3 . Bafilomycin A1 was dissolved in DMSO and applied to the Huh 7 . 5 cells at concentrations of 12 . 5 nM , 25 nM , 50 nM , and 100 nM , while the concentration of DMSO in the final treatments was 0 . 01% . One hour later , HCV virus suspension and HCV infected exosomes ( captured with anti-CD63 antibody ) were added to the cells . After 24 h , the cells were washed 3 times and assessed for viral RNA entry . The LDH toxicity assay kit ( Abcam Cat . # ab65393 ) was used according to the manufacturer's specification . Briefly , released LDH in culture supernatants of Huh7 . 5 cells after 24 h co-culture with different concentration of Bafilomycin A1 and Lansoprazole was measured as the indicator of lysed cells . The percentage of cytotoxicity was measured by subtracting LDH content in remaining viable cells from total LDH in untreated controls . Staurosporine ( 20 nM ) ( Abcam , Cambridge , MA ) treatment of Huh7 . 5 cells for 12 h was used as positive control . The final absorbance was measured at 490 nm . All experiments were performed in triplicate . Cells , as indicated , were treated with blocking antibodies to target HCV host receptors for one hour prior to infection with either HCV exosomes , HCV J6/JFH-1 virus or not as indicated . Blocking antibodies used included: anti-CD81 antibody ( Santa Cruz Biotechnology cat . # sc-23962 ) , anti-Scavenging Receptor ( SR-BI ) antibody ( Abcam , cat . # ab52629 ) , anti-HCV E2 antibody ( GeneTex cat . # GTX103353 ) and anti-ApoE antibody ( Millipore Cat . #: AB947 ) . Culture supernatants of Huh7 . 5 cells infected or non-infected with HCV ( J6/JFH-1 ) were centrifuged at 1 , 000× rpm for 10 minutes to remove cells followed by another spin at 2 , 000× rpm for 15 minutes to remove cellular debris . Exosomes were positively selected with CD63 immunomagnetic beads as described above and the flow through collected which included cell free virus and viral particles . Levels of APOE and APOB proteins in the exosomes were identified by using Apolipoprotein E ( APOE ) Human ELISA Kit ( Abcam cat # ab108813 ) and Human Apolipoprotein B ( APOB ) Quantikine ELISA Kit ( R&D Systems cat # DAPB00 ) according to the manufacturers' protocols . The same number of control exosomes ( obtained from non-infected Huh 7 . 5 cells ) , exosomes derived from HCV infected Huh 7 . 5 cells and viral particles were used for the experiment and quantified by Nanosight measurements . The optical density of the color reactions for both plates was read on plate reader at 450 nm . Standard curves were generated and concentrations of APOE and APOB were calculated as stipulated in the manufacturer's protocol . Liver cell protein lysate was used as positive control . Data are representative of at least 3 independently repeated experiments presented as mean + standard error of the mean ( SEM ) . A non-parametric Mann-Whitney U test and multiple comparisons for repeated-measures were done using ANOVA performed with GraphPad Prism Version 5 . 0 ( GraphPad Software ) . A p value of <0 . 05 was considered significant . | Since its first isolation and identification in 1989 , Hepatitis C virus ( HCV ) , has caused significant disease burden to humans worldwide . So far , there is no vaccine against HCV , and neutralizing antibody therapies to block receptor–mediated transmission of HCV to liver cells have so far achieved limited therapeutic benefits . This indicates that HCV can transmit infection via receptor-independent mechanisms . Evidence suggests that small host extracellular vesicles ( exosomes ) can mediate receptor-independent transfer of genetic material between cells , though their role in HCV transmission remains uncertain . Here , we found that the HCV virus can utilize host exosomes to transmit infection to naïve liver cells , even in the presence of potent blocking anti-HCV receptor antibody treatments . Additionally , we identified alternative treatment strategies that can block host exosomes from transmitting HCV infection . Our study provides novel insights to an alternative mechanism of HCV transmission that can compromise anti-HCV immune therapies and proposes potential therapeutic approaches to block exosome-mediated transmission of HCV infection . | [
"Abstract",
"Introduction",
"Results",
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] | [
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] | 2014 | Exosomes from Hepatitis C Infected Patients Transmit HCV Infection and Contain Replication Competent Viral RNA in Complex with Ago2-miR122-HSP90 |
The complex life cycle of oncogenic human papillomavirus ( HPV ) initiates in undifferentiated basal epithelial keratinocytes where expression of the E6 and E7 oncogenes is restricted . Upon epithelial differentiation , E6/E7 transcription is increased through unknown mechanisms to drive cellular proliferation required to support virus replication . We report that the chromatin-organising CCCTC-binding factor ( CTCF ) promotes the formation of a chromatin loop in the HPV genome that epigenetically represses viral enhancer activity controlling E6/E7 expression . CTCF-dependent looping is dependent on the expression of the CTCF-associated Yin Yang 1 ( YY1 ) transcription factor and polycomb repressor complex ( PRC ) recruitment , resulting in trimethylation of histone H3 at lysine 27 . We show that viral oncogene up-regulation during cellular differentiation results from YY1 down-regulation , disruption of viral genome looping , and a loss of epigenetic repression of viral enhancer activity . Our data therefore reveal a key role for CTCF-YY1–dependent looping in the HPV life cycle and identify a regulatory mechanism that could be disrupted in HPV carcinogenesis .
Human papillomaviruses ( HPVs ) are a family of small , double-stranded DNA viruses that infect epithelia at specific anatomical sites . Infection with any of the 12 mucosal oncogenic HPV types is a risk factor for the development of epithelial cancers such as cancer of the uterine cervix and oropharynx [1] . The majority of these epithelial cancers are caused by infection with the HPV16 and 18 viral subtypes . The HPV life cycle is dependent on the differentiation of infected keratinocytes . Infection is established in the undifferentiated basal cells of epithelia , allowing the virus access to the cellular DNA replication machinery required to replicate viral episomes . To maintain the cell in a proliferative state , the viral E6 and E7 oncoproteins work synergistically to delay differentiation and prevent cell cycle exit . These essential viral proteins are encoded by transcripts that initiate from a short promoter situated immediately upstream of the early transcription start site , termed P105 in HPV18 [2] . The activity of P105 is controlled by enhancer and silencer sequences upstream of the promoter in the 850 basepair ( bp ) viral long control region ( LCR ) . P105 contains a canonical TATA box , essential for the recruitment of the general transcription factor II D ( TFIID ) and the initiation of RNA polymerase II ( RNA Pol II ) -dependent transcription [3] . Proximal to the TATA box is a keratinocyte-specific 3′ enhancer , which recruits cellular transcription activators such as Sp1 and AP-1 ( Fos/Jun ) [4–6] . Situated within the 3′ enhancer is a silencer region that contains an array of Yin Yang 1 ( YY1 ) binding sites . YY1 recruitment within this region has strong repressive effects on early gene transcription by the exclusion of AP-1 binding [7 , 8] . It has also been shown in the related HPV31 that the transcription elongation factor TEF-1 and YY1 work cooperatively to activate a second 5′ distal enhancer within the viral LCR [9] , and YY1 binding at this site increases as cells differentiate [10] . However , YY1 binding to the 5′ distal enhancer has minimal effects on transcription in HPV16 [7] . The episomal papillomavirus genome associates with histones to form nucleosomes that are subject to epigenetic modification through the specific recruitment of cellular transcription factors that regulate viral transcription [11] . In HPV31 , levels of acetylated histone H3 and H4 within the LCR increased upon cellular differentiation , particularly in the keratinocyte-specific enhancer , and correlated with increased transcription [10] . It is clear that epigenetic regulation of HPV transcription plays an important role in the HPV life cycle and in enhanced viral oncogene expression during disease progression [10 , 12]; however , the mechanisms involved in this regulation have not been determined . We have previously shown that the chromatin-organising transcriptional insulator protein CCCTC-binding factor ( CTCF ) associates with oncogenic HPV16 and 18 in the E2 open reading frame ( ORF ) , approximately 3 , 000 nucleotides downstream of the viral LCR [13] . CTCF is a ubiquitously expressed host-cell chromatin-binding protein that associates with tens of thousands of sites within the human genome [14] . Depending on the context of the binding site , CTCF can function as an epigenetic insulator or coordinate long-range interactions between gene promoters and distant enhancers [15 , 16] . Notably , mutation of the CTCF binding site within the E2 ORF of HPV18 resulted in increased production of E6/E7 encoding transcripts , leading to hyperproliferation of viral genome–containing keratinocytes in organotypic raft culture [13] . CTCF also binds to the DNA genomes of much larger herpesviruses such as Epstein-Barr virus ( EBV ) , Kaposi sarcoma–associated herpesvirus ( KSHV ) , and herpes simplex virus ( HSV-1 ) , and CTCF recruitment in these viruses is important in the regulation of epigenetic silencing of latency-associated genes [17–22] . This regulation is in part brought about by the ability of CTCF to coordinate long-range chromosomal interactions within the viral episomes [19 , 23] . However , in other contexts , CTCF functions to insulate epigenetic boundaries in these large DNA viruses [18 , 24] . The mechanism by which CTCF regulates HPV oncogene expression is not known . In this study , we identify CTCF- and YY1-dependent loop formation in the HPV18 genome as the mechanism through which viral oncogene expression is restricted in the early stages of infection in undifferentiated keratinocytes . We show that down-regulation of YY1 following differentiation results in loss of loop formation and reversal of epigenetic silencing and facilitates increased oncogene expression and completion of the viral life cycle .
To analyse the function of CTCF in the life cycle of HPV18 , primary human keratinocytes , the natural host cell of HPV , were transfected with religated HPV18 genomes , and replicating episomes were established that were stably maintained at approximately 50 copies per cell . We previously showed that mutation of the CTCF binding site within the E2 ORF of HPV18 ( HPV18 ΔCTCF ) results in a marked reduction of CTCF binding at this site with no effect on the establishment of HPV18 episomes in primary keratinocytes [13] . However , the long-term persistence of herpesvirus saimiri ( HVS ) has been shown to be dependent on CTCF [25] . Therefore , we serially passaged HPV18 wild-type ( WT ) - and ΔCTCF-genome–containing human foreskin keratinocytes ( HFKs ) and performed Southern blot analysis to examine HPV18 episome copy-number variation over time . The genome copy number at all passages analysed ( 9–11 ) was similar between HPV18 WT and ΔCTCF genomes , demonstrating that CTCF binding within the E2 ORF does not play a role in the persistence of HPV18 episomes ( Fig 1A ) . It is important to note that all of the experiments included in this study were performed on cells between passages 9 and 11 to ensure consistent episomal copy number between HPV18 WT and ΔCTCF cultures since viral episomes can integrate into the host genome in long-term culture [26] . To determine whether HPV18 genome establishment alters CTCF protein expression , we quantified CTCF protein in isogenic primary HFKs . We observed a >2 . 5-fold increase in CTCF protein expression following establishment of HPV18 episomes ( Fig 1B ) . This was consistent in two independent donors and is in agreement with a previous study that demonstrated an increase in CTCF protein expression in HPV31-positive neoplastic cervical keratinocytes compared to HFKs [27] . Interestingly , the HPV18-induced increase in CTCF protein is post-transcriptional since quantitative RNA-Sequencing ( RNA-Seq ) and quantitative reverse transcriptase-PCR ( qRT-PCR ) analysis of CTCF transcripts did not show any significant differences in CTCF transcript levels following establishment of HPV18 episomes ( Fig 1C and 1D and S1 Table ) . To determine whether abrogation of CTCF binding at the E2 ORF affects CTCF recruitment elsewhere in the viral episome , we performed chromatin immunoprecipitation followed by quantitative PCR ( ChIP-qPCR ) to specifically amplify CTCF-bound regions throughout the HPV18 genome ( Fig 1E ) . CTCF binding was enriched at the previously identified E2 ORF binding site in cells containing HPV18 WT genomes . In addition , CTCF-enriched regions were identified within the viral LCR , close to the late promoter , and within the L2 ORF . Interestingly , abrogation of CTCF binding at the E2 ORF by mutation resulted in an almost complete loss of CTCF recruitment to all regions of the viral genome , suggesting that CTCF binding at the E2 ORF influences recruitment to regulatory regions that do not contain CTCF binding sites . This phenomenon was consistent in both keratinocyte donors tested . We previously concluded that CTCF recruitment is important in the regulation of HPV18 oncogene expression in differentiated epithelia [13] . Consistent with these results , we found that in undifferentiated cells , transcripts originating from the early promoter were increased in abundance in quantitative RNA-Seq experiments ( Fig 1F and S2 Table ) , which was confirmed by qRT-PCR ( Fig 1G ) . Importantly , our RNA-Seq analysis showed that this increase in early transcripts is specific to E6/E7 encoding spliced transcripts and not to alternatively spliced E2 encoding mRNA species ( Fig 1F and S2 Table ) , which is in agreement with our previous observation that E2 protein expression is not altered in HPV18 ΔCTCF genomes compared to WT [13] . E6 and E7 protein translated from the polycistronic message increased 11 . 3- and 1 . 9-fold , respectively , when the CTCF site was mutated ( Fig 1H ) . To exclude the possibility that abrogation of CTCF binding by mutation of the E2–CTCF binding site results in increased E6/E7 transcription by inadvertently affecting the binding of other factors involved in an alternative regulatory network , CTCF protein levels were depleted by doxycycline-induced expression of two independent CTCF-specific shRNA molecules in HPV18 WT-genome–containing cells ( Fig 1I ) . qRT-PCR analysis of E6/E7 encoding transcript levels demonstrated that partial depletion of CTCF protein resulted in a significant increase in E6/E7 encoding transcripts ( Fig 1J ) . This increase in E6/E7 transcripts was not observed following induction of a nontargeting shRNA control ( Fig 1J ) . Our data show that recruitment of CTCF within the E2 ORF represses HPV18 early gene expression , and we hypothesised that this was due to repression of early promoter activity . Regulatory genomic elements are depleted of nucleosomes , and the remaining nucleosomes are enriched in active chromatin marks ( e . g . , acetylated lysine residues in histone H3 and H4 ) [28] . Formaldehyde-assisted isolation of regulatory elements ( FAIRE ) can be used to identify open and nucleosome-depleted enhancer regions of DNA [29] . To gain mechanistic insight into the control of HPV early promoter activity by distal CTCF binding , the chromatin accessibility of HPV18 episomes was analysed by FAIRE . We consistently observed a higher FAIRE-to-input amplification ratio , indicative of open chromatin at the HPV18 WT viral enhancer and early promoter ( Fig 2A ) . Notably , there was a significant enrichment of open chromatin at the early promoter of HPV18 ΔCTCF genomes ( Fig 2A; p < 0 . 001 ) . This increased chromatin accessibility was consistent between independent donor lines and suggests a mechanism by which CTCF binding at the distal E2 binding site influences nucleosome occupancy within the viral LCR . Interestingly , we observed that immediately downstream of the open chromatin area in the HPV18 WT genome is a region of closed chromatin in the E6 and E7 ORFs and the late promoter , P811 . These findings are in agreement with previous DNase I footprinting experiments that demonstrated dynamic nucleosome binding in the viral enhancer and tightly held nucleosomes at the viral late promoter [11] . FAIRE analysis of the HPV18 episome also revealed an area of open chromatin within the E1 ORF , the specific function of which remains unknown . We next investigated whether the increased accessibility of chromatin within the viral LCR following disruption of CTCF binding was associated with any change in active and repressive epigenetic marks . Using ChIP-qPCR , we analysed levels of the active-promoter–associated H3K4Me3 mark and the polycomb repressor complex ( PRC ) -associated repressive H3K27Me3 mark across the HPV18 genome . These experiments revealed an enrichment of H3K4Me3 in HPV18 ΔCTCF genomes compared to WT , particularly within the viral enhancer and immediately downstream of the early promoter , indicative of active transcription ( Fig 2B ) . In contrast , enrichment of the repressive H3K27Me3 mark was detected in the enhancer and early gene region of HPV18 WT genomes . This finding was surprising , given that expression of E6/E7 has been shown to cause a global reduction in cellular H3K27Me3 [30] . The enrichment of H3K27Me3 was markedly decreased to almost undetectable levels on HPV18 ΔCTCF genomes ( Fig 2C ) . This epigenetic switching of viral genomes unable to bind CTCF at the E2 ORF is consistent with increased transcriptional activity of the viral early promoter in ΔCTCF genomes and explains the observed alterations in chromatin accessibility identified by FAIRE ( Fig 2A ) . Since H3K27Me3 has been shown to inhibit recruitment of the general transcription machinery , we examined RNA Pol II recruitment . Indeed , enrichment of RNA Pol II was observed at the early promoter and within the early gene region in HPV18 ΔCTCF compared to WT , consistent with increased transcription levels ( Fig 2D ) . Numerous host-cell transcriptional regulators have been shown to specifically bind to the HPV LCR and regulate transcription of viral early genes . To determine whether CTCF influences recruitment of specific cellular regulators of HPV18 transcription , we analysed enrichment of transcription factors that regulate early gene transcription using ChIP-PCR . Our analysis revealed that mutation of the CTCF binding site resulted in significantly reduced binding of the YY1 transcription factor at both the 5′ and 3′ enhancers in the viral LCR and at the early and late promoter regions ( Fig 3A ) . Since YY1 functions in the sequence-specific recruitment of PRCs PRC1 and PRC2 , and we detected enrichment of the PRC2-associated H3K27Me3 repressive mark in WT HPV18 genomes , we examined PRC1 and PRC2 binding in the HPV18 genome in WT- and ΔCTCF-genome–containing cells . We found that the PRC2 subunit embryonic ectoderm development ( EED ) was significantly depleted at the viral enhancer and early promoter in HPV18 ΔCTCF genomes compared to WT , consistent with the observed loss of H3K27Me3 ( Fig 3B ) . Reinforcement of repressed chromatin is achieved via recruitment of PRC1 to the H3K27Me3 mark and ubiquitylation of K119 on histone H2A by the PRC1 E3 ubiquitin ligase , ring finger protein 1B ( Ring1B ) [31] . Our data demonstrated that Ring1B was associated with the early promoter in HPV18 WT genomes , but binding was dramatically reduced in ΔCTCF genomes ( Fig 3C ) . Loss of Ring1B was coincident with an almost complete loss of histone 2A lysine 119 ubiquitinylation ( H2AK119Ub ) ( Fig 3D ) . These data indicate that in addition to reduced PRC2 recruitment , PRC1 recruitment to the viral LCR is significantly reduced in HPV18 ΔCTCF episomes . Our data are consistent with a model in which the abrogation of CTCF binding in the E2 ORF results in a loss of YY1 binding to the viral LCR , causing reduced PRC1 and PRC2 recruitment . This leads to reduced H3K27Me3 and H2AK119Ub deposition , de-repression of the HPV18 early promoter , and the up-regulation of viral oncogene expression . Studies have shown that CTCF and YY1 are able to directly interact and that the assembly of this protein complex induces chromatin loop formation between distant loci [32 , 33] . We therefore hypothesised that the repressive effects of CTCF binding in the E2 ORF is mediated through loop formation between the YY1-bound viral enhancer and the downstream CTCF-bound E2 ORF . To test this hypothesis , we used chromosome conformation capture ( 3C ) , a method that can be used to directly measure inter- and intramolecular interactions between specific distal loci . HPV18 WT and ΔCTCF genomes were cross-linked in situ , and chromatin was extracted and digested with the NlaIII restriction enzyme , which restricts the viral DNA at multiple sites ( Fig 4A ) . Digestion efficiency was determined for each sample by qPCR analysis of amplicons that are sensitive to digestion compared with a PCR amplicon that is insensitive to digestion . Samples were only processed further if the digestion efficiency was above 90% . Proximity ligation at low dilution was then carried out to ligate restriction fragments containing DNA loci that are physically associated through interactions between chromatin-bound factors . We designed unidirectional PCR primers to amplify a 346 bp amplicon across the ligation junction that would be formed if the restriction fragments containing the CTCF site in the E2 ORF and the LCR were ligated together as a result of the formation of a chromatin loop ( Fig 4A and 4B ) . 3C analysis of HPV18 WT genomes consistently detected a PCR product of the correct size formed by ligation of the E2 ORF to the LCR . In addition , the 346 bp PCR products were excised from the gel and sequenced to confirm ligation between the YY1-bound viral LCR and CTCF-bound E2 ORF ( S1 Fig ) . Given the small size of the HPV genome , we controlled for nonspecific interactions by carrying out PCR using primers designed to amplify ligation products between the CTCF-bound E2 ORF and the L2 ORF . No interactions were detected between these regions of the genome , although the E2–L2 primers efficiently amplified synthesised DNA molecules containing this ligation junction ( Fig 4B , middle panel ) . We also performed a PCR reaction with primers that anneal within the E1 ORF that are insensitive to NlaIII digestion to ensure equal amplification of digested input chromatin in all 3C experiments ( Fig 4B , lower panel ) . Notably , we found that looping between the YY1-bound LCR and the E2 ORF was significantly reduced in HPV18 ΔCTCF genomes in both donor lines tested ( Fig 4C ) . To confirm that the reduction in E2 ORF–LCR interactions in HPV18 ΔCTCF was due to abrogation of CTCF binding , CTCF protein levels were depleted in HPV18-genome–containing keratinocytes using three independent shRNA lentiviral vectors following induction of shRNA expression with doxycycline ( Fig 4D ) . While E2–LCR interactions were consistently detected in control shRNA-expressing cells , this interaction was significantly reduced following partial depletion of CTCF with all three independent shRNA molecules ( Fig 4E and 4F ) . These data therefore demonstrate that CTCF directs the formation of a chromatin loop between the viral LCR and E2 ORF . We next assessed the role of YY1 in the formation of this chromatin loop by shRNA-mediated depletion of YY1 . HPV18-genome–containing cells were transduced with lentivirus expressing doxycycline-inducible YY1-specific shRNA ( Fig 4G ) . 3C analysis demonstrated that depletion of YY1 resulted in a consistent and significant reduction in E2–LCR interactions ( Fig 4H and 4I ) . Together , our data demonstrate that E2–LCR loop formation in the HPV18 genome requires both CTCF and YY1 . To confirm that YY1-CTCF–dependent chromatin loop formation within the HPV18 episome regulates chromatin topology , we used FAIRE to assess the chromatin structure within the viral LCR and flanking regions following shRNA-mediated depletion of CTCF ( Fig 5A ) and YY1 ( Fig 5B ) . These experiments revealed a significant increase in chromatin accessibility within the LCR following depletion of either CTCF or YY1 , consistent with the increase in chromatin accessibility in HPV18 ΔCTCF genomes that are unable to bind CTCF . Our data show that CTCF and YY1 contribute to chromatin loop formation within the HPV18 genome , resulting in epigenetic repression of early gene expression . To determine whether CTCF and YY1 binding to the viral genome are interdependent , we depleted CTCF protein by shRNA induction and performed ChIP for CTCF and YY1 at the viral LCR and E2–CTCF binding site ( Fig 5C ) . Depletion of CTCF protein resulted in reduced recruitment of CTCF to the E2–CTCF binding site and also a reduction in binding in the viral LCR . Notably , CTCF depletion also resulted in reduced YY1 recruitment to the LCR , and YY1 depletion resulted in reduced CTCF binding at the E2–CTCF binding site ( Fig 5D ) . These data suggest that CTCF binding at the E2 ORF stabilises YY1 binding at the LCR and vice versa and that the enrichment of these proteins within the viral genome is interdependent . It has previously been shown using transcriptional reporter plasmids that YY1 plays a pivotal role in the repression of HPV enhancer activity [7 , 8] . To confirm that YY1 is an essential repressor of HPV18 early gene expression in the context of the HPV genome , YY1 protein was depleted by YY1-specific shRNA expression as previously described and E6/E7 encoding viral transcripts quantified by qRT-PCR . Depletion of YY1 resulted in an over 20-fold increase in E6/E7 transcript levels ( Fig 6A ) , confirming the role of YY1 as a transcriptional repressor in the HPV life cycle . HPV gene expression during the virus life cycle is dependent on keratinocyte differentiation . Differentiation of infected keratinocytes in the midlayers of epithelia corresponds to an increase in early promoter activity [34–37] . In agreement with these studies , synchronous differentiation of keratinocytes by suspension of keratinocytes in semisolid medium for 48 hr resulted in a 2 . 6-fold increase in E6/E7 encoding transcripts in both keratinocyte donors ( Fig 6B ) and protein expression of the intermediate–early keratinocyte differentiation marker involucrin and a marker of the productive phase of the HPV life cycle , E1^E4 ( Fig 6C ) . It has previously been shown that CTCF protein is localised to the nucleus of human keratinocytes and that expression is reduced in differentiated layers of human skin [38] and following morphological differentiation of human corneal epithelial cells [39] . Because of the known interaction between CTCF and YY1 and the functional role of this interaction in 3D chromatin loop formation , we also analysed YY1 expression in HPV18-genome–containing keratinocytes grown in monolayer ( undifferentiated ) or synchronously differentiated by suspension in methylcellulose for 48 hr . Western blot analysis of lysates of undifferentiated and differentiated cells revealed no difference in CTCF protein expression ( Fig 6C ) , and importantly , CTCF protein was expressed at similar levels in HPV18 WT- and ΔCTCF-genome–containing cells ( expression in HPV18 ΔCTCF compared to WT was 0 . 94-fold ± 0 . 18 SD; p = 0 . 97 ) . However , a significant reduction of YY1 protein expression was observed in both HPV18 WT- and ΔCTCF-genome–containing cultures ( Fig 6C and 6D ) , and this was consistent in two independent keratinocyte donors . Since keratinocyte differentiation results in a marked reduction in YY1 protein expression , we examined whether cellular differentiation results in reduced CTCF and YY1 recruitment to the HPV18 genome . HPV18 WT-genome–containing cells were differentiated in methylcellulose and CTCF , and YY1 recruitment was analysed by ChIP . Differentiation of the cells resulted in reduced CTCF recruitment throughout the viral genome ( Fig 6E ) and a dramatic and complete loss of YY1 recruitment to the viral LCR ( Fig 6F ) . To determine the effect of the differentiation-induced reduction in YY1 expression and recruitment to the HPV18 genome on the interaction between the E2–CTCF binding site and the viral LCR , 3C analysis was carried out using undifferentiated HPV18 WT-genome–containing cells harvested after growth in monolayer culture or genome-containing cells differentiated through incubation in methylcellulose for 48 hrs . We found that E2–LCR interactions were significantly reduced following cellular differentiation in two independent keratinocyte donors ( Fig 6G and 6H ) . We next tested whether the decreased looping between the E2 ORF and the LCR we observed on keratinocyte differentiation resulted in changes in the epigenetic status and chromatin accessibility of HPV18 WT genomes . ChIP-qPCR analysis of H3K4Me3 and H3K27Me3 levels revealed enrichment of the active H3K4Me3 mark and loss of H3K27Me3 modifications , indicative of transcriptional de-repression following differentiation ( Fig 7A ) . In contrast , differentiation of ΔCTCF HPV18-genome–containing cells that we previously showed were in enriched H3K4Me3 histone marks when undifferentiated ( Fig 2B ) did not result in any further enrichment of H3K4Me3 following differentiation , indicating aberrant regulation of epigenetic changes upon cellular differentiation in genomes unable to recruit CTCF ( Fig 7B ) . We next examined whether this switching of epigenetic modifications results in increased accessibility of the chromatin using FAIRE . Our data demonstrated that differentiation of HPV18-genome–containing cells resulted in significant depletion of nucleosomes in the HPV18 LCR ( Fig 7C ) . Taken together , our data demonstrate that upon cellular differentiation , reduced YY1 protein expression and recruitment to the viral LCR leads to the loss of chromatin loop formation , depletion of repressive epigenetic marks , and an associated increase in LCR chromatin accessibility . These observations therefore elucidate the mechanism underlying the progressive up-regulation of HPV18 E6 and E7 expression during keratinocyte differentiation , a mechanism likely to play a critical role in the successful completion of the viral life cycle .
CTCF is a major regulator of host and virus transcription and mediates many of its functions by the coordination of dynamic long-range chromosomal interactions [15] . In this study , we show that mutation of the CTCF binding site with the E2 ORF of HPV18 results in a significant depletion of CTCF binding throughout the HPV genome . This interesting phenomenon suggested that intramolecular interactions occur between distinct regions of the HPV18 episome and that these interactions are stabilised by CTCF bound at the E2 ORF . For example , CTCF association with LCR-specific sequences , devoid of CTCF consensus binding sites [13] , could occur via indirect interaction with the CTCF-bound E2 ORF , suggesting that the viral genome is organised by distinct intramolecular interactions . The global reduction of CTCF binding , by either mutation of the E2 binding site or by induction of CTCF-specific shRNA , resulted in increased E6/E7 transcript and protein production . We therefore hypothesised that CTCF mediates intrachromosomal interactions that are important for controlling the activity of the viral early promoter ( depicted in Fig 8 ) . Analysis of the epigenetic status of the HPV18 WT episome revealed several important features . H3K4Me3 enrichment , indicative of active promoter regions , was observed at the early and late promoter regions in comparison to other regions in the HPV18 genome . In contrast , repressive H3K27Me3 levels were low at the early promoter , consistent with a previous study in HPV31 [10] . However , higher levels of H3K27Me3 were observed in the viral LCR , suggesting epigenetic repression of enhancer activity in undifferentiated cells , which has not previously been shown . Enhanced enrichment of H3K27Me3 was also observed at the late promoter . Quantitative analysis of these epigenetic marks in HPV18 ΔCTCF episomes demonstrated that attenuation of CTCF binding resulted in dramatic epigenetic switching in the viral genome , as evidenced by increased accessibility of the viral enhancer within the LCR and an enrichment of H3K4Me3 alongside a global loss of H3K27Me3 marks . This alteration in chromatin accessibility and epigenetic status correlated with enhanced recruitment of RNA Pol II . We previously reported that mutation of the CTCF binding within the E2 ORF of HPV18 results in a reduction in exon 416 to 929 inclusion [13] . Cotranscriptional splicing of RNA is physically linked to RNA Pol II activity , and it has been clearly demonstrated that transcription elongation dynamics influence intron identification and processing by the spliceosome ( reviewed by [40] ) . We therefore hypothesise that the observed alteration of HPV transcript splicing [13] is due to altered RNA Pol II dynamics , and we will formally test this hypothesis in future studies . YY1 in part functions as a cellular transcriptional repressor by mediating the recruitment of PRC1 and PRC2 to specific enhancer loci [41 , 42] . PRC2 catalyses H3K27Me3 deposition while PRC1 catalyses H2AK119Ub deposition , together resulting in transcriptional repression . Since a dramatic loss of H3K27Me3 enrichment was observed in HPV18 genomes unable to bind CTCF , we hypothesised that PRC2 was depleted . We demonstrated significant loss of the PRC2 component EED , providing evidence that PRC2 recruitment is reduced in HPV18 ΔCTCF genomes , resulting in reduced H3K27Me3 deposition . In addition , a significant reduction of the PRC1 catalytic subunit Ring1B to the viral early promoter was observed , resulting in reduced H2AK119Ub deposition . The reduction in both PRC1 and PRC2 recruitment to the viral LCR following abrogation of CTCF binding explains the loss of repressive epigenetic marks and increased chromatin accessibility and activity of the P105 early promoter . CTCF and YY1 can physically associate to stabilise chromatin loops , and organisation of the host cell genome in this manner controls specific gene-expression switching in X chromosome inactivation and neural cell differentiation [32 , 33] . Combined with our data showing the global loss of CTCF recruitment within the HPV18 genome when the dominant E2 ORF binding site was mutated and CTCF-mediated regulation of LCR topology and YY1 enrichment , we hypothesised that CTCF and YY1 mediate an intramolecular interaction between the E2 ORF and the LCR to stabilise an epigenetically repressed chromatin domain . We demonstrated a specific interaction between the E2 ORF and LCR that was dependent on CTCF and YY1 expression . We show that disruption of the E2–LCR interaction results in transcriptional de-repression of the viral early promoter through a dramatic alteration of the epigenetic status of the viral episome and increased chromatin accessibility . Such short-range interactions have been previously identified in cellular loci using similar methods , including interactions between the insulin gene promoter and distal enhancer and within the 2 . 5 kbp CD68 gene [43 , 44] . In addition , a recent study has demonstrated genomic interactions within the KSHV genome , ranging from 5 kbp to >80 kbp in size [45] . These studies provide evidence that short-range genomic loci are important in the regulation of host cell and episomal virus transcription regulation . However , it is important to note that the 3C analysis used in our studies could also detect interchromosomal interactions between multiple viral episomes in the same cell . Such interactions between viral episomes could stabilise the formation of viral super enhancers [46] and/or function in the homologous recombination-dependent replication of episomes in replication centres [47] . Our results provide important insight into YY1 and CTCF function in transcriptional control . We demonstrate that depletion of CTCF reduces YY1 recruitment and vice versa , suggesting that CTCF and YY1 bind to the viral genome in a cooperative manner . CTCF and YY1 have been shown to physically interact [33] , and studies have shown that YY1 and CTCF can anchor loops via homo- and heterodimerisation [32 , 48] . It has previously been shown that over 30% of YY1-occupied sites in the human genome are at locations directly adjacent to CTCF-occupied sites , but that the binding of these factors do not directly colocalise , suggesting that these factors work together to cooperatively influence occupancy at adjacent binding sites [32 , 49] . YY1 is enriched at sites within the host chromatin that engage in 3D looping , and YY1 enrichment at these sites is reduced when these elements are not connected , suggesting that YY1 binding is stabilised by 3D chromatin interactions [32] . It has therefore been suggested that CTCF binding initially serves as an architectural ‘seed’ and that YY1 binding then connects CTCF-bound nearby regulated genes and enhancers . Our results show that depletion of CTCF reduces YY1 recruitment , consistent with this hypothesis , but also suggest that CTCF binding is also influenced by YY1 recruitment . In the HPV life cycle , the E6 and E7 oncoproteins are expressed at low levels in basal keratinocytes , presumably to limit host immune activation and because their combined functions in the cell cycle to maintain expression of the cellular DNA replication machinery are less important in these undifferentiated , cycling cells . Host cell differentiation is associated with increased viral early transcript production , resulting in increased E6/E7 protein expression as well as activation of the late promoter [35] . E6 and E7 act to maintain host cell proliferation , maintaining viral access to the host cell DNA replication machinery , and it has been shown that viral genome amplification in differentiated epithelia requires robust E6 expression [50] . Our data demonstrated a significant reduction in E2–LCR loop formation in differentiated keratinocytes and an associated reduction in epigenetic repression of the viral genome and increased accessibility of the LCR . Our results support a model in which the level of YY1 protein expression controls viral oncogene expression during differentiation . The in-depth analysis of the epigenetic status and topology of HPV18 episomes in a physiological model of the HPV18 life cycle has provided mechanistic insight into the underlying differentiation-dependent control of HPV18 early gene expression . We have demonstrated that CTCF and YY1 together function in coordinating a transcriptional switch that is directly linked to host cell differentiation ( Fig 8 ) . This ensures low-level expression of viral proteins in the basal cells , which presumably facilitates persistence in vivo . As infected cells differentiate , the epigenetically repressed chromatin loop responsible for attenuating activity of the viral enhancer is disrupted , and the repressive epigenetic marks are lost . We show that this mechanism of controlling viral gene expression is regulated by CTCF-YY1 interactions within the HPV18 episome; as cells differentiate , YY1 protein expression is repressed and loop formation is disrupted , promoting enhancer activation . Whether this mechanism of differentiation-dependent regulation of HPV oncogene expression plays a role in HPV-driven cancer is not clear , but it is tempting to speculate that this is the case . YY1 binding sites are often mutated in the HPV16 genome in cervical cancer [51 , 52] , and a recent study has demonstrated that an open chromatin state of the viral LCR correlates with high E6/E7 expression in a model of HPV16-driven carcinogenesis [12] . Since the CTCF binding site within the E2 ORF is conserved in HPV16 [13] , we predict that a similar mechanism of oncogene repression exists in HPV16 . In addition , CTCF recruitment to the E2 ORF within integrated HPV18 DNA in HeLa cells is very low even though the CTCF binding site is intact [53] . Low CTCF binding in HeLa cells is coincident with low H3K27Me3 and high H3K4Me3 marks at the viral LCR and early promoter , combined with high E6/E7 transcript production , which is in agreement with our findings in HPV18 ΔCTCF episomes . It will therefore be of importance to determine whether CTCF-mediated attenuation of viral oncogene expression is disrupted in HPV-driven cancers . To begin to answer this question , we have analysed CTCF binding-site mutations in a cohort of 3 , 215 HPV16 positive lesions and correlated our findings with clinical outcome [54] . A variation in the CTCF binding-site motif 2 was discovered in 357 individual HPV16 sequences ( A2938 to G ) , which we predict would enhance CTCF binding [55] . Interestingly , the presence of this genetic variation in the HPV16 genome is significantly associated with decreased cancer incidence when compared to the cases with no variation in the vicinity of the binding site ( p = 0 . 050 , one-tailed Fisher’s test ) . We therefore speculate that in lesions that contain this variant of HPV16 , CTCF may bind with higher affinity and have a more significant effect on the attenuation of E6/E7 expression , thereby reducing the risk of cancer development . In addition to genetic variations within the CTCF binding site , we also found many sequences that had no sequence information at and around the E2 ORF CTCF binding site . This could be due to integration of the virus such that the E2 coding sequence is disrupted , but in addition to this widely reported mechanism of HPV-driven carcinogenesis , it will be important to determine the mechanism and consequence of CTCF exclusion in cancers with integrated HPV DNA .
The collection of circumcised foreskin tissue from newborns for the isolation of primary HFKs for investigation of HPV biology was approved by Southampton and South West Hampshire Research Ethics Committee A ( REC Reference number 06/Q1702/45 ) . Written consent was obtained from the parent or guardian . The study was approved by the University of Birmingham Ethical Review process ( ERN_16–0540 ) . pGEMII-HPV18 ( gift from F . Stubenrauch , University of Tübingen , Germany ) contains the complete HPV18 genome cloned into the EcoRI site of pGEMII and was used to create pGEMII-HPV18-ΔCTCF , which contains three conservative nucleotide substitutions ( C2993T , G3005A , T3020C ) within the E2 coding region to abolish CTCF binding as previously described [13] . CTCF ( 61311 ) , H3K4Me3 ( 39915 ) , H3K27Me3 ( 39155 ) , RNA Pol II ( 61081 ) , SP1 ( 39058 ) , TEF1 ( 61644 ) , EZH2 ( 39901 ) , EED ( 61203 ) , and Ring1B ( 39663 ) and antibodies were purchased from Active Motif ( La Hulpe , Belgium ) . YY1 antibody ( SC-7341X ) was purchased from Santa Cruz Biotechnology ( Dallas , TX , United States of America ) . H2AK119Ub ( D27C4 ) was purchased from Cell Signaling Technology , Inc ( Danvers , MA , USA ) . FLAG M2 was purchased from Sigma-Aldrich ( Gillingham , United Kingdom ) , E7 clone 8E2 ( ab100953 ) was purchased from Abcam ( Cambridge , UK ) , and E6 clone G7 ( SC-365089 ) and Glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) antibody were purchased from Santa Cruz Biotechnology ( Dallas , TX , USA ) . Monoclonal HPV18 E1^E4 antibody 1D11 was produced by S . Roberts [56] . All horseradish-peroxidase–conjugated secondary antibodies were purchased from Jackson Laboratories ( Bar Harbor , ME , USA ) . The transfection of normal primary HFKs from neonatal foreskin epithelia with recircularised HPV18 WT and ΔCTCF genomes was performed in S . Roberts’ laboratory by J . Parish as previously described [13 , 57] . To eliminate donor-specific effects , primary cells from two foreskin donors were used: one isolated in house and one commercially available ( Lonza , Basel , Switzerland ) . Episome copy number in each cell line was determined by Southern blotting as described previously [26] and calculated by densitometry of three technical repetitions of each HPV18-transfected keratinocyte donor compared to the 50 copies per cell loading control . Digestion of the DNA with EcoRI results in linearisation of episomes , whereas digestion with BglII restricts the host cell DNA but not the viral DNA to reveal integrated or multimeric virus . DpnI digests input DNA only . Organotypic raft cultures were prepared as previously described [13 , 57 , 58] and cultured for 14 d in E medium [58] without epidermal growth factor to allow cellular stratification . Rafts were fixed in 3 . 7% formaldehyde and paraffin embedded prior to sectioning ( Propath Ltd . , Hereford , UK ) . HPV18-genome–containing keratinocytes ( 3 × 106 cells ) were suspended in E medium containing 10% FBS and 1 . 5% methylcellulose and incubated at 37°C , 5% CO2 for 48 h . Cells were then harvested by centrifugation at 250 × g and then thoroughly washed with ice-cold PBS . Cells were then either resuspended in medium containing 1% formaldehyde to cross-link for ChIP and 3C or in urea lysis buffer for protein extraction . ChIP assays were carried out using the ChIP-IT Express kit ( Active Motif ) following the manufacturer’s instructions . Briefly , cells were fixed in 1% formaldehyde for 3 min at room temperature , quenched in 0 . 25 M glycine , and washed in ice-cold PBS . Nuclei were released by 40 strokes in a tight dounce homogeniser . Samples were sonicated at 25% amplitude for 30 s on/30 s off for a total of 15 min using a Sonics Vibracell sonicator fitted with a microprobe . ChIP efficiency was assessed by quantitative PCR ( qPCR ) using SensiMix SYBR master mix using an MXPro 3000 ( Agilent Technologies , Santa Clara , CA , USA ) . Primer sequences for ChIP experiments are shown in Table 1 . Cycle threshold ( CT ) values were calculated at a constant threshold for each experiment , and fold-enrichment–compared to negative control FLAG antibody was calculated using the following formula: Fold binding over IgG = ( 2ΔCT target ) / ( 2ΔCT IgG ) , where ΔCT target = Input CT−Target CT and ΔCT IgG = Input CT−IgG CT . Each ChIP experiment was performed in triplicate , and data shown are the mean ± SD of a representative experiment . Biological repeats were performed for each experiment a minimum of three times with similar results . Cells were lysed in urea lysis buffer ( 8 M Urea , 100 mM Tris-HCl , pH 7 . 4 , 14 mM β-mercaptoethanol , protease inhibitors ) and protein concentration determined by Bradford assay . Equal amounts of protein were separated by SDS-PAGE and western blotting carried out using conventional methods . RNA was extracted with an RNeasy Mini Kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s protocol and DNase treated . For RNA-Seq , libraries were prepared using TruSeq Stranded mRNA Library Prep kit for NeoPrep ( Illumina , San Diego , CA , USA ) using 100 ng total RNA input according to manufacturer’s instructions . Libraries were pooled and run as 75-cycle–pair end reads on a NextSeq 550 ( Illumina ) using a high-output flow cell . cDNA was synthesised using Superscript III ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer’s instructions . qPCR was performed using a Stratagene Mx3005P detection system with SyBr Green incorporation and the primers listed in Table 2 . Cells were fixed and chromatin extracted and sheared as described above for ChIP . FAIRE analysis was then carried out as previously described [29] . Briefly , two aliquots of chromatin were taken , each containing chromatin from approximately 2 × 106 cells , one for Input and one for FAIRE . To the FAIRE samples , 150 μl water was added . To the Input sample , 150 μl water and 10 μl of 5 M NaCl were added , and the samples were incubated at 95°C for 15 min to reverse the crosslinks . RNaseA ( 10 μg/μl ) was added , and the samples incubated at 37°C for 15 min . Proteinase K ( 0 . 5 μg/μl ) was added followed by incubation at 67°C for 15 min . Both Input and FAIRE samples were then extracted with 200 μl phenol:chloroform:isoamylalcohol ( 25:24:1 ) and the aqueous layer retained . DNA was precipitated by conventional methods and the pellet resuspended in 50–150 μl 50 mM Tris-HCl , pH 7 . 4 , 10 mM EDTA . Recovery of FAIRE-extracted DNA in comparison to Input DNA was then determined by qPCR using the ΔΔCT method . Primer sequences are shown in Table 1 . A total of 1–1 . 5 × 107 cells were trypsinised and resuspended in 1 ml 10% ( v/v ) FCS/PBS . Cells were passed through a 70 μm cell strainer and 9 . 5 ml of 1% formaldehyde in 10% FCS/PBS added before incubation for 10 min at RT with end-to-end rotation . Glycine was added to a final concentration of 125 μM before the cells were pelleted at 4°C . Cells were resuspended in 5 ml of ice cold lysis buffer ( 10 mM Tris-HCl , pH 7 . 7 , 10 mM NaCl , 5 mM MgCl2 , 0 . 1 mM EGTA , protease inhibitors ) and incubated on ice for 10 min . Samples were centrifuged at 400 × g for 5 min at 4°C to pellet the nuclei . Five hundred μl 1 . 2× restriction enzyme buffer and 0 . 3% ( final concentration ) SDS were added and samples incubated at 37°C for 1 hr while shaking at 900 rpm . Fifty μl of 20% Triton X-100 was then added , followed by incubation at 37°C for 1 hr with shaking at 900 rpm . Prior to digestion , an aliquot was removed for assessment of digestion efficiency . Four hundred units of NlaIII restriction enzyme were added , and the samples were incubated at 37°C overnight with shaking at 900 rpm . An aliquot of each sample was removed and assessment of digestion efficiency performed by adding 500 μl of 5 mM EDTA , pH 8 . 0 , 10 mM Tris-HCl , pH 8 . 0 , 0 . 5% SDS , and 20 μg proteinase K and incubating at 65°C for 30 min . One μg RNase A was added , followed by incubation at 37°C for 2 hr . The DNA was extracted with PCI and ethanol precipitated using conventional methods and the pellet resuspended in 60 μl dH20 . Digestion efficiency of the viral genomes was assessed by qPCR of genome regions sensitive and insensitive to containing NlaIII restriction sites ( Table 3 ) and comparison of CT values as described in [59] . For ligation , 40 μl of 20% SDS was added to the samples , followed by incubation for 25 min at 65°C with shaking at 900 rpm . A total of 6 . 125 ml 1 . 15× ligation buffer and 1% ( final concentration ) Triton X-100 was added . Samples were incubated for 1 hr at 37°C with gentle shaking . One hundred units of T4 DNA ligase were added and the samples incubated for 4 hr at 16°C , followed by 30 min at RT . Three hundred μg proteinase K was added , and the samples were incubated at 65°C overnight . To purify the digested DNA , 300 μg RNase A was added and the samples incubated for 45 min at 37°C . The DNA was extracted with PCI twice and ethanol precipitated . Finally , the DNA pellet was resuspended in 10 mM Tris-HCl , pH 7 . 5 . Ligation of specific regions of the HPV18 genome was assessed by PCR using sense primers specific to the L1 and E2 ORFs to detect interactions between the CTCF-bound E2 ORF and the viral LCR and sense primers specific for the L2 and E2 ORFs to detect ligation events that could occur by chance ( Table 3 ) . PCR products were assessed by agarose gel electrophoresis and compared to products obtained with a synthesised DNA template equivalent to the predicted ligation product ( GeneStrings ) . Products were sequenced and quantified with a Fusion FX imaging system . Human embryonic kidney 293T ( HEK293T ) cells were grown in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% FBS and transfected with second-generation lentiviral packaging plasmids , psPAX2 and pMD2 . G , and pTRIPZ-shRNA expressing plasmid using polyethylenimine ( PEI ) Max at a DNA-to-reagent ratio of 1:3 . Medium containing lentiviral particles was recovered at 48 and 72 hr post transfection and passed through a 0 . 45 μM filter . The resulting recombinant lentiviruses were concentrated using Vivaspin ultrafiltration spin columns ( 50 , 000 MWCO PES ) and used to spin infect HPV18-genome–containing keratinocytes growing in 6-well plates in E medium containing 8 μg/ml polybrene after feeders had been removed . Plates were spun at 3 , 220 × g for 90 min to facilitate infection , after which media were replaced with E medium . Twenty-four hr later , lentiviral infection was repeated as described above before cells were detached and seeded onto fresh irradiated feeder cells in 10 cm dishes . Puromycin to a final concentration of 1 μg/ml was added to the cells 72 hr later to select infected cells . shRNA expression was induced with 1 μg/ml doxycycline for 48 hr . | Oncogenic human papillomavirus ( HPV ) infection causes cancers of the anogenital and oropharyngeal tracts . HPV infects undifferentiated basal cells of the epithelium at these body sites and expresses low levels of viral early genes , required for replication of the viral genome . In normal epithelia , cellular migration away from the basal layer induces cell cycle exit and differentiation . However , in an HPV-infected cell , differentiation induces increased transcription of the viral early genes to prevent cell cycle exit , supporting amplification of the viral DNA . In this study , we show that the HPV genome recruits the cellular transcriptional regulators CTCF and YY1 , which coordinate an epigenetically repressed chromatin loop between the YY1-bound viral transcriptional enhancer and CTCF-bound early gene region to attenuate early gene expression in undifferentiated cells . As the cells differentiate , YY1 protein expression and recruitment to the viral genome is dramatically reduced . This results in a loss of chromatin loop formation , epigenetic de-repression of the viral genome , and enhanced viral early gene expression . The coordination of viral gene expression with cellular differentiation is vital for persistence of infection and completion of the virus life cycle , and disruption of HPV transcriptional control is also a key step in the development of cancer . | [
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"genome... | 2018 | Disruption of CTCF-YY1–dependent looping of the human papillomavirus genome activates differentiation-induced viral oncogene transcription |
During the last eight years , North and South Kivu , located in a lake area in Eastern Democratic Republic of Congo , have been the site of a major volcano eruption and of numerous complex emergencies with population displacements . These conditions have been suspected to favour emergence and spread of cholera epidemics . In order to assess the influence of these conditions on outbreaks , reports of cholera cases were collected weekly from each health district of North Kivu ( 4 , 667 , 699 inhabitants ) and South Kivu ( 4 , 670 , 121 inhabitants ) from 2000 through 2007 . A geographic information system was established , and in each health district , the relationships between environmental variables and the number of cholera cases were assessed using regression techniques and time series analysis . We further checked for a link between complex emergencies and cholera outbreaks . Finally , we analysed data collected during an epidemiological survey that was implemented in Goma after Nyiragongo eruption . A total of 73 , 605 cases and 1 , 612 deaths of cholera were reported . Time series decomposition showed a greater number of cases during the rainy season in South Kivu but not in North Kivu . Spatial distribution of cholera cases exhibited a higher number of cases in health districts bordering lakes ( Odds Ratio 7 . 0 , Confidence Interval range 3 . 8–12 . 9 ) . Four epidemic reactivations were observed in the 12-week periods following war events , but simulations indicate that the number of reactivations was not larger than that expected during any random selection of period with no war . Nyiragongo volcanic eruption was followed by a marked decrease of cholera incidence . Our study points out the crucial role of some towns located in lakeside areas in the persistence of cholera in Kivu . Even if complex emergencies were not systematically followed by cholera epidemics , some of them enabled cholera spreading .
Numerous factors have been postulated to increase the risk of cholera outbreaks in a given area where cholera is already circulating among the population . The main environmental risk factors identified include heavy rainfall , blooms of plankton , and an increase in sea surface temperatures [1] . However , most studies have been performed in coastal areas and very little is known about environmental factors involved in the recurrence of cholera epidemics in inland areas . In this context , it has been recently shown that the lake areas have been the source of iterative cholera outbreaks in the inland areas of Katanga , a province located south-east of the Democratic Republic of Congo ( DRC ) [2] . Deadly cholera outbreaks have also been reported during complex emergencies ( CEs ) that are defined as “a humanitarian crisis in a country , region or society where there is a total or considerable breakdown of authority resulting from internal or external conflict , and which requires an international response that goes beyond the mandate or capacity of any single and/or ongoing United Nations ( UN ) country programme” [3] . Lastly , probably by analogy with CEs , the risk of cholera epidemics is also an often-repeated assertion in the aftermath of large-scale natural disasters [4] . The provinces of North and South Kivu , bordering Lake Kivu in the east of the DRC , present an exceptional accumulation of these risk factors . They have been the site of numerous dramatic events , including invasion and occupation by foreign forces , civil war , population displacements , a major volcano eruption and earthquakes . According to a recently published mortality survey , it is estimated that the conflicts and humanitarian crises , which ravaged the eastern part of the DRC , have taken the lives of 5 . 4 million people since 1998 , and continue to leave as many as 45 , 000 dead every month [5] . Despite the tragic events encountered in the Kivu provinces , an epidemiological surveillance system was set up at the end of the 1990's , and it is still recording data on cholera and other communicable diseases . Here , we present a study designed to describe epidemiological patterns of cholera outbreaks in the Kivu provinces and to elucidate the influence of specific environmental and geographical factors , CEs and disasters on outbreaks of cholera . This study and the previous work that described the patterns of cholera outbreaks in Katanga and Eastern Kasaï [2] constitute the first step of a plan aiming to fight cholera in the DRC .
From January 2000 through December 2007 , reports of cholera cases and deaths were collected weekly from each health district ( HD ) of North and South Kivu provinces . Case-patients of cholera were defined as recommended by the World Health Organization ( WHO ) : “any person 5 years of age or older in whom severe dehydration develops or who dies from acute watery diarrhoea” , with an age limit lowered to 2 years for cases associated with confirmed cholera outbreaks [6] . Also as recommended by the WHO , each new important outbreak was confirmed by culture and identification of Vibrio cholera O1 from stool samples [6] . North Kivu ( 53 , 855 km2 , 4 , 667 , 699 inhabitants , 19 HDs ) and South Kivu ( 65 , 000 km2 , 4 , 670 , 121 inhabitants , 14 HDs ) are located in the Great Rift Valley , and border Lake Edward , Lake Kivu , and the north edge of Lake Tanganyika . In Kivu , the climate is characterized by a rainy season from October to the end of May and a dry season the rest of the year . However , the rainy season is partially interrupted by a short dryer period in January and February . The relief of the Kivu provinces is dominated by several volcanic chains . Nyiragongo , the most active volcano , is located approximately 20 kilometres north of the city of Goma ( 400 , 000 inhabitants ) near Lake Kivu . Nyiragongo last major eruption occurred on January 17 , 2002 , when lava flow destroyed one third of the city of Goma [7] , claiming 147 victims ( Didier Bompangue , personal data ) . In response to this disaster , the international community brought quick and massive help to the population by providing safe drinking water . Moreover , during the 12-week period following the disaster , access to health care facilities was improved , due to the humanitarian response . In particular , a program of drug supply was implemented to support the primary health centres in Goma and health facilities were made free for a six-week period , followed by another six-week period with reduced prices ( 0 . 2 $ instead of 1 $ for ambulatory care services , including drugs ) . Reports on CEs which occurred in the Kivu provinces from 2000 to 2007 were collected from the Reliefweb Website , which compiles information from a variety of sources , including UN Agencies and non-governmental organizations ( NGOs ) [7] . Among them , we further selected the CEs which were subjected to a medical assessment by humanitarian organizations and which involved more than 1000 internally displaced persons ( IDPs ) . A geographic information system was established , based on the data collected from the 33 HDs of the two provinces . Following a previously described procedure [2] , we statistically examined the relationship between the number of cholera cases in each HD and geographic and environmental variables ( area , population , and presence/absence of cities with >100 , 000 inhabitants , of at least one commercial port , of major tracks or roads , and of lakeside location for each HD ) . Population and area were log transformed and log ( population ) included as an offset term in the model . Due to the overdispersion of cholera incidence , several kinds of generalized linear models were compared using quasi-Poisson , and type I and type II negative binomial distributions and they were checked for spatial structure . Stepwise selection of variables was performed in each case and the best models of each family were compared using the Akaïke index criterion , according to Venables and Ripley [8] and Rigby et al . [9] The relationship between the number of cholera cases in health districts and geographical variables was finally modelled using the type II negative binomial family ( log link function for both the mean and the distribution parameter ) . The residuals were checked for spatial structure by plotting an empirical variogram where the distances were computed depending on the geographical coordinates of the centroid of each health district . A variogram envelope was then computed by performing 1000 permutations of the residual values on the spatial locations and the envelope limits were then compared to the variogram . In the present study , we failed to detect residual spatial autocorrelation . The rate of the cholera cases was mapped for the 33 HDs using ESRI shapefiles . Cross-correlations between time-series of HDs were computed [10] . Time series , which were synchronous ( i . e . with no time lag ) in a geographical area , were merged . This led to define 5 zones ( zone 1: Mutwanga; zone 2: Goma , Kirotshe; zone 3: Bukavu , Katana; zone 4: Uvira , Nundu , Fizi; zone 5: Pinga , Walikale ) . The time-series obtained were decomposed into a trend , a seasonal component and a remainder using a seasonal-trend decomposition procedure based on loess regression ( STL ) following Cleveland et al . ( 1990 ) [11] . In time series analysis , non-parametric STL methods have the advantage of robustness and simplicity but do not allow predictions and detailed quantification of time-series parameters , not necessary for our purpose in the present study . The remainder was examined and , each week , zones with an above-average number of cases were marked as an epidemic reactivation . If epidemic reactivation was typically fostered by war events in a non-epidemic period , one would expect more epidemic reactivations within the 12 weeks following a war event than within the 12 weeks following any randomly selected no-war week . This hypothesis was tested on a basis of 1000 simulations , checking for the occurrence of at least one ( or the absence of any ) epidemic reactivations during the 12 weeks following each week randomly selected during a non-epidemic period . The number of randomly selected weeks considered was proportional to the number of war events actually observed in each zone . The impact of the Nyiragongo 2002 eruption was also analysed in relation to the dynamics of cholera epidemics . In addition , during the 12-week period following the eruption of Nyiragongo , an epidemiological survey was implemented in primary care patients of Goma . During this period , all cases of acute diarrhoea , upper and lower respiratory tract infection and fever were recorded from five local health centres located within the western area of Goma . These data were compared to the records of the centres corresponding to the three weeks before the disasters . Computing and graphical displays were done using ArcGIS 8 . 3 and R 7 . 2 [12] , and the following additional packages: MASS version 44 [8] , maptools [13] , sp [14] , GAMLSS [15] , and geoR [16] .
From January 2000 through December 2007 ( 416 weeks ) , a total of 73 , 605 cases and 1 , 612 deaths ( lethality: 2 . 2% ) from cholera were reported in North and South Kivu . Vibrio cholerae O1 El Tor Ogawa was isolated in 8 samples collected from North Kivu ( among 38 samples ) and 3 from South Kivu ( among 29 samples ) . Vibrio cholerae O1 El Tor Inaba was found only in South Kivu ( 6 positive samples among 29 ) . In Bukavu ( South Kivu ) , Ogawa and Inaba serotypes were isolated during the same outbreak of cholera in 2005 . All isolates were found to be sensitive to ciprofloxacin , erythromycin and nalidixic acid , and resistant to tetracycline , ampicillin and cotrimoxazole . During the eight-year study , both provinces experienced at least one outbreak of cholera per year , with peaks ranging from 130 to more than 700 cases a week ( Figure 1 ) . In South Kivu , cholera cases were reported in every week except for two short periods in 2001 and 2002 ( Figure 1 ) . Time-series decomposition showed a marked seasonal influence , with a greater number of cases during the rainy season . In North Kivu , no period without cholera could be identified . Seasonal patterns were notably different in North Kivu from those in South Kivu , with epidemics occurring during both dry and rainy seasons ( Figure 1 ) . Occasionally , periods of partial remission occurred , during which the weekly incidence of the disease was below 1/50 , 000 inhabitants ( in 2001 , weeks 29 , 30 , 31 , 32 , 33 , 35 and 40 , in 2004 , weeks 23 and 28 ) . Each time , cholera epidemics again appeared , stemming from residual cases located in Goma ( North Kivu ) and Uvira ( South Kivu ) . The spatial distribution of cholera cases was heterogeneous , with a higher number of cases in HDs bordering lakes whereas two remote HDs , Kaziba and Shabunda reported no cases of cholera ( Figure 2 ) . Table 1 shows that the number of cholera cases was significantly higher in the presence of a lake ( odds ratio [OR] 7 , 95% confidence interval range [CI] 3 . 8–12 . 9 ) . According to UN and NGOs reports , a total of 18 large-scale population displacements related to CEs has been recorded during the period . In six cases , these population displacements occurred during already ongoing cholera epidemics . Among the 12 remaining war events with displacements of population , four were followed by a cholera outbreak within a period of 12 weeks . Two of these cholera epidemics occurred in IDP camps , starting six and eight weeks after the arrival of the first IDPs in the settlements . However , simulations indicate that the number of reactivations was not larger than expected after any random selection of a week with no war event in a non-epidemic period ( Figure 3 ) . In 2002 , the Nyiragongo volcanic eruption was not followed by any exacerbation of cholera incidence . A survey performed in five health centres located in the western area of Goma showed that , during this period , diarrhoeas accounted for only 6% of patients who were seen in the health centres . In the entire city of Goma , only 140 cholera cases ( 8 cases per week ) , without any deaths , were reported from January to April 2002 . This low number of cases contrasts with an average of 29 cases per week usually encountered in Goma at this period of the year .
Since the beginning of the 1990's , Kivu provinces have been identified as one of the most active foci of cholera in the world . In 1994 , the refugee camps located around Goma and Bukavu experienced the deadliest cholera epidemics recorded during the last hundred years . This explosive outbreak of cholera , which affected Rwandan refugees , resulted in about 70 , 000 cases and 12 , 000 deaths [17] . Since that period , cholera epidemics have been common in the Kivu provinces , and , in a review of reported cholera outbreaks worldwide , from 1995 to 2005 , Griffith et al . notes that the eastern DRC provinces are among the most affected zones in Africa [18] . Notwithstanding the inherent limitations associated with epidemiological data collected in developing countries , especially in a context of civil war , our study is the first that provides spatial data collected weekly during an eight-year period for a population of ten million living in an area severely affected by cholera . Even though the number of cases reported in this study is impressive ( more than 73 , 000 cases ) , it is likely to be underestimated due to the situation of insecurity which prevailed in this region during the studied period , and which led to poor access to health facilities . By contrast , one cannot exclude that some patients suffering from other diarrhoeal diseases may have been included in the study: indeed , only a few clinical cases have been confirmed by culture , even if that may be due to inappropriate sampling procedures ( samples collected from buckets containing chlorine ) as well as an excessive delay to handle the samples to the laboratories . Moreover , given the lack of local laboratory facilities , other causes of acute watery diarrhoea could unfortunately not be investigated . For instance , in Bangladesh a number of epidemics of watery diarrhoea are actually caused by ETEC ( Enterotoxigenic Escherichia coli ) , which would have a similar clinical presentation to that of the cholera cases here [19] . Therefore , our results obtained using a clinical definition of cases , represent only an estimate of the real cholera burden in the Kivu provinces . Nevertheless , we believe our results to highlight some significant aspects of cholera epidemiology that need further discussion . First , our findings , obtained from a study performed in a lakeside region , are consistent with what was recently found in the province of Katanga , located on the south side of Kivu in the eastern part of the DRC [2] . This previous study showed that 60 percent of cases that were observed in Katanga and Eastern Kasaï between 2002 and 2005 actually occurred in a few lake areas . In these two provinces , the number of cholera cases was significantly higher in the presence of a lake , ( OR 7 . 5 , 95% CI: 3 . 9–14 . 2 ) . The present study confirms that the same trends can be observed in the Kivu Provinces . Similar to the role played by the towns of Kalemie and Bukama in Katanga , the cities of Goma , Bukavu and Uvira seem to act as the main sources of cholera epidemics in the Kivu provinces . From an operational point of view , this finding implies that more attention should be paid to cholera in these towns , especially in periods when outbreaks are starting with a small , but rising number of cases . Here , the influence of seasons , and the effect that some lakeside areas have on the persistence of the disease , could make the epidemiological pattern of cholera in Kivu comparable to patterns studied more comprehensively in Asian coastal areas [1] , [20] , [21] . Two studies have stressed the link between having cholera and living on the shores of a lake or a river , which includes drinking the water and bathing in it . One of these studies was carried out in Rumonge , a city in Burundi bordering Lake Tanganyika , the same lake where the port of Uvira is located [22] . Another one , carried out in nearby Lake Victoria in Kenya , suggested the possible existence of at least a transient environmental reservoir for cholera in the lake and evoked the possible role of water hyacinths in maintaining environmental sources of toxigenic cholera strains during inter-epidemic periods . [23] . However , lake water differ from estuarine brackish water that is known to be the natural reservoir of V . cholerae [24]–[26] . Even though lake water can sometimes be rich in plankton [27] , the role of lakes as reservoirs for V . cholerae is not formally established because no study has yet demonstrated a long term persistence of toxigenic V . cholerae in the East African Rift Valley lakes . Our results show that there is a need for further studies to explore the role of lake environments in the persistence of cholera in inland Africa . Indeed , some endemic V . cholerae strains have long been isolated from fresh water [28] and Kirschner et al . recently demonstrated the permanent existence of non-toxigenic V . cholerae strains , which can rapidly grow in a free-living state in one natural lake water in Austria [29] . The eruption of Nyiragongo , the largest natural disaster reported during this period , was not followed by a cholera outbreak , or by any other disease outbreak . On the contrary , during the months that followed the disaster , the rates of cholera infection were among the lowest that have been recorded in the city of Goma during the 8 years of the study . Several hypotheses can be advanced to explain this low number of cholera cases . This can be due to the impact of the emergency program , but other possible explanations cannot be ruled out , including the fact that the volcanic eruption could have decreased the likelihood of a cholera outbreak secondary to the alterations in water sources and usage patterns . Our finding is in agreement with a recent study showing that earthquakes , tsunamis and volcano eruption were not usually followed by epidemics [30] . In particular , for 20 years , not a single cholera outbreak has been recorded in the aftermath of geophysical disaster , even after the cataclysmic tsunami in Asia in 2004 . Here , we show that even in a place where cholera outbreaks are common and during a period known to be favourable for epidemics ( the rainy season ) , a disaster that destroyed approximately 12 , 000 homes and partially destroyed the water supply pipelines of a town of 400 , 000 inhabitants , the occurrence of cholera epidemics was not unavoidable . The search for the impact of the CEs indicates that they do not systematically represent a triggering factor for cholera outbreaks . However , in our study , we also saw that in four cases , the occurrence of a CE led to the exacerbation of cholera , including , in two cases a cholera outbreak , which started in a displaced settlement a few weeks after the arrival of IDPs . Actually , several conditions are necessary for a CE to father a cholera outbreak . Among these conditions , there is the fact that some of the IDPs fleeing from the conflict area should have a previously acquired cholera infection ( symptomatic or in incubation ) , and/or the fact that the IDPs should move into areas where cases of cholera are already present . It would also be necessary for there to be insufficient or no assistance to the IDPs ( i . e . provisions for safe drinking water and a system of free health care ) . These circumstances were met in 1994 when one million refugees coming from Rwanda settled in makeshift camps around Goma , overwhelming the capacities of humanitarian staff already present in the town . More recently , due to the insecurity that prevailed around Goma in the summer and fall of 2008 , a cascade of cholera outbreaks that began in Rutshuru in the North of Goma have been recorded in North Kivu: Rutshuru ( beginning on week 37 ) , Goma and Karisimbi ( beginning on week 40 ) , Walikale and Birambizo ( beginning on week 44 ) . In each of these HDs , the outbreak was introduced by people escaping from battle-hit areas located North of Goma ( D . Bompangue , personal data ) . Simultaneously , due to excessive danger for fieldworkers , numerous NGOs fled from Goma and neighbouring areas as cholera outbreaks started , leading to a disorganization of the programs aimed to limit the spread of the epidemic . In conclusion , the epidemiology of cholera in both Kivu provinces confirms our previous findings in Katanga and eastern Kasaï , and highlights the role of some towns located in lakeside areas as sources of cholera outbreaks . The results of this study show that , even if each CE with numerous IDPs is not systematically followed by a cholera outbreak , CEs may facilitate spreading of already existing outbreaks due to the fleeing of infected IDPs to new areas where NGOs cannot reach them due to an excessive danger for fieldworkers . By contrast , even in a context of CE and natural disaster , the occurrence of epidemics is not unavoidable . For example , the number of cholera cases was lower than expected after the partial destruction of the town by the Nyiragongo eruption followed by the implementation of an emergency program . We think that this low number of cholera cases is one more argument to implement programs aiming to restore , and if possible to improve , drinking water access following natural disasters . | With the number of cholera cases up to 73 , 000 during the last eight years and successive wars that have persisted for fifteen years , the North and South Kivu provinces of the Democratic Republic of Congo are currently heavily hit by both cholera outbreaks and war-related population displacements . Prior to this study , no research had been done to identify the sources of epidemics and the pathways used by cholera to spread throughout the Kivu provinces . Here we show that a few cities located on the lakeshore of Lake Kivu and Lake Tanganyika act as the main sources of cholera epidemics and that the number of cholera cases tends to increase during the rainy season . We also found that only a minority of population displacements were followed by cholera outbreaks . Finally , we think that the low number of cholera cases recorded after the Nyiragongo eruption is one more argument to implement programs aiming at restoring , and if possible improving , drinking water access following natural disasters | [
"Abstract",
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"Methods",
"Results",
"Discussion"
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] | 2009 | Cholera Epidemics, War and Disasters around Goma and Lake Kivu: An Eight-Year Survey |
Although neurological manifestations associated with dengue viruses ( DENV ) infection have been reported , there is very limited information on the genetic characteristics of neurotropic DENV . Here we describe the isolation and complete genome analysis of DENV serotype 3 ( DENV-3 ) from cerebrospinal fluid of an encephalitis paediatric patient in Jakarta , Indonesia . Next-generation sequencing was employed to deduce the complete genome of the neurotropic DENV-3 isolate . Based on complete genome analysis , two unique and nine uncommon amino acid changes in the protein coding region were observed in the virus . A phylogenetic tree and molecular clock analysis revealed that the neurotropic virus was a member of Sumatran-Javan clade of DENV-3 genotype I and shared a common ancestor with other isolates from Jakarta around 1998 . This is the first report of neurotropic DENV-3 complete genome analysis , providing detailed information on the genetic characteristics of this virus .
Dengue viruses ( DENV ) are among the most important mosquito-borne viruses from the genus Flavivirus , family Flaviviridae and have been a major public health problem in many parts of the world , including Southeast Asia and the Americas [1] . In Indonesia , DENV has become a significant public health problem with a trend toward increasing numbers of outbreaks [2–5] . Infection with any of the four DENV serotypes can be asymptomatic or cause a spectrum of clinical symptoms from mild fever to a more severe , potentially life-threatening disease including dengue haemorrhagic fever and shock syndrome [6] . Although DENV is not a classical neurotropic virus , evidence of DENV neurotropism and neurological dengue have increased over the past five decades [7] . In laboratory-confirmed cases of DENV infection with admission to the hospital , the frequency of neurological involvement has ranged between 0 . 5% to 21%; while in patients admitted to the hospitals with encephalitis or suspected central nervous system ( CNS ) infection , DENV was identified in 4⎼47% of patients in endemic areas [7] . To address this expanding clinical manifestation of DENV infection , the latest WHO dengue guidelines published in 2011 have included CNS involvement in the definition of severe disease [8] . Nevertheless , the molecular and biological characteristics of neurotropic DENV strains is extremely limited despite its important role in deciphering the neuropathogenesis of dengue . Here , we report the isolation and complete genome analysis of DENV-3 from the CSF on a paediatric encephalitis patient in Jakarta , Indonesia .
Informed consent was obtained from the parents of the patient . The patient’s identity and personal information has been de-identified from the sample number , test results , or GenBank accession number . The study was approved by the Eijkman Institute Research Ethic Commission ( Ethical Approval No . 66 ) . In June 2016 , a 10-year old boy was referred to Dr . Cipto Mangunkusumo National Central Hospital , Jakarta , Indonesia with the chief complaint of decreased consciousness on the second day of illness . He had history of high grade fever for two days accompanied with headache , nausea , vomiting , seizures and altered consciousness before admission . His laboratory examination showed hemoconcentration , thrombocytopenia , positive anti-DENV serum IgM and NS1 antigen , and normal blood chemistries . Lumbar puncture ( LP ) was performed on day two post illness onset to obtain CSF sample from the patient . There was no evidence of blood in the CSF indicating that the LP was not traumatic . The CSF was clear with 5 polymorphonuclear cells/μl , 2 mononuclear cells/μl , 60 mg/dl protein , 51 mg/dl glucose , and 121 mEq/l chloride . The patient was diagnosed with dengue encephalitis based on the neurological symptoms and detection of anti-DENV antibody and NS1 antigen in serum and was given supportive therapy . His consciousness improved on day 5 of illness after intensive supportive care and he was discharged after 12 days without any neurological sequelae . The CSF sample ( designated as 201610225 ) , was submitted to Eijkman Institute where it was analysed by using pan-alphavirus and pan-flavivirus RT-PCR targeting nSP4 and NS5 genes , respectively [9 , 10] . Pan-alphavirus RT-PCR was negative , while pan-flavivirus RT-PCR was positive . A flavivirus-specific 265-bp amplicon was sequenced from the sample and showed 99% nucleotide identity with DENV-3 in subsequent BLAST analysis . Further examination of the CSF and serum samples confirmed the presence of DENV-3 RNA by using Simplexa Dengue Real-time RT-PCR Kit ( Focus Diagnostics , Cypress , CA ) . The anti-dengue IgM and anti-dengue IgG ( Panbio Dengue Duo IgM and IgG Capture ELISA , Alere , Brisbane , Australia ) were absent from CSF although the serum sample was DENV IgM-positive/IgG-negative , suggesting a primary DENV infection . DENV NS1 antigen was detected in the serum and CSF samples by using rapid NS1 detection kit ( Panbio Dengue Early Rapid ) . The DENV viral load was measured by using SYBR Green qRT-PCR as described previously [11] and was estimated to be 19 , 800 and 975 , 000 PFU equivalent/ml in the CSF and serum samples , respectively . Virus isolation was performed by using Vero cells ( African green monkey kidney epithelial ) as described previously [12] . Cell monolayers were inoculated with the CSF and serum samples and incubated for 1 hour at 37°C . Infected monolayers were subsequently maintained in MEM medium supplemented with 10% FBS , 500 U/ml penicillin , 500 μg/ml streptomycin , and 2 mM L-glutamine ( Gibco , Carlsbad , CA ) . The presence of cytopathic effect ( CPE ) was investigated daily and DENV-3 RNA in culture supernatant was detected by pan-flavivirus RT-PCR and sequencing of the amplicon . Virus isolation from CSF sample was also performed by using Aedes albopictus C6/36 cells . Cell monolayer was inoculated with CSF sample and incubated for 1 hour at 28°C and subsequently maintained in RPMI medium supplemented with 10% FBS , 500 U/ml penicillin , 500 μg/ml streptomycin , and 2 mM L-glutamine ( Gibco ) . The presence of CPE was investigated daily and DENV-3 infection was confirmed by IFA staining with anti-DENV-3 monoclonal antibody . In the absence of CPE within 10 days , culture supernatant was blind passaged to new C6/36 cell monolayer . For IFA staining , infected C6/36 cells were spotted onto 12-well Teflon coated slides , air-dried and then fixed in cold 100% acetone for 10 min . Fixed cells were incubated with mouse anti-DENV-3 monoclonal antibody ( clone 5D4 ) for 30 minutes at 37°C , followed by FITC-conjugated goat anti-mouse IgG ( Sigma , St . Louis , MO , USA ) for 30 minutes at 37°C . Cells were rinsed twice with PBS and counterstained with 0 . 05% Evans blue . Next-generation sequencing ( NGS ) was employed to sequence the complete DENV-3 RNA genome from Vero cell culture supernatant by using Ovation RNA-Seq System V2 library preparation kit ( NuGEN , San Carlos , CA ) according to the manufacturer’s recommendations . The Ion PGM Hi‐Q OT2 Kit ( Life Technologies , Carlsbad , CA ) was used to prepare and enrich template-positive particles from the cDNA library . Enriched cDNA library was sequenced using the Ion PGM sequencer , Ion PGM Hi‐Q Sequencing Kit , and an Ion 316 Chip v2 BC . Reads from the Ion PGM sequencer were dereplicated and filtered based on their quality and length with PRINSEQ lite version ( prinseq-lite . pl ) [13] , with following settings: -derep 5 -max_len 250 -min_len 125 -min_qual_mean 30 . These settings were used to remove reverse complement 5'/3' duplicates as well as to screen for reads with Q>30 and length range 125–250 bp . SPAdes assembler v . 3 . 7 . 1 meta-mode [14] was used to assemble the filtered reads , with following settings:--iontorrent -k 21 , 33 , 55 --meta . The extreme 5’ and 3’UTR of the genome were obtained by Sanger sequencing using DENV-3-specific primer sets as described previously [15] . The full length 10 , 707-nt sequence of DENV-3 strain 201610225 was annotated with Genome Annotator tools in Virus Pathogen Database and Analysis Resource ( ViPR , www . ViPRbrc . org ) . The complete genome sequence of DENV-3 strain 201610225 was assembled and aligned with DENV-3 prototype strain H87 and all available DENV-3 complete genome sequences retrieved from the GenBank database with known host and country of isolation as of December 6 , 2016 ( n = 787 ) ( S1 Table ) . Multiple sequence alignment was performed using MUSCLE v . 3 . 8 [16] and trimmed to generate a dataset of 10 , 170-nucleotide ( 3 , 390 amino acid ) encoding the complete DENV-3 open reading frame ( ORF ) . The aligned sequences were used to analyse amino acid sequence variation visualized in MEGA v . 7 . 0 . 21 . The 3’UTR RNA secondary structures of 201610225 and prototype strain H87 were predicted using the Mfold web server ( http://unafold . rna . albany . edu/ ? q=mfold ) [17] . Mfold predictions were constrained to preserve consistency of RNA structures with bioinformatics and biochemical predictions as described previously [18] . The optimal energy folding patterns from the Mfold predictions were visualized with VARNA [19] . To identify representative and closely related reference sequences for deep phylogenetic analysis , a maximum-likelihood phylogenetic tree was constructed from the initial alignment by using FastTree v . 2 . 1 . The evolutionary history and the time to most common ancestor ( TMRCA ) of strain 201610225 was analysed using Bayesian Markov chain Monte Carlo ( MCMC ) method as implemented in BEAST v . 1 . 7 . 4 [20] . The evolution model was evaluated using jModelTest v . 2 . 2 . 7 [21] and the generalised time reversible ( GTR ) model with invariant and gamma sites ( GTR+I+G ) was used , as recommended by Akaike ( AIC ) and Bayesian information criteria ( BIC ) from jModelTest output . BEAST analysis was subsequently performed with following parameters: GTR+I+G , lognormal relaxed clock , uniform clock rate , coalescent Bayesian skyride tree prior , and 50 million MCMC repetitions ( sampling every 1 , 000 trees ) [22] . The collection of trees from this process was annotated using TreeAnnotator v . 1 . 8 . 2 to get the most credible tree . FigTree v1 . 2 . 2 was used to view and evaluate the most credible tree . Similarly , for further grouping of 201610225 relative to other recent Indonesian DENV-3 strains , 112 available DENV-3 envelope gene sequences ( 1 , 479 nt ) were analysed including prototype reference strains for each DENV-3 genotype as well as recent Indonesian and Southeast Asian strains . Amino acid changes mapping onto existing homologous structural data from Protein Data Bank ( PDB ) was done by using FeatureMap3D ( http://www . cbs . dtu . dk/services/ ) [23] . The multiple sequence alignment dataset was also used to check for the presence of recombination in our isolate by using the Recombination Detection Program version 4 ( RDP4 ) which used seven different recombination detection methods [24] . A sequence was considered as a potential recombinant only if it was detected as significant ( with p-value cutoff of 0 . 00001 ) by at least two methods . The complete genome sequence of DENV-3 isolate 201610225 is available in GenBank under accession number KY863456 .
Neurotropic DENV-3 strain was isolated and identified from CSF sample of a paediatric encephalitis patient in Jakarta , Indonesia . Inoculation of CSF sample from the patient into Vero cells caused apparent CPE after day 5 ( Fig 1B ) , while inoculation of the serum sample caused distinguishable CPE after day 10 ( Fig 1C ) . No changes were observed in the uninfected Vero cells ( Fig 1A ) . Infection of the Vero cell was confirmed by detection of DENV-3 RNA in the culture supernatant by pan-flavivirus RT-PCR followed by sequencing of the amplicon . Furthermore , DENV-3 infection at third passage in C6/36 cells inoculated with CSF sample was confirmed by detection of intracellular DENV-3 antigen using DENV-3-specific IFA staining ( Fig 1E ) . The presence of DENV-3 antigen was not observed in uninfected C6/36 cells ( Fig 1D ) . By using NGS strategy , 94% of the complete genome was determined from the virus isolate recovered from Vero cell culture supernatant , while the remaining 5’ and 3’UTR were sequenced by classical Sanger sequencing . The extreme terminal 5’ and 3’UTR were not confirmed independently ( i . e . using RACE method ) and assumed to be of the same length as the prototype DENV-3 strain H87 . The complete genome of isolate 201610225 consists of 94 nucleotides of 5’UTR , 440 nucleotides of 3’UTR , and a 10 , 173-nucleotide ORF located from 95 to 10267 . Compared with the prototype DENV-3 strain H87 , 46 amino acid changes were found within the genome of 201610225 ( Table 1 ) . Among these , 14 and 20 amino acid changes were shared with the historical DENV-3 strain collected in Sleman in 1978 ( Sleman/78 ) and Jakarta in 1988 ( den3_88 ) , respectively . Two novel amino acid changes , R3259K and F3369Y , located in the NS5 protein coding sequence were not observed in any other complete DENV-3 genome . However , when these two changes were mapped to the 3D structure of the NS5 proteins , we found that R3259K and F3369Y are not predicted to significantly affect the physicochemical property of the NS5 protein although the R3259K change is located in the protein bend structure . Moreover , while the remaining 44 amino acid changes were also observed in other DENV-3 strains in our complete genome data set , nine amino acid changes were only found in relatively few other DENV-3 strains ( less than 1% occurrence ) , including G76R ( found in 4 others ) , A624V ( found in 2 others ) , I896V ( found in 3 others ) , R1176K ( found in 2 others ) , H1180Q ( found in 1 others ) , K1658R ( found in 6 others ) , Q1940R ( found in 1 other ) , D2764N ( found in 2 others ) , and Q3052R ( found in 7 others ) . In addition to amino acid changes within the ORF , we also examined the nucleotide changes that occurred in the 5’UTR and 3’UTR of the neurotropic DENV-3 genome . Compared with the prototype DENV-3 strain H87 , a G to A change at position 62 was observed; this change was also seen in other DENV-3 strains in our data set . The G62A nucleotide change was found to be associated with DENV-3 genotype I specifically [25] . No other nucleotide changes were found in the 5’UTR of our neurotropic DENV-3 genome . The 3’UTR of 201610225 genome has an insertion of an 11-nucleotide sequence ( AGTGAAAAAGA ) after position 10275 and seven nucleotide changes including A10269C , C10294T , G10425A , T10476C , A10504G , T10567G , and C10585T when compared with the prototype DENV-3 strain H87 . The RNA structure of complete 3’UTR 201610225 was predicted and compared with the strain H87 ( Fig 2 ) . The nucleotide changes present in our isolate were not predicted to alter the two stem-loop ( SLI and SLII ) , the conserved duplicated dumbbell structures ( DBI and DBII ) , or the essential terminal structure small hairpin 3’ stem-loop ( SHP-3’SL ) which are important for viral RNA synthesis [18] . However , the 11-nucleotide insertion observed in our isolate was predicted to form bigger stem-loop structure downstream of the stop codon compared with the strain H87 . Furthermore , when we compared these nucleotide insertion and changes to all other DENV-3 3’UTR sequences , we found that none were unique to isolate 201610225 . The complete ORF of DENV-3 phylogenetic tree generated using the Bayesian MCMC method showed that 201610225 belongs to genotype I based on Lanciotti et al and Wittke et al classification [26 , 27] and is closely related to isolate TB16 , KJ30i , and TB55i ( GenBank acc . no . AY858047 , AY858042 , and AY858048 , respectively ) sampled in Jakarta , Indonesia in 2004 ( Fig 3 ) . This phylogenetic history was supported by high posterior probability values ( 1 . 0 ) . Molecular clock analysis suggested that these strains shared a common ancestor dated from approximately 1998 ( 95% HPD: 1996–1999 ) . Furthermore , our phylogenetic tree also indicates that 201610225 was part of Sumatran-Javan clade which includes DENV-3 genotype I strains from Sumatra , Jawa , and Sulawesi islands in Indonesia as well as strains from Timor Leste ( formerly East Timor ) described previously [28] . The time to the most recent common ancestor ( TMRCA ) of this Sumatran-Javan clade was approximately 1978 ( 95% HPD: 1977–1983 ) supported by high posterior probability values ( 1 . 0 ) . Similar to the complete ORF phylogenetic tree , MCMC phylogenetic tree based on the envelope gene with additional recent DENV-3 Indonesian and Southeast Asian reference strains suggested that strain 201610225 is closely related to the same Jakarta strains mentioned above as well as to strains from Bandung ( GenBank acc . no . AY265857 ) and Semarang ( GenBank acc . no . KC589013 and KC589012 ) , Jawa island , Indonesia ( S1 Fig ) . The TMRCA of this clade based on the envelope region is estimated to be 1997 ( 95% HPD: 1996–1998 ) with high posterior probability values ( 1 . 0 ) . The envelope phylogenetic tree also confirms the classification of our isolate as DENV-3 genotype I . Additionally , screening for recombination by RDP4 analysis found no evidence of recombination in 201610225 genome .
Over the last five decades , numerous studies have reported DENV-associated neurological manifestations in prospective and retrospective as well as in outbreak and case report study settings [7] . Despite the recent increase in the number of cases of neurological dengue , the pathogenesis of neurotropism is still controversial and the neurotropic virus are not well characterized , although some studies suggest a direct virus-induced destruction of neurons [29–31] . The encephalitis case presented here is similar to cases described in some of the earliest reports of DENV-associated encephalitis/encephalopathy in Indonesia [32–35] . However , assays to detect virus or antibody were either not performed or not successful in these studies . Our report describes the isolation and complete genome analysis of DENV-3 from CSF of an encephalitis patient in Jakarta , Indonesia . Although a number of studies have reported the presence of DENV RNA and infectious virus in the CSF of encephalitis patients , there are only few published studies detailing the findings on the genetic characteristics of the neurotropic DENV strains , including one DENV-4 complete genome sequence , one DENV-3 partial capsid-prM sequence , one DENV-3 envelope , and two DENV-2 partial envelope-NS1 sequences isolated directly from CSF of encephalitis patients [36–39] . To our knowledge , this is the first complete genome analysis of DENV-3 isolated from CSF in a patient with neurological manifestations , underscoring the need for more molecular studies to better characterize neurotropic DENV strains . The identification of serotype DENV-3 in an encephalitis case is not unexpected , as DENV-3 , together with DENV-2 , appeared to be more frequently associated with encephalitis and other neurological complications compared with other serotypes [29 , 37–43 , 35] . Although all DENV serotypes are endemic in Indonesia and cycling occurs between DENV serotypes , DENV-3 has been historically reported as the predominant circulating serotype since 1975 [44] . A meta-analysis study encompassing 15 , 741 cases of dengue from 31 reports of primary infections found that these primary infections with DENV-3 are associated with severe cases under WHO new classification [45] , thus emphasizing the need for appropriate clinical consideration for DENV-3 cases . It is also noteworthy that the patient in this report experienced primary DENV infection as evidenced by the absence of anti-DENV IgG both in serum and CSF . Antibody-dependent enhancement ( ADE ) in secondary DENV infections has been proposed to increase viral replication leading to over activation of immune response [46] . However , the neurological complications seen in this patient might be the consequences of direct viral invasion in the CNS , rather than non-specific immune complications of DENV infection . The finding of DENV in CSF also supports a more direct cause of viral invasion as with earlier studies [31 , 36 , 37] . The presence of relatively high viral load in the serum compared to CSF is also striking in contrast to other flaviviruses such as Japanese encephalitis virus and West Nile virus infections , where viremia is undetectable by the time they invade the CNS [47–49] . Nevertheless , dengue encephalitis could be due to host genetic or immunologic differences rather than viral mutations although evidence to support this possibility is scarce . Many studies have shown that dengue disease severity can be influenced by host genetic variation and immune status [reviewed in 6] . However , these studies did not specifically compare dengue encephalitis and non-encephalitis patients and further data from dengue encephalitis cases is still needed . In this study , two unique and nine uncommon amino acid changes were shown to be present in isolate 201610225 . The two unique amino acid changes in NS5 region ( R3259K and F3369Y ) have never been reported before , while the nine uncommon amino acid changes including G76R ( capsid ) ; A624V ( envelope ) ; I896V ( NS1 ) ; R1176K and H1180Q ( NS2A ) ; K1658R and Q1940R ( NS3 ) ; D2764N and Q3052R ( NS5 ) were found in a few other strains with complete genomes listed in GenBank . The putative amino acid changes associated with DENV-3 neurovirulence in mouse [50] , including A18S , A54E , F277S , E401K , and T403I were not found in isolate 201610225 . Similarly , the amino acid change in the envelope protein ( E406K ) which has been identified as a neurovirulence determinant of DENV-2 and DENV-3 [51 , 52] was not found in isolate 201610225 . Furthermore , no amino acid changes are shared between isolate 201610225 and partial genome sequences of DENV-3 strains isolated from the CSF of encephalitis patients described earlier ( DENV-3 partial capsid-prM and envelope sequence; GenBank acc . no . KM024988 and KP893717 , respectively ) . Interestingly however , amino acid changes F3369Y and K1658R found in our neurotropic DENV-3 were also found in corresponding position of neurotropic DENV-4 ( GenBank acc . no . JX024757 ) [36] . The amino acids F3369Y and K1658R shared similar properties; F and Y are aromatic hydrophobic amino acids with Y being more polar , while K and R are positively-charged polar amino acids with K being more hydrophobic . Whether these amino acid changes represent genetic determinant of DENV neurovirulence is unknown . Genetic characterization of additional neurotropic DENVs will allow further insights into the role of these unique amino acid changes found in isolate 201610225 . In addition , further studies are warranted to study the virulence of this isolate in animal models and its tropism for neural cells in vitro . Complete genome phylogenetic analysis placed the 201610225 strain in the DENV-3 genotype I group , reported to be circulating in Indonesia for almost four decades . It has been suggested by Ong et al [28] that the strains from this particular clade are likely to cause another dengue epidemic if they remain in circulation . Not surprisingly , within the Sumatran-Javan clade of DENV-3 genotype I , isolate 201610225 was found to be more closely related to isolates from Jakarta and Singapore than to those from Makassar , a city on eastern Indonesia’s Sulawesi island . Together , this result suggests the endemicity of Sumatran-Javan clade of DENV-3 with sustained transmission in Indonesia . In summary , we described here the isolation and the first complete genome analysis of DENV-3 from the CSF of an encephalitis patient . Our findings complement those of others in suggesting the greater pathogenic potential of DENV-3 to cause neurological complications . Furthermore , DENV should be included in the laboratory diagnostic algorithm for encephalitis and other CNS infections especially in dengue endemic areas , despite the challenges in laboratory confirmation as isolation of virus or antibody from CSF is often difficult . | Dengue viruses ( DENV ) are viruses that can cause asymptomatic infection to life-threatening haemorrhagic fever disease . Although DENV are not classically known to infect and invade central nervous system ( CNS ) in human , numerous cases of DENV infection in the CNS have been reported with limited information about the characteristics of the infecting virus . Here , we report the isolation and first complete genome analysis of DENV serotype 3 ( DENV-3 ) from cerebrospinal fluid of a patient diagnosed with dengue encephalitis in Jakarta , Indonesia . By using next-generation sequencing strategy , we recovered the complete genome of the virus isolate and identified unique amino acid changes not found in any other recovered DENV-3 strains . The virus was determined to be closely related to isolates from Jakarta , Indonesia , which have been circulating for almost four decades . | [
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"man... | 2018 | Isolation and complete genome analysis of neurotropic dengue virus serotype 3 from the cerebrospinal fluid of an encephalitis patient |
The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy ( VE ) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1 . The impact of vaccine-induced immune responses can be investigated through sieve analysis of HIV-1 breakthrough infections ( infected vaccine and placebo recipients ) . A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups . Here we extended the V1/V2 analysis to the entire HIV-1 genome using an array of methods based on individual sites , k-mers and genes/proteins . We identified 56 amino acid sites or “signatures” and 119 k-mers that differed between the vaccine and placebo groups . Of those , 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine ( Env-gp120 , Gag , and Pro ) . The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites ( p = 0 . 0021 ) . In particular , site 317 in the third variable loop ( V3 ) overlapped with a hotspot of antibody recognition , and sites 369 and 424 were linked to CD4 binding site neutralization . The identified signature sites significantly covaried with other sites across the genome ( mean = 32 . 1 ) more than did non-signature sites ( mean = 0 . 9 ) ( p < 0 . 0001 ) , suggesting functional and/or structural relevance of the signature sites . Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing , we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure . In addition to presenting results of the first complete-genome analysis of the breakthrough infections in the RV144 trial , this work describes a set of statistical methods and tools applicable to analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens .
The HIV pandemic is responsible for more than 34 million deaths worldwide . Analysis of the RV144 vaccine trial yielded an estimated efficacy to prevent HIV infection of 31% , with a 95% confidence interval ( CI ) of 1% to 51% [1] . In this phase III efficacy trial , 16 , 402 Thai HIV-1-negative volunteers were randomized to receive a prime-boost vaccine regimen that consisted of four priming injections of a recombinant canarypox vector [ALVAC-HIV vCP1521: subtype B gag , pro ( from HIV-1 strain LAI ) and CRF01_AE gp120 ( 92TH023 ) ] , and two booster injections of a recombinant gp120 subunit vaccine [AIDSVAX B/E: subtype B ( MN ) and CRF01_AE ( CM244 ) ] . Follow-up studies highlighted possible mechanisms behind the modest RV144 protection . Multiple sources of evidence indicated a role for vaccine-induced antibody responses targeting the V2 region of the envelope glycoprotein ( Env ) : ( 1 ) the case-control study of immune correlates of risk showed that the magnitude of IgG antibodies binding to the V1/V2 region of Env was inversely correlated with risk of infection [2–5]; ( 2 ) the magnitude of binding of IgG antibodies to linear peptides in the V2 loop was inversely correlated with risk of infection [3 , 6]; and ( 3 ) sieve analysis targeted to the V2 region ( described below ) demonstrated vaccine pressure at two sites [7] . The case-control correlates study also showed that IgA antibodies to envelope and to the C1 region of Env were directly correlated with risk of infection [3] . In addition , among vaccine recipients with low IgA antibody responses to Env , HIV-1 infection risk was inversely correlated with IgG Env antibody avidity , antibody-dependent cellular cytotoxicity , neutralizing antibodies , and Env-specific CD4+ T cell responses [3] , as well as with IgG to V3 linear peptides [6] . “Sieve analysis” is the statistical assessment of whether and how the efficacy of a vaccine depends upon characteristics of the pathogen . Genomic sieve analysis compares breakthrough HIV-1 sequences between the infected vaccine and infected placebo groups . A sieve analysis of the HVTN 502/Step trial , with a vaccine inducing cytotoxic T-lymphocyte ( CTL ) responses , found evidence of CTL epitope-specific variation [8 , 9] . Based on HIV-1 breakthrough infections in the RV144 trial sequenced at the time of HIV-1 diagnosis , a sieve analysis that focused on the V1/V2 region of Env identified two sites in the V2 loop ( HXB2 amino acids Env 169 and 181 ) at which the level of efficacy of the vaccine significantly differed depending on whether the genome of the infecting HIV-1 virus matched the vaccine immunogen sequence at the site [7] . Here we present a comprehensive genome-wide exploratory sieve analysis of the breakthrough HIV-1 sequences of 109 of the 110 RV144 participants who were infected with CRF01_AE ( excluding one subject whose infection was epidemiologically linked and secondary to another study participant’s infection [7] ) . Our investigation was based on a pre-specified analysis plan , and included multiple sieve analysis methods , each of which evaluates a different immunological hypothesis ( Fig . 1 ) . The analysis focused on amino acid ( AA ) site- , peptide- , and protein-specific methods , with investigation of ( 1 ) differential deviation ( vaccine versus placebo ) from the immunogen sequences at specific loci or in peptide regions that are relevant to antibody binding; ( 2 ) differential vaccine efficacy versus HIV-1 sequences that do not match immunogen sequences at individual sites and in each of several pre-specified antibody-relevant protein regions; ( 3 ) differential codon selection , and differences in physico-chemical properties across treatment groups; ( 4 ) greater or more rapid viral escape ( vaccine versus placebo ) at predicted class I and class II HLA-restricted T cell epitopes; and ( 5 ) differences in phylogenetic diversity of the breakthrough amino acid sequences or differential evolutionary divergence from the vaccine immunogen sequences . The results of these analyses generate testable hypotheses about the mechanisms underlying the modest protection induced by the RV144 vaccine regimen and about potential paths to more effective HIV-1 vaccines to be investigated in future research .
We applied an array of methods designed to evaluate distinct hypotheses regarding vaccine-induced effects on the genetic sequences of the breakthrough HIV-1 viruses . The methods and their relative merits are outlined in Fig . 1 and in the Materials and Methods section . All analysis methods used here have been described previously except for the “Expected Gilbert , Wu , Jobes” ( EGWJ ) method , the “Quasi-Earth Mover’s Distance” ( QEMD ) method , and the “Physico-chemical Properties” method ( PCP ) , which are described here for the first time . The EscapeCount method was also developed for this analysis; it has been reported previously [10] , but is described more thoroughly here . This paper is the also first published application of the PRIME method ( http://hyphy . org/w/index . php/PRIME ) . All results reported here have not been reported previously except for the V2 crown signature sites ( Env 169 and Env 181 ) [7] , and the V3 signature site ( Env 317 ) [11] . In addition , one epitope region found by the EscapeCount method ( at the crown of the V2 loop ) was reported previously [10] . The SmoothMarks method has been applied to evaluate a related genetic distance , but as described below the results shown here are novel . Our dataset includes genome sequences from 109 of the 110 subjects previously identified with HIV-1 CRF01_AE infections [7] . All sequences were obtained from the earliest available sample for sequencing , and all were prior to the subjects’ initiation of antiretroviral therapy . There were a median of 10 sequences available for analysis per subject in vaccine proteins ( S1 Table ) and also a median of 10 in non-vaccine proteins ( S2 Table ) . We found 19 signature AA sites contained within the vaccine immunogens ( Fig . 2 ) – 12 in Env-gp120 , 3 in Gag and 4 in Pro – that showed a p-value ≤ 0 . 05 in at least one of the three primary site-scanning methods ( Table 1 ) . In addition , proteins that were not included in the vaccine immunogens were scanned for sieve effects versus the consensus CRF01_AE sequence ( CON-AE ) [12]: 37 sites were significant by at least one of the primary methods , and these were distributed across all non-vaccine proteins ( Table 2 and Fig . 2; complete results in S1 Dataset ) . Four pairs of sites overlap different proteins across reading frames: one pair in the immunogen region , three pairs outside of the immunogen region . These overlapping sites are described in the “context” columns in Tables S3 and S4 . 10 of the 19 immunogen signature sites were more likely to match a vaccine immunogen AA in the placebo group ( a “vMatch” or “typical” sieve effect ) , and 10 of 19 in the vaccine group ( a “vMismatch” or “atypical” sieve effect ) , with one site ( Env 369 ) having both a vMatch effect vs the CRF AE immunogen AA and a vMismatch effect versus the subtype B immunogen AA , see Fig . 3; additional information about each site is provided in S3 Table ( see Figs . 4 and 5 and S4 Table for non-immunogen signature sites ) . In contrast to the hypothesis-driven V1/V2 study , in this exploratory analysis we used uncorrected p-values at the 0 . 05 significance level to identify putative signature sites , a strategy taken to maximize sensitivity . To control for false positives , we used a conventional 0 . 20 false discovery rate ( FDR ) significance threshold [13] , evaluated separately by gene for each analysis method . Only 1 of the 19 signature sites within the immunogen region , Pol 51 , had q-value < 0 . 20 ( Table 1 , Fig . 2 ) . The first of the three primary site-scanning sieve analysis methods , differential vaccine efficacy ( DVE ) , uses Cox survival analysis to test whether vaccine efficacy ( VE ) for preventing infection by viruses that are AA-matched to the vaccine immunogen sequence at a particular locus is significantly different from the VE versus mismatched infections , [14 , 15] . Point estimates of VE associated with each mutation at which the DVE is significant show that VE can be eliminated or greatly strengthened with the mutation of a single residue ( Table 3 and Table 4 ) . Because this method evaluates all trial time-to-event data ( including all randomized subjects HIV negative at baseline ) and yielded a p-value for differential VE close to that for testing overall VE , the strength of evidence is comparable to the strength of evidence for overall efficacy , with the important caveat that multiple testing could lead to false discoveries due to chance variation . The other two primary methods compare the AA distribution of breakthrough infections at an individual site . Both methods employ numeric weights determined by the substitution frequency of the immunogen AA to the breakthrough sequence AA [16] . The Gilbert , Wu , Jobes ( GWJ ) method compares these substitution weights across treatment groups [17] , and the Model-Based Sieve ( MBS ) method employs the weights in a Bayesian model comparison that is more sensitive to detect treatment effects that alter the distribution among non-vaccine-matched amino acid categories [18] . These three primary methods evaluate a single representative sequence ( the mindist sequence ) per subject . This sequence , chosen as the closest actual sequence to the consensus of a subject’s multiple sequences ( S1 Text ) , is selected to represent the founder of the subject’s infection . To more fully represent the viral population , two secondary site-scanning methods utilize all available sequence data: the Mismatch Bootstrap ( MMBootstrap , or simply MMB ) method , which compares the frequency of vaccine-mismatched AAs in all of the subjects’ sequences across treatment groups ( this is the method employed previously in the V2-focussed analysis [7] ) , and the new Expected GWJ ( EGWJ ) method that generalizes the GWJ method by replacing subject weights with weight averages over a subject’s multiple sequences ( Table 1 and Table 2 ) . Only 9 of the 19 sites identified in proteins represented in the vaccine were significant with all five scanning methods: five in Env ( 19 , 181 , 317 , 369 , 424 ) , along with site 11 in Gag and sites 12 , 44 and 51 in Pro , reflecting the variety of alternative hypotheses tested by the five methods ( Fig . 1 ) . In a related analysis , we used 9-mer scanning ( the KmerScan method as previously described [8] ) to compare all 9-mers in subjects’ sequences to the corresponding 9-mer in each reference sequence ( the vaccine sequences for immunogen proteins and the CRF01_AE consensus sequence , CON-AE , for non-immunogen proteins ) . This analysis evaluates contiguous amino acids that could be the target of a CTL response , but does so without incorporating subject-specific HLA information . For a given pair of 9-mers the similarity score was the sum of HIVb similarity scores [16] over the nine sites . We compared the distribution of these scores for all of the individual sequences between the infected vaccine group and the infected placebo group . S5 Table lists 9-mers that had significantly different similarity to a vaccine immunogen sequence 9-mer across treatment groups ( 38 9-mers ) and S6 Table lists 9-mers outside of the vaccine immunogen regions that significantly differed versus CON-AE ( 82 9-mers ) . Thirty 9-mers in Tat ( involving sites 1–72 ) and one 9-mer in Vpu ( sites 30–38 ) passed the pre-specified 20% q-value multiplicity adjustment threshold . The significant 9-mers in Tat comprised seven distinct contiguous regions ranging in length from 9 to 25 AA . In four of these seven regions there was a signature site that could explain the 9-mer scanning results ( with concordance of “vMatch” or “vMismatch” sieve effects ) while in three of the regions at least one of the 9-mers did not overlap a signature site . We sought to test the hypothesis that vaccine efficacy declined as a function of the distance between the HIV-1 breakthrough viruses and the immunogen sequences . The SmoothMarks method [19 , 20] ( S2 Text ) evaluates VE as a continuous function of each of several distances between the mindist sequences and each immunogen sequence . In addition to distances corresponding to all gp120 sites , we considered four of the pre-specified immunologically-relevant subsets of gp120 amino acid sites: contactsites , contactsites-augmented , hotspots , EPIMAP ( Table 5 ) . For all of these analyses the first 41 sites of Env were excluded , because they were not present in CM244 and MN and the first approximately 30 sites corresponded to the signal peptide , which is cleaved from the mature protein . S1 Fig . shows boxplots of the genetic distances for the vaccine and placebo groups for the five sets of sites against the two CRF01_AE vaccine sequences ( 92TH023 and CM244 ) , computed using the HIVb substitution matrix . These distances are tightly correlated with Hamming distances ( percent amino acid mismatch ) , with Spearman rank correlations ranging between 0 . 91 and 0 . 95 across the 10 distances . The distances are approximately equal when measured to the 92TH023 and CM244 reference sequences , and all of the distances except hotspots are approximately equal across the sets , whereas the hotspots distances tend to be lower . The median ( range ) number of amino acid mismatches to the reference sequences are 13 . 4 ( 4 . 3–24 . 6 ) per 100 sites for all of the distances except the hotspots distances , and are 9 . 2 ( 3 . 6–14 . 8 ) per 100 sites for the hotspots distances . The likely reason for the closer hotspots distances is that the linear peptides used to measure antibody binding reactivity included 7 distinct HIV-1 subtypes , indicating that hotspots sites are sites with cross-subtype-reactivity , and such sites are expected to be relatively conserved because the vaccine can more readily induce cross-reactive antibodies to more conserved peptides . While the hypothesis testing analyses presented next are of main interest given they assess vaccine efficacy directly , we note that the distances between HIV-1 breakthrough and immunogen sequences did not significantly differ between infected vaccine recipients and infected placebo recipients . Vaccine efficacy was estimated as a function of genetic distance v for each of the ten distances ( Fig . 6 for contactsites , S2 Fig . for all ten distances ) . A similar analysis of a subset of these antibody contact sites was previously reported in Gilbert and Sun [20] , using a set of monoclonal antibody contact sites that was current through 2011; here we analyzed distances of Ab contact sites based on information that is up-to-date as of August 2014 . The tests for distance-variability of vaccine efficacy were all non-significant ( p-values > 0 . 20 ) . These results support no strong sieve effects but cannot rule out moderate sieve effects , as power calculations showed that for the setting of the RV144 trial , the SmoothMarks method has only 50% power to detect VE declining from 67% to 0% . However , these distance-based analyses contribute additional evidence supporting the hypothesis that the vaccine regimen conferred some protection . In particular , overall vaccine efficacy against CRF01_AE HIV-1 ignoring the genetic distances resulted in a p-value for positive VE of 0 . 026 , whereas the tests of Gilbert and Sun [20] for positive vaccine efficacy against at least one HIV-1 genotype ( 10 tests ) gave p-values ranging from 0 . 006 to 0 . 024 , with median p = 0 . 013 . This shows that accounting for the genetic distances increased the evidence for positive vaccine efficacy against CRF01_AE HIV-1 . To complement the site-scanning sieve analysis that identified individual Env-gp120 signature sites as potential discriminators of vaccine efficacy , we combined the signature sites into a global distance and assessed how the vaccine efficacy varied with this distance . In particular , the global sieve analysis above was repeated for the distances calculated over just the 10 identified Env-gp120 signature sites ( listed in Table 1 ) , excluding Env 6 and Env 19 because they are not in the CM244 and MN inserts and they are part of the signal peptide . Because 5 of the 10 signature sites had “vMatch” sieve effects and 5 had “vMismatch” sieve effects , we do not expect the vaccine efficacy curves to exhibit the “classical” sieve effect shape with vaccine efficacy highest for smallest distances and waning to zero for the greatest distances; rather the vaccine efficacy curves could take many shapes depending on the joint distribution/covariation of the amino acids at the 10 sites , and the curves provide new information about the aggregate impact of the non-contiguous decapeptides on vaccine efficacy . S3 Fig . shows the distributions of the signature-site distances to 92TH023 and CM244 together with the estimated vaccine efficacy curves . For 92TH023 , the estimated curve is approximately horizontal , indicating that the 5 “vMatch” and 5 “vMismatch” signature sites have a “balancing” effect , with the net impact being that the combined decapeptide patterns do not associate with vaccine efficacy . However , for CM244 the estimated VE peaks against HIV-1s with an intermediate number of mismatches to the vaccine ( zenith at estimated VE = 59% for genetic distance 0 . 28 , an average of 2 mismatched residues ) and declines to zero against HIV-1s with increasing distance ( estimated VE = 0% for genetic distance 0 . 53 , an average of 5 mismatched residues ) . To help interpret this relationship , S4 Fig . shows the signature site decapeptide AA patterns for each of the 109 infected subjects , aligned to the vaccine efficacy curve for reference . S4 Fig . indicates that the “vMismatch” signature site Env 413 has the greatest influence to create the increasing VE curve in the distance region 0 . 066 to 0 . 166 and the vMismatch signature sites Env413 , Env 268 , and Env 317 have the greatest influence to create the declining VE curve in the distance region 0 . 28 to 0 . 53 . To search for functional sequence differences in the vaccine and placebo groups , we evaluated treatment-group differences in the physicochemical properties of amino acids in the mindist sequences . Unlike the methods with results presented in Table 1 and Table 2 , which compare divergences of breakthrough AA from a vaccine AA between treatment groups , the physicochemical properties ( PCP ) method compares the sequences between groups directly , without regard for the vaccine reference sequences , on a per-property basis . We evaluated each of two different property scales: ( 1 ) the vector of ten indicator values determined by Taylor [21] , indicating the presence or absence of ten particular physicochemical properties for each amino acid; and ( 2 ) the vector of five “z scales” for each amino acid , principal components of observed physicochemical properties used to determine quantitative structure-activity relationships between peptides [22–24] . We scanned the sequences at individual sites ( S7 and S8 Table ) as well as at contiguous 3-mers ( S9 and S10 Table ) and 9-mers ( S11 and S12 Table ) across the HIV-1 proteome , comparing counts of each of the ten Taylor properties and five z-scale components across treatment groups ( complete results are included in S1 Dataset ) . The results of this method can help interpret the physicochemical and structural differences between the vaccine and placebo viral populations . Of the 19 vaccine immunogen signature sites shown in Table 1 , only Pol 51 was also found to have site-specific significant PCP results ( S3 Table ) , and of the 37 out-of-immunogen signature sites shown in Table 2 , eight coincided with site-specific PCP results ( S4 Table ) . A total of 16 individual sites were significant at the p ≤ 0 . 05 level ( S7 and S8 Table ) , two of which with q-values below 0 . 2: Pol 51 as noted above ( property z3 , q = 0 . 19 ) and Vpu 30 ( hydrophobicity , q = 0 . 10 ) . These two sites were also the only locations with q-values below 20% in the scanning of 3-mer peptides ( Peptide starting at Pol 49: property z5 , q = 0 . 024; Peptides starting at Vpu 28 , 29 and 30: hydrophobicity , q = 0 . 050 ) . Additional sites had q-values below 20% when scanning 9-mer peptides , all of which were located in the non-immunogen proteins , concordant with the KmerScan 9-mer results . A negatively charged region in the vicinity of Nef 150 differed between the treatment groups ( q = 0 . 059 ) as did component z4 in the vicinity of Tat 73 ( q = 0 . 19 ) . In addition , hydrophobic residues in the vicinity of Vpu 30 spanning positions Vpu 25 through Vpu 30 differed between the treatment groups ( the q-values in this region ranged from a minimum of q = 0 . 021 for the 9-mer starting at Vpu 25 to a maximum of q = 0 . 23 for the 9-mer starting at position Vpu 29 ) . To further elucidate the role of selection for particular physicochemical properties , we conducted a codon-based phylogenetic analysis that detected Env-gp120 sites at which natural selection has operated to preserve or change one or more of five physicochemical properties: chemical composition , polarity , volume , iso-electric point or hydropathy [25 , 26] . To do so , we modeled the rate of nonsynonymous substitution from codon x to codon y , β ( x , y ) at a site as a function of the difference in properties between x and y: β ( x , y ) = exp ( −∑pcpdp[x , y] ) , where p indexes the five properties . If cp is significantly different from 0 for a particular property p at a site ( measured by a likelihood ratio test , with 5-fold multiple testing correction at each site using the Holm-Bonferroni procedure ) along the vaccine-group lineages , then we conclude that the property is preserved ( cp >0 ) or driven to change ( cp <0 ) by natural selection . Fig . 7 shows the sites found to have selection acting on one or more properties , along with signature sites on a crystal structure of Julien et al . [27] , and S5 Fig . provides an alternate viewing angle . Notably , at several sites almost all physicochemical properties tested were under selection , including Env 85–87 , 353 , 365 , and 425 ( S13 Table and S1 Dataset ) . Many of the signature sites described above are located in genomic regions of known functional or immunological relevance . Specifically , those in HIV-1 Env-gp120 include sites in the antibody binding regions at the crowns of the V2 ( Sites 169 , 181 ) and V3 ( Site 317 ) variable loops , sites in the co-receptor binding site ( Sites 317 , 353 ) , and in the CD4 binding loop motif ( Site 369 ) . We considered six pre-specified subsets of Env sites known to be immunologically relevant ( Table 5 ) . We found that the Env-gp120 signature sites were significantly more likely to be found in a subset of Env sites known for their relevance to neutralization potency ( nAb-sites set ) ( Fisher’s exact test p = 0 . 0035 ) . Signature sites were also more likely to be part of the focus set that includes only those sites that were identified as hotspots of antibody binding reactivity in RV144 and are either in the known antibody contactsites set or are in the nAb-sites set ( p = 0 . 0049 ) . Results for all six site sets are presented in Table 6 . Using tests for codon selection that identify important biochemical properties as discussed above , as well as a method that estimates the ratio of non-synonymous and synonymous substitution rates ( dN/dS ) separately in internal and terminal branches of the tree connecting these sequences [25] , we found 91 sites in gp160 that were under differential selective pressure between the vaccine and the placebo groups ( 54 among the 511 sites of gp120 and 37 among the 345 sites of gp41 , S13 Table and S1 Dataset ) , including signature sites Env 6 and Env 353 . Interestingly , a high proportion of these sites were located in V3 ( 8/54 , significantly more than the proportion of sites located outside of V3: Fisher’s exact test two-sided p-value = 0 . 049 ) . Covariation was assessed within proteins between pairs of sites , both pooling over the vaccine and placebo groups and separately , together with a test for whether the degree of covariation differed for vaccine versus placebo , which could imply vaccine-induced functional or structural constraints ( S3 Text and S2 Dataset ) . Covariation was generally weak , and there was no evidence that covarying site pairs were restricted to the vaccine group . Among gp160 residues , there were 630 covarying site pairs with p < 0 . 05 but only two had a q-value below 0 . 2: between sites 276 and 343 ( q = 0 . 10 ) , and between sites 65 and 181 ( q = 0 . 12 ) . Both sites 181 and 343 were identified above as signature sites . An interesting pattern was seen when we considered how many associations ( defined as treatment-arm-pooled covariation p < 0 . 05 ) linked each residue . While most Env sites ( n = 584 ) showed no association with any other site , 22 sites interacted with more than five other sites ( 19 , 169 , 181 , 276 , 307 , 308 , 317 , 332 , 343 , 347 , 353 , 360 , 365 , 369 , 379 , 412 , 413 , 424 , 465 , 564 , 658 , 822 ) . Ten of these 22 sites were also signature sites , and there is evidence for a hub of covariation in V2 ( S3 Text ) . Importantly , the signature sites identified showed significantly more associations with other signature sites ( mean = 32 . 13 ) than with non-signature sites ( mean = 0 . 88 ) ( p < 0 . 0001 ) . This difference was also significant if we considered only gp120 ( p < 0 . 0001 ) . Interestingly , there were more associations between residues in gp120 ( mean = 2 . 03 ) , which corresponded to the vaccine sequence , than in gp41 , which was not included in the vaccine ( mean = 0 . 63 ) ( p = 0 . 10; p = 0 . 001 if zero values are excluded ) . We have developed multiple methods to evaluate potential T cell-driven sieve effects based on comparisons of computationally-predicted epitopes in sequences from vaccine and placebo recipients . Results are shown in S14 and S15 Tables . For all methods , we begin by predicting T cell epitopes in the vaccine and breakthrough sequences using the HLA haplotypes of the infected trial participants . First we evaluated each viral protein using the novel EscapeCount method ( S4 Text ) , which counts the number of high-affinity predicted epitopes in the vaccine sequence that bind with much lower affinity to the corresponding k-mer in the subject’s mindist sequence . Since the power of this method is improved when there is more variability in the predicted epitope binding affinities , for class I predictions we used the adaptive double threading ( ADT ) epitope prediction software [28] rather than the more well-known NetMHCpan software [29] that we used in this and previous applications of the EpitopeDistance method ( discussed below ) . Using the EscapeCount method , we found significant evidence of greater class I binding escape among placebo recipients in Env versus the CM244 reference sequence than among vaccine recipients ( p = 0 . 031 ) . We also applied the EscapeCount method to evaluate individual k-mers for evidence of greater class I ( 9-mers ) and class II ( 15-mers ) binding escape in vaccine versus placebo groups ( S14 and S15 Tables ) . Twelve 9-mers ( ten in Env and two in Gag ) showed an unadjusted p-value <0 . 05 for differential binding escape , though none of these surpassed the q-value threshold of 20% ( 0 . 24 < q-value < 0 . 93 ) . Seven 9-mers with a q-value < 0 . 5 were found in Env-gp120 ( start positions: 5 , 128 , 299 , 328 , 335 , 363 , 445 ) . In S6 Fig . we show as an example the V3 loop 9-mer “PSNNTRTSI” ( PI9 , HXB2 start position 299 ) at which there was a greater number of HLA binding escapes in vaccine versus placebo recipients ( p = 0 . 0084 ) . Vaccine to placebo differences are concentrated in the 9th position , site 307 , which forms part of the core of the V3 loop . Although site 307 did not qualify as a signature site , its DVE p-value was 0 . 065 and its EGWJ p-value was significant at 0 . 029 . This site forms ( with Env 308 and 317 ) the core of the V3 loop and is a well-studied target of antibody binding [6 , 30] . Since also many of the infected RV144 subjects had HLA types capable of binding the CRF01_AE vaccine sequence epitope , and the variation at site 307 abrogated class I HLA binding in vaccine but not placebo recipients , the V3 sieve effect may be partially due to T cell mediated effects . As the second of three methods , we applied the PercentEpitopeMismatch procedure , which was applied previously to the HVTN 502/Step sieve analysis [8] ( S14 and S15 Tables ) . This method complements the EscapeCount method by considering any class I epitope that is predicted ( for a given subjects’ HLA type ) in the vaccine sequence , regardless of whether it is also predicted in the breakthrough sequence , and regardless of the number of changes between the breakthrough and vaccine k-mers . The percent of vaccine-predicted epitopes for which there is any change in the corresponding breakthrough sequence was computed for all of a subject’s sequences and these were compared across treatment groups as previously described [8] ( S5 Text ) . Using the PercentEpitopeMismatch method , we found no evidence of T cell escape in insert-relevant genes when using the NetMHCpan- or ADT-predicted epitopes . For the third method we applied the EpitopeDistance procedure that was also previously applied for the HVTN 502/Step trial [8] . This method compares the predicted epitopes in each subject’s breakthrough sequences to HLA-matched epitopes estimated in the vaccine sequence ( S6 Text ) . In summary , we found no concordant evidence for a T cell-driven sieve effect across Gag , Pro and Env ( S14 Table ) or the non-vaccine proteins ( S15 Table ) . However , some significant results were found in the V2 region of Env when binding affinities were considered ( CM244 p = 0 . 022; 92TH023 p = 0 . 047 ) , although there was only a trend suggesting a difference between the vaccine and placebo groups when evolutionary distances were considered ( CM244 p = 0 . 058; 92TH023 p = 0 . 23 ) . To analyze sequences at the gene/protein level , we assessed whether sequences from vaccine recipients were ( a ) more phylogenetically diverse or ( b ) more divergent from the vaccine insert sequences than sequences found in placebo recipients . For the phylogenetic diversity , we constructed maximum-likelihood phylogenetic trees using all amino acid sequences available for each subject; we then subset the leaves of these trees to retain only the mindist sequences . For each tree , we then computed the differential amino acid phylogenetic diversity ( PD ) [31] , defined as the difference in the total branch length of two subtrees: one corresponding to placebo recipients and the vaccine insert , and the complementary sub-tree corresponding to vaccine recipients and the vaccine insert , and compared this to an estimated null distribution as described in Methods . We found trending evidence of greater phylogenetic diversity in Gag for the vaccine group compared to the placebo group ( p = 0 . 089 ) ( S17 Table ) . In addition to the phylogenetic diversity , we also calculated the phylogenetic divergence from the vaccine sequence based on the same mindist trees ( S7 Fig . shows the distributions of these distances to the CM244 reference sequence for each tree ) . We compared these values across treatment groups for each tree , and found trending evidence of greater divergence from the LAI sequence in Pro among placebo-recipient sequences ( p = 0 . 059 ) ( S18 Table ) , a “vMismatch” result . We repeated this analysis using the median distance over the multiple sequences available from each subject ( n = 3–14 ) rather than the mindist sequence distance ( as previously described [8] ) , and found that the results were consistent . When applying a variant of this analysis using nucleotide sequence trees , we found a trend toward greater divergence of vaccine recipient Gag sequences to the LAI insert sequence ( p = 0 . 072 ) and no significant or trending result in Pro . In addition to the phylogenetic analyses , we also evaluated whether the pairwise alignment similarity scores between all vaccine recipient sequences versus all placebo recipient sequences were different than what would be expected under the null hypothesis that the vaccine and placebo sequences came from the same distribution . We computed the Quasi-Earth Mover’s Distance ( QEMD ) using BLOSUM90 [32] pairwise alignment scores and compared it to the null distribution of the QEMD computed based on repeatedly permuting treatment assignments; this approach does not use the vaccine reference sequences and does not depend on an estimated phylogeny . While the PD analysis evaluates the across-group difference between within-group phylogenetic diversity , the QEMD analysis evaluates the between-group sequence variation . We found significantly less QEMD similarity in Gag sequences than would be expected under the null hypothesis ( p = 0 . 041 ) , consistent with the trend toward a sieve effect found via the phylogenetic analysis . We did not find significant evidence for QEMD dissimilarity for Pol ( p = 0 . 16 ) or Env ( p = 0 . 54 ) . We compared variable loop lengths , numbers of cysteines , and frequencies of potential N-linked glycosylation sites ( PNG sites ) between vaccine and placebo recipient sequences . There were no significant differences in the mindist variable loop lengths in Env-gp120 between vaccine and placebo recipients in any of the five variable loops ( Wilcoxon rank sum p-values > 0 . 20 ) . Next , based on mindist sequences we compared the per-subject median number of cysteines in gp120 between the treatment groups; this analysis was motivated by the finding in Vax004 that 20% of trial participants had atypical cysteine variants [33] . The distributions of per-subject median numbers of cysteines were similar in the two treatment groups ( average number of cysteines = 19 . 45 in vaccine recipients and 19 . 73 in placebo recipients ) . Next , we identified PNG sites by searching each breakthrough sequence for tripeptide motifs of the form N-X-S or N-X-T , where X is any amino acid other than proline [34] . We compared the numbers of PNG sites between the treatment groups using the mindist sequences as well as with all sequences using the multiple outputation ( MO ) method [35] , and found no significant or trending difference . We also tested for a difference across treatment groups in the distribution of PNG sites at each of the sites at which any subject had a PNG site , restricting to sites with sufficient diversity ( defined as at least 4 sequences with a PNG site and at least 4 sequences without a PNG site ) . Of the 75 sites tested , only one had an unadjusted Fisher’s exact test p ≤ 0 . 05 ( site 186s , p = 0 . 04 ) . S8 Fig . shows the percentage of mindist sequences with a PNG site at each alignment position that had one or more PNG site .
Sieve analysis is a powerful tool for the evaluation of breakthrough infections in vaccine studies and complements related studies of immune correlates of infection risk among vaccine recipients . Sieve analysis leverages the randomized design of the trial by comparing features of infections across treatment groups , and can further suggest testable hypotheses about the targets of vaccine-induced immunity . By comparing HIV-1 breakthrough viruses that were isolated from vaccine and placebo recipients in the RV144 trial , we identified HIV-1 genetic determinants potentially associated with ( unmeasured ) vaccine-induced immune responses . Scanning across the HIV-1 proteome , we identified 19 signature sites in the vaccine proteins Env-gp120 , Gag , and Pro that differed between the vaccine and placebo groups . In addition , we identified 37 signature sites in parts of the proteome that were not included in the vaccine . Four pairs of signature sites overlapped in different proteins across reading frames , resulting in a total of 52 unique sites across the proteome . Because our exploratory study was designed to identify all potential sieve effects , we reported all sites with unadjusted p-values below 0 . 05 . Of the signature sites identified in vaccine immunogen regions , only Pol 51 passed the q-value ≤ 0 . 20 threshold . Sieve analysis , by comparing breakthrough HIV-1 viruses across treatment groups , can test some of the specific hypotheses generated by correlates of risk analyses , such as the Haynes et al . [3] study that identified anti-V2 antibodies as a correlate of risk . For example , sieve analysis can test whether the breakthrough infections in the vaccine group are less viable targets for the vaccine-induced anti-V2 antibodies than the infections in the placebo group . The V1/V2 focused sieve analysis that identified V2 signature sites 169 and 181 [7] and follow-up studies [36] lent support to the hypothesis that anti-V1/V2 antibodies were involved in a mechanism of partial vaccine protection and that these sites are important for antibody binding . Confirming our previous study [7] , the full proteome site-scanning analysis also identified signature sites 169 and 181 , although unlike the previous V1V2-focused analysis results , the exploratory results reported here did not pass multiplicity correction , partly due to the much larger number of analyzed sites ( 8 compared to 248 in Env alone ) . We hope that additional follow-up experiments will further elucidate the role , if any , of the other newly-identified signatures in the partial protection conferred by the vaccine regimen . Among all the signature sites , some are worth singling out because they were found by multiple methods and/or there is biological evidence supporting their potential vaccine-associated immunological relevance . In particular , Env 19 , 169 , 181 , 317 , 413 and 424 appear important because they are known antibody contact sites or belong to functionally important regions of the HIV envelope ( S3 Table ) . The finding that Env signatures preferentially map to sites known to have a role in antibody neutralization or binding supports the hypothesis that the results are biologically meaningful . For example , the tridimensional structure of Env-gp120 showed that site 169 was in the vicinity of site 317 . Env 169 is located at the crown of the V2 loop and was previously identified in the V1/V2-focused sieve analysis [7] . It is contained in a linear binding antibody epitope hotspot for RV144 vaccine-induced antibodies , and is a known contact site for neutralizing and binding antibodies . It is also part of a predicted HLA binding hotspot in the MN vaccine immunogen for both class I and class II alleles . Furthermore , this position is in the seventh position of a 9-mer that had significant treatment group differential binding escape versus the subtype B immunogen sequence ( MN ) , a surprising discovery that motivated further analysis , leading to the finding that the differential vaccine efficacy at Env 169 was significantly associated with the class I HLA allele A*02 [10] . Env 317 , identified by all three of the primary site-specific sieve methods , is in the core of the V3 loop and is part of the conserved co-receptor binding site . It is also known to be a contact site for neutralizing antibodies ( nAb-sites ) , is part of an antibody hotspot defined using antigen microarrays [6] , and is predicted to be on antibody interfaces using the EPIMAP method [7] . It exhibited a “vMismatch” sieve effect , in that there was greater divergence from the vaccine immunogen AA among the placebo recipient sequences than among the vaccine recipient sequences . It has been shown previously that mutations in V2 can interact with V3 , and thereby have an impact on phenotypic changes such as co-receptor usage [37 , 38] . In addition , mutations in V3 can modulate the neutralization sensitivity of the conserved V2 epitope that is recognized by PG9/PG16-like antibodies . Interestingly , some antibodies isolated in RV144 vaccine recipients mapped to the same V2 region as PG9/PG16-like antibodies , implying that the mutations that we identified in V2 and V3 may have a synergistic impact on the neutralization sensitivity of breakthrough viruses [39] . Signature site Env 413 had the strongest influence on the variation of vaccine efficacy against HIV-1 as a function of genetic distance to CM244 computed using the Env-gp120 signature sites , and exhibited a vMismatch sieve effect . Env 413 is close to the CCR5 and 17b binding sites , and , together with signature site Env 424 , it surrounds the binding motif RIKQ ( residues 419–422 ) . Most importantly , Env 413 was linked to the breadth of neutralizing antibody responses in a study that compared subjects with strong or weak neutralizing antibody responses [40]: an increase in breadth was associated with asparagine ( N ) at position 413 . Here the consensus residue in CRF01_AE was T and the second most frequent residue found at that site was asparagine ( N ) , which creates a site for potential N-linked glycosylation . The signature sites identified with the site-scanning methods were characterized by greater amino acid covariation with other sites; there were more interactions with signature sites than at other sites as well as more interactions in gp120 ( in the vaccine ) than in gp41 ( not in the vaccine ) . When across-protein interactions were considered , vaccine proteins showed greater connectedness: they covaried with more proteins . We conjecture that a vaccine-induced constraint at a highly connected site would have a greater impact on viral fitness than a change at a weakly connected site . The differential vaccine efficacy ( DVE ) analysis allowed us not only to identify sites that differed between the infected vaccine and infected placebo groups but also to estimate the site-specific vaccine efficacy against viruses with a matched or mismatched residue to that present in a vaccine reference sequence at this given site . In Env , the DVE analysis identified six sites where estimated vaccine efficacy was increased to at least 43% ( Env 19 ) and up to 85% ( Env 317 ) against viruses with a specific residue at that site . Conversely , the vaccine efficacy was abolished with a different residue at these sites ( point estimates ranging from less than zero percent to 17% ) . These results suggest that vaccine efficacy can essentially disappear with a single mutation . Better evaluating the VE/mutation relationship is critical for our understanding of vaccine immunity as it pertains to HIV-1 . Knowing the genetic diversity of HIV-1 , the disappearance of vaccine efficacy with a point mutation raises important questions as to the future efficacy of a successful vaccine . By analogy with drug-resistance mutations , we can envisage that the broad usage of a vaccine may lead to the increased frequency of mutations such as those that we found to be associated with null vaccine efficacy , and that such mutations would rapidly be selected in the population , hence reducing the vaccine efficacy . This also emphasizes that it may be necessary to have vaccines with multiple specificities in order to avoid the focusing of immune responses , which may lead to more rapid escape from vaccine-induced immunity , or that it may be important to include only essential protein sequences that cannot be mutated without impacting viral fitness . The SmoothMarks sieve analysis did not find significant evidence that vaccine efficacy varied against HIV-1 genotypes with genotype defined by the genetic distance of breakthrough viruses to the CRF01_AE vaccine inserts . However , we found evidence of global T-cell based sieve effects relative to the CM244 and MN Env gp120 vaccine inserts using the EscapeCount T-cell sieve method . These results are surprising , since CD8+ T-cell response rates of RV144 vaccine recipients were low overall; depending on the sample time point and the assay that was used , 12–63% of vaccine recipients had a T-cell response to Env peptides , but these responses were predominantly CD4 responses [1 , 41 , 42] . One explanation is that the vaccine primed natural infection and an anamnestic response caused earlier escape in the vaccine group . We also note that these results were not found with the EpitopeDistance method . This may be due to differences in the definition of a T-cell sieve effect by the two methods . The global effects found here are also related to a V2-specific T-cell sieve analysis using the EscapeCount method that reported evidence of a T-cell sieve effect in the V2 region of the MN immunogen sequence [10] . By employing methods that incorporate T cell epitope binding predictions , our analysis indicates that vaccine-primed T cells and participants’ HLA alleles may have played a role either in early T cell escape or in modulating vaccine efficacy , even possibly at sites that are part of known antibody binding epitopes , such as the tips of the V2 and V3 loops and the CD4 binding site . Identification of potential sieve effects and vaccine-induced T cell epitopes motivates further study both experimentally and computationally , including , for example , testing for amino acid covariation within the PI9 peptide among infected participants ( S6 Fig . ) . The putative effect within Env 299 PSNNTRTSI , along with those identified by the EscapeCount method in other 9-mers ( S16 Table ) , generates hypotheses that can be further investigated computationally and tested experimentally with T cell assays . The suggestion of a trend toward greater phylogenetic diversity and divergence in Gag sequences for vaccine than placebo recipients could reflect the genetic effect of some T-cell mediated responses , although the signal is weak and there was no evidence of a sieve effect at predicted T-cell epitopes in Gag . In addition , our finding of 30 9-mers in Tat with a T cell sieve effect ( passing the 20% q-value multiplicity adjustment ) could possibly be explained by the fact that Tat is a viral regulatory factor for HIV gene expression and CD8 T-cell responses have been shown to select for viral escape variants in Tat during acute HIV and SIV infection [43] . It remains unclear whether the observed vaccine sieve effects are due to an acquisition barrier , reflecting the selection of viruses that managed to break through the protective effects of vaccination by the RV144 vaccine regimen , versus reflecting early post-acquisition immune pressures that affected within-host viral evolution after infection , or some combination of the two . We note that significant results found using methods that focus on T cell epitopes are not necessarily driven by T-cell pressure , and that signatures in Env may be driven by either T cell pressure , antibody pressure , or by a combination of the two . Given that one could expect that sieve effects would be restricted to the proteins included in the RV144 vaccine , how can the 37 non-vaccine signature sites be interpreted ? In addition , how can we explain that there are an approximately equal number of “vMismatch” and “vMatch” effects ? Additionally , the physico-chemical property sieve effect sites tended to occur in non-vaccine proteins , as did all thirty-one sieve effect 9-mers that were significant after multiplicity correction . While some of these signature sites and 9-mers are false positives , others may be true effects . Certain non-immunogen sites/9-mers may be implicated because they are in linkage with other mutations in vaccine sequences; for instance such sites could act as compensatory mutations for vaccine-associated mutations that would be destabilizing . In addition , certain residues that are matched to the vaccine may be required for HIV-1 to be infectious/transmittable . Alternatively , some non-vaccine-immunogen signature sites/9-mers and vMismatch effects could reflect true effects of post-acquisition immune pressure that affect vaccine recipients more strongly or more rapidly than placebo recipients . This comprehensive whole-genome sieve analysis generates additional testable hypotheses about the nature and mechanism of the vaccine’s partial efficacy , by identifying individual sites , peptide regions and proteins at which the genomic sequences significantly differed between vaccine and placebo recipients . By using a variety of methods , each tailored to detect different types of signals , we both increased the chance of finding differences and provided means for potentially explaining the differences . With additional support from independent analyses , such as viral inhibition experiments based on individual amino acid substitutions , a subset of the site-specific sieve effects identified here may prove to reflect vaccine immune pressure and thus be significant for future vaccine design and analysis . Directions for future research include experimental determination of vaccine-induced antibody binding in identified Env regions , evaluating functional consequences of the observed mutations , and further elucidating the extent to which differences in non-vaccine-immunogen regions of the breakthrough viruses could be directly attributed to vaccination , or indirectly attributed to constraints on the virus or to chance sampling variability . The list of specific testable hypotheses includes evaluation of all of the signature sites for evidence of vaccine-induced immune pressure targeting each site . While in the absence of an additional efficacy trial it is not possible to directly evaluate the statement that “vaccine efficacy can essentially disappear with a single mutation” , it is possible to test the hypothesis that the vaccine-induced antibodies bind viruses differentially depending on individual mutations . This has been done for V2-targeting antibodies by Liao and colleagues [36] as well as for V3-targeting antibodies as reported by Susan Zolla-Pazner [11] ) and could in principle be done for any of the ( Env ) signature sites . Neutralization assays could also be applied to assess differential neutralization , though the antibodies induced by RV144 appear to be non-neutralizing . More generally , effector function assays ( e . g . , ADCC ) could be applied to assess differential functional responses . After over 30 years of effort to develop an effective public health vaccine to prevent infection by HIV-1 , the only vaccine to show statistically significant efficacy was the regimen used in the RV144 Thai trial . The borderline significant p-value of this result leaves open the possibility that the regimen had no overall efficacy . It would be possible for a vaccine with no overall vaccine efficacy to nevertheless have differential efficacy against different viruses . One example is a balancing effect , in which the vaccine has both protective and harmful effects , depending on the virus . Another possibility is that subjects who experience multiple exposures to HIV are protected against some viruses but ultimately become infected despite this partial protection ( but later than they otherwise would have been ) ; if the time delay is short ( if the multiple exposures are close together in time ) , this could lead to negligible overall efficacy despite true acquisition sieve effects . In our view the strength of evidence for overall VE is increased by the findings of this study , for example the SmoothMarks analysis provided smaller p-values for overall VE by accounting for viral distances . However , experimental confirmation is still crucial , both because we are not able to prove that the observed sieve effects are acquisition effects and because of the expectation that many of these are false discoveries . Even if an effect is a true discovery ( in that the treatment group differences are not due to chance variation ) , it may be an effect of vaccine-induced changes to the evolutionary course of the virus after infection ( post-acquisition effects ) rather than effects to prevent infection ( acquisition effects ) . There were significant effects found in the sieve analysis of the Step 502 trial that are presumed to be post-acquisition effects because the vaccine immunogen had no Envelope component ( and no evidence of antibody induction ) and because the strongest effect was found at a known T-cell epitope and was strongest in subjects with the necessary HLA types to target that epitope [8 , 9] . In the absence of confirmatory studies , the signature site analysis would not increase the strength of evidence . However , the strength of evidence for overall efficacy has already been increased , in our view , by the experimental confirmation of RV144-induced V2-targeting mAbs that differentially bind depending on the amino acid at site Env 169 , in conjunction with the significant correlation of vaccine efficacy with induction of V2-targeting Abs . It has become clear that future vaccine studies should be designed for a more rapid iterative process , to maximize the information gleaned from each trial and ultimately to minimize the time to an effective global intervention strategy [44 , 45] . The correlates and sieve analyses of the RV144 trial demonstrate the importance of designing future trials with sufficient power to conduct such analyses . In particular , both types of analyses are improved by more precise resolution of the timing of HIV infection ( e . g . , accomplished through more frequent visits for diagnostic testing of HIV-1 infection that capture a sizable fraction of HIV infection events in the pre-seroconversion acute phase ) , which would allow use of statistical methods that can help tease apart acquisition sieve effects from post-acquisition differential within-host viral evolution [19] .
The protocol was approved by the Institutional Review Boards of the Ministry of Public Health , the Royal Thai Army , Mahidol University , and the US Army Medical Research and Materiel Command . Written informed consent was obtained from all participants . The study conduct and results have been published previously [1] . The vaccine regimen was a combination of HIV-1 subtype B and HIV-1 CRF01_AE: the prime corresponded to HIV-1 Gag and Pro of subtype B LAI and the CRF01_AE HIV-1 gp120 ( strain 92TH023 ) linked to the subtype B transmembrane domain of gp41 ( strain LAI ) ; the boost corresponded to the CRF01_AE HIV-1 gp120 strain CM244 with the subtype B HIV-1 gp120 strain MN . ( CRF01_AE is subtype E in the HIV-1 Env . ) We aligned these three sequences to the breakthrough sequences for analysis . Of the 16 , 395 participants who entered the trial HIV-1 negative [the modified intention-to-treat ( MITT ) cohort] , 125 acquired HIV-1 infection during the 3 . 5-year follow-up period . Of the 125 MITT infected subjects , we analyzed the subset of subjects who were infected by HIV-1 CRF01_AE viruses , for whom we have sequence data , and who were not infected by another trial participant ( n = 109 subjects ) . Subjects infected with subtype B viruses ( n = 11 ) were excluded because of the much larger HIV-1 genetic distances to the vaccine immunogen sequences compared to CRF01_AE , such that their inclusion would likely reduce statistical power of the sieve analysis by contributing genetic variation unrelated to a sieve effect . Four of the 125 infected subjects had no sequence data , three because the Sanger sequencing technology failed to deliver a result due to low HIV-1 viral load , and one because of drop-out . Finally , we excluded subject AA100 because this subject was the second to acquire HIV in the AA118/AA100 transmission pair; excluding AA100 avoids complications arising from the non-independence of these infections , and helps maintain plausibility of the ‘sieve conditions’ , which are sufficient assumptions to justify interpretability of observed genotype-specific vaccine efficacies and infected-case sequence differences as prospective , per-contact estimates of genotype-specific vaccine efficacy [46] . The other linked transmission pair was subtype B , so both of those subjects were excluded on the grounds of their infecting subtype . Note that while most of the sieve analyses conditioned on infection ( and therefore truly excluded from analysis all subjects other than the 109 ) , the estimates of genotype-specific vaccine efficacy and differential vaccine efficacy , as well as the SmoothMarks multi-site acquisition sieve analyses , were time-to-infection analyses that included the entire MITT cohort ( and right-censored , rather than truly excluded , the infected subjects outside of the 109 ) . The vaccine efficacy to prevent acquisition of CRF01_AE HIV-1 ( based on the MITT cohort and these 109 infections ) was estimated to be 35 . 2% ( 95% CI 4 . 8% to 55 . 8% , score test p = 0 . 026 ) . The RV144 HIV-1 sequencing methodology has been published previously[8] , and further information is provided in S1 Table and S2 Table . Sequences are available under GenBank accession numbers JX446645–JX448316 . For each subject , we defined the mindist sequence to be the closest actual sequence to the consensus of that subject’s full-genome nucleotide sequences , as measured by the Tamura-Nei ‘93 ( TN93 ) distance correction model [47] . A full description of our mindist selection process is presented in S1 Text . In short , subjects with a full-length nucleotide sequence that measured closest to their consensus with TN93 had that sequence used and translated for all mindist protein sequences . For subjects with only right-half or left-half sequences that measured closest to their consensus , the closest right- and left-half genomes were selected and thence translated into the appropriate mindist protein sequences . Ties were broken by ( a ) excluding sequences with the most ambiguous , incomplete or stop codons , ( b ) for right-half genomes , selecting the sequence with the shortest env distance , and ( c ) for left-half genomes , selecting the sequence with the shortest gag distance . Five ties remained after this procedure , which were broken randomly . Only the SmoothMarks and vaccine efficacy ( VE ) and differential VE ( DVE ) analyses utilize the entire “Modified Intent-to-Treat” ( MITT ) cohort of the RV144 trial , including subjects who did not become infected and subjects lost to followup . These methods are particularly well-suited to detect acquisition sieve effects , because under fairly general conditions these have been shown to be robust to post-hoc selection biases engendered by conditioning on infection . The other methods only include in the analysis infected subjects ( who by definition are the only subjects with HIV-1 sequences available for analysis; see the Trial Data subsection of Methods for details ) , and ( while generally applicable ) are best-suited to evaluate post-acquisition sieve effects . Because of the six-monthly sampling scheme employed in the RV144 trial , the evaluated sequences are likely to have evolved between acquisition and sampling , and , of the methods applied here , only the SmoothMarks method attempts to recapitulate the genetic distance of the founder variant using missing-data methods . To our knowledge there is no existing method that can differentiate between acquisition and post-acquisition effects without incorporating longitudinal sequence data , which are not available for this trial . The DVE method is designed to detect acquisition sieve effects of differential VE by Match vs . Mismatch of breakthrough sequences to the immunogen sequences , and the SmoothMarks method is designed to detect acquisition sieve effects of differential VE by continuous genetic distance of breakthrough sequences to the immunogen sequences . The other methods are designed to detect post-acquisition effects such as weighted mutation rates at single sites ( GWJ uses a T-type test comparing AA substitution costs versus the vaccine immunogen , MBS uses a Bayesian model of post-acquisition sieve effects ) , incorporate multiple sequences per subject ( SMMB and EGWJ ) , employ a phylogenetic model of sequence relatedness ( divergence , diversity , PRIME , and FEL ) , evaluate codons for selection pressure ( dN/dS and PRIME ) , and/or evaluate immunological hypotheses such as physico-chemical selection ( PCP and PRIME ) , T cell escape ( EscapeCount , EpitopeDistance , and PercentEpitopeMismatch ) , and antibody binding ( signature site set enrichment , and SmoothMarks when applied to Ab site sets ) . The application of these varied methods provides a comprehensive exploratory evaluation of the effects of vaccination on breakthrough HIV-1 sequences . Maximum likelihood phylogenetic trees were constructed ( one tree per protein and per vaccine immunogen sequence ) using PhyML ( version 3 . 0 ) [48] , using the HIV-between ( HIVb ) PAM substitution matrix[16] , invariant sites , and four gamma-distributed rate categories . For each tree , the differential amino acid phylogenetic diversity ( PD ) [31] was defined as the difference in the total branch length of two subtrees ( defined by holding the tree fixed and excluding a subset of leaves corresponding to one treatment group ) : the subtree excluding placebo recipients ( retaining only sequences from vaccine recipients and the vaccine immunogen sequence ) ; and the complementary subtree excluding vaccine recipients ( but retaining the vaccine immunogen sequence ) . We estimated a null distribution by randomly permuting vaccine/placebo labels 10 , 000 times , and computed a ( two-sided ) p-value by comparing the observed difference in PDs to this null distribution . The phylogenetic divergence analysis computed shortest-path distances between each subject’s sequence ( s ) and the vaccine immunogen sequence . For mindist analyses we used the trees computed for the PD analysis . For multiple-sequence-per-subject analyses , we constructed AA trees as above using all available sequences , and we constructed nucleotide trees using the GTR + I + G nucleotide substitution model using PhyML ( version 3 . 0 ) [48] implemented in DIVEIN [49] ( http://indra . mullins . microbiol . washington . edu/DIVEIN/diver . html ) . Tree-based distances were extracted from these trees using the NewickTermBranch algorithm ( http://indra . mullins . microbiol . washington . edu/perlscript/docs/NewickTermBranch . html ) and the ape package in the R computing language [50] , and per-subject median distances were computed to each reference sequence . These distances were compared between the vaccine and placebo groups using a Wilcoxon rank sum test ( one test per gene/reference combination ) . We introduce a new application of the Earth-Mover’s Distance statistic to sieve analysis . The QEMD statistic equals the maximum over W of ∑ ( S * W ) where S is the n by m matrix containing the pairwise alignment scores between vaccine and placebo mindist sequences , * denotes entrywise multiplication and W is an n by m weight matrix subject to the following constraints: W > 0 , every row sums to 1/n and every column sums to 1/m . Note that in this case the QEMD statistic measures similarity ( not distance ) . The QEMD hypothesis test reports two-sided “mid p-values” [51] based on random permutation of treatment assignments . We applied this approach with n and m , the total number of vaccine and placebo recipient sequences , respectively . We used the mindist sequence as an approximation of the founder virus , and we computed distances between the immunogen sequences and the mindist sequence measured from blood samples drawn at or before the HIV diagnosis date . The SmoothMarks method [19 , 46] was used for estimation and testing of VE ( v ) over the range of distances v from 0 to 1 , where the vaccine efficacy against HIV-1 with distance v , VE ( v ) , is one minus the distance v-specific hazard ratio ( vaccine/placebo ) of HIV-1 infection multiplied by 100% . This method employs a missing-data framework to analyze VE ( v ) as a function of the “true distance” v between the transmitted founder sequence and the vaccine immunogen sequence . This can in principle improve the analysis over the other analysis methods that analyze the “observed distances” of available sequences that are measured weeks or months after infection; by not accounting for post-acquisition evolution these methods may obscure acquisition sieve effects . Since we do not have longitudinal sequence data , we are limited in our ability to estimate the transmitted founder sequence , so for the present analysis we defined “true” genetic distances as the HIVb-computed distance between the immunogen sequences and the mindist sequence measured from blood samples drawn at or before the HIV diagnosis date , where the 10% of infected subjects ( 11 of 109 ) with later sampled sequences were treated as missing data . See S2 Text for additional details about the method and its implementation . We assessed genotype-specific VE using the Cox proportional hazards model and score test as described by [52] , and we assessed differential VE ( DVE ) by genotype using the same model , via the procedure described by Lunn and McNeil [14] . These were the primary analysis methods used previously [7] . Negative VE values are shown in symmetrized form ( as the negative of the VE value calculated with vaccine and placebo groups interchanged ) . Two additional primary site-scanning methods were used that assess at each site whether the amino acid distances to a reference immunogen at that site differ for vaccine compared to placebo recipient sequences: a nonparametric weighted distance comparison test ( GWJ ) [17] , and a model-based method ( MBS ) [18] . Both of these methods were based on the mindist amino acid sequences . Code for these methods was published previously [7] . We introduce the PCP analysis method , which compares counts of each of the ten Taylor properties [21] , and five z-scale components [22–24] across treatment groups using parametric two-sample pooled-variance two-sided t-tests . The analysis can apply to individual sites or to arbitrary site sets ( we evaluated 3-mers and 9-mers ) , in the latter case by summing counts over sites . The resulting p-values are then Bonferroni-corrected across the properties for each of the two property scales at each site ( for all k-mers overlapping that site , separately for each value of k ) . Peptide microarrays designed to cover the entire gp160 consensus sequences for HIV-1 Group M , subtypes A , B , C , D , CRF01_AE and CRF02_AG for a total of 1423 peptides ( 15-mers overlapping by 12 amino acids ) were used to detect reactive regions for RV144 vaccine recipients . Using the analysis method of Imholte et al . [53] , four dominant responses were detected in the C1 , V2 , V3 and C5 regions of gp120 [6] . These sites are listed in S3 Dataset . This is a set of known and published monoclonal antibody contact sites provided by Ivelin Georgiev , Peter Kwong , Robin Stanfield , and Ian Wilson [54 , 55] . They are listed in S3 Dataset . These are the sites identified as relevant to the neutralization activity of known neutralizing antibodies in Wei et al . ( 2003 ) [54] , Moore et al . ( 2009 ) [55] , and Tomaras et al . ( 2011 ) [56] . They are listed in S3 Dataset . These are the union of sites in contactsites and nAb-sites . Potential antibody contact “patches” were calculated by the method described previously [7] , but considering all of the Env protein rather than only the V1/V2 region . Sites were sorted by frequency of inclusion in these patches ( by the mean of their frequency of inclusion in patches versus the 92TH023 sequence and the maximum of their frequencies of inclusion in patches versus the CM244 and MN sequences ) , as shown in S9 Fig . . The same threshold used previously [7] was used to select top-scoring sites . The EPIMAP site set contains the 71 sites that passed this threshold , 38 of which overlap vaccine sequence sites . They are listed in S3 Dataset . We defined two sets of sites where selective pressure by T cells was putatively highest . These analyses considered only the vaccine immunogen sequences ( and the HLA types of the subjects ) , and were conducted blinded to subject treatment assignment . They are listed in S4 Dataset . | We present an analysis of the genomes of the HIV viruses that infected some participants of the RV144 Thai trial , which was the first study to show efficacy of a vaccine to prevent HIV infection . We analyzed the HIV genomes of infected vaccine recipients and infected placebo recipients , and found differences between them . These differences coincide with previously-studied genetic features that are relevant to the biology of HIV infection , including features involved in immune recognition of the virus . The findings presented here generate testable hypotheses about the mechanism of the partial protection seen in the Thai trial , and may ultimately lead to improved vaccines . The article also presents a toolkit of methods for computational analyses that can be applied to other vaccine efficacy trials . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Comprehensive Sieve Analysis of Breakthrough HIV-1 Sequences in the RV144 Vaccine Efficacy Trial |
The evolutionary history of biological pathways is of general interest , especially in this post-genomic era , because it may provide clues for understanding how complex systems encoded on genomes have been organized . To explain how pathways can evolve de novo , some noteworthy models have been proposed . However , direct reconstruction of pathway evolutionary history both on a genomic scale and at the depth of the tree of life has suffered from artificial effects in estimating the gene content of ancestral species . Recently , we developed an algorithm that effectively reconstructs gene-content evolution without these artificial effects , and we applied it to this problem . The carefully reconstructed history , which was based on the metabolic pathways of 160 prokaryotic species , confirmed that pathways have grown beyond the random acquisition of individual genes . Pathway acquisition took place quickly , probably eliminating the difficulty in holding genes during the course of the pathway evolution . This rapid evolution was due to massive horizontal gene transfers as gene groups , some of which were possibly operon transfers , which would convey existing pathways but not be able to generate novel pathways . To this end , we analyzed how these pathways originally appeared and found that the original acquisition of pathways occurred more contemporaneously than expected across different phylogenetic clades . As a possible model to explain this observation , we propose that novel pathway evolution may be facilitated by bidirectional horizontal gene transfers in prokaryotic communities . Such a model would complement existing pathway evolution models .
The evolution of biological pathways has attracted increasing attention in recent years [1]–[3] . Since this research area originated more than 60 years ago [4] , several models have been proposed for the evolutionary mechanisms of biological pathways , to explain the building principles of biological systems ( e . g . , [5] ) . Recently , the advance in genome sequencing technologies and the development of novel computational tools have enabled researchers to study pathway evolution ( i . e . , the evolution of pathways ) in a comprehensive and systematic manner . Unlike the evolution of individual genes , the evolution of genes that function cooperatively ( e . g . , genes constituting biological pathways ) cannot be understood intuitively in many cases . As genetics has shown , biological pathways sometimes lose their positive effects on the survival of the host species when some parts of their component genes are absent from genomes . This would suggest that , to acquire a novel biological pathway by inventing its genes individually , a genome might have to retain those genes until it completes all components of the pathway , a process that would be evolutionarily disadvantageous [4] . To date , several noteworthy models of pathway evolution have been proposed . Recent studies have shown that the patchwork model , in which enzymes from different pathways are recruited and combined to generate a novel pathway [5] , could play a major role in pathway acquisition events , compared to other models ( e . g . , the pathway duplication , enzyme specialization , and retro-evolution models; for a review , see [3] ) . These observations come mainly from sequence similarity analyses between biological pathways [6] and close investigations into individual pathways [7] . Nevertheless , the reconstruction of pathway evolution on a genomic scale and over the tree of life has suffered from several obstacles . To trace pathway evolution , it is necessary to estimate the pathways of ancestral species . In general , this can be achieved by estimating the ancestors' gene-content vectors , whose elements represent the existence or nonexistence of genes belonging to each ortholog group , and then projecting them onto metabolic databases . An intuitive solution for gene-content estimation uses methods based on maximum-parsimony [8]–[10] . Given a phylogenetic tree and an ortholog table ( a matrix comprised of the gene-content vectors of all extant species investigated ) , these methods estimate the existence or nonexistence of each ortholog group at each branching point on the tree , just as the maximum parsimony method does in molecular phylogenetics . However , the ancestral gene content estimated by using these methods is drastically affected by changing the relative penalties between gene gain and gene loss events [10] . This artificial effect requires iterative experiments with a number of different penalty parameter sets , a process whose complexity has prevented researchers from investigating the unambiguous history of pathway evolution . The substantial difficulty in choosing these penalties arises from the fact that gene gain/loss rates are actually highly variable depending on the biological background of the host species ( e . g . , massive horizontal gene transfers [HGTs] and parasitization sometimes result in exceptional numbers of gene gains and losses , respectively [11] , [12] ) , which is incompatible with the universal application of one set of gene gain/loss rates . Recently , we developed an algorithm that can estimate ancestral gene content precisely without an artificial effect by estimating the most likely rates of gene gains and losses over a phylogenetic tree [13] . We applied this algorithm to the present problem and investigated the general tendencies of pathway evolution on the scale of whole genomes and the tree of life . In the present study , we focused on prokaryotic rather than eukaryotic lineages , because prokaryotic and eukaryotic modes of genome evolution are highly divergent and should be treated separately [13] . Metabolic pathways were chosen for analysis because they are the best understood and best described biological pathways in prokaryotes .
To reconstruct the gene content of ancestral prokaryotic species , we adopted an algorithm that can effectively estimate gene-content evolutionary history [13] . This algorithm , which is summarized in Figure 1 , estimates the gene content of each ancestral species ( i . e . , each intermediate node on a phylogenetic tree ) as an integer vector whose elements represent the numbers of genes for each ortholog group . To deal with the heterogeneous gene gain/loss modes , the algorithm's evolution model can estimate rates of gene gains and losses for each individual branch on a tree automatically without given artificial parameters . The estimated rates are then used to calculate the probabilities of gene gain/loss events over the phylogenetic tree ( e . g . , the probability of gene gains on a branch with a high gene gain rate becomes large , the reverse occurs with gene losses ) , and a set of ancestral gene-content vectors with the highest probability is computed . This algorithm requires pre-computation of a species phylogenetic tree and an expanded ortholog table that describes numbers of genes instead of the usual existence/nonexistence [13] . We used the phylogenetic tree of life produced by rigorous phylogenetic analysis [14] as the most comprehensive and most resolved tree of life available today , and we used an extended ortholog table derived from the manually annotated KEGG Orthology database [15] . The intersection of the phylogenetic tree and the expanded ortholog table encompassed 142 species of Eubacteria and 18 species of Archaea ( Figure 2; for complete names , see Table S1 ) . Using these as the input data set , we reconstructed the most likely gene content of the ancestral prokaryotic species . Although many of the branches on the phylogenetic tree used in the present study [14] had strong bootstrap supports , some branches had rather weak supports . Hence , for the statistical tests conducted below , we tested whether the observations were robust when we used alternative tree topologies and confirmed that they were supported as well ( see Methods ) . To investigate general tendencies of pathway evolution , we traced how metabolic pathways have evolved to date over all prokaryotic lineages ( Figure 3 ) . First , the probable reaction catalog of each of the ancestral and extant species was deduced using the KEGG annotations between metabolic reactions and genes ( enzymes ) that constitute the species gene content . We discarded information on reaction directionalities because our focus was not on orders , but on the cooperativeness of genes . Second , for each pair of an ancestral species and its direct/indirect descendant species on the phylogenetic tree , we subtracted the ancestor's gene content from the descendant's . The resultant genes corresponded to a set of genes acquired during the evolutionary period between the two species . Finally , every gene pair in each of the gene sets acquired together was connected if its catalyzing reactions shared at least one compound that was not contained in the ancestor's reaction catalog , which may have comprised a set of chemical compounds that was already exploited . This procedure not only prevented us from connecting genes that shared universal metabolites only ( e . g . , H2O and ATP ) but also let us systematically define which metabolites were trivial to each species ( i . e . , if a species already metabolized compound X , the acquisition of two genes connected by X would not necessarily mean that these genes function cooperatively; otherwise , they are likely to function in a coordinated manner ) . Because pathway definitions that connect genes via such trivial or species-specific compounds cause substantial problems in pathway analyses [16] , the procedure described here was developed . We did not require that the ultimate substrates and/or products of the pathways already be represented in the metabolic network of the ancestor , because there would be no a priori reason to assume pathways not connected to the existing network do not contribute to the host survival . This is because ( 1 ) ultimate substrates can be absorbed from the environment , ( 2 ) ultimate products can be useful in contexts other than metabolism ( e . g . , they can function as signaling molecules ) , and ( 3 ) alternative unknown pathways may connect the newly acquired pathways and the existing networks . We treated the genes/enzymes whose reaction products would be consumed in the consecutive spontaneous reactions according to the KEGG annotation as if they also catalyze the corresponding spontaneous reactions . We adopted this procedure because our focus was not on whether the enzymes directly catalyze the reactions , but on whether the enzymes would function cooperatively . In addition , we allowed genes on the same reaction ( e . g . , genes constituting multi-subunit enzymes ) to be connected , because they should also be the genes that function cooperatively . In this way , the history of pathway evolution over the tree of life was deduced as a set of graphs whose nodes and edges corresponded to genes and reactions , respectively , at the resolution of phylogenetic tree branches . Hereafter , the terms edges and branches are confined to metabolic reactions and phylogenetic relationships , respectively . Because the subject of our study was pathway evolution , we were interested in sets of genes that function coordinately and appeared together on a genome . Hence , we searched among each of the deduced graphs for ones that contained at least five connected genes . Changing this number in the range of three to eight did not affect the results . Over the entire phylogenetic tree , we extracted 379 such connected graphs by excluding redundancies , and we call these graphs “acquired pathways . ” The functional distribution of the acquired pathways is shown in Figure 4; the functional categories above the dashed line ( i . e . , glycan metabolism , environmental information processing/signaling , and cofactor/vitamin metabolism ) were more frequently observed than expected . Next , we used this comprehensive evolutionary history to examine whether a phenomenon that can be called pathway evolution really exists . That is , if the independent acquisition of genes can also result in a comparable number of acquired pathways , pathway evolution would be anything but a remarkable phenomenon . To test this possibility , we shuffled the relationships between genes and reactions and investigated whether a comparable number of acquired pathways was observed . To avoid bias due to genes not being assigned to the previously identified pathways , we used only genes that were associated with reactions in the data set . In addition , we preserved the original gene gain/loss numbers on each branch to eliminate bias from the heterogeneous distribution of gene gain/loss events ( e . g . , an exceptional number of gene gains would itself lead to the acquisition of pathways ) . As a result , there were 31 . 9±18 . 9 acquired pathways in the shuffled data sets , which was significantly fewer than the original 379 pathways ( N = 1000 , p<0 . 05 ) . This indicates that pathways have actually grown beyond the independent acquisition of genes over the history of genome evolution . As a general rule , to acquire multiple genes that function cooperatively , evolving genomes might be able to choose from two possible tactics: the gradual acquisition of the genes or their rapid acquisition and retention . As stated earlier , the former scenario might raise the difficulty of keeping genes that have weak effects on host survival , especially in the case of prokaryotic evolution , in which the evolutionary pressure on genome size is very strong and such genes can be promptly discarded from the genomes [4] . Nonetheless , this gradual evolution scenario may also be supported by the fact that even a well-studied genome such as that of Escherichia coli contains thousands of non-essential genes [17] , suggesting that the existence of genes that have weak effects may be less disadvantageous than generally thought . Therefore , we examined whether the gradual acquisition scenario would hold true . To examine how pathway acquisition actually took place , we visualized its mode in Figure 5 . Its horizontal axis represents relative evolutionary time , and the vertical axis represents the proportion of acquired genes to all genes constituting the pathway being investigated . The history shown seems to support the rapid acquisition scenario , and this observation was statistically supported , as described in the Methods section ( p<0 . 05 ) . This rapid gene gain scenario is consistent with the highly heterogeneous modes of gene-content evolution; that is , genomes change drastically by sometimes expanding or shrinking quickly [13] . The next question is how the rapid acquisition of pathways was achieved . To focus on the rapidly evolved portions of the acquired pathways , we searched for graphs that were acquired within one branch , and found 156 “rapidly acquired pathways” that contained at least three connected genes over the phylogenetic tree ( Figure 2 , colored symbols ) . Increasing this cutoff value to four reduced the number to 62 , which was not sufficient to conduct the statistical tests described below . We hypothesized that HGTs contribute to rapid evolution because they play a key role in prokaryotic genome evolution [18] . Probable HGT instances were detected if genes belonging to the same ortholog group ( i . e . , gene sets that are exclusively similar to each other among all genes in the data set ) appeared independently at different places on the phylogenetic tree [10] . In particular , we investigated the independent appearances of pathways , which we defined as rapidly acquired pathways that ( 1 ) appeared at different places on the tree and ( 2 ) shared at least three genes in common . Among the rapidly acquired pathways , we found that a significant proportion appeared independently several times , as 95 of the 156 rapidly acquired pathways were compiled to give 33 unique pathways ( Figure 2 , large symbols; those of the same shape and color indicate the independent acquisition of the same pathway at different places on the phylogenetic tree ) . This value was significantly larger than that expected by chance ( p<0 . 05; see Methods ) . This suggests that such HGTs , not as individual genes but as gene groups functioning coordinately , promote rapid pathway evolution . Because this observation is consistent with comparative genomics studies of closely related species in which recent pathway acquisition via operon transfers has been detected [19] , some of the pathway transfers might be due to horizontal transfers of operons , at least those that occurred relatively recently . Operon transfers might be able to carry existing pathways , but they do not seem to be able to develop novel pathways . Therefore , the next question is how these pathways originally appeared . Because we reconstructed pathway evolution at the depth of the tree of life , it may be possible to shed light on this question . We focused on the oldest acquisition of the 33 repeatedly acquired pathways . Here , the pathway acquisition time periods were estimated by using a linearized tree , which assumes a constant rate of evolution and enables the inference of temporal relationships between evolutionary events on a phylogenetic tree [20] . Note that the following statistical evaluation was confined to bacterial taxa only , because it is quite difficult to assess temporal relationships between bacterial and archaeal taxa using the molecular clock . We found that , among the repeatedly acquired pathways , the oldest acquisition was soon followed by the second-oldest acquisition . In other words , the first and second acquisitions of a rapidly acquired pathway seem to have occurred more contemporaneously in different phylogenetic clades than expected by chance . The mean difference was 0 . 13 time units , which was a significantly smaller value than the background . This was confirmed to be neither because of artificial effects in selecting old branches nor because of intrinsic bias in the original pathway-acquiring branches ( p<0 . 05; see Methods ) . The fact that branch lengths of a linearized tree can be affected by some sort of artifacts is noteworthy . Nonetheless , the present analysis would have been robust to such effects , because observations were based on the comparison against the background time difference calculated by using the same linearized tree , and it is expected that such artifacts would have been canceled out in the comparison .
In this study , we traced and analyzed the evolutionary history of metabolic pathways on prokaryotic genomes at the depth of the tree of life . The reconstruction was conducted carefully , by considering the trivial and species-specific compounds and estimating the most likely gene gain/loss rate for each phylogenetic branch . This process yielded four findings: ( 1 ) Pathways have grown beyond the random acquisition of individual genes , ( 2 ) obstacles to pathway acquisition would be overcome by the rapid acquisition of genes that would function cooperatively , ( 3 ) this rapid evolution was due to massive horizontal transfers as gene groups , and ( 4 ) the original acquisition of the pathways seems to have occurred more contemporaneously than expected across different phylogenetic clades . Importantly , the functional categories that were emphasized in the pathway acquisition events ( i . e . , glycan metabolism , environmental information processing/signaling , and cofactor/vitamin metabolism ) are the categories that are likely to be substantially affected by the environment . Preferable glycan structures would be influenced by cohabiting organisms and the chemical/physical properties of the environment , environmental information processing/signaling should respond to the environment adequately by definition , and the usefulness of cofactor/vitamin metabolism pathways would also be deeply affected by the existence of related molecules in the environment . Thus it would be reasonable to assume that these pathways have been more acquired than other pathways including ones in the central metabolism , by corresponding to changes in the environment . One possible explanation for the contemporaneous acquisition of pathways might be that some portions of the original pathway acquisition occurred through bidirectional gene transfers instead of unidirectional operon transfers . If this is the case , pathways could evolve in a prokaryotic community and likely be acquired contemporaneously in multiple phylogenies . A possible advantage of this pathway evolution model is that the recruitment of genes from different phylogenies could expand available gene/enzyme space , which would be preferable in quickly developing pathways before the evolutionary pressure discards genes , possibly by complementing the effect of the patchwork model [5] . To our knowledge , this is the first study to propose this pathway evolution model , based on a comprehensive analysis of actual pathway evolutionary history . As a possible instance of such pathway evolution in prokaryotic communities , we found a pathway for lysine biosynthesis via α-aminoadipate and N2-acetyl-l-lysine among the repeatedly acquired pathways estimated in this study . This pathway was estimated to have been acquired three times in the ancient era , in the ancestors of Crenarchaeota , Deinococcus-Thermus , and Pyrococcus ( Figure 2 , large green circles ) . It is suggested that there was no proper lysine biogenesis pathway at the last common ancestor of life , and at least five different pathways , including the one above , are believed to have developed after the diversification of life [21] , [22] . It is interesting that all three of these phylogenetic clades live in similar hot environments [23] , which does not contradict the assumption that this pathway evolved in a prokaryotic community . Another interesting observation is the repeated acquisition of pathways on carbohydrate metabolism among intestinal ( e . g . , Enterobacteria and Bacteroides ) and pathogenic ( e . g . , Pseudomonas , Xanthomonas , and Ralstonia ) bacteria ( Figure 2 , red symbols ) . The habitats of these bacteria are characterized by rich source of carbohydrate , and thus it is reasonable that many carbohydrate metabolic pathways have evolved among these phylogenetic clades . The recent technical advances in environmental genomics are remarkable , and more than 100 metagenomics projects are ongoing . Although many contigs detected in such projects cannot currently be assigned to individual taxa , making it difficult to discuss pathway evolution in each phylogenetic clade by using those data sets , in some studies , most of the environmental genes are binned to individual species [24] . Moreover , this situation might be improved in the future with the use of sequencers that can read longer sequences than can existing sequencers . Above all , metagenomic data have an important advantage in that they can ensure the certain existence of prokaryotic communities . We therefore expect that the emergence of biological pathways in prokaryotic communities might be further studied experimentally and computationally , for example by taking advantage of metagenomics technologies .
To prepare the expanded ortholog table , we downloaded the KEGG Orthology data from the KEGG database [15] and counted the numbers of genes for each ortholog group over the whole 160 species . The phylogenetic tree was downloaded from the supplementary website of the paper reporting the tree [14] . To prepare the metabolic reaction set , we downloaded the KEGG Pathway database [15] , which contains information on the chemical reactions catalyzed by each KEGG ortholog group . To reconstruct the gene content of the ancestral prokaryotic species , we adopted a previously developed algorithm that is summarized in Figure 1 and fully described in a separate paper [13] . We converted the ortholog table and the phylogenetic tree into table format in the R language and ran the R implementation of the algorithm . Then , the program reconstructed the most likely gene content of the ancestral species by estimating the most likely gene gain/loss rates on all branches of the phylogenetic tree . The computation took about 20 h on a Linux machine with a 3-GHz Intel Pentium 4 processor . To enumerate the acquired pathways , we adopted the following procedure . First , we selected every pair of an ancestral species and its descendant from the phylogenetic tree: The ancestor was always an internal node , and the descendant could be either an internal or an external node . The descendant did not need to be a direct descendant ( i . e . , child ) of the ancestor . Second , for every species pair , we counted all the pathways that were acquired through the evolutionary process from the ancestor to the descendant , as described in the main text . Third , to avoid counting the same event redundantly , we excluded any pathway that was a subgraph of another pathway whose acquisition periods covered that of the former . To enumerate the rapidly acquired pathways , we adopted the following procedure: First , we selected every pair of an ancestral species and its direct descendant ( i . e . , child ) from the phylogenetic tree . Then , for every pair , we counted all the pathways that were acquired through the evolutionary process from the ancestor to the descendant , as described in the main text . For the repeatedly acquired pathways , we counted the rapidly acquired pathways that ( 1 ) appeared at different places on the tree and ( 2 ) shared at least three genes in common . When genes belonged to the same ortholog group and were shared beyond phylogenetic clades , three possibilities could be considered: multiple geneses , multiple losses , and HGTs . Among these , the second and third possibilities should be more likely , because genes in the same ortholog group are exclusively similar to each other among all genes in the data set and thus are likely to have some sort of evolutionary relationship , although the possibility of convergent evolution might remain . Between the second and third possibilities , we exploited the advantage of our algorithm . As described above and in Figure 1 , our algorithm can estimate the most likely gene gain/loss rates on each phylogenetic branch , and thus it provides the probabilities of the multiple loss scenario and the HGT scenario based on those gene gain/loss rates . If the probability of the HGT scenario is greater than the probability of the multiple loss scenario , the algorithm will estimate the acquisition of the same ortholog group at difference places on the tree , and the HGT scenario was adopted . Statistical support for the rapid pathway acquisition scenario was obtained by evaluating how the acquisition sequential lines in Figure 5 were displaced from the 45° line . If areas surrounded by the sequential lines and the 45° line were larger than expected by chance , the pathway acquisition could be judged to have occurred rapidly . This indicator was borrowed from the Gini coefficient in economics , which uses the area surrounded by Lorenz curves to measure inequality of wealth distribution . The mean area was 0 . 409 for our data , and we examined the significance of this value by shuffling the gene acquisition periods for all ancestral-descendant species pairs where the pathway acquisition occurred . We generated real numbers that were randomly distributed between 0 and the total length of the phylogenetic branches between the two species , and these random numbers were treated as the acquisition periods of each gene constituting the acquired pathways . Then , the mean area of the acquisition sequential lines was calculated for the randomized data in the same way . The experiment was repeated 1 , 000 times and the expected area was 0 . 206±0 . 003 , which showed that the pathway acquisition was actually biased to the rapid acquisition scenario with a significance level of 0 . 05 . Statistical support for the HGTs in groups was obtained as follows . For all 156 rapidly acquired pathways , the same number of genes that existed in the descendant but not in the ancestor were chosen randomly , as in the above test to eliminate the bias originating from species selection . Instances of the independent acquisition of the same three genes at different places on the phylogenetic tree were then counted . The experiment was repeated 1 , 000 times , and the expected number of such instances was 0 . 11±0 . 33 , which showed that rapid pathway acquisition accompanied HGTs in groups ( p<0 . 05 ) . The linearized tree was constructed as described [20] with the branch lengths produced by the maximum likelihood method [14] . The pathway-acquisition periods were expected to be the midpoints of the branches . As described in the main text , the following statistical evaluation was confined to bacterial taxa . Two statistical tests for contemporaneous pathway evolution were performed . First , to test the possibility that selecting two branches of the oldest and second oldest acquisition simply resulted in temporally close branches , we randomly selected 95 pathways from all of the rapidly acquired pathways and grouped them into 33 groups as in the original grouping . The oldest and second oldest branches were then extracted from each group , and periodical differences between them were calculated . The mean difference was 0 . 20±0 . 03 ( N = 1000 ) , which was significantly larger than 0 . 13 ( p<0 . 05 ) Second , to test the possibility that the original 95 branches were biased to close relationships in themselves , we randomly selected 33 pairs from the 95 branches and investigated their mean differences . The result was 0 . 17±0 . 02 ( N = 1000 ) , which also was significant ( p<0 . 05 ) . The alternative tree topologies were constructed as follows . We downloaded the amino acid sequence alignment created by concatenating the orthologous genes that are conserved over the tree of life from the supplementary website of the paper reporting the comprehensive phylogenetic tree [14] . Then we constructed 20 bootstrap alignments and their phylogenetic trees by applying the maximum likelihood method ( PhyML , [25] ) . We conducted the same series of analyses using the 20 alternative phylogenetic trees to test whether the same observations were obtained . | Many biological functions , from energy metabolism to antibiotic resistance , are carried out by biological pathways that require a number of cooperatively functioning genes . Hence , underlying mechanisms in the evolution of biological pathways are of particular interest . However , compared to the evolution of individual genes , which has been well studied , the evolution of biological pathways is far less understood . In this study , we used the abundant genome sequences available today and a novel algorithm we recently developed to trace the evolutionary history of prokaryotic metabolic pathways and to analyze how these pathways emerged . We found that the pathways have experienced significantly rapid acquisition , which would play a key role in eliminating the difficulty in holding genes during the course of pathway evolution . In addition , the emergence of novel pathways was suggested to have occurred more contemporaneously than expected across different phylogenetic clades . Based on these observations , we propose that novel pathway evolution can be facilitated by bidirectional horizontal gene transfers in prokaryotic communities . This simple model may approach the question of how biological pathways requiring a number of cooperatively functioning genes can be obtained and are the core event within the evolution of biological pathways in prokaryotes . | [
"Abstract",
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... | 2009 | Rapid Pathway Evolution Facilitated by Horizontal Gene Transfers across Prokaryotic Lineages |
Infection of the mammalian host by schistosome larvae occurs via the skin , although nothing is known about the development of immune responses to multiple exposures of schistosome larvae , and/or their excretory/secretory ( E/S ) products . Here , we show that multiple ( 4x ) exposures , prior to the onset of egg laying by adult worms , modulate the skin immune response and induce CD4+ cell hypo-responsiveness in the draining lymph node , and even modulate the formation of hepatic egg-induced granulomas . Compared to mice exposed to a single infection ( 1x ) , dermal cells from multiply infected mice ( 4x ) , were less able to support lymph node cell proliferation . Analysis of dermal cells showed that the most abundant in 4x mice were eosinophils ( F4/80+MHC-II− ) , but they did not impact the ability of antigen presenting cells ( APC ) to support lymphocyte proliferation to parasite antigen in vitro . However , two other cell populations from the dermal site of infection appear to have a critical role . The first comprises arginase-1+ , Ym-1+ alternatively activated macrophage-like cells , and the second are functionally compromised MHC-IIhi cells . Through the administration of exogenous IL-12 to multiply infected mice , we show that these suppressive myeloid cell phenotypes form as a consequence of events in the skin , most notably an enrichment of IL-4 and IL-13 , likely resulting from an influx of RELMα-expressing eosinophils . We further illustrate that the development of these suppressive dermal cells is dependent upon IL-4Rα signalling . The development of immune hypo-responsiveness to schistosome larvae and their effect on the subsequent response to the immunopathogenic egg is important in appreciating how immune responses to helminth infections are modulated by repeated exposure to the infective early stages of development .
Schistosomiasis is an important tropical disease caused by the parasitic helminth Schistosoma and affects 200 million people [1] , [2] with a further 779 million at risk of infection [3] . Infection of the host proceeds via the rapid penetration of exposed areas of skin by invasive aquatic cercariae , and people living in endemic areas are likely to repeatedly come into contact with infective cercariae . However , it is not known whether repeated exposure to cercariae affects the development of immune responses in the skin , or responses to later stages of the parasite such as the egg which is the primary agent of Th2 biased immunopathology [2] , [4] , [5] . The mouse model of schistosome infection provides an important tool with which to examine the early immune response to larval schistosomes . Studies in this model have almost exclusively examined responses to a single infection which are associated with the development of mixed Th1/Th2 responses against normal larvae , although vaccination with live radiation-attenuated cercariae induces a Th1 biased response [6] , [7] . Infection elicits an initial neutrophil influx into the skin [8] , followed by MHC-II+ macrophages ( MΦ ) and dendritic cells ( DC ) orchestrated by a cascade of chemokines and pro-inflammatory cytokines [9] . Both MΦ and DC in the dermis take up antigenic excretory/secretory ( E/S ) material released by invading larvae and are subsequently detected in the skin draining lymph nodes ( sdLN ) [10] where they have the potential to present parasite antigen to CD4+ cells . However , invading larvae and their E/S products can also modulate the dermal immune response [9] , [11] , [12] , [13] and condition DC towards a ‘modulated’ phenotype [14] which prime CD4+ cells towards a Th2 phenotype in vitro and in vivo [15] , [16] . A common feature of chronic exposure to helminth infections is the modulation of host immune responses which over time leads to a state of hypo-responsiveness [17] , [18] , [19] . However , little is known about whether immune responsiveness to helminth infections is determined by the frequency of exposure to infective larvae . In particular it is not known whether multiple exposures to schistosome larvae , and/or their E/S products , deviate innate immune events in the skin , or shape the subsequent development of acquired immune responses [12] . Here , evidence is provided to support the view that multiple exposures of the host to schistosome cercariae modulate the skin immune response and induce hypo-responsiveness of the adaptive response . Two distinct APC populations at the dermal site of infection appear to have a critical role . The first population comprises arginase-1+ ( Arg-1 ) Ym-1+ AAMΦ-like cells , and the second are functionally compromised MHC-IIhi cells . These suppressive myeloid cell phenotypes form as a consequence of events in the skin , most notably an enrichment of IL-4 and IL-13 co-incident with an influx of RELMα-expressing eosinophils . We further show that the development of these suppressive dermal cells is dependent upon IL-4Rα signalling . The importance of immune down-regulation caused by multiple exposures to larvae extends beyond the immediate infection site to distant lymphoid tissues and even modulates the formation of hepatic granulomas elicited by the egg stage of the parasite .
The immune responses in the sdLN of mice exposed to four percutaneous doses ( 4x ) of S . mansoni cercariae at weekly intervals were compared with those in mice exposed to a single ( 1x ) infection ( Figure 1A ) . This revealed that following stimulation in vitro with larval parasite antigen , CFSE-labelled cells from the sdLN of 4x mice were hypo-responsive in terms of their ability to proliferate and divide , compared to cells from 1x mice ( Figure 1B ) . The hypo-responsive state in 4x mice was particularly marked in the CD4+ cell population ( 4x = 4 . 8% cf . 1x = 30 . 1%; Figure 1B ) . Furthermore , while sdLN cells from 1x mice produced abundant antigen-specific IL-4 , IFNγ and IL-10 , very little or no cytokine was produced by cells from 4x mice ( Figure 1C ) . Hypo-responsiveness in the sdLN was also evident in vivo since CD4+ cells from 1x mice presented significantly greater uptake of BrdU compared to 4x mice ( 26 . 6% cf . 16 . 9% , p<0 . 001; Figure 1D ) . However , analysis of the CD4+ cell population in the sdLN failed to provide any evidence of expanded Foxp3+ regulatory T cell populations ( Figure 1E ) . Hypo-responsiveness was not dependent on the total dose ( i . e . 4x 100 cercariae ) , as a single dose of 400 cercariae induced abundant cell proliferation ( data not shown ) . The duration after the initial infection was not a cause of hypo-responsiveness as CD4+ cells from 1x mice infected on day 0 and sampled on day 25 ( Figure S1A ) which failed to proliferate extensively in response to antigen , ( Figure S1B ) , released abundant antigen-driven IFNγ showing that the cells were responsive to antigenic re-stimulation ( Figure S1C ) . To assess whether hypo-responsiveness was evident in lymphoid tissues distant from the site of infection , mice were exposed to 4x doses of cercariae on the right pinna ( 4xR ) while the left pinna was exposed to only one dose ( 1xL ) . Mice exposed to 4x or 1x dose ( s ) on both pinnae served as controls . As predicted , cells from the sdLN draining 4xR pinnae were hypo-responsive , comparable to mice exposed to 4x doses on both ears ( Figure 2A ) . However , sdLN cells draining the 1xL pinna from the same mouse as 4xR pinna were also hypo-responsive ( Figure 2A ) . This suggests that immune events in the skin exposed to multiple doses of larvae induce hypo-responsiveness even in distant non-draining sdLN ( i . e . 1xL pinnae ) and is not just confined to the local site of infection ( i . e . 4xR pinnae ) . Multiple infections also modulated the immune response after maturation of larvae into adult worms and commencement of oviposition . Five weeks ( 35 days ) after the initial infection ( Figure 2B ) , cells from the mesenteric LN of mice exposed to 4x infections were hypo-responsive in terms of their ability to proliferate in vitro to stimulation with SEA compared to cells from mice exposed to a single infection ( p<0 . 05; Figure 2C ) . Modulation was observed even when a lower infection dose ( 25 cercariae ) was employed ( data not shown ) . At 6 weeks ( 42 days ) after the first infection , 4x mice produced significantly lower levels of IL-4 than cells from 1x mice ( p<0 . 05; Figure 2D ) . IFNγ was not detectable in either 1x or 4x mice , while only limited amounts of IL-10 were detected , supporting the thesis that multiple infections induce lymphoid hypo-responsiveness . The timing of the infection regime ( Figure 2B ) ensured that the only source of egg antigens came from the primary and not subsequent infections . Significantly , inflammatory granulomas surrounding embolised eggs in the livers of 4x mice at day 42 were on average 38% smaller in area ( µM2 ) than in 1x mice ( Figure 2E; p<0 . 001 ) . This demonstrates that repeated percutaneous exposure to schistosome cercariae causes immune hypo-responsiveness to later developmental stages of the parasite and can down-regulate egg-induced pathology . Multiple exposures to schistosome cercariae caused a significant thickening of the skin infection site ( Figure S2A ) . This was largely due to a pronounced infiltrate of inflammatory cells within epidermal and dermal layers ( Figure S2B ) . Therefore , we hypothesised that MHC-II+ APC populations within this infiltrate might play an important role in mediating the observed hypo-responsiveness following their migration to the sdLN and presentation of antigen to CD4+ lymphocytes [9] . Skin biopsies from 1x and 4x infected mice were cultured in vitro overnight to obtain populations of spontaneously migrating dermal exudate cells ( DEC ) and then used as APC during co-culture with CD4+ cells from the sdLN [20] . The advantage of this isolation technique is that migratory cells can be recovered without having to use a potentially damaging enzymatic digestion step . Significantly , DEC from 1x mice supported much greater levels ( >60% ) of antigen-specific CD4+ cell proliferation than DEC from 4x mice ( p<0 . 001; Figure 3A ) . Moreover , the superior antigen presenting capacity of 1x DEC was evident with CD4+ cells from either 1x or 4x infected mice ( data not shown ) . The total numbers of DEC obtained from 4x mice between days 1 to 4 post-infection were much greater compared to 1x mice ( p<0 . 01; Figure 3B ) , although the proportions that were CD45+ across both groups of mice , at all time points , were similar ( 60–80%; Figure 3B ) . Very few ( <0 . 2×105 ) spontaneously migrating DEC were recovered from naïve mice , indicating that the DEC recovered from 1x and 4x mice represented the infection-induced inflammatory immune cell populations of the skin . DEC consist primarily of neutrophils immediately after infection but an increasing number of DC and MΦ are present during the time that larvae remain in the skin [8] , [9] , [10] . On the basis of MHC-II and F4/80 expression , four discrete cell populations ( R1–R4 ) were identified ( Figure 3C ) . R1 cells were F4/80− and MHC-II− , and comprised a smaller proportion of 4x compared to 1x DEC ( p<0 . 001 ) . The majority of R1 cells were Ly6GhiLy6ChiSiglecFloCD11clo ( Figure 3E ) , suggesting the majority are neutrophils . Cytospins of R1 cells recovered using a MoFlo cell sorter ( DakoCytomation ) confirmed that morphologically they predominantly consisted of neutrophils ( Figure 3D ) and that very few lymphocytes were present . R2 cells ( F4/80+MHC-II− ) constituted the majority ( >60% ) of DEC from 4x mice , and comprised a much greater proportion of the DEC population than from 1x mice ( ∼5 fold increase; p<0 . 001; Figure 3C ) . Moreover , when the numbers of DEC recovered from the two groups of mice ( Figure 3B ) are taken into account , R2 cells in 4x mice were 15 . 8-fold more numerous than in 1x mice . R2 cells were the only cells to express high levels of SiglecF ( Figure 3E ) , a marker of eosinophils [21] . R2 cells were also Ly6GloLy6ChiCD11clo , displayed high granularity and cytospins of sorted R2 cells identified them as eosinophils ( Figure 3D ) . The abundance of eosinophils in 4x compared to 1x or naïve mice was confirmed following probing of pinnae sheets with FITC-labelled anti-SiglecF mAb ( Figure S3A ) . Toluidine blue staining of skin sections showed that while the occasional mast cell was detected in the dermis of both naïve and 1x skin , there was a substantial increase in the numbers detected in the skin of 4x mice ( p<0 . 01; Figure S3B & S3C ) . Mast cells were particularly abundant adjacent to the basement membrane separating the epidermis from the dermis , and many appeared to be degranulating ( Figure S3D ) . However , mast cells were retained in the pinnae and did not migrate during overnight culture as very few IgeR+ SiglecF− cells were present in 4x DEC , and only ∼4% were c-kit+ ( data not shown ) . Two further populations of DEC were defined on the basis of differential MHC-II expression: R3 ( MHC-IIlo ) and R4 ( MHC-IIhi ) . R3 cells were also F4/80+ , while R4 comprised both F4/80+ and F4/80− cells ( Figure 3C ) . Both R3 and R4 cells were Ly6G−SiglecF− showing this fraction did not contain granulocytes ( Figure 3E ) . Cytospins showed that R3 and R4 cells were largely mononuclear with a large cytoplasm ( Figure 3D ) and since R3 cells had increased Ly6C expression compared to R4 cells we conclude that R3 cells were likely to be inflammatory MΦ . Whilst both R3 and R4 cells expressed CD11c , the geometric mean fluorescence intensity ( MFI ) was highest on MHC-IIhi R4 cells ( Figure 3E ) , indicating that most R4 cells were DC with high antigen presenting capabilities . Although DEC from 4x mice comprised smaller proportions of both R3 and R4 cells compared to 1x mice , this was presumably due to the massive expansion of R2 eosinophils ( 53% and 80% decrease respectively; p<0 . 001; Figure 3C ) . The MFI of expression for a number of activation/regulatory factors ( i . e . CD40 , CD80 , CD86 , PDL1 , PDL2 , Fas , and FasL ) on R3 and R4 cells was examined , and several were found to be differentially expressed between 1x and 4x mice , and between R3 and R4 cells ( Figure S4 ) . CD80 , and to a lesser extent CD86 , were down-regulated in 4x compared to 1x mice , although the MFI for CD40 was either slightly up-regulated ( on R3 cells ) , or not altered ( R4 cells ) . Together , this suggests that R4 rather than R3 cells are the primary APC population in the DEC population , and that APCs in the skin have reduced expression co-stimulatory molecules following four infections . Both R3 and R4 cells from 4x mice expressed lower MFI of regulatory factor PDL1 but significantly increased PDL2 and Fas ( Figure S4 ) . The expression of PDL1 and PDL2 was greater for R4 cells , whilst the MFI for Fas and FasL was much greater on R3 cells; all four of these markers have been associated with regulation of the immune responses but PDL2 is specifically associated with AAMΦ [22] . The cytokine milieu of the infection site is likely to be important in determining the composition and activation status of the DEC populations . Indeed , supernatants recovered from in vitro cultured skin biopsies of infected compared to naive mice contained elevated levels of several soluble immune mediators including TNFα , IL-12/23p40 , IL-4 , IL-13 , IL-10 and TSLP ( Figure 4A ) ; IFNγ was not detectable . The supernatants from 4x infected mice were particularly rich in Th2-type cytokines , and over the first 4 days after infection contained 3- to 5-fold increased levels of IL-4 and IL-13 compared to 1x mice , as well as significantly greater quantities of IL-10 ( Figure 4A ) . Though levels of IL-12/23p40 were significantly increased from 4x skin biopsies compared to 1x , it was a less dramatic increase compared to IL-4 , IL-13 and IL-10 . Furthermore , there were no significant differences between 1x and 4x mice in the levels of TNFα and , perhaps surprisingly , TSLP . The Th2-like environment in the skin infection site of 4x mice appeared to trigger switching of dermal MΦ from being ‘classically-activated’ ( CAMΦ ) to ‘alternatively-activated’ as quantitative ( q ) RT-PCR analysis of mRNA from 4x DEC showed that transcripts for Arg-1 , Ym1 and RELMα , which typically characterise AAMΦ [23] , [24] , [25] , were all significantly up-regulated compared to 1x DEC ( Figure 4B ) . Transcripts for IL-4 and IL-13 were also elevated in DEC from 4x mice . Conversely , the expression of iNOS and IFNγ mRNA was significantly lower in 4x compared to 1x DEC . When DEC were sorted into the R2 , R3 and R4 populations as described above ( see Figure 3C ) , only R3 cells ( F4/80+MHC-IIlo ) from 4x DEC expressed an abundance of Arg-1 and Ym1 transcripts; they did not express RELMα ( Figure 4C ) . This indicates that the R3 fraction comprised a RELMα negative ‘AAMΦ-like’ cell population . In contrast , R3 cells from 1x DEC are likely to be CAMΦ due to their high levels of iNOS transcript combined with low expression of Arg-1 , RELMα and Ym1 ( Figure 4C ) . R2 cells ( F4/80+MHC-II− ) , particularly from 4x DEC , expressed the greatest levels of IL-4 and IL-13 mRNA , and also expressed RELMα transcript . As R2 cells from 4x mice comprised an abundance of eosinophils , this suggests that these RELMα+ granulocytes are a source of the Th2-biassed cytokine environment in multiply-infected skin , which in turn may be crucial in driving the formation of the R3 AAMΦ-like cells . To test which DEC population mediates suppression of sdLN lymphocytes , R2 ( eosinophil ) , R3 ( MHC-IIlo AAMΦlike ) and R4 ( MHC-IIhi DC ) cells from 1x or 4x mice were isolated and co-cultured with CD4+ cells from 1x infected mice . Whilst R2 and R3 cells from 1x mice induced only low levels of CD4+ proliferation , this was even lower when they were obtained from 4x mice in which proliferation was not significantly above that by CD4+ cells alone . However , MHC-IIhi R4 cells from 1x and 4x DEC were the only cells able to support substantially elevated levels of antigen-specific CD4+ cell proliferation ( Figure 5A ) . Strikingly , R4 cells from 4x DEC supported significantly lower ( ∼3-fold ) levels of proliferation compared to R4 cells from 1x mice ( p<0 . 001; Figure 5A ) . This suggests that the R4 cells from 4x mice , despite expressing high levels of MHC-II , are functionally compromised and that their intrinsic APC potential is impaired . To establish whether R2 eosinophils from 4x mice modulate the APC potential of MHC-II+ cells ( i . e . R3 and R4 combined ) , R2 cells were added to MHC-II+ cells and used to drive CD4+ cell proliferation . The level of CD4+ cell proliferation in the presence of R2 cells was similar to that achieved by MHC-II+ cells , or unsorted 1x and 4x DEC ( Figure 5B ) . Therefore , the R2 cells do not adversely affect in vitro CD4+ cell proliferation , either by acting directly on CD4+ cells , or by modulating putative APCs present in the R4 population . In vivo however , eosinophils may modulate the immune response indirectly as a source of IL-4 and IL-13 . Significantly , addition of R3 ( MHC-IIlo ) AAMΦlike cells from 4x DEC to co-cultures of R4 ( MHC-IIhi ) and CD4+ cells suppressed cell proliferation by ∼70% ( p<0 . 05; Figure 5C ) . Indeed , CD4+ proliferation following co-culture with both R3 and R4 cells from 4x mice was reduced to near the level achieved with unsorted 4x DEC and was 82% lower than the level achieved with unsorted 1x DEC . Together , these results show that AAMΦ-like R3 cells from 4x mice are unable to support antigen-specific CD4+ proliferation and have a suppressive function on MHC-IIhi R4 cells . Thus , R3 but not R2 DEC from multiply infected mice mediate the suppression of CD4+ cells from the sdLN . Removal of phagocytic cells in the skin infection site via clodronate liposome ( CL ) treatment ( Figure 6A ) , substantially reduced the number of both R3 and R4 DEC from 4x mice , although the numbers of eosinophils was only slightly reduced ( Figure 6B ) . Moreover , the proliferative response of sdLN cells from CL-treated mice was increased compared to PBS-liposome-treated 4x mice ( p<0 . 05 ) and the production of IFNγ , albeit limited , was also significantly increased ( Figure 6C ) . This further shows that R3 and R4 phagocytes in the skin are compromised in their ability to support lymphocyte responsiveness in the sdLN . In order to prevent the dominant Th2-type response in the skin of 4x mice and thereby determine whether it drives the formation of modulated APC , recombinant IL-12 ( rIL-12 ) was administered 48 hours after the 1st , 2nd and 3rd infections ( Figure 7A ) . DEC from the pinnae of rIL-12 treated 4x mice had much reduced levels of IL-4 and IL-13 transcripts ( 11- and 5-fold reduction respectively; both p<0 . 01 ) , but also less RELMα ( p<0 . 01 ) and Ym1 ( p<0 . 05; Figure 7B ) . Although the levels of Arg-1 mRNA in 4x DEC were not affected by rIL-12 treatment , levels of iNOS transcript were up-regulated ( 4 . 5-fold; p<0 . 01; Figure 7B ) . IL-12 treatment had no impact on the number of DEC recovered but it altered the cellular composition of DEC from 4x mice substantially by reducing the proportions of eosinophils ( p<0 . 01; Figure 7C ) . Moreover , as judged by the expression of iNOS , rIL-12 promotes conditioning of MΦ toward a ‘classically-activated’ status rather than ‘alternatively-activated’ as seen in the PBS-treated control 4x mice . In contrast , the pattern of expression of CD40 , CD80 , CD86 , PD-L1 , PD-L2 , Fas and FasL by R3 and R4 cell populations ( Figure S5 ) showed that while there were clear differences in expression between R3 and R4 cells obtained from 1x versus 4x mice , there were only minor changes in the expression of these molecules between 4x versus rIL-12-treated 4x mice . The only significant , albeit slight , changes were up-regulation of CD40 , CD80 , and PDL2 by R3 cells from rIL-12-treated 4x mice , and Fas by R4 cells . Conversely , PDL2 was down-regulated by R4 cells . Together , this suggests that an obvious marker of ‘modulation’ has not yet been identified . On the other hand , in vitro proliferation of sdLN cells from rIL-12-treated 4x mice was 3 . 6-fold greater than for cells from sham-treated ( PBS ) 4x mice ( p<0 . 01 ) , and was similar to that in both groups of 1x mice ( Figure 7D ) . Moreover , the sdLN cells secreted abundant IFNγ ( unlike sham-treated 4x mice ) , which was ∼7 . 5 fold greater than 1x mice ( p<0 . 001 , Figure 7D ) : delivery of exogenous IL-12 also caused the detection of small quantities of IL-4 compared to sham-treated 4x mice ( p<0 . 01 ) . These data indicate that exogenous IL-12 delivery to the skin prevents the development of sdLN hypo-responsiveness whilst simultaneously modulating dermal eosinophil influx and Th2-conditioning of dermal macrophage populations . To further investigate the role of dermal cytokines in conditioning DEC phenotype and the generation of lymphocyte hypo-responsiveness , mice deficient for IL-4Rα were exposed to multiple infections . DEC recovered from 4x IL-4Rα−/− mice contained only a small SiglecF+ eosinophil population compared to 4x WT mice ( p<0 . 05; Figure 7E ) , demonstrating that IL-4Rα expression is critical for mediating the influx of eosinophils into the 4x skin infection site . DEC from 4x IL-4Rα−/− mice also had significantly down-regulated levels of mRNA for Arg-1 , Ym1 and RELMα but up-regulated levels of iNOS ( Figure 7F ) , confirming that signalling via IL-4Rα is required for the expression of these molecules [23] and the generation of the AAMФ-like population in 4x mice . Our data reveals an essential role for IL-4Rα in the regulation of RELMα , which is confined to the eosinophil population . The proliferation of sdLN cells from 4x IL-4Rα−/− mice was restored to near the levels achieved by cells from 1x wild-type ( WT ) mice , clearly showing that IL-4/IL-13 signalling is important in the development of lymphocyte hypo-responsiveness ( Figure 7G ) . Combined , this provides evidence that IL-4Rα+ cells contribute towards the generation of lymphocyte hypo-responsiveness and demonstrates that IL-4 and IL-13 cytokine signalling through the IL-4Rα is an important mediator in dampening the immune responses in multiply infected mice .
In this study , we demonstrate that mice multiply-infected with schistosome larvae have increased expression of ‘Th2-associated’ cytokines in the skin-exposure site leading to hypo-responsive lymphoid activity in the sdLN and down-regulated hepatic pathology to schistosome eggs . We conclude that the altered cytokine environment in the infection site of multiply-exposed mice most likely results from an influx of RELMα+ eosinophils , which as a source of IL-4 and IL-13 condition dermal MHC-II+ myeloid cells with an alternatively-activated and modulated phenotype and makes them inefficient at supporting CD4+ lymphocyte activity . We have established an experimental model of schistosome infection in which the immune response to multiple-exposure with S . mansoni larvae can be investigated prior to oviposition and hence in the absence of egg antigens . Mice exposed to four doses of cercariae exhibit lymphocyte hypo-responsiveness supporting earlier studies on multiple infection with the bird schistosome T . regenti [26] . The hypo-responsive state extends to sdLN of distant ‘non-exposed’ skin and the mesenteric LN responses at the acute stage of infection leading to the modulation of granulomatous inflammation against eggs in the liver . The down-regulated activity of lymphocytes in the sdLN appears not to involve Foxp3+ Treg cells as there was no difference in their frequency in the sdLN of 1x and 4x mice . Rather , it appears to result partially from the development of anergy as in vitro responsiveness of sdLN cells can restore lymphocyte activity to a limited extent through the addition of IL-2 ( Cook et al . MS in preparation ) . Modulation of the acquired immune response to chronic schistosome infection is a well accepted immune phenomenon and the presence of eggs and their released antigens are the primary agent [27] . However , whilst modulation of the immune response to multiple schistosome infections has been reported previously [28] , [29] , parasites were allowed to mature and lay eggs before drug-cure , thereby obscuring the cause of hypo-responsiveness . Here our study clearly demonstrates that multiple exposures of the skin to infective larvae ( prior to egg deposition ) predispose the host to immune regulation against larval antigens and later developmental stages of the parasite ( namely the egg ) . This suggest that the exposure history of individuals in endemic areas who frequently come into contact with infective parasites [30] is likely to be an important factor in the development of immune responsiveness and hence egg-induced immunopathology . Typically , chronic helminth infections are associated with the induction of a biased Th2 associated immune response [31] , although the response to schistosome parasites prior to egg-laying is thought to comprise a mixed Th1/Th2 phenotype with IFNγ production alongside IL-4 and IL-5 [4] . It is widely accepted that the immune response only becomes dominated by Th2 cells after the start of egg laying [4] , although it has also been suggested that exposure to adult worms and their released antigens in the absence of egg antigen can initiate polarisation towards a Th2-phenotype [32] . In light of these observations , we specifically examined whether multiple exposures to infective larvae is conducive to the development of Th2 polarisation . While a Th2 bias was observed in the skin and sdLN in response to non-maturing bird schistosome T . regenti larvae [26] , we did not observe a Th cell subset bias in the sdLN of mice exposed to 4x doses of S . mansoni cercariae since hypo-responsiveness was evident for all the cytokines tested . Nevertheless , analysis of the skin-infection site demonstrated that multiple exposures to cercariae caused dramatically increased levels of IL-4 and IL-13 secretion , as well as increased levels of transcript for these cytokines . Lymphocyte responsiveness was also restored in 4x IL-4Rα−/− mice demonstrating that signalling via IL-4Rα , which is required for both IL-4 and IL-13 , has a major influence on the development of hypo-responsiveness . As the most abundant cell population in the skin after 4x infections were SiglecF+ eosinophils , and R2 eosinophils sorted from total DEC expressed abundant mRNA for IL-4 and IL-13 , we propose that eosinophils may be the primary source of the copious IL-4 and IL-13 released by 4x skin biopsies . Eosinophils release other pro-Th2/down regulatory molecules such as eosinophil-derived neurotoxin [33] , although the expression of RELMα by eosinophils may represent a feedback mechanism to dampen the abundance and potency of Th2-type cytokines [34] , [35] . Other tissue resident cells in the skin , such as mast cells and endothelial cells , may release additional polarising mediators such as TSLP [36] , [37] but no difference was detected in the levels secreted by the skin of 4x versus 1x mice . This implies that TSLP is not likely to be important in conditioning the dermal immune response in our multiple infection model but does not rule out other cytokines such as IL-25 or IL-33 recently described to be important for Th2 induction [38] , [39] , [40] . It might be argued that the abundance of eosinophils in 4x DEC simply dilutes the number of potential APC accounting for the inability of the total DEC population to support lymphocyte responsiveness . However , R3 an R4 cells from 4x mice in the absence of R2 eosinophils were deficient at supporting lymphocyte proliferation . Moreover , we found no evidence that purified eosinophils from 4x DEC directly or indirectly down-regulate in vitro lymphocyte responses supported by putative APCs . Instead , eosinophils may contribute towards the development of hypo-responsiveness in our infection model by conditioning dermal cells that subsequently traffic to the sdLN where they mediate the extent of the acquired immune response . MФ are especially sensitive to high levels of IL-4 and IL-13 and become ‘alternatively-activated’ [41] . In fact , AAMΦ-like cells ( R3 ) are a major constituent of the DEC population of 4x mice , and while most studies on AAMΦ elicited by helminth infections have been on cells in the intestines , lungs or peritoneal cavity [24] , [25] , [34] , [41] , [42] , [43] , [44] , [45] , our study is the first to report their presence in the skin . Conventional AAMΦ observed following helminth infection are IL-4/IL-13-dependent , and analyses of the DEC mRNA transcript levels demonstrated that the AAMΦ-like population was absent in 4x IL-4Rα−/− mice . However , although RELMα has been previously thought to be a defining characteristic of AAMΦ [41] , we note that our AAMΦ-like cell population obtained from the skin does not express abundant RELMα and may represent a tissue-specific sub-population of MФ . The MФ population in 4x IL-4Rα−/− mice instead displayed a CAMΦ phenotype accompanied by increased levels of MHC-II . AAMФ are required for the induction of protective memory Th2 responses against gut helminths [46] , possibly via increased Ym1 [47] . However , sdLN cells from our repeatedly infected mice displayed down-regulated Th2 cytokine production suggesting that the AAMΦ-like cells in our infection model are not involved in the promotion of Th2 responses . AAMΦ-like cells may be required for eosinophil recruitment [48] . Indeed , 4x mice treated with clodronate liposomes to deplete phagocytic cells had a reduced influx of eosinophils , although the remaining population was still substantial in number . The AAMΦ-like cells revealed in our studies were functionally suppressive and mediated hypo-responsiveness of sdLN cells . They expressed arginase and Ym1 but not RELMα transcript which may highlight the heterogeneity of AAMΦ depending upon their tissue location ( i . e . the skin ) , and/or reflect a ‘wound healing’ phenotype defined as M2c MΦ within a ‘colour wheel’ of immune function [49] , [50] . The sorted R3 AAMΦ-like DEC population in 4x mice down-regulated CD4+ T cell responses supported by MHC-IIhi APCs , a feature previously described for conventional AAMΦ [51] . Removal of the dermal AAMΦ-like population by clodronate liposomes also lead to significant increases in the proliferative responses of sdLN cells . Therefore , we conclude that irrespective of their precise classification , the AAMΦ-like cells in our model are an important component causing down-regulation of lymphocyte proliferation and cytokine production . In addition to the AAMΦ-like cells , we show that dermal MHC-IIhi APCs from 4x mice were less efficient at supporting the lymphocyte response compared to R4 cells from 1x mice on a ‘cell-to-cell’ basis ( Figure 5A ) . The mechanism by which these cells were functionally impaired is unclear and may be related to decreased expression of MHC-II , CD80 or CD86 , or elevated expression of PDL2 and Fas . However , after IL-12 treatment of 4x mice , the expression of activation versus regulatory factors was not markedly altered , suggesting that other as yet un-identified molecule ( s ) play a critical role . Expression of Arg-1 and Ym1 transcripts , indicative of an ‘alternatively-activated’ population , were greater in MHC-IIhi DEC from 4x compared to 1x mice ( Figure 4C ) and , although expression of these markers by DC has been previously identified [25] , [52] , it is not known what impact this has on their ability to support lymphocyte responsiveness . The large quantities of IL-10 released by 4x skin biopsies may impair DC activation of CD4+ cells as IL-10 can generate tolerogenic DC [53] . Furthermore , we speculate that since clodronate treatment did not completely ablate the R4 cell population , the remaining cells represent modulated APCs such as Langerhan's cells which are not affected by clodronate treatment [54] . This could explain why the sdLN response of CL-treated mice was not restored to the levels seen in 1x mice . The ability of APCs , and DC in particular , to support T cell proliferation needs to also be viewed in the context of how they are stimulated by parasite specific antigens . Like schistosome egg antigens [55] , molecules released by the invading cercariae ( named 0–3hRP ) stimulate limited maturation of bone marrow-derived DC [14] which drive Th2 responses both in vitro and in vivo [15] . Recognition of 0–3hRP by potential APCs occurs via TLRs [16] and/or C-type lectin receptors , such as the mannose receptor ( Paveley et al . , MS in preparation ) , drives arginase production by cultured DC and MΦ , suggestive of alternative activation [10] . Repeated exposure to these cercarial complexes may accentuate their properties and so interfere with the ability of APCs to support T cell responsiveness . This study provides evidence that the skin-infection site of mice frequently exposed to an infectious pathogen is important in determining the nature of subsequent acquired immune responses . Formation of AAMΦ-like and modulated MHC-IIhi cells in the skin represent previously unknown mechanisms by which the host immune response limits harmful pathology to subsequent doses of an infectious agent . In the context of schistosome infection , our studies show that exposure to larvae and their antigens , prior to the arrival of eggs , can initiate immune hypo-responsiveness against different stages of the parasite . This has important consequences in the development of future vaccination strategies but also has implications in the prevention of immune-related pathology to embolised eggs .
All experiments were carried out in accordance with UK Animal's Scientific Procedures Act 1986 and with approval of the University of York Ethics Committee . Female C57BL/6 mice were bred in house at the University of York and used aged 8–12 weeks . IL-4Rα−/− on a BALB/c background were kindly provided by Dr F . Brombacher and experiments were performed at the University of Cape Town . A Puerto Rican strain of S . mansoni was maintained by routine passage through outbred NMR-I mice and Biomphalaria glabrata snails maintained at University of York . Mice were exposed to either a single ( 1x ) , or four ( 4x ) dose ( s ) of 100 S . mansoni cercariae via each pinna [56] at weekly intervals between day 0 and 21 ( Figure 1A ) . Penetration rates were approximately 50% , therefore , the combined infection dose per mouse after 4x infections was approximately 400 larvae . To assess in vivo cell proliferation , mice were given 5-Bromo-2′deoxyuridine ( BrdU; Sigma-Aldrich ) via the drinking water ( 0 . 8 mg/ml ) , for four days prior to sdLN removal . To ablate phagocytic cells from the skin infection site , clodronate liposomes ( CL ) , or PBS-loaded liposomes in 10 ml , were administered intradermally to the pinnae 72 hours prior to the 1st , 2nd and 3rd infection . Liposomes were prepared as previously described by Dr N . van Rooijen [57] using phosphatidylcholine ( LIPOID E PC; Lipoid GmbH ) and cholesterol ( Sigma ) . Clodronate ( Cl2MDP ) was a gift of Roche Diagnostics GmbH , ( Mannheim , Germany ) . In some experiments , rIL-12 ( gift of Dr S . Wolf , Genetics Institute , Cambridge , MA USA ) , or an equivalent volume of PBS ( 10 µl ) , was delivered intradermally into the pinnae and intraperitoneally ( 0 . 25 µg and 0 . 2 µg , respectively ) 48 hours after the 1st , 2nd , and 3rd S . mansoni infection ( Figure 6A ) . In mice receiving a single infection , rIL-12 was given once 48 hrs prior to infection . Cells from the sdLN were cultured ( 1×106 cells/ml ) for 4 days in RPMI-1640 containing 10% low endotoxin FCS ( Harlan Sera labs ) , 2 mM L-Glutamine , 200 U/ml penicillin , 100 µg/ml streptomycin and 50 µM 2-ME ( all Invitrogen ) , in the presence of soluble Ag prepared from larval schistosomes ( 50 µg/ml ) [9] and cell proliferation measured by [3H]thymidine incorporation ( 18 . 5 kBq/well; Amersham Biosciences ) [20] . Alternatively , sdLN cells were labelled with 3 µM CFSE ( Molecular Probes ) for 15 min , washed and after chase incubation , cultured for 3 days with or without Ag . Culture supernatants were collected at 72 hr for cytokine detection by ELISA . Inflammation of pinnae was measured using a dial gauge micrometer ( Mitutoyo , Japan ) . For histological analysis , pinnae were removed , fixed in 10% neutral buffered formal saline , wax-embedded , sectioned at 5 µm and stained with Hematoxylin and Eosin , or Toluidine Blue ( Department of Veterinary Pathology , University of Liverpool , UK ) . Pinnae sheets separated from the central cartilage were incubated with optimal concentrations of anti-Siglec-F FITC labelled antibody ( BD Pharmingen ) prior to mounting and imaging using a Zeiss confocal LSM 510 meta microscope . For the recovery of dermal exudate cells ( DEC ) , freshly excised pinnae were split in two along the central cartilage , and cultured in vitro for 18 hr in the absence of added Ag as described previously [9] , [56] . DEC were then recovered and prepared for phenotyping , or cell sorting as below . Culture supernatants from the skin biopsies were stored at −20°C for cytokine detection by ELISA . To assess the immune response at skin sites distant to the site of infection , mice were infected at weekly intervals as above with 100 cercariae via the right pinna . At the 4th infection , both the right ( = 4xR ) and the previously uninfected left ( 1xL ) pinnae were infected and immune assays performed on the pinnae ( i . e . 4xR and 1xL ) and their respective sdLN 4 days later . To assess the effect of multiple infections on immune responses to later stages of parasite development , one group of mice ( denoted as 1x ) were exposed to 100 cercariae on the pinnae on day 0 , and then sacrificed at days 35 or 42 , by which time adult worms had matured and commenced egg deposition . A parallel group of mice ( denoted as 4x ) was similarly infected on day 0 , and again on days 10 , 17 , and 24 , before sacrifice on days 35 or 42 . The mesenteric LN were removed and cultured as for sdLN but the parasite Ag was soluble egg antigen ( SEA ) . Lymphocyte proliferation and cytokine production from LN cell cultures were measured as above . The liver was wax-embedded , sectioned at 5 µm and stained with Hematoxylin and Eosin; granuloma areas surrounding individual eggs were determined using AxioVision 4 . 3 ( Zeiss UK Ltd ) and expressed as mm2 . ELISAs were used to quantify IL-12/23p40 , IL-6 , IL-4 , and IFNγ in the pinnae biopsy and sdLN culture supernatants as previously described [9] . IL-13 and TSLP were measured by DuoSet ELISA kit ( R&D Systems ) , TNFα and IL-10 by Cytoset ( Invitrogen ) . DEC were blocked with anti-CD16/32 mAb ( BD Pharmingen ) in PBS ( supplemented with 1% FCS & 5 mM EDTA ) and subsequently labelled with the following conjugated antibodies; F4/80 FITC , Pacific Blue or PE-Cy7 ( BM8 ) , CD11c APC-eFlour® 780 ( N418 ) , SiglecF PE ( E50-2440 ) , Ly6C APC ( AL-21 ) , Ly6G PerCP-Cy5 . 5 ( 1A8 ) , CD40 PE ( 3/23 ) , CD80 APC ( 16-10A1 ) , CD86 PerCP-Cy5 . 5 ( GL1 ) , and I-Ab biotin or FITC ( 28-16-8S ) , PDL1 biotin ( MIH5 ) , PDL2 PE ( 122 ) , Fas PE ( 15A7 ) , FasL biotin ( MFL3 ) ( Ab from BD Pharmingen , BioLegend , Caltag Medsystems , eBioscience and GeneTex Inc . ) . Biotin conjugated antibodies were probed with streptavidin APC ( Caltag Medsystems ) . Cells isolated from the sdLN were stained CD4 FITC ( RM4-5 ) , Foxp3 PE ( FJK-16s ) . BrdU staining was performed using FITC-conjugated anti-BrdU with DNase according to manufacturer's instructions ( BD Pharmingen ) . All antibody concentrations were optimised and labelling performed alongside relevant isotype controls . Flow cytometric acquisition was performed using a Cyan ADP analyser and analysed with Summit v4 . 3 ( DakoCytomation ) or FlowJo software ( Tree Star , Inc . ) . DEC labelled with F4/80 and I-Ab mAb were separated using a MoFlo cell sorter ( Dako ) revealing 4 populations of live cells gated to give purity >70–90% . Cytospins of the cell fractions ( Cytospin 2 , Shandon ) were stained with Diff-Quik ( Dade ) to determine cell morphology . CD4+ cells from 1x and 4x infected mice were isolated via negative selection ( MACS LS column; Miltenyi Biotec ) ; cell purities were >95% . CD4+ cells ( 5×104 cells ) were co-cultured with unsorted DEC ( 2×104 cells ) , or sorted R2 , R3 and R4 DEC ( 1 or 2×104 cells ) , for 4 days in round-bottom 96 well plates in the presence of soluble larval parasite Ag ( 50 µg/ml ) [20] . Cell proliferation and cytokine analysis was performed as described above . Cells were re-suspended in TRIzol ( Invitrogen ) and total RNA extracted . After synthesis of cDNA using Superscript III DNA polymerase ( Invitrogen ) , various genes were analysed by qRT-PCR ( ABI PRISM 7000; Applied Biosystems ) using Taqman probes ( Sigma-Aldrich ) . The relative expression of each gene was normalised to the values for the GAPDH before statistical analysis . The primer pairs and probes were; Arg-1: 5′-TCACCTGAGCTTTGATGTCG , 5′-CTGAAAGGAGCCCTGTCTTG , Probe 5′-TTCTGGGAGGCCTATCTTACAGAGAAGGTCTCTAC , RELMα: 5′-TGCTGGGATGACTGCTACTG , 5′-CTGGGTTCTCCACCTCTTCA , Probe 5′-CAAGATCCACAGGCAAAGCCACAA , Ym1: 5′-CTCAATATACACAGTGCAAGTTG , 5′TGGGATTCAATTTAGGAAAGTTCA , Probe TCCACAGTGCATTCTGCATCATGCT , iNOS: 5′-CTGCATGGACCAGTATAAGG , 5′-CTAAGCATGAACAGAGATTTCTTC , Probe: 5′-AGTCTGCCCATTGCTG , IL-4: 5′-CTCACAGCAACGAAGAACAC , 5′-TAAATAAAATATGCGAAGCACCTTG , Probe 5′-AAGCCCTACAGACGAGC , IL-10: 5′-GGTCTTGGGAAGAGAAACCAG , 5′-GCCACAGTTTTCAGGGATGA , Probe 5′-CTTTGATGATCATTCCTGCAGCAGCTC , IL-13: 5′-TTATTGAGGAGCTGAGCAAC , 5′-GAGATGTTGGTCAGGGAATC , Probe 5′-TACACAGAACCCGCCAG , IFNγ: 5′-GCGTCATTGAATCACACCTG , 5′-TGAGCTCATTGAATGCTTGG , Probe 5′-TTGAGGTCAACAACCCACAGGTCCA , GAPDH: 5′-CCATGTTTGTGATGGGTGTG , 5′-CCTTCCACAATGCCAAAGTT , Probe 5′-CATCCTGCACCACCAACTGCTTAGC . Statistical analysis was performed using Student's t test , or one-way ANOVA . Values of p<0 . 05 were considered significant: * p<0 . 05; ** p<0 . 01; *** p<0 . 001 . | Schistosomiasis is a major helminth disease that infects more than 200 million people in the tropics . Free-swimming aquatic cercariae infect through the skin after contact with contaminated water , and in endemic areas this can occur frequently . However , nothing is known about how multiple exposures affects innate immunity in the skin , and/or whether it impacts the acquired immune response . Consequently , we have developed an infection model in the mouse to examine the immune response to multiple infections prior to the production of eggs . We show that multiple exposures to schistosome larvae cause lymphocyte hypo-responsiveness , partly mediated by macrophages and dendritic cells from the skin which have a ‘down-regulated’ phenotype and are not able to act as efficient antigen presenting cells ( APCs ) . These regulated APCs are conditioned amongst high levels of the cytokines IL-4 and IL-13 which follow an influx of abundant eosinophils . In the absence of the regulatory APCs , and in the absence of the common receptor chain for IL-4 and IL-13 ( i . e . IL-4Rα ) , lymphocyte proliferation is restored . These findings are important in understanding how dermal immune responses are modulated so that we can devise new strategies for vaccine delivery , or the treatment of chronic inflammatory conditions of the skin . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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"Methods"
] | [
"immunology/innate",
"immunity",
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] | 2011 | Multiple Helminth Infection of the Skin Causes Lymphocyte
Hypo-Responsiveness Mediated by Th2 Conditioning of Dermal Myeloid
Cells |
Sedentary endoparasitic nematodes are obligate biotrophs that modify host root tissues , using a suite of effector proteins to create and maintain a feeding site that is their sole source of nutrition . Using assumptions about the characteristics of genes involved in plant-nematode biotrophic interactions to inform the identification strategy , we provide a description and characterisation of a novel group of hyper-variable extracellular effectors termed HYP , from the potato cyst nematode Globodera pallida . HYP effectors comprise a large gene family , with a modular structure , and have unparalleled diversity between individuals of the same population: no two nematodes tested had the same genetic complement of HYP effectors . Individuals vary in the number , size , and type of effector subfamilies . HYP effectors are expressed throughout the biotrophic stages in large secretory cells associated with the amphids of parasitic stage nematodes as confirmed by in situ hybridisation . The encoded proteins are secreted into the host roots where they are detectable by immunochemistry in the apoplasm , between the anterior end of the nematode and the feeding site . We have identified HYP effectors in three genera of plant parasitic nematodes capable of infecting a broad range of mono- and dicotyledon crop species . In planta RNAi targeted to all members of the effector family causes a reduction in successful parasitism .
Plant parasitism by nematodes is a major threat to global food security , with at least one nematode species targeting each of the world's most economically important crops [1] . Damage to crops caused by plant-parasitic nematodes worldwide has been valued at over £75 billion each year [2] . A better understanding of the mechanisms by which these organisms parasitise plants has the potential to make a significant contribution to global food security . Plant parasitic nematodes display a wide range of parasitic strategies , from simple migratory ectoparasites that live in soil and feed on root epidermal cells , to migratory endoparasites that feed destructively as they move through roots . However , the most complex , well-adapted , economically important , and consequently most widely studied are the sedentary endoparasites , including the root-knot and cyst nematodes of Clade 12 of the phylum Nematoda [3] . These biotrophic pathogens invade the host roots as second stage juveniles ( J2 ) and migrate to cells near the vascular cylinder . A suite of “effector proteins” , which modify host tissues to create a feeding site , are injected into root cells via a needle-like stylet . The female nematodes will feed from these sites for a period of 4–6 weeks while they develop and swell into mature , egg producing adults [4] . At the time of induction of the feeding site the nematode becomes sedentary , losing the ability to move . If at any time during these 4–6 weeks the feeding site is compromised the nematode cannot survive . Nematodes , like other biotrophic plant pathogens , have therefore evolved the ability to suppress host defences ( reviewed in [5] , [6] ) . The ability to manipulate host processes and induce complex feeding sites appears to have evolved independently in the root-knot and cyst nematodes [3] . Root-knot nematodes induce the formation of giant cells while cyst nematodes induce syncytia . Although giant cells and syncytia show similar cellular features including reduced vacuoles and extensive proliferation of the smooth endoplasmic reticulum , ribosomes , mitochondria and plastids [7] , [8] , their development and ontogeny are entirely different . Root-knot nematodes induce multiple rounds of mitosis in the absence of cytokinesis to generate the multinucleate giant cell . Cyst nematodes , on the other hand , promote dissolution of cell walls and protoplast fusion of hundreds of adjacent cells to generate the syncytium [8] . Much recent work for both cyst and root-knot nematodes has focused on identification of effectors and their localisation within the host tissue . It is becoming increasingly clear that the apoplasm is an important recipient compartment for effectors during both the migratory and sedentary stages . The CLAVATA-like or CLE peptides of Heterodera schachtii are expressed in the dorsal pharyngeal gland cell throughout infection [9] , and are apparently secreted into syncytia then subsequently transported to the apoplasm by existing plant mechanisms [10] . The MAP-1 protein of Meloidogyne incognita , initially identified as a putative avirulence factor is secreted by the amphids [11] and accumulates in the apoplasm [12] , [13] . Similarly the Mi-ASP2 and Mi-PEL3 proteins are secreted into the apoplasm by the subventral glands during both migration of the juvenile and the sedentary feeding stages of mature females [12] . In addition , electron microscopy studies of feeding cyst nematodes have identified a structure produced by endoparasitic nematodes that is directly associated with the plant-nematode interface; the feeding plug [14] , [15] . Feeding plugs appear as electron dense material located in the apoplasm between the anterior end of the nematode and the feeding site [14] . Until recently , nematode effector identification was centred on the dorsal or subventral pharyngeal gland cells , either by localisation of sequenced gene expression to these structures or by direct isolation of RNA from the pharyngeal gland cells [16] , [17] . These have successfully identified various effectors with a range of functions [5] , [6] however these strategies may fail to identify effectors involved in maintenance and suppression of host defences throughout the biotrophic phases , in particular those that do not originate from the gland cells . Indeed , nematodes have numerous other tissues with the capacity to secrete proteins into their host . The amphids are the primary sense organs and their function was thought to be confined to the migratory stages of sedentary endoparasitic nematodes ( reviewed in [18] ) . However , structural changes occur in the amphids during the transition from migratory juvenile to sedentary feeding stages [19] , suggesting distinct roles at each stage . Two previous studies showed the feeding plugs of cyst nematodes are continuous with the amphid openings of sedentary females [14] , [15] , one of which concluded that the feeding plug originates from the amphidial canal [14] . In Meloidogyne species , MAP-1 proteins are secreted into the host from the amphid opening [12] , [20] . Moreover , a functional glutathione peroxidase is secreted from the hypodermis of G . rostochiensis , and may play a role in breaking down host reactive oxygen species during infection [21] . Finally , a Cellulose Binding Module2-bearing protein accumulates near the vagina of gravid female M . incognita [12] and may originate from the rectal glands . We therefore hypothesise there would be a class of effectors , crucial to the successful biotrophic interaction , that would require continual renewal throughout biotrophy and may not originate from the gland cells . The advent of next generation sequencing , in particular RNA sequencing , has provided new approaches to many questions in biology . Here we describe the use of RNAseq data to identify and characterise effectors with continual expression throughout the biotrophic phases of cyst nematodes . We present the first description and characterisation of a hyper-variable extracellular effector gene family , termed HYP effectors .
Based on the genomic sequence of GPLIN_001208400 ( ftp://ftp . sanger . ac . uk/pub/pathogens/Globodera/pallida/Gene_Predictions/ ) , primers were designed to amplify the coding region after the predicted cleavage site of the signal peptide to the stop codon ( Table S1 ) . This primer pair amplified a range of different sized products , which when sequenced could be grouped into three subfamilies -1 -2 and -3 , each sharing considerable stretches of conserved bases at the 5′ and 3′ ends of the genes . Each subfamily was compared back to the genome by BLAST . Two complete genes were present , the previously identified GPLIN_001208400 and GPLIN_001025300 , corresponding to subfamily -1 and -3 respectively . There were also two gene fragments ( either a partial sequence or containing poly-N regions ) , named GPLIN_001135100 and GPLIN_000907700 , corresponding to subfamilies -1 and -3 respectively . Based on the genomic sequence of gene GPLIN_001208400 , primers were designed to amplify from the start to the stop codon . These specifically amplified a range of different sized products that could be grouped into subfamily -1 and -3 only . No subfamily-2 members were present in the assembled genome sequence , however two Gp-hyp sequences were present in the de novo transcriptome assembly of early sedentary stage nematodes ( 7 days post infection ) , both of which corresponded to subfamily -2 . Primers designed to amplify from the start to the stop codon amplified a range of products that all corresponded to subfamily -2 . Subfamilies -1 and -3 were not separable by PCR of coding regions . 3′ RACE was carried out for each of the three subfamilies , which identified a single specific UTR per subfamily ( Figure 2 ) . Each single subfamily specific 3′ UTR corresponded to the expected 3′ UTR sequence present for that subfamily from either the assembled genome or de novo transcriptome . Subfamily-specific PCR primers from start codon to 3′ UTR ( Figure 2 ) were able to differentiate between subfamilies . In general subfamilies and locations of primer combinations can be described by Figure 2 . In total , 75 unique genomic sequences were identified across Gp-hyp-1 , -2 and -3 , where Gp-hyp-1 is numerically dominant . All cloned Gp-hyp genes , irrespective of subfamily , shared stretches of 410 and 94 nucleotides with >90% identity at the 5′ and 3′ ends respectively . Between highly conserved regions , subfamilies are characterised by a series of variable number subfamily-specific tandem repeats , summarised in Figure 2 . No Gp-hyp sequences identified to date encode any annotated domains with the exception of a predicted signal peptide at the N-terminus of the protein . Within each subfamily multiple different genomic DNA sequences were amplified , cloned and sequenced . In all cases , the entire tandem repeat region consists of a single open reading frame . For Gp-hyp-1 and -3 only , large variation is observed in the number , sequence and order of tandem repeats in the deduced amino acid sequences corresponding to different genomic sequences . Within this region , Gp-hyp-1 genes contain four motifs , two of which are present as tandem repeats with complex organisations ( Figure 3 ) . The most common motif ( 1 . 1 ) consists of 6 amino acids , the first two are variable followed by a highly conserved RGGG . This glycine rich motif is present on average 12 times per gene , although this varies greatly . The second motif ( 1 . 2 ) consists of 5 amino acids , with a variable first position followed by conserved DRGD . This motif is present approximately 4 times per gene . The other two motifs ( 1 . 3 and 1 . 4 ) are usually present no more than once , often at the start or end of the tandem repeat domain ( Table S2 ) . Including all variable regions of all motifs in subfamily-1 , 20 unique amino acid sequences are encoded by 43 unique nucleotide sequences . The Gp-hyp-2 tandem repeat region always contains four proline rich tandem repeats , comprised of three different motifs ( 2 . 1 , 2 . 2 and 2 . 3 ) . Motif 2 . 1 consists of 7 amino acids in the sequence EKPPPKY . Motif 2 . 2 is identical , except for the inclusion of an additional proline in the proline rich repeat ( EKPPPPKY ) and has no variation in sequence . Motif 2 . 3 consists of 9 amino acids , also has no sequence variation and is usually present only once ( Figure 3 ) . If motif 2 . 1 is present in positions 1 and 3 , the position 3 motifs are more similar at the nucleotide level to other 2 . 1 motifs in position 3 , than they are to 2 . 1 motifs in position 1 , even if they are identical at the amino acid level . The four subfamily-2 tandem repeat variants are encoded by 10 unique nucleotide sequences . Interestingly , both subfamily-1 and -3 also contain the first and last of this type of tandem repeat . Gp-hyp-3 tandem repeat regions contain two lysine and glutamic acid rich motifs ( 3 . 1 and 3 . 2 ) the first of which ( 3 . 1 ) occurs in tandem repeats on average 15 times , although this varies greatly . Motif 3 . 1 consists of 11 amino acids with highly conserved amino acids in position 2 , 4 , 5 , 7 , 10 and 11 . Motif 3 . 2 consists of 17 amino acids and is always present as a single copy in the final position of the tandem repeat domain of subfamily-3 ( Figure 3 and Table S2 ) . Including all variable regions of all motifs in subfamily-3 , 27 unique amino acid sequences are encoded by 42 unique nucleotide sequences . Organisation of the various motifs within the tandem repeat region of all subfamilies is summarised in Table S2 , although no pattern can describe all the variation . The above analysis was carried out using amino acid sequences deduced from genomic DNA sequences . Representatives of all subfamilies are present in cDNA synthesised from mRNA extracted from feeding females and these display similarly variable numbers and arrangements of tandem repeats . However , these are not studied in any detail due to the introduction of non-canonical apparent splice events as a direct result of the reverse transcriptase enzyme activity ( described in more detail in Supporting information S2 and Figure S3 ) . In addition to the variable tandem repeats , within the highly conserved regions several non-synonymous polymorphic sequences have been identified that lead to amino acid changes . Figure 4 shows a schematic representation of these domains and does not represent the order in which they appear in the genes . Each combination of domains can be referred to as a “type” . Types are not subfamily specific . Moreover , sequences encoding similar tandem repeat regions can have different amino acid sequence at every domain locus , and similarly identical types at every domain locus can have different tandem repeat regions ( Figure 4 and Table S2 ) . The large number of unique Gp-hyp genomic DNA sequences were cloned from a pool of thousands of individual nematodes . To determine the complement of Gp-hyp sequences present in each nematode , a method was developed to extract high quality DNA , sufficient for multiple PCR reactions , from individual feeding female cyst nematodes . PCR with Gp-hyp subfamily-specific primers ( Gp-hyp-1 F-1/3 and R-UTR-1; Gp-hyp-2 F-2 and R-UTR-2; Gp-hyp-3 F-1/3 and R-UTR-3; summarised in Table S1 ) revealed that individuals within a population differ in the number of Gp-hyp sequences , the length of Gp-hyp sequences , and in some cases even the presence or absence of Gp-hyp-3 genes . No two nematodes tested had the same complement of HYP effectors ( Figure 5 ) . Amplicons from individual PCR reactions were sequenced and this confirmed that a single nematode can contain more than one type , even within subfamilies . These data indicate a profound difference between individuals at the genetic level . In situ hybridisation was used to determine the spatial expression patterns of Gp-hyp genes in feeding G . pallida females . A probe designed to target a conserved region of all subfamilies localised transcripts to a paired structure anterior to the metacorpal pump chamber . No such staining pattern was seen using the negative control probe ( Figure 6 ) . When compared to the anatomy of a typical tylenchid nematode , the position of the signal is consistent with expression in the amphidial sheath cells . No other paired structures are present in this region of the nematode . Amphid sheath cells are large secretory cells that produce material present in the amphidial canal and at the anterior surface of the nematode [14] . No expression was observed in pre-parasitic J2 nematodes ( Figure 6 ) , as expected from the highly significant up-regulation of HYP effectors specifically in all biotrophic stages ( p-values range from 1 . 68*10−3 to 5 . 08*10−9 depending on the genomic copy; Figure S1 ) . Polyclonal rabbit antibodies were raised against a synthetic peptide derived from Gp-HYP sequences . Anti-Gp-HYP antibody was able to detect several proteins in a pooled nematode extraction and did not detect a signal when using similar quantities of plant root protein tested by western blot ( Figure S2 B ) . Pre-immune serum was unable to detect the same proteins ( Figure S3 B ) . Anti-Gp-HYP antibody specifically detected recombinant Gp-HYP-2 protein expressed and purified from bacteria , but not an unrelated negative control candidate effector protein similarly produced ( Figure S3 A ) . The anti-Gp-HYP antibody localised a protein present in the apoplasm 14 days post infection at the plant-nematode interface , between the nematode and the syncytial cell wall ( Figure 7 ) . A representative Gp-hyp-1 cDNA sequence was used to create an inverted repeat ( IR ) construct to express double stranded RNA in planta under the control of the CaMV35S promoter . As a result of the highly conserved regions , this construct should target all Gp-hyp-1 , -2 and -3 transcripts . RT-PCR was used to confirm expression of the inverted repeat constructs in planta ( Figure 8B ) . All potato hairy root lines tested that expressed the Gp-hyp-1 hairpin construct resulted in a 50–60% reduction in the number of nematodes that successfully infected and induced a feeding site compared to GFP IR control ( correcting for multiple T-tests p<0 . 05 ) , suggesting a role of HYP effectors during parasitism ( Figure 8A ) . Hairy root line 2b was not tested as its growth phenotype was not comparable to the control roots . We used bioinformatic analysis to investigate the distribution of the HYP effectors in a variety of plant-parasitic nematodes . The genome assembly of the closely related yellow potato cyst nematode Globodera rostochiensis ( unpublished ) contained Gp-hyp-1 , -2 and -3 like sequences . Due to the nature of HYP effectors and the apparent difficulty in assembly , their presence in G . rostochiensis was confirmed by PCR ( primers Gp-hyp-1 F-1/3 and R-UTR-1; Gp-hyp-2 F-2 and R-UTR-2; Gp-hyp-3 F-1/3 and R-UTR-3; summarised in Table S1 ) . Amplification products from G . rostochiensis were sequenced to confirm they did indeed correspond to HYP effectors . However , notably fewer HYP effectors are present in G . rostochiensis compared to G . pallida ( Figure 9 ) . Gp-hyp-1 , -2 and -3 like sequences were present in the genome sequence of the more distantly related cyst nematode Heterodera glycines ( patent WO 2007095469 ) . Two partial Gp-hyp-1-like and one Gp-hyp-2-like sequences were also present in the transcriptome of Rotylenchulus reniformis ( unpublished and [23] ) . No HYP effector-like sequences were identifiable from the EST database of the migratory endoparasite Radopholus similis [24] or the genome sequence of the root-knot nematodes Meloidogyne incognita [25] or Meloidogyne hapla [26] . Interestingly the Gp-hyp–like sequences from the various nematode species described show a remarkable level of conservation between species and indeed between genera ( Figure 10 ) .
Until recently , nematode effector identification was centred on the dorsal or subventral gland cells . We exploited the highly conserved structure of HYP effectors to design an in situ hybridisation probe that would target all members of all subfamilies , irrespective of the individual genetic complement of hyp genes each nematode had . We were able to demonstrate expression in the amphid sheath cells , large secretory cells associated with the amphids . Previous studies have shown that the cyst nematode feeding plug is continuous with the amphid openings of sedentary females [14] , [15] and may originate from the amphidial canal [14] . Gp-HYP proteins are secreted from the amphids , however the proteins extend further in the apoplasm than expected when compared to electron micrographs of feeding plugs and so they are probably not structural components of the feeding plug . In Meloidogyne species the importance of the amphids and of the apoplasm as a site of effector action is now being recognised [12] , [20] . The data presented here also suggest a changing role for the amphids in G . pallida throughout the life cycle . Interestingly , map-1 genes of M . incognita also contain tandem repeats that differ between populations [27] , although these have no sequence or structural similarity to HYP effector tandem repeat domains and are relatively simple in comparison . Recent data suggest these genes may in fact encode CLE-like peptides that originate from the pharyngeal glands [28] . HYP effector tandem repeats have no similarity to CLE peptides . Despite the presence of just three complete Gp-hyp sequences across the assembled genome and transcriptome sequences of G . pallida ( one corresponding to each subfamily ) , the complexity of the HYP effector family was identified by conventional PCR and sequencing of individual clones . In addition , each time more clones were sequenced more unique sequences were identified . This suggests that the 75 unique genomic sequences identified represent a far from exhaustive list , and that the full complexity of HYP effectors is yet to be catalogued . The absence of the full gene family in the G . pallida genome assembly , and the presence of two fragmented Gp-hyp genes in poly-N regions , highlights a limitation of sequencing and assembly of short reads . All Gp-hyp sequences , irrespective of subfamily , share stretches of 410 and 94 nucleotides with >90% identity at the 5′ and 3′ ends respectively which may underlie the difficulty in assembly . All unique genomic HYP effector sequences identified to date can be readily assigned to one of three subfamilies primarily , although not only , based on the amino acid sequence of the tandem repeat region . Numbers of subfamily-specific tandem repeats range greatly . It is unclear what the function of the tandem repeats is in the context of plant parasitic nematodes . Variable tandem repeat Transcription Activator-Like ( TAL ) effectors have been described for Xanthomonas spp [29] . TAL effectors are highly adaptable phytobacterial virulence factors that contain a 34 amino acid tandem repeat present in 17 . 5 iterations . DNA-binding base pair specificity is conferred by variable di-residues in positions 12 and 13 within each tandem repeat . HYP effectors contain numerous shorter tandem repeats that are considerably more variable in both sequence and organisation . Tandem motif 1 . 1 does contain highly conserved regions directly preceded by variable di-residues . However , HYP effectors are localised in the apoplasm , and so DNA binding is unlikely , although other ligand binding may be possible . It is unclear what the role of the different subfamilies , variable number/organisation of tandem repeats , or domains is in the context of ligand binding . HYP effector tandem repeats of subfamilies -1 , -2 , and -3 contain conserved glycine , proline and lysine residues . Glycine residues often create flexible linkers between domains . Taken together , these data may allow suggest that variable residues interspersed by highly conserved linker regions may play a role in ligand binding . Several non-synonymous SNPs were identified in HYP effectors , within the highly conserved regions , that do not group by subfamily . Various combinations of these domains are described here as “types” . Modularity of effectors has been demonstrated for the Crinkler and Necrosis ( CRN ) effectors of oomycete species [30] , [31] . It is suggested that different domains in CRN effectors have discrete functions . The amino acid changes of HYP effector domains are usually physiochemically similar , and may be structurally superficial . The “type” structure of HYP effectors may reflect evolutionary origins and rearrangements rather than function , whereas the different subfamily tandem repeats , and the different number of tandem repeats within subfamilies , may reflect different functions . It has been noted however , that subtle amino acid changes that should conserve physiochemical properties can have an impact on effector function [32] . Similarly in oomycete species , the RXLR effectors are a large family of modular effectors with a highly conserved domain involved in membrane translocation [32] . HYP effectors are not characterised by a short conserved amino acid domain: stretches of hundreds of highly conserved amino acids are present at both the C and N-terminal ends of the protein . These highly conserved regions are not just conserved between species , but are conserved across at least three Genera of plant-parasitic nematodes . Despite the superficial genetic structural similarity of the HYP effector tandem repeats to TAL effectors , and the similar modularity compared to RXLR and CRN effectors , no effector families , from any pathosystem to date , have been identified with such complex variation between individuals of the same population . We have demonstrated un-paralleled genomic diversity of HYP effectors between individuals: no two nematodes tested had the same genetic complement of Gp-hyp-1 or -3 sequences . Nematodes differed in the length , number , and even presence/absence of entire gene subfamilies . It is unclear what the underlying genetic mechanism is that allows such variation between individuals . Due to the nature of the draft genome assembly of G . pallida we are unable to confirm that Gp-hyp sequences are paralogues , although the fact that individual nematodes differ in the size , and particularly number , of sequences within subfamilies suggests this is the case . The number and variation suggests that Gp-hyp genes are under high selection pressure . Gene expansions of cytochrome P450 genes have been described in Anopheles species [33] , where estimates are of approximately 30 to 40 genes [34] . This is a good example as CYP450 genes , and in particular their copy number , have been linked to the extremely high selection pressure of resistance to DDT [34] , [35] , [36] . It is possible that the expansion of the HYP effector gene family in G . pallida may reflect a similarly high selection pressure from the host . Secreted components are the pathogen factors that are recognised by both pattern recognition receptors and resistance gene products . Diversity in Gp-hyp sequences may reflect the need to evade recognition in order to avoid detection . We have found that a UK population of G . pallida has considerably more variation in HYP effectors than G . rostochiensis . It has been suggested that the UK populations of G . pallida are from a broader genetic introduction than G . rostochiensis . If the diversity seen between individuals of G . pallida for the Gp-hyp genes is true on a wider genomic scale , this may explain the difficulty in identifying a broad spectrum resistance against G . pallida compared to G . rostochiensis [37] . Recent technological advances in sequencing the transcriptome/genome of single nematodes may identify wider differences between individuals [38] . The genome sequence of G . pallida was challenging to assemble [22] . This may also be due to the inherent genetic variation that we have demonstrated between individuals of the same population , even infecting the same plant . Genetic diversity in plant parasitic nematodes has never been studied on such a scale , the most relevant works being between ( as opposed to within ) populations [27] , [39] , [40] , [41] , [42] . Interestingly it has been suggested that genetic variability observed at the scale of a field or even of a region is already observed at the scale of a single plant within a field [40] . Transformation of cyst nematodes is not currently possible: In order to carry out gene knockout studies J2s can be soaked in double stranded RNA ( dsRNA ) or dsRNA can be delivered to feeding nematodes through in planta expression [43] , [44] . RNA soaking was not attempted in this study as expression of the Gp-hyp genes is specific to all feeding stages ( Figure S1 ) . Instead , a hairpin construct was expressed in potato hairy roots . It has been reported that dsRNA expression in hairy roots is not ubiquitous and that phenotypes of the root cultures can vary , which may affect nematode infection [45] , [46] . Phenotypes of empty vector control , GFP IR control , and Gp-hyp IR hairy root lines were therefore matched to the best of our ability before infecting with nematodes . Three independent lines expressing dsRNA targeting HYP effectors had reduced infection compared to the controls , suggesting HYP effectors play a role in the nematode infection . Despite the inherent noise of the hairy root system [45] , RNAi targeting the Gp-hyp genes resulted in a significant reduction in nematode numbers . Although the dsRNA construct used has the potential to knock out all members of all subfamilies characterised to date , the list of cloned Gp-hyp sequences we have characterised is unlikely to be complete . Future work will focus on generating transgenic potato plants expressing the inverted repeat constructs of all three Gp-hyp gene subfamilies , potentially creating durable resistance across all cyst and reniform nematodes . Due to the patchy nature of hairy root transgene expression [45] , [46] , it is possible that successfully established nematodes had induced feeding sites from cells not expressing the dsRNA . It is therefore challenging to confirm the knockdown of Gp-hyp expression from the nematodes that successfully establish . Similarly due to the amplification step and transitive nature of RNAi in nematodes [47] , subfamily-specific knockout studies are not possible . Using bioinformatic analysis we have shown that HYP effectors are present in all cyst nematode species sampled and are also present in the closely related reniform nematode R . reniformis . Given the phylogenetic proximity of the reniform nematodes to the cyst nematodes [3] , and the similarities in the feeding sites that they induce [48] , it is likely that they share the same origin of sedentary parasitism . HYP effectors were not identifiable from the most closely related migratory plant parasitic nematode [49] , Radopholus similis [24] , which does not form specific biotrophic interactions [48] . Similarly , no HYP effectors were identified from Meloidogyne species [25] , which have an independent origin of sedentary plant parasitism [3] . The high degree of similarity between all Gp-hyp genes , and in particular the similarity of 3′ UTRs within subfamilies , suggests that this is a very recent gene expansion . Presumably the high conservation in the coding region of the genes is linked to protein function . The function of the high degree of conservation in the 3′ UTR within subfamilies is unknown , although it may be related to regulation of gene expression [50] , [51] . Using assumptions about the characteristics of genes involved in plant-nematode biotrophic interactions to inform identification strategy , we provide the first description and characterisation of the HYP effectors . HYP effectors may play an important role in plant-nematode interactions and represent a class of effectors with continual expression , and presumably continual function , throughout biotrophy . Future work will focus on elucidating the function/s and specificity of the various gene family members , variable number tandem repeats and various domain arrangements . There are many economically unimportant , and consequently poorly characterised , nematode species that induce syncytial feeding sites in their host roots [52] , [53] , [54] , [55] , [56] . HYP effectors appear to be specific to plant-nematode interactions involving syncytia; identifying HYP effectors in these species may provide an insight into a common underlying feature of a complex endo-parasitic relationship .
Genomic DNA was extracted from a pool of thousands of frozen J2 G . pallida ( “Lindley” ) or G . rostochiensis ( Ro1 ) according to the protocol described by Cotton et al . [22] . PCR was routinely carried out using BioTaq Red DNA polymerase following the manufacturer's instructions for cycling conditions ( Bioline ) and the oligonucleotide primers and annealing temperatures listed in Table S1 . PCR products were cloned by T-A cloning into the pGEM-T Easy vector ( Promega ) following the manufacturer's suggestions . Plasmid DNA was extracted from bacterial culture using a QIAprep Spin Miniprep Kit ( Qiagen ) following the manufacturer's instructions . Individual plasmids were sequenced at the service provided by Beckman Coulter Genomics . Individual G . pallida females , collected 14 days post infection ( dpi ) of potato plants , were flash frozen in liquid nitrogen in individual 1 . 5 ml microfuge tubes . Nematodes were suspended in 200 µl “Chaos” buffer [57] ( 4 . 5 M guanidine thiocyanate , 2% N-lauryl sarcosine , 50 mM EDTA ( pH 8 . 0 ) , 0 . 1 M 2-mercaptoethanol , 0 . 2% antifoam-A ) disrupted with a pipette tip , and lysed by vortexing . One volume of phenol∶chloroform∶isoamyl alcohol ( 25∶24∶1 ) was added to the sample , vortexed , and centrifuged at 10 , 000 g for 5 minutes . The upper aqueous phase was transferred to 1 volume of 70% ethanol with the addition of 20 ng carrier RNA ( NucleoSpin RNA XS ) . Total nucleic acid was extracted from the sample using a NucleoSpin RNA XS column , and the NucleoSpin RNA/DNA Buffer Set following manufacturer's instructions . Two microliters of purified DNA was sufficient per 50 µl PCR reaction . RNA was extracted from 14 dpi feeding females of G . pallida using an RNeasy mini kit ( Qiagen ) following the manufacturer's instructions for animal tissues . 3′ RACE was carried out using the 5′/3′ RACE Kit ( Roche ) according to manufacturer's instructions . The supplied oligo dT primer was used to prime cDNA synthesis . Subfamily-specific forward primers GAGGTTATGACGAGCATCATC , GAAAGGGCGGAGACAAAG and TGAGCATCGTCTCCGTGCTG for subfamily -1 , -2 and -3 respectively were identified by aligning all cloned sequences and visualising in an alignment viewer ( BioEdit ) . The entire 3′ RACE PCR reaction was purified with a Qiaquick PCR purification kit ( Qiagen ) following the manufacturer's instructions . Purified amplification products were cloned by T-A cloning into the pGEM-T Easy vector ( Promega ) following the manufacturer's suggestions , and positive clones were confirmed by sequencing . In situ hybridisation probes were designed to target a region of 134 conserved nucleotides at the 3′ end of the translated region of Gp-hyp transcripts using oligonucleotide forward ( AACACGGAGGTTATGACGAG ) , and reverse ( GCTTGCGAATGCAAATAT ) primers respectively . Template was amplified from feeding nematode cDNA , and single stranded probes were synthesised from the template to incorporate digoxigenin labelled dUTP ( Roche ) using PCR by incubating at 94°C for 2 minutes followed by 35 cycles of 94°C for 15 seconds , 55°C annealing for 30 seconds , and 72°C extension for 90 seconds . Incorporation of DIG-labelled dUTP was confirmed by an apparent increase in size on agarose gel electrophoresis compared to template dsDNA . In situ hybridisation was carried out according to the methods described by de Boer et al . [58] with the following alterations . Cleaned potato roots heavily infected with 7–14 dpi feeding female G . pallida were lightly macerated using a bench top blender , and soaked in 10% formaldehyde for 3 days at room temperature . Fixed nematodes were collected by additional blending and subsequent sucrose gradient centrifugation ( 40% w/v ) . Feeding females were collected between 200 and 150 µm mesh sieves . The protocol was continued as described from the cutting stage . Lengths of potato root 14 days post infection with J2 of G . pallida were fixed in 4% paraformaldehyde in PEM buffer ( 50 mM PIPES , 10 mM EGTA , 10 mM MgSO4 pH 6 . 9 ) for 3 days at 4°C . Samples were dehydrated , resin embedded and sectioned according to Davies et al . [59] with the following alterations . Primary antibodies were raised to a 31 amino acid synthetic peptide VVRVARGEYENKCPAGPAGDVGPPGPPGPSG in a conserved region common to all Gp-HYP proteins predicted to have high antigenicity using [60] . The first 20 of which match with 100% identity to a consensus Gp-HYP-1 protein , the first 29 of which match with 100% identity to a consensus Gp-HYP-2 protein and the last 13 of which match with 100% identity to a consensus Gp-HYP-3 protein . Primary antibodies , or pre-immune sera , were hybridised at a dilution of 1 in 5 in 0 . 5% milk powder in PBS , and detected with a FITC-conjugated anti-rabbit secondary antibody at a dilution of 1 in 100 . Plant cell walls were stained using Calcoflour-White at 1 mg/ml . Antibody specificity was tested against protein expressed in , and extracted from , the heterologous E . coli system ( Supporting information S1 ) . An inverted repeat ( IR ) construct was generated using an entire Gp-hyp-1 coding region . To clone in forward direction the oligonucleotide primers CTCGAGATGGTCGGCAACAATTTG and GGTACCTTAATATTTGCATTCGCAAGC introduced a 5′ Xho I and 3′ Kpn I site respectively . To clone in the inverted direction the oligonucleotide primers TCTAGAATGGTCGGCAACAATTTG and AAGCTTTTAATATTTGCATTCGCAAGC introduced a 5′ Xba I and a 3′ Hind III site respectively . Correct amplification was confirmed by sequencing . Sequences with no errors were cloned into the vector pHannibal [61] under the control of a CaMV 35S promoter and OCS terminator using the relevant restriction enzymes . The entire construct from promoter to terminator was cloned into the plant binary vector pART27 [62] using Sac I and Spe I . Four hundred overlapping 21 nucleotide fragments could hypothetically be generated from the IR construct . These were compared by alignment with the Gp-hyp-2 and -3 consensus transcripts . 26 individual 21 nucleotide fragments matched the Gp-hyp-2 consensus sequence with 100% identity while 10 matched to the Gp-hyp-3 consensus sequence . As a control , a full length GFP inverted repeat was created as above using the oligonucleotide primers CTCGAGATGAGTAAAGGAGAAGAACTTTTC and GGTACCCTATTTGTATAGTTCATCCATGCC for the forward direction and TCTAGAATGAGTAAAGGAGAAGAACTTTTC and AAGCTTCTATTTGTATAGTTCATCCATGCC for the reverse direction . A single colony of Agrobacterium rhizogenes strain R1000 containing the relevant pART27 IR construct was incubated in 5 ml liquid Luria Bertani medium overnight at 28°C . Potato hairy root transformation was carried out by incubating 1 cm squares of Solanum tuberosum ( ‘Desirée’ ) leaf material in liquid MS20 medium ( 4 . 3 g/l Murashige and Skoog ( with vitamins ) , 20 g/l sucrose , pH 5 . 3–5 . 6 ) containing 100 µl of Agrobacterium culture , for 3 days at room temperature . Leaf squares were dried on filter paper and placed on MS20 agar plates ( 2 . 4 g/l agar ) , containing 50 µg/ml kanamycin and 400 µg/ml cefotaxime . Roots originating from different locations on each leaf square were considered individual transformation events , and were removed and cultured on MS20 agar plates containing 50 µg/ml kanamycin . Inverted repeat construct expression was confirmed by RNA extraction ( RNeasy Plant Mini Kit , Qiagen ) , cDNA synthesis ( SuperScript II Reverse Transcriptase , Invitrogen ) , and PCR using the inverted repeat specific oligonucleotide primers ACGGAGGTTATGACGAG and GCTTGCGAATGCAAATATTAA . Hatched J2 of G . pallida were sterilised for 20 minutes in an appropriate volume of hexadecyltrimethylammonium bromide ( CTAB , 0 . 5 mg/ml – Sigma ) containing 0 . 1% v/v chlorhexidine digluconate ( Sigma ) and 0 . 01% v/v Tween-20 , followed by three washes in sterile tap water . J2s were suspended at a concentration of approximately 1 nematode/µl and 30 µl of suspension was pipetted onto each infection point . Three infection points were used per hairy root plate and 10 plates were used per line , with three independent lines for the Gp-hyp-IR , one line for GFP control , and one line for empty vector control . Two weeks after infection roots were stained by soaking in 1% sodium hypochlorite for 5 minutes , washing with tap water for 1 . 5 minutes 3 times , followed by boiling in 1× acid fuchsin stain for 2 minutes . Total nematode numbers per root system were counted . Analysis of tandem repeats was carried out on genomic clones of Gp-hyp-1 -2 and -3 genes . For each subfamily , the middle region of the protein ( between the two introns ) was translated so that frame would be consistent with a cDNA clone . The software XSTREAM ( http://jimcooperlab . mcdb . ucsb . edu/xstream/2013-8-8 ) was used to analyse the tandem repeats present for each subfamily . Putative signal peptides and transmembrane domains were predicted using SignalP v 4 . 1 ( http://www . cbs . dtu . dk/services/SignalP/ ) and TMHMM v 2 . 0 ( http://www . cbs . dtu . dk/services/TMHMM-2 . 0/ ) , from the Centre for Biological Sequence analysis [63] . HYP effectors present in the G . pallida genome assembly ( GPLIN_001208400 , GPLIN_001025300 , GPLIN_001135100 , GPLIN_000907700 ) available at: ftp://ftp . sanger . ac . uk/pub/pathogens/Globodera/pallida/Gene_Predictions/ Unique genomic sequences for all amplified , cloned G . pallida HYP effectors are available at GenBank under accession numbers KM206198 to KM206272 . | Sedentary plant parasitic nematodes are pathogens that invade plant roots and establish a feeding site . The feeding site is a specialist structure used by the nematode to support its development within the plant . The nematode secretes a suite of proteins , termed ‘effector proteins’ that are responsible for initiating and maintaining the feeding site . The nematode must also evade recognition by the plant defence systems throughout its lifecycle that can last for many weeks . We describe a diverse and variable effector gene family ( HYP ) , the products of which are secreted into the plant by the nematode and are required for successful infection . The variability and modular structure of this gene family can lead to the production of a large array of effector proteins . This diversity may allow the nematodes to combat any resistance mechanisms developed by the plant . Each nematode tested within a population is genetically unique in terms of these effector genes . We found huge variation in the number , size and type of HYP effectors at the level of the individual . This may explain some of the difficulties in breeding nematode resistant plants and has profound implications for those working with other plant pathogens . | [
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] | 2014 | Identification and Characterisation of a Hyper-Variable Apoplastic Effector Gene Family of the Potato Cyst Nematodes |
Mucorales are an emerging group of human pathogens that are responsible for the lethal disease mucormycosis . Unfortunately , functional studies on the genetic factors behind the virulence of these organisms are hampered by their limited genetic tractability , since they are reluctant to classical genetic tools like transposable elements or gene mapping . Here , we describe an RNAi-based functional genomic platform that allows the identification of new virulence factors through a forward genetic approach firstly described in Mucorales . This platform contains a whole-genome collection of Mucor circinelloides silenced transformants that presented a broad assortment of phenotypes related to the main physiological processes in fungi , including virulence , hyphae morphology , mycelial and yeast growth , carotenogenesis and asexual sporulation . Selection of transformants with reduced virulence allowed the identification of mcplD , which encodes a Phospholipase D , and mcmyo5 , encoding a probably essential cargo transporter of the Myosin V family , as required for a fully virulent phenotype of M . circinelloides . Knock-out mutants for those genes showed reduced virulence in both Galleria mellonella and Mus musculus models , probably due to a delayed germination and polarized growth within macrophages . This study provides a robust approach to study virulence in Mucorales and as a proof of concept identified new virulence determinants in M . circinelloides that could represent promising targets for future antifungal therapies .
Mucormycosis is a fungal infection caused by species of the order Mucorales that represents the third most common angio-invasive fungal infection after candidiasis and aspergillosis . Due to the unusual antifungal drug resistance of Mucorales , mucormycosis is considered one of the most important medical complications in immunocompromised patients [1] . Among current antifungal drugs , fluconazole , voriconazole , posaconazole and itraconazole are potent agents of choice used in aspergillosis and candidiasis that , unfortunately , present poor activity against mucormycosis [2] . More specifically , amphotericin B , an old-known macrolide antifungal compound with severe adverse effects , and more recently isavuconazole are used against mucormycosis although they only achieve partial activity [3–5] . As a consequence of this lack of efficient antifungal drugs , mortality rates of mucormycosis remain higher than 50% and reach up to 90% in disseminated infections [6 , 7] . Another negative aspect of mucormycosis is its emerging condition . Only a few years ago , mucormycosis was considered a rare infection limited to immunocompromised patients suffering diabetes , organ transplant or other diseases associated with immunosuppression [8] . However , the current improvement in the diagnostic techniques has revealed an alarming number of mucormycosis cases in immunocompetent patients that have severe trauma ( e . g . burn patients , traumatic injuries ) , since it is now rarely misdiagnosed as aspergillosis [9] . Thus , the isolation of new strains that are capable of infecting healthy individuals and the increasing number of reported cases have raised the alarm on this emerging disease . Together , the lack of effective treatments and the emerging character of this devastating disease are urgently demanding new strategies to prevent and/or treat mucormycosis . The development of therapies to treat mucormycosis is restricted by the lack of knowledge about the disease and the organisms that cause the infection . One of the main reason explaining the scarce information about mucormycosis is the high reluctance of Mucorales to modern molecular genetics techniques . Among Mucorales , Rhizopus oryzae and Mucor circinelloides are two study models in which genetic transformation is available [10 , 11] . Study of pathogenesis in these two models has revealed iron uptake , spore size , spore coat proteins and dimorphism as virulence determinants in mucormycosis [12–18] . In M . circinelloides , along with genetic transformation , the application of molecular tools has allowed the dissection of its RNAi mechanism , which has become a useful tool for functional genetics in this fungus [19–22] . Besides its applications as a genetic tool , the RNAi mechanism of M . circinelloides has a regulatory role that controls complex physiological processes such as growth , sexual and asexual sporulation and death by autolysis [19 , 23–27] . Moreover , the extensive study of the RNAi mechanism in M . circinelloides led to the discovery of the first link between this endogenous regulatory mechanism and the unusual antifungal drug resistance of Mucorales [28] . This novel mechanism generates spontaneous resistance to the antifungal drug FK506 by epigenetic RNAi-mediated post-transcriptional silencing of the fkbA gene encoding the protein FKBP12 , which is the natural target of FK506 . As a result , the lack of target blocks the action of FK506 and the fungus becomes resistant to this drug , suggesting that similar mechanisms could be behind of the exacerbated resistance to antifungal drugs in Mucorales . The limited knowledge about Mucorales , mainly due to the phylogenetic distance and genetic differences of these basal fungi with other well-known fungi like Ascomycota and Basidiomycota [29] , together with the low efficiency of the current antifungal drugs , makes it urgent the development of novel strategies to study this group of organisms and , more specifically , the finding of new virulence determinants that could become future antifungal drug targets in Mucorales . Consequently , the main purpose of this work has been the establishment of a functional genomic approach based on the RNAi mechanism of M . circinelloides to select phenotypes relevant for the biology of Mucorales and related to virulence , and subsequently to identify the genes responsible for these phenotypes . RNAi has been used as a powerful reverse genetic tool to develop functional whole-genome studies in many organisms , including worms [30] , flies [31] and mammal cells [32] . In these reverse genetic approaches , a defined library is laboriously constructed by designing a silencing vector for each annotated gene of the studied organism . In these studies , the model organism requires an easy transformation method ready to be arrayed in large-scale assays in which each silencing vector/molecule is individually delivered . Unfortunately , this is not the case of M . circinelloides or any other emerging Mucoral . These inconveniences have led us to design a different approach that uses RNAi as a forward genetic tool in which a library representing M . circinelloides whole genome was constructed in a vector that silenced the cloned inserts . Transformation with this silencing library generated a collection of silenced transformants ready for phenotype screenings in a similar way as in classic chemical or insertional mutagenesis approaches . This transformant collection and the silencing library represent a new genetic tool in Mucorales for forward genetics and functional analysis at whole genome level . Using this approach we have isolated phenotypes related to virulence leading to the identification of two new virulence determinants in M . circinelloides , the enzyme Phospholipase D ( PLD ) and a Myosin 5 ( Myo5 ) motor protein , which are required for full virulence in Galleria mellonella and Mus musculus host models . Overall , this work illustrates a new approach to study virulence in Mucorales at the whole genome level .
A vector capable of inducing RNAi from any random DNA fragment was designed previously to the construction of the gDNA library for phenotypic screening based on RNAi . RNAi can be triggered in M . circinelloides by using self-replicative plasmids containing either complete or fragmented genes with their own promoters , obtaining silencing frequencies ranging between 3% and 30% [21] . However , highest silencing frequencies ( nearly 95% ) can be achieved when the plasmid contains a strong promoter and hairpin structures that directly transcribe dsRNA [22] . To circumvent the limitations of constructing a hairpin producing vector for each gene of M . circinelloides genome , we designed a high-throughput silencing vector ( pMAT1700 ) with two convergent promoters and no terminator sequences that are flanking a multiple cloning site ( MCS ) in which random gDNA fragments can be cloned ( Fig 1A ) . In addition , a sequence of 0 . 5 kb of carB gene was cloned next to the MCS to be used as a reporter of silencing ( Fig 1A ) . This gene encodes a phytoene dehydrogenase involved in the production of β-carotene , a pigment responsible for the typical yellow color of M . circinelloides [33] . Triggering of RNAi after transformation by plasmids containing this reporter produces albino transformants that are easily detectable and also signalize silencing of any other sequence cloned next to it . Fragments of 0 . 5–4 kb were isolated from M . circinelloides gDNA partially digested with Sau3A and filled in with dGTP-dATP to avoid self-ligation . The genomic fragments thus obtained were ligated with vector pMAT1700 digested with XhoI and filled in with dCTP-dTTP to make their ends compatible with Sau3A filled fragments and avoid self-ligation , thus favoring the frequency of recombinant clones in the library [34] . Ligation mixtures were introduced into Escherichia coli cells to generate the genome-wide RNAi library ( Fig 1B ) consisting in roughly 83 , 000 clones , which determined a confidence level higher than 99% . Pooled plasmids were directly purified from the E . coli colonies and used to transform M . circinelloides MU402 ( pyrG- and leuA- ) strain . The empty vector pMAT1700 and a version of this vector lacking the carB fragment ( pMAT1701 ) were used as controls to monitor silencing efficiency . Up to sixty transformations following this approach were required to obtain a collection of 51 , 657 silenced transformants ( S1 Table ) , which ensured a 95% confidence level . Comparison of silencing frequencies obtained with the empty vector and the high-throughput silencing library showed a pronounced increase of carB silenced transformants among those obtained with the library ( 87% ) relative to the empty plasmid pMAT1700 ( 43% ) ( S1 Table ) . As expected , the plasmid pMAT1701 did not trigger silencing in any of the transformants ( S1 Table ) . The increase in silencing frequency among transformants obtained with the library could be explained if the 0 . 5 kb fragment of carB gene that is cloned between the two promoters was not long enough in the empty plasmid to allow efficient convergent transcription from both promoters . Nevertheless , once the convergent cassette assimilates new fragments in the library , the silencing efficiency increases close to the maximum previously observed with hairpin triggering molecules [22] . These results demonstrated a high silencing efficiency of our high-throughput library in M . circinelloides . The main purpose of generating a high-throughput functional genomic tool in M . circinelloides was to use it as a new approach to find unknown virulence determinants in Mucorales ( Fig 1B ) . In order to find candidate genes involved in M . circinelloides pathogenesis , we focused the screening of silenced strains on abnormal growth and morphology , since those aspects of fungal physiology have been related to pathogenesis in M . circinelloides and other fungi [14 , 35] . Special attention was paid to transformants growing as yeast-like colonies and showing altered dimorphism and strong reduction of the growth rate , as it is one of the few processes previously associated with virulence in M . circinelloides [13] . Plates from the transformations with the silencing library were directly screened for abnormal phenotypes ( 51 , 657 silenced transformants ) , resulting in the selection of fifteen transformants with different abnormalities . Later , the transformation plates were further incubated and vegetative spores were pooled together to obtain the collection of silenced spores harboring the high-throughput silencing library described in the previous section . A total of 1x104 viable spores from this collection were grown in new plates for a second screening , resulting in the selection of eleven abnormal candidates . In addition , the second screening confirmed that silencing is maintained in the collection of silenced spores , since the frequency of silenced colonies ( 79±4% of albino colonies ) was similar to the previously described in the original transformants ( S1 Table ) . Growth rate and sporulation efficiency of the twenty six isolated candidates from the two screenings were quantified and classified into five categories based on the different morphological abnormalities that they presented ( Fig 2 and Table 1 ) . The first category , the most abundant with 16 isolates , presented a reduced growth ( RG1-16 ) compared with control transformants , but wild-type sporulation . Two transformants presenting a highly reduced growth ( HRG1 and HRG2 ) were included in the second category , as they showed clear differences with the first category , including a reduced vegetative sporulation . The third category comprised five transformants that showed a strong lack of vegetative sporulation ( LVS1-5 ) . In addition to the lack of sporulation , some of these five transformants also presented a reduced growth similar to the first category ( Table 1 ) . The fourth category contained only one transformant that showed a yeast-like growth ( YLG1 ) . This transformant presented the slowest growth , forming small colonies similar to yeasts rather than mycelial colonies . The morphology of YLG1 under the optical microscope also showed strongly deformed cell walls incapable of forming regular filaments ( Fig 2 ) . These filaments appeared to be septated , although one of the main characteristics of Mucorales is their coenocytic mycelium . This contradictory observation could be explained if this transformant is immersed in a hyphae-yeast transition state in which the tip of the hyphae produces yeast cells that resemble a septated structure before the yeast cells are liberated ( Fig 2 , yeast-like growth ) . The last category included two transformants showing a satellite growth phenotype ( SG1 and SG2 ) . These two transformants grew slower than control transformants , producing long sporangia that bent to the media to form new colonies , acquiring this unusual satellite phenotype in transformation plates at pH 3 . 2 ( Fig 2 ) . When SG1 and SG2 were grown in MMC medium at pH 4 . 5 ( a rich medium but selective for uracil auxotrophy , [36] ) , they showed reduced growth and sporulation , but not the satellite phenotype . HRG and SG transformants also presented abnormal mycelia under the optical microscope , showing swollen hyphae with abnormal branching ( Fig 2 ) . Accordingly with the mechanism of silencing previously described in M . circinelloides [21] , the twenty six transformants showed a reversible phenotype and they lost the abnormalities when they were grown in a non-selective medium for several vegetative cycles , confirming that the phenotypes were caused by the silencing of some genes harbored in the plasmids . As this work focuses on the identification of new virulence determinants in M . circinelloides , we performed virulence tests with all the selected transformants in a heterologous host , Galleria mellonella , which was previously established as a host model for M . circinelloides [14] . The viability of larvae infected with two thousand spores was monitored at one day intervals for all the transformants except YLG1 , which was unable to produce spores for this assay and , therefore , yeast-like cells were used for the infection assay [13] ( Fig 3 and S1 Fig ) . Among the twenty six transformants , only three isolates were significantly less virulent than the virulent control strains , the two HRG transformants ( Fig 3A , HRG1 and HRG2 ) and the single YLG transformant ( Fig 3B , YLG1 ) . The isolation of these three transformants with reduced virulence confirmed that our RNAi-based functional genomics strategy can be used to select phenotypes related to virulence and pathogenesis in M . circinelloides . The self-replicative nature of M . circinelloides plasmids used to construct the RNAi libraries facilitates the identification of the silencing sequences responsible for the phenotypes in the selected transformants , since library plasmids are maintained as episomes . Thus , the gDNA sequence present in the silencing plasmids can be identified by PCR amplification and sequenced using oligonucleotides flanking the cloning site . Alternatively , silencing plasmid can be re-cloned in E . coli and sequenced . In order to validate this hypothetical forward genetic approach in Mucorales , five independent transformants ( HGR1 , HGR2 , YLG1 , SG1 and SG2 ) were selected for gene identification and validation of the silencing phenotype . Three transformants ( HGR1 , HGR2 and YLG1 ) were selected due to their avirulent phenotype , whereas the transformants presenting the satellite growing phenotype ( SG1 and SG2 ) were selected to demonstrate that genes involved in other physiological processes can also be identified following this approach . Amplifications from gDNA of these five transformants generated PCR products only in YLG1 , SG1 and SG2 . After purification and sequencing of these PCR products , the DNA sequences were analyzed and compared to the genome database of M . circinelloides v1 . 0 and v2 . 0 ( http://genome . jgi-psf . org/Mucci1/Mucci1 . home . html and http://genome . jgi-psf . org/Mucci2/Mucci2 . home . html , respectively ) . The two strains sharing the satellite growth phenotype , SG1 and SG2 , exhibited both equal size PCR products and DNA sequences , indicating that these two transformants harbored the same plasmid . The analysis of the sequence amplified from this plasmid revealed the presence of three different ORFs: ID 84675 ( CLIP-associated proteins ( CLASPs ) , v1 . 0 ) , ID 156742 ( intracellular protein transport , v2 . 0 ) and ID 145873 ( DNA repair protein RAD51/RHP55 , v2 . 0 ) ( Table 2 ) . The analysis of the sequence obtained from transformant YLG1 also unveiled a DNA insert containing two different ORFs: ID 51513 ( myosin class V heavy chain , v1 . 0 ) and ID 166338 ( no description in either v1 . 0 or v2 . 0 ) . For the analysis of transformants HGR1 and HGR2 , plasmid re-cloning in E . coli was required , revealing that both transformants shared a plasmid with the same insert sequence ( pMAT1726 ) . In this case , the sequence of the plasmid insert harbored only one ORF , ID 134906 , which encoded a Phospholipase D like protein ( v2 . 0 ) . The analysis of the sequences found in the plasmids of the five selected transformants has been summarized in Table 2 , which shows that six different candidate genes could be responsible for three selected phenotypes . In order to identify which genes are behind the phenotypes , a silencing validation experiment was performed for each of the six candidate genes . Five new silencing validation plasmids were engineered by cloning a 1 kb fragment of each candidate gene in the MCS of pMAT1700 ( Table 2 ) . After transformation of the recipient wild type strain with these five plasmids and pMAT1726 , only three plasmids reproduced the three phenotypes previously observed in the original transformants obtained with the high-throughput silencing libraries ( Table 2 ) . Silencing of gene ID 84678 resulted in the satellite growing phenotype previously observed in the transformants SG1 and SG2 , whereas the yeast like growth phenotype of YLG1 was reproduced only by silencing of gene ID 51513 . As expected , silencing of the only gene ( ID 134906 ) found in the transformants HRG1 and HRG2 resulted in the highly reduced growth phenotype . To confirm that the phenotypes obtained after the introduction of plasmids harboring sequences of genes IDs 84675 , 51513 and 134906 are due to the lack of function of these genes through a canonical RNAi mechanism , we checked the mRNA levels and the production of siRNAs for the three candidate genes in both the original transformants containing the plasmids from the high-throughput RNAi libraries and the transformants obtained with the validation plasmids ( Fig 4 ) . All transformants for the three genes showed a reduction of mRNA levels and a production of siRNA for the corresponding gene , confirming the expected mechanism of action of the RNAi high-throughput library . These results demonstrated that the RNAi high-throughput library can be used as a new means to perform forward genetics and functional genomics in the study of virulence of Mucorales . The approach of RNAi-based functional genomics in M . circinelloides resulted in the identification of two genes that could be new virulence determinants in Mucorales ( Fig 3 ) . The role of these two genes was confirmed through the generation of the corresponding knockout strains and the study of their phenotype and virulence in a heterologous host model . The gene ID 51513 ( v2 . 0 ) encodes a Myosin class V protein that contains the three characteristic domains of this protein family: a motor domain , the IQ motifs and the cargo-binding globular tail . Thus , the gene encoding M . circinelloides Myosin 5 was denominated mcmyo5 . In order of adding more evidence to the identity of mcmyo5 gene , we performed a detailed phylogenetic analysis that included myosin proteins identified in other fungi ( S3 Table ) . This analysis revealed that gene mcmyo5 encodes a myosin protein that is perfectly clustered among other fungal myosin 5 proteins ( S2A Fig ) . The second gene , ID 134906 ( v2 . 0 ) , encodes a Phospholipase D like protein ( accordingly denominated mcplD ) , that contains the characteristic domains ( C2 , PX , PH ) , the active site and other functionally important parts of the enzyme [37] . Similarly to mcmyo5 , we performed a detailed phylogenetic analysis that included phospholipase proteins identified in other fungi ( S4 Table ) . This analysis revealed that gene mcplD encodes a phospholipase protein that is perfectly clustered among other fungal phospholipases type D ( S2B Fig ) . In addition , the gene ID 84675 ( v1 . 0 ) was also mutated , as mentioned above , to prove that the strategy presented in this work is also valid to study other fungal processes different than virulence , and also as a control to prove that not all growth defects are related to reduced virulence . This gene , named as mcclasp , encodes a CLIP-associated protein like ( CLASPs ) , as the CLASP N-terminal domain is the main conserved region , which shares an 87% identity with a hypothetical CLASP protein of Mucor ambiguous . The disruptions of these three genes were carried out through the construction of knockout vectors designed to replace each candidate gene with pyrG gene , which was used as a selective marker ( S3 Fig ) . These knockout vectors contained an engineered cassette with adjacent regions of the target genes flanking the pyrG gene ( S3A Fig ) and were used to transform MU402 strain ( pyrG- , leuA- ) . After transformations , candidates presenting the phenotype previously associated with silencing of each gene were isolated and the disruptions analyzed by Southern blot analysis ( S3B Fig ) . The two transformants selected from mcplD disruption ( MU466 and MU467 ) and the transformant selected from mcclasp disruption ( MU464 ) only showed the DNA fragments corresponding to the correct integration of the disruption fragment at the corresponding loci ( S3B Fig ) , indicating that they were homokaryons for the mutant allele . In order to confirm the identity of the product encoded by the gene mcplD , activity of the enzyme PLD was measured in the mutant ΔmcplD and compared to the wild type strain ( “Phospholipase D Assay Kit” , from Sigma-Aldrich ) . This assay showed a significant reduction ( p = 0 . 0017 ) of PLD activity of almost 30% in the mutant strain , but not a total lack of PLD activity ( S4 Fig ) . These results could be explained if there are other proteins with similar activity in the crude extracts of this mutant . Regarding the deletion of mcmyo5 gene , two transformants showing the yeast-like growth phenotype were selected after transformation with a replacement cassette for the gene mcmyo5 . One of these transformants , MU468 , probably harbored a chromosomal rearrangement at the mcmyo5 locus , whereas the second one , MU465 , showed the correct pyrG insertion of 4 . 1 kb replacing mcmyo5 gene ( S3B Fig ) . However , it was impossible to obtain a homokaryotic knockout strain for the gene mcmyo5 , as the transformant containing the mutant allele maintained some wild type nuclei even after ten vegetative cycles on selective media ( 3 . 4 kb fragment in S3B Fig ) . These results suggested that mcmyo5 gene may play an essential role in the viability of M . circinelloides and a homokaryotic state of the mutant nuclei might be lethal . The heterokaryotic strain containing mcmyo5 mutant nuclei was named Δmcmyo5 ( - ) ( + ) . The phenotype of each knockout strain was equivalent to those observed in the silencing transformants ( Fig 5A ) . The three mutants showed a reduction of growth and sporulation rates , as well as an increase in the production of β-carotene ( Fig 5C , 5D and 5E , respectively ) . The accumulation of β-carotene in the three mutants might be due to the growth stress present in these strains , since diverse stress factors have been previously linked to the production of β-carotene in other organisms [38] . Regarding virulence , infection assays in G . mellonella larvae with sporangiospores from mutants ΔmcplD and Δmcclasp , and yeast cells from mutant Δmcmyo5 ( - ) ( + ) , revealed a significant reduction in virulence of mutants Δmcmyo5 ( - ) ( + ) and ΔmcplD but not in Δmcclasp ( p = 0 . 0007 , p = 0 . 0002 and p = 0 . 4420 , respectively ) ( Fig 5B ) , as expected from the results previously obtained with the strains containing silencing vectors ( Fig 3 ) . The moth G . mellonella is a convenient model to study virulence when numerous candidates have to be tested . However , the immune system of this invertebrate model presents several differences compared to vertebrates , especially with warm blooded animals like mammals . Thus , the avirulent phenotype of mutants Δmcmyo5 ( - ) ( + ) and ΔmcplD was tested in a mouse model , where temperature and immune system components and action mechanisms are similar to humans . Yeast cells and spores of mutants Δmcmyo5 ( - ) ( + ) and ΔmcplD ( respectively ) were injected in immunodepressed mice and survival was daily monitored during twenty days after the infection with inocula of both 1x105 ( S5 Fig ) and 1x106 ( Fig 6 ) . Both inocula generated similar results , confirming the avirulent phenotype of mutant ΔmcplD in the murine model with a strong statistical significance ( p = 0 . 0081 in S5A Fig and p = 0 . 0065 in Fig 6A ) . However , although a reduced virulence was also observed for the heterokaryotic strain Δmcmyo5 ( - ) ( + ) compared to the wild type R7B strain , difference was not statistically significant ( p = 0 . 1595 in S5B Fig and p = 0 . 0526 in Fig 6B ) . This was probably due to the long time-course of the virulence assay in mice , since heterokaryotic Δmcmyo5 ( - ) ( + ) cells might segregate to a wild type phenotype by losing mutant nuclei when grown under non-selective conditions . In fact , growing of mutant Δmcmyo5 ( - ) ( + ) in non-selective culture medium gave rise to patches of wild-type phenotypes after few days of incubation ( S6A Fig ) . To test this hypothesis , retrieved CFUs from infected organs of both agonizing mice that showed signs of an imminent death and apparently healthy mice were analyzed in a Southern blot assay that distinguishes between the mutant and wild type genotypes ( S6B Fig ) . Quantification of the proportion of wild type and mutant nuclei in these retrieved CFUs showed correlation between the segregation to wild type genotype and the restitutions of virulence , which supported the role of the gene mcmyo5 in the pathogenesis of M . circinelloides ( S6C Fig ) . In order of acquiring more insights about the virulence of the strains ΔmcplD and Δmcmyo5 , the fungal burden was quantified in the relevant organs of mice infected with wild type R7B and both mutant strains . Quantification of fungal gDNA on relevant target organs ( brain and lung ) revealed prevalence of R7B in tissues from mice infected with both yeast and spore forms , showing a more significant presence in lung tissues ( Fig 6C and 6D ) . In particular , the presence of R7B in lung tissue was higher at day 2 post infection ( Fig 6C ) than at five days ( Fig 6D ) , indicating a decrease of fungal biomass in mice over time . Such fungal burden decrease was less accentuated on infection with R7B yeasts , suggesting that this fungal form is more persistent in mice during the infection progression . Despite these differences in fungal load , symptoms and mortality rates were similar after infection with R7B spores and yeasts ( Fig 6A and 6B ) . Conversely , a low amount of fungal DNA in mice infected with NRRL3631 and mutant strains ΔmcplD and Δmcmyo5 was detected , even below the limit of detection ( 0 . 005 ng ) after five days of infection ( Fig 6C and 6D ) . These results , together with survival outcomes , indicated a greater capacity of R7B strain to infect and invade mice tissues , causing higher mortality rates than the mutants ΔmcplD and Δmcmyo . The determinants of virulence in M . circinelloides have been studied during the initial interaction of spores with macrophages , in which the main factor distinguishing virulent and avirulent strains was the size of the spores [14] . Therefore , spore and yeast cell sizes of virulent and avirulent strains were determined . The size of yeast cells produced by the avirulent control strain NRRL3631 ( + ) was pronouncedly reduced compared with yeasts produced by virulent control strain R7B ( - ) ( S7B Fig ) , in the same manner as occurred with the size of the spores [14] ( S7A Fig ) . However , the sizes of the spores or yeast cells of the mutant strains ΔmcplD , Δmcmyo5 ( - ) ( + ) and Δmcclasp were not significantly reduced when compared to the virulent strain R7B ( S7 Fig ) . These results suggested that the reduction of virulence in the strains ΔmcplD and Δmcmyo5 ( - ) ( + ) might be due to other factors that are independent of the initial size of the fungal spore or yeast cells . In order to find these factors , the interaction between macrophages and the mutant strains ΔmcplD and Δmcmyo5 ( - ) ( + ) was also studied . Spores and yeast cells ( from ΔmcplD and Δmcmyo5 ( - ) ( + ) , respectively ) were co-cultured with the mouse macrophage cell line J774A . 1 ( ATCC , TIB-67 ) , during four hours . At this time of interaction , all the spore/yeast cells have been phagocytized by macrophages and virulent strains initiate germination and polar growth trying to escape before being inactivated [14] . Thus , we quantified the germination rate and polarity index ( a quotient between cell length and cell width [39] ) as a measure of virulence of the different strains tested here . A germination delay and a reduced polarity index were observed in mutants ΔmcplD and Δmcmyo5 ( - ) ( + ) relative to wild type ( Fig 7 ) . Mutant ΔmcplD presented a germination delay and polarity index similar to the avirulent strain NRRL3631 , whereas mutant Δmcmyo5 ( - ) ( + ) showed a reduction of the polarity index even more pronounced than the avirulent control strain , as well as a similar germination delay . Mutants grown in absence of macrophages also presented the same delay in germination and polar growth . The knockout strain in the mcclasp gene showed a non-significant reduction of the polarity index and no changes in the germination rate when compared to the wild type . As mutant Δmcclasp is not affected in virulence , these results highlight the relevance of spore germination and hyphal growth rates within macrophages for M . circinelloides virulence .
Here , we have developed a new approach based on RNAi high-throughput libraries that allows the identification of genes responsible for virulence in M . circinelloides . This work represents the first application of a functional genomic approach to identify virulence determinants in Mucorales . RNAi-based reverse genetics has allowed successful whole-genome functional studies in animals ( see Introduction ) , but this technology presents several restrictions in fungi that have prevented its use at whole genome level . Following this reverse genomic approach , the only known study carried out in fungi was firstly reported in a plant pathogen , Magnaporthe oryzae , in which the function of a calcium-signaling family of 37 genes was studied using silencing plasmids for each gene of this family [40] . Our platform presents an opposite approach in which a collection of phenotypes are generated by transformation with a whole-genome RNAi library and afterward the genes responsible for a particular phenotype are identified . This approach represents the first forward genetic strategy to study gene function at the whole genome level in Mucorales . Following this strategy , a general screening of a collection of silenced M . circinelloides transformants led us to the isolation of twenty-six strains showing a wide range of distinct phenotypes in fungal processes such as virulence , growth and sporulation . One advantage of this approach versus traditional chemical or insertional mutagenesis is that essential genes could be isolated , since RNAi usually reduces the expression of the target gene rather than a total inhibition . Another advantage of this RNAi-based functional genomic platform is the possibility of designing conditional screenings . The screenings shown here were performed under non-specific growth conditions , as it was intended to prove the general utility of the platform to apply functional genomics and to show how genes related to virulence can be identified from general screenings . However , the collection of silenced transformants offers the opportunity to carry out specific screenings under particular conditions to select phenotypes related to virulence , such as yeast growth , thermotolerance , protease over-production , etc . Moreover , another attractive advantage of this approach is its exportability to other Mucorales or fungi from different groups like Ascomycota and Basidiomycota , since the only conditions required are the existence of a functional RNAi mechanism and an efficient transformation method , which are both present in many fungal groups [41] . The application of the RNAi-based functional genomic platform has facilitated the identification of the genes mcplD as an essential factor to maintain full virulence in M . circinelloides , both in a heterologous model like G . mellonella and a murine host model . Knockout mutants in mcplD showed deficient growth accompanied with sporulation reduction and increased production of β-carotene , and more importantly , reduced virulence in the host models G . mellonella and M . musculus . The gene mcplD codes for a Phospholipase D enzyme ( PLD ) , a well-known protein that is highly conserved in different organisms . This enzyme catalyzes the hydrolysis of the phosphodiester bond of glycerophospholipids to generate phosphatidic acid and a free headgroup [37] . Phosphatidic acid functions as an intracellular lipid messenger that activates different target kinases , which in turn activate a broad range of cellular processes such as receptor signaling , control of intracellular membrane transport , and reorganization of the actin cytoskeleton [37] . This pleiotropic function of PLD could explain the complex phenotype observed in the M . circinelloides mutant for the mcplD gene . In addition , PLD has been described as a major virulence factor in Corynebacterium pseudotuberculosis , being involved in macrophage death and systemic dissemination of this pathogen [42] . In fungi , Aspergillus fumigatus PLD regulates its internalization into lung epithelial cells , and the pld gene of Purpureocillium lilacinum is significantly up-regulated during infection of Meloidogyne incognita eggs [43 , 44] . In M . circinelloides , mcplD may regulate some signaling pathways involved in germination and hyphal growth , since mutants in this gene showed delayed germination and reduction of the polarity index . The other gene related to virulence that has been found in this study , mcmyo5 encodes a processive cargo transporter belonging to the Myosin V Class ( Myo5 ) . Myosins play important roles in morphogenesis of filamentous fungi , since they are involved in the establishment and/or maintenance of polarity [45] . Among Myosins , Myo5 supplies a constant transport of organelles , membranous cargo , secretory vesicles , mRNA , lipid and protein vesicles on actin tracks [46] . In M . circinelloides , Myo5 might play an essential role in viability , since the knockout mutant was viable only as a heterokaryon containing a small proportion of wild type nuclei . This heterokaryotic strain was unable to produce regular hyphae and presented a yeast-like phenotype with no polar growth . Likely , the lack of a continuous transport mediated by Myo5 impairs the correct formation of the mycelium . Since filamentous growth is a major determinant of virulence in M . circinelloides [13] , the yeast-like knockout strain Δmcmyo5 ( - ) ( + ) presented a pronounced reduced virulence in G . mellonella . Similarly , in the dimorphic plant pathogenic fungus Ustilago maydis , a single Myosin class V protein encoded by myo5 was involved in hyphal growth and pathogenicity [47] . However , the reduction of virulence shown by the Δmcmyo5 ( - ) ( + ) heterokaryotic mutant in a murine host model did not reach the significance level stablished , although the reduction in the fungal burden was similar to the mutant mcplD . These partially contradictory results obtained from the two host models could be precisely due to the heterokaryotic state of mutant Δmcmyo5 ( - ) ( + ) . The virulence assays in G . mellonella are performed during eight days , whereas in M . musculus the assays are prolonged until the twentieth day , which could be time enough for the heterokayotic strain under non selective conditions to segregate and lose mutant nuclei , reverting to the wild type phenotype . According to this hypothesis , wild type patches segregating from Δmcmyo5 ( - ) ( + ) mutant cells growing under non selective culture conditions are easily observed after several days of incubation . In addition , a correlation between the segregation to wild type genotype and the restitutions of virulence was observed after genotyping the nuclei proportion of several retrieved CFUs , which further supported a role of gene mcmyo5 in the virulence of M . circinelloides . Similar results were obtained in Rhizopus oryzae when FTR1 gene was disrupted by double cross-over homologous recombination , but multinucleated R . oryzae could not be forced to segregate to a homokaryotic null allele [48] . The heterokaryotic strain Δmcmyo5 ( - ) ( + ) showed the strongest reduction of polar growth and germination rates when phagocyted by macrophages , since it was tested during only four hours in non-selective medium , which is not time enough for segregation . Our results from the in vitro analysis and the intravenous infection model showed the potential role of mcmyo5 in virulence of M . circinelloides . Although Myo5 is a highly conserved protein , which disqualifies it as a specific antifungal target , the fungal cargo domain and the proteins that interact with this domain could represent a promising target for future antifungal developments . The analysis of germination and polar growth of Δmcmyo5 ( - ) ( + ) and ΔmcplD mutants and their interaction with mouse macrophages revealed a delayed germination and reduced polarity index of those strains relative to the wild type strain , although the size of the infecting particles ( spores or yeast cells ) was not reduced in these mutants . These results suggested that a big size of the infecting particle ( spore or yeast cells ) is not enough to counteract a delayed germination and reduced polarity index . Time of germination and polarity index are two values that measure the velocity of the pathogen growing inside the macrophage and escaping from it . Thus , a possible explanation of the reduced virulence observed in ΔmcplD and Δmcmyo5 ( - ) ( + ) mutants might be that delayed germination and reduced polarity index concede macrophages time enough to inactivate the pathogen before it escapes from its cytoplasm . Along with the size of the spore previously described [14] , our work demonstrated that the time required for germination and the hyphal elongation rate , measured as polarity index , are two new factors to be considered in the analysis of M . circinelloides virulence . Besides genes related to virulence , a third gene named mcclasp was selected from the RNAi-based functional genomic screenings for further studies . Mutant Δmcclasp presented a complex phenotype affecting several fungal processes like vegetative growth , carotene production and sporulation in a similar manner to the Δmcmyo5 ( - ) ( + ) and ΔmcplD mutants , although the Δmcclasp strain was not affected in virulence . Besides that , the main differences between Δmcclasp and those mutants were the formation of sporangiophores , which were normal in length in Δmcclasp mutants , allowing the formation of satellite colonies in low pH media , along with a less pronounced reduction of growth and sporulation . The gene product of mcclasp is similar to CLASP proteins that are involved in the attachment of microtubules to the cell cortex in animals and plants , thereby contributing to self-organization of cortical microtubules [49] . During mitosis , CLASP proteins control the interactions of astral microtubules with the cell cortex , helping the proper positioning and orientation of the spindle [50] . This important role of CLASP proteins during cell division might be behind the reduced growth and decreased sporulation observed in the Δmcclasp mutants . In addition , the role of CLASP proteins in the stability of microtubules is essential for the motility of motor proteins , such as kinesins and dyneins . Kinesins participate in the maintenance of the polarity of filamentous fungi ( reviewed by Harris , 2006 ) , which could explain the reduction in the polarity index of the Δmcclasp mutant relative to the wild type strain . However , unlike Myo5 , kinesins does not seem to be involved in polarity establishment in M . circinelloides , since strains without CLASP protein are still able to generate hyphal growth , although their elongation rate is lower than the wild type strain . Mutant Δmcclasp showed a reduced growth rate and vegetative sporulation and an increase in β-carotene production compared to control strain R7B ( Fig 5C , 5D and 5E , respectively ) , similarly to the phenotypes observed in ΔmcplD and Δmcmyo5 mutants . However these phenotypes are not associated with reduced virulence in mutant Δmcclasp , indicating that growth defects are not necessarily linked to attenuated virulence and suggesting a possible specific role of mcplD and mcmyo5 genes in pathogenesis . The identification and analysis of mcclasp gene demonstrated that along with virulence , other fungal processes can be studied and genetically dissected with the RNAi-based functional genomic platform developed in this work . In summary , the absence of classic molecular genetic tools and the scarce information about virulence and pathogenesis in Mucorales encouraged us to develop a robust system for RNAi-based functional genomics in M . circinelloides . It is a new genetic tool that can be used in the study of a wide range of biological processes , including the identification and study of genes related to virulence and pathogenesis . As a result of its implementation , we have identified new virulence determinants in Mucorales that could represent new targets for future antifungal therapies .
The leucine auxotroph R7B , derived from the ( - ) mating type M . circinelloides f . lusitanicus CBS 277 . 49 ( syn . Mucor racemosus ATCC 1216b ) , was used as the wild type strain . Strain MU402 is a uracil and leucine auxotroph derived from R7B used as recipient strain of the silencing library [36] . The M . circinelloides f . lusitanicus strain of the ( + ) mating type NRRL3631 was used in virulence assays as an avirulent control . M . circinelloides cultures were grown at 26°C in complete YPG medium or in MMC medium as described previously [36] . Media were supplemented with uridine ( 200 μg/ml ) when required . The pH was adjusted to 4 . 5 and 3 . 2 for mycelial and colonial growth , respectively . Transformation was carried out as described previously [10] . Macrophage cells , J774A . 1 ( ATCC , TIB-67 ) , were cultured in L15 medium ( Capricorn Scientific GmbH ) supplemented with 10% FBS at 37°C and without CO2 supplementation . Plasmid pMAT1726 was recovered from gDNA of HRG1 and HRG2 transformants . In order to construct dsRNA-expressing vectors with the target candidate genes , plasmid pMAT1700 was used as cloning vector . Insert fragments corresponding to the 5’ end of each candidate gene ( 0 . 5–2 kb ) were amplified with primers containing NotI and XhoI restriction sites to facilitate cloning into pMAT1700 ( S2 Table ) . Plasmid pMAT828 harbors a 2 kb fragment of gene ID 51513 which was PCR-amplified using primer pairs FYL1 and RYL1 ( S2 Table ) . Plasmid pMAT798 contains a 0 . 9 kb fragment of gene ID 166338 amplified by PCR reactions using primer pairs FYL1 . 2 and RYL1 . 2 ( S2 Table ) . To identify the candidate gene responsible for the satellite growth phenotype , three different plasmids were constructed: pMAT823 , pMAT824 and pMAT825 . These plasmids contain 1 . 2 kb , 0 . 9 kb and 0 . 5 kb fragments corresponding to genes ID 84675 , ID 156742 and ID 145873 , which were amplified using primer pairs FYL10 . 1/RYL10 . 1 , FYL10 . 2/RYL10 . 2 and FYL10 . 3/RYL10 . 3 , respectively ( S2 Table ) . To calculate the coverage of the genomic libraries in the genome of M . circinelloides and confidence levels , we followed the formula: N = ln ( 1-P ) /ln ( 1-f ) ; where N is the necessary number of recombinants , P is the desired probability that any fragment of the genome is represented in the library at least one time , and f is the fractional proportion of the genome in a single recombinant . “f” can be further shown to be f = i/g , where i is the insert size and g is the genome size [51] . To disrupt the candidate genes , a pyrG selective marker ( 2 kb fragment amplified from gDNA using primers F-pyrG and R-pyrG ( S2 Table ) was fused with adjacent sequences of the candidate coding regions using fusion PCRs , generating a gene replacement fragment . This fragment was cloned into pGEMT-easy vector ( Promega ) and used to disrupt the candidate genes via homologous recombination . Plasmid pMAT833 was constructed to disrupt the mcclasp gene . It contains a 4 . 2 kb fragment that includes the pyrG gene flanked by 1 . 3 kb of upstream and downstream sequences of mcclasp gene , amplified with primers FYL10U/RYL10-pyrG and FYL10-pyrG/RYL10D ( S2 Table ) , respectively . The 4 . 2 kb fusion fragment was amplified with internal primers FYL10 and RYL10 ( S2 Table ) . Plasmid pMAT832 was constructed to disrupt mcmyo5 gene , following the same strategy described for pMAT833 , but using primers FYL1U/RYL1-pyrG and FYL1-pyrG/RYL1D to amplify 1 . 3 kb of upstream and downstream sequences of the mcmyo5 gene , respectively ( S2 Table ) . In the case of gene mcplD , plasmid pMAT1733 was constructed also following the same fusion strategy , but with the specific primers FPLD/RPLD-pyrG and RPLD/FPLD-pyrG , for two fragments of 0 . 95 kb from upstream and downstream regions of mcplD ( S2 Table ) , respectively . Genomic DNA from M . circinelloides mycelia was extracted as previously described [36] . Recombinant DNA manipulations were performed as reported [52] . Total RNA was extracted from mycelia grown during 48 hours at 26°C in liquid MMC pH 4 . 5 medium under light conditions using RNeasy Plant Mini Kit following the supplier’s recommendation ( Qiagen ) . Southern blot and Northern blot hybridizations were carried out under stringent conditions [22] . DNA probes were labeled with [α-32P] dCTP using Ready-To-Go Labeling Beads ( GE Healthcare Life Science ) . For Southern and Northern blot experiments , DNA probes were directly amplified from genomic DNA using the primer pairs FPLD/RPLD-pyrG , FYL10/RYL10-pyrG and FYL1N/RYL1-pyrG for genes mcplD , mcclasp and mcmyo5 , respectively ( S2 Table ) . For siRNA analysis , small RNA samples were extracted from mycelia grown 72 hours in liquid MMC medium pH 4 . 5 at 26°C using the miRVana kit ( Ambion ) , following the instructions of the supplier . Northern blots for siRNAs were performed as previously described using antisense specific riboprobes generated by in vitro transcription of the DNA probes described above ( MAXIscriptsT7 , Ambion ) [21] . Quantifications of signal intensities were estimated from autoradiograms using a Shimadzu CS-9000 densitometer and the ImageJ application ( rsbweb . nih . gov/ij/ ) . Computational phylogenetic analyses were performed using Phylogeny software ( http://phylogeny . lirmm . fr ) [53] . Multiple protein sequence alignments were conducted with ClustalW program and phylogenetic trees were inferred by maximum likelihood statistical methods using a bootstrapping procedure of 1000 iterations . Vegetative sporulation , growth rate , carotene production and virulence measurements were carried out as previously described [14 , 19 , 36 , 54] . Interactions between different strains of M . circinelloides and J774A . 1 macrophage cells were carried out in L15 medium , during 4 hours at 37°C . Regarding polarity index , ten images were taken from each interaction and a total of fifty germinating spores were measured from each image with ImageJ [55] . From the same images , germination was calculated considering germinated spores all those that presented a protuberant bud from the spherical spore . For the phospholipase D activity measurements , spores of the wild type strain and mutant mcplD were grown in MMC medium at pH = 4 . 5 during six hours . The mycelia from five biological replicates were filtrated , washed and weighted to perform the assay with exactly 100 mg of biomass from each strain . The virulence assays in G . mellonella were performed by injection of 5 μl of phosphate buffered saline ( PBS ) containing 2000 spores or 20 , 000 yeast cells into the wax moth larvae ( 10 per strain ) . For the murine host model , groups of 8 four-week-old OF1 male mice ( Charles River , Criffa S . A . , Barcelona , Spain ) weighing 30 g were used . Mice were immunosuppressed 2 days prior to the infection by intraperitoneal ( i . p . ) administration of 200 mg/kg of body weight of cyclophosphamide and once every 5 days thereafter . Animals were housed under standard conditions with free access to food and water . Mice were challenged intravenously ( i . v . ) via the lateral tail vein with a suspension consisting on 1x105 sporangiospores or 1x105 yeast cells per animal . Animals were checked twice daily for 20 days . Surviving animals at the end of the experimental period or those meeting criteria for discomfort were euthanized by CO2 inhalation . Significance of mortality rate data was evaluated by using the Kaplan-Meier ( Graph Pad Prism 4 . 0 for Windows; GraphPad Software , San Diego California USA ) . Differences were considered statistically significant at a P value of <0 . 05 . For absolute DNA quantification and genotyping , organs were ground up on liquid nitrogen and gDNA was extracted as previously described [56] . For DNA quantification by real-time PCR ( qRT-PCR ) specific primers of M . circinelloides chitin synthase gene ( ID153118 ) and mice β2 microglobulin gene ( ID12010 ) were used ( S2 Table ) . Samples analyses were carried out in triplicate in 15 μl PCR reactions containing 180 ng of test sample gDNA form three individuals using SybrGreen kit ( Fast SYBR Green Master Mix -ABI ) in a StepOne Real-Time PCR System ( ABI ) . gDNA from non-infected mice was used as negative control . Relative amount of fungal and mice gDNA was quantified on the basis of their standard curves , elaborated with known fungal DNA concentrations ( 0 . 005 ng—10 ng ) in a background of 150 ng of non-infected mice gDNA and mice DNA concentrations ( 1 ng—200 ng ) and their corresponding amplification cycle threshold ( Ct ) . Animal care procedures were supervised and approved by the Universitat Rovira i Virgili Animal Welfare and Ethics Committee . The experimental animal facilities are registered under reference T9900003 of the Generalitat de Catalunya in compliance with the regulations of Real Decreto 53/2013 , of February 1st ( BOE of 8 February ) . Procedures included into the project number 280 were supervised and approved by L . Loriente Sanz ( ID 39671243 ) of the Veterinary and Animal Welfare Advisory of the Universitat Rovira i Virgili Animal Welfare and Ethics Committee ( Reus , Spain ) . | Mucormycosis is an infectious disease caused by organisms of the order Mucorales . It is a lethal infection that is raising the alarm in the medical and scientific community due to its high mortality rates , unusual antifungal drug resistance and its emerging character . Among the reasons explaining the nescience about this disease is the lack of knowledge on the biology of the organisms that cause mucormycosis , which is encouraged by the reluctance of these species to genetic studies . In this work , we have developed an RNAi-based functional genomic platform to study virulence in Mucorales . It is a powerful tool available for the scientific community that will contribute to solve the reluctance of Mucorales to genetic studies and will help to understand the genetic basis of virulence in these organisms . Secondly , and as a proof of concept , we have used this genetic tool to identify two new virulence determinants in Mucor circinelloides . Lack of function of these determinants delays germination and growth of spores , conceding time to macrophages for the inactivation of the pathogen . The two genes identified , mcplD and mcmyo5 , represent promising targets for future development of new antifungal therapies against mucormycosis . | [
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"genetic... | 2017 | RNAi-Based Functional Genomics Identifies New Virulence Determinants in Mucormycosis |
Despite utilizing the same chymotrypsin fold to host the catalytic machinery , coronavirus 3C-like proteases ( 3CLpro ) noticeably differ from picornavirus 3C proteases in acquiring an extra helical domain in evolution . Previously , the extra domain was demonstrated to regulate the catalysis of the SARS-CoV 3CLpro by controlling its dimerization . Here , we studied N214A , another mutant with only a doubled dissociation constant but significantly abolished activity . Unexpectedly , N214A still adopts the dimeric structure almost identical to that of the wild-type ( WT ) enzyme . Thus , we conducted 30-ns molecular dynamics ( MD ) simulations for N214A , WT , and R298A which we previously characterized to be a monomer with the collapsed catalytic machinery . Remarkably , three proteases display distinctive dynamical behaviors . While in WT , the catalytic machinery stably retains in the activated state; in R298A it remains largely collapsed in the inactivated state , thus implying that two states are not only structurally very distinguishable but also dynamically well separated . Surprisingly , in N214A the catalytic dyad becomes dynamically unstable and many residues constituting the catalytic machinery jump to sample the conformations highly resembling those of R298A . Therefore , the N214A mutation appears to trigger the dramatic change of the enzyme dynamics in the context of the dimeric form which ultimately inactivates the catalytic machinery . The present MD simulations represent the longest reported so far for the SARS-CoV 3CLpro , unveiling that its catalysis is critically dependent on the dynamics , which can be amazingly modulated by the extra domain . Consequently , mediating the dynamics may offer a potential avenue to inhibit the SARS-CoV 3CLpro .
Severe acute respiratory syndrome ( SARS ) is the first emerging infectious disease of the 21st century and was caused by a novel coronavirus termed SARS-CoV . It suddenly broke out in China in 2002 and then rapidly spread to 32 countries , causing ∼8500 infections and over 900 deaths ( http://www . who . int/csr/sars/en/ ) . So far neither a vaccine nor an efficacious therapy has been available . Therefore , it remains highly demanded to design the potential therapeutic agents against SARS . Coronaviruses are enveloped , positive-stranded RNA viruses with the largest single-stranded RNA genome ( 27–31 kilobases ) among known RNA viruses . Its large replicase gene encodes two viral polyproteins , namely pp1a ( 486 kDa ) and pp1ab ( 790 kDa ) , which are processed into active subunits for genome replication and transcription by two viral proteases [1] , [2] , namely the papain-like cysteine protease ( PL2pro ) and 3C-Like protease ( 3CLpro ) . Previously , SARS 3CLpro has been extensively considered to be a key target for development of antiviral therapies . The coronavirus 3CLpro , also known as main protease ( Mpro ) , is so named to reflect the similarity of its catalytic machinery to that of the picornavirus 3C proteases [1]–[3] . Perceptibly , both 3C and 3CL-Like proteases utilize the two-domain chymotrypsin fold to harbor the complete catalytic machinery , which is located in the cleft between domains I and II [1] , [3] . However , in the coronavirus 3CLpro , a ∼100-residue helical domain was additionally acquired at its C-terminus during evolution [1] , [2] , [4] . Moreover , unlike 3C protease , only the homodimeric form is catalytically competent for the SARS-CoV 3CLpro [1] , [4] , [5]–[20] . Immediately after the SARS outbreak , by a domain dissection approach , we have experimentally identified that the extra domain plays a key role in mediating the dimerization and intriguingly even the isolated extra domain could dimerize itself in solution [15] . Furthermore , detailed mutagenesis studies led to identification of different groups of extra-domain residues critical for both dimerization and catalysis [8] , [12] , [14] , [20] . Now it is clear that both chymotrypsin fold and extra domain are critical for dimerization . Recently , we have succeeded in determining the high-resolution structure of R298A , a monomeric mutant in both solution and crystal [21] . In the R298A structure , the most affected regions are within the catalytic machinery , in addition to the several N- and C-tail residues as well as the orientation between the chymotrypsin fold and extra domain . Most importantly , we revealed that R298A has the completely collapsed and inactivated catalytic machinery structurally distinguishable from that in wild-type ( WT ) enzyme , characteristic of a chameleon formation of a short 310-helix by residues Ser139-Phe140-Leu141 within the oxyanion-binding loop [21] . The collapsed catalytic machinery observed in R298A appears to represent a universal inactivated state intrinsic to all inactive enzymes because almost identical collapsed machinery was found in other two monomers , G11A and S139A , with the mutations located on the chymotrypsin fold [22] , [23] . On the other hand , previously we also identified another mutant N214A , which owns a dramatically abolished activity but appeared to be largely dimeric as assessed by NMR spectroscopy . However , previous N214A construct contains two extra N-terminal residues Gly-Ser leftover from the thrombin cleavage of the GST-fusion protein . Since N-terminus has been shown to be critical for both activity and dimerization [17] , in the present study , we generated the N214A enzyme without the two extra residues by reengineering the expression vector . Interestingly , the new N214A form only has a doubled dissociation constant ( Kd ) for the dimer-monomer equilibrium but almost abolished activity . Although initially we characterized it by NMR spectroscopy , many NMR peaks were undetectable because of its large size and conformational exchange on µs-ms time scale , thus preventing further characterization by NMR . As a result we subsequently determined the crystal structure of N214A but unexpectedly it adopts a dimeric structure almost identical to that of the WT protease . To explore whether the N214A mutation will trigger dynamical changes which account for the activity loss , we initiated 30-ns molecular dynamics ( MD ) simulations for the WT , N214A and R298A enzymes , as well as two artificial monomers derived from the dimeric WT and N214A structures . The obtained results unveil that for the SARS 3CLpro , the activated and inactivated states of the catalytic machinery are dynamically well separated . Very surprisingly , the N214A mutation triggers the dynamical instability of the catalytic machinery in the context of the dimeric form , with many key residues jumped to sample the conformations characteristic of the inactivated state .
Recently the extra N-terminal residues leftover from the cleavage of fusion proteins were demonstrated to significantly disrupt dimerization as well as to affect the enzymatic activity [17] . On the other hand , the SARS 3CLp we previously studied had two extra residues Gly-Ser after the thrombin cleavage of the GST-3CLp fusion proteins [14] , [15] . In order to remove the two extra residues , in the present study we transferred the gene encoding SARS 3CLp from the pGEX-4T-1 GST-fusion expression vector ( Amersham Biosciences , GE Healthcare , Little Chalfont , UK ) to the His-tagged pET28a vector . Subsequently , site-directed mutagenesis was utilized to shorten the thrombin cleavage sequence LVPR|GS ( CTG GTT CCG CGT GGA TCC ) engineered by the company to LVPR| ( CTG GTT CCG CGT ) , which only constituted the thrombin cleavage site in conjunction with the first two N-terminal residues Ser-Gly of SARS 3CLp . Interestingly , thrombin cleaved this new site ( LVPR|SG ) very efficiently to release the authentic wild-type 3CLpro . To produce N214A mutant , site-directed mutagenesis was further used to mutate Asn214 to Ala [14] , [21] . Benefited from this re-engineered cleavage site , we were able to produce both authentic WT 3CLpro and its N214A mutant without any extra residues from the fusion tag . Recently , two structures were determined for the authentic SARS-CoV 3CLpro with PDB codes of 2H2Z [17] and 2GT7 [24] . The recombinant His-tagged WT and N214A proteases were expressed in E . coli strain BL21 ( DE3 ) with induction by 0 . 4 mM isopropyl-1-thio-d-galactopyranoside ( IPTG ) at 20°C overnight . The WT and N214A proteases were obtained by affinity binding of the His-tagged proteins to the Nickel-NTA beads ( QIAGEN ) , followed by the in-gel cleavage with thrombin to release the WT and N214A proteases , which was further purified by FPLC on a gel filtration column ( HiLoad 16/60 Superdex 200 ) . The molecular weight of the WT and N214A proteases were determined by a Voyager STR MALDI-TOF mass spectrometer ( Applied Biosystems ) . The enzymatic activities of the WT and N214A proteases were measured by a fluorescence resonance energy transfer ( FRET ) -based assay using a fluorogenic substrate peptide as previously described [13] , [17] , [25] . Briefly , the reaction mixture contained 50 nM protease and fluorogenic substrate with concentrations ranging from 1 µM to 30 µM in a 5 mM Tris-HCl buffer with 5 mM DTT at pH 6 . 0 , which is identical to the crystallization condition . The enzyme activity was measured by monitoring the increase of the emission fluorescence at a wavelength of 538 nm with excitation at 355 nm using a Perkin-Elmer LS-50B luminescence spectrometer . The Km and kcat values were deduced from data analysis using Graphpad prism . ITC experiments were carried out to determine the monomer-dimer dissociation constants of the WT and N214A proteases as previously described [7] using a Microcal VP ITC machine . Briefly , the protease samples and buffers were span at 13 . 3 k rpm for one hour to remove the tiny particles and degas thoroughly . In titrations , the WT or N214A sample in 5 mM Tris-HCl buffer at pH 6 . 0 containing 5 mM DTT were loaded in the syringe , which was subsequently titrated into the same buffer in the cell . The obtained titration data with endothermic peaks were analyzed by the built-in Microcal ORIGIN software using a dimer-monomer dissociation model to generate the dissociation constants and the enthalpy changes . The N214A protease with a concentration of 10 mg/ml was crystallized in a 2 µl hanging drop using a condition identical to that previously reported except for a minor variation of the PEG6000 concentration [4] , [17] . The crystals were grown up to three days . 20% glycerol was supplemented with the mother liquid as a cryoprotectant . A single crystal was picked up from the cluster of crystals using the nylon loop and the X-ray diffraction data were collected at Bruker X8 PROTEUM in-house X-ray system . The collected data set was processed using the program HKL2000 up to 2 . 3 Å resolution . The N214A was crystallized in the space group P21 . The phase determination for the mutant structure was done by the molecular replacement method by using the WT SARS-CoV 3CLpro structure ( PDB code: 2H2Z ) as the searching model by the program Phaser [26] in the program suite Phenix [27] . The Ala mutating residues ( Asn214 ) were corrected in the program COOT [28] . The refinements and the addition of the solvent molecules of the models for the mutant were done in the program suite Phenix [27] . The final model was analyzed by PROCHECK [29] . The data collection and refinement statistics are provided in the Table S1 . The atomic coordinates have been deposited in the Protein Data Bank with the PDB code of 3M3S [30] . The structure overlay was done by LSQKAB from CCP4 suite [28] . All the figures were prepared using Pymol [31] . The crystal structures of the WT ( PDB code: 2H2Z ) [17] , the monomeric mutant R298A ( PDB code: 2QCY ) [21] , and N214A determined in the present study were selected as the initial models for molecular dynamics simulations . In simulations , the WT and N214A proteases were in the dimeric forms while the R298A protease was a monomer . Furthermore , artificial monomers of WT and N214A obtained by splitting their dimeric structures were also subjected to the MD simulations under the same conditions . The simulation cell is a periodic cubic box with a minimum distance of 10 Å between the protein and the box walls to ensure the protein would not directly interact with its own periodic image given the cutoff . The water molecules , described using the TIP3P model , were filled in the periodic cubic box for the all atom simulation . To neutralize the system , some Na+ and Cl- ions were randomly placed far away from the surface of the proteases . At the end , each system contained about more than 75 , 000 atoms . Three independent 30-ns MD simulations were performed for each of five forms of the protease by the program GROMACS [32] with the AMBER-03 [33] all-atom force field . The long-range electrostatic interactions were treated using the fast particle-mesh Ewald summation method [34] , with the real space cutoff of 9 Å and a cutoff of 14 Å was used for the calculation of van der Waals interactions . The temperature during simulation was kept constant at 300 K by Berendsen's coupling . The pressure was held at 1 bar . The isothermal compressibility was 4 . 5*10−5 bar-1 . The time step was set as 2 fs . All bond lengths including hydrogen atoms were constrained by the LINCS algorithm [35] . Prior to MD simulations , all the initial structures were relaxed by 500 steps of energy minimization using steepest descent algorithm , followed by 100 ps equilibration with a harmonic restraint potential applied to all the heavy atoms of the protease .
By reengineering the thrombin cleavage site of the His-tagged pET28a vector , we succeeded in obtaining both WT and N214A proteases without any extra residues leftover from the fusion tag . The two enzymes were characterized by far-UV CD spectroscopy and their spectra ( spectra not shown ) had no detectable difference from those with two extra residues Gly-Ser that we previously studied , thus indicating that the two extra residues have no detectable effect on their secondary structures . By a fluorescence resonance energy transfer ( FRET ) -based assay , we have measured the enzymatic activities of both WT and N214A proteases . As shown in Figure 1a , the WT protease is fully active with the Km and kcat values very similar to that previously reported on the authentic enzyme [17] . However , the activity of the N214A mutant is extremely low and consequently had no detectable increase of the fluorescence intensity within 3-minute incubation . Only after 2 hours , a slight increase of the fluorescence intensity could be detected ( data not shown ) . We have also tested on the activity at higher N214A concentrations , no significant activity enhancement was observed . Because of this , we were unable to fit out their precise Km and kcat values although we collected a large set of data for N214A . The dimerization of the SARS 3CLpro has been extensively characterized to be absolutely required for the enzymatic activity . As such , previously the dimer-monomer equilibrium has been exhaustively assessed by a variety of methods , including enzyme kinetics , chemical cross-linking , dynamic light scattering ( DLS ) , isothermal titration calorimetry ( ITC ) , analytic ultra-centrifuge ( AUC ) and small-angle X-ray scattering [5] , [7]–[12] , [14] . Interestingly , the obtained dissociation constant ( Kd ) values have an extremely-large variation , ranging from <1 nM to more than 200 µM in the literature . Recently , a study specifically aimed to reexamine these discrepancies by measuring the Kd values with three independent methods . Strikingly , the obtained Kd values for the WT SARS 3CLpro were very similar , 5 . 8–6 . 8 µM by small-angle X-ray scattering; 12 . 7 µM from chemical cross-linking and 5 . 2 µM from enzyme kinetics [10] . Previously , despite being catalytically active , the Kd value of a WT form of the SARS 3CLpro was derived to be 227 µM by using ITC method [7] . However , this protease form contained a 14-residue N-terminal tag with a sequence of MRGSHHHHHHGSTM . Here we used the same ITC method to measure the Kd values and the obtained values were 21 . 4 µM ( Figure 1b ) for the authentic WT protease and 47 . 4 for its N214A mutant ( Figure 1c ) respectively . Notably , the Kd value obtained here for the WT protease is ∼10-fold less than the previous one [7] . This difference may be attributed to the presence of the 14-residue His-tag in the protease previously characterized , as it is now well established that the presence of the extra N-terminal residues would significantly destabilizes the dimerization [10] , [11] , [17] , [36] . Noticeably , the Kd value of the N214A mutant is only ∼2 fold higher , indicating that without the extra N-terminal tag residues , the N214A mutation itself has a very limited effect on the dimerization . On the other hand , the enzymatic activity of N214A is severely abolished . As a consequence , the observed loss of the N214A activity can not be completely attributed to the slight disruption of the dimerization . This conclusion is strongly supported by the fact that several forms of the enzyme containing different N-terminal tags have much more destabilized dimerization but still retain high enzymatic activity [4] , [7] , [9]–[11] , [14] , [17] . For example , the form with the extra 14 residues has a ∼10-fold increase of Kd value but still has very high enzymatic activity [7] . This result thus strongly implies that the extra domain may mediate the catalysis of the SARS 3CLpro by unknown mechanisms other than controlling the dimerization . To gain insights into the structural consequence of the N214A mutation , we determined its crystal structure at a resolution of 2 . 3 Å in P21 space group . The R factor of the final model for N214A was 19 . 6% , with the Rfree factor of 25 . 7% . Details of the data collection and refinement statistics are presented in Table S1 . Remarkably , the N214A mutant still adopts the classic dimeric structure with the same packing of the two protomers as observed in all previously-determined dimeric structures of the coronavirus 3CLpro [1] , [2] , [4] , [17] , [18] , [24] , [31] , [37]–[40] . In the electron density map of the N214A mutant , all residues are visible except for the last four residues Val303-Thr304-Phe305-Gln306 of the protomer B , which could not be identified due to the poorly defined electron densities . The dimeric N214A structure is highly similar to those of the WT protease previously reported ( Figure 2 ) . If compared to the WT crystal structure ( 2H2Z ) with the authentic sequence in the C2 space group [17] , the overall RMS deviation is 0 . 6 Å . A close examination reveals that the main difference is over the C-terminal 7 residues of the protomer A . Strikingly , if compared with another WT crystal structure ( 2GT7 ) also with the authentic sequence but in the P21 space group [24] , even the C-terminal residues have no marked difference and as a consequence the overall RMS deviation is only 0 . 2 Å . Interestingly , in the 2GT7 structure , the disordered regions include the last four residues Val303-Thr304-Phe305-Gln306 of the protomer B as well as additionally Thr45-Ala46-Glu47-Asp48-Met49 of the protomer A . Unexpectedly , in the N214A mutant , even for the key residues constituting the catalytic machinery including the catalytic dyad His41-Cys145 , oxyanion-loop Phe140-Cys145 , His163 and Glu166 critical for binding substrates; and Phe140 , His172 in holding the substrate binding-pocket open , their backbones and side-chains are almost superimposable to those in the WT structures ( Figures 2c and 2d ) . This observation strongly implies that the loss of the N214A activity can not be readily rationalized only by the static structure . Instead , it may be due to the change of the dynamics of the enzyme as triggered by the N214A mutation . Therefore , we examined the B-factors of N214A , R298A and WT proteases ( Figure S1 ) and indeed some regions in the N214A mutant do have higher B-factors . However , it appears not straightforward to establish a precise correlation between the B-factors and catalytic activity for the three enzymes . Molecular dynamics simulation is a powerful tool to pinpoint the dynamical factors underlying protein functions . To gain insights into their dynamical behaviors , we initiated 30-ns MD simulations for the WT , N214A and R298A , as well as artificial WT and N214A monomers . For R298A , we used the monomeric crystal structure we previously determined at 1 . 75 Å resolution [21] . For the WT enzyme , two structures with the authentic sequence are available: 2H2Z determined in C2 and 2GT7 in P21 space group . Here , we selected the 2H2Z structure but not 2GT7 for MD simulations because residues Thr45-Met49 are missing in 2GT7 . Figure 3 presents the root-mean-square deviations ( RMSD ) of all heavy atoms for three parallel simulations for WT , R298A and N214A . It appears that for all simulations , the RMSD values increased very rapidly during the first 0 . 6 ns . This is mostly due to the relaxations of the crystal structures upon being solvated in solution as previously observed on MD simulations of the SARS 3CLpro [7] , [22] , [39] , [41]–[43] . Very strikingly , three proteases display different dynamic behaviors . The WT enzyme appears to have the largest conformational rigidity , with the lowest RMSD values averaged over three simulations ( 1 . 75 and 1 . 76 Å for two protomers respectively ) . By contrast , R298A shows the highest overall conformational flexibility , with the largest average RMSD ( 2 . 50 Å ) . This feature may be largely due to the exposure of the interfacial residues to the solvent upon losing the counter monomer , as previously observed [22] , [41] , [43] . Indeed , much high RMSD values are observed for the simulations of the artificial WT and N214A monomers ( Figure S2 ) . More surprisingly , despite having the initial structure very similar to that of WT , both N214A protomers have much larger conformational flexibility than the WT enzyme , as clearly evidenced from their large average RMSD ( 2 . 20 and 2 . 34 Å for two protomers respectively ) . The RMSD values of the N214A dimer are also similarly larger than those of the WT dimer in three parallel simulations , implying no significant dissociation in N214A ( Figure S3 ) . Noticeably , similar dynamic behaviors are also captured by the root-mean-square fluctuations ( RMSF ) of the Cα atoms in the MD simulations ( Figures 3j–3r ) . R298A has the highest overall average RMSF value ( 1 . 10 Å ) while both WT protomers have the lowest values ( 0 . 88 and 0 . 81 Å respectively ) . Although overall two N214A protomers have the average RMSF values ( 1 . 00 and 1 . 06 Å respectively ) larger than two WT protomers but slightly smaller than R298A , it is noteworthy to point out that both N214A protomers have unusual large RMSF values over residues Ala46-Asn51 . This region may be intrinsically flexible because residues Thr45-Met49 were totally disordered in the 2GT7 structure . We have extensively analyzed the conformational changes of individual residues in the MD simulations . It appears that the most dramatic and relevant changes are located within the catalytic machinery composed of the catalytic dyad and substrate binding subsite S1 . As seen in Figure 2b , although the SARS 3CLpro acquires an extra domain at the C-terminus ( domain III , residues 201–303 ) , like the 3C protease it still uses the chymotrypsin fold made up of domains I ( residues 8–101 ) and II ( residues 102–184 ) to harbor the complete catalytic machinery , with the catalytic dyad His41-Cys145 and substrate binding-pocket located in a cleft between domains I and II . As a consequence , below we will be mostly focused on the analysis of the dynamical behaviors of the catalytic dyad and S1 substrate-binding subsite . Figure 4 presents the structural snapshots for side chains of the key residues forming the catalytic dyad and S1 substrate-binding subsite in WT , N214A and R298A at the MD simulation time points of 0 , 10 , 20 and 30 ns . Overall in the both WT protomers , the conformational fluctuation of these side chains is relatively small except for that of Glu166 . The relatively large fluctuation for Glu166 is understandable because it functions to bind the substrate but in the present simulations , the enzyme is not in complex with any substrate , unlike previous MD simulations [41] , [43] . Consequently the Glu166 side chain is accessible to the bulk solvent and is expected to have a large fluctuation . By contrast , although N214A owns a crystal structure almost identical to that of WT , it has the much larger fluctuations in both protomers . On the other hand , in R298A due to the formation of a characteristic 310-helix over residues Ser139-Phe140-Leu141 , its catalytic machinery has become completely collapsed , as we previously demonstrated [21] . The collapsed machinery is structurally very different from that of the activated WT enzyme , as exemplified by the dramatic movements of the Phe140 and Tyr126 sidechains . Consequently the Phe140 aromatic ring only remains interacting with the Tyr126 aromatic ring but no longer interacts with the aromatic rings of His163 and His172 . Amusingly , as shown in Figures 4 , even with the simulation time up to 30 ns , the R298A catalytic machinery still remains highly trapped in the collapsed state . In the previously-determined crystal structures of SARS CoV 3CLpro , the distance between NE2 of His41 and SG of Cys145 ranges from 3 . 6 to 3 . 9 Å [43] . Furthermore , previous MD simulations also revealed that the dynamic stability of this distance is extremely critical for the stable formation of a hydrogen bond , which appears to be pivotal for maintaining the catalytic competency of the SARS 3CLpro [7] , [22] , [39] , [41]–[43] . Also in previous MD simulations for the active WT enzyme , this distance has been demonstrated to range from 1 . 8–3 . 3 Å and 3 . 5–4 . 5 Å . Figure 5 presents the time-trajectories of this distance in the present simulations for WT , R298A and N214A . For the WT enzyme , the average value of the distance is 3 . 83 Å , while is 4 . 18 Å for R298A . Very surprisingly , for N214A , this distance has the largest average value of 4 . 26 Å . In particular , in the trajectory of one simulation ( Figure 5g ) , there are several very large fluctuations . Consistent with this observation , the occupancy of the hydrogen bond between NE2 of His41 and SG of Cys145 , which was calculated based on the dynamic behavior of both distance and angle , yields to 28 . 40% and 15 . 82% respectively for the WT protomers A and B; 5 . 42% for R298A , and 3 . 83%–8 . 55% respectively for the N214A protomers A and B . This result implies that only as judged from the occupancy of this hydrogen bond , the catalytic competency of both R298A and N214A enzymes is significantly inactivated . We also analyzed the time-trajectories of the angles Chi1 ( N-CA-CB-CG ) and Chi2 ( CA-CB-CG-CD2 ) of His41 for WT , R298A and N214A . Interestingly in both WT protomers , the Chi1 ( Figures 5j–5l ) and Chi2 ( 5s–5u ) are dynamically stable . For R298A , the Chi1 ( Figures 5m–5o ) and Chi2 ( 5v–5x ) also appears relatively stable . Very unexpectedly , in one simulation , both protomers of N214A , Chi1 ( Figure 5p ) and Chi2 ( Figure 5y ) , are dynamically very unstable and jumping among several conformational clusters . Intriguingly , the instability observed in the simulations of the dimeric N214A largely disappears in those of the artificial N214A monomers ( Figure S4 ) . One key component of the catalytic machinery of the SARS 3CLp is the substrate-binding pocket composed of six subsites , namely S1–S6 , corresponding to the P1–P6 residues of the substrate [1] , [2] , [4] . Out of them the S1 subsite is the most critical which confers an absolute specificity for a Gln at the P1 position of the substrate . As such , maintaining the intact conformation of S1 subsite is especially vital for catalysis . Briefly , the S1 substrate can be divided into four parts: the oxyanion hole , His163 , Glu166 and Phe140 and its stabilizing elements [1] , [2] , [4] , [23] . The oxyanion hole refers to a structural element to donate two hydrogen bonds from main-chain amides of Gly-143 and Cys-145 to accommodate the main-chain oxygen of Gln-P1 as well as the tetrahedral intermediate during catalysis [1] , [2] , [4] , [23] . Previously we demonstrated that in the inactive monomer R298A , the most distinguishable change is the collapse of the oxyanion hole as triggered by the chameleon formation of a short 310-helix from a loop over residues Ser139-Phe140-Leu141 [21] . As seen in Figures 6a–6c , 6j–6l and 6s–6u , in WT these three residues mostly maintain the initial extended backbone conformations in the simulations . On the other hand , in R298A , even with the simulation time up to 30 ns , these residues also only sample the helical backbone conformations characteristic of the initial collapsed catalytic machinery in the R298A crystal structure . By contrast , in N214A , although residues Ser139-Phe140 sample the WT-like extended backbone conformations , Leu141 jumps to sample the helical conformation resembling that of R298A . Also it is worthy to point out that although the backbone conformations for the three residues of the artificial WT and N214A monomers are much more dynamic than those in the R298A monomer , they appears less dynamic than those for the dimeric N214A ( Figure S5 ) . The imidazole side chain of His163 plays a key role in determining the SARS 3CLpro specificity for glutamine at P1 by interacting with the P1 carboxamide side chain of the substrate . Previous MD simulations also showed that the dynamic behavior of the His163 side chain is critical for interacting with the substrate [7] , [22] , [39] , [41]–[43] . Figure S6 presents the dynamic properties of the His163 backbones and side chains in WT , R298A and N214A . Interestingly , it appears that all three enzymes have the similar dynamic behaviors for the backbone conformations and Chi1 angles over the 30-ns simulations . Nevertheless , the Chi2 angles show some differentiations in three enzymes . In R298A , the His163 imidazole ring appears to flip rapidly among different conformations , probably due to the complete loss of the aromatic stacking interaction to the Phe140 ring in the collapsed catalytic machinery . On the other hand , in WT , the Chi2 angles only jumps to sample another conformational cluster . Interestingly , in all three N214A simulations , the Chi2 angle behaves much more dynamically than those in WT , as well as those in the artificial N214 monomer ( data not shown ) . Glu166 is located at the entrance of the substrate binding pocket in the active enzyme , specifically recognizing the side-chain NE2 of Gln-P1 . Figure 7 shows the dynamic behaviors of the distances between the Gln166 and aromatic ring of His172 , as well as Chi1 and Chi2 angles of Gln166 . Interestingly , WT has the shortest average distance ( 3 . 95 Å ) and smallest fluctuations . By contrast , N214A has the highest fluctuations and consequently has the average distance ( 4 . 88 Å ) even larger than that of R298A ( 4 . 46 Å ) . Also the Chi1 and Chi2 angles in three N214A simulations are the most dynamical . Interestingly again , the fluctuations reduce significantly for both the distance and angles in the simulations of the artificial N214A monomer ( Figure S7 ) . To maintain the catalytic machinery activated , Phe140 plays a key role by inserting its large aromatic ring into the S1 subsite to hold it open and active . The correct orientation of the Phe140 aromatic ring is maintained by the stacking interactions mainly with aromatic residues His163 , His172 and Tyr126 . Previously we have demonstrated that in the collapsed catalytic machinery of R298A , both backbone and aromatic ring of Phe140 underwent a remarkable movement and consequently Phe140 loses the stacking interactions with His163 and His172 [21] . Figure 8 shows the dynamical behaviors for the aromatic interaction between Phe140 and His172 . In WT , the centroid distance remains short and dynamically stable in three simulations , with average values of 5 . 16 and 5 . 12 Å respectively for two protomers . By contrast , in R298A , this distance remains large as observed in the crystal structure , with an average value of 8 . 15 Å , indicating the total absence of this stacking interaction in the whole 30 ns simulations . For N214A , the dynamic behavior of this distance is very similar to that for WT , despite having slightly larger average values of 5 . 42 and 5 . 14 Å respectively for two protomers . As for the side chain conformations , the His172 Chi1 angles have both a very similar value as well as similar dynamical behavior in all three enzymes except that in one simulation of N214A , one protomer jumps to sample another conformation ( Figure 8r ) . However , significant dynamics are observed for the N214A Chi2 , which jumps to sample several conformational clusters in simulations ( Figures 8y–8aa ) . In addition to the residues constituting the catalytic machinery , we have also extensively analyzed the dynamic behaviors of other residues in the MD simulations . The majority of them have similar values as well as dynamic behaviors in WT , R298A and N214A . For example , even for Tyr126 directly involved in interacting with Phe140 , it has very similar conformations and dynamic behaviors for its backbones and side chains in WT , R298A and N214A ( data not shown ) . Nevertheless , although in the crystal structures , the backbones of the residues Asn214 ( in WT ) and Ala214 ( in N214A ) have very similar conformations , they display distinctive dynamic behaviors in the simulations ( Figure 9 ) . For both WT protomers as well as R298A , the Phi and Psi angles remain dynamically stable , only with slight fluctuations . By a sharp contrast , the backbone conformation of Ala214 in N214A becomes highly dynamic in all three simulations ( Figures 9p–9r and 9y–9aa ) . However , the high dynamics of the Ala214 backbone disappear in the simulations of the artificial N214A monomer ( Figure S8 ) . In the crystal structure of the WT enzyme , Asn214 is located at the end of the first helix of the extra domain which is sitting in between the N-finger and C-tail of the same protomer ( Figure 2b ) . Its side-chain ND atom forms a hydrogen bond with the Gly2 backbone oxygen atom . Although no significant conformational change over these regions was detected in the crystal structure of the N214A mutant , the replacement of Asn214 by Ala did result in the elimination of the hydrogen bond between Asn214 and Gly2 . Furthermore , we have calculated the hydrogen bond occupancy in the simulations and found that indeed in WT , the Asn214 was able to dynamically establish a variety of long-range intra-protomer hydrogen bonds with N-terminal residues Ser1 and Gly2 , as well as with C-terminal residues Cys300 in both protomers , with some having relatively large average occupancies . By contrast , in N214A , only one long-range intra-protomer hydrogen bond could be found between the Gly2 backbone NH and Ala214 backbone oxygen atoms , with only an average occupancy of 2 . 85% . This suggests that in N214A , the packing would be weakened between the N- and C-termini , as well as between the N-finger with the rest part of the protein within the same protomer . On the other hand , previous studies have elegantly revealed the extremely critical involvement of the N-finger residues in both dimerization and catalysis due to their extensive contacts with residues composed of the active-site pocket of the counter protomer , which include Lys137-Phe140 , Glu166 , His172 [4] , [7] , [11] , [12] , [14] , [17] , [24] . Here we further analyzed the occupancy of the inter-protomer hydrogen bonds associated with the N-finger residues . Strikingly , in the simulations , the N-finger residues in N214A form more hydrogen bond contacts with the active-site residues in the counter protomers which also have higher occupancy than those in WT ( Table S2 ) . Also it appears that the backbone conformations of the N-finger residues in N214A are slightly more dynamic than in WT ( Figure S9 ) . These observations together may rationalize why the Asn214Ala mutation only slightly weakens the dimerization but drastically inactivates the catalysis . It might be likely that upon replacing Asn214 with Ala , the hydrogen bonds of Asn214 with the N- and C-termini in the same protomer are mostly eliminated . As a result , the N-finger residues would be liberated to some degree from interacting with the residues in the same protomer and as such have more capacity to contact the active-site residues in the counter protomer , as exemplified by the increased formation of hydrogen bonds between them . This enhanced inter-protomer interaction between the N-finger and active-site residues might cause some dynamical rearrangements of the active-site residues which may trigger the dynamical instability of the catalytic machinery , as captured by the above analysis . On the other hand , the N214A mutation may also have some globular effect as to slightly weaken the dimerization and to provoke the dynamics of the residues Ala46-Asn51 . These effects may also play additional roles in destabilizing the catalytic machinery . Our present results nicely agree with the previous observation that despite being highly conserved among different coronaviruses , the IBV 3C-like protease has Ser instead of Asn in the corresponding position of Asn214 but it still has a dimeric structure [44] . In the future , it would be interesting to investigate whether this substitution in the context of the IBV protease will weaken the intra-protomer interactions associated with the N-finger residues , and if yes , how the enzyme evolves the mechanism in IBV to prevent the active-site pocket being significantly destabilized .
By acquiring additional non-catalytic domains during evolution , enzymes have been shown to gain altered catalytic mechanisms or/and be connected to cellular signaling networks [45] , [46] . Indeed , upon having the C-terminal extra domain , the SARS-CoV 3CLpro suddenly requests the dimerization to activate its catalysis . By contrast , the monomeric form is enzymatically inactive because its catalytic machinery is collapsed into the highly-conserved inactivated state in all monomeric structures [21]–[23] . Previously , our determination of the monomeric R298A structure reveals how the extra domain controls the dimerization which is ultimately coupled to the catalysis [21] . Recently , the isolated C-terminal domain has been found to form a domain-swapped dimer which may underlie the formation of the intermediate dimer structurally differential from the classic one [19] , [47] . Notably , this non-classic dimer was recently proposed to be capable of performing N-terminal autocleavage which might mimic the initial autocleavage of the proprotein in vivo [48] . On the other hand , previously we have also identified N214A , another mutant which had significantly abolished activity but appeared to remain highly dimeric by our NMR characterization . In the present study , we examined the new version of the N214A mutant without the two extra residues leftover from the GST-fusion protein . Similarly , the new N214A protease again owns severely abolished activity but its dimer-monomer dissociation constant only slightly increased , from 21 . 4 to 47 . 4 µM . As such , it appears that in addition to modulating the dimerization , the extra domain may be able to regulate the catalysis by other unknown mechanisms . In an attempt to understand its structural basis , we determined the crystal structure of the N214A mutant and unexpectedly it still adopts a dimeric structure highly similar to that of the wild-type enzyme . In particular , N214A has the catalytic machinery almost identical to that of the wild-type enzyme . This result thus raises an intriguing question as how the N214A mutation on the extra domain is able to dramatically inactivate the catalytic machinery without significantly affecting its three dimensional structure . The dynamics of the enzyme molecules have been extensively revealed to modulate the catalysis [49]–[53] . As such , we conducted 30-ns MD simulations for the WT , R298A and N214A enzymes , which , to the best of our knowledge , represent the longest MD simulations for the SARS-CoV 3CL proteases reported so far . Detailed analysis reveals that the three enzymes have very distinctive dynamic behaviors in the simulations . Although in the present study , the simulation time increased up to 30 ns , the WT enzyme not only displays the highest overall dynamic stability , but also has the catalytic machinery highly retained in the activated state , completely consistent with the previous MD results [7] , [22] , [39] , [41]–[43] . On the other hand , despite showing an increased overall conformational flexibility of R298A , largely due to the additional exposure of interfacial residues to bulk solvent upon losing the counter protomer , as evidenced by the increase of the RMSD values in the simulations of the artificial WT and N214A monomers , the catalytic machinery of R298A remains largely trapped in the inactivated state . This result strongly implies that for the catalytic machinery of the SARS-CoV 3CLpro , the activated and inactivated states are not only structurally distinguishable , but also dynamically well separated . As a result , upon losing the dimerization , the catalytic machinery will be collapsed and subsequently permanently frozen in the inactivated state as we previously proposed [21] . Furthermore , our MD simulations also reveal the first dynamic picture of an experimentally-determined monomer of the SARS-CoV 3CLpro , which is slightly different from the previous [41] , [43] as well as our present MD simulation results for two artificial monomers obtained by simply removing the counter protomer from the WT and N214A dimeric structures . In those simulations , the artificial WT and N214A monomers showed more dynamical instability than R298A for some key components of the catalytic machinery . The discrepancy may be mainly due to the possibility that in a 10-ns period , the artificial monomers with an initial WT structure could not even reach the real monomeric structure , which has marked changes in C- , N-termini and catalytic machinery , as well as the orientation between the extra domain and the chymotrypsin fold [21]–[23] . Most surprisingly , although the N214A mutant has the three-dimensional structure highly similar to that of the WT enzyme , in the simulations it has much larger conformational fluctuations than WT and its catalytic machinery is even more unstable than that of the R298A mutant and its own artificial monomer . In particular , during the simulations , the key distance between His41 and Cys145 , which has been characterized to be an indicator of the competency of the catalytic dyad [7] , [22] , [39] , [41]–[43] , displays the largest fluctuation in the N214A mutant . This implies that even only based on the dynamic behavior of the catalytic dyad , the N214A catalytic machinery is largely inactivated . Furthermore , many N214A residues made up of the catalytic machinery are dynamically unstable and some even jump to sample the conformations resembling those of R298A . Therefore , even within the 30-ns simulation , the N214A mutant already displays dynamically unstable behaviors which may at least partly rationalize the observed abolishment of the catalytic activity . Furthermore , it is possible that more dynamical changes would be disclosed by MD simulations with longer simulation times such as over µs to ms . Previously , it has been documented that mutations far away from the active site were able to affect the catalysis by triggering long-range dynamical changes but it remains extremely challenging to define the exact pathway of the transmission of dynamics [9] , [50] , [52] , [53] . In the case of N214A , we show that the mutation of Asn214 to Ala will lead to eliminating most of the hydrogen bonds between the Asn214 and N-/C-terminal residues within the same protomer , as observed in the crystal structures as well as captured by the MD simulations . As a consequence , the N-finger residues would have more capacity to contact the key residues constituting the catalytic machinery of the counter protomer , such as Lys137 , Gly138 , Ser139 , Phe140 , Glu166 and His 172 . It seems that the enhanced interaction in N214A may act as a perturbation which significantly triggers the dynamical instability of the catalytic machinery , which ultimately leads to the severe abolishment of the catalytic activity . In this regard , the N-finger appears to play dual roles in regulating the catalytic machinery . Namely , the maintenance of the competent catalytic machinery absolutely needs the supportive interactions from the N-finger residues of another protomer as previously well demonstrated [4] , [7] , [11] , [12] , [14] , [17] , [21]–[24] . Nevertheless , if these interactions are overwhelming , the catalytic machinery would also be dynamically destabilized . It is possible that the slight weakening of the dimerization in N214A may contribute to the observed instability of its catalytic machinery to some degree . However , this contribution may not be significant because within the present 30-ns simulations , no obvious dissociation of two protomers occurs , as judged from the comparison of the RMSD values for the WT and N214A dimers , as well as the fact that even more inter-protomer hydrogen bonds with high occupancy are found over the key dimerization interface , namely between the N-finger and active-site residues of N214A . In particular , the artificial N214A monomer with the dimerization interface fully exposed to solvent has much less fluctuations than the N214A dimer for the trajectories of most key residues constituting the catalytic machinery . In conclusion , our present experimental and MD simulation studies unveil that the activated and inactivated states of the catalytic machinery of the SARS-CoV 3CLpro are not only structurally distinctive , but also dynamically separated . Furthermore , although the N214A mutation only slightly disrupts the dimerization and has no notable alteration on the static three-dimensional structure , it does trigger the dynamic instability of the catalytic machinery , thus rationalizing the severe abolishment of the enzymatic activity . Our current study thus signifies the potential to pinpoint the dynamical consequence of the mutations on the enzymatic catalysis by a combined use of experimental and computational approaches . Furthermore , the present results imply that to modulate the dynamics of the SARS-CoV 3CLpro may represent a promising avenue for design of its inhibitors for the anti-SARS therapeutics . In the future , it would be of fundamental interest to test this possibility by using NMR or/and Mass spectrometry to identify compounds which can significantly enhance the dynamics of enzymes and subsequently to correlate their effect on catalysis to the dynamic alteration . | Severe acute respiratory syndrome ( SARS ) is the first emerging infectious disease of the 21st century which has not only caused rapid infection and death , but also triggered a dramatic social crisis . Its 3C-like protease is crucial for reproducing virus and thus represents a top target for drug design . Interestingly , unlike 3C protease such as from picorovirus , the SARS protease evolutionarily acquired a C-terminal extra domain with previously-unknown function . Immediately after SARS outbreak , we revealed that the extra domain was able to regulate the catalysis by controlling the dimerization essential for activity . Here , we studied one mutant with only slightly-weakened dimerization but almost completely abolished activity . We determined its three-dimensional structure but very unexpectedly it is almost identical to that of the wild-type enzyme . Therefore , we initiated 30-ns molecular dynamic simulations for five forms of the enzyme and the results demonstrate that the dynamical changes in this mutant are responsible for its inactivation . Therefore , the extra domain can also control the catalysis by modulating the enzyme dynamics . This is not only of fundamental significance to understanding how enzymes evolve , but also implies a novel avenue for design of anti-SARS molecules . | [
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] | 2011 | Dynamically-Driven Inactivation of the Catalytic Machinery of the SARS 3C-Like Protease by the N214A Mutation on the Extra Domain |
Chlamydiae are obligate intracellular pathogens that must coordinate the acquisition of host cell-derived biosynthetic constituents essential for bacterial survival . Purified chlamydiae contain several lipids that are typically found in eukaryotes , implying the translocation of host cell lipids to the chlamydial vacuole . Acquisition and incorporation of sphingomyelin occurs subsequent to transport from Golgi-derived exocytic vesicles , with possible intermediate transport through endosomal multivesicular bodies . Eukaryotic host cell-derived sphingomyelin is essential for intracellular growth of Chlamydia trachomatis , but the precise role of this lipid in development has not been delineated . The present study identifies specific phenotypic effects on inclusion membrane biogenesis and stability consequent to conditions of sphingomyelin deficiency . Culturing infected cells in the presence of inhibitors of serine palmitoyltransferase , the first enzyme in the biosynthetic pathway of host cell sphingomyelin , resulted in loss of inclusion membrane integrity with subsequent disruption in normal chlamydial inclusion development . Surprisingly , this was accompanied by premature redifferentiation to and release of infectious elementary bodies . Homotypic fusion of inclusions was also disrupted under conditions of sphingolipid deficiency . In addition , host cell sphingomyelin synthesis was essential for inclusion membrane stability and expansion that is vital to reactivation of persistent chlamydial infection . The present study implicates both the Golgi apparatus and multivesicular bodies as key sources of host-derived lipids , with multivesicular bodies being essential for normal inclusion development and reactivation of persistent C . trachomatis infection .
The genus Chlamydia is composed of obligate intracellular prokaryotic pathogens that cause a range of clinical sequelae in humans encompassing ocular , genital , and respiratory tract infections . Consequences of subsequent chronic disease include blindness , infertility , arthritis , and possible coronary heart disease [1] , [2] . Despite their notoriety clinically , the molecular interactions between Chlamydia and its host cell that allow for propagation , persistence , and subsequent pathology , remain elusive . The defining biological characteristic of these successful pathogens is a unique process of intracellular development , with an infectious elementary body ( EB ) initiating uptake into a target host cell . The chlamydial EB subsequently differentiates to the noninfectious , metabolically active reticulate body ( RB ) within the confines of a membrane-bound vacuole termed an inclusion . Successive growth and replication , giving rise to a large inclusion body containing a multitude of infectious EBs , is contingent upon the acquisition of biosynthetic constituents from the nutrient-rich host cell cytosol . In response to nutrient or immunological stress [3] , Chlamydiae can also enter into a persistent phase of development characterized by morphologically altered RBs that can be maintained intracellularly for extended periods of time . Alternating infectious and persistent phases of chlamydial growth correlate with acute and chronic infections in vivo [4] . The cellular biosynthetic constituents that sustain persistent chlamydiae , and allow for emergence from a persistent state , are poorly understood . The intricacies of this host-pathogen interaction , which allow for acquisition of biosynthetic precursors from the host cell , remain largely undefined . Vacuole-bound chlamydiae attain nucleotides , amino acids , and lipids from the host cell [5] . Eukaryotic-derived phospholipids , sphingomyelin , and cholesterol are found within purified chlamydiae , suggesting that these host-derived constituents traverse the inclusion membrane with subsequent incorporation into the bacterium [6] , [7] . Translocation of lipid droplets to the chlamydial inclusion lumen represents one potential source of neutral lipids [8] , [9] . Host cell sphingolipids are required for the intracellular growth of C . trachomatis [10] , with sphingomyelin attained via the intersection of the chlamydial inclusion with Golgi-derived exocytic vesicles destined for the plasma membrane [11]–[13] . Multivesicular bodies ( MVBs ) , late endocytic organelles abundant in sphingolipids and central to intracellular lipid segregation , also serve as a source for host-derived lipids and a potential intermediate in Golgi to inclusion transport [14] , [15] . To further delineate lipid acquisition pathways pirated by the chlamydial inclusion , specific inhibitors of host cell lipid biosynthesis and/or trafficking were evaluated for their effects on chlamydial growth and inclusion development . The present study focuses on sphingomyelin biosynthesis , a host cell pathway validated as essential for growth and replication of chlamydiae by Engel and colleagues [10] . Our studies indicate that sphingomyelin biosynthesis is requisite to inclusion membrane biogenesis and stability , and demonstrate that MVBs are a major source for this essential lipid .
Specific inhibitors of sphingomyelin biosynthesis and trafficking were evaluated for effects on chlamydial growth and inclusion development . Treatment of infected cells with 25 µM myriocin , a potent inhibitor of serine palmitoyltransferase ( SPT ) , the initial enzyme in the biosynthesis of sphingomyelin ( Figure 1 ) [16] , revealed striking morphological alterations in inclusion maturation . Confocal analysis of untreated Chlamydia-infected cells revealed normal inclusion development with the vacuole expanding in size from 24 to 36 hr postinfection ( pi ) ( Figure 2 ) . Infected cells cultured in the presence of myriocin , revealed a marked loss of inclusion membrane integrity with disruption of the inclusion and release of intracellular bacteria , initially evident at 24 hr pi ( 22% of infected cells with disrupted inclusions ) and most notable at 30 hr pi ( 61% ) ( Figure 2 ) . At 36 hr pi , myriocin-treated cells contained small multiple inclusions of heterogeneous size , rather than the large single inclusion typical of untreated cells ( Figure 2 ) . The concentration of myriocin used in these studies had no effect on host cell viability . The CHO-K1 mutant cell line , LY-B [17] , which contains a mutation in the LCB1 gene and therefore does not express SPT , was used to independently test the role of sphingomyelin . C . trachomatis inclusions in LY-B cells showed a collapse of membrane integrity , similar to myriocin treatment ( Figure 2 ) . In addition , at 36 hr pi , LY-B-infected cells contained small multiple inclusions comparable to those observed in myriocin-treated HEp-2 cells . The complemented cell line , LY-B/LCB1 , supported normal inclusion development comparable to that observed in both CHO-K1 and HEp-2 cells ( data not shown ) , confirming that maintenance of inclusion membrane integrity was dependent on host cell SPT activity . To confirm that the loss of inclusion membrane integrity was a consequence of a deficiency in host cell sphingomyelin rather than an indirect effect of depleted SPT activity , cells were cultured in the presence of 5 µM dihydroceramide or 5 µM sphingosine prior to infection . Dihydroceramide and sphingosine are precursors of sphingomyelin , positioned downstream of SPT , allowing for the restoration of sphingomyelin synthesis under conditions of SPT inactivity ( Figure 1 ) [10] , [18] . These sphingomyelin precursors reversed the detrimental effects of SPT-deficiency in LY-B cells or myriocin-treated HEp-2 cells , with growth and expansion of intact inclusions morphologically comparable to those present in untreated control cells at 24 to 36 hr pi ( Figure 2 ) ( data for sphingosine not shown ) . The intracellular developmental cycle of C . trachomatis E requires approximately 72 hr to complete , with redifferentiation of RBs to infectious EBs occurring prior to release of infectious progeny . At 24 to 36 hr pi , the expanding inclusion contains predominantly noninfectious RBs that , if released indiscriminately from the infected cell , are incapable of initiating an infectious cycle . The presence of multiple small inclusions at 36 hr pi , under conditions of disrupted host cell sphingomyelin biosynthesis , suggested premature release of infectious progeny and subsequent reinfection . To analyze possible early emergence of infectious EBs , the expression of OmcB , an EB-specific protein detectable late in the developmental cycle , was analyzed . In untreated cells , low levels of OmcB were evident at 30 to 36 hr pi ( Figure 3 ) , with peak levels emerging at 48 to 72 hr as inclusions reached maximal size and approached lysis ( not shown ) . Myriocin treatment resulted in expression of OmcB as early as 24 hr pi with EBs being dispersed upon premature loss of both inclusion and host cell membrane integrity ( Figure 3 ) . Infected SPT-deficient LY-B cells also displayed early emergence of OmcB-positive EBs , temporally similar to those observed under conditions of myriocin treatment ( not shown ) . Western blot analysis confirmed the higher levels of OmcB at 27–36 hr pi in infected cells treated with myriocin as compared to control cells ( Figure 3 ) . In addition , higher levels of infectious progeny were released from myriocin-treated cells versus control cells at early times post infection ( Figure 3 ) . Collectively , these results indicate that the absence of sphingomyelin results in loss of inclusion membrane integrity , early redifferentiation , and premature release of infectious chlamydiae . A distinguishing trait of prototypic C . trachomatis strains is homotypic fusion of inclusions [19] . Infecting a single cell with multiple EBs of a defined serovar , results in multiple bacterial-containing vacuoles that fuse early in the developmental cycle to form a single inclusion . The presence of multiple inclusions at 36 hr pi in sphingomyelin-depleted cells , suggests reinfection with subsequent disruption of homotypic fusion . To analyze the effect of sphingomyelin deficiency on homotypic fusion , cells were infected with a high MOI of five bacteria per cell and inclusion numbers were determined at 16 hr pi ( Figure 4 ) . HEp-2 and CHO-K1 cells generally contained a single inclusion per infected cell as shown in the histogram inserts . HEp-2 cells cultured in the presence of 25 µM myriocin or 5 µg/ml fumonisin B1 ( a potent inhibitor of sphingonine and sphinosine N-acetyltransferase , Figure 1 ) , or the SPT-deficient LY-B cells , revealed multiple inclusions per cell . Complementation of the LY-B cells with the LCB1 gene , resulted in the restoration of host cell sphingomyelin biosynthesis , and the recovery of the inclusion fusion phenotype as shown by a single inclusion per infected cell ( Figure 4 ) . To confirm that lack of inclusion fusion was a consequence of a deficiency in host cell sphingomyelin rather than an indirect effect of depleted SPT activity , cells were cultured in the presence of dihydroceramide and sphingosine prior to infection . These sphingomyelin precursors restored the fusion capability to infected cells cultured under conditions of SPT-deficiency with a majority of cells containing a single inclusion ( Figure 4 ) . Collectively , these findings indicate that host cell sphingomyelin biosynthesis is required for homotypic fusion of chlamydia inclusions within a single infected cell . Persistence is a hallmark of natural chlamydial diseases , and is characterized by the retention of nonreplicating , aberrant reticulate bodies within the host cell for extended periods of time [3] . Host cell sphingomyelin biosynthesis is essential for maintenance of inclusion integrity during normal chlamydial development , and is likely essential during reactivation of persistent infection , a process concurrent with inclusion membrane expansion . The role of host cell sphingomyelin was tested in a model system of IFN-γ-induced persistence [20] . HEp-2 cells were infected with C . trachomatis B , a strain sensitive to IFN-γ-mediated alterations in intracellular growth [21] . Untreated Chlamydia-infected cells revealed normal inclusion development with large inclusions at 48 hr pi , while IFN-γ-treated cells harbored smaller inclusions containing enlarged RBs as confirmed by fluorescence and electron microscopy ( Figure 5 ) . The persistent state was reversible as shown by the expansion of the inclusion and reactivation of infectious EBs following removal of IFN at 48 hr pi and culturing in fresh medium for an additional 48 hr ( Figure 5 ) . In contrast , culturing in the presence of myriocin during the recovery phase resulted in disruption in inclusion membrane integrity and failure of persistent forms to completely reactivate to infectious EBs ( Figure 5 ) . These results were confirmed in an alternate in vitro model system of penicillin-induced persistence [22] , [23] . In C . trachomatis serovar B- and servovar E-infected cells treated with penicillin to induce aberrant , persistent chlamydial development , the presence of myriocin during the recovery phase prevented the recovery of infectious EBs ( not shown ) . These studies implicate host cell-derived sphingomyelin as an essential component for maintenance of inclusion membrane integrity during reactivation of persistent chlamydial infection . The precursors of sphingomyelin are synthesized in the endoplasmic reticulum with subsequent transfer of ceramide to the Golgi apparatus , the site of the final step in sphingomyelin biosynthesis ( Figure 1 ) . Hackstadt and colleagues have demonstrated the transport of sphingomyelin from the Golgi to the chlamydial inclusion , with incorporation of the sphingolipid into the inclusion membrane and the cell wall of chlamydiae [12] , [13] . MVBs , late endocytic organelles abundant in sphingomyelin , have been proposed to provide essential lipids to the chlamydial inclusion and may be an intermediate in Golgi to inclusion transport [14] , [15] . To decipher the source of Chlamydia-acquired sphingomyelin , the phenotypic effects of inhibitors of Golgi and MVB transport on inclusion maturation were compared to inclusion development under conditions of sphingomyelin deficiency . The inhibitors were used at concentrations that disrupt transport of ceramide-derived sphingomyelin from the Golgi apparatus to the chlamydial inclusion , but have no effect on host cell viability [13] , [14] . HEp-2 cells were infected with a high MOI of five bacteria per cell and treated with the indicated inhibitors at 1 hr pi , then analyzed for homotypic fusion at 16 hr pi ( Figure 6 ) . Control cells generally contained a single inclusion per infected cell as shown in the histogram inserts . HEp-2 cells were cultured in the presence of golgicide A ( GCA ) , a potent , highly specific inhibitor of GBR1 ( Golgi BFA resistence factor 1 ) that disrupts both anterograde and retrograde transport through the Golgi [24] . GCA-treatment revealed a slight disruption in vacuole fusion with a mean of 2 . 6 inclusions per infected cell ( Figure 6 ) , with a similar result observed upon treatment with 1 µg/ml brefeldin A ( BFA ) another inhibitor of Golgi function [25] ( not shown ) . HEp-2 cells cultured in the presence of 10 µM U18666A , a pharmacological agent that disrupts trafficking from MVBs [26]–[28] , revealed multiple inclusions per infected cell ( Figure 6 ) , similar to the conditions of sphingomyelin deficiency ( Figure 4 ) . Therefore , interruption of sphingomyelin trafficking from the Golgi delayed inclusion fusion , while a block in MVB trafficking completely impeded fusion , implicating MVBs , an organelle abundant in sphingolipids , as a principle source of chlamydiae-acquired sphingomyelin . To analyze the effect of inhibitors on inclusion maturation , HEp-2 cells were infected with a low MOI of C . trachomatis E , treated with the indicated inhibitors at 1 hr pi and analyzed at 36 hr pi . Confocal analysis of GCA-treated Chlamydia-infected cells revealed a slight delay in inclusion maturation with smaller inclusions compared to those in untreated control cells ( Figure 6 ) . There was no evidence of inclusion membrane instability as observed under conditions of sphingomyelin deficiency ( Figure 2 ) , indicating that sphingolipids may be acquired from an alternate source such as MVBs . Infected cells cultured in the presence of the MVB inhibitor U18666A , revealed a dramatic interruption in inclusion development with significantly smaller inclusions ( Figure 6 ) . There was no evidence of inclusion membrane instability as observed under conditions of sphingomyelin deficiency ( Figure 2 ) . However , the complete interruption in RB division and subsequent inclusion expansion , implicates additional MVB-derived constituents necessary for normal chlamydial inclusion expansion and development . Host cell sphingomyelin biosynthesis is essential for maintenance of membrane integrity during expansion of the inclusion following reactivation of persistent infection ( Figure 5 ) . Because trafficking from MVBs was essential to sphingomyelin-dependent inclusion expansion , the potential significance of these sphingolipid-rich organelles in reactivation of persistent infection was analyzed . Following induction of the persistent state by IFN-γ treatment for 48 hr , removal of IFN and subsequent culturing in the presence of the MVB inhibitor U18666A for an additional 48 hr , resulted in a lack of inclusion expansion , disruption in inclusion membrane integrity , and complete failure of aberrant persistent forms to reactivate to infectious EBs ( Figure 5 ) . These studies implicate MVB-derived sphingomyelin , and potentially other MVB constituents , requisite to inclusion membrane integrity during reactivation of persistent chlamydial infection .
The present studies were initiated to identify lipid biosynthetic and transport pathways essential to the intracellular propagation of chlamydiae . These studies revealed novel effects on the intracellular development of chlamydiae under conditions that inhibit sphingomyelin biosynthesis . As demonstrated in classic studies by Hackstadt and colleagues , sphingomyelin synthesized in the Golgi apparatus is transported from the trans-Golgi to the chlamydial inclusion with successive incorporation into the bacterial cell wall [12] , [13] . In subsequent studies by Engel and colleagues , host cell-derived sphingomyelin was shown to be essential for intracellular development of C . trachomatis and optimal production of infectious progeny [10] . In the present study , we further explore this requirement and demonstrate that sphingomyelin biosynthesis is necessary for stability and expansion of the inclusion membrane during both normal intracellular development and reactivation of persistent infection . Blockage of this pathway results in premature egress , reduced bacterial output , and failure to emerge from a persistent state . Hence , disruption of lipid trafficking may provide a novel means to thwart intracellular pathogens . Chlamydiae undergo their entire intracellular developmental cycle within an inclusion that is bound by a membrane , providing a protected intracellular environment for bacterial replication . Treatment of infected cells with myriocin interrupted inclusion membrane functionality , with complete disruption of membrane integrity resulting in premature dispersal of intracellular bacteria from their protected niche ( Figure 2 ) . Myriocin is a potent inhibitor of SPT , the initial enzyme in sphingomyelin biosynthesis ( Figure 1 ) [16] . Analysis of inclusion development in SPT-deficient LY-B cells , and under conditions of concurrent pretreatment with precursors of sphingomyelin , revealed that the compromise in inclusion membrane integrity was a direct result of host cell sphingomyelin deficiency ( Figure 2 ) . Actin and intermediate filaments have been shown to stabilize the chlamydial inclusion , with disruption of these host cytoskeletal structures resulting in loss of inclusion membrane integrity and release of bacteria into the host cell cytosol [29] . In the present studies , immunofluorescence analyses of actin and intermediate filaments of both uninfected and chlamydiae-infected cells revealed no obvious morphological alterations in the cytoskeletal structure upon inhibition of sphingomyelin biosynthesis ( data not shown ) . The disruption of inclusion membrane integrity under conditions of sphingomyelin deficiency occurred concomitantly with the early redifferentiation of noninfectious RBs to infectious EBs ( Figure 3 ) . This implies that the procurement of host cell sphingomyelin may be required for inclusion membrane expansion and stability , and programmed conversion to infectious forms . The signals that trigger the replicative RBs to convert to infectious EBs remain elusive . However , it is clear that this developmental transformation coincides with a contact-dependent interaction of the type III secretion ( TTS ) system with the inclusion membrane . RBs amass at the periphery of the inclusion , with projections of the TTS system mediating intimate contact between the bacteria and the inner face of the inclusion membrane [30] , [31] . The proposed chlamydial injectisome acts as a molecular syringe , translocating effector proteins directly from the intrainclusion chlamydiae to the host cell cytosol [32] . This association may be requisite to RB replication and potentially inclusion expansion allowing for nutrient acquisition from the host cell cytosol [33] . The physical detachment of RBs from the inclusion membrane , coupled to inactivation of TTS , signals the initation of late redifferentiation [32] . In the present studies , lipid deprivation may signal the loss of TTS intimate contact and RB detachment leading to premature conversion of RBs to infectious EBs . Host cell-derived sphingomyelin associates transiently with the chlamydial inclusion membrane and incorporates into the bacterial cell wall [12] . Failure of this sphingolipid to incorporate into the inclusion membrane may cause the normally contiguously intact membrane to become indiscriminately permeable to environmental changes that potentially signal RB to EB conversion . Alternately , incorporation of sphingomyelin into the chlamydial cell wall may be essential to RB division and proliferation , with lack of available sphingomyelin being a potential cue for premature redifferentiation . A secondary function of the inclusion membrane of C . trachomatis , distinct from inclusion membrane integrity , is homotypic fusion of multiple inclusions to a single vacuole in multiply-infected cells . The resulting multiple inclusions with greater surface area would require more lipid incorporation into the chlamydial inclusion membrane , indicating that early in infection other host cell lipids are available for incorporation into the expanding inclusion under conditions of sphingomyelin deficiency . Fusion of inclusions is a temperature-dependent process that requires export of the chlamydial incA protein to the inclusion membrane [34] , [35] . Characteristic homotypic fusion of inclusions was interrupted when multiply-infected cells were cultured in the presence of myriocin ( Figure 4 ) . Analysis of the fusion of multiple inclusions in SPT-deficient LY-B cells , and under conditions of concurrent pretreatment with precursors of sphingomyelin , revealed that the disruption in homotypic fusion was a direct result of host cell sphingomyelin deficiency ( Figure 4 ) . These studies did not reveal an alteration in IncA incorporation into the inclusion membrane under conditions of sphingomyelin deficiency , implicating a role for host cell sphingolipids in homotypic fusion independent of incA . Culturing C . trachomatis-infected cells under conditions of sphingomyelin deficiency has two distinct phenotypic effects on chlamydial inclusion biogenesis . Interruption in homotypic fusion is observed early in chlamydial inclusion development , while a compromise in inclusion membrane integrity occurs later . These distinct anomalies may result from the failure of sphingomyelin incorporation into the inclusion membrane , implicating a direct role for host cell lipid in maintaining normal inclusion functionality . However , the effect of sphingomyelin deficiency on other lipid biosynthetic or signaling pathways that indirectly alter inclusion biogenesis cannot be disregarded . Further studies determined the source of sphingomyelin essential to inclusion biogenesis , which includes membrane stability and the capacity for homotypic fusion . As described previously , inhibition of sphingomyelin transport from the Golgi apparatus using the inhibitor BFA , results in smaller , compact inclusions that retain a burst size comparable to untreated controls [12] . In the present studies , this observation was reproduced using both BFA and GCA . In addition , treatment of infected cells with concentrations of BFA or GCA that prevent the incorporation of newly synthesized Golgi-derived sphingomyelin into the chlamydial inclusion , failed to completely disrupt inclusion fusion or inclusion membrane integrity ( Figure 6 ) . This implicates another source of sphingomyelin available to the chlamydial inclusion under conditions of disrupted Golgi transport . These studies identify MVBs , late endocytic organelles abundant in sphingolipids and pivotal for intracellular distribution , as a potential source of sphingomyelin essential to homotypic fusion and maintenance of inclusion membrane integrity . U18666A treatment of infected cells , utilizing concentrations that block MVB transport and prevent the incorporation of newly synthesized Golgi-derived sphingomyelin into the chlamydial inclusion [14] , [15] , revealed complete inhibition of homotypic fusion of inclusions ( Figure 6 ) . These findings were identical to the disruption of inclusion fusion observed under conditions of sphingomyelin deficiency ( Figure 4 ) . However , inhibition of MVB transport had much more profound effects on RB division and normal inclusion development than what was observed under conditions of sphingomyelin deficiency . A deficit in host cell sphingomyelin resulted in RB division and the expansion of the chlamydial inclusion to a moderate size with subsequent loss of inclusion membrane integrity at 24 to 36 hr pi ( Figure 2 ) . In contrast , interruption in MVB transport impeded early RB division and inclusion membrane expansion at a stage in development prior to imposing stress on inclusion membrane integrity . Collectively these studies implicate sphingomyelin , and potentially additional constituents derived from MVBs , essential for inclusion expansion during normal development and the reactivation of persistent C . trachomatis infection . However , a pleiotropic effect of inhibitors of MVB transport , on cellular function or potential acquisition of sphingomyelin from alternate sources , cannot be disregarded . Within the confines of a protected intracellular environment , chlamydiae coordinate the expansion of the inclusion and acquisition of biosynthetic constituents from the host cell cytosol . In the presence of eukaryotic protein synthesis inhibitors , intracellular development proceeds normally , indicating that inclusion expansion may be linked to host cell lipid biosynthesis . These studies identify host cell sphingomyelin biosynthesis as a requisite to C . trachomatis inclusion membrane biogenesis and functionality . This encompasses inclusion membrane expansion , homotypic fusion , and stability during normal inclusion development and reactivation of a persistent chlamydial infection . In addition , identification of potential sphingomyelin transport pathways may have important implications when deciphering this unique host-pathogen interaction .
Rabbit anti-incG was kindly provided by Dr . Ted Hackstadt ( Rocky Mountain Laboratories , NIH , NIAID , Hamilton , MT ) . Rabbit anti-outer membrane complex protein B ( OmcB ) was generously provided by Dr . Thomas Hatch ( University of Tennessee Health Science Center , Memphis , TN ) . Monoclonal antibody ( mAb ) L2I-10 to the major outer membrane protein ( MOMP ) of C . trachomatis , was kindly provided by Dr . Harlan Caldwell ( Rocky Mountain Laboratories , NIH , NIAID , Hamilton , MT ) . MAb A57B9 against the chlamydial heat shock protein-60 ( hsp60 ) , was generously provided by Dr . Richard Morrison ( University of Arkansas for Medical Sciences , Little Rock , AK ) . Antibodies to chlamydial LPS and eukaryotic actin ( clone C4 ) were obtained from Chemicon International ( Billerica , MA ) . TOPRO-3 ( monomeric cyanine nucleic acid stain ) , and secondary antibodies conjugated to Alexa Fluor 488 and Alexa Fluor 568 were obtained from Invitrogen ( Eugene , OR ) . Myriocin , fumonisin B1 , dihydroceramide , sphingosine , 3-β- ( 2-diethylaminoethoxy ) -androstenone HCl ( U18666A ) , and brefeldin A were obtained from BioMol International ( Plymouth Meeting , PA ) . Recombinant human IFN-γ was purchased from BD Biosciences ( San Jose , CA ) . Golgicide A was kindly provided by Dr . David Haslam ( Washington University School of Medicine , St . Louis , MO ) . C . trachomatis serovar E ( provided by Dr . Harlan Caldwell ) and C . trachomatis serovar B ( provided by Dr . Ted Hackstadt ) were propagated in HEp-2 cells ( ATCC , Manassas , VA ) and elementary bodies ( EBs ) were purified by Renografin gradient centrifugation as previously described [36] . HEp-2 cells were maintained in Iscove's DME medium supplemented with 12 . 5 mM HEPES , 10% ( vol/vol ) FBS , and 10 µg/ml gentamicin , and grown at 37°C with 5 . 5% CO2 . CHO-K1 , LY-B , and LY-B/LCB1 cells , obtained from Dr . Kentaro Hanada ( National Institute of Infectious Disease , Tokyo , Japan ) , were maintained in Ham's F12 medium supplemented with 10% ( vol/vol ) FBS , and 10 µg/ml gentamicin at 37°C with 5 . 5% CO2 . Cells were infected by incubating monolayers with Chlamydia EBs at a multiplicity of infection ( MOI ) of 0 . 2 or 5 for 1 hr at 37°C , washed and incubated in fresh culture medium for the times indicated . For immunofluorescence analyses , infected cells were fixed and permeabilized for 1 min with cold methanol . Cells were then incubated with the indicated primary and fluorophore-conjugated secondary antibodies , labeled with the nucleic acid stain TOPRO-3 , and mounted in ProLong Anti-Fade ( Invitrogen ) , as previously described [14] . Images were acquired using a Zeiss LSM510 Meta laser scanning confocal microscope ( Carl Zeiss Inc . , Thornwood , NY ) equipped with a 63X , 1 . 4 numerical aperature Zeiss Plan Apochromat oil objective . Confocal Z slices of 0 . 5 µm were obtained using the Zeiss LSM510 software . One hour post infection ( pi ) , infected HEp-2 cells were incubated with medium containing inhibitors and the effects on inclusion development were determined by immunofluorescence , Western blot analysis , and infectivity assays , when indicated . To quantify the disruption of inclusions , one hundred infected cells were scored by fluorescence microscopy as indicated . Data are presented as the mean percent of disrupted inclusions . To quantify the number of inclusions per cell , one hundred infected cells were scored by fluorescence microscopy at 16 hr pi and presented as the mean number of inclusions per infected cell . Infected monolayers cultured in the presence of myriocin or IFN-γ were scraped from culture dishes , and supernatant and cells were analyzed to determine the number of infectious forming units ( IFU ) per ml ( per 7 . 5×105 infected cells ) . Data are presented as the mean+/−standard error of mean ( s . e . m . ) from one of three representative experiments . At the times indicated , infected monolayers were dissolved in Laemmli buffer and equivalent protein concentrations were analyzed by 10% SDS-PAGE . Western blots were probed with antibody to chlamydial OmcB , and antibody to host cell actin , which served as a loading control . HEp-2 cells were pretreated with 1 ng/ml IFN-γ for 48 hr prior to infecting with C . trachomatis B . Infected cells were then cultured in the presence of 1 ng/ml IFN for 48 hr , IFN was subsequently removed , and cells were incubated for an additional 48 hr with fresh culture medium with or without 25 µg/ml myriocin or 10 µM U18666A . At the indicated time points , inclusion development and infectivity were analyzed by immunofluorescence analysis and infectivity assays , respectively . For ultrastructural analysis , infected HEp-2 cells were fixed in 2% paraformaldehyde/2 . 5% glutaraldehyde ( Polysciences Inc . , Warrington , PA ) in 100 mM phosphate buffer , and processed as described previously [14] . | The genus Chlamydia is composed of a group of obligate intracellular bacterial pathogens that cause several human diseases of medical significance . C . trachomatis is the most commonly encountered sexually transmitted pathogen , as well as the leading cause of preventable blindness worldwide . The prevalence of chlamydial infections , and the extraordinary morbidity and health care costs associated with chronic persisting disease , justifies the research efforts in this area of microbial pathogenesis . Despite their clinical importance , the mechanisms by which these intracellular bacteria obtain nutrients essential to their growth remain enigmatic . Acquisition of sphingolipids , from the cells that chlamydiae infect , is essential for bacterial propagation . This study identifies a requirement for the lipid sphingomyelin from the infected host cell for bacterial replication during infection , and for long-term subsistence in persistent chlamydial infection . Blockage of sphingomyelin acquisition results in premature release of bacteria , a reduced bacterial number , and failure of the bacteria to cause a persisting infection . In this study , we have identified and subsequently disrupted specific sphingomyelin transport pathways , providing important implications on therapeutic intervention targeting this successful microbial pathogen . | [
"Abstract",
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] | 2009 | Inclusion Biogenesis and Reactivation of Persistent Chlamydia trachomatis Requires Host Cell Sphingolipid Biosynthesis |
Speciation events often occur in rapid bursts of diversification , but the ecological and genetic factors that promote these radiations are still much debated . Using whole transcriptomes from all 13 species in the ecologically and reproductively diverse wild tomato clade ( Solanum sect . Lycopersicon ) , we infer the species phylogeny and patterns of genetic diversity in this group . Despite widespread phylogenetic discordance due to the sorting of ancestral variation , we date the origin of this radiation to approximately 2 . 5 million years ago and find evidence for at least three sources of adaptive genetic variation that fuel diversification . First , we detect introgression both historically between early-branching lineages and recently between individual populations , at specific loci whose functions indicate likely adaptive benefits . Second , we find evidence of lineage-specific de novo evolution for many genes , including loci involved in the production of red fruit color . Finally , using a “PhyloGWAS” approach , we detect environment-specific sorting of ancestral variation among populations that come from different species but share common environmental conditions . Estimated across the whole clade , small but substantial and approximately equal fractions of the euchromatic portion of the genome are inferred to contribute to each of these three sources of adaptive genetic variation . These results indicate that multiple genetic sources can promote rapid diversification and speciation in response to new ecological opportunity , in agreement with our emerging phylogenomic understanding of the complexity of both ancient and recent species radiations .
Speciation—the origin of new species—occurs when diverging lineages accumulate ecological , functional , and/or reproductive differences that result in their evolutionary independence from close relatives . Rates of speciation vary widely among groups , but the underlying causes of this rate variation , especially the conditions that promote bursts of adaptive divergence across short timescales ( “adaptive radiations” ) , are still under debate [1–6] . New ecological opportunity , although likely essential , appears to be insufficient as the sole explanation for many contemporary cases of species radiation [7 , 8] . Instead , intrinsic factors might be more critical , including the availability of sufficient genetic variation to respond to ecological conditions or of novel traits that accelerate rates of diversification [2 , 9] . To understand these factors requires a detailed understanding of both the ecological transitions and the underlying molecular genetic changes that accompany , and potentially facilitate , speciation . Whereas classical genetic studies of speciation were often limited to relatively few loci or genomic regions , modern sequencing can interrogate genome-wide patterns of molecular differentiation during speciation and can potentially reveal the genetic substrate of associated trait changes . Recent studies have begun to uncover several intriguing patterns of phylogenomic divergence , especially in rapidly radiating groups . One such pattern is a persistent discordance among genes for particular phylogenetic relationships , regardless of the quantity or quality of molecular data sampled . This discordance is often caused by incomplete lineage sorting ( ILS ) , in which shared ancestral variation fails to fix between closely timed speciation events [10 , 11] . This sorting of ancestral variation causes conflicting phylogenetic signals , and is especially common in groups radiating both in recent history ( e . g . , African rift cichlids [7] , Drosophila simulans group [12] , platyfish [13] , and horses [14] ) and at deeper timescales ( major land plant families [15] and major bird lineages [16] ) . A second emerging pattern is that postspeciation hybridization ( introgression ) appears to be substantially more commonplace than previously appreciated . A diverse range of animal groups—including butterflies , horses , fish , flies , mosquitoes , and Galápagos finches [8 , 12–14 , 17 , 18]—all show evidence of postspeciation gene flow . The frequency and extent of introgression is remarkable given that introgressive hybridization has played little role in conventional models of animal speciation and diversification [19] . Overall , both ILS and postspeciation introgression contribute to generating more complex evolutionary histories than can be represented by simple bifurcating trees . In response , new approaches are being developed to account for these potential sources of gene tree discordance , including tools that can infer the underlying species tree even when there are high levels of ILS ( e . g . , MP-EST [20] , ASTRAL [21] ) . Accordingly , despite these complexities , several genome-wide studies of diversification have successfully clarified ambiguous species relationships and highlighted loci that might underpin specific functional or ecological changes accompanying rapid phylogenetic transitions . These include loci contributing to ecologically and reproductively significant traits , such as beak size differentiation among Galápagos finches [18] . Here , we examine genome-wide patterns of lineage divergence among all species in the wild tomato clade ( Solanum sect . Lycopersicon ) using whole transcriptome sequencing ( RNA-Seq ) . In addition to domesticated tomato ( S . lycopersicum ) and its conspecific wild relative , the group includes 12 species native to the Galápagos Islands and Andean South America , a biodiversity hotspot ( Fig 1 and S1 Fig ) , and the clade has been estimated to share a common ancestor ~2 million years ago ( Ma ) [22] . All lineages are diploid and chromosomally homosequential , except for a few small rearrangements that distinguish some species [23–25] . Wild tomato species are differentiated for numerous functional , ecological , and reproductive traits , and display different habitat associations at macroecological scales ( Fig 1 ) [26–29] . They also exhibit strong , but often incomplete , reproductive isolating barriers at various pre- and postzygotic stages [30–35] . Nonetheless , efforts to infer species phylogenies and the timing of lineage divergence have met with mixed success and have revealed chronically challenging taxa in this group [23 , 27 , 36 , 37] . Given its ecological and reproductive diversity , the wild tomato group presents a unique opportunity to examine the genome-wide signatures of rapid recent divergence . While the timescale of speciation is comparable to other recent phylogenomic studies of radiating animal clades ( e . g . , [7 , 17 , 18 , 38] ) , it is unclear whether plant clades such as wild tomatoes—equally rapidly radiating , but also classically perceived as having greater tendency to hybridize [39]—will differ in their genomic patterns of diversification and introgression and in the genetic variation on which this diversification is based . The aims of our study were , first , to clarify species phylogenetic relationships; second , to assess postspeciation gene flow between lineages; and third , to investigate the genomic basis of lineage-specific and environment-specific adaptation . We find evidence consistent with at least three genetic sources of adaptive variation: introgression among species , de novo mutation , and recruitment from ancestral variation . Our results indicate that a combination of all three of these evolutionary factors facilitated rapid adaptive expansion in response to ecological opportunity .
We sequenced whole transcriptomes ( mRNAs ) for 29 accessions from 13 tomato species and 2 outgroup species ( Fig 1 and S1 Table ) . Although these sequences came from different species , high sequence similarity allowed us to confidently align ~90% of RNA-Seq read-pairs from all accessions to the reference genome of the domesticated tomato , S . lycopersicum [40] . We aligned an average of 31 . 6 Mb per accession , covering 21 , 896 genes with an average of >26 accessions per gene . This corresponds to an average coverage of 76% of total annotated coding regions per accession , but only 3 . 9% of the full genome due to the high proportion of gene-poor heterochromatin in the tomato genome [40 , 41] . We inferred phylogenetic relationships among species using several data partitions , including: whole transcriptome concatenated , each chromosome concatenated , nonoverlapping 1 Mb and 100 kb genomic windows , and trees inferred from individual genes ( Fig 2 , S2 Fig , and S2 Table ) . We also used a majority rule method ( as implemented in RAxML [42] ) , a coalescent method ( MP-EST [20] ) , and a coalescent-based quartet method ( ASTRAL [21] ) to infer phylogenies using the 100-kb window trees ( S2D–S2F Fig ) . All concatenation , majority rule , and coalescent methods inferred a generally consistent species tree topology ( Fig 2A ) , identifying four main groups that recapitulate relationships found in previous studies [23 , 27 , 36 , 37] . As in other recent analyses , we find that S . habrochaites and S . pennellii are placed together ( the “Hirsutum” group ) and split from the other wild tomatoes at the base of the tree [37] , that S . arcanum groups with other members of our inferred “Arcanum” group rather than with the “Peruvianum” group [43] , and that some members of the “Peruvianum” group have ambiguous taxonomic placement , especially accessions of S . huaylesense [37] . In particular , one of our lineages of S . huaylesense shows evidence of extensive and recent reticulation ( as discussed further below ) , so it was omitted from our reconstructed consensus tree ( Fig 2A ) . Using molecular clock estimates , we dated several nodes that define major groups and distinct species . Our inferred date for the basal node ( 2 . 48 Ma ) agrees well with a recent fossil-calibrated estimate of 2 Ma [22] , and we confirm that some groups within the clade have very recent divergence times ( e . g . , <0 . 5 Ma for the Esculentum or “red-fruited” group; Fig 2 ) . Because of the large amount of sequence used in the concatenated whole transcriptome alignment ( 46 . 5 Mb with at least 10 accessions represented ) and the large number of loci used in coalescent-based methods ( n = 2 , 745 100-kb windows ) , our phylogeny shows strong bootstrap support for almost all nodes ( Fig 2A ) , as expected [44] . However , these summary support measures conceal rampant phylogenetic complexity that is evident when examining the evolutionary history of more defined genomic partitions ( Fig 2B , S2 Fig , and S2 Table ) . Among the 2 , 745 trees generated from nonoverlapping 100 kb segments of the genome , we inferred 2 , 743 different topologies and found wide variation in support for the specific placement of individual accessions and species ( S2 Table ) . For example , the Esculentum group is supported by ~99% of 100-kb trees , while the more diffuse Peruvianum group is supported by only 21 . 3% of trees . Gene trees show discordance both within subclades and across deeper nodes and , when examined spatially within the genome ( using “chromoplots” [17 , 45] ) , discordant topologies are observed to be interdigitated across all chromosomes ( S2 , S4 and S5 Figs ) . None of the trees generated from 100-kb segments ( Fig 2B ) matched the topology of the species tree ( Fig 2A ) . We find that shorter internodes exhibit more discordance ( S3C Fig ) , indicating that homoplasy can be excluded as major contributor to the observed discordance [46] , but consistent with high levels of ILS due to rapid speciation in the group ( S1 Text Section 3 . 1 ) . As such , our results are clearly concordant with several other recent studies of contemporary ( e . g . , [7 , 14 , 18 , 47] ) and more ancient ( e . g . , [15 , 16 , 46 , 48] ) adaptive radiations that also detect abundant evidence for genome-wide ILS . To investigate these patterns of discordance further and to more accurately assess heterozygosity in these wild species , we used a high-depth ( HD ) dataset of 12 . 1 million sites with ≥10X sequencing coverage for all samples . Consistent with very recent divergence , tomato species differ on average by ~1% nucleotide divergence , ranging from 0 . 05% between Galápagos species to 1 . 58% between the most distantly related pairs ( full table in S1 Data 1 . 2 ) . Within-accession variation ranged from 0 . 05%−1 . 1% heterozygous sites ( Fig 3A ) and was higher in outcrossing ( self-incompatible ) lineages compared to more inbreeding ( self-compatible ) lineages , as expected [49 , 50] . In contrast , the proportion of loci that showed shared genetic variation across major subclades was approximately the same across all accessions ( Fig 3B ) ; that is , all lineages appear to exhibit equivalent levels of shared ancestral genetic variation , regardless of their overall proportion of heterozygous sites . Since both ILS and introgression manifest as discordant phylogenetic relationships , distinguishing these two factors is challenging , even with new methods developed specifically to address this issue [51 , 52] . Nonetheless , we detected evidence for a highly variable history of cross-species introgression , including one clearly reticulate lineage , a few cases of clearly demarcated and chromosomally localized introgressions between lineages , and many lineages with little or no evidence of introgression ( S4 and S5 Figs ) . These observations in wild accessions are in addition to observed evidence of intentional introgression of wild alleles into domesticated accessions , which are presumably for crop improvement [53] and well documented in other studies [54 , 55] but excluded here to focus on introgression in nature . In the case of reticulate lineages , hua-1360 ( in particular ) , and hua-1364 and per-2744 ( to a lesser extent ) show extensive phylogenetic conflict and patterns of recent hybridization ( S4 Fig ) . In hua-1360 , 48% of gene trees indicate that this lineage has a closer relationship with the Esculentum group than the Peruvianum group , where it has traditionally been placed based on morphological and reproductive characters [56] . Our finding agrees with another recent study that found this accession to be admixed [43] , and with our analysis indicating that , of all accessions analyzed here , this lineage has the highest taxonomic instability index [57] , a metric of the consistency of topological placement of individual taxa in a phylogeny ( S1 Data 1 . 26 ) . In addition , 40% of heterozygous sites in hua-1360 contain at least one allele that is otherwise Peruvianum- or Esculentum-specific ( S4 Fig ) , indicating that the hybridization event that produced this accession is relatively recent . Unsurprisingly , including this reticulate lineage when inferring the whole-transcriptome phylogeny causes the Peruvianum group to appear to be paraphyletic with respect to the Esculentum and Arcanum groups ( S2A Fig ) . While the level of reticulation observed in these three lineages was surprisingly high , it is consistent with both the history of contested species definitions in the Peruvianum group and the particularly uncertain status of S . huaylasense—a recently described species with populations that have had conflicting taxonomic designations [43 , 58] . To assess introgression across all species in the clade , we calculated genome-wide D-statistics [51 , 59] . In addition , for nonoverlapping genomic windows , we computed D-statistics and DFOIL statistics [52] to identify spatially localized regions of introgression . Because there are 2 , 925 trios of taxa that can be analyzed in the D-statistic framework , we inferred the timing of introgression based on shared signals among related species . Based on genome-wide D-statistics , we inferred that the majority of introgression occurred among relatively ancient lineages rather than across more recent splits ( S5 Fig and S1 Text Sections 1 . 5 and 4 . 2 ) . To estimate the proportion of the euchromatic fraction of the genome exchanged in these ancient events , we calculated the frequencies of discordant gene trees for trios of accessions , using one representative from each lineage that was implicated in introgression by the D-statistics . Other than the reticulate genomes previously described , we noted two likely ancient introgression events ( S1 Text Section 4 . 2 and S4E and S4F Fig ) . First , extrapolating from the frequency of windows with significant D-statistics observed in our transcriptome data , the ancestor of S . habrochaites is inferred to have exchanged 8 . 7% of the euchromatic portion of its genome with the lineage that gave rise to the Esculentum and Arcanum groups . The other ancient introgression involved an estimated 8 . 8% genome exchange between the lineages ancestral to the Esculentum+Arcanum groups and the Peruvianum group , though these patterns are more difficult to interpret because of both ancestral population structure and interbreeding among Peruvianum group species ( S4 and S5 Figs ) . Except in the case of very recent introgression between several Peruvianum group accessions ( S5E Fig ) , evidence of more recent introgression between species or accessions is limited to a few cases and involves <1% of our analyzed loci ( Fig 4A , S5 Fig , and S1 Text Section 4 . 2 ) . In particular , each of the two S . neorickii accessions has a different region introgressed from a red-fruited clade donor ( Fig 4A ) . Another case involves introgression from the red-fruited clade into only one S . pennellii accession ( S1 Text Section 4 . 2 and S5D Fig ) . These cases are particularly interesting because of their recent timing , since population-specific introgressions must postdate the common species ancestor . Based on the function of the genes involved , they may also represent strong candidates for adaptive introgression [60–62] . For example , the two independent introgressions into S . neorickii correspond to different regions within the Cf-4/NL ( “Northern Lights” ) locus that is associated with resistance to the pathogenic leaf mold Cladosporium fulvum [63 , 64] . Because these two accessions of S . neorickii were sampled from ecologically distinct habitats ~1 , 350 km apart , it is plausible that the introgressions occurred in response to different local fungal pathogens . In contrast , the introgression into S . pennellii involved transfer of a gene currently without a described environment-specific adaptive role ( Solyc08g005190; pre-mRNA-splicing factor cwc22 ) . Finally , looking across all branches and all possible trios of species within the wild tomatoes , we can infer a coarse clade-wide estimate of the frequency with which introgression appears in our dataset , if we assume an arbitrary but reasonable general cutoff for inferring significant evidence of introgression . For example , across all 26 lineages that we queried within the wild tomato tree , we found 1 , 147 windows where |D| ≥ 0 . 2 , p < 1 × 10−4 , and |ABBA − BABA| ≥ 10 for any trio of three species ( out of 2 , 596 100-kb windows with 100 or more aligned sites ) . That is , about 44% of windows show some evidence of introgression over at least one branch in the tree , as expected , given that our overall sampling of taxa was found to include admixed taxa . On the basis of these criteria , then , per branch we find that 1 . 76% of our 100-kb windows show evidence of introgression . Note that if we remove the substantially admixed taxa ( hua-1360 , hua-1364 , and per-2744 ) from these calculations , we find 672 windows that are significant over the 20 remaining possible branches , and therefore that an estimated 1 . 29% ( 672 / [ ( 2 , 596 ) ( 20 ) ] ) of windows show evidence of introgression . These calculations rely on several simplifying criteria , but they permit a crude estimate of the genome-wide proportion of 100-kb windows that show evidence of past introgression , and therefore that could contribute to adaptive allele sharing between lineages . Nonetheless , it is clear that a major computational need for future phylogenomic studies is a method to simultaneously integrate data from more than four taxa in order to infer the number and specific timing of introgression events among all members of a clade . Regardless , the substantial but small estimate of clade-wide introgression we infer here also suggests that the pervasive genome-wide discordance we detect across the clade is predominantly due to the effects of ILS . Despite the extensive phylogenetic complexity observed in our genome-wide data , wild tomato species and subclades are separated by clear diagnostic ecological preferences , functional traits , and various pre- and postzygotic isolating barriers [26 , 27] . Therefore , in addition to shared ancestral variation and introgressed alleles , there should also be mutations that uniquely diagnose well-supported groups within the clade , including in loci that confer species- and group-specific traits . To identify candidates for such loci , we examined patterns of protein-coding changes to distinguish genes that showed high rates of group-specific protein-coding changes relative to group-specific synonymous changes . Because the high level of ILS detected here , in addition to lineage-specific introgression , produces highly discordant gene trees , standard approaches for inferring the timing of nucleotide substitutions may be inaccurate [65] . Therefore , we used a more conservative dN/dS-like test to identify genes with high numbers of unambiguously clade-specific sequence changes . This test requires that an amino acid substitution be exclusively observed in a particular group and be common to all members of the group ( S1 Text Section 5 ) . For each of the four main groups within wild tomatoes ( Fig 2B ) , we found hundreds to thousands of genes with protein-coding changes that were unique to all species within a group and not found in other groups ( including the outgroup ) ( S1 Data 1 . 8−1 . 20 ) . These changes are inferred to have occurred exclusively on the ancestral branch of each of our four main clades , and therefore arose during the emergence of that clade . Of these , we detected significant evidence for positive selection ( dN/dS > 1; p < 0 . 01 ) on the Esculentum ( red-fruited ) group ancestral branch in 3 . 08% of genes ( 137 out of 4 , 447 testable genes; False Discovery Rate ( FDR ) = 32 . 5% ) , 4 . 69% in the Arcanum group ( 179 out of 3 , 819 genes; FDR = 21 . 3% ) , and 3 . 96% in the Hirsutum group ( 38 out of 958 genes; FDR 25 . 2%; see S1 Data 1 . 9–1 . 12 for all genes and p-values ) . Due to the variability in the gene tree topologies particular to the Peruvianum group , the ancestral branch appeared in only 10% of genes , so this group was not tested . Results for all genes tested , regardless of the presence of a lineage-specific nonsynonymous substitution , are presented in S1 Text Section 5 . In some instances , there are clear functional consequences for these group-specific amino acid changes . For example , all members of the red-fruited Esculentum group share such changes in 10 enzymes within the carotenoid biosynthesis pathway , which is responsible for red coloration ( Fig 4C ) [40 , 66–68] . Although not all elements of this pathway have been functionally characterized , current estimates are that it contains ~31 enzymes [67]; therefore , we find nearly a third of the enzymes in the carotenoid biosynthesis pathway have novel amino acid changes specific to the group that has evolved red-colored fruits . This includes four amino acid substitutions each in Solyc06g036260 ( β-carotene hydroxylase 1; p = 0 . 005 ) and Solyc04g040190 ( lycopene β-cyclase 1; p = 0 . 043 ) . Other examples of adaptively evolving genes include 10 Arcanum-group-specific amino acid substitutions in Solyc02g067670 , an ortholog of the Arabidopsis gene UVR1 ( Ultraviolet Repair Defective 1 ) , which may be connected to adaptation to increased solar radiation at the high altitudes characteristic of these species ( Fig 1; p < 10−5 ) . We also found many putative species-specific substitutions across the tree , although more extensive intraspecific sampling will be required to confirm species-specificity . For example , both S . chmielewskii accessions shared six nonsynonymous changes in Solyc06g051460 ( ATP-dependent chaperone ClpB ) , a gene implicated in temperature stress response [69] . In addition to genes with obvious phenotypic consequences , these analyses also revealed group-specific loci with many amino-acid changes , but whose ecological functions are less clear . For example , five Esculentum-group-specific amino acid substitutions were observed in Solyc09g082460 ( a homocysteine S-methyltransferase , p = 4 . 64 × 10−4 ) . This and other cases demonstrate the potential of this analysis to discover new candidate genes whose adaptive functional consequences are currently unknown , but that are intriguing targets for follow-up work ( S1 Data 1 . 8–1 . 20 ) . Overall , across all of the loci for which we could test clade-specific sites , we found 3 . 8% of genes had evidence for positive selection ( within PAML at p < 0 . 01 ) on at least one of our three well-supported branches . Though this number includes variable fractions of false positives ( depending upon the branch involved ) , and we have conditioned on seeing lineage-specific nonsynonymous changes , it provides a crude estimate of the potential contribution of de novo mutation to new genetic variation in this clade . In addition to lineage-specific changes , the close genetic relationships among wild tomato species make it possible to conduct a clade-wide , genome-wide investigation of genetic variants associated with broad-scale ecological factors ( Fig 1 ) rather than shared genealogical history . Our expectation for this “PhyloGWAS” approach is that ancestrally segregating variants that confer an advantage to specific ecological conditions will be differentially fixed among current populations that share common environments , regardless of their phylogenetic relatedness . These genes will therefore show polyphyletic topologies that group species or accessions according to common environments . Note that this approach does not aim to detect molecular convergence ( e . g . , [70] ) , instead aiming to identify parallel selection on standing variation ( e . g . , [71] ) . Such surveys have been previously conducted within wild S . lycopersicum populations [72 , 73] , but not among the clade as a whole . While the accessions used in our study were sampled from a broad geographic and environmental range ( Fig 1 ) , these analyses are only informative when ecological conditions are not confounded with phylogenetic relationships ( i . e . , when all members of a clade are not found in similar environments ) . This requirement excludes several broad ecological variables from testing , including variation in salinity , island versus mainland , and East versus West of the Andes . In addition , many potential environmental variables are highly correlated with each other , and data are often only available at relatively coarse environmental scales ( S1 Text Section 6 ) . With these limitations in mind , we examined allelic associations with four ecological factors that were distributed among species within each of the major groups: altitude/temperature , a composite measure of seasonal climate variability , water pH , and soil heavy metal content . These factors capture broad axes of environmental variation among our samples while minimizing strongly correlated environmental variables ( S1 Text Section 6 ) . For all factors except altitude/temperature , we found numerous genes with environmentally associated alleles , and more loci than are expected to be environmentally associated by chance ( see Materials and Methods , S1 Text Section 6 ) , thereby generating a list of genes for which selection has putatively sorted functional allelic variants from variation ancestral to the entire group ( S1 Data 1 . 22–1 . 25 ) . Overall , we found 12 nonsynonymous variants ( in 12 loci ) associated with our second environmental factor ( seasonal climate variation ) , 44 nonsynonymous variants ( in 43 loci ) associated with our third factor ( soil pH ) , and 455 nonsynonymous variants ( in 401 loci ) associated with our fourth factor ( variation in heavy metals ) . None of the loci identified to have nonsynonymous variation uniquely associated with differences in environmental factors is colocalized with a chromosomal region inferred to be introgressed between specific lineages . This indicates that loci putatively subject to selection from standing variation are not associated with inferred cases of cross-species introgression . We found 12 genes with distinguishing amino acid differences between two groups of accessions that are found in distinct categories of seasonal climate variation ( Fig 4C ) described by a composite measure of latitudinal differences in temperature , precipitation seasonality , and the intensity of photosynthetically active radiation ( PAR ) ( p < 2 . 5 × 10−4; S1 Text Section 6 and S1 Data 1 . 23 ) . This list of genes includes several with potential roles in seasonal and latitudinal adaptation , including Solyc02g069460 ( photosystem I reaction center subunit III ) and Solyc12g014040 ( chloroplast protein HCF243 ) . Even more strikingly , using mineral survey data from Peru to identify four populations sampled from habitats with high environmental levels of heavy metals ( As , Cu , Hg , Ni , and Pb ) and four from areas with low levels ( Fig 4C ) , we found 401 genes with protein differences between the high and low metals groups ( p < 2 . 5 × 10−4 ) . These include a likely heavy metal binding/detoxification protein ( Solyc04g015030 ) , and two genes that require copper as a cofactor: Solyc01g005510 ( Laccase-2 ) and Solyc08g079430 ( Primary amine oxidase ) ; these and other detected loci suggest that geographical variation in heavy metals in the Andean region may be a factor in local selection for functionally important ancestral variants . We also found environmentally sorted ancestral allelic variation associated with soil pH ( S1 Text Section 6 ) , enriched above that expected due to random association . Note that , unlike in our cases of lineage-specific de novo adaptive evolution , these genes are generally characterized by only one or few nucleotide differences ( S1 Data 1 . 22–1 . 25 ) , as might be expected of alleles that are recruited from standing ancestral variation; that is , there is little reason to expect that functionally differentiated alleles would be segregating many sequence variants in the ancestral population . This small number of differences also makes it easier to determine whether introgression has contributed to the observed patterns of allele sharing . Under a model of introgression , we expect evidence for a localized block of variants that exhibit discordant phylogenetic signal regardless of whether changes are synonymous or nonsynonymous , whereas this is not expected for selection from standing variation . In our analysis , very few of the loci with environmentally associated nonsynonymous variants also had associated synonymous variants . For environmental factor 2 ( seasonality ) , none of our candidate genes had synonymous variants in addition to the identified nonsynonymous variant ( S1 Data 1 . 23 ) . For factor 3 ( soil pH ) , only 4 of 43 genes also had a single synonymous variant associated with the detected nonsynonymous variant ( s ) . Associations between SNPs within candidate loci were slightly more common for environmental factor 4 ( soil heavy metal content ) : of 401 genes with at least one environmentally associated nonsynonymous SNP , 55 loci also had 1 associated synonymous SNP . Of these 55 loci , 19 had >1 associated synonymous SNP . In these latter cases ( ~5%–14% of the identified candidates for this factor ) , we cannot unambiguously differentiate the relative contributions of standing variation and introgression . However , given the conditions of our tests of environmental association , it is unlikely that our detected candidates are frequently affected by introgression . This is because our tests for selection on standing variation explicitly required that variation in focal environmental factors be distributed among species and clades . For introgression to explain the distribution of these loci across distantly related ( and often geographically distant ) accessions would therefore require a mechanism involving multiple interspecific introgression events across different branches of the phylogeny , and in multiple geographical locations . Similarly , it is also unlikely that these results are generally explained by convergent de novo molecular changes , because each change would have to arise many times in many independent taxa , although we cannot exclude the possibility that some fraction of our loci might have been subject to convergence in one or a few taxa . While the extensive shared variation detected in this group makes phylogenomic reconstruction much more complex , it also provides a novel opportunity to use a genome-wide association approach to identify candidate loci . Accordingly , in addition to lineage specific changes , we can point to potential examples of ecological selection on ancestral alleles as another mode of adaptation in this clade . Overall , across all the loci that could be compared for associations with our four environmental factors , 2 . 6% were found to have at least one nonsynonymous variant in perfect association with at least one of these factors ( S1 Data 1 . 21–1 . 25 ) , providing a provisional estimate of the potential for selection from standing variation across the clade . Lineages of closely related species can occupy diverse ecological roles , but the conditions that promote this rapid adaptive radiation are still under debate . Given multiple examples where only one of two closely related lineages experienced a burst of diversification under the same conditions , new ecological opportunity alone is likely to be insufficient [7 , 74–76] . This suggests that intrinsic factors—such as the availability of appropriate genetic variation—are equally critical for facilitating adaptive responses , although conditions that promote the origin and sharing of this variation remain largely speculative [77 , 78] . Here , we have found evidence for at least three significant sources of genetic variation that might facilitate adaptive diversification in response to ecological opportunity . First , we inferred introgression both between early lineages in the radiation and recently between specific populations . Second , we observed rapid lineage-specific adaptation from de novo mutation in genes related to functional traits that differ between groups . Finally , we find evidence of environment-specific sorting of ancestral variation . Analyses of other rapid radiations have also inferred the role of one or more of these three mechanisms in facilitating rapid diversification . For example , analyses of radiating African cichlids suggest widespread recruitment of potentially adaptive coding and regulatory variants from standing ancestral variation [7] . Clade-wide variation in Equids revealed evidence for both the rapid accumulation of de novo substitutions and for both ancient and recent introgression events between species [14] . In Darwin’s Finches [18] , hybridization appears to play a role both in the origin of new lineages and potentially in the adaptive introgression of functional loci ( e . g . , for beak shape ) between species . Because each of our analyses relies on different assumptions and varies in power , directly comparing the relative contribution of our three detected sources of genetic variation requires caution . Nonetheless , based on our crude estimates within each analysis , we infer that relatively small yet substantial fractions of the euchromatic genome are implicated in each source of genetic variation . We find little evidence that one of these processes predominates in its contribution , although our estimates suggest that de novo mutation might be relatively more influential and cross-species introgression relatively less so . This latter observation is in interesting contrast with several recent studies of animal adaptive radiations , including in Darwin’s Finches [18] , Equids [14] , and fish [13] , where evidence suggests that hybridization and introgression might be much more pervasive and influential than previously suspected , and more abundant than we detect in Solanum . This is despite a greater historical emphasis on the role and importance of post-speciation gene flow in plant groups [79 , 80] and suggests that the dynamics of adaptive radiation might be less shaped by classical expectations of differences between broad taxonomic groups like plants and animals than expected . Rather , as with other studies that also detect one or more of these sources of genetic variation [7 , 14 , 17 , 18] , we detect evidence for all three within the same diversifying clade , suggesting that these mechanisms may be universal in their facilitation of rapid adaptation to diverse environmental niches . Rapid diversification via these three modes within wild tomatoes was likely ecologically driven by the extremely variable environments of the Andes and Galápagos . Notably , most of the significant geo-climatological transitions of this region substantially predate the entire history of wild tomato diversification . These events include major uplifts of the Central Andes [81–83] and the formation of biogeographic zones such as the Atacama Desert ( at least ~14 Ma , though possibly up to ~150 Ma ) and the Peruvian coastal desert [84 , 85] . Therefore , geographical and ecological expansion of wild tomato species was almost certainly due to migration into new environments rather than in situ adaptation during more ancient geological and climatic transitions . The timing of major lineage splits , in addition to the current distributions of extant species , can be used to infer the progression of these migratory steps ( S1 Text Section 7 . 6 and S6 Fig ) . This south-to-north range expansion and diversification has been suggested by phylogenies of other plant and animal groups in the Central Andes [85–89] . More broadly , Solanum is one of the most speciose and widespread angiosperm genera , with ~1 , 500 extant species found on all continents except Antarctica . The last common ancestor of the genus is estimated to be only ~15 . 5 Ma [22 , 90–93] . Therefore , the rapid speciation rates that we see in the tomato clade , and the accompanying genetic and genomic changes , could be symptomatic of the factors facilitating sustained divergence and diversification across the entire Solanum genus around the globe .
Our sampling included 29 accessions from 13 species of tomato and two outgroup species ( representing the entire clade and accepted outgroups; S1 Table ) . Seeds of each accession were obtained from the C . M . Rick Tomato Genetics Resource Center at the University of California , Davis ( http://tgrc . ucdavis . edu ) . Seeds were germinated following standard guidelines ( http://tgrc . ucdavis . edu ) and then transplanted to 7 . 56-L pots containing a 1:1 mix of standard soil and Metro Mix 360 ( http://www . hummert . com/ ) in the Department of Biology greenhouse at Indiana University under supplemental lighting to maintain a constant 14:10 h light:dark cycle . Plants were watered to field capacity daily to prevent drought stress and fertilized weekly . To capture a wide set of transcripts , we harvested RNA from five different tissues: roots , leaf primordia and young/unexpanded leaves , mature leaves ( fully expanded , the fifth leaf from the meristem ) , floral buds , and mature ( open ) unfertilized flowers . Tissue was collected in sterile 15 or 50 mL conical vials ( VWR: 89039–666 , 89039–658 , respectively ) . Floral and leaf tissue was immediately placed into liquid nitrogen . Root tissue was washed in cold water for <60 s to remove large soil particles , blotted with paper towel for 10 s , and then frozen with liquid nitrogen . All tissues were pulverized under liquid nitrogen using a mortar and pestle; 50–100 mg fresh weight of ground tissue was used for total RNA extraction . Extraction of the poly-A fraction of total RNA from ground tissue was performed using RNeasy Plant Mini Kits from Qiagen ( catalog number 74904 ) . Resuspended RNA was stored at −80°C until all samples were collected . Tissue-specific total RNA was equimolar pooled using the RiboGreen RNA quantitation assay ( Life Technologies: R11491 ) and then quality checked using an Agilent 2200 TapeStation System prior to library construction . Stranded , paired-end libraries of total RNA were generated from these pools for each accession using Illumina TruSeq Stranded total RNA HT Sample Preparation Kits ( Illumina: RS-122-2203 ) , these libraries were pooled and distributed evenly ( < 6-fold difference among libraries , S1 Data 1 . 1 ) across three lanes of Illumina HiSeqTM 2000 ( Illumina Inc . , San Diego , CA , US ) . RNA QC , library preparation , and pooling was performed by the Indiana University Center for Genomics and Bioinformatics ( http://cgb . indiana . edu ) . Raw reads were filtered and trimmed using the SHEAR program ( http://www . github . com/jbpease/shear ) . RNA-Seq reads were mapped to the S . lycopersicum reference genome v . SL2 . 50 ( ftp://www . solgenomics . net ) [40 , 41] , reference chloroplast ( NCBI accession NC_007898 . 3 ) , and mitochondrial scaffolds ( http://mitochondrialgenome . org/ ) using STAR [94] . Alignments were processed into multisample Variant Call Format ( VCF ) using SAMtools [95] , then converted/filtered into Multisample Variant Format ( MVF ) using MVFtools ( http://www . github . com/jbpease/mvftools ) [45] . Two primary alignments were filtered: a high-quality ( HQ ) set requiring sequencing depth ≥ 3 and mapping quality ≥ 30 , and a HD set with depth ≥ 10 and mapping quality ≥ 30 ( see S1 Text Section 2 . 1–2 . 3 and S1 Data 1 . 1 for additional details ) . Phylogenies were inferred using several methods ( RAxML [42] , ASTRAL [21] , MP-EST [20] ) and partitions of the data . Using RAxML , whole-transcriptome and whole-chromosome concatenated phylogenies were inferred from all sites with alleles represented in ≥10 accessions . Molecular clock estimates were performed using r8s [96] with calibrated time points from Särkinen , Bohs [22] . RAxML was also used to infer phylogenies for 1 Mb and 100 kb genomic windows , and for annotated reference genes ( ITAG v . 2 . 4 , https://www . solgenomics . net ) with four or more accessions represented ( S1 Text Sections 3 . 1–3 . 3 ) . From 100-kb window trees , a majority rule tree ( S2D Fig ) was computed using RAxML [42] and annotated with percentage of window tree support and IC/ICA scores for each node [44] . Coalescent trees were inferred with ASTRAL [21] and MP-EST [20] ( S2E and S2F Fig ) . MP-EST was run for 100 replicates; the tree with the strongest likelihood score is shown in S2F Fig , as in [16] . Options for all three programs were set to default , and no consensus tree was used as input . RAxML was run with the “-J MRE” option for Majority Rule Extended . Majority rule and coalescent topologies agreed with the consensus phylogeny for the major subclades , with the exception of hua-1364 ( see discussion below on Peruvianum group ) . The proportions of 100-kb trees supporting various nodes are shown in S2D Fig and S2 Table . We calculated the proportion of heterozygous sites sampled from each accession and the patterns of alleles shared among groups from the HD alignment . Pairwise sequence distances between all pairs of the 29 sequenced accessions and the reference ( S1 Data 1 . 2 ) were calculated from the HQ dataset using MVFtools . At heterozygous sites in an accession , one of the two alleles represented was selected randomly; random allele selection was also done for all analyses described below . Accessions in section Lycopersicon differ from accessions in Lycopersicoides by 2 . 10%–2 . 71% sequence divergence . Accessions within Lycopersicon have pairwise distances of 0 . 05%–1 . 7% , with the closest relationships between different accessions within S . galapagense ( gal-3909/gal-0436 ) and within domesticated tomato ( lyc-3475/lyc-ref ) ( S1 Text Section 3 . 2 ) . Using MVFtools [45] , we calculated the D-statistic [51 , 59] for nonoverlapping 1-Mb windows of the HQcomp dataset , for all possible trios of the 27 Lycopersicon accessions and the reference . The consensus tree ( Fig 2A ) was used to determine expected tree topologies and to assign P1 , P2 , and P3 . ABBA and BABA site patterns were combined for all windows to calculate a transcriptome-wide average D-statistic ( S1 Data 1 . 5 ) . Many cases were observed in which transcriptome-wide D values appeared to be driven almost entirely by a small number of 1 Mb windows , consistent with recent introgression at a localized chromosomal location against a background of generally low divergence ( i . e . , few ABBA and BABA patterns genome wide ) . To more directly assess whether the D values observed represented a genome-wide pattern of gene flow , we performed a bootstrap resampling analysis . For each trio of accessions , we randomly resampled 1 Mb windows with replacement and recomputed D ( n = 10 , 000 replicates ) . From the distribution of resulting D-values , we assessed whether the 95% CI of the resampled distribution included D = 0 . From the D-statistics , we inferred putatively introgressing lineages ( S4E and S4F Fig ) . We further investigated cases where trios of accessions showed evidence of widespread or significant amounts of introgression based on D-statistic calculations . For each putatively introgressed trio , we inferred gene trees for each protein-coding region using sequences from the trio and lyd-4126 as the outgroup ( S1 Data 1 . 5 , S4 and S5 Figs ) using RAxML [42] . From these gene trees , we counted the proportion of gene trees of each of the three possible rooted topologies . In each introgression case , we estimated the proportion of genes that were introgressed as the difference in the proportions of trees with the two discordant topologies . In the case of the introgressions involving neo-2133 , neo-1322 , and S . pimpinellifolium , we also calculated DFOIL statistics for 100 kb windows to infer the direction of introgression [52]; these cases involved tree topologies appropriate for the use of this 5-taxon method ( S5A Fig ) . This window size is large enough to avoid problems associated with the sampling of trees from smaller windows [52 , 97] . The high levels of incompletely sorted ancestral variation and variability of the gene-by-gene phylogenies presented a particular challenge to estimating genes with lineage- or species-specific substitutions . A standard tree-based dN/dS model implicitly reconstructs ancestral states , which in our dataset would be subject to high error because of the pervasive background of ILS [65] . Instead , we used a more conservative variant of a dN/dS test to infer which genes show high relative frequencies of nonsynonymous substitutions ( and therefore are likely under positive selection ) for the four well-supported subclades within Lycopersicon ( Esculentum , Arcanum , Peruvianum , and Hirsutum groups ) , as well as some specific species ( below ) . For a given gene , we counted only substitutions that could be placed unambiguously on the branch leading to a particular lineage . For example , when testing the Esculentum group as the target lineage of interest , substitutions were counted as lineage-specific only when the set of sites sampled from Esculentum group and the set of sites sampled from all other accessions ( including the outgroup ) were completely nonoverlapping in identity . These substitutions were tabulated as synonymous or nonsynonymous , depending on whether a change in amino acid occurred . For all tests , the outgroup accessions were included in the nontarget group . Sites were only considered when at least one allele was available for each ingroup species and at least one outgroup accession . We tested for changes on the branch separating section Lycopersicon versus section Lycopersicoides and for all other samples against these groups/species: Esculentum group , Arcanum group , Peruvianum group , Hirsutum group , the Galápagos species , S . pennellii , S . habrochaites , S . chilense , S . chmielewskii , and S . neorickii . The domesticated accessions were not included since they have experienced intentional introgression of wild alleles for crop improvement . hua-1360 and hua-1364 were only included in the Peruvianum group-specific test because of their high incidence of reticulation . We evaluated evidence for positive selection on the set of genes that showed lineage-specific substitutions for each of our well-supported branches ( as outlined above ) using the branch-site test in PAML 4 . 8a [98] . For each protein-coding gene , the codon alignment for that gene was extracted from the MVF-translated alignment file and accepted for testing only if at least one sequence was represented for each species ( not including hua-1360 and hua-1364 ) . To maximize the alignment tested , only the sequence for each ingroup species ( among available accessions ) with the most aligned codons represented was retained . Similarly , only the outgroup accession with the most aligned codons was also retained . Phylogenies for each 14-species gene alignment were then inferred using RAxML v . 8 . 1 . 16 [42] using standard parameters and the GTRGAMMA model . For each of the four major groups ( Esculentum , Arcanum , Peruvianum , Hirsutum ) , we verified in the gene tree that all accessions in the given group are a monophyletic clade ( i . e . , that the gene tree has an appropriate branch ancestral to the group being tested ) . If this ancestral branch was not present , the gene was not tested for that particular group . Otherwise , the ancestral branch was marked as the target “foreground” branch and tested using the branch-site test in PAML . We ran both null and alternative tests , and recorded dN/dS values and likelihood scores . Since branch lengths were fixed at the values provided by the RAxML tree , the null model has four free parameters and the alternative test has five . Therefore , significance was assessed by a likelihood ratio test ( LRT ) assuming a χ2 distribution with one degree of freedom ( see S1 Text Section 5 for full PAML control file parameters ) . From these tests , we calculated the proportion of genes that showed significance under the LRT ( p < 0 . 01 ) both for ( 1 ) a set of genes where we had sampled at least one site where all accessions within the target group had alleles that differed from all accessions outside the target group ( i . e . , an exact allele pattern that indicated a nonsynonymous substitution on the branch leading to the target group; see Results ) and ( 2 ) for all genes containing the target branch according to the RAxML gene trees ( see S1 Text Section 5 ) . Geographical coordinates and sampling location information for each accession were obtained from the TGRC database ( http://tgrc . ucdavis . edu ) . Altitude and temperature for each population location were extracted from the WorldClim database ( www . worldclim . org ) ; because many environmental factors in this database are strongly correlated across the natural range of wild tomatoes [26] , we limited our analyses on WorldClim data to these two broadly representative factors . Soil solution pH data at 1 km resolution was obtained from the ISRIC SoilGrids project ( http://soilgrids . org/ ) . Metal abundances for the Peruvian accessions were estimated from data in GEOCATMIN ( http://geocatmin . ingemmet . gob . pe ) provided by the Instituto Geologico Minero y Metalurgico de Peru . In combination with topographic and hydrological data from the same database , metal abundances ( in ppm ) were averaged for all sample points located within a 100 km2 centroid surrounding each accession’s coordinates , for sites directly upstream or downstream of the population location; this area corresponds to locations within ~11 km of each accession’s geographical location . Metal concentrations were taken from the “Geochemistry: Serie B: Prospecting Geochemistry Sediment ravine” survey data collected between 2002 and 2011 . PAR values for mainland South America were obtained from Insituto Nacional de Pesquisas Espaciais de Brasil ( http://www . inpe . br/ ) . These data are available in units of kWh/m2/d for 40-km resolution in monthly averages from data spanning 1995–2005 . At this resolution , each accession inhabited a unique data cell except hua-1358 and hua-1360 . PAR values were unavailable for Galápagos populations . Seasonality of PAR was estimated as the standard deviation of monthly averages . To identify genomic targets of selection in response to abiotic factors , we treated all the accessions in section Lycopersicon as a population and looked for alleles that differentiated environmentally classified populations in phylogenetic genome-wide association study ( “PhyloGWAS” ) . These tests required that accessions from the same species or group occurred in different ecological categories , thus allowing detection of abiotic effects over lineage-specific effects . In our dataset , some environmental/geographical factors were intrinsically correlated with each other and thus were combined into a single composite environmental axis . Therefore , for our PhyloGWAS analysis , we selected four environmental axes that met the requirements for this approach: ( 1 ) altitude/temperature , ( 2 ) latitude/climate seasonality , ( 3 ) interpolated water pH , and ( 4 ) heavy metal abundance . In our sampled accessions , each environmental axis identified two clearly separable groups of populations ( see S1 Text Section 6 for additional details ) . For each of these four comparisons , we asked whether there were nonsynonymous variants completely correlated with each environmental condition . For instance , at a single position there might be an arginine present in all accessions experiencing high heavy metals , and a glycine present in all accessions in environments with low heavy metal concentrations . We examined all sites with nonsynonymous variants between any of the accessions used in each of the four environmental contrasts . This led to four sets of variants that were queried for environmental-specific changes; the size of the datasets examined were 233 , 567 nonsynonymous variants for abiotic Factor 1 , 253 , 161 for Factor 2 , 160 , 255 for Factor 3 , and 198 , 908 for Factor 4 . We found nonsynonymous variants perfectly associated with our environmental factors for all four contrasts , except for Factor 1 ( altitude/temperature ) , which was nonetheless associated with three synonymous variants . The numbers observed were 0 nonsynonymous variants for Factor 1 , 12 for Factor 2 ( in 12 genes ) , 44 for Factor 3 ( in 43 genes ) , and 455 for Factor 4 ( in 401 genes ) . To assess the significance of observing these patterns , we used the program ms [99] to simulate 109 genes with a single variable site over the consensus phylogeny ( Fig 2A ) using Ne = 105 and 2 . 5 generations per year . For each environmental factor , we determined the number of times we could expect a perfect association between variants and the environment due to ILS alone , out of the specific number of variants examined for that contrast . To do this , we simulated many datasets of the same size as the ones we tested , and for each dataset recorded the number of perfectly associated variants observed . The p-values for all four environmental contrasts are the proportion of simulated datasets that have a greater number of genes perfectly associated with environmental variables than our observed values ( see S1 Text Section 6 for additional details ) . MVFtools is freely available at http://www . github . com/jbpease/mvftools . Plots were generated with the Python matplotlib ( http://www . matplotlib . org ) and Veusz ( http://home . gna . org/veusz/ ) . Phylogenies were prepared with FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) . “Cloudogram” diagrams were generated with DensiTree ( https://www . cs . auckland . ac . nz/~remco/DensiTree/ ) . All other analyses were performed with custom Python scripts , using the BioPython , NumPy , and SciPy libraries . Read trimming , mapping , and large-scale file conversions were performed on the Mason High Performance Computing Cluster at Indiana University . All raw sequence reads are available on NCBI SRA at Bioproject PRJNA305880 . VCF , MVF , and phylogeny files are deposited in the Dryad repository http://dx . doi . org/10 . 5061/dryad . 182dv [100] . The tomato reference genome is available from SolGenomics ( http://www . solgenomics . net ) . List of Ultra-Conserved Orthologs can be found at the Compositae Genome Project ( http://compgenomics . ucdavis . edu ) . Additional geographic , ecological , and sampling information on the accessions used in this study is available at http://www . tgrc . ucdavis . edu . | The formation of new and distinct species during evolution often occurs in rapid bursts of diversification in which many species arise within a short time frame . The ecological and genetic factors that promote these radiations are much debated . Here , we examine genome-wide patterns of molecular evolution that accompanied a rapid adaptive radiation among 13 species of wild tomato—the ecologically and reproductively diverse group that gave rise to the domesticated tomato . By analyzing patterns of genetic variation in thousands of expressed genes from multiple populations and species , we identify genome-wide signatures of rapid consecutive speciation events during 2 . 5 million years of diversification in this group . These signatures include pervasive shared ancestral variation and frequently discordant signals of relatedness among different parts of the genome . Our analyses find evidence for three unique sources of genetic variation that fuel adaptive diversification in this group—postspeciation hybridization , rapid accumulation of new mutations , and recruitment from ancestral variation—and identify specific examples of putatively adaptive loci drawn from each source . Recent analyses of other rapid radiations have also inferred a role for at least one of these mechanisms; our finding of all three simultaneously at work within the same diversifying clade suggests that they might be a universal feature of rapid adaptation to diverse environmental niches . | [
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... | 2016 | Phylogenomics Reveals Three Sources of Adaptive Variation during a Rapid Radiation |
Entomopathogenic nematodes ( EPNs ) employ a sophisticated chemosensory apparatus to detect potential hosts . Understanding the molecular basis of relevant host-finding behaviours could facilitate improved EPN biocontrol approaches , and could lend insight to similar behaviours in economically important mammalian parasites . FMRFamide-like peptides are enriched and conserved across the Phylum Nematoda , and have been linked with motor and sensory function , including dispersal and aggregating behaviours in the free living nematode Caenorhabditis elegans . The RNA interference ( RNAi ) pathway of Steinernema carpocapsae was characterised in silico , and employed to knockdown the expression of the FMRFamide-like peptide 21 ( GLGPRPLRFamide ) gene ( flp-21 ) in S . carpocapsae infective juveniles; a first instance of RNAi in this genus , and a first in an infective juvenile of any EPN species . Our data show that 5 mg/ml dsRNA and 50 mM serotonin triggers statistically significant flp-21 knockdown ( -84%*** ) over a 48 h timecourse , which inhibits host-finding ( chemosensory ) , dispersal , hyperactive nictation and jumping behaviours . However , whilst 1 mg/ml dsRNA and 50 mM serotonin also triggers statistically significant flp-21 knockdown ( -51%** ) over a 48 h timecourse , it does not trigger the null sensory phenotypes; statistically significant target knockdown can still lead to false negative results , necessitating appropriate experimental design . SPME GC-MS volatile profiles of two EPN hosts , Galleria mellonella and Tenebrio molitor reveal an array of shared and unique compounds; these differences had no impact on null flp-21 RNAi phenotypes for the behaviours assayed . Localisation of flp-21 / FLP-21 to paired anterior neurons by whole mount in situ hybridisation and immunocytochemistry corroborates the RNAi data , further suggesting a role in sensory modulation . These data can underpin efforts to study these behaviours in other economically important parasites , and could facilitate molecular approaches to EPN strain improvement for biocontrol .
Entomopathogenic nematodes ( EPNs ) borrow their name from the entomopathogenic bacteria ( Photorhabdus , Serratia and Xenorhabdus spp . ) with which they form a commensal relationship . These nematodes provide a stable environment for the bacteria , and act as a vector between insect hosts . Once the nematode has invaded an insect , the nematode exsheaths ( or ‘recovers’ ) and entomopathogenic bacteria are regurgitated into the insect haemolymph; the bacteria then rapidly kill and metabolise the insect , providing nutrition and developmental cues for the nematode . These entomopathogenic bacteria are then transmitted between nematode generations [1] . The entomopathogenic lifestyle has been found to arise independently in nematodes , at least three times , spanning significant phylogenetic diversity . Heterorhabditis and Oscheius spp . [2] reside within clade 9 along with major strongylid parasites of man and animal [3]; Steinernema spp . reside within clade 10 alongside strongyloidid parasites [4] . Nictation is a dispersal and host-finding strategy , enacted by nematodes which stand upright on their tails , waving their anterior in the air [5] . This behaviour is shared amongst many economically important animal parasitic and entomopathogenic nematodes , alongside the model nematode C . elegans , for which nictation is a phoretic dispersal behaviour of dauer larvae , used to increase the likelihood of attachment to passing animals . Nictation is regulated by amphidial IL2 neurons in C . elegans , which occur in lateral triplets either side of the pharyngeal metacorpus [5 , 6] . IL2 neurons display significant remodelling from C . elegans L3 to dauer ( the only life-stage to enact nictation behaviours ) such that connectivity with other chemosensory and cephalic neurons is enhanced [6] . It has been shown that IL2 neurons express the DES-2 acetylcholine receptor subunit , and that cholinergic signalling is requisite for nictation [5 , 7–9] . Additionally , the central pair of IL2 neurons express the FMRFamide-like peptide ( FLP ) receptor , NPR-1 [10] . To date there are two known NPR-1 agonists; FLP-18 and FLP-21 [11] . However , there is also known redundancy of FLP-18 and FLP-21 in signalling through other neuropeptide receptors ( NPR-4 , -5 , -6–10 , -11 , and NPR-2 , -3 , -5 , -6 , 11 , respectively ) in heterologous systems [12 , 13] , making functional linkage difficult . Steinernema spp . also display a highly specialised jumping behaviour which is thought to enhance both dispersal and host attachment . Jumping occurs when a nictating infective juvenile ( IJ ) unilaterally contracts body wall muscles bringing the anterior region towards the posterior region , forming a loop . This generates high pressure within the IJ pseudocoel , and differential stretching and compression forces across the nematode cuticle . Release of this unilateral contraction , in conjunction with the correction of cuticle pressure , triggers enough momentum for an IJ to jump a distance of nine times body length , to a height of seven times body length [14] . Here we aimed to study the function of Sc-flp-21 in coordinating nictation and other behaviours relevant to host-finding . The recent publication of five Steinernema spp . genomes , along with stage-specific transcriptomes [15] represents a valuable resource , alongside the previously published genomes of Oscheius sp . TEL-2014 [16] and Heterorhabditis bacteriophora [17] . The genome of Steinernema carpocapsae is the most complete , at an estimated 85 . 6 Mb , with predicted coverage of 98% [15] . S . carpocapsae was selected as a test subject for our study due to the quality of genome sequence . The close phylogenetic relationship between Steinernema spp . coupled with a diverse behavioural repertoire , particularly in terms of host-finding [18 , 19] , make this genus an extremely attractive model for comparative neurobiology . The aim of this study was to examine RNAi functionality in S . carpocapsae IJs , and to probe the involvement of FLP-21 in coordinating sensory perception ( host-finding , nictation , jumping and dispersal phenotypes ) , as a prelim to probing the neuronal and molecular underpinnings of host-finding behaviour in this genus .
S . carpocapsae ( ALL ) was maintained in Galleria mellonella at 23°C . IJs were collected by White trap [20] in a solution of Phosphate Buffered Saline ( PBS ) . Freshly emerged IJs were used for each experiment . BLAST analysis of RNAi pathway components was conducted as in Dalzell et al . [21] , using a modified list of core RNAi pathway components from C . elegans , against predicted protein sets and contigs of the S . carpocapsae genome , through the Wormbase Parasite BLAST server [22 , 23] . Sc-flp-21 ( Gene ID: L596_g19959 . t1 ) dsRNA templates were generated from S . carpocapsae IJ cDNA using gene-specific primers with T7 recognition sites ( see Table 1 ) . Neomycin phosphotransferase ( neo ) and Green Fluorescent Protein ( gfp ) dsRNA templates were generated from pEGFP-N1 ( GenBank: U55762 . 1 ) . All dsRNA templates were size matched ( 200–220 bp ) . Template PCR products were generated as follows: [95°C x 10 min , 40 x ( 95°C x 30 s , 60°C x 30 s , 72°C x 30 s ) 72°C x 10 min] . PCR products were assessed by gel electrophoresis , and cleaned using the Chargeswitch PCR clean-up kit ( Life Technologies ) . dsRNA was synthesised using the T7 RiboMAX Express Large Scale RNA Production System ( Promega ) , and quantified by a Nanodrop 1000 spectrophotometer . 1000 S . carpocapsae were incubated in 50 μl PBS with dsRNA and 50 mM serotonin ( to stimulate pharyngeal pumping ) across four experimental regimes; ( i ) 24 h in 5 mg/ml dsRNA / serotonin / PBS; ( ii ) 24 h in 5 mg/ml dsRNA / serotonin / PBS , followed by washes to remove the initial dsRNA , and 24 h recovery in PBS only; ( iii ) 48 h in 5 mg/ml dsRNA / serotonin / PBS; and ( iv ) 48 h in 1 mg/ml dsRNA and serotonin . Each experiment was conducted as five replicates at 23°C . Total RNA was extracted from 1000 IJs using the Simply RNA extraction kit ( Promega , UK ) and Maxwell 16 extraction system ( Promega , UK ) . cDNA was synthesised using the High Capacity RNA to cDNA kit ( Applied Biosystems , UK ) . Each individual qRT-PCR reaction comprised 5 μl Faststart SYBR Green mastermix ( Roche Applied Science ) , 1 μl each of the forward and reverse primers ( 10 μM ) , 1 μl water , 2 μl cDNA . PCR reactions were conducted in triplicate for each individual cDNA using a Rotorgene Q thermal cycler under the following conditions: [95°C x 10 min , 40 x ( 95°C x 20 s , 60°C x 20 s , 72°C x 20 s ) 72°C x 10 min] . Primer sets were optimised for working concentration , annealing temperature and analysed by dissociation curve for contamination or non-specific amplification by primer—dimer as standard . The PCR efficiency of each specific amplicon was calculated using the Rotorgene Q software . Relative quantification of target transcript relative to two endogenous control genes ( Sc-act and Sc-β-tubulin ) was calculated by the augmented ΔΔCt method [24] , relative to the geometric mean of endogenous references [25] . The most similar non-target gene ( L596_g5821 . t1 ) was identified using BLASTn against the S . carpocapsae genomic contigs ( supplemental S1 Text ) , and primers Sc-L596_g5821 . t1-f and Sc- L596_g5821 . t1-r were used to assess transcript abundance relative to Sc-act across control and experimental conditions for the 48h dsRNA exposure experiments only ( Table 1 ) . Approximately 5 g of fresh waxworm ( Galleria mellonella ) and mealworm ( Tenebrio molitor ) larvae were placed into 20 mL glass tubes and sealed . The holder needle was exposed to the headspace of the tube over a 120 min timecourse ( extraction time ) at room temperature ( 22°C ) . After this time , the SPME syringe was directly desorbed in the GC injection port for 5 min . A fused silica fibre coated with a 95 μm layer of carboxen—polydimethylsiloxane ( CAR—PDMS; Supelco ) was used to extract the volatile compounds from the samples . Fibres were immediately thermally desorbed in the GC injector for 5 min ( with this time we desorb the analytes and re-activated the fiber for the next analysis ) at 250°C and the compounds were analysed by GC-MS . A CTC Analytics CombiPal autosampler was coupled to a 7890N Agilent gas chromatograph ( Agilent , Palo Alto , California ) and connected to a 5975C MSD mass spectrometer . The manual SPME holder ( Supelco , Bellefonte , PA , USA ) was used to perform the experiments . Chromatographic separation was carried out on 30 m x 0 . 25 mm I . D . ZB-semivolatiles , Zebron column ( Phenomenex , Macclescfield , UK ) . The oven temperature was set at 40°C for 3 min , temperature increased from 40 to 250°C at 5°C min-1 and set at the maximum temperature for 4 min . Helium was used as carrier gas at 1 ml min-1 . Mass spectra were recorded in electron impact ( EI ) mode at 70 eV . Scan mode was used for the acquisition to get all the volatile compounds sampled . Quadrupole and source temperature were set at 150 and 230°C respectively . Compounds were identified using MS data from the NIST library ( >95% confidence ) . 100 S . carpocapsae IJs were placed in the centre of a 90 mm PBS agar plate ( 1 . 5% w/v ) in a 5 μl aliquot of PBS . Plates were divided into four zones; a central zone 15 mm in diameter , and three further zones equally spaced over the remainder of the plate . Plates were allowed to air dry for ~5 min . Evaporation of the PBS allowed the IJs to begin movement over the agar surface . Lids were then placed back onto the Petri dishes , and plates were incubated at 23°C in darkness for one hour . IJs were counted across central and peripheral zones and expressed as percentage of total worms . Our subsequent analysis was conducted on total IJs found within the two central zones . Relative to those found in the two peripheral zones . Five replicate assays were conducted for each treatment . 3 . 5 g of compost ( John Innes No . 2 ) was placed in a petri dish ( 55 mm ) , and dampened evenly with 150 μl PBS . Approximately ten IJs were pipetted onto the compost in 5 μl of PBS , and left for 5 minutes at room temperature; this enabled IJs to begin nictating . For the waxworm volatile challenge , one healthy waxworm ( UK Waxworms Ltd . ) was placed inside a 1 mL pipette tip , without filter . For the mealworm volatile challenge , two mealworms ( Monkfield Nutrition , UK ) , weight-matched to the waxworm , were placed inside a 1 mL pipette tip , without filter . Blank exposure data were captured using an empty 1 ml pipette tip , without filter . In each case , the pipette was set to eject a volume of 500 μL , comprising air and the corresponding insect volatiles . A binocular microscope was used to record IJ behavioural responses following up to five volatile exposures each , on gentle ejection from the pipette within a distance of ~1 cm of the S . carpocapsae IJs . A five second period was allowed between each volatile exposure . Recording ended for any individual when jumping was observed or the IJ abandoned a nictating stance ( this always corresponded with migration away from the stimulus ) . A jumping index was calculated for each treatment group by counting the number of IJs which jump in response to any of the five volatile exposures [1] . Additional behavioural observations were recorded , and subsequently reported as percentage IJs displaying the behaviour over the course of up to five volatile exposures , or until the IJ migrated / jumped out of the field of vision . Five replicate assays were conducted for each treatment . Two circular holes ( approx . 6 mm diameter , centred 4 mm from edge of lid ) were drilled either side of a 90 mm petri dish lid . Two microcentrifuge tubes ( 1 . 5 ml ) with a small hole cut out the bottom ( approx . 2mm diameter ) , were also used for each assay . 200 S . carpocapsae IJs were placed in the centre of a 90 mm PBS agar plate ( 1 . 5% w/v ) in a 5 μl aliquot of PBS . The arena was segmented into positive and a negative zones either side of the plate ( 25 mm in length from the edge , circling off the plate at a point 60 mm apart; see Fig 1A ) . Plates were allowed to air dry for ~5 min , allowing the IJs to begin migration . The lid was placed on top of the plate , and sealed with parafilm . The 1 . 5 ml tubes were secured in the holes with parafilm; one remained empty , which we term the blank tube , and the other held four live Galleria mellonella fourth instar larvae , or four Tenebrio molitor larvae as appropriate . The lid of the tubes were then closed . The plates were incubated at 23°C in darkness for one hour . IJs were counted in the positive ( host side ) and negative ( blank side ) zones and then scored using a chemotaxis index [26] . The assay format was adapted from Grewal et al . [1994] [27] . Five replicate assays were conducted for each treatment . Freshly emerged S . carpocapsae IJs were fixed in 4% paraformaldehyde overnight at 4°C , followed by a brief wash in antibody diluent ( AbD; 0 . 1% bovine serum albumin , 0 . 1% sodium azide , 0 . 1% Triton-X-100 and PBS pH 7 . 4 ) . The fixed specimens were roughly chopped on a glass microscope slide with a flat edged razor , and incubated in primary polyclonal antiserum raised against GLGPRPLRFamide , N-terminally coupled to KLH , and affinity purified ( 1:800 dilution in AbD ) for 72 h at 4°C . Subsequently , chopped IJs were washed in AbD for 24 h at 4°C , and then incubated in secondary antibody conjugated to fluorescein isothiocyanate ( 1:100 dilution in AbD ) for 72 h at 4°C . A further AbD wash for 24 h at 4°C was followed by incubation in Phalloidin—Tetramethylrhodamine B isothiocyanate ( 1:100 dilution in AbD ) for 24 h at 4°C . Finally , chopped IJs were washed in AbD for 24 h at 4°C . Specimens were mounted onto a glass slide with Vectasheild mounting medium and viewed with a Leica TCS SP5 confocal scanning laser microscope . Controls included the omission of primary antiserum , and pre-adsorption of the primary antiserum with ≥250 ng of GLGPRPLRFamide . Pre-adsorption of the primary antiserum in GLGPRPLRFamide resulted in no observable staining . PCR primers were designed to amplify a 200 bp region of Sc-flp-21 ( Gene ID: L596_g19959 . t1 ) from S . carpocapsae IJ cDNA . Template PCR products were generated as follows: [95°C x 10 min , 40 x ( 95°C x 30 sec , 60°C x 30 sec , 72°C x 30 sec ) 72°C x 10 min] . PCR products were assessed by gel electrophoresis , and cleaned using the Chargeswitch PCR clean-up kit ( Life Technologies ) . Amplicons were quantified by a Nanodrop 1000 spectrophotometer . Sense and antisense probes were generated using amplicons in an asymmetric PCR reaction . The components of each reaction were as follows: 2 . 0μl of Reverse primer ( or Forward primer for control probe ) ; 2 . 5μl 10X PCR buffer with MgCl2 ( Roche Diagnostics ) ; 2μl DIG DNA labelling mix ( Roche Diagnostics ) ; 0 . 25μl 10x Taq DNA polymerase ( Roche Diagnostics ) ; 20ng DNA template; distilled water to a volume of 25μl . Probes were assessed by gel electrophoresis . Freshly emerged S . carpocapse IJs were fixed in 2% paraformaldehyde in M9 buffer overnight at 4°C followed by 4h at room temperature . Nematodes were chopped roughly using a sterile razor blade for 2 minutes and washed three times in DEPC M9 . Subsequently , the chopped nematodes were incubated in 0 . 4 mg/ml proteinase K ( Roche Diagnostics ) for 20 minutes at room temperature . Following three washes in DEPC M9 , the nematodes were pelleted ( 7000g ) and frozen for 15 minutes on dry ice . Subsequently the nematode sections were incubated for 1 minute in -20°C methanol and then in -20°C acetone for 1 minute . The nematodes were then rehydrated using DEPC M9 and incubated at room temperature for 20 minutes , after which three wash steps in DEPC M9 were carried out to remove any acetone . The nematodes were pre-hybridised in 150 μl hybridisation buffer [prepared as detailed by Boer et al . , 1998] for 15 minutes . The hybridisation probes were heat denatured at 95°C for 10 minutes , after which they were diluted with 125 μl hybridisation buffer . The probe-hybridisation mixture was then added to the nematode sections which were incubated at 50°C overnight . Post hybridisation washes were carried out as follows: three washes in 4x Saline Sodium Citrate buffer ( 15 minutes , 50°C ) ; three washes in 0 . 1x SSC/0 . 1x Sodium dodecyl sulphate ( 20 minutes , 50°C ) and; 30 minute incubation in 1% blocking reagent ( Roche Diagnostics ) in maleic acid buffer ( 50°C ) . Subsequently the nematodes were incubated at room temperature for 2 h in alkaline phosphatase conjugated anti-digoxigenin antibody ( diluted 1:1000 in 1% blocking reagent in maleic acid buffer ) . Detection was completed with an overnight incubation in 5-Bromo-4-chloro-3-indolyl phosphate/Nitro blue tetrazolium at 4°C . The staining was stopped with two washes in DEPC treated water . The nematode sections were mounted on to glass slides for visualisation . Data pertaining to both qRT-PCR and behavioural assays were assessed by Brown-Forsythe and Bartlett’s tests to examine homogeneity of variance between groups . One-way or two-way ANOVA was followed by Bonferroni’s multiple comparisons test . All statistical tests were performed using GraphPad Prism 6 .
As is the case for other parasitic nematode species , S . carpocapsae was found to encode a less diverse RNAi pathway than that of C . elegans , in terms of gene for gene conservation [21] . However , the apparent reduction in AGO homologue diversity is offset by significant expansions across several putative ago genes , to give a predicted overall increase in the S . carpocapsae AGO complement ( 38 in total ) , relative to C . elegans ( 24 , not including pseudogenes ) [28]; WAGO-1 ( nineteen ) , ALG-1 ( three ) , ALG-3 ( two ) , WAGO-5 ( four ) , WAGO-10 ( two ) , WAGO-11 ( three ) are all expanded relative to C . elegans . Notably , no identifiable homologue of RDE-1 , the primary AGO for exogenously triggered RNAi events in C . elegans , could be identified ( refer to S1 and S2 Tables ) . The presence of PRG-1 and components of the piwi interacting ( pi ) RNA biosynthetic machinery suggests that a functional piRNA ( or 21U RNA ) pathway may be present . Whilst ERGO-1 is not conserved , two putative ALG-3 orthologues suggest that a functional endo-siRNA ( 26G RNA ) pathway may also exist , which is supported by broad conservation of associated proteins . MicroRNA-associated AGOs , ALG-1 and ALG-2 are conserved , with a small apparent expansion of ALG-1 to three related proteins in S . carpocapsae . Further understanding of how RNAi pathway complements influence functionality will require small RNA sequencing efforts , and functional genomics approaches . The RNA-dependent RNA Polymerase ( RdRp ) , RRF-3 is conserved , and known to function antagonistically to exogenously primed RNAi , through competing activity for pathway components required for both exogenous RNAi , and the endo-siRNA ( 26G RNA ) pathway within which RRF-3 operates [29–31] . The RdRps , RRF-1 and EGO-1 , which are involved in the biosynthesis of secondary siRNAs ( 22G RNAs ) are also conserved . Loss of the argonaute ERGO-1 which functions upstream of secondary siRNA biogenesis in the endo-siRNA ( 26G RNA ) pathway in C . elegans , also leads to an exogenous ERI phenotype ( Enhanced RNAi ) , but is not conserved in S . carpocapsae , suggesting that ALG-3 / -4 may be solely responsible for endo-siRNA functionality [32 , 33] . The apparent absence of the intestinal dsRNA transporter , SID-2 is consistent with findings from other parasitic nematodes [21 , 34 , 35] . SID-1 also appears to be absent , however CHUP-1 , a putative cholesterol uptake protein which contains a SID-1 RNA channel is present , and may assist in the intercellular spread of dsRNA . RSD-3 , which also effects intercellular spread of dsRNA is conserved ( see Fig 2 for pathway overview and S2 Text ) . Various treatment regimens were employed in order to assess the responsiveness of S . carpocapsae IJs to exogenous dsRNA . 24 h incubation in 5 mg/ml dsRNA , with 50 mM serotonin was not sufficient to trigger statistically significant Sc-flp-21 knockdown ( Fig 3A ) , however a 24 h dsRNA / serotonin incubation followed by a 24 h recovery in PBS only , did trigger a small decrease in Sc-flp-21 relative to Sc-act when compared to gfp and neo dsRNA controls ( 0 . 70 ±0 . 11 , P<0 . 05 ) ( Fig 3B ) . Extended incubation of S . carpocapsae IJs in 5 mg/ml dsRNA and 50 mM serotonin for 48 h triggered robust knockdown of Sc-flp-21 ( 0 . 16 ±0 . 07 , P<0 . 0001 ) ( Fig 3C ) . 48 h incubation in 1 mg/ml dsRNA , with 50 mM serotonin also triggered significant levels of Sc-flp-21 knockdown ( 0 . 49 ±0 . 27 , P<0 . 01 ) , however this was not as effective as the 5 mg/ml dsRNA treatment ( Fig 3D ) . A BLAST analysis identified predicted S . carpocapsae transcript L596_g5821 . t as the non-target gene with most similarity to the Sc-flp-21 dsRNA ( S1 Text ) . The relative expression level of L596_g5821 . t1 was unaffected by a 48 h incubation in 5 mg/ml Sc-flp-21 dsRNA with 50 mM serotonin , relative to neo and gfp dsRNA ( 1 . 013 ±0 . 04 , P>0 . 05 ) ( Fig 3E ) . Comprehensive volatile signatures were characterised , and significant differences noted between G . mellonella and T . molitor larvae . In total , we identified 9 compounds unique to G . mellonella , four compounds unique to T . molitor , and 15 compounds shared between both species ( Table 2 ) . These profiles significantly expand on the number of volatiles identified from headspace GC-MS data presented by Hallem et al . [36] for the same insect species . A semi-quantitative analysis of detected volatiles can be found in supplementary S1 Data . S . carpocapsae IJs were challenged by exposure to volatiles from G . mellonella or T . molitor following RNAi ( 48 h 5 mg/ml dsRNA , 50 mM serotonin ) and control treatments . A decrease in hyperactive nictation following Sc-flp-21 knockdown was observed ( 10% ±5 . 774 ) relative to untreated ( 40 . 75% ±6 . 75; P<0 . 01 ) and neo dsRNA treatment ( 47 . 5% ±2 . 5; P<0 . 01 ) following G . mellonella volatile challenge ( Fig 4A ) . Likewise , a decrease in hyperactive nictation was observed following T . molitor volatile challenge to Sc-flp-21 RNAi IJs ( 5 . 0% ±2 . 9 ) , relative to untreated ( 57 . 25% ±2 . 8; P<0 . 0001 ) and neo dsRNA treatment ( 35 . 0% ±6 . 5; P<0 . 001 ) ( Fig 4B ) . A decrease in the jumping index of IJs following Sc . flp-21 dsRNA treatment was observed when challenged by G . mellonella volatiles ( 0 . 08 ±0 . 02 ) relative to untreated ( 0 . 72 ±0 . 09; P<0 . 001 ) and neo dsRNA treated ( 0 . 55 ±0 . 06; P<0 . 01 ) ( Fig 4C ) . Similarly , a decrease in jumping index as a response to T . molitor volatiles was observed following Sc-flp-21 RNAi ( 0 . 03 ±0 . 02 ) relative to untreated ( 0 . 46 ±0 . 05; P<0 . 001 ) and neo dsRNA treatment ( 0 . 4 ±0 . 04; P<0 . 001 ) ( Fig 4D ) . An agar host-finding assay was used to further assess the impact of flp-21 knockdown . A decrease in G . mellonella finding ability was observed ( 0 . 06 ±0 . 08 ) relative to untreated ( 0 . 53 ±0 . 03; P<0 . 001 ) and neo dsRNA treated ( 0 . 42 ±0 . 08; P<0 . 01 ) ( Fig 4E ) . Likewise , a decrease in T . molitor finding ability was observed ( 0 . 01 ±0 . 06 ) relative to untreated ( 0 . 32 ±0 . 04; P<0 . 01 ) and neo dsRNA treated ( 0 . 26 ±0 . 1; P>0 . 01 ) ( Fig 4F ) . It was also found that Sc-flp-21 RNAi resulted in significantly decreased lateral dispersal , relative to both untreated and neo dsRNA treatment ( P<0 . 0001 ) ( Fig 4G ) . In all instances , dsRNA treatment regimens which triggered lower levels of Sc-flp-21 knockdown relative to the 48h 5 mg/ml dsRNA , 50 mM serotonin approach , failed to trigger null phenotypes . flp-21/FLP-21 was localised exclusively to paired neurons within the central nerve ring region of S . carpocapsae IJs . Without additional neuroanatomical information on S . carpocapsae IJs it is impossible to further define these cells , however , based on the immuocytochemical localisation the cells appear to project posteriorly ( Fig 5 ) . These data suggest that FLP-21 must act as a modulator of sensory function , downstream of the primary chemosensory neurons ( amphids ) .
RNA interference is an extremely important tool for the study of gene function in parasitic nematodes [37 , 38] . Three independent reports of a functional RNAi pathway in the entomopathogenic nematode Heterorhabditis bacteriophora have been published . Ciche and Sternberg [39] assessed the efficacy of RNAi through soaking egg / L1 stage H . bacteriophora in 5–7 . 5 mg/ml dsRNA targeting a number of genes which had been selected on the basis of phenotypic impact on the model C . elegans . Demonstrable phenotypes and target transcript knockdown signified an active pathway . Moshayov , Koltai and Glazer [40] employed the methodology of Ciche and Sternberg [39] to study the involvement of genes in the regulation of IJ exsheathment ( or ‘recovery’ ) . Subsequently , Ratnappan et al . [41] demonstrated that microinjection was also a suitable method for introducing dsRNA into hermaphrodite gonads , effectively triggering the RNAi pathway in F1 progeny . To date , no such assessment of a functional RNAi pathway has been published for Steinernema spp . The RNAi pathway of S . carpocapsae has been characterised by BLAST and validated through silencing Sc-flp-21 in IJs . Our data indicate that neuronal cells are sensitive to RNAi in S . carpocapsae IJs , and that knockdown is highly sequence specific . Like other parasitic nematodes S . carpocapsae encodes an expanded set of WAGO-1 ( R06C7 . 1 ) family AGOs ( 19 in total ) which function primarily with secondary siRNAs ( 22G RNAs ) in C . elegans , along with CSR-1 which is also conserved . Whilst RDE-1 is primarily responsible for triggering the onset of an exogenous RNAi response , acting upstream of secondary siRNAs ( 22G RNAs ) , it is not conserved in S . carpocapsae [21 , 31] . Our observation of RNAi sensitivity in S . carpocapsae reveals that RDE-1 is not required to trigger an exogenous RNAi response , however the functional significance of AGO homologue expansions relative to C . elegans remains to be determined . The lack of SID-2 seems to correlate with our observation that relatively high amounts of dsRNA are required to trigger the RNAi pathway by oral delivery . The nearest non-target gene sequence within the S . carpocapsae genome represents an uncharacterised predicted gene ( L596_g5821 . t1 ) . The Sc-flp-21 dsRNA shared high levels of sequence similarity over a 21 bp stretch of L596_g5821 . t1 ( 20 of 21 bp shared ) , however qRT-PCR indicates that L596_g5821 . t1 had not been silenced , which could suggest: ( i ) the level of sequence similarity was either insufficient for gene knockdown; ( ii ) dsRNA was not diced in the correct register to produce this exact 21 bp sequence within a significant population of siRNAs; or ( iii ) the L596_g5821 . t1 gene is not expressed in cells / tissue which is sufficiently susceptible to dsRNA delivered under the conditions tested . In order to trigger significant knockdown of Sc-flp-21 , 48h continuous exposure to dsRNA was required in the presence of 50 mM serotonin . Reducing dsRNA exposure time lead to a corresponding reduction in Sc-flp-21 knockdown , as did a reduction of dsRNA amount from 5mg/ml to 1mg/ml over a 48h time-course . Phenotypes which developed following 48h dsRNA exposure were not observed across any of the experimental variations which resulted in decreased gene knockdown ( shorter exposure timeframes / lower dsRNA amounts ) . This has potentially important implications for RNAi experimental design in other parasitic nematodes , and notably in C . elegans , for which the validation of gene knockdown by qRT-PCR is not common across the literature . Undoubtedly false negative determinations of gene function will be a problem in this context . Our data demonstrate that statistically significant gene knockdown levels are not necessarily sufficient to reveal gene function; careful consideration should be given to the design of RNAi experiments as a result . The neuronal RNAi sensitivity of S . carpocapsae IJs , and the ease of behavioural assays makes these species ideal models for studying the neurobiology of sensory perception and host-finding behaviours . Within the Steinernematid EPNs , a number of species also display a highly specialised jumping behaviour which can be triggered in nictating IJs on exposure to host Insect volatiles [18] . Silencing Sc-flp-21 triggers pleiotropic effects on sensory behaviours of relevance to host-finding , lateral dispersal , hyperactive nictation and jumping phenotypes . The waxworm and mealworm headspace SPME GC-MS profiles are significantly expanded relative to those presented by Hallem et al . [36] These data could provide a valuable tool for comparative analysis of neurobiology and host-finding behaviours across EPN species . We find that flp-21 / FLP-21 is localised exclusively to paired neurons in the anterior of the IJ using whole mount in situ hybridisation and immunocytochemistry . This represents the most restricted flp-21 / FLP-21 expression pattern observed in a nematode to date . FLP-21 is expressed in several anterior sensory and motor neurons in C . elegans , where it is known to coordinate aspects of sensory perception [11] . The FLP-21 homologues of two plant parasitic nematodes , Globodera pallida , and Meloidogyne incognita ( GSLGPRPLRFamide ) are expressed in the sensory amphid neurons and across the central nerve ring of infective stage J2s [42] . These data support a broad role for FLP-21 in coordinating sensory perception across different nematode species . The ICC localisation of FLP-21 in S . carpocapsae reveals positive immunostaining within the two cell bodies , and posteriorly along the axons , terminating at paired synapses at points around the terminal bulb of the pharynx . Additional effort must focus on understanding the neuroanatomy of entomopathogenic nematodes in order to understand these data more fully , and exploit this platform for comparative neurobiology . Collectively , these data provide the first mechanistic insight to EPN sensory behaviour , which may have implications for biocontrol efficacy . Through isolating genes and signalling pathways which coordinate these behaviours , efforts to identify molecular markers of desired behaviours and traits could facilitate the identification of more suitable isolates and strains for biocontrol use , and the enhancement of current strains through selective breeding / mutagenic approaches . The selection or manipulation of behavioural tendencies could lead to strains which are capable of operating within new ecological niches , expanding their utility . More broadly , these data suggest a broad role for FLP-21 in coordinating sensory perception and host-finding behaviours which may be relevant to other economically important parasites of plant and mammal . | Entomopathogenic nematodes ( EPNs ) use a range of behaviours in order to find a suitable host , some of which are shared with important mammalian parasites . The ethical burden of conducting research on parasites which require a mammalian host has driven a move towards appropriate ‘model’ parasites , like EPNs , which have short life cycles , can be cultured in insects or agar plates , and have excellent genomic resources . This study aimed to develop a method for triggering gene knockdown by RNA interference ( RNAi ) , which would allow us to study the function of genes and the molecular basis of behaviour . We have successfully knocked down the expression of a neuropeptide gene , flp-21 in S . carpocapsae infective juveniles . We find that it is involved in the regulation of behaviours which rely on sensory perception and relate to host-finding . This study provides a method for employing RNAi in a promising model parasite , and characterises the molecular basis of host-finding behaviours which could be relevant to economically important mammalian parasites . EPNs are also used as bioinsecticides , and so understanding their behaviour and biology could have broad benefits across industry and academia . | [
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... | 2017 | A neuropeptide modulates sensory perception in the entomopathogenic nematode Steinernema carpocapsae |
Recently introduced , exotic plant pathogens may exhibit low genetic diversity and be limited to clonal reproduction . However , rapidly mutating molecular markers such as microsatellites can reveal genetic variation within these populations and be used to model putative migration patterns . Phytophthora ramorum is the exotic pathogen , discovered in the late 1990s , that is responsible for sudden oak death in California forests and ramorum blight of common ornamentals . The nursery trade has moved this pathogen from source populations on the West Coast to locations across the United States , thus risking introduction to other native forests . We examined the genetic diversity of P . ramorum in United States nurseries by microsatellite genotyping 279 isolates collected from 19 states between 2004 and 2007 . Of the three known P . ramorum clonal lineages , the most common and genetically diverse lineage in the sample was NA1 . Two eastward migration pathways were revealed in the clustering of NA1 isolates into two groups , one containing isolates from Connecticut , Oregon , and Washington and the other isolates from California and the remaining states . This finding is consistent with trace forward analyses conducted by the US Department of Agriculture's Animal and Plant Health Inspection Service . At the same time , genetic diversities in several states equaled those observed in California , Oregon , and Washington and two-thirds of multilocus genotypes exhibited limited geographic distributions , indicating that mutation was common during or subsequent to migration . Together , these data suggest that migration , rapid mutation , and genetic drift all play a role in structuring the genetic diversity of P . ramorum in US nurseries . This work demonstrates that fast-evolving genetic markers can be used to examine the evolutionary processes acting on recently introduced pathogens and to infer their putative migration patterns , thus showing promise for the application of forensics to plant pathogens .
Plant pathogens that have been introduced to a new environment may be characterized by low genetic diversity due to a genetic bottleneck experienced during the process of introduction and establishment , given that only one or a few genotypes are usually introduced [1]–[5] . Genetic diversity may also be lower on the margins of an epidemic or in founder compared to older populations [6]–[9] . In some cases the absence of a mating type may limit the pathogen to clonal reproduction and contribute to its reduced genetic diversity , yet clonality does not necessarily prevent continued evolution . Phytophthora infestans , causal agent of potato and tomato late blight , is a well known example of a plant pathogen able to adapt while reproducing clonally , as observed by changing virulence on host cultivars [10] . Stepwise evolution of new pathotypes in a single clonal lineage has also been observed for stripe rust of wheat , Puccinia striiformis f . sp . tritici , in Australia and New Zealand [4] . The increasing development and availability of polymorphic neutral genetic markers have allowed for detailed exploration of the genetic variation contained within clonal lineages [11]–[13] . Genetic markers are also beginning to be used for forensic purposes in human pathogens . Microbial forensics is “the detection of reliably measured molecular variations between related microbial strains and their use to infer the origin , relationships , or transmission route of a particular isolate” [14] . This approach has been taken to examine high-profile HIV outbreaks and transmission events [15] , [16] and characterize anthrax strains associated with bioterror attacks [17] , [18] . Forensics requires a sound scientific foundation , including knowledge of the genetic diversity within and among populations of the organism of interest and the evolutionary forces and genetic mechanisms that shape this diversity [19] , [20] . The population genetic base required for forensic work remains weak for many plant pathogens that pose economic or environmental threats [19] . Phytophthora ramorum , the causal agent of sudden oak death , was recently introduced to North America and is responsible for the rapid decline of forest populations of tanoak ( Lithocarpus densiflorus ) and coast live oak ( Quercus agrifolia ) in northern California coastal forests and parts of coastal southern Oregon [21] , [22] . P . ramorum is also a foliar and twig pathogen on common ornamentals , such as Rhododendron , Viburnum , Pieris , and Camellia . Thus , P . ramorum has been found in nurseries in North America and Europe , and nursery shipments have been implicated in the movement of the pathogen . There is serious concern about the inadvertent transfer of P . ramorum to other susceptible ecosystems , such as the Appalachians [23] . P . ramorum has had significant economic and societal impacts [22] , [24] , [25] . P . ramorum is a diploid oomycete , located in the kingdom Stramenopila along with diatoms , golden-brown algae , and brown algae [26] , [27] . Fast-evolving microsatellites in P . ramorum have confirmed the clonal reproduction of this pathogen and have proved valuable for examining its population structure [12] , [13] , [28] , [29] . Three distinct clonal lineages of P . ramorum have been found in nurseries [28] , [30] . These lineages appear to have been evolutionarily isolated for at least 100 , 000 years [31] , which together with their initial geographic distributions suggests that there were three introductions of this pathogen to North America and Europe [32] . The lineages have been given the names NA1 , NA2 , and EU1 by consensus agreement within the P . ramorum research community [33] . The NA1 lineage has been the most frequently isolated lineage from US nurseries and is the cause of oak and tanoak mortality in US forests [13] , [28] . The EU1 lineage was initially confined to European nurseries , but is now also found in European parks and North American nurseries [34]–[36] . The third lineage , NA2 , has only been documented in North American nurseries [28] , [36] . P . ramorum is self-sterile; sexual reproduction requires contact between two different mating types . All tested NA1 and NA2 isolates have been mating type A2 and EU1 isolates mating type A1 with the exception of rare finds of A2 in Belgium [37] . Sexual reproduction has not yet been observed in nurseries where both mating types have been found [34] . Most of the P . ramorum-positive nurseries have been in California , Oregon , and Washington , where annual inspection and sampling is required for nurseries that ship interstate and contain host or associated host plants on the P . ramorum host lists per the Federal Interim Rule of 2007 ( 7 CFR 301 . 92 ) . West Coast nurseries that ship non-host nursery stock interstate are also required to be inspected annually . When found , infected plants are quarantined and destroyed under the authority of the Plant Protection Act of 2000 . P . ramorum has also been found in states that received shipments from infected West Coast nurseries . For example , shipments of millions of potentially infected plants were made from a large California nursery to over 1 , 200 nurseries in 39 states in 2004 [25] . When a nursery has been confirmed as infested with P . ramorum and it has been determined that the nursery shipped potentially infected P . ramorum host or associated host plants , the nursery is required to provide to the US Department of Agriculture's Animal and Plant Health Inspection Service ( USDA APHIS ) a list of all host and associated host plants that were shipped from the nursery during the preceding 12 months . A trace forward protocol ( http://www . aphis . usda . gov/plant_health/plant_pest_info/pram/ ) is implemented to determine whether the receiving nurseries or landscapes have become infested . Similarly , a trace back protocol is implemented at the infested shipping nursery to investigate the potential source of P . ramorum . Previous studies examining neutral genetic variation in nursery populations of P . ramorum using mitochondrial DNA sequence , AFLP , or microsatellites have focused on the broad diversity of a worldwide sample of P . ramorum isolates [28] , [30] , [38] and specifically on Oregon [13] , California [12] , or West Coast [29] populations using isolates collected through 2005 . These studies have shown genetic similarity between 2004 nursery isolates and early California forest infestations [12] and migration among West Coast populations in the first half of this decade [29] . The Oregon forest population is an apparent exception to the frequent migration between California , Oregon , and Washington , as it is genetically differentiated from both California forest and Oregon nursery populations [13] . Thus far , microsatellites have been the most informative markers for examining population structure and migration . Here we report on the population genetic analysis of P . ramorum in US nurseries using 279 isolates collected from infected nurseries from across the US between 2004 and 2007 . There is interest in the P . ramorum community in using genetic markers to link new detections of P . ramorum in both nursery and wildland settings to possible sources; therefore , we typed microsatellite loci known to show variation within and between the P . ramorum clonal lineages to examine their utility in confirming or contributing to trace forward and trace back investigations and , more generally , the potential for forensic analysis of P . ramorum . We specifically address four major questions regarding nursery populations of P . ramorum: 1 ) Do nursery populations show genetic diversity and population structure or are they dominated by a single dominant or founding genotype ? 2 ) Are West Coast infestations more genetically diverse than those in other states , as might be expected if infestations are older and effectively larger in Oregon , Washington , and California ? 3 ) Have the populations of the West Coast states changed between 2004 and 2007 in a way that would indicate that eradication measures have or have not been effective ? 4 ) Can we use these genetic markers to infer the major migration pathways and potential sources of recent migrants ?
All 279 isolates produced multilocus genotypes that could be unambiguously assigned to one of the three known P . ramorum lineages and no recombinant multilocus genotypes were observed that would be indicative of sexual reproduction between lineages . Thirty-four EU1 isolates and 17 NA2 isolates were identified in the sample ( Table 1 ) . EU1 isolates were found in California ( CA ) , Oregon ( OR ) , and Washington ( WA ) and produced two genotypes ( Figure 1 ) , which differed by two repeats at locus 64 . OR and WA isolates were all identical , while all but one of the CA isolates were the second genotype . All of the NA2 isolates were from WA and produced identical genotypes except for one isolate from 2004 ( Table 1 , Figure 1 ) , which differed by one repeat at both alleles of locus PrMS43a . The NA1 lineage was the most common and genetically variable lineage in US nurseries , found in all sampled states . We found 53 different multilocus genotypes among the 228 NA1 isolates , including three genotypes with null alleles at PrMS43b ( Figure 1 , Tables S1 and S2 ) . Unique to the NA1 lineage was apparent uniform homozygosity at loci PrMS39b , PrMS43a , and PrMS43b . These loci also exhibited high numbers of alleles among NA1 isolates relative to the other genotyped loci ( Table S1 ) . Loss of heterozygosity was observed for two isolates at locus PrMS45 and one isolate at locus 64 . Sample sizes from many states were very small , e . g . one isolate from one infested nursery in the state ( Tables 1 and S3 ) . For sample sizes up to about 15 isolates , there was a positive linear relationship between sample size and number of resulting multilocus genotypes , such that for the NA1 clonal lineage every five additional isolates produced around 3 additional multilocus genotypes ( Figure S1 ) . The relationship between sample size and genotypes changed at higher sample sizes and the number of multilocus genotypes was instead correlated with the number of infected nurseries in the state . The lineages are separated by large genetic distances ( Figure 1 ) and reproduction appears to be completely clonal [12] , [13] , [28] , therefore the three lineages were considered separately . Furthermore , the paucity of EU1 and NA2 isolates and genotypes precluded the need for extensive analysis of these lineages and hence our analyses focused on NA1 isolates . We examined the genotypic diversity , genotypic evenness , and genotypic and allelic richness of NA1 samples by state and year for those with sample sizes of five or more isolates ( Table 1 ) . Importantly , given the variation in sample sizes among states , we used rarefaction to estimate genotypic and allelic richness for a standardized sample size of five isolates . Interestingly , genotypic richness in the Connecticut ( CT ) , Georgia ( GA ) , Texas ( TX ) , and Virginia ( VA ) samples were at levels seen in the West Coast states . Evenness is expected to be influenced by differences in sampling intensity , but tended to decrease over the sampled years in CA and WA . Private alleles were found in OR , WA , TX , and VA . A larger number of states produced multilocus genotypes that were not observed elsewhere ( Table S2 ) . Minimum spanning networks revealed qualitative differences among states and years for the West Coast ( Figure 2 ) . By 2007 , samples from all three states produced relatively compact networks , indicating that these populations had been limited to a small number of mostly closely related genotypes . The change over time was most evident in the Washington networks , in which there were long chains of genotypes prior to 2007 . Private genotypes were generally on the margins of the networks and sometimes were only distantly related to the other genotypes , suggesting that they were either the result of rare mutation events or were immigrants from locations with intermediate genotypes . We also examined the minimum spanning networks for other states represented by five or more isolates to compare them to the West Coast states ( Figure 3 ) . These networks generally showed populations of closely related genotypes . The most common multilocus haplotype in each of the four networks corresponded to one of the two most common multilocus genotypes in the overall sample ( either MG 1 or 2 in Table S2 ) and may thus be the founding genotype . The outlying haplotypes in the networks were often private genotypes . We tested for significant genetic variation among West Coast states and years using analysis of molecular variance . We found significant variation among years within states , but more variation among states and within states and years ( Table 2 ) . Examination of CA , OR , and WA individually showed that variation among years accounted for 0% ( P = 0 . 27 ) , 3 . 0% ( P = 0 . 13 ) , and 4 . 9% ( P<0 . 0001 ) of the total variation , respectively . When data were clone corrected there was significant variation among states but not among years within states ( Table 2 ) . Structure 2 . 2 and BAPS 5 . 2 were used to cluster NA1 isolates , without regard to state or year of isolation , into underlying groups . The Structure analysis produced the highest likelihood for two groups ( posterior probability that K = 2 was 1 . 00 ) . AMOVA confirmed significant variation between these groups , which accounted for 33% of the variation . Ten isolates could not be assigned to one or the other group with a probability greater than 0 . 75 and 31 isolates were not assigned with a probability greater than 0 . 95 ( Figure 4 ) . The optimal partitioning of isolates by BAPS produced 18 clusters ( Figure 5 ) . However , these 18 clusters formed two overall groups that largely coincided with the two Structure groups ( Figure 5 ) . AMOVA on the BAPS groups indicated that the two overall groups were responsible for 27% of the variation and the clusters within the larger groups explained another 27% of the variation . K-means clustering of individuals based on either allele frequency or AMOVA also produced the best result for two groups based on Calinski and Harabasz's pseudo-F [39] . Differences in group assignment between Structure , BAPS , and k-means clustering were limited to twelve isolates , all of which produced low posterior probabilities for group assignment in Structure . Many states were represented by mostly one group or the other , but there were also mixed populations ( Figures 4 and 6 ) . Both groups were represented in WA in all years , OR in 2004 , CA in 2006 , and GA with high probability . Structure outputs the overall allele frequencies and frequencies within each resulting group , which showed that particular loci and alleles were highly influential in determining group assignments ( Table 3 ) . For example , allele 246 of locus PrMS39b had an overall frequency of 0 . 303 , but a frequency of 0 . 936 in group 2 . The influential alleles differed by only one repeat from each other , suggesting that these groups may not be robust to repeated and reverse mutation . The relative rates of immigration to mutation among West Coast states and from these states eastward were estimated using a coalescent-based approach , as implemented in the program Migrate . We used a migration model in which the three West Coast source populations could both send and receive migrants , but the combined population representing all other states could only receive immigrants . This migration model is consistent with nursery industry shipment patterns . The ratio of immigration rate to mutation rate ( m/µ ) tended to be higher for the non-West Coast sample , but with a large amount of uncertainty in the estimates ( Figure 7 ) . Many of the estimates were not significantly greater than 1 . 0 , indicating that mutation and drift were often more important than migration in generating population genetic variation . When a nursery that ships P . ramorum host and associated host plants out of state is confirmed to be infested with P . ramorum , the USDA APHIS trace forward protocol is implemented by the receiving state ( s ) . Shipping records are obtained for all host and associated host plants that were shipped in the preceding 12 months . These shipping records are used to conduct inspections to determine whether receiving nurseries or landscapes have become infested . Trace forward shipments from P . ramorum infested nurseries in CA , OR and WA to non-West Coast states resulted in the detection of P . ramorum in 12 states from 2004 to 2007 ( Figure 6 ) . In 2004 , all but three of the confirmed trace forward detections originated in CA . The remaining three were from OR to CT ( 2 detections ) and MD ( 1 detection ) . Additional states received shipments from P . ramorum infested nurseries; however , the movement of any infected plants was not determined or confirmed .
Our analysis of the genotypic diversity of P . ramorum isolates from US nurseries revealed two genetic groups in the NA1 lineage . The composition of these groups suggests that many of the isolates collected in non-West Coast states were associated with California genotypes whereas the Connecticut infestation more closely resembled Oregon and Washington genotypes . This is in agreement with trace forward analyses by USDA APHIS , which established major shipments of P . ramorum-positive plants from California to nurseries across the country and smaller shipments from an Oregon nursery to Connecticut in 2004 . The 2004 California shipments also sent P . ramorum-positive plants to Oregon and Washington , perhaps explaining representation from both genetic groups in these states' samples . Migration between all three West Coast states was also inferred by Prospero et al . [29] based on genotyping of California forest isolates and Oregon and Washington nursery isolates collected from 2003 to 2005 . Yet , the clustering of isolates into two groups appeared to be highly influenced by three loci that show rapid evolution in NA1 . This suggests that over time isolates could mutate between groups and thus grouping based on these markers may not be robust in the long term . The states with representatives from both groups tended to be those with higher numbers of multilocus genotypes and higher genotypic diversities , which could be explained by either more migration to these states or larger populations with more opportunities for mutation . For example , the networks of Washington isolates included chains of genotypes differing by single mutational steps yet assigned to different groups , suggesting that these mixed populations could be the result of large and diverse infestations . P . ramorum isolates from nineteen states were examined and only five states were found that did not contain the most common genotype in the overall sample ( NA1 multilocus genotype 1 ) . Two of these , Connecticut and North Carolina , produced isolates with the second and third most common NA1 genotype , respectively . This suggests that only a few genotypes may be responsible for initiating P . ramorum infestations across the US . This is again consistent with USDA APHIS analysis , which indicated that shipments of infected plant material occurred only a few times . P . ramorum is also present in nurseries in British Columbia , Canada [36] , [40] and there has been movement of the pathogen between BC and West Coast states every year since 2003 based on USDA APHIS trace data . The most genetically variable populations were on the West Coast , as expected based on the large number of infected nurseries that have been found in these states ( Table S3 ) , yet we also found relatively diverse samples when we had five or more isolates from other states . The observed variation is likely related to the number of infested nurseries sampled and perhaps also to how long the infestations went undiagnosed , information that we do not have . Georgia and Texas had 14 and 11 confirmed positive nurseries , respectively , which could help explain the observed levels of variation , but Connecticut had only three and Virginia two positive nurseries ( Table S3 ) . More extensive sampling within nurseries would be required to elucidate the population structure in infested nurseries as our results suggest that we did not achieve saturation in sampling the diversity of nursery populations . In general , rapid detection and eradication should result in small effective population sizes and low genetic diversities . As the genotyping of nursery isolates becomes increasingly routine , more samples per nursery are being retained for genotyping . In fact , sampling appeared to be nearing saturation in 2007 for California and Washington nurseries . Providing an interesting contrast to the single genotype shared among many states , we identified 36 NA1 multilocus genotypes that were unique to a state . Destruction of infected plants should ensure that populations in individual nurseries do not have the opportunity to grow large and small populations are subject to genetic drift . The observed genetic diversity and number of private genotypes suggests that there is also rapid mutation following the founding of a new nursery population and little to no gene flow following initial introduction . Interestingly , in California , several recently established P . ramorum forest populations ( <5 yrs old ) were observed to be as diverse as older forest populations ( >10 yrs ) and the genetic distance among new populations was greater than that observed among older populations [12] , suggesting that a similar process of rapid mutation , genetic drift , and limited gene flow may characterize newly founded populations in both forest and nursery environments . From 2004 to 2007 NA1 populations in West Coast nurseries appeared to become increasingly dominated by a few closely related genotypes and in 2007 all three states produced compact minimum spanning networks . This pattern is particularly striking for Washington , from which we had the largest numbers of isolates and observed high genotypic and allelic richnesses , and suggests that in 2007 there were fewer nodes of infection or earlier detection and eradication of infections . In fact , West Coast states had many fewer P . ramorum-positive nurseries in 2007 than in previous years ( Table S3 ) . Prospero et al . [13] examined P . ramorum isolates from Oregon nurseries collected in 2003 and 2004 , finding four NA1 genotypes in 2003 and six in 2004 . Although each year was dominated by two closely related genotypes , there were no genotypes in common between years , which suggested that the 2003 nursery infestations were eradicated and the 2004 infestations were new introductions . In our sample of Oregon nursery NA1 isolates from 2004 through 2007 , we did not find significant genetic variation across years . Of 13 multilocus genotypes found in Oregon , 4 of these were found in more than one year and 3 additional genotypes differed by one repeat from a genotype found in multiple years . Thus , some genotypes may have persisted in Oregon nurseries . However , the most common Oregon multilocus genotype ( NA1 MG 2 ) was found in 2004 , 2005 , and 2006 but not 2007 . California nursery populations were dominated by a single genotype ( NA1 MG 1 ) , comprising 20 of the 36 isolates from the state . Mascheretti et al . [12] found the same dominant genotype in their nursery sample , which was also a common genotype in the California forests . This genotype has been observed in nurseries since 2004 , thus it is either not being eradicated from nurseries or is re-colonizing nurseries from forest populations . Given the levels of heterozygosity observed at most of the microsatellite loci [13] , [28] and in the nuclear genome [41] , the consistent homozygosity at loci PrMS39b , PrMS43a , and PrMS43b is unexpected . Loci PrMS45 and 64 were also heterozygous in all but three isolates and had large differences in allele sizes , therefore this limited homozygosity was likely a result of mitotic recombination . Mitotic recombination generally refers to crossing-over during mitosis , which results in the loss of heterozygosity at all loci distal to the chromosomal breakpoint . Loss of heterozygosity may also be the result of mitotic gene conversion , in which case only a small segment of the chromosome is altered . Mitotic recombination is thought to be responsible for frequent observations of loss of heterozygosity in P . infestans allozymes [42] and P . cinnamomi microsatellites [11] . Mitotic gene conversion has been observed in P . sojae [43] . Mitotic recombination or gene conversion may also provide an explanation for the homozygosity at PrMS39b , PrMS43a , and PrMS43b , where it must occur at a very rapid rate as these are also fast-evolving loci . It is also possible that these three loci are hemizygous or heterozygous for a null allele [11] or that intermediate genotypes have simply not been sampled . Mitotic recombination may purge deleterious mutations from Phytophthora populations in the absence of sexual reproduction and unmask recessive traits or advantageous new mutations [11] , [42] . However , the eradication of infections in US nurseries results in small effective population sizes and populations likely to be structured by genetic drift rather than natural selection . The major effect of mitotic recombination on nursery populations may be to increase the genetic distance between isolates as new mutations are made homozygous and passed on to asexual progeny . The genetic diversity among populations that could conceivably be created by this process may benefit the pathogen in the long term if in the future these populations are allowed to grow unchecked , which would allow natural selection to weed out the more fit recombinants from the less fit . Meiotic recombination through sexual reproduction would further benefit these populations by breaking linkages between beneficial and detrimental mutations . Limiting the distribution of the EU1 lineage , which is primarily the A1 mating type , and its proximity to NA1 and NA2 lineages ( A2 mating type ) will reduce the possibility of sexual reproduction . The EU1 clonal lineage has now been found in all three west coast states [34] , yet detectable genetic diversity in both this lineage and the NA2 lineage remain low . This could be due to the hypervariability of several of the microsatellite loci in NA1 but not EU1 and NA2 , the more recent introduction of EU1 and NA2 to North America , and/or smaller population sizes of these lineages in US nurseries compared to the NA1 lineage . The recent finding of a single nucleotide polymorphism in the mitochondrial DNA of the NA1 lineage implies that this lineage may have a larger effective population size than the other two lineages [30] . The rapid mutation rates of these microsatellite loci has proven valuable for population genetic analyses , but poses a challenge for forensic tracing of P . ramorum when mutation rates are as high as appears to be the case for the PrMS43a and PrMS43b loci in the NA1 lineage . For example , an isolate of interest may differ from a suspected source population at one of these loci , thus raising doubts about their connection . Alternatively , convergence through repeat or reverse mutations may also have caused some Washington isolates to cluster with isolates from California and other states , which could falsely imply a direct connection between states where there is none . On the other hand , the relative homogeneity of the EU1 and NA2 lineages in US nurseries may hinder genetic-based tracing of isolates in these lineages . Nevertheless , our results were consistent with trace forward analyses and thus these microsatellites should be informative when used in conjunction with other data . The identification of more microsatellite loci that exhibit variation within the clonal lineages would strengthen these inferences [32] , [44] , [45] . Continued genotyping of P . ramorum from nurseries will be necessary to track the movement and diversification of the lineages and to identify new dominant genotypes , newly introduced lineages , or recombinant genotypes . As part of our efforts , the clonal lineage of each P . ramorum isolate genotyped , with permission from the provider of the isolate , is posted to a public website along with its county and state of origin at http://oregonstate . edu/~grunwaln/index . htm . Ongoing genotyping will also be valuable in evaluating how effective eradication efforts are in restricting migration , lowering effective population size , and increasing the effect of genetic drift .
Isolates of P . ramorum were obtained from scientists with State Departments of Agriculture , the US Department of Agriculture's Animal and Plant Health Inspection Service , universities and research institutions as new or recurring findings of infected nurseries occurred . Newly infected sites are subject to federal quarantine and could not be systematically sampled . Thus sampling intensity likely varied by state . For example , isolates from non-West Coast states may each represent one infested nursery , whereas recent samples from OR and WA include multiple isolates per nursery . Isolates for which we had detailed host information came from Camellia japonica , C . sasanqua , C . bonsai , Kalmia latifolia , Laurus noblis , Osmanthus heterophyllus , O . fragrans , Pieris japonica , Rhododendron spp . , Viburnum tinus , and from soil and water baits . The 2004 shipments from CA to 39 states contained Camellia species . We do not know how many nurseries with recurrent infestations that were sampled over 2 or more years are represented in our dataset . Upon receipt , isolates were transferred to cleared 20% V8 agar medium ( 200 ml V8 juice; 2 g CaCO3; 30 mg/L β-sitosterol ( EMD Chemicals , Inc . , San Diego , CA ) ; 15 g agar; 800 ml deionized water ) and stored at 20°C in the dark . All isolates were handled following the standard operating procedures associated with corresponding USDA APHIS permits and an exemption from the Director of the Oregon Department of Agriculture for work with P . ramorum under containment conditions . Six microsatellite loci were genotyped that had previously shown variation among isolates within the P . ramorum clonal lineages: PrMS39b , PrMS43a , PrMS43b , PrMS45 [13] , 18 , and 64 [28] . These loci are also differentiated between lineages . Three additional loci that are invariable within lineages , PrMS6 , Pr9C3 , and PrMS39a , were also genotyped . Genomic DNA was extracted from mycelia grown in cleared 20% V8 broth using the FastDNA SPIN kit ( MP Biomedicals LLC , Solon , OH ) following the protocol for yeast , algae , and fungi . Loci were amplified using primers and protocols as outlined in [28] and [13] . PrMS6 , Pr9C3 , PrMS39a and b , and PrMS45 were amplified using a PCR program of 1 cycle of 92°C for 2 min , followed by 30 cycles of 92°C for 30 s , 52°C for 30 s , 65°C for 30 s , and 1 cycle of 65°C for 5 min . Fluorescent multiplex PCR reactions were performed in 10-µL volumes with the following final concentrations: 1× GenScript PCR Buffer ( 10 mM Tris-HCl; 50 mM KCl; 1 . 5 mM MgCl2; 0 . 1% Triton X-100 buffer ) , 0 . 2 µM dNTPs , 3–6 µM of primer pairs , 0 . 5 U GenScript Taq DNA polymerase ( Genscript Corporation , Piscataway , NJ ) , and 0 . 5 µL ( ∼50 ng ) DNA template . Loci PrMS43a and b were amplified using the following PCR program: 1 cycle of 92°C for 2 min , 35 cycles of 92°C for 30 s , 52°C for 30 s , and 72°C for 1 min , and 1 cycle of 72°C for 45 min . The final concentrations of the reaction mixture for PrMS43 ( 10 µL volume ) were 1× PCR Buffer , 0 . 4 µM dNTPs , 0 . 3 µM forward and reverse primers , 1 . 0 U DNA polymerase , and 0 . 5 µL DNA template . Loci 18 and 64 were amplified with the PCR program: 1 cycle of 94°C for 2 min , 30 cycles of 94°C for 20 s , 55°C for 20 s , and 72°C for 30 s min , and 1 cycle of 72°C for 10 min . The final concentrations of the 10 µL reaction mixture for 18 and 64 were 1× PCR Buffer , 0 . 2 µM dNTPs , 0 . 2 µM forward and reverse primers , 0 . 5 U DNA polymerase , and 0 . 5 µL DNA template . Three isolates were used as positive controls in identification of the three clonal lineages and to ensure consistency among runs: PR-04-001 ( aka 2027 . 1 , lineage NA1 from Curry County , Oregon ) , PR-04-020 ( aka 03-74-D12-A , EU1 from an Oregon nursery ) , and PR-04-015 ( aka wsda3765 , NA2 from a Washington nursery ) . PCR products were sized using capillary electrophoresis on an 3100 Avant Genetic Analyzer ( Applied Biosystems , Foster City , CA ) using the internal size-standard LIZ 500 ( Applied Biosystems ) . Results were analyzed using GeneMapper 3 . 7 software packages ( Applied Biosystems ) . Genotyping was replicated for a subset of isolates with independent DNA extractions , PCR , and sizing of fragments . Reproducibility of novel allele sizes was confirmed . Genetic distances among all identified multilocus genotypes were calculated over eight of the nine loci using Wright's modification of Roger's genetic distance [46] , [47] as implemented in the program TFPGA [48] . PrMS39a was excluded from the calculation because it was invariable in NA1 isolates and did not amplify in the other two lineages . Null alleles were coded as missing data . A UPGMA dendrogram was inferred from the distance matrix and visualized using MEGA version 4 [49] . Bootstrapping of data was conducted in TFPGA using 1 , 000 permutations . For the NA1 lineage and states and years with sample sizes of at least five isolates , we estimated multilocus genotypic diversity using Stoddart and Taylor's index G [50] , multilocus genotypic evenness ( the distribution of genotypes in a sample ) using the index E5 [51] , [52] , and multilocus genotypic richness and allelic richness ( average number of alleles per locus ) corrected for sample size using rarefaction as implemented in ADZE [53] . Analysis of molecular variance ( AMOVA ) [54] was conducted using Arlequin 3 . 1 [55] to test for significant variation among years in CA , OR , and WA . The analyses used the standard data setting and 10 , 000 permutations . In order to examine genetic distances among isolates as measured by mutational differences , rather than mutation plus mitotic recombination , we collapsed the data to the haploid state . Three loci , PrMS39b , PrMS43a , and PrMS43b , were consistently homozygous . Loci 18 and 64 had two distinct size classes of alleles and only the larger of the two was variable among isolates for both loci . Thus when collapsed to haploid , the larger allele was retained . Three additional isolates appeared to exhibit mitotic recombination rather than mutation at otherwise uniformly heterozygous loci . These were two WA 2004 isolates ( locus PrMS45 ) and a SC 2004 isolate ( locus 64 ) , multilocus genotypes 49 , 53 , and 35 , respectively ( Tables S1 and S2 ) . These isolates were excluded from the haploid data set . PrMS45 was monomorphic across remaining NA1 isolates and PrMS6 and Pr9C3c were invariable within NA1 . To examine the relationships among isolates , minimum spanning networks were constructed using the genetic distance of Bruvo et al . [56] , which incorporates microsatellite repeat number . Here , a distance of 0 . 10 is equivalent to one mutational step ( one repeat ) but larger distances do not strictly correspond to a given number of mutational steps . Genetic distance matrices were calculated for the three West Coast states for all available years and for the 2004 samples from CT , GA , TX , and VA using the haploid dataset . MINSPNET [57] was used to generate minimum spanning networks from genetic distance matrices . All tied trees were included in the network , which was visualized using the neato program in the Graphviz package [58] . To examine genetic structure in the NA1 sample , the clustering programs Structure 2 . 2 [59] , [60] and BAPS 5 . 2 [61] were run using the haploid data set . For Structure 2 . 2 we used the no admixture model , because the NA1 lineage appears to be completely clonal , and assumed that allele frequencies among populations were correlated . However , very similar results were obtained using the admixture model and independent allele frequencies . Lambda was set to 1 . 0 and 100 , 000 MCMC replicates were used after a burn-in of 20 , 000 . The number of underlying groups ( K ) was varied from 1 to 5 and replicated five times . The posterior probability of the most likely K was calculated assuming a uniform prior as described in the Structure 2 . 2 documentation . Genetic mixture analysis was run at the individual level in BAPS 5 . 2 for maximum number of populations ( K ) from 2 to 31 , replicated 3 times . A UPGMA dendrogram of the resulting clusters was produced using Nei's distance as implemented by the program . AMOVA was conducted on the resulting Structure and BAPS clusters . Structure and BAPS results were also compared to those obtained from k-means clustering of individuals as implemented in Genodive [62] , which does not assume Hardy-Weinberg equilibrium within populations . Maximum likelihood estimates of the ratio of immigration rate to mutation rate ( m/µ ) for West Coast states compared to the non-West Coast sample were obtained using the program Migrate version 2 . 4 . 3 [63]–[65] . Isolates from all years were divided into four populations: CA , OR , WA , and all other states . All years of collection were combined to obtain larger population sizes for parameter estimates . The data were coded such that the homozygous loci had one missing allele , to account for the possibility of homozygosity by mitotic recombination rather than mutation . The analysis used a migration model in which the three West Coast source populations could both send and receive migrants , but the fourth combined population could only receive immigrants . We used the Brownian motion approximation to the stepwise mutation model and a search strategy of 10 short chains of 500 steps followed by 3 long chains of 10 , 000 steps at the default sampling increments with 3 heated chains using the adaptive heating scheme . The search strategy was replicated five times for each locus within each run such that the last chains of each replicate were combined for parameter estimation . Runs for which the profile likelihood calculation failed were discarded . A total of four runs were examined to account for possible variation among runs . | Sudden oak death , caused by the fungus-like pathogen Phytophthora ramorum , has caused devastating levels of mortality of live oak and tanoak trees in coastal California forests and in urban and suburban landscapes in the San Francisco Bay Area . This pathogen also causes non-lethal disease on popular ornamental plants , including rhododendrons , viburnums , and camellias . P . ramorum was discovered in California in the late 1990s and is exotic to the United States . Recently , presence of the disease in wholesale nurseries in California , Oregon , and Washington has led to shipments of diseased plants across the US , thus risking the introduction of the pathogen to other vulnerable forests . We examined the genetic diversity of this pathogen in US nurseries in order to better understand its evolution in nurseries and movement between states . We found that California populations were genetically different enough from Oregon and Washington populations that infestations of the pathogen found in nurseries in other states could be distinguished as having originated from California or the Northwest . Our inferences were consistent with trace forward investigations by regulatory agencies . | [
"Abstract",
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] | [
"evolutionary",
"biology"
] | 2009 | Population Genetic Analysis Infers Migration Pathways of Phytophthora ramorum in US Nurseries |
Infected animals will produce reactive oxygen species ( ROS ) and other inflammatory molecules that help fight pathogens , but can inadvertently damage host tissue . Therefore specific responses , which protect and repair against the collateral damage caused by the immune response , are critical for successfully surviving pathogen attack . We previously demonstrated that ROS are generated during infection in the model host Caenorhabditis elegans by the dual oxidase Ce-Duox1/BLI-3 . Herein , an important connection between ROS generation by Ce-Duox1/BLI-3 and upregulation of a protective transcriptional response by SKN-1 is established in the context of infection . SKN-1 is an ortholog of the mammalian Nrf transcription factors and has previously been documented to promote survival , following oxidative stress , by upregulating genes involved in the detoxification of ROS and other reactive compounds . Using qRT-PCR , transcriptional reporter fusions , and a translational fusion , SKN-1 is shown to become highly active in the C . elegans intestine upon exposure to the human bacterial pathogens , Enterococcus faecalis and Pseudomonas aeruginosa . Activation is dependent on the overall pathogenicity of the bacterium , demonstrated by a weakened response observed in attenuated mutants of these pathogens . Previous work demonstrated a role for p38 MAPK signaling both in pathogen resistance and in activating SKN-1 upon exposure to chemically induced oxidative stress . We show that NSY-1 , SEK-1 and PMK-1 are also required for SKN-1 activity during infection . Evidence is also presented that the ROS produced by Ce-Duox1/BLI-3 is the source of SKN-1 activation via p38 MAPK signaling during infection . Finally , for the first time , SKN-1 activity is shown to be protective during infection; loss of skn-1 decreases resistance , whereas increasing SKN-1 activity augments resistance to pathogen . Overall , a model is presented in which ROS generation by Ce-Duox1/BLI-3 activates a protective SKN-1 response via p38 MAPK signaling .
Infection by pathogenic microorganisms requires a coordinated response from the host to cope with the multitude of physiological challenges presented by the attack . In addition to producing compounds that have direct antimicrobial activity and countering pathogen virulence strategies , the host must also initiate stress responses to protect cellular resources and processes from the negative consequences of “friendly-fire . ” Damage , disease , and sometimes death of the host can occur if immune responses are not controlled or protected against properly . Septic shock and various autoimmune diseases are examples of immune responses gone awry . In this work we explore the connections between infection , immune response and cellular stress response using the well-studied model host Caenorhabditis elegans . A general response to microbial challenge that most animals possess is the production of reactive oxygen species ( ROS ) . The best-studied example is the production of ROS as an antimicrobial response in the phagolysosome of phagocytic cells by the NADPH oxidase gp91phox . However , this response is not limited to phagocytes , and NADPH oxidases are present in the skin as well as the mucosal epithelium of the oral cavity , respiratory and gastrointestinal tracts of humans [1] , [2] . Less complex organisms that lack innate immune cells , such as C . elegans , also encode for NADPH oxidases . For instance , the dual oxidase Ce-Duox1/BLI-3 is present in the hypodermis and in the intestine of C . elegans [3] , [4] . Our laboratory and others recently demonstrated that an intestinal infection triggers the release of ROS by Ce-Duox1/BLI-3 in what appears to be a protective response [3] , [5] . Presumably due to the production of ROS however , there was also evidence of cellular damage as shown by lipofuscin accumulation and loss of protein homeostasis , which was worsened by the knock-down of certain oxidative stress enzymes [6] , [7] . The goal of this work was to determine if infection , by triggering ROS release by Ce-Duox1/BLI-3 , induces an oxidative stress response in the host as part of the overall response to the pathogen . SKN-1 is a transcription factor that senses oxidative stress and functionally affects resistance to oxidative stress and lifespan in C . elegans . It is an Nrf ortholog , a protein family found in all eukaryotes that upregulates the Phase 2 genes of the three-phase detoxification system [8] . Phase 2 genes encode enzymes that defend against ROS and other reactive compounds [9] . A large number of genes are regulated by SKN-1 , including many glutathione-S-transferases that are important for detoxifying reactive compounds such as xenobiotics and peroxidized lipids by conjugating glutathione to electrophilic centers [10] , [11] . SKN-1 transcriptional activity is regulated by phosphorylation by the p38 MAPK , PMK-1 , which promotes its localization to the nucleus [12] . Interestingly , PMK-1 is a major regulator of C . elegans innate immunity and loss of this protein results in a strong susceptibility phenotype [13] . Work on PMK-1 showed that it is regulated by a phosphorylation cascade involving the activation of a Toll/IL-1 receptor ( TIR ) domain protein , TIR-1 , which leads to the activation of a MAPKKK called NSY-1 , which then activates a MAPKK called SEK-1 , culminating in PMK-1 phosphorylation [13] , [14] . Phosphorylation of the transcription factor ATF-7 by PMK-1 is thought to ultimately promote innate immune gene expression by turning this repressor of innate immune gene expression into an activator [15] . While SEK-1 is also required for activation of SKN-1 by oxidative stress [12] , the roles of TIR-1 and NSY-1 are controversial . One report shows that NSY-1 and TIR-1 are dispensable for activation of PMK-1 in response to oxidative stress created by sodium arsenite [12] , whereas two other publications show that NSY-1 is required for resistance to oxidative stress caused by paraquat [16]–[18] . In earlier studies , loss of SKN-1 was not observed to affect the overall susceptibility of the worm to infection by Pseudomonas aeruginosa , and it was postulated that the oxidative stress transcriptional response mediated by SKN-1 is not involved in pathogen defense [15] , [19] . However , because our data suggested that oxidative stress is present during infection [3] , [6] , [7] , we postulated that SKN-1 is activated and conducted experiments to investigate this hypothesis . Specifically , this study examined SKN-1 directed gene expression and localization in C . elegans infected with Pseudomonas aeruginosa and Enterococcus faecalis . We establish that bacterial infection stimulates SKN-1 activity in a manner dependent on the p38 MAPK signaling pathway . We show that components of the p38 MAPK signaling pathway previously established as necessary for responding to pathogen are involved , including NSY-1 , SEK-1 and PMK-1 , but not TIR-1 . In contrast to previous work , we find that loss of SKN-1 activity increases susceptibility to the pathogens whereas constitutive activation results in increased resistance . Of key significance is the demonstration that ROS produced by Ce-Duox1/BLI-3 is the source of oxidative stress triggering SKN-1 activity during infection . Overall , this work establishes that a protective SKN-1 response is activated during infection via p38 MAPK signaling as a result of the mucosal oxidative burst generated by the host .
Previous work in our laboratory demonstrated that C . elegans releases significant amounts of ROS during infection and that expression of oxidative stress response genes such as sod-3 is induced [6] . Additionally , we have shown that cells at the site of the infection , the intestine , display a loss of protein homeostasis indicative of cellular stress [7] . SKN-1 is a transcription factor that responds to cellular stressors , including ROS [8] , and we predicted that its activity is likely to be induced as a result of pathogen exposure . We tested genes previously characterized as being regulated by SKN-1 , including gst-4 , gst-5 , gst-7 , gst-10 and gcs-1 . The gst genes encode for glutathione-S-transferases , which are important for detoxifying reactive compounds by conjugating reduced glutathione to electrophilic centers , while gcs-1 encodes for gamma-glutamine cysteine synthetase heavy chain , a protein involved in glutathione biosynthesis [10] , [11] , [20] . Expression of these genes was examined by qRT-PCR following a 24-hour exposure of L4 worms to both P . aeruginosa and E . faecalis ( Figure 1A ) . All of the reporter genes were induced between two and five-fold more when the animals were feeding on the pathogens , as compared to those feeding on their normal laboratory food source ( E . coli OP50 ) . As shown in Figure 1B , in which exposure of the animals to skn-1 RNAi preceded exposure to E . faecalis , the induction was prevented , indicating that expression of these genes retains dependence on SKN-1 under conditions of pathogen exposure . The Blackwell and Johnson laboratories have generated several C . elegans strains containing GFP reporter fusions to genes that are markers for SKN-1 activity including gst-4 [21] , gcs-1 [8] , [22] and gst-7 [20] . We obtained these strains to further examine pathogen-induced SKN-1 activity . L4 animals were exposed to P . aeruginosa , E . faecalis or non-pathogenic E . coli for 18 hours . Fluorescent micrographs were taken , and the animals were scored for low , medium or high expression of the reporters as described in Methods . For each condition , several worms in a representative micrograph are shown ( Figures 1C-E , 1G-I ) , and additionally the quantification of the scoring with statistical analysis is included ( Figures 1F and 1J ) . Figures 1C-F show that significantly more animals had high levels of expression of gst-4::gfp when infected with E . faecalis ( Figure 1C ) and P . aeruginosa ( Figure 1D ) than when allowed to feed on E . coli ( Figure 1E ) . Significantly higher expression of gcs-1::gfp was also observed on the pathogenic strains compared to the controls as shown in Figures 1G-J . We also examined the levels of GST-7::GFP using a transgenic strain containing a translational fusion of gfp to the gst-7 gene , and again we observed higher levels when the animals were exposed to the pathogens ( Figure S1 in Text S1 ) . To ensure that the expression was SKN-1 dependent , the levels of fluorescence were observed in strains exposed to skn-1 RNAi prior to pathogen exposure; the pathogen-induced increase in fluorescence was abolished , supporting the use of these reporters as read-outs for SKN-1 expression levels ( see below , S2E in Text S1 and data not shown ) . In independent experiments looking at number of transcripts by RNAseq experiments that examine transcript numbers in worms exposed to another human pathogen , Staphylococcus aureus , both gcs-1 and gst-4 were expressed at higher levels compared to animals on E . coli ( Javier Irazoqui , personal communication , data not shown ) . In conclusion , several different experimental methodologies indicate that SKN-1 regulated genes have increased levels of expression in C . elegans fed on pathogenic bacteria . To investigate whether or not the strength of the SKN-1 response is affected by any known bacterial virulence factors , we tested some well-studied mutants . GacA is a response regulator in P . aeruginosa that strongly affects the virulence of this pathogen and a gacA mutant is greatly attenuated in a variety of hosts including C . elegans [23] . As shown in Figure 2B , a gacA deletion mutant significantly reduced the expression level of the gcs-1::gfp reporter as compared to the isogenic parental strain of P . aeruginosa shown in Figure 2A . One of the GacA-dependent virulence factors is pyocyanin [24] , a secreted compound that is redox active and has been shown to oxidatively stress host cells [25]–[27] . We examined a pyocyanin-deficient mutant , which is deleted in phzM , a gene that encodes an enzyme critical for pyocyanin biosynthesis [28] , [29] . As seen in Figure 2C , this mutant also results in less gcs-1::gfp reporter expression as compared to wild-type . The quantification of the effects of these P . aeruginosa mutants on gcs-1::gfp expression is shown in Figure 2D . In E . faecalis , FsrB is a component of a quorum sensing system that has been found to affect virulence in every animal model studied , including C . elegans [30] , [31] . An fsrB deletion mutant also resulted in less SKN-1 activity , as measured by gst-4::gfp expression compared to the isogenic parental strain ( Figure 2E-G ) . These results show that the level of SKN-1 activity is sensitive to the presence or absence of major virulence factors and that SKN-1 activity may be a way to discern the overall virulence of the infecting organism . SKN-1 activity is regulated by localization to the nucleus [8] , [32] . If pathogen exposure increases SKN-1 activity , one would expect to see localization of this transcription factor to the nucleus . We obtained a SKN-1B/C::GFP transgenic line used in previous studies to examine SKN-1 activity and localization [8] . As shown in Figure 3 , exposure to P . aeruginosa ( Figure 3A ) or E . faecalis ( Figure 3B ) causes significant nuclear localization of SKN-1B/C::GFP , similar to what is observed by exposure to paraquat ( Figure 3C ) , a chemical that causes oxidative stress and is documented to promote SKN-1 nuclear localization [8] . In contrast , when feeding on their normal laboratory food source , the animals do not display significant SKN-1B/C::GFP nuclear localization ( Figure 3D ) . Utilizing chemicals known to generate oxidative stress ( paraquat , sodium arsenite and t-butyl peroxide ) , previous work demonstrated that activation of SKN-1 is dependent on p38 MAPK signaling components [12] , [16] , [17] . We asked whether or not pathogen-induced activation of SKN-1 is also dependent on p38 MAPK signaling . To investigate this question , the levels of GFP were scored in the gst-4::gfp transgenic worms after RNAi knock-down of the genes-of-interest followed by 18 hours of exposure to E . faecalis ( Figure 4 ) . Additionally , we assayed gcs-1::gfp transgenics exposed to P . aeruginosa following RNAi ( Figure S2 in Text S1 ) . As a control , the effects of RNAi knock-down on animals exposed to the non-pathogenic control , E . coli , were also assayed and found to be minimal ( Table S1 in Text S1 ) . The p38 MAPK , PMK-1 , and the upstream MAPKK , SEK-1 , are absolutely necessary to activate SKN-1 as a result of oxidative stress [12] . As shown in Figure 4 , reduction in the expression of sek-1 ( Figure 4C ) or pmk-1 ( Figure 4D ) by RNAi prior to exposure to E . faecalis resulted in significantly less fluorescence of the SKN-1 dependent reporter , gst-4::gfp , in the worm intestine , with similar levels to the skn-1 RNAi control ( Figure 4E ) . These data suggest that PMK-1 and SEK-1 are also crucial for SKN-1 activation as a result of pathogen exposure . RNAi of these genes had similar effects on gcs-1::gfp transgenics exposed to P . aeruginosa ( Figure S2C and S2D in Text S1 ) . In addition to its role in responding to oxidative stress , it was previously demonstrated that PMK-1 is activated by pathogen exposure and plays a very important role in host defense . The infection protective activity of PMK-1 is dependent not only on SEK-1 , but also on the upstream MAPKKK NSY-1 and the Toll/IL-1 receptor ( TIR ) domain protein , TIR-1 [13] , [14] . The role of NSY-1 and TIR-1 in PMK-1 activation under conditions of oxidative stress is less clear and may be dependent on the stressor utilized [12] , [16] , [17] . To investigate the possible roles of NSY-1 and TIR-1 on SKN-1 activation during pathogen exposure , we exposed the animals to nsy-1 or tir-1 RNAi prior to placing them on pathogen . Knock-down of nsy-1 caused loss of SKN-1 activity on both pathogens as assayed by the gst-4::gfp and gcs-1::gfp reporters , ( Figure 4B , Figure S2B in Text S1 ) . In contrast , knock-down of tir-1 , resulted in only a non-statistically significant trend towards less expression , suggesting no , to minimal involvement , at best ( Figure S3B in Text S1 and data not shown ) . To confirm the RNAi results using another methodology , we also examined expression in genetic mutants using qRT-PCR to measure the expression of gst-4 and gcs-1 . In null mutants of nsy-1 , sek-1 and pmk-1 , expression of these two genes was down relative to wild-type worms on both E . faecalis and P . aeruginosa ( Figure 4G ) . In contrast , expression levels were not significantly different in a tir-1 mutant . Our previous studies demonstrated that part of the response of the worm to pathogen challenge is the release of ROS into the intestinal lumen by the dual oxidase Ce-Duox1/BLI-3 . Rather than a tangential consequence of cell death , this response is purposeful and protective [3] , [6] . Since SKN-1 is known to respond to oxidative stress , and the above-described experiments conducted on the p38 MAPK signaling pathway are most consistent with an oxidative stress response , we postulated that the ROS generated by Ce-Duox1/BLI-3 during pathogen exposure may trigger signaling through the p38 MAPK pathway , resulting in SKN-1 activation . To test this , we knocked down the expression of bli-3 by RNAi in the gst-4::gfp background and then exposed the strain to E . faecalis . As shown in Figure 5A-C , SKN-1 activity is reduced to a level comparable to worms exposed to skn-1 RNAi ( Figure 4E ) , suggesting that the presence of Ce-Duox1/BLI-3 is necessary for SKN-1 activity . We propose that the ROS produced by Ce-Duox1/BLI-3 in response to pathogens and the resulting oxidative stress activates SKN-1 activity . To test if SKN-1 activity could be rescued in the bli-3 knock-down worms by providing ROS from an alternative source , we exposed the animals to paraquat . Paraquat generates ROS by redox cycling in vivo and has been used in previous studies as a trigger for SKN-1 activity in C . elegans [8] . L4 worms were exposed to either 1/30 bli-3 RNAi or vector control RNAi and were placed for 30 minutes in M9 solution with or without 100 mM of paraquat . As shown in Figure 5E , SKN-1 activity , as measured by fluorescence of the gst-4::gfp fusion , was activated in response to paraquat in worms fed vector control RNAi , in agreement with previous work [8] . Knock-down of bli-3 had no effect on activation of SKN-1 by paraquat ( Figure 5G ) . These data indicate that SKN-1 activation induced by a chemical ROS generator does not require Ce-Duox1/BLI-3 , unlike activation by pathogen exposure . We were unable to perform these experiments on P . aeruginosa using the gfp-expressing transgenics because we discovered that the bli-3 knock-down caused a severe susceptibility phenotype on P . aeruginosa , much more severe than on E . faecalis , and more than half the worms were dead by the 24 hour time point ( see below ) . Instead we used qRT-PCR to look at SKN-1 regulated genes on both E . faecalis and P . aeruginosa at an earlier time point ( 6 hours ) . By this methodology , we observed that both gst-4 and gcs-1 expression was significantly reduced in the bli-3 knock-downs compared to wild-type on both pathogens ( Figure 5I and 5J ) . In conclusion , our results support a model in which ROS generated by Ce-Duox1/BLI-3 as a result of pathogen exposure trigger SKN-1 activity . In previous investigations , loss of skn-1 did not influence susceptibility to P . aeruginosa [15] , [19] . One study examined loss of skn-1 by RNAi [19] . However , in this work the animals were not exposed to RNAi until the L4 stage , at which point RNAi is likely less effective since the SKN-1 protein is produced in significant quantities during larval development [8] . We reduced skn-1 expression using RNAi , but began the exposure at the L1 stage and continued exposure through the L4 stage . Following this experimental procedure , the animals exhibited a statistically significant and reproducible susceptibility phenotype to both E . faecalis ( Figure 6C , Table S2A in Text S1 ) and P . aeruginosa ( Table S2B in Text S1 ) . We additionally tested the possible role in susceptibility of some individual SKN-1 targets such as gst-4 , gst-7 and gcs-1 by reducing the expression of these genes by RNAi . A significant difference compared to control RNAi was not observed , suggesting that these are not the critical SKN-1 targets or more than one gene contributes to SKN-1 pathogen resistance ( data not shown ) . Another study examined loss of skn-1 by mutation using loss of function alleles zu67 and zu135 [15] . Using these same strains , we also examined susceptibility to P . aeruginosa and additionally E . faecalis . In a wild-type background , there was no significant difference in susceptibility , as previously reported; in fact zu135 was slightly more resistant ( Figure 6A , 6B , Table S2 in Text S1 ) . However , the zu67 and zu135 strains are sterile and do not produce viable embryos . This eliminates a major mechanism of killing , the internal hatching of the embryos during exposure to pathogen , a process called “bagging” [33] , [34] . We found that RNAi of skn-1 did not cause a severe sterility phenotype until the second generation , likely due to its maternal effect [35] , so the RNAi experiments described above were not affected by this problem . To render all the strains equally sterile so that they would be directly comparable , we exposed them to cdc-25 . 1 RNAi prior to pathogen exposure [36] , [37] . Under these conditions , both skn-1 mutants were significantly more susceptible compared to wild-type ( Figure 6A , 6B , Table S2 in Text S1 ) . Note that the cdc-25 . 1 RNAi targets germline mitosis/meiosis , while a skn-1 mutation affects cell division within the embryo . Preventing development of the germline is known to increase resistance [34] and likely accounts for the increase in susceptibility observed when the already sterile skn-1 mutants are exposed to cdc-25 . 1 RNAi . Shivers et al . also attempted to control for sterility by adding FUDR to their assay plates , a compound that prevents cell division [15] . We speculate that this procedure may have affected the virulence of the pathogen , as noted in previous reports [36] , [38] . Since the activation of skn-1 is very sensitive to the overall virulence of the pathogen ( see Figure 2 ) , this may have confounded the results . To further test the role of SKN-1 on pathogen susceptibility , we used another approach . If loss of SKN-1 causes a susceptibility phenotype , constitutively active SKN-1 is predicted to increase resistance . We increased SKN-1 activity by two known means . First , we reduced expression of gsk-3 by RNAi , which causes constitutive nuclear localization of SKN-1 and therefore , greater transcriptional activity . By phosphorylation , GSK-3 normally inhibits nuclear localization of SKN-1 [39] . Secondly , we reduced expression of wdr-23 , which encodes a WD40 repeat protein that targets SKN-1 to an ubiquitin ligase for proteasomal degradation . Loss of WDR-23 results in increased SKN-1 protein levels and greater output of its transcriptional program [40] . Reducing the expression of both of these genes significantly increased resistance to E . faecalis and P . aeruginosa ( Figure 6C , Table S2 in Text S1 ) . As a control , the expression of skn-1 was additionally reduced by RNAi , which abrogated the phenotypes , confirming that the increased resistance was dependent on skn-1 . In previous work , loss of gsk-3 or wdr-23 both increased survival upon exposure to oxidative stress , however there was little ( wdr-23 ) to no ( gsk-3 ) concomitant increase in longevity when lifespan was assayed on non-pathogenic E . coli [39] , [40] . Therefore , the significant increase in pathogen resistance observed in Figure 6A is not just a byproduct of causing a long-lived phenotype in general . To look at the effect of increased SKN-1 activity by another means , we examined two strains in which SKN-1 is over-produced because the strains carry extra copies of SKN-1::GFP . One strain carried wild-type SKN-1 , fused to GFP ( Figure 6D , Table S2 in Text S1 ) , whereas the second expressed a mutant form of SKN-1 , which is constitutively active ( Figure 6E , Table S2 in Text S1 ) [8] , [20] . Both strains exhibited increased resistance to both E . faecalis and P . aeruginosa . The effect was stronger utilizing the strain that produces constitutively active SKN-1 ( Figure 6E , Table S2 in Text S1 ) . Overall the data in this section demonstrates that that the level of SKN-1 activity significantly influences how long the worm survives on pathogen; less SKN-1 activity reduces resistance whereas more SKN-1 activity increases resistance . In previous work , loss of bli-3 was shown to increase susceptibility to E . faecalis [3] . To determine if this effect could be completely dependent on skn-1 , phenotypic analysis of the loss of both genes on susceptibility was performed . In a skn-1 background , loss of bli-3 by RNAi caused an increase in susceptibility to E . faecalis compared to skn-1 animals not exposed to bli-3 RNAi ( P<0 . 0001 ) . These data suggest that skn-1 is not completely epistatic to bli-3 , ie not all of Ce-Duox1/BLI-3's protective effects are mediated through SKN-1 ( Figure 7A , Table S2A in Text S1 ) . However , on P . aeruginosa , we observed some differences ( Figure 7B , Table S2B in Text S1 ) . First , we discovered that a bli-3 knock-down caused a profound susceptibility phenotype – much more severe than that observed on E . faecalis in this work and in our previous publication [3] . Loss of skn-1 ameliorated this severe phenotype and showed the same level of susceptibility as a skn-1 mutant plus bli-3 RNAi ( P = 0 . 3429 ) . The data is consistent with skn-1 being epistatic to bli-3 on P . aeruginosa , despite the difference in phenotypic consequences compared to E . faecalis . In this case , loss of skn-1 protects against the severe susceptibility phenotype caused by the loss of bli-3 on P . aeruginosa , even though the skn-1 mutant is still more susceptible than the wild-type strain ( P<0 . 0001 ) . Understanding why there is a difference in the bli-3 phenotypes of animals exposed to these two different pathogens will require further investigation . Overall , the results are consistent with the model shown in Figure 8 that postulates that SKN-1 acts downstream of BLI-3 . However , the experiments do not rule out the possibility that SKN-1 and BLI-3 have other roles independent of each other .
We have shown for the first time that the oxidative stress response transcription factor , SKN-1 , plays an important role in C . elegans innate immunity . A model for the activation of SKN-1 is shown in Figure 8 . Upon exposure to intestinal pathogens , Ce-Duox1/BLI-3 is activated by an unknown mechanism to produce extracellular ROS . In addition to possibly having direct antimicrobial properties , ROS generated by Ce-Duox1/BLI-3 ( likely in the form of membrane diffusible H2O2 ) activates the p38 MAPK signaling cascade , which results in the phosphorylation and nuclear localization of SKN-1 . SKN-1 carries out a transcriptional program to produce proteins with detoxification functions that eliminate ROS and help repair or recycle damaged molecules . Overall , we demonstrated that this response is functionally important during infection; loss of skn-1 increased susceptibility of the worms to pathogen , whereas increasing SKN-1's activity increased resistance Interestingly , there is evidence for Nrf-related transcription factors like SKN-1 protecting against immune-derived oxidative stress in other systems , suggesting broad conservation of this protective mechanism . A study by Jain et al . showed that the SKN-1 ortholog , Yap1 in Saccharomyces cerevisae , protected this microbe against Ce-Duox1/BLI-3-derived ROS produced by C . elegans . Specifically , it was demonstrated that a dar ( distended anal region ) phenotype caused by colonization with S . cerevisae was abrogated by a Yap1 mutation . The dar phenotype was dependent on bli-3 [5] . Therefore it appears that Nrf-related transcription factors not only protect the host against oxidative stress during host-pathogen interactions , but also protect eukaryotic pathogens and enable their virulence . In higher animals , there is also evidence that Nrf-related transcription factors respond to immune-induced oxidative stress . For example , increasing concentrations of HOCl were shown to increase activation of Nrf2 and immune responsive genes in mouse macrophages [41] . Because Nrf transcription factors generally upregulate the production of “Phase 2” detoxification enzymes with activities such as metabolizing free radicals and conjugating xenobiotics and peroxidized lipids [10] , [11] , it is logical that their activity would be helpful during pathogen attack when there is often excess ROS production and cellular stress . It is therefore not surprising that one central signaling pathway , p38 MAPK signaling , has a crucial role in the C . elegans response to both pathogen and oxidative stress . When considering the components of the p38 MAPK pathway that govern oxidative stress response , SEK-1 and PMK-1 have well-established functions [12] . The importance of two upstream components , NSY-1 and TIR-1 is less clear and may depend on the oxidative stressor used to assay function [12] , [16] , [17] . On pathogen , we observed that loss of nsy-1 significantly reduced SKN-1 activity ( Figure 4B , 4G , S2B in Text S1 ) . The loss of tir-1 did not cause a significant change in expression , suggesting that it is not involved ( Figure 4G , S3B in Text S1 ) . These data suggest that other components may feed into the pathway upstream of SEK-1 to activate SKN-1 . Though we tested several potential candidate kinases with previously established roles in p38 MAPK signaling including MEK-1 , MKK-4 and IKKε-1 [42] , [43] , none of them reduced SKN-1 activity ( data not shown ) . In mammalian systems , a thioredoxin redox sensor inhibits the mammalian homolog of NSY-1 , ASK-1 [44] , [45] . ROS production causes the disassociation of the thioredoxin and allows an active signaling complex to form with other adaptor molecules enabling p38 MAPK signaling [46] . We are actively exploring if a thioredoxin is involved in activating NSY-1 in C . elegans . In addition to SKN-1 , p38 MAPK signaling in C . elegans was previously shown to regulate another bZIP transcription factor , ATF-7 . Loss of atf-7 enhances susceptibility to pathogen . However , rather than simply acting to induce immune activation , ATF-7 normally represses innate immune activation . Phosphorylation by PMK-1 turns ATF-7 from a repressor into an activator [15] . Since ROS released from Ce-Duox1/BLI-3 activates SKN-1 activity via p38 MAPK signaling , one could postulate that ROS also activates ATF-7 . Alternatively , the signaling cascade might have some way of distinguishing between different inputs to selectively activate these transcription factors depending on the stimulus . Such a model might allow better coordination of gene expression to meet specific challenges . One gene shown to be regulated by ATF-7 , T24B8 . 5 , encodes a ShK-like toxin peptide which has predicted antimicrobial activity [15] . Perhaps SKN-1 coordinates a “defensive” arm of the innate immune response by regulating genes encoding enzymes involved in protecting against and repairing cellular damage , whereas ATF-7 regulates an “offensive” arm by controlling genes encoding for activities that are directly antimicrobial . A more thorough study of the genes regulated by ATF-7 and SKN-1 on pathogen would need to be carried out to investigate this hypothesis . The precise role ( s ) of ROS generated by Ce-Duox1/BLI-3 in protecting the worm from infection is not yet completely understood , though this study implicates an important signaling function . On E . faecalis , reducing expression of bli-3 increased the susceptibility phenotype of skn-1 . The incomplete epistasis suggests that Ce-Duox1 has additional roles in protecting the worm from the pathogen independent of activating skn-1 . One role could be activating other transcription factors regulated by p38 MAPK signaling such as ATF-7 , as mentioned above . A potential role , independent of signaling , is in host physical barrier function . The ROS generated by Ce-Duox1/BLI-3 could be utilized by peroxidases to increase the impermeability of the ECM ( extracellular matrix ) in the worm intestine , analogous with how peroxidases use ROS generated by Ce-Duox1/BLI-3 to cross-link the cuticle [47] . There is some evidence for NADPH oxidases contributing to barrier function in the mosquito gut [48] . Another possibility is that ROS generated by Ce-Duox1/BLI-3 , is turned into a more potent antimicrobial , as is known to happen in other systems , including the oral and respiratory mucosa of animals , in which DUOXs generate the H2O2 necessary for lactoperoxidase ( LPO ) to oxidize thiocyanate to create the microcidal compound hypothiocyanite [49] , [50] . Our susceptibility analysis indicated that the genetic interactions between skn-1 and bli-3 are different on P . aeruginosa than on E . faecalis . The data in Figure 7 demonstrated that in the absence of Ce-Duox1/BLI-3 , and only on P . aeruginosa , SKN-1 is activating a transcriptional program that is harmful to the worm , but in the presence of Ce-Duox1/BLI-3 , SKN-1 is protective , as expected . On E . faecalis , in contrast , the presence of SKN-1 is protective in both the bli-3 and wild-type backgrounds . Perhaps the explanation for the difference lies in the fact that P . aeruginosa is actively manipulating the host innate immune response through production of redox-active factors such as pyocyanin . In previous work using human respiratory epithelial cells , pyocyanin production by P . aeruginosa was shown to cause increased oxidative stress by potentiating the intracellular production of superoxide in the host cells . Pyocyanin , like Ce-Duox1/BLI-3 uses NADPH and molecular oxygen to create ROS , so these host and pathogen factors are potentially competing for the same substrates [27] . We postulate that intracellular superoxide production by pyocyanin , in contrast to the extracellular production of H2O2 by Ce-Duox1/BLI-3 , activates a different SKN-1 transcriptional program that is very harmful to the worm . In a previous investigation , different oxidative stressors caused significant differences in SKN-1's transcriptional program , so this idea is not with out precedent [10] . This hypothesis could explain the results in Figure 7B . When Ce-Duox1/BLI-3 is present , the H2O2 produced activates SKN-1 to carry out a protective response . It may also inhibit pyocyanin activity by decreasing availability of NADPH and oxygen . Loss of skn-1 is detrimental , but loss of bli-3 when skn-1 is present allows this transcription factor to be influenced by the ROS generated by the pathogen resulting in a transcriptional program that is actively harmful to the host . Testing this hypothesis will require further study . In conclusion , we have demonstrated for the first time that the Nrf-family transcription factor SKN-1 is induced by exposure to pathogen and has a protective function during infection . We additionally showed that components of the p38 MAPK pathway , including NSY-1 , SEK-1 and PMK-1 , are necessary for this response . Finally , ROS produced by the dual oxidase Ce-Duox1/BLI-3 in response to pathogen was shown to trigger SKN-1 activity . Because Ce-Duox1/BLI-3 plays an important role in activating p38 MAPK signaling and SKN-1 activity , defining how this dual oxidase's activity is triggered will be an important area of future investigation .
C . elegans strains were grown and maintained as previously described [51] . The following bacterial strains were used in this study: E . coli OP50 [52] , E . faecalis OG1RF [53] , P . aeruginosa PA14 [23] . C . elegans strains used in this study are indicated in Table S3 in Text S1 . In Figure 1A and 4G , RNA was extracted from L4 larvae exposed to E . faecalis OGIRF , P . aeruginosa PA14 and E . coli OP50 for 24 hours . In Figure 5I and 5J , RNAi treated eri-1 ( mg336 ) worms were exposed to E . faecalis OGIRF , P . aeruginosa PA14 and E . coli OP50 for 6 hours . The RNA was extracted using Trizol ( Invitrogen ) as indicated by the manufacturer . Samples were treated with DNase I to remove DNA contamination using the Turbo DNA free kit ( Applied Biosystems ) as described by the manufacturer . qRT-PCR was performed on an ABI 7500 instrument using the Power SYBR Green RNA-to-CT 1 step kit ( Applied Biosystems ) . Comparative CT method was used to determine fold changes in gene expression normalized to act-1 [20] . Primers used are listed in Table S4 in Text S1 . RNAi was induced by feeding L1 worms through L4 stage with bacteria producing dsRNA to target genes . RNAi expressing clones were obtained from the C . elegans library ( Geneservices , UK ) [54] , [55] . All clones were verified by sequencing . Clones absent in the library were constructed as follows . Briefly , RNA was extracted from C . elegans L4 larvae using Trizol ( Invitrogen ) according to the manufacturer's protocol . cDNA was synthesized using SuperscriptII reverse transcriptase ( Invitrogen ) with oligodT and random hexamer primers . Gene specific primers were used to amplify regions of target genes , cloned into the vector pL4440 [56] and transformed into E . coli HT115 ( DE3 ) . Constructs were verified by sequencing . Sequences of gene specific primers are listed in Table S5 in Text S1 . To induce bli-3 knockdown , the bacterial strain expressing bli-3 RNAi was diluted in a 1∶30 ratio using the vector control . Double RNAi knockdowns were obtained by mixing bacteria expressing dsRNA to each target gene in a 1∶1 ratio . To render worms sterile prior to killing assays , larvae were exposed to cdc-25 . 1 RNAi . To investigate the expression of gst-4::gfp , gcs-1::gfp and GST-7::GFP , worms were exposed to E . faecalis , P . aeruginosa and E . coli strains for 24 hours at 25°C and paralyzed with 1mM levamisole . Anesthetized worms were mounted on 2% agarose pads and imaged using an Olympus IX81 automated inverted microscope and Slidebook ( version 5 . 0 ) software . The levels of GFP expression were scored as previously described [8] , [12] . Briefly , little or no expression of GFP , expression of GFP in the anterior or posterior of the worm and expression throughout the intestine of the worm are categorically indicated by low , medium and high respectively for gst-4 , gcs-1 and gst-7 . To determine the effect of hydrogen peroxide and paraquat on gst-4::gfp expression , worms were exposed to 5mM hydrogen peroxide or 100mM paraquat in M9 for 20 and 30 minutes respectively , then transferred to seeded NG plate to recover for 4 hours before imaging . As controls , worms were exposed to the same period of time in M9 . Higher background expression was observed in the control worms using the latter procedures . SKN-1B/C::GFP expression was analyzed by fluorescence microscopy in worms exposed to E . faecalis for 24 hours and P . aeruginosa for 6 hours . Imaging was performed using the FITC , TRITC , DAPI and YFP filter sets to exclude the signal from autofluorescence in the worms . Percentages of worms indicating the degree of nuclear localization in the intestinal cells were scored as previously described [8] , [12] . Briefly , no nuclear localization , localization of SKN-1B/C::GFP in the anterior or posterior of the worm and nuclear localization of SKN-1B/C::GFP in all intestinal cells are categorically indicated by low , medium and high , respectively . All fluorescence microscopy experiments shown were independently repeated at least three times . Killing assays were generally performed as previously described [28] , [30] , [57] . Briefly , for E . faecalis killing assays , E . faecalis OG1RF grown in Brain Heart Infusion ( BHI ) medium for 5 hours was seeded on BHI plates and incubated at 37°C for 24 hours . While for P . aeruginosa killing assays , P . aeruginosa PA14 was cultured in Luria broth ( LB ) , seeded on slow-killing plates and incubated first for 24 hours at 37°C and then for 24 hours at 25°C . A total of 90 L4 larvae were transferred to three replica plates . Worms were scored as live and dead at various points along the time course . After scoring the fluorescent micrographs , statistical differences were determined by Chi square and Fisher's exact tests using GraphPad Prism version 5 . 0 ( GraphPad Software , San Diego , CA ) . Each experimental condition was compared pairwise to the control condition . P-values of <0 . 05 were considered to be statistically significant . Statistically significant differences are indicated in the figures with asterisks next to the experimental condition . Kaplan-Meier log rank analysis was used to compare survival curves pairwise and to calculate the median survival . P-values <0 . 05 were considered to be statistically significant . | To fight infection , an animal's immune response produces a variety of toxic compounds that are directed at the invading pathogen . However , these toxic compounds can also damage the host's tissue . Therefore an important job of the immune response is to prevent and repair damage caused by “friendly fire . ” In this study , we explore this damage control function of the immune response in a small worm called C . elegans , which can be infected by feeding on human pathogens and serves as a model of human infection . Upon exposure of C . elegans to reactive compounds , a factor called SKN-1 was shown to induce the production of protective , detoxifying enzymes . Here , we demonstrate that SKN-1 also becomes active in infected animals . The cause of SKN-1 activation is reactive compounds produced by the animal's immune system , i . e . a source of “friendly fire . ” We additionally show that a highly conserved signaling pathway , called the p38 MAPK pathway , controls SKN-1 activity during infection . Finally , we demonstrate that SKN-1 activity is beneficial to the worm during infection , enhancing survival . Because humans share many of the components of the interaction we describe , this study provides broad insights into the principals of damage control during immune response . | [
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] | 2011 | Ce-Duox1/BLI-3 Generated Reactive Oxygen Species Trigger Protective SKN-1 Activity via p38 MAPK Signaling during Infection in C. elegans |
Protein-protein interactions ( PPIs ) are essential to all biological processes and they represent increasingly important therapeutic targets . Here , we present a new method for accurately predicting protein-protein interfaces , understanding their properties , origins and binding to multiple partners . Contrary to machine learning approaches , our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation , physico-chemical properties and local geometry . The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites . Beyond its predictive power , the approach permits to dissect interaction surfaces and unravel their complexity . We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign . The approach is implemented in JET2 , an automated tool based on the Joint Evolutionary Trees ( JET ) method for sequence-based protein interface prediction . JET2 is freely available at www . lcqb . upmc . fr/JET2 .
Proteins regulate biological processes through a complex network of dynamical interactions . Protein-protein interactions ( PPIs ) are considered as increasingly important therapeutic targets [1–4] . However protein-protein interfaces are notably more difficult to characterize than typical drug design targets ( e . g . small-molecule binding pockets ) . Numerous studies have described some structural properties of PPIs sites [5–13] . By analogy to the interior-surface dichotomy for protein structure folding , a core-rim dichotomy was proposed for protein-protein interfaces [14 , 15] . The amino acids forming the interface core tend to be more hydrophobic than over the rim [14–17]; they are also more frequently hotspots [18] and , therefore , usually more conserved [19–23] . Starting from these observations , a formal structural definition of these regions was proposed and a new structural region , the support , was introduced [24] . An effort was also engaged to define multiple recognition patches in large protein interfaces [25] . Many questions regarding PPIs cannot be answered by just knowing the approximate location of the interaction site at the protein surface but demand an understanding of the geometrical organization of the interacting residues . For instance , one would like to estimate the number of interactions for a protein , identify precisely the borders of each interaction site possibly overlapping other sites , understand the structure and the usage of a moonlighting protein interaction site shared with several partners , identify the anchor points in an interaction site that allow for strong versus weak binding , identify the locations on a protein surface where artificial molecules ( e . g . drugs ) could best interfere with protein partners . To answer these questions , a detailed description of the interaction at the atomic level is needed and any computational tool bringing insights on such a description becomes extremely useful . The challenge of understanding PPIs on the one hand , and , on the other , the knowledge accumulated on experimental protein interfaces , have stimulated a growing interest in the development of computational methods to predict protein-protein interfaces . Pioneering works relied on physico-chemical and geometric descriptors of protein structures [26] , and on residue conservation [19 , 27] . More recent methods [28–35] exploit diverse types of information—including sequence conservation , side-chain flexibility , secondary structures—and employ various algorithms—including neural networks , Bayesian networks , support vector machines . For instance the VORFFIP method [36] makes use of tens of descriptors and integrates them in a two-step random forest classifier . Other machine learning approaches , such as PredUs [37] and eFindSitePPI [38] , rely on the hypothesis that protein-protein interfaces are structurally conserved: they map experimentally characterized interfaces of structurally similar proteins onto the target protein . Although these machine learning approaches sometimes perform very well , they generally do not provide a clear understanding of the molecular determinants of protein-protein association . We previously developed Joint Evolutionary Trees ( JET ) for protein interface prediction [39] . JET relies on the assumption that protein interfaces are composed of a core , formed by highly conserved residues having particular physico-chemical properties , and extends through concentric layers of gradually less conserved amino acids ( S1 Fig ) . JET showed good performance on diverse reference data sets and compared favorably to other methods [39] . However our recent complete cross-docking study [40] highlighted the need for a very precise definition of the predicted binding sites to have discriminating power in evaluating docking poses of protein partners versus non-partners . The present work revisits the idea formalized in JET , by defining a protein interface as formed by three structural regions , called seed , extension , and outer layer , that approximate the structural notions of support , core , and rim defined for experimental interfaces [24] ( S1 Fig ) . Intuitively , protein interfaces are comprised of residues issued by a combination of conservation and/or physico-chemical properties ( seed-extension/support-core ) and extend through neighboring protruding regions ( outer layer/rim ) . We propose this three-layer structure to be characteristic of protein-protein interfaces ( in contrast to interfaces with other chemical compounds or biomolecules ) and , based on it , we provide a large-scale predictive pipeline , called JET2 . Our basic goal is to unravel the complexity of protein interaction surfaces by pinpointing characteristics that can define their structures and distinguish their multiple properties . We show that interaction sites can be described by very simple and general rules of organisation of the interacting residues , indicating that evolutionary constraints on this organisation exist and can be revealed . JET2 is a new tool that takes as input the three-dimensional structure of a single protein and exploits both sequence and structural information , in contrast to JET . The strategy implemented in JET2 does not rely on learning and the method is computationally much simpler than any machine learning approach . We show that the use of a very straightforward geometric descriptor ( circular variance ) captures with remarkable precision the intrinsic geometry of protein-protein interfaces . Moreover different combinations of structural and sequence descriptors allow the detection of different protein surface regions and to deconstruct protein interfaces . We provide a completely versatile tool that enables the user to tune all parameters depending on the biological question one wants to answer . JET2 is freely available to the community and can be downloaded at www . lcqb . upmc . fr/JET2 .
First , we analyzed experimentally determined protein complex structures to gain knowledge on protein-protein interfaces and identify their characteristic features . We computed the residue conservation levels ( TJET ) , interface physico-chemical propensities ( PC ) and circular variances ( CV ) ( see Materials and Methods ) in the 176 protein complexes from the Protein-Protein Docking Benchmark version 4 ( PPDBv4 ) [41] ( S1 Table ) . The values obtained for the interacting residues were compared to those obtained for the rest of the protein ( Fig 1A ) . Interacting residues are divided in support ( the most buried , in yellow ) , core ( intermediate , in brown ) and rim ( the most exposed , in green ) [24] ( Fig 2A , on the left; see Materials and Methods ) . TJET distributions reveal that support residues are significantly more conserved than the rest of the protein whereas rim residues are significantly less conserved ( Fig 1A ) . Core residues display intermediate profiles . PC values also decrease gradually from support through core to rim . For 1-CV , a striking contrast is observed between rim residues , that display a narrow distribution up to 1 , indicating that they tend to be located in protruding protein regions , and support residues that have very low values . Overall , this analysis showed that different signals are encoded in the three different structural components of protein experimental interfaces . Small ligand-binding sites generally form more concave and deeper cavities than protein-protein interfaces and those used by natural ligands are also expected to display strong conservation signals [42] . These characteristic features were verified on the 43 proteins from PPDBv4 that contain a bound small molecule ( S2 Table ) . Interestingly , in most of those cases , the region of the protein surface targeted by the small ligand is close to or even overlaps with the protein-protein interface . The small ligand-binding site was defined as the set of residues located less than 5Å away from the small molecule ( in grey ) . For TJET , a narrow distribution is observed around 0 . 84 while values for 1-CV are not higher than 0 . 58 ( Fig 1B ) . These results support the assumption that small ligand-binding sites tend to be overall more conserved and more buried than protein-protein interfaces . Hence evolutionary information and geometric criteria could be used to distinguish the two types of binding sites . We [40] and others [43 , 44] have previously observed that antibody-antigen interfaces constitute particularly difficult cases for protein binding site prediction using evolutionary information . Fig 1C reports distributions for TJET , PC and 1-CV focusing on the 25 antibodies from PPDBv4 . It is clear from the TJET boxplots that the evolutionary signal that can be extracted from antibody interfaces is very poor . By contrast , PC and 1-CV distributions show trends similar to the other proteins from the dataset . This analysis revealed that evolutionary information may not be helpful for detecting protein binding sites in peculiar cases such as antibody-antigen interfaces . Based on our analysis of signals encoded in experimental protein interfaces , we defined a predictive model ( Fig 2A , right; see Material and Methods and S3 Table ) and we used it to detect putative PPI sites . In this model , we consider only surface residues and ignore completely or almost completely buried residues ( asa<5% ) that are generally highly conserved and would not contribute significantly to the interface ( S3 Table ) . Predicted protein interfaces are defined as residue clusters composed of a seed , forming the core of the predicted site , several concentric layers forming an extension , and an additional outer layer . Intuitively , seed/extension/outer layer approximate support/core/rim , although we do not seek a perfect match between the individual components of the two models . The variable nature of protein interfaces , represented in PPDBv4 , emphasizes the need for adapted modeling approaches to correctly predict them . Consequently , we devised specific clustering strategies , aiming at detecting support , core and rim residues in a wide range of interfaces ( Fig 2B ) . Namely , we developed three scoring schemes ( SC ) that combine evolutionary information from the sequence ( TJET ) , amino-acid interface propensities ( PC ) and local geometry of the structure ( CV ) , as follows ( Fig 2C ) : In each SC , TJET , PC and/or CV are combined in a very straightforward manner without any specific weight ( S3 Table ) . Depending on the SC employed , the predicted patch may be highly conserved , display peculiar physico-chemical and/or geometrical properties . We developed an algorithm that determines the best strategy depending on the system studied ( Fig 2D; see Materials and Methods ) . Examples of predictions are illustrated in Fig 3 . They were obtained by running our tool JET2 , that implements the fully automated clustering procedure . Let us stress that JET2 also allows the user to manually choose a particular scoring scheme . In the following we report JET2 performance on a number of different interfaces . To describe the local geometry of the protein surface , we use the measure of circular variance ( CV ) that evaluates the density of protein around an atom . This simple geometric descriptor captures the structural properties of interacting residues . To properly assess the predictive role of CV , we compared JET2 and its iterative version iJET2 ( see Materials and Methods ) to JET/iJET , which use only sequence information . We applied both methods to two testing sets , namely PPDBv4 and the Huang dataset of 62 protein complexes [20] ( S1 Table ) . One should notice that PPDBv4 was also used for the analysis of the signals encoded in experimental interfaces ( see above ) . Although this analysis conceptually inspired the detection strategies implemented in JET2 , the method was not trained on PPDBv4 and no JET2 parameter was set based on PPDBv4 analysis . Hence , PPDBv4 could be used for assessing JET2 predictive power . Lowly conserved interacting residues are found in the antigen-binding sites of the 25 antibodies from PPDBv4 ( Fig 1C ) . JET2/iJET2 dramatically improves the detection of these interfaces compared to JET/iJET ( Table 1 and Fig 4A ) . The scoring scheme SC3 enables to increase sensitivity by almost 50 and precision by more than 30 for this group of proteins . The cases of the Fab fragment of an anti-VEGF antibody ( 1BJ1:R ) and the shark new antigen receptor PBLA8 ( 2I25:R ) illustrate the power of SC3 in predicting with high precision ( PPV = 72% and 60% ) antigen-binding sites while iJET failed to correctly detect them ( Fig 5 ) . In addition to antigen-binding sites , SC3 detects 52% of the FCG-binding site of FCG receptor ( 1E4K:L ) with a very high precision value of 86% ( Fig 3 ) . It also successfully predicts the binding sites of two trypsin inhibitors to their enzyme target , namely the Kunitz soybean trypsin inhibitor ( 1AVX:L , Sens = 55% , PPV = 69% ) from PPDBv4 and alaserpin ( 1K9O:I , Sens = 79% , PPV = 48% ) from Huang ( Fig 5 ) . By contrast , iJET could not detect any interacting residue of the former and predicted about 20% of the latter but with low precision ( PPV = 24% ) . Over all proteins , the most exposed interface residues , constituting the rim , are generally less conserved than the rest of the protein ( Fig 1A ) . JET2 greatly improves the detection of these residues for both testing sets ( Fig 4C ) . Sensitivity values obtained for the rim residues over 10 iterative runs of iJET2 are increased by 20 to 32 compared to iJET . These results show that JET2 , by exploiting the local geometry of the protein surface and combining it it with amino acids physico-chemical properties , is able to specifically detect lowly conserved protruding interacting residues , as those forming the rim of the interface or those involved in e . g . antigen-binding sites , which represent particularly difficult cases . We observed in PPDBv4 that small ligand-binding sites were often located in the vicinity of protein-binding sites or even overlapped with them . This can make the specific detection of protein-binding sites a difficult task . The ability of JET2/iJET2 to correctly detect protein-protein interfaces was evaluated on the 67 proteins—43 from PPDBv4 and 24 from Huang—that contain a bound small molecule ( in the complexed conformational state ) . For 80% of these proteins , SC2 enabled to get a more precise definition of the protein interface ( Table 1 and Fig 4B ) . JET2/iJET2 predictions display improved precision by up to 17 and increased sensitivity by up to 20 on average compared to JET/iJET . Furthermore , in the large majority ( 83% ) of these cases , JET2/iJET2 automated procedure successfully detected the presence of a small-ligand binding pocket and consequently chose SC2 for protein interface prediction ( Fig 2D; see Materials and Methods ) . For example , iJET2 detects 60% and 79% of the residues involved in the protein-protein interfaces of adrenoxin reductase ( 1E6E:R ) and the Ras-related protein Rab-33B ( 2G77:L ) with precision values of 50% and 64% ( Fig 5 ) . By contrast , iJET patches are wrapped around the small molecule ligand . Among the 67 analyzed proteins , 13 have a protein interface whose prediction is not improved when using SC2 . This can be explained by three reasons: ( 1 ) the protein interface displays very low evolutionary signal , hence it is correctly detected using SC3 ( 1ATN:R , 1RLB:L , 1XQS:L , 1WEJ:L from PPDBv4; 1QOR:A , 1RRP:C from Huang ) , ( 2 ) the protein interface does not contain a significant number of protruding residues and is thus correctly captured by SC1 ( 1F6M:R , 1R8S:R , 2A9K:R , 2J7P:R from PPDBv4; 1ALL:A , 1ALL:B from Huang ) , ( 3 ) the protein interface displays very peculiar features and is not detected by JET2/iJET2 ( 1AZS:R and 1XQS:L from PPDBv4 ) . When JET2 was run on the entire PPDBv4 ( except for antibodies ) and Huang datasets , it automatically chose SC2 for 126 proteins—90 from PPDBv4 and 36 from Huang . 62 of those proteins have a bound small molecule in their PDB structure ( complexed conformational state ) . For 67% of the remaining proteins , we could find experimental evidence reported in the literature that the proteins do interact with a small molecule ( see S17 Table for the list of proteins along with manually collected information about their ligands ) . Overall , this analysis revealed that ( i ) exploiting the local geometry of the protein surface in combination with evolutionary information often permits to better define protein-binding patches and segregate them from small ligand-binding pockets , ( ii ) JET2/iJET2 automated clustering procedure is efficient for selecting the most appropriate scoring strategy ( SC2 ) . A single ( typically large ) protein interface may be comprised of multiple recognition patches . Janin and co-authors [25] previously proposed to detect multiple patches within an interface by clustering the positions of the interacting atoms in 3D space [25] . They applied this clustering on experimentally known interaction sites . By contrast , JET2 considers the whole surface of the protein where it predicts interacting residues and groups them based on their shared evolutionary , physico-chemical and/or geometric properties and on their 3D proximity . We observed that SC1 or SC2 ( in short SC1-2 ) and SC3 often point to different surface regions ( S2 Fig ) . This suggests that JET2 could be employed to revisit the way recognition patches are defined within protein interfaces . To test this hypothesis , we analyzed JET2 multi-patch predictions and we compared their SC-driven decomposition with the 3D position-based clustering [25] of experimental interacting residues ( Fig 6 ) . JET2 complete automated clustering procedure ( Fig 2D; see Materials and Methods ) , by combining patches detected using different scoring schemes , yielded predicted interacting surfaces of different types ( insert in Fig 6 ) . For both Huang and PPBDv4 , in more than half of the cases , the SC3-predicted patches were smaller and partially or totally included in the SC1-2-predicted patches ( a , d ) . We also observed cases where SC1-2- and SC3-predicted patches were either almost disjoint ( b , e ) or largely overlapping ( c , f ) . For 60 proteins—44 from PPDBv4 and 16 from Huang—JET2 predicted a site comprised of multiple patches , i . e . the second round of the clustering procedure added at least 5 residues and the added residues represent more than a quarter of the whole predicted site . 57% of those predictions match experimental interfaces comprised of multiple recognition patches , according to the definition of Janin and co-authors [25] . iJET2 predictions for ferritin ( 1IES:B ) and lumazine synthase ( 1NQV:A ) contain two patches , one detected by SC1 and the other by SC3 , that match well one or several experimental recognition patches ( Fig 6 , numbered black circles ) . For example , in lumazine synthase ( 1NQV:A ) , the SC1-predicted patch matches the experimental patch numbered 1 ( highly conserved ) with an accuracy value of 87% . The SC3-predicted patch matches the experimental patches 3 and 4 ( highly protruding ) with an accuracy value of 88% . For tryptophan synthase beta chain 1 ( 1WDW:R ) , iJET2 predicted a site composed of two patches issued from SC2 and SC3 . The SC2-predicted patch corresponds to the experimental patches 1 and 2 ( Acc = 96% ) while the SC3 prediction corresponds to patch 3 ( Acc = 98% ) . Interestingly the experimental patches 1 and 2 display different characteristics: the former is highly conserved while the latter comprises a concave conserved region ( in red ) and lowly conserved highly protruding residues ( in green ) . This example illustrates the power of SC2 to detect interacting residues having different properties . For the remaining proteins for which JET2 predictions are composed of multiple patches , only one predicted patch matches the experimental interface . For 2 out of 3 proteins ( 66% ) from Huang and 11 out of 23 proteins ( 48% ) from PPDBv4 , the JET2 predicted patch that would be considered as false positive actually corresponds to an interaction with another partner , based on experimental data available in the literature ( see S18 Table for the list of proteins along with manually collected information about their partners ) . This analysis showed that JET2 is able to capture the spatial organization of interacting residues at the protein surface based on their evolutionary conservation , physico-chemical and geometric properties . JET2 provides insights on the ( possibly multiple ) origin ( s ) of an interaction , i . e . the set ( s ) of interacting residues forming the core ( s ) of the physical contact between the two partners . It reveals how these multiple origins shape large protein interfaces and , in doing so , it revisits the way recognition patches are defined . Moreover , it enables to characterize the different properties of these recognition patches . Such information cannot be obtained by using purely 3D position-based clustering of experimentally known interaction surfaces . Some proteins present several binding sites , to different partners , that have a lot of residues in common . In such cases , we expect predicted patches to encompass residues involved in the interaction ( s ) with one , several or all partners . Consequently , given an experimental complex , residues that would be considered as false positives might actually participate in the interaction with another partner . We used JET2 to understand the geometry of these patches by analyzing two proteins that interact with multiple partners via the same region of their surface . Transducin is a heterotrimeric guanine-nucleotide-binding ( G ) protein composed of three polypeptide chains α , β and γ ( 1GOT , Huang ) . Once activated , the Gα subunit dissociates from the complex and triggers phototransduction signaling cascade . The regulatory protein RGS9 terminates the signal by binding to Gα ( 1FQJ , PPDBv4 ) . By using SC2 , iJET2 predicted the Gβγ- and RGS9-binding patches of Gα with high sensitivity ( Sens = 58% and 62% ) and high precision ( PPV = 78% and 54% ) . The two patches predicted over the two structures share 73% of their residues ( Fig 7A ) . Among the true positives , 64% ( 9/14 ) of the seed residues are involved in both interactions whereas most if not all residues from the extension and the outer layer are specific of the interaction with one partner ( S4 Table ) . Thioredoxin ( Trx ) from E . coli facilitates the reduction of other proteins such as PAPS reductase ( 2O8V , PPDBv4 ) . Trx is itself reduced by Trx reductase ( 1F6M , PPDBv4 ) . In addition to its anti-oxidant function , Trx also plays a structural role upon bacteriophage T7 infection by associating with T7 DNA polymerase ( PDB code: 1X9M ) [45] . This ability to perform different autonomous functions via the same domain is typical of moonlighting proteins [46] . 86% of Trx interacting residues in structures 1F6M:L and 2O8V:L were detected by JET2 ( SC2 ) . The two predicted patches share 84% of their residues and T7 DNA polymerase also binds to this region ( Fig 7B ) . 60% ( 9/15 ) of the seed residues are involved in all three interactions ( S5 Table ) . By contrast 46% of the extension and 67% of the outer layer participate in only one interaction . This analysis of the usage of an interaction site by several partners showed that the residues located in the outer layer , and to a smaller extent the extension , of JET2 predicted patches tend to specifically interact with one partner . Consequently , the predictive model implemented in JET2 , composed of a seed , an extension and an outer layer , provides insight into the determinants of molecular recognition specificity . Thanks to the experimental knowledge on the two proteins studied here , we could validate JET2 predictions . This further suggests that JET2 could be useful for identifying moonlighting proteins and help to learn about a protein’s partners by looking at the characteristics of the interaction site . For instance , given a protein for which we know a partner and the localisation of its binding site , the presence of a significant number of false positives located in the extension and/or outer layer of a patch predicted by JET2 may indicate that this patch is involved in the association with another partner—and that the false positive residues actually interact with this other partner . To test this hypothesis , we selected the proteins from PPDBv4 whose interaction site was predicted by SC1 with high sensitivity ( > 80% ) but rather low precision ( < 60% ) and looked for additional structural data in the Protein Data Bank ( PDB ) . Among the 11 identified proteins , we could identify another partner for 6 ( 55% ) of them and match the false positives of JET2 prediction with the corresponding interaction site ( see S19 Table for the list of proteins along with manually collected information about their partners ) . A large scale assessment of the role played by structural information in JET2 was realized by comparing the results of JET2 to those of JET , which is based on sequence information only . Proteins may interact with different partners via several binding sites located in distinct regions of the protein surface . An example is given by acetylcholinesterase ( 1MAH , PPDBv4 ) for which SC1 predicted a patch that corresponds to the binding site of the snake toxin fasciculin and SC3 predicted well the protein homodimeric interface ( Fig 7C ) . Consequently , to compute JET2 performance on the testing sets , we ran the tool by selecting manually each of the three scoring schemes and we considered the best patch or combination of patches . iJET2 consensus predictions ( 2 runs out of 10 , best precision/recall balance ) cover about 50% and 60% of all protein interfaces from Huang and PPDBv4 , with precision of 66% and 43% respectively ( Table 1 and Fig 8 ) . These values are increased by 15 to 25 compared to values computed for iJET consensus predictions ( 7 runs out of 10 ) . This significant improvement of JET2 over JET performance is consistently observed when considering consensus predictions obtained from 1 to 10 runs out of 10 and the different functional and structural classes of the two testing sets ( S6 and S7 Tables and S3 Fig ) . The improvement is less striking when we consider the predictions obtained from JET2 complete automated clustering procedure ( see iJETA u t o C o m p l e t e 2 in Table 1 ) . This reflects the fact that the entirely automatic process may detect patches that are not present in the testing sets , i . e . patches possibly involved in interactions with other partners ( see Fig 7C and S18 Table for such cases ) . Performance values for individual proteins are reported in S8 , S9 and S10 Tables for Huang and S11 , S12 , S13 and S14 Tables for PPDBv4 . The strategy employed in JET2 combines three physically and biologically meaningful residue-based descriptors in a rational way to predict protein-protein interfaces at large scale . To evaluate the relevance of our approach , we compared the results of iJET2 to those of three state-of-the-art methods that use machine learning , namely VORFFIP , PredUs and eFindSitePPI . The VORFFIP method [36] integrates a broad set of residue descriptors—including solvent accessibility , energy terms , sequence conservation , crystallographic B-factors and Voronoi Diagrams-derived contact density—in a two-steps random forest ensemble classifier . We applied VORFFIP on the Huang dataset and PPDBv4 , after having removed the proteins that were used for training the method ( Table 1 and Fig 8 ) . VORFFIP predictions were defined from residues having a normalized score ( or probability ) greater than 0 . 5 . The overall performance of iJET2 ( 2 or 8 runs out of 10 ) is comparable to that of VORFFIP on the Huang dataset ( S6 Table ) . On PPDBv4 , iJET2 achieved higher sensitivity and higher precision than VORFFIP , with similar specificity and accuracy values ( S7 Table ) . iJET2 performed particularly better on the antibody-antigen and bound antibody-antigen complexes and on the proteins with other function from PPDBv4 . Fig 3 shows examples where iJET2 single-patch predictions give a better coverage ( 1TMQ:R ) , better overall localization ( 1K5D:L ) or more precise definition ( 1E4K:L ) of the experimental protein interfaces than VORFFIP predictions . iJET2 also detects more experimental interacting residues in the multi-patch predictions reported on Fig 6 . The PredUs method [37 , 47] predicts protein interfaces by using only information from structural neighbors of the query protein for which experimental data on their interacting surface is available . The development of the method was motivated by the observation that experimentally known interfaces are conserved across proteins that adopt similar structures [37] . Given a query protein structure , PredUs maps residues of structural neighbors involved in an interaction to residues on the surface of the query . Scores are calculated for all residues based on the mapped contacting frequencies by using a support vector machine [47] . The method was ranked first in a recent comparative evaluation of different protein-protein interfaces prediction tools [48] . We used the PredUs web server [47] to predict binding sites on the proteins from Huang and PPDBv4 . We excluded proteins from PPDBv4 that were used for training the method ( proteins from PPDBv3 [49] ) and proteins containing several chains , as PredUs can only treat single chains . The predictions were defined from residues with strictly positive raw scores ( default settings ) . PredUs achieved an average sensitivity of 80% and precision of 53% on all but 2 proteins from Huang , for which no structural neighbors could be found ( S4 Fig ) . Among the 92 tested proteins from PPDBv4 , PredUs calculation failed because of the absence of any structural neighbor for 11 proteins ( 12% ) . On the remaining proteins , PredUs predictions cover on average 65% of the interaction sites with a precision of 34% , while iJET2 average sensitivity and precision values are 59% and 43% . Consequently , PredUs predicted overall more interacting residues than iJET2 but with lower precision . In addition , there are a significant number of cases where the PredUs calculation failed . For the majority of these cases , iJET2 performed very well: 10/13 predictions have sensitivity values above 50% ( up to 100% ) and 7/13 predictions have precision values above 50% ( up to 82% ) . In 9 cases over 13 , SC3 alone or in combination with SC1 or SC2 yielded the best prediction . The eFindSitePPI method [38] combines meta-threading , structural alignment and machine learning algorithms to predict the residues involved in protein-protein interfaces and their molecular interactions—hydrogen bonds , hydrophobic contacts , salt bridges , aromatic interactions . Given a protein query structure , eFindSitePPI applies meta-threading to identify structurally and functionally related templates and maps the known interfaces of the templates to the query protein using structural alignment . Then , the algorithm combines the information from the templates with four residue-based descriptors ( relative accessible area , generic and position-specific interface propensities , sequence entropy ) to compute interfacial probability scores for every residues of the query protein , by using support vector machines and a naive Bayes classifier . The method was shown to outperform PredUs when protein models are given as inputs instead of experimentally determined structures [48] . eFindSitePPI was applied to the proteins from the Huang dataset , for which it showed better performance values than iJET [38] . On average , eFindSitePPI achieved a sensitivity of 62% and a precision of 62% ( see Table 5 in [38] ) . iJET2 displays a lower sensitivity of 48% ( resp . 42% ) but a higher precision of 66% ( resp . 69% ) with 2 ( resp . 8 ) runs out of 10 . We used the eFindSitePPI web server [38] to predict binding sites on the three examples illustrating JET2 ability to predict and dissect multi-patch interaction sites ( Fig 6 ) . About one third of the interface residues of ferritin ( 1IES:B , Sens = 33% ) and lumazine synthase ( 1NQV:A , Sens = 36% ) were detected by eFindSitePPI with very high precision of 96% and 94% respectively ( S5 Fig ) . The predicted residues are sparsely distributed at the protein surface and do not form multiple identifiable patches , as was observed for iJET2 predictions ( Fig 6 ) . The case of tryptophan synthase beta chain 1 ( 1WDW:R ) could not be treated by eFindSitePPI due to a too large number of residues in the protein structure . Overall , these analyses showed that the performance values computed for JET2/iJET2 on Huang and PPDBv4 are comparable to those computed for state-of-the-art methods that use machine learning algorithms . An advantage of our method is that it predicts interacting residues that form recognition patches , it gives insights into the origins of these patches and it enables to contrast the different properties of the interacting residues and their role for the molecular recognition specificity . It can be applied to proteins for which a structurally similar protein with experimentally characterized interface ( s ) cannot be found , to proteins of arbitrary length and to multi-chain complexes .
Antibody Fab fragments are comprised of framework regions , that are absolutely or very strongly conserved [51] , and hypervariable loops that recognize the antigen . They represent a paradigm for protein recognition specificity as one antibody usually specifically targets one antigen [52] . Thornton and co-authors previously demonstrated that the “antigenic” residues of antibodies tend to be located in loops which protrude from the surface of the protein [43 , 44] . In agreement with these early studies , we showed that JET2 predicts remarkably well the antigen-binding sites of the 25 antibodies from PPDBv4 by exploiting the local geometry of the protein surface ( SC3 ) . Interestingly , the evolutionary trace-driven strategy ( SC1 ) captured with high accuracy the interfaces between light and heavy chains ( S6 Fig ) . This observation suggests that JET2 different scoring schemes permit to detect patches involved in different types of interactions and subject to different evolutionary constraints at the surface of a protein . JET2 can be used to predict and learn about the different partners of a protein in several ways . First , it can detect the presence of a putative small ligand binding site at the surface of the protein based on the specific properties of these sites compared to protein-protein interaction surfaces . When the cluster seed detected by SC1 is highly conserved , then a small ligand binding site is suspected and the program will automatically switch to SC2 on order to specifically detect the protein binding site . Second , a prediction , obtained by running JET2 complete automated procedure , comprised of multiple patches that do not or slightly overlap , may indicate that the protein can interact with two different partners binding to the two detected locations . Thirdly , a large predicted patch matching a known interaction site with high sensitivity but low precision can reveal the shared usage of the detected region of the protein surface by several partners . Lymphocyte function-associated antigen-1 ( LFA-1 ) is an integrin that plays a crucial role in antigen-specific responses [53] by recognizing intercellular adhesion molecule 1 ( ICAM-1 ) ( 1MQ8 , PPDBv4 ) . By using SC1 , JET2 successfully predicted ICAM-1 binding site of LFA-1 ( Sens = 67% and PPV = 48% ) . Another patch was predicted by using SC3 and the overlap between the two patches is 50% . We could identify five residues in the SC3-predicted patch that are not involved in the interaction with ICAM-1 but are found in the interface with Efalizumab ( PDB code: 3EOA ) ( S7 Fig ) . Efalizumab is an antibody drug used in the treatment of psoriasis which inhibits the association between LFA-1 and ICAM-1 [54] . This example suggests that JET2 , by exploiting surface local geometry and physico-chemical properties , could be useful for identifying putative sites at the surface of therapeutic targets . This opens new perspectives for the design of highly specific drugs These examples illustrate the predictive potential of JET2 to detect multiple interactions involving different partners of a protein . By manually inspecting the predictions and looking for additional experimental data reported in the literature , we could assess JET2 predictive power beyond the interactions present in the studied datasets . It is likely that the increasing availability of experimental structures of protein complexes will further validate JET2 predictions in the future . Let us stress however that many aspects of protein regulation ( e . g . post-translational modification , differential expression… ) , which are out of the scope of this work , may also influence protein binding specificity . We have carefully compared the performance of JET2 with state-of-the-art methods for predicting protein-protein interfaces based on machine learning algorithms . JET2 is computationally much simpler than any machine learning approach . It combines in a rational way three residue-based descriptors that have straightforward biological interpretability . This strategy allows to reach comparable or even better results than dozens of features used to train unsupervised classifiers . We thereby demonstrate that machine learning is not a necessary algorithmic approach for protein-protein binding site prediction . To have few features instead of dozens is important while exploring the very large landscape of protein interaction sites , since many of these sites might not be classifiable yet , and many others might be overlapping , making their recognition a difficult task for learning algorithms . A clear advantage of JET2 is that it provides a detailed residue-based characterization of the conservation , physico-chemical and geometrical properties of the recognition patches . By characterizing different signals at the surface of a protein , it enlightens the complexity of this surface and more specifically of the surface regions involved in functional interactions . It can be used to discover novel interfaces , which is not possible with methods based on the learning of experimentally known interfaces of structurally similar proteins . Our evaluation of iJET2 performance shows that the first step of the clustering procedure , i . e . seed detection , yields very precise predictions with zero or very few false positive ( s ) for a large number of proteins ( S15 Table ) . The subsequent steps , extension and addition of an outer layer , essentially contribute to increasing the coverage of the interface . Consequently the tool can be used in a broad set of contexts depending on whether one is interested in achieving high coverage of experimental interfaces , or obtaining very high precision for further use of the predictions to discriminate partners within the cell [40] . Furthermore , the predicted interfaces can be used to guide mutation studies . Specifically , the core residues are particularly well predicted ( best precision/recall balance ) by JET2 ( Fig 4C ) . The method can also be useful to probe the evolutionary landscape of protein interfaces , and to propose allosteric sites that could be targeted in the context of drug development . Last , it can be used to provide a straightforward understanding of the molecular descriptors that are characteristic of protein interfaces .
We describe experimental protein interfaces by using the Support-Core-Rim model information obtained from complex structures ( Fig 2A , on the left ) . The Levy model [24] argues that interface residues play different roles in the interaction depending on the geometry of the interacting surface . Interface residues are defined based on relative solvent accessible surface area ( rasa ) changes , as computed by NACCESS [55] with a probe radius of 1 . 4Å , between the unbound and bound protein in complexed conformational state ( S3 Table ) . The interface is divided in three structural components: support residues are buried in unbound and bound protein ( rasau < 0 . 25 , rasab < 0 . 25 ) ; core residues become buried upon binding to the partner ( rasau ≥ 0 . 25 , rasab < 0 . 25 ) ; rim residues are exposed in unbound and bound protein ( rasau ≥ 0 . 25 , rasab ≥ 0 . 25 ) . The support and the rim resemble protein interior and surface respectively , while the core has a specific amino-acid composition . Two datasets were used to analyze experimental protein interfaces and evaluate JET2 performance: the Huang dataset of 62 protein complexes [20] which was previously used to assess JET performance [39] , and the Protein Protein Docking Benchmark version 4 ( PPDBv4 ) of 176 protein complexes [41] ( S1 Table ) . The Huang dataset comprises 41 homodimeric chains , 24 heterodimeric chains and 19 transient chains . PPDBv4 proteins can be grouped in four functional classes—antibody-antigen ( 26 ) , bound antibody-antigen ( 24 ) , enzyme-inhibitor ( 104 ) and proteins with other functions ( 198 ) . PPDBv4 collects both their free and complexed states . Experimental interfaces were defined using the complexed conformations while JET2 predictions were performed on the free conformations . Given the variety of proteins and protein complexes represented and the presence of both free and complexed states , these testing data sets constitute a rich mine of information . JET2 development is based on the large-scale method Joint Evolutionary Trees ( JET ) [39] . JET predicts binding patches for protein families by combining residue conservation ( TJET ) and amino-acid physico-chemical properties ( PC ) . JET does not need to know the binding partner but only requires information on a single query protein . The tool was designed to ( i ) detect very different types of interfaces ( with a protein , a small molecule , DNA or RNA ) , ( ii ) provide predictions even with weak signal thereby ensuring broad applicability , ( iii ) provide robust consensus predictions from iterative runs ( iJET ) . An important characteristics of JET algorithm is that it uses suitable heuristics to detect and extend binding patches . Consequently alternative predictions are produced by different JET runs . Consensus predictions are defined from the likelihood of a residue to belong to the interface predicted in several JET independent runs . The JET2 method requires as input a protein query sequence for which three-dimensional structural coordinates are available in the Protein Data Bank ( PDB ) [56] . In a first step , JET2 determines the conservation level ( TJET ) of every amino acid in the query sequence . The calculation is performed on a set of homologous sequences ( ideally 100 or more ) . This step , essentially unchanged compared to JET , was extensively described in [39] . In a second step , JET2 detects and extends putative binding patches at the surface of the three-dimensional structure of the query protein . For this , residues are scored using a mixture of three descriptors: the evolutionary traces ( TJET ) computed in step 1 , interface propensities ( PC ) specific to each amino acid and circular variances ( CV ) computed from the protein 3D structure and representing the density of protein around each residue . JET2 implements three different scoring schemes , i . e . combinations of the three descriptors ( Fig 2C and S3 Table ) . They can be used alternatively depending on the system studied , according to the user’s choice or through an automated procedure ( Fig 2D and Table 2 ) . JET2 clustering algorithm unfolds as follows: ( 1 ) highly-scored residues are selected and grouped together based on 3D proximity ( < 5Å ) to form cluster seeds; ( 2 ) seeds are extended by progressively adding highly-scored neighboring residues until a cluster mean score threshold is reached down; ( 3 ) an outer layer is added comprised of highly protruding residues . Seeds too small to be considered as putative protein binding sites are filtered out , as described in [39] . The strategy employed for seed extension and the addition of the outer layer are new compared to JET and are described in details below . The obtained residue clusters represent predicted binding patches . Patches that are in close proximity at the surface of the protein can form a single interaction site . JET2 allows to automatically combine different binding patches predicted using complementary scoring schemes in order to form multi-patch binding sites ( Fig 2D and Table 2 ) . In JET2 , the clustering procedure can be performed in two modes: ( 1 ) either the user let JET2 automatically decide which scoring scheme is appropriate for the studied system , or ( 2 ) the user can manually choose a particular scoring scheme . The user can also decide to run only one round of JET2 clustering procedure ( patches will be detected by the automatically or manually chosen main scoring scheme ) or to run the complete procedure where a second round is performed to detect additional patches by using a scoring scheme complementary to the main one ( see below ) . Finally , as described above for JET , JET2 can be run multiple times , in an iterative mode of the program ( iJET2 ) , to get more robust predictions . Three measures , TJET , PC and CV , were introduced in JET2 to evaluate single residues in a protein . Conservation levels ( TJET ) are computed from phylogenetic trees constructed using sequences homologous to the query sequence and sampled by a Gibbs-like approach [39] . N trees are constructed from N representative subsets of sequences . For each position in the query sequence , a tree trace is computed from each tree T: it corresponds to the level n in the tree T where the amino acid at this position appeared and remained conserved thereafter [39] . Let us recall that JET definition of evolutionary trace is notably different from the measure defined by Lichtarge and co-authors to rank protein residues [57 , 58] . Tree traces are averaged over the N trees to get more statistically significant values , which we denote relative trace significances . The final TJET value of amino acid aj at position j is obtained by accounting for aj’s environment and is expressed as follows [39]: T J E T ( j ) = w I * ( 1 | I | ∑ h ∈ I d h ) + w j * d j w I + w j ( 1 ) where I is the set of residue positions which are neighbors of aj ( i . e . with at least one atom distant by less than 5Å to at least one atom of aj ) and where dj is the relative trace significance of aj . The weights were fixed at wI = 3 and wj = 4 , as in [39] . TJET values are scaled between 0 ( least conserved residue of the protein ) and 1 ( most conserved residue of the protein ) for the calculation of residue scores . Physico-chemical properties ( PC ) are derived from propensities specific to every amino acid to be located at a protein interface , taken from [59] . The original values , ranging from 0 to 2 . 21 , are scaled between 0 and 1 for the calculation of residue scores . Circular variance ( CV ) is a measure of the vectorial distribution of a set of neighboring points around a fixed point in 3D space [60] . For a given residue , CV reflects the density of protein around it . CV has the advantage of changing more smoothly than surface accessibility in passing from the surface to the interior of the protein [61] , making it less sensitive to small conformational changes . CV can be applied equally well to atomic or coarse-grain representations [60] . The CV value of an atom i is computed as: C V ( i ) = 1 - 1 n i ∑ j ≠ i , r i ≤ r c r i j → ∥ r i j → ∥ ( 2 ) where ni is the number of atoms distant by less than rcÅ from atom i . The CV value of a residue j is then computed as the average of the atomic CVs , over all the atoms of j . A low CV value indicates for a residue that it is located in a protruding region of the protein surface . CV values are scaled between 0 ( most protruding residue of the protein ) and 1 ( least protruding residue of the protein ) for the calculation of residue scores . The cutoff distance rc directly influences the resolution of the protein surface . Here we chose to use two different values of rc . We set rc = 100Å when using SC3 as the main scoring scheme , to be able to capture the most protruding regions of the protein surface at a global level . Otherwise we set rc = 12Å to obtain a local description of the geometry of the protein surface . Values in the range 10–14Å for rc give similar results . CV complement ( 1-CV ) was preferred to CV to visualize the results because it permits to better contrast this measure with the two other descriptors , TJET and PC . The descriptors TJET , PC and CV are combined in a very straightforward way in the different scoring schemes , without any specific weight . Each residue score is simply computed as the arithmetic mean of the scaled TJET , PC and/or ( 1-CV ) values ( Fig 2C and S3 Table ) . Let us stress that the scaling of TJET , PC and ( 1-CV ) values between 0 and 1 is performed by considering the variability of the values for residues in the query protein . As a consequence , interface predictions are protein-specific . The expected size of a protein interface is computed as fintfrac ( N ) = ( 26 . 54/N ) + 0 . 03 , where N is the number of surface residues [39] . The parameters in this analytical expression were determined in [39] based on a dataset of 1256 protein chains collected from the PDB [62] . The function also approximates well the experimental data from PPDBv4 ( S8 Fig ) . fintfrac ( N ) is used in JET2 clustering procedure to define two thresholds ( Table 2 ) . The s c o r e r e s C U T threshold is the score determined with a confidence level of 2 × fintfrac ( N ) on the distribution of score values . It is used to select appropriate residues for constructing the clusters . The s c o r e c l u s C U T threshold is the score determined with a confidence level of fintfrac ( N ) /x , where x values 2 or 4 , on the same distribution . It is used to decide when to terminate the cluster seed construction step ( x = 4 ) and the cluster extension step ( x = 2 ) . These thresholds are the same as those used in JET and more details on how they are determined and employed can be found in [39] . JET2 algorithm for extending cluster seeds reflects the decreasing gradient of conservation and interface propensities observed in the experimental interfaces from support through core to rim ( Fig 1A and Table 2 ) . Specifically , at each iteration n of the JET2 extension step , we list all neighboring residues , i . e . residues that are distant by less than 5Å from any residue of the considered cluster . Among this ensemble of neighbors , residues are included in the cluster extension only if they display scores lower than the maximum score computed among the residues that were added in the previous iteration n − 1 ( scoreMax , in Table 2 ) . This constraint imposed on individual residues was not used in the extension algorithm of JET [39] . The algorithm stops when the average score of the cluster reaches down the threshold s c o r e c l u s C U T , determined with a confidence level of fintfrac ( N ) /2 on the distribution of residue scores [39] . Following the extension step , clusters that neighbor each other ( distant by < 5Å ) are merged . The final step of JET2 clustering procedure runs in only one iteration and the strategy adopted is quite different from that of the regular extension step ( described above ) . Each cluster is extended toward neighboring residues ( distant by < 5Å ) that have a high score—combining PC and CV , provided that the inclusion of the considered residue leads to a mean cluster score as high as or greater than before the inclusion ( Table 2 ) . The purpose of this additional step is to include highly exposed protein regions—protruding loops typically—that often compose the rim of experimental interfaces . Clusters are not merged anymore at this stage . Notice that this step is completely new compared to JET clustering procedure [39] and it handles clusters whose residues are detected using CV , a measure that was not considered in JET . The user can let JET2 automatically choose the scoring scheme appropriate to the studied system ( Fig 2D ) . The implemented algorithm proceeds as described in Table 2 . First , the proportion of highly conserved residues ( TJET > 0 . 6 ) is compared to the expected size of the interface , fintfrac ( N ) . If the protein surface is characterized by evolutionary signals too low to help detect protein interfaces , then the strategy is to look for sites comprised of protruding residues that satisfy expected physico-chemical properties ( SC3 ) . Otherwise , SC1 is used to detect conserved seeds . If the search finds no seed with satisfiable size , then the strategy is switched for SC3 . Otherwise , if a seed displays a very homogeneous conservation signal , i . e . the ratio of the dispersion to the mean of the conservation score computed over the seed is much smaller ( <1/6 ) than the ratio computed over the whole surface , then a protein-small ligand binding site is suspected and the strategy is to use SC2 . Otherwise , the seeds are considered to display good characteristics and SC1 is further employed for their extension and the addition of the outer layer . Alternatively the user can select the scoring scheme of his/her choice . He/She can decide to run only the seed detection step , the seed detection and extension steps , or all three steps of JET2 clustering procedure . Whether JET2 procedure is run in the automated mode or the manual mode , the user can decide to run only one round of the clustering algorithm ( main clusters will be detected by the automatically or manually chosen scoring scheme ) or the complete procedure where a second round of the clustering is performed to detect complementary clusters by using a scoring scheme different from the main one ( Table 2 ) . SC3 will be used in the second round if SC1 or SC2 was chosen as the main scoring scheme , and SC2 will be coupled with SC3 when SC3 is chosen as the main scoring scheme ( Fig 2D ) . If a new site is located sufficiently close ( less than 5Å ) to the first predicted site , then it will be fused together with the first site . For a given query protein and a fixed set of homologous protein sequences , different independent runs of JET2 may produce slightly different results ( typically differing by a few amino-acid residues ) . This is due to the heuristics employed to compute evolutionary traces TJET and to filter out small cluster seeds ( see [39] ) . To get more robust predictions , JET2 can be run multiple times , in an iterative mode of the program which we call iJET2 . For each residue , we count the number of runs where it was detected in a cluster and divide it by the total number of runs . The resulting number is comprised between 0 and 1 and corresponds to the probability of each residue to be predicted at an interface . In order to define interaction patches , we set up a threshold from which we get a consensus prediction . Varying the threshold permits to shift the balance between sensitivity and precision . The optimal number of independent runs for establishing the consensus was evaluated to be 7 out of 10 for JET [39] . In JET2 , the predictions are stable from 2 runs out of 10 . We previously showed that consensus predictions obtained by running multiple independent runs of JET were more accurate than predictions from a single run [39] . Consequently we used the iterative mode of JET2 ( iJET2 ) to evaluate the performance of the tool . We relied on the following quantities: the number of residues correctly predicted as interacting ( true positives , TP ) , the number of residues correctly predicted as non-interacting ( true negatives , TN ) , the number of non-interacting residues incorrectly predicted as interacting ( false positives , FP ) and the number of interacting residues incorrectly predicted as non-interacting ( false negatives , FN ) . We used the four standard measures of performance: sensitivity Sens = TP/ ( TP + FN ) , specificity Spe = TN/ ( TN + FP ) , accuracy Acc = ( TP + TN ) / ( TP + FN + TN + FP ) and positive predictive value PPV = TP/ ( TP + FP ) . We also evaluated the statistical significance of Sens , Spe , PPV and Acc by using scores that account for the expected values of these measures . Expected numbers of true/false positives/negatives are computed as: TPexp = C ⋅ S , TNexp = ( 1 − C ) ( N − S ) , FPexp = C ⋅ ( N − S ) , FNexp = ( 1 − C ) ⋅ S , where C = P/N in the coverage obtained with JET2 , P is the number of surface residues predicted by JET2 , N is the total number of surface residues and S is the number of residues in the experimental interaction site . Note that the calculation of expected values assumes that C ⋅ N residues have been selected at random as being positives on the structure of the protein under study . This means that expected values depend on the protein studied . The expected sensitivity , specificity , positive predictive value and accuracy are then computed as: Sensexp = C , Speexp = 1 − C , PPVexp = S/N , Accexp = ( ( 1 − C ) ⋅ ( 1 − S/N ) ) + C ⋅ S/N . Pertinence scores are expressed as: ScSens = Sens − Sensexp , ScSpe = Spe − Speexp , ScPPV = PPV − PPVexp and ScAcc = Acc − Accexp . The R software [63] was used to compute all performance values and produce the corresponding graphs . Multiple recognition patches within protein experimental interfaces from the Huang dataset and PPDBv4 were detected by applying hierarchical clustering to interface residues using the average linkage method , as shown in [25] . Calculations were performed with the R software [63] . A threshold distance of 20Å enabled to obtain values similar to those reported in [25] . However , contrary to [25] , for each complex we consider the two interfaces from the receptor and the ligand individually . The two datasets show very different trends: while the majority of the proteins from all functional classes of PPDBv4 contain one single recognition patch , more than 70% of the homodimers , heterodimers and transients from the Huang dataset have multi-patch interaction sites , containing up to 7 patches ( S16 Table ) . The Multi-VORFFIP webserver [64] was used to predict binding sites on the proteins from PPDBv4 and Huang . The proteins listed in the O333 dataset [65] , which was used to train the method , were excluded . Given a query protein , Multi-VORFFIP gives four predictions corresponding to putative interfaces with a protein , DNA , RNA or a peptide . We considered only predictions related to protein-protein binding sites . The prediction consists in two scores assigned to each residue of the query protein: a raw score and a normalized score ( or probability ) . Following the recommandations of the authors of the method , we used the normalized score and set up a threshold of 0 . 5 , which seemed to yield the best balance between sensitivity and precision . The PredUs web server [47] was also used to predict binding sites on the proteins from Huang and PPDBv4 . Proteins from PPDBv4 that were used for training the method ( proteins from PPDBv3 [49] ) and proteins containing several chains were excluded . The prediction consists in an interfacial score attributed to each residue reflecting the distance above ( positive score ) or below ( negative score ) the hyperplane defined by the Support Vector Machine classifier of the method . Residues displaying positive scores were considered as predicted to be at the interface ( default settings ) . Finally , the eFindSitePPI webserver [38] was used to predict the protein binding sites of selected proteins . The prediction consists in a list of confidently predicted interfacial residues determined from several interfacial probability scores obtained by using five features integrated in two different classifiers . | Many questions regarding Protein-Protein Interactions ( PPI ) cannot be answered by just knowing the approximate location of the interaction site at the protein surface but demand an understanding of the geometrical organization of the interacting residues . For instance , one would like to estimate the number of interactions for a protein , identify precisely the borders of each interaction site possibly overlapping other sites , understand the structure and the usage of a moonlighting protein interaction site shared with several partners , identify the anchor points in an interaction site that allow for strong versus weak binding , identify the locations on a protein surface where artificial molecules ( e . g . drugs ) could best interfere with protein partners . To answer these questions , a detailed description of the interaction at the atomic level is needed and we present a novel computational approach , JET2 , bringing insights on such a description . Beyond its highly precise predictive power , the approach permits to dissect the interaction surfaces and unravel their complexity . It fosters new strategies for protein-protein interactions modulation and interaction surface redesign . | [
"Abstract",
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] | [] | 2015 | Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions |
The presence of treatment-resistant cells is an important factor that limits the efficacy of cancer therapy , and the prospect of resistance is considered the major cause of the treatment strategy . Several recent studies have employed mathematical models to elucidate the dynamics of generating resistant cancer cells and attempted to predict the probability of emerging resistant cells . The purpose of this paper is to present numerical approach to compute the number of resistant cells and the emerging probability of resistance . Stochastic model was designed and developed a method to approximately but efficiently compute the number of resistant cells and the probability of resistance . To model the progression of cancer , a discrete-state , two-dimensional Markov process whose states are the total number of cells and the number of resistant cells was employed . Then exact analysis and approximate aggregation approaches were proposed to calculate the number of resistant cells and the probability of resistance when the cell population reaches detection size . To confirm the accuracy of computed results of approximation , relative errors between exact analysis and approximation were computed . The numerical values of our approximation method were very close to those of exact analysis calculated in the range of small detection size M = 500 , 100 , and 1500 . Then computer simulation was performed to confirm the accuracy of computed results of approximation when the detection size was M = 10000 , 30000 , 50000 , 100000 and 1000000 . All the numerical results of approximation fell between the upper level and the lower level of 95% confidential intervals and our method took less time to compute over a broad range of cell size . The effects of parameter change on emerging probabilities of resistance were also investigated by computed values using approximation method . The results showed that the number of divisions until the cell population reached the detection size is important for emerging the probability of resistance . The next step of numerical approach is to compute the emerging probabilities of resistance under drug administration and with multiple mutation . Another effective approximation would be necessary for the analysis of the latter case .
Oncogenic pathways have been investigated using molecular biology techniques , which have helped elucidate the molecular mechanism of cancer growth , invasion , and metastasis among other aspects . The findings from these investigations have encouraged the development of anti-cancer drugs that inhibit specific oncogenic pathway and have helped improve clinical outcomes dramatically [1] [2] . But there exists some percentage of patients who have no response to these kinds of drugs . One of the reasons for the lack of response is the point mutation of a specific gene . Cancer cells mutate and acquire resistance to the anti-cancer drug , posing a significant obstacle for curing cancer [3] [4] . Several recent studies have attempted to understand the proliferation of cancer cells by employing mathematical models for the process of biological evolution [4] [5] . The mutation occurs randomly in the cell population and the number of resistant cells in the population increases as the cancer cells grow . Because expanding process of resistant cells in cancer cell population is like the dynamics of biological evolution , this process of expanding mutation cells can be viewed as an evolutionary process within the body occurring within a short span of time [5] . Mathematical models are often used to elucidate the dynamics of evolutionary process and have been studied to understand the mechanism through which cancer cells develop drug resistance [4] [6] [7] [8] [9] [10] . Iwasa et al . [11] analyzed the dynamics of resistant mutants in the exponential growth of cancer cells . The authors used a continuous-time branching process to calculate the probability of resistance , and the probability distribution of resistant cells when the population of cancer cells attains a certain detection size in the absence of therapy . They observed that the probability of resistance is an increasing function with the product of detection size and mutation rate . They concluded that the probability of resistance and the average number of resistant cells increase with the number of cell divisions over the course of the cancer . Haeno et al . [12] extended Iwasa’s model to cancer cells carrying two mutations . They also used a continuous-time branching process to calculate the probability of formation of at least one cell carrying two mutations , and the probability distribution when the population reaches a certain size . Their findings were similar to those from Iwasa’s study . Foo et al . [13] [14] modelled the cancer cell population during treatment with a continuous-time birth and death process . They measured the effect of drugs in reducing the proliferation rate of drug-sensitive cells . They studied resistance dynamics during therapy under a general time-varying treatment schedule . They coupled their stochastic framework with pharmacokinetic models incorporating the processes of drug absorption and elimination within the body . They calculated the probability of resistance arising during continuous and pulsed administration strategies . They used their estimates of probability of resistance and population size of drug-resistant cells to determine an optimum drug administration schedule that would minimize the risk of resistance . The mathematical approach used in these models were analytical ones . The probability of resistance was obtained by solving differential equations and confirmed by computer simulations . To obtain the analytical solution , various ways to derive the solution of equations were devised . For example , the authors in [11] [12] [13] [14] handle the cell size as continuous variables in their calculations , though it is considered appropriate for addressing the discrete state space when the dynamics of cell size is discussed . The purpose of this paper is to present numerical approach for computing the emerging probability of resistant cells . We model the process of cancer progression through a discrete state space , continuous time Markov chain . Then , we transform it into an embedded Markov chain to reduce the computational effort for computing the emerging probabilities of resistant cells . This paper first explains our models for computing the number of resistant cells . Then the two ways of computation are presented: Exact analysis and aggregation approximation . The approximation method is introduced as efficient way to compute . Second , the computed values of exact analysis and those of approximate are compared . At the same time the computed values of exact analysis also are compared with those of previous study . The relative errors are used for evaluating the accuracy of these computed values . Third , values of computer simulation are compared with those of approximation . This comparison is performed at detection size over 10000 . The 95% confident intervals are used to confirm and evaluate the accuracy of computed values of approximation . The execution time of both methods are also compared . Lastly , the parameter dependency on the computed values of emerging probabilities of resistant cells . The parameter dependency of division rate , death rate and mutation rate are investigated by computing the probabilities of resistance with changing these values of parameters . Factors of effecting the emerging probabilities of resistance are discussed .
Consider an expanding cancer cell population . There are two types of cancer cells: drug-sensitive and drug-resistant . The sensitive cells divide and die at a rate of λ and μ , respectively . The probability of mutation when a sensitive cell divides ( i . e . , the probability of formation of a resistant cell ) is γ , and the probability of formation of a sensitive cell is 1 − γ . The resistant cells divide and die at a rate of α and β , respectively . Our objective is to obtain the distribution of resistant cells when the total cell population reaches detection size , which is denoted by M . Let us denote the total number of cells ( both sensitive and resistant ) and the number of resistant cells by m and n , respectively . Then , the number of sensitive cells increases at the rate of λ ( 1 − γ ) ( m − n ) and decreases at the rate of μ ( m − n ) . The number of resistant cells increases at the rate of λγ ( m − n ) + αn and decreases at the rate of β n . Let us consider the two-dimensional Markov chain with states ( m , n ) , where m = 0 , 1 , 2 , ⋯ , M; n = 0 , 1 , 2 , ⋯ , m . Let us consider the process starting at the state ( m , n ) = ( 1 , 0 ) . The process can end either at extinction ( m = 0 ) or when the cell population reaches detection size ( m = M ) . Let us denote the state of the process ( m , n ) after the t-th event ( cell division or death ) as ( mt , nt ) . The transition probabilities of this process are given as follows: P r { ( m t + 1 , n t + 1 ) = ( i + 1 , j ) | ( m t , n t ) = ( i , j ) } = λ ( 1 − γ ) ( i − j ) / Γ i , j ( 1 ) P r { ( m t + 1 , n t + 1 ) = ( i + 1 , j + 1 ) | ( m t , n t ) = ( i , j ) } = { λ γ ( i − j ) + j α } / Γ i , j ( 2 ) P r { ( m t + 1 , n t + 1 ) = ( i − 1 , j ) | ( m t , n t ) = ( i , j ) } = μ ( i − j ) / Γ i , j ( 3 ) P r { ( m t + 1 , n t + 1 ) = ( i − 1 , j − 1 ) | ( m t , n t ) = ( i , j ) } = j β / Γ i , j ( 4 ) Here Γi , j = ( i − j ) ( λ + μ ) + j ( α + β ) is the sum of the rates , normalizing the total probability to 1 . Note that the Markov chain ( m , n ) is homogeneous . Let us define the set of states in which the total number of cells is i , { ( i , 0 ) , ( i , 1 ) ⋯ ( i , i ) } as level i . Let us denote the transition probability sub-matrix from level i to level i + 1 , and from level i to level i − 1 as Pi and Qi , respectively . The element of Pi in the k-th row and i-th column is the transition probability from ( i , k ) to ( i + 1 , k ) . The corresponding element of Qi is the transition probability from ( i , k ) to ( i − 1 , k ) . Pi and Qi are expressed as follows: P i = ( i λ ( 1 − γ ) Γi 0 i λ γ Γi 0 0 ⋯ ⋯ ⋯ ⋯ 0 0 ( i − 1 ) λ ( 1 − γ ) Γi 1 ( i − 1 ) λ γ + α Γi 1 0 ⋯ ⋯ ⋯ 0 0 0 ( i − 2 ) λ ( 1 − γ ) Γi 2 ( i − 2 ) λ γ + 2 α Γi 2 ⋯ ⋯ ⋯ 0 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 0 0 ⋯ ⋯ ⋯ λ ( 1 − γ ) Γi i − 1 λ γ + ( i − 1 ) α Γi i − 1 0 0 0⋯⋯⋯⋯⋯⋯0 i α Γi i ) Q i = ( i μ Γi 0 0 0 ⋯ ⋯ 0 β Γi 1 ( i − 1 ) μ Γi 1 0 ⋯ ⋯ 0 0 2 β Γi 2 ( i − 2 ) μ Γi 2 ⋯ ⋯ 0 ⋯⋯⋯⋯⋯⋯⋯ 0 0 0 ⋯ ( i − 1 ) β Γi i − 1 μ Γi i − 1 0 0⋯⋯0 i β Γi i ) where Pi is the ( i + 1 ) × ( i + 2 ) matrix , and Qi is the ( i + 1 ) × i matrix . We can now express the transition probability matrix of the process ( m , n ) by using Pi and Qi as follows: S = ( 1 0 0 0 0 ⋯ ⋯ ⋯ 0 Q 1 0 P 1 0 0 ⋯ ⋯ ⋯ 0 0 Q 2 0 P 2 0 ⋯ ⋯ ⋯ 0 0 0 Q 3 0 P 3 ⋯ ⋯ ⋯ 0 ⋯⋯⋯⋯⋯⋯⋯⋯ 0 ⋯ ⋯ ⋯ ⋯ ⋯ Q M − 1 0 P M − 1 0 ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ 0 I M ) Note that the states in levels 0 and M are absorbing states . We take the submatrix T from S as follows: T = ( 0 P 1 0 0 ⋯ ⋯ ⋯ 0 Q 2 0 P 2 0 ⋯ ⋯ ⋯ 0 0 Q 3 0 P 3 ⋯ ⋯ ⋯ 0 ⋯⋯⋯⋯⋯⋯ 0 ⋯ ⋯ ⋯ ⋯ ⋯ Q M − 1 0 ) The submatrix corresponds to the transition probability matrix from transient states to transient states . Then , mt denotes the total number of cancer cells and nt denotes the number of resistant cells after the t-th event ( cell division or death ) . Here , we define ( mt , nt ) as the state of the number of total cells and resistant cells after the t-th event . In addition , the probability being at the state is expressed as π ( i , j ) t = P r { ( m t , n t ) = ( i , j ) } . Now , we define the probability distribution vector: π i t = ( π t ( i , 0 ) , π t ( i , 1 ) , π t ( i , 2 ) , ⋯ , π t ( i , i − 1 ) , π t ( i , i ) ) Here , we consider the process starting at one sensitive cell and no resistant cell , so the initial state of the process is ( m0 , n0 ) = ( 1 , 0 ) i . e . , { π0i= ( 1 , 0 ) ( i=1 ) π0i=0 ( i≠1 ) Our objective is to obtain the probability distribution and the emerging probabilities of resistant cells when the total number of cells reaches M . As level 0 and level M are absorbing states , the emerging probability of resistant cells is expressed by lim t → ∞ π ( M , j ) t 1 − π ( 0 , 0 ) t ( j = 0 , 1 , 2 , 3 , ⋯ , M ) . In general , to calculate the distribution of probability in the absorbing states , we should obtain the fundamental matrix as shown below: I + T + T 2 + T 3 + ⋯ = ( I − T ) − 1 ( 5 ) The matrix T is large , and the calculation of the fundamental matrix involves high computational complexity as the matrix becomes large . Therefore , we propose another algorithm to reduce complexity . Let πi = ( π ( i , 0 ) , π ( i , 1 ) , π ( i , 2 ) , ⋯ , π ( i , i−1 ) , π ( i , i ) ) be the probability distribution of the process at first arrival to level i , where ∑ k = 0 iπ ( i , k ) = 1 . The discrete-time chain {πi} is said to be embedded in {π i t} , so it is referred to as embedded Markov chain . Then the probability distribution when the total number of cells reaches M is equivalent to πM . Let us now denote the probability matrix that the state at first arrival to level i + 1 is ( i + 1 , ⋅ ) under the condition that the state at first arrival to level i is ( i , ⋅ ) by Fi . There are two paths of transition for the state in level i to reach level i+1 . In one , the cells transition directly from level i to level i + 1 in a single step . In the other , the cells first transition from level i to level i − 1 and then reach level i + 1 through level i . Using the transition probability matrices Pi and Qi , we obtain the recurrence formula as follows: F i = P i + Q i F i − 1 F i ( i = 2 , 3 , ⋯ , M ) ( 6 ) Thus , we have F i = ( I i + 1 − Q i F i − 1 ) − 1 P i ( 7 ) where F1 = P1 , and Ii+1 is the ( i + 1 ) × ( i + 1 ) identity matrix . When the total number of cells reaches M , the probability distribution πM is calculated by the following formula: { π2= ( 1 , 0 ) F1πi+1=πiFi ( i=3 , 4 , ⋯ , M−1 ) Here , Fi is an i × ( i + 1 ) matrix . Thus , the complexity of calculation is much lower in this case than in the case of the fundamental matrix . The algorithm proposed in the previous section still includes the inverse of the ( M − 1 ) × M matrix . We propose an approximate aggregation to further reduce the complexity of the calculation . If the level of states is greater than m , we aggregate the states in which the number of resistant cells is greater than m to a single state ( Fig 1 ) . The level m + k is the set of states as follows: { ( m + k , 0 ) , ( m + k , 1 ) , … , ( m + k , m − 1 ) , ( m + k , m ) , … , ( m + k , m + k ) } From among these , we aggregate the states ( m + k , m ) , ( m + k , m + 1 ) , … , ( m + k , m + k ) to one state and denote the aggregated state by ( m + k , m* ) . Then , state ( m + k , m* ) can transit to ( m + k + 1 , m* ) , ( m + k − 1 , m* ) , or ( m + k − 1 , m − 1 ) . Let us denote the rate of transition from state ( m + k , m* ) to each of state ( m + k + 1 , m* ) , ( m + k − 1 , m* ) , and ( m + k − 1 , m − 1 ) by R A k , R B k , and R C k , respectively . These rates can now be expressed as follows: R A k = ∑ x = 0 k { ( k − x ) λ ( 1 − γ ) + ( k − x ) λ γ + ( m + x ) α } × π t ( m + k , m + x ) ∑ v = 0 k π t ( m + k , m + v ) = [ 1 2 k ( k + 1 ) λ + { ( k + 1 ) m + 1 2 k ( k + 1 ) } α ] × π t ( m + k , m + x ) ∑ v = 0 k π t ( m + k , m + v ) ( 8 ) R B k = ∑ y = 0 k { ( k − y ) μ × π t ( m + k , m + x ) ∑ v = 0 k π t ( m + k , m + v ) + ∑ y = 1 k { ( m + y ) β × π t ( m + k , m + x ) ∑ v = 0 k π t ( m + k , m + v ) = [ 1 2 k ( k + 1 ) μ + { ( k m + 1 2 k ( k + 1 ) } β ] × π t ( m + k , m + x ) ∑ v = 0 k π t ( m + k , m + v ) ( 9 ) R C k = m β × π t ( m + k , m + x ) ∑ v = 0 k π t ( m + k , m + v ) ( 10 ) Here , we assume that the following probabilities are the same: π t ( m + k , m ) = π t ( m + k , m + 1 ) = ⋯ = π t ( m + k , m + k ) = 1 k + 1 ( 11 ) Now we have R A k = ∑ x = 0 k { ( k − x ) λ ( 1 − γ ) + ( k − x ) λ γ + ( m + x ) α } × 1 k + 1 = [ 1 2 k ( k + 1 ) λ + { ( k + 1 ) m + 1 2 k ( k + 1 ) } α ] × 1 k + 1 ( 12 ) R B k = ∑ y = 0 k { ( k − y ) μ × 1 k + 1 + ∑ y = 1 k { ( m + y ) β × 1 k + 1 = [ 1 2 k ( k + 1 ) μ + { ( k m + 1 2 k ( k + 1 ) } β ] × 1 k + 1 ( 13 ) R C k = m β × 1 k + 1 ( 14 ) Using the sum of the rates Γ ( k ) , which is described as Γ ( k ) = R A k + R B k + R C k = [ 1 2 k ( k + 1 ) ( λ + μ ) + { ( k + 1 ) m + 1 2 k ( k + 1 ) } ( α + β ) ] , ( 15 ) we can obtain the probabilities of transition from state ( m + k , m* ) to each of ( m + k + 1 , m* ) , ( m + k − 1 , m* ) , and ( m + k − 1 , m − 1 ) through the following equations: R A k Γ ( k ) = k λ + ( 2 m + k ) α k ( λ + μ ) + ( 2 m + k ) ( α + β ) ( 16 ) R B k Γ ( k ) = k ( k + 1 ) μ + { 2 k m + k ( k + 1 ) } β ( k + 1 ) ( λ + μ ) + { 2 m ( k + 1 ) + k ( k + 1 ) ( α + β ) } ( 17 ) R C k Γ ( k ) = 2 m β ( k + 1 ) ( λ + μ ) + { 2 m ( k + 1 ) + k ( k + 1 ) ( α + β ) } ( 18 ) After the aggregation , the transition probability submatrix from level m + k to level m + k + 1 , and from level m + k to level m + k − 1 , which are denoted by P ˜ m + k and Q ˜ m + k , respectively , become: P ˜ m + k = ( i λ ( 1 − γ ) Γi 0 i λ γ Γi 0 0 ⋯ ⋯ ⋯ 0 0 ( i − 1 ) λ ( 1 − γ ) Γi 1 ( i − 1 ) λ γ + α Γi 1 0 ⋯ ⋯ 0 0 0 ( i − 2 ) λ ( 1 − γ ) Γi 2 ( i − 2 ) λ γ + 2 α Γi 2 ⋯ ⋯ 0 ⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ 0 0 0 ⋯ ⋯ λ ( 1 − γ ) Γi i − 1 λ γ + ( i − 1 ) α Γi i − 1 0 0 ⋯⋯⋯⋯ ⋯ 0 R A k Γ ( k ) ) Q ˜ m + k = ( i μ Γi 0 0 0 ⋯ ⋯ 0 β Γi 1 ( i − 1 ) μ Γi 1 0 ⋯ ⋯ 0 0 2 β Γi 2 ( i − 2 ) μ Γi 2 ⋯ ⋯ 0 ⋯⋯⋯⋯⋯⋯⋯ 0 0 0 ⋯ ( i − 1 ) β Γi i − 1 0 0 0 ⋯⋯ R C k Γ ( k ) R B k Γ ( k ) ) where P ˜ m + k and Q ˜ m + k are ( m + k ) × ( m + k ) matrices . Then we can approximately calculate Fi ( i = m , m + 1 , m + 2 , ⋯ ) using F i = ( I i + 1 − Q ˜ i F i − 1 ) − 1 P ˜ i ( 19 ) The largest size of the matrix Fi is ( m + 1 ) × ( m + 1 ) , so we can obtain the probability more easily than through exact analysis .
Fig 2 shows the results of emerging probabilities of resistance using the exact analysis method and the aggregation approximation method . We set the detection size M at 500 , 1000 , and 1500 because the exact analysis took a considerable time when M was greater than 1500 . We computed each relative error at variable aggregation size; m = 10 ⋯ 100 . Computations using the aggregate approximation and the formula in previous study . [11] were executed and computed the relative errors . The relative errors of approximation method were no lower than 10−6 order regardless of aggregation size . For comparison , the relative error of the previous study . [11] was no less than the order of 10−4 . The relative error became smaller as the detection size becomes larger because they regarded the number of cells as continuous valuables when they calculated the probability of resistance . These results are shown in Figs 2 and 3 and Supporting information S1 Table .
In this study , we modeled the cell progression process by a two-dimensional Markov process that was characterized by the total number of cells and the number of resistant cells . We calculated the emerging probability of resistance when the total number of cells reached detection size M , starting from one drug sensitive cell . This probability was equivalent to the probability of being absorbed in the absorbing state wherein the total number of cells was M . To calculate this probability , we needed to inverse matrix of size ( M + 2 ) ( M − 1 ) /2 × ( M + 2 ) ( M − 1 ) /2 and the complexity of calculation was O ( M6 ) . We employed the embedded Markov analysis approach and observed only the timepoint at which the total number of cells change . We also derived the recurrence formula for state transition probabilities of the first visit to the set of states wherein the total number of cells was n + 1 from the set of states wherein the total number of cells was n . Using this approach and the formula , we proposed an efficient calculation method for emerging probabilities of resistant cells that required M times calculation of the ( M + 1 ) × ( M + 1 ) inverse matrix only . Then , we calculated the emerging probabilities of resistance when the number of cells reached M = 1000 . However , it took significant execution time for realistic detection sizes such as M = 10000 or 100000; thus , we designed a more practical method for calculation . The approximation approaches inverting a matrix of large dimension have been intensively studied , and this approach may be useful to reduce the execution time for emerging probabilities of resistance . However , as shown above , once the number of resistant cells reached 100 , the probability of extinction of resistant cells is under 10−5 , the information of probability distribution of over 100 resistant cells was not very valuable from the viewpoint of treatment strategy . Hence , we aggregated the states ( m + k , m ) , ( m + k , m + 1 ) , … . , ( m + k , m + k ) to a single state for each k ( k = 1 , 2 , … , M − m ) . This approximation method required computing M times calculation of ( m + 1 ) × ( m + 1 ) inverse matrix to obtain the approximate solutions for the emerging probabilities of resistant cells . We set m = 100 in the numerical analysis , because the calculation was completed in a practical execution time for a realistic detection size such as100000 . The numerical analysis showed that approximation errors of emerging probability are negligibly small when M = 500 and M = 1000 and that our approximation method demonstrated the same level of accuracy when M = 1000 or the higher level of accuracy when M = 500 compared to the result of a previous study . We also performed a stochastic computer simulation to confirm the results of the approximation method . The simulation performed 1000000 × 10 runs to obtain a 95% confidence interval of the emerging probability of resistance . The results of emerging probability of resistance by the approximation method fell within the 95% confidence interval , and the execution time of our approximation method was considerably shorter than that obtained for our simulation . The numerical results demonstrated that the probability of resistance was chiefly dependent on the number of cell divisions until the cell population reached the detection size . It is reasonable as drug-resistant cells are generated by mutation in the process of cell division . A large population of cancer cells would have a greater likelihood of generating resistant cells via mutation . As only a few resistant cells exist in the early stage of cancer , if any , the possibility of extinction of resistant cells owing to natural causes cannot be disregarded . The proposed method in this paper was able to track the transition of cell size . By applying this method , we would be able to follow the transition of the number of cells under drug administration . The next step in our study is to design the treatment strategy based on the analysis under drug administration . In many cases , multiple different mutations can confer resistance and the mutagenic processes leading to such mutations may be different . Then , clones may have different growth and death rates in accordance depending on the types of mutations . It is of interest to compute the number of resistant cells of multiple types when the cell population reaches the detection size . However , it would be more difficult to extend the framework in this paper to the case of multiple resistance mutations . For example , it is necessary to analyze a three-dimensional Markov process in the case of two types of mutations . Thus , another effective approximation would be necessary for the analysis . | Drug therapies for cancer have dramatically succeeded since molecular-targeted drugs have been introduced in medical practice; however , drug treatment often fails owing to the emergence of drug-resistant cells . A variety of approaches , including mathematical modeling , has been undertaken to clarify the mechanism of resistance and subsequently avoid resistance to therapy . This paper proposes one of the mathematical approaches that uses a stochastic model and provides the emerging probabilities of resistance at detection size . | [
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... | 2019 | A numerical approach for a discrete Markov model for progressing drug resistance of cancer |
The eukaryotic nicotinamide riboside kinase ( Nrk ) pathway , which is induced in response to nerve damage and promotes replicative life span in yeast , converts nicotinamide riboside to nicotinamide adenine dinucleotide ( NAD+ ) by phosphorylation and adenylylation . Crystal structures of human Nrk1 bound to nucleoside and nucleotide substrates and products revealed an enzyme structurally similar to Rossmann fold metabolite kinases and allowed the identification of active site residues , which were shown to be essential for human Nrk1 and Nrk2 activity in vivo . Although the structures account for the 500-fold discrimination between nicotinamide riboside and pyrimidine nucleosides , no enzyme feature was identified to recognize the distinctive carboxamide group of nicotinamide riboside . Indeed , nicotinic acid riboside is a specific substrate of human Nrk enzymes and is utilized in yeast in a novel biosynthetic pathway that depends on Nrk and NAD+ synthetase . Additionally , nicotinic acid riboside is utilized in vivo by Urh1 , Pnp1 , and Preiss-Handler salvage . Thus , crystal structures of Nrk1 led to the identification of new pathways to NAD+ .
NAD+ functions both as a co-enzyme for hydride transfer reactions and as a substrate for NAD+-consuming enzymes including Sirtuins and poly ( ADPribose ) polymerases [1] . Most fungal and animal cells have redundant pathways for NAD+ biosynthesis that consist of a de novo pathway from tryptophan [2] and salvage pathways that utilize the vitamin precursors of NAD+ , namely nicotinic acid ( Na ) , nicotinamide ( Nam ) [3] , and nicotinamide riboside ( NR ) [4] . Because NAD+ biosynthesis is required for the function of Sirtuins [5–9] and given the evidence that Sirtuins play roles in life span extension [10–12] , increased mitochondrial function [13] , and energy expenditure [14] , there has been a resurgence of interest in NAD+-boosting drug therapies and nutritional interventions [1] . NR , a natural product present in milk [4] , increases NAD+ biosynthesis , increases Sir2-dependent gene silencing , and extends yeast life span via two NR salvage pathways [15] . The first NR salvage pathway depends on NR phosphorylation by a specific kinase , encoded by the products of the yeast and human NRK1 genes or the human NRK2 gene [4] . The second NR salvage pathway is Nrk-independent and is initiated by the activity of yeast Urh1 , Pnp1 , and , to a slight degree , Meu1 , which split NR into a ribosyl product and Nam for resynthesis of NAD+ via Nam salvage [15] . Although the second pathway of NR salvage has yet to be investigated in mammalian systems , Pnp1 and Meu1 are the yeast homologs of human purine nucleoside phosphorylase and methylthioadenosine phosphorylase , suggesting that human NR salvage may depend on Nrk1 , Nrk2 , and Pnp [15] . Na , Nam , and NR have been investigated in an ex vivo model of murine dorsal root ganglion neurodegeneration [16] . Prompted by genetic evidence that increased neuronal NAD+ biosynthesis protects against Wallerian degeneration [17 , 18] , NR was shown to be the only NAD+ precursor vitamin that protects against axonopathy without engineered overexpression of a biosynthetic gene , apparently because the NRK2 gene is transcriptionally induced by nerve damage [16] . NR kinases are ∼200–amino acid polypeptides related to human uridine/cytidine kinase 2 [19] and Escherichia coli pantothenate kinase [20] . To establish that yeast Nrk1 and no other enzyme phosphorylates NR in vivo , Saccharomyces cerevisiae mutants without the QNS1 gene , encoding glutamine-dependent NAD+ synthetase [21] were shown to be entirely dependent on NR and Nrk1 for viability [4] . The human homologs Nrk1 and Nrk2 were validated in the same assay [4] . The presence of a human Nrk pathway suggests the means by which the anticancer prodrug tiazofurin [22] may be converted to the toxic NAD+ antagonist tiazofurin adenine dinucleotide ( TAD ) . Although yeast and human Nrk1 and human Nrk2 were purified and characterized with respect to NR , cytidine , uridine , and tiazofurin phosphorylation in specific activity terms [4] , the kinetics of nucleoside and nucleoside triphosphate specificity have not been carefully quantified . Here we report the structure–activity relationships for human NR kinases with nucleoside and nucleoside triphosphate substrates . Nrk1 and Nrk2 both strongly discriminate against cytidine phosphorylation by 500-fold in kcat/KM . However , Nrk1 effectively phosphorylates NR with ATP or GTP and discriminates against uridine , whereas Nrk2 discriminates against GTP as a phosphodonor but does not strongly discriminate against phosphorylation of uridine . To dissect the structural basis for specificity , we crystallized selenomethionyl human Nrk1 bound to Mg2+·ADP and solved the 1 . 95 Å structure of the Nrk1 monomer by single-wavelength anomalous scattering . Using a series of crystal structures of human Nrk1 bound to NR , NR·Mg2+·adenosine-5′-[ ( β , γ ) -imido]triphosphate ( AppNHp ) , and nicotinamide mononucleotide ( NMN ) , we resolved snapshots of the catalytic cycle and identified two conserved carboxylate groups that we establish as essential for biological activity . From a structure of Nrk1 bound to tiazofurin , we gained further understanding of nucleoside specificity . However , at the site where we expected to find specific enzyme features that would recognize the distinctive carboxamide portion of NR and tiazofurin substrates , we found only steric complementarity and solvent exposure . Accordingly , we synthesized nicotinic acid riboside ( NaR ) and found this molecule to be as specific a biochemical substrate as is NR . Finally , we showed that NaR is a synthetic vitamin precursor of NAD+ that supports the growth of yeast cells through each of the salvage pathways—Nrk and Urh1/Pnp1—also used by NR . Thus , NR kinases are actually dual-specificity salvage enzymes that may play a role in another unanticipated biosynthetic pathway to NAD+ .
To assess Nrk specificity in vitro , recombinant human Nrk1 and Nrk2 were expressed in E . coli and purified by immobilized metal chelate affinity chromatography . As shown in Table 1 , the enzymes discriminate between substrates almost entirely in the KM term . Nrk1 has a kcat of approximately 0 . 5 s−1 irrespective of substrate , and Nrk2 possesses a kcat of approximately 1 s−1 irrespective of substrate . Nrk1 strongly favors NR as a substrate , displaying a 340-fold preference for NR over cytidine in the KM term and a ∼500-fold preference over either cytidine or uridine in the kcat/KM term . Tiazofurin , the prodrug form of the toxic NAD+ analog TAD , is a relatively good Nrk1 substrate with a kcat/KM of 1300 s−1M−1 , which represents 19% of the second-order rate for NR conversion to NMN ( 6800 s−1M−1 ) . Moreover , Nrk1 shows little preference for ATP ( 6800 s−1M−1 ) over GTP ( 5000 s−1M−1 ) as phosphodonor in formation of NMN . Whereas these data would classify Nrk1 as an NR and tiazofurin:ATP or GTP kinase , the data establish Nrk2 as an ATP-specific NR , tiazofurin , and uridine kinase . As shown in Table 1 , with GTP as the phosphodonor , Nrk2 has only 1 . 5% of the NR phosphorylating activity with respect to ATP . Tiazofurin ( 4500 s−1M−1 ) is phosphorylated as well as NR ( 3900 s−1M−1 ) , and uridine ( 850 s−1M−1 ) is within 5-fold of NR . The fact that Nrk1 and Nrk2 are distinct from uridine/cytidine kinases [19] is underscored by the poor cytidine monophosphate-forming activity of each enzyme . To understand the basis for substrate specificity of human Nrk1 , we prepared a selenomethionyl form of human Nrk1 and grew single crystals of a complex of the enzyme with Mg2+·ADP . A crystal , which had the symmetry of C2221 , was subjected to 0 . 9793-Å synchrotron X-radiation , and produced nearly complete diffraction data to 1 . 9-Å resolution ( Table 2 ) . Single-wavelength anomalous scattering [23] allowed location of Se sites [24] and phasing [25] to produce an interpretable experimental electron density map of the Nrk1 monomer prior to model building . The 1 . 95-Å refined protein model includes residues 1–82 and 92–189 of the 199–amino acid polypeptide , plus ADP , Mg2+ , and 72 water molecules with B factors between 9 and 37 Å2 . As shown in Figure 1A , Nrk1 consists of a five-stranded β sheet flanked on one side by α helices , E and A , and on the other side by helix B . Additionally , the monomeric enzyme contains a lid domain consisting of helix C and D connected by a 12– amino acid loop . The five-stranded sheet is entirely parallel and is formed from strands 2 , 3 , 1 , 4 , and 5 in the primary sequence . Earlier [4] , we detected sequence similarity with uridine/cytidine kinase and pantothenate kinase . Indeed , the DALI structural similarity server [26] revealed Nrk1 to be a structural homolog of a variety of Rossmann fold-containing metabolite kinases including human uridine/cytidine kinase 2 Uck2 [27] , E . coli pantothenate kinase panK [20] , Bacillus stearothermophilus adenylate kinase [28] and E . coli gluconate kinase [29] . A structural superposition of Nrk1 and Uck2 is provided in Figure 1B . The ADP-binding site , including P-loop [30] sequence Gly-Val-Thr-Asn-Ser-Gly-Lys-Thr ( residues 10–17 ) , is shown in close-up in Figure 2A . The guanidino group of Arg132 , the ε amino group of Lys16 , and the hydroxyl of Thr18 coordinate the β and α phosphates of ADP , whereas the hydroxyl of Thr17 , a β phosphate oxygen , and four well-ordered water molecules coordinate the magnesium ion . The adenine ring of ADP lies between Arg128 and Glu174 . Accounting for the ATP/GTP nonspecificity of Nrk1 , the 2 carbon of adenine is solvent exposed such that the 2 amino group of guanine would not appear to preclude binding in the same manner . However , in the ATP-specific Nrk2 sequence , Glu174 is replaced with Arg . Indeed , in a large study of amino acid propensity in adenine and guanine-binding sites , Arg was found to be localized at adenine sites and to be largely excluded from guanine sites [31] . To determine the nature of nucleoside phosphorylation by Nrk1 , we co-crystallized the Nrk1 enzyme with NR , NR·Mg2+·AppNHp , NMN , and tiazofurin , and performed molecular replacement and refinement to obtain high-resolution models . Despite the typical hinge motions identified in Rossmann fold-containing metabolite kinases upon substrate-binding [32] , Nrk1 was neither opened nor closed by any ligands examined . Pairwise comparisons of the α carbon coordinates indicated that all structures are within a root mean square difference of less than 0 . 4 Å . As shown in Figure 2B , which is derived from a 1 . 32-Å crystal structure , NR is bound with the 2′ and 3′ hydroxyl groups recognized by bidentate interactions from Asp56 and Arg129 and with the carboxylate of Asp36 accepting an apparent hydrogen bond from the NR 5′ hydroxyl . Such a hydrogen bond could serve to activate the 5′ oxygen toward bond formation with the γ phosphorous atom of a bound ATP substrate . As shown in Figure 2C , in the 1 . 92-Å refined crystal structure of Nrk1 bound to the hydrolysis-resistant ATP analog AppNHp with Mg2+ and NR , the γ phosphate—recognized by side chains of Thr12 , Lys16 , Tyr134 , and Arg132—is positioned for potential in-line transfer to the 5' oxygen of NR . In this structure , the carboxylate of Asp36 is a direct Mg2+ ligand . In the NMN product complex ( Figure 2D ) , all four side chains “formerly” associated with the γ phosphate of AppNHp are associated with the α phosphate of NMN , suggesting that these residues may be optimally aligned to stabilize a putative pentacoordinate phosphorane transition state that is resolved either by collapse to ATP + NR or by the formation of ADP + NMN products . In the NR ( Figure 2B ) and NMN ( Figure 2D ) substrate and product complexes , Asp36 is oriented toward the 5′' oxygen , suggesting a role in activating the acceptor oxygen and promoting bond formation . In the absence of NR or NMN and in the presence of the ADP product ( Figure 2A ) , Asp36 stabilizes a Mg2+-associated water molecule . Curiously , Asp36 has yet a third unique conformation in the inactive bi-substrate analog complex ( Figure 2C ) . In addition , Glu98 appears to have a key role in organizing a stable water ligand of Mg2+ . To test the hypothesis that Asp36 and Glu98 ( residues 35 and 100 in Nrk2 ) might be essential for function , we constructed human nrk1-D36A , nrk1-E98A , nrk2-D35A , and nrk2-E100A alleles for evaluation in yeast . These mutants , alongside wild-type NRK1 and NRK2 controls , were introduced into yeast strain BY278 in which NRK alleles were expressed from the GAL1 promoter on a LEU2 plasmid , the endogenous NRK1 gene was deleted , and a QNS1 gene was provided on a URA3 plasmid . In this system , a functional NRK gene allows a yeast cell grown in the presence of 10 μM NR to lose the QNS1 gene with associated URA3 marker , as scored by resistance to 5-fluoro-orotic acid [4] . As shown in Figure 3 , the conserved Asp and Glu residues are required for function of Nrk enzymes in vivo . To exclude the possibility that the conserved Glu residues are required for folding and are potentially dispensable after protein biosynthesis , we expressed and purified nrk1-E98A and nrk2-E100A mutant proteins in E . coli . As shown in Figure S1 , the conserved Glu is not required for soluble expression , accumulation , purification , or concentration . Although nrk1-E98A and nrk2-E100A proteins behaved precisely as did wild-type enzymes in purification , their activity in ATP-dependent phosphorylation of NR was below the level of detection of our assay . Thus , Asp36 ( Asp35 in Nrk2 ) and Glu98 ( Glu100 in Nrk2 ) are essential residues for function in vivo . The demonstrated post-biosynthetic role for the conserved Glu and the conserved active-site positions of Glu and Asp strongly argue for roles in catalysis . Data in Table 1 show that Nrk1 has strong specificity for nucleosides containing a carboxamide group two bond lengths away from N1 , such as NR and tiazofurin , and features that may discriminate against the 2- and/or 4-substitutions found in cytidine and uridine . Indeed , in crystal structures of Nrk1 bound to NR and NMN , it is clear that the 4-amino group of cytidine or the 4-oxy group of uridine could not be accommodated without rearrangement , because these constituents would clash with the carbonyl oxygen of Gln135 , which is in a cis peptide linkage with Pro136 . This unique backbone conformation is unlikely to be conserved by the uridine-accepting Nrk2 enzyme , which has a Thr-Val sequence in this position , likely to be in the typical trans conformation . The van der Waals clash between a modeled 4-oxy group of uridine and the Gln135 carbonyl oxygen is shown in Figure 4 . The carboxamide-containing preferred nucleoside substrates of Nrk1 are tiazofurin and NR . As shown in Figure 5A , the two nucleosides are bound almost superimposably by Nrk1 . The pyridine and thiazole moieties of NR and tiazofurin , respectively , are stacked between the phenol rings of Tyr55 and Tyr134 . An additional aromatic interaction with Phe39 allows the polar carbonyl oxygen and amino groups of NR and tiazofurin to be exposed to solvent ( Figure 5B ) . Because carbonyl oxygen and amino groups are isosteric at 1 . 9-Å resolution , we looked for an electrostatic interaction that might uniquely orient the carboxamide function of NR and tiazofurin , and found no interacting residue within hydrogen-bonding distance . We therefore considered the possibility that Nrk enzymes might phosphorylate the isosteric but nonisoelectronic NR analog , NaR . Consequently , NaR was synthesized and examined as an in vitro substrate of Nrk1 and Nrk2 . As shown in Table 1 , NaR is phosphorylated by Nrk1 and Nrk2 with highly similar kinetics with respect to those for NR phosphorylation . In assays of each human enzyme , NaR is favored over NR by slight KM advantages offset by slight kcat disadvantages . In kcat/KM terms , Nrk1 has 60% of the activity and Nrk2 has 138% of the activity with NaR versus NR . Thus , human Nrk1 and human Nrk2 are dual-specificity , NR and NaR kinases . The steric complementarity of Nrk1 with NR and the lack of electrostatic exclusion of NaR by human Nrk1 and Nrk2 suggested that NaR might be a synthetic NAD+ precursor vitamin . Should NaR be utilized by yeast cells , it would be conceivable that NaR is a previously unrecognized metabolite , such that the utility of NaR salvage might have played a role in the evolution of Nrk specificity . Additionally , our discovery of Nrk1-dependent [4] and Nrk1-independent [15] NR utilization suggested that , should NaR support the vitamin requirement of de novo pathway–deficient yeast cells , there could be two different metabolic pathways for NaR utilization . As shown in Figure 6A and 6B , the bna1 mutant in de novo biosynthetic enzyme 3-hydroxyanthranilic acid dioxygenase is a Na auxotroph [33] that can also be supported by 10 μM NR , thus providing an assay for vitamin activity of NaR . As reported earlier [4] , NR can bypass the requirement of glutamine-dependent NAD+ synthetase , Qns1 [21] . Also shown in Figure 6B , NR keeps a bna1 mutant alive in two different ways because cells have two NR salvage pathways [15] . The first NR salvage pathway goes through Nrk1 , which allows NR-dependent viability in a bna1 mutant deleted for Npt1 , which is the Na phosphoribosyltransferase . The second NR salvage pathway depends on the NR-splitting activities of Urh1 and Pnp1 , followed by nicotinamidase and Npt1 activities . This pathway allows a bna1 nrk1 double mutant to retain viability . The two NR salvage pathways are schematized in the lower right section of Figure 7 . As shown in Figure 6C , 10 μM NaR can also be used by bna1 mutants , establishing that NaR is a transportable NAD+ precursor vitamin . Genetic control over NAD+ biosynthesis in the yeast system allowed us to establish that NaR is not simply used as Na , not contaminated by or converted to NR , and is used via a unique set of enzymes including Nrk1 , Urh1 and Pnp1 , and Qns1 . If NaR were merely a source of Na , the bna1 npt1 mutant would fail to grow on NaR . However , Figure 6C clearly shows that NaR supports the growth of bna1 npt1 mutant yeast cells . Moreover , if NaR were either contaminated by NR or converted to NR by any cellular process , then NaR would support the growth of the qns1 mutant . As shown in Figure 6C , NaR fails to support the growth of the qns1 mutant . Whereas NaR shares with NR the ability to be utilized by bna1 npt1 and bna1 nrk1 mutants , the Qns1 requirement for NaR indicates that NaR metabolites must flow through nicotinic acid mononucleotide ( NaMN ) and nicotinic acid adenine dinucleotide ( NaAD ) as schematized in Figure 7 . Vitamin activity of NaR in bna1 npt1 and bna1 nrk1 mutant strains can be explained by two pathways for NaR utilization . NaR is phosphorylated by Nrk1 with highly similar kinetics to those of NR ( Table 1 ) . Thus , phosphorylation of NaR by Nrk1 produces NaMN in a pathway that is independent of Npt1 . By analogy with the recently described Nrk1-independent NR utilization pathway , we hypothesized that NaR is a substrate of the nucleoside hydrolase and nucleoside phosphorylase activities of Urh1 and Pnp1 , which are responsible for virtually all Nrk1-independent NR salvage [15] . However , whereas yeast Nrk1-independent NR salvage requires nicotinamidase , the corresponding pathway for NaR would simply produce Na from NaR , which would be salvaged by the Preiss-Handler pathway [34] , consisting of Na phosphoribosyltransferase Npt1 [7] , Nma1 , 2 , and Qns1 . To test the hypothesis that NaR utilization depends on Nrk1 , Urh1 , and Pnp1 , i . e . , the same enzymes that initiate NR salvage [15] , we grew wild-type , npt1 mutant , nrk1 mutant , urh1 pnp1 mutant , and nrk1 urh1 pnp1 mutant cells in vitamin-free media and in vitamin-free media supplemented with 10 μM NaR . As shown in Figure 6D , NaR elevates NAD+ levels in all strains except the nrk1 urh1 pnp1 mutant . In the wild-type strain , NaR elevated intracellular NAD+ from 0 . 70 ± 0 . 04 mM to 1 . 18 ± 0 . 10 mM , an increase of 480 μM . This can be compared with the 1 . 21 mM increase in intracellular NAD+ that is produced by growing wild-type cells in 10 μM NR [15] . Elimination of Nrk1 or both Urh1 and Pnp1 produced an identical 40% decline in the ability of NaR to elevate NAD+ . In the nrk1 mutant , NAD+ was elevated from 0 . 70 ± 0 . 03 mM to 0 . 99 ± 0 . 05 mM , whereas NAD+ was elevated from 0 . 70 ± 0 . 01 mM to 0 . 99 ± 0 . 02 mM in the urh1 pnp1 double mutant . Two strains , namely the npt1 mutant ( 0 . 59 ± 0 . 001 mM ) , which is deficient in the NAD+ salvage necessitated by Sirtuin activity [7] , and the nrk1 urh1 pnp1 mutant ( 0 . 60 ± 0 . 02 mM ) , which is deficient in NR utilization and salvage [15] , have baseline NAD+ concentrations in vitamin-free media that are 110 μM and 100 μM lower than those of the other strains , respectively . However , whereas the npt1 mutant was increased to 0 . 73 ± 0 . 03 mM with addition of NaR , the triple mutant in NR utilization was unable to obtain an increase in NAD+ concentration in response to NaR ( 0 . 59 ± 0 . 02 mM ) . Thus , NaR is a synthetic vitamin precursor of NAD+ that is phosphorylated by Nrk enzymes in vitro ( Table 1 ) and utilized in vivo ( Figure 6 ) . In vivo utilization of NaR is not limited to the Nrk pathway producing NaMN , because NaR can fulfill the vitamin requirement of a bna1 nrk1 mutant , and NaR can elevate NAD+ in cells without Nrk1 . Just as NR salvage goes through Nrk and Urh1/Pnp1 pathways [15] , the nucleoside-splitting activities of Urh1 and Pnp1 and Nrk1 must be eliminated to block NaR utilization . Yeast NAD+ biosynthetic pathways updated to include NaR utilization are schematized in Figure 7 .
The experiments performed herein establish that Asp36 and Glu98 have essential roles in Nrk function . Structures of Nrk1 bound to adenosine nucleotides and pyridine and thiazol nucleoside substrates provided information of the basis for ATP/GTP nondiscrimination and pyrimidine exclusion by Nrk1 . Structural analysis of Nrk2 is expected to shed light on how Nrk2 excludes GTP and phosphorylates uridine . It has been established that no yeast enzyme can substitute for Nrk1 in conversion of NR to NMN in vivo [4] . Moreover , the postulated role for Nrk enzymes in phosphorylating NR-mimetic prodrugs has created expectations for strong specificity in carboxamide recognition . Structures of Nrk1 with NR and tiazofurin , however , indicated that the nucleosides are recognized by polar interactions with the 2′ , 3′ , and 5′ hydroxyl groups and aromatic interactions with the base . Whereas there are steric clashes that would appear to destabilize the 4-substitutions found in cytosine and uracil ( Figure 4 ) , there is steric complementarity for the 3 carboxamide moiety in NR ( Figure 5B ) . Recognizing the absence of an enzyme feature that would specifically orient the carboxamide group , we synthesized NaR and discovered that both Nrk1 and Nrk2 are dual-specificity NR and NaR kinases ( Table 1 ) . To determine whether this specificity is merely a biochemical curiosity or might indicate another cellular function , we determined whether yeast cells deficient in de novo NAD+ biosynthesis can use NaR in vivo . According to genetic data presented in Figure 6 and schematized in Figure 7 , NaR is used via Na salvage and via Nrk1-dependent production of NaMN . The Nrk-dependent and Nrk-independent salvage of NaR is precedented by the processes by which NR is utilized in yeast cells . NR is utilized in an Nrk-dependent process that does not depend on the glutamine-dependent NAD+ synthetase [4] . However , NR is also utilized in a process that depends on NR splitting by Urh1 , Pnp1 and Meu1 , the Pnc1 nicotinamidase , and the Preiss-Handler pathway [15] . The unique feature of NaR as an in vivo substrate of the Nrk pathway is that NaR requires Nrk and NAD+ synthetase . The importance of the dual specificity of Nrk enzymes at phosphorylating NR and NaR is three-fold . First , there are active programs to design prodrugs of NAD+-antagonistic compounds such as TAD and benzamidine adenine dinucleotide . It has been assumed that such prodrugs must not stray far from NR to allow phosphorylation , adenylylation , and inhibition of the target dehydrogenases [35] . However , the discovery that Nrk1 and , apparently , Nrk2 exhibit steric but not electrostatic recognition of the carboxamide group will allow a wider range of prodrugs to be synthesized and evaluated . Second , the abilities of yeast to use NaR and of human Nrk enzymes to phosphorylate NaR in vitro suggest that NaR might be useful as a vitamin precursor to NAD+ . However , because Nrk-independent salvage requires expression of all three Preiss-Handler enzymes , Nrk-independent utilization of NaR would amount to supplementing with a very expensive form of Na . This is impractical because Na is already readily available in the diet and may be tissue-limited by the expression of Na phosphoribosyltransferase . Because maturation of NaR to NAD+ through the Nrk pathway requires the activity of NAD+ synthetase , NaR might be more tissue-restricted than NR or constitute a slower-release niacin-equivalent than NR . Thus , it is conceivable that NaR could be a useful supplement , particularly if it largely evades Nrk-independent phosphorolysis to Na or , as suggested for NR [15] , if NaR phosphorolysis can be inhibited , presumably with a Pnp inhibitor . Finally , the biotransformation of NaR by the two NR salvage systems in yeast prompts us to ask whether NaR might be an endogenous metabolite , such that the utility of NaR phosphorylation could have played a role in maintaining dual NR/NaR substrate specificity . NR was initially characterized as a compound produced in the laboratory and found in milk that can provide for qns1-independent yeast cell growth when added exogenously [4] . Exogenously applied NR protects against transection-induced degeneration of murine dorsal root ganglion neurons [16] . In the yeast system , exogenously applied NR increases NAD+ levels , Sir2 function , and replicative life span [15] . Additionally , the yeast study provided evidence for an endogenous NAD+ catabolic process that creates a requirement for NR salvage enzymes to maintain NAD+ levels [15] . By deleting the NRK1 , URH1 , and PNP1 genes , which account for virtually all NR utilization through both the Nrk-dependent and the Nrk-independent pathways , we showed that there is a significant ( 0 . 8 mM ) deficiency in NAD+ levels in cells grown in standard media , which does not contain any NR [15] . These data strongly argue for an endogenous process that produces NR and/or NaR at the expense of NAD+ . Indeed , because npt1 and nrk1 urh1 pnp1 mutants have the same deficiency in baseline NAD+ levels in vitamin-free media ( Figure 6D ) , we suggest that the rate of NAD+ catabolism to NR and/or NaR is comparable to the rate of Sirtuin-dependent consumption of NAD+ to Nam .
His-tagged human Nrk1 and Nrk2 proteins were expressed and purified from E . coli strain BL21 ( DE3 ) as described [4] . Kinetic analyses were performed in 20 mM HEPES , pH 7 . 5 , 100 mM NaCl , 5 mM MgCl2 with 1 mM ATP or GTP as phosphodonor and with varying concentrations of nucleoside substrates . Reactions were initiated by Nrk1 or Nrk2 enzyme sufficient to convert 1% to 10% of the input nucleoside to nucleoside monophosphate in 30 min incubations at 37 °C . Products were quantified by anion exchange high-performance liquid chromatography ( HPLC ) as described [4] and kinetic parameters were determined from Lineweaver-Burke plots . Nrk1 ( 30 mg/ml ) was crystallized by 1:1 sitting drop vapor diffusion ( 18 °C ) against the reservoir solutions listed in Table 2 . Crystals were cryo-protected in 1:1 paratone and mineral oil . Diffraction data ( Table 2 ) were reduced to intensities with the HKL2000 suite [36] , and the first Nrk1 structure was solved de novo as described in the text . ARP/wARP [37] was used for model building , and PHASER [38] was used for molecular replacement of subsequent Nrk1 structures . Geometric restrains for NR , NMN , and tiazofurin were generated on the PRODRG server [39] . Restrained refinement using REFMAC [40] , geometric validation using MOLPROBITY [41] , and manual rebuilding using COOT [42] were performed iteratively until convergence ( Table 2 ) . Coordinate alignments were performed by secondary structure matching [43] within COOT . Molecular graphics were produced with PyMOL [39] . Structure factors and coordinates have been deposited in the Protein Data Bank . Trimethylsilyl trifluoromethanesulfonate ( 1 . 039 g , 4 . 4 mmol; Sigma-Aldrich; http://www . sigmaaldrich . com ) was slowly added to ethyl nicotinate ( 0 . 9 ml , 6 . 6 mmol; Sigma-Aldrich ) and 1 , 2 , 3 , 5-tetra-O-acetyl-β-D-ribofuranose ( 1 . 4 g , 4 . 4 mmol; Sigma-Aldrich ) in 50 ml anhydrous methylene chloride at room temperature , stirred under argon . The mixture was heated to reflux for 8 h . TLC ( CH2Cl2: MeOH: TEA=5: 0 . 3: 0 . 05 ) stained with 10% H2SO4 in MeOH showed the disappearance of the ribofuranose and appearance of the presumed product , 2′ , 3′ , 5′-triacetyl ethyl NaR in a single spot at lower mobility relative to the front . After evaporation of methylene chloride , product ( 25 mg , 0 . 05 mmol ) was added into 0 . 9 ml of 312 mM NaOEt in EtOH on ice to form O-ethyl β-NaR . After mixing well , the reaction was stored at −20 °C overnight . The reaction was quenched with addition of acetic acid to neutralize the pH . After organic solvent was removed in vacuum , the residue was dissolved in water and extracted with cyclohexane to remove organic impurities . The aqueous phase was then concentrated 10-fold , made to 150 mM in phosphate buffer , and provided with 10 μl of pig liver esterase ( 13 units; Sigma-Aldrich ) to release NaR in a 25 °C overnight incubation . NaR was purified by C-18 HPLC . NaR was assayed by MALDI MS , in positive ion detection mode , and was observed as the protonated molecular ion ( predicted mass-to-charge ratio m/z= 256 . 08 , observed m/z= 256 . 1 ) . Other assignable fragmented ions detected included protonated Na . The entire m/z spectrum ( % peak height ) was 256 . 1 ( 1 . 81% ) , 228 . 0 ( 48 . 5% ) , 207 . 1 ( 25 . 4% ) , 146 . 1 ( 10 . 3% ) , and 124 . 0 ( 13 . 9% ) . We used a molar extinction coefficient of 6411 cm−1 ( 260 nm ) for NaR and 4305 cm−1 ( 259 nm ) for NR . Yeast strain BY278 , which contains qns1 deletion covered by plasmid pB175 ( QNS1 and URA3 ) and which contains nrk1 deletion , has been described [4] . pB450 and pB459 , which are LEU2 plasmids for expression of human NRK1 and NRK2 cDNAs under GAL1 promoter control [4] , were used as templates for site-directed mutagenesis to produce nrk1-D36A ( pHC12 ) , nrk1-E98A ( pHC10 ) , nrk2-D35A ( pHC13 ) , and nrk2-E100A ( pHC11 ) . BY278 was transformed with each plasmid and the empty p425GAL1 control . After passage on galactose media , transformants were streaked on synthetic complete , galactose media with 5-fluoroorotic acid and 10 μM NR [4] to score the function of NRK alleles . Isogenic strains for the NaR utilization study were BY165-1d ( qns1 ) [4] , KB046 ( bna1 in the deletion consortium background [44] ) , KB056 ( nrk1 deleted from KB046 ) , and JS949 ( bna1 npt1 , a gift of Jeffrey S . Smith , University of Virginia , United States ) . The four strains were grown in synthetic media with 3 μM Na plus 10 μM NR , washed in saline , and then cultured to exhaustion in vitamin-free media [15] . To assay utilization of NR or NaR , strains grown to exhaustion in vitamin-free media were streaked on vitamin-free synthetic media supplemented with 10 μM NR or NaR and photographed after 3 d at 28 °C . NAD+ measurements were performed as described [15] with isogenic yeast strains grown in vitamin-free media and vitamin-free media supplemented with 10 μM NaR to an optical density ( OD ) 600 nm of 1 . Strains were wild-type BY4742 , KB009 ( nrk1 in the deletion consortium background [44] ) , KB008 ( npt1 in the deletion consortium background [44] ) , PAB047 ( urh1 pnp1 [15] ) and PAB038 ( urh1 pnp1 nrk1 [15] ) .
The Swiss-Prot ( http://www . ebi . ac . uk/swissprot ) accession numbers for proteins in this paper are: human MTAP ( Q13126 ) ; human Nrk1 ( Q9NWW6 ) ; human Nrk2 ( Q9NPI5 ) ; human NP ( P00491 ) ; human Uck2 ( Q9BZX2 ) ; S . cerevisiae Bna1 ( P47096 ) ; S . cerevisiae Meu1 ( Q07938 ) ; S . cerevisiae Npt1 ( P39683 ) ; S . cerevisiae Nrk1 ( P53915 ) ; S . cerevisiae Pnc1 ( P53184 ) ; S . cerevisiae Pnp1 ( Q05788 ) ; S . cerevisiae Qns1 ( P38795 ) ; S . cerevisiae Urh1 ( Q04179 ) ; E . coli gntK ( P46859 ) ; E . coli panK ( P0A615 ) ; and B . stearothermophilus adk ( P27142 ) . The Protein Data Bank ( PDB ) ( http://www . rcsb . org/pdb ) accession numbers for human Nrk1 are 2QSY , 2QT1 , 2QT0 , 2QSZ , and 2P0E; for human Uck2 is 1UJ2 . | Biosynthesis of nicotinamide adenine dinucleotide ( NAD+ ) is fundamental to cells , because NAD+ is an essential co-factor for metabolic and gene regulatory pathways that control life and death . Two vitamin precursors of NAD+ were discovered in 1938 . We recently discovered nicotinamide riboside ( NR ) as a third vitamin precursor of NAD+ in eukaryotes , which extends yeast life span without caloric restriction and protects damaged dorsal root ganglion neurons from degeneration . Biosynthesis of NAD+ from NR requires enzyme activities in either of two pathways . In one pathway , specific NR kinases , including human Nrk1 and Nrk2 , phosphorylate NR to nicotinamide mononucleotide . A second and Nrk-independent pathway is initiated by yeast nucleoside-splitting enzymes , Urh1 and Pnp1 . We solved five crystal structures of human Nrk1 and , on the basis of co-crystal structures with substrates , suggested that the enzyme might be able to phosphorylate a novel compound , nicotinic acid riboside ( NaR ) . We then demonstrated that human Nrk enzymes have dual specificity as NR/NaR kinases in vitro , and we established the ability of NaR to be used as a vitamin precursor of NAD+ via pathways initiated by Nrk1 , Urh1 , and Pnp1 in living yeast cells . Thus , starting from the structure of human Nrk1 , we discovered a synthetic vitamin precursor of NAD+ and suggest the possibility that NaR is a normal NAD+ metabolite . | [
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Generation of reactive oxygen species ( ROS ) during infection is an immediate host defense leading to microbial killing . APE1 is a multifunctional protein induced by ROS and after induction , protects against ROS-mediated DNA damage . Rac1 and NAPDH oxidase ( Nox1 ) are important contributors of ROS generation following infection and associated with gastrointestinal epithelial injury . The purpose of this study was to determine if APE1 regulates the function of Rac1 and Nox1 during oxidative stress . Gastric or colonic epithelial cells ( wild-type or with suppressed APE1 ) were infected with Helicobacter pylori or Salmonella enterica and assessed for Rac1 and NADPH oxidase-dependent superoxide production . Rac1 and APE1 interactions were measured by co-immunoprecipitation , confocal microscopy and proximity ligation assay ( PLA ) in cell lines or in biopsy specimens . Significantly greater levels of ROS were produced by APE1-deficient human gastric and colonic cell lines and primary gastric epithelial cells compared to control cells after infection with either gastric or enteric pathogens . H . pylori activated Rac1 and Nox1 in all cell types , but activation was higher in APE1 suppressed cells . APE1 overexpression decreased H . pylori-induced ROS generation , Rac1 activation , and Nox1 expression . We determined that the effects of APE1 were mediated through its N-terminal lysine residues interacting with Rac1 , leading to inhibition of Nox1 expression and ROS generation . APE1 is a negative regulator of oxidative stress in the gastrointestinal epithelium during bacterial infection by modulating Rac1 and Nox1 . Our results implicate APE1 in novel molecular interactions that regulate early stress responses elicited by microbial infections .
The gastrointestinal epithelium serves as an initial interface between the host and luminal microbiota [1] and initiates innate immune responses to infection . Gastric and intestinal epithelial cells infected by microbial pathogens or commensal microbiota typically activate Rho GTPases leading , amongst other effects , to the production of reactive oxygen species ( ROS ) [2 , 3] that arise from the activation of the NADPH oxidase complex ( Nox1 ) [4] . Nox1 family proteins are the catalytic , electron transporting subunits of Nox1 in non-phagocytic cells that produce superoxide [5 , 6] . While production of microbicidal levels of ROS in professional phagocytes via Nox2 is well-studied , information on ROS generation by gastric and intestinal epithelial cells in response to microbial signals via epithelial Nox1 is limited . The levels of ROS produced by epithelial cells are much lower than in phagocytes , and are more important in redox-sensitive signaling than direct antimicrobial killing . Nox1 is associated with the membrane-integrated protein p22phox , NOXA1 and NOXO1 to form superoxide [5] . Nox1 is expressed in gastric tissues [4] and is thought to play a role in ROS production in H . pylori-infected human gastric epithelial cells . While NADPH oxidase can be activated in epithelial cells throughout the gut , little is known about its responses to enteric infection . Helicobacter pylori causes a lifelong infection that can lead to gastric and duodenal ulceration and gastric cancer , one of the major causes of cancer mortality worldwide [7 , 8 , 9] . Following H . pylori infection of guinea pigs [10] , humans [11] and cultured gastric epithelial cells [12] , an increase in oxidative stress occurs . H . pylori lipopolysaccharide ( LPS ) activates the small GTPase , Rac1 , leading to Nox1 activation and production of superoxide [10 , 13 , 14 , 15] . Since H . pylori is a persistent infection , chronic ROS exposure eventually leads to oxidative DNA damage [4 , 16 , 17] and activation of signaling pathways implicated in the pathogenesis of cancer [18 , 19] . Accumulation of ROS increases APE1 activation [20] which in turn , mediates vital functions designed to protect the host [18] . APE1 is a multifunctional protein that is widely express in epithelial cells and that regulates multiple responses to bacterial infections , including chemokine production , apoptosis , cell proliferation and responses to hypoxia . The carboxy-terminus of APE1 is responsible for repairing DNA damage induced by ROS , while its N-terminal region regulates transcription [18] . Another distinct transcriptional regulatory role of APE1 is mediated by the N-terminal Lys6/Lys7 acetylation , which modulates certain promoter activities [21 , 22 , 23] . We have shown that APE1 is upregulated in gastric epithelial cells in the context of H . pylori infection [20] and contributes to the activation of AP-1 and NF-κB that regulate cell responses , including IL-8 production [24 , 25] and inhibition of cell death during H . pylori infection [26] . Interestingly , in a model of mouse hepatic ischemia/reperfusion , overexpression of APE1 resulted in suppression of reperfusion-stimulated oxidative stress [27] . While infection of gastric epithelial cells with H . pylori is a suitable model system to study the mechanisms of APE1-mediated regulation of ROS , Salmonella enterica serovar Typhimurium can be used as model to study the mechanisms of ROS production by intestinal epithelial cells ( IEC ) . The pathogenicity of Salmonella is in part dependent on the presence of the Salmonella pathogenicity island 2 ( SPI2 ) that interferes with ROS production by Nox2 in macrophages [28 , 29] . As many of the established infection-induced effects on gastrointestinal physiology are mediated by ROS-dependent mechanisms , we sought to compare the role of APE1 in ROS generation following infection with gastric or enteric pathogens . In the current study , we provide evidence that H . pylori- and Salmonella-induced ROS is inhibited by APE1 in gastric and intestinal epithelial cells respectively . We also demonstrate that the Lys residues at the N-terminus of APE1 at positions 6 and 7 , are required for Rac1 binding . This interaction inhibits Rac1 activation and Nox1 expression , decreasing ROS generation that results from infection . Together , our findings show a novel role of APE1 in regulating ROS levels in gastrointestinal epithelial cells following infection .
Empty retroQ vector ( pSIREN ) , APE1 shRNA expressing ( shRNA ) cells , or non-transfected AGS ( AGS ) cells obtained from American Type Culture Collection were harvested and cultured in Ham’s F/12 medium ( Hyclone ) supplemented with 10% heat-inactivated FBS ( Hyclone ) [21] . NCI-N87 cells obtained from ATCC were maintained in RPMI supplemented with 10% FBS . T84 and HT-29 cells ( a kind gift from Dr . K . Barrett , University of California San Diego ) were maintained respectively in L-Glutamine containing F12/DMEM supplemented with 5% FBS and in McCoy’s 5A medium supplemented with 10% FBS . H . pylori 26695 , a cag PAI+ strain ( ATCC ) and its isogenic mutants , cag PAI− strain 8–1 and VacA ( kind gift from Dr R . M . Peek , VanderBilt University , Tennessee , USA [30] ) , were maintained as previously described [21]; a MOI of 100 was used for all the experiments in this study as this was the highest dose with minimal necrotic cell death [26] . Previously , we reported that infection of gastric epithelial cells with H . pylori longer than 6h cause cell death and therefore , longer infection times do not result in reliable ROS data [26] . Gastric antrum-derived primary epithelial cells were isolated and maintained in culture according to the procedures developed by Dr . Stappenbeck [31] . Briefly , biopsy samples were obtained from consenting adult patients undergoing esophagogastroduodenoscopy ( IRB UCSD HRPP 150476 ) were minced in small pieces and treated with collagenase at 37°C for approximately 1h . Then cells were washed and filtered . Cultures were maintained in matrigel and medium containing Wnt3a , R-spondin and Noggin , which was refreshed or passaged every other day . For luminol experiments , wells were coated with 1/30 matrigel for 30 min , which was removed immediately before cells were added and for imaging , glass slides were coated with 10 μg/cm2 with Collagen IV for 1 . 5h at 37°C , and washed with warm PBS prior to the addition of cells . Salmonella enterica serovar Typhimurium strain SL1344 and a ΔSPI2 mutant ( kind gifts from Drs . Olivia Steel Mortimer NIAID , Rocky Mountain Laboratory , Montana , USA and Brett Finlay , University of British Columbia , Canada ) , were used at MOI 30 in cultures of T84 cells and HT-29 cells . Salmonella cultures were grown as described previously [32] . Briefly , a single colony was inoculated into LB broth and grown for 8h under aerobic conditions and then under oxygen-limiting conditions overnight . Wild type APE1 , an N-terminal acetylation mutant of APE1 ( N-K6R/K7R ) , and a C-terminal DNA repair mutant of APE1 ( C-H309N ) constructs were used as previously reported [26] . Active Rac1 V12 and dominant negative Rac1 N17 plasmids were kind gifts from Dr . Jim Casanova University of Virginia , Charlottesville , Virginia , USA . All epithelial cells were seeded in six-well plates 18–24h before transfection . For overexpression studies , cells were transfected using 2 μg of plasmid DNA with Lipofectamine 2000 reagent ( Invitrogen ) as per the manufacturer’s protocol . In keeping with the manufacturer’s recommendation cells were used for infected experiments 40h post-transfection . Nox1 expression was suppressed with human NOX1 siRNA ON-TARGETplus SMARTpool ( Dharmacon RNAi technologies , L-010193-00-0005 ) . AGS cells in 6 well plates were transfected using Lipofectamine RNAiMAX transfection reagent according to the protocol and luminol oxidation was measured after 48h . Antibodies used include the following: anti-APE1 , mouse monoclonal anti-Nox1 ( Novus Biologicals ) , rabbit polyclonal anti-APE1 , mouse monoclonal anti-Rac1 clone 28A ( Millipore ) followed by incubation with anti-rabbit or anti-mouse HRP-conjugated IgG ( Cell Signaling Technology ) . NADPH oxidase inhibitor diphenyleneiodonium ( DPI ) and Rac inhibitor NSC23766 were purchased from Calbiochem . ROS in AGS and T84 cells were measured according to the protocol described in Lumimax Superoxide Anion Detection Kit ( Stratagene ) . See S1 Supplementary Materials and Methods for details . Measurements of ROS in NCI-N87 , HT-29 and primary gastric epithelial cells were performed using 1 mM luminol ( Sigma A8511 , without additional enhancers ) dissolved in borax buffer ( pH 9 ) and the Spectramax L ( Molecular Devices ) reader for detection . For microscopic detection of ROS , cells were loaded with 5 μM CM-H2DCFDA ( Invitrogen ) for 30 min in an incubator ( 5% CO2 37°C ) . Following loading with CM-H2DCFDA cells were washed and infected . Protein expression of APE1 , Rac1 and Nox1 was assessed by western blot . Co-immunoprecipitation experiments were performed using anti-FLAG M2 agarose beads ( Sigma ) to analyze components that bind to FLAG-APE1 or FLAG-Rac1 . See S1 Supplementary Materials and Methods for details . Densitometry was performed using ImageJ ( National Institutes of Health ) . The levels of the protein of interest were corrected for the levels of the loading control ( e . g . α-Tubulin ) . Rac1 activity was measured as described previously [32] ( see S1 Supplementary Materials and Methods ) . Densitometry was performed using ImageJ . The levels of active Rac1 were normalized for levels of total Rac1 . cDNAs obtained from antral gastric biopsies of H . pylori infected and uninfected patients were kindly provided by Richard Peek , Vanderbilt University ( Tennessee , USA ) . Additionally , antral gastric mucosa biopsy specimens were collected from H . pylori-infected and uninfected individuals during diagnostic esophagogastroduodenoscopy following a University of Virginia Human Investigation Committee ( HIC ) ( IRB number 9686 ) approved protocol into HBSS with 5% FBS [21] . All patient samples were de-identified apart from being known to be H . pylori infected or uninfected . The samples were analyzed at the University of Virginia , Virginia USA . See S1 Supplementary Materials and Methods for details . APE1-Rac1 interactions were detected with Duolink PLA Kit ( Olink Bioscience , Uppsala , Sweden: PLA probe anti-rabbit plus; PLA probe anti-mouse minus; Detection Kit orange ) according to the manufacturer's protocol . See S1 Supplementary Materials and Methods for details . Biopsy specimens for immunohistochemistry were obtained with Institutional Review Board approval of the Pontifical Catholic University , Santiago , Chile ( IRB number 12–236 ) from adult subjects with abdominal symptoms in Santiago , Chile . Samples were collected and H . pylori status was determined by rapid urease test and microscopic evaluation , and a study subject was judged colonized with H . pylori if one or both tests were positive for the bacteria . In collaboration with Dr . Harris , these snap frozen samples were shipped to UCSD where PLA was performed . Quantification of co-localization was performed using the colocalization plugin ( JACoP ) for ImageJ which calculates Pearson’s coefficient . ImageJ was used to quantify the amount of PLA signal , which was corrected for the number of cells present in each field of view . Results are expressed as mean ± SEM . Statistical differences were calculated using ANOVA for multiple comparisons and Bonferroni post-hoc testing in Graphpad Prism . Levels of significance are indicated as follows: * p<0 . 05 , ** p<0 . 01 and *** p< 0 . 001 . Proteins studied in this manuscript are given below with a reference to the SwissProt database: APE1 ( gene name APEX1 ) , P27695 Rac1 ( gene name RAC1 ) , P63000 Nox1 ( gene name NOX1 ) , Q9Y5S8
We observed a rapid increase in superoxide production in the human gastric adenocarcinoma-derived cell line AGS following infection with H . pylori ( Fig 1A ) . To determine whether the production of ROS observed was not unique to AGS cells additional experiments were performed in an alternative cancer-derived cell line NCI-N87 and non-transformed antral-derived primary epithelial cells . Induction of ROS production was also observed in NCI-N87 cells and primary human gastric epithelial cells isolated from the antrum ( Fig 1B and 1C respectively ) following infection with wild type H . pylori strain 26695 although the kinetics where somewhat different from AGS cells . Superoxide generation by luminol oxidation was independent of the vacA and cagA pathogenicity island ( PAI ) status of H . pylori since no significant differences were seen when AGS cells were infected with wild type H . pylori or the vacA or cagA PAI mutant strain , 8–1 ( S1 Fig ) . Prolonged infection studies showed that ROS is generated early following infection and not observed at 4h of infection or later ( S2 Fig ) . It is known that H . pylori activates Rac1 , and another report shows that Rac1 activation initiates ROS production in guinea pig gastric cells [13 , 33] . To determine if Rac1 regulates H . pylori-mediated ROS generation in human gastric epithelial cells , a constitutively active Rac1 plasmid ( V12 ) was overexpressed in AGS cells or cells were treated with the Rac1-specific inhibitor NSC23766 , before H . pylori infection . Overexpression of active Rac1 resulted in increased ROS generation , while the Rac1 inhibitor reduced ROS generation compared to vector-transfected cells ( Fig 1D ) . To confirm Rac1 activation during H . pylori infection , active Rac1 was assessed using a pulldown assay . As shown in Fig 1E and 1F , H . pylori 26695 infection increased Rac1 activation in AGS cells in 30 min and in NCI-N87 cells at 60 min after infection . Since there was no difference in ROS generation or Rac1 activation by H . pylori strains 26695 and 8–1 ( S3 Fig ) , subsequent experiments were performed with H . pylori 26695 only . Although activation of Rac1 by H . pylori has been previously reported , here we expand this finding by showing that Rac1 is involved in the production of ROS by gastric epithelial cells following infection with H . pylori . After establishing that H . pylori induce ROS production by gastric epithelial cells through activation of Rac1 , we investigated whether the ROS were generated by Nox1 as a major NADPH oxidase expressed in gastric epithelial cells [4] . Our results demonstrate that AGS cells infected in the presence of the general ROS inhibitor N-acetyl-L-cysteine ( NAC ) , showed significant inhibition of superoxide production ( Fig 2A ) . Also , infection in the presence of the NADPH oxidase inhibitor diphenyleneiodonium ( DPI ) resulted in inhibition of superoxide production , suggesting that NADPH oxidase is involved in H . pylori-induced ROS generation . As DPI is not a specific inhibitor of Nox1 , we used siRNA-mediated suppression of Nox1 to show a comparable decrease in luminol oxidation following infection with H . pylori ROS ( Fig 2B ) . To evaluate the relative contributions of Nox1 and Rac1 in H . pylori-induced ROS generation , luminol oxidation was measured in AGS cells in the presence of DPI , the Rac1 inhibitor NSC23766 or overexpression of active Rac1 . Comparable inhibition of ROS generation was observed when NSC23766 or DPI was used alone or in combination , indicating that Rac1 and NADPH oxidase share the same pathway to generate ROS . The increase in ROS in the presence of V12 was abrogated by DPI suggesting that Rac1 activation alone is not sufficient to generate ROS when NADPH oxidase activity is inhibited ( Fig 2C ) . In a parallel experiment , Nox1 protein expression was increased in AGS cells within 1h of H . pylori infection . This induction was further enhanced in the presence of active Rac1 but decreased in the presence of the Rac1 inhibitor NSC23766 ( Fig 2D ) . Together , our data demonstrate that NOX1 is the major source of ROS in gastric epithelial cells infected with H . pylori . It is known that ROS induces APE1 , but whether APE1 modulates ROS generation has not been previously examined . As illustrated in Fig 3A , luminol oxidation was increased in APE1 suppressed cells indicating regulation of ROS by APE1 . Corroborating the findings with luminol , immunofluorescence with CM-H2DCFDA demonstrated increased ROS generation in APE1 suppressed cells following infection ( Fig 3E ) . The additional increase of ROS in APE1 suppressed cells was absent in the presence of NSC23766 or DPI suggesting that both Rac1 and NADPH oxidase act downstream of APE1 in the pathway of ROS generation ( Fig 3B ) . Furthermore , overexpression of exogenous APE1 in cells with suppressed endogenous APE1 significantly reduced H . pylori-induced ROS generation ( Fig 3C ) . APE1 overexpression was also sufficient to inhibit ROS generation in the presence of V12 overexpression implicating APE1 as a major regulator of Rac1-mediated oxidative stress ( Fig 3D ) . To address if APE1 directly regulates Rac1 , Rac1 activity was compared in vector control and APE1 suppressed cells . Fig 4A and 4B demonstrate a significant increase in active Rac1 in APE1 suppressed AGS or NCI-N87 cells within 60 min of infection . Overexpression of exogenous APE1 in APE1 suppressed AGS cells resulted in a decrease in Rac1 activity ( Fig 4C ) . To establish whether APE1 binds to Rac1 to inhibit its activity , we immunoprecipitated APE1 and demonstrated that Rac1 interacted with APE1 . This interaction was augmented within 30 min of H . pylori infection ( Fig 4D ) . The enhanced association between APE1 and Rac1 after H . pylori infection was further confirmed by confocal microscopy showing co-localization of Rac1 and APE1 staining in the cytosol as indicated in the merged image ( Fig 4E ) . Using in situ proximity ligation assay ( PLA ) we confirmed cytosolic co-localization of APE1 and Rac1 following H . pylori infection in AGS , NCI-N87 and antral-derived primary gastric cells ( Fig 4F ) . To demonstrate that the findings in cell lines also occur in native human gastric epithelial cells we performed PLA in primary gastric epithelial cells from gastric mucosal biopsy samples ( Fig 4G ) . Our experiments showed that the APE1-Rac1 interaction was greater in biopsy samples from patients infected with H . pylori compared to those from uninfected control subjects ( Fig 4F right panel ) . Moreover , by performing Co-IP experiments we observed that APE1 interacted with the constitutively active form of Rac1 ( V12 ) but not with the dominant negative form ( N17 ) ( S4 Fig ) . From these observations we conclude that APE1 negatively regulates activation of Rac1 . To examine the effect of the level of APE1 on the previously reported increase of Nox1 after H . pylori infection [13] , levels of Nox1 were assessed by western blot in AGS cells with varying APE1 levels after infection at various times . Increased levels of Nox1 were observed in the APE1 suppressed cells compared to the vector control cells within 1h of H . pylori infection ( Fig 5A ) . This was confirmed by immunofluorescence staining that showed increased Nox1 after infection in APE1 suppressed cells compared to controls ( Fig 5B ) . To determine if the observations found in cell lines could be translated to native human gastric epithelial cells , real time RT-PCR for Nox1 and APE1 were performed with the total RNA isolated from gastric antral biopsies from uninfected or H . pylori infected patients . The expression of Nox1 and APE1 was significantly increased in tissue from infected patients ( Fig 5C ) . These in vivo data suggest a role for Nox1 and APE1 in the response to infection of human stomach with H . pylori . Earlier we established that various regulatory functions of APE1 are largely regulated by its N-terminal lysines ( K6K7 ) and C-terminal histidine ( H309 ) [26] . Therefore , co-immunoprecipitation was performed in AGS cells to determine the binding of Rac1 with the acetylation mutant ( N-K6R/K7R ) and the DNA repair mutant ( C-H309N ) of APE1 . Our results showed that the N-terminal acetylation mutant of APE1 had minimal binding with Rac1 whereas the binding of the DNA repair mutant was comparable to that of WT APE1 ( Fig 6A ) . To establish if this interaction between APE1 and Rac1 is essential in regulating H . pylori-induced ROS generation , ROS were measured in APE1 suppressed AGS cells that were transfected with WT APE1 , N-terminal mutant or C-terminal mutant and then infected with H . pylori . We observed a greater than 2-fold ROS increase in the N-terminal mutant overexpressing cells compared to WT APE1 . Although overexpression of the C-terminal mutant also showed increased ROS generation compared to WT APE1 , this was significantly less than the N-terminal mutant ( Fig 6B ) . To determine if Rac1 activity is modulated by the non-acetylatable mutant of APE1 , APE1 suppressed AGS cells were similarly transfected as described in Fig 6B , and Rac1 activation was measured after 30 min of H . pylori infection . Analogous to the findings of ROS generation , we observed that the non-acetylatable mutant was unable to inhibit Rac1 activation compared to WT APE1 or the DNA repair mutant of APE1 ( Fig 6C ) . To determine if the suppression of ROS production by APE1 occurs in other epithelial cells within the gastrointestinal tract and with other infections , we generated stable APE1 suppressed human colonic epithelial T84 cells and compared responses to wild type Salmonella SL1344 and the Salmonella ΔSPI2 mutant . The ΔSPI2 mutant of Salmonella was used for the ability of the pathogenicity island 2 of Salmonella to inhibit ROS production in phagocytes [34] . For both HT-29 and T84 colonic epithelial cells , we found that infection with ΔSPI2 mutant of Salmonella generated ROS that was further increased in corresponding APE1 suppressed cells ( Fig 7A and 7B ) . Compared to the ΔSPI2 mutant , limited amounts of ROS were induced by wild type Salmonella . Also , immunofluorescence with CM-H2DCFDA demonstrated increased ROS generation in APE1 suppressed T84 cells following ΔSPI2 mutant infection ( Fig 7C ) . This finding suggests that in addition to interfering with Nox2 in macrophages , Salmonella may also interfere with the Nox1 complex in intestinal epithelial cells .
In this study we show that APE1 regulates the induction of reactive oxygen species ( ROS ) by gastroenteric pathogens in a panel of relevant human gastrointestinal epithelial cells . Multifunctional APE1 was demonstrated to inhibit Nox1-mediated ROS production through its direct interactions with Rac1 . In addition to preventing formation of the functional NADPH complex , APE1 limits ROS production by decreasing Nox1 expression . Together , these data support the concept that through its molecular interactions with Rac1 , APE1 provides negative feedback on Nox1 and oxidative responses in the gastrointestinal epithelium during bacterial infection . These data implicate APE1 in novel molecular interactions that regulate early stress responses elicited by microbial infections . Microbial pathogens affect host cells through the generation of various radicals [3 , 35 , 36] . For example , we and others have demonstrated that H . pylori infection stimulates the accumulation of intracellular ROS in human gastric epithelial cell lines and freshly isolated native human gastric epithelial cells [37 , 38] . The potential roles of VacA and CagA in regulating ROS production in cells are also illustrated by other reports showing VacA-dependent regulation of autophagy and associated ROS production [39 , 40] . In our studies H . pylori lacking VacA had no significant effect on ROS production as assessed by luminol oxidation . Although CagA has been implicated in increased levels of ROS , the 8–1 mutant lacking CagA did not significantly alter ROS production in our assays [41] . Since dyes that detect ROS species have varying sensitivities and detect ROS in intracellular or extracellular compartments the role of VacA or CagA in the generation of ROS was not conclusively demonstrated in our studies [38 , 41] . Commensal bacteria that reside in the gut are reported to induce ROS generation from intestinal epithelial cells [42] . High levels of ROS are associated with molecular damage to cellular components and consequent tissue injury but APE1 may represent an important host factor to limit this damage . The differences in the kinetics of ROS generation in the various cell lines employed in this study could be resolved in future studies in animal models . This is particularly relevant to model the persistent infection of humans with H . pylori . Advancing our prior observations showing that H . pylori-induced apoptosis is inhibited by APE1 [26] , the present work establishes a novel role of APE1 , mediating the inhibition of oxidative stress . This function of APE1 may contribute to its ability to inhibit oxidative stress-induced cell death as well as a fine-tuning of the redox-sensitive responses induced during infection [43] . Although APE1 is referred to as a stress response molecule [44] , concordant with a recent report showing the regulation of stress by APE1 in the mitochondria of neuronal cells [45] our work demonstrates its role in regulating stress generation in gastrointestinal epithelial cells . To understand the mechanism of APE1 as a determinant of ROS regulation , we focused on Rac1 and Nox1 , two major contributors of ROS generation in non-phagocytic cells . The small GTPases , Rac1 and Rac2 , are common mediators of NADPH-dependent ROS production in diverse signaling pathways that lead to mitogenesis , gene expression and stress responses [18 , 46] . Our findings corroborate the dependence of Rac1 on ROS production as we show that H . pylori-induced ROS generation is downregulated by the APE1-Rac1 interaction that subsequently inhibits Nox1 . Further characterization of the molecular association between active Rac1 , cellular ROS levels and APE1 provides new mechanistic insight into the control of redox-sensitive host responses with potential relevance to the development of novel therapies for gastrointestinal infections and associated inflammation . As Rac1 is an integral part of the functional NADPH oxidase complex [14] , inhibition of Rac1 activity by APE1 is expected to interfere with this assembly , thereby providing negative feedback on ROS generation . Regulation of Rac1 by APE1 was observed in AGS and NCI-N87 cells , however , the kinetics of the regulation of Rac1 and APE1 were different . Although those kinetics varied somewhat , intracellular co-localization of APE1 and Rac1 following infection assessed by using the proximity ligation assay showed a significant increase in both AGS and NCI-N87 at 1h after infection . This co-localization of APE1 and Rac1 was also observed in antrum-derived primary gastric epithelial cells . Overexpression of APE1 decreased ROS comparable with the effect of DPI or of NSC23766 ( Fig 3C and 3D ) , underlining that APE1 is a major regulator of the Rac1-NADPH oxidase axis of ROS production . In addition to Rac1 inhibition , we identified another level of inhibition by APE1 when APE1 suppressed cells were found to express significantly more Nox1 compared to the vector control cells . The observation of an augmentation of H . pylori-induced ROS generation in two different APE1 suppressed gastric epithelial cells supports a broadly relevant role for APE1 in regulating ROS . Interestingly , APE1 and the phytochemical Ginko biloba both regulated mitochondrial oxidative stress in neuronal cells [45] . APE1 and phytochemical-mediated regulation of mitochondrial oxidative stress could also be of relevance in Helicobacter-induced ROS generation in gastric epithelial cells . Given APE1’s multiple functions , it is not surprising that interacting molecular partners of APE1 have already been identified . It appears likely that acetylation-mediated conformational changes in APE1's N-terminal domain modulate its interaction with partner proteins , including Rac1 [47] . We have not manipulated the various redox-responsive cysteine residues of APE1 in our study . As various reports show a role for the redox function of APE1 in regulating responses to cell stress , the redox function of APE1 may also be involved in cellular responses to oxidative stress . Unlike the stable interaction between APE1 and Rac1 , the minimal association between Rac1 and the N-terminal acetylation mutant of APE1 underscores the necessity of the Lys residues for the interaction . Our data indicate that this interaction is essential for the ability of APE1 to inhibit the production of ROS since significantly increased ROS generation was found with the non-interacting acetylation mutant compared to WT APE1 ( Fig 6B ) . Taken together with our previous observation that H . pylori induced APE1 acetylation [21] , this finding highlights a previously unrecognized modification of regulatory molecules during infection . We speculate that the role of APE1 could be similar to the Rho-GDP dissociation inhibitors ( Rho-GDI ) , which translocates Rac1 from the membrane to the cytoplasm , effectively deactivating NADPH oxidase [48 , 49] . Our observation of the inhibition of Rac1 by APE1 in intestinal cell lines indicates that APE1-regulated ROS generation is conserved between gastric and intestinal epithelial cells . These data suggest a common role for APE1 in the pathogenesis of various prolonged gastrointestinal bacterial infections . Unlike the robust ROS generation typically induced by acute infection , lower levels of ROS produced by host epithelial cells are increasingly recognized to play a critical physiological role [18] including regulation of the molecular machinery of epithelial secretory lineages and autophagy [50] . As such , redox signaling through Nox1 represents a unique intracellular regulator of diverse signaling pathways involved in normal cell physiology , inflammation and carcinogenesis . Due to the nature of in vitro infection models , including uncontrolled bacterial growth and related cell stress-induced mitochondrial ROS production in cell models , future experiments in vivo are needed to determine the physiological importance of acute versus chronic infections with H . pylori in relation to regulation of oxidative stress by APE1 . In summary , we have shown that APE1 controls the regulation of epithelial responses to gastroenteric infections and the subsequent generation of oxidative stress . Our findings provide new insights into APE1’s role as a host molecule that modulates ROS generation via negative regulation of Rac1 and Nox1 . Our future studies will aim to examine models of prolonged infection and the physiological responses to infections . | Helicobacter pylori infection of the gastric mucosa is largely lifelong leading to continued stimulation of immune cells . This results in the generation of reactive oxygen species ( ROS ) which are produced to kill bacteria , but at the same time ROS regulate cellular events in the host . However , prolonged generation of ROS has been implicated in damage of DNA , which ultimately could lead to the development of cancer . We studied a molecule known as APE-1 in gastric and intestinal cells , which is activated upon encounter of ROS . Our results show that APE1 limits the production of ROS in cells that form the lining of the gastrointestinal tract . APE1 regulates ROS production by inhibiting activation of the molecule Rac1 . Inhibition of ROS production by APE1 occurred after infection of gastric cells with Helicobacter pylori and after Salmonella infection of intestinal cells . These data demonstrate that APE1 inhibits production of ROS in cells that line the inside of the digestive tract . | [
"Abstract",
"Introduction",
"Materials",
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"Results",
"Discussion"
] | [] | 2016 | Regulation of Rac1 and Reactive Oxygen Species Production in Response to Infection of Gastrointestinal Epithelia |
Protein-protein interaction networks provide a global picture of cellular function and biological processes . Some proteins act as hub proteins , highly connected to others , whereas some others have few interactions . The dysfunction of some interactions causes many diseases , including cancer . Proteins interact through their interfaces . Therefore , studying the interface properties of cancer-related proteins will help explain their role in the interaction networks . Similar or overlapping binding sites should be used repeatedly in single interface hub proteins , making them promiscuous . Alternatively , multi-interface hub proteins make use of several distinct binding sites to bind to different partners . We propose a methodology to integrate protein interfaces into cancer interaction networks ( ciSPIN , cancer structural protein interface network ) . The interactions in the human protein interaction network are replaced by interfaces , coming from either known or predicted complexes . We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network . The results reveal that cancer-related proteins have smaller , more planar , more charged and less hydrophobic binding sites than non-cancer proteins , which may indicate low affinity and high specificity of the cancer-related interactions . We also classified the genes in ciSPIN according to phenotypes . Within phenotypes , for breast cancer , colorectal cancer and leukemia , interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71% , 67% , 61% , respectively . In addition , cancer-related proteins tend to interact with their partners through distinct interfaces , corresponding mostly to multi-interface hubs , which comprise 56% of cancer-related proteins , and constituting the nodes with higher essentiality in the network ( 76% ) . We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3 , a multi interface , and Raf1 , a single interface hub . The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub . These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates .
Protein–protein interaction networks provide valuable information in the understanding of cellular function and biological processes . With the tremendous increase in human protein interaction data , network approach is used to understand molecular mechanisms of disease [1] particularly to analyze cancer phenomenon . To date , attempts at providing insights into distinct topological features of cancer genes [2]–[5] have illustrated how to improve cancer classification [6] , [7] and identified cancer-related subnetworks [8] . Thus , abstract network representation , where proteins are nodes and interactions are edges , is useful for the comprehension of biological processes and protein function in a global sense . However , to characterize interactions with respect to their physical and chemical properties and in particular , to understand how a function is exerted , it is essential to include structural details in the networks; such details come from three dimensional protein structures and from protein interfaces . Proteins interact with each other through binding sites [9]–[13] . Interface characteristics are important in determining the specificity and strength of interactions . For example , conserved modes are used to distinguish biological from crystal interactions [14] . Different in residue composition , transient and obligate complexes have different strength of interactions; the former mostly rely on salt bridges and hydrogen bonds whereas for the latter , hydrophobic forces are more dominant [15] , [16] . In terms of geometrical concern , if two proteins interact through a large interface with high complementarity , they will probably interact with high specificity and high affinity [17] . Physical interactions through interface residues also determine whether the binding will be promiscuous or specific . Structural knowledge of proteins is also critical in identifying whether a binding site is specific or multiply used . Since each protein has almost a fixed surface area , it can have a limited number of binding sites . How can a hub protein interact with tens of other proteins through its binding sites ? This question implies that whereas some binding sites are distinct , others should be used to bind to several different proteins . Therefore , the same or overlapping binding sites should be frequently and repeatedly used in hub proteins making them promiscuous [18] . With this in mind , Kim et al . [19] distinguished overlapping from non-overlapping interfaces in their structural interaction network to determine interaction behavior . They classified network hubs into single-interface and multi-interface . The former have at most two distinct binding interfaces and the interactions exclude each other whereas the latter have more than two binding interfaces with most of the interactions being possible simultaneously . Knowing that cancer-related proteins are more likely to act as hubs [2] in protein interaction networks , the questions that arise are what features of cancer-related proteins make them act as hubs and how is it possible for them to bind to many different proteins with varying affinity . To address these questions , as distinct from previous structural studies [19]–[25] , here we integrate protein-protein interfaces into a structural network , focus on cancer-related proteins and investigate the interface properties of cancer/noncancer protein interactions in order to shed light on the details of interaction . We provide a detailed analysis and comparison of six interaction networks: 1 ) the human protein-protein interaction network , ( PIN ) , 2 ) the human cancer-related protein-protein interaction network , cPIN , a sub-network of the first . Then , we characterize the interactions in these networks by combining three-dimensional protein structures . Thus , we have: 3 ) the network constructed by selecting genes for which three-dimensional protein data is available , SPIN , a sub-network of the first , 4 ) the human cancer-related structural protein-protein interaction network , cSPIN , a sub-network of SPIN . We map the known structural data into these networks whenever a complex structure is available . For the rest , we predict the complex structures of the interactions through structural templates and hot spots using PRISM [26] , [27] . The last two resulting networks are “structural interface” networks: 5 ) human structural protein interface network ( iSPIN ) and 6 ) structural cancer-related protein interface network ( ciSPIN ) . These six networks are analyzed and compared to highlight the advantages of using structures . Our results reveal that cancer-related proteins tend to interact with their partners through distinct interfaces , corresponding mostly to multi-interface hubs and constituting the nodes with higher essentiality in the network . In addition , they have smaller , more planar and more hydrophilic binding sites compared to those seen in non-cancer proteins which may indicate low affinity and high specificity of the cancer-related interactions .
We illustrate how to obtain a structure-integrated network from PIN: The seed network is the human protein-protein interaction network ( PIN ) where the nodes are proteins and the edges are interactions . We determined which proteins in this network have structural information in Protein Data Bank ( PDB ) [28] and constructed a subnetwork with the extracted structures called SPIN ( see Methods for the details ) . To further integrate protein interfaces into SPIN , we mapped the known structural data of complexes into SPIN whenever a complex structure was available . If a known structure was not available for an interaction , we predicted the complex structures of the two interacting proteins using structural templates and hot spots through PRISM [26] , [27] . The resulting network , which includes known complexes in PDB and predicted complexes ( from PRISM ) contains interface knowledge and is called iSPIN . The subsets of PIN , SPIN and iSPIN , which contain cancer-related interactions , are called cPIN , cSPIN and ciSPIN , respectively ( See Methods section for further information ) . Table 1 lists the number of proteins and interactions in each network . In Table 1 , “known complex in PDB” column represents the number of interactions for which three dimensional protein structures are available in PDB . The three networks ( PIN , SPIN , iSPIN ) are illustrated in Figure 1 . We should note that there was a dramatic decrease in the number of proteins when going from PIN to SPIN . As seen in Figure 1 , while PIN contains information about gene interactions , SPIN only contains those with PDB IDs . And finally iSPIN contains the information at the residue level; protein interfaces . Although we provide a topological analysis of the networks , the main concern of this study is to present interface analysis of cancer-related proteins and , in addition , to predict which interactions can and cannot occur simultaneously and ultimately , to emphasize the importance of using structures in network studies . We present the interface properties of interactions such as the accessible surface area ( ASA ) , planarity , gap volume index ( see definitions below ) and residue composition at the interfaces in iSPIN ( both predicted and known PDB interfaces ) . To analyze the properties of interfaces , we used PROTORP [29] ( see Methods ) . First , the analysis of the interface properties throughout the whole network ( iSPIN ) is presented . Next , the analysis is restricted to subsets of genes having common phenotype , molecular function or biological process . Physical properties of interfaces were computed for the interactions in iSPIN . We classified the interactions into two groups: “cancer-related interactions” are those in which at least one partner in a binary interaction is a cancer-related protein and “noncancer interactions” are those in which none of the proteins are known to be involved in cancer . According to these designations , there were 363 cancer-related and 186 non-cancer interactions . Change in ASA ( ΔASA ) is the difference between the total ASA of monomers and that of the complex . Cancer proteins on average were observed to have smaller ΔASAs ( 1009 . 1 Å2 ) than that of noncancer proteins ( 1242 . 9 Å2 ) ( standard deviations and p-values are summarized in Table 2 ) . Next , we calculated the interface ASA as the sum of ASAs of each interface residue in the complex state . When the interface ASA of the complex structures is considered , it was found that ASA of cancer proteins ( 2210 . 9 Å2 ) were smaller than that of noncancer proteins ( 2628 . 1 Å2 ) . These results indicate that the complex interfaces which are formed through the interactions of cancer proteins are less buried , or likewise , the monomeric surfaces of cancer proteins are less exposed . It is known that transient complexes have smaller interface areas [30] . Our results show that cancer proteins use a smaller surface area while interacting and we know that they have many interaction partners [3] , thus it may be hypothesized that they are more likely to be involved in transient interactions . Here , we should note that although standard deviations of the two datasets are high in all cases , i . e . the distributions of the data sets are highly disperse , p-values at 5% confidence interval are small indicating the significance of the difference between two means of cancer-related and noncancer interfaces . We also investigated the complementarity of the interfaces . Gap volume provides a measure of complementarity and closeness of packing of the interface between the two interacting proteins by measuring the volume of empty space between them . Gap volume index is the ratio of gap volume to the interface area; it estimates the volume enclosed between any two molecules , delimiting the boundary by defining a maximum allowed distance from both interfaces [17] . For the cancer related interactions , the average gap volume ( 5076 . 8 Å3 ) was found to be smaller than the average gap volume of noncancer interactions ( 5574 . 5 Å3 ) ( p-value = 0 . 038 at α = 0 . 05 ) . This is an outcome of the smaller interfaces of the cancer proteins since volume is proportional to the surface area . On the other hand , the average gap volume indices for these two categories were 2 . 76A° and 2 . 54 A° , respectively ( p-value = 0 . 07 at α = 0 . 05 ) . This means cancer related interactions are less optimized in terms of complementarity indicating that , the complementarity and packing of two types ( cancer/noncancer ) are distinguishable from each other . Planarity indices are used to analyze the shapes of the interfaces . The planarity of the interface is defined as the rmsd of the interface atoms from the least-squares plane fitted through all interface atoms . The larger the planarity index , the less planar the interface , and , conversely , the smaller the planarity index , the more planar the interface [9] . For cancer-related interactions , the average planarity index ( 2 . 84 ) was smaller than that of non-cancer interactions ( 3 . 06 ) with p-value 0 . 04 indicating that cancer-related interfaces are more planar . It is known that there is a high correlation between the planarity of the interfaces and their ASAs [18] . As the ASAs of the interfaces increase , the planarity index also increases , and the interfaces become less planar , deviating from their principal axes . It is also known that transient complexes usually have more planar interfaces [30] . Here , consistent with previous findings , we observed that cancer proteins use more planar binding sites in their complexes . The results are summarized in Table 2 . Previously , smaller interfaces were shown to display a reduced hydrophobic effect [31] . Residue compositions of interfaces ( polar , non-polar or charged ) were analyzed in iSPIN and were normalized by the ASA in the complex structures ( see Methods ) . The results revealed that cancer-related interactions show a reduction in hydrophobicity and an increase in charged interactions , and thus have more hydrophilic interfaces than non-cancer interactions . Although , in general , it is agreed that protein-protein interfaces are highly hydrophobic and hydrophobicity is a dominant force in protein-protein interactions [32] , there are also studies indicating the importance of hydrophilic interface regions . Tormo et al . ( 1999 ) studied the interactions of NK ( natural killer ) receptors ( which regulates NK cell function ) and determined the interface of C-type-lectin-like receptor family ( Ly49 A ) to be highly hydrophilic and dominated by charged interactions [33] . Charged interactions appear to play important role in our iSPIN interfaces as well , which implies that electrostatics are significant in binding . A recent study indicated that favorable electrostatic interactions were not a prerequisite for stable complex formation between proteins whereas hydrophobic effects were found to be favorable in native complexes [34] . Here , we also observed that cancer related proteins , which are intrinsically more disordered and transient [35] , had less hydrophobic interactions than other proteins . We also classified interactions as “hub-involved” or “non-hub-involved” . In hub-involved interactions , at least one protein of the binary interaction is a hub protein , whereas in non-hub-involved interactions , none of the proteins correspond to a hub . There were 455 hub-involved interactions and 94 non-hub-involved interactions . As hub proteins in iSPIN , we considered the hubs of SPIN . We found that , on average , hub proteins tended to form smaller , more planar interfaces with their partners . In contrast to previous studies [36] , [37] , we found no significant difference in the residue composition of the interfaces ( including charged residue content ) of hub proteins . In terms of complementarity of the interfaces , hub proteins formed looser complexes ( gap volume index of 2 . 72 versus 2 . 49 ) . The results are summarized in Table 3 . ( See first lines in each row ) Some hubs are single-interface ( communicating with their partners by using the same interface ) whereas others are multi-interface . The hub proteins of SPIN with more than two interactions in iSPIN were classified as either multi-interface or single-interface hubs resulting in 79 hub proteins , of which 42 were multi-interface and 37 were single-interface . Interestingly , when we compared the interfaces of these two types of hubs , we observed that they had different compositions . Interfaces of multi-interface hubs were usually similar to non-hub interfaces ( data not shown ) . On the other hand , interfaces of single-interface hubs were more polar and less charged than multi-interface hubs and non-hub proteins ( See the second lines in each row of Table 3 ) . The most populated phenotypes observed among cancer genes in iSPIN are leukemia , breast cancer and colorectal cancer , for which there are 55 , 22 and 23 related interactions in iSPIN , respectively . Phenotype information was obtained from OMIM [38] which is a compendium of human genes and genetic phenotypes . We compared the interface properties of these cancer related interactions with the same number of interfaces of non-cancer interactions . For all of the phenotype groups , cancer related interfaces showed a reduction in interface ASA and ΔASA compared to noncancer ones . In addition , cancer related interfaces were more planar and less tightly packed . If the difference in interface properties is important enough , it would be possible to classify a protein as cancer-related or non-cancer by analyzing its interface . Thus , to check whether the data on interface properties can be assessed for classification purposes , we used Weka [39] , a machine learning software for data analysis . The training sets included equal number of cancer-related and non-cancer interfaces . The experiments were performed using 10-fold cross validation with several classifiers using four interface features; interface ASA , ΔASA , planarity and gap volume index . ( See Methods for the details of the classification procedure ) For example , using support vector machine ( SVM ) as the classifier algorithm , interfaces were ranked as cancer or noncancer related with an accuracy of 61% , 71% and 67% for leukemia , breast cancer and colorectal cancer , respectively . The relatively poorer accuracy of leukemia might be the outcome that there are many distinct subgroups of leukemia which we combined all in one here . The results obtained using SVM classifier are summarized in Table 4 . The results using all classifiers are given as supplementary information ( Text S1 ) . We also classified the genes in iSPIN according to the molecular function and biological process of each protein obtained from the Gene Ontology slim terms [40] . Among the most common molecular functions were signal transducer activity , catalytic activity , nucleic acid binding and transcription regulator activity . Interfaces were classified as cancer related with an accuracy of 53% , 58% , 58% and 63% for signal transducer activity , catalytic acitivity , nucleic acid binding and transcription regulator activity , respectively . For the last three molecular functions , interface properties showed noticeable differences for cancer and noncancer interactions . However , for signal transducer activity function ( 65 cancer related-65 noncancer interfaces ) , the interface properties were quite similar . We observed that cancer/noncancer interfaces can be distinguished to a greater extent when the genes are classified according to common phenotype rather than molecular function . For the common phenotype case , in our interface datasets , only cancer genes share the phenotype and noncancer genes would have different phenotype properties . On the other hand , for molecular function case , all genes share the same molecular function irrespective of being cancer/noncancer . The relatively poor classification performance by using molecular functions indicates that functionally related proteins might have similar interface characteristics regardless of being cancer-related . Similarly , no discriminative characteristics between cancer-related and noncancer interface datasets were observed when the proteins were classified according to the biological processes . The last four rows of the Table 4 shows the results of classification performances without grouping genes according to their phenotypes or functions . When we used all the data in iSPIN ( with an unbalanced training set ) , the performance is poorer than the clustered cases . However , when a more appropriate method ( adaboost instead of SVM ) was used , comparable performances were obtained ( Text S1 ) . Topological properties of protein-protein interaction networks are shown to be useful to characterize proteins functionally [41] and to understand molecular mechanisms of diseases [3] , [4] . To address the topological properties of each of our network , we calculated the degree distribution of proteins , which is a measure of the number of proteins' interaction partners . In Figure 2 , the topological properties are visualized for SPIN and listed in Table 5 . For each network , the degree distribution of the proteins decreases following a power-law ( P ( k ) ∼kγ where k is the number of partner proteins ) . This implies that the networks have scale-free properties [42] . The average number of neighbors is the average degree of a node in the network . On average , proteins in SPIN have 6 . 24 interaction partners . A normalized version of average degree is the network density showing how densely the network is populated with edges . When structure information was integrated , network density increased . This might indicate that less connected nodes in PIN might be absent in PDB ( Table 5 ) . In Figure 2B , the average clustering coefficient , which is a measure of proteins to form clusters in the network [42] is shown . The average clustering coefficient decreases as the number of protein interactions increases , since sparsely connected proteins are neighbors of highly connected proteins ( hub proteins ) . For the hub proteins , the number of neighbors increased , however , the number of connected pairs did not increase as much as the number of neighbors which caused the average clustering coefficient to decrease . This behavior indicates a hierarchical organization in the protein interaction network [42] . In Figure 2B , we see an exception for this case , although some nodes are highly connected , their average clustering coefficients are also high ( >0 . 30 ) ( upper right corner of the figure ) . This indicates the occurrence of dense subnetworks , in which hubs mostly interact with other hub proteins . ( Such subnetworks in SPIN are explained and visualized in the next section ) In Figure 2C , the topological coefficient which is a relative measure for the extent to which a protein shares neighbors with other proteins , [43] is displayed . The decreasing behavior of the topological coefficient as the number of interactions of a protein increases confirms the modular network organization; neighbors of hub proteins are not more connected than the neighbors of sparsely connected proteins . Figure 2D shows the shortest path length distribution and indicates that proteins are closely linked . The topological properties of other networks ( PIN , cPIN , cSPIN , iSPIN , ciSPIN ) showed similar trends to those of SPIN explained above . When cancer related networks were compared with the whole networks ( cPIN with PIN , cSPIN with SPIN and ciSPIN with iSPIN ) , the average clustering coefficient values were lower; i . e . , the proteins have a lower tendency to form clusters . This is reasonable since cancer proteins are the key nodes that link different pathways and they spread throughout the network to function in these pathways . For example , the Cancer Cell Map ( http://cancer . cellmap . org/cellmap/ ) , which is a collection of human-focused cellular pathways implicated in cancer , contains ten pathways each having around 100–400 interactions and cancer genes usually function in more than one pathway . Another parameter related to shortest path length is network diameter , which is the largest shortest path length between two nodes providing information about the accessibility of the nodes . The network parameters calculated for each network are displayed in Table 5 . Functionally related proteins are more connected than randomly chosen protein pairs [43] . Here , we analyzed the distributions of molecular function of cancer and noncancer proteins and biological process in which they are involved ( shown in Figure 3 ) . The results show that in PIN and SPIN , cancer proteins and hub proteins are over-represented in protein binding , signal transducer activity , kinase activity and transcription regulator activity . Previously , Jonsson et al [3] performed a cluster analysis of the human interactome ( the so-called ‘PIN’ in this study ) . They observed that cancer proteins , on average , belonged to more highly populated clusters compared to non-cancer proteins and were involved in multiple cellular processes . Here , we performed a clustering analysis of SPIN using MCODE [44] and obtained subnetworks ( see Methods ) . The first six subnetworks , which were ranked as top six , are shown in Figure 4 ( proteins are colored according to four categories; cancer-hub , noncancer-hub , cancer-nonhub , noncancer-nonhub and shown in purple , green , blue and white color , respectively ) . These subnetworks were compared to SPIN to check if some molecular functions and biological processes were over/under-represented . We observed a common molecular function; signal transduction activity , which is over-represented in three of the subnetworks ( subnetworks 2 , 4 and 6 ) . In terms of topological properties , these subnetworks showed similarity in the way that they contain hub proteins; subnetworks 2 and 4 contain only hub proteins ( cancer or noncancer ) and in subnetwork 6; 14 nodes out of 17 are hubs . Thus , we wondered if hub proteins prefer to interact with other hub proteins . Maslov and Sneppen [45] argued that hub proteins do not tend to interact with other hub proteins , but rather prefer to interact with lowly connected proteins . In contrast , Coulomb et al . [46] found that the average degree of nearest neighbors is independent of node degree . We calculated the average degree of hub proteins; we divided the partners of hub proteins into two class; hubs and nonhubs . We found that , on average , hub-nonhub average degree ( 7 . 04 ) was greater than hub-hub average degree ( 5 . 06 ) indicating that hubs do not have a preference to interact with other hub proteins in SPIN . On the other hand , we found that cancer hubs prefer to interact with other hub proteins rather than interacting with non-hubs . Cancerhub – hub average degree and cancerhub – nonhub average degree were 8 . 49 and 7 . 16 , respectively . The same results are valid for PIN as well . The results support that cancer proteins play central role in the networks and show distinct topological properties than noncancer proteins . Recently , Yu et al ( 2007 ) [47] have analyzed the significance of hubs , proteins with high degree distribution , and bottlenecks , proteins with high betweenness , in the yeast protein-protein interaction network and regulatory networks . They have investigated which quantity , degree distribution or betweenness , is a better predictor of protein essentiality . It was reported that in directed networks , for example in regulatory networks , betweenness is a more important feature in terms of essentiality . In yeast regulatory networks , Yu et al . observed that bottlenecks ( both hub-bottlenecks and nonhub-bottlenecks ) are generally products of essential genes , whereas hub-nonbottlenecks are not essential at all . When they analyzed the protein-protein interaction network in yeast ( undirected network ) , they found that degree is a much better predictor of essentiality than betweenness since hub-nonbottlenecks are much more essential than nonhub-bottlenecks . We also investigated how degree and betweenness correlate with essentiality in protein-protein interaction network in human . We classified all proteins into four categories; hub-bottleneck , hub-nonbottleneck , nonhub-bottleneck and nonhub-nonbottleneck . Figure 5 ( A , B ) show the essentiality of different categories of proteins , in PIN and in SPIN . In addition to these networks , a random network , which is the same size as SPIN and has the same average degree distribution , was generated from PIN . First a protein from PIN was selected randomly . Then , some of the interactions of this protein were randomly selected . The same procedure was applied to the newly selected neighbors until the network size and average degree distribution values were satisfied . As shown in Figure 5 , the hub-bottlenecks were found to be the most essential category in all networks . The fraction of essential gene percentages for hub-bottlenecks in SPIN , random network and PIN were 54% , 35% and 31% , respectively . Hub-nonbottlenecks were found to be more essential than nonhub-bottlenecks; i . e . degree is a more important parameter in terms of essentiality in PIN , SPIN and the random network . This finding confirms the hypothesis stated by Yu et al ( 2007 ) [47] . Essentiality fractions in SPIN were much higher than the ones in PIN ( y-axes of Figure 5A and Figure 5B ) . The reason for higher fraction of essential genes in SPIN may stem from a possible bias towards well-studied proteins for which structural information is available . Another reason could be a physical bias due to the fact that PIN is a large-scale data . To investigate the reason for this bias , we generated a random network from PIN , which is the same size as SPIN and has the same average degree distribution . Figure 5C displays the fraction of essential genes in this random network . We observed that the fraction of essentiality was higher for the random network than for PIN . However , the values were still much smaller than those for SPIN . Thus , we concluded that the reason for higher essentiality in SPIN probably arose from a bias towards well-studied proteins rather than a physical bias . Hub proteins are more likely to be encoded by essential genes [48] , [49] . In addition , somatic cancer genes are more likely to encode hub proteins [2] . From these , we can hypothesize that essential cancer genes are more likely to encode hub proteins than non-essential cancer genes . Thus , we classified all cancer genes in the networks as hub and non-hub , and observed that cancer-hubs were more essential than cancer-nonhubs , which confirms our above hypothesis; essential cancer genes are more likely to encode hub proteins than non-essential cancer genes . The essentiality percentage in each category , hubs and non-hubs are 50% ( total 532 ) and 37% ( total 650 ) for PIN , 66% ( total 158 ) and 44% ( total 286 ) for SPIN , 47% ( total 85 ) and 37% ( total 140 ) for random network , respectively . The essentiality percentage values are visualized in Figure 6 . Another question is whether cancer or non-cancer hubs are more essential . We found that when we classified the hub proteins as cancer-hubs and non-cancer-hubs , there was a significant difference in essentiality . In SPIN , there were 158 cancer hubs , 66% of which were essential . In contrast , only 28% of the 197 non-cancer hubs were essential . Similarly , in both PIN and the random network cancer hubs were much more essential than non-cancer hub proteins . In PIN the 50% of the 532 cancer hubs were essential , whereas only 24% of the 1801 total non-cancer hub proteins were essential . In the random network , 47% of 85 total cancer hubs were essential , whereas 30% of 246 total non-cancer hub proteins were essential . The fraction of essential genes in cancer hubs and non-cancer hubs for each network are shown in Figure 6 . The numbers of essential and nonessential genes are given for each category in PIN , SPIN and random network as supplementary information ( Text S1 ) . We should note that essential gene list is obtained on optimal growth/living conditions and if the conditions are changed , for example in case of a disease state such as cancer , a nonessential gene would become essential or vice versa . However , due to the lack of data on essential gene information in cancer cells , we assigned the same set of essential genes to cancer state and non-cancer state . Recently , Luo et . al [50] had an effort to identify the genes essential for growth and related phenotypes in different cancer cells by genetic screening strategy . Since a small fraction of these genes appear in our networks , it is not appropriate to use them in statistical analysis . As discussed above , some hubs are single-interface , that is , they communicate with their partners by using the same interface , whereas others are multi-interface . We investigated to which category , hub-bottleneck or hub-nonbottleneck , multi-interface and single-interface proteins belong . We observed that multi-interface proteins generally corresponded to hub-bottleneck proteins rather than hub-nonbottlenecks ( 71% of multi-interface proteins are hub-bottlenecks . ) When the single-interface proteins were considered , the percentage of hub-bottleneck correspondence decreased to 59% . In other words , 58% of hub-bottleneck proteins were multi-interface and 42% are single-interface . Previously we showed that hub-bottlenecks were the most essential category of proteins in SPIN and in PIN . Here , in the structural interface network , we found that the essentiality of multi-interface hubs ( 68% ) was higher than that of single-interface ( 52% ) . This result agrees with a previous finding [19] indicating that the number of interfaces leads to higher essentiality . In addition , Aragues et al . ( 2007 ) found that yeast hubs with multiple interacting motifs were more likely to be essential than hubs with one or two interacting motifs [51] . Being more essential and corresponding mostly to hub-bottlenecks , multi-interface hubs are the key points in the protein-protein interaction network . Cancer proteins in our network are more enriched in multi-interface proteins: 56% of cancer proteins are multi-interface , while 44% being single-interface . This is reasonable since on average , cancer proteins are longer [52] with larger surface areas . To cope with many interactions at the same time , they tend to be multi-interface hubs with distinct interfaces interacting with different proteins . Although cancer proteins tend to have more than one distinct interface , we found that on average their interfaces were smaller , which can indicate that their binding behavior acts similar to that of hub proteins . In addition , the average number of interfaces of cancer multi-interface hubs and noncancer multi-interface hubs were 2 . 5 and 2 . 3 , respectively . Cancer multi-interface hubs have a greater average number of interfaces . The correspondence of hub-bottlenecks and hub-nonbottlenecks to multi/single interface proteins and the essentiality percentage in cancer/noncancer & multi/single interface proteins are displayed in Table 6 . The interface information is an asset in predicting which interactions can and cannot co-exist . In other words , it will help to deduce which interactions can occur simultaneously and which are mutually excluded . Addressing this question may add a fourth dimension to interaction maps , that of sequence of processes . Including the sequence dimension in structural networks is an immense asset; transforming network node-and-edge maps into cellular processes , and assisting in the comprehension of cellular pathways and their regulation . Here , to characterize the interactions and to infer the order of processes , we present two case studies , first a multi-interface cancer protein and an inhibitor of the protein , and second , a single-interface cancer protein in iSPIN . For the first case study , multi-interface cancer protein , most of the interactions are simultaneously possible whereas for the latter , the interactions are mutually exclusive . In addition to geometrical justification for simultaneous and exclusive interaction behavior , dynamic nature of protein-protein interactions are taken into account . The interacting complexes were refined using FiberDock http://bioinfo3d . cs . tau . ac . il/FiberDock/ , which models both side-chain and backbone flexibility . Next , to obtain a quantitative estimation of the importance of the interactions , we used FoldX algorithm [53] , [54] for calculating the interaction energy between two proteins , which serves as an estimate for the affinity of the interactions . In Figure 7 , a visualization of iSPIN is displayed together with multi-interface and single-interface proteins . Here we show how the interface information is used to deduce which interactions can and cannot co-exist . If each interaction partner of a hub protein uses a distinct interface on the hub while interacting , then these interactions are more likely to occur simultaneously . In addition , the quaternary structure of the complex should be considered carefully to ensure that the interaction partners do not collide . To demonstrate this idea , we present a so-called ‘multi-interface’ hub protein: ERBB3 ( or HER3 ) , which is one of the hub proteins in SPIN . The receptor tyrosine-protein kinase erbB-3 precursor ( ERBB3 ) belongs to EGF receptor subfamily and acts as a heregulin receptor and as an epidermal growth factor receptor . Amplification of this gene and/or overexpression of its protein have been reported in numerous cancers , including prostate , bladder , and breast tumors [55] . According to the KEGG database [56] , ERBB3 functions in the ErbB signaling pathway and the Calcium signaling pathway . In the ErbB signaling pathway , NRG1 ( neuregulin 1 , heregulin ) , which is a direct ligand for ERBB3 , binds and activates ERBB3 . We modeled this interaction using the PDB accession codes 1hae_A ( NMR structure of heregulin ) for NRG1 and 1m6b_A ( crystal structure of ERBB3 taken from a homodimer structure ) for ERBB3 , respectively . PRISM results indicate that these two proteins ( 1hae_A and 1m6b_A ) interact , and using NOXclass [57] , we found that the interaction is biologically relevant . After applying flexible refinement by FiberDock , FoldX server [53] , [54] was used to calculate the interaction energy ( −4 . 08 kcal/mol ) . Predicted binding sites on both proteins and interacting residues for NRG1-ERBB3 interaction are shown in Figure 8A . The interaction was experimentally studied in a previous study by Jones et al ( 1998 ) [58] , where they mutated individual residues of the egf domain of heregulinβ ( the same as egf domain of heregulinα-NRG1- except four residues ) to alanine in order to determine residues critical for binding receptors and initiating signal transduction . They found that when His2 , Leu3 , Val4 , Phe13 , Val15–Gly18 , Val23 , Arg31 , Lys35 , Gly42–Gln46 residues were changed to alanine , binding affinity for ERBB3 was dramatically reduced . We observed that most of these critical residues were included in our predicted binding site for NRG1 . In Figure 8A , these residues are labeled . In the ErbB signaling pathway , NRG1 also binds to ERBB4 , and the binding affinity was reported to be similar to that of ERBB3 [58] . According to our interface prediction , ERBB3 and ERBB4 binding interfaces on NRG1 are overlapping; i . e . , the same binding site is used for the ERBB3 and ERBB4 interactions . Therefore , NRG1-ERBB3 and NRG1-ERBB4 interactions are mutually exclusive; they cannot occur at the same time . According to the calcium signaling pathway in KEGG [56] , ERBB3 interacts with PLCG1 . Although the interaction is not reported in public databases as in DIP [59] , BIND [60] , in a recent study , it was observed on protein microarrays [61] . PLCG1 ( Phospholipase C-gamma-1 ) is a major substrate for heparin-binding growth factor 1 ( acidic fibroblast growth factor ) -activated tyrosine kinase . The PDB structure of SH3 domain of PLCG1 is 1hsq . The predicted interface residues of ERBB3-PLCG1 ( 1m6b_A-1hsq_A ) interaction are displayed in Figure 9 labeled as A . The interaction energy between proteins was calculated as −12 . 62 kcal/mol . The two other possible interactions of ERBB3 occur with EPOR ( Erythropoietin receptor ) and ACK1 ( Activated CDC42 kinase 1 ) according to the human interactome constructed by Jonsson and Bates . No experimental confirmation is available for these interactions yet , however , they have high confidence scores to occur in Jonsson and Bates's network [3] . These interactions of ERBB3 were also predicted to interact and further investigated . Subcellular location for ERBB3 , EPOR and ACK1 is the cell membrane . EPOR and ERBB3 function as single-pass type I membrane protein . The predicted interfaces for these interactions are illustrated in Figure 9 , labeled as B and C . Our results show that ERBB3 uses at least three different binding sites while interacting . Of these interactions , we propose that ERBB3 cannot interact with EPOR and ACK1 at the same time , because if we model the quaternary structure of ERBB3-EPOR-ACK1 complex , the residues of EPOR and ACK1 will collide . Thus , they cannot bind simultaneously . But , we should keep in mind that proteins are dynamic , and a hinge-like motion of the two domains of ERBB3 can eliminate the collision between EPOR and ACK1 . If we compare their interaction energy , which were calculated as −16 . 37 kcal/mol and −6 . 12 kcal/mol for ERBB3-EPOR and ERBB3-ACK1 , respectively , ERBB3-EPOR interaction is more favorable . In addition , when ACK1 interacts with ERBB3 , it also blocks the interaction of NRG1 . In terms of geometrical and energy concern , the simultaneously possible interactions would be ERBB3-PLCG1 ( interaction energy: −12 . 62 kcal/mol ) and ERBB3-EPOR , for which the affinity predictions are higher than those of other interactions . To illustrate the importance of the sequence of processes , we further focused on ERBB3 interactions and investigated how it functions if its partners use the same interface while interacting . In this case the interactions cannot occur at the same time . In general , the HER/erbB family of proteins ( EGFR ( HER1 ) , HER2 , HER3 , and HER4 ) activate intracellular signaling pathways in response to extracellular signals [55] . The signaling mechanism is as follows: first EGFR and HER3 are activated by ligand binding ( ligands are EGF and NRG1 for EGFR and HER3 , respectively ) , and then EGFR or HER3 forms heterodimer with HER2 followed by the transphosphorylation of their C-terminal tails . Heterodimer formation of HER2 with EGFR and HER3 induces different pathways . For example , The PI3K/Akt pathway , which is critically important in tumorigenesis , is activated by phosphorylated HER3 . The deregulation of signaling functions of the HER family of proteins causes cell transformation and tumorigenic growth [55] . In anti-cancer drug development , EGFR and HER2 proteins are the main targets . For example , pertuzumab , which targets HER2 dimerization region , attempts to inhibit HER2-HER3 or HER2-EGFR interactions . In a recent study [62] investigating the effect of pertuzumab in lung cancer cells , it was found that pertuzumab blocked NRG1-stimulated phosphorylation of HER3 . In contrast , it failed to block epidermal growth factor ( EGF ) -stimulated phosphorylation of EGFR in human non-small cell lung cancer cell line 11_18 . This is somewhat interesting since HER2 uses the same binding region for dimerization with HER3 and EGFR and this region is assumed to be blocked by pertuzumab . However , it may be hypothesized that in addition to its inhibiting effect on dimerization region of HER2 , pertuzumab should also affect the ligand binding region of HER3 and EGFR , namely HER3-NRG1 interaction and EGFR1-EGF interaction . In order to investigate the effect of pertuzumab on HER3-NRG1 interaction , pertuzumab heavy chain ( PDB ID 1s78 ) was docked to HER3 ( PDB ID 1m6b ) . The docked conformation is visualized in Figure 8B . NOXclass results indicate that the docked conformation is biological ( biological score is 70% ) . Although HER2 and HER3 are similar in structure , the interface region on HER2 and HER3 through which the interaction with pertuzumab occurs are not exactly the same in structure , but rather use overlapping regions . We observed that pertuzumab binding interferes with NRG1 binding region , which indicates that pertuzumab may also block ligand binding to HER3 and thus prevent HER3 activation . 36% of interface residues ( 8 out of 22 ) of HER3-NRG1 interface are also used by pertuzumab , which makes the interactions of HER3 with NRG1 and pertuzumab mutually exclusive . Both interactions are visualized together and the black surface region shows the shared interface region ( see Text S1 ) . Thus , our results indicate that pertuzumab may block the NRG1 interaction region of HER3 . Probably , pertuzumab would not affect the binding of EGF to EGFR and thus it is not effective against ( EGF ) -stimulated phosphorylation of EGFR in the aforementioned lung cancer cells . If the interaction partners of a hub protein use the same interface region , then these interactions are more likely to be mutually exclusive . For example , in iSPIN , RAF1 has 9 interactions partners which compete for binding . RAF proto-oncogene serine/threonine-protein kinase participates in the transduction of mitogenic signals from the cell membrane to the nucleus and protects cells from apoptosis mediated by STK3 . Among its interaction partners , we were able to predict interaction interfaces for CDC25 , YWHAZ and MAP2K2 , for which interaction energies were calculated as −1 . 91 kcal/mol , −8 . 35 kcal/mol , −2 . 92 kcal/mol , respectively . We should note that all interaction energies were calculated for the comparison of the interactions and the numeric values may not be precise since these are not experimental results . Interaction with RAP1A is a known structure with PDB ID 1c1y . Additional possible interactions of RAF1 in iSPIN are with RALA , DIRAS1 , DIRAS2 , CCNA2 and RRAD . Although the interface region is not completely the same for each interaction partner , most interface residues are shared ( the shared percentage >20 , which is the cutoff value for assigning the interface as distinct or same ) . Thus , these interactions cannot occur at the same time . All three interactions ( RAF1-CDC25A , RAF1-MAP2K2 , RAF1-YWHAZ ) are cancer-cancer related and their affinities are lower compared to ERBB3-EPOR and ERBB3-PLCG1 interactions which are cancer-noncancer related and simultaneously possible . Friedler et al . ( 2005 ) [63] observed a highly electrostatic binding site in a cancer protein , p53 , interacting with Rad51 and other peptide sequences with different affinity . The results imply that cancer proteins and hubs interact with their partners with high specificity and low affinity . Therefore , it becomes possible for them to bind to many different proteins with varying affinity . Three predicted binding sites are illustrated in Figure 10 . In Text S1 , RAF1 is displayed with its three binding partners: RAF1 ( 1c1y_B ) is shown in blue , the partners YWHAZ ( 1qja_A ) , MAP2K2 ( 1s9i_A ) and CDC25A ( 1c25_A ) are colored in red , cyan and purple respectively . The interface is highly shared which hypothesize that RAF1 is a single-interface protein and involved in mutually exclusive interactions . RAF1 is a protein kinase and a signaling protein; thus , it probably interacts transiently with most of its targets . A recent study confirms this interaction behavior of RAF1 , showing that the binding of Cdc25 and of Rad24 ( 14-3-3 homolog that is important in the DNA damage checkpoint ) to Raf-1 is mutually exclusive [64] . In this work , we analyzed cancer proteins and hub proteins in human protein-protein interaction networks from a structural perspective , and by considering their global behavior in the network . Integrating three-dimensional protein structures into human protein-protein interaction network revealed important aspects about hubs and cancer-related proteins . Interface property analysis identified the structural tendencies of cancer proteins that assist their binding to multiple proteins . Interfaces of cancer proteins , on average , are smaller in size , more planar , less tightly packed and more hydrophilic than those of non-cancer proteins . Within phenotypes , for breast cancer , colorectal cancer and leukemia , interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71% , 67% , 61% , respectively . Hub proteins also have smaller , less tightly packed and more planar interfaces than non-hub proteins . Similar or overlapping binding sites should be used repeatedly in hub proteins , single interface hub proteins , making them promiscuous . Alternatively , multi-interface hub proteins make use of several distinct binding sites to bind to different partners . Interfaces of multi-interface hubs are usually similar to non-hub interfaces . On the other hand , interfaces of single-interface hubs are more polar and less charged than multi-interface hubs and non-hub proteins . In addition cancer-related proteins tend to interact with their partners through distinct interfaces , corresponding mostly to multi-interface hubs , which comprise 56% of cancer-related proteins , and constituting the nodes with higher essentiality in the network ( 76% ) . Cancer proteins are more enriched in multi-interface proteins: 56% of cancer proteins are multi-interface , while 44% being single-interface . This is reasonable since it is known that , on average , cancer proteins are longer with larger surface areas . To cope with many interactions at the same time , they tend to be multi-interface hubs with distinct interfaces interacting with different proteins . Cancer multi-interface hubs have a greater average number of interfaces . We found that , on average , hub-nonhub average degree ( 7 . 04 ) is greater than hub-hub average degree ( 5 . 06 ) indicating that hubs do not have a preference to interact with other hub proteins in SPIN . On the other hand , we found that cancer hubs prefer to interact with other hub proteins rather than interacting with non-hubs . Cancerhub – hub average degree and cancerhub – nonhub average degree are 8 . 49 and 7 . 16 , respectively . The same results are valid for PIN as well . The results reveal the well known information that cancer proteins play central role in the networks and show distinct topological properties than noncancer proteins . Finally , we illustrated , in detail , the interface related affinity properties of two cancer-related hub proteins: Erbb3 , a multi interface , and Raf1 , a single interface hub . The results revealed that affinity of interactions of the multi-interface hub tend to be higher than that of the single-interface hub . These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates .
We studied the human interactome constructed by Jonsson & Bates ( 2006 ) [3] and referred to this network as ‘PIN’ . They used an orthology-based method in which BLAST [65] searches were run for the human genome against all proteins in the DIP [59] and MIPS Mammalian Protein-Protein Interaction databases [66] . They analyzed their putative interactions giving confidence scores based on the level of homology to proteins found experimentally to interact and the amount of experimental data available . After ROC curve analysis , with a sensitivity of 85% and specificity of 82% , the human interactome consisted of 108113 binary gene-gene interactions and 13584 genes . From these interactions , the redundant ones , i . e . the interactions for which the RefSeq ID corresponding to the same genes , were omitted . Thereby , the network ( PIN ) consists of 85083 interactions . The list of cancer genes was taken from the comprehensive census of human cancer genes provided by Futreal et al ( 2004 ) [67] . 10724 interactions were cancer-related in this interactome . In addition , we collected a set of known cancer genes from the Memorial Sloan Kettering computational biology website CancerGenes ( http://cbio . mskcc . org/CancerGenes/Select . action ) using the queries of “tumor suppressor” , “oncogene” and “stability” genes . We combined that list with the known cancer genes of Futreal et al . [67] . Thus , cancer related interactions number increased to 27413 . We used Swiss-Prot Knowledgebase [68] to map the binary interactions to known structures . The human genes for which 3D structures are known were compiled from the Swiss-Prot Knowledgebase . For each gene-gene interaction in the human interactome , a known complex structure was searched . If a known structure was not available for the interaction , we searched for the structures of each gene and mapped each gene to the corresponding structure as a single chain . If any of the genes in the binary interaction did not have a structural representation , then that interaction was omitted . For example , in the human interactome , one of the binary interactions is TP53-MDM2 interaction . The interaction is represented by a known complex structure in PDB [28] as 1ycr . However , for the TP53-MDM4 interaction , there occurs no known complex structure . In this case , TP53 was represented by its corresponding structure with the highest resolution for which the PDB ID is 1aie_chain A . Similarly , for MDM4 , the structure is 2cr8_chainA . In total , 206 interactions were mapped to known complexes . The summary of the mapping procedure is illustrated in Figure 11 . The mapped protein-protein interaction network called the “structural protein interaction network” ( SPIN ) consists of 1702 nodes ( proteins ) and 5312 edges ( interactions ) . From 5312 interactions , 206 interactions were mapped to known 3D structures . Therefore , the interfaces of these 206 interactions were known . On the other hand , the interfaces of the remaining 5106 interactions were left for further prediction . When the list of cancer-related proteins were searched through 1702 proteins , 466 of them were found to be encoded by cancer-related genes ( cancer gene information from Futreal et al . [67] and the Memorial Sloan Kettering computational biology website CancerGenes ( http://cbio . mskcc . org/CancerGenes/Select . action ) , the rest ( 1236 ) were taken as encoded by noncancer genes . As a result , we defined the ‘cancer structural subnetwork’ ( ‘cSPIN’ ) , as the one consisting of cancer-cancer and cancer-noncancer gene interactions . Our cSPIN contains 1303 proteins and 3221 interactions . The total number of proteins and interactions for each network is summarized in Table 5 . Degree represents the number of interaction partners of a protein . Betweenness is a measure of the total number of shortest paths going through a certain node or edge in the network [69] . We defined as hubs the proteins that are in the top 20% of the degree distribution in PIN and SPIN . That corresponds to proteins with ≥9 interactions . Accordingly , we defined bottlenecks as the top 20% proteins with the highest betweenness values . ( Varying the threshold from 10% to 30% had no significant impact on our results; see Text S1 for hub/non-hub interface statistics ) . To calculate betweenness within the network , we used NetworkX ( NX ) ( https://networkx . lanl . gov/wiki ) , a Python package . Hubs were classified as hub-bottlenecks and hub-nonbottlenecks according to high betweenness or low betweenness , respectively . Goh et al ( 2007 ) [2] predicted the essentiality of a human gene using phenotype information of the corresponding mouse orthologs . A human gene was defined as “essential” if a knock-out of its mouse ortholog results in lethality . Here embryonic/prenatal lethality and postnatal lethality are considered lethal phenotypes , and the rest of the phenotypes are considered non-lethal . We obtained the human-mouse orthology and mouse phenotype data from Mouse Genome Informatics ( http://www . informatics . jax . org ) on May 10 , 2008 . Of 1702 proteins in our SPIN , 1536 have mouse orthologs and phenotype information . According to our classification , we found 497 genes to be essential and the rest to be non-essential . PRISM ( protein interactions by structural matching ) [26] , [27] is a web server to predict protein-protein interactions and protein interfaces . The prediction algorithm uses structural and evolutionary similarities to find possible binary interactions between proteins , “targets , ” through similar known interfaces , “templates . ” Here , target proteins were the proteins in our SPIN dataset for which we wanted to predict the interaction interfaces . As template interfaces , we used the representative interfaces generated from the nonredundant data set of protein-protein interfaces [13] available at http://prism . ccbb . ku . edu . tr/interface , for which the interactions are biological according to NOXclass [57] outputs . There are 1478 template interfaces . The PRISM prediction algorithm starts by extracting the surfaces of target proteins by invoking NACCESS [70] . Template interfaces are split into their complementary partner chains and these partners are structurally aligned with the surfaces of the target proteins . Similarity between the target surface and one partner of the template interface is measured using a scoring function based on two factors . The first is structural similarity , in which RMSD and residue match ratio between target protein and the template interface is scored . The other factor considers evolutionary similarity in which a hotspot match ratio is scored . ( Critical residues at the interface which account for the majority of the binding free energy are called hotspots [71] . PRISM obtains the information on hotspots from Hotsprint [72] , [73] a web server for predicting hotspots at protein interfaces . ) Then , combining these scores , PRISM predicts the most possible interactions occurring between the target proteins . After we obtained the interfaces of the proteins in our network using PRISM , non-biological interfaces , if any , should be eliminated . Interfaces having a biological score greater than 60% according to the NOXclass [57] outputs were accepted as biologically relevant . Thus , 357 interaction interfaces were predicted and most of them ( 80% ) had biological scores greater than 80% . Also , including the known interfaces coming from 3D structures , the resulting network which includes interface information is called ‘iSPIN’ . It consists of 534 proteins and 563 interactions . The subnetwork of cancer-related interactions ( ciSPIN ) includes 381 proteins and 375 interactions . The protein and interaction numbers are given in Table 5 . Kim et al . ( 2006 ) [19] classified protein hubs as singlish-interface and multi-interface hubs . The former has at most two distinct binding interfaces , whereas the latter has more than two binding interfaces . In this study , we also classified the hubs in iSPIN according to the number of distinct binding interfaces; we defined single-interface hubs as protein hubs with only one distinct binding interface and multi-interface hubs as those with more than one distinct binding interface . To distinguish overlapping interfaces from non-overlapping interfaces , we looked at the shared residue percentage of the interfaces of hub proteins . We defined shared residue percentage as the ratio of number of shared residues to the number of total interface residues . If the interface residues are shared at a percentage greater than 20% , then the corresponding interface is an overlapping one and interactions occurring through this interface are mutually exclusive . On the other hand , if the interface is not shared at all , meaning that the shared residue percentage is less than 20% , then this is a non-overlapping interface and the interaction through this interface is simultaneously possible , independent of each other . For interface analysis , we used PROTORP [29] which invokes NACCESS [70] , SURFNET [74] and PRINCIP ( SURFNET ) [74] for interface accessible surface area and gap volume and planarity calculation , respectively . PROTORP calculates the amino acid composition of residues defined in the interface as a percentage value of those classified as polar , non-polar and charged as described previously by Jones and Thornton [75] . The amino acid compositions were weighted and then normalized by the interface ASA values which were calculated using NACCESS . Mann-Whitney test ( also called Wilcoxon rank sum ) , which is a nonparametric test that compares the distributions of two unmatched groups , was performed to compare cancer and non-cancer related interface properties . Two-tailed p values were calculated at α = 0 . 05 . To check whether the differences in cancer & noncancer related interface properties are significant in practice or not , Weka [39] , which is a machine learning software , was used . Training set contained equal number of cancer-related ( positive set ) and noncancer interfaces ( negative set ) . To equalize the number of data in the positive and negative set , a Weka filter called “Resample” which creates a stratified subsample of the given dataset , was used . “Resample” filter ensures that overall class distributions are retained within the sample . 10 runs of 10-fold cross validation were performed using four different classifier algorithms; decision stump , naïve bayes , support vector machine ( SVM ) and adaboostm1 . Decision stump is a machine learning algorithm consisting of a single-level Decision Tree . It is mostly used as a component in boosting algorithms such as Adaboostm1 . In Weka , Adaboostm1 functions as a meta-classifier which uses decision stump by weighting several iterations of it . Naïve Bayes is a simple probabilistic classifier whereas SVM is a supervised learning classifier . The statistical measures of the tests are Accuracy and Precision . Accuracy is the percentage of correctly classified instances calculated by TP+TN/ ( TP+TN+FN+FP ) . For cancer class predictions , TP is the number of correctly predicted cancer interfaces and FP is the number of non-cancer interfaces which are predicted as cancer-related . TN is the number of correctly predicted noncancer interfaces and FN is the number of cancer-related interfaces which are predicted as being non-cancer . Precision is the proportion of the instances which are correctly predicted among all predictions and calculated by TP/ ( TP+FP ) for cancer class . For noncancer class , precision is calculated by TN/ ( TN+FN ) . Average of two precision values ( for cancer and noncancer ) comes out to be Precision of the tests . For the case studies , interaction energies were calculated using FoldX [53] , [54] . Firstly , the complex structures were subjected to an optimization procedure using the repair function of FoldX . During this step , all side chains were moved slightly to eliminate small van der Waals' clashes . Next , AnalyzeComplex function was used to determine the interaction energy between the proteins . Throughout the FoldX calculations , the default parameters were used . All the parameters describing the network topology were calculated using NetworkAnalyzer , which is a Java plugin for Cytoscape [76] . Another Cytoscape plugin MCODE [44] , which detects densely connected regions in protein-protein interaction networks based on a vertex weighting method by local neighborhood density , was used to find highly connected subnetworks in the network . BINGO [77] , being also a Cytoscape plugin , determines which Gene Ontology terms are significantly overrepresented in subgraphs of biological networks . | Protein-protein interaction networks provide a global picture of cellular function and biological processes . The dysfunction of some interactions causes many diseases , including cancer . Proteins interact through their interfaces . Therefore , studying the interface properties of cancer-related proteins will help explain their role in the interaction networks . The structural details of interfaces are immensely useful in efforts to answer some fundamental questions such as: ( i ) what features of cancer-related protein interfaces make them act as hubs; ( ii ) how hub protein interfaces can interact with tens of other proteins with varying affinities; and ( iii ) which interactions can occur simultaneously and which are mutually exclusive . Addressing these questions , we propose a method to characterize interactions in a human protein-protein interaction network using three-dimensional protein structures and interfaces . Protein interface analysis shows that the strength and specificity of the interactions of hub proteins and cancer proteins are different than the interactions of non-hub and non-cancer proteins , respectively . In addition , distinguishing overlapping from non-overlapping interfaces , we illustrate how a fourth dimension , that of the sequence of processes , is integrated into the network with case studies . We believe that such an approach should be useful in structural systems biology . | [
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"genomics/... | 2009 | Human Cancer Protein-Protein Interaction Network: A Structural Perspective |
Terminal differentiation of B cells is an essential process for the humoral immune response in vertebrates and is achieved by the concerted action of several transcription factors in response to antigen recognition and extracellular signals provided by T-helper cells . While there is a wealth of experimental data regarding the molecular and cellular signals involved in this process , there is no general consensus regarding the structure and dynamical properties of the underlying regulatory network controlling this process . We developed a dynamical model of the regulatory network controlling terminal differentiation of B cells . The structure of the network was inferred from experimental data available in the literature , and its dynamical behavior was analyzed by modeling the network both as a discrete and a continuous dynamical systems . The steady states of these models are consistent with the patterns of activation reported for the Naive , GC , Mem , and PC cell types . Moreover , the models are able to describe the patterns of differentiation from the precursor Naive to any of the GC , Mem , or PC cell types in response to a specific set of extracellular signals . We simulated all possible single loss- and gain-of-function mutants , corroborating the importance of Pax5 , Bcl6 , Bach2 , Irf4 , and Blimp1 as key regulators of B cell differentiation process . The model is able to represent the directional nature of terminal B cell differentiation and qualitatively describes key differentiation events from a precursor cell to terminally differentiated B cells .
Adaptive immunity in vertebrates depends on the rapid maturation and differentiation of T and B cells . While T cells originate cell-mediated immune responses , B cells are responsible for the humoral response of the organism by means of the production of high-affinity antibodies . B cells develop in the bone marrow from hematopoietic progenitors , and migrate as mature B cells ( Naive ) to the germinal centers ( GCs ) , which are highly specialized environments of the secondary lymphoid organs [1] . There , B cells are activated by antigens ( Ag ) and undergo diversification of the B cell receptor ( BCR ) genes by somatic hypermutation ( SHM ) , as well as the subsequent expression of distinct isotypes by class switch recombination ( CSR ) [2] . After the activation due to Ag recognition , Naive and GC B cells differentiate into antibody-producing plasma cells ( PC ) , as well as memory cells ( Mem ) [3] . Cytokines secreted by T-helper cells , such as IL-2 , IL-4 and IL-21 as well as the direct contact with these cells , mediated by the union CD40 receptor on B cells with its ligand CD40L , play a key role in the determination of B cell fate [4] , since these external signals act as instructive cues that promote the differentiation from a cell progenitor to multiple cell types ( Fig 1 ) . Terminal differentiation of B cells is controlled by the concerted action of multiple transcription factors that integrate physiologic signals in response to BCR cross-linking , extracellular cytokines , and the direct interaction with T cells , thus creating a complex regulatory network . These factors appear to regulate mutually antagonistic programs and can be divided into those that promote and maintain B cell identity , such as Pax5 , Bcl6 and Bach2 , and those that control differentiation into memory cells or plasma cells , i . e . , Irf4 , Blimp1 and XBP1 , as has been shown by multiple functional , biochemical and gene expression analysis [5–7] . A type is characterized by the expression of a specific set of master transcriptional regulators . Naive B cells express Pax5 and Bach2 , which are induced at the onset of B cell development , and are maintained through all developmental stages upon plasma cell differentiation [8 , 9] . Furthermore , Pax5 is essential for the maintenance of B cell identity , since Pax5 deficiency results in the acquisition of multilineage potential [10] . Both Pax5 and Bach2 are required to inhibit PC differentiation [11 , 12] . In addition to Pax5 and Bach2 , GC cells express Bcl6 , a transcription factor necessary for germinal center formation that allows the SHM and CSR processes to occur [13–15] . Development of B cells toward Mem cells requires Bcl6 downregulation and the induction of Irf4 [16 , 17] . Conversely , PCs are characterized by the expression of Blimp1 and XBP1 that along with Irf4 , inhibit the B cell identity program [5 , 18] . Although a number of molecules that play a key role in the process of the terminal differentiation of B cells are known , it is not completely clear how such molecules regulate each other to ensure the proper appearance of GC , Mem , and PC from progenitor Naive B cells . There exist models describing several aspects of the differentiation of B cells such as the decisions promoting the developmental processes of CSR and SHM [19 , 20] , the response to environmental contaminants that disrupt B cell differentiation [21 , 22] , the B cell exit from the GC phase for the differentiation into plasma or memory cells [23] , as well as the dynamics of B cell differentiation inside the complex microenvironment of germinal centers [24 , 25] . Nonetheless , a general consensus about the regulatory network controlling cell fate decisions of B lymphocytes is lacking . The modeling of regulatory networks has been shown to be a valuable approach to understand the way cells integrate several signals that control the differentiation process [26 , 27] . In particular , the logical modeling approach has been useful to qualitatively describe biological processes for which detailed kinetic information is lacking [28] . This type of modeling usually focus on the nature and number of steady states reached by the network , which are often interpreted as stable patterns of gene expression that characterize multiple cell fates [29] . In this paradigm , the transit from one steady state to another occurs when cells receive a specific external stimuli , such as hormones , cytokines , changes in osmolarity , etc . These external stimuli are sensed and integrated to create an intracellular response that may trigger a global response such as cell growth , division , differentiation , etc . External signals are usually continuous in nature , i . e . , they are present as concentration gradients of external molecules that may attain different values of strength and duration . Therefore it becomes desirable to develop models that incorporate the possibility of following the response of the network to continuous signals while at the same time , describe qualitatively the directional and branched nature of cell differentiation processes . In this work we infer the regulatory network that controls the terminal differentiation of B cells . We then construct two dynamical systems , one discrete and one continuous , to analyze the dynamical properties of the regulatory network . Specifically , we find the stationary states of the models , and compare them against the known stationary molecular patterns observed in Naive , GC , Mem , and PC cells , under wild type and mutant backgrounds . Finally , we show that the dynamical models are able to describe the cellular differentiation pattern under a variety of external signals . Importantly , the models predict the existence of several interactions necessary for the network to ensure the proper pattern of terminal differentiation of B cells . Furthermore , the continuous model predicts the existence of intermediary states that could be reached by the network , but that have not been reported experimentally .
We studied the dynamical behavior of the discrete and the continuous systems so as to obtain their attractors . The discrete version of the B cell regulatory network was studied by exhaustively testing the behavior of the network from all possible initial conditions . The system reaches exactly four fixed point attractors , shown in Table 1 . Notably , there is a one-to-one relation of these four attractors with the expression patterns of the cell types shown in Fig 1 . We labeled the attractors as Naive , GC , Mem , and PC . It is important to remember that each attractor represents a different configuration pattern of the network at the steady state . Specifically , the first attractor , where the nodes Pax5 and Bach2 are active , can be interpreted as the activation pattern of Naive cells . The second attractor , with high levels of Bcl6 , Pax5 , and Bach2 , corresponds to the GC cell type . The third attractor , with high levels of Irf4 , Pax5 , and Bach2 , along with the absence of Bcl6 can be interpreted as the Mem cell fate . Finally , the fourth attractor , with high Blimp1 , Irf4 , and XBP1 , corresponds to the pattern of the cell type PC ( Fig 3 ) . In the discrete version of the model , the set of initial states draining to the attractors , i . e the basins of attraction , do not partition the state space evenly . The percentage of initial states leading to each of the attractors were as follows: Naive = 56 . 25% , GC = 6 . 25% , Mem = 6 . 25% , PC = 31 . 25% . The size of the basins reflects how an attractor can be attained from different initial configurations , and may indicate the relative stability of such steady state [38] . It has been suggested that different basins represent stable or semistable cellular differentiation states [29] . Moreover , in order to transit form one steady state to another , a specific external signal would need to trigger a response in order to overcome the basin of attraction such that a different attractor could be reached by the system . Configurations with larger basins can be easily reached from many initial states , therefore , different perturbations could be buffered and canalized by the network towards a particular steady state [39] . Since the Naive and PC states have larger basins than that for GC or Mem attractors , it is possible to suggest that the former are relatively more stable than the later . Importantly , the proportion of basin sizes of the Naive , GC and Mem attractors agree with in vivo measures of B cells where the Naive progenitor is more abundant in proportion than the other three cell types [40 , 41] . However in spite of the low abundance of PC cells in vivo as a result of a selection process of B lymphocytes during the germinal center reaction , the largest basins corresponding to the progenitor Naive and terminally differentiated PC cells suggest that the regulatory network assures the formation of these cell types in a robust manner . Contrary to discrete systems , continuous dynamical systems have an infinite number of possible initial states so that the search for attractors by sampling a large number of random initial states can lead to the possibility to miss attractors with small basins of attraction . Indeed , the sampling of initial states resulted in the finding of only four attractors for the continuous model , which resulted identical to the attractors of the discrete model , see Table 1 . Therefore , to find possible missing attractors we made an exhaustive perturbation study by temporarily modifying the activation state of each node in the four attractors found by random sampling [42] . With this approach we found three more fixed point attractors in the continuous model . These extra attractors are characterized by intermediate values of activation of the nodes conforming the network core and do not have a counterpart in the discrete model , since the discrete model can attain only 0 or 1 activation values ( Table 2 ) . These attractors with intermediate values may represent possible unstable activation states that can be reached by the system but have not been yet experimentally observed or may correspond to transient differentiation states . Indeed , one of the attractors ( “New3” attractor ) found in Table 2 shows intermediate levels of Bcl6 and Irf4 , in spite of the antagonistic role of these two factors , suggesting that low levels of Irf4 controls the establishment of stationary states prior to Bcl6 downregulation . This attractor may correspond to the known activation pattern of centrocytes , which are Irf4int , Bcl6hi B cells exiting the GC reaction that represent an intermediate cellular state between GC and PC cells [43] . This result supports the role of Irf4 as a regulator of the differentiation process prior the terminal differentiation to PCs since it has been observed that intermediate levels of Irf4 promote the GC program , whereas high levels of Irf4 promote Bcl6 downregulation and further PC differentiation as B cells exit the germinal center [44] . The B cell regulatory network is able to describe the differentiation process outlined in Fig 1 , from the Naive precursor to any of the GC , Mem , or PC cell types by means of sequential pulses of extracellular signals known to direct terminal B cell differentiation ( Fig 4 ) . The system is initialized starting from the Naive attractor , and the system is perturbed at a time t ≈ 25 with a single high pulse of IL-2 or IL-4 for 2 or more time units . Computationally , this is achieved by fixing the variable IL − 4 = 1 and the equation d I L - 4 d t = 0 for the indicated period of time . This signal was intended to mimic the effect of subjecting the Naive cell to a saturating extracellular concentration of IL-2 or IL-4 for a brief incubation time . After the pulse , the entire system was left to evolve until it converged . This perturbation is sufficient to move the dynamical system to the GC attractor which is in agreement with the observations that IL-2 and IL-4 promote B cell proliferation and germinal center formation , and are also necessary signals for the transition of Naive B cells to GC B cells [45–47] . Differentiation of either Naive or GC cells to Mem cells is mediated by the activation of the CD40 receptor by its ligand CD40L [48] , which leads to Irf4 induction and to the repression of Bcl6 [16] . Our model recovers these differentiation routes with a saturating activation of CD40L for ≈2 or more abitrary time units , which leads to the activation of Irf4 node when the Pax5 node is active and Blimp1 is not present . Activation of Irf4 downregulates Bcl6 and directs the transition from the GC to the Mem attractor of the dynamical system , see Fig 4 . Similarly , starting from any of the Naive , GC , or Mem attractors , the system is able to move to the PC attractor by applying a saturating signal of either IL-21 or Ag . This is consistent with the experimental reports where BCR activation by Ag induce Blimp1 upregulation , as well as Pax5 and Bcl6 downregulation thus promoting plasma cell differentiation from either Naive , GC , or Mem cell types [49–51] . This process is facilitated by the presence of IL-21 which is transduced by STAT3 [52 , 53] . For both the discrete and continuous models we obtained the same biological relevant transition paths that describe the wild type differentiation pattern outlined in Fig 1 . However , given that the continuous model has 7 fixed-point attractors , its complete fate map is larger than that for the discrete model ( S1 Fig ) . Nonetheless , the continuous model also presents the known biologically relevant transitions . It has been suggested that progression toward a terminal differentiated state involves several epigenetic changes that reduce the options of a cell to differentiate to other cell types , possibly by several mechanisms that constraint the function of the components of a regulatory network thus reducing the dimensionality of the state space and controlling the compartmentalization of this space into basins of attraction with different sizes [29] . Therefore , the presence of external signals could affect the way the nodes of the network activate in response to these signals which in turn regulate the activation of multiple parts of the network to control the establishment of stationary states of the system and the transitions between these states . Interestingly , no transitions from the PC state to other attractors were obtained in any of the two models , suggesting that the network controls B cell differentiation towards an effector cell fate in an irreversible manner while allowing the transition between precursor cell fates ( Fig 5 ) . To gain further insight of the dynamical behavior of the B cell regulatory network we systematically simulated all possible single loss- and gain-of-function mutants and evaluated the severity of each mutation by comparing the resulting attractors with those of the wild type model . Loss-of-function mutations were simulated by fixing at 0 the value of a node , whereas gain-of-function was simulated by fixing at 1 the same activation state of a node . For each mutant , its attractors were found , exhaustively in the case of the discrete model , and for the continuous version , by running the dynamical system from 5000 random initial states and solving the equations numerically until the system converged . Tables 3 , 4 and 5 shows that the mutants can be grouped according to whether its effect results in the loss of one or more attractors with respect to the wild type model or if it results in the appearance of atypical attractors not found in the wild type model . Importantly , both the discrete and continuous versions of the model were able to describe most of the reported mutants for the six master regulators that conform the core of the network . For instance , the simulated loss-of-function of the Blimp1 node results in the disappearance of the PC attractor , which is in accordance with the experimentally acknowledged role of Blimp1 as an essential regulator for PC differentiation [54] . Although absence of Blimp1 in B cells impedes PC differentiation , it does not affect the establishment of Naive , GC or Mem cell types [54–56] , which is in turn reflected by the model since the network reaches all the Naive , GC and Mem attractors in spite of the loss-of-function of the Blimp1 node ( Table 3 ) . Additionally , for Blimp1 null mutant a distinct attractor was found showing low Pax5 and high Irf4 levels . It has been reported that Pax5 inactivation along with Irf4 induction precedes Blimp1 expression and while Irf4 activation is not sufficient to rescue PC differentiation in the absence of Blimp1 , the coordinate expression of both factors is necessary for complete terminal B cell differentiation [56] . Therefore , this attractor may represent a cellular state prior to the PC state . Similarly to the Blimp1 null mutation , the simulated gain-of-function mutants for the Pax5 , Bcl6 or Bach2 nodes also result in the loss of the PC attractor but the other three wild type activation patterns are still reached by the network ( Table 4 ) , the constitutive activation of any of these nodes maintains the system in attractors corresponding to precursor B cell fates , in accordance with the observations showing that forced expression of Pax5 or Bach2 in mature B cells inhibit terminal differentiation to PCs and are required to maintain the B cell identity program [12 , 66 , 67] . Moreover , for the Bach2 gain-of-function model an additional attractor was found . This attractor is characterized by high levels of Bach2 and Irf4 and low Pax5 in a pattern similar to the Mem attractor , this attractor may correspond to a state previous to PC differentiation where Bach2 avoids Blimp1 activation when Pax5 is inactive [56 , 68] . The simulated Irf4 loss-of-function results in the loss of PC and Mem cell attractors ( See Table 3 ) . Since Irf4 deficient B cells are unable to differentiate into Mem and PCs , the attractors found for this mutant support the role of Irf4 in the formation of PC and Mem cell types [18 , 37 , 60 , 61] . Induction of Irf4 promotes the formation of Mem cells and PC differentiation [18 , 44 , 70] , which is also described by the model as simulated gain-of-function of the Irf4 recovers only two attractors corresponding to the Mem and PC states . Therefore , constitutive activation of the Irf4 node drives the system to the Mem and PC cell fate states . Conversely , constitutive activation of the Bcl6 node results into three attractors , one of them corresponds to the GC cell pattern , the other two attractors correspond to patterns where Bcl6 is active along with Irf4 . These activation patterns coincide with the expression patterns observed for centrocytes , which are Bcl6+ Irf4+ B cells exiting from the GC reaction [79] . This result suggest that sustained activation of the Bcl6 node drives the system to a GC or GC-like state , in accordance with the reported observations where Bcl6 enforced expression in B cells blocks terminal differentiation and regulates GC formation [34 , 66 , 71 , 80] . Bach2 null mutation does not affects the formation of any of the Naive , GC , Mem or PC cell types , thus confirming its role as a dispensable regulator of B cell terminal differentiation , but a necessary negative regulator for Blimp1 expression and PC formation . Only similar attractors to the wild type fates were found [9 , 12 , 19] . Bcl6 null mutant mice does not form GC cells but differentiation to Naive , Mem or PC cell types is not affected . Also , Bcl6-deficient B cells can differentiate into Mem cells or PC independently of germinal center reactions . Accordingly the GC attractor is lost in the simulated Bcl6 loss-of-function mutant [13–15 , 57 , 58] . Deletion of Pax5 in mice results in the loss of B cells from early pro-B stage . Inactivation of Pax5 in mature B cells results in the repression of genes necessary for B cell identity . Pax5 deficient B cells differentiate towards the PC cell fate and show Blimp1 up-regulation . Conditional inactivation of Pax5 in mice mature B cells promotes differentiation toward PCs , in line with the PC attractor found for this mutant . An attractor not reported in literature was found which may correspond to the total loss of expression of the B cell lineage factors [8 , 30 , 62 , 63] . XBP1 is not strictly required for initiation of PC cell differentiation or for previous differentiation stages of terminal B cell differentiation . The network reaches all the wild type attractors [64 , 65] . Forced expression of Blimp1 promotes terminal differentiation to PC cells . Only the PC attractor was found for this simulated mutant [5 , 72–76] . Loss-of-function of XBP1 affects subsequent PC development but it does not impairs B cell differentiation or the establishment of any of the Naive , GC , Mem and PC cell types [54] . Accordingly , similar attractors to the wild type patterns were found . It is important to note that not all single loss- or gain-of-function mutants have a severe effect on the dynamics of our B cell differentiation model , since simulated Bach2 and XBP1 constitutive and null mutations result in attractors similar to the wild type , suggesting that these nodes have only a mild effect on the global behavior of the network . However , the Bach2 node is not dispensable since the constitutive activation of this node avoided the network for reach the PC attractor , in accordance with its biological role as an inhibitor of PC differentiation [12] . These results show the contribution of each node to the dynamics of network and therefore indicate the importance of these factors as regulators of the differentiation process . Given that the expression patterns defining each cell type are controlled by the core module of the regulatory network , the attractors found for the wild-type models as well as for the single loss- and gain-of-function mutants persist even in the absence of external signals . However , as mentioned in the above paragraphs , external stimuli can drive the system from one steady state to another , thus affecting the way the network controls the establishment of different expression patterns . Therefore , we simulated the continuous presence of external signals by fixing the activation value of the nodes representing signaling pathways , namely Ag , BCR , CD40 , CD40L , ERK , IL-2 , IL-2R , IL-4 , IL-4R , IL-21 , IL-21R , NF-κB , STAT3 , STAT5 , and STAT6 , in order to analyze how its continued activation influences the behavior of the core regulatory network affecting the appearance and maintenance of multiple cell fates . For clarity , the effect of the continued stimulation by external signals and the effect of the simulated mutants on the stationary patterns reached by the network is summarized in Table 5 .
The hematopoietic system is well characterized at the cellular level , and there exist several efforts to reconstruct and analyze parts of its underlying molecular regulatory network to understand the differentiation process of multiple cell types . Network modeling has become an appropriate tool for the systematic study of the dynamical properties of specific regulatory networks and signaling pathways . The dynamic behavior of even relatively simple networks is neither trivial nor intuitive . Moreover , experimental information about the kinetic parameters of the molecules conforming such networks is generally lacking . However , the use of qualitative methods shows that it is possible to predict the existence of expression patterns or pointing at missing regulatory interactions . The model presented in this work describes the activation states observed experimentally for Naive , GC , Mem and PC cell types . This model is also able to describe the differentiation pattern from Naive B cells to GC , Mem and PC subsets in response to specific external signals . Despite the lack of qualitative information it was possible to reconstruct the regulatory network of B cells and propose a basic regulatory architecture . This model propose the existence of some missing regulatory interactions and activation states not documented in the literature that might play an important role in the context of terminal B cell differentiation . Importantly , these interactions constitute specific predictions that can be tested experimentally . It is also relevant to stress that the proposed regulatory interactions might be attained by way of intermediary molecules not included in the regulatory network . This is so because the whole network modeling approach is based upon the net effect of one node over another , focusing on whether the flow of information is known , rather than relying on the direct physical contact between molecules . Furthermore , the results suggest that the dynamical behavior of the B cell regulatory network is to a large extent determined by the structure of the network rather than the detail of the kinetic parameters , in accordance to analyses of related models [42 , 81 , 82] . While Boolean networks constitute a valuable modeling approach of choice whenever there is only qualitative data available , for this biological system we wanted to incorporate qualitative continue variables that in addition to the identification of the stationary states as in the discrete model , allows for the analysis of the effect of gradients of external signals . The dynamical behavior of the model resembles the qualitative behavior of the differentiation process by recovering the transition of the system from a Naive state to the terminally differentiated PC state under the presence of external signals . This result recapitulates the directional and branched nature of B cell differentiation events and supports the key role of extracellular signals in the maintenance and instruction of the differentiation process . Importantly , the model allows the exploration of system transitions that describe the differentiation form one cell type to another , it is interesting to note that no transitions from the PC state to other attractors were obtained , suggesting that the B cell regulatory network assures the differentiation towards an effector cell fate in an irreversible manner whereas allowing plasticity of the precursor cell fates . There are several ways in which our model could be improved in future versions . One general change may be the implementation of the model as a stochastic dynamical system . Although both the stochastic and deterministic models retain the same steady states , the implementation as a stochastic system could be useful to generate information about the probability of the cells to transit from one state to another . Another possible route of refinement of the models would be the inclusion of a specific time scale . Both the discrete and continuous models presented here use qualitative modeling frameworks , with results having arbitrary time units . In order to incorporate phenomena with specific timescales , it will be necessary either to calibrate the continuous dynamical system by scanning for appropriate values for the parameters , or alternatively make use of a quantitative modeling framework . Also , it possible to add other layers of regulation to the model , for example by incorporating the effect of chromatin remodeling on the availability of some genes . However , given that we were able to recover with a small qualitative network the basic patterns of activation , it is possible that the role played by the levels of regulation not included in the present model may significantly reduce the number of possible transitory trajectories of the system , instead of determining nature and number of the stationary states themselves . Finally , despite the qualitative nature of the model presented here , we believe it might be used as seed to analyze important biological and clinical phenomena , given that deregulation of the master regulators included in the network are known to be involved in oncogenic events occurring in multiple lymphomas . For instance , aberrant expression of Bcl6 may lead to constitutive repression of genes necessary for exit of the GC program and normal differentiation , therefore contributing to lymphomagenesis [83] . In addition , activation of Irf4 leads to extensive cell proliferation and survival [84] . The present model could serve as a starting framework to test different hypothesis regarding the possible routes by which the expression of the aforementioned factors and other components of the network could be regulated in order to find therapeutic intervention strategies or to test how deregulation of the known mechanisms could lead to pathological conditions , thus contributing to our knowledge on the development of lymphomas .
We inferred the regulatory network controlling terminal B cell differentiation from experimental data available in literature . The evidence used to recover the nodes and interactions of the B cell regulatory network ( Fig 2 ) is summarized in the following paragraphs . The transition from Naive B cells to GC , Mem , and antibody-secreting PCs is regulated by the coordinated activity of transcription factors that act as key regulators of the differentiation process . These factors appear to regulate mutually antagonistic genetic programs and can be divided into those that promote and maintain the B cell program , such as Pax5 , Bcl6 , and Bach2 , and those that control terminal differentiation into memory cells or plasma cells , such as Irf4 and Blimp1 and XBP1 [7] . Pax5 functions as the master regulator of B cell identity , it is expressed at the onset of B cell differentiation and is maintained in all developmental stages of B cells upon commitment to plasma cells . Pax5-deficiency results in the loss of B cell identity and the acquisition of multilineage potential [10] . Pax5 directly inhibits Blimp1 transcription by binding to the promoter of Prdm1 the gene encoding Blimp1 [11] . In turn , Blimp1 represses Pax5 [78] , thus conforming a mutually exclusive regulatory circuit . Along with Pax5 , Bach2 avoids PC differentiation and promotes class switch recombination by repressing Blimp1 through binding to a regulatory element on the Prdm1 gene [12] . Bach2 is positively regulated by Pax5 [31] , while being repressed by Blimp1 in PCs , thus creating a mutual inhibition feedback loop [19] . Bcl6 expression is induced upon arrival of Naive B cells into the germinal centers . Bcl6 is a transcription factor essential for germinal center formation , since deficiency of Bcl6 results in the absence of germinal centers in mice [14 , 15] . The signals that promote high Bcl6 expression in GC cells are not fully understood . However , it has been shown that mutations that disrupt a negative autoregulatory circuit of Bcl6 deregulate its expression and promote the proliferation of GC cells in dense large B cell lymphomas ( DLBCL ) [34] . Moreover , it has been reported that there exists a positive regulatory mechanism controlling high Bcl6 expression during the GC phase that overcome its negative autoregulation [35 , 36] . In accordance with these data , we found necessary to include in our model a positive autoregulatory interaction for Bcl6 ( Bcl6 → Bcl6 ) in order to account for the required signals that maintain high Bcl6 activation levels in GC cells . Additionally , the presence of IL-2 and IL-4 produced by follicular T helper cells play an important role in the transition from Naive to GC cells , as these signals are required for the maintenance and proliferation of GC cells . IL-2/IL-2R and IL-4/IL-4R signals are transduced by STAT5 and STAT6 , respectively , thus positively regulating the expression of Bcl6 [46] . Bcl6 binds directly to the Prdm1 promoter and down-regulates the expression of Blimp1 in GC cells , thus preventing the terminal differentiation to PCs [85] . Conversely , Bcl6 is a direct target of Blimp1 . This creates a mutual inhibition circuit among Bcl6 and Blimp1 [86] . Maturation of GC cells towards the Mem or PC cell fates requires the downregulation of Bcl6 [17] . This process also depends on the activation of BCR by Ag recognition , as well as on the direct contact of B cells with T helper cells which leads to BCR activation and the proteosomal degradation of Bcl6 , mediated by ERK [49] . The direct contact between B and T cells is mediated by the union of CD40 with its ligand CD40L , which in turn activates NF-κB , a positive regulator of Irf4 [16] . Irf4 is a key regulator required for the development of Mem cells from Naive and GC cells , and is involved in the control of CSR and PC differentiation [18 , 37] . It has been shown that low levels of Irf4 promote CSR while high Irf4 levels promote PC differentiation . Irf4 inhibits Bcl6 by binding to a regulatory site in the Bcl6 gene promoter in response to the direct contact of B and T cells [16] . Conversely , Bcl6 is a direct negative regulator of Irf4 in GC cells [87 , 88] , thus generating a mutual inhibition circuit between Bcl6 and Irf4 . Moreover , high Irf4 expression is maintained through direct binding of Irf4 to its own promoter creating a positive autoregulatory circuit [61] . Irf4 also plays an important role in early stages of B cell development where it regulates Pax5 expression through the formation of molecular complexes in the Pax5 enhancer region [89] . Similarly , Pax5 activation during B cell development is maintained by the transcription factor Ebf1 [33] which in turn is activated by Pax5 [32] , therefore conforming a mutually positive regulatory circuit . However , the role of the regulatory circuits between Pax5 , Irf4 and Ebf1 during terminal B cell differentiation is not clearly understood . Nevertheless , we found necessary to include these interactions in our model ( Pax5 → Pax5 and Irf4 ⊣ Pax5 ) in order to account for the known activation patterns for these two regulators . Therefore , these interactions constitute predictions of the model that may support an important role of these regulatory interactions during the late stages of B cell differentiation . The processes of CSR and SHM are controlled by the action of AID [90] which is regulated by the direct binding of Pax5 , NF-κB and STAT6 to its regulatory regions in response to IL-4 and CD40 signals [91–93] . AID expression is inhibited in PCs by Blimp1 [5] . Finally , PC differentiation program is regulated by the coordinated activity of Blimp1 , Irf4 and XBP1 . Blimp1 is specifically expressed in PCs and its activation is sufficient to drive mature B cell differentiation towards the PC fate [56] . Blimp1 is induced by the direct binding of Irf4 to an intronic region of the Prdm1 gene [18 , 61] . Also Blimp1 is involved in Irf4 activation conforming a double positive regulatory circuit . Deficient B cells do not express Irf4 and fail to differentiate into PCs [94 , 95] . In turn , Blimp1 activates XBP1 [64] which is normally repressed by Pax5 in mature B cells [65] . Boolean networks constitute the simplest approach to modeling the dynamics of regulatory networks . A Boolean network consists of a set of nodes , each of which may attain only one of two states: 0 if the node is OFF , or 1 if the node is ON [96 , 97] . The level of activation for the i-th node is represented by a discrete variable xi , which is updated at discrete time steps according to a Boolean function Fi such that xi ( t+1 ) = Fi[x1 ( t ) , x2 ( t ) , … , xn ( t ) ] , where [x1 ( t ) , x2 ( t ) , … , xn ( t ) ] is the activation state of the regulators of the node xi at time t . The Boolean function Fi is expressed using the logic operators ∧ ( AND ) , ∨ ( OR ) , and ¬ ( NOT ) . In our model , all Fis are updated simultaneously , which is known as the synchronous approach . The resulting set of Fis is shown in Table 6 . We obtained all the attractors of the Boolean model by testing all possible initial states under a synchronous updating scheme using the R package BoolNet [99] . Moreover , we simulated all possible single loss- and gain-of-function mutants by fixing the value of each node to 0 or 1 , respectively . The complete discrete model is available for testing in The Cell Collective ( http://www . thecellcollective . org/ ) model B cell differentiation [98] . Furthermore , the model is available as the accompanying file S1 File ( Bcells_model . xml ) in SBMLqual format . The B cell regulatory network was converted into a continuous dynamical system by using the standardized qualitative dynamical systems method ( SQUAD ) [104 , 105] with the modification by Sánchez-Corrales and colaborators [42] to include into the equations a version of the regulatory logic rule for each node . This methodology offers two main advantages , first , it allows to construct a qualitative model in spite of the lack of kinetic information , making use only of the regulatory interactions of the network , and second , since the external signals are continuous in nature , this methodology permit to study the response of the network to such signals while at the same time allowing a direct comparison with the Boolean model . Moreover , due to its formulation as a set of ordinary differential equations , it may find additional unstable steady states , cyclic behavior , or attraction basins with respect to Boolean approaches [105] . The SQUAD method approximate a Boolean system with the use of a set of ordinary differential equations , where the activation level of a node is represented by a variable xi which is normalized in the range [0 , 1] . This is a dimensionless variable since it represents the functional activation level of a node , but it may be used to represent the normalized concentration of the active form of a molecule or a macromolecular complex . The change of the xi node over time is controlled by an activation term and a decay term as described by: d x i d t = - e 0 . 5 h i + e - h i ( ω i - 0 . 5 ) ( 1 - e 0 . 5 h i ) ( 1 + e - h i ( ω i - 0 . 5 ) ) - γ i x i ( 1 ) In Eq ( 1 ) parameters hi and γi are the gain of the input of the node and the decaying rate , respectively . The term ωi is the continuous form of the logical rule describing the response of the node xi to its regulatory inputs , as defined for the discrete dynamical system in the previous section . The logical statements defined for the discrete model are converted into their continuous equivalent by changing A ∧ B , A ∨ B , and ¬A in an expression of classic logic into min ( A , B ) , max ( A , B ) , and 1−A , respectively , thus creating a fuzzy-logic expression . Note that the term ωi cannot be applied to all nodes of Fig 2 , because there are five of them that do not have any regulatory inputs , therefore equations representing these nodes contain only the term for the decaying rate . The activation term for Eq ( 1 ) has the form of a sigmoid as a function of the total input to a node ωi , and was constructed so as to pass through the points ( 0 , 0 ) , ( 0 . 5 , 0 . 5 ) , ( 1 , 1 ) for any positive value of h . We found that for values of h ≥ 50 , the curve is very close to a step function; for intermediate values of h the function is similar to a logistic curve and as h approaches 0 the function is almost a straight line ( Fig 6 ) . This characteristic allows the study of different qualitative response curves on the overall behavior of the regulatory network , while at the same time conserving the direct comparison against a Boolean model due to the three fixed points mentioned above . Since there is a lack of published quantitative data that could be used to estimate the values of either of the hi and γi parameters to solve the system of equations , we decided to use a set of default values . Therefore , all h’s were set to 50 and γ = 1 so as to obtain steep response curves , thus making an easy comparison of the discrete model against the current continuous model and/or forthcoming models . We found that values h ≠ {4 , 8} and γ = 1 recover the experimentally observed patterns of expression S2 Fig . In contrast to the relative insensitivity of changes in the strength of interactions h , the attractors are highly sensitive to changes in values of the decay rate γ . Eq ( 1 ) is constructed in such a way that γ has to have a value equal to 1 in order for xi’s to lie in the closed interval [0 , 1] . Now , values of γ different than 1 make all attractors to disappear S3 Fig . The attractors of the B cell regulatory network model , therefore , are highly dependent on the value used in the parameter specifying the decaying rate . The resulting dynamical system in shown as S2 Table in the Supporting Information , and available as the supplementary S1 File . Due to the high non-linearity of the continuous system of equations , we located the steady states of this model by numerically solving the system of equations from 500 , 000 random initial states and letting it converge , with the use of the R package deSolve [106] , the detailed attractors found for both the wild type and the mutant models are shown in S2 File . | Generation of antibody-producing cells through terminal B cell differentiation represents a good model to study the formation of multiple effector cells from a progenitor cell type . This process is controlled by the action of several molecules that maintain cell type specific programs in response to cytokines , antigen recognition and the direct contact with T helper cells , forming a complex regulatory network . While there is a large body of experimental data regarding some of the key molecules involved in this process and there have been several efforts to reconstruct the underlying regulatory network , a general consensus about the structure and dynamical behavior of this network is lacking . Moreover , it is not well understood how this network controls the establishment of specific B cell expression patterns and how it responds to specific external signals . We present a model of the regulatory network controlling terminal B cell differentiation and analyze its dynamical behavior under normal and mutant conditions . The model recovers the patterns of differentiation of B cells and describes a large set of gain- and loss-of-function mutants . This model provides an unified framework to generate qualitative descriptions to interpret the role of intra- and extracellular regulators of B cell differentiation . | [
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Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging infectious disease that is caused by a novel bunyavirus , SFTSV . We assessed whether the single nucleotide polymorphisms ( SNPs ) in the tumor necrosis factor-alpha ( TNF-α ) were associated with risk to severity of SFTS . Five TNF-α SNPs ( SNP1: T-1031C; SNP2: C-863A; SNP3: C-857T; SNP4: G-308A; SNP5: G-238A ) were genotyped in 987 hospitalized SFTS patients and 633 asymptomatic/mild SFTSV-infected subjects of Chinese Han origin . Multivariate logistic regression analysis was used to calculate adjusted odds ratios ( ORs ) and 95% confidence intervals ( 95% CIs ) . The hospitalized SFTS patients had significantly lower frequency of G-238A A allele than those with mild/asymptomatic infection ( P = 0 . 006 ) . Furthermore , T-1031C C allele ( P < 0 . 001 ) and G-238A A allele ( P < 0 . 001 ) were significantly associated with decreased risk of death . Multiple haplotypes were significantly associated with decreased risk of SFTS hospital admission ( SNP1-2 , CC; SNP1-3 , CCC; SNP1-4 , CCCG; SNP1-5 , CCCGA; SNP2-4 , CCGA; SNP3-5 , CGA; SNP4-5 , GA ) and death ( SNP1-2 , CA; SNP1-3 , CAG; SNP1-4 , CACG; SNP1-5 , CACGG; SNP2-3 , AC; SNP2-4 , ACG; SNP2-5 , ACGG ) after correction for multiple comparisons . By using the ELISA assay , we observed that TNF-α concentration of hospitalized patients was significantly increased in acute phase than in convalescent phase ( P < 0 . 001 ) . Elevated TNF-α concentration was also revealed from fatal patients ( P < 0 . 001 ) . The -238A allele was associated with decreased serum TNF-α levels in SFTS patients in acute phase ( P = 0 . 01 ) . Our findings suggest that polymorphisms in TNF-α gene may play a role in mediating the risk to disease severity of SFTS in Chinese Han population .
Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging infectious disease that is caused by a novel bunyavirus named SFTS virus ( SFTSV ) [1] , a novel phlebovirus belongs to the Phenuiviridae family ( https://talk . ictvonline . org/taxonomy ) . Since its discovery in 2009 , over three thousands of cases have been reported from at least 23 provinces in China [2] . Recent case report in South Korea and Japan demonstrated its existence outside of China , thus indicating the imminent public health impact of this emerging infectious disease [3–5] . Host genetic variations may contribute to severity and death of SFTS . Although large amounts of individuals had been exposed to the SFTSV in endemic areas , only a small proportion developed symptomatic disease , with their clinical manifestations ranging widely from an acute self-limited febrile illness to complications of various severity and even death [6 , 7] . Among all the studies that reported risk factors for adverse disease outcome , older age has been consistently found to increase the death risk [8 , 9] , suggesting the role of host immunity in determining the clinical disease . Based on the current knowledge , inflammatory cytokines and chemokines , the first ramification of activation of the innate immune cells , play important roles in the pathogenesis of SFTS [10–12] . As for patients with adverse disease outcome , the altered production of these cytokines has constantly been found , which process might be genetically determined [13] . Actually , both peripheral cytokine level and their determined genetic polymorphism have been explored for their relationship with the risk of acquiring infection and related disease severity , such as hepatitis B and fungal infections [14 , 15] . Among the cytokines , TNF-α is a major regulator of the inflammatory response that acts locally to trigger a cascade of other pro-inflammatory and chemotactic cytokines and adhesion factors . TNF-α has been putatively implicated in the pathogenesis of a variety of diseases including infectious disease , autoimmune disorders , neoplasia , and malignant diseases [16] . The increased levels of TNF-α was observed in SFTS patients than in healthy individuals , and to an even higher level in fatal patients [17–19] . A number of single nucleotide polymorphisms ( SNPs ) , which are thought to affect the TNF-α production , have been found to alter individual susceptibility to a wide spectrum of infectious disease . Among all the genetic polymorphisms that determined the TNF-α expression in human serum , those located in the promoter region have been most frequently implicated in the regulation of TNF-α expression [20–22] . On the basis of the functional role of TNF-α in the pathogenesis of SFTS , we are impelled to explore the possible role of the TNF-α promoter polymorphisms in determining the disease severity of SFTS in Chinese Han population .
A total of 987 virologically confirmed SFTS patients who needed hospitalization and 633 asymptomatic/mild SFTSV-infected subjects of Chinese Han origin were recruited for the study . Fatal outcome developed in 106 hospitalized SFTS patients . By checking the medical records and by interviewing the participants’ guardians , we determined that all cases and controls were genetically unrelated Han Chinese . The selected characteristics of subjects are shown in Table 1 . Compared with the asymptomatic/mild SFTSV-infected subjects , SFTS hospitalized patients were significantly older ( P < 0 . 001 ) , more often to be female ( P < 0 . 001 ) and with more presence of underlying medical conditions ( P = 0 . 009 ) . Compared with non-fatal patients , significantly older age ( P < 0 . 001 ) , more male gender ( P = 0 . 046 ) and over-presence of underlying medical conditions ( P = 0 . 001 ) were found in fatal patients ( Table 1 ) . Sequencing of the ~1 . 2-kb genomic region in the TNF-α gene in 174 individuals revealed 8 polymorphisms ( Table 2 ) . To ensure enough statistical power , a value of 0 . 03 of minor allele frequency was set as the threshold value of inclusion in this study . Finally , five polymorphisms ( SNP1 , T-1031C; SNP2 , C-863A; SNP3 , C-857T; SNP4 , G-308A; and SNP5 , G-238A ) were selected in the subsequent genotyping analysis . The genotyping results for the five TNF-α polymorphisms were shown in Table 3 . The observed genotype frequencies for the five polymorphisms conformed to Hardy-Weinberg equilibrium in two groups , respectively ( all P > 0 . 05 ) . When compared with asymptomatic/mild SFTSV-infected subjects controls , significantly decreased frequencies of SNP1 and SNP5 were observed in hospitalized SFTS patients by using multivariate logistic regression model to adjust for the effect from age , sex , and underlying medical conditions ( P = 0 . 043 and 0 . 006 respectively ) ( Table 3 ) . After multiple corrections , only G-238A was significantly associated with hospital admission of SFTS . The associations between the G-238A polymorphism and hospital admission of SFTS were further examined with stratification by age , sex , and underlying medical conditions ( S1 Table ) . Although the effect appeared to be more pronounced in subjects who were females , younger ( ≤60 years ) , and without underlying medical conditions , these differences could be attributed to chance ( all P > 0 . 07 , test for homogeneity ) , indicating that these potential confounding factors had no modification effect on the risk of SFTS hospital admission related to the G-238A genotypes . By using multivariate logistic regression model to adjust for the effect from age , sex , and underlying medical conditions , significant associations with fatal outcome were observed for the T-1031C and G-238A polymorphisms ( Table 4 ) . For T-1031C polymorphism , the genotypes containing C allele ( TC + CC genotypes ) were significantly associated with decreased risk to death when compared with the TT genotype ( OR = 0 . 43 , 95% CI = 0 . 26–0 . 71; P < 0 . 001 ) ( Table 4 ) . For G-238A polymorphism , when compared with the -238GG genotype , the genotypes containing A allele ( GA + AA genotypes ) were significantly associated with decreased risk to SFTS related death ( P < 0 . 001 ) . The significant associations remained after correction for multiple comparisons . No association between risk of SFTS related death and other investigated polymorphisms were found after multiple testing . In the stratification analyses , sex , age , and underlying medical condition had no modification effect on the risk of SFTS related death related to the -1031 TC + CC genotypes and -238 GA + AA genotypes respectively ( S2 Table ) . The pairwise disequilibria measures ( D´ and r2 ) of the five TNF-α polymorphisms ( Fig 1A ) were calculated . Fig 1B and 1C showed that two polymorphisms , T-1031C and C-863A , were in strong LD . We next performed haplotype analysis to derive haplotypes specifically correlated with disease severity of SFTS . When compared with asymptomatic/mild SFTSV-infected subjects , the frequencies of seven multi-SNP haplotype systems ( Fig 1D ) and multiple haplotypes ( SNP1-2 , CC; SNP1-3 , CCC; SNP1-4 , CCCG; SNP1-5 , CCCGA; SNP2-4 , CCGA; SNP3-5 , CGA;SNP4-5 , GA ) ( Fig 1E ) were found to be significantly lower in hospitalized SFTS patients after correction for multiple comparisons . When comparison was made between non-fatal and fatal patients , several multi-SNP haplotype systems ( Fig 1F ) and seven multi-SNP haplotypes ( SNP1-2 , CA; SNP1-3 , CAG; SNP1-4 , CACG; SNP1-5 , CACGG; SNP2-3 , AC; SNP2-4 , ACG; SNP2-5 , ACGG ) were found to be associated with decreased susceptibility to death of SFTS , after correction for multiple comparisons ( Fig 1G ) . Altogether 61 hospitalized SFTS patients at acute phase and 25 hospitalized SFTS patients at convalescent phase were evaluated for the serum TNF-α level . The serum TNF-α levels from acute phase were significantly higher than that obtained from convalescent phase ( P < 0 . 001; Fig 2A ) . In addition , the TNF-α levels from fatal hospitalized SFTS patients were significantly increased compared with non-fatal hospitalized SFTS patients ( P < 0 . 001; Fig 2B ) . The TNF-α serum levels in 61 SFTS patients were also evaluated for their association with T-1031C and G-238A genotypes . No significant difference of TNF-α serum levels was observed between T-1031C TC + CC genotypes and TT genotype carriers ( P = 0 . 096; Fig 2C ) . However , among the 61 hospitalized SFTS patients , those carrying the -238A allele ( n = 4 ) had significantly lower TNF-α level than the GG genotype carriers ( n = 18 ) at acute phase ( P = 0 . 01; Fig 2D ) .
In this study , we found two SNPs ( T-1031C and G-238A ) in the promoter of TNF-α gene were associated with disease severity of SFTS in Chinese Han population . Multi-SNP haplotypes derived from the TNF-α polymorphisms was also shown to be associated with the decreased risk to hospital admission and death of SFTS . Furthermore , consistent with the population-based association study , the decreased TNF-α serum levels from the -238A carriers were also observed . These findings suggest that TNF-α gene polymorphisms might contribute to the severity of SFTS by influencing TNF-α expression in Chinese Han population . Our observed genetic associations are plausible from a biological perspective . TNF-α is a potent pro-inflammatory and immunoregulatory cytokine that plays a key role in the initiation , regulation , and perpetuation of the inflammatory response . As for SFTS , we found that the TNF-α concentration of hospitalized patients was higher in acute phase as than in convalescent phase . Elevated TNF-α concentration was also revealed from fatal patients . Our results are consistent with those of previous studies , regarding the abnormally increased expression of TNF-α in the severe and especially fatal SFTS [10–12 , 17–19] . TNF-α has been suggested to act on the endothelium , inducing vasodilating substances , stimulating nitric oxide synthase , increasing capillary endothelial permeability . This process might be responsible for the occurrence of haemorrhagic manifestations , eventually resulting in DIC or MOF , and even death in SFTS [23] . The TNF-α T-1031C and G-238A polymorphisms are reportedly capable of altering TNF-a expression , however with controversy among various studies . In vitro studies showed that the -1031C and -238A alleles conferred increased transcriptional activation of the TNF promoter [20 , 24 , 25] . In contrast , other studies showed no associations between two polymorphisms and TNF-α expression [21 , 22]; one study revealed that the -238A-allelic TNF-α promoter was associated with a reduced transcriptional activity by luciferase assays and this allele was associated with decreasing TNF-α expression in psoriasis patients [26] . In the present study , we indeed found decreased TNF-α serum levels in hospitalized SFTS patients with the -238A allele , which is consistent with the results of population-based association study , however , no such significant association was determined between T-1031C genotypes and TNF-α levels . Further studies with large sample size are warranted to elucidate the molecular mechanism of these two important polymorphisms . This study demonstrated how the appropriate choice of control groups might impact on the results of population based study . The genius control group might be mixed up with individuals who have not been exposed to the virus at all , thus masking the association . The current study chose asymptomatic SFTSV-infected subjects as controls . The asymptomatic SFTSV-infected controls might represent a real control , who have been similarly challenged with the SFTSV , while remained apparently healthy , or at least with very mild disease not to be recalled by the individual . Because asymptomatic SFTSV-infected subjects cannot necessarily recall all the possible flu-like symptoms that might be a mild or very mild SFTS disease within a period of 5 years , we have defined the asymptomatic SFTSV-infected subjects as asymptomatic/mild SFTSV-infected subjects . Recently , several association studies have shown that the TNF-α polymorphisms were related to the susceptibility to various specific infections , including pulmonary tuberculosis , leprosy , severe sepsis in trauma patients , HBV , and HIV [27–32] . Some of the results , however , could not be replicated in subsequent studies . The lack of reproducibility may be ascribed to multiple factors , such as small sample sizes , the different ethnicities of study populations and/or different genetic background . The design and results of our study include many of the features that are considered desirable components of an ideal association study , including large sample size , small P values , and an association that makes biological sense . We acknowledge the potential limits of the study . Firstly , due to the low minor allele frequency of G-238A , the number of the -238A allele carriers for the ELISA assay was very small . Consequently , the effects of the G-238A polymorphism on TNF-α serum expression should be interpreted in caution . Secondly , considering that lower level of TNF-α expression in -238A allele was associated with decreased risk to severe SFTS , G-238A cannot be used to identify individuals with high-risk of becoming severe . Thirdly , only five polymorphisms in the TNF-α promoter were studied . Without performing a systematic screen for variants in the whole TNF-α gene , we cannot exclude the possible linkage disequilibrium that existed between these two polymorphisms and other nearby causative variant . Deep resequencing of this gene may help to uncover additional associated variants and facilitate selection of potential causal variants for further functional studies . In conclusion , our results reveal , for the first time , an association between the TNF-α polymorphisms and lower risk to severe of SFTS in Chinese Han population . These findings provided evidence supporting the importance of TNF-α in the pathogenesis of SFTS . If confirmed by other studies , knowledge of genetic factors contributing to the pathogenesis of SFTS as presented here would be important for the assessment of one’s susceptibility to SFTS and other infectious diseases , especially those sharing a mode of action similar to that of SFTS .
The study was performed in a SFTS designated hospital ( The 154 Hospital of People’s Liberation Army ) in Xinyang administrative district of Henan Province between 2011 and 2014 . All SFTS patients were newly diagnosed and virologically confirmed hospitalized patients . Definitive diagnosis of SFTS patients was based upon typical clinical and epidemiological findings and by detection of SFTSV genomic segments using reverse transcription polymerase chain reaction ( RT-PCR ) ( detailed in SFTSV RNA detection ) . Information regarding demographic characteristics , medical history , clinical manifestation , laboratory test results were prospectively collected using a standard questionnaire . Asymptomatic/mild SFTSV-infected subjects ( no need of medical attention at a hospital ) were selected from healthy subjects who underwent routine physical examination in the same hospital during the same period when the cases were recruited . Their sera samples were subjected for SFTSV specific IgG antibody test by enzyme-linked immunosorbent assay ( ELISA ) . Only those positive for SFTSV IgG antibody while negative for SFTSV genomic segments and denied clinical manifestations resembling SFTS were included as eligible asymptomatic/mild SFTSV-infected subjects . By checking the medical records or by interviewing the participants , we determined that all asymptomatic/mild SFTSV-infected subjects were genetically unrelated Han Chinese and have not been hospitalized for febrile disease in the past five years . For each participant , peripheral blood and sera were collected and immediately stored at -80°C until genomic DNA/RNA extraction . The study was performed with the approval of the Ethical Committee of Beijing Institute of Microbiology and Epidemiology and conducted according to the principles expressed in the Declaration of Helsinki . All participants were adults and provided written informed consent . Viral RNA was isolated from serum samples using QIAamp Viral RNA Mini Kit ( Qiagen , Germantown , MD , USA ) , according to the manufacturer’s instructions . One step Primer Script RT-PCR Kit ( TaKaRa ) was used according to the manufacturer’s instructions for SFTSV detection according to the method described previously [33] . The reaction was performed on an ABI 7500 Real Time PCR System ( Applied Biosystems , USA ) . The primers and TaqMan probes used for the SFTSV detection were as follows: 5′-TTCACAGCAGCATGGAGAGG-3′ ( forward primer ) , 5′-GATGCCTTCACCAAGACTATCAATG-3′ ( reverse primer ) , 5′-AACTTCTGTCTTGCTGGCTCCGC-3′ ( probe ) . Nested RT-PCR and sequencing of the M- segment were performed on randomly selected positive samples to verify the real-time RT-PCR results . The primer set covering the genomic sequence of the promoter region of the TNF-α gene , which spans 1 . 2 kb ( from nt 2935 to nt 4137; GenBank accession no . M16441 . 1 ) , was designed on the basis of size and overlap of polymerase chain reaction ( PCR ) amplicons . The screening panel included DNA from 174 individuals randomly selected , without regard to disease status , from the total study population of 2270 individuals . The primers for the target regions were designed using the Web-based software Primer3 [34 , 35] ( S3 Table ) . DNA samples from the 174 individuals were amplified and purified . PCR conditions were identical to those used for the SNP discovery described previously [36] . Briefly , PCR was performed with a 25 mL reaction mixture containing 20ng DNA , 1 . 0mmol/L each primer , 0 . 2 mmol/L each dNTP , 2 . 0 mmol/L MgCl2 , and 1 . 0 U Taq DNA polymerase in 1X reaction buffer ( Takara Biotech , Dalian , China ) . The reaction for amplification was carried out in the following conditions: an initial melting step of 2 min at 95°C , followed by 35 cycles of 30 s at 94°C , 30 s at 57°C , and 30 s at 72°C and a final elongation of 7 min at 72°C . Then the PCR products were sequenced using an ABI PRISM Dye Terminator Sequencing Kit with Amplitaq DNA polymerase ( ABI ) and loaded onto an ABI 3730 sequencer . Polymorphism candidates were identified by the PolyPhred program and were inspected by 2 observers . Polymorphism positions and individual genotypes were confirmed by reamplifying and resequencing the polymorphism sites from the opposite strand . The primers are available on request . The five promoter polymorphisms were selected for genotyping by use of PCR direct sequencing in the case-control population . The primers for PCR and sequencing and the reaction parameters were identical with those used for the polymorphism validation procedure mentioned above . Genotyping was done in a blind manner that the performers did not know the subjects’ case and control status . The accuracy of genotyping data for each polymorphism was validated by masking , choosing at random , and resequencing 15% of the samples from case patients and control subjects . To compare the differential expression of TNF-α among genotypes , hospitalized SFTS patients and asymptomatic/mild SFTSV-infected subjects were randomly selected to measure the serum concentrations of TNF-α by using TNF-α ELISA assay ( GenWay Biotech , USA ) . The assays were performed according to the instructions of the manufacturers . All measurements were performed in duplicate . Genotype and allele frequencies for polymorphisms were determined by gene counting . The fitness to the Hardy-Weinberg equilibrium was tested using the χ2 test . Associations between polymorphisms and risk of SFTS were estimated by use of logistic regression analyses . Odds ratios ( ORs ) and 95% confidence intervals ( CIs ) were used to measure the strength of association . In view of the multiple comparisons , the correction factor n ( m − 1 ) ( n loci with m alleles each ) was applied to correct the significance level . This method showed that P values of 0 . 01 and below can be considered statistically significant after correction for multiple testing . The TNF-α serum concentrations were log transformed , and tested for differences between different groups by two-sample Wilcoxon rank-sum test . These analyses were performed using SPSS software ( version 17 . 0 , SPSS Inc . , Chicago , IL ) . The pairwise LD calculation ( Lewontin’s D´ and r2 ) and haplotype blocks construction were performed using the program HaploView 4 . 2 . Haplotypes based on the polymorphisms in the TNF-α gene were inferred using PHASE 2 . 1 software . Haplotype frequencies of the cases and controls were compared using χ2 tests . The haplo . glm program was then used to calculate adjusted ORs for each haplotype , and the number of simulations for empirical P values was set at 1000 . | Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging infectious disease that is caused by a novel bunyavirus . The current study disclosed the single nucleotide polymorphisms ( SNPs ) in the tumor necrosis factor-alpha ( TNF-α ) were associated with risk to disease severity of SFTS . These findings suggest that polymorphisms in TNF-α gene may play a role in mediating the risk to disease severity of SFTS in Chinese Han population . The study will be of interest to either the clinicians devoted to the prevention and therapy of SFTS or the geneticists devoted to studying the genetic susceptibility mechanisms of common diseases . | [
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"and... | 2018 | Polymorphisms and haplotypes in the promoter of the TNF-α gene are associated with disease severity of severe fever with thrombocytopenia syndrome in Chinese Han population |
Zipper interacting protein kinase ( ZIPK , also known as death-associated protein kinase 3 [DAPK3] ) is a Ser/Thr kinase that functions in programmed cell death . Since its identification eight years ago , contradictory findings regarding its intracellular localization and molecular mode of action have been reported , which may be attributed to unpredicted differences among the human and rodent orthologs . By aligning the sequences of all available ZIPK orthologs , from fish to human , we discovered that rat and mouse sequences are more diverged from the human ortholog relative to other , more distant , vertebrates . To test experimentally the outcome of this sequence divergence , we compared rat ZIPK to human ZIPK in the same cellular settings . We found that while ectopically expressed human ZIPK localized to the cytoplasm and induced membrane blebbing , rat ZIPK localized exclusively within nuclei , mainly to promyelocytic leukemia oncogenic bodies , and induced significantly lower levels of membrane blebbing . Among the unique murine ( rat and mouse ) sequence features , we found that a highly conserved phosphorylation site , previously shown to have an effect on the cellular localization of human ZIPK , is absent in murines but not in earlier diverging organisms . Recreating this phosphorylation site in rat ZIPK led to a significant reduction in its promyelocytic leukemia oncogenic body localization , yet did not confer full cytoplasmic localization . Additionally , we found that while rat ZIPK interacts with PAR-4 ( also known as PAWR ) very efficiently , human ZIPK fails to do so . This interaction has clear functional implications , as coexpression of PAR-4 with rat ZIPK caused nuclear to cytoplasm translocation and induced strong membrane blebbing , thus providing the murine protein a possible adaptive mechanism to compensate for its sequence divergence . We have also cloned zebrafish ZIPK and found that , like the human and unlike the murine orthologs , it localizes to the cytoplasm , and fails to bind the highly conserved PAR-4 protein . This further supports the hypothesis that murine ZIPK underwent specific divergence from a conserved consensus . In conclusion , we present a case of species-specific divergence occurring in a specific branch of the evolutionary tree , accompanied by the acquisition of a unique protein–protein interaction that enables conservation of cellular function .
Orthologs are corresponding genes in different species . Such genes evolve from a common ancestral gene and usually have similar functions . The degree of divergence of orthologs can provide information on the taxonomic relationship between organisms and the similarity of their function . Orthologs from organisms that are closely related display a higher degree of similarity in the nucleotide sequence than distant organisms in the evolutionary tree . Here , we present an unusual exception to this rule in the ZIP-kinase genes . Zipper interacting protein kinase ( ZIPK , also called DAPK3 or DAPK like kinase ( DLK ) ) is a member of the death associated protein kinase ( DAPK ) family . The family consists of a group of cell death-promoting Ser/Thr kinases homologous in their catalytic domains , including DAPK ( DAPK1 ) and DRP-1 ( DAPK2 ) [1] . Notably , the C-terminal extra-catalytic domains of these family members differ substantially from each other . In ZIPK , it comprises several putative nuclear localization signal ( NLS ) sequences and a leucine zipper domain , required for homo-oligomerization , interaction with other leucine zipper–containing proteins , and also critical for its death-promoting effects [2–5] . A long-standing debate exists in the literature concerning the intracellular localization of ZIPK , as well as its molecular mode of action . Several publications report that ectopic expression of catalytically active human ZIPK induces cell death , characterized by membrane blebbing and cell rounding [6] . These reports indicate that ZIPK is mostly localized to the cytoplasm , with a small fraction of cells also showing nuclear staining in a diffuse pattern . The catalytically inactive mutant ZIPK K42A was shown to localize exclusively to the cytoplasm , further suggesting that the kinase activity may have an impact on the intracellular localization [7] . In epithelial cell lines , ZIPK was shown to induce membrane blebbing through phosphorylation of the regulatory light chain of myosin II ( MLC ) , and in some circumstances , to induce the formation of autophagic vesicles [6–8] . A second cytoplasmic function for ZIPK was observed in smooth muscle cells , where ZIPK-dependent phosphorylation of MLC led to Ca+2 sensitization and smooth muscle contraction . This was attributed to direct phosphorylation of MLC as well as inactivation of smooth muscle myosin phosphatase ( SMMP-1M ) , through phosphorylation of the phosphatase's myosin binding subunit , and phosphorylation of its inhibitor protein CPI17 ( PPP1R14A ) [9 , 10] . In contrast , other studies from various researchers suggest a completely different mode of action and intracellular localization for ZIPK . It has been reported that ZIPK localizes predominantly to the nucleus , mostly appearing in a speckled staining pattern identified as promyelocytic leukemia oncogenic bodies ( PODs ) [2–4 , 11] . At the molecular level , ZIPK was shown by these groups to bind DAXX ( also known as Fas death domain-associated protein 6 ) and thus regulate its recruitment to the PODs . ZIPK was also shown to interact with two transcription factors , ATF4 , a member of the activating transcription factor/cyclic AMP-responsive element-binding protein ( ATF/CREB ) family and STAT3 , a latent cytoplasmic transcription factor that can be activated by cytokines and growth factors [2] . Interestingly , one group reported that the prostate apoptosis response 4 ( PAR-4 ) , a substrate of ZIPK , is capable of translocating ZIPK from the PODs to the actin microfilaments [4 , 12 , 13] . Different arguments have been raised in the literature in an attempt to explain this basic discrepancy in localization/function , suggesting that differences in the type of cells or expression vectors used in these experiments accounted for the disparate results . Others argued that the differences were species specific , and depended on whether the human or mouse/rat orthologs were used in these studies [6 , 14] . Since the latter possibility has not been studied in a direct manner , we have undertaken integrated bioinformatics and experimental analyses of ZIPK orthologs to test this hypothesis . Here , we report that through accelerated evolution of the ZIPK locus in the mouse and rat , these genes diverged considerably from the common consensus , highly conserved from fish to human . Among the various changes , the loss of a critical phosphorylation site that influences the protein's intracellular localization was identified . We also demonstrate that PAR-4/ZIPK interaction occurs in the murine system , but not in human or in the zebrafish ortholog , which , like the human ZIPK , also localizes to the cytoplasm . We suggest that the PAR-4–mediated cytoplasmic translocation in the murine system may provide a compensatory mechanism for conserving the membrane blebbing function of mouse and rat orthologs , an important feature in the mode of action of these kinases .
The ZIPK gene is present in various vertebrates , from fish to mammals ( Figures 1 and 2; accession numbers listed in Text S1 ) , and its product is highly conserved in all the species in which it was found . Identities are at least 88% in the N-terminal kinase domain of ZIPK proteins ( 268 amino acids long ) , and at least 46% in the C-terminal regulatory domain ( approximately 185 amino acids long ) , between fish and mammals ( Figure 1 ) . However , careful examination reveals that both mouse and rat ZIPK proteins are more divergent than expected . A dendogram computed from an alignment of ZIPK protein sequences shows that both rat and mouse sequences are on a long branch within the mammalian ZIPK protein cluster ( Figure 2; the alignment of the protein sequences from the 25 species used to calculate the dendogram is shown in Figure S1 , and the amino acid and DNA sequences are shown in Figures S2 and S3 , respectively ) . Calculating the phylogenetic distance between the different ZIPKs shows that the rat and mouse sequences diverged from the human sequence to the same extent as chicken ZIPK , much further than expected . The dendogram has no other notable discrepancies from accepted taxonomy in either its topology or branch lengths . This finding was robust , being observed in all dendograms obtained using different organism sets and ZIPK regions ( unpublished data ) . Most of the murine-unique sites ( 82% ) appear in the C-terminal regulatory domain of the kinase ( aa 269–454 , according to the human nomenclature ) . Altogether , the murine C-terminal region contains 47 unique sites , as compared to the mammalian consensus , corresponding to 25% of the sequence . The rat and mouse sequences are very conserved between themselves , having only one nonidentical position ( Val/Ala at aa 358 ) . The two other rodents whose ZIPK sequence we found , Cavia ( guinea pig ) and kangaroo rat , possess only a small portion of the murine unique sites , and are more similar to other mammals ( Figure 1 ) . In the dendogram , they are grouped together with the rat and mouse to form the group of rodents , but their shorter branch length indicates that they have diverged from the mammalian consensus to a lesser extent ( Figure 2 ) . The shrew , an even smaller mammal , which also has a short life span and numerous progeny , does not show extended branch length on the dendogram ( Figure 2 ) . Thus , it is unlikely that the extent of the divergence we found in the murine ZIPK sequences emerged from a more general process of accelerated evolution due to short life span and numerous progeny of murines . Additionally , two other proteins , which were examined in a similar manner , did not show exceptional murine branch lengths . These were PAR-4 ( a protein interacting with ZIPK , see below ) and DAPK ( a ZIPK family member sharing some similar functions due to a common kinase domain and capable of interacting and transphosphorylating ZIPK [8] ) proteins ( Figures S4 and S5 for DAPK and PAR-4 , respectively ) . A large-scale analysis of mammalian evolution also found that the rate of molecular evolution in rodent and carnivore lineages is the same and is ∼11%–14% faster than in the primate lineage [15] . Other works also did not find a particularly hyper-accelerated evolution of murines , such as what we found for ZIPKs [16–18] . The divergence therefore appears to be specific to the ZIPK gene and to the murine system . To understand the significance of the changes in the protein sequence of rat and mouse ZIPK , we superimposed on the ZIPK multiple sequence alignment the functional data generated in our lab and others regarding human , rat , and mouse ZIPK proteins ( Figure 1 ) , and examined which of the murine unique sites lie in these functional domains . Functional annotations included the potential NLSs , the leucine zipper domain , and Ser/Thr residues previously shown to be targets for autophosphorylation and/or trans-phosphorylation by DAPK . The kinase domain contains only two notable divergences in the rat and mouse ZIPK: a serine to proline substitution at position 50 located in a basic loop within the catalytic domain , and a 4-aa substitution at positions 151–154 ( NVPN to HAAS ) . The basic loop ( aa 45–57 , mostly positively charged ) , known as the fingerprint of the DAP kinase family of proteins , is exposed on the surface of the catalytic domain , as shown in the crystal structure of another member , DAPK [19] . Previous work has indicated that the basic loops of human ZIPK and DAPK are essential for a physical interaction between the two kinases , a process followed by trans-phosphorylation and amplification of the death signals [8] . Yet , we found that the physical interaction between the catalytic domain of DAPK and the full-length rat ZIPK was similar to that of human ZIPK/DAPK interaction , suggesting that the serine-to-proline substitution had no effect on the binding property ( unpublished data ) . Recently , though , it has been suggested that serine 50 in human ZIPK undergoes autophosphorylation ( A . P . Turnbull et al . , Protein Data Bank entry 2J90; http://www . rcsb . org/pdb/explore/explore . do ? structureId=2J90 ) . The putative functional implications of this phosphorylation would be absent in the murine ZIPK proteins , a point to be addressed in the future . The 4-aa substitution at positions 151–154 falls within another loop , between helices VI and VII of the kinase , and its significance is , as yet , unknown . In the extra-catalytic part of the protein , the most apparent change in murine ZIPK is a 5-aa deletion ( between position 277–284 in human ) , which falls within a putative bipartite NLS , previously classified as NLS II [4] . The rat and mouse ZIPK proteins have lost part of the linker region between the positively charged ends of this potential NLS . Yet , in light of previous work showing that NLS IV , at the C terminus of the protein ( position 409–416 of the human protein ) , is the functionally relevant NLS , required and sufficient for nuclear localization of murine ZIPK [4] , the possible functional implication of the loss of the linker region in NLS II in influencing the nuclear localization of ZIPK is unclear . NLS IV contains two murine substitution sites , but the effect of these substitutions is predicted to be minor , as all of the positively charged amino acids important for the nuclear localization signal are conserved . The leucine zipper structure , at the C-terminal part of the extra catalytic domain , is conserved between human and rodents , as the heptad Leu/Val repeats at positions 427/434/441 , previously shown to compose the leucine zipper [2] , are unchanged . Another site of significant divergence noted among the functionally relevant regions in the protein relates to phosphorylation sites on ZIPK . Human ZIPK was previously shown to undergo both autophosphorylation as well as trans-phosphorylation by DAPK at multiple sites ( Figure 1 ) [5 , 8] . These phosphorylation events , two of which result from both auto- and trans-phosphorylation , have been shown to have prominent functional effects on either the catalytic activity or the cellular localization of human ZIPK . As human ZIPK and DAPK share several common substrates , it has been shown that the trans-phosphorylation by DAPK , which occurs following the physical interaction between the two kinases , creates a feed-forward regulatory process leading to amplification of the cell death–promoting signals [8] . Examining the conservation of these sites revealed that most are conserved in murine ZIPK . The only nonconserved phosphorylation site is threonine 299 , a site that was identified as a common target of both auto- and trans-phosphorylation [5 , 8] . The phosphorylation of threonines 299–300 was shown to have an impact on the cellular localization of human ZIPK; ZIPK bearing the T299A/T300A phospho-silencing mutation is mainly nuclear , while the T299D/T300D phospho-mimicking mutant is a mainly cytoplasmic form [5] . The murine ZIPK protein thus lacks a critical phosphorylation site that affects cellular localization . Notably , rat , mouse , and Cavia ZIPKs have alanines in positions 299 and 300 , while the kangaroo rat , the other rodent ZIPK sequence found , has the conserved threonines at these positions . Within the rodents , the kangaroo rat , rat , and mouse are in a different cluster than the cavia [16] . This indicates either convergent mutations in the rat/mouse and cavia , or a reversion in the kangaroo rat . As discussed above , there are conflicting data in the literature concerning the intracellular localization of ZIPK protein . In order to directly test whether this reflects differences between species , we expressed either human or rat FLAG-tagged ZIPK in human HeLa cells , at sublethal concentrations , and followed their intracellular localization by immunostaining . Rat ZIPK was localized to the nucleus , showing in 50%–60% of the cells a punctate nuclear staining that was previously defined as being associated with PODs ( Figure 3BI and 3BII for diffuse and punctate nuclear staining , respectively ) . The human ortholog was mainly cytoplasmic and was excluded from nuclei ( Figure 3AI ) , with a small fraction also showing a diffuse nuclear staining , yet with no punctate staining at all ( Figure 3AII ) . We repeated the experiment using GFP-conjugated proteins , with similar results ( unpublished data ) . To our knowledge , this is the first time the localization of both human and rat ZIPK was examined and compared in the same cells with the same vectors , and thus it is clear that the differences in localization stem from the proteins themselves . To determine whether the difference in cellular localization was due to the specific divergence of murine ZIPK from a common evolutionarily conserved consensus , FLAG-tagged zebrafish ZIPK was cloned and expressed in the same cellular setting described above . Zebrafish ZIPK immunostaining was very similar to human ZIPK , localizing mostly to the cytoplasm ( Figure 3CI ) with a small percentage showing diffused nuclear staining in addition to the cytoplasmic staining ( Figure 3CII ) . To check whether the loss of the phosphorylation sites at positions 299 and 300 in rat ZIPK might influence murine protein nuclear localization , we generated an A299T/A300T rat ZIPK mutant , thus recreating the phosphorylation sites . Comparing the cellular localization of ectopically expressed wild type and mutant rat ZIPK in HeLa cells revealed that the localization of the protein changed in a specific manner ( Figure 4A ) . Both wild-type and mutant rat ZIPK are still exclusively nuclear , but whereas the wild type is mostly associated with PODs , the mutant rat ZIPK is mostly nucleoplasmic . Thus , it seems that the A299T/A300T mutation reduced the localization to PODs characteristic of the murine orthologs , yet was not sufficient by itself to impose a massive translocation to the cytoplasm . A small yet functionally significant fraction of the rat ZIPK mutant molecules , which have lost the POD staining , might translocate to the cytoplasm while still remaining below the immunostaining threshold sensitivity . To approach this possibility we turned to a more sensitive assay that measures the membrane blebbing effects of ZIPK , previously shown to be caused by MLC phosphorylation on microfilaments . We compared the membrane blebbing potency of wild type and A299T/A300T rat ZIPK , and of human ZIPK . As shown in Figure 4B , mutant rat ZIPK is more potent in the induction of blebbing than wild-type rat ZIPK , although not as potent as human ZIPK . All the tested ZIPK proteins were expressed to the same extent in these experiments ( Figure 4C ) . To examine more carefully the intracellular localization of mutant ZIPK in the blebbing cells we turned to HEK 293 cells , in which the blebbing phenotype is more pronounced , and ectopically expressed proteins reach higher levels . In some of the blebbing cells , we could detect the mutant rat ZIPK within the bleb structures , suggesting that some nuclear-to-cytoplasm translocation takes place in the mutant rat ZIPK ( Figure S6 ) . Altogether , the small yet statistically significant increase in blebbing capacity and the dissociation from PODs in the A299T/A300T mutant indicate that the loss of the phosphorylation sites at aa 299 and 300 in the rat and mouse may partially explain the phenotypic differences between the human and murine proteins . Still , the blebbing-inducing capacity of mutant rat ZIPK is significantly lower than that of human ZIPK , suggesting that additional sequence changes may be involved , and are most probably part of the other unique structural differences discussed above . The observed significant differences in the intracellular localization of the wild-type ZIPK proteins suggest that the human and murine ZIPK reside in different microenvironments , accessible to different interacting proteins and probably subjected to different regulatory mechanisms . This raises the question of how the cellular function of these divergent proteins has been conserved in spite of these changes . We therefore searched for an adaptive mechanism exclusive to the murine system that may allow the nuclear murine ZIPK to exit the nucleus and perform its cytoplasmic functions , thus compensating for the sequence divergence . Previous studies have shown that rat and mouse ZIPK both bind PAR-4 protein [4 , 11 , 13] . PAR-4 contains nuclear localization and export sequences , and is also able to bind actin microfilaments ( MF ) . Upon overexpression , rat PAR-4 was shown by most studies to shuttle rat ZIPK from the PODs in the nucleus to the MFs , where ZIPK phosphorylates MLC and induces membrane blebbing [12 , 13] . The ability of PAR-4 to bind rat ZIPK , its ability to bind actin MF , and the actual ZIPK catalytic activity were all shown to be necessary for the induction of this phenotype . It therefore became of interest to test whether the PAR-4 binding capacity is unique to the murine system . To this end , we examined whether the two other orthologs , human and zebrafish ZIPK , also interact with PAR-4 . We also examined in these experiments the possible influence of the mutations in position 299 and 300 of the rat ZIPK on PAR-4 binding . FLAG-tagged human ZIPK , wild-type , and A299T/A300T mutant rat ZIPK , and zebrafish ZIPK proteins were coexpressed with HA-tagged PAR-4 in HEK 293 cells , and ZIPK proteins were immunoprecipitated and assessed for their ability to pull down PAR-4 . We used the human PAR-4 ortholog for these binding experiments in light of the high degree of conservation of PAR-4 in evolution ( see Figure S7 for full sequence alignment ) , and considering the fact that the most critical question was to study whether ZIPK/PAR-4 interactions exist in the human system . The similarity between PAR-4 orthologs is extremely high , especially along the region of PAR-4 that binds to ZIPK ( this region corresponds to the leucine zipper domain of PAR-4 spanning between aa 279–332 [20] ) , and , as shown in the alignment in Figure 5E , this domain displays 98% and 94% similarity between human PAR-4 and rat or zebrafish orthologs , respectively . Thus , it was not surprising to learn that human PAR-4 strongly interacted with the rat ZIPK . Also the mutations at position 299/300 in the rat ZIPK did not interfere with this binding ( Figure 5A ) . Furthermore , PAR-4 coexpression displayed very pronounced effects on both the cellular localization and the membrane blebbing capacity of rat ZIPK , consistent with previous reports . As shown in Figure 5B and 5D , rat ZIPK translocated to the cytoplasm upon coexpression with PAR-4 . Scoring the transfected cells for membrane blebbing indicated that rat ZIPK induced massive membrane blebbing when co-expressed with PAR-4 , yielding values which approached those obtained upon ectopic expression of human ZIPK alone ( Figure 5C ) . In contrast , we found that neither human nor zebrafish ZIPK could pull down human PAR-4 ( Figure 5A ) . Although the PAR-4 binding region in ZIPK has not been well defined , it notably involves regions from the C-terminal part of the kinase ( between aa 337–417 [13] or between aa 397–448 [11] ) . Our experimental data therefore suggests that the overall high sequence divergence within these C-terminal regions of murine ZIPK ( Figure 1 ) dictates the PAR-4 interaction . This result , along with the differences in localization , proves that the divergence of the murine ZIPK sequence from the common conserved consensus changes the biochemical properties of the protein relative to that of human or zebrafish ZIPK . We suggest that the interaction between murine ZIPK and PAR-4 evolved as a mechanism to compensate for the localization of ZIPK to the PODs by exporting it from the PODs in the nucleus to the cytoplasm , where it can fulfill one of the major functions of ZIPK , which is membrane blebbing . To conclude , we present here an interesting case in which a highly conserved gene in all vertebrates has diverged considerably and specifically in the murine lineage . We show that murine ZIPK protein differs significantly from its human ortholog , losing an important auto- and trans-phosphorylation site and displaying distinct altered cellular localization . All this affects the regulation and , possibly , the activity of murine ZIPK . Yet , a different protein interaction capacity , with an important protein partner in the apoptosis pathway , evolved in the murine system to maintain the basic membrane blebbing function of these kinases . Research on mouse and rat proteins in order to gain insights into the function of their human orthologs is widespread . The common conviction is that these three mammalian systems are close enough in evolution to make these deductions valid . Many studies have demonstrated that human and murine orthologs act and are regulated in the same manner . Here , we present an extraordinary exception to that rule , where data learned in one system could not be fully projected onto the other . Hence , the possibility of divergence and dissimilarity should be kept in mind when transferring knowledge even between seemingly closely related organisms . Still , the basic rule of functional conservation is valid through circumventing mechanisms .
Accession numbers for the ZIPK sequences used are in the Accession Numbers list under Supporting Information . ZIPK sequences extracted by us from genomic contigs , ESTs , or assembled raw sequencing traces ( the latter indicated by TI ) , are available from the National Center for Biotechnology Information ( NCBI ) trace archive database and are listed in the Accession Numbers section . Assembly was done with the CAP3 program [21] . When needed , sequences from different sources for the same organism were combined . The coding regions on the genomic contigs are detailed in Figure S3 . Multiple sequence alignments were generated using the DIALIGN2 program [22] . ZIPK alignments were corrected to allow the arginine triplet at position 278–280 of the rodent sequences to align with similar triplets in the other sequences; this change was evident using other alignment programs ( unpublished data ) . Sequence dendograms were calculated based on the full-length ZIPK multiple alignment , and the indicated PAR-4 and DAPK alignments , using the PHYML v . 2 . 4 . 4 program [23] . Human ZIPK plasmid was previously described [8] . GFP-conjugated rat ZIPK was kindly provided by Karl Heinz Scheidtmann , from which the rat ZIPK ( unfused to GFP ) was subcloned into a FLAG-tagged pcDNA3 expression plasmid . A299T/A300T mutations were generated by PCR-mediated site-directed mutagenesis , using the QuikChange kit from STRATAGENE , following the provided protocol . All mutations were confirmed by direct sequencing . Human PAR-4 cDNA clone was purchased from RZPD , the German Resource Center for Genome Research , Berlin , Germany ( I . M . A . G . E . Consortium [LLNL] cDNA clones ) ( clone ID IRAUp969D0743D6 ) and subcloned into an HA-tagged pcDNA3 expression plasmid . Zebrafish ZIPK was cloned from a 48-h embryo cDNA library , kindly provided by Nataliya Borodovsky and Gil Levkowitz , into a FLAG-tagged pcDNA3 expression plasmid . 293 Human Embryonic Kidney cells and HeLa cells were grown in DMEM ( Biological Industries ) supplemented with 10% fetal bovine serum ( Hyclone ) and 1% L-glutamine ( GibcoBRL ) and a mixture of antibiotics ( 100 U/ml penicillin and 0 . 1 mg/ml streptomycin ) . For transient transfections , 1 . 2 × 106 ( 293 ) or 0 . 8 × 106 ( HeLa ) cells were plated on 9-cm plates 24-h prior to transfection . Transfections were done by the calcium phosphate method with 10 μg DNA per plate . To assess the membrane-blebbing potency of ZIPK , HeLa cells were transfected with 9 μg of the appropriate ZIPK construct and 1 μg of peGFP expression vector . After 24 h , green cells were counted and the percent of blebbing cells was calculated . Cells were washed twice in PBS and then suspended in cold lysis buffer ( 20 mM Tris pH 7 . 5 , 0 . 5% NP-40 , 150 mM NaCl ) with protease inhibitors ( 1% protease inhibitor cocktail [Sigma] , 1% PMSF ) and passed through a 21-gauge needle 20 times to break nuclei . Lysates were centrifuged for 15 min at 14 , 000 rpm at 4 °C . The pellet was discarded and the supernatant was precleared for 1 h at 4 °C on a slurry of protein G-PLUS Agarose beads ( Santa Cruz Biotechnology ) . The precleared extracts were incubated with agarose-conjugated anti-FLAG M2 gel beads ( Sigma ) for 2 h at 4 °C . Immunoprecipitates were washed four times with lysis buffer containing protease inhibitors , and resolved by standard SDS-PAGE . Blots were reacted with anti-FLAG-M2 monoclonal antibody ( dilution 1:500 ) ( Sigma ) ; anti-FLAG polyclonal antibody ( dilution 1:800 ) ( Sigma ) ; anti-HA monoclonal antibody ( dilution 1:1000 ) ( Babco ) ; or anti-Actin monoclonal antibody ( dilution 1:5000 ) ( Sigma ) . HeLa cells ( 0 . 8 × 106 ) were seeded on glass cover slips in 9-cm plates and transfected the next day with the appropriate constructs , 10 μg DNA per plate . After 24 h , cells were fixed in 3 . 7% formaldehyde for 15 min . After blocking and permeabilization with 10% normal goat serum ( Biological Industries ) , 0 . 4% Triton X-100 in PBS , the cells were incubated for 1 h with anti-FLAG polyclonal antibody ( Sigma; 1:600 dilution ) followed by RRX-conjugated goat anti-rabbit secondary antibody ( Jackson ImmunoResearch; dilution 1:800 ) . The cover slips were finally stained with DAPI ( 0 . 5 μg/ml , Sigma ) and mounted with ImmuMount ( Thermo Shandon ) embedding media . Stained cells were viewed by fluorescent microscopy ( Olympus BX41 ) equipped with a 100× oil immersion objective , using excitation wavelengths of 530–550 nm ( for RRX ) and 360–370 nm ( for DAPI ) . Digital imaging was performed with a DP50 CCD camera using Viewfinder Lite and Studio Lite software ( Olympus ) . Final composites were prepared in Adobe Photoshop ( Adobe Systems ) .
The NCBI Entrez ( http://www . ncbi . nlm . nih . gov/sites/gquery ) accession numbers for the ZIPK sequences used in this paper are Bos taurus , 76622257; Canis familiaris , 73987437; Danio rerio , 68356496; Homo sapiens , 4557511; Macaca mulatta , 109122939; Mus musculus , 6681133; Pan troglodytes , 114674687; Rattus norvegicus , 11968142; Tetraodon nigroviridis , 47223108; Xenopus laevis , 66911521; and Xenopus tropicalis , 62860094 The ZIPK sequences extracted by us from genomic contigs , ESTs , or assembled raw sequencing traces ( the latter indicated by TI and available from the NCBI trace archive database ( http://www . ncbi . nlm . nih . gov/Traces ) under NCBI accession numbers Astatotilapia burtoni , 46489038; Cavia porcellus , TI| ( 812701167 + 816150362 ) + TI| ( 1589066801 + 816143923 + 1588856408 + 812549142 + 1586592910 + 760990457 + 1593835765 + 1587132918 ) + TI|1583699030 + 91731898; Dipodomys ordii , TI| ( 1542302641 + 1541211393 + 1578971935 + 1562912636 + 1552706462 ) + TI| ( 1569549916 + 1566271599 + 1585076393 + 1585061768 + 1542035913 + 1556201828 + 1541200922 + 1586154871 + 1585648556 + 1535177412 ) + TI|1587085697 + TI|1569544450; Fugu rubripes , 22419072; Gallus gallus , 51039807 + 25936497 + 25365707 +TI|227294367; Gasterosteus aculeatus , 86297297; Monodelphis domestica , 84819837; Myotis lucifugus , TI| ( 976759098 + 964301557 + 981846460 ) + 105860175; Ornithorhynchus anatinus , 91473547; Oryzias latipes , 16991872; Pimephales promelas , 72422020 + 73556779 + 73634852 + 73512480 + 73721434 + 72761900 + 73439047 + 73721433; Sorex araneus , 80402909 + 80402905 + TI| ( 838222606 + 892417228 ) + TI| ( 873967520 + 875830371 + 894062723 + 845045749 + 848969792 ) ; Taeniopygia guttata , TI| ( 1401310278 + 1277343563 + 1253220430 + 1397767862 + 1249786453 + 1290200497 + 1398826774 + 1397636686 + 1423694077 ) + TI| ( 1242613131 + 1422732028 + 1404775456 + 127731875 1 ) + TI|1241607171; and Tupaia belangeri , 108226212 + 107812812 . | Mammals are a fairly young class of animals , first appearing about 70 million years ago . Such recent common descent does not allow the evolutionary process to create much diversity within the class , and indeed , the physiology among different mammals is remarkably similar . This similarity enables the use of various small mammals , especially rats and mice , as model systems for the study of biological phenomenon and disease . Experiments unfeasible or unethical to perform on humans are conducted on these model animals , with the postulation that insights gained from them are applicable to the human system . In this article , we present an exception to this rule . We bring evidence that ZIPK , a gene with important roles in programmed cell death , has undergone accelerated evolution in the rat and mouse , thus diverging considerably from a well-conserved consensus in all vertebrates , from fish to man . We also show that this sequence divergence caused changes in the protein's properties , including its localization within the cell , and the proteins with which it interacts . Still , the basic biologic function of ZIPK is conserved in both systems , and we propose an adaptive mechanism that compensates for the sequence divergence in rodents . | [
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] | 2007 | ZIPK: A Unique Case of Murine-Specific Divergence of a Conserved Vertebrate Gene |
The mitochondrial free radical theory of aging ( mFRTA ) implicates Reactive Oxygen Species ( ROS ) -induced mutations of mitochondrial DNA ( mtDNA ) as a major cause of aging . However , fifty years after its inception , several of its premises are intensely debated . Much of this uncertainty is due to the large range of values in the reported experimental data , for example on oxidative damage and mutational burden in mtDNA . This is in part due to limitations with available measurement technologies . Here we show that sample preparations in some assays necessitating high dilution of DNA ( single molecule level ) may introduce significant statistical variability . Adding to this complexity is the intrinsically stochastic nature of cellular processes , which manifests in cells from the same tissue harboring varying mutation load . In conjunction , these random elements make the determination of the underlying mutation dynamics extremely challenging . Our in silico stochastic study reveals the effect of coupling the experimental variability and the intrinsic stochasticity of aging process in some of the reported experimental data . We also show that the stochastic nature of a de novo point mutation generated during embryonic development is a major contributor of different mutation burdens in the individuals of mouse population . Analysis of simulation results leads to several new insights on the relevance of mutation stochasticity in the context of dividing tissues and the plausibility of ROS ”vicious cycle” hypothesis .
Mitochondria are the main energy producing organelles present in eukaryotic cells . Mitochondria are the only organelles aside from the nucleus which harbor their own genetic material . Mitochondrial DNA ( mtDNA ) encodes a small number of polypeptides needed for the electron transfer chain ( ETC ) . The ETC is responsible for cellular energy synthesis via oxidative phosphorylation ( OXPHOS ) , during which some of the electrons leak from the ETC and are captured by oxygen to form reactive oxygen species ( ROS ) [1] . Most ROS are detoxified by cellular antioxidant defenses , but some escape and cause damage to cellular biomolecules like lipids , protein and nucleic acids [2] . Mitochondrial DNA may be particularly susceptible to such oxidative insult due to its proximity to the ROS production sites of the ETC [3] . Oxidative damage of mtDNA and its implications on cellular aging form the basis of the mitochondrial Free Radical Theory of Aging ( mFRTA ) [3] . One of the predictions of the mFRTA is the possibility of ROS ‘vicious cycle’ ( Figure 1 ) , referring to the hypothesized positive feedback mechanism in which mtDNA mutations cause an increase in the ROS production resulting in a higher de novo mutation rate [3] . Major challenges and questions with respect to the mFRTA have been summarized in some of the recent reviews [4] , [5] . Despite uncertainties related to the assumptions of mFRTA , the importance of mitochondria as both the source and target of ROS in aging is supported by some transgenic mouse studies . For example , a 15% increase in the maximum and median lifespan is observed in knock-in mice expressing human catalase , an enzyme that decomposes H2O2 into water and oxygen , in mitochondria ( MCAT ) , but not in the nucleus or the peroxisome [6] . Furthermore , MCAT mice heart tissue accumulates less than 50% of the mtDNA point mutations of age-matched wild-type mice [7] . Also , studies of homozygous knock-in mice with an error-prone polymerase-γ ( POLG mutator mice ) show that a dramatic increase in mtDNA mutation burden , most importantly deletions [7] , is associated with shortened lifespan and some phenotypes that may resemble accelerated human-like aging [8] , [9] . Although there is reasonable evidence for an age-dependent increase in mtDNA mutations , the dynamics by which these mutations accumulate is still largely unclear . Inferring dynamics and more importantly , the mechanism by which mtDNA mutations accumulate critically depends on accurate quantification of oxidative and mutational burden , which poses significant experimental challenges [4] . Many of these challenges stem from the limitations associated with experimental protocols in measuring oxidative damages and mutational frequency [10] , [11] , which typically exist at extremely low magnitude . Consequently , published reports show conflicting results regarding the levels of oxidative damages and mutation dynamics of mtDNA during aging [12]–[14] . A highly sensitive method based on the random mutation capture ( RMC ) assay has recently been developed for the quantification of mtDNA mutation frequency [15] . This method is based on restriction enzyme digestion and amplification of mtDNA molecules carrying mutations at the corresponding recognition site [12] . Application to wild-type mice has revealed mtDNA mutation burdens that were two orders of magnitude lower than previously determined using PCR-cloning and sequencing protocols [8] , [9] . This indicates that PCR artifacts may have been a major contributor of errors in the past reports . Furthermore , quantification of age-dependent accumulation of point mutation burdens using the RMC assay in wild-type mice suggested an exponential increase , apparently supporting the existence of a ‘vicious cycle’ in the mutation accumulation [3] , [13] . However , the low levels of burden suggest that point mutations may not be a major determinant of lifespan [12] and it is difficult to see how a positive feedback mechanism could set in at such a miniscule level of point mutation burden . One requirement for addressing these uncertainties is a better understanding of the inherent stochasticity of cellular processes [16] . The accumulation of mtDNA mutations likely involves complex stochastic factors , such as the inherent random nature of mutations and related cellular processes in the context of aging . For instance , enzyme staining for ETC deficient tissue of substantia nigra neurons in aged subjects and Parkinson patients revealed a high degree of mosaicity of COX respiratory deficient cells [17] . This mosaicity has also been seen in skeletal muscle cells associated with sarcopenia in aged subjects [18] . Also , studies on Caenorhabditis elegans indicate that individual worms and their cells harbor a wide spectrum of mtDNA deletion loads [19] . Here we aim to address these challenges using a systems approach by way of constructing mathematical models that encompass the most relevant biological processes and also features related to experimental protocols to comprehend the origin and consequence of mutation variability that arises in individuals of a mouse population . Additionally , we seek to better understand the influence of intrinsic stochasticity of the mutation process on the variability observed in the experimental data . Such understanding may reveal possible causes of disagreements amongst published reports and further facilitate optimization of experimental design . In this study , we have constructed an in silico stochastic mouse model using the Chemical Master Equation ( CME ) [20] . Here , the accumulation of point mutations in mtDNA is simulated to arise as a consequence of what we believe to be a minimal process required for the maintenance of mtDNA integrity .
The in silico mouse model accounts for the accumulation of mtDNA point mutations across two stages of mouse life: development and postnatal ( Figure 2 ) . In this study , the number of wild-type mtDNA ( W ) and mutant mtDNA ( M ) molecules are tracked for each cell in whole mouse heart ( ∼2 . 5×107 cells ) and liver tissues ( ∼4×108 cells ) [21] . Each mutant mtDNA molecule is assumed to contain only a single mutation in the TaqI recognition site ( TCGA ) , following the RMC experimental design [12] . The probability of finding two or more mutations at the same site is negligible [15] . The model simulates two mtDNA-related maintenance processes: mitochondrial turnover , comprising of relaxed replication and degradation of mitochondria , and de novo point mutation , based on a minimal conservative assumptions . First , the mtDNA population of each cell is assumed to exist as a well-mixed pool due to fast fusion and fission dynamics of mitochondria [22] . Second , due to the low overall mutation burden , point mutation burden is assumed to remain below the level of functional significance ( i . e . no nuclear retrograde signaling [23] , [24] ) . While the latter assumption is conservative , our simulations indicate that the incorporation of functional effects of mutations into the model , by assuming that mutant mtDNA are non-functional and cells respond to a decrease in the number of wild-type ( WT ) mtDNA by increasing replication , does not result in any significant changes to the mutation burden ( see Text S1 and Figure S3 ) . A Langevin formulation using relaxed replication assumption demonstrated that stochastic drift can lead to a clonal expansion of mtDNA mutations in human [25] . Following experimental evidence , each mitochondrion is assumed to carry 10 mtDNA molecules and these mtDNA are assumed to undergo replication and degradation due to mitochondrial turnover [26] . In a turnover event ( Figure 2B ) , ten molecules of mtDNA are chosen randomly from a well mixed population of mtDNA in a cell and are either degraded or replicated according to the CME described below . The selection of ten wild-type and mutant mtDNA molecules from the population can be described as a hypergeometric random sampling following the probability distribution: [27] ( 1 ) where x represents the number of wild type mtDNA chosen for replication or degradation . De novo point mutation can occur during replication of mtDNA due to mis-pairing associated with ROS-induced mutagenic lesions such as 8-hydroxy-2-deoxyguanosine ( 8OHdG ) [2] or as random errors arising due to finite polymerase-γ ( POLG ) fidelity [28] . Consequently , each replication of a wild-type mtDNA has a finite probability , given by the mutation rate constant ( km ) , to produce a mutant . Here , the number of de novo mutant mtDNA is randomly chosen from a binomial distribution: [27] ( 2 ) where y denotes the number of de novo mutations resulting from replication of x wild-type mtDNA . Based on these probabilities , the in silico mouse model is formulated as a CME in which each mtDNA-related process: replication without mutation , replication with de novo mutations and degradation , is described as a jump Markov process with the following state transitions: ( 3 ) The first two transitions reflect replication without mutation , the third represents de novo mutation , and the last pair represents degradation . A general formulation of CME is given by: [20] ( 4 ) where is the state vector denoting the total number of each molecular species present in the system and the function denotes the probability of a system to assume the state configuration at time t , given the initial condition at time . The function aj denotes the propensity function , while is the state change associated with a single j-th event . The propensity function gives the probability of the j-th event to occur in the time interval [t , t+dt ) . As analytical solution to CME is usually not available even for moderately sized systems [29] , Monte Carlo algorithms have been employed to solve the CME numerically [30] , e . g . using Gillespie's SSA ( Stochastic Simulation Algorithm ) [31] . In SSA , two random variables ( , j ) determine the temporal evolution of the states in a system , where is the time for the next event to occur and j is the type of event that will take place . The probability density functions of and j are evaluated based on the propensity function of the events involved [29] . A modified version of the SSA is used in this work for simulating in silico mice tissues based on the following CME: ( 5 ) The density function denotes the probability of a cell in a given tissue to contain W and M number of wild-type and mutant mtDNA , respectively , given the initial conditions of the states ( not explicitly stated here for brevity , refer Equation 4 ) . The parameters kR , kd and km represent the specific probability rate constants for mtDNA replication , degradation and de-novo point mutations , respectively . The terms in the curly braces describe the hypergeometric sampling of mtDNA from the population . Particularly , the first two terms of the CME above represent mtDNA replication without mutation , the second pair of terms corresponds to replication with de-novo mutation , and the last two terms represent the degradation of mtDNA . The CME can be solved numerically using a Monte Carlo approach following the SSA . The implementation of the modified SSA is described below: To predict mtDNA mutation burden in a single organ or tissue , millions of such simulations are performed to capture the mtDNA dynamics of all cells in a tissue . Simulations were performed using an IBM high performance computing cluster with 112 Intel 1 . 6 GHz processors . The simulation code ( Text S2 ) was compiled using GNU FORTRAN compiler G77 ( v4 . 1 . 1 ) and run on a CentOS Linux platform . On average , a single simulation of a heart tissue ( ∼25 million cells ) from development to 3 years of age required approximately 3 hours . The embryonic cell divisions begin after fertilization of an oocyte . Mouse oocytes harbor a large number of mitochondria ( ∼1 . 5×105 mtDNA ) [32] , which allow the zygote to multiply initially without the need to replicate mtDNA [33] , [34] . Mouse embryos with dysfunctional mitochondrial replication are able to proceed through the implantation and gastrulation stages , but eventually die , presumably when the mtDNA synthesis becomes necessary to maintain ATP level [35] , [36] . Furthermore , the total mtDNA number in mouse embryo does not increase until the late stage of blastocyst , which is roughly the 7th to 8th cell divisions in development ( i . e . , 4 . 7 to 5 . 5 days post coitum ( d . p . c ) ) [33] , [34] , [37] . During these stages , mtDNA are segregated among the dividing progenitor cells ( Figure 2A ) . Consequently , each progenitor cell of the developing embryo has only few copies of mtDNA at the early egg-cylinder stage [33] , [34] . In order to account for the mtDNA segregation without replication during the initial cell divisions , the developmental simulations start from the end of the 8th stage ( 5 d . p . c ) with an initial wild-type mtDNA count of roughly 580 molecules per cell ( W = 580 , M = 0 ) [33] . Mitochondrial DNA replication is tied to the cellular division to maintain a steady state number of total mtDNA after each division [38] . Mouse development lasts until 20 d . p . c [39] with a doubling time of roughly 15 . 5 hours [40] . The mtDNA replication rate is estimated assuming that mtDNA doubles its population every 15 hours while still undergoing degradation . Here , a cell division occurs when the total number of mtDNA count reaches twice the steady state homeostatic count ( Table 1 ) . The segregation of wild-type and mutant mtDNA between the daughter cells is assumed to occur at random , without any selective advantage according to a hypergeometric distribution: [27] ( 6 ) where denotes the number of wild-type mtDNA in one of the daughter cells after segregation and n is the total number of mtDNA in a single daughter cell ( i . e . , n = ( W+M ) /2 ) . During development , polymerase-γ , the care taker of the mtDNA replication fidelity , is the main contributor for point mutations in mtDNA , with negligible oxidative activity and damage [28] , [41] . After birth , many tissues like heart do not undergo further cellular division . However , mtDNA in these tissues are still continuously turned over independent of cellular division , a process called “relaxed replication” [26] . The functional significance of relaxed replication in postmitotic tissues like heart and brain is to maintain a healthy population of mtDNA to satisfy the cellular energy requirements [26] , [42] . The postmitotic simulations continue from cells produced at the last stage of development ( Figure 2A ) , in which each cell maintains mitochondrial biogenesis to balance degradation . The mutation rate in this stage is a summation of contributions from oxidative damage and POLG-related error . The in silico mouse model is also used to simulate POLG mutator heterozygous ( POLG+/mut ) and homozygous ( POLGmut/mut ) mice by changing the rate of de novo point mutations . Mutator mice carry a proofreading-deficient allele of POLG which has 200 times the error rate of the wild-type enzyme [28] , [43] . Thus , in the simulations of POLG mutator mice , the model formulation remains the same in all aspects with the exception that the POLG error rate corresponding to the mutant allele is assumed to be 200 times higher ( Table S2 and S3 ) . In heterozygous POLG mutator mouse , the replication of mtDNA molecules is carried out by either wild-type or mutant allele with equal probability . Model parameters are compiled from published data for mice and we have ensured that they are consistent with the current literature and the state of the art techniques . The basic model parameters are listed in Table 1 , while more detailed information of the rest of parameters used in all mouse models is given in Tables S1 , S2 and S3 .
In silico wild-type ( WT ) mouse population of 1100 individuals was generated starting from embryo up to three years of age , the approximate life span of mice ( Figure 2 ) . The overall point mutation frequency in 2 . 5×107 cells of whole heart tissues was recorded at the end of each cell division during development and every fortnight during the postnatal stage . Figure 3 illustrates the percentile and distribution function of the mutation frequency arising from two important sources of variability related to the quantification of mtDNA point mutations . The probability density functions indicate the distribution of mutation frequencies in the population as a function of time . Each contour on the percentile plot represents the maximum mutation frequency that a given percentage of the population harbors ( e . g . 99% of mice harbor mutation frequencies up to and including the level indicated by the 99th percentile curve ( Figure 3A , 3C ) ) . The main source of randomness is the intrinsic stochastic nature of the aging process , which arises from the mtDNA maintenance processes ( Figure 2B ) . Note that the intrinsic stochasticity prevailing in the in silico population has a long tailed non-Gaussian density function ( Figure 3B , 3D ) , indicating that a small fraction of the population harbors a significantly higher mutation burden . Cell-to-cell variability of mtDNA mutation load is also observed as a result of the random processes ( Figure S1 ) . Figure 4 illustrates the evolution of mtDNA states ( W and M ) in two cardiomyocytes during the postnatal stage of a mouse . Random fluctuation of wild-type mtDNA can be seen in the population with regular bursts and decay of mutant mtDNA . Furthermore , it is interesting to observe that despite the significant cell-to-cell variability of mutation load being large ( Figure S1 ) , the average accumulation of mtDNA mutation in tissue remains linear after birth ( Figure 3A ) . Also , the variance due to the natural aging process remains roughly constant during the mouse life span , indicating that the variability among individuals is inherited at birth . However , for comparison with data derived from RMC assay , a second source of variability has to be considered due to the intrinsic statistical properties of the assay protocol . This is because , the determination of point mutation burden by the RMC assay involves drawing a random sample of mtDNA copies ( ∼840 , 000 ) from tissue homogenates [12] . This sampling procedure introduces additional variability that becomes significant due to the low overall count of total mtDNA mutations . This statistical feature of the RMC protocol can be described as sampling from a hypergeometric distribution [27] . ( 8 ) where m denotes the number of mutant mtDNA molecules present in a random sample of mtDNA of size n ( n = 840 , 000 mtDNA molecules in this case ) . Thus , for low mutation frequencies and sample sizes , the RMC protocol introduces significant additional variability in the data . For example , in heart tissue homogenate containing 1010 molecules of mtDNA with a mutation frequency of 10−6/bp ( a total of 4×105 mutant mtDNA ) , samples of 840 , 000 mtDNA drawn from the same homogenate will have a mean value of 3 . 36 mutants with a standard deviation of 1 . 83 molecules or 54 . 6% coefficient of variation from the RMC sampling alone . The compounded effect of the two sources of variabilities ( intrinsic aging related and RMC assay ) can be expressed by , ( 9 ) where denotes the underlying probability distribution of mtDNA mutations predicted by the mouse model simulations and is the overall probability function of measured mtDNA mutations . Importantly , the additional variability associated with the sampling of mtDNA in the RMC protocol causes the mutation frequency variance to increase as a function of the average mutation frequency ( Figure 3C ) , a result expected from a hypergeometric distribution . This is particularly relevant here because of the age-dependent increase in mean mutation burden and the fact that the distribution describing the mutation process is long-tailed ( Figure 3A , 3B ) . When this underlying mutation dynamics is sampled using the RMC assay , the resulting data will exhibit an age-dependent increase in variance . Due to low number of replicates ( typically n<5 per age group ) , it is highly probable to obtain data that are best approximated by a non-linear , possibly exponential model ( Figure 3C ) . However , this apparent exponential increase is not actually a feature of the underlying mutation dynamics , which may be in fact , linear ( Figure 3A ) . This has important implications for the interpretation of the available experimental data . In accordance with the interpretation reached in the original experimental work [12] , the variance in the in silico data as well as the experimental data for low n-values appears to suggest an exponential dynamics supporting the ‘vicious cycle’ theory [3] , [13] . However , on careful consideration ( Figure 3 ) , the apparent exponential increase of the mutational burden is actually an artifact of: ( a ) intrinsic stochasticity of aging process ( Figure 3A , 3B ) , coupled with ( b ) the random sampling variability introduced by the statistical properties of the RMC protocol ( Figure 3C , 3D ) . Experimentally , it is not possible to carry out 100 s or 1000 s of repeats and it is therefore difficult to distinguish between a truly exponential and a linear increase of age dependent point mutation burden . In summary , while the RMC assay is able to quantify extremely low levels of mutations , its discrete nature ( in terms of mutant mtDNA count ) introduces significant challenges in data analysis and interpretation . The interpretation of the data can be flawed if the statistical properties of the RMC assay are not considered . Taking both processes into consideration , the fundamental mtDNA maintenance processes modeled by our in silico mice are in excellent agreement with the published data ( Figure 3C ) . However , the last data point of mutation burden from an old mouse ( 980 days ) deviated from in silico mouse population ( p-value = 0 . 064 ) , suggesting that other processes not predicted by our model may be involved during the last months of life ( e . g . , inflammation or other disorders that can accelerate oxidative DNA damage [58] ) . Transgenic mouse studies on POLG mutator mouse have recently shed some light on the role of mtDNA in aging [8] , [9] , [12] . However with these mutator models , many open questions still remain about the role of mtDNA mutation in aging . For example , only the homozygous mutator mice exhibited accelerated human-aging-like phenotypes ( e . g . , anemia , alopecia , kyphosis ) and shortened lifespan , while the heterozygous mice have no obvious aging phenotypes , despite significantly elevated mutation burdens [9] . After successfully validating the in silico mouse model against wild-type mouse data , we further simulated 1 , 100 hetero- and homozygous POLG mouse heart and liver tissues by elevating the baseline POLG error rate to 200 times that of wild-type [28] , [43] . We found an excellent agreement of our in silico results with the reported mutation burdens from two different laboratories [9] , [12] ( Figure 5 and Figure S4 ) . As with the wild-type mice , the point mutation increase was linear with age ( Figure S4 ) . Again , mitochondrial turnover and de novo point mutations alone were sufficient to explain the accumulation of mtDNA point mutations . These results indicate that even at the elevated levels of point mutations ROS-mediated acceleration of point mutations with age is not necessary to explain the data presented in [8] , [9] . This is consistent with additional experimental observation suggesting that the levels of ROS in POLG mice are not significantly elevated in the mutator mice [8] . Crucially , no modification of mtDNA maintenance rate constants was required to reproduce the experimental data [8] , [9] . That is , one does not have to resort to assumptions such as the existence of a vicious cycle or other possible feedback mechanism [59] , [60] . The stage in an organism's life from which the accumulation of mtDNA mutations starts to become functionally significant ( if at all ) is unclear . During development , mtDNA replication is tied to the cellular division , and as a consequence , initial mutations may arise as soon as mtDNA replication begins . In fact , the total number of replications during development is comparable to that during the entire adult life . In mice , the heart tissue develops in about 20 days [39] . Considering the degradation rate described in Table 1 and the mouse heart to contain ∼2 . 5×107 cardiomyocytes [21] , [61] arising from 22 cell divisions ( 6 progenitor cells ) , the total number of mtDNA replications needed to maintain homeostatic value of mtDNA ( Table 1 ) [21] per cell should exceed 9×1010 times during the development . On the other hand , based on the degradation rate of mtDNA in postnatal stages ( Table 1 ) [45] , the number of mtDNA replications events over the three years lifespan of mice is about 1 . 3×1011 . Thus depending on their source ( ROS , POLG errors ) , the development period may carry comparable contributions in de novo mtDNA mutations as does the entire adult life . POLG errors have been postulated to be the main cause of de novo point mutations in murine embryonic fibroblast [28] , [41] . Therefore , the POLG baseline error rate was used as mutation rate during development . Generally , our in silico mouse data highlight that mutations occurring in the early embryonic cells have a strong impact on the mutation load at birth ( Figure 6 ) and that the variability among individuals is set during development ( Figure S2 and Figure S4 ) . Since the mtDNA replication is several folds higher than the degradation during development , de-novo point mutations generated during the early cell divisions can accumulate very quickly , resulting in a high mutation load at birth in some individuals ( Figure 6 ) . These results highlight that the stochastic drift of mutation dynamics during the early developmental cell divisions may be a deciding factor of the organism's mutation trajectory , and also a major contributor of the mutation variability in a population , including isogenetic individuals [19] . The variability generated during development is conserved throughout the organism's life ( see Figure 2A and Figure S4A , S4B ) . In postmitotic tissues , like heart , mtDNA are continuously turned over independent of cellular division [26] . Although the turnover rate of mtDNA is lower during the postnatal stage than during development , the higher mutation rate due to oxidative damage ( Table 1 ) can lead to 2–3 fold increase in the mutation load between birth and old age in wild-type mice ( see Figure 2A and Figure 7 ) . The in silico POLG mice however differ from the wild-type because in these mice , the POLG error is the dominant contributor of de novo point mutations , both during embryonic and postmitotic stages ( Supplementary Table S2 and S3 ) . Due to faster mtDNA replication ( tied to cell division ) , most of the mutations in mutator mice therefore arise during development ( Figure 6B , 6C and Figure 7 ) . This is consistent with the experimental data which shows clearly that mutator mice are born with significantly elevated mutation burden [9] , [62] . However , during their adult life , the accumulation is relatively lower compared to their development , due to the slow turnover of mtDNA [45] . Furthermore , the above observation leads to an interesting insight , largely unappreciated in the original work [8] , [9] , [63] , regarding the point mutation load in tissues that remain mitotic ( epidermal , stem cells , spleen ) . Since in POLG mice the point mutation burden of mtDNA is dominated by POLG errors , mutation accumulation in fast dividing cells is expected to be several fold faster than in postmitotic tissues such as heart . This is consistent with the experimental observation in POLG mutator mice , where some of the most prominent pathologies associated with the fast dividing tissues manifest in the form of alopecia , spleen enlargement and anemia . However it should be appreciated that such mechanistic hypothesis is speculative , because we have not included the simulation of mtDNA turnover of any fast dividing tissues in the present work . Treatment of cell division and selection pressure for mitochondrial turnover might be a promising area of investigation for the future work . By thinking carefully about the different sources of stochasticity in each process from early development all the way to experimental sampling , we have identified the RMC assay procedure as a major contributor to the overall uncertainty . In contrast to the original interpretation of the data , our analysis reveals that the existence of an exponential dynamics in point mutations cannot be inferred with certainty , and thus no contradiction between the observed point mutation dynamics and the apparent absence of evidence for elevated oxidative stress exists . A detailed , quantitative understanding of the relevant sources of noise also allows optimization of experimental designs , thereby opening avenues for maximizing information return and minimizing cost , time and animal use . The fact that the reproduction of the POLG mouse data requires no modifications to the wild type model , other than the obvious decrease of the polymerase fidelity , suggests that elevation of the point mutation burden does not trigger fundamentally new processes . In particular , neither mutant replicative advantage nor the elevation of the ROS dynamics resulting from the increase of the point mutation burden is required to explain the POLG data . This is consistent with our current view on the mFRTA [4] , showing little evidence for the existence of vicious cycle mechanism . Two further observations related to the POLG mice that have originally been seen as somewhat surprising , can also be explained . The first is the observation that dividing tissues seem to be more severely affected in POLG mice than postmitotic tissues [9] , [63] . The second is the fact that most mtDNA mutations in the POLG mice are already present at birth with comparatively little further accumulation during adult life , when compared to its development [9] , [62] . Quantitative analysis shows both of these observations to be consequences of the low turnover of mtDNA in postmitotic tissues of adult mice . Finally , our in silico analysis reveals the importance of early development in determining the trajectory of mtDNA mutation burden . This is in sharp contrast to the common assumption that health and diseases are determined predominantly by the genome interacting with the environment . Here , we have demonstrated that in silico modeling can contribute significantly to analysis and understanding of experimental data as well as potentially help to design more effective methodology . We believe that this approach of “Computer Aided Thought” can contribute towards a fundamentally improved understanding of intrinsically challenging biological problems such as aging . | Aging is characterized by a systemic decline of an organism's capacity in responding to internal and external stresses , leading to increased mortality . The mitochondrial Free Radical Theory of Aging ( mFRTA ) attributes this decline to the accumulation of damages , in the form of mitochondrial DNA ( mtDNA ) mutations , caused by free radical byproducts of metabolism . However , there is still a great deal of uncertainty with this theory due to the difficulties in quantifying mtDNA mutation burden . In this modeling study , we have shown that a random drift in mtDNA point mutation during life , in combination with the experimental sampling can explain the variability seen in some of the reported experimental data . Particularly , we found that while the average mutation increases in a linear fashion , the variability in the mutation load data increases over time , and thus a low number of data replicates can often lead to a deceptive inference of the mutation burden dynamics . The model also predicted a significant contribution from the embryonic developmental phase to the accumulation of mtDNA mutation burden . Furthermore , the model revealed that the replication rate of mtDNA is a major determinant of new mutations during development and in fast-dividing tissues . | [
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] | [
"mathematics/statistics",
"developmental",
"biology/aging",
"biophysics/theory",
"and",
"simulation"
] | 2009 | Stochastic Drift in Mitochondrial DNA Point Mutations: A Novel Perspective Ex Silico |
Leprosy is a disease of the skin and peripheral nervous system caused by the obligate intracellular bacterium Mycobacterium leprae . The clinical presentations of leprosy are spectral , with the severity of disease determined by the balance between the cellular and humoral immune response of the host . The exact mechanisms that facilitate disease susceptibility , onset and progression to certain clinical phenotypes are presently unclear . Various studies have examined lipid metabolism in leprosy , but there has been limited work using whole metabolite profiles to distinguish the clinical forms of leprosy . In this study we adopted a metabolomics approach using high mass accuracy ultrahigh pressure liquid chromatography mass spectrometry ( UPLC-MS ) to investigate the circulatory biomarkers in newly diagnosed untreated leprosy patients . Sera from patients having bacterial indices ( BI ) below 1 or above 4 were selected , subjected to UPLC-MS , and then analyzed for biomarkers which distinguish the polar presentations of leprosy . We found significant increases in the abundance of certain polyunsaturated fatty acids ( PUFAs ) and phospholipids in the high-BI patients , when contrasted with the levels in the low-BI patients . In particular , the median values of arachidonic acid ( 2-fold increase ) , eicosapentaenoic acid ( 2 . 6-fold increase ) and docosahexaenoic acid ( 1 . 6-fold increase ) were found to be greater in the high-BI patients . Eicosapentaenoic acid and docosahexaenoic acid are known to exert anti-inflammatory properties , while arachidonic acid has been reported to have both pro- and anti-inflammatory activities . The observed increase in the levels of several lipids in high-BI patients may provide novel clues regarding the biological pathways involved in the immunomodulation of leprosy . Furthermore , these results may lead to the discovery of biomarkers that can be used to investigate susceptibility to infection , facilitate early diagnosis and monitor the progression of disease .
Leprosy is caused by Mycobacterium leprae , an obligate intracellular pathogen , which infects the skin and peripheral nerves . M . leprae invasion of Schwann cells leads to nerve damage , disability and deformity [1]–[2] . However , not all infected patients have the same clinical course . The course of the disease may be punctuated by spontaneous and/or recurring episodes of immunological imbalances that need immediate medical attention and immune suppressive treatment . There are no routine laboratory tests for monitoring clinical improvement , response to treatment or evolution of drug resistance , aside from monitoring the reduction of bacillary levels in skin smears . Even after several decades of multidrug therapy programs to reduce leprosy transmission , incidence is not declining at expected rates in some of the most endemic countries [3] . This persistent incidence in some regions is commonly believed to be due to undetected and undiagnosed subclinical cases [4] . Leprosy is conventionally described as a spectral disease using the well-established Ridley-Jopling scheme [5] . At one pole is the limiting form termed tuberculoid ( TT ) leprosy . In tuberculoid leprosy the bacterial load is low due to effective cell mediated immunity ( CMI ) , and the infection is usually localized to either a skin patch or nerve trunk . At the site of infection , the immune response is dominated by Th1 associated pro-inflammatory cytokines ( IFN-γ and IL-2 ) and granuloma formation . The opposite profile form is lepromatous ( LL ) leprosy , which shows a high bacterial load , poor CMI , and is characterized by Th2 associated anti-inflammatory cytokines ( IL-4 and IL-10 ) and antibody production . Between the poles are borderline tuberculoid ( BT ) , borderline ( BB ) and borderline lepromatous ( BL ) . There are multiple known and undefined factors that modulate the range of susceptibility to clinical outcomes , including metabolic and immune functions . The individual contributions of host and bacterium are not yet fully defined , although many human genetic loci and bacterial components have been implicated in the process of infection and perturbation of the immune response [6]–[10] . Host factors include single nucleotide polymorphisms ( SNPs ) in genomic regions associated with a variety of products such as TNF-α , IL-10 , vitamin D receptor ( VDR ) , parkin ( PARK2 ) and parkin co-regulated gene protein ( PACRG ) [11]–[12] . Nutritional and metabolic factors may also play a role in regulating the host immune response [13]–[14] . The pathogen M . leprae is unique in that its genome has undergone massive decay , particularly in catabolic pathways and energy generating processes , and is therefore thought to be highly dependent on the host system for growth [15] . Novel overlapping mechanisms have been described by which M . leprae modulates its environment for nutrition and immune evasion [16] . In this context , where leprosy is a product of complex host-pathogen relationships , there is a need for modern approaches to uncover underlying and/or novel biochemical signals that may be informative regarding those pathways that contribute to disease . Though leprosy is a disease of the skin and peripheral nerves , there may be biomarkers in the blood ( circulatory biomarkers ) which may indicate systemic factors . Several investigators have studied plasma and serum lipid composition in patients using traditional analytical methods such as thin layer chromatography ( TLC ) or gas chromatography ( GC ) [17]–[18] . With the advent of sensitive ultrahigh pressure liquid chromatography ( UPLC ) quadrupole time-of-flight ( Q-TOF ) mass spectrometry ( UPLC-MS ) , separation and detection of large numbers of small molecules ( metabolites ) in complex starting mixtures has become feasible . UPLC-MS provides rapid screening with accurate mass measurement , is of high resolution , has low-detection limits , permits ion fragmentation , and does not require a large amount of sample or a combination of different techniques to identify metabolites . This technology has made it possible to rapidly identify biomarkers which distinguish normal states from various disease states using biological specimens such as urine , plasma and serum [19]–[21] . We sought to use this metabolomics approach to contrast the serum metabolome of patients with high and low bacterial indices ( BI ) using UPLC-MS . In the high-BI serum we discovered greater levels of the polyunsaturated fatty acids ( PUFAs ) eicosapentaenoic acid ( EPA ) , arachidonic acid ( AA ) and docosahexaenoic acid ( DHA ) . We discuss these findings in the context of emerging models regarding the interactions between lipid metabolism and immunity . The methods and findings have implications for discovery of novel biomarkers for diagnosis , identification of therapeutic targets and elucidation of pathogenic mechanisms .
Ethical approval for the use of these stored samples for research was obtained from the Institutional Review Board of Colorado State University and the Cebu Skin Clinic . Patient samples were collected following written informed consent . Sera were selected from a sample bank generated for ongoing research into the molecular epidemiology of leprosy involving newly diagnosed leprosy patients at the Cebu Skin Clinic in Cebu , Philippines [22] . Samples were taken prior to the initiation of multidrug therapy . Blood samples were drawn into a plain ( no additive ) evacuated tube ( BD Vacutainer Serum ) and centrifuged at 1 , 500 rpm for 10 min at 4°C in a refrigerated centrifuge . The serum samples were aliquoted into multiple vials at 1 ml per vial and frozen at −20°C until shipment . The sera were shipped on dry ice to Colorado State University and stored at −20°C until subsequent use in the laboratory . Serum samples were selected from two groups of patients , those with BI<1 ( n = 10 ) and those with BI>4 ( n = 13 ) ( Table 1 ) . Sample selection was randomized and without consideration of clinical or demographic data aside from BI . Though factors such as age , gender , clinical presentation and medical history were not considered in the study design or analysis , such data were collected during patient intake and are presented in Table 1 . BI was measured at the Cebu Skin Clinic by microscopic examination of acid-fast stained slit-skin smears taken from six sites , including representative active lesions . BI was ranked on a log 10 scale from 0 to 6 [23] . A volume of 50 µl from each serum sample was prepared for analysis by UPLC-MS . Sera proteins were precipitated by the addition of 3 volumes ( 150 µl ) of cold 100% methanol . The samples were vortexed , placed at −20°C for two hours , then centrifuged for 10 minutes at 15 , 000 rpm to pellet the protein precipitate . The supernatants were carefully transferred to new Eppendorf tubes . From each supernatant , 1 µl was analyzed by UPLC-MS in both negative and positive modes with duplicate injections . To confirm the observations and mass spectrometry methods a subset of the selected sera were reanalyzed using new aliquots and triplicate injections . Five each from the low-BI ( L32 , L40 , L76 , L79 , L85 ) and high-BI ( L1 , L11 , L19 , L58 , L88 ) groups were pooled and retested; two low-BI ( L40 , L85 ) and two high-BI ( L15 , L88 ) samples were retested individually . The serum methanol extracts were separated on a Waters ACQUITY UPLC coupled with a Q-TOF under the control of MassLynx v4 . 1 [Waters . Millford , MA , USA] . Sample injections ( 1 µl ) were performed on a Waters ACQUITY UPLC system . Separation was performed using a Waters ACQUITY UPLC C8 column ( 1 . 7 µM , 1 . 0×100 mm ) , using a gradient from solvent A ( 89% water , 5% acetonitrile , 5% isopropanol , 1% 500 mM ammonium acetate ) to solvent B ( 49 . 5% acetonitrile , 49 . 5% isopropanol , 1% 500 mM ammonium formate ) . Injections were made in 100% A , which was held for 0 . 1 min . A 0 . 9 min linear gradient to 40% B was applied , followed by a 10 min gradient to 100% B which was held for 3 min , then returned to starting conditions over 0 . 1 min , and then allowed to re-equilibrate for 5 . 9 min . Flow rate was constant at140 µl/min for the duration of the run . The column was held at 50°C; samples were held at 5°C . Mass data were collected between 50 and 1200 m/z at a rate of two scans per second . The voltage and temperature parameters were as follows: 3000 V capillary , 30 V sample cone , 2 . 0 V extraction cone , 350°C desolvation temperature and 130°C source temperature . Calibration was performed prior to sample analysis via infusion of sodium formate solution , with mass accuracy within 5 ppm . For MS/MS , the parent ion was selected by quadrupole and fragmented via collision-induced dissociation ( CID ) with argon at collision energy of 20 eV for fatty acids and 30 eV for phospholipids . UPLC-MS data were aligned , extracted and viewed using MarkerLynx v4 . 1 [Waters . Millford , MA , USA] . Chromatographic peaks were detected between 0 and 28 min with a retention time ( RT ) window of 0 . 1 min . Apex track peak detection parameters were used , with automatic detection of peak width and baseline noise . The spectrometric features were assigned by m/z and RT , while the relative intensity was based on the area of all features . Initial screening for compounds with significant differences in abundance between the low-BI and high-BI groups was performed by orthogonal projection onto latent structures ( OPLS ) with the software SIMCA-P+ v12 . 0 [Umetrics . Umeå , Västerbotten , Sweden] , using a Po ( corr ) cut-off of 0 . 5 . Further statistical analysis was performed using several R packages within R 2 . 12 . 1 [24] . Principal component analysis ( PCA ) was performed using the package stats::prcomp with both scaling and centering of the variables . Generation of receiver operating characteristic ( ROC ) curves for selected compounds was performed using the R package pROC v1 . 4 . 3 [25]; a 95% confidence interval was generated for sensitivity using 2 , 000 bootstrap replicates . For each selected compound , histogram bins were calculated using the Freedman-Diaconis rule and kernel density estimates were calculated using Gaussian smoothing . In order to compare the first ( individual serum ) and second ( pooled sera ) runs , data for each compound was standardized by subtracting the mean and dividing the result by the standard deviation ( standard score ) to account for shifts in instrument sensitivity over time; the data were then compared for statistically significant differences using the Mann-Whitney test . Tentative compound class assignments ( free fatty acid , glycerolipid , phospholipid , etc . ) were made by querying the exact mass against the LIPID MAPS database [26] and the online web server MassTRIX: Mass Translator into Pathways [27] . The compounds that showed significant differences in intensity between the low-BI and high-BI groups , based on exact m/z and 0 . 05 min RT differences , were further fragmented by MS/MS in both positive and negative ion modes . Metabolite identities were manually examined for signature ions and verified by comparing the fragment spectra to those in LIPID MAPS and published data [28] . MassTRIX was also used to explore related pathways that may be associated with selected metabolites . After preliminary assignments were made for some of the selected compounds , pure standards were obtained and analyzed by the previously described chromatographic methods . Pooled sera were rerun along side the standards . Eicosapentaenoic acid ( EPA , 20∶5 ) , arachidonic acid ( AA , 20∶4 ) and docosahexaenoic acid ( DHA , 22∶6 ) were purchased from Sigma-Aldrich [Saint Louis , MO , USA]; 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine ( PAPC ) was purchased from Avanti Polar Lipids [Alabaster , Alabama , USA] . All standards were dissolved in 75% methanol prior to UPLC-MS analysis .
The UPLC-MS data were first characterized globally . Across both the low-BI ( n = 10 ) and high-BI ( n = 13 ) samples a total of 1668 features in the positive mode and 2489 features in negative mode were observed ( Supplement S1 ) . A PCA , generated from abundance data of all positive and negative mode m/z-RT pairs ( features ) , showed low-BI and high-BI patient sera clustering away from each other ( Figure 1 ) . The separation of patient groups indicates that there are m/z-RT pairs that are quantitatively distinct in the two groups . Close clustering of injection duplicates is also seen , which is the expected behavior . To identify the compounds that distinguished the low-BI from high-BI samples , the dataset was first pared down to features which exhibited the greatest difference in abundance between the two sample groups with OPLS ( not shown ) . This yielded 48 features with masses up to approximately 1 kDa: 19 from the positive mode and 29 from the negative mode . From these 48 features , 18 compounds were tentatively identified using the online databases LIPID MAPS and MassTRIX ( Table 2 ) . The data indicate an increase in the level of several lipids in the high-BI sera compared to those in the low-BI sera . All of the 18 identified compounds were more abundant in the high-BI samples except for those with m/z 518 . 3245 , 558 . 3196 and 566 . 3192 , which were more abundant in the low-BI samples . A confirmatory second UPLC-MS analysis was performed using a pooled subset of samples . Though m/z and RT values shifted slightly , due to expected operational variability , we found the same 18 compounds again showed quantitative distinctions between low-BI and high-BI groups . We also queried the complete list of m/z values against the MassTRIX annotation system , which performs a search for potential compound identities and associated pathways curated in KEGG: Kyoto Encyclopedia of Genes and Genomes [29] . MassTRIX assigned a total of 74 negative mode features to 143 compounds in 40 pathways , and 79 positive mode features to 89 compounds in 51 pathways . The predominant hits were pathways involved in AA metabolism ( 29 compounds ) and synthesis of unsaturated fatty acids ( 13 compounds ) ; not all compounds were unique . The 18 significant compounds that we tentatively identified were further characterized by MS/MS . From these 18 compounds , the compounds of the most interest to us - given their role in modulation of the inflammatory response - were the n-6 PUFA AA , the n-3 PUFAs EPA and DHA , and the compound with structural similarity to PAPC . Commercial standards of EPA , AA , DHA and PAPC were obtained and submitted to mass spectrometry in parallel with the serum samples . Not all of the 18 ions fragmented , but compound confirmation was achieved via MS/MS for 9 compounds by referencing the ion fragmentation pattern against published spectra and/or available standards ( Table 2 ) [30]–[31] . The chemical structures and fragmentation patterns of compounds listed in Table 2 are shown in Figures 2 , 3 , 4 and Supplements S2 , S3 . In each of Figures 2 , 3 , 4 and Supplement S2 the chemical structure of the compound is shown in Panel A , the fragmentation pattern of the commercial standard is shown in Panel B , and the fragmentation pattern of the corresponding compound in the pooled serum sample is shown in Panel C . Supplement S3 shows the fragmentation pattern in the pooled serum sample for the remaining compounds putatively identified by MS/MS . The spectra illustrations have been adjusted from the original MassLynx output files for clarity; the font of the axes and labels has been changed , the line width of the spectra has been increased , and extraneous text and borders have been removed . The parent and daughter ions of EPA , AA and DHA appear as expected in both the standards and patient samples . However , we could not conclusively identify the feature we observed in the serum samples with m/z of 798 . The molecular weight of PAPC is 781 , with an observed value of 766 under negative ionization due to loss of the methyl group from choline ( Supplement S2B ) . The feature with m/z of 798 produced several fragments consistent with the PAPC standard; specifically , ions with m/z 255 , 303 and 480 ( Supplement S2C ) , which correspond to palmitic acid , arachidonic acid and lysophosphocholine ( 16∶0/0∶0 ) , respectively . The RT for the PAPC standard was 8 . 5 min , while the RT for the feature with m/z 798 was 4 . 3 min . It is possible , but not confirmed , that the observed feature with an m/z of 798 is an oxidized form of PAPC [32]; additional investigation was performed , but did not yield satisfactory results . The diagnostic accuracy of each feature , as measured by the extent to which each feature accurately distinguishes low-BI from high-BI samples , was determined using receiver operating characteristic ( ROC ) curves [33] . The ROC curve for the feature compares the distribution of abundance between low-BI and high-BI samples . The more the curve is pulled toward the upper-left corner [higher sensitivity , higher specificity and higher area under the curve ( AUC ) ] the less overlap between the distributions in each group , and thus the more effective the feature is at discriminating low-BI from high-BI sera . The AUC for each significant feature , along with a 95% confidence interval indicated as a ± value , is shown in Table 2 . ROC curves for features of interest are shown in Panel D of Figures 2 , 3 , 4 and Supplement S2 . The distribution of abundance values of the individual samples ( first experiment ) can be seen in Panel E of Figures 2 , 3 , 4 and Supplement S2 , as both a histogram and kernel density estimate . In addition to comparing the abundance values across patient groups , we also compared the first ( individual sample ) and second ( pooled sample ) experiments for statistically significant differences . Although the same qualitative differences were seen across patient groups in both experiments , the two experiments showed marked differences in mean abundance values , which we believe is due to variation in instrument sensitivity between runs . The distribution of abundance values between experiments was compared using the Mann-Whitney test following standardization to account for variation between runs . Between the two experiments , the distributions for EPA ( low-BI p = 0 . 15 , high-BI p = 0 . 07; Figure 2E ) , AA ( low-BI p = 0 . 65 , high-BI p = 0 . 34; Figure 3E ) , DHA ( low-BI p = 0 . 83 , high-BI p = 0 . 53; Figure 4E ) and the PAPC-like compound ( low-BI p = 0 . 62 , high-BI p = 0 . 38; Supplement S2E ) were not found to differ significantly at 95% confidence . We note that there is a sampling bias with regards to both age and sex in the selected patients ( Table 1 ) . Specifically , the median age is 37 in the low-BI and 28 in the high-BI , and the ratio of male to female is 6∶4 in the low-BI and 12∶1 in the high-BI . The parent study from which these patients were randomly selected ( n = 310 ) shows a concordant bias . In the parent study , the median age of a low-BI patient ( n = 63 ) is 37 and the median age of a high-BI patient ( n = 123 ) is 29 . The odds of selecting a male patient from the low-BI group are 37∶26 ( 1 . 42 ) , and the odds of selecting a male patient from the high-BI group are 111∶12 ( 9 . 25 ) . Though patient age and sex were not considered in the study design or the analysis as a whole , we investigated the diagnostic accuracy of the features listed in Table 2 when considering only the male patients . Though some shifts were seen in the AUC and median abundance in the two BI groups , the same set of features still showed statistically significant differences between the low-BI and high-BI groups ( data not shown ) .
The goal of our research was to explore the applicability of non-targeted metabolomics to the study of leprosy . Most research aimed at understanding variations in clinical presentations have been studies of gene expression profiles and immune response mechanisms using a variety of assays on whole blood , serum , plasma , peripheral blood mononuclear cells or skin biopsies [17] , [34]–[36] . To date , metabolite profiles in leprosy have only been explored using target-based assays of blood samples [37]–[38] . These techniques are limited in terms of sample throughput , the ability to resolve individual metabolites in complex specimens , the sensitivity of feature detection , and the accuracy of compound identification . By using a metabolomics approach based on mass spectrometry , we were able to discover several metabolites in serum with differential levels in low-BI and high-BI patient groups . In particular , we found that in the high-BI group there was a statistically significant increase in abundance of the n-3 PUFAs EPA and DHA , and the n-6 PUFA AA . The identification of differential levels of PUFAs in high-BI patients is intriguing , as lipid metabolism and lipid mediators have been implicated in many disease models , both infectious and non-infectious . It has been widely thought that n-3 PUFAs ( DHA and EPA ) are beneficial to human health , because of their association with mitigation of the inflammatory response in conditions such as autoimmune disorders , heart disease , arthritis and graft-versus-host disease [13] , [39]–[40] . Conversely , the n-6 PUFAs ( including AA ) are generally considered deleterious in chronic diseases because they exert pro-inflammatory effects [39] . Ironically , it is this pro-inflammatory property that would provide the necessary anti-microbial activity to combat bacterial infections . However , new research indicates that this is only a generalized model for the properties of the n-6 versus n-3 PUFAs . Consensus is absent on their strict pro- versus anti-inflammatory phenotypes , due to their interconnected metabolic pathways and the production of downstream products ( eicosanoids ) . Recent studies have pointed to the benefits of AA and derived eicosanoids , finding that they had both pro- and anti-inflammatory roles . Deckelbaum and Calder found that prostaglandin E2 ( PGE2 ) may inhibit the production of pro-inflammatory cytokines ( TNF-α and IL-1 ) from monocytes and macrophages [41] . They also found that PGE2 inhibits production of leukotrienes ( LTs ) , through control of 5-lipoxygenese , and induces production of lipoxins , through 15-lipooxygenase; leading to anti-inflammatory and pro-resolution activities by the action of lipoxins [41] . Based on these results , AA n-6 PUFAs may control the inflammatory response by regulating both the pro- and anti-inflammatory cytokine networks . It has also been suggested that both n-3 and n-6 PUFAs play an anti-inflammatory role due to inactivation of reactive oxygen species by the unsaturated double bond . Furthermore , PUFAs may bind to peroxisome proliferator activated receptors , thus interfering with signaling molecules such as NF-κB , and repressing transcription of a variety of genes [42] . Zeyda et al found that both n-3 and n-6 PUFAs inhibit cytokine production ( TNF-α and IL-12 ) , T cell stimulation and dendritic cell differentiation at the gene level . PUFA treated dendritic cells were shown to be associated with altered membrane lipid composition , specifically an increase in unsaturated lipids , which implicates AA and EPA as anti-inflammatory mediators [14] . In the mycobacterial disease models , enrichment of n-3 PUFAs enhances susceptibility to Mycobacterium tuberculosis infection in vitro ( infected macrophages ) [43]–[44] . Anes et al showed that the pro-inflammatory effect of AA promotes increased bacteria killing inside macrophages by stimulating phagosomal actin assembly . In contrast , the same authors also showed that EPA and DHA promote bacterial survival and growth inside macrophages by lowering the levels of pro-inflammatory cytokines ( IFN-γ , TNF-α , IL-1 and IL-6 ) , weakening the oxidative response and hindering phagosome maturation [45] . In leprosy , Cruz et al postulated that the fatty acids and phospholipids which accumulate in lepromatous lesions are of host origin . They found a pronounced upregulation of host genes involved in lipid metabolism , such as phospholipase A2 ( PLA2 ) and phospholipase C ( PLC ) , for which functional counterparts are not encoded in the M . leprae genome [16] . An increase in phospholipase activity may contribute to the increased serum levels of AA we observed in our high-BI patients; PLA2 catalyzes the hydrolysis of phospholipids to release arachidonate in a single-step reaction , and PLC generates diacylglycerols , from which AA can be subsequently released by diacylglycerol- and monoacylglycerol-lipases . Several other metabolites which modulate immunity either for or against mycobacterial survival have been described in the literature . These include cholesterol ( HDL or LDL derived ) , triglycerides and vitamin D [17]–[18] , [46]–[47] . We did not observe differential levels of these metabolites in our patient groups , though this does not imply variations were not present . The nature of the starting sample and the fractionation conditions may affect the metabolite pools; this study was based only on a simple one-step methanol extraction followed by C8 reverse phase UPLC-MS . Lysophosphatidylcholines ( Lyso PCs ) have been shown to have a potential role in immunomodulation , particularly pro-inflammatory functions [48] . We tentatively assigned some significant features as Lyso PCs - three of which were more abundant in the low-BI sera ( Table 2 ) . However , these identifications are preliminary and unconfirmed at his stage . In this study we focused solely on identifying compounds with differential levels in patient sera based on the quantitative criterion of BI , rather than the more qualitative Ridley-Jopling and paucibacillary/multibacillary systems which are not always consistent across clinics [49] . Sex was not a controlled factor in our study . Though our results do not specifically indicate whether the serum signatures we found can be explained by sex differences , there is an inherent sex bias in leprosy [3] , as also evidenced in our sample sets . Further investigations which delve into whether there are specific metabolites that differentiate leprosy patients based upon other classification criteria , such clinical presentation , sex , age or genetics , would provide valuable insight into the intrinsic biological factors that contribute to bacterial growth in leprosy . The metabolomic fingerprint we identified - higher levels of AA , DHA and EPA in the sera of high-BI leprosy patients - is consistent with diminished host innate immunity to infection [16]; reaffirming the role of altered host lipid metabolism in infection and immunity . The increased serum levels of n-3 and n-6 PUFAs we identified in high-BI patients may promote M . leprae survival through inhibition of both the innate and adaptive immune response of the host . These novel preliminary findings lend themselves to pathway specific genome expression analysis and further characterization of the AA derived lipid mediators . For instance , the leukotriene A4 hydrolase ( lta4h ) gene has been implicated as a susceptibility locus in leprosy and tuberculosis [50] . It is thought that the fine balance of lipoxin B4 and leukotriene B4 controls the propensity to infection or immunity . Of the 9 compounds we identified by MS/MS , those with m/z 317 and 335 are candidate AA derivatives suitable for further analysis . A longitudinal study that employs a metabolomics approach may shed light on the origins and dynamics of the lipid profile . By collecting and analyzing sera before multidrug therapy , during treatment , at the onset of reaction states and after the patient is released , we may discover fluctuations in the lipid profile over the course of the infection; enabling the ultimate aim of improving diagnostics , treatment options and creating a deeper understanding of the pathogenesis of leprosy . | Leprosy is an infectious disease caused by the obligate intracellular bacterium Mycobacterium leprae . M . leprae infects the skin and nerves , leading to disfigurement and nerve damage , with the severity of the disease varying widely . We believe there are multiple factors ( genetic , bacterial , nutritional and environmental ) , which may explain the differences in clinical manifestations of the disease . We studied the metabolites in the serum of infected patients to search for specific molecules that may contribute to variations in the severity of disease seen in leprosy . We found that there were variations in levels of certain lipids in the patients with different bacterial loads . In particular , we found that three polyunsaturated fatty acids ( PUFAs ) involved in the inhibition of inflammation were more abundant in the serum of patients with higher bacterial loads . However , we do not know whether these PUFAs originated from the host or the bacteria . The variations in the metabolite profile that we observed provide a foundation for future research into the explanations of how leprosy causes disease . | [
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... | 2011 | Serum Metabolomics Reveals Higher Levels of Polyunsaturated Fatty Acids in Lepromatous Leprosy: Potential Markers for Susceptibility and Pathogenesis |
In oviparous animals , the egg yolk is synthesized by the mother in a major metabolic challenge , where the different yolk components are secreted to the hemolymph and delivered to the oocytes mostly by endocytosis . The yolk macromolecules are then stored in a wide range of endocytic-originated vesicles which are collectively referred to as yolk organelles and occupy most of the mature oocytes cytoplasm . After fertilization , the contents of these organelles are degraded in a regulated manner to supply the embryo cells with fundamental molecules for de novo synthesis . Yolk accumulation and its regulated degradation are therefore crucial for successful development , however , most of the molecular mechanisms involved in the biogenesis , sorting and degradation of targeted yolk organelles are still poorly understood . ATG6 is part of two PI3P-kinase complexes that can regulate the recruitment of the endocytic or the autophagy machineries . Here , we investigate the role of RpATG6 in the endocytosis of the yolk macromolecules and in the biogenesis of the yolk organelles in the insect vector Rhodnius prolixus . We found that vitellogenic females express high levels of RpATG6 in the ovaries , when compared to the levels detected in the midgut and fat body . RNAi silencing of RpATG6 resulted in yolk proteins accumulated in the vitellogenic hemolymph , as a consequence of poor uptake by the oocytes . Accordingly , the silenced oocytes are unviable , white ( contrasting to the control pink oocytes ) , smaller ( 62% of the control oocyte volume ) and accumulate only 40% of the yolk proteins , 80% of the TAG and 50% of the polymer polyphosphate quantified in control oocytes . The cortex of silenced oocytes present atypical smaller vesicles indicating that the yolk organelles were not properly formed and/or sorted , which was supported by the lack of endocytic vesicles near the plasma membrane of silenced oocytes as seen by TEM . Altogether , we found that RpATG6 is central for the mechanisms of yolk accumulation , emerging as an important target for further investigations on oogenesis and , therefore , reproduction of this vector .
Oocytes are remarkable cells in the matter of accumulating macronutrients . Oviparous animals have evolved to produce germline cells that not only enter meiosis to generate a gamete , but also differentiate into a giant cell designed to support embryo growth . Once fertilized , the egg is able to fulfill embryo development away from the maternal body . To accomplish that , an oocyte typically grows up to 5 , 000x its original size by accumulating macromolecules in a highly specialized cytoplasm with maternal mRNAs , proteins , ribosomes , mitochondria and so on . That stock of macronutrients is referred to as yolk . The yolk components are entirely made by the mother in a major metabolic challenge where proteins , lipids and carbohydrates are synthesized and delivered to the oocytes mostly by endocytosis . The yolk macromolecules are stored in a wide range of endocytic-originated vesicles which are collectively referred to as yolk organelles and occupy more than 99% of the mature oocytes cytoplasm [1–3] . After fertilization , the programmed degradation of the contents stored in the yolk organelles is crucial to support the anabolic metabolism of the growing embryo and , therefore , successful development . ATG6/beclin1 is a multifunctional protein that appears to participate in several functions including endocytosis [4 , 5] , autophagy [6–10] , aging [11] , immunity [12] , cell death [13–16] and others . ATG6 contains a BCL-2 homology domain ( BH3 ) , a coiled-coil domain ( CCD ) and an evolutionarily conserved domain ( ECD ) , which together enable multiple interactions with different target proteins . Through ECD , two distinct PI3K complexes can be formed , the complex I binds ATG14 and initiates the autophagosome formation by accumulation of PI3P , and the complex III that binds VPS15 , a vacuolar protein sorting protein , which also results in the accumulation of PI3P but now recruiting the endocytic machinery [17] . Genetic loss-of-function studies have revealed that ATG6 orthologues can participate in endocytosis and autophagy in different organisms including A . thaliana , D . melanogaster , C . elegans and mice [5 , 18–20] , but the molecular mechanisms that regulate the formation of the two distinct PI3K complexes are still poorly understood . Because the oocyte is an endocytosis-specialized cell , we investigate here the role of ATG6 in the uptake of the yolk components using the oocytes of the insect Rhodnius prolixus as a model . R . prolixus is an insect vector of Chagas disease , which is one of the neglected tropical diseases that are important in Latin America and Africa ( WHO , 2017 ) . Currently 8 million people are estimated to be infected by Chagas disease , and vector control is still the most useful method to prevent this illness [21] . As embryo development relies entirely on the yolk nutritional reserves , mobilization of the yolk components is crucial for embryo viability , and we hope that molecular investigations on its mechanisms may reveal targets for interference in vector’s reproduction and eventually contribute to the elaboration of new strategies for population control . Rhodnius genome was recently published and transcriptome databases are available at Vector Base [22] . Parental gene silencing can be consistently performed via RNAi [23 , 24] and , since it is a strictly hematophagous insect , oogenesis is highly synchronized with the blood meal , which is convenient if one aims to monitor oocyte development . We silenced the one isoform of ATG6 found in the genome of R . prolixus ( RpATG6 ) and found that it results in the accumulation of yolk proteins in the insect hemolymph during vitellogenesis as a consequence of poor uptake by the oocytes . The mature oocytes from silenced females are not able to properly form the endocytic-originated yolk organelles in the cortex , and , as a result , they are smaller and embryos are unviable . The amounts of the main yolk proteins , vitellin and RHBP , and the non-protein yolk components , TAG and polyphosphate ( PolyP ) , are severely compromised . The importance of ATG6 in the yolk organelles biogenesis and proper oocyte formation is discussed .
The sequence of RpATG6 was obtained from the R . prolixus genome and transcriptome databases ( Rpro C3 . 2 ) from Vector Base ( www . vectorbase . org ) . The sequence of RpATG6 was identified by a local BLAST database made on Bio Edit software . The Drosophila ATG6 orthologue ( AAF56227 . 1 ) was used as the first template for identification . Values of similarity and identity were predicted using SIA software and conserved domains were predicted using PFAM . Insects were maintained at a 28 ± 2°C controlled temperature and relative humidity of 70–80% . The experimental animals used were adult females directly fed in live-rabbit blood at 21 days intervals . All animal care and experimental protocols were approved by guidelines of the institutional care and use committee ( Committee for Evaluation of Animal Use for Research from the Federal University of Rio de Janeiro , CEUA-UFRJ #01200 . 001568/2013-87 , order number 155/13 ) , under the regulation of the national council of animal experimentation control ( CONCEA ) . Technicians dedicated to the animal facility conducted all aspects related to animal care under strict guidelines to ensure careful and consistent animal handling . All organs were dissected 6 days after blood meal and homogenized in Trizol reagent ( Invitrogen ) for total RNA extraction . Reverse transcription reaction was carried out using the High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) , using 1 μg of total RNA after RNase-free DNase I ( Invitrogen ) treatment , all according to the manufacture’s protocol . Specific primers for RpAtg6 sequence were designed to amplify a 226 bp fragment in a PCR using the following cycling parameters: 10 min at 95°C , followed by 35 cycles of 15 s at 95°C , 45 s at 52°C and 30 s at 72°C and a final extension of 15 min at 72°C . Amplifications were observed in 2% agarose gels . Quantitative PCR ( qPCR ) was performed in a StepOne Real-Time PCR System ( Applied Biosystems ) using SYBR Green PCR Master Mix ( Applied Biosystems ) under the following conditions: 10 min at 95°C , followed by 40 cycles of 15 s at 95°C and 45 s at 60°C . qPCR amplification was performed using the specific primers 5’CCGCTCCTGTAGACTGGTC3’ ( F ) , 3’GCCACCATCGCAGCATCAAATTTTG5’ ( R ) . Rp18S was used as endogenous control , with the following primers: 5’TCGGCCAACAAAAGTACACA3’ ( F ) , 3’TGTCGGTGTAACTGGCATGT5’ ( R ) . The relative expression and ΔCt values were calculated from obtained Ct ( cycle threshold ) values [25] . dsRNA was synthesized by MEGAScript RNAi Kit ( Ambion Inc ) using primers for RpATG6 specific gene amplification with the T7 promoter sequence 5’GCAGTTTGGGAGAACATACTCTCG3’ ( F ) ; 3’CTGTACACTTCTGTGTTCATCTTCC5’ ( R ) designed to target a region of 595bp . Unfed adult females were injected with 1 μg dsRNA [26] and fed 2 days later . Knockdown efficiency was confirmed by PCR and qPCR at different days after blood meal . The bacterial MalE gene was used as a control dsRNA [27] . Adult females injected with dsRNA were fed and transferred to individual vials . The mortality rates and the number of eggs laid were recorded daily and weekly , respectively . Additional measurements are described below . Silenced and control females were fed with blood enriched with 32Pi [28] using a special feeder [29] . On the tenth day after a blood meal , the chorionated oocytes were collected and subjected to lipid extraction [30] . The lipid extracts were chromatographed on oxalate-impregnated , thin-layer silica plates ( G-60; 0–25 mm thickness ) using chloroform–acetone–methanol–acetic acid–water ( 40: 15:13:12:8 , by vol . ) developing solvent [31] . The radioactivity was analyzed in a laser scanner Cyclone® Plus Storage Phosphor System ( Perkin Elmer ) . To visualize the lipids , the plates were immersed for 10 s in a charring solution consisting of 3% CuSO4 and 8% H3PO4 ( v/v ) and heated to 110°C for 10 min . The charred TLC plates were then subjected to densitometric analysis using Adobe Photoshop CC 2015 software . The phospholipids spots were identified by comparison with standards run in parallel . Control and silenced eggs were collected at 24h of embryogenesis . Pools of 4 eggs were homogenized in 100 μl of phosphate buffered saline ( PBS ) containing a cocktail of protease inhibitors ( Aprotinin 0 . 3 μM , leupeptin 1 μg/μl , pepstatin 1 μg/μl , PMSF 100 μM and EDTA 1 mM ) . 30 μg of total protein were loaded in each lane of a 13% SDS-PAGE . Gels were stained with silver nitrate [32] . Hemolymphs of silenced and control females were collected at 7 days after blood feeding from a pool of 3 insects . Once collected , the hemolymph was diluted 2x in PBS containing protease inhibitors ( aprotinin 0 . 3 μM , leupeptin 1 μg/μl , pepstatin 1 μg/μl , PMSF 100 μM and EDTA 1 mM ) , and approximately 8 mg of phenylthiourea . 2 μl of hemolymph was loaded in each lane of a 13% SDS-PAGE , corresponding to 30–40 μg of protein for dsMal samples and 60–70 μg for dsRpATG6 samples . The total amount of protein in the silenced and control eggs and hemolymphs was measured by the Lowry ( Folin ) method , using as standard control 1–5 μg of BSA [33] in a E-MAX PLUS microplate reader ( Molecular devices ) using SoftMax Pro 5 . 0 as software . Glycogen content was determined in 100 μl of the egg homogenate described above using the Glucox 500 kit ( Doles reagents ) following the manufacture’s protocol . The samples were incubated for 4h at 40 C° in the presence of 20 μl ( 1U ) of amyloglicosidase . Controls were prepared under the same conditions , but in the absence of enzyme . Eggs homogenates were prepared , as described above , and were used to determine the total amount of neutral lipids such as TAG . Lipid extraction was performed for 2 h in a tube containing 5 mL chloroform-methanol-water solution ( 2:1:0 . 8 , v/v ) , with intermittent shaking . The mixture was centrifuged at 1500 g for 30 min at 4°C , the supernatant was collected and the pellet subjected to a second lipid extraction ( 1 h ) . To the pooled supernatants , 5 mL water and 5 mL chloroform were added , the mixture was shaken and , after centrifugation , the organic phase was removed and dried under nitrogen . Extracted lipids corresponding to 1 egg were analyzed by one-dimensional thin-layer chromatography ( TLC ) for neutral lipids [34] . To visualize the lipids , the plates were immersed for 10 s in a charring solution consisting of 3% CuSO4 and 8% H3PO4 ( v/v ) and heated to 110°C for 10 min . The charred TLC plates were then subjected to densitometric analysis using Adobe Photoshop CC 2015 software . The endogenous TAG spots were identified by comparison with TAG standards run in parallel . Quantification of PolyP in egg homogenates was done using a general protocol based in the fluorimetric analysis of the characteristic emission of the DAPI-PolyP complex , as described before by [35] . Excitation wavelength was 420 nm and the emission wavelength was of 535 nm . PolyP65 was used for a standard curve ranging from 90 to 540 ng of PolyP . Measurements were made using a 2030 Victor X5 fluorometer ( Perkin Elmer ) . Opercula of the eggs were carefully detached using a sharp razor blade under the stereomicroscope . Vitellogenic oocytes and opercula-free eggs were fixed by immersion in 4% freshly prepared formaldehyde in 0 . 1 M cacodylate buffer , pH 7 . 2 for 12 h at room temperature . Samples were washed 3 times for 10 minutes in the same buffer and embedded in increasing concentrations ( 25% , 50% , 75% and 100% ) of OCT compound medium ( Tissue-TEK ) plus 20% glucose as a cryoprotectant , for 12 h for each of the concentrations . Once infiltrated in pure OCT , 14 μm transversal sections of the oocytes and eggs were obtained in a cryostat . The slides were mounted in glycerol 50% followed by observation in a Zeiss Observer . Z1 equipped a Zeiss Axio Cam MrM operated in a differential interferencial contrast ( DIC ) mode . Vitellogenic oocytes were fixed for 4–6 hours in 2 . 5% glutaraldehyde ( Grade I ) and 4% freshly prepared formaldehyde in 0 . 1 M cacodylate buffer , pH 7 . 2 . Samples were washed in cacodylate buffer , dehydrated in an ethanol series and embedded in a Polybed 812 resin . Thin sections were stained with lead citrate and uranyl acetate followed by observation in a Zeiss EM 900 transmission electron microscope , operating at 80 kV . 10 μl of hemolymph of silenced and control females were collected 7 days after the blood meal and diluted in 250 μl TBS ( Tris-HCl 10 Mm , NaCl 150 mM , pH 7 . 4 ) containing approximately 8 mg of phenylthiourea . The samples were centrifuged 2x at 13 . 800 x g for 5 minutes at 4°C and 200 μl of the supernatants were loaded in the column for analysis as described below . The secreted protein samples from whole mount fat bodies were obtained as previously described by [36] . Briefly , dissected fat bodies from 3 days after the blood meal ( day where the secretion of yolk proteins is at its maximum ) were incubated in culture medium for 2 h at 28°C ( 5 organs for 250 μl of medium ) . The secreted proteins in the culture medium ( 200 μl ) were loaded in the column and analyzed as described below . For the oocytes , 5 chorionated oocytes were homogenized in 250 μl of TBS . After centrifugation , 200 μl of the supernatant were loaded in the column . All samples ( hemolymphs , fat bodies and chorionated oocytes ) were loaded on a Superdex 75 10/300GL ( GE HealthCare ) column and analyzed by HPLC using a LC-10AT device ( Shimadzu ) . For RHBP , the area of its 412 nm-absorbing Soret peak was used for relative quantifications . For vitellogenin , its characteristic peak , as compared to the profile of a standard purified vitellogenin detected at 280 nm , was used for the quantifications . FITC stock solution was prepared in DMSO at 0 . 5 μg/ ml . 4 μl of this solution ( 2 μg ) were injected in the hemocoel of silenced and control vitellogenic females 8 days after the blood meal . The ovaries were dissected 18 h after the injection and observed under the fluorescence stereomicroscope ( LEICA M165 FC ) . The relative expression and ΔCt values were calculated from obtained Ct ( cycle threshold ) values . The Ct mean values obtained from the experiments were compared using One-way ANOVA followed by Tukey's multiple comparison test . Differences were considered significant at P< 0 . 05 . The relative expression values ( 2-ΔCt ) were used only for graph construction . Other results were analyzed by Student’s t-Test for the comparison of two different conditions and One-way ANOVA followed by Tukey's test for the comparison among more than two conditions . Differences were considered significant at p<0 . 05 . All statistical analyses were performed using the Prism 5 . 0 software ( GraphPad Software ) .
We first identified the sequence of RpATG6 from the R . prolixus digestive tract transcriptome database [37] ( GAHY01001036 . 1 ) . We found one isoform of the gene ATG6 in the Rhodnius genome assembly ( Rpro C3 ) , with a total of 9 exons in the scaffolds KQ0344093 ( exons 1–7 ) and ACPB3032647 ( exons 8–9 ) . RpATG6 predicted protein has 80%/ 70% similarity/ identity with the human ATG6 ( Beclin1 ) ( S1 Fig ) , where all the expected Atg6 conserved domains ( BCL2 , NES , CCD and ECD , Pfam: PF04111 ) were detected ( Fig 1A ) . Quantitative PCR showed that the ovary of R . prolixus expresses an average of 2x and 5x more RpATG6 than the midgut and fat body , the other two major organs of the adult insect ( Fig 1B ) . Throughout oogenesis , RpATG6 mRNA was detected in the tropharium ( structure where the germ cell cluster and the nurse cells are located ) and in all stages of the developing oocytes ( pre-vitellogenic , vitellogenic and chorionated ) ( Fig 1C ) . In this experiment , we dissected the ovarioles and separated the follicles ( oocyte plus follicular epithelium ) to extract the total RNA . To investigate if the detected mRNA was from the oocyte or from the follicular epithelium , we dissected the follicular epithelium alone , and performed qPCRs in this tissue separately . Interestingly , we found that it accumulates only 10% of the RpATG6 mRNA detected in the whole follicle ( Vit ) , showing that most of the mRNA detected is present in the oocyte ( Fig 1C ) . To investigate the role of RpATG6 during oogenesis , we synthesized a specific double-stranded RNA designed to specifically target the sequence of RpATG6 and injected it directly to the females haemocoel two days before the blood meal . To check the efficiency of the RpATG6 knockout we dissected the ovary and fat body ( the two organs directly related to the yolk synthesis and accumulation ) at different days after dsRNA injection . RpATG6 was 98% silenced in the ovary at day 7 after the blood meal ( Fig 2B ) , and its expression levels were partially recovered at day 14 ( Fig 2A ) . On the other hand , at day 7 , RpATG6 was only moderately silenced in the fat body ( Fig 2A ) . Accordingly , PI3P , the product of PI3K complexes ( where RpATG6 is functional ) , was detected by TLC and shown to be 28% decreased in silenced ovaries ( Fig 2C ) . We did not detect any difference in the main physiological characteristics of R . prolixus such as protein blood digestion and longevity ( median survival of 33 days for control females and 29 days for silenced females , p>0 . 05 ) , when compared to control animals ( Fig 2D and 2E , respectively ) . Because day 7 after blood meal was the day where gene silencing was more effective , we dissected the silenced females at this time point and found that their oocytes were smaller and white , contrasting to the standard pink oocytes from control animals ( Fig 3 ) . The absence of the red pigment indicates the lack of one of the main yolk proteins , named RHBP ( Rhodnius heme-binding protein ) . RHBP is known for being the one red molecule from the oocytes that go through the classic vitellogenesis route: they are synthesized by the fat body , secreted to the hemolymph and endocytosed by the oocytes during oogenesis [38–40] . We asked if the absence of RHBP in the oocytes was the result of poor uptake of macromolecules from the hemolymph or a defect in the synthesis of RHBP by the fat body . Thus , we looked at the total protein profile in the hemolymph of silenced vitellogenic females 7 days after the blood meal . High concentrations of RHBP were already apparent in the freshly extracted hemolymph from silenced females , as they were pink ( Fig 4A ) , and had roughly twice the protein concentration of the hemolymph from control animals ( Fig 4B ) . SDS-PAGE of the hemolymph shows that silenced females accumulated the major secreted yolk proteins—RHBP ( arrow ) and vitellogenin ( two subunits , arrowheads ) ( Fig 4C ) . Quantifications of RHBP and vitellogenin/ vitellin in the hemolymph showed that silenced females accumulate approximately twice the levels of both proteins , when compared to control females ( Fig 4D , upper panel ) . Accordingly , chorionated oocytes store only half the amount of the two major yolk proteins ( Fig 4D , middle panel ) . To test the levels of the secreted yolk proteins by the fat bodies , the dissected organs were incubated for 2 h in culture medium ( using a protocol previously described by [36] ) and the secreted protein profiles were analyzed by HPLC . We found that the levels of both secreted vitellogenin and RHBP by the fat bodies were similar between silenced and control females ( Fig 4D , lower panel ) . To investigate the role of RpATG6 on oviposition and hatching , we quantified the eggs laid individually by females and maintained the collected eggs under ideal conditions to ensure embryo development . Surprisingly , our results show a 28% increase in the number of eggs laid by silenced females ( Fig 5A and 5B ) . Despite the higher oviposition , silenced females transiently laid eggs presenting two main types of abnormal morphology: an average of 18 oocytes ( 31% of the total number of eggs ) where white and 8 ( 13% of the total number of eggs ) where collapsed ( Fig 5D and 5E ) . In total , we observed a decrease in 50% in embryo viability ( Fig 5C ) , but all eggs that presented one of the phenotypes described above ( white or collapsed ) are unviable ( Fig 5D , see numbers on the top of the bars for the hatching % for each of the phenotypes ) . RpATG6 RNAi silencing triggers a gradual phenotype that can be easily seen in the varying color of the laid eggs ( conveniently , because RHBP is red ) . Starting at day 7 after blood meal , silenced females started to lay eggs with a lighter pink color . In the next 5–6 days , the eggs were laid with a progressive lighter pink color; up to the day when they were laid completely white ( Fig 6A ) . White eggs accumulated only 35% of the total protein found in the control eggs ( Fig 6B ) , and the accumulation of the main yolk proteins , vitellin ( arrowheads ) and RHBP ( arrow ) , was severely compromised ( Fig 6C ) . We also predicted the volume of the silenced eggs and found that the white eggs are 38% smaller , with an average volume of 1 . 03 ± 0 . 06 mm3 versus 1 . 66 ± 0 . 09 mm3 of the eggs laid by control females . In addition to the yolk proteins , carbohydrates and lipids are also an important part of the yolk storage . To test the effect of RpATG6 in the buildup of non-protein yolk macromolecules we measured the content of TAG and glycogen in the white eggs . We found no differences in the glycogen accumulated in white silenced eggs ( Fig 7B ) and a 20% reduction in the TAG reserves in white eggs ( Fig 7A ) . Another major yolk component is the polymer PolyP , which is known to provide indispensable phosphate supply to the high metabolic demands of the embryo . Silenced eggs showed a 50% decrease in its PolyP levels when compared to control eggs ( Fig 7C ) . Because the lack of RpATG6 is apparently affecting the endocytosis of the yolk macromolecules by the oocytes , we decided to look at the yolk organelles in the silenced ( white ) vitellogenic oocytes and 24 h eggs . Transversal cross sections showed an accumulation of larger yolk organelles in the core cytoplasm and an irregular distribution of these organelles in the periphery of oocytes and eggs ( Fig 8 , left panel ) . This morphology suggests problems in the biogenesis/ sorting of the yolk organelles , compatible with the hypothesis that silenced oocytes were not able to properly recruit the endocytosis machinery during oogenesis . Vitellogenic oocytes were also processed for transmission electron microscopy . High resolution images from the plasma membrane and cortex showed that the silenced oocytes do not present the prominent microvilli and the endocytic vesicles ( arrowheads ) that can be found in the control oocytes ( Fig 8 , right panel ) . To further investigate the morphology of the yolk organelles in silenced oocytes we injected FITC in the vitellogenic females and dissected their ovaries 18 h later . We found that the FITC labeled yolk organelles presented , as previously observed in the cross sections , irregular morphology and distribution in the cytoplasm of oocytes from silenced females , when compared to control oocytes ( Fig 9 ) .
The mRNA of the one single isoform of the RpATG6 gene in the Rhodnius genome assembly was detected in all major organs of the adult insect , but its expression levels are 2x and 5x higher in the ovaries of vitellogenic females than in the midgut and fat body . Accordingly , the only apparent phenotype observed ( even though silencing was systemic ) was in the impairment of the uptake of yolk macromolecules by the oocytes . No effects on the major physiological routes of the adult animal were apparent: blood digestion , yolk protein synthesis and longevity were not altered . Similar results were previously found in the hard tick Haemaphysalis longicornis , where the lack of ATG6 resulted in a decrease of internalization yolk proteins by the oocytes [18] . Atg6 is a key component of two different PI3K complexes that are essential for autophagy or endocytosis , but how the formation of the different complexes is regulated is still unclear [9 , 17] . Endocytosis and the subsequent membrane endosomal traffic are integral processes to eukaryotic cells and are especially important in the context of the oocyte maturation and yolk accumulation during oogenesis . PI3P-kinases complexes are central to this process as the local production of PI3P is known to recruit specific effector proteins that promote endocytosis , endosome fusion , endosome motility and endosome maturation [41] , as well as actin dynamics , which allows membrane deformations for cell protrusions and vesicle formation [42] . Because RpATG6 is part of a class III PI3P kinase complex , it was expected that its silencing would result in a decrease in the levels of PI3P in the oocytes , as we detected by TLC , and the lack of the that signal molecule is likely the reason why microvilli , endocytic vesicles and the yolk organelles in the cortex of silenced oocytes are not properly formed . Although our data indicates impairment in the recruitment of the endocytic machinery in the oocytes , it does not exclude the possibility of deleterious effects also in autophagy . It is important to note that our experiment was set to focus on vitellogenesis , so we triggered the knockout right at the time that females were fully fed and , therefore , producing the oocytes . At this nutritional status , somatic tissues are only going through background levels of autophagy , so it is expected that the lack of RpATG6 ( or any other ATG ) would not result in any major autophagy-related phenotype . RpATG6 high expression levels in the tropharium , when compared to the oocytes , is interesting in the sense that in a meroistic-telotrophic type of ovary , the accessory nurse cells are located in the tropharium being connected to the oocytes through cytoplasmic bridges . Since the oocyte itself is undergoing meiosis during oogenesis most of its accumulated mRNA is synthesized by the nurse cells , and it is possible that the high levels of RpATG6 mRNA found in the tropharium is targeted for delivery to the oocytes [43] . It is also interesting to notice that we observed a transient phenotype ( probably because of silencing recovering ) , where the silenced females produced a batch of oocytes ( from 7–12 days after feeding ) that were not able to properly take up the yolk macromolecules . So , it is possible that the spare yolk accumulated in the hemolymph was used for the animal to build more eggs after the silencing effect was lost , and that this is the reason why the silenced females laid more eggs in total when compared to control females . Apart from the main yolk proteins vitellin and RHBP , we also observed a decrease in the storage of TAG and PolyP , both major non-protein yolk components of the oocytes . 30–40% of the dry weight of the full grown oocyte is comprised by lipids , mostly fatty acid , cholesterol , phospholipids and triacylglycerol ( TAG ) [44] . Among those lipids , TAG is the main yolk lipid and one of the major sources of energy to the embryo . PolyP is a polymer of phosphate residues linked by high-energy phosphoanydride bonds , which has been found in a wide diversity of organisms [45] . Amongst several other functions , PolyP has been described as one of the yolk storage macromolecules in insects , as its pools are degraded during early embryogenesis as a source of Pi to the embryo anabolic metabolism [46] . Because it is known that not all yolk macromolecules are delivered to the oocytes through an endocytic pathway , our findings suggest that: 1 ) the silencing of RpATG6 may impair a different mechanism that regulates the delivery of TAG and PolyP to the oocytes; or 2 ) TAG and PolyP may be carried into the oocyte by a macromolecule that is being internalized through endocytosis . The yolk vitellin is the internalized post-translationally altered form of the circulating vitellogenin . It is know that TAG and phospholipids are bound to the internalized vitellogenin [47] , and that large amounts of vitellogenin are taken up by insect oocytes . Thus , it is tempting to hypothesize that the decrease in the vitellin pools in the silenced oocyte could be at least partially responsible for the reduction in the TAG storage in the silenced eggs . However , because of its low lipid content , it is known that insects vitellogenins contribute only to a small amount of the lipids in the egg storage [44] . In this context , the possibility of the internalization of lipophorin ( Lp ) , the main insect lipoprotein , by endocytosis via Lp Receptor ( RLp ) , should be taken into account . Previous works on R . prolixus excluded this process based on the evidence that 1 ) injected [32P] Lp was not accumulated inside the oocytes [48] and 2 ) immunogold electron microscopy labeled only the surface of the oocytes [49] . However , recent data found evidence that endocytosis of Lp by oocytes seems to occur in most insect species [44] . Also , a Lp receptor was recently found in the genome of R . prolixus ( RLp Gene: LpR1 RPRC011390 , Vector Base ) . Thus , we cannot rule out the possibility that maybe a small fraction of the lipids in the oocytes of R . prolixus is delivered through Lp endocytosis . As for PolyP , it is possible that it is also linked to vitellin , but it has never been properly tested . Further experiments testing the synthesis and delivery mechanism of PolyP to the oocytes are necessary to understand their fate in the oocytes . Glycogen also comprises the yolk , but while extra ovarian organs produce proteins and lipids , glycogen is synthesized in the ovary itself [50] . The accumulation of carbohydrates by the oocytes is still not clear but it is known that a glycoside hydrolase activity is required to the transport of sugar during oogenesis in R . prolixus [51] . Accordingly , we found that the levels of glycogen in the silenced oocytes remain unaltered , gathering evidence that the delivery of carbohydrates to the oocytes is independent from an endocytic pathway . Our findings that the microvilli and endocytic vesicles are not properly formed in the silenced oocytes , and that part of the yolk organelles in the oocyte cortex have abnormal morphology further support the assumption that endocytosis was compromised . It is generally acknowledged that the mature oocyte comprises different types of what we collectively call yolk organelles , and that this is the result of distinct packaging routes during oogenesis where the size and localization of the yolk vesicles within the oocyte is maternally-determined . Several groups have described that the yolk organelles population is not homogeneous in the fertilized egg . The vesicles can vary in its macromolecule contents , density and size , and for several models it is possible to fractionate the yolk organelles in accordance with their difference of size and density [52–56] . In the stick insect , Carausius morosus , differential acidification of the yolk organelles was already described and correlated with proteolytic activity [57] . In Periplaneta americana and R . prolixus , some small yolk organelles were described as acidocalcisome-like organelles , and they can be fractioned by differential centrifugation [58 , 59] . For the hard tick Boophilus microplus the proteolytic activity has also been correlated with differential acidification of yolk granules and with vesicles in the cortex of the fertilized egg [60] . At early development , the periphery of the egg is the region where the blastoderm is formed , so it makes sense that cortex organelles would be the ones acidifying during development to trigger the yolk mobilization . Because the silencing of RpATG6 apparently leads to defects in the formation of primarily a set of yolk organelles ( the ones in the cortex ) , it is possible that RpATG6 may be involved in the biogenesis of the different populations of yolk organelles . Altogether , we found that RpATG6 has a key role in the uptake of the main yolk proteins and in the biogenesis of the yolk organelles in R . prolixus . Since the yolk storage and degradation are crucial events for proper oocyte formation and embryo development , understanding the role of RpATG6 could contribute to the elaboration of vector control strategies . This is especially important in the context of Chagas Disease , in which , to this day , the main forms of control and prevention in endemic areas are vector-avoiding tactics , as the use of insecticide sprays and bednets ( www . who . int/chagas/en/ ) . Such approaches are virtually the same ones that have been done in the past decades , so it becomes evident how little accomplishments we have made in our efforts to manage this insect population and dispersion . Certainly , that lack of progress is at least partially due to our scattered knowledge regarding the insect reproduction biology from a molecular point of view . In this context , we think that contributions to this field are of key importance and may help us to further understand vectors biology and to elaborate new tactics for population control and the prevention of vector-borne NTDs such as Chagas Disease . | In oviparous animals all the nutrients and energy needed for the embryo cells are previously stored in the eggs , in the form of what we call yolk . The yolk is the food of the embryo , and , like our food , it needs to be broken down into nutrients and energy to supply the embryo cells rapid growth and division . The yolk-macromolecules are maternally synthesized , accumulated in the oocytes into specific organelles and , after fertilization , they are degraded in a regulated manner throughout early embryogenesis . The cellular and molecular mechanisms that govern these components accumulation and degradation are still mostly unknown . This is our interest in this work . We found that one specific gene ( RpATG6 ) is crucial for the endocytosis of the yolk components by the oocytes in the insect vector of Chagas disease R . prolixus . Insects silenced for this gene generate oocytes lacking most of their yolk components , and , as a consequence , their F1 embryos are not viable . Thus , these findings are important in the context of vector population control and can help us to further understand the molecular mechanisms of yolk uptake and oocyte formation in these animals . | [
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"cells",... | 2018 | Silencing of RpATG6 impaired the yolk accumulation and the biogenesis of the yolk organelles in the insect vector R. prolixus |
A fatal human case of Duvenhage virus ( DUVV ) infection in a Dutch traveller who had returned from Kenya was reported in 2007 . She exhibited classical symptoms of rabies encephalitis with distinct pathological findings . In the present study we describe the isolation and characterization of DUVV in vitro and its passage in BALB/c mice . The virus proved to be neuroinvasive in both juvenile and adult mice , resulting in about 50% lethality upon peripheral infection . Clinical signs in infected mice were those of classical rabies . However , the distribution of viral antigen expression in the brain differed from that of classical rabies virus infection and neither inclusion bodies nor neuronal necrosis were observed . This is the first study to describe the in vitro and in vivo isolation and characterization of DUVV .
Infection with Duvenhage virus ( DUVV ) causes lethal encephalitis in humans and animals . Although DUVV infection is prevalent among bats in Africa , reports of human infections are rare and limited to three fatal cases to date , two from South Africa and one from Kenya [1]–[3] . The clinical manifestations of human rabies encephalitis , caused by any of the lyssaviruses , are typically divided into four stages: 1 ) prodromal phase ( local neuropathic reactions at the inoculation site ) ; 2 ) acute neurological phase ( signs of aggression , fear for water and air , fluctuating consciousness , weakness and inspiratory spasms ) ; 3 ) comatous phase; and 4 ) death . No effective treatment is available for rabies to date . The prototype virus of the lyssavirus genus; rabies virus ( RABV ) has a world-wide distribution and is usually transmitted through the bite of a rabid carnivore . Bat species are important reservoirs for RABV in North and South America . Ten additional virus species have been recognized within the Lyssavirus genus , which are mainly carried by bats ( with the notable exception of Mokola virus ) and are geographically more restricted . African lyssaviruses include Lagos bat virus , ( LBV ) Mokola virus , ( MOKV ) and DUVV . European bat lyssaviruses 1 and 2 ( EBLV 1 and 2 respectively ) , Irkut ( IRKV ) , Aravan ( ARAV ) , Khujant ( KHUV ) and West Caucasian bat virus ( WCBV ) cause sporadic cases in Europe and Asia . Australian bat lyssavirus ( ABLV ) is restricted to Australia . DUVV although genetically closely related to RABV , causes different lesions in humans: RABV infection is associated with eosinophilic cytoplasmic inclusion bodies in neurons ( Negri bodies ) while inflammation is usually not prominent [4] , [5] . In contrast , Negri bodies have not been observed in human DUVV infections while extensive inflammation and cell death were found [1] , [2] . Similarly , extensive cell death and neuronal damage has been described in human cases of EBLV infection [6] . These differences in lesions suggest differences in the pathogenesis of the infection by the different members of the lyssavirus genus . However , data from human cases of DUVV infections should not be generalized since only three human cases have been described to date . DUVV has not been previously propagated in vitro and in vivo , hence the limited studies on DUVV pathogenesis . The present study describes the isolation and characterization of the DUVV that caused a fatal infection in a Dutch traveler who had visited Kenya in 2007 [2] , [3] . Furthermore we describe passage of the virus in BALB/c mice , thus describing an animal model to further study the pathogenesis of DUVV infection . In the present paper we compare the virulence of DUVV in mice with that of two rabies viruses: the highly pathogenic , wild-type silver-haired bat rabies virus ( SHBRV ) and the laboratory adapted , attenuated Pasteur rabies virus ( RABV-PV ) . Since different rabies virus isolates vary considerably in their pathogenic potential and our knowledge of DUVV pathogenesis is limited , we chose to compare DUVV-NL07 with two very different rabies virus isolates in order to be able to place DUVV in the spectrum of rabies pathogenesis .
Samples taken for diagnostic purposes from different parts of the human brain [2] were inoculated onto N2a cells and the cultures were followed for 28 days . Virus was isolated from a sample taken from the thalamus as shown by the increase of viral RNA over time in the absence of cytophathic changes . The culture supernatant ( primary isolate ) was subsequently used for RNA isolation and sequencing of the complete genome of the virus ( DUVV-NL07 ) . To confirm that the primary isolate could be propagated in mice , intracranial inoculation of 3-day old BALB/c mice ( n = 12 ) was performed using 100–200 TCID50 of the primary isolate of DUVV-NL07 , which resulted in 100% mortality within 5 days post infection ( DPI ) . Virus could be recovered from the brain of infected animals and after three in vivo passages the virus was still 100% lethal in newborn mice . The complete nucleotide sequence of the primary isolate was deposited in GenBank ( accession number JN986749 ) . We next determined the phylogenetic relationship of DUVV-NL07 with other members of the genus Lyssavirus . A phylogenetic tree constructed with whole genome sequences of members of all 11 lyssavirus species confirmed that DUVV-NL07 indeed belongs to DUVV species ( Figure 1 ) . The DUVV-NL07 isolate proved to be 93% identical on nucleotide level to the other three DUVV isolates from South Africa for which the complete genome sequences are available . The identity between the three South African isolates is up to 99% . The position of DUVV-NL07 outside the South African cluster suggests a genetic variant that circulates in Kenya . When the deduced amino acid sequences of the individual genes were compared with published sequences , the highest identity of DUVV-NL07 with other DUVV isolates was observed in the nucleoprotein and matrix proteins ( 99% identity with eight published sequences of each gene ) followed by the phosphoprotein and the polymerase ( 96% identity with seven and three published sequences of the respective genes ) . The glycoprotein G was the most divergent protein ( 95% identity with six published sequences ) . An earlier in-frame initiator was seen in the matrix protein resulting in 12 additional amino acids at the N-terminal of matrix protein , similar to what has been described for ABLV [7] . The sequence of the primary DUVV-NL07 isolate was also compared with the partial sequence of the N protein obtained directly from the brain material of the patient [2] . These sequences were 99% identical on the nucleotide level with only three nucleotide differences reported between the sequence deposited from the original brain material and the primary isolate of DUVV-NL07 ( position 542: G vs A , position 545: Y vs C , position 551: T vs C ) . Virus stocks were prepared by two additional passages of the primary isolate on human neuroblastoma cells ( SK-N-SH ) . The nucleotide sequence of the passaged virus ( P3 ) was 98% identical to the primary isolate . The differences between primary isolate and P3 virus were mainly silent with only one amino acid substitution ( a phenylalanine was substituted into a tyrosine in the P3 virus ) in the L gene . All subsequent experiments described here were carried out with the primary isolate that had been passaged twice on SK-N-SH cells . In the focal infection experiment , the replication kinetics of the three different viruses were compared on N2a cells infected at an m . o . i . of 0 . 01 . DUVV-NL07 and RABV-PV showed peak virus titres by day 3 post infection ( DPI; Figure 2a ) , a day earlier compared to SHBRV-18 . Infection with any of the three viruses proved to be non-cytopathic in these cells , which remained persistently infected for the 17 days follow up period , although virus titers started to decrease from 7 DPI onwards . In contrast , peak virus titers were measured by 4 DPI in SK-N-SH cells for DUVV-NL07 and SHBRV-18 viruses and by 7 DPI for RABV-PV ( Figure 2a ) . In addition , similar titers were obtained from DUVV-NL07 and RABV-PV in SK-N-SH cells compared to slightly higher titers obtained from SHBRV-18 infected N2a cells . In contrast , infection of SK-N-SH cells with DUVV-NL07 resulted in almost 2log10 higher titers compared to the titers obtained from RABV-PV and SHBRV-18 infected cells . To study the short term dynamics of DUVV-NL07 replication , a one-step growth curve was performed on mouse and human neuroblastoma cells at an m . o . i . of 10 . The eclipse period for all three viruses was between 14 and 16 hours on N2a cells . Slightly different eclipse periods were found on human neuroblastoma cells for DUVV-NL07 and SHBRV-18 ( between 14 and 16 hours ) compared to RABV-PV ( between 16 and 18 hours ) ( Figure 2b ) . At 24 hours , 100% of the cells were infected ( Figure S1 and S2 ) and high titers were detected in the culture supernatant ( Figure 2b ) . Similar titers were found on SK-N-SH cells and N2a cells . Several studies have demonstrated that neuro-invasiveness and neurovirulence of RABV are strain-dependent [8]–[10] . We sought to determine the kinetics and minimum dose of DUVV-NL07 that can cause disease upon peripheral inoculation in 3-week and 8-week old BALB/c mice . We found that DUVV-NL07 was neuroinvasive for both 3-week and 8-week old mice inoculated with 106 TCID50 or 104 TCID50 either i . m or s . c ( Table 1 ) . Virus was detected in the brains of animals as early as 7 days post inoculation and clinical signs ( including ruffled hair , hunched position and muscle weakness ) were apparent from 10 days post inoculation onward . On day 10 post inoculation seven out of 60 inoculated animals developed hind limb paralysis . As shown in Table 1 , more animals that received 106 TCID50 developed paralysis compared to animals receiving 104 TCID50 . There was no strong association between development of paralysis and route of inoculation , age or level of viral RNA in the brain . Virus was not detected in the brain samples of animals inoculated with 102 TCID50 . In addition , no viral RNA was detected in samples taken from the site of inoculation ( muscle for i . m . inoculation or skin for s . c . inoculations ) and the draining lymph nodes , early after inoculation ( 3–7 DPI . ) . In order to assess whether virus replication was necessary to induce antibody response or whether the inoculated antigen burden alone was sufficient to induce antibody response , we compared antibody production in animals inoculated i . m . and s . c . with DUVV-NL07 and BPL-inactivated virus controls . As depicted in Figure 3 , on day 7 antibodies could be measured in 30/40 mice inoculated with 104 ( Figure 3b ) or 106 ( Figure 3c ) TCID50 of DUVV-NL07 . On the day that animals had to be euthanized because of development of symptoms ( day 11 or 10 post inoculation ) , antibodies were detected in 33/40 animals which had received 104 ( Figure 3b ) or 106 ( Figure 3c ) TCID50 of DUVV-NL07 respectively . In animals inoculated with 102 TCID50 of DUVV-NL07 no specific antibody response was detected ( Figure 3a ) . Similarly , all animals that received the same antigenic burden of BPL-inactivated virus did not induce specific antibody response ( Figure 3a , b , and c ) . The route of inoculation did not influence significantly the antibody production of animals receiving any dose of DUVV-NL07 at 5 , 7 or >10 days post inoculation ( P>0 . 05; Figure 3 ) . The use of RABV-PV antigen in the ELISA could explain the low antibody titers that DUVV-NL07 inoculated mice developed after inoculation with 106 TCID50 or 104 TCID50 . Since the differences in outcome of infection between the respective routes of inoculation and age groups were subtle , we decided to study virulence of DUVV-NL07 in 8-week old mice ( n = 25 ) infected with 106 TCID50 virus via the i . m . route . SHBRV-18 and RABV-PV were used as reference controls . Eight-week old mice were chosen for this purpose since they have a fully developed immune system and fully myelinated brain . As shown in Table 2 , DUVV-NL07 reached the brain by day 9 after i . m inoculation ( as determined by PCR ) , slightly later than RABV-PV and SHBRV-18 ( both on day 5 p . i ) . Infection with DUVV-NL07 followed a similar course as infection with RABV-PV: clinical manifestations appeared at about the same time ( between days 8 and 15 p . i . ) and mortality rates were similar ( 52% and 60% respectively; Figure 4a ) . Clinical signs were typical of rabies encephalitis including lethargy , ruffled hair , muscle weakness , loss of body-weight , and progressive paralysis of one or both hind-limbs . DUVV-NL07 infected animals developed paralysis in both hind-limbs and became moribund approx . 3 days after onset of clinical signs . RABV-PV infected animals developed paralysis of both hind-limbs by day 3 post onset of clinical signs without becoming moribund . Clinical signs were first observed in SHBRV-18 infected animals on day 5 and infection was more severe resulting in mortality rates up to 95% by day 9 p . i . ( Figure 4a ) . The clinical manifestations of SHBRV-18 infected animals differed significantly from those observed in DUVV-NL07 or RABV-PV infected animals: signs developed very rapidly ( within 12 hours post onset of clinical signs animals had to be euthanized ) and included aggressive behaviour , myoclonus and torticollis . Hind-limb paralysis was not frequently observed and animals continued to be very active . Next , we compared virus titers in the brain of infected animals at the time of euthanasia . As depicted in Figure 4b , significantly higher virus titers were recovered from animals infected with SHBRV-18 as compared to animals infected with DUVV-NL07 ( P = 0 . 011 ) or RABV-PV ( P = 0 . 015 ) . No significant differences were observed in the viral titers recovered from brains of DUVV-NL07 and RABV-PV infected animals ( P = 0 . 45 ) . Most wild type RABV strains are known to induce few pathological changes in the brain of infected humans or animals . The pathological changes and tropism of DUVV for brain compartments are unknown . HE and LFB-staining of brains from DUVV-NL07 , SHBRV-18 and RABV-PV infected animals showed no evidence of necrosis or demyelination . Staining with anti-RABV-NP antibodies showed extensive antigen expression in infected brains . Antigen expression was seen as characteristic granular staining in the cytoplasm , axons and dendritic processes of infected neurons of moderate ( DUVV-NL07; Figure 5A ) to strong ( RABV-PV; Figure 6A , SHBRV-18; Figure 7A ) signal . All three virus infections cumulated in the presence of infected neurons in all parts of the brain: brainstem , cerebellum and cerebrum . However , the pattern of spread differed among the three viruses . DUVV-NL07 infection was first observed in few neurons in the molecular layer of the cerebellum at 9 DPI and spread to the cortex of the cerebrum in the following days ( up to 10 DPI ) . By the time that hind-limb paralysis was observed ( 9–16 DPI ) , many neurons were positive in cerebrum , cortex hippocampus and brainstem ( and somewhat less in the cerebellum ) . RABV-PV infection was first observed in a few neurons in the brainstem at 5 DPI , and spread to the cortex of the cerebrum in the following days ( 7 DPI ) . By 10 DPI many neurons were positive in all areas of the brainstem and cerebrum , but only in the Purkinje cell layer of the cerebellum . By the time hind-limb paralysis was observed ( 9 DPI onwards ) , many neurons were still positive in all areas of brainstem and cerebrum . However , in the cerebellum , positive neurons were found only in the granular layer and not in the Purkinje cell layer . SHBRV-18 infection was first observed in a few neurons in brainstem and cerebrum at 5 DPI and spread to the dentate nucleus of the cerebellum in the following days . By 9 DPI many neurons were positive in all areas of brainstem , cerebrum and cerebellum , including the Purkinje cell layer . At the time of paralysis all three viruses were present in the spinal cord and wide-spread antigen expression was seen in cervical , thoracic and lumbar section of the spinal cord ( illustrated for RABV-PV in Figure 6B ) . Histopathological changes in the brain consisted of perivascular cuffing by mononuclear cells and increased cellularity in the neuropil , both of which generally colocalized with lyssavirus antigen expression . However , the severity and distribution of these changes differed per virus ( Table 3 ) . In DUVV-NL07 infection , perivascular cuffs were 1–2 cells thick and consisted mainly of T cells ( CD3 positive ) . Infiltration of the neuropil by T cells was most prominent in the cerebral cortex where 2 to 4 T cells per high power field ( HPF; objective 40× ) were observed . Increased numbers of astrocytes ( GFAP positive ) i . e . mild astrocytosis , was observed in white matter of cerebellum , around perivascular cuffs and in hippocampus but not in brainstem . Increased numbers of activated microglia cells ( Iba-1 positive ) i . e . microgliosis was observed in the brainstem , grey matter of the cerebrum and cerebellum mainly around infected neurons ( Figure 5D ) . In RABV-PV infection , perivascular cuffs were 1 to 2 cells thick . Infiltration of the neuropil by T cells was minimal , less than one per HPF . Mild astrocytosis was visible in white matter of cerebellum , around perivascular cuffs , in cerebral cortex and in brainstem ( Figure 6D ) . Microgliosis was seen mainly in cerebrum and brainstem and somewhat less in the cerebellum ( around the Purkinje cell layer ) . In SHBRV-18 infection , perivascular cuffs were more than two cells thick and consisted of many T cells . Infiltration of the neuropil by T cells was most prominent in the brainstem , where 2 to 3 cells per HFP were observed . Astrocytosis was visible in cerebral cortex , cerebral nuclei and brain stem but not in the cerebellum ( Figure 7D ) . Microgliosis was seen mainly around perivascular cuffs and not in the neuropil . Neuronal necrosis was not seen in the brains of any of the infected mice , irrespective of the infecting virus . Although neither visible by routine HE staining nor with Seller's staining ( Figure S4 ) , we did observe inclusion bodies reminiscent of Negri bodies when staining for antigen expression with IHC . In the spinal cords of infected animals acute neuronal necrosis was seen after infection with all three viruses ( illustrated for SHBRV-18 in Figure 7B ) . Perivascular cuffing and mild infiltration of T cells indicated acute inflammation . In DUVV-NL07 infection , perivascular cuffs were 1 cell layer thick and consisted mainly of T cells ( CD3 positive ) . Infiltration of the neuropil by T cells was prominent in cervical , thoracic and lumbar sections ( Figure 5C ) with more than 2 positive cells per HPF . Astrocytosis and microgliosis were seen mainly in lumbar and thoracic sections of the spinal cord . In RABV-PV infection , perivascular cuffs were 1 cell layer thick and consisted mainly of T cells ( Figure 6C ) . Infiltration of the neuropil by T cells was moderate with 1 to 2 cells per HPF and astrocytosis and microgliosis was mainly seen in thoracic and lumbar sections . In SHBRV-18 infection , perivascular cuffs were 1 cell layer thick consisting mainly of T cells and neutrophils ( Figure 7C ) . Infiltration of the neuropil was minimal with less than 1 cell per HPF and astrocytosis and microgliosis were seen mainly in the thoracic sections of the spinal cord .
Here we describe the in vitro isolation of DUVV from the brain of a human patient [3] and its subsequent molecular and biological characterization . DUVV infection in humans is rare and restricted to Africa , with only three cases reported so far . Phylogenetic analysis of lyssaviruses has been largely based on the complete genome or the N gene [11]–[15] . The DUVV-NL07 strain described in this study clusters differently from the bat and human DUVV's reported so far ( Figure 1 ) [1] . At the time of analysis there were only three complete genome sequences and eight N gene sequences available from other DUVV isolates . Although the variation among DUVV strains ( including the DUVV-NL07 isolate ) is low compared to other lyssavirus species [11] , [16] , DUVV-NL07 is the most divergent among DUVV isolates both on the nucleotide and amino acid level . Further studies are needed to elucidate the biological relevance of the observed differences between DUVV isolates as has been proposed for different RABV isolates [17] , [18] . The one-step growth curve experiment did not reveal any significant differences in replication kinetics between the different viruses , neither on mouse or human neuroblastoma cells . Therefore , the replication kinetics are not an in vitro correlate of virulence . However , we cannot exclude that different amounts of defective interfering particles in the different virus stocks may have influenced the replication kinetics of these viruses . In order to study the spreading potential of the respective viruses , focal experiments were performed . Reduced virus titers were observed in mouse neuroblastoma cell lines as compared to human cell lines . The mechanisms underlying these differences are not clear . It is possible that the two different cell lines differ in viral entry-receptor density or qualitative or quantitative differences in anti-viral response to infection . Alternatively , differences in the rate of cell death due to prolonged culture could also explain the differences observed in the spreading potential of the three viruses in human and mouse cells . In the case report of van Thiel et al . [2] the crude brain material from the patient did not cause disease in outbred mice with the mouse inoculation test . Consistently , virus or RNA could not be detected in inoculated animals . Similarly , virus was not isolated from newborn inbred mice ( BALB/c and C57/Bl6 ) inoculated with the same crude brain material ( data not shown ) . Even though we could not determine the infectious virus titer in the brain material of the patient by endpoint dilution , the quantitative PCR data suggest that the brain sample used for inoculation of mice contained approximately 10 TCID50/ml of infectious virus . Our data confirm the low sensitivity of the mouse inoculation test [19] , [20] . Therefore , prior in vitro propagation of the virus proved to be necessary to further study the characteristics of the virus . We have shown that DUVV-NL07 is pathogenic for BALB/c mice upon i . c . and peripheral inoculation . DUVV-NL07 replicated well in mouse and human neuroblastoma cells as well as in hamster fibroblasts ( BHK-21-C13 , data not shown ) . Further , the virus proved to be both neuroinvasive and neurovirulent in juvenile and adult BALB/c mice upon infection via the i . m or s . c routes . It was previously shown that neuroinvasiveness of a bat-derived RABV was dependent on the route of the inoculation [17] . The authors speculated that the increased neuroinvasion observed in their study was likely a reflection of the peripheral cell tropism . It is plausible that bat-derived lyssaviruses , such as DUVV-NL07 could replicate better in sites such as the dermis or the subcutaneous space compared to muscles since the natural route of infection via bat scratches would not implicate direct inoculation of the virus into the muscle . In our studies we did not see differences in neuroinvasion between the two different routes of inoculation , neither could we detect replicating virus at the site of inoculation early after infection . This suggests that neuroinvasion might not be dependent on abundant peripheral virus replication . Given the nature of transmission via usually superficial bat-scratches , as was the case in the human DUVV-NL07 infection , it is generally believed that bat-derived lyssaviruses are neuroinvasive at low doses . Indeed , peripheral infection with as little as 104 TCID50 of DUVV-NL07 caused encephalitis in a substantial number of mice . Further comparisons revealed that neither route of infection , nor age of the mice led to marked differences in the clinical outcome , antibody response or amount of viral RNA recovered from the brain . Comparison of in vitro characteristics and in vivo neuroinvasive characteristics and virulence of DUVV-NL07 ( after peripheral inoculation ) with those of RABV-PV ( a mouse adapted strain of RABV ) and SHBRV-18 , a wild-type highly pathogenic bat-derived RABV strain that had been recovered from a human rabies case [17] , showed clear similarities with RABV-PV . In contrast , SHBRV-18 infection exhibited a different tropism for neuronal cells , different clinical manifestations and nearly 100% lethality . These data indicate that DUVV-NL07 has intermediate neuroinvasiveness and virulence while exhibiting characteristics of some of the fixed RABV strains [21] . Similar to RABV-PV and SHBRV-18 , the major pathological change observed after DUVV-NL07 infections was perivascular cuffing , whereas no neuronal necrosis ( in the brain ) was observed . Different lines of research in mouse models have indicated that several aspects of RABV pathogenesis are largely strain dependent , such as activation of the immune system , infiltration of leukocytes into infected brains , gene expression patterns and cell death ( reviewed by Fu and Jackson [22] ) . Some studies have suggested that level of expression of G protein inversely correlates with pathogenicity [8] , [23] . However , more recent data suggest that G protein expression levels are not critical for pathogenicity [24] and that it is the capacity of the G protein to promote survival or death signals in the infected neurons that contributes to pathogenicity [25] . It remains to be seen if the G protein of DUVV-NL07 would have the capacity to induce such signals and confer a pathogenic or attenuated phenotype . Our findings are in agreement with previous studies suggesting that inflammation is not a significant determinant of pathogenesis of DUVV-NL07 or bat-derived RABV [10] , [23] . Also tropism of lyssaviruses for different brain compartments has been shown to be largely strain dependent [4] , [26] , [27] . The mouse cerebellum did not seem to be an important site of replication for DUVV-NL07 , since only few cells stained positive for NP-antigen at all time points analysed after infection . The infection appeared largely localized in certain areas of the cerebral cortex and hippocampus even in mice that had reached advanced stages of paralysis . In contrast , both RABV-PV and SHBRV-18 showed a clearly different pattern of spread into the CNS , with Purkinje cells and the dentate nucleus in the cerebellum being the primary site of replication and more than 70% of neurons throughout the brain being infected at the time of euthanasia . Interestingly these histopathological findings largely paralleled the differences in clinical signs observed between infections with the respective virus strains . Overall , all animals exhibited signs of aggression reflecting infection of the cerebrum by the three viruses . Extensive infection of the cerebellum by RABV-PV and SHBRV-18 may have contributed to paralysis whereas infection of the dentate nucleus by SHBRV-18 correlated with the characteristic signs of torticollis . Given the differential tropism patterns of the three viruses for neurons in the cerebellum , it would be interesting to compare the receptor usage of DUVV-NL07 with RABV-PV and SHBRV-18 . Despite the differences seen in the brain , the pathology of the spinal cord of the infected animals seemed similar between the three viruses . Inflammation and necrosis were of similar extent between the three viruses and antigen expression was wide-spread in all sections of the spinal cord . These findings suggest that the differences in pathogenesis of the three viruses are most likely linked to the brain . In the three human cases of DUVV infection documented so far , the clinical manifestations did not differ from those of classical rabies . We therefore speculate that the severity of clinical outcome seen in rabies is probably attributable to infection or dysfunction of ( yet unidentified ) highly specialized compartments of the brain , common to all lyssaviruses . It should be noted that the original sequence of the virus from crude brain material was not available . However , the sequence of the primary isolate was 98% identical ( on the nucleotide level ) and only one amino acid substitution with the P3 virus used for the in vitro and in vivo studies described in this paper . Studies using a molecular clone of DUVV would be needed to further determine the significance of the single amino acid substitution in the L gene after in vitro passage of DUVV . It would be interesting to evaluate the implications of such differences in patient management and identify treatment protocols . Both the in vitro and in vivo systems for DUVV-NL07 propagation described in the present study will be valuable tools to further elucidate the pathogenesis of infections with bat lyssaviruses and possible treatment options .
All animal experiments described in this paper have been conducted according to Dutch guidelines for animal experimentation and approved by the Animal Welfare Committee of the Erasmus Medical Centre , Rotterdam , The Netherlands . All efforts were made to minimize animal suffering . Mouse neuroblastoma ( N2a ) cells and baby hamster kidney ( BHK-21 clone 13 ) , a kind gift from Dr . F . Cliquet , ( AFSSA , Nancy France ) , were grown in DMEM supplemented with 5% heat inactivated fetal bovine serum ( HI-FBS ) and GMEM supplemented with 10% HI- FBS , respectively . Human neuroblastoma cells ( SK-N-SH; a kind gift from Dr . C . Prenaud; Institute Pasteur , Paris France ) were cultured in DMEM with Glutamax supplemented with 10% HI-FBS . All media were supplemented with antibiotics ( 100 U penicillin , 100 µg/ml streptomycin ) and 2 mM L-glutamine . Cell culture reagents were obtained from LONZA ( Lonza Benelux BV , Breda , The Netherlands ) . All cell lines tested negative for mycoplasma . The SHBRV-18 ( a kind gift of Dr . B Dietzschold , Tomas Jefferson University , Philadelphia , USA ) , RABV strain Pasteur ( RABV-PV ) , and DUVV isolated from a Dutch patient ( DUVV-NL07 , described in this study ) were grown to high titres on SK-N-SH cells . SHBRV-18 was originally isolated from the brain of an infected human and subsequently passaged in vivo and in vitro to obtain variant SHBRV-18 [18] . The passage history of RABV-PV has not been documented adequately . Virus titrations were performed in BHK-21-C13 cells as previously described [28] and titres were calculated with the Kärber-Kaplan method [29] . Viruses were inactivated with beta-propiolactone ( BPL ) . To this end , BPL was introduced to a final dilution of 1∶4000 ( v/v ) and incubated overnight at 4°C to ensure complete viral inactivation . Subsequently , BPL was inactivated for 1 h at 37°C . Inactivated viruses did not replicate on BHK-21-C13 cells in virus titration assays . RNA was isolated from the primary DUVV-NL07 isolate with the Qiagen RNeasy mini kit according to the instructions of the manufacturer ( Qiagen Benelux , Venlo , The Netherlands ) . cDNA was synthesized using random hexamer primers ( Invitrogen , Breda , The Netherlands ) or oligo dT primer ( Invitrogen ) and superscript III RT enzyme ( Invitrogen ) according to the instructions of the manufacturer . Twelve sets of primers spanning the complete genome sequence of RABV-PV were designed in areas conserved with other members of plylogroup I lyssaviruses including the previously known DUVV sequences [14] . Primers were designed using the Primer select module of DNASTAR software ( DNASTAR , Madison WI , USA ) and adjusted manually to obtain highest identity with the known DUVV sequences . Primer sequences are available from the authors upon request . cDNA was amplified using Taq DNA polymerase ( Invitrogen ) and DNA fragments were purified from gel and cloned into the pCR4-TOPO vector ( Invitrogen ) . Colonies were sequenced using M13 primers in an ABI3130XL sequencer . Sequences were analysed using the SeqMan module of DNASTAR software and aligned so that the complete DUVV-NL07 genome was obtained from the consensus sequence of at least five colonies . Real-time PCR for the detection of viral RNA was done using the Taq-Gold TaqMan kit ( Applied Biosystems , Nieuwerkerk aan den Ijssel , The Netherlands ) and primers/probe combinations previously described [2] , [30] . RNA copy numbers were quantified using a standard curve of in-vitro transcribed RNA of known quantities . The detection limit of the qPCR used to determine virus load in the brains of infected animals was 1000 copies/ml . The replication kinetics of DUVV-NL07 , RABV-PV , and SHBRV-18 were studied in vitro using two type of experiments: a one-step growth curve experiment in which all cells were infected at high m . o . i . , and a focal-infection experiment using a low m . o . i . To this end , mouse or human neuroblastoma cells were inoculated in suspension with either of the three viruses at an m . o . i . of 0 . 01 ( focal infection ) or m . o . i . of 10 ( one step growth curve ) for one hour at 37°C . Cells were washed five times with serum free medium , resuspended in growth medium , seeded in plates and incubated at 37°C ( T = 0 ) . Supernatant samples were collected in triplicate at the indicated time points , centrifuged at 1000 rpm for 5 min to remove cell debris and stored in −80°C until determination of virus titers on BHK-21-C13 cells . High-binding COSTAR 96-well ELISA plates ( Sigma Aldrich , St . Luis , MO , USA ) were coated overnight at 4°C with BPL-inactivated RABV-PV antigen . Plates were washed four times with PBS containing 0 . 05% Tween-20 ( PBS-T ) to remove unbound antigen and blocked for one hour at 37°C with PBS containing 0 . 1% ( w/v ) bovine serum albumin ( Sigma ) and 0 . 2% ( w/v ) skim milk powder ( ELK Campina , Eindhoven , The Netherlands ) ( ELISA buffer ) . After a washing step , plates were incubated for one hour at 37°C with mouse serum samples serially diluted ( 2-fold ) in ELISA buffer . After removal of unbound antibodies with a washing step plates were incubated with a rabbit anti-mouse IgG HRPO conjugate ( DAKO , Glosturp , Denmark ) for one hour at 37°C . After a final washing step plates were developed with tetra-methyl-benzidine substrate for 10 min at RT and the reaction was stopped by adding 0 . 5 N of sulphuric acid . Absorbance was measured at 450 nm with an Infinite F200 TECAN instrument ( TECAN Benelux , Giessen , The Netherlands ) . Antibody ratio was calculated as O . D . sample/cutoff ( where cutoff: mean O . D . of the negative controls+3× the standard deviation ) . Neuroinvasive characteristics and virulence of DUVV-NL07 was studied in newborn ( 3 days old ) , 3-week or 8-week old BALB/c mice . Newborn mice were euthanized when they reached humane end-points ( both hind-limb paralysis ) . Three-week or 8-week old mice were euthanized on day 3 , 5 , 7 , 10 ( n = 5 for each time point ) and on the humane end-point ( n = 25 ) . Mice were anesthetized with isoflurane prior to inoculation via the indicated route ( intracranial; i . c . intramuscular; i . m . or subcutaneous; s . c ) . Animals were euthanized under anaesthesia by cervical dislocation at the indicated time points and samples were collected immediately for further processing . Brain samples were collected for immunohistochemistry , virus isolation and quantification whereas blood was collected for determination of antibody levels . Eight-week old BALB/c mice were inoculated i . m . with RABV-PV or SHBRV-18 and euthanized on day 3 , 5 , 7 ( n = 5 for each time point ) and at the time of the humane end point ( n = 25 for RABV-PV and n = 20 for SHBRV-18 ) . Samples were collected as described for DUVV-NL07 . Animals were housed in cages of 5 animals per cage , had 12-hour day-night cycle and had constant access to food and water . All animal experiments were approved by the Animal Ethics Committee of Erasmus MC , The Netherlands . Brains and spinal cords were removed and fixed in 10% neutral-buffered formalin , embedded in paraffin and sectioned at 4 µm . Spinal cords were divided in parts of approx . 1 cm thick to obtain cervical , thoracic and lumbar sections . Slides were stained with hematoxylin and eosin ( HE ) and Luxor Fast blue ( LFB ) for analysis by light microscopy . Immunohistochemical analysis for virus nucleoprotein and cell-type markers was performed on brain and spinal cord sections using the streptavidin-biotin-peroxidase technique . Briefly , sections were deparaffinized in xylane , re-hydrated in descending concentrations of ethanol and incubated for 10 min in 3% H2O2 diluted in PBS to block endogenous peroxidase activity . Antigen exposure was performed by incubation for 15 min at 121°C in citrate buffer ( 0 . 01 M , pH 6 . 0 ) . Primary antibodies included goat anti-rabies NP antibody ( 1∶500 Rabies polyclonal DFA Reagent; CHEMICON ) , anti-mouse CD3 ( T cell marker; 1∶1000 DAKO ) , rabbit anti-Iba1 ( microglial marker , 1∶500; WAKO ) and rabbit anti-GFAP ( astrocyte marker , 1∶500; ZYMED ) . A streptavidin-biotin-peroxidase kit ( UltraVision Large Volume Detection System Anti-polyvalent , HRP Lab Vision , USA ) was used as secondary antibody ( goat anti-polyvalent/streptavidin enzyme complex ) and 3-amino-9-ethyl carbazole ( AEC , Sigma ) was used as a substrate . Sections were counterstained with Mayer's hematoxylin and mounted with Kaiser's glycerin-gelatin . Sections incubated without the primary antibody ( omission control ) , isotype controls and use of negative brain tissue confirmed specificity of staining . Sections from animals inoculated with RABV-PV were considered as positive controls . One complete brain section from all infected animals ( as demonstrated by positive viral antigen staining ) was screened at high power field ( objective 40× , approximately 30 high power fields/cerebellum , 30 high power fields/cerebrum and 30 high power fields/brainstem ) for determination of CD3 , GFAP and Iba1 positive cells . One cervical , one thoracic and one lumbar section of the spinal cord of nine representative animals ( n = 3 for DUVV , n = 3 for RABV-PV and n = 3 for SHBRV-18 ) were screened at high power field as described for the brain sections ( approximately eight high power fields per spinal cord section ) . Survival curves were made with the Kaplan-Meier method and analyzed with a two-tailed logrank test ( Graph Pad version 4 ) . Viral and antibody titers were compared with a two-tailed , non-parametric Mann-Whitney test ( Graph Pad version 4 ) . | Lyssaviruses have been known for centuries to cause lethal encephalitis in animals and humans , representing a serious public health problem especially in developing countries . Little is known about the way that lyssaviruses in general , and Duvenhage virus in particular cause disease . Studies of pathogenesis have been hampered by the fact that the virus has not yet been propagated and characterized extensively . In this paper , we describe the characterization of Duvenhage virus in vitro . Further , we characterized the virus in BALB/c mice . We compared Duvenhage virus with a wild type rabies virus ( silver-haired bat rabies virus ) and we found that while in vitro the differences of these two viruses were not significant , the in vivo characteristics of these two viruses differed significantly . Histological analyses of infected mouse brains suggest that differences in virulence may be associated with difference in tropism . Elucidating the differences in pathogenesis between different lyssaviruses might help us in the design of novel treatment protocols . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"rabies",
"neglected",
"tropical",
"diseases",
"viral",
"diseases"
] | 2012 | In Vitro and In Vivo Isolation and Characterization of Duvenhage Virus |
Chronic Kidney Disease of uncertain etiology ( CKDu ) is an emerging epidemic among farming communities in rural Sri Lanka . Victims do not exhibit common causative factors , however , histopathological studies revealed that CKDu is a tubulointerstitial disease . Urine albumin or albumin-creatinine ratio is still being used as a traditional diagnostic tool to identify CKDu , but accuracy and prevalence data generated are questionable . Urinary biomarkers have been used in similar nephropathy and are widely recognised for their sensitivity , specificity and accuracy in determining CKDu and early renal injury . However , these biomarkers have never been used in diagnosing CKDu in Sri Lanka . Male farmers ( n = 1734 ) were recruited from 4 regions in Sri Lanka i . e . Matara and Nuwara Eliya ( farming locations with no CKDu prevalence ) and two CKDu emerging locations from Hambantota District in Southern Sri Lanka; Angunakolapelessa ( EL1 ) and Bandagiriya ( EL2 ) . Albuminuria ( ACR ≥ 30mg/g ) ; serum creatinine based estimation of glomerular filtration rate ( eGFR ) ; creatinine normalized urinary kidney injury molecule ( KIM-1 ) and neutrophil gelatinase-associated lipocalin ( NGAL ) were measured . Fourteen new CKDu cases ( 18% ) from EL1 and nine CKDu cases ( 9% ) from EL2 were recognized for the first time from EL1 , EL2 locations , which were previously considered as non-endemic of the disease and associated with persistent albuminuria ( ACR ≥ 30mg/g Cr ) . No CKDu cases were identified in non-endemic study locations in Matara ( CM ) and Nuwara Eliya ( CN ) . Analysis of urinary biomarkers showed urinary KIM-1 and NGAL were significantly higher in new CKDu cases in EL1 and EL2 . However , we also reported significantly higher KIM-1 and NGAL in apparently healthy farmers in EL 1 and EL 2 with comparison to both control groups . These observations may indicate possible early renal damage in absence of persistent albuminuria and potential capabilities of urinary KIM-1 and NGAL in early detection of renal injury among farming communities in Southern Sri Lanka .
Chronic Kidney Disease of unknown etiology ( CKDu ) is an endemic disease among dry zone farming communities in Sri Lanka . First cases were reported in early 1990s in North Central Province ( NCP ) predominantly among male farmers [1] . It has reached epidemic proportions with ever increasing numbers of patients and deaths , thus becoming a new and emerging health issue that would eventually inflict adverse consequences on food security , merely for the fact that affected populations constitute the major rice farming communities in Sri Lanka . It is a global epidemic as similar types of kidney diseases are reported from Andhra Pradesh in India [2] and in Central America including Nicaragua [3] , El Salvador [4] and Costa Rica [5] . Hypertension , diabetes , glomerulonephritis and other traditional causes are not associated with CKDu . However , multiple causes have been suggested such as chronic low dose exposure to multiple heavy metals and agrochemicals [6 , 7] , heat stress and recurrent dehydration [8–11] , heat driven pathophysiologic mechanisms [12] , nephrotoxic drugs [13] , hyperuricemia and hyperuricosuria [14–17] , leptospirosis [18 , 19] and genetic susceptibility [20 , 21] . Based on clinical and pathological studies , CKDu cases in Sri Lanka show glomerular and tubulointerstitial injury in kidneys [1 , 22 , 23] and similar glomerular and tubulointerstitial injury have also been reported in Mesoamerican nephropathy [24 , 25] . A recent study by the World Health Organization ( WHO ) has used albumin creatinine ratio ( ACR ) as the diagnostic criteria for CKDu in Sri Lanka [26] . However , ACR ≥ 30 mg/g Cr may not detect early renal injury [27–29] hence may underestimate disease prevalence . Several novel biomarkers such as human neutrophil gelatinase-associated lipocalin ( NGAL ) , kidney injury molecule 1 ( KIM-1 ) , N-acetyl beta glucosaminidase ( NAG ) , interleukin 18 ( IL-18 ) , insulin like growth factor-binding protein 7 ( IGFBP7 ) and tissue inhibitor of metalloproteinases-2 ( TIMP-2 ) are being used to diagnose acute kidney injury [29] and their use in chronic kidney disease was evident in Mesoamerican nephropathy [30 , 31] . However , tubular markers such as KIM -1 and NGAL have not been used for CKDu diagnosis in Sri Lanka . KIM-1 also known as T-cell immunoglobulin and mucin-containing molecule is a type 1 trans-membrane protein with molecular weight approximately 100 kDa . Proximal tubular cells in kidneys are the main source of KIM-1 in the urine and it is up-regulated during acute kidney injury [29] . NGAL , also known as siderocalin , lipocalin-2 ( lnc2 ) or lipocalin 24p3 is a 22–25 kDa glycoprotein that belongs to superfamily “lipocalin” [32] . Leucocytes , loop of Henle and collecting ducts are some of the major sources of NGAL in the body [29] . NGAL is released from lysosomes , brush-border and cytoplasm of proximal tubular epithelial cells during chronic kidney disease [29] . Urinary KIM-1 and NGAL are good predictors of renal injury prior to detectable changes in eGFR [33–35] . Moreover , urinary KIM-1 and NGAL are potential biomarkers in predicting chronic kidney disease due to tubulointerstitial damage [36] . The first objective of this study was to determine the prevalence of CKDu using case definition by Jayathilaka et al , [26] mainly focussing on albuminuria ( ACR ≥ 30mg/g ) and eGFR in disease emerging locations in Hambantota district ( EL1 & EL2 ) and non-endemic areas Matara and Nuwara Eliya ( CM & CN ) in Sri Lanka . The second objective was to determine the levels of tubular markers KIM-1 and NGAL in the same study populations to assess potential early renal injury among CKDu subjects and healthy farmers from the selected locations .
Ethical approval was obtained from the ethics review committee of the Faculty of Medicine , Rajarata University , Sri Lanka . Farmers from all four locations were made aware about the study verbally and with information leaflets . A written consent ( n = 1701 ) was obtained from each farmer . There were 33 farmers that were unable to write , in such cases thumb print consent was obtained following ethical guidelines . The study was conducted in accordance with Helsinki declaration . Urine and blood samples were collected from individuals to measure creatinine , eGFR , urine ACR , KIM-1 , NGAL and HbA1c . Fresh morning first void urine samples were collected in sterile containers from each farmer and stored temporarily at 4°C . In the lab , urine samples were centrifuged for 15 minutes at 4000 rpm , 4°C and samples were stored in aliquots at -80°C until analysis . Blood samples were collected in labelled serum separator tubes and allowed to rest and clot at room temperature for 30 minutes . Blood sample tubes were then centrifuged at 2500 rpm for 10 minutes at 4°C . Later , the supernatant ( serum ) was labelled and transferred into cryogenic vials and stored at -80°C until analysis . Creatinine was measured in urine and serum by modified kinetic Jaffe reaction to minimize interference of non-creatinine and jaffe-positive compounds [37 , 38] in Dimension clinical chemistry system ( Siemens , New York , U . S . A . ) . Picrate reacts with creatinine to produce a red chromophore in the presence of a strong base ( NaOH ) . Absorbance was measured at 510 nm ( assay range: 0 mg/dl– 20 mg/dl ) . Urinary or serum creatinine levels were expressed in mg/dl . eGFR was calculated by using both CKD-EPI ( CKD Epidemiology Collaboration ) and modified MDRD equation [39] . GFR was expressed in mL/min/1 . 73 m2 of body surface area . A medical doctor in the study group measured the blood pressure after fifteen minutes’ rest , using a mercury sphygmo-manometer . The average of two readings taken five minutes apart was used . Microalbumin was measured in urine by particle-enhanced turbidimetric inhibition immunoassay ( PETINIA ) and Dimension clinical chemistry system ( Siemens , Newark , U . S . A . ) . In the presence of human albumin bound particle reagent ( PR ) , albumin present in the sample competes for monoclonal antibody ( mAb ) and reduces the rate of PR–mAb aggregation . Therefore , rate of aggregation was inversely proportional to albumin concentration in urine samples . Rate of aggregation was measured using bichromatic turbidimetric reading at 340 nm ( assay range: 1 . 3 mg/L– 100 mg/L ) . Ion-exchange high-performance liquid chromatography ( HPLC ) principle based separation of HbA1c on a cation exchange cartridge method was used for the measurement of HbA1c in human anti-coagulated whole blood samples . The absorbance of separated HbA1c was then measured at 415 nm using Bio-Rad D-10 as per manufacturer’s instructions . Human KIM-1 was measured in early morning urine samples using ELISA ( CUSABIO , P . R . China; Cat#: CSB-E08807h ) according to the manufacturer’s instructions . KIM-1 ELISA kit employs quantitative sandwich enzyme immunoassay technique for high sensitivity and specificity for human KIM-1 detection . Minimum detectable dose of human KIM-1 was typically less than 0 . 043 ng/ml . Intra-assay precision was ( CV%: <8% ) while inter-assay precision was ( CV%: <10% ) . Detection range of the kit was ( 0 . 312 ng/ml—20 ng/ml ) . Absorbance was measured at 450 nm using micro plate reader ( Utrao microplate reader–SM600 , Shanghai Yong Chuang , P . R . China ) . Human NGAL/Lipocalin-2 was measured in early morning urine samples using ELISA ( Ray Biotech , Inc . Norcross , GA; Cat: ELH-Lipocalin2-001 ) according to the manufacturer’s instructions . NGAL ELISA kit employs quantitative sandwich enzyme immunoassay technique for specific detection of human Lipocalin-2 or NGAL . Minimum detectable dose of human Lipocalin-2 was 4 pg/ml . NGAL ELISA kit intra-assay precision was ( CV%: <10% ) and inter-assay precision was ( CV%: <12% ) . Detection range of the kit was ( 4 pg/ml—1000 pg/ml ) . Absorbance was measured at 450 nm using microplate reader ( Utrao microplate reader—SM600 , Shanghai Yong Chuang , P . R . China ) . Data were analysed using IBM statistics ( version 22 . 0 ) . Continuous variables were reported as means ( SEM ) whereas categorical variables were reported as proportions . Renal biomarkers ( KIM-1 & NGAL ) were adjusted for urine creatinine concentrations prior to analysis . All comparisons between groups were performed by one-way ANOVA test with normally distributed parameters or transformed to natural log parameters . Kruskall–Wallis test and the Mann–Whitney U-test were performed to compare the significance between groups when deviated from the normality . Association between renal biomarkers with Albumin- creatinine ratio ( ACR ) and eGFR were performed using linear regression models . In all analysis , P < 0 . 05 was considered as significant .
Baseline characteristics and information of the study sample were given in Table 1 . Overall , 439 male farmers ( age ≥ 20 years ) participated in the cross-sectional study representing four different farming locations of Sri Lanka . Age of individuals ranged between 26–49 years in CM , 20–83 years in CN , 27–70 years in EL1 and 36–79 years in EL2 . Most participants were paddy Farmers . Farmers from CN and CM were involved in vegetable farming in addition to paddy . Lower education level hence low socio—economic status has been reported in emerging locations EL1 and EL2 than CM and CN . The most significant factor was the previous source of drinking water where EL1 and EL2 farmers mainly consumed well water that has been categorized as very hard ( ≥ 181 ppm ) . However , most of the farming locations now have access to tap water . Co-morbid diseases ( i . e . diabetes , hypertension , arthritis , gastritis , renal calculi etc . ) were reported in certain farmers from all four farming locations with CM ( 4% ) , CN ( 11% ) , EL1 ( 27% ) and EL2 ( 25% ) . Diabetes and hypertension were not reported within the study sample from CM . However , cases of diabetes and hypertension were reported within certain farmers from CN ( 1% and 6% ) , EL1 ( 6% and 12% ) and EL2 ( 10% and 8% ) respectively . Other co-morbid diseases such as arthritis , gastritis , renal calculi etc . , were also reported in CM ( 4% ) , CN ( 4% ) , EL1 ( 9% ) and EL2 ( 8% ) respectively . Healthy subjects ( i . e . no diabetes , hypertension , other kidney disease etc . ) from CM , CN , EL1 and EL2 were 96% ( n = 102 ) , 89% ( n = 104 ) , 81% ( n = 63 ) and 90% ( n = 86 ) respectively . Albuminuria ( ACR ≥ 30 mg/g Cr ) in repeated occasions was not reported in both non-endemic locations ( CM and CN ) and therefore no CKDu cases were identified under WHO case definition . However , fourteen ( 18% ) and nine ( 9% ) individuals from emerging locations EL1 ( Angunakolapelessa ) and EL2 ( Bandagiriya ) were reported with repeated ACR ≥ 30 mg/g Cr . These cases were defined as CKDu patients and grouped into EL1-CKDu and EL2-CKDu in further biomarker analysis . The range reported was 31–124 . 4 mg/g Cr in EL1-CKDu and 35 . 2–82 . 2 mg/g Cr in EL2-CKDu . Healthy subjects ( ACR ≤ 30 mg/g Cr ) in EL 1 and EL 2 were considered as controls from the emerging locations and grouped into C-EL1 and C-EL2 . ACR of farmers from CM , CN , C-EL1 and C-EL2 was not significant with each other ( p>0 . 05 ) except isolated two CKDu groups . Mean eGFR in both CM and CN was 108 ml/mi/1 . 73m2 and slightly lower eGFR was recorded at emerging locations EL1 and EL2 . The lowest eGFR was recorded in EL2-CKDu . No cases of eGFR ≤ 60 ml/min/1 . 73m2 , were reported in non-endemic locations ( CM & CN ) but three individuals ( 4% ) from EL1 and two individuals ( 2% ) from EL2 had eGFR ≤ 60 ml/min/1 . 73m2 Table 2 . Summarised statistics of KIM-1 normalized to urinary creatinine along with respective ACR , eGFR and HbA1C are presented in Table 3 . The lowest mean values of urinary KIM-1 were reported in CN ( 0 . 17 μg/g Cr ) followed by non-endemic controls in CM ( 1 . 26 μg/g Cr ) . Higher urinary KIM-1 was reported in CKDu emerging locations EL-1 and EL–2 with compared to both CM and CN . The highest KIM-1 levels ( 64 . 27 μg/g Cr and 48 . 53 μg/g Cr ) were reported by CKDu subjects in both EL-1 and EL-2 . As seen in Fig 3 , urine-KIM-1 concomitantly increased in CKDu cases identified in farming locations ( EL1- CKDu and EL2-CKDu ) . KIM-1 values reported at EL1-CKDu and EL2-CKDu were 50 fold and 38 fold higher than the control farming location ( CM ) . They also reported significantly higher concentrations in comparison with CN ( P < 0 . 001 ) . Higher KIM-1 was also reported in healthy farmers ( ACR ≤ 30 mg/g Cr ) at EL1 and EL2 . The highest urinary KIM-1 ( 16 . 07 μg/g Cr ) recorded in C-EL1 was twelve times higher than the control farming location ( CM ) . However , a lower KIM-1 concentration ( 10 . 52 μg/g Cr ) was recorded at farming location C-EL2 . KIM-1 levels in C-EL1 and C-EL2 were also significantly higher than the KIM-1 levels in non-endemic farming controls in CN ( P < 0 . 001 ) . Creatinine adjusted NGAL levels in non-endemic farming locations ( CM , CN ) and emerging farming locations ( EL1 and EL2 ) were given in Table 3 . The lowest mean NGAL ( 0 . 30 μg/g Cr ) was reported in non- endemic farmers in Matara ( CM ) followed by non-endemic farmers in Nuwara Eliya ( CN; 0 . 47 μg/g Cr ) . Both CKDu groups in emerging locations , EL1-CKDu ( 2 . 46 μg/g Cr ) and EL2-CKDu ( 6 . 95 μg/g Cr ) reported the highest NGAL . Pairwise comparisons revealed that NGAL levels at both CKDu groups were significantly higher than the control farming locations ( CM & CN , P < 0 . 001; Fig 4 ) . NGAL levels of C -EL1 and C- EL2 were approximately 2 . 5 fold and 4 fold higher than the control group CM and 1 . 6 fold and 2 . 4 fold higher than the CN . However , NGAL levels in C -EL1 and C- EL2 were not significant ( P > 0 . 05 ) with comparison to both control groups CM and CN . CKDu cases in both emerging locations ( EL1-CKDu and EL2-CKDu ) were also reported significantly higher NGAL when compare to healthy subjects ( C -EL1 & C- EL2 ) in the same location ( P < 0 . 001 ) . Analysis of urinary biomarkers ( KIM-1 and NGAL ) following the isolation of CKDu cases ( based on urinary albumin-to-creatinine ratio ( ACR ) ≥ 30 mg/g Cr ) revealed a significant correlation between increased urinary renal biomarker levels and albuminuria . Elevated levels of urinary KIM-1 were positively correlated with increased ACR in EL1 and EL2 ( rs = 0 . 57 , P < 0 . 001; Fig 5A ) however , no significant correlation was found between Urinary KIM-1 and eGFR ( rs = -0 . 12 , P = 0 . 30; Fig 5B ) . Similarly , elevated levels of urinary NGAL was positively correlated with increased ACR in CKDu emerging locations ( EL1 and EL2 ) ( rs = 0 . 49 , P < 0 . 001; Fig 5C ) and significant negative correlation was observed between urinary NGAL and eGFR in EL1 and EL2 ( rs = -0 . 37 , P < 0 . 001; Fig 5D ) .
The current study contributes to reveal the role of novel urinary biomarkers ( KIM-1 and NGAL ) in CKDu detection for the first time in Sri Lanka . This is also the first cross-sectional study exploring chronic kidney disease of uncertain etiology ( CKDu ) adopting WHO guidelines based CKDu case definition in Hambantota district , Southern Province , Sri Lanka . No studies have been previously reported using combination of urinary ACR , KIM-1 and NGAL together in determining early CKDu cases among Sri Lankan farming community . New CKDu cases ( 6% , 23/363 ) were identified during the study based on WHO study group definition . Albuminuric groups ( EL 1-CKDu & EL 2 –CKDu ) reporting the highest , non-endemic control subjects ( CM & CN ) showing the lowest and healthy subjects in emerging locations ( C-EL1 &C-EL 2 ) showing intermediate values of KIM -1 and NGAL indicate occurrence of sub-clinical renal injury . A gradient with clear separation of KIM -1 and NGAL values in albuminuric , non-endemic controls and endemic controls was evident . Overall , higher levels of urinary KIM-1 may indicate the proximal tubular damage whereas higher levels of urinary NGAL might be likely due to detectable damage occurring in loop of Henle and distal convoluted tubule . In Sri Lanka , men are more vulnerable to CKDu than women . A study conducted in NCP showed CKDu prevalence is higher in males ( 6% ) than in females ( 2 . 9% ) [1] . Similarly , in El Salvador , the prevalence of CKDu was reported higher in men ( 25 . 7% ) than in women ( 11 . 8% ) [4] . A meta-analysis based investigation using 68 studies concluded that males with non-diabetic renal disease showed significantly rapid kidney function deterioration over time than females [40] . Rapid progression of males from early stages of renal damage to chronic stages of kidney injury was most probably due constant exposure towards occupational or environmental factors [3–5 , 26] . As a consequence , the current study was precisely focused on male farmers excluding females and children . In 2011 , a single suspected case of CKDu ( 0 . 025% ) was reported in Hambantota district despite it was previously considered as a non-endemic region [27] . In the same year , six ( 0 . 43% ) CKDu cases were identified in Hambantota district using non-specific qualitative dipstick test followed by sulfosalicylic acid test [1] . Hambantota , located in the dry zone , shares similar socio-economic background and identical farming practices to the CKDu endemic NCP in Sri Lanka . Therefore , there is a looming possibility of emergence of CKDu in Hambantota district , Southern Province , Sri Lanka . Here , we report distinct 23 CKDu cases from EL1 and EL2 in Hambantota district , Southern Province , Sri Lanka , based on both CKDu definition by WHO [26] and increased levels of KIM-1and NGAL . Co-morbid diseases ( i . e . diabetes , hypertension , pyelonephritis , renal calculi etc . ) may influence levels of urinary ACR , KIM-1 and NGAL [41–45] . Consequently , we utilized questionnaire and medical history of individuals based assessment to identify and eliminate cases with co-morbid diseases . HbA1c was also measured in individuals with ACR ≥ 30 mg/g in EL1 and EL2 to exclude diabetes cases . Therefore , reported 23 new cases can be confirmed as CKDu . All CKDu cases ( EL1-CKDu and EL2-CKDu ) had ACR ≥ 30 mg/g on repeated testing . Measurement of albumin levels is a well-known early non-invasive biomarker to detect CKD [46 , 47] . An epidemiological study also supports the notion of testing albuminuria as an indicator of renal disease in general population [48] . Urinary albumin levels define the glomerular integrity and proximal tubule function in kidneys [49] . CKDu cases were also confirmed by significantly higher levels of urinary KIM-1 and NGAL . Both markers could easily isolate the suspected cases . Apparently healthy farmers at emerging locations ( C -EL1 & C- EL2 ) who had ACR < 30 mg/g , healthy eGFR and normal serum creatinine also showed elevated levels of KIM-1 and NGAL compared to both non-endemic control groups ( CM & CN ) indicating possible early renal damage . It may suggest that tubular damage expressed by KIM-1 is present before albuminuria appeared in farmers from C -EL1 & C- EL2 Similar cases have been reported among sugarcane cutters in Nicaragua with elevated urinary NGAL , IL-18 and NAG [30] . Urinary KIM-1 detection using micro-urine nanoparticle detection technique has been recently reported in Sri Lanka [50] but comparison is not possible due to smaller sample size of the study . KIM-1 is markedly up regulated in kidneys due to ischemic insult [35 , 51] . Up regulation of KIM-1 is a well-known consequence of proximal tubular damage in the nephron . Until more recently , detecting glomerular KIM-1 expression could also be a useful tool in identifying glomerular injury [52] . Increased levels of KIM-1 may also represent its involvement in phagocytosis of damaged proximal tubule epithelial cells by converting epithelial cells into semi-professional phagocytes [53 , 54] . KIM-1 up regulation may also be responsible in restoring functional and morphological integrity of kidneys following ischemic insult [35] . Our study shows that KIM-1 may be used to detect early CKDu cases in susceptible farming communities in Sri Lanka other than the conventional markers . However , NGAL elevation was only notable in EL1-CKDu and EL2-CKDu with 8 fold and 23-fold increase with compared to CM and 5 fold and 14-fold increase with CN . Similar results have been reported in El-Salvador where 26% higher NGAL was reported in CKD cases [55] . Laws et al . , reported 1 . 49 times higher NGAL among sugarcane farmers in Nicaragua [30] . However , no studies have been reported using NGAL in Sri Lankan population . NGAL elevation suggest , re-epithelialisation of damaged tubules and reabsorption of iron that was leaked due to damage of proximal epithelial tubule cells and also to induce iron-dependent nephrogenesis [51 , 56] . NGAL was not significantly increased in C—EL1 & C—EL2 when compared to CM and CN . This suggests elevation of KIM-1 may be more sensitive in detecting early tubular damage when compared with NGAL . Therefore , this study does not support the use of NGAL to detect early cases of CKDu in susceptible populations however , further studies are necessary . There are some limitations in our study . We initially recruited 1734 farmers . We used precise inclusion criteria to limit the study population i . e . , continuous farming ( > 10 years ) with long working hours ( > 600 hours per year ) . These inclusion criteria at the beginning of the study lead to a smaller sample size ( n = 439 ) . Some farmers ( n = 140 , 38 . 6% ) were not present at the time of urine collection therefore the study left with modest sample size for the biomarker analysis ( n = 223 ) . Other main limitation of the study was lack of established urinary biomarker levels that reflect sub clinical damage in Sri Lankan nephropathy . No previous studies have been done under local conditions and comparable occupational cohorts are even difficult to find in Mesoamerican nephropathy except a few recent studies [30 , 31] . Short term individual variation within the subjects was also unknown and a follow up study is required . The current study was conducted only on native male farmers in selected farming locations in Sri Lanka ignoring children and females therefore might hinder generalized applicability of the findings in other geographical locations and general population . In conclusion , this study reports 23 new CKDu cases for the first time in Hambantota district , Sri Lanka in spite of previously being considered as a non-endemic location . This is the first study to identify CKDu suspected cases and detection of early kidney damage in Sri Lankan farming communities using urinary biomarkers KIM-1 and NGAL . New cases were defined by WHO study group criteria for CKDu diagnosis in Sri Lanka along with urinary markers KIM-1 and NGAL . The results of our cross-sectional study shows that the tubular damage predicted by urinary KIM-1 and NGAL were significantly correlated with high urinary ACR levels . Strikingly , early tubular damage as seen by higher urinary KIM-1 and NGAL was also observed in healthy farmers despite normal ACR levels ( < 30 mg/g ) . Urinary tubular markers reconfirm tubulointerstitial disease with repeated tubular injury in CKDu among farming communities in Sri Lanka . However , longitudinal cohort studies are needed to predict use of tubular markers for precise prognosis , optimized treatment and patient management . | Chronic Kidney Disease ( CKD ) is a challenging global health issue around the world . Impairment of kidney function with time is eminent , but indications of CKD may not be seen until considerable damage to kidney functions . Two main causes of CKD are diabetes and high blood pressure . However recently new form of CKD has been reported among agricultural works in the tropics other than known factors . The causes for this mysterious CKD are unknown and termed as Chronic Kidney Disease of uncertain etiology ( CKDu ) . Clinical diagnosis depends on urinary markers and conventional creatinine based markers may underestimate the prevalence of the disease . Therefore , development of new sensitive markers for the early detection may certainly improve the treatment and patient management around the world . | [
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"pl... | 2016 | Urinary Biomarkers KIM-1 and NGAL for Detection of Chronic Kidney Disease of Uncertain Etiology (CKDu) among Agricultural Communities in Sri Lanka |
The human malaria parasite Plasmodium vivax is more resistant to malaria control strategies than Plasmodium falciparum , and maintains high genetic diversity even when transmission is low . To investigate whether declining P . vivax transmission leads to increasing population structure that would facilitate elimination , we genotyped samples from across the Southwest Pacific region , which experiences an eastward decline in malaria transmission , as well as samples from two time points at one site ( Tetere , Solomon Islands ) during intensified malaria control . Analysis of 887 P . vivax microsatellite haplotypes from hyperendemic Papua New Guinea ( PNG , n = 443 ) , meso-hyperendemic Solomon Islands ( n = 420 ) , and hypoendemic Vanuatu ( n = 24 ) revealed increasing population structure and multilocus linkage disequilibrium yet a modest decline in diversity as transmission decreases over space and time . In Solomon Islands , which has had sustained control efforts for 20 years , and Vanuatu , which has experienced sustained low transmission for many years , significant population structure was observed at different spatial scales . We conclude that control efforts will eventually impact P . vivax population structure and with sustained pressure , populations may eventually fragment into a limited number of clustered foci that could be targeted for elimination .
The international intensification of malaria control over the last 15 years has reduced the global malaria burden by more than 50% with rapidly declining transmission in many endemic regions [1] . Plasmodium falciparum and Plasmodium vivax are the major agents of human malaria however P . vivax is becoming the main source of malaria infection and disease in co-endemic areas because it is more resilient to control efforts [1–9] . These shifts in species dominance may result from the fact that P . vivax employs unique transmission strategies including dormant liver-stage infections that relapse months to years after the primary infection [10] . These biological characteristics suggest that P . vivax will be the far more challenging species to eliminate [10–13] , and that interventions and monitoring approaches originally developed for P . falciparum malaria may not be sufficient or suitable for P . vivax [6 , 14–17] . Surveillance tools that monitor the impact of antimalarial interventions are central to determining the success of disease control programs . Population genetics has been successfully harnessed to understand local changes in P . falciparum transmission dynamics in response to sustained control [18] , but this has not yet been applied extensively to P . vivax . Plasmodium parasites are haploid in the human host and replicate asexually for most of the lifecycle but undergo sexual replication and a brief period of diploidy within the mosquito vector . During this stage , meiosis produces haploid recombinant progeny that are then inoculated back into the human host . The co-transmission of multiple genetically distinct clones to the vector is thus central to the generation and maintenance of diversity via sexual recombination [19 , 20] . As infections decline both within and among hosts , it is expected that effective population size , genetic diversity and gene flow will decrease , eventually leading to inbred , structured populations [21–23] . Conversely , in areas of high transmission , recombination between distinct clones and gene flow are more common , resulting in diverse , unstructured populations [22] . Whilst P . falciparum fits this expectation [22] , P . vivax populations retain high levels of diversity and large effective population sizes at low transmission [7 , 24–28] and have higher diversity than P . falciparum populations [5 , 29–31] . P . vivax population structure has been reported for some areas [27 , 32–34] , but is absent in others [5 , 30 , 35 , 36] and does not appear to be associated with the level of transmission . The population structure observed in countries such as Peru [27] , Colombia [32] and Malaysia [33] , can be explained by multiple independent introductions of the parasite [37] , historically low P . vivax transmission [27 , 34] , non-overlapping vector species refractory to non-autochthonous P . vivax strains [38] and historically focal transmission combined with recent reductions due to control [33] . In regions with past hyperendemic P . vivax transmission and recent upscaling of malaria control efforts , population structure has not been observed [5] . The relationship between P . vivax transmission and population genetic parameters thus remains poorly understood , and requires systematic investigations with declining transmission and in the context of long-term intensified control . Historically , the Southwest Pacific region , in particular Papua New Guinea ( PNG ) and Solomon Islands , has endured some of the highest P . vivax transmission anywhere in the world [39 , 40] . This region has a natural , gradual decline in malaria endemicity from west to east with high transmission in PNG , moderate-to-high in Solomon Islands and low transmission in Vanuatu [39] , that has been accentuated by recent control efforts [7 , 41 , 42] . Our previously published population genetic data from PNG and Solomon Islands [30 , 35] , combined with new samples from ongoing studies within Solomon Islands and Vanuatu , presents a unique opportunity to understand the population genetics of P . vivax in context with declining transmission . Here we have defined P . vivax population genetic structure at different transmission intensities , spatial scales and in the context of successful long-term malaria control . We analysed almost 900 P . vivax microsatellite haplotypes from P . vivax isolates collected from infected humans throughout the Southwest Pacific region , including dense spatial and temporal sampling in the Solomon Islands [7] . The results demonstrate that P . vivax exhibits significant changes in population genetic parameters with declining transmission over space and time , highlighting the importance of maintaining control efforts , and the key role that population genetic surveillance of P . vivax can play in malaria control and elimination .
P . vivax positive samples from PNG , Solomon Islands and Vanuatu were used in this study ( Fig 1 ) . PNG has traditionally had the highest burden of the three countries and control has only been intensified in the last ten years through universal access to long lasting insecticide treated bednets ( LLIN ) and access to artemisinin combination therapy ( ACT ) [43] . In Solomon Islands , sustained and intensified malaria interventions in the last two decades including LLIN , indoor residual spraying and ACT have resulted in an approximately 90% reduction in malaria incidence [1 , 2 , 7 , 44] . The small country of Vanuatu harbors the southern boundary of malaria transmission in the Pacific , as it is crossed by the Buxton Line , which defines the limit of Anopheline breeding [45] resulting in a very low clinical infection rate that is dominated by P . vivax infections [1] . At the time of sampling , transmission ranged from high in PNG ( prevalence = 17 . 0–31 . 7% ) , moderate-high in Solomon Islands ( 3 . 9–31 . 7% ) and low in Vanuatu ( <1% [39] , S1 Table ) . Genotyping data from total of 887 P . vivax isolates from PNG ( n = 443 ) , Solomon Islands ( n = 420 ) and Vanuatu ( n = 24 ) were obtained ( Table 1 , S2 File ) . P . vivax positive samples were from both clinical and asymptomatic infections collected during different epidemiological surveys ( S1 Table ) . Data included previously published genotyping data from PNG collected in 2005–6 ( n = 443 ) and Solomon Islands in 2004–5 ( Tetere 2004 , n = 45 ) [30 , 35] in addition to 375 newly typed P . vivax isolates from three provinces of the Solomon Islands collected in 2012–2013 [7] , and 24 genotypes from one province of Vanuatu collected in 2013 ( Fig 1A ) . Dense sampling of the central region of the Solomon Islands allowed analyses at different spatial scales in three neighbouring island provinces including Guadalcanal ( Tetere 2013 , n = 39 ) , Malaita ( Auki , n = 13 ) and Central Province ( Ngella , n = 323 ) ( Fig 1B ) . In Ngella , sampling included 19 villages organized into five geographically and ecologically distinct areas including Bay ( n = 83 ) , South ( n = 35 ) , Channel ( n = 46 ) , North ( n = 136 ) and Anchor ( n = 23 , Fig 1C ) . In Vanuatu , samples were collected from the province of Espiritu Santo and included the villages of Port Orly ( n = 7 ) , Luganville ( n = 7 ) and Nambauk ( n = 10 ) . Further details of the samples and study sites are summarised in S1 Table and S1 File . All data and samples were de-identified for the analysis . The study was approved by The Walter and Eliza Hall Institute Human Research Ethics Committee ( 12/01 , 11/12 and 13/02 ) , the Papua New Guinea Institute of Medical Research Institutional Review Board ( 11–05 ) , the Papua New Guinea Medical Research Advisory Committee ( 11–06 ) , the Solomon Islands National Health Research Ethics Committee ( 12/022 ) and the Vanuatu Ministry of Health ( 19-02-2013 ) . To allow the selection of low complexity infections for confident reconstruction of haplotypes , we first determined the multiplicity of infection ( MOI ) in each P . vivax isolate by genotyping with the highly polymorphic microsatellites , MS16 and msp1F3 [30 , 35] . MOI data was previously published for the PNG [30 , 35] , Tetere 2004 [35] , and Ngella datasets [7] . The MOI in the Tetere 2013 , Auki 2013 , and Vanuatu P . vivax populations was determined for this study , and done according to previously published protocols [30 , 35] . Sample numbers that were genotyped using this approach are indicated in S1 Table . To measure population structure , all confirmed monoclonal infections ( MOI = 1 ) from new Solomon Islands ( Tetere 2013 , Ngella and Auki ) and Vanuatu samples were genotyped with nine genome-wide and putatively neutral microsatellites loci ( MS1 , MS2 , MS5 , MS6 , MS7 , MS9 , MS10 , MS12 and MS15 ) [46] . Due to small sample size , Auki and Vanuatu populations were supplemented by genotyping additional low complexity polyclonal infections ( MOI = 2 ) . Previously published data on the nine microsatellite markers for PNG and Tetere 2004 isolates was also derived from low complexity samples ( MOI = 1 or 2 , [30 , 35] ) . A semi-nested PCR strategy was employed , whereby a multiplex primary PCR was followed by nine individual secondary reactions , with a fluorescently labelled forward primer , as previously described [30 , 35] . PCR products were sent to a commercial facility for GeneScan fragment analysis on an ABI3730xl capillary electrophoresis platform ( Applied Biosystems ) using the size standard LIZ500 . Electropherograms resulting from the fragment analysis were visually inspected and the sizes of the fluorescently labeled PCR products were scored with Genemapper V4 . 0 software ( Applied Biosystems ) , with the peak calling strategy done as previously described [30] . Raw data from the published dataset was added to the new dataset and binned together to obtain consistent allele calls . Automatic binning ( i . e . rounding of fragment length to specific allele sizes ) was performed with Tandem [47] . After binning , quality control for individual P . vivax haplotypes and microsatellite markers was conducted to confirm the markers were not in linkage disequilibrium ( LD ) and to identify outlier haplotypes and/or markers ( i . e . haplotypes or markers which are disproportionately driving variance in the dataset ) . Isolates with one allele at all markers , or more than one allele at only one microsatellite marker were considered “confirmed monoclonal infections” . For isolates with more than one allele at any of the loci , the dominant alleles ( highest peaks ) were used to construct “dominant haplotypes” as previously described [30] . Both monoclonal infection and dominant haplotypes were combined for population genetic analyses [30 , 35] . Allele frequencies and input files for the various population genetics software programs were created using CONVERT version 1 . 31 . Allele frequencies and genetic diversity parameters including the number of alleles ( A ) and Nei’s unbiased estimator of gene diversity ( Hs ) [48] were measured using FSTAT version 2 . 9 . 3 . 2 [49] . Because A is influenced by sample size we also calculated the allelic richness ( Rs ) , which is normalized on the basis of the smallest sample size and based on the rarefaction method developed by Hurlbert [50] as implemented in FSTAT version 2 . 9 . 3 . 2 [49] . In addition , we measured the pairwise relatedness between haplotypes ( PS ) , calculated by determining the proportion of alleles shared between haplotypes as a function of the total number of markers genotyped . The proportion of pairs with Ps values greater than 0 . 50 ( Ps>0 . 50 ) , was then used as an indicator of relatedness within populations , and is analogous to Identity by Descent measures used by Taylor et al . and shown to decay with geographic distance [51] . Effective Population Size ( Ne ) was calculated using the stepwise mutation model ( SMM ) and infinite alleles model ( IAM ) , as previously described [22] . Mutation rates for P . vivax were not available and thus the P . falciparum mutation rate was used [52] . For SMM , Ne was calculated as follows: Ne=18x{[11−HEmean]2−1}μ where HE mean is the expected heterozygosity averaged across all loci . For the IAM , Ne was calculated using the formula: Ne= ( HEmean4 ( 1−HEmean ) ) x1μ . As a measure of inbreeding in the populations studied , multilocus LD ( non-random associations between alleles of all pairs of markers ) was estimated using the standardized index of association ( IAS ) in LIAN version 3 . 6 . IAS compares the observed variance in the number of shared alleles between parasites with that expected under equilibrium , when alleles at different loci are not in association [53] . The measure was followed by a formal test of the null hypothesis of LD and p-values were derived . Only unique haplotypes with complete genotypes were used and Monte Carlo tests with 100 , 000 re-samplings were applied [53] . The number of unique haplotypes was assessed using DROPOUT [54] . To confirm that LD was not artificially reduced by false reconstruction of dominant haplotypes , the analysis was performed for the combined dataset of dominant and monoclonal infection haplotypes ( i . e . all haplotypes ) , and for monoclonal infection haplotypes only . MS2 and MS5 both localize to chromosome 6 and MS12 and MS15 to chromosome 5 thus , analyses were repeated on datasets where MS5 and MS15 were excluded ( chosen due to a greater degree of missing data ) using the remaining seven loci spanning seven chromosomes . Where sample size permitted ( n > 5 ) , multilocus LD was also estimated at the village level . To investigate geographic population structure , for each metapopulation we measured the weighted average F-statistics over all loci using the distance method [55] using global AMOVA implemented in Arlequin version 3 . 5 . 2 . 2 [56] . Pairwise comparisons among populations were done using three measures of genetic differentiation , namely FST , GST and Jost’s D . FST was estimated using FSTAT version 2 . 9 . 3 . 2 [49] . GST [57] and Jost’s D [58] were estimated using the R package DEMEtics , as previously described [59] . Population structure was further confirmed by Bayesian clustering of haplotypes implemented in the software STRUCTURE version 2 . 3 . 4 [60] , and was used to investigate whether haplotypes cluster into distinct genetic populations ( K ) among the defined geographic areas . The analyses were run for K = 1–20 , with 20 independent stochastic simulations for each K and 100 , 000 MCMCs , after an initial burn-in period of 10 , 000 MCMCs using the admixture model and correlated allele frequencies . The results were processed using STRUCTURE Harvester [61] , to calculate the optimal number of clusters as indicated by a peak in ΔK according to the method of Evanno et al . [62] . The programs CLUMPP version 1 . 1 . 2 [63] and DISTRUCT 1 . 1 [64] were used to display the results . To assess phylogenetic clustering of haplotypes in each geographic area , the R software ( APE ) package was used to draw an unrooted phylogenetic tree using pairwise distances between multilocus haplotypes [65] . Statistical analysis of epidemiological and population genetic parameters was done using Graphpad Prism version 7 .
Based on infection prevalence data , PNG , Solomon Islands and Vanuatu represent high , moderate to high , and low transmission areas respectively ( [39] , S1 Table , S1 File ) . Because reliable prevalence data was not available for all populations , as an additional measure of transmission intensity we determined the multiplicity of infection ( MOI ) and examined the frequency distribution of samples with 1 , 2 , 3 or >3 clones , and the proportions of polyclonal infections in each population . MOI was determined by genotyping of all available P . vivax infections using the highly polymorphic markers MS16 and msp1F3 , and the proportion of polyclonal infections in each population [66] ( S1 Table ) . The MOI frequency distribution varied significantly across the Southwest Pacific ( Fig 1D , Chi Squared test: p<0 . 0001 ) with polyclonal infections ranging from high in PNG ( 52 . 2%-74 . 3% ) , and moderate to high in Solomon Islands ( 28 . 6–88 . 2% ) to low in Vanuatu ( 12% , S1 Table ) . The Solomon Islands population of Tetere experienced a significant change in the frequency distribution over a period of intensive control with polyclonal infections declining between 2004 ( 88 . 2% ) to 2013 ( 58 . 6% , Fig 1D , S1 Table , Chi Squared test: p = 0 . 0014 ) . There was significant variability in the distribution of polyclonal infections also among subpopulations of both PNG and Solomon Islands ( Chi Squared test: p<0 . 0001 ) . In the Solomon Islands , Tetere 2013 ( 58 . 6% ) had a higher proportion of polyclonal infections than Auki ( 28 . 6% ) and Ngella ( 30 . 0% ) , consistent with lower transmission in the latter two regions . Within Ngella , an area of dense sampling divided into five distinct ecological zones ( Anchor , North , Channel , South and Bay ) , the proportion of polyclonal infections ranged between 20 . 0–36 . 9% ( S1 Table ) , and was significantly associated with prevalence ( Linear regression: r2 = 0 . 97 , p = 0 . 002 ) . Low complexity isolates ( MOI = 1 or 2 ) were selected for further characterization with the full panel of nine-microsatellites . This strategy increases confidence in multilocus haplotypes , and since the majority of infections are MOI = 1 it was possible to reconstruct haplotypes from large numbers of samples with high confidence . New haplotypes were obtained for all monoclonal infections ( MOI = 1 ) from the Solomon Islands Tetere 2013 , Ngella and Auki populations , and from Vanuatu by genotyping an overlapping set of nine microsatellite markers . Two low complexity polyclonal infections ( MOI = 2 ) each from Auki and Vanuatu were also genotyped to boost sample numbers in those populations . Published microsatellite haplotype data was available for PNG ( n = 443 ) and the Tetere 2004 population ( n = 45 , [30 , 35] ) . Only high-quality haplotypes with data for at least five out of nine microsatellite loci were retained for population genetic analysis [30 , 35] , resulting in seven haplotypes being excluded . Two further haplotypes were identified as outliers ( i . e . those that do not conform to the expected distribution ) due to rare singleton alleles at the MS2 locus , and were discarded for subsequent analyses . The final dataset comprised a total of 887 haplotypes including 443 from PNG , 420 from Solomon Islands and 24 from Vanuatu ( Table 1 , S1 Table ) . The microsatellite haplotype dataset is available as a supporting file ( S2 File ) for further analyses however caution is needed if comparing to other datasets , since allele calls need to be binned together using raw data . Although most samples were initially identified as monoclonal , multiple alleles were detected after genotyping the additional nine markers . Therefore , the data includes 555 confirmed monoclonal infection haplotypes and 332 “dominant” haplotypes comprising the dominant allele calls ( highest peaks ) from samples with multiple alleles . The 887 haplotypes were distributed across all catchment areas , as were the 332 dominant haplotypes , however small sample sizes were available for lower prevalence regions of Auki and Vanuatu ( Table 1 ) . Note that MS16 and msp1F3 were used only to determine MOI and are not recommended for analysis of population structure due to their extreme diversity [67 , 68] and therefore they were excluded for the following analyses . Mean genetic diversity of the microsatellite markers showed a modest but significant trend of declining diversity from PNG ( HS = 0 . 81–0 . 84 , RS = 7 . 37–9 . 62 ) to Solomon Islands ( HS = 0 . 79–0 . 85 , RS = 6 . 51–9 . 20 ) and Vanuatu ( HS = 0 . 72 , RS = 5 . 45 ) ( One way ANOVA test of trend: p <0 . 05 , Table 1 ) . There was a trend of decreasing population diversity ( HS , RS ) and increasing proportions of closely related haplotypes ( Ps>0 . 50 ) with declining polyclonal infections but this was not significant ( Fig 2A–2C ) . In addition , effective population sizes ( Ne ) reflect the high diversity across the different parasite populations . The Solomon Islands and PNG populations showed moderate to high Ne , while Vanuatu had 1 . 5–5 fold lower Ne than any of the other populations ( Table 1 ) . The patterns observed suggest that sustained low transmission , such as that seen in Vanuatu , is needed for significant reductions in diversity and effective population size . Multilocus linkage disequilibrium of asexual blood stage parasites is an indirect measure of the rate of recombination between related individuals ( inbreeding ) in the mosquito stages , which is expected as transmission declines and infections become increasingly clustered . In previously published data from the high transmission sites of PNG and the earlier Solomon Islands timepoint ( Tetere 2004 ) there were no identical haplotypes and no significant multilocus LD was observed indicating limited inbreeding and random associations between alleles in those populations [30 , 35] . In the later data from Solomon Islands ( i . e . Tetere 2013 , Ngella and Auki ) and Vanuatu , seven haplotypes were found repeatedly amongst 22 isolates , suggesting clonal transmission due to self-fertilization and no detectable recombination , or alternatively , a single mosquito infecting several individuals . All repeated haplotypes were found in Ngella , and four were distributed among different villages or regions ( S1 Fig ) , making the latter scenario unlikely . Repeated and incomplete haplotypes were excluded for the analysis of multilocus LD retaining only the unique , complete microsatellite haplotypes comprised of all nine markers ( n = 248 ) . Significant multilocus LD was observed in the contemporary Solomon Islands populations ( Tetere 2013 , Ngella and Auki ) , including the five Ngella subpopulations , and in Vanuatu ( Table 2 ) [30 , 35] . The pattern of multilocus LD was retained when only monoclonal haplotypes from the dataset were considered ( n = 93 , Table 2 ) , as well as when only one locus per chromosome was analyzed , confirming that LD was not the result of false reconstruction or physical linkage , respectively ( S2 Table ) . Multilocus LD was significantly inversely associated with the proportion of polyclonal infections ( Fig 2D ) . Thus , multilocus LD is present only in the post-control Tetere 2013 population and in low transmission populations of Auki , Ngella and Vanuatu . This suggests increasing LD with declining transmission due to geospatial variability , and malaria intervention in Tetere . To measure population structure across the study area , patterns of genetic differentiation among populations and clustering of haplotypes was investigated . Average F-statistics over all loci indicated the presence of low levels of population subdivision amongst countries ( FST = 0 . 049 ) and a gradient of increasing structure from high to low transmission . Negligible differentiation was observed among provinces in PNG ( East Sepik , Madang and Simbu: FST = 0 . 013 ) , low levels among Solomon Islands provinces ( Tetere 2013 , Auki , Ngella: FST = 0 . 035 ) , and slightly higher genetic differentiation was observed among Ngella regions ( Bay , South , Channel , North , Anchor: FST = 0 . 042 , Fig 3A ) . Very high genetic differentiation was found among the three Vanuatu villages ( Port Orly , Nambauk , Luganville: FST = 0 . 348 , Fig 3A ) , however sample sizes were much smaller for this country ( n per village = 7–10 ) , making this analysis less reliable , with potentially inflated FST . Despite dense sampling within the Ngella regions , sample sizes were too small for village-level analysis within all but the North Coast region where it was similar to that found among the five Ngella zones ( FST = 0 . 045 , Fig 3A ) . Pairwise Jost’s D statistics , which account for the high diversity of microsatellites [30 , 58] ) , confirm moderate to high differentiation among countries with 22–42% private alleles ( Fig 3B ) . Within Solomon Islands , moderate to high proportions of private alleles were observed for Ngella: Channel ( 21–31% ) and Auki ( 27–40% ) compared to other populations . In addition , there was moderate genetic differentiation between villages on the Ngella:North Coast ( 18–24% ) and high differentiation between Channel villages ( 49% ) ( Fig 3B ) . Pairwise GST and FST values are also provided in the Supporting Information for comparison to other studies ( S3 Table ) . To investigate haplotype clustering patterns , we used the program STRUCTURE to define up to 20 genetic clusters ( K = 1–20 ) within the entire dataset , as well as for Solomon Islands and its sub-regions . The analysis identified a small number of sub-populations at various spatial scales down to the village level ( Fig 4 , S2 Fig ) . A major subdivision in Southwest Pacific parasites occurs at K = 2 between PNG and Solomon Islands and is supported by the ΔK analysis , whilst Vanuatu appears to be a mixture of the two ( Fig 4 , S2 Fig ) . A ΔK peak at K = 2 can be an artifact of STRUCTURE analyses especially where strong population structure occurs at the highest hierarchy . At K = 3 however , samples from the three countries cluster into three genetically distinct populations ( K = 3 ) ( Fig 4 , S2 Fig ) . For Solomon Islands , further substructure was observed at K = 4 , and within Ngella: Channel , with the Hanuvavine and Vutumakoilo village haplotypes forming distinct clusters; and on the North Coast , with some genetic clustering observed amongst villages ( Fig 4 , S2 Fig ) . Vanuatu haplotypes appear to cluster into two major groups also ( Fig 4 ) . High levels of recombination in Plasmodium lead to large star-shaped phylogenetic trees , however genetically differentiated clades ( populations ) can be observed with short internal and long external branches , and when isolates are relatively closely related , structure can also be observed . Phylogenetic analysis can thus be used for recombining organisms to detect clusters of parasites that may result from local population structure or focal transmission . Phylogenetic trees support the spatial structuring of haplotypes in Solomon Islands ( Ngella ) and in Vanuatu ( Fig 5 ) . The tree for PNG shows no spatial clustering and thus is not shown . In Ngella , the North Coast and Bay haplotypes radiate from distinct internal branches of the tree . Two distinct clusters were also observed for Channel isolates , one of which contains a number of closely related haplotypes and falls within a clade of North Coast isolates , the other with Bay isolates ( Fig 5A ) . In Vanuatu , haplotypes clustered by village of origin ( Fig 5B ) . The availability of samples from two time points during intensive malaria control in the Solomon Islands population ( Tetere 2004 and 2013 ) allowed the investigation of changes in P . vivax population genetic parameters in association with antimalarial interventions . In Tetere 2004 , genetic diversity was high ( mean Hs = 0 . 84 , Table 1 ) and there was no significant multilocus LD [35] ( Table 2 ) . By 2013 however , diversity was lower with borderline significance ( Hs = 0 . 79 , p = 0 . 055 , Wilcoxon signed rank test ) , the proportion of closely related haplotypes ( Ps>0 . 50 ) more than doubled , effective population sizes halved ( Table 1 ) and multilocus LD increased to significance ( Table 2 ) . There were also low but significant levels of genetic differentiation between the two years ( Jost’s D = 19 . 7% , Fig 2B , FST = 0 . 029 , S2 Table ) . This suggests significant changes in the population structure of P . vivax in Tetere due to intensified malaria control .
As malaria-endemic countries move towards elimination of the disease , measuring changing transmission dynamics will inform control programs when to switch from broad ranging to targeted control efforts [28 , 69] . Classical epidemiological studies that define the prevalence of infection are valuable as a monitoring tool , but only population genetic analyses such as those described here can detect the perturbation of transmission patterns , as indicated by the presence of inbreeding and fragmented population structure [28] . Moreover , understanding the geographic distribution and connectivity of malaria parasite populations will help to prioritize specific geographic regions for elimination [23] . Tracking the impact of control on P . vivax populations may be challenging given its more stable transmission , allowing populations to maintain high levels of diversity and gene flow relative to P . falciparum [5 , 30 , 70 , 71] . Using the largest and most densely sampled dataset of P . vivax microsatellite genotypes to date , across a geographic region with a strong , natural gradient of transmission intensities , our results reveal a modest decrease in diversity and limited changes in the proportions of closely related haplotypes , but significant increases in multilocus LD and population structure with declining transmission . Changes in population structure were also observed between two time points in the Solomon Islands population of Tetere , revealing a similar pattern due to declining transmission with intensifying malaria control during the intervening period . Together the results suggest that sustained control efforts are needed to reduce P . vivax transmission to the point where diversity and gene flow are interrupted . This provides one possible explanation for P . vivax resilience to control and a strong incentive to maintain intensive control efforts for P . vivax for longer periods of time relative to P . falciparum . Even with the wide range of transmission intensities investigated , the within population genetic diversity and relatedness values observed for PNG and Solomon Islands populations were similar . In Vanuatu , where P . vivax transmission has been sustained at low levels for many years , and after intensive control efforts in Tetere , Solomon Islands , lower levels of diversity and higher proportions of closely related haplotypes were observed . However , associations with multilocus LD and sub-population structure were consistently detected with declining transmission either over space or time . High P . vivax genetic diversity at low transmission was first recognized in Sri Lanka [25] and has also been observed together with significant multilocus LD in Peru [26] , Malaysia [33] , Indonesia [5] and Vietnam [72] . Multilocus LD and local population structure may therefore be more sensitive signals to detect changes in P . vivax transmission than diversity or relatedness . The relationship of these population genetic parameters with the proportion of polyclonal infections , suggests that polyclonality may be used as a proxy for these analyses . However , relatively small numbers of samples ( n = 30–50 ) would need to be genotyped for the more informative population genetic analysis providing a relatively cost-effective approach to understand transmission dynamics as well as to discern connectivity between parasite populations . The presence of identical and closely related haplotypes and significant multilocus LD in the context of high diversity is consistent with focal inbreeding which occurs as a result of low and increasingly clustered transmission . In most endemic regions , identical P . vivax haplotypes are rare and have only been seen only at very low transmission in Central Asia where the P . vivax population is nearly clonal , or at low transmission in the Amazon [19 , 73] . With sustained low transmission , opportunities for recombination between diverse strains will be reduced , resulting in multilocus LD and population structure . The patterns we have observed in lower transmission areas of Solomon Islands and Vanuatu may also reflect the contribution of relapse and increasingly related clones within polyclonal infections over a sustained period of low transmission [30 , 74] . Even in the high transmission setting of PNG , relapse has been shown to account for up to 80% of P . vivax infections [75] and would be expected to be even higher in a low transmission area . For some time after a reduction in transmission , the re-activation of parasites from a pool of genetically diverse parasites in the liver from numerous past infections will continue to provide opportunities for the exchange and dissemination of diverse alleles , sustaining genetic diversity in the population . As the liver reservoir is depleted over time , focal clusters of infection may be composed of more recent infections and subsequent relapses with highly related parasites [76] . Therefore , relapse is likely to maintain diverse meta-populations with high evolutionary potential . Other biological characteristics of P . vivax that are likely to sustain transmission and resilience to intervention include the pre-symptomatic and continuous production of transmission forms , coupled with efficient transmissibility at lower infection density that drives high rates of human-to-vector transmission [77 , 78] . In addition , the rapid acquisition of clinical immunity early in life and low density of infection [13] would lead to a larger population reservoir of asymptomatic carriers that would not be treated [2 , 7 , 28] . However , unlike relapse , these features of P . vivax biology do not fully explain the patterns of population structure that we have observed in the context of declining transmission . Across the Southwest Pacific , measures of genetic differentiation and clustering patterns using Bayesian analysis demonstrated that the diversity amongst P . vivax populations was predominantly partitioned by country of origin , which reflects both restricted gene flow and high LD in Solomon Islands and Vanuatu . One caveat is the 7–10 years gap between collections in PNG ( 2003–6 ) and the other two countries ( 2012–13 , not including Tetere 2004 ) , which may lead to the overestimation of population structure . However , low population structure between the 2003 PNG and 2004 Solomon Islands data was previously reported [30 , 35] . In addition , comparison of PNG data from 2003 and 2005/6 revealed no evidence of genetic differentiation [30] , suggesting that there are negligible changes in population structure across periods of high transmission . Population structure between the combined 2003–6 PNG data and the 2012–13 Solomon Islands data may therefore be attributed to multiple factors including the different time points , the much larger sample size from multiple Solomon Islands locations , and the intervening intensification of antimalarial interventions in Solomon Islands . The latter possibility is supported by the comparison of two time points for Tetere that reveal a decline in polyclonal infections , lower diversity and effective population size and an increase in closely related haplotypes and multilocus LD , which are the same changes that occurred with declining transmission over geographical space . Still , we cannot be certain that the population structure observed in the Solomon Islands ( and Vanuatu ) is the result of control efforts , because temporal population genetic data were only available for one site ( Tetere ) . The genetic structure of malaria parasite populations has previously been investigated with P . vivax populations over large spatial scales ( e . g . between countries or distant locations within countries ) [30 , 32 , 35 , 73 , 79 , 80] . Local population structure was also observed with the high-resolution analyses of P . vivax population structure in the central zone of Solomon Islands , a region spanning the three island provinces of Guadalcanal ( Tetere ) , Ngella and Malaita ( Auki ) , an area of around 100 km2 . Ngella P . vivax populations were also found to have moderate levels of genetic differentiation from populations of the other island provinces . Ngella is connected via a direct and popular shipping route that exists between Guadalcanal ( Tetere ) and Malaita ( Auki ) Provinces . This suggests that despite a significant level of human movement among these three provinces , importation of P . vivax cases into Ngella may be sufficiently reduced , contributing to the observed population structure . We also observed local population structure within Vanuatu . The small number of samples from each Vanuatu village limits the analysis of population structure somewhat , however , the high LD and the spatial clustering observed in the Bayesian and phylogenetic analyses would be unlikely if population structure was not present . Another caveat is that Vanuatu is at the edge of the species range , so it cannot be assumed that the low diversity and highly fragmented population structure is solely due to sustained low transmission . Indeed , fewer immigrants would be expected for a population at the edge of a species distribution , thus it is not surprising that the gene pool is smaller in this region . On an even finer scale , within Ngella , dense sampling was done allowing resolution of population structure amongst different ecological zones and villages within each zone . P . falciparum has almost disappeared due to ongoing control interventions , but P . vivax transmission remains at a cross-sectional prevalence of around 13% by PCR [7] . Ngella P . vivax parasite populations were spatially structured among different zones and even villages within the same region . Parasite populations within Ngella ( 20–50km ) were subdivided into four genetic clusters: Anchor/Bay/South , North Coast , and the two Channel villages . The Channel area has comparable prevalence and proportions of polyclonal infections to other Ngella areas , however the villages lay in an extensive mangrove system on both sides of a channel , suggesting that the relative isolation of these villages influences population structure . Population structure was also observed among neighbouring villages of the North Coast . Thus , P . vivax in Ngella consists of a metapopulation of several partially fragmented sub-populations [81] . No earlier samples were available from Ngella , however evidence from malaria surveys indicate a 90% reduction in cases from 1992 to 2013 ( Solomon Islands National Vector Borne Diseases Control Program ) , consistent with the structure observed being influenced by malaria control . Sustained interventions may have led to the relatively inbred and fragmented parasite populations observed , and indicate that a critical turning point may be within reach . Overall the results demonstrate changing P . vivax population structure with declining transmission across a gradient of high to low transmission , and to a limited extent , over time in concert with intensifying control efforts . Comparison to other populations to inform regional malaria elimination is now the subject of an ongoing study by the authors and collaborators . This systematic survey demonstrates the utility of multilocus LD and population structure to monitor P . vivax transmission . While the data indicates that transmission is high in the PNG populations , inbreeding and population substructure was observed at all spatial scales within Solomon Islands and Vanuatu , consistent with increasing recombination of related clones within populations and hampered gene flow between populations . The fact that these patterns are observed after documented transmission decline and that temporal observations in one area suggest that long term malaria control has led to these patterns , however further investigations with later time points are needed to confirm this . We conclude that while P . vivax may be more resistant to control efforts than P . falciparum [10–13] , long-term sustained malaria control will reduce transmission to low levels and lead to inbreeding and fragmentation of parasite subpopulations . The results emphasize the need for interventions aiming to eliminate P . vivax to be sustained for very long periods , well beyond the time frame required for P . falciparum . Given the proposal to eliminate malaria from the Asia-Pacific by 2030 [82] , intensive control pressure must be maintained to capitalize on these successes and avoid rebound . Enhanced control efforts including targeted control of fragmented populations will help to reach these goals . | Plasmodium vivax is a major human malaria parasite , common in endemic areas outside sub-Saharan Africa , and more difficult to control than other malaria parasite species . The distinct lifecycle biology of P . vivax is thought to contribute to its more stable and efficient transmission allowing the maintenance of high diversity and potentially , gene flow . Independent studies are therefore needed to understand how P . vivax populations respond to changing transmission levels , in order to inform malaria control and elimination efforts . Here we have determined parasite population genetic structure in three countries of the Southwest Pacific , an island chain with a natural west to east decline in transmission intensity ( Papua New Guinea > Solomon Islands > Vanuatu ) . With declining transmission , P . vivax populations experience only a modest decline in diversity but a significant increase in multilocus linkage disequilibrium and population structure , indicating that parasite populations become more inbred and begin to fragment into clustered foci . Analysis of two time points in one study area ( Tetere , Solomon Islands ) also show similar changes in association with intensifying malaria control . The results indicate that with long term sustained malaria control P . vivax populations will eventually fracture into population clusters that could be targeted for elimination . | [
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... | 2018 | Increasingly inbred and fragmented populations of Plasmodium vivax associated with the eastward decline in malaria transmission across the Southwest Pacific |
Recent outbreaks of Ebola and Zika have highlighted the possibility that viruses may cause enduring infections in tissues like the eye , including the neural retina , which have been considered immune privileged . Whether this is a peculiarity of exotic viruses remains unclear , since the impact of more common viral infections on neural compartments has not been examined , especially in immunocompetent hosts . Cytomegalovirus is a common , universally distributed pathogen , generally innocuous in healthy individuals . Whether in immunocompetent hosts cytomegalovirus can access the eye , and reside there indefinitely , was unknown . Using the well-established murine cytomegalovirus infection model , we show that systemic infection of immunocompetent hosts results in broad ocular infection , chronic inflammation and establishment of a latent viral pool in the eye . Infection leads to infiltration and accumulation of anti-viral CD8+ T cells in the eye , and to the development of tissue resident memory T cells that localize to the eye , including the retina . These findings identify the eye as an unexpected reservoir for cytomegalovirus , and suggest that common viruses may target this organ more frequently than appreciated . Notably , they also highlight that infection triggers sustained inflammatory responses in the eye , including the neural retina .
The concept that the eye , and other tissues in which immune privilege has been predicated , can act as a reservoir for viral infection has received increased attention recently . Ebola virus was detected in the eye of a patient who survived the initial infection and was considered fully recovered , but presented with inflammation of uveal tissues i . e . uveitis [1 , 2] . Subsequently , ocular complications were noted in almost 60% of Ebola virus survivors [3] . Ocular inflammation ( uveitis ) has also been reported in patients with a history of dengue fever that had subsided without other complications [4] . More recently , Zika virus has been shown to affect the eye , causing severe eye disease ( optic neuritis , chorioretinal atrophy ) and blindness in newborns . In adults , Zika virus can induce uveitis , a finding noted recently in animal models [5] . Human CMV ( HCMV ) is a very common pathogen , with a worldwide sero-prevalence ranging from 40 to > 90% [6 , 7] . HCMV infection is usually contracted early in life , and after an initial , generally asymptomatic infection , and partial viral control , the virus establishes a persistent and then latent infection that lasts for life [8] . Ocular inflammation has been noted in HCMV infected individuals , however , despite the presence of CMV DNA in the aqueous humor of the eye , it remains unclear whether active CMV infection , as might occur during CMV reactivation , is the primary cause of the inflammation [9] . Similarly , CMV has also been implicated in some forms of acute aseptic meningitis and encephalitis in immunocompetent patients [10] , but there are no reports of live virus being cultured from cerebrospinal fluid samples . Life-threatening complications arising from HCMV reactivation are a common occurrence in immunosuppressed individuals ( reviewed in [11 , 12] ) and in critically ill immunocompetent patients [13] in whom the infection is associated with prolonged hospitalization and/or mortality . HCMV reactivation in immunosuppressed patients manifests as an array of clinical syndromes including pneumonitis , hepatitis and colitis [14] . CMV infection of the central nervous system ( CNS ) and the retina are prominent features of immune-incompetence or severe immunosuppression , as occurs in neonates [15–17] , untreated AIDS patients or patients who do not respond to HAART or discontinue therapy [18] , and patients with haematological malignancies and graft versus host disease [19–21] . CMVs are strictly species specific , thus , in vivo experimental studies using HCMV are not possible . Due to the similarities in sequence and in vivo pathogenesis , murine CMV ( MCMV ) is widely utilized as a model of HCMV infection . In this model , initial acute infection lasts less than 2 weeks . Viral titres peak at 5 days , and by 10 days the virus is cleared from the blood and most target organs . By day 14 replicating virus is only detectable in the salivary gland , where MCMV establishes a persistent infection that lasts 30–60 days depending on the mouse strain . The virus then enters a state of latency , where replicating virus is not detectable [22] . Importantly , the mouse model of CMV infection does not require manipulation of the host . Unlike many other viruses , MCMV , a natural mouse pathogen , provides a unique model to study a medically important virus in vivo after infection of its biological host . Indeed , preclinical mouse models of CMV infection have provided valuable insights into the immunobiology of allogeneic haematopoietic stem cell or bone marrow transplantation , with much of the information obtained from these models already translated into clinical practice [23] . The relevance of inflammation at immune privileged sites , including the CNS , is increasingly appreciated . However , it remains unclear what the triggers of such inflammation are , and how sustained these responses may be . Several models have been used to study the etiology and pathogenesis of CMV infection in the eye , including the neural retina , in vivo . These have relied primarily on intraocular injection of virus [24] , infection of mice in which the blood-retinal barrier has been disrupted [25] , or infection of severely immunosuppressed mice [26] . By contrast , the impact of CMV on ocular compartments , including the neural retina , in immunocompetent hosts following systemic infection has been poorly characterised . Here , we demonstrate that following systemic infection of immunocompetent mice , ( a ) MCMV causes a chronic uveitis beginning within 48 hours and lasting indefinitely , even after viral replication is no longer detectable , ( b ) MCMV infects the anterior segment of the eye , with replicating virus detected throughout the uveal tract ( iris , ciliary body and choroid ) , but not the retina , for up to 10 days post infection and ( c ) MCMV can be cultured from explants of uveal tissues months after the initial infection has been controlled . Notably , persistent inflammatory changes , including infiltration of virus-specific CD8+ T cells and the development of tissue-resident memory cells ( TRM ) were noted in the retina despite the absence of detectable viral replication in this compartment . Thus , the eye is both a site of viral replication and viral latency for a common viral pathogen . These findings highlight the need to consider the ocular microbiome when investigating not only pathogenic processes , but also ‘normal’ physiological situations , as these viruses are common ‘residents’ in the eye . The data also demonstrate that immunosuppression is not a prerequisite for CMV-mediated eye disease and that severe and sustained inflammation is triggered by this infection .
To evaluate the impact of systemic MCMV infection on the eye we used adult BALB/c mice that are genetically susceptible to MCMV infection . Mice were systemically infected with MCMV by intraperitoneal injection ( IP ) and spectral domain optical coherence tomography ( SDOCT ) was used to monitor the impact of infection on the eye . SDOCT is a non-invasive technique that uses near-infrared light to generate high-resolution cross-sectional images of the eye . The eye can be grossly divided into two major anatomical components: the anterior and posterior segments . The anterior segment comprises the cornea , iris , ciliary body and lens; the posterior segment includes the vitreous , retina , choroid and optic nerve . A fluid ( aqueous humor ) filled chamber ( anterior chamber ) separates the cornea from the iris . A representative image of the anterior segment from an uninfected mouse is shown , with the relevant components identified ( Fig 1A ) . As early as day 5 post-infection ( pi ) , cells were visible in the anterior chamber , and cellular deposits were observed on the corneal endothelium ( Fig 1B , white arrows ) ; these became markedly obvious by day 10 pi , and were still apparent at day 25 pi ( Fig 1B , white arrows ) . Patchy iris thickening and vessel dilation were also evident ( Fig 1B , yellow arrows ) . Synechiae , a condition where the iris adheres to the lens , were frequently observed after infection ( Fig 1C , yellow arrow ) . Formation of synechiae blocks the flow of aqueous humor resulting in an increase in intraocular pressure that manifests as forward bowing of the iris ( iris bombé ) ( Fig 1C ) . The frequency of the various pathological features observed after MCMV infection was quantified at several times pi ( Fig 1D ) . Pathological changes associated with MCMV infection were highest at day 5–10 pi and decreased by day 25 pi . Retinal imaging microscopy identified vascular changes from day 10 pi ( Fig 1E , black arrows ) with significant increases in the diameter of the retinal vessels ( vessel dilatation and calibre variation ) observed after infection ( Fig 1F ) . Histological analysis was undertaken to evaluate the extent of inflammation and potential pathology in various eye compartments . The major eye compartments are schematically depicted ( Fig 2A ) . Inflammatory changes were noted in the anterior chamber from day 5 to day 25 pi , including cell infiltration ( Fig 2B , black arrows ) and synechiae ( Fig 2B , red arrows ) . The retinal structure was also affected following infection; at day 10 pi retinal vessel enlargement ( Fig 2C , double tailed arrow ) and occasional retinal folding ( Fig 2C , asterisk ) were observed , together with inflammatory cells in the subretinal space ( Fig 2C , black arrows ) . Mild vitritis ( Fig 2C , red arrows ) and rare inflammatory cells in the subretinal space were present at day 25 pi ( Fig 2 , black arrow ) . Whether MCMV can infect and be detected in the eye after systemic infection of immunocompetent hosts is unclear . To address this question and determine whether the virus is present in the eye , we infected mice with a recombinant MCMV engineered to express the fluorescent protein mCherry , that aids the identification of infected cells . At various times pi , mice were injected with fluorescein prior to analysis to identify blood vessels . mCherry-positive cells were detected in vivo in the iris at day 5 and 7 pi ( Fig 3A ) . The majority of mCherry positive cells were detected outside the vasculature , indicating that MCMV had infected the iris tissue . To extend this finding , eyes , along with other target organs , were harvested at day 5 pi and homogenates prepared . Infectious viral loads within organs were measured by plaque assay , and significant levels of virus were detected in organs such as spleen , liver , lungs and salivary glands ( Fig 3B ) . Importantly , infectious MCMV was also reproducibly detected in eye homogenates ( Fig 3B ) indicating that the virus can effectively replicate in some compartments of the eye . Eye whole-mounts provide important topographic information , including the distribution and morphology of infected cells in specific compartments of the eye . Whole-mounts of the eye were therefore prepared and stained with antibodies to the viral antigen IE1 , to identify virally infected ( IE1+ ) cells in situ and to distinguish between virus directly infecting ocular tissue and virus trafficking within the eye vasculature . At day 5 pi , foci of infected cells were readily detected in the iris with focal distribution around vessels ( Fig 3C ) . The number of infected cells detected within the iris decreased thereafter , with no infected cells detectable by day 25 pi ( Fig 3C ) . In order to determine if the anterior segment is a prominent target for MCMV infection , whole-mounts were prepared from a large cohort of mice infected in multiple independent experiments . This analysis indicates that MCMV infection within the iris occurred in approximately 80% of immunocompetent mice systemically infected with MCMV ( Fig 3D ) . The percentage of mice with an active MCMV infection in the iris began to decrease by day 7 pi , with the infection no longer detectable in this compartment by day 25pi ( Fig 3D ) . The capacity for MCMV to infect other anatomical compartments within the eye ( Fig 4A ) was then assessed . Whole-mounts were prepared at day 5 pi and stained with antibodies specific for the viral IE1 protein . In addition to the iris , foci of infected cells were detected in the ciliary body , choroid and cornea ( Fig 4B ) , while infection of the retina could not be detected by this method . To consolidate these findings and localise virus within the distinct compartments of the eye , mice were infected with MCMV-LacZ , a recombinant virus engineered to express β-galactosidase . Tissue sections of eyes from mice infected with MCMV-LacZ confirmed that multiple compartments of the eye harbour MCMV . Specifically , virally infected cells were detected in the choroid , iris and ciliary body ( Fig 4C ) . Together these data clearly demonstrate that , following systemic infection , MCMV gains access to select compartments of the eye and is capable of effectively replicating within these sites . MCMV is known to be capable of infecting a wide array of cell types , including endothelial cells , macrophages and fibroblasts . To define the nature of the ocular cells targeted by MCMV , phenotyping of infected cells in the eye was performed . Given that MCMV infection was most prominent in the iris , this tissue was selected for analysis . Iris whole-mounts were stained with anti-IE1 antibodies ( to detect viral antigen ) , in combination with cell lineage specific antibodies and analysed by microscopy . Infection , denoted by nuclear IE1 staining , was detected in individual CD31+ vascular endothelial cells as early as 24h pi ( Fig 5A ) with clusters of infected endothelial cells detected by day 2 pi ( Fig 5B ) . In addition to endothelial cells , MCMV-infected PDGFRβ+ pericytes were detected from day 2 pi ( Fig 5C ) , indicating that the virus can exit the vasculature and infect surrounding tissue . As the infection proceeded , in addition to endothelial cells and pericytes , virally infected , IE1+ cells were found to express MHC-II and macrophage markers ( F4/80 and IBA1 ) ( Fig 5D ) . Thus , vascular endothelial cells within the iris are initially infected by MCMV , with the virus then spreading to pericytes and infiltrating monocytes within the iris as the infection proceeds . The results of the OCT analysis indicated systemic MCMV infection causes inflammation in some compartments of the eye . Eye whole-mounts were used to characterize changes in the distribution of cellular components , including cells that may be infiltrating specific compartments of the eye after infection . Normal uveal tissue contains a rich population of MHC-II+ dendritic cells ( DC ) and dendritiform macrophages , while the vasculature is MHC-IIlo or negative ( [27] and Fig 6A , uninfected ) . At day 5 pi , expression of MHC-II on the vessels within the iris was significantly increased , with expression returning to baseline by day 25 pi ( Fig 6A ) . In addition , by day 10 pi an increase in the number of MHC-II+ cells in the iris stroma was apparent ( Fig 6A ) , a finding consistent with our previous data showing that IBA1+ macrophages infiltrate the iris after MCMV infection [28] . These cells persisted to day 25 pi , by which time they were found to form large clusters ( Fig 6A ) . To confirm these findings and detect leukocytes infiltrating the iris , whole-mounts were also stained with anti-CD45 antibodies . An increase in the number of CD45+ cells was apparent at day 5 pi with large clusters of CD45+ cells still evident in approximately 40% of the infected mice at day 25 pi ( Fig 6B ) , a time when virus is no longer detectable at this site . Quantitative analysis of the cellular infiltrate in the iris was performed by flow cytometry to better define the cells infiltrating this site in response to systemic MCMV infection . The leukocytes in an uninfected iris consisted predominantly of resident macrophages , with a small number of CD4+ T cells present , a finding consistent with previous work ( [27] and Fig 6C ) . At day 5 pi an influx of inflammatory monocytes and neutrophils was evident , with no significant change in the proportion of other cell lineages observed ( Fig 6C ) . At day 10 and 25pi , a substantial increase in the number of CD45+ cells localised to the iris was evident , with CD8+ and CD4+ T cells being the major infiltrating cell types ( Fig 6C ) . MCMV infection did not have any significant impact on the number of B cells or NK cells localised to the iris . In order to confirm that the observed changes in T cell numbers reflected infiltration of the iris rather than an increase in T cells within the vasculature , whole-mounts from mice at day 25 pi were prepared . Staining of iris whole-mounts with anti-CD4 or anti-CD8 antibodies clearly demonstrated that these lymphocytes have exited the vasculature and localise to iris tissue ( Fig 6D ) . Together , these data establish that systemic infection with MCMV induces a strong inflammatory response in the anterior segment of the eye . In contrast to the iris , and in agreement with previous findings [28] , we did not detect discernible replicating MCMV in the retina . Retinal tissue was assayed for the presence of viral genome and viral transcripts at the peak of infection ( day 5 and day 9 ) , when virus was clearly identified in other eye compartments both by this method ( S1 Fig ) , as well as by plaque assay ( Fig 3B ) and immunofluorescence analyses for viral antigen ( Figs 3 , 4 and 5 ) . We were unable to detect viral genome , replicating virus or viral antigens in retinal tissue . Despite the absence of actively replicating virus at this site , the inflammatory response to systemic MCMV infection was exuberant , and similar to that observed in the iris , where replicating virus and viral antigen were detected . In uninfected mice , the retina is devoid of leukocytes and no significant MHC-II staining is evident ( Fig 7A and 7B uninfected ) . Following MCMV infection , increased expression of MHC-II was observed on vessels within the retina ( Fig 7A ) and CD45+ leukocytes were also detected in the retina ( Fig 7B ) . Flow cytometric analysis revealed that neutrophils and monocytes represent the bulk of the infiltrating cells early after infection , with CD8+ T cells becoming the predominant population by 25 pi ( Fig 7C ) . Staining of retina whole-mounts confirmed that CD45+ leukocytes had exited the vasculature and entered the retina ( Fig 7D ) . Thus , despite there being no detectable MCMV within the retina , leukocytes and lymphocytes infiltrate this compartment after systemic infection and , remarkably ongoing inflammation is noted , even though the host is immuno-competent . Having demonstrated that systemic infection leads to ongoing inflammatory responses both in the iris and in the retina , we examined whether these responses included virus-specific CD8+ T cells , a population essential for the control of acute infection and postulated as critical to prevent viral reactivation . The number of CD8+ T cells increased over the course of MCMV infection in both the iris and the retina ( Fig 8A and 8B ) , as did the number of virus-specific CD8+ T cells which were detected using MHC-I tetramers for MCMV IE1 ( Fig 8A and 8B ) . In BALB/c mice , which is the model used in our studies , viral latency is established by day 40 pi , when viral replication is not detected in any of the target organs , including the salivary gland [29] . We therefore extended our analyses to day 60 pi to examine inflammatory responses during viral latency . Non-recirculating TRM represent long-lived memory T cells that reside in tissues well after initial antigen encounter [30] . TRM typically express CD69 and/or CD103; following MCMV infection CD8+ T cells with a TRM phenotype were detected in both the iris ( Fig 8A ) and retina at day 60 pi ( Fig 8B ) . Thus , a pool of tissue resident memory T cells is established in the eye after systemic MCMV infection . Clusters of CD45+ leukocytes were found in the iris at day 25 pi ( Fig 6B ) . Since MCMV infection is rapidly controlled at this site , we assumed that the aggregates of leukocytes would disperse after resolution of the infection . Surprisingly , large clusters of CD45+ cells resembling granulomas were still detectable at later times after infection , and as late as day 250 pi ( Fig 9A ) . No IE1+ ( virally-infected ) cells could be detected in these sections , indicating that this inflammation was occurring in the absence of active viral replication . The persistence of leukocytes at this site could signify that a latent infection has been established . To test this possibility , mice were infected with MCMV for at least 70 days , a time point where no active viral replication is detectable . Eyes were then isolated from latently infected mice , the cornea , iris and choroid dissected , and explant cultures established . The cultures were assayed for the presence of MCMV by plaque assay on a weekly basis . No virus was detected in the first week of culture , confirming that active viral replication is not taking place at this time point . Iris explants from uninfected mice never showed any changes in the cellular monolayer ( Fig 9B ) . After two weeks of culture , cytomegalic cells and foci of viral cytopathy were noted in several of the iris explants from latently infected mice ( Fig 9B ) . Titration of culture supernatants on permissive fibroblasts was used to confirm the presence of reactivated infectious virus . MCMV reactivation was frequently observed in iris and choroid cultures , and rarely in corneal cultures ( Fig 9C ) . These data provide unequivocal evidence that MCMV can establish latency in the eye , and highlight the eye as a reservoir of reactivating , infectious virus in immunocompetent hosts .
MCMV infection in mice is a reliable model for HCMV infection and considerable data on pathogenesis and disease in humans have been derived from the MCMV mouse model . We report here that systemic infection with MCMV in immunocompetent mice induces ocular inflammation in the form of uveitis ( iritis , cyclitis and choroiditis ) , which is characterised by an acute replicative viral phase lasting 7–10 days and a chronic , granulomatous inflammatory phase which persists long-term , even when replicating virus is no longer detectable in eye tissues . Although no discernible replicating virus was detected in the retina , unexpected and sustained inflammatory changes were noted in this neural compartment . Notably , latent infectious virus could be readily reactivated from explanted uveal tissues , indicating that cytomegalovirus establishes an unexpected reservoir of virus in the eye of immunocompetent hosts . These findings provide support for the notion that in situations of immunosuppression , the presence of virus in the eye is unlikely to require spread of reactivating virus from non-ocular tissues and highlight the fact that in immunocompetent hosts sustained inflammatory changes can be elicited by infection . Based on these data , several general observations relevant to host-viral interaction can be made . Firstly , the characteristic pathology of myeloid cell aggregates , which contain virally infected cells within focal areas located around vessels , resemble similar , if larger , lesions in the lung [31] . The nature of the tissue pathology induced in the iris by CMV is also reminiscent of damage in several other tissues , in that vasculitis is a prominent component of the inflammatory response [32–35] . Inflammation in the retina after systemic infection is similar to data obtained from studies in the brain [15 , 35–37] , with the notable difference that brain inflammation has only been reported either ( i ) after direct infection of the brain or ( ii ) after systemic infection of severely immunosuppressed or immune-incompetent hosts , and concomitantly with overt viral infection . Indeed , in mouse models , systemic infection leads to rapid infection of the brain parenchyma , as well as meningeal tissue ( equivalent to retina and uveal tract respectively in the eye ) in neonatal mice and in severely immunocompromised mice , but not in adult immunocompetent mice [15 , 37–39] . These studies suggest that in adult mice the blood-brain barrier and/or immune-mediated mechanisms prevent intracerebral spread of MCMV . By contrast , in neonatal mice , where both the blood-brain barrier and the immune system are not fully developed , MCMV can infect neural cells [35 , 37] . Similar control mechanisms may prevent infection of the retina; while we found that in immunocompetent mice MCMV infection was readily detected in choroidal cells which abut the retinal pigment epithelial ( RPE ) component of the blood-retinal barrier , infection of retinal parenchymal cells was not observed by the methods used here . Consistent with these findings , in humans , retinal pathology is observed in the severely immunocompromised [40] . Notably , overt and sustained inflammation ( vasculitis and lymphocyte infiltration ) was noted in the retina of mice after systemic viral infection and in the absence of discernible viral replication . Our results provide evidence that CMV infection leads to sustained inflammatory changes in the eye and suggest that immunocompetent CMV-seropositive individuals may experience long-lasting low grade ocular inflammation . HCMV infection or reactivation is generally regarded as being asymptomatic in healthy individuals . This assumption is coming under increased scrutiny , with HCMV infection of apparently immunocompetent individuals reported to cause disease in organs including the gastrointestinal tract , the central nervous system and the eye [10] . In recent years , HCMV has been increasingly implicated in anterior uveitis and corneal endotheliitis [41–45] . In support of HCMV being the etiologic agent in these conditions , some patients respond to antiviral drugs [42 , 44] . Many clinicians remain sceptical about the relevance of HCMV in the aetiology of anterior segment disease and therefore it is likely that the incidence of disease is underestimated , resulting in inappropriate treatment which can severely compromise vision . Our data provide evidence for the role of CMV infection in these diseases . OCT analyses performed after day 60 pi in our mouse model showed that the on-going inflammatory changes detected by immunohistology and FACS analyses could not be detected by OCT ( S2 Fig ) , indicating that this technology is not sufficiently sensitive to monitor relevant inflammatory changes . Notably , the ongoing inflammation observed following CMV infection may predispose to other conditions , including age-related macular degeneration [46] . Our current study also addresses the important question as to which is the initial cell to be infected . Notably , the nature of the initially infected cell is likely to have relevance to the cellular source of latent infection . As shown in this study , endothelial cells in the iris are the first cells in which virus is detected; this is followed by infection of perivascular cells , including pericytes . Importantly , the areas of focal infection are associated with patches of vascular occlusion ( vasculitis ) . As the virus is contained in foci around the vessels during the productive stage , and this remains the site where eventually viral gene expression is shut off , it is likely that uveal endothelial cells , and/or the associated resident myeloid cells , become the source of latent infection . Other possibilities are uveal tract stromal fibroblasts . Since the choroid tissue samples used in the reactivation assays also contain RPE , the involvement of RPE cells in supporting latent infection cannot be excluded . In previous studies , liver sinusoidal endothelial cells , as well as myeloid cells have been identified as potential sites of initial infection and latency [47] . As is well known , CMV is a superbly opportunistic virus with the potential to infect many cell types , and this may explain why the virus remains latent in such a large proportion of the population , although requiring a sustained inflationary T cell response to prevent the virus from reactivating and causing pathology in vivo [48] . Previous studies , using models where the virus was delivered directly in the eye have established that ocular compartments are permissive to CMV infection [24 , 49] . Our studies have expanded the previous body of knowledge and provide novel evidence that systemic CMV infection can lead to viral replication in specific eye compartments , namely the anterior segment and choroid , but not the retina . Unlike previous studies , we have also shown that infection in the eye occurs in immunocompetent hosts . Importantly , our data demonstrate that systemic CMV infection of immunocompetent hosts leads to sustained inflammation in the eye , including the neural retina . Retinal tissue is permissive to CMV infection as demonstrated by intraocular delivery of virus in vivo , as well as infection of retinal pigment epithelial cells in vitro [24 , 50] . Clearly , in the setting of a functional immune system and an intact blood retinal barrier , as modeled in our current studies , the virus does not appear to be able to infect the retina . Despite the lack of detectable infection , an inflammatory response is elicited in the retina , which includes the presence of virus-specific CD8+ T cells and the formation of TRM populations . TRM are non-circulating subsets of memory T cells that mediate localized protective immunity to site-specific infections . They mount potent recall responses and accelerate the control of pathogens , especially at barrier sites . At mucosal sites , such as the salivary gland , the formation of MCMV-specific TRM CD8+ T cells can occur independently of viral replication , and this population can be supplemented in an ongoing manner by circulating TRM [51] . The presence of MCMV-specific TRM in the retina is more difficult to reconcile given the absence of viral replication noted in this tissue and the fact that the retina is protected by a tight retinal blood barrier . Two possibilities come to mind . The retina is a highly vascularized tissue and thus in one scenario it is possible that TRM exit the vasculature and access the retinal parenchyma . The signals that drive egress of virus-specific TRM from the retinal vasculature to the parenchyma remain to be identified . Alternatively , it is possible that the deposition of TRM in retinal tissue is driven by viral antigen presented to these T cells independently of local viral replication . In such a situation it is possible that viral antigens are phagocytosed by microglia during the process of ‘cleaning’ up lysed MCMV infected cells in the anterior segment and/or retinal pigment epithelium and that these cells then present viral peptides . Whether the inflammatory milieu induced by infection is sufficient to cross-present these viral peptides in an immunogenic manner that stimulates local T cell proliferation and TRM formation remains unknown . Thus , the cues required for the generation of retinal TRM , their functionality and role in protective immunity to CMV , as well as to potential collateral damage responses are important considerations that require further investigation . In summary , the role of micro-organisms that inhabit and establish ‘residency’ in various tissues has received much attention recently because of the profound impact that they have on an extensive range of functions , including immunological , metabolic and hormonal responses . The microbiota of the eye remains poorly characterized and the impact of pathogens that reside in this tissue is largely unknown . The current findings clearly identify the eye as a reservoir for a common viral pathogen , and provide support for the notion that viral infection of ‘privileged sites’ may be a general phenomenon . Importantly , the ongoing inflammation observed in the eye following MCMV infection of immunocompetent hosts is unexpected and highlights the need to consider CMV as a pathogen capable of inducing long-lived inflammatory sequelae in the eye , including the neural retina .
Inbred female BALB/c mice at 8–10 weeks of age were obtained from the Animal Resource Centre ( Perth , Western Australia ) and kept in specific pathogen free conditions . Mice were infected by intraperitoneal administration of 1x104 plaque-forming units of salivary gland propagated virus stock of MCMV diluted in phosphate-buffered saline ( PBS ) containing 0 . 05% fetal bovine serum . The viruses used were: MCMV-K181-Perth [52] , MCMV-K181-Perth-LacZ [29] , and MCMV-K181-Perth-mCherry . The mCherry virus was constructed by inserting the DNA sequence for the mCherry fluorescent protein in frame with C-terminus of IE1 . A self-cleaving 2A peptide sequence separates the IE1 and mCherry sequences allowing for simultaneous expression of mCherry and IE1 . Mice were euthanized and eyes removed and fixed in Davidson’s fixative for 12hr and stored in 10% neutral buffered formalin . Tissue sections of 5 μm were cut and stained with hematoxylin and eosin . X-gal staining was performed by freezing eyes in Tissue-Tek OCT compound ( Sakura Finetek USA , Torrance , CA ) , and 7 μm sections cut . Staining of sections with X-gal was performed as described . Mice were euthanized , and eyes collected and fixed in 2% paraformaldehyde for 1 hr before transferring into cold PBS . For staining of endothelial or pericyte markers eyes were fixed in ice cold methanol . After dissection of the various compartments of the eye ( choroid , retina , iris ) the tissues were incubated in 20 mM EDTA at 37°C for 30 minutes . Tissues were then incubated in a solution containing 0 . 3% Triton-X , 2% bovine serum albumin and 10% of normal goat serum in PBS at room temperature for 30 min . Tissues were incubated with the primary antibody overnight at 4°C followed by incubation with the secondary antibody at room temperature for 1 hr . Detection of the immediate-early ( IE1 ) protein of MCMV was performed with the 6/58/1 monoclonal antibody [53] . Monoclonal antibodies specific for the following cell surface markers were used to stain tissue sections: MHC-class II ( clone M5/114 ) , CD45 ( clone 30F11 ) , CD31 ( clone Mec 13 . 3 ) , PDGFRβ ( clone APB5 ) , F4/80 ( clone BM8 ) , CD4 ( clone RM4-5 ) , CD8 ( clone 53–6 . 7 ) , IBA1 ( Wako Pure Chemicals Industry , Osaka , Japan ) , CollagenIV ( Biorad-2150-1470 ) . Slides were counterstained with Hoechst to visualize nuclei . Assessment of stained specimens was performed using an epifluorescence microscope ( Olympus BX60 microscope: Olympus , Tokyo , Japan ) or a Nikon C2 Upright Confocal microscope . Organ were collected and homogenized in cold Minimum Essential Medium ( MEM ) supplemented with 2% neonatal calf serum ( NCS ) . After homogenisation insoluble material was removed by centrifugation ( 300 g for 15 min at 4°C ) and the resulting supernatants were stored at -80°C . Viral titres were determined by absorbing serial dilutions of organ homogenates on confluent monolayers of M2-10B cells in 24-well tissue culture trays for 1hr at 37 oC . The homogenate was removed by aspiration and cells overlaid with 1 ml of MEM 2% NCS containing 0 . 7% w/v carboxymethylcellulose and incubated for 4 days at 37 oC . Plates were stained with a solution of 4% formaldehyde and 0 . 5% Methylene blue and plaques counted . The viral load in different compartments of the eye was determined by real-time quantitative PCR ( qPCR ) . Briefly , eyes were dissected and DNA extracted from the iris/ciliary body , choroid/sclera and retina using the DNeasy Blood and Tissue Kit ( Qiagen ) . Iris/ciliary body and choroid/sclera were pooled from 5 mice while retinas from both globes were pooled for each mouse . qPCR was performed with 100ng of purified DNA , glycoprotein B ( gB ) specific primers ( F: ttggctgtcgtctagctgttt and R: taaggcgtggactagcgataa ) and the Sso Advanced Universal SYBR Green system ( Biorad ) . Serial dilutions of a synthesized MCMV gB sequence were used to generate a standard curve . The presence of MCMV mRNA was determined by isolating total RNA from dissected compartments of the eye using the PureLink RNA kit ( Ambion ) according to the manufacturer’s protocols . Briefly , tissue was collected and placed directly into lysis buffer containing β-mercaptoethanol and homogenised using the Tissue Lyzer II ( Qiagen ) for 3 x 30 seconds at 23 Hz . The resulting lysate was passed through a PureLink RNA column , treated with DNase I , and eluted in RNase free water . The purity and quantity of RNA was assessed by a BioPhotometer ( Eppendorf ) . The expression of IE1 and gB viral mRNA was determined using a two-step RT-qPCR assay; the expression of IE1 and gB was compared with that of ribosomal protein L32 mRNA . First , cDNA was generated from 2 μg of total RNA using Random primers and M-MLV Reverse Transcriptase ( Promega ) . The cDNA samples were then used in the qPCR assay as described above using IE specific primers used ( F: tgacttaaactccccaggcaa and R: taggtgaggccatagtggcag ) or gB primers indicated above . Single cell suspensions of retinal tissue and iris/ciliary body were prepared at the indicated time points after infection . Briefly , eyes were dissected to separate the anterior from the posterior segment . The anterior segment was dissected further to yield the iris and the ciliary body while the posterior segment was dissected to separate the retina from the choroid/sclera . Retinas from both globes were pooled for each mouse . The iris and ciliary body tissue samples were pooled from multiple mice ( up to five/group ) before all tissue was minced and digested in a mixture of 10μg/ml Liberase ( Roche , Germany , Cat No #05401119001 ) and 10μg/ml DNAse I ( Sigma , USA ) in PBS for 40 minutes at 37°C . The resulting single cell preparations were stained with antibodies specific for CD45 ( 30F11 ) , CD11b ( M1/70 ) , CD3 ( 145-2C11 ) , CD4 ( RM4-5 ) , CD8 ( 53–6 . 7 ) , NKp46 ( 29A1 . 4 ) , CD11c ( HL3 ) , CD19 ( 6D5 ) , CD64 ( X54-5/7 . 1 ) , F/480 ( BM8 ) , MHC-II ( M5/114 ) , Ly6C ( AL . 21 ) , Ly6G ( 1AB ) ; CD69 ( H1 . 2F3 ) , CD103 ( M290 ) . Virus-specific CD8+ T cells were identified using tetramers for H-2Ld-YPHFMPTNL MCMV-IE1 from Immuno ID Tetramers ( Melbourne , Victoria ) . Antibodies were obtained from BD Biosciences , BioLegend , or eBioscience . Fixable viability stain 620 ( BD Biosciences ) was used for live/dead discrimination . Samples were analysed using an LSRFortessa X-20 instrument ( BD Biosciences ) . The gating strategies used to identify immune cell populations in the iris and retina are shown in S3 Fig . All data analysis was performed using the FlowJo software package ( FlowJo , LLC ) . Fluorescent images were obtained using a Phoenix Micron IV Retinal Imaging Microscope ( Phoenix Research Laboratory San Ramon , CA-USA ) . Fluorescent images were captured after anesthetising mice with a mixture of ketamine and xylazine ( Troy Laboratories , Australia ) . Genta-Gel lubricant eye gel was used on the eye surface to prevent drying of the cornea . Vessels were visualised by subcutaneous injection of 20 μl of 10% sodium fluorescein solution ( Alcon , Australia ) . OCT images of the anterior segment were obtained using an EnvisuR2200 SD-OCT system with a 12mm-telocentric lens ( Leica Microsystems ) . Anterior segment OCT analysis was performed in the absence of anaesthesia with lubricant eye drops ( Refresh Tears Plus , Allergan , USA ) applied throughout the procedure to maintain corneal clarity . OCT fundus images of the retina were acquired using the EnvisuR2200 SD-OCT system with the mouse retina lens . The mice were anesthetized as described above . Vessel size on the fundus images were measured using the caliper function available in Bioptigen OCT software . Eyes from infected mice were removed between day 70 and 170 pi and iris/ciliary body , cornea , retina and choroid dissected . Each component of the eye was placed in a well of a 48 well tray and 100 μl of minimum essential medium ( MEM ) containing 10% FCS and 7g/L carboxymethyl cellulose added . After 24 hr of culture at 37°C , 500 μl of MEM supplemented with 10% FCS was added to each well . Medium was collected from each well every 7 days thereafter and assayed for the presence of MCMV by plaque assay for up to 35 days . Reactivated infectious virus was detected between day 7 and day 14 of culture . All experiments were performed in accordance with the recommendations in the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes and the National Health and Medical Research Council of Australia Guidelines and Policies on Animal Ethics . Experiments were approved by the Animal Ethics Committee of the University of Western Australia ( Animal Ethics Research Protocol RA/3/100/1094 ) and the Harry Perkins Institute of Medical Research Animal Ethics Committee ( AEC Project Number AE031/2015 ) . All data except for measurements of vessels size data was assessed using a two-tailed Mann-Whitney test . Changes in vessel diameter were assessed using a one-way ANOVA test with Dunn’s correction . Statistical tests were performed using the statistical software package InStat ( GraphPad Software , San Diego California USA ) . | Cytomegalovirus ( CMV ) is a common viral pathogen which is highly prevalent , but does not cause clinical disease in hosts with a fully competent immune system . After infection the virus remains with the host life-long in a chronic and then latent state . Latency is thought to establish primarily in the lung and in the salivary glands , and immune privileged tissues , such as the eye and the brain are considered inaccessible to CMV unless the host is severely immunocompromised . Here Voigt et al show that following a systemic infection of immunocompetent hosts CMV accesses the eye and establishes a reservoir of latent virus in this tissue . Ongoing inflammation in the eye , including the neural retina , is then sustained long-term in the absence of viral replication . This study reveals that virally induced inflammation in immune privileged tissues may be a general phenomenon and can occur despite a fully competent immune system . | [
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"disea... | 2018 | Cytomegalovirus establishes a latent reservoir and triggers long-lasting inflammation in the eye |
The three-dimensional ( 3D ) structure of neural circuits is commonly studied by reconstructing individual or small groups of neurons in separate preparations . Investigation of structural organization principles or quantification of dendritic and axonal innervation thus requires integration of many reconstructed morphologies into a common reference frame . Here we present a standardized 3D model of the rat vibrissal cortex and introduce an automated registration tool that allows for precise placement of single neuron reconstructions . We ( 1 ) developed an automated image processing pipeline to reconstruct 3D anatomical landmarks , i . e . , the barrels in Layer 4 , the pia and white matter surfaces and the blood vessel pattern from high-resolution images , ( 2 ) quantified these landmarks in 12 different rats , ( 3 ) generated an average 3D model of the vibrissal cortex and ( 4 ) used rigid transformations and stepwise linear scaling to register 94 neuron morphologies , reconstructed from in vivo stainings , to the standardized cortex model . We find that anatomical landmarks vary substantially across the vibrissal cortex within an individual rat . In contrast , the 3D layout of the entire vibrissal cortex remains remarkably preserved across animals . This allows for precise registration of individual neuron reconstructions with approximately 30 µm accuracy . Our approach could be used to reconstruct and standardize other anatomically defined brain areas and may ultimately lead to a precise digital reference atlas of the rat brain .
The morphology of neurons has been of interest for three main reasons . First , the morphology of the soma , dendrites , and axon is commonly used to identify types of neurons [1] , [2] , [3] , [4] , [5] . Secondly , the detailed morphology of soma and dendrites has been analyzed with respect to their biophysical effect on electrical properties [6] , [7] , [8] , [9] . Thirdly , the morphology of axons and dendrites has been used to infer synaptic connectivity between neurons by structural overlap [3] , [4] , [10] , [11] , [12] , [13] . For classification of neuronal cell types and analyzing biophysical properties , a single neuron may be a sufficient reference frame for morphological analysis . In contrast , the aim of inferring synaptic connectivity from structural overlap of neuronal morphologies requires the placement of neurons into a reference frame that is sufficiently precise and invariant to variability between experiments . Quantitative registration methods have been applied to neurons in the mammalian cortex from in vitro preparations with two-dimensional ( 2D ) registration [14] , [15] . Three-dimensional ( 3D ) registration approaches were so far limited to various insect model systems , such as the bee and fly brains [16] , [17] . In contrast to the stereotypic layout of these insect brains [18] , where the number of neurons and even the neuronal projection patterns are often preserved across animals [19] , [20] , the mammalian cortex is likely to be more heterogeneous and variable across animals . For the analysis of cortical neuron ensembles in 3D , for example from experiments carried out in vivo ( relieving the restriction to a tissue slice ) , a 3D registration is required , especially if neurons from many different experiments are to be combined . We therefore developed a set of tools that allow ( i ) reconstructing anatomical landmarks with 1 µm resolution , ( ii ) generating a standardized average 3D cortex model and ( iii ) precise registration of 3D neuron morphologies , obtained from in vivo preparations . Due to its well-defined structural and functional layout , subdivided vertically into cortical barrel columns and horizontally into six cortical layers ( L1–6 ) , the rodent vibrissal cortex is a natural starting point for generating a precise 3D anatomical model of the mammalian cortex . A cortical column is thought to be the elementary functional unit of sensory cortices [21] , [22] . The barrel columns in the vibrissal area of rodent somatosensory cortex ( S1 ) are regarded as cytoarchitectonic equivalents of these functional columns [23] , [24] . In the present study , we define the dimensions of barrel columns by staining for Cytochrome-oxidase in L4 and extrapolating the circumference of the respective L4 barrels along their vertical axes towards the pia and white matter . This cylindrical approximation renders one way to describe the 3D extent of cortical barrel columns , but alternative definitions , for example , based on thalamocortical projections [25] , dendrite innervations [3] or intracortical connectivity patterns [26] , [27] exist . Despite multiple studies that investigated the geometry of the rodent vibrissal cortex [25] , [28] , [29] , [30] , a quantitative 3D description of the variability of barrel column dimensions and orientations within the vibrissal cortex and across animals is lacking . Here we developed criteria to automatically extract the dimensions of the barrels , the pia and white matter surfaces and the orientation of the barrels and respective cortical columns with high precision . We find that the variability of individual anatomical parameters is surprisingly small across different animals . In contrast , the parameters within individual animals differ substantially . The large anatomical variability within the vibrissal cortex of individual animals demands that neuron reconstructions need to be registered as close as possible to their original location . The automated registration tool presented here meets this demand . Rigid transformations and stepwise linear scaling along the vertical column axis are used to match the reference landmarks of a reconstructed neuron to their respective counterparts in the standardized cortex model . The 3D reconstructions of somata , dendrites and axons from in vivo preparations can thus be placed at their true cortical location with a precision of approximately 30 µm .
The vibrissal cortex in rats comprises 30 large barrels in L4 , separated by septa between them ( Figure 1A ) . The layout of the barrel field , as revealed by Cytochrome-oxidase staining [31] , resembles the layout of the large facial whiskers on the animal's snout , which are organized into rows ( A–E ) and arcs ( α-δ , A1–4 , B1–4 , C/D/E1–6 ) ( Figure 1B ) . Taking a coronal section through the barrel field reveals that the curvatures of the pia and white matter ( WM ) surfaces ( Figure 1C ) differ across the vibrissal cortex . This results in location-specific cortical thickness and barrel depth , as well as tilted orientations of the vertical barrel column axes with respect to each other ( Figure 1D ) . We defined five parameters for each barrel column to describe this location-specific 3D layout of the vibrissal cortex: ( i ) the barrel area , defined as the maximal circumference of the L4 barrel in the tangential plane ( Figure 1B ) , ( ii ) the barrel top ( BT ) , defined as the closest point of the barrel to the pia in the coronal plane ( Figure 1D ) , ( iii ) the barrel bottom ( BB ) , defined as the closest point of the barrel to the WM . Together , BB and BT define the vertical extent ( i . e . , height ) of a barrel . The maximal barrel circumference and the height yield the definition of its centroid ( i . e . , barrel center , BC ) . The two remaining parameters are ( iv ) the barrel column orientation ( BC axis ) , defined as the shortest perpendicular axis from the BC to the pia above the barrel ( Figure 1D ) and ( v ) the barrel column height , defined by extrapolating the barrel circumference along the BC axis towards the WM and pia , respectively ( i . e . , pia-WM distance ) . We determined these five parameters for 984 barrels from 104 different rats . To do so , brains were cut approximately tangential to the barrel field into 50 or 100 µm thick vibratome sections ( Figure 1E ) . Ranging from the pia to the WM , the resulting 24 or 48 brain sections ( S01–S48 ) were stained for Cytochrome-oxidase to reveal the barrel field in L4 ( e . g . , S13 in Figure 1E ) . We manually traced 637 individual barrels on low-resolution images from 100 µm thick sections in 92 rats using Neurolucida software ( MicroBrightfield , Williston , VT , USA ) . Only clearly stained barrels were traced , one contour per brain section . In addition , pia and WM contours were traced for all sections . The resultant average barrel area was 9 . 8±1 . 9×104 µm2 ( mean ± SD ) . The average barrel height was 299±92 µm . The average pia-WM distance was 1949±100 µm . The manual determination of the vertical extent of the barrel ( i . e . , BT and BB ) proved to be difficult , because barrels were tilted with respect to the vertical cortex axis within a brain section . Consequently , we decided to determine the barrel dimensions by more objective criteria . Using an automated image processing pipeline ( Figure S1 , S2 , S3 ) and high-resolution image stacks , the contrast between barrels and the septum was enhanced ( Figure 2 ) . This allowed determining the BT and BB as local minima in diameter of the extracted barrel contours ( Figure 3 ) . Using this automated tracing method , we reconstructed 347 barrels from 50 µm thick sections in 12 different rats ( 6 male , 6 female ) . The average area of the automatically extracted barrels was 9 . 9±1 . 7×104 µm2 . The average pia-WM distance was 1929±99 µm . Because the mean values as well as the standard deviations ( SDs ) of the two parameters were identical to their manually determined counterparts , we regard our automated algorithms as sufficiently accurate to reconstruct the five anatomical parameters describing the barrel field . The automatically determined barrel height ( 348±34 µm ) was slightly different from its manual counterpart ( 299±92 µm ) . Given the difficulties in manually determining the vertical borders of the barrels , we regard the automated result as more accurate . Further , we determined a systematic error of ∼10 µm for the automated detection of BT and BB , respectively . Thus , the automatically determined SD of 34 µm in barrel height likely reflects the ‘true’ biological variability between animals . In contrast , the 3-fold larger manually determined SD in barrel height of 92 µm may primarily reflect systematic limitations of the manual tracings and hence conceals the biological variability . Consequently , the automated pipeline of imaging and image processing , presented here , is a fast and precise alternative to extract the 3D geometry of the vibrissal cortex , reaching at least the same accuracy as manual tracings , by using a smaller sample size . Therefore , only the 12 automatically reconstructed vibrissal cortices were subsequently used for quantification and standardization of the five geometrical parameters . The cortical column and its cytoarchitectonic equivalent in the vibrissal cortex , the barrel column , has been regarded as an elementary building block of sensory cortices [32] , [33] . Accordingly , assuming a stereotypic column layout throughout the cortex , average dimensions were used to describe the 3D column dimensions . Here , we determined an average barrel area and height of ∼100 , 000 µm2 and 300 µm , respectively , yielding a barrel volume of ∼0 . 03 mm3 . Combined with an average column height of ∼2 , 000 µm , we obtained an average barrel column volume of 0 . 2 mm3 . These values were in good agreement with previous 2D measurements [25] . However , the automated 3D reconstruction of 12 complete barrel fields , now allowed comparing the parameters of individual barrel column across the vibrissal field in a quantitative manner ( Table 1 ) . Barrel columns up to arc number 4 were evaluated ( Figure 4A–B ) . Barrels in higher arcs were not clearly visible in all animals . We found that the five evaluated barrel column parameters varied substantially across the vibrissal cortex of individual animals ( Figure 4B–E ) . First , BT and BB ranged from 455 µm to 580 µm and 777 µm to 924 µm distances below the pia , respectively . Both parameters varied in a codependent manner ( Figure 4C ) . While the depth locations of the barrels varied across the vibrissal cortex , the barrel height was preserved ( 348±34 µm ) , suggesting that the thickness of granular L4 was constant across the vibrissal field . Consequently , the thickness of the remaining cortical layers varied . The changes in barrel depths were not random but followed a well-defined gradient ( arrow in Figure 4C ) , from BC locations closer to the pia at lower row and arc numbers ( minimum at A1 ) to BC locations deeper within the cortex at higher row and arc numbers ( maximum at E4 ) . Second , the barrel areas displayed substantial location-specific variations across the vibrissal field , ranging from 64 , 800 µm2 to 158 , 900 µm2 . The ∼2 . 5-fold difference in barrel area again followed a well-defined gradient ( Figure 4D , left panel ) . However , barrel areas were smaller at lower row and higher arc numbers ( minimum at A4 ) and increased towards higher row and lower arc numbers ( maximum at E2 ) . Because the barrel height was preserved across the vibrissal cortex , barrel volumes followed the same gradient as the barrel areas , ranging from 0 . 02 mm3 to 0 . 06 mm3 ( Figure 4D , right panel ) . Third , the column heights ( i . e . , pia-WM distance ) displayed a gradient similar to the ones obtained for BT and BB ( Figure 4E , left panel ) . The differences in average column heights were however four times more pronounced ( ranging from 1 , 600 µm in the α-column to 2 , 117 µm in the E4-column ) than the average differences in barrel depth . Consequently , the fraction of supragranular-to-granular-to-infragranular ( s-g-i ) layers was column-specific . For example , the average A1-column was 1 , 651 µm high . Average BT and BB in the A1-column were located at 455 µm and 777 µm , respectively . The thickness of the supragranular , granular and infragranular layers in A1 was thus 455 µm , 322 µm and 873 µm , respectively ( s-g-i: 27%-20%-53% ) . In contrast , the average height of the E4-column was 2 , 111 µm . Average BT and BB were located at 580 µm and 924 µm , respectively . The thickness of the supragranular , granular and infragranular layers in E4 was thus 580 µm , 344 µm and 1187 µm , respectively ( s-g-i: 28%-16%-56% ) . Hence , defining granular L4 by the vertical extent of the barrels [24] yielded that supra- and infragranular layers in columns with lower row and arc numbers were relatively thinner compared to L4 and relatively thicker in columns with higher row and arc numbers . Fourth , the volumes of the barrel columns displayed a location-specific gradient , different from the ones observed for barrel areas or column heights ( Figure 4E , right panel ) . The gradients of the barrel areas and column heights compensated each other yielding approximately constant barrel column volumes within the same whisker row ( A-row: 0 . 12–0 . 15 mm3; B-row: 0 . 15–0 . 16 mm3; C-row: 0 . 18–0 . 21 mm3; D-row: 0 . 21–0 . 24 mm3; E-row: 0 . 26–0 . 33 mm3 ) . Specifically , the average SD in column volume within the same row was 0 . 017 mm3 . In contrast , the average SD in column volume between rows was four times larger , i . e . , 0 . 067 mm3 . Finally , the orientation ( i . e . , vertical axis ) of the barrel columns with respect to each other was not parallel , but tilted , following the curvature of the pia . We defined the vertical axis of the C2-column as the Null direction and determined the tilt of the remaining columns with respect to this axis ( Figure 4B , bottom panel ) . All barrel columns were tilted with respect to C2 . The gradient describing the column orientations across the vibrissal cortex followed a symmetric relationship along an axis approximately parallel to the medial axis of the brain ( i . e . , α→B1→C2→D3→E4 ) . The tilt along this ‘1-2-3’ axis was substantially smaller ( 3–6° ) than the tilt along the perpendicular ‘3-2-1’ axis ( i . e . , A4→B3→C2→D1→δ ) ( Figure 5A ) , which was between 8° and 16° . For example , the A4-column displayed a maximal average tilt of 17° . In contrast , the average tilt of the E4-column within the same arc was only 6° . This relationship reflects the fact that the rodent brain is elongated , resulting in a smaller curvature of the cortex along the medial axis than along an axis perpendicular to it . The curvature of the cortex and the resulting tilts of the BC axes yielded barrel columns that started to overlap in deep cortical layers ( Figure 5B , top panels ) , using the cylindrical extrapolation of the barrel towards the pia and WM . We quantified this overlap as a function of cortical depth for neighboring columns within the same whisker row , arc or along the 3-2-1 axis , respectively . The overlap was measured as the ratio of volume shared by neighboring columns to the total volume of a central column in 50 µm bins along the BC axis ( Figure 5B , bottom panels ) . Barrel columns began to overlap right below the granular layer . The overlap increased monotonically with increasing cortical depths , reaching a maximum of ∼25% at the WM . The magnitude of the overlap was different along the three investigated axes . The overlap within the same whisker row reached on average a maximum of ∼10% at the WM and ∼5% within the same whisker arc . The largest cortical curvature along the 3-2-1 axis resulted in the largest overlap along this direction , reaching on average a maximum of about 15% at the WM . In consequence , the overlap between neighboring columns resulted on average in 7% smaller column volumes in infragranular layers , when compared to the cylindrical extrapolation of the barrels towards the WM . The increasing overlap with cortical depth can alternatively be described by a depth-dependent change in volume that separates cortical barrel columns . The volume separating the barrels in L4 is commonly referred to as the septum . We adopted this terminology for the entire volume of the vibrissal cortex that was not covered by any cortical barrel column . We subdivided the entire volumes of the 12 reconstructed vibrissal cortices into voxels of size 10×10×10 µm3 and assigned each voxel either to a barrel column or the septum . The resultant volume of the entire vibrissal cortex ( α-δ , A1-E4 ) was 6 . 53±0 . 75 mm3 , with 4 . 58±0 . 54 mm3 ( ∼70% ) belonging to barrel columns and consequently 1 . 95±0 . 28 mm3 ( ∼30% ) belonging to the septum . The total volume of the supragranular , granular and infragranular layers was 2 . 02±0 . 19 , 1 . 29±0 . 15 and 3 . 06±0 . 46 mm3 , respectively ( Figure 5C ) . Further , the relative fraction of the septum from the total volume increased from the granular layer ( 33% , 0 . 42±0 . 05 mm3 ) towards the supragranular layers ( 37% , 0 . 75±0 . 09 mm3 ) and decreased towards the infragranular layers ( 20% , 0 . 62±0 . 16 mm3 ) . We investigated whether the anatomical variability of the respective five parameters describing the dimensions of each barrel column was sufficiently small across animals ( Figure 6A ) to allow for registration of anatomical data to a standardized 3D model of the entire vibrissal cortex . As a first qualitative assessment , we calculated the mean and SD of each parameter for each individual column ( Table 1 ) . The resultant average variability of the five parameters across animals was as follows: ( i ) the barrel area deviated on average by 16 , 600 µm2 ( SD in percent of the mean: 17% ) , ( ii ) the BT deviated by 51 µm ( 10% ) , ( iii ) the BB deviated by 53 µm ( 6% ) , ( iv ) the column orientation with respect to the C2 column deviated by 4 . 1° and ( v ) the barrel column height deviated by 98 µm ( 5% ) . Further , we checked for differences between male ( n = 6 ) and female ( n = 6 ) animals . Male rats had slightly , but consistently thinner vibrissal cortices ( 1860±185 vs . 1919±171 , p<0 . 01 , 2-way ANOVA ) . However , none of the other barrel or barrel column parameters were significantly different ( p>0 . 1 , 2-way ANOVA ) . In addition , we correlated the column parameters , as well as the volume of the vibrissal cortex , with the weight of the respective animal , but found no significant relationships ( p>0 . 05 , non-directional t-test ) . We thus pooled all reconstructed cortices for the subsequent analyses . To obtain a more quantitative measure of the anatomical variability of the vibrissal cortex , we registered 12 reconstructed vibrissal cortices into a common coordinate system and created an average 3D cortex model by using only rigid transformations ( i . e . , translations and rotations ) ( Figure 6B–C ) . The variability in barrel location across animals can then be determined quantitatively by computing the covariance of the 12 corresponding BT and BB coordinates with respect to the standard cortex model . Specifically , by diagonalizing the covariance matrix and computing the square root of the eigenvalues , deviations in barrel locations can be investigated along the whisker row , arc ( Figure 6D ) and BC axis ( Figure 6E ) , respectively . The average variability across BT locations in the tangential plane , as measured by the respective square root of the eigenvalues , was 67 µm along whisker rows and 49 µm along whisker arcs . The variability of the BB ( row: 64 µm; arc: 48 µm ) locations followed the variability of the BT ( along rows: r = 0 . 96 , along arcs: r = 0 . 92 ) . In general , the variability of barrel locations in the tangential plane was much smaller than the average extent of the barrels ( ∼355 µm diameter ) ( Figure 6D ) . The variability in barrel location along the vertical column axis is exemplarily illustrated for the C2 barrel ( Figure 6E ) . There , the average variability of BT and BB locations were 28 µm and 19 µm , respectively . This is illustrated by the vertical extent of the respective error ellipses . For the remaining barrels , similarly small variability values were obtained , being on average 35 µm . Finally , combination of the 3D variability values of the BT and BB locations allowed determining the variability of the BC axes , which was 4 . 5° ( Figure 6E ) . This variability in orientation results in an additional depth-dependent horizontal uncertainty ( dashed region in Figure 6E ) of the column location . However , this uncertainty is small compared to the horizontal variability of the BT and BB and is therefore neglected . The variability between the 12 individually registered BT and BB locations from their counterparts in the standardized model , as measured by the respective square root of the eigenvalue , were similar to the respective average SDs determined across animals ( e . g . , BT: 51 µm vs . 35 µm ) . Hence , the precision of the BC axes in the standardized model was close to the variability in orientation across animals ( i . e . , 4 . 5° vs . 4 . 1° ) . Consequently , the rigid transformations and optimizations used to create the 3D standard model did not introduce any systematic biases . Taken together , the two quantitative measures indicate that the 3D geometry of the rat vibrissal cortex was preserved across animals and that the standardized model captures its average 3D layout . So far , we considered the parameters from each barrel column individually , neglecting that positioning of the barrel columns with respect to each other may change between animals . We therefore introduced a set of three non-linear functions ( 2nd order polynomials ) to parameterize the 3D layout of the entire vibrissal cortex separately for each reconstructed cortex and the standardized model . The 15 coefficients of the three functions may be interpreted as specific geometrical properties of the vibrissal cortex; for example measuring the deviation of the barrel field from a rectangular grid ( see Materials and Methods ) . The coefficients were determined by fitting the functions to the 24 BC locations of the standardized model of the vibrissal cortex ( Figure 7A ) . The fitting was further applied to each of the 12 reconstructed cortices , individually . The resulting mean and SD of each coefficient is shown in Figure 7B . A quantitative measure of the quality of the standard model can then be expressed as the difference between each mean coefficient and the corresponding value from the fit to the standardized barrel field ( Figure 7C ) . For example: The coefficient describing the deviation of the barrel field from rectangular grid ( f10 , see Materials and Methods ) was −100 . 77 for the fit to the standard model . Fitting the three functions to the 12 cortices individually yielded an average coefficient of −102 . 02±112 . 15 . Thus , the difference between the standardized and the average coefficient was 1 . 25 ( i . e . , 102 . 02–100 . 77 ) . Compared to the variability across animals ( SD: 112 . 15 ) , the difference between the two coefficients was small ( i . e . , ∼1% of the SD ) . The quality of the standard model in capturing the average 3D layout of the vibrissal cortex was hence defined in units of SD of the 15 coefficients . This measure was below 12% for all coefficients and on average around 5% . The finding that not only the 3D dimensions of the respective barrel columns , but also the 3D layout of the entire vibrissal cortex , is preserved with approximately 5% accuracy across animals , is somewhat counter-intuitive when visually comparing individual cortices reconstructed in the present ( Figure 6A ) or previous studies ( e . g . [24] ) . For example , the barrel shape , the size of the septum between rows ( in particular between the D- and E-row ) or the curvature of the arcs ( in particular of the greek arc ) vary between individual animals . Consequently , while the present standard model of the vibrissal cortex captures its average layout with approximately 5% accuracy ( i . e . , SD ) , the deviation of one individual barrel field from this average model may be larger . To assess how each individual cortex reconstruction matches the average model ( i . e . , standard layout of the vibrissal cortex ) we performed a ‘leave-one-out’ cross validation analysis . Specifically , we determined the average coefficients from only 11 cortices and then computed the root mean squared error ( RMSE ) between the predicted and the actual 3D BC locations of the remaining cortex . The procedure was repeated 12 times , i . e . , for each reconstructed vibrissal cortex . The average RMSE was 146 µm , but varied between animals and barrel columns ( Table 2 ) . For example , the average RMSE of ‘Male 6’ was 187 µm , compared to 92 µm for ‘Female 1’; the average RSME for the greek arc was 187 µm , compared to 111 µm for the B-row . The latter is consistent with the analysis of BT and BB locations , as illustrated by large SD-ellipses in the greek arc and small ones at centrally located barrels ( Figure 6D ) . The quantifications of the variability of the vibrissal cortex and the quality of its standardized model suggest that 3D reconstructions of neuron morphologies can be registered with high precision , if the respective reference landmarks are present in each tracing . Unfortunately , the high-contrast Cytochrome-oxidase staining needed to automatically extract the barrel landmarks prevents tracing biocytin-labeled [34] dendrite and in particular axon morphologies . In turn , the low-contrast Cytochrome-oxidase staining needed to reliably trace neuron morphologies prevented us from automatically extracting the barrel landmarks . Thus , to assess how accurate 3D neuron tracings can be registered to the standard model by rigid transformations , systematic differences between manually and automatically extracted reference landmarks needed to be quantified . To do so , we manually traced all visible anatomical landmarks for 94 reconstructed neuron morphologies with somata located randomly within the vibrissal cortex and at varying cortical depth between L2 and L6 ( recording depth: 222–1727 µm [3] ) . Using this set of morphologies we developed a precise registration pipeline that automatically compensates for differences between manually and automatically extracted landmarks . The individual steps of the pipeline are exemplarily illustrated for one L5 thick-tufted pyramidal neuron [2] in Figure 8 . The BC of the manually reconstructed principal column ( i . e . , containing the neuron's soma ) was aligned with the respective BC of the standard cortex . Then , the remaining BC locations were registered by using only rigid transformations ( Figure 8C , left panel ) . This step resulted in a rotation of the principal BC axis of 14 . 0±7 . 6° ( 1 . 6–32 . 8° , Figure 8C , top-right panel ) . Because the BC axis of an unregistered tracing is defined by the cutting plane of the vibratome , the rotation of the ‘global orientation’ of the neuron compensated for systematic differences introduced by cutting the brain into sections . The orientation of the BC axis after the first registration step was on average more variable ( SD: 7 . 6° ) than the 4 . 5° deviation in column orientation determined for the standard vibrissal cortex . This likely reflected the observation that the manually determined contours defining BT and BB were less precise than their automated counterparts . We thus introduced a second rotation step . The apical dendrite of pyramidal neurons in the cortex usually projects along an axis perpendicular to the pia surface and thus , parallel to the large blood vessels in its immediate surrounding [35] . The local blood vessel pattern can consequently be used to determine the vertical axis of a barrel column and hence of a reconstructed neuron . To do so , we reconstructed the blood vessels throughout the vibrissal cortex and determined local vertical axes with 50 µm precision ( i . e . , 50 µm spacing between neighboring vertical axes , see Materials and Methods and Text S1 ) . Further , we determined the smallest moment of inertia of the apical dendrite and rotated the tracing until this ‘dendrite orientation’ matched the vertical axis closest to the respective soma . In cases where no clear apical dendrite was present ( e . g . , for L4 spiny stellate neurons [36] ) , the direction of the main axon leaving the soma in a straight direction towards the WM was defined as the neuron's orientation . The additional rotation of the ‘neuron orientation’ was small ( 0 . 8–20 . 0° , 7 . 3±4 . 5° , Figure 8C , bottom-right panel ) compared to the global orientation step . In particular , the resultant variability in neuron orientation of 4 . 5° matched the previously determined variability in BC axis orientation across animals . After translations and rotations , the new BT , BB , pia and WM locations were systematically compared to their counterparts in the standardized cortex model . The average vertical locations of all landmarks deviated from the standard model ( Figure 8D , right panel ) . All parameters varied independently for different columns . For example , for the D2 column , the manual BB deviated on average 59 µm from the respective standard landmark ( manual: 947 µm vs . standard: 888 µm depth below the pia surface ) . The BT deviated on average by 68 µm ( manual: 594 µm vs . standard: 526 µm ) and the depth location of the WM deviated on average by 7 µm ( manual: 1950 µm vs . standard: 1957 µm ) from the respective standard landmarks . Consequently , we shifted the contours of the principal column in each tracing by the respective differences between the mean values of the manual tracings and the standard cortex ( Figure 8D , left panel ) . Further , we measured the distance between the apical tuft endings and the reconstructed pia surface exemplarily for four neurons where the apical tufts reached the upper most part of L1 ( i . e . , true distance to the pia surface was zero ) . We found that the average distance of the apical tuft endings to the reconstructed pia surfaces was 39±5 µm . Thus , we shifted all manually traced contours by −39 µm with respect to the neuron tracing . In addition , the thickness of the first vibratome section may deviate from the assumed 100 µm thickness . We therefore compared the average distance to the pia for four neurons whose apical tufts ended within the first vibratome section and ten neurons with tufts already reaching the pia in deeper sections . We found that the reconstructed pia of the first section was on average 20 µm too high and corrected the vertical pia location accordingly . In the final registration step , differences between the registered vertical locations of BT , BB , pia and WM of each individual neuron tracing were compared to the respective standardized landmarks ( Figure 8E , top-right panel ) . BT , BB , pia and WM deviated independently from each other . Therefore , we chose a stepwise linear scaling to match the respective landmarks of each tracing with the standardized counterparts ( Figure 8E , left panel ) . Three scaling factors were determined between: ( i ) the pia and the BT ( i . e . , supragranular layers ) , ( ii ) the BT and BB ( i . e . , granular layer ) and ( iii ) the BB and the WM ( i . e . , infragranular layers ) . The scaling factors were on average very close to 1 ( i . e . , 1 . 05±0 . 27 , 1 . 09±0 . 31 and 1 . 01±0 . 11 in supragranular , granular and infragranular layers , respectively ) . In summary , by ( i ) coarse registration of BC locations , ( ii ) fine tuning of neuron orientation , ( iii ) shifting the vertical locations of BT , BB , pia and WM by their respective average differences between manually and automatically determined landmarks and ( iv ) stepwise linear scaling of the neuron along the BC axis , we found that the manually reconstructed vibrissal cortices could be matched to the standard cortex as precisely as the automatically reconstructed versions . The precision of registering individual neurons to the standardized model may thus be expressed as the standard error ( SE ) of the average BC location as determined by the covariance matrix above , multiplied with the respective scaling values in supragranular , granular and infragranular layers , respectively . Specifically , the vertical precision of the supragranular layers can be determined as the SE of the BT locations , which was 15 µm , multiplied with the average scaling of 1 . 05 , resulting in SEz , supra = 16 µm . The vertical precisions of the granular and infragranular layers can be determined accordingly by the SE in barrel and column heights ( i . e . , SEz , granular = 10 µm and SEz , infra = 28 µm ) , respectively . Combined with the precisions along the row and arc ( SErow = 19 µm , SEarc = 14 µm , see above ) , we obtained a 3D registration accuracy for neurons located in supragranular layers of 28 µm , in the granular layer of 26 µm and in infragranular layers of 37 µm . Consequently , the 3D location of the soma , as well as dendrites and axons close to the principal column , can on average be determined with ∼30 µm accuracy . However , the registration was optimized to match the BC location of the principal column . The registration accuracy of neuronal branches that project out of the principal column ( i . e . , long-range projections into septa and surrounding columns ) was hence not determined by the SE of the surrounding BC locations , but by their average SDs . The average 3D registration accuracy of neuronal ( long-range ) projections within surrounding columns was thus ∼89 µm . At this stage it should be emphasized that the present registration precisions are to be considered with respect to the average dimensions of the vibrissal cortex , i . e . , SE and SD of the barrel location describe the precision of registered local and long-range projections , respectively . However , since the 3D layout of an individual cortex may deviate more from the standard cortex than the average of the 12 cortices , the ‘minimal’ precision of registration may be given as the average RMSE of the BC locations from the ‘leave-on-out’ analysis , i . e . , 146 µm . For a summary of the column-specific registration precisions see Table 2 . As a first application of the registration method , we compared the vertical locations of the somata after registration with their respective recording depths ( i . e . , penetration depth of the pipette , Figure 8F ) . In general , the recording depth slightly deviated from the registered depth . Some neurons were deeper within in the cortex than suggested by their recoding depths; others were closer to the pia . On average , the recording depth deviated by −46±102 µm from the registered soma depth ( i . e . , unregistered neurons appeared to be deeper within the cortex ) . The surprisingly small difference of on average 46 µm between the registered depth of the soma and the penetration depth of the recording pipette suggest that tissue shrinkage due to perfusion , fixation and histology ( see Materials and Methods ) , which can be up to 20% [37] , is largely compensated by the present approach of generating a standard model of the vibrissal cortex . Consequently , the recording depth may be used as a predictor of a neuron's location within the present reference frame of the vibrissal cortex with approximately ±102 µm precision .
Various attempts to quantify the geometry of individual barrels in the rodent vibrissal cortex have been reported previously [25] , [28] , [29] , [30] . In these studies , anatomical barrel parameters were measured in 2D using manual reconstructions on low-resolution images of single or a few consecutive brain sections , either in the tangential or thalamocortical plane . In contrast to these 2D approaches , we determined five parameters ( barrel area , BT , BB , column height , BC axis ) describing the geometry of almost 1 , 000 barrels across 104 rats in 3D . Going beyond the scope of the previous 2D studies , we found that the five 3D column parameters varied substantially across the vibrissal cortex ( e . g . , the barrel area ranged from 65 , 000 to 160 , 000 µm2 or the cortical thickness ranged from 1 , 600 to 2 , 100 µm ) . Further , the differences in column dimensions were not random , but followed well-defined gradients . In contrast , the variability of the five parameters was remarkably small across different animals ( i . e . , the SD was usually ∼5% of the mean ) . Moreover , we found that the precision of cutting the brain with exactly the same orientation into tangential sections was only around 14 . 0±7 . 6° . Hence , 2D reconstructions of the barrel geometry will likely be subject to systematic errors , because the vertical axes along which parameters , such as barrel area and height , are determined vary between preparations . Further , the curvatures of the pia and WM resulted in column orientations not parallel , but tilted with respect to each other . The tilt deviated along different axes and was most pronounced for neighboring columns along an axis perpendicular to the medial axis of the brain ( i . e . , 3-2-1 axis ) . Hence , even if the cutting angle would be identical across preparations , 2D measurements of barrel area and height will still be affected by systematic errors due to the curvatures of the cortex . However , when evaluating the dimensions of only a single column , the tilt of the neighboring columns can be neglected and systematic errors in cutting angle may be compensated by large numbers of reconstructed barrel columns . Thus , the previously reported dimensions of the D2 column in rats , based on 2D tracings of axonal projections from the posterior medial division of the vibrissal thalamus ( POm ) [25] , were in remarkably good agreement with the respective dimensions reported here , based on automated 3D reconstructions of Cytochrome-oxidase stained barrels ( i . e . , barrel area: 124 , 000 vs . 124 , 000 µm2 and barrel column height ( i . e . , pia-WM distance ) : 1 , 977 vs . 1 , 957 µm ) . Several attempts to create 3D reference frames for precise registration of single neuron morphologies have been reported for various animal models previously . For example , reconstructing stereotypical anatomical landmarks from multiple complete brains resulted in an average 3D reference frame of the entire bee brain [18] . Using nonlinear deformations and averaging of 3D label fields , individual 3D neuron morphologies could be registered by matching the labeled landmarks to the standardized 3D Bee Brain [18] . Similar approaches have been reported for other insect models , such as the Drosophila brain [16] . While the general idea of ( i ) determining the 3D dimensions of stereotypic anatomical landmarks , ( ii ) generating an average 3D model from these landmarks and ( iii ) registering neurons by matching anatomical landmarks to the average model are similar between the insect models and the model of vibrissal cortex presented in this study , there is one major difference: The registration to the insect brains uses non-rigid transformations ( i . e . , nonlinear deformations of 3D label fields ) , while our registration approach was based on rigid transformations ( i . e . , translations , rotations and stepwise linear scaling ) . Typically , the 3D anatomical layout and even the number of neurons , as well as the 3D dendrite/axon projection patterns of individual neurons are stereotypic across insect brains [19] , [20] . The use of label fields and nonlinear deformations may thus be justified for the reconstruction of average anatomical models , if the 3D structure of the brain of interest is sufficiently stereotypic [16] . However , the mammalian cortex is different . Neither the numbers of neurons ( e . g . , per cortical column [37] , [38] ) , nor the 3D dendrite and in particular axon projection patterns [2] , [3] , [39] display such large degrees of stereotypy across animals . Thus , in the case of the vibrissal cortex , we argue that nonlinear deformations would certainly result in a perfect match of all anatomical landmarks , but the resultant non-rigid transformations of neuron tracings may introduce uncontrollable systematic morphological changes ( e . g . , in path length or innervation volume ) . The variability across animals of all parameters describing the 3D layout of the vibrissal cortex was sufficiently small to create an average cortex model . Further , the set of linear transformations , introduced here , was sufficient to create a standard model , which captured the average 3D layout of the vibrissal cortex . Specifically , we showed that all parameters describing the 3D layout of the standard model were very close to the respective parameters averaged across all reconstructed cortices ( i . e . , SD within 5% of the mean ) . Thus , the precision of the standard model was basically identical to the variability between animals . Therefore , the standard model can be regarded as an optimal reference frame for the vibrissal cortex . Finally , the precision of soma/dendrite/axon locations after rigid registration to the standard cortex was on average ∼30 µm within the principal column and ∼90 µm in surrounding columns , but at least ∼140 µm ( see Table 2 for column-specific values ) . The registration accuracy was hence in the range of the anatomical variability of the vibrissal cortex across animals . In conclusion , lacking a sufficiently high density of reproducible anatomical landmarks , non-rigid deformations would artificially minimize the measured , true anatomical variability of the vibrissal cortex across animals , but would not improve the accuracy of the registration . Moreover , non-rigid transformations would deform the morphology of the cortical neurons , changing their path lengths , innervation domains and even electrotonic properties [40] in an uncontrollable manner . Thus , in the case of the mammalian cortex , non-rigid transformations should be replaced by rigid ones when the true anatomical variability across animals is known and sufficiently small . Recently , a first attempt to register neuron morphologies to the mammalian brain has been reported , using a 3D model of the hippocampus in rats [41] . There , 3D reconstructions of two hippocampi were obtained by manually tracing anatomical outlines from low-resolution images of several consecutive 16 µm thick brains sections . Registration of individual neuron morphologies was then performed by placing the somata at the recording location , determined by the coordinates of the pipette , and correction of dendritic orientation and scaling . This approach renders an important step in standardizing this large structure in the rat brain . Our results suggest , however , that the recording location in vivo can be systematically biased , and that large sample sizes may be required to estimate the underlying anatomical variability . Finally , magnetic resonance imaging ( MRI ) has been used to generate anatomical reference frames with voxel dimensions of ∼60 µm in vitro [42] and ∼100 µm in vivo [43] . While MRI allows imaging the entire rodent brain at once , the limited spatial resolution , at present , prevents from using this imaging technique to register individual 3D neuron morphologies to the vibrissal cortex with sufficient precision to determine structural overlap between axons and dendrites . Here we presented a novel , largely automated approach to ( i ) reconstruct the precise 3D geometry of the vibrissal cortex in rats , ( ii ) generate a standardized average cortex model and ( iii ) register dendrite and axon morphologies obtained from in vivo preparations to the standard vibrissal cortex . Our results yielded five major insights: First , the automated reconstruction of the barrel cortex geometry from high-resolution image stacks allowed extracting five parameters describing the geometry of each barrel column with higher precision than manual reconstructions . This allowed estimating the ‘true’ biological variability of column geometry within the vibrissal cortex and across animals . Second , the parameters of a respective column and the 3D layout of the entire vibrissal cortex were remarkably preserved across animals . This allowed generating a standard model that captured the average layout of the vibrissal cortex . Third , the accuracy of the standard model resembled the variability across animals , which rendered the maximal precision possible for registering single neuron morphologies . Fourth , the rigid registration approach allows placing soma/dendrites/axon at their true cortical position with ∼30 µm and ∼90 µm precision within the principal and surrounding columns , respectively . Finally , the dimensions and orientations of individual barrel columns varied substantially across the vibrissal cortex , following well-defined gradients . This finding raises the question whether a cortical barrel column can be regarded as a stereotypical anatomical unit of the vibrissal cortex . In particular , two findings argue against this theory . First , the cortical column volume increases from the A- towards the E-row by ∼2 . 5-fold . Previous studies demonstrated that the average neuron density is rather constant across cortical columns [37] , [38] . Hence , assuming an average neuron density of 80 , 000 neurons per cubic millimeter , the number of neurons would increase from ∼10 , 000 per column in the A-row to ∼25 , 000 per column in the E-row . Second , the curvature of the pia and WM surfaces resulted in tilted orientations of the BC-axes , converging towards the WM . Consequently , cylindrically extrapolated barrel columns started to overlap in deeper layers , sharing up to 25% of their volume with their surrounding barrel columns at the WM . Thus , the column-specific ( i ) volume , ( ii ) number of neurons , ( iii ) overlap with surrounding columns and ( iv ) relative proportion of supragranular-to-granular-to-infragranular layers suggest that each barrel column is a unique anatomical and potentially functional unit , as was suggested previously by functional measurements in different barrel columns in freely behaving mice [44] . Averaging of barrel column dimensions across different whisker rows and arcs may therefore be unjustified . In contrast , the geometry of the entire vibrissal cortex is remarkably stereotypic across animals . This suggests that the vibrissal cortex itself may be regarded as an anatomical and functional unit .
All experiments were carried out in accordance with the animal welfare guidelines of the Max Planck Society and VU University Amsterdam , the Netherlands . Neurons were filled with biocytin in urethane-anaesthetized or fentanyl-sedated Wistar rats either extracellularly by using juxtasomal recording and electroporation [45] or via whole-cell recording [46] . Spiking profiles [47] , [48] and morphology of these neurons have been published previously [3] . The recorded neurons were targeted with standard patch electrodes ( 5 MΩ ) that were positioned at ∼35° with respect to midline . Vibratome sections were cut approximately tangential to the barrel field by positioning the brains at an angle of ∼45° with respect to midline . Neurons were revealed with the chromogen 3 , 3′-diaminobenzidine tetrahydrochloride ( DAB ) [34] . Dendrite and axon morphologies were obtained between postnatal days 25–35 . Automated barrel field reconstructions were obtained at postnatal day 28 . Animal weights ranged from 68 g to 93 g ( mean 77±8 g ) . No obvious differences in morphologies and cortex dimensions were observed at different ages and weights . Cytochrome-oxidase staining was performed on 50 or 100 µm thick sections using phosphate-buffered saline ( 0 . 05 M ) containing 0 . 2 mg/ml Cytochrome C ( Sigma ) , 0 . 2 mg/ml catalase ( Sigma ) and 0 . 5 µg/ml DAB . To perform manual tracings of barrel outlines in Cytochrome C positive sections , Cytochrome-oxidase staining was performed for 45–60 minutes at 37° C . For automated detection of barrels , Cytochrome-oxidase staining was performed overnight at 37°C . Neuron tracings were performed on 50 or 100 µm thick vibratome sections , cut approximately tangential to the D2 barrel column . Ranging from the pia surface to the white matter , 40 or 24 sections were reconstructed per neuron . DAB-stained dendrites were detected manually using Neurolucida software ( MicroBrightfield , Williston , VT , USA ) . Axons were detected and traced in each brain section using a previously described automated method [49] , [50] . Manual post-processing of individual sections [51] , as well as automated alignment of reconstructed branches across sections [52] , were performed using a custom-designed 3D editing environment based on ZIBamira visualization software v2010 . 06 ( Zuse Institute Berlin ) . Pia and barrel outlines were manually traced in each section at low resolution ( Olympus 4× UPLAN S APO; 0 . 16 NA ) and added to the tracings in Neurolucida software ( MicroBrightfield , Williston , VT , USA ) . A standard transmitted light brightfield microscope ( Olympus BX-52 , Olympus , Japan ) equipped with a motorized x-y-z stage ( Märzhäuser , Wetzlar , Germany ) was used for automated mosaic/optical-sectioning image acquisition , using Surveyor Software ( Objective Imaging Ltd , Cambridge , UK ) . A 435±70 nm band-pass illumination filter , was attached to the diaphragm of the lighthouse to provide high contrast of the barrels . A 4× air objective ( Olympus 4× UPLFLN; 0 . 3 NA ) with a pixel size of 2 . 33 µm was used for reconstruction of pia , WM and blood vessels . A 40× oil immersion objective ( Olympus 40× UPLFLN; 1 . 3 NA ) with a pixel size of 0 . 23 µm and optical sectioning of 1 µm spacing was used for reconstructing the barrel field ( Figure 1C , usually in 11–13 sections ) . Individual image planes were down-sampled to a pixel size of 1 . 85 µm . All processing was carried out on workstations with Intel Xeon processors ( 8 cores/12GB RAM ) or compute-servers with Intel Xeon processors ( 24 cores/256GB RAM ) . Segmentation and reconstruction of blood vessel , pia , WM and barrel outlines was performed automatically using custom written C++ routines , in part based on ITK [53] , VTK [54] , OpenMP and GSL [55] libraries . All image processing algorithms , filters and parameters were determined by systematic testing . The individual steps of the image processing and 3D reconstruction pipelines are described in detail in the Supplemental Materials ( Figure S1 , S2 , S3 , Text S1 ) . Briefly , blood vessels are automatically extracted from low-resolution images and median projections of the high-resolution image stacks . Outlines of the pia and WM are automatically extracted from low-resolution images in each brain section using thresholding and region growing methods ( Figure S1 ) . Barrel outlines are automatically detected in each optical section of the high resolution image stacks during three processing steps ( Figure 2 ) . First , a set of gray value-based filters enhances the contrast between the barrels and the septum , by reducing the noise introduced by structures at small spatial frequencies ( e . g . , blood vessels or unstained neuron somata , Figure 2D–F , Figure S2 ) . Second , seed points are manually assigned for each barrel in one brain section where all barrels are visible ( Figure 2A ) . Based on these seed locations , Voronoi Regions ( VR ) are calculated as a first order approximation of each barrel ( Figure 2B ) . Within each VR , a set of landmark-based segmentation filters ( Figure S2 ) extracts the barrel circumference for each optical section ( Figure 2B–I ) . Third , the quality of each barrel contour is evaluated by set a model-based correction filters ( Figure 3A–C , Figure S2 ) . This step guarantees that barrel diameters decrease towards the respective BT and BB points and again increase outside the barrels . Consequently , the vertical extent of each barrel can be objectively determined as local minima in barrel diameter ( Figure 3D–E ) . All automatically extracted contours ( i . e . , vessel , pia , WM , barrel ) are converted into closed graphs . By manually or automatically matching the extracted blood vessel patterns [50] , [52] , the contours from all low- and high-resolution images from the same animal are aligned and merged into a single file . Application of 2D distance transforms to the individual sections allows transforming the pia and WM contours into 3D isosurfaces , respectively ( Figure 4 , Figure S3 ) . Using the pia surfaces , the orientation of the blood vessels is determined . Vessels that are not perpendicular to the pia ( i . e . , angle between the vessels and the normal vector of the pia triangle at the intersection point is larger than 10° ) are deleted . The remaining vessels are used to constrain the vertical BC axes . For each BC , a set of candidate axes is determined for each triangle of the pia surface within a 2 mm radius . The quality of each axis is scored . The shorter the axis and the more perpendicular to the pia surface , the higher the score . Finally , from all candidate axes that are parallel to the average vessel orientation within the respective barrel , the one with the highest score is automatically chosen as the BC axis . Finally , the reconstructed barrel contours are projected to the respective BC axis , defining the BT and BB points . Calculating the average barrel circumference and extrapolating it towards the pia and WM , completes the reconstruction pipeline and allows extracting the five parameters per column needed to quantitatively describe the 3D geometry of the rat vibrissal cortex ( BT , BB , barrel area , BC axis and pia-WM distance ) . The first step in generating a standard barrel cortex is registration of all reconstructions to a common coordinate system . Only translations and rotations are used for registration . Corresponding BT and BB points from all reconstructions are used to align different reconstructions . Further , the BC axis passes through these points . Aligning all corresponding BT and BB points therefore implicitly aligns the BC axes from different reconstructions . The transformations for each reconstruction are computed by minimizing the sum of squared differences S between BT and BB points of corresponding barrels for all reconstructions: . Here , i = 1 , 2 , … , n enumerates the corresponding BT and BB points and u , v = 1 , 2 , … , m refer to different reconstructions to be matched at each corresponding point i . This is equivalent to minimizing the sum of squared differences between the BT and BB points of all reconstructions and the centroids of the corresponding points of all reconstructions [56] . An analytic solution to this problem exists . However , because the centroid itself depends on the desired transformations for each reconstruction , an iterative algorithm is used to find an approximate solution [56] . Briefly , one barrel reconstruction is arbitrarily selected as a reference . For all other barrel reconstructions , the optimal translation and rotation with respect to the reference reconstruction are computed separately . The centroids of all corresponding points are computed and used as reference during the next iteration . For every iteration step the optimal translation is given by aligning the overall centroid of the reference with the overall centroid of the reconstruction to be matched . The optimal rotation is then computed from the singular value decomposition of the product of the two point position matrices set up from the positions of all BT and BB points of the reference and the reconstruction to be matched . No scaling was allowed . Only one iteration step was necessary , because the change in BT and BB positions was less than 1 µm after the first iteration . The position of the BT and BB points of each barrel in the standardized cortex model is set to the average centroid of the respective BT and BB after registration , resulting in average BC axes . In addition , a vector field representing the local orientation is created with a resolution of 50×50×50 µm3 voxels . The vector at each voxel is computed by linear interpolation of the orientation of the three nearest BC axes . The standard barrel contour is created as a circle with cross-sectional area equal to the average cross-sectional area of the respective barrels ( Table 1 ) . The standard barrel columns are created by extrapolation of the barrel contours along the standard barrel axes by the average distances of the barrels to pia and WM ( Table 1 ) . This approach is justified , because only length- and angle-preserving transformations are applied to the individual reconstructions before computing the average barrel field . The resultant top and bottom points of all standard barrel columns are triangulated , yielding the standard pia and standard WM , respectively ( Figure 5B , D ) . When registering neuron morphologies to the standard cortex , the BC of the principal column ( i . e . , containing the neuron's soma ) is aligned to the respective BC in the standard model of the vibrissal cortex . The remaining registration steps ( i . e . , minimizing the squared differences of all BC locations to obtain the optimal rotation angle ) are as for generating the standard cortex . This method was chosen to guarantee the highest possible registration accuracy of soma/dendrites/axon within the principal column , at the cost of achieving less precision in surrounding columns ( see Results ) . The somatotopic layout of the barrel field in rows and arcs can be described as a map from the 2D discrete ( Row , Arc ) space to a 3D one , using the centroid location of each barrel . To do so , we chose a coordinate system as shown in Figure 1B . The origin is located at the centroid of the C2 barrel . The z-axis points vertically along the C2 BC axis , the x-axis points towards the centroid of the C3 barrel , approximately along the row . Thus , the y-axis points approximately along the arc . Using this description , the layout of the 12 registered and the standardized barrel field is described by 24×3 = 72 parameters . The 2D–3D mapping of the barrel cortex layout is then described by three functions f , g , h of row and arc index: where are unit vectors along the axes of the coordinate system . The three functions are modeled as polynomials of 2nd order , i . e . , they are of the form: Linear functions proved insufficient to describe the non-linear spacing between rows or the curvature of the barrel cortex . Higher-order polynomials showed no obvious improvement in the description of the 3D layout of the vibrissal cortex . The 15 coefficients of the three functions describe different features of the barrel field layout: The linear coefficients f01 and g10 describe regular spacing of arcs and rows along the x- and y-axis , respectively . The linear coefficients f10 and g01 describe the relative shift between barrels in neighboring arcs or rows and can be used to measure the deviation of the barrel field layout from a rectangular grid . The second-order coefficients of f and g describe nonlinear effects such as curved rows and arcs or septa of different sizes between rows . The coefficients hij describe the cortical depth of the BC points . The constant coefficients can be neglected . For a numerical description of the 15 coefficients , the Row and Arc coordinates of each barrel are mapped on integer numbers , such as A row→0 , B row→1 , greek arc→0 , arc 1→1 , etc . The barrels of the greek arc are mapped on half-integer Row coordinates . The coefficients are determined by fitting the functions to the barrel centroids ( Figure 7 ) . | For studying the neural basis of perception and behavior , it would be ideal to directly monitor sensory-evoked excitation streams within neural circuits , at sub-cellular and millisecond resolution . To do so , reverse engineering approaches of reconstructing circuit anatomy and synaptic wiring have been suggested . The resulting anatomically realistic models may then allow for computer simulations ( in silico experiments ) of circuit function . A natural starting point for reconstructing neural circuits is a cortical column , which is thought to be an elementary functional unit of sensory cortices . In the vibrissal area of rodent somatosensory cortex , a cytoarchitectonic equivalent , designated as a ‘barrel column’ , has been described . By reconstructing the 3D geometry of almost 1 , 000 barrel columns , we show that the somatotopic layout of the vibrissal cortex is highly preserved across animals . This allows generating a standard cortex and registering neuron morphologies , obtained from different experiments , to their ‘true’ location . Marking a crucial step towards reverse engineering of cortical circuits , the present study will allow estimating synaptic connectivity within an entire cortical area by structural overlap of registered axons and dendrites . | [
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] | 2012 | 3D Reconstruction and Standardization of the Rat Vibrissal Cortex for Precise Registration of Single Neuron Morphology |
Secondary lymphoid organs ( SLO ) , such as lymph nodes and the spleen , display a complex micro-architecture . In the T cell zone the micro-architecture is provided by a network of fibroblastic reticular cells ( FRC ) and their filaments . The FRC network is thought to enhance the interaction between immune cells and their cognate antigen . However , the effect of the FRC network on cell interaction cannot be quantified to date because of limitations in immunological methodology . We use computational models to study the influence of different densities of FRC networks on the probability that two cells meet . We developed a 3D cellular automaton model to simulate cell movements and interactions along the FRC network inside lymphatic tissue . We show that the FRC network density has only a small effect on the probability of a cell to come into contact with a static or motile target . However , damage caused by a disruption of the FRC network is greatest at FRC densities corresponding to densities observed in the spleen of naïve mice . Our analysis suggests that the FRC network as a guiding structure for moving T cells has only a minor effect on the probability to find a corresponding dendritic cell . We propose alternative hypotheses by which the FRC network might influence the functionality of immune responses in a more significant way .
Secondary lymphoid organs ( SLOs ) , such as lymph nodes ( LN ) or the spleen , are anatomical structures important for the establishment and proper functioning of immune responses . In the absence of these SLO , an organism fails to control an infection [1] . SLOs are strongly connected to the blood , and thus facilitate cell-cell interactions across the entire body . Of particular importance for immune responses are interactions between naïve T cells and antigen-presenting cells , such as dendritic cells ( DC ) [2] , as well as interactions between activated T cells and infected cells . Lymph nodes and the spleen have themselves a highly organized architecture with different anatomical compartments for specific subsets of lymphocytes ( reviewed in [3] , [4] ) . Recently developed two-photon microscopy methods enable us to observe how the cells move inside of LNs and the spleen ex vivo or in vivo [5]–[10] . Using this method , it has been observed that lymphocytes move along the fibroblastic reticular cell ( FRC ) network – a network formed by FRC and filaments between them [11] . Observations made by electron microscopy had already revealed the detailed structure of the FRC network which forms “corridors” , through which lymphocytes migrate [12]–[14] . The FRC network provides guidance for T cells , which preferentially move along the network filaments due to certain chemokines expressed by the FRC network [11] , [14] , [15] . Especially the lymphoid chemokines CCL19 and CCL21 , which interact with the CCR7 receptor on naïve T cells and activated dendritic cells , and other soluble factors presented by FRCs influence T cell motility in lymph nodes [16]–[18] . Additionally , dendritic cells tend to reside on FRCs [19] , [20] . It is found that the FRC network is disrupted or changed in several infections . For example , infections with the lymphocytic choriomeningitis virus ( LCMV ) Clone-13 strain or with visceral leishmaniasis in mice are associated with a disrupted FRC network [21] , [22] . On the other hand , chronic human immunodeficiency virus ( HIV ) infection leads to additional deposition of collagen in the lymphoid tissue ( fibrosis ) , consolidating the existing FRC network [23] . However , it is not clear to what extent changes in the FRC network affect the efficacy of the immune response [24] , [25] . In this study , we examine quantitatively how the FRC network influences cell-cell interactions . To this end , we extended a recently published 3D cellular automaton model for lymphatic tissue to allow for a more detailed description of the FRC network [26] . We simulate the spatial movement of T cells in lymphnodes that are structured by FRC network with different characteristics . To investigate the effect of FRC networks we manipulate the density of FRCs and the number of filaments between them . We then study with what probability a T cell encounters a static dendritic cell with dynamic dendrites or a motile target . Our analysis reveals that the influence of the FRC network structure on this probability is only minor . Based on our analysis , we conclude that enhancement of the contact probability of a single T cell with a target is not the most important contribution of the FRC network to secondary lymphoid organs to make them an efficient environment for the establishment and proper functioning of immune responses . We propose other hypotheses and experimental methods to test them in order to reveal the importance of the FRC network .
In Figure 1C , we show the average fraction of the simulated space which would be surveyed by a cell crawling along all filaments of the FRC network . In a dense as well as in a sparse network , we observe an exponential increase in this value for increasing densities . If 10% of the space is occupied by FRC , the whole simulated volume can be surveyed , independent of a dense or sparse network structure . This increasing coverage is associated with a reduced centrality value of a single FRC inside the FRC network ( Figure 1D ) . The centrality , , quantifies how likely a node in the network is reached by a cell which performs a random walk along the network ( see Materials & Methods for a detailed description of the calculation ) [27] , [28] . In contrast , the average centrality of a random node in the cube is not affected by varying levels of . The average distance between two intersections of FRC filaments in our simulations is around similar to experimental observations [15] ( see Supporting Information ( SI ) Figure S2 ) . Mueller et al . [21] used a tracer molecule to label the conduit system inside the white pulp in the spleen of mice . This basically represents the filaments of the FRC-network as they surround this system [24] . Image analysis revealed that roughly of the white pulp in the spleen of naïve mice is occupied by FRC and their filaments [21] . In our simulations , this would correspond to a dense FRC network constructed by , counting the frequency of edges representing FRC-filaments . The same parameterization for , but in a sparse network , would occupy of the simulated space , comparable to the amount of FRC components observed in mice persistently infected with the LCMV Clone-13 strain [21] . In addition , we analyzed how different FRC networks affect the motility of moving cells . For the start , we assume that the FRC network only provides directional guidance for the movement of the cells and does not interfere with cell velocity . Simulated cells perform a random walk in the long term , as seen from the mean displacement of those cells ( see SI Figure S3 ) and comparable to experimental observations [6] . In Figure 1E–F , the turning angle distribution of moving cells calculated over 500 independent simulation runs are shown , either for a dense or sparse network structure given different levels of . Each run followed 400 simulated time steps which corresponds to 280 min in real time . The rough pattern of the turning angle distribution , especially in a dense network situation with , corresponds to the distribution observed experimentally for naïve T cells in a lymph node [7] , [29] . Simulated T cells prefer small turning angles as determined by the way a new moving direction is chosen ( see corresponding paragraph in Materials & Methods ) . There is nearly no change in the distribution of the turning angle between a situation without FRC network ( ) and when a FRC network is covering the whole simulated space ( , compare Figure 1C ) . These observations are robust against changes in the average length of filaments between FRC , . In a first series of simulations , we investigate how a network structure affects the contact probability of two cells distinguishing between two different scenarios: ( i ) a naïve T cell interacting with a static dendritic cell with extracting dendrites , and ( ii ) a cytotoxic T cell hunting for a moving target . We determined the number of successfully established contacts for different FRC network scenarios after a maximum of 400 time steps . With one simulated time step corresponding to 0 . 7 min in real time , we are determining the contact rate of cells after they enter the lymphoid tissue . This is at the lower boundary of estimates for the dwell time of T cells in lymphoid organs , with these estimates varying between [30] , [31] . We examined the search of a naïve T cell or CTL in the light of a dense or sparse underlying FRC network . If no FRC network is present , a contact between a naïve T cell and its corresponding static dendritic cell is established in around 64% of the cases after 400 simulated time steps ( Figure 2 ) . The success rate stays constant on this level ( ) for different frequencies of FRC if these cells form a dense network structure ( Figure 2A ) . In the scenario where a CTL hunts for a moving target cell , the probability of the hunting cell to find its target within 400 time steps equals without any FRC network present . The contact probability slightly increases for increasing numbers of FRC , reaching a maximum value of 64% for ( Figure 2A ) . With , defining a FRC network covering the whole simulated compartment , the probability of a cell to find its target equals a scenario without any FRC network ( ) . If the network structure is disrupted and connecting filaments between FRC are lost , the contact probability of a cell with a static or motile target is reduced ( Figure 2B ) . In the scenario with a naïve T cell interacting with a static DC , the success rate reaches a local minimum in . There , only of the T cells will find their target during the simulated time period . A similar observation is made for the case of a CTL hunting for a motile target . For , the success rate is only two third of the one observed in a dense network scenario . We additionally examined , if the impaired contact probability of two cells is also reflected in a longer “hunting time” of the cells among those which succesfully established a contact to another cell . Figure 3A shows the histograms of the time until a contact is made among all successful contacts , either for a dense or sparse FRC network which is build up by a frequency of FRC . We fitted an exponential distribution function , which best describes such waiting time distributions , to the simulated data using a maximum likelihood approach . Our data indicate that the number of sufficiently established contacts follows an exponential distribution over time . The rate constants of the exponential distribution do slightly differ between the intact and impaired network ( naïve T cell - DC: /CTL - motile target: ) . Numbers in brackets represent 95% confidence intervals of the estimates ( see also Table 1 ) . The results with do not qualitatively change across the range of that we considered with . Furthermore , we examined if the time the naïve T cell or CTL needs to initially reach the FRC network affects the successful establishment of a contact to a DC or motile target cell , respectively . In neither of the scenarios analyzed did we find an influence of this time , or the initial distance of the two interacting cells at the beginning , on the establishment of a contact ( Figure S5 ) . So far we assumed that the FRC network only affects the moving direction of the cells . Several experiments showed that chemokines , such as the CCR7-ligands CCL19 and CCL21 , are released by FRC and directly influence the motility of T cells [17] , [18] . Worbs et al . [17] could show that these CCR7-ligands increased the median cell velocity by a factor of . We incorporated this aspect into our simulations by increasing the velocity of cells which are connected to the FRC network . As long as a cell crawls along the simulated fibroblastic reticular fibres , the basic velocity is increased by a factor , , allowing a cell to perform more moves/swaps per time step . Results for are shown in Figure 3B . With one simulated time step corresponding to in real time , this leads to average cell velocities in the range of and ( compare to [17] ) . With and , corresponding to cell velocities observed in vivo , the motility coefficient of simulated cells shows reasonable values ( see SI Figure S3 ) . In a dense network scenario , the contact probability of two cells is slightly increased for increasing cell velocities ( compare Figure 3B ) . When a CTL is hunting for a moving target and a dense FRC network is provided ( Figure 3B , lower panel ) , the maximal probability to successfully find their counterpart is observed for , irrespectively of the network affecting cell velocity . If the network structure is disrupted , the probability of a cell to find its target is maximally reduced in this parameterization . This latter observation is also valid in the case of a naïve T cell - DC scenario . The increase of the cell velocity by a factor of due to the FRC network does not significantly affect the time until a contact between two cells is made ( Figure 3C , Table 1 ) ( naïve T cell - DC: /CTL - motile target: ) . If , there is a slight tendency for cells to find their counterpart earlier than for lower values of ( naïve T cell - DC: /CTL - motile target: ) . The same analyses were performed using a larger simulated volume of cells , which led to the same results ( see SI Figure S4 ) .
Several studies showed that disruption of the FRC network correlates with a disturbed organization of the lymphoid tissue and leads to the loss of control of an ongoing infection . The network of fibroblastic reticular cells inside secondary lymphoid organs affects cell motility , and is assumed to facilitate the interaction between different cells in the LN or spleen [11] , [21] , [24] , [32] , [33] . However , a “road system” for cells , as e . g . represented by the FRC network , provides guidance , but also constraints . A sparse road system is difficult to reach , but once a cell moves on the roads it will easily find its target . A dense road system , on the other hand , is reached fast , but it may not be any easier to find a target than in free space because there are too many routes available . This trade-off between guidance and constraints gives rise to an optimal network density . The quantitative relationship between cell interaction and FRC network cannot be investigated experimentally at present because it requires the manipulation of the FRC network structure . Therefore , we investigated this relationship and the trade-off as it is described above using a simulation model for the movement of T cells in a lymphatic tissue . We extended a 3D cellular automaton of a lymphoid region inside the spleen which we developed previously [26] . We specified the FRC network structure and rules which define the movement of simulated cells along an FRC network . The resulting network and cell motility characteristics is consistent with experimental observations [6] , [15] , [29] . To simulate biologically plausible cell behaviour in our model , the moving direction of a cell depends on two components . The first component is the intrinsic movement behaviour of a cell . As a change in moving direction requires a costly restructuring of the actin-cytoskeleton [34] , cells prefer small turning angles . The second component influencing the moving direction of cells in our model is the FRC network . The fraction of time a simulated cell follows the filaments of the FRC network is around 80% and varies dependent on the density of the FRC network . While not consistently moving along the network , this fraction is always higher as you would expect by just random movement without influence of the FRC network on the movement direction . The turning angle distribution is therefore influenced by the FRC network and , hence , the frequency of filaments in the simulated volume ( see Figure 1E and F ) . However , the intrinsic movement behaviour prevents cells to solely follow this network as this might require cell turns that are overruled by the preference for small turning angles . This reduces the differences between the turning angle distributions for different kind of networks . We neglected to model cell movement without the intrinsic movement behaviour solely following the FRC network in order to simulate more biologically plausible cell behaviour . Our analysis suggests that the fibroblastic reticular network as a guidance system has only a small effect on the probability of cell encounters . However , if the FRC network is disrupted by losing some of the filaments the probability of two cells to find each other is reduced . This is observed for static targets , such as dendritic cells , and moving targets alike . It has been previously shown that the absence or alteration of the FRC network inside LNs and the spleen also impairs the motility of moving lymphocytes [16] , [18] , [32] . Therefore , we investigated how an increase of cell velocity along the filaments of the FRC network affects the contact probability . Assuming this additional property of the FRC network , the influence of varying FRC network densities on the probability of a cell to find either a moving or static target slightly increases . However , this did not affect the relative effect of a disruption of the FRC network on the contact probability between cells . In general , we found that the presence of FRC network increases the contact probability between two moving cells by approximately 20–25% , or even up to 40% if the FRC network also contributes to cell velocity . In contrast , the probability of a moving T cell to encounter a static dendritic cell is unaffected by the FRC network . This is probably due to the fact that in the latter situation the protruding dendrites of the dendritic cell survey a large fraction of the simulated volume . Even considering a larger simulated volume ( cells ) led to the same results ( see SI Figure S4 ) . However , we have to emphasize that our simulations represent a more challenging environment for the activation of a specific naïve T cell than in biological plausible conditions . The T cell - DC ratio in our simulations ( 1∶1 ) is even lower than observed in two-photon microscopy experiments which study T cell and DC interaction . In these experiments , usually 1–2% of all T cells in a lymph node are fluorescently labelled and appropriate DC make up about 0 . 5% of all cells ( roughly 10–30% of all DC ) . Based on these estimates , roughly 2–5% of the cells in a LN are DC and , for simplicity , we assume that the rest of the cells are T cells as we are modeling the T cell zone of a secondary lymphoid organ [35] . The total volume of a DC is assumed to be around [8] , [35] . Under the assumption that of the simulated space is occupied by reticular network or represents free space , we would have to model 2–15 DC and 60–120 specific T cells in a cube of nodes ( 6–50 DC vs . 200–400 T cells for ) . We study the first contact between two cells given a fixed ratio of the two cell populations considered . In order to leave space unconfined , we use periodic boundary conditions in our cellular automaton . We checked that the periodic boundary conditions do not affect our results . While during a simulation on average around of the moves of a cell “pass” the periodic boundary , we found no difference in this value , or its variation , among all the different scenarios considered ( static/moving target , dense-sparse FRC networks , different FRC-frequencies ) . There is also no difference between the runs which ended in a contact between the cells or those which did not after a period of . Therefore , the comparisons we show are not biased in the one way or the other by the periodic boundary conditions of the simulation system . Quantification of the first-passage time , the time a random walker needs to reach a certain target point , plays an important role in different kinds of target search processes [36] , [37] or the dynamics for the spread of diseases [38] . Applying previously developed mathematical theory calculating first-passage times [39] , [40] to the scenario described here can be used to corroborate the simuation results . However , such a theoretical approximation has to be carefully compared to the complex migration characteristics and dynamics , which was beyond the scope of this study , and will be subject to future work . While in total the observed increase in cell contact probability due to a dense FRC network is only small , which is in line with recent simulation results confining the movement of cells to the network [41] , it remains to be investigated how an increase of 20% to 30% in the efficacy of cell encounter affects the susceptibility to infection , the morbidity due to infection , and finally the fitness of the host . We found that the FRC network density observed in the spleen of naïve mice [21] corresponds to a density that is maximally vulnerable to disruption . However , the evolutionary pressure evoked by this disadvantage might be too low to favour the development of alternative network structures . Furthermore , if the FRC network does not enhance the contact probability of an individual naïve T cell to a DC substantially as shown in the simulations , the question remains if the FRC network provides additional factors for the proper function of the immune response which are not captured in the simulations so far . In the following , we propose several hypotheses and possible experimental approaches to test them . One hypothesis porposes the FRC-network to represent something like a crowd control system to improve DC scanning rates . The average distance between two filaments of the FRC-network is observed to resemble the average diamter of a T cell [11] , [12] , [15] . This could force T cells to move in streams through the lymph node . While the time of an individual T cell to reach a certain DC might not be increased by this , it could allow DC , which are attached to fibroblastic reticular cells , to scan more different T cells per minute . Without the network , cells might clump around DC , limiting access to the dendritic cell . This hypothesis is corroborated by several experimental and simulation studies [8] , [26] , [42] , which proposed that the contact duration , i . e . the time a DC needs to scan a T cell ( attach - detach ) , is more important for efficacy than the time it actually takes to find a corresponding cell . Previous simulation studies have already observed the occurence of T cell streams along the filaments of the FRC network [35] . The modeling frameworks used in this study might be useful to examine this hypothesis theoretically . One possibility to investigate experimentally the hypothesis that the FRC network works as a crowd control system could be to perfom two-photon imaging analysis of T cell - DC interactions in lymph nodes of naïve mice and those that show an impaired FRC network structure , as e . g . evoked by persistent infections in mice [21] . Accounting for confounding factors , such as the presence of an infection in the persistenly infected mice , one could check if the number of individual T cells scanned by one DC differs between the two situations . However , one has to ensure that no other factors of the persitstent infection impair the comparison . Besides interferring with the motility of T cells , the FRC network could also facillitate cell encounters by trapping immune cells inside the lymphoid organs . Estimates for the dwell time of a T cell in a lymphoid organ vary around , for B cells between [30] , [31] . By presenting “homing” molecules , such as CCL19 and CCL21 , the FRC might contribute to the length of the time a lymphocyte will stay in the spleen or LN , respectively . Without the network , the transit time of a T cell through the spleen and thereby its chance to find its cognate antigen could be reduced . This might even increase the damage induced by network impairment , as we found that , for biologically relevant cell velocities , cells find their counterpart earlier in a dense than in a sparse network ( see Table 1 , ) . Transfer of labelled T cells into the spleen of naïve mice and those showing an impaired FRC network can be used to estimate the dwell time of T cells in the spleen of the two different animal populations , and to detect differences . Recently , a new experimental approach was presented to construct FRC networks in vitro on a macroporous polyurethane scaffold [43] . Constructing FRC networks on this scaffold with different doses of FRC clones and using appropriate imaging techniques might show if varying FRC network densities affect the number of contacts between cells . Varying the FRC network density , and analyzing cell motility and interactions in vitro could allow us to quantify the effect of FRC network disruption on immune cell dynamics in more detail .
The interaction of naïve T cells/CTL with dendritic cells ( DC ) /target cells , as well as with the FRC network is simulated on two separate but interacting three-dimensional lattices of nodes and edges , unlike our previous model [26] . Each node denotes the body of a cell and has 26 direct neighbours . Edges define paths on which FRC fibres can grow or cells are able to move . We define periodic boundary conditions in which a cell , or a dendrite or fibre of a DC or a FRC , respectively leaving the simulated space on the one side of the lattice reappears on the opposite side . The cellular automata were implemented in the C++ programming language . Each cell that is capable to move , such as naïve T cells or CTL , possesses a certain moving direction . The moving direction , pointing to one of the 26 neighbouring cells , can change during a simulation run as described below . We distinguish between two types of movement as done before [26]: Either cells move into free space or they swap their place with a neighbouring cell . Thereby , a naïve T cells or CTL swaps its place with an unspecific splenocyte irrespectively of the moving direction of this cell . The movement of cells is constrained by the underlying FRC network . Each edge in the lattice of the FRC network is weighted according to the connection and distance to an FRC . This weight , , represents the amount of chemokines released and defines the level of attraction of a moving cell into this direction . The weight is determined in such a way that the attraction to a FRC is higher than the movement away from it . Edges without FRC filaments receive a weight , roughly 100 times lower than those with FRC elements . This assumption ensures , that cells preferentially crawl along the FRC network as observed experimentally . Some edges close to FRC are assigned a weight of which accounts for paths that are blocked by spatial obstacles [26] , [35] . The probability of an edge connected to a FRC to have a weight of depends on the frequency of filaments connected to this FRC in accordance to the biological situation . The more filaments are connected , the more likely it is that some pathways into other directions are blocked by those filaments . The number of pathways blocked is sampled from a uniform distribution over , where denotes the number of different filaments connected to this FRC . However , we have to emphasize that , although edges are blocked , each node in the lattice can be reached , e . g . from a different side . Dependent on the frequency of FRC and the density of the FRC-network , on average of all possible moving directions are blocked . As cell movement requires a complex restructuring of the actin-cytoskeleton [34] , cells prefer small turning angles . Therefore , a second weight is assigned to every moving direction which incorporates the turning angle between and the previous moving direction . The calculation of is similar to the direction assignment performed in [29] and comparable to the assignment done before [26] . The weight into direction is determined by the distance to the surface of a composite ellipsoid which includes and and is defined by ( 1 ) The parameters and are predetermined and fixed for all simulations . The default parameterization is defined by ( see SI Figure S1C for an illustration of the weight assignment ) . With this default parameterization we ensure a preference of the moving cell for small turning angles . Varying the actual values , but still keeping a preference for small turning angles , did not change our general results . Before a cell makes a move , the direction of the cell is determined according to both these weights . The probability to move into direction is calculated by: ( 2 ) The actual new moving direction of the cell is then sampled from this multinomial distribution . A simulated CTL is assumed to be in contact to a target cell if these cells are on neighbouring nodes [26] . As the average distance between two nodes is assumed to be , which corresponds to the average diameter of a T cell , the membranes of cells on neighbouring nodes would attach to each other and , therefore , a contact is counted . A simulated naïve T cell is assumed to have established a contact with a static dendritic cell either if the naïve T cell is located on a node directly connected to the node were the core of the DC is situated , or if the position of the naïve T cell is reached by a dendrite of the DC . Thereby , a cell is assumed to be reached by the dendrite if the dendrite reaches or covers the node the T cell is occupying . If the position of the naïve T cell is only insufficiently reached by one of the continuously varying dendrites , we calculate the distance of the tip of the dendrite to the node the T cell is occupying , defined by the parameter , with . The probability that a contact is established in this situation is then sampled from a binomial distribution with . This scenario refers to the fact that the membrane of the T cell might as well be in connection to the dendrite as our model does not cover the actual flexible shape of a T cell . Simulation runs are performed as follows: First , the cellular automaton with the FRC network is constructed according to the assigned frequency of FRC , . Second , the cellular automaton containing the cells which actually move along the FRC network is initialized . Thereby , we examine either a situation with a motile naïve T cell and a static DC ( in the following referred to as static target ) or with two motile cells ( moving target ) . The second scenario corresponds to a CTL hunting for an infected target cell . During one simulated time step , each motile cell will sample a moving direction as described above and perform a move into this direction if possible . Furthermore , each cell is able to move into free space ( compare to [26] ) . While motile cells move , a DC will extend and retract its dendrites . After each time step , it is checked if the specific cell pair has made a contact . A simulation will run for 400 time steps or until a contact is established . A time step corresponds to in real time , hence , 400 time steps correspond to in real time . Estimates for the dwell time of T cells in lymphoid organs such as lymph nodes or the spleen vary around [30] , [31] . We chose the lower boundary of the estimates of for our analyses to determine the success rate for the fastest passage time of T cells . For each randomly constructed FRC network , 50 independent runs of cell pairs are performed . In our simulation environment , we have to distinguish between two types of networks on which the cells can move . The lattice-network comprises all nodes and edges in the 3D cellular automaton cube . This network specifies all possible moving directions of a cell , 26 possible edges at each node . The actual FRC network is a subset of those edges and only consists of the FRC and the connecting edges which represent the filaments of the FRC network . These ones include the weights of the FRC network . The FRC network can be characterized by several parameters . First of all , as described above , it is defined by the frequency of FRC spanning up the network , , as well as the connectivity of the FRC determined by . Additionally , we define different parameters to describe the structure of the FRC network: ( i ) the centrality of a node or a FRC in the total lattice-network or FRC-network , respectively , and ( ii ) the “area surveyed by the FRC network” . The centrality of each node is a quantity used in general network analysis . This measurement quantifies , how likely a node in the network is reached by a cell which is randomly crawling along the lattice [27] , [28] . To calculate , we determine the degree of each node which determines the sum over all weights of the edges connected to this node , . The centrality for node is then calculated by the following formula [27]: ( 3 ) Hereby , the sum over is taken over all neighbouring nodes of node . For simplicity , we define . We calculate the centrality among all nodes in the 3D lattice as well as only for the fibroblastic reticular cells in the FRC network ( see Figure 1D ) . The second parameter , the “area surveyed by the FRC network” , ( see Figure 1B ) is defined by the volume a T cell is able to scan while crawling along all edges of the FRC network ( see Supporting Information , Figure S1B ) . | The interaction between lymphocytes and antigen presenting cells or infected cells is thought to be enhanced by the complex microarchitecture of secondary lymphoid organs such as the spleen or lymph nodes . In the T cell zone the micro-architecture is provided by a network of fibroblastic reticular cells ( FRC ) and their filaments which are assumed to work as a “road system” on which T cells can migrate . However , the effect of the FRC network on cell interaction cannot be quantified experimentally to date because of limitations in immunological methodology . We use computational models to study the influence of different kinds of FRC networks on the probability that two cells meet . We can show that the structure of the FRC network has only a small influence on this probability . However , disruption of the FRC network as observed in persistent infections maximally impairs the contact probability between cells in FRC densities corresponding to those observed in the spleen of naïve mice . Our analysis suggests that the FRC-network as a guiding structure has only a limited effect on the probability of a single cell to find its appropriate counterpart . Further analysis is suggested to reveal the importance of the FRC-network . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"immunology",
"biology",
"computational",
"biology",
"population",
"biology"
] | 2012 | Influence of the Fibroblastic Reticular Network on Cell-Cell Interactions in Lymphoid Organs |
Rhinoscleroma is a human specific chronic granulomatous infection of the nose and upper airways caused by the Gram-negative bacterium Klebsiella pneumoniae subsp . rhinoscleromatis . Although considered a rare disease , it is endemic in low-income countries where hygienic conditions are poor . A hallmark of this pathology is the appearance of atypical foamy monocytes called Mikulicz cells . However , the pathogenesis of rhinoscleroma remains poorly investigated . Capsule polysaccharide ( CPS ) is a prominent virulence factor in bacteria . All K . rhinoscleromatis strains are of K3 serotype , suggesting that CPS can be an important driver of rhinoscleroma disease . In this study , we describe the creation of the first mutant of K . rhinoscleromatis , inactivated in its capsule export machinery . Using a murine model recapitulating the formation of Mikulicz cells in lungs , we observed that a K . rhinoscleromatis CPS mutant ( KR cps- ) is strongly attenuated and that mice infected with a high dose of KR cps- are still able to induce Mikulicz cells formation , unlike a K . pneumoniae capsule mutant , and to partially recapitulate the characteristic strong production of IL-10 . Altogether , the results of this study show that CPS is a virulence factor of K . rhinoscleromatis not involved in the specific appearance of Mikulicz cells .
Rhinoscleroma is a chronic granulomatous infectious disease that affects the nose and other parts of the respiratory tract down to the trachea [1] . Although few sporadic cases are typically described in Western Europe and in the USA , this disease is still endemic in impoverished areas of the Middle East , Eastern Europe , tropical Africa , South East Asia , Central and South America . A delay in the diagnosis can lead to complications such as physical deformity , upper airway obstruction and , rarely , sepsis . Treatment can be challenging and includes surgery and prolonged course of antibiotics to avoid relapses . The bacterium implicated as the causative agent of rhinoscleroma is Klebsiella pneumoniae subsp . rhinoscleromatis ( hereafter mentioned as K . rhinoscleromatis or KR ) , a subspecies of Klebsiella pneumoniae . Despite being geographically broadly distributed , K . rhinoscleromatis has been isolated mainly in human [2] although three recent reports mention the identification of K . rhinoscleromatis in cockroaches [3 , 4] or chickens [5] in low hygiene settings . K . rhinoscleromatis is very closely related to Klebsiella pneumoniae subsp . pneumoniae but can be distinguished from K . pneumoniae sensu stricto by biochemical properties and multilocus sequence typing [6] . Rhinoscleroma development is typically described clinically and pathologically into three overlapping stages: catarrhal stage , proliferative stage , and sclerotic stage [7] . The catarrhal stage is marked by purulent rhinorroea and nasal obstruction , which persists for months . Histological examination shows evidence of squamous metaplasia with a subepithelial infiltrate of polymorphonuclear cells . However , in the subepithelial layer , bacteria are incompletely digested and further released into tissues . The proliferative stage is characterized by symptoms of epistaxis , nasal deformity and other problems depending on the other areas affected . In addition , histology shows the appearance of Mikulicz cells , a hallmark of rhinoscleroma [8] . These cells are large foamy macrophages with numerous enlarged vacuoles containing viable or non-viable bacteria . Finally , the sclerotic stage is characterized by increasing deformity , granulomatous areas and scar formation . Most patients are diagnosed in the proliferative stage , when the lesion appears as a bluish-red , rubbery granuloma and the typical Mikulicz cells can be observed . Mikulicz cells are only documented in rhinoscleroma and have been described as atypical inflammatory monocytes specifically recruited from the bone-marrow upon K . rhinoscleromatis infection [9] . These cells represent a peculiar state of highly vacuolated inflammatory monocytes unable to digest bacteria . Moreover , it has been shown that IL-10 , an anti-inflammatory cytokine , is essential in the establishment of a proper environment leading to the phenotypic maturation of Mikulicz cells [9] . Different virulence factors have been implicated in the pathogenesis of K . pneumoniae . Capsule polysaccharide ( CPS ) is recognized as one of the most important virulence determinants of this pathogen . The presence of CPS inhibits the deposition of complement components onto the bacterium [10–12] , impedes adhesion and reduces phagocytosis of the bacterium by macrophages and epithelial cells [10 , 12–17] . Using in vivo models of colonization and pathogenesis , CPS mutants have been shown to be unable to colonize either pulmonary or systemic tissues [13 , 18 , 19] . Clearly , CPS plays an important role in the interplay between K . pneumoniae and the innate immune system . K . pneumoniae and K . rhinoscleromatis are heavily capsulated bacteria . K . pneumoniae express 134 different capsular serotypes that they are easily transferred via homologous recombination [20 , 21] . Interestingly , despite their scattered geographical distribution , all K . rhinoscleromatis isolates belong to capsular type 3 ( K3 ) [6] . This is raising the question of whether the K3 serotype capsule composition plays any specific role in rhinoscleroma pathology . Indeed the K3 capsule repeated unit is rich in mannose residues , its repeated unit being composed of →2-[ ( 4 , 6- ( S ) -pyruvate ) -α-D-Man- ( 1→4 ) ]-α-D-GalA- ( 1→3 ) -α-D-Man- ( 1→2 ) -α-D-Man- ( 1→3 ) -ß-D-Gal- ( 1→ [22] . This is also suggestive of possible interaction of the bacteria with mannose receptors mainly carried by macrophages and dendritic cells . Indeed , the K3 capsule has been shown to be one of the few Klebsiella K types able to bind to the mannose receptor [23] . The complete sequence of the genomic region comprising the capsule polysaccharide synthesis gene cluster was determined [24] . However , to date , the link between CPS and K . rhinoscleromatis virulence remains to be elucidated . The role of the K . rhinoscleromatis CPS has never been tested in vivo since , currently , there are no K . rhinoscleromatis CPS mutants available . As CPS is a prominent factor in other bacteria , here we explored the possibility that K . rhinoscleromatis CPS is implicated in the peculiar pathophysiological aspects of rhinoscleroma . We have previously established an intranasal mouse model of K . rhinoscleromatis infection recapitulating the formation of Mikulicz cells , the major histological feature of the disease [9] . In this work , we successfully constructed a K . rhinoscleromatis CPS mutant strain , representing the first report of the use of genetic tools in K . rhinoscleromatis . Further , using our mouse model , we compared the host responses to wild-type and K . rhinoscleromatis CPS mutant infections by examining cytokine production and pulmonary histology . We report that the K . rhinoscleromatis CPS mutant is attenuated in vivo but also that Mikulicz cells are observed upon infection with high dose of K . rhinoscleromatis CPS mutant . Our data indicate that capsule is a virulence factor of K . rhinoscleromatis but is not involved in the specific appearance of Mikulicz cells .
All protocols involving animal experiments were carried out in accordance with the ethical guidelines of Pasteur Institute , Paris and approved by the Comité d'Ethique de l'Institut Pasteur ( CETEA ) ( comité d'éthique en expérimentation animale n°89 ) under the protocol license number: 2013–0031 . All mice had free access to food and water and were under controlled light/dark cycle , temperature and humidity . Animals were handled with regard for pain alleviation of suffering . Animals were anesthetized using ketamine and xylazine , and euthanized with CO2 . Bacterial strains and plasmids used in this study are listed in Table 1 . The K . pneumoniae subsp . rhinoscleromatis SB3432 strain ( KR WT ) was isolated in 2004 at the Avicenne hospital , Bobigny , France , from a biopsy of the left nasal cavity of an 11-years old patient diagnosed with rhinoscleroma . The K . pneumoniae subsp . pneumoniae Kp52145 strain is a previously described clinical isolate ( serotype O1:K2 ) [25] . The Escherichia coli strains used in the cloning experiments were DH5α λpir ( Invitrogen ) and ß2163 , kind gift from Didier Mazel ( Institut Pasteur , France ) . pGEM-T ( Promega ) is TA cloning vector used for cloning PCR products . pDS132 was a kind gift from Dominique Schneider ( Université Joseph Fourier , France ) . A kanamycin cassette was PCR amplified from the plasmid pKD4 [26] and recombineering plasmid pSIM6 expressing Red system was used to create mutant in Kp52145 [27] . The plasmid pAT881 carrying the luxABCDE operon was used to make bioluminescent strains [28] . Bacteria were grown in Lysogeny Broth ( LB ) medium at 37°C with shaking . When appropriate , antibiotics were added at the following concentrations: ampicillin ( Amp ) 100 μg/ml; chloramphenicol ( Cm ) 30 μg/ml; kanamycin ( Kan ) 50 μg/ml . When necessary , DAP was supplemented to a final concentration of 0 , 3 mM . For selection against sacB , LB medium was supplemented with sucrose to a final concentration of 5% ( wt/vol ) . Inocula were prepared from overnight bacterial cultures grown on a loan on LB plates at 37°C resuspended in physiological saline . Capsule K . rhinoscleromatis mutant ( KR cps- ) was obtained by insertion of the plasmid pAM2 in the wzc gene . Briefly , a kanamycin cassette flanked by 1kb of upstream ( wzb ) and 1 kb of downstream ( wbaP ) sequences of wzc using a three-step PCR method [29] was cloned into pGEM-T and then subcloned into pDS132 suicide vector . The resulting plasmid was introduced in the E . coli ß2163 donor strain ( DAP- ) and the recombinant strain was used for conjugation with K . rhinoscleromatis . KR cps- mutants were selected onto Kan/DAP- plates . K . pneumoniae 52Δwzc ( Kp52Δwzc ) was generated using the λ RED recombination technique [26] . Briefly , a kanamycin cassette was amplified by PCR from the pKD4 plasmid using primers Kp52WzcUpKan ( 5’-ATCAGTGTTCAAACTTATTGAGCAATCTGCACTGTTATGGGCTGAGAAATTAAAAGCTTAGAAATTCAGGAAATAATGCATGATTGAACAAGATGGATTG -3’ ) and Kp52WzcDownKan ( 5’- CGATATGGATGACGTTCATTATTATCCTTTTATTATATATTTTAAAAAAGGGGATTCTTCGTCCCCTTCTTGAGTAACTCAGAAGAACTCGTCAAGAAGG -3’ ) . The PCR product was purified onto a column , digested with DpnI , repurified and electroporated into K . pneumoniae carrying pSIM6 , which encodes the λ RED recombinase . Kan-resistant clones were screened for successful genomic replacement of the entire wzc . Deletion of wzc on the K . pneumoniae 52145 chromosome was confirmed by PCR and sequencing . Female BALB/cJ mice were purchased from Janvier ( Le Genest-Saint-Isle , France ) . Inocula of WT and mutant bacteria used in this study are 2 . 107 bacteria for KR WT , 2 . 107 , 4 . 108 and 109 for KR cps- and 109 for Kp52Δwzc . When appropriate , similar inocula of the respective bioluminescent strains were used . Bacterial counts were determined as colony forming units ( CFU ) by plating serial dilutions of lung homogenates in 3 ml ice-cold PBS supplemented with 0 , 5% Triton X-100 and EDTA-free protease inhibitors ( Fisher Scientific ) . For survival studies , mice received either 2 . 107 KR WT or 2 . 107 , 4 . 108 , 109 KR cps- by the intranasal route . Following infection , animals were returned to standard housing and observed for 14 days . A census of survivors was taken daily . In order to maintain the plasmid conferring luciferase expression , mice were injected intraperitoneally twice daily from 1 day post-infection with 20 mg/kg spectinomycin ( Spectam ) . Following isoflurane anesthesia , bioluminescence imaging was performed using an IVIS Spectrum ( Perkin Elmer ) . Analysis and quantification of bioluminescence were done using Living Image ( Perkin Elmer ) . At 96h post-infection lungs were inflated with 4% PFA and fixed overnight at 4°C . Paraffin-embedded tissue blocks were cut into 7 μm sections and stained with hematoxylin-eosin ( HE ) . Images were acquired with the AxioScan . Z1 ( Zeiss ) using the Zeiss Zen2 software . FISH staining was performed as follows . Paraffin lung sections were deparaffinized , rehydrated in PBS and covered with a solution of lysozyme at 10 mg/ml in PBS during 30 min at 37°C . Slides were then washed twice in PBS , preincubated 30 min at 42°C in hybridization buffer ( 20 mM Tris-HCl [pH 8] , 0 . 9 M NaCl , 0 . 01% SDS , 30% formamide ) and incubated overnight at 55°C in hybridization buffer containing 50 nM of the pan-bacteria probe Eub338-Alexa555 5′-GCTGCCTCCCGTAGGAGT-3 [30] . After washing in 1X SSC ( 1 SSC is 0 . 15 M NaCl plus 0 . 015 M sodium citrate ) , slides were covered for 1 min with DAPI to visualize the nuclei , washed in PBS and mounted in Prolong Gold reagent . Images were acquired on an upright fluorescence microscope equipped with the Apotome technology ( Zeiss AxioImager with Apotome2 , Carl Zeiss Jena ) . The number of Mikulicz cells was estimated from HE stained sections by manually segmenting region containing high number of Mikulicz cells in Zen Blue software ( Zeiss ) . Regions containing Mikulicz cells within dense infiltrate of inflammatory cells were not included . Mikulicz cells-containing region was quantified as % area of the total lung area . The number of bacteria present in the tissue section was quantified from fluorescence images using the Fiji plugin TrackMate [31] . Bacteria were defined as spots of 1 . 5 μm after Laplace Gaussian fitting . Capsule was quantified as the concentration of uronic acid in the samples from a standard curve of D-glucuronic acid as described by Favre-Bonte et al [14] . The uronic acid content was expressed in nanograms per 106 CFU . At various time post-infection the five pulmonary lobes were removed and collected in ceramic-beads containing tubes ( Precellys lysing kit CK28 ) with 2 , 5 ml of ice cold PBS supplemented with 0 , 5% Triton X-100 and EDTA-free protease inhibitors ( Fisher Scientific ) . Samples were then crushed using the Precellys homogenizer with the following program: 3 cycles of 15 sec at 5 . 000 × g with 10 sec pause . Twenty microliters were removed to determine the number of CFU/lung . After adding 10 μl of Pen/Strep ( 100X , Sigma ) , samples were centrifuged at 300 × g for 10 min and left on ice for 30 min . The supernatants were frozen rapidly in dry-ice ethanol bath and stored at -80°C . The following cytokines were measured: IL1ß , IL-10 , IL-17 , TNFα ( Duoset , all from R&D Systems ) . Assays were performed according to the manufacturer’s instructions . Correlation between bioluminescence signal and CFU number was analyzed by Pearson correlation using GraphPad Prism 5 .
To investigate the role of capsule in K . rhinoscleromatis virulence , we constructed a KR capsule mutant ( KR cps- ) from the K . rhinoscleromatis wild-type strain SB3432 ( KR WT ) by insertion of a suicide plasmid . It has been shown that inactivation of wzc gene , whose product is involved in capsular polysaccharide export machinery , leads to a capsule-minus phenotype in K . pneumoniae [13] . We decided thus to mutate the capsular operon in SB3432 by replacing the wzc gene by a kanamycin cassette by using the suicide plasmid pAM2 . Although this suicide plasmid can be normally excised following double crossover using sacB counter-selection , we did not manage to obtain the desired gene replacement , possibly because KR does not grow on media without salt which is required for sacB counter-selection . Nevertheless , sequencing of KR cps- confirmed the integration of the suicide plasmid in the wzb gene leading to a polar effect and one base deletion in the sacB gene leading to the production of a truncated SacB protein , hence explaining the selection of this mutant during the counter-selection step . A schematic representation of the wild-type KR and the capsule mutant KR cps- capsule export portion of cps operon is shown in Fig 1A . As expected , colonies of KR cps- did not show the slimy and mucoid phenotype characteristic of surface polysaccharide-producing KR colonies ( Fig 1B ) . We also quantified the amount of capsule produced and observed a drastic reduction from 329±59 to 10±9 ng uronic acid / 106 bacteria for KR WT and KR cps- respectively . Altogether , these results indicated that this KR mutant is an effective capsule mutant . Capsule is a well-characterized virulence factor of K . pneumoniae . K . pneumoniae capsule mutants are avirulent and they are not able to cause pneumonia or urinary tract infections [13 , 19 , 34] . We sought to analyze whether the KR cps- strain was attenuated in vivo . Anticipating that at identical inoculum of KR WT and KR cps- this would be the case , we wondered whether we could recapitulate part of the disease by increasing the infectious dose of the KR cps- . BALB/c mice were thus infected intranasally with 2 . 107 KR WT or 2 . 107 , 4 . 108 or 109 KR cps- and survival was monitored over 14 days ( Fig 1C ) . While all mice infected with 2 . 107 bacteria of KR WT strain succumbed within 6 days post-infection , mice infected with 2 . 107 or 4 . 108 KR cps- bacteria recovered from the infection and survived . However , a 50% death rate was observed with the highest dose of 109 KR cps- . Altogether , these findings show that the KR cps-strain is attenuated in vivo , confirming the crucial role of capsule in KR virulence . In order to compare KR WT and KR cps- infections , we tested the capacity of bioluminescent bacteria to colonize the lungs after intranasal instillation . Mice were infected with either 2 . 107 bioluminescent KR WT or 2 . 107 , 4 . 108 or 109 bioluminescent KR cps- , and bioluminescence imaging was performed and quantified 6 , 24 , 48 , 72 , 96 hours post-infection ( Fig 2A and 2B ) . Mice infected with bioluminescent KR WT showed a gradual increase in lungs bioluminescence with a 430 fold signal increase at 4 days post-infection as compared after 6 hours . On the other hand , the bioluminescence signal started to decrease from 6 hours post-infection with 2 . 107 KR cps- and reached background level at day 1 . A similar but less pronounced decrease was observed in mice infected with 4 . 108 or 109 KR cps- indicating a higher persistence of the mutant bacteria in the lungs . Moreover , because of a more viscous inoculum at high infection doses leading to difficulties to achieve proper intranasal infection , some mice swallowed part of the inoculum and showed a bioluminescent signal in the gut that disappeared in most of the animals at day 4 , indicating that the bacteria transited in the gut before being eliminated . To correlate the bioluminescent signal with the bacterial load , we quantified the number of CFU in the lungs after bioluminescence imaging 96 hours post-infection . After subtraction of the background signal , we observed a significant correlation between bioluminescence and CFU in mice infected with 2 . 107 KR WT and 4 . 108 or 109 KR cps- ( S1 Fig ) , allowing a good estimate of CFU greater than 5 . 105 bacteria in the lungs from the bioluminescence signal . We also directly monitored the lungs bacterial load during the same time course in mice infected with inocula of 2 . 107 KR WT or 2 . 107 , 4 . 108 KR cps- ( Fig 2C ) . While the number of bacteria in mice infected with 2 . 107 KR WT gradually increased from 4 . 107 bacteria per lungs 6 hours post-infection to reach 4 . 109 bacteria at 96 hours , the number of bacteria in animals infected with the same inoculum of KR cps- decreased gradually until the bacteria were being completely cleared from the organ in 72 hours . However , lungs from mice infected with a higher inoculum of 4 . 108 KR cps- presented a still significant amount of bacteria in the organ 4 days post-infection , providing a more relevant comparison to the wild-type infection . By 96 hours after infection with 4 . 108 KR cps- , 33% of mice successfully cleared the infection while the others were still being colonized and had between 5 . 105 and 109 bacteria in their lungs . These results indicated that the KR cps- mutant is strongly attenuated but that at a higher inoculum , after a certain threshold , KR cps- is able to persist and proliferate within the host . To examine the pathology induced by KR cps- , lungs of mice were also examined histologically at 4 days post-infection ( Fig 3A ) . Animals infected by 2 . 107 KR WT presented the classical extensive but moderately destructive inflammation of the lungs characterized by the recruitment and formation of large Mikulicz cells filling alveoli . By contrast , mice infected with 2 . 107 KR cps- showed localized dense inflammatory lesions with signs of hemorrhages and recruitment of monocytic and polymorphonuclear cells . No classical Mikulicz cells could be observed . This phenotype reflects the inflammatory response that was required to eradicate the bacteria . Interestingly , when mice were challenged with 4 . 108 or 109 KR cps- , many alveoli were filled almost exclusively with Mikulicz cells , similarly to what is observed with KR WT , although the alveolar lining was more often disrupted . Regions with dense and localized inflammatory regions , characterized by infiltration of numerous polymorphonuclear cells , were also observed ( Fig 3A , highlighted zone ) . Of note , all mice infected with 4 . 108 or 109 KR cps- out of 9 examined histologically presented Mikulicz cells . Altogether , these observations suggested that while capsule is a virulence factor in KR , it is not required to induce the formation of Mikulicz cells in KR pathogenesis . As mice infected with the KR cps- strain showed variations in the intensity of the Mikulicz cells infiltrate observed by histology , we wondered whether this variation was correlated to the bacterial burden . Because we cannot directly quantify total CFU and perform an histological analysis on the same sample , we estimated the number of bacteria by fluorescence in situ hybridization and quantified the Mikulicz cells infiltrate by manually segmenting regions containing highly visible Mikulicz cells on adjacent lungs sections ( S2 Fig ) . Mice infected with KR cps- showed different number of bacteria spots and extend of Mikulicz cells infiltrate in the lung section ( Fig 3B ) . Both parameters were significantly correlated , suggesting that the local bacterial load drives the intensity of recruitment of Mikulicz cells . Cytokines are key mediators of immune responses and the anti-inflammatory cytokine IL-10 has been shown to be highly produced after K . rhinoscleromatis infection and to play a crucial role in the establishment of a proper environment leading to Mikulicz cells maturation [9] . Therefore , we characterized the production of some major cytokines in mouse lung extracts upon KR cps- infection . When BALB/c mice were infected with 2 . 107 KR WT or 4 . 108 KR cps- , the pro-inflammatory cytokines IL-1β , IL-17 and TNF-α were produced in high amounts from 6 hours post-infection onwards ( Fig 4A and S3 Fig ) . However , although produced in similar amounts at the beginning of the infection in mice infected with 2 . 107 KR cps- , the level of these cytokines diminished overtime because bacteria were progressively cleared from the organ . As previously shown , the anti-inflammatory cytokine IL-10 was highly produced upon infection with 2 . 107 KR WT but not in mice infected with 2 . 107 KR cps- . IL-10 was also produced in mice infected with 4 . 108 KR cps- , but to a lower extend and in more variable manner as compared to KR WT ( Fig 4B ) . These observations indicate that a high inoculum of KR cps- allows recapitulating a high production of IL-10 , thereby suggesting that capsule does not have a direct role in IL-10 production upon KR infection . Because we observed a high variability in the production of IL-10 in mice infected with 4 . 108 KR cps- , we wondered whether it was correlated with the burden of the infection . We thus compared the production of IL-1β , IL-17 , TNF-α and IL-10 to the number of CFU in the lungs at 96 hours post-infection for each animal . While a high production of IL-1β ( > 3 . 104 pg/ml ) is indicative of the presence of bacteria in the lungs ( mainly ranging from 105−109 bacteria ) , IL-1β is expressed at intermediate levels ( 500–3 . 000 pg/ml ) when mice managed to clear the infection ( Fig 4C ) . Similar observation was made for IL-17 and TNF-α ( S4 Fig ) . On the other hand , this is different for IL-10 ( Fig 4D ) . A first group of mice mildly colonized ( between 5 . 105 and 2 . 107 CFU ) showed intermediate level of IL-10 ( between 70 and 200 pg/ml ) while a second group of mice that were unable to control the infection ( > 2 . 107 CFU ) were characterized by an intense production of IL-10 ( > 103 pg/ml ) suggesting that KR is able to induce an intense production of IL-10 only above a certain threshold of KR bacteria in the lungs . To establish that the occurrence of Mikulicz cells observed with 4 . 108 and 109 KR cps- was not due to the higher inoculum of KR cps- as compared to KR , we measured bacterial loads and cytokines expression in animals inoculated with the same high inoculum ( 109 bacteria ) of Kp52Δwzc 4 days post-instillation . The Kp52Δwzc strain is a similar capsule mutant from K . pneumoniae strain Kp52145 obtained after deletion of the wzc gene showing a drastic reduction of capsule expression from 256±22 ng uronic acid / 106 bacteria for Kp52145 to 34±13 ng uronic acid / 106 bacteria ( S5 Fig ) . We observed that the bacterial load of mice infected with 109 Kp52Δwzc was around 105 bacteria per organ and was lower than the bacterial load of mice infected with 109 KR cps- indicating that a wzc mutant in Kp52145 is less virulent that its counterpart in KR ( Fig 5A ) . We then measured the cytokines levels in lungs of infected animals ( Fig 5B and S6 Fig ) . Pro-inflammatory cytokines IL-1β , IL-17 and TNF-α were expressed in similar amounts in mice infected with Kp52Δwzc or KR cps- . However , IL-10 was expressed at low level after 109 Kp52Δwzc infection ( 53–63 pg/ml ) , contrasting the higher amount observed after 109 KR cps- infection in some mice . By histology , we observed an intense and dense inflammation characterized by a strong recruitment of monocytes and polymorphonuclear cells and an absence of Mikulicz cells formation ( Fig 5C ) . Altogether , and combined with the histological data , these observations suggest that when present in high concentration in the lungs from 3 days of infection without being lethal , KR or its capsule mutant are able to induce the recruitment and maturation of Mikulicz cells and drive a strong production of IL-10 .
The diversity of capsule types in Klebsiella pneumoniae species is strikingly very large , as 134 different capsule loci have been identified up to now [21] . This tends to indicate that K . pneumoniae species is under strong selection pressure to diversify its capsule . However , and strikingly , all K . rhinoscleromatis strains isolated so far are of the KL3 ( K3 ) serotype despite having been isolated from diverse geographical locations [6] . Because of this homogeneity , we speculated that this specific K3 serotype could be an important factor driving the rhinoscleroma disease . By creating a capsule mutant in K . rhinoscleromatis , we showed that if capsule is an important virulence factor for this species , it is not necessary to induce the formation of Mikulicz cells , the hallmark of rhinoscleroma , as these cells have been observed when using high inocula of this mutant . The saccharide composition of the capsule has been linked to some extent to K . pneumoniae virulence . K1 and K2 serotypes have been suggested to be major determinants in liver abscess-causing K . pneumoniae [35 , 36] . Strains from other serotypes , including K5 , K16 , K20 , K54 and K57 , have also been described as highly virulent [37] . In addition , switching the capsular serotype of a highly virulent K2 strain to a weakly virulent K21a strain has been shown to lead to a decrease in virulence in mouse and in survival in blood and to an increased binding to macrophages . Conversely , switching the capsule serotype of the K21a strain to virulent K2 resulted in an increased virulence in mouse and in survival in blood and to a lower binding to macrophages [38 , 39] . In addition , switching highly conserved genes of the capsule cluster involved in capsule export from K1 into K20 hypervirulent strain strongly reduced its bacterial virulence in mice while increasing its neutrophil phagocytosis and survival in macrophages , although it is still not known whether this is due to a change in capsule expression[40] . However , a recent pan-genomic analysis did not reveal any correlation between capsule serotype and strains responsible of invasive community-acquired infection but rather suggested that the presence of one or several siderophores explains bacterial virulence [41] . Thus the exact role of capsule composition in virulence still remains to be clearly determined . Capsule plays an important role in immune cells evasion by preventing binding of complement and antibodies to the bacteria thereby decreasing opsono-phagocytosis and complement-mediated killing [10–13 , 42–45] . Moreover , Klebsiella capsule composition has been shown to influence the binding of the bacteria to macrophages . K3 , K46 and K64 K . pneumoniae capsule are binding more to the mannose receptor , which is highly expressed on macrophages , than other serotypes , in a mannose-dependent manner , while other serotypes presented no binding [23] . A common feature of these three different serotypes is that they have two or three mannose residues in their repeated unit . Though , other K serotypes that present also two mannose residues did not show any binding to the mannose receptor , suggesting that binding of mannose-bearing capsule to the mannose receptor is influenced by other factors than its mannose composition . However , as all K . rhinoscleromatis strains are of the K3 serotype , and even though K3 capsule interacts with mannose receptor , our results obtained with a high infection dose of KR cps- suggest that this step is not important in driving the development of Mikulicz cells . Some results obtained with the high inocula of KR cps- were heterogeneous: the bacterial load 4 days post-infection was spread over 4 logs , IL-10 levels in the lungs were quite variable and some mice showed some bacteria in the digestive tract by bioluminescence . This variability is a consequence of the use of higher inocula , which are thicker and more viscous than lower inocula used for the KR WT and of KR cps- strains that are more fluid . As a consequence , part of the inoculum is swallowed by mice and passes into the digestive tract . This is also suggesting that above a certain threshold of cps- bacteria delivered to the lungs , the animal cannot control the infection and the bacteria are able to multiply and maintain themselves in high number , although to a lower burden than WT bacteria . We wondered whether there was a correlation between the number of bacteria in the lungs and the level of IL-10 produced . Indeed we observed that IL-10 was produced in high amount when the bacterial load was high , raising the possibility that high IL-10 expression was the result of a high bacterial burden and not specific to K . rhinoscleromatis . To verify this one needs to compare IL-10 production upon similar bacterial burdens , greater than 108 bacteria , at 4 days post-infection with different bacteria . We first thought to use a high dose of a K . pneumoniae mutant inactivated in the same gene as the KR cps- strain , but showed that the bacterial load was lower ( 104−105 bacteria ) than the lowest ones obtained with high KR cps- ( 106 to 108 bacteria ) and that IL-10 levels were also quite low . This showed that this Kp52Δwzc mutant was actually more attenuated than KR cps- and suggested that K . rhinoscleromatis is better adapted to surviving in lungs . Some virulent K . pneumoniae strains can cause intense and severe and acute pneumonia in mice with high burden . We had previously observed that a variable bacterial load can be achieved 3 and 5 days post-infection with a low dose of the virulent strain Kp52145 [9] and that about 30% of mice were presenting a high bacteria burden 3 and 5 days post-infection . By measuring CFU loads and cytokines in mice infected with Kp52145 we observed that mice that had a high bacterial load were producing IL-10 in amount similar to those that were less colonized . Comparable high bacterial burden were obtained with the widely used K . pneumoniae strain 43816 [18 , 19 , 46] and IL-10 was produced in similar low amounts 3 days post-infection [18] . Hence these observations indicate that the intense IL-10 production observed upon infection with KR WT or KR cps- is specific of K . rhinoscleromatis and does not result from a global high bacterial load . Moreover , all high dose KR cps—infected mice out of 9 observed by histology show the presence of Mikulicz cells in their lungs , although to various extent . We also observed that the density of the Mikulicz cells infiltrate is correlated to the number of bacteria . We also tried to see whether there was a similar correlation with the amount of IL-10 on a mouse to mouse basis , but were unable to detect directly this cytokine by immunohistochemistry . Nevertheless , the variation in the host response to KR cps- infection is likely correlated to the amount of IL-10 produced: lower number of bacteria lead to fewer Mikulicz cells and low amounts of IL-10 whereas an intense IL-10 production is accompanied by high number of bacteria and Mikulicz cells and less destructive inflammation . Recently , IL-10 has been shown to regulate metabolic processes in activated macrophages and thus control the inflammatory response . IL-10 impedes glycolysis and promotes oxidative phosphorylation maintaining mitochondrial fitness . This metabolic reprogramming of macrophages is controlled by IL-10 through inhibition of mechanistic target of rapamycin ( mTOR ) signaling pathway [47] . Interestingly , deregulation of mTOR signaling , such as prolonged mTORC1 activation , leads to metabolic changes , hyperproliferation of macrophages and granuloma formation , contributing to disease progression in human granulomatous sarcoidosis [48] . These mechanisms might be associated with formation of granulomas in rhinoscleroma , where Mikulicz cells could undergo similar metabolic remodeling mediated by IL-10 . The fact that the capsule is not required for Mikulicz cells recruitment and formation indicates that the factors responsible of this process are still unknown and remain to be identified . Current in vivo screening approaches , such as signature tagged mutagenesis , cannot be used as they identify mutants unable to grow in specific experimental conditions , but not those that are required for the expression of a particular phenotype , such as the appearance of Mikulicz cells . Therefore an in vitro screening assay has to be developed . However , in vivo phagocytosis assays can often be difficult to set up and standardize due to the high expression of capsule in K pneumoniae species and its strong anti-phagocytic effect . Our results show that capsule is not required for the formation of Mikulicz cells , opening the way to in vitro assays of Mikulicz cells formation and to in vitro screening of factors that are driving this maturation in vivo . | Rhinoscleroma is a human specific chronic infection characterized by the formation of granuloma in the nose and upper airways . It is a rare disease endemic in low-income countries where hygienic conditions are poor and caused by the Gram-negative bacterium Klebsiella pneumoniae subsp . rhinoscleromatis . A hallmark of this pathology is the appearance of atypical foamy monocytes called Mikulicz cells . Very little is known about the cellular and molecular mechanisms underlying this disease and the bacterial virulence factors of K . rhinoscleromatis are unknown . In this study , we created the first mutant made in K . rhinoscleromatis and inactivated the production of capsule , an outer-membrane-anchored polysaccharide . Using a murine model recapitulating the formation of Mikulicz cells and this bacterial capsule mutant , we observed that capsule is a virulence factor for K . rhinoscleromatis which is not required for the formation of Mikulicz cells , indicating that other specific virulence factors are present in the genome of the bacterium . This works opens the way to further genetic analysis of K . rhinoscleromatis and identification of new specific virulence factors . | [
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"carbohydra... | 2018 | Rhinoscleroma pathogenesis: The type K3 capsule of Klebsiella rhinoscleromatis is a virulence factor not involved in Mikulicz cells formation |
Genetic interactions help map biological processes and their functional relationships . A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations . In Saccharomyces cerevisiae , most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions . Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions . How different are these networks and what can we learn from their differences ? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions . We find that networks obtained using different phenotypic readouts , in different conditions and from different laboratories overlap less than expected and provide significant unique information . To exploit this information , we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance , demonstrating that individual networks provide complementary information . Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens .
A genetic interaction is defined as an unexpected phenotype for a combination of mutations given each mutation's individual effect [1] . Genetic interactions provide valuable information about gene function and are useful to study the organization of biological processes in the cell [2] . Experimental techniques are now available to map genetic interactions at a large scale , in particular in Saccharomyces cerevisiae [3] . A genetic interaction is obtained in an experiment using a particular phenotypic readout and set of experimental conditions in a given species . Typically , a single , easy to observe phenotype , such as cell growth , is used to measure genetic interactions on a large scale [3] . As most yeast genes have no deletion mutant defect in rich media , but have a defect in at least one environmental condition [4] , and individual genetic interactions change under different phenotypic readouts [5] , it has been postulated that many unknown genetic interactions could be uncovered by performing the same interaction mapping experiment under different conditions [6] . However , no large-scale quantification of this effect has been undertaken . Here we ask how much more genetic interaction and gene function information is gained by mapping genetic interactions using different phenotypic readouts and experimental conditions . A handful of recent studies have examined parts of this question . Linden et al . developed a normalization method to maximize the similarity between genetic interaction networks mapped by different laboratories so they can be combined [7] , but this was only applied to networks obtained using the same phenotypic readout ( growth phenotype ) . St . Onge et al . showed that mapping genetic interactions in multiple environmental conditions ( standard laboratory and compound-induced DNA damage ) provides useful information to infer functional relationships and order pathways [8] , however this study was based on only 26 genes . An identical comparison involving almost 400 genes revealed differences between conditions and many ( 60–80% ) condition-specific interactions [9] , and methods have been developed to identify genetic interactions changing between conditions [10] . In a complementary approach , Carter et al . defined multiple types of genetic interactions in order to extract as much biological information as possible from raw data [11] . These studies show that changing environmental conditions and interaction definition provides additional information about genetic interaction . However , none have yet considered other aspects of experimental conditions , such as different phenotypic readouts , or how much overlap between networks is expected given known false positive and negative rates . While most genetic interaction studies in budding yeast assess cell fitness by measuring cell growth in standard laboratory conditions , an increasing number have mapped genetic interactions under other experimental conditions . These include environmental conditions such as DNA damage [8]–[10] or low-ammonium agar [12] , and phenotypic readouts such as gene expression [13] , filamentous growth [12] , endocytosis [5] and unfolded protein response [14] instead of normal growth . Earlier studies focused on small gene sets ( less than 150 ) but recent studies have increased that number [5] , [9] , [14] to about 300–500 genes per study , which enables a systematic comparison . We use this recently available data to conduct a systematic analysis of quantitative genetic interaction networks in budding yeast mapped under different conditions , phenotypic readouts and laboratories ( Figure 1A ) , while considering false positive and false negative rates . We chose the largest available network as the reference [3] and compare it to a network mapped in a different environmental condition ( DNA damage ) [9] , as well as two networks mapped using different phenotypic readouts ( endocytosis and unfolded protein response ) [5] , [14] . A set of networks mapped under similar experimental conditions was used as a control [9] , [15] , [16] . We find that networks obtained in different experimental conditions overlap less than expected by chance and provide unique and complementary information . We also find that the laboratory where the experiments are carried out has an important effect on the resulting genetic interaction network . Finally , we develop a method to combine all networks together in a way that improves gene function prediction .
We collected seven different quantitative genetic interaction data sets ( Figure 1 ) . Unfortunately , even though these data sets are reasonably large ( more than 300 genes each , Text S1 ) , no gene was included in all of them and only a few genes were present in four studies ( Figure 1B ) , eliminating the possibility of a direct global comparison . However , the very large Synthetic Genetic Array ( SGA ) genetic interaction data set [3] , which was obtained in standard laboratory conditions using colony growth as the phenotypic readout , is comprehensive enough to contain most ( 80–90% ) of the genes tested in each of the other data sets ( Figure 1C ) and has a relatively high precision ( 0 . 63 for negative interactions and 0 . 59 for positive interactions ) . Thus , we used SGA as a reference and compared each of the other data sets to it ( Figure 1D ) . This approach enables us to consider most of the genes tested in each study , though it doesn't consider possible bias from function-based gene selection across most studies . Thus , we additionally analyzed pairs of genes tested across three studies that used different phenotypic readouts and conditions . We hypothesized that networks obtained using different phenotypic readouts or in different conditions would be more different than expected , whereas networks obtained in similar experimental conditions would be similar . To investigate the effect of using different phenotypic readouts on the resulting genetic interaction network , we compared two networks ( PHENO ) that used non-growth phenotypes to define genetic interactions ( endocytosis defect [5] and the unfolded protein response [14] ) to SGA . Both networks are independently biologically informative as shown in the original analysis [3] , [5] , [14] . Genetic interactions are also known to be dependent on environmental condition , such as temperature , starvation , or DNA damage induced by a small molecule [8] , [9] . To investigate the effect of condition on the resulting genetic interaction network , we compared our reference SGA network , mapped in standard laboratory conditions , to the Bandyopadhyay et al . genetic interaction network , mapped in the presence of methyl methanesulfonate ( MMS ) , a DNA damage-inducing compound [9] . The three networks obtained using different phenotypes or in different environmental conditions are referred to as the PHENO/MMS set . We also collected a set of three networks similar to the reference ( similar ‘growth’ phenotypic readout and environmental conditions ) obtained by other research groups , referred to as CONTROL . To perform meaningful comparisons ( network of interest vs . SGA and SGA vs . CONTROL vs . PHENO/MMS ) , analyses were limited to the set of gene pairs tested in two or three data sets , respectively ( Text S1 ) . In quantitative genetic interaction networks , nodes represent genes and weighted edges quantify the deviation of the double mutant phenotype from what is expected from the single mutant phenotypes . Edge weight is positive if the phenotypic readout is significantly higher than expected and negative if it is significantly lower . We treated the networks as undirected and did not consider the query or array role . We used four measures to compare networks: These measures were computed only for genes and gene pairs present in both network of interest vs . SGA and in three networks SGA vs . CONTROL vs . PHENO/MMS . We also evaluated how different the resulting measures are for a given network pair from what is expected based on a statistical model that considers known experimental interaction detection error rates . Analyzing networks obtained using different phenotypic readouts , we find that SGA and PHENO networks have quantitative genetic interaction scores that are less correlated ( 0 . 037 on average ) than SGA and CONTROL networks ( 0 . 13 on average ) ( Figure 2 ) . This shows that SGA and PHENO networks contain different information . The lack of SGA-PHENO correlation could in part be due to error and noise differences between experiments , though the higher SGA-CONTROL correlation between networks from different research groups suggests that this is not simply due to laboratory specific effects . We also find that SGA and PHENO networks overlap less ( 0 . 10 on average ) than SGA and CONTROL networks ( 0 . 19 on average ) ( Figure 2 ) . These results could be due to experimental errors in both data sets or to genuinely complementary biological information . To distinguish between these two cases , we estimated the expected level of overlap given the experimental error rates of the networks , following previous work on network error modeling [17] . Positive and negative interaction networks have different properties and error rates [3] , thus we analyzed them separately . Since we limited our study to genetic interactions involving gene pairs that were tested in both data sets , the absence of an interaction indicates that no genetic interaction was detected between the corresponding two genes . This provides us with an accurate number of negatives for the error model . Based on an estimation of the error rates of the data sets , we computed the overlap expected by chance ( Methods ) . We find that SGA and PHENO overlap less than expected ( ratio observed/expected 0 . 53 on average , Text S1 ) . As a control , we compare SGA to each of our ‘similar phenotype’ CONTROL networks and find that they overlap more than expected ( ratio 1 . 55 on average , Text S1 ) . In agreement with this , SGA and PHENO have more unique interactions and are more unique than expected while SGA and ‘similar’ CONTROL networks are less unique than expected ( Figure 2 , Text S1 ) . We also found that SGA and PHENO networks disagree more on interaction sign than ‘similar phenotype’ networks ( SGA vs . CONTROL ) ( Figure 2 ) . Values obtained for PHENO networks are also significantly different to those of the CONTROL networks in general ( Figure 2 , Text S1 ) . Taken together , we observe substantial differences between genetic interaction networks mapped using different phenotypic readouts and these are not simply due to network error rates . We repeated the analysis on networks obtained in different environmental conditions , and found similar results: SGA and MMS have a lower correlation , lower overlap , higher unique ratio and higher disagreement ratio than networks in the control set ( Figure 2 ) . In addition , SGA and MMS overlap less and provide more unique information than expected ( Text S1 ) . Values obtained for the MMS network are also significantly different to those of the CONTROL networks in general ( Figure 2 , Text S1 ) . While we observe a consistent trend across PHENO and MMS vs . reference and CONTROL vs . reference comparisons , it is possible that function-based gene selection in PHENO , MMS and CONTROL networks could bias the data in a way that artificially causes the results we observe . To gain more confidence in our results , we additionally analyzed all gene pairs that were tested in the reference SGA network and one of the PHENO/MMS networks and one of the CONTROL networks . For the 48 , 499 gene pairs tested in these three categories ( SGA , PHENO/MMS , CONTROL ) , we found that the correlation between SGA reference and PHENO/MMS is lower than between SGA and CONTROL values ( paired T-test p<0 . 003 , Figure S1 ) . Similarly , the overlap is lower ( paired T-test p<0 . 029 ) and the agree ratio is lower ( paired T-test p<0 . 011 ) . Each network seems to provide a similar level of unique information in this analysis , as the unique ratios are not significantly different . Altogether , our results show that genetic interaction networks mapped using different phenotypic readouts and in different environmental conditions provide unique information . We have shown that genetic interaction networks obtained under different experimental conditions ( phenotype readout or environmental condition ) provide unique information . We next examined if this unique information is complementary . Since a major goal of mapping genetic interactions is to discover new gene function information , we used gene function prediction performance as a measure of biological information contained in a genetic interaction network . Two genes that genetically interact with a similar set of genes ( two genes with similar genetic interaction profiles ) are more likely to be in the same pathway or complex [16] , [18] . Thus , the function of a gene in a genetic interaction network can be predicted based on genes with similar genetic interaction profiles ( a guilt-by-association approach ) . The quantitative genetic interaction network can be transformed into a genetic profile correlation network useful for gene function prediction by computing a correlation coefficient of the genetic interaction profiles for all gene pairs . We can then measure gene function prediction performance by holding out a fraction of a set of genes known to have the same function ( e . g . cell budding ) , using the remaining genes to predict additional genes with the same function ( based on genetic interaction profile similarity ) , and then assessing how many known ( held out ) genes were in the prediction list . This can be repeated with all available gene function categories and is automated using the GeneMANIA gene function prediction software system [19] , [20] . We reasoned that if gene function prediction performance improves when genetic interaction networks are combined then they must contain complementary information . To combine a network of interest with the reference network , we computed a genetic interaction profile similarity network for each one ( using Spearman correlation ) and then chose the maximum correlation value for a pair of genes to include in the ‘combined’ network . To make the comparison fair , we analyzed just the set of genetic interactions tested in all the networks we compared . We quantified the utility of the individual correlation networks and the combined correlation network for gene function prediction using GeneMANIA with all available Gene Ontology ( GO ) terms [21] . Since we used five-fold cross validation , we limited our analysis to GO terms with at least five genes . We measured gene function prediction performance using the area under the receiver-operating characteristic ( ROC ) curve and the area under the precision recall ( PR ) curve statistic for each term in the three gene ontologies ( Biological Process , Molecular Function , Cellular Component ) . We find that PHENO/MMS networks each enable a significant performance improvement in PR values when combined with the reference network ( Figure 3A , Table 1 ) , whereas CONTROL networks do not provide a significant improvement . The difference between PHENO/MMS and CONTROL is highly significant ( Wilcoxon p-value<0 . 0043 ) . This suggests that the unique information provided by the PHENO/MMS networks is complementary to the information from the reference network and combining them improves gene function prediction . However the ROC results are less clear ( Figure 3B ) where the set of networks providing significantly complementary information ( Schuldiner , Bandyopadhyay-mms and Bandyopadhyay-un ) does not correspond directly to the set of PHENO/MMS networks . Also , when considering all networks , gene function prediction performance is improved when combining a given network with the reference both for PR ( Table 1 , p<2 . 2e-4 ) and ROC ( Table 2 , p<7 . 6e-5 ) . This suggests that other factors , such as laboratory effects , may also contribute to the presence of complementary information . To investigate the differences between the combined networks and the reference , we selected the GO terms with the highest gene function prediction PR value differences ( adjusted p-value<0 . 05 ) ( Figure S2 ) . We found that Burston performs significantly better on ‘actin filament organization’ , ‘late endosome to vacuole transport via multivesicular body sorting pathway’ and ‘endoplasmic reticulum unfolded protein response’ ( Figure S3 ) . The members of the ‘actin filament organization’ biological process are more densely connected in the correlation network in the Burston data set leading to better gene function prediction as compared to the reference SGA data set where PBS2 is not connected at all . The Jonikas data set performs better on ‘protein glycosylation’ and ‘Hrd1p ubiquitin ligase ERAD-L complex’ ( Figure S4 ) . For the latter complex , the subunits are generally better connected in the Jonikas dataset , leading to better gene function prediction for this GO term . For instance , Jonikas shows a strong correlation between YOS9 and HRD3 subunits , which physically interact , but this correlation is not strong in the reference . Similarly , the members of the lipid-linked oligosaccharide biosynthesis pathway ( ALG9 , ALG6 , ALG3 , ALG12 ) are strongly connected in the Jonikas data set , leading to better gene function prediction for this GO term . Jonikas shows strong correlations between those four genes , which all physically interact , but those correlations are not present in the SGA reference . For the control networks , Collins performs better on ‘loop DNA binding’ , ‘mismatch repair’ and ‘histone exchange’ while Schuldiner is worse on ‘dolichyl-diphosphooligosaccharide-protein glycotransferase activity’ and ‘Hrd1p ubiquitin ligase ERAD-L complex’ . Both Bandyopadhyay networks ( untreated and in presence of MMS ) perform better on ‘regulation of transcription’ but the untreated network performs worse on ‘regulation of cyclin-dependent protein kinase activity’ ( it only contains one correlation between MIH1 and PTC3 protein phosphatase genes , while the reference contains many more correlations ( Figure S5 ) . ROC values did not distinguish GO terms enough to identify significant differences between networks ( Figure S6 ) . As noted above , it is possible that function-based gene selection in PHENO , MMS and CONTROL networks could bias our results . In particular , gene selection bias causes a different set of GO terms to be tested for each network . Thus , we repeated our gene function prediction analysis on triplets of gene pairs tested across SGA , PHENO/MMS and CONTROL networks . The combination of the PHENO/MMS correlation network with the reference correlation network tends to perform better in terms of gene function prediction as compared to that of the CONTROL and reference networks ( Figure S7 ) , for example for ‘response to stress’ in both PR and ROC measurements ( Text S1 ) . As before the trend is significant on the PR measurements ( paired Wilcoxon test p<0 . 012 ) but not on the ROC measurements . Altogether , our results show that genetic interactions mapped in different conditions provide complementary information . The above results hinted that there may exist factors other than phenotypic readout or condition that explain genetic interaction data set differences . To gain a better understanding of these potential other factors , we generalized our analysis to compare all pairs of networks , by clustering the all data set by all data set comparison matrices for our four measures: correlation , overlap , unique and disagree . The two networks obtained with different phenotypes ( Burston and Jonikas ) are clearly outliers in this analysis , in particular for the correlation values ( Figure 4A ) , reinforcing our above results . Surprisingly , the Bandyopadhyay et al . MMS network is always grouped with its associated untreated network , which are both separated from the control networks and very close to each other ( 4A–D ) . Indeed their correlation ( r = 0 . 58 ) is the second highest in the correlation matrix . This suggests that factors , such as the laboratory environment external to the experiment , also affect network mapping . This may be due to the ‘batch effect’ recently described for large-scale genetic interactions [22] . In agreement with this , the most correlated networks ( Schuldiner and Collins , r = 0 . 65 ) were obtained in the same laboratory . Since these two networks are both in the control group ( similar phenotype , similar conditions ) , we were originally not surprised to find that they are always grouped together . However , the fact that they are more similar each other than they are to the SGA network suggests an important laboratory effect is present . As an additional analysis , we compared genetic interaction profiles for individual genes across all data sets ( Methods ) . For a given gene and a given pair of networks , we computed the correlation ( Spearman ) between the genetic interaction profiles of that gene in both networks . This measure was previously used , for example , to identify genes with different profiles between untreated and DNA damage condition genetic interaction networks [9] . Clustering all networks based on their average correlation measures across all genes shows similar results to those above ( Figure S8 ) . Thus , in addition to phenotypic readout and internal experiment condition , external factors in the laboratory where the experiment is performed contribute to the unique information present in each network . To create a fair comparison , we previously reduced each set of networks analyzed to common tested gene pairs . However , all of the information available in all networks should be considered for gene function prediction . Thus , we repeated our analysis of gene function prediction performance using genetic interaction profile correlation networks computed using all genes in each data set and combined all seven of them using the same correlation network building methodology described above ( max correlation ) . We find that the combined network provides substantially better results , on average , across GO terms for both ROC and PR performance measures ( Figure 5 ) . To illustrate the complementarity of the individual correlation networks , we examined the SWR1 complex , one of the annotation categories that the combined network predicts better than any individual network ( Figure 6 ) . The SWR1 complex ( GO:0000812 ) is a multi-subunit complex involved in chromatin remodeling and is required for the incorporation of the histone variant H2AZ into chromatin . All of its 13 subunits are connected when combining all networks , whereas only subsets of those are connected in each individual network ( five genes in Jonikas et al . , 10 in Costanzo et al . , 12 in Collins et al . ) . In some cases the missing genes were not present in the original screen ( Jonikas and Costanzo ) , while in others they were mostly present ( Collins ) , illustrating the benefit of the new combined network to gather information and genes from different studies to get a more complete view of functional connections among all genes in a system .
Genetic interaction experiments are performed using a particular phenotypic readout and set of experimental conditions in a given species . Using recently available data , we conducted a systematic analysis of quantitative genetic interaction networks in budding yeast mapped under different experimental conditions . We showed that genetic interaction networks mapped in different environmental and laboratory conditions or using different phenotypic readouts provide unique and complementary information . The functional interactions defined by genetic interaction profile correlations can be combined using a simple ‘max correlation’ procedure to aid gene function prediction . Given the low overlap between the data sets , we adopted a reference-based comparison approach where each data set is in turn compared to a common high confidence reference . While this enables a global comparison , it is possible that the reference network is biased towards certain gene sets present in only some compared networks and this could affect our results . Thus , we repeated our analysis on a set of gene pairs present across three networks under comparison . While these results agree , there a many fewer gene pairs tested across three networks than there are for two networks . The SGA dataset continues to grow and will be complete in the future . Also , we expect additional networks to be mapped under different conditions . Ideally , an additional global genetic interaction map of the scale of SGA in different conditions would be available to analyze , but this is unlikely to be available anytime soon , as SGA cost millions of dollars and has already taken more than a decade to achieve a 30% coverage rate of all interactions . Smaller genetic interaction networks mapped under different environment and phenotypic readout among comparable gene sets are more likely to be available in the near future and would help test our results . We propose a simple method to combine diverse genetic interaction networks and show that this improves gene function prediction . We chose to combine data sets at the level of genetic interaction profile correlations instead of individual genetic interactions for a number of reasons: correlation can be computed for all gene pairs in a sufficiently large genetic interaction map not just those pairs tested in both maps , no tuning of parameters is needed , no normalization of individual data sets is needed as would be required if combining data at the level of genetic interactions [7] , correlation is the primary type of relationship used for gene function prediction from genetic interaction networks [3] , [18] , and similar methods are established in the gene expression field that we can draw from [23] . We chose gene function prediction as a means to assess and compare the biological content of each network , as it is one of the main goals of genetic interaction mapping . However , other measures could be used such as the overlap with benchmark data sets [7] . Moreover , it is likely that the method we propose could be improved to yield even better gene function prediction results , for instance by tuning the weight of each network to optimize gene function prediction for a given gene function , as is done in the multi-network version of GeneMANIA [19] ( we only used GeneMANIA on a single combined genetic interaction profile correlation network ) . It will also be interesting to evaluate the gene function prediction improvement gained by combining genetic interactions with other types of network data , such as protein-protein interactions . We provide our combined network as a resource at http://baderlab . org/Data/GeneticInteractionComparison . We expect our results to extend to other organisms , which are increasingly targeted for genetic interaction mapping [24]–[30] with traditional growth assays and diverse phenotypic readouts [31] . Analysis of additional multi-condition and multi-phenotype data will eventually enable us to select experimental conditions that maximize discovery of gene function information , as has been accomplished with gene expression data [32] .
All genetic interaction data sets were downloaded from original publications or requested from the authors ( Figure 1 , Text S1 ) . The measures used to compare a network to the reference are: ‘correlation’ is the Spearman correlation coefficient of genetic interaction scores for all compared pairs; ‘overlap’ is the percentage of binary interactions in common among all observed interactions; ‘unique’ is the percentage of interactions observed in only one network among all observed interactions; ‘disagree’ is the percentage of interactions of different type ( positive , negative ) among all interactions observed in common . Gene profile correlation is computed for a given gene as the Spearman correlation coefficient of the genetic interaction profiles of that gene in two data sets , limited to genetic interaction partners found in both data sets . The similarity between two data sets used for clustering is the mean of the gene profile correlation distribution ( Figure S3 ) . We only consider gene pairs tested in all data sets to enable a fair comparison . For the stochastic model , we use the error rates estimated by Costanzo et al . for positive ( sensitivity = 0 . 18 and precision = 0 . 59 ) and negative ( sensitivity = 0 . 35 and precision = 0 . 63 ) genetic interactions . Since such estimates for the other data sets are not available , we use the Costanzo values for all data sets . This information is then used to compute the expected number of interactions present in zero , one or two data sets and compared to the observed numbers of interactions ( Text S1 ) . We compare those measures between networks in the CONDITION group to networks in the CONTROL group with a Student's t-Test . To limit the analysis to the best associations , correlation networks only contain correlation values higher than 0 . 1 . To assess each network , we use the command line version of the GeneMANIA Cytoscape plugin ( version 2 . 11 ) [20] . We use five-fold cross validation with the function ‘CrossValidator’ and then compared the results for the different networks . The validation was run on a set of 3618 GO terms ( 1789 BP , 1299 MF , 530 CC ) , though only a subset of these terms are tested in each network ( according to which genes are present ) . To avoid circularity in the analysis and annotations potentially coming from the networks we are studying , we only considered annotations that were derived from direct assays/experiments ( evidence codes EXP , IDA , IPI , IMP , IGI , IEP ) . We manually checked that IGI annotations were not derived from genetic interactions from networks we analyze ( only three IGI annotations from these studies were found ) . For both the PR and ROC assessments , each network is associated with a score . The relative improvement of the combined network C obtained from two individual networks A and B is computed as follows:where is the mean score of the two individual networks A and B . | Genetic interactions map functional dependencies between genes , under a given phenotype . In the budding yeast Saccharomyces cerevisiae , most genetic interactions have been measured under a single phenotype - growth rate in standard laboratory conditions . Recently , genetic interactions have been collected under different phenotypic readouts and experimental conditions . How different are these networks and what can we learn from their differences ? We analyzed quantitative genetic interaction networks mapped in yeast under different experimental conditions and phenotypic readouts and found that they provide significant unique information . We next asked if this unique information is complementary . As a measure of complementarity , we asked if combining networks mapped under different experimental conditions could improve gene function prediction . Two genes that genetically interact with a similar set of genes ( two genes with similar genetic interaction profiles ) are more likely to be in the same pathway or complex and this can be used for gene function prediction . We found that combining multiple genetic interaction profile correlation networks using a simple ‘maximum correlation’ approach improved gene function prediction , demonstrating that the networks provide complementary information . Thus , using diverse phenotypic readouts and experimental conditions will likely increase the amount of information produced by genetic interaction screens . | [
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Sexually reproducing organisms halve their cellular ploidy during gametogenesis by undergoing a specialized form of cell division known as meiosis . During meiosis , a single round of DNA replication is followed by two rounds of nuclear divisions ( referred to as meiosis I and II ) . While sister kinetochores bind to microtubules emanating from opposite spindle poles during mitosis , they bind to microtubules originating from the same spindle pole during meiosis I . This phenomenon is referred to as mono-orientation and is essential for setting up the reductional mode of chromosome segregation during meiosis I . In budding yeast , mono-orientation depends on a four component protein complex referred to as monopolin which consists of two nucleolar proteins Csm1 and Lrs4 , meiosis-specific protein Mam1 of unknown function and casein kinase Hrr25 . Monopolin complex binds to kinetochores during meiosis I and prevents bipolar attachments . Although monopolin associates with kinetochores during meiosis I , its binding site ( s ) on the kinetochore is not known and its mechanism of action has not been established . By carrying out an imaging-based screen we have found that the MIND complex , a component of the central kinetochore , is required for monopolin association with kinetochores during meiosis . Furthermore , we demonstrate that interaction of monopolin subunit Csm1 with the N-terminal domain of MIND complex subunit Dsn1 , is essential for both the association of monopolin with kinetochores and for monopolar attachment of sister kinetochores during meiosis I . As such this provides the first functional evidence for a monopolin-binding site at the kinetochore .
Meiosis is a specialized form of cell division that results in the formation of haploid gametes from diploid cells . Two nuclear divisions following one round of DNA replication results in halving of ploidy during meiosis . Four innovations during meiosis allow cells to achieve this remarkable step [1] , [2] . Firstly , recombination between homologs results in covalent connections between them , which are cytologically manifested as chiasmata . This is required for bi-orientation of homologs during meiosis I . Secondly , sister kinetochores mono-orient during meiosis I namely that they bind to microtubules emanating from the same spindle pole . Thirdly , centromeric cohesion is protected from separase cleavage during meiosis I . Centromeric cohesion is required for bi-orientation of sister centromeres during meiosis II . Fourthly , a second round of DNA replication is prevented between the two meiotic divisions . Understanding how meiotic cell cycle works is crucial for understanding the molecular basis of infertility , spontaneous abortions and aneuploidy-related disorders such as Down syndrome in humans . Monopolar attachment of sister kinetochores is essential for setting up the reductional mode of chromosome segregation during meiosis I . During mitosis , sister kinetochores bind to microtubules from opposite spindle poles , a process referred to as bi-orientation . During meiosis I , homologs connected by chiasmata bi-orient on the meiosis I spindle . Tension created by sister chromatid cohesion distal to chiasmata stabilizes the bi-oriented state . For homologs to segregate towards opposite spindle poles , it is essential that sister kinetochores bind to microtubules originating from the same spindle pole . Research over the last twelve years has shown that monopolar attachment in budding yeast is mediated by the ‘monopolin’ complex , which is composed of the Csm1 , Lrs4 , Mam1 and Hrr25 proteins [3]–[5] . Csm1 and Lrs4 are nucleolar proteins expressed during the mitotic cell cycle . They are required for rDNA silencing and for preventing unequal sister chromatid exchange at the rDNA repeats [6] . Csm1 and Lrs4 interact with Tof2 which binds to rDNA via interaction with the RENT ( regulator of nucleolar silencing and telophase ) complex composed of Net1 , Cdc14 and Sir2 [6] . However during meiosis I , Csm1 and Lrs4 are released from the nucleolus [4] and this requires the activity of polo-like kinase Cdc5 [7] . Csm1 and Lrs4 associate with meiosis-specific protein Mam1 and casein kinase-1 Hrr25 to form the monopolin complex which binds to kinetochores . The kinase activity of Hrr25 is required for monopolar attachment but not for monopolin binding to kinetochores [5] . Following nucleolar release Lrs4 is hyperphosphorylated by Dbf4-dependent kinase Cdc7 and Cdc5 in league with a meiosis-specific protein called Spo13 [8] . Hyperphosphorylation of Lrs4 helps association of monopolin with kinetochores . Monopolin complex was suggested to work by crosslinking the sister kinetochores together about ten years ago [4] . Crystal structure and electron microscopic analysis of Csm1/Lrs4 complex indicated that it forms a V-shaped structure with 2 kinetochore binding globular domains of Csm1 separated by 10 nm [9] . It was therefore proposed that monopolins could crosslink the sister kinetochores via the 2 globular kinetochore-binding domains such that they face the same spindle pole . Recently , the structure of a fragment of Mam1 bound to Csm1 was determined which shows that Mam1 wraps around the globular domain of the Csm1 dimer [10] . Homologs of monopolin subunits Csm1 , Lrs4 and Mam1 exist only in some fungi . In fission yeast Pcs1 and Mde4 are homologs of Csm1 and Lrs4 respectively . Interestingly , Pcs1 and Mde4 are not required for monopolar attachment during meiosis I but are necessary to prevent merotelic attachments ( where a single kinetochore binds to microtubules from both spindle poles ) during mitosis and meiosis II [4] , [11] , [12] . The fission yeast kinetochore has 2–4 MT binding sites compared to just one site for the budding yeast kinetochore . An attractive hypothesis was proposed to explain the contrasting phenotypes of monopolin mutants in budding and fission yeasts [4] . While monopolins were proposed to clamp microtubule binding sites from two sister kinetochores in budding yeast , they crosslink two adjacent microtubule binding sites from the same sister kinetochore in fission yeast . It was recently reported that Pcs1 and Mde4 prevent merotelic attachment in fission yeast by targeting condensin complex to the kinetochores [13] . Determining how monopolin associates with the kinetochore is essential for elucidating the mechanism of monopolar attachment . The budding yeast kinetochore is made up of several multi-subunit protein complexes that assemble on centromeres and help in the segregation of chromosomes towards spindle poles [14] , [15] . The kinetochore can be classified into three layers namely inner , central and outer . While the inner kinetochore directly interacts with centromeric DNA , the outer kinetochore interacts with the microtubule ends . The inner and outer layers are connected by the central kinetochore . The monopolin subunit Csm1 has been found to interact with MIND complex ( a part of the central kinetochore composed of Mtw1 , Nsl1 , Nnf1 and Dsn1 proteins ) and CENP-C homolog Mif2 ( a part of the inner kinetochore ) in vitro [9] . Replacement of amino acid residues in a conserved hydrophobic loop of Csm1 blocks its interaction with Dsn1 and Mif2 in vitro and prevents monopolar attachment during meiosis I [9] . However the amino acid replacements also prevent interaction of Csm1 with the nucleolar protein Tof2 and affect rDNA silencing [9] suggesting that they might perturb the overall structure of Csm1 . Moreover , the interaction of Csm1 with Mif2 has been recently questioned [10] . Csm1 has also been shown to interact with Ctf19 , a non-essential central kinetochore protein and the MIND complex subunit Dsn1 in a high throughput yeast 2-hybrid screen [16] . However the functional significance of these interactions has not been analysed . By carrying out an imaging based screen , we show that the MIND complex is required for stable association of monopolins with kinetochores during meiosis I . By coupling targeted mutagenesis to a binding assay , we have identified a ‘Csm1-interaction domain’ ( CID ) in Dsn1 . Deleting CID had no effect on mitotic growth but severely compromised meiotic chromosome segregation . Furthermore we show that cells lacking the CID do not localize monopolins to the kinetochore and attempt to bi-orient sister kinetochores on the meiosis I spindle . Our work provides a mechanism for monopolar attachment during meiosis I in budding yeast .
To identify the binding sites of monopolin at the kinetochore , we first established an assay for detecting association of monopolins to the kinetochore during meiosis I . To do this we attached Green Fluorescent Protein ( GFP ) to Mam1 ( to visualize monopolins ) and Red Fluorescent Protein ( RFP ) to Cep3/Mtw1 ( to visualize kinetochores ) . To arrest cells in metaphase I ( which increases the proportion of cells with Mam1 at kinetochores ) , we replaced the promoter of CDC20 ( which encodes an activator of Anaphase-Promoting Complex ) with the mitosis-specific promoter PCLB2 [17] . We find that Mam1 co-localizes with Cep3 at the kinetochore in 90% of GFP-positive MAM1-GFP CEP3-RFP PCLB2-CDC20 cells following 7 hours of incubation in sporulation medium ( SPM ) ( Figure 1A ) . Co-localisation of Mam1 and Cep3/Mtw1 was dependent on Lrs4 ( Figure 1A and Table 1 ) , confirming that binding of monopolin subunits to the kinetochore is inter-dependent [4] . Importantly , addition of benomyl abolished formation of metaphase I spindles but did not affect association of Mam1 with kinetochores ( Figure 1B and 1C ) , indicating that binding of monopolin to kinetochores is not dependent on kinetochore-microtubule interaction . To determine which kinetochore protein ( s ) are required for monopolin complex recruitment , we inactivated 14 genes encoding kinetochore proteins either by gene deletion ( for non-essential genes ) or meiosis-specific suppression of gene expression by replacement of native promoters with PCLB2 ( for essential genes ) . We find that inactivation of Ctf19 and other components of the Ctf19 complex including Mcm21 , Chl4 , Ctf3 , Mcm22 , Nkp1 , Nkp2 , Mcm16 and Iml3 had little or no effect on association of monopolin with kinetochores ( Table 1 , Figure 1A ) . These results are consistent with the observation that about 80% of ctf19Δ cells segregate chromosomes reductionally during meiosis I [18] . Similarly , inactivation of Spc24 and Ask1 , which are components of the Ndc80 and Dam1 complexes respectively , did not affect Mam1 binding to kinetochores ( Table 1 , Figures 1A ) . We confirmed that both Spc24 and Ask1 were efficiently depleted in PCLB2-SPC24 and PCLB2-ASK1 strains following transfer to SPM ( Figure S1 ) . This suggests that monopolin recruitment to kinetochores does not require the outer kinetochore components . In contrast , inactivation of MIND complex components Mtw1 and Dsn1 and the CENP-C homolog Mif2 severely affected association of monopolin with kinetochores ( Table 1 and Figure 1A ) . We note that depletion of Mif2 during meiosis also affected recruitment of MIND complex to kinetochores ( Figure 1D ) . This is consistent with the observations that CENP-C is required for stable association of the Mis12 ( counterpart of MIND ) complex with kinetochores in both human and fly cells [19] , [20] . These results suggest that the MIND complex is required for monopolin association to the kinetochore . The hypothesis that the MIND complex directly recruits monopolin to the kinetochore makes three key predictions . Firstly , at least one of its subunits should contain a monopolin-interacting domain . Secondly , mutation of this domain should not affect mitotic chromosome segregation . Thirdly , mutation of the domain however should prevent monopolin binding to kinetochores and monopolar attachment during meiosis I . The MIND complex subunit Dsn1 has been reported to interact with Csm1 in a 2-hybrid assay [16] . We first confirmed the 2-hybrid interaction between full length Csm1 and Dsn1 ( Figure 2B Top panel ) . We then used deletion mutagenesis to map the domain in Dsn1 that interacts with Csm1 ( Table 2 ) . While Dsn1 ( 1–352 ) interacted with Csm1 as efficiently as full length Dsn1 ( 1–576 ) , Dsn1 ( 353–576 ) did not . We further narrowed down the interaction domain to the first 220 residues of Dsn1 ( Table 2 and Figure 2B ) . However neither Dsn1 ( 1–130 ) nor Dsn1 ( 131–220 ) interacted with Csm1 suggesting that the N-terminal 220 residues of Dsn1 represents a minimal region sufficient for binding Csm1 ( Table 2 ) . To identify motifs crucial for Csm1-Dsn1 interaction , we scrutinized the N-terminal 220 residues of Saccharomyces cerevisiae ( S . c . ) Dsn1 . The N-terminal domain is conserved amongst the Saccharomycetes class of ascomycetes but absent in Schizosaccharomyces pombe and human Dsn1 ( Figure 2A ) . A multiple sequence alignment of the N-terminal domain of Dsn1 identified a highly conserved stretch between residues 70–110 in S . c . Dsn1 that contains three conserved elements , which we termed Box 1 , 2 and 3 ( Figure 2A ) . We find that mutation to alanine of Box 2 and Box 3 , but not Box 1 , completely abolished Csm1-Dsn1 interaction ( Figure 2B Bottom panel ) . To test whether the N-terminal domain of Dsn1 interacts with Csm1 in vitro , we incubated yeast extracts containing either full length Dsn1 or a version lacking the first 110 residues ( Dsn1-Δ110 ) , with beads coupled to recombinant Csm1 . Full length Dsn1 interacted with Csm1-coated beads , but not to control beads , whereas Dsn1-Δ110 failed to interact with either Csm1-coated or control beads ( Figure 2C ) . This indicates that the first 110 residues of Dsn1 are required for interaction with Csm1 . We refer to this region as the Csm1-Interaction Domain ( CID ) . To determine the reciprocal Dsn1-interacting domain in Csm1 , we tested the ability of different segments of Csm1 to interact with Dsn1 in an in vitro binding assay . Crystal structure of Csm1 indicates that the first N-terminal 82 residues form a coiled-coil domain and the C-terminal residues 83–181 form a globular domain [9] . While regions of Csm1 encoding residues 88–190 , 141–190 and 1–140 interacted with Dsn1 , the N-terminal 87 residues did not ( Figure 2D ) . This suggests that the globular , but not coiled-coiled , domain of Csm1 interacts with Dsn1 . This is consistent with the finding that point mutations that disrupt a conserved hydrophobic patch in globular domain of Csm1 abolished its interaction with Dsn1 in vitro [9] . Since Corbett et al . [9] used purified Csm1 and Dsn1 proteins in their in vitro binding assays , it is quite likely that Csm1 directly interacts with Dsn1 in vivo . To determine whether the Csm1-interacting domain ( CID ) of Dsn1 is required for monopolar attachment during meiosis I , we first tested whether mutations in CID suppress the poor spore viability phenotype of spo11Δ spo12Δ strains . Deletion of SPO12 causes a failure to exit from meiosis I resulting in production of dyads [21] , [22] . Strains lacking SPO11 ( whose product initiates meiotic recombination by producing double-strand breaks on DNA ) segregate homologs randomly [23] . However sister centromeres are co-oriented in spo11Δ cells and migrate towards the same spindle pole . Therefore spo11Δ spo12Δ strains produce dyads which have low ( <5% ) spore viability . Crucially disrupting mono-orientation , either by deleting or inactivating monopolin genes , rescues the spore viability of spo11Δ spo12Δ strains ( Figure 3A ) [4] , [5] . We find that the poor spore viability of PCLB2-DSN1 spo11Δ spo12Δ cells is rescued by transformation with DSN1 mutants lacking Box 2 ( 30% ) , Box 3 ( 11% ) or the entire CID ( 80% ) , but not by transformation of either wild type DSN1 or a DSN1 mutant lacking the N-terminal 75 amino-acids ( Figure 3A ) . These results suggest that the CID is required for mono-orientation of sister kinetochores during meiosis . We then tested whether the CID is required for accurate chromosome segregation during wild type meiosis . Since expression of dsn1-Δ110 has a dominant effect on meiotic chromosome segregation ( see below ) and thereby precluded us from generating strains by crossing , we constructed dsn1-Δ110 strains by direct transformation . We expressed either full length Dsn1 or Dsn1-Δ110 ( that lacks the CID ) from the GPD1 promoter that is active during both mitotic and meiotic cell cycles . PGPD1-DSN1 and PGPD1-dsn1-Δ110 strains were indistinguishable from DSN1 strains in terms of benomyl sensitivity ( Figure S2A ) and mitotic chromosome loss rates ( Figure S2B ) . These results indicate that neither GPD1 promoter replacement of DSN1 nor deletion of the CID affects kinetochore function during mitosis . This is consistent with recent biochemical studies which show that deletion of the first 172 residues of Dsn1 is dispensable for MIND complex assembly [24] . While PGPD1-DSN1 and PGPD1-dsn1-Δ110 strains were very similar in terms of their mitotic growth , their meiotic phenotypes were quite different . Only PGPD1-dsn1-Δ110 but not PGPD1-DSN1 completely rescued the poor spore viability of spo11Δ spo12Δ strains ( Figure 3A ) . Whilst PGPD1-DSN1 cells display spore viability ( 96% ) comparable to wild type cells , the spore viability of PGPD1-dsn1-Δ110 strains was dramatically reduced ( 5% ) ( Figure 3B ) . To visualize chromosome segregation , we tagged homologous chromosomes with GFP and followed their segregation by fluorescence microscopy . While 100% of nuclei in PGPD1-DSN1 cells had 1 GFP dot per nucleus , 70% of PGPD1-dsn1-Δ110 cells had nuclei containing more than 1 GFP dot ( Figure 3B ) . These results indicate that the CID is dispensable for mitotic cell proliferation , but required for accurate chromosome segregation during meiosis . Notably , this is the first mutant allele of a core essential kinetochore protein that is differentially required for mitotic and meiotic divisions in any organism . To determine the precise role of the CID in meiotic chromosome segregation , we transferred PGPD1-DSN1 and PGPD1-dsn1-Δ110 cells to SPM . To visualize chromosome segregation , we tagged the URA3 locus ( located 30 kb from the centromere ) in one of the two parental chromosome V's with GFP using the tetO/tetR system [25] . We also attached 18 copies of Myc epitope to Pds1 ( securin ) and 3 copies of HA epitope to Rec8 ( cohesin subunit ) . Accumulation of Pds1 and Rec8 , their destruction , and the formation of metaphase I spindles was indistinguishable in PGPD1-DSN1 and PGPD1-dsn1-Δ110 strains ( Figure 3C ) . In PGPD1-DSN1 cells , the progression from metaphase I-anaphase I coincided with destruction of Pds1 , spindle elongation , nuclear division and co-segregation of sister URA3 sequences towards the same spindle pole ( Figure 3D ) . Pds1 re-accumulated in these bi-nucleate cells accompanied by formation of two sets of bipolar spindles . The progression from metaphase II-anaphase II was accompanied by a second round of Pds1 destruction , segregation of sister URA3 sequences towards opposite poles and formation of 4 nuclei ( Figure 3D ) . Although PGPD1-dsn1-Δ110 cells formed metaphase I spindles with paired sister URA3-GFP signals , they failed to undergo the first nuclear division following destruction of Pds1 ( Figure 3D ) . We observed a high proportion of mono-nucleate cells ( 95% compared to 20% in wild type cells ) lacking Pds1 with stretched DNA and anaphase I-like spindles ( Figure 3D and E ) . In these cells sister URA3 sequences were frequently split ( 18% ) , suggesting that cells had attempted to bi-orient sister centromeres on the meiosis I spindle ( Figure 3D ) . Additionally , PGPD1- dsn1-Δ110 cells formed two sets of bipolar spindles in mono-nucleate cells and underwent a highly abnormal nuclear division where a single DNA mass was split along two sets of bipolar spindles resulting in four unequal DNA masses ( Figure 3D ) . The phenotype of PGPD1-dsn1-Δ110 cells is highly reminiscent to monopolin mutant cells in which sister centromeres bi-orient on the meiosis I spindle . To confirm this , we analysed meiotic chromosome segregation in mam1Δ and dsn1-Δ110 strains in parallel and found that they were indeed strikingly similar ( Figure S3 ) . Monopolin mutant cells fail to undergo the first meiotic division as the microtubule pulling forces exerted on sister centromeres are resisted by centromeric cohesion which is protected during meiosis I [3]–[5] . We reasoned that if centromeric cohesion was responsible for preventing the first nuclear division in PGPD1-dsn1-Δ110 cells , then its ectopic destruction should rescue the nuclear division defect in PGPD1-dsn1-Δ110 cells . To test this we replaced the meiotic cohesin subunit Rec8 by Scc1 which compromises protection of centromeric cohesion , but does not affect monopolar attachment [3] . Since Scc1 cannot substitute for Rec8 in meiotic recombination , we performed our experiments in the absence of Spo11 to prevent the formation of double strand breaks . We transferred diploid spo11Δ rec8Δ:PREC8-SCC1 cells that were heterozygous for URA3-GFP and expressing either DSN1 or dsn1-Δ110 into SPM . In the absence of recombination , homologs are not connected and therefore segregate randomly . However sister centromeres are mono-oriented and move towards the same spindle pole . Consistent with this anaphase I spindles were formed in the absence of Pds1 destruction and sister URA3 sequences moved towards the same spindle pole in spo11Δ rec8Δ:PREC8-SCC1 cells expressing DSN1 ( Figure 4A and 4B ) . In contrast , nuclei divided only after Pds1 degradation and sister URA3 sequences moved towards opposite spindle poles in spo11Δ rec8Δ:PREC8-SCC1 cells expressing dsn1-Δ110 ( Figure 4A and 4B ) . Thus the meiotic nuclear division defect of dsn1-Δ110 cells can be rescued by deprotection of centromeric cohesion . These results formally demonstrate that the CID is required for monopolar attachment of sister kinetochores during meiosis I . To test whether the CID is required for association of monopolin with kinetochores during meiosis I , we induced MAM1-GFP MTW1-RFP PCLB2-CDC20 diploid cells expressing either wild type DSN1 or dsn1-Δ110 to enter meiosis and examined the binding of Mam1 to kinetochores by fluorescence microscopy and chromatin immunoprecipitation ( ChIP ) . Mam1 was expressed after 5 hours following transfer to SPM in both PGPD1-DSN1 and PGPD1-dsn1-Δ110 strains ( Figure 5B ) . While Mam1-GFP dots coincided with Mtw1-RFP signals in 90% of PGPD1-DSN1 cells , co-localisation of Mam1 and Mtw1 was observed in only 3% of PGPD1-dsn1-Δ110 cells ( Figure 5A ) . To rule out the possibility that lack of Mam1 binding to kinetochores in the dsn1-Δ110 strain was due to our fixation protocol , we also performed time-lapse video microscopy of wild type and dsn1-Δ110 cells after 6 h in SPM . In 100% of wild type cells ( n = 60 ) analysed , Mam1-GFP signal was strongly enriched at kinetochore and this enrichment was maintained for the entire length of imaging analysis ( Figure S4 and Supplemental Video S1 ) . In contrast in 100% of dsn1-Δ110 cells ( n = 200 ) the Mam1-GFP signals were diffuse and did not show any enrichment at kinetochores during the entire experiment ( Figure S4 and Supplemental Video S2 ) . To confirm that CID is required for monopolin binding to kinetochores , we performed Chromatin Immunoprecipitation ( ChIP ) . Consistently , we find that Mam1 binds to centromeric and pericentric DNA in PGPD1-DSN1 , but not in PGPD1-dsn1-Δ110 , cells by ChIP ( Figure 5C ) . We also examined the association of Csm1 with kinetochores in PGPD1-DSN1 and PGPD1-dsn1-Δ110 cells by chromosome spreading . Diploid CSM1-myc9 PCLB2-CDC20 cells expressing either Dsn1-pk6 or Dsn1-Δ110-pk6 were induced to enter meiosis by transferring them to SPM for 7 hours . Chromosome spreads were prepared and stained with anti-myc and anti-PK antibodies to detect Csm1 and Dsn1 , respectively . We find that Csm1 foci coincide with Dsn1 in 70% of wild type cells , but in only 2% of mutant cells , even though comparable proportions of wild type and mutant cells formed metaphase I spindles ( Figure 5D ) . Together these results indicate that the CID is required for stable association of monopolin with kinetochores during meiosis I . We also note that the CID is required for association of Csm1/Lrs4 complex with kinetochores during mitotic anaphase ( Figure S5 ) . It was recently reported that the binding of Csm1/Lrs4 complex to kinetochores during anaphase is required for accurate chromosome segregation during mitosis [26] . However this notion is inconsistent with our observation that PGPD1-dsn1-Δ110 cells do not have an increased chromosome loss rate ( Figure S2B ) . However , we find that , consistent with an earlier report [4] , lrs4Δ strain was indistinguishable from wild type strain in terms of its chromosome loss rate ( Figure S2B ) . We observed that dsn1-Δ110 had a semi-dominant effect on meiotic chromosome segregation and spore viability ( Figure S6A and S6B ) . The semi-dominant effect of dsn1-Δ110 was not due to sub-optimal association of monopolin with kinetochores ( Figure S6C ) but is consistent with the notion that Dsn1-Δ110 protein binds efficiently to kinetochores , but interferes with the cross-linking of sister kinetochores ( Figure S6D ) .
By performing a microscopic screen , we have identified the MIND complex to be required for monopolin association with kinetochores . In particular , we have demonstrated an interaction between monopolin subunit Csm1 and the N-terminal domain of Dsn1 . Furthermore we have shown the CID ( the N-terminal 110 amino acid residues of Dsn1 ) is required for mono-orientation of sister kinetochores and localization of monopolin to kinetochores . Cross-linking of kinetochores could be achieved by association of monopolin with the CID of Dsn1 . Alternatively , the CID of Dsn1 could simply target monopolins to kinetochore which then crosslink kinetochores via a different mechanism . The fact that we see a dominant effect of Dsn1 lacking CID on meiotic chromosome segregation is consistent with the former possibility . Based on the structural data of the monopolin complex [9] and our findings , we propose that monopolin crosslinks sister kinetochores during meiosis I in wild type cells and this is mediated by an interaction between Csm1's globular domain and the CID in Dsn1 ( Figure 6 ) . Sister kinetochores are thereby constrained to face the same spindle pole and thus mono-oriented on the meiosis I spindle . In cells lacking the CID , monopolin fails to associate with kinetochores and the sister kinetochores are consequently bi-oriented on the meiosis I spindle ( Figure 6 ) . Quantitative imaging microscopy has revealed that a single budding yeast kinetochore has 6–8 copies of the MIND complex [27] . If monopolin works by cross-linking Dsn1 molecules , it is intriguing how monopolin distinguishes Dsn1 molecules on the same kinetochore from those bound to two different kinetochores . The catalytic activity of casein kinase Hrr25 is required for monopolar attachment but not for kinetochore recruitment [5] . It will be interesting to determine whether MIND complex is phosphorylated by Hrr25 kinase during meiosis I . Hyperphosphorylation of Lrs4 by the Dbf4-dependent kinase Cdc7 facilitates monopolin binding to kinetochores [8] . It will be worth investigating whether Lrs4 hyperphosphorylation enhances the Csm1-CID interaction . Intriguingly , the CID is not present in fission yeast Dsn1 ( Mis13 ) even though fission yeast expresses orthologues of Csm1 ( Pcs1 ) and Lrs4 ( Mde4 ) . Notably Pcs1 interacts with Cnp3 ( homolog of Mif2/CENP-C ) and requires Cnp3 for its stable association with kinetochores [28] . However , the domain of Pcs1 that interacts with Cnp3 is not present in budding yeast Mif2 ( Figure S7 ) . Moreover , we and others have not detected any interaction between Mif2 and Csm1 in vitro ( [10] and data not shown ) . It is quite possible that budding and fission yeast monopolin complexes have distinct kinetochore binding sites . This is not unprecedented as there are many species–specific variations in the kinetochore architecture despite a conserved building scheme . For instance , the vertebrate Ndc80 complex contacts the Mis12 complex by interacting with the C-terminal domain of Nsl1 subunit [29] . However the budding and fission yeast Nsl1 homologs do not have this interaction domain . It has been suggested that Pcs1/Mde4 establishes chromosome bi-orientation in fission yeast by targeting condensin to the centromere [13] . Curiously , the Csm1/Lrs4 complex targets condensin to rDNA regions in budding yeast [30] . Unravelling the precise relationship between the abilities of monopolins to mono-orient kinetochores and direct condensin to defined regions of the chromosome is a crucial challenge for the future . Notably , monopolar attachment of sister kinetochores in plants occurs by cross-linking of Mis12 complexes [31] . It will be important to determine whether a mechanism involving cross-linking of Mis12 complexes also operates during co-orientation of sister kinetochores during mammalian meiosis .
Diploid SK1 strains were used for most experiments except for the 2-hybrid assays ( AH109 strain -Clontech ) and the chromosome loss assay experiments ( BY4741 ) . A list of strains used is provided in the Table S1 . PCR-based modifications of the yeast genome including gene deletions , epitope tagging and promoter replacements were performed using standard yeast techniques . Meiotic cultures were performed at 30°C as previously described [32] . One ml sporulation culture was spun in a microfuge tube at 13 , 000 rpm for a minute and the cells were re-suspended in 100 µl fixative ( 4% paraformadehyde , 3 . 4% sucrose ) and incubated at room temperature for 15 minutes . Cells were then spun and washed once in Solution I ( 100 mM Potassium phosphate pH 7 . 5 1 . 2 M Sorbitol ) and then resuspended in 100 µl of Solution I . Cells were sonicated for 5 sec and then 3 µl cells were mounted on a slide for fluorescence microscopy . Images were acquired using an inverted microscope ( TE-2000 Nikon ) with a 100×1 . 49 NA objective lens equipped with a cooled charge-coupled device camera ( CoolSNAP HQ2; Photometrics ) . 16 z-stack ( spacing = 0 . 2 µm ) were used at exposure times of 1 sec for Cy3 , Cy5 , RFP and Alexa Fluor 488/GFP and 0 . 25 sec for DAPI . Images were analyzed using MetaMorph ( version 7 . 5 . 2 . 0; MAG Biosystems Software ) . Cells were induced to enter meiosis by transferring them to sporulation medium for 6 hours . Then 20 µl of cells were added onto a Y04D CellASIC plate ( CellASIC ONIX microfluidic perfusion system ) and imaged inside an environmental chamber set at 30°C . A flow rate of 8 psi was used to load the cells and a steady-state flow rate of 2 psi was used for the duration of the experiment . Time-lapse microscopy was carried out using a Personal DeltaVision ( Applied Precision ) with solid-state illumination , using associated proprietary software ( SoftWoRx software; version 4 . 0 . 0 , Applied Precision ) . Images were captured using an UPLS Apochromat 1 . 4 numerical aperture , ×100 magnification oil immersion objective ( Olympus ) , standard DeltaVision filter sets FITC ( Excitation 490 nm , Emission 525 nm ) and TRITC ( Excitation 490 nm , Emission 525 nm ) yielding approximate resolutions ( Rayleigh's d ) of ∼229 nm and 264 nm in the xy , respectively , whereas axial resolutions were approximately 811 and 935 nm . Photon detection was carried out using a Cascade2 1 K EMCCD camera ( Photometrics ) using a gain of 4 . 00 and no binning . Effective pixel size was ∼0 . 0645 µm in the xy . Images were taken using exposure times of 0 . 1 sec and 32% transmission ( FITC ) and 0 . 15 sec exposure ( TRITC ) . Final images for sporulation were carried out with DIC , 32% transmission and 0 . 08 sec exposure . 8 z-stacks of 0 . 5 µm thickness were taken of each nucleus . Images were recorded every 10 minutes for 2 hours . Five ml of 37% formaldehyde was added to 50 ml yeast culture and the mixture was incubated at 30°C for 30 minutes . 2 . 5 ml of 2 . 5 M glycine was added and the mixture was incubated further for 5 minutes . Cells were spun at room temperature at 3 , 000 rpm ( 1 , 620 g ) for 3 minutes . The supernatant was discarded and cells were resuspended in 25 ml 1× PBS and pelleted at 3 , 000 rpm ( 1620 g ) for 3 minutes . The supernatant was discarded and cells were resuspended in 1 ml 1× PBS and transferred to a fresh eppendorf tube . Cells were spun at room temperature at 10 , 000 rpm ( 9 , 500 g ) for 2 minutes and the pellet was frozen at −80°C . Cells were re-suspended in lysis buffer A ( 50 mM HEPES-KOH pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate , 1 mM PMSF , EDTA free complete mini protease inhibitor mix ) . An equal volume of glass beads was added to the cells and the mixture was vortexed in a bead beater ( 1 minute on and 1 minute off , 20 times ) . This was followed by sonication ( 30 sec on and 30 sec off , 6 times ) . The lysate was cleared using protein G-Sepharose beads for 1 hour at 4°C . The cleared lysate was incubated with mouse anti-GFP antibody overnight at 4°C followed by 2 hours incubation with protein G-Sepharose beads on a rotary wheel at 4°C . The beads were then washed 3 times with lysis buffer A . Proteins were eluted off the beads by heating the beads in TES buffer ( 50 mM Tris pH 8 . 0 , 1 mM EDTA , 10% SDS ) overnight at 65°C . The beads were discarded and the supernatant was treated with RNAse A for 1 hour at room temperature and proteinase K in 50 mM Tris pH 8 . 0 for 2 hours at 37°C . DNA was purified using Qiagen columns as per the manufacturer's instructions . Primers used for amplifying the centromeric , pericentric and arm regions of Chromosome III have been previously described [4] . Whole-cell extract ( WCE ) DNA was diluted 25 times and 1 µl was used as a template in a 50 µl Polymerase Chain Reaction ( PCR ) . The immunoprecipitated DNA was diluted 5 times and 1 µl was used in a 50 µl PCR . The PCR products were run on a 2% agarose gel and visualized by staining with ethidium bromide . Either pMAL-c2x vector or pMAL-c2x vector containing Csm1 ORF ( and its variants ) was introduced into E . coli Rosetta ( DE3 ) pLysS Competent Cells ( Novagen ) by transformation . Transformants were grown in 100 ml LB medium at 37°C until the OD600 was 0 . 6 . Expression of Csm1-MBP/MBP was induced by addition of IPTG ( 0 . 2 mM ) and cultures were incubated for 3 hours at 37°C . Cells were harvested and resuspended in 5 ml lysis buffer B ( 50 mM HEPES-KOH pH 7 . 5 , 140 mM NaCl , 1 mM PMSF , Roche EDTA-free complete mini protease inhibitor mix ) and sonicated for 4 minutes ( 1 min on , 1 min off ) using the Sonics . Cell lysates were spun at 18 , 000 rpm for 30 minutes . The supernatant was collected and mixed with 500 µl of Amylose resin ( New England Biolabs ) and incubated for 1 hour at 4°C . Beads were pelleted and washed with lysis buffer B three times and then resuspended in 500 µl of lysis buffer B . This was used for the binding assay ( see below ) . Dsn1 or its truncated version Dsn1 ( 111–576 ) , carrying 6 copies of PK tag attached to its C-terminus was expressed in S . cerevisiae from the GPD1 promoter . Yeast cells from an overnight culture were inoculated into 50 ml YEPD with a starting OD of 0 . 2 and incubated at 30°C in a shaker at 170 rpm . Cells were harvested and resuspended in 600 µl of lysis buffer A and were mixed with an equal volume of glass beads . This mixture was vortexed using the VIBRAX-IKA for 16 minutes ( 1 min on , 1 min off , 4 times , 4 cycles ) . The lysate was separated from the cell debris by spinning at 14 , 000 rpm for 10 minutes . 100 µl of lysate was mixed with 100 µl of Csm1-MBP/MBP beads ( purified above ) and kept at 4°C for 30 minutes . Beads were then washed thrice with 1 ml of lysis buffer A containing 1% NP40 . Beads were re-suspended in 100 µl of 2xSDS-sample buffer and incubated at 95°C for 5 minutes to elute the bound proteins . The ORF encoding CSM1 was cloned into EcoRI and SalI sites of pGADT7 ( Clontech ) . The ORF encoding Dsn1 was cloned into NcoI and BamHI sites of pGBKT7 ( Clontech ) . Mutant versions of Dsn1 in pGBKT7 were generated by gap-repair . Briefly oligos with 50 base homology to regions upstream and downstream of BamHI and NcoI sites respectively of pGBKT7 at their 5′ ends followed by 15 nucleotides specific to DSN1 were designed to amplify specific regions of DSN1 ORF . 1 µg of the PCR product was co-transformed with 100 ng of BamHI/NcoI digested pGBKT7 into AH109 cells containing either pGADT7 or pGADT7-CSM1 . Transformants were tested for interaction by replica plating them on SD–LEU-TRP-ADE-HIS plates . Genomic DNA from four randomly chosen transformants was used as a template for amplifying the insert and the PCR products obtained were sequenced to confirm the identity of the mutants created by gap repair . Details of oligonucleotides used in the study are available upon request . Immunoblotting , chromosome spreads and in situ immunofluorescence were performed as previously described [32] . For Westerns , mouse anti-PK antibody ( Serotec ) , Goat anti-Cdc28 antibody ( Santa Cruz ) and mouse anti-GFP antibody ( Roche ) were all used at 1∶1000 dilutions . For staining in situs , mouse anti-myc 9E10 ( 1∶500 ) , mouse anti-HA16B12 ( 1∶500 ) and rat anti-tubulin YOL1/34 ( 1∶500 ) were used . For chromosome spreads the rabbit anti-myc ( Gramsch; 1∶500 ) and mouse anti-PK ( 1∶500 ) antibodies were used . Mitotic chromosome loss assay was performed as previously described [33] . Strains used for chromosome loss assay harbour the ade2-101 ochre mutation and a supernumerary chromosome that contains URA3 marker and SUP11 ( an ochre-suppressing tRNA ) . Cells containing the supernumerary chromosome produce white colonies as SUP11 suppresses the ade2-101 ochre allele . Loss of the supernumerary chromosome results in the formation of a red sector in an otherwise white colony . For the assay , cells were grown overnight on minimal medium lacking uracil and then plated at a density of 300 colonies per plate on YEP ( 1 . 1%Yeast extract , 2 . 2% Peptone and 2% Glucose ) medium . After 2–3 days of incubation at 30°C , colonies that were at least half red were counted . The chromosome loss rate was computed by dividing the number of half-sectored colonies by the total number of colonies scored . | All sexually reproducing organisms produce haploid gametes from diploid cells via meiosis . During meiosis , one round of DNA replication is followed by two rounds of nuclear division ( called meiosis I and II ) . This is unlike mitotically proliferating cells wherein one round of DNA replication is followed by one round of nuclear division . During meiosis I , sister chromatids move towards the same spindle pole unlike in mitosis where they move towards opposite spindle poles . Poleward chromosome movement is achieved by association of kinetochores ( a complex network of proteins assembled at centromeres on chromosomes ) with microtubule ends emanating from spindle poles . The basis of the contrasting fate of sister chromatids during mitosis and meiosis I is best studied in budding yeast in which a protein complex called monopolin binds to sister kinetochores during meiosis I and ensures that they face the same spindle pole . But precisely how monopolin binds to kinetochores was unknown . In this study , we have identified a monopolin's receptor at the kinetochore . Disabling the receptor did not affect mitotic growth but severely compromised meiotic chromosome segregation . Cells lacking the monopolin receptor attempt to segregate sister chromatids towards opposite spindle poles during meiosis I with catastrophic genetic consequences . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"chemistry",
"biology"
] | 2013 | Monopolin Subunit Csm1 Associates with MIND Complex to Establish Monopolar Attachment of Sister Kinetochores at Meiosis I |
Gene expression differences between the sexes account for the majority of sexually dimorphic phenotypes , and the study of sex-biased gene expression is important for understanding the genetic basis of complex sexual dimorphisms . However , it has been difficult to test the nature of this relationship due to the fact that sexual dimorphism has traditionally been conceptualized as a dichotomy between males and females , rather than an axis with individuals distributed at intermediate points . The wild turkey ( Meleagris gallopavo ) exhibits just this sort of continuum , with dominant and subordinate males forming a gradient in male secondary sexual characteristics . This makes it possible for the first time to test the correlation between sex-biased gene expression and sexually dimorphic phenotypes , a relationship crucial to molecular studies of sexual selection and sexual conflict . Here , we show that subordinate male transcriptomes show striking multiple concordances with their relative phenotypic sexual dimorphism . Subordinate males were clearly male rather than intersex , and when compared to dominant males , their transcriptomes were simultaneously demasculinized for male-biased genes and feminized for female-biased genes across the majority of the transcriptome . These results provide the first evidence linking sexually dimorphic transcription and sexually dimorphic phenotypes . More importantly , they indicate that evolutionary changes in sexual dimorphism can be achieved by varying the magnitude of sex-bias in expression across a large proportion of the coding content of a genome .
Complex sexually dimorphic phenotypes are largely the result of gene expression differences between males and females for loci that are present in both sexes [1] , [2] , and the study of sex-biased gene expression provides a link between sexual conflict and sexual selection acting on the phenotype with the genetic loci that underpin it . It is often assumed that genes expressed more in either sex encode sexually dimorphic phenotypes that are then subject to sex-specific selection . Studies in a range of animals have demonstrated that sex-biased gene expression is widespread across the genome [3]–[7] , most evident in adults as would be expected as this is when sexual phenotypes are most manifest [8]–[10] , variable among closely related species [11] and subject to rates of evolution consistent with sexual selection acting primarily on males [2] . However , despite this mounting circumstantial evidence , the relationship between gene expression and the phenotype is complex , and direct connections linking sex-biased gene expression to sexually dimorphic phenotypes have remained elusive . This relationship between sex-biased transcription and sexual dimorphism is key to studies of sexual conflict and sexual selection , which are increasingly focused on sex-specific regulation , and to the broader question of the regulatory control of complex phenotypes . The relationship between sex-biased gene expression and sexual dimorphism has been difficult to test directly , primarily because sexual dimorphism is often envisaged as a dichotomous comparison between female and male forms . Additionally , many of the model systems for sex-biased gene expression studies lack multiple sex-specific morphs , precluding detailed tests of the association between sex-biased gene expression and dimorphic phenotypes . However , sexual dimorphism is far more complex for many species , with some individuals occupying intermediate points along an axis . The wild turkey exhibits two male phenotypes in the forms of dominant and subordinate males . The species is strongly sexually dimorphic , with dominant males showing greater body size than females , along with a constellation of sexually selected traits including iridescent plumage , a long beard , vivid coloration on the head and neck , enlargement of the caruncles , wattle and snood ( Supplemental Fig . 1 ) , and distinct mating behaviours [12]–[14] . Dominance among sibling males is established via male-male competition during the winter prior to sexual maturation [15] , and at this point , many males develop the subordinate male phenotype , which includes iridescent plumage and long beards similar to dominant males , but with less vivid head and neck coloration and less developed wattles , caruncles and snoods . The length of the latter appears to be key to intra-sexual and inter-sexual selection in this species [12] , [13] . Although subordinate males can mate and sire offspring [15]–[17] they rarely obtain mating opportunities . Their role is mainly to assist their dominant brothers in attracting mates , and as such has been held up as an example of Hamilton's rule of kin selection [15] , [16] , [18] . Importantly , subordinate males can become dominant males later in life if the dominant dies , emphasising the plastic nature of the male phenotype . Subordinate males are therefore clearly male in phenotype , but occupy an intermediate position on the continuum of sexual dimorphism . The two male phenotypes in the wild turkey make it possible to test for the first time whether the magnitude of sexual dimorphism in the phenotype is associated with the magnitude of sex-biased expression . Male-biased genes are often assumed to encode male-specific phenotypes , while female-biased genes are thought to encode female-specific phenotypes . Within this framework , the subordinate male phenotype could be the product of reduced expression of male-biased genes ( demasculinized ) , increased expression of female-biased genes ( feminized ) , or a combination of both , compared to the dominant male phenotype . We therefore used the female and subordinate male and dominant male phenotypes in order to directly test for the first time whether the degree of sex-biased gene expression is correlated with sexual dimorphism , and to understand the role of demasculinization and feminization in gene expression in encoding the subordinate male form .
Our analyses provide the first correlative support linking magnitude of sex-biased gene expression to the degree of phenotypic sexual dimorphism . Our data show a clear and strikingly direct concordance between relative expression of male sexually selected traits and transcriptional masculinization and feminization at multiple levels , in both the gonad and the soma . Synthesis and decay rates can differ for transcription and translation , which can break down the correlation between mRNA abundance and protein titer . However , in some studies , up to 70% of the variance in protein abundance is explained by mRNA levels [28] . Additionally , the broad , genome wide pattern we observe suggests that many of the differences in gene expression levels between male morphs will have functional consequences . It is not clear whether this axis of dimorphism extends to systems with alternative male mating strategies , as observed in some fish species , where sneaker males and female mimics seek to steal fertilization events from dominant males . In these cases , males with alternative morphs likely divert effort from sexually selected somatic traits to reproductive function and sperm production [29] , and so it is difficult to predict what we might expect in transcriptomic comparisons . However , our results suggest that evolutionary changes in the magnitude of sexual dimorphism , which affect a large number of species in many clades , may be achievable by changes in the magnitude of sex-biased transcription .
Two-year-old wild turkeys were obtained in the breeding season of their first reproductive year , after social dominance was established , from Vicvet Farms ( Yorkshire , UK ) . Although the population is natural in that is has not been subject to selection for domestication traits , it is kept under controlled semi-natural conditions , allowing us to control for age , diet and many environmental influences that can potentially affect gene expression . All samples were collected under permission from institutional ethical review committees and in accordance with national guidelines . In each case , the telencephalon , spleen and left gonad were collected separately , homogenized and stored in RNAlater . RNA was prepared from the same volume of starting material with the Animal Tissue RNA Kit ( Qiagen ) . Library and RNA-Sequence samples were prepared and barcoded by the Wellcome Trust Centre for Human Genetics , University of Oxford , using standard methods and sequenced on an Illumina HiSeq 2000 as paired-end 100 bp reads . The resulting data was assessed for quality using FastQC ( http://www . bioinformatics . bbsrc . ac . uk/projects/fastqc ) . Trimmomatic [30] was used to remove read pairs with residual adaptor sequence and conduct quality filtering . Reads were trimmed if the leading or trailing bases had a Phred score <4 , and were also trimmed if a sliding window average Phred score over four bases was <15 . Post filtering , reads where either pair was <25 bases in length were removed from subsequent analyses , leaving on average more than 26 million mappable paired-end reads per sample . The genome of Meleagris gallopavo [31] version 2 . 01 ( GCA_000146605 . 1 ) , was obtained from Ensembl release 67 [32] . Filtered reads were mapped to the genome ( excluding rRNA regions ) using RSEM , version 1 . 1 . 20 [33] , which leverages the short-read aligner bowtie , version 0 . 12 . 8 [34] . To remove non- and lowly-expressed genes , a minimum expression filter of four reads per million mappable reads was applied to the raw counts , as we have previously implemented for deep RNA-Seq datasets [35]–[36] . All genes expressed lower than this threshold in less than half the female , dominant male or subordinate male individuals were removed from further analysis to prevent our results being biased by the noise inherent in very lowly expressed genes . Fragments per kilobase per million mappable reads ( FPKM ) , which corrects for variations in contig length and read depth between samples was calculated from these raw counts for each sample [37] . To explore the expression differences among the three sexual phenotypes in the gonad , we calculated average log2 expression for all females , dominant males and subordinate males for each gene , and tested for sex-bias in several ways using the R package , DESeq [38] , which calculates differential expression in a pairwise fashion by negative binomial modelling and adjusts for multiple testing using the Benjamini-Hochberg method . For the gonad , we first tested for sex-bias by identifying significant expression differences ( >2-fold difference , p<0 . 05 ) between females and dominant males . However , in order to verify that our results were not artefacts of how we defined sex-bias , and regression toward the mean , we also identified those genes with significant expression differences between females and subordinate males , and between females and all males . Due to the reduced level of transcriptional dimorphism in the soma , we reduced our fold-change thresholds considerably for the spleen ( Supplemental Materials ) . We performed hierarchical clustering using Euclidean distance with complete linkage , as implemented in Cluster 3 . 0 [39] and visualized in TreeView ( v . 1 . 1 . 6 ) [40] . Heat maps were separately constructed for male-biased , female-biased and unbiased autosomal genes and Z-linked genes . The reliabilities of the inferred trees were tested by bootstrap resampling ( 1000 replicates ) using the R package , Pvclust [41] . We separated autosomal and Z-linked genes for two reasons . First , the sex chromosomes in birds show incomplete dosage compensation [21] , therefore they exhibit an overall male-bias due to gene dose effects . Additionally , the unbalanced sex-specific selection acting on the sex chromosomes has been shown in chicken to masculinize Z chromosome expression [42]–[43] . These patterns mean that although the Z chromosome is interesting in its own right , it cannot be directly compared in terms of sex-bias to the autosomes . Therefore , sex-bias for autosomal genes was defined as those genes expressed two-fold higher in dominant males or females , with an adjusted p-value<0 . 05 ( unpaired t-test , Benjamini-Hochberg correction for multiple comparisons [44] ) . Unbiased genes were all those not classified as either male- or female-biased . When average log2 expression values for quartiles based on sex-bias were calculated , the fold change criteria was dropped so as to include genes with a lower fold change than 2 . This prevented restriction of the quartile analysis to solely the most sex-biased genes but allowed comparison to genes differentially expressed between the sexes but sex-biased to a lesser degree . GO term enrichment analysis was performed by taking mouse Ensembl gene IDs for those genes with a 1∶1 mouse ortholog , identified via Biomart . The target list ( i . e . 21 significantly differentially expressed genes between dominant and subordinate males , or genes shared between two morphs ) were compared to a background list ( either all expressed autosomal genes or all expressed genes ) using Gorilla [45]–[46] . P-values were calculated using a hypergeometric model and corrected for multiple testing . In order to investigate dN and dS , the turkey genome was compared to the genomes of chicken ( Gallus gallus ) and zebra finch ( Taeniopygia guttata ) , obtaining 16 , 496 , 22 , 194 and 18 , 204 peptides and corresponding cDNA sequence for each species respectively from Ensembl . Proteinortho [47] , with default parameters , was used to identify single copy orthologs held in all three species . These 7 , 854 orthologous groups were aligned with PRANK using a guide tree obtained from Superfamily 1 . 75 [48] . This orthologous set was filtered with Repeatmasker ( http://www . repeatmasker . org ) to remove seven retrotransposons and perl scripts were used to remove two genes with in frame stop codons and 13 genes with less than 100 bp in aligned gapless length . PAML , version 4 . 4b [49] , was used to analyse the remaining 6 , 839 one-to-one orthologs , utilizing the phylogeny used for PRANK above . Alignments where dS>2 were removed as this represents the point of mutational saturation in avian sequence data [50] . For those alignments that passed filtering , the number of potential nonsynonymous substitutions ( NdN ) , the number of nonsynonymous substitutions ( N ) , the number of potential synonymous substitutions ( SdS ) and the number of synonymous substitutions ( S ) were extracted for each orthologous group for the turkey-specific branch of the three-species phylogeny . These values were summed for each expression category in order to calculate average dN and dS for male-biased , female-biased and unbiased genes . This has the advantage of simultaneously avoiding the problem of infinitely high dN/dS values for genes lacking synonymous substitutions while weighting the data by alignment length [9] . The location of androgen transcription factor binding sites ( tfbs ) in the turkey genome were predicted using amniote androgen tfbs motifs [51] . The predicted tfbs locations were then compared to the start sites of all turkey genes in 2 kb , 5 kb and 10 kb upstream windows and matching hits recorded . | Males and females exhibit many differences in morphology , behavior and physiology , yet they share the vast majority of their genomes . Most differences between the sexes are therefore thought to be the product of gene expression differences between females and males . Studies of sex differences in expression assume that genes expressed more in males encode male traits , and genes expressed more in females encode female traits , and this assumption is a key foundation to genetic studies of sexual dimorphism and sexual conflict . Despite this key assumption , this relationship has yet to be empirically tested , as the main model organisms for studies of sex-biased gene expression lack multiple male and female morphs . Here , we use the two male morphs in the wild turkey to show that the magnitude of male-biased gene expression correlates with the manifestation of sexually dimorphic traits . Males with less manifestation of sexual dimorphism in phenotype were both demasculinized for male-biased genes , as well as feminized for female-biased genes . This pattern encompassed the majority of expressed loci , suggesting that evolutionary changes in the magnitude of sexual dimorphism may be achieved by small changes in the magnitude of sex-biased transcription across thousands of genes . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
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"and",
"Methods"
] | [
"gene",
"expression",
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"evolutionary",
"biology",
"evolutionary",
"genetics"
] | 2013 | Masculinization of Gene Expression Is Associated with Exaggeration of Male Sexual Dimorphism |
The insertion Sequence IS6110 , only present in the pathogens of the Mycobacterium tuberculosis Complex ( MTBC ) , has been the gold-standard epidemiological marker for TB for more than 25 years , but biological implications of IS6110 transposition during MTBC adaptation to humans remain elusive . By studying 2 , 236 clinical isolates typed by IS6110-RFLP and covering the MTBC , we remarked a lineage-specific content of IS6110 being higher in modern globally distributed strains . Once observed the IS6110 distribution in the MTBC , we selected representative isolates and found a correlation between the normalized expression of IS6110 and its abundance in MTBC chromosomes . We also studied the molecular regulation of IS6110 transposition and we found a synergistic action of two post-transcriptional mechanisms: a -1 ribosomal frameshift and a RNA pseudoknot which interferes translation . The construction of a transcriptionally active transposase resulted in 20-fold increase of the transposition frequency . Finally , we examined transposition in M . bovis and M . tuberculosis during laboratory starvation and in a mouse infection model of TB . Our results shown a higher transposition in M . tuberculosis , that preferably happens during TB infection in mice and after one year of laboratory culture , suggesting that IS6110 transposition is dynamically adapted to the host and to adverse growth conditions .
Tuberculosis ( TB ) is the largest infectious cause of death in history having claimed more deaths than smallpox , malaria , plague , influenza and AIDS together [1] . In addition to the alarming 1 . 7 million deaths and 10 , 4 million of new TB cases in 2016 , the emergence of multi-drug resistant strains is an increasing threat which makes TB treatment difficult or occasionally impossible [2] . Thus , early diagnostics and identification of transmission chains greatly contribute to control the TB epidemic . The adaptation of M . tuberculosis to the host is extremely complex . Most of the infected individuals are chronically infected in the form of latent TB infection ( LTBI ) and only one of 10 will develop clinical TB disease . The essential , yet unanswered question , on the natural history of TB is when M . tuberculosis decides to establish either LTBI in the host , resembling the lysogenic cycle of lambda phage , or to cause pulmonary TB disease , like the lytic cycle of lambda phage . In this latter case , M . tuberculosis decide to kill the host with the aim of achieving transmission to new hosts [3] . Seminal studies by Barbara McClintock deciphered the key role of mobile genetic elements in chromosome remodelling of maize in 1950 [4] . In the late 60’s insertion sequences were described by the groups of Shapiro , Malamy , Sybalsky and Starlinger and in 1974 Robert W . Hedges and Alan E . Jacob coined the term “transposition” in bacteria [5] . The insertion sequence IS6110 is a mobile genetic element exclusively found in the M . tuberculosis Complex ( MTBC ) [6] , the causative agent of TB in humans and other mammals including farm animals responsible for zoonotic TB transmission . This feature makes IS6110 a valuable tool in the diagnosis of MTBC in biological samples [7 , 8] . In addition , IS6110 is present in multiple copies in the chromosome of M . tuberculosis and IS6110 restriction fragment length polymorphism ( RFLP ) analysis of strains isolated from patients who developed TB showed identical patterns over years [9] . On the other side a high degree of polymorphism was observed between strains of the MTBC isolated from different patients due to IS6110 transposition [10] . Standardized IS6110 RFLP typing has been the gold standard for more than 25 years , being the most reliable TB epidemiological marker [11] . IS6110 typing allows the detection of TB outbreaks as well as to identify transmission chains using conventional and molecular methods [12] . To date tens of thousands of MTBC stains all around the world have been typed by this method but the biological role , if any , of IS6110 remains elusive . In the last 5–10 years IS6110 typing is being replaced by less time-consuming methods based in PCR amplification of mycobacterial interspersed repetitive units ( MIRU ) [13 , 14] , or more recently by whole genome sequencing ( WGS ) [15 , 16] . The MTBC comprises eight defined phylogenetic lineages . M . tuberculosis sensu-stricto includes lineages L1–L4 and L7 . These human-adapted lineages are responsible for the vast majority of global human TB cases , whereas M . africanum lineages ( L5 , L6 ) are mainly restricted to humans from West Africa . The L8 comprises animal-adapted strains with ecotypes adapted to different mammals , such as M . caprae and M . bovis , which branched from the M . africanum lineage [17] . All these lineages are classified into sub-lineage / clonal complexes or families on the basis of different spoligotyping profiles [18] or on specific genomic signatures [19 , 20] . The more distantly related M . canettii is outside the clonal population of the MTBC and it is considered the most ancestral progenitor from which the above mentioned MTBC members emerged [21] . According to the IS6110 content , MTBC members are classified into high ( >6 ) and low ( <7 ) IS6110 copy number strains [22] . It is not clear whether differences in IS6110 content account for biological phenotypic consequences in bacterial physiology and pathogenesis . However , it is well known that the M . tuberculosis Beijing/W lineage ( L2 ) , with a remarkably high content of IS6110 [23] , is associated with higher virulence and massive spread of drug resistant strains , being possibly better adapted to high density populations [19] . Beijing/W lineage was originally described in the 1990’s as a predominant genotype found in countries of East Asia designated Beijing-family [24] and after observing an interstate spread from New York of the multidrug-resistant M . tuberculosis clone family named W [25] . During its transposition , the IS6110 promotes a number of important genetic modifications in MTBC strains . This confers plasticity to the MTBC genomes and could have significant biological implications . As for other IS , insertion of IS6110 into a coding region frequently renders the gene inactive , the basis of transposon mutagenesis , or the recombination between two IS6110 copies can lead to either inversion or deletion of the chromosomal fragment between them [26–28] . Furthermore , it has been demonstrated that IS6110 acts as a mobile promoter and this phenotype is selectively activated during in vitro infection of monocytes/macrophages [29 , 30] . This latter finding has extraordinary consequences in the host-pathogen evolution of the MTBC , as will be discussed below . It has been suggested that a moderate number of IS6110 might translate into strain-specific phenotypes that provide selective advantages during the course of the infection [26] . Conversely , it has been demonstrated that excessive accumulation of IS6110 copies could result in inactivation or deletion of essential genetic regions , being deleterious to the bacterium [31] . This later finding implies that transposition rates of IS6110 should be finely regulated and maintained at relatively low levels ( 7 . 9x10-5 events per site per generation ) [32] . Considering the clonal evolution of the MTBC , the rate of point mutations is estimated at 10−9 events per site per generation and comparatively the mutation rate of IS6110 is orders of magnitude higher . This reinforces the notion that IS6110 transposition is under positive selection when infecting or causing disease to the host [32] and accordingly it constitutes an excellent TB epidemiological marker . At the genetic level the IS6110 belongs to the IS3 family and it is annotated as two open reading frames: ORF1 ( 327 bp ) and ORF2 ( 987 bp ) which overlap in 52 bp and are flanked by 28 bp imperfect Inverted Repeats ( IR ) . The 3–4 bp boundaries of IS6110 are duplicated upon transposition [10] . Despite the massive use of WGS , the repetitive nature of IS6110 makes difficult to finely map their localizations in the MTBC chromosomes . Although some studies have attempted to localize IS6110 in M . tuberculosis genomes [27 , 33–35] , little is known about its involvement in other MTBC members including M . canettii , M . africanum and ecotypes responsible for animal TB ( i . e . M . bovis and M . caprae ) which possess a zoonotic risk . Similar to other members of the IS3 family , it is thought that transposition of IS6110 occurs when its two constituent ORFs are translationally fused producing an active transposase [36] . Former studies using M . smegmatis ( a non-pathogen fast growing mycobacteria ) as surrogate host to demonstrate that IS6110 transposition occurs more readily when this element is located in transcriptionally active locations and also upon exposure to a microaerobic environment [37 , 38] . However , there is a definite lack of evidence about the precise mechanisms leading to the production of an active IS6110 transposase and the physiological conditions that promote transposition in the MTBC . In the present study , we analyse biological data from more than two-thousand clinical isolates covering the MTBC to dissect the molecular mechanism of IS6110 transposition and its dynamic distribution between the different MTBC lineages . We discuss its biological significance in the tubercle bacillus and also in the clinical presentations of TB .
Different members from the MTBC have evolved by accumulation of genomic deletions and specific polymorphisms [39 , 40] . Accordingly , the MTBC phylogeny is the result of a genomic decay after an evolutionary bottleneck which led to speciation [41] . Upon examination of fully sequenced and assembled MTBC genomes , we observe that the M . bovis AF2122/97 reference strain contains a single IS6110 while M . africanum and M . tuberculosis have higher copy numbers of this element ( an average of 6 and 17 respectively ) ( Fig 1A ) . When interrogating M . canettii , considered as the most ancestral linage known from which all MTBC members emerged , we only found potentially functional IS6110 sequences in subgroups STB-A , -D , and–L . Those subgroups that show greater phylogenetic distances ( STB–J and–K ) have no traces of IS6110 [21] . Only STB-L carries identical IS6110 sequences to the MTBC ( S1 Fig ) . Supporting this finding , another study demonstrated the presence of IS6110 in evolutionarily closer M . canettii isolates [42] . The IS6110 content in MTBC genomes suggested to us that the copy number of this transposon might be lineage-specific . However , since a limited number of genomes have been fully sequenced and assembled , we decided to investigate this hypothesis in a representative collection of TB causing strains . We systematically genotyped clinical isolates from TB patients during the last 25 years and subdivided them in families according to spoligotyping profiles . A total of 2 , 236 clinical samples from our data base covering the MTBC were analysed by standardised Restriction Fragment Length Polymorphism ( RFLP ) of IS6110 . Results confirmed that the average IS6110 content is lineage-specific , ranging from low copy number ( M . bovis , L1 , the L4 sub-lineage X and M . africanum L5 and L6 ) to high copy number in modern M . tuberculosis lineages ( LAM , CAS and Beijing from L4 , L3 and L2 respectively ) ( Fig 1B ) . Among the M . tuberculosis human-adapted species , these high copy number families are globally distributed and accordingly they could be considered as generalists capable of infecting and causing disease in many different human populations [43] . Of these , the LAM family is distributed in America , Africa , Europe , Oceania and East Asia [20] , the CAS family affects the Indian continent and East Africa [44] and the Beijing family is amply distributed in East Asia and East Europe [19] . Once we established that MTBC phylogenetic clades have different IS6110 content , we interrogated the molecular mechanisms underlying this observation . First , we selected representative isolates of the MTBC ( BCG , M . bovis , M . caprae , M . africanum and M . tuberculosis ) . Then we analysed their global IS6110 mRNA levels and found that animal-adapted and M . africanum species have lower levels of IS6110 mRNA than M . tuberculosis L2 and L4 ( Fig 2A ) . We also found that ORF1 and ORF2 were similarly expressed ( S2 Fig ) , with is compatible with the presence of a single RNA molecule with two out-of-phase reading frames translated into a single ORF by way of a translational frameshift . This result resembles other IS3 family members [36 , 45] . Altogether these results indicated a proportional relationship between the copy number content and IS6110 mRNA expression , which led us to quantitate the “normalised mRNA expression” by calculating expression ratios relative to the IS6110 content in every MTBC strain ( IS6110 mRNA / IS6110 copy number ) . First , the IS6110 copy number in the above-mentioned strains was checked by RFLP ( Fig 2B ) and our previous results were reanalysed considering this IS6110 content . Our results demonstrated that expression per IS6110 copy is lower in animal adapted strains and in M . africanum than in M . tuberculosis ( Fig 2C ) . IS6110 distribution in representative MTBC members indicates a proportional relationship between the copy number content and the normalised expression of this element and this relation follows an exponential trend ( r2 = 0 . 80 ) ( Fig 2D ) . To gain further insight into the transposition dynamics of IS6110 , we analysed M . bovis and M . tuberculosis isolates showing an uncommon copy number of this element . We selected M . tuberculosis clinical isolates from X and T families of lineage 4 containing 1 , 2 , 3 and 11 IS6110 copies . Beijing strains from lineage 2 known to possess the highest copy number of IS6110 were also included [34] . We also selected M . bovis strains causative of human TB with 3 , 4 and 5 copies of IS6110 [46] , which represent an unusually high copy number for the animal adapted lineage ( S3 Fig ) . Results demonstrated that M . tuberculosis strains having a single IS6110 expresses this mRNA similarly to M . bovis BCG . Accumulation of additional copies of IS6110 resulted in higher mRNA expression of the transposase to reach expression levels comparable to M . tuberculosis H37Rv ( S3 Fig ) . On the other hand , accumulation of more than one IS6110 in M . bovis resulted in exacerbated expression of its coding gene . This expression was 5-fold higher than that observed in M . tuberculosis H37Rv even if the latter contains 15 IS6110 compared to the 3–5 copies in these atypical M . bovis isolates ( S3 Fig ) . The use of IS6110 as molecular epidemiological marker is useful due to its relatively low frequency of transposition which allows investigators to distinguish between currently circulating strains ( transmission ) and older episodes of TB ( reactivation ) in individual patients . Since transcription per IS6110 copy is within the range of other genes producing physiological phenotypes in M . tuberculosis ( S4 Fig ) , it is predictable that low transposition rates must be subjected to some type of post-transcriptional regulation . Our results show that both ORFs are similarly transcribed ( S2 Fig ) . The transposase is composed of a DNA binding domain ( N-term ) and a catalytic integrase domain ( C-term ) which contains the residues forming the putative active site ( D310 , D350 , E379 ) ( S5A and S5B Fig ) . By analysing the IS6110 genetic sequence we found that the intergenic region of the constituent ORFs contained a putative translational frameshift that could produce an active transposase as described for other members of the IS3 family [47] . Since the precise translational frameshift has not been documented for IS6110 , we searched for heptanucleotide U/A-rich sequences defined by the motif XXX-YYY-Z [47] in the overlapping region of ORF1 and ORF2 since these sequences are prone to ribosomal slippage . A auUUU-AAA-Gac sequence was located in the appropriate location ( Fig 3A and 3B ) . This sequence codes for Ile91 ( AUU ) , Leu92 ( UUA ) and Lys93 ( AAG ) codons of ORF1 and upon translational slippage it codes for Lys1 ( AAA ) and Asp2 ( GAC ) of ORF2 ( Fig 3B ) . Additionally , we found a tight RNA secondary structure known as pseudoknot immediately downstream of the slippage sequence ( Fig 3A and 3B ) . Pseudoknots are very complex and stable RNA structures with diverse biological functions , which include self-catalytic activity or the induction of ribosomal frameshifting [48] To validate these mechanisms , we constructed three genetic variants of IS6110 fused to a 3xFLAG epitope in order to detect the functional transposase by western blot . These variants were: the wild type ( WT ) sequence containing the UUU-AAA-G slippage region ( IS6110-WT-FLAG ) , a construct with an A insertion in the slippage sequence ( UUU-AAAA-G ) to produce a complete transposase in the absence of ribosomal frameshift ( IS6110-FS-FLAG ) and a third construct including the previous A insertion and several mutations to disrupt pseudoknot formation without affecting the coding sequence ( IS6110-FS+PK-FLAG ) ( S6 Fig ) . These variants were introduced in Escherichia coli to detect IS6110 protein expression . We barely detected the IS6110 using the WT sequence . In contrast , by introducing an A insertion a transcriptionally active transposase was detected as a discrete band ( Fig 3C ) . Further , introduction of mutations in the pseudoknot sequence resulted in even more increased translation of the functional transposase ( Fig 3C ) . Based on these findings , we infer that post-transcriptional regulation of IS6110 occurs by the combined action of two genetic mechanisms inherent to its coding sequence . The presence of a slippage sequence and a downstream pseudoknot would favour ribosome stalling at the appropriate location and the subsequent -1 translational frameshift ( Fig 3D ) . In addition , the 5’ end of the IS6110 transcript is predicted to form a hairpin structure which occludes the ribosome binding sequence ( S5C Fig ) and possibly interferes with translation . Our next step was to demonstrate that the IS6110 transposase produced after translational frameshift is biologically active when mycobacteria are grown under laboratory conditions . To avoid homologous recombination or other potential confusing effects that could be produced from orthologue IS6110 sequences , we decided to study transposition in M . smegmatis mc2155 , a fast growing , non pathogen mycobacterial surrogate host in which the IS6110 is not present [49] . It is important to remark that IS6110 is exclusive of the MTBC and albeit a related IS6110 ( 67% aminoacid identity ) has been found in the MKD8 strain of M . smegmatis , this copy is non functional [49] . We cloned in a mycobacterial integrative plasmid either the wild type ( pIS6110-WT ) or a variant carrying the A insertion in the slippage region ( pIS6110-FS ) expected to be transcriptionally active ( Fig 4A ) . Active transposases recognize the ends flanking the transposon , which in the case of IS6110 are the IR , and catalyse “copy-out-paste-in” transposition [50 , 51] . Accordingly , we constructed a third plasmid to act as a transposition reporter . A kanamycin resistance cassette flanked by the IR regions of the IS6110 was cloned in a conditionally replicating plasmid with thermosensitive origin and sacB counter-selectable marker and named pIR-Km ( Fig 4A ) . Plasmid pIR-Km was introduced in M . smegmatis mc2155 carrying either pIS6110-WT or pIS6110-FS and maintained at 30°C . We confirmed that both strains grew at comparable rates ( Fig 4B ) . To measure transposition frequency , aliquots were plated on 7H10 medium to enumerate total CFU or on 7H10 medium containing kanamycin and sucrose and incubated at 42°C . Under these latter conditions pIR-Km does not replicate and consequently kanamycin and sucrose resistant colonies arise from transposition of the IR-Km-IR construct into the chromosome ( Fig 4C ) . Our results revealed that the transcriptionally active transposase in pIS6110-FS exhibited 20-fold higher transposition rates than the wild type IS6110 ( Fig 4D ) . Differences in transposition frequencies between both transposase variants were notably significant during the exponential and early stationary growth with a higher proportion of colonies resulting from transposition in this latter phase ( Fig 4D ) . This result opens the door to hypothesize whether transposition in vitro is phase-dependent or conversely it results from accumulation of transposition events during mycobacterial growth . In order to confirm that transposition occurs randomly across M . smegmatis chromosome , we used a similar RFLP-IS6110 analysis to that used in MTBC clinical isolates . Several kanamycin and sucrose resistant colonies were chosen at random and their restriction fragments were hybridised with a probe against the IR-Km-IR fragment . The RFLP showed loss of signal from the pIR-Km indicative of the appropriate plasmid loss . A polymorphic RFLP pattern was observed , indicative that IS6110 transposition occurred at random locations in the chromosome ( Fig 4E ) . Once studied the IS6110 distribution in more than 2 . 000 strains covering various MTBC lineages and after we have experimentally demonstrated that low transposition frequencies of IS6110 are due to a post-transcriptional mechanism in M . smegmatis , we pursued our investigations in analysing potential biological conditions” promoting IS6110 transposition in slow growing MTBC . We chose as reference strains M . tuberculosis H37Rv ( 15 IS6110 copies ) belonging to L4 and M . bovis AF2122/97 ( 1 IS6110 copy ) as representative of the animal-adapted L8 . Each strain was transformed with the IS6110 transposition reporter pIR-Km plasmid to measure transposition during growth on laboratory media or in a mouse infection model of TB . Aliquots of the culture or from organ homogenates at different time points were plated on conventional 7H10 medium to enumerate total CFU or on 7H10 supplemented with sucrose and kanamycin to recover colonies resulting from IS6110 transposition ( Fig 5A ) . For in vitro transposition experiments , we first confirmed that both strains carrying pIR-Km grew at comparable rates at 30°C , a permissive temperature for this plasmid ( S7 Fig ) . Then , we selected 1 , 4 and 12 months’ time points as representative for exponential , stationary and starvation periods in in vitro cultures according to growth curves at 30°C ( S7 Fig ) . Our results for M . tuberculosis H37Rv indicate that transposition rates were 10- and 60-fold higher in stationary and starvation periods respectively relative to exponential growth ( Fig 5B ) . When examining M . bovis AF2122/97 , similar transposition frequencies were observed under exponential growth with respect to M . tuberculosis . However , although transposition in M . bovis strain was 5-fold higher in stationary and starvation periods , this was noticeably lower than that observed for M . tuberculosis ( Fig 5B ) . We also quantitated M . tuberculosis IS6110 expression during laboratory growth and we found higher mRNA transcription in the starvation period ( Fig 5C ) . This result indicates that even if high mRNA expression does not necessarily imply high translation rates , there is a remarkable correlation between the transposase expression and the transposition frequencies . These results indicate that transposition increases starting from the stationary growth and similarly to that observed in M . smegmatis we cannot rule out the possibility that transposition events accumulate during growth in vitro . Further , the comparison of both strains allows us to establish lineage-defined transposition frequency . These results are remarkably comparable with our previous findings indicating that normalised expression of IS6110 is lineage-specific , being 35-fold higher in M . tuberculosis than in M . bovis ( Fig 2C ) . Our transposition experiments in mice correlate with our findings during laboratory growth with an interesting exception: transposition rates for M . bovis AF2122/97 did not differ in the mouse model relative to exponential growth in laboratory medium , both being in the order 10−6 ( Fig 5B and 5D ) . This result agrees with current biological and clinical data indicating that the single IS6110 copy of M . bovis strains has been maintained during evolution with rare cases of transposition in this lineage . Further supporting these observations , our previous results demonstrate marginal levels of normalised IS6110 mRNA expression in M . bovis isolates ( Fig 2C ) . Conversely , for M . tuberculosis H37Rv , we observed a 10-fold increase in the transposition rates during mouse infection relative to exponential growth ( Fig 5D ) . This increase was observed not only in the lung -the primary site of infection- , but also in the spleen of infected animals ( Fig 5D ) . Finally , we also demonstrate that normalised expression of IS6110 increases upon infection of murine alveolar macrophages ( S8 Fig ) and this result supports our transposition experiments in the mouse model of TB .
The IS6110 belongs to the IS3 family whose more representative member is IS911 . In this work , we first demonstrate that similarly to IS911 , the IS6110 is subjected to -1 ribosomal frameshifting [36 , 52] , contains a RNA pseudoknot [47] and its transposition occurs by a copy-out-paste-in mechanism [53–55] . Then , we go a step ahead to understand the biological role of IS6110 transposition in the MTBC biology . The reliability of IS6110 as a clinical epidemiological marker is unquestionable . In 1993 DNA fingerprinting using IS6110 was standardized and became the gold standard for epidemiological studies of TB in the last 25 years . Since then tens of thousands MTBC strains have been studied by IS6110 RFLP [11] . IS6110 RFLP requires extraction of DNA from pure cultures/sputum samples which is then used in a Southern-Blot hybridization . Consequently , this is a laborious and time-consuming technique that in the last 5–10 years is being replaced by PCR methods based on amplification of MIRU [13 , 14] or even more recently , by WGS [15 , 16] . However , MIRU does not allow to know the number and position of IS6110 insertions in the MTBC strains and most WGS studies fail to determine the number and localizing repeated sequences in the genome , such as the insertion sites of IS6110 . Hopefully , new PacBio and Oxford Nanopore sequencing technologies will improve the resolution of WGS . After an in depth systematic analysis of 2 , 236 clinical isolates typed by IS6110-RFLP our findings show the different distribution of IS6110 between the various MTBC lineages . Our results reveal that modern lineages of the MTBC ( L2 , L3 and L4 ) have accumulated higher IS6110 copy number than ancient lineages ( L1 , L5 and L6 ) ( Fig 1 ) . Since modern lineages are widely distributed and consequently they are more successfully adapted to high density populations they have been referred to as generalists [43] . Conversely , lineages geographically restricted to certain regions are considered specialists [43] . Given the role of mobile genetic elements in providing chromosomal variability , it is tempting to think that the higher IS6110 number in generalists might represent a strategy of the MTBC to adapt to different populations . A potential limitation of our study is the predominance of strains corresponding to L4 , more frequent in Europe , Africa and America . Similar studies in other places of the world using larger number of the remaining MTBC lineages would be important to confirm the results of the present study . As with any mutational event , transposition could be deleterious , neutral or advantageous and these events might impact on the pathogen fitness . Accordingly , another limitation of our study is inherent to the use of clinical isolates since only advantageous phenotypes are selected and we might be observing only those IS6110 transposition events providing benefits in terms of enhanced transmissibility or pathogenicity . In this context , those transposition events observed during our mouse infection experiments might be the result of enhanced fitness in vivo . Accordingly , serially infecting batch of mice with those bacteria resulting from transposition events would surely enrich the bacterial population for IS6110 locations conferring selective phenotypes . The transposition dynamics of IS6110 imply an exponential relationship between the copy number content and mRNA expression per IS6110 copy , ( Fig 2D ) . Accordingly , the increased expression per IS6110 copy observed in high copy number strains ( Fig 2C ) provide more messenger molecules and this probably results in increased probability of ribosomal frameshift and translation of a functional transposase , leading to accumulation of this mobile element across the chromosome . On the other hand , even if transposition generally occurs at random across the MTBC chromosomes , it remains to be answered why some genomic regions such as a 600Kb close to the origin of replication lack IS6110 , pointing to the detrimental effect of insertions in this location [56] , while other regions such as plcD are prone to accumulate IS6110 insertions and result in IS6110-mediated deletions such as RvD2 [57] . Since only subgroups STB-A , -D and–L ( but not–J and–K ) of the MTBC progenitor M . canettii contain IS6110 , we can hypothesize about the origin of this transposon prior to or during the evolution of the MTBC progenitor . Recent evidence has shown that M . canettii strains , in contrast to the MTBC , are not clonal and could exchange DNA [58] . M . canettii STB-D , -A and -L share adjacent C-term and N-term truncated regions of IS6110 separated by 1 , 2 Kb ( S1 Fig ) . DNA binding and integrase domains are located in the opposite ends of the IS6110 coding sequence ( S5 Fig ) and we can hypothesize about the origin of IS6110 by a recombination between these adjacent regions ( S1 Fig ) . Reinforcing this hypothesis , similar recombination events leading to surface remodelling have been recently documented in M . canettii [59] . The low transposition frequencies observed during the natural infection support the remarkable value of IS6110 as a molecular epidemiology marker . Transposition is probably maintained at low levels by the action of several mechanisms . Here , we found two regulatory pathways involving translational slippage or the formation of secondary RNA structures , such as pseudoknot , but we cannot discard other regulatory mechanisms . The putative ribosome binding sequence of the IS6110 is occluded by a stem loop ( S5C Fig ) and formation of this structure is expected to have some impact over translation of the transposase . An important question is whether the mRNA initiates within the own IS6110 or whether it initiates upstream from an adjacent promoter in the MTBC chromosome . In this latter case , IS6110 transcription might very well depend on the precise location of this transposon within the host chromosome . This assumption would justify why high copy number strains are associated with higher expression rates per IS6110 copy and vice versa ( Figs 1 and 2 ) . The exploration of M . bovis RNA-seq data indicates negligible transcription of the unique IS6110 copy in this species with no presence of neighbour transcription start sites [60 , 61] . The position of the IS6110 copy in M . bovis is shared by most members of the MTBC and it is located within the Direct Repeats ( DR ) region of the CRISPR-Cas locus . Since this region is subjected to a complex post-transcriptional regulation involving RNA processing steps , this might explain the low expression of this IS6110 copy . This is congruent with our expression data ( Fig 2 ) and reinforces the hypothesis that lower transcription is likely associated with decreased probability of translational frameshift and consequently with low transposition rates in M . bovis ( Fig 5B and 5C ) . Our results with M . tuberculosis H37Rv indicate that transposition of IS6110 is not limited to the natural infection and also occurs during growth in vitro ( Fig 5B ) . Supporting this finding , the examination of H37Rv reference strains across multiple laboratories worldwide indicate different transposition events of IS6110 [62] . Other example of changes in the IS6110 pattern is the presence of 19 and 15 IS6110 copies in H37Ra and H37Rv respectively . Since these strains arose during laboratory subculture of the original H37R strain in the 1930’s , differential IS6110 are likely the result of separate and individual transposition events during in vitro passage . It is interesting to observe higher transposition frequencies in long-term cultures ( Fig 5B ) , but it remains to be answered whether this is the result of cumulative transposition events during the growth curve or transposition increases as a consequence of starvation signals . Examination of the IS6110 mRNA expression indicates a strong upregulation during starvation ( Fig 5C ) which could indicate the presence of yet unknown stimulating signals triggering IS6110 mobilization . To rule out that differences in the mutation rate of starved bacteria influence the transposition frequency , we should have performed a fluctuation test or similar . Nevertheless , a previous work demonstrated similar mutation rates during latency and during active disease or in a logarithmic growing culture [63] , which agrees with the low mutation rates observed for MTBC chromosomes [40] . Several lines of evidence support a possible role for IS6110 during adaptation to different hosts . Diverse epidemiological studies have demonstrated that IS6110 RFLP presents distinct profiles in M . tuberculosis transmission clusters [28 , 64–67] . Since these studies involve isolates from different patients isolated during prolonged periods of time , it is plausible to think that IS6110-mediated adaptive mechanisms might be involved in the patient-to-patient transmission of M . tuberculosis . Supporting this idea , our results indicate higher transposition rates during infection ( Fig 5D ) . Another evidence comes from the observation that M . bovis are able to infect humans but rarely transmits between this population . However , a specific M . bovis strain was responsible of a deathly human MDR TB outbreak [68 , 69] and this phenotype is largely related to transposition of a second IS6110 [70] . This second IS6110 is located upstream the phoPR virulence genes and acts as a mobile exogenous promoter increasing virulence phenotypes in M . bovis [71] . A very recent study demonstrates that IS6110-mediated deletions in the ppe38-ppe71 genes are widespread in “modern” Beijing strains . This genotype result in lack of secretion of PE_PGRS and PPE-MPTR proteins and lead to increased virulence . Accordingly , this specific deletion mediated by IS6110 may have contributed to the success and global distribution of this Beijing sublineage [72] . A previous study confirmed that Beijing ( L2 ) strains have higher mutation rates than L4 strains , which result in increased acquisition of drug resistance in the former [73] . It is at present unknown whether varying mutational rates across MTBC lineages can impact on transposition rates . Further work is needed to confirm whether higher mutation rates provide the driving force for increased transposition or viceversa . In conclusion , our findings indicate that the lineage-specific number of IS6110 results from differential transcriptional and posttranscriptional mechanisms inherent to the MTBC chromosomes in order to control the copy number of this transposon . Our results show that IS6110 transposition increases during mouse infection and during growth in starvation suggesting the potential role of IS6110 transposition during the MTBC adaptation to the host . In the future , many MTBC strains are being massively sequenced , this opportunity should be taken into consideration to locate the IS6110 insertion sites , which would lead us to a better understanding of its biological role in TB pathogenesis and life cycle .
All procedures were carried out under Project Licence PI14/14 approved by the Ethic Committee for Animal Experiments from the University of Zaragoza . The care and use of animals were performed accordingly with the Spanish Policy for Animal Protection RD53/2013 , which meets the European Union Directive 2010/63 on the protection of animals used for experimental and other scientific purposes . Strains from the MTBC and M . smegmatis mc2155 were routinely 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 MTBC strains different from M . tuberculosis , 40 mM sodium pyruvate was added to the medium . E . coli DH5α used for cloning procedures was grown at 37°C in LB broth or on LB agar plates . Ampicillin ( 100 μg/ml ) , kanamycin ( 20 μg/ml ) and hygromycin ( 20 μg/ml ) were used as appropriate . For transposition experiments , cultures were incubated at 30°C or 37°C and sucrose was added to 7H10 plates at a final concentration of 2% for M . tuberculosis and M . bovis and 10% for M . smegmatis if appropriate . All chemicals were purchased from Sigma-Aldrich , unless otherwise stated . IS6110 containing IR was PCR amplified from M . tuberculosis H37Rv DNA using primers NheI-IS6110-fw ( GCTAGCTGAACCGCCCCGGCATG ) and NheI-IS6110-rv ( GCTAGCTGAACCGCCCCGGTGAGT ) . The PCR product was digested with NheI and cloned into NheI cut pMV361 to yield pIS6110-WT . To construct IS6110 carrying the -1 translational frameshift , a two-step overlapping PCR strategy was used . ORF1 was amplified with primers NheI-IS6110-fw and IS6110-FS-rv ( CGACGCGGTCTTTTAAAATCGCGT ) and ORF2 with NheI-IS6110-rv and IS6110-FS-fw ( ACGCGATTTTAAAAGACCGCGTCG ) . Both PCR products overlap in 24 nucleotides and carry the extra nucleotide required for the translational frameshifting ( underlined nucleotides ) . These products were used as self-templates in a PCR reaction that was amplified using the flanking primers NheI-IS6110-fw and NheI-IS6110-rv , digested with NheI and introduced in the NheI site of pMV361 to yield pIS6110-FS . The resulting constructs were confirmed by Sanger sequencing , introduced in M . smegmatis mc2155 by electroporation and colonies carrying a chromosome-integrated vector were checked by PCR . To construct the transposition reporter , a kanamycin resistance cassette from pKD4 was amplified with primers BamHI-IR1-P1 ( cgcggatccgcgTGAACCGCCCCGGCATGTCCGGAGACTCgtgtaggctggagctgcttc ) and BamHI-IR2-P2 ( cgcggatccgcgTGAACCGCCCCGGTGAGTCCGGAGACTCcatatgaatatcctccttag ) , which include the IR from IS6110 indicated in capital letters . The PCR product was confirmed by Sanger sequencing , digested with BamHI and introduced into pPR27 cut with the same enzyme . The final plasmid was named pIR-Km and was introduced into M . smegmatis mc2155 , M . tuberculosis H37Rv and M . bovis AF2122/97 by electroporation . Transformants were selected with kanamycin at 30°C and cultured at this permissive temperature to allow plasmid replication . Tagged variants of IS6110 were obtained by gene synthesis ( Genescript ) as follows: a 3xFLAG epitope ( DYKDHDGDYKDHDIDYKDDDDK ) with codons optimized for M . tuberculosis was placed in frame immediately after the IS6110 coding sequence . To construct the transcriptionally active IS6110-FS , an A insertion was placed after the Leu92 codon . To construct the IS6110-FS+PK variant carrying mutations disrupting the pseudoknot , the original sequence ( cgcggccgagctcgaccggccagcacgctaattacccggttcatcgccgatcatcagggccaccgcgagggccccgatggtttgcggtggggtgtcgag ) was replaced by ( cgggggcgtgcacgtcccgcgagtacgctaattacgcggtttattgccgaccaccaagggcaccgcgaggggcccgacggcttaaggtggggagtggaa ) to maintain the aminoacid sequence . The final constructs were flanked by XmnI and EcoRI sites at the 5’ and 3’ ends respectively and cloned between these sites in pMV361 . These plasmids were introduced in E . coli DH5α for subsequent experiments . DNA from MTBC strains or M . smegmatis mc2155 were extracted by the CTAB/NaCl procedure . DNA integrity was confirmed by agarose gel electrophoresis . For standard IS6110 RFLP , DNA was digested with PvuII and separated overnight in 0 . 8% agarose gels . DNA was transferred from the gel to a positively charged nylon membrane ( Hybond N+ , Amersham ) by using a vacuum transfer device . The membrane was hybridized with a probe amplified with primers INS-1 ( CGTGAGGGCATCGAGGTGGC ) and INS-2 ( GCGTAGGCGTCGGTGACAAA ) . After hybridization with labeled DNA probes , the bound probes were detected with an enhanced chemiluminescence direct nucleic acid detection system ( Amersham ) according to the manufacturer's recommendations . For RFLP of colonies resulting from transposition of the IR-Km-IR cassette from the pIR-Km transposition reporter , these modifications were introduced in the RFLP protocol: DNA was digested with PstI and hybridized with a probe amplified with P1 ( GTGTAGGCTGGAGCTGCTTC ) and Km-pKD4-out1 ( CCACGATAGCCGCGCTGCCTCG ) primers using pIR-Km as template . Genome sequences were retrieved from NCBI GenBank ( http://www . ncbi . nlm . nih . gov/ ) . The copy number content and genomic polymorphisms in IS6110 were calculated using nucleotide BLAST ( https://blast . ncbi . nlm . nih . gov/Blast . cgi ) . Secondary RNA structures were predicted using the RNA fold WebServer ( http://rna . tbi . univie . ac . at/cgi-bin/RNAWebSuite/RNAfold . cgi ) . Pseudoknot structures and their estimated free energy were located and computed using DotKnot ( http://dotknot . csse . uwa . edu . au/ ) . Mycobacterial cultures 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 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 . One microgram of MTBC RNA was converted to cDNA using SuperScript III Reverse Transcriptase ( Invitrogen ) according to the manufacturer’s recommendations . The 10 μl PCR reaction consisted of 1X 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 StepOnePlusTM Sequence Detection System ( Applied Biosystems ) according to the manufacturer’s instructions . Melting curves were constructed to ensure that only one amplification product was obtained . Normalization was obtained to the number of sigA molecules in each sample . To obtain normalized expression values per IS6110 copy number , data normalized with respect to sigA were subsequently divided by the total number of IS6110 for every strain used . All qRT-PCR primers were designed using Primer Express software ( Applied Biosystems ) and sequences are as follows: RT-IS6110-1-fw ( TCAGCACGATTCGGAGTGG ) , RT-IS6110-1-rv ( CCAAGTAGACGGGCGACCT ) , RT-IS6110-2-fw ( CGCAAAGTGTGGCTAACCCT ) , RT-IS6110-2-rv ( GCATCTGGCCACCTCGAT ) , RT-sigA-fw ( CCGATGACGACGAGGAGATC ) and sigA-rv ( CGGAGGCCTTGTCCTTTTC ) . The pelleted fraction of bacterial cultures was resuspended in PBS containing 1% triton X100 and a cocktail of protease inhibitors ( Roche ) and disrupted using a Fast-Prep during three pulses , 1 minute each , cooling on ice between pulses . Samples were then centrifuged and the upper phase containing whole-cell lysate was quantitated using the RC DC protein assay ( BioRad ) . 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 . Anti-FLAG ( M2 clone , Sigma ) antibody was used at 1:10 , 000 dilution and horseradish peroxidase ( HRP ) conjugated IgG secondary antibody ( Sigma-Aldrich ) was used at a 1:20 , 000 dilution . Signals were detected using chemiluminescent substrates ( GE Healthcare ) . All mice were kept under controlled conditions and observed for any sign of disease . Experimental work was conducted in agreement with European and national directives for protection of experimental animals and with approval from the competent local ethics committees ( approved protocol PI14/14 ) . We performed a single biological replicate using 3 mice per group . Female C57BL/6 mice ( Janvier Biolabs ) were intranasally inoculated with 104 CFU of M . tuberculosis H37Rv or M . bovis AF2122/97 ( both carrying the transposition reporter ) . Infection was left to progress for 4 weeks and bacterial burden was determined by plating homogenized lungs and spleen on solid medium . Transposition events were enumerated as described in the “transposition experiments” section . Liquid cultures grown at 30°C or organ homogenates from infected mice were serially diluted and plated on 7H10 medium without sucrose at 30°C to enumerate viable bacteria . In parallel , appropriate dilutions were plated on 7H10 medium containing kanamycin and sucrose at 37°C to obtain colonies resulting from transposition of the IR-Km-IR cassette in the mycobacterial chromosome . The transposition frequency was calculated as the number of bacteria resulting from a transposition events divided by the number of total viable bacteria . | Since the pioneering discovery of transposition by Barbara McClintock in eukaryotes and later in prokaryotes by Robert W . Hedges and Alan E . Jacob , it has become clear the key role of mobile genetics elements in chromosome remodelling , microbial evolution and host adaptation . The insertion sequence IS6110 is widely recognized for its utility in TB diagnosis and epidemiology because it is only present in the M . tuberculosis Complex ( MTBC ) and its transposition provides an excellent chromosomal polymorphic variability allowing the study of recent TB transmission . This inherent feature of IS6110 leads us to hypothesize that IS6110 plays a crucial role during the TB infectious cycle . However , the biological significance of IS6110 has been hindered by its almost exclusive use as an epidemiological marker . Here , we study the regulatory mechanisms and the distribution of IS6110 in the different MTBC lineages . We discuss the potential biological implications of IS6110 , that is much more than an excellent TB epidemiological tool . Since IS6110 could play an important role in the adaptation of MTBC to the host , this study opens new avenues to decipher the biological roles of IS6110 in TB pathogenesis . | [
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"structur... | 2018 | New insights into the transposition mechanisms of IS6110 and its dynamic distribution between Mycobacterium tuberculosis Complex lineages |
Circadian-regulated gene expression is predominantly controlled by a transcriptional negative feedback loop , and it is evident that chromatin modifications and chromatin remodeling are integral to this process in eukaryotes . We previously determined that multiple ATP–dependent chromatin-remodeling enzymes function at frequency ( frq ) . In this report , we demonstrate that the Neurospora homologue of chd1 is required for normal remodeling of chromatin at frq and is required for normal frq expression and sustained rhythmicity . Surprisingly , our studies of CHD1 also revealed that DNA sequences within the frq promoter are methylated , and deletion of chd1 results in expansion of this methylated domain . DNA methylation of the frq locus is altered in strains bearing mutations in a variety of circadian clock genes , including frq , frh , wc-1 , and the gene encoding the frq antisense transcript ( qrf ) . Furthermore , frq methylation depends on the DNA methyltransferase , DIM-2 . Phenotypic characterization of Δdim-2 strains revealed an approximate WT period length and a phase advance of approximately 2 hours , indicating that methylation plays only an ancillary role in clock-regulated gene expression . This suggests that DNA methylation , like the antisense transcript , is necessary to establish proper clock phasing but does not control overt rhythmicity . These data demonstrate that the epigenetic state of clock genes is dependent on normal regulation of clock components .
Virtually all eukaryotes have the ability to control temporal gene expression as a function of time-of-day . The molecular mechanism of circadian rhythms is conserved among animals and fungi and involves a transcriptional negative feedback loop [1]–[5] . In Neurospora , the positive elements are the White Collar ( WC ) proteins , which drive rhythmic expression of the negative element frq . WC-1 and WC-2 are GATA-type zinc finger transcription factors that heterodimerize via PAS ( Per-Arnt-Sim ) domains to form the White Collar Complex ( WCC ) [6]–[9] . Rhythmic and light-activated WCC-driven expression of frq is regulated by two cis-acting sequences in the promoter , the Clock Box ( C-box ) and the proximal light regulated element ( PLRE ) [10] , [11] . In the negative arm of the loop , translated FRQ protein associates with the FRQ-interacting RNA helicase , FRH [12] , and undergoes regulated nuclear entry [13] modulated in part by the phosphorylation state of FRQ [14] . FRQ-FRH is believed to inhibit frq expression by promoting inactivation of the WCC through phosphorylation [15] , [16] and via direct binding to the WCC [17] . In addition , circadian regulated frq expression is partially controlled by a chromatin-remodeling event that promotes accessibility to the C-box promoter element [18] . Once FRQ is synthesized , it undergoes progressive phosphorylation [19]–[21] while feeding back to inhibit its expression [22] and it is eventually turned over in a reaction that involves an F-box/WD40 protein FWD [23] and the SKP-cullin complex [24] , thus releasing its negative effects on transcription . A plethora of data indicates FRQ phosphorylation and turnover establish period but less is known about the molecular mechanisms underlying phase determination . Rhythmic changes in chromatin structure have been correlated with changes in transcriptional activity of clock-associated loci . In mammalian cells , rhythmic modifications include acetylation of histone H3 at Per1 , Per2 and Cry , and acetylation of H4 at Per1 , with the peak in acetylation occurring during the transcriptional activation phase [25] , [26] . In contrast , during the repressive phase , histone H3 associated with Per1 and Per2 becomes methylated at K27 [27] . There are also rhythmic changes at the clock-controlled gene ( ccg ) encoding the D-element binding protein ( Dbp ) , including H3K9me2 and HP1 binding , suggesting that at least one ccg is regulated by facultative heterochromatin formation [28] . In addition , the role of CLOCK as a histone acetyltransferase [29] , and the acetylation of BMAL1 [30] , combined with the observation that the NAD+-dependent histone deacetylase SIRT1 modulates the amplitude of circadian regulated gene expression [31] , [32] has garnished attention . More recently , the mixed-lineage leukemia ( MLL1 ) lysine methyltransferase ( KMT2A ) has been show to associate with CLOCK-BMAL1 and is required for rhythmic expression of clock genes [33] . Additionally , the histone demethylase JMJD5 ( KDM8 ) is rhythmically expressed and is required for normal rhythms in both the Arabidopsis and mammalian clock suggesting that oscillations in H3K36 methylation are also an important component of clock function [34] . Current models of circadian gene regulation suggest that the clock controls its own phase-specific chromatin modifications , which help confer levels of expression appropriate for the time-of-day . How these “marks” are established after entrainment , transferred after replication , and ultimately interpreted so that rhythms are maintained and asynchronous expression is prevented are active areas of research . It is clear that chromatin structure is instrumental for appropriate circadian regulation of transcription . In fact , circadian changes in nucleosome location are known to occur at both frq and mDbp [18] , [35] , indicating that the clock controls chromatin architecture at a subset of clock-regulated loci . Consistent with this idea , circadian regulated remodeling at frq involves the ATP-dependent chromatin-remodeling enzyme CLOCKSWITCH ( CSW-1 ) [18] . Moreover , Drosophila KISMET protein , a member of the CHD subfamily of ATP-dependent remodeling enzymes , was shown to be a key regulator of photoresponses [36] . RNAi knockdowns of the kis gene resulted in activity rhythms in constant light similar to the cryb mutation [36] . In this report , we show that an additional remodeling enzyme CHD1 , remodels chromatin at frq , is necessary for normal frq expression , and is involved in regulating chromatin structure at frq . Surprisingly , we report that DNA in the frq promoter is methylated and we observe an expanded domain of DNA methylation at frq and other light/clock regulated genes in Δchd1 . This was unexpected because previously , only repeated DNA sequences were found to be methylated and presumably silenced ( reviewed in [37] ) . We show transient and reversible methylation that is associated with regulatory and coding sequences of genes that are actively expressed . This normal DNA methylation at frq is altered in strains bearing mutations in circadian clock genes and the frq antisense transcript qrf , and it is catalyzed by the DNA methyltransferase DIM-2 . Strains lacking dim-2 exhibit a small phase advance , suggesting that the methylation serves to limit the onset of circadian regulated transcription . These results demonstrate that transient , facultative , and presumably regulated DNA methylation also occurs in fungi and that chromatin remodeling and DNA methylation combine to fine turn circadian regulated gene expression .
Previously , we generated and screened 19 Neurospora knockout strains in which proposed ATP-dependent chromatin-remodeling enzymes were deleted , to identify genes necessary for remodeling chromatin structure at frq [18] . As is typical for circadian research in Neurospora , these knockouts were generated in a ras-1bd genetic background in order to facilitate subsequent screening of the overt rhythm in conidiation [38] , [39] . In this genetic background , we found that two remodeling enzymes ( orthologues of Sth1 and Chd1 ) appeared essential for viability and a third , CSW-1 , whose closest homologues are Fun30 in Saccharomyces cerevisiae , ETL1 in mice , and SMARCAD in humans , remodels chromatin at frq [18] . Subsequent analysis indicated that Δchd1 is synthetically lethal with ras-1bd , explaining our inability to isolate viable ascospores that contain both Δchd1 and ras-1bd . We have since obtained a viable homokaryotic knockout of the chd1 homologue , NCU03060 in a WT background , from the Neurospora Genome Project knockout consortium [40] . Verification of the Δchd1 knockout strain and characterization of its associated phenotypes can be found in Figure S1 . Because chd1 is essential when combined with ras-1bd , we were unable to assay clock phenotypes with race tubes , the standard assay used to measure circadian regulated conidiation . Therefore , circadian rhythmicity was analyzed molecularly after a standard light to dark transfer by examining the normally rhythmic expression of frq mRNA transcript and protein . Northern blot analysis ( Figure 1A ) and quantitative RT PCR ( Figure 1B ) revealed that in the Δchd1 strain , frq expression was diminished and the amplitude of rhythmic RNA accumulation was significantly reduced and rapidly graded to arhythmicity . Western blot analysis ( Figure 1C ) confirmed that clock function was disrupted in the Δchd1 strain . FRQ was detected as multiple phosphorylated forms , indicating newly synthesized FRQ was continually replacing protein destined for turnover . Quantification of the western blot is shown in Figure 1D and is consistent with the RNA expression data showing that CHD1 is required for sustained rhythmicity . We also observed a striking defect in chromatin remodeling at the antisense promoter ( see below ) so we examined expression of the antisense transcript by quantitative RT-PCR using a primer specific for antisense in the reverse transcriptase reaction . We found no significant differences between WT and Δchd1 strains ( Figure 1E ) . Although we did not see a rhythm in qrf in a true wild-type strain , we did confirm the low amplitude rhythm in the ras-1bd strain previously reported [41] ( data not shown ) . To further define frq expression , we used chromatin immunoprecipitation ( ChIP ) to assay the association of WC-2 with the frq promoter in Δchd1 ( Figure 2A and 2B ) . We consistently detected a subtle , low-amplitude rhythm in WC-2 association with the C-box , but binding was reduced approximately 50% when compared with WT , consistent with the observed reduction in frq mRNA ( Figure 2C and 2D ) . The low amplitude rhythms in frq message and rhythmic WC-2 binding to the C-box suggest a more supportive rather than immediately essential role for CHD1 in clock function; however , these analyses only monitor clock function for two circadian cycles . To confirm the effects of Δchd1 on clock function and to examine frq expression over an extended circadian time course , we used an indirect method to measure rhythmicity of the core oscillator in real time using a frq promoter-luciferase reporter construct [42] . WT and Δchd1 strains containing the pVG110 construct integrated at the his-3 locus were grown on mini-race tubes and LUC activity was initially monitored over the entire tube and at region near the inoculation point ( Figure S2 ) . While clear robust rhythms were observed in WT , the Δchd1 strain typically displayed a single peak of frq-driven LUC activity with no evidence of sustained circadian regulation . Together , these data demonstrate that CHD1 is required to maintain normal high amplitude rhythms of WC-2 binding and frq mRNA accumulation , and is essential for persistent circadian expression of frq in synchronized Neurospora cultures . Previously , we showed that chromatin in the frq promoter region , near the transcriptional start site ( TSS ) and the PLRE , is remodeled in response to light treatment but that this remodeling is independent of CSW-1 [18] . We suspected that this light-dependent remodeling might be catalyzed by CHD1 because of the failure to properly express frq in Δchd1 strains . Therefore , we asked if CHD1 is responsible for catalyzing the opening and closing of chromatin structure by comparing Δchd1 and WT nuclei using a limited MNase I digestion . Nuclei were isolated from 48-hour old cultures grown in the dark for 12 hours ( CT0 ) and 24 hours ( CT12 ) , and treated with a 15 minute light-pulse ( LP15 ) given after 24 hrs in darkness . Regions in frq were examined by partial MNaseI digestion followed by indirect end-labeling ( Figure 3A ) . Within the region surrounding the PLRE and TSS , we consistently only observed a very minor remodeling defect in Δchd1 compared to WT ( Figure S3 ) . We extended our analysis to regions near the C-box promoter element , a region shown to be remodeled by CSW-1 [18] , and did not observe any CHD1-dependent remodeling in that region ( data not shown ) . In contrast to the subtle effect seen in the sense promoter , we detected unequivocal CHD1-dependent changes in chromatin structure in the antisense promoter ( Figure 3B ) . A sequence near the 1250 site in the qrf promoter closely matches the imperfect repeat consensus sequence of the C-box and PLRE previously described [10] . WC-2 binding to the antisense light response element ( aLRE ) has been observed in WC-2 ChIP-seq experiments [43] . As indicated above , chromatin remodeling in the frq promoter persists in Δchd1 and may account for the low amplitude rhythms in frq mRNA and WC-2 binding observed in Δchd1 mutants ( Figure 1A and Figure 2A ) . This observation is consistent with previous work and suggests that multiple chromatin remodeling enzymes regulate frq including the previously identified CSW-1 protein [18] . These data cumulatively indicate that chromatin structure at frq in a Δchd1 strain is different from that in WT and implicates CHD1 in maintaining chromatin architecture at the frq locus . Curiously , in the Southern blot data associated with the MNase I remodeling assay described in Figure S3 , we routinely observed that a large percentage of the DNA isolated from Δchd1 was resistant to digestion with the restriction endonuclease NcoI . NcoI is sensitive to cytosine methylation , which suggested the possibility that frq DNA from Δchd1 is hypermethylated . Tests using other methylation-sensitive enzymes strongly supported this notion ( Figure 4A–4D , and data not shown ) . This apparent DNA methylation at frq is surprising; in Neurospora , about 2% of cytosines are methylated , but in a variety of strains and under all conditions previously tested methylation has been shown to exist almost exclusively in relics of RIP ( repeat-induced point mutation ) and rDNA [37] , [44] , [45] . A common assay for examining DNA methylation in Neurospora utilizes the methylation sensitive restriction endonuclease Sau3AI and its methylation insensitive isoschizomer , DpnII , followed by Southern blot analysis [46] . We first examined DNA methylation in WT and Δchd1 strains grown in constant light for 48 hrs at 30°C , which results in constitutive , non-rhythmic frq expression , and compared this with DNA methylation observed in WT and Δchd1 strains grown in constant darkness for two circadian cycles . Cultures were synchronized by a transfer from constant light at 25°C to constant darkness at 25°C and samples were collected every four hours ( Figure 4 ) . Southern blots were probed with DNA corresponding to the C-box ( CBmeP ) and PLRE ( PLREmeP ) to check for methylation at these sites ( Figure 4A ) . The appearance of partially digested DNA in the Sau3AI digests suggested that the frq promoter is methylated ( Figure 4B and 4C ) under these conditions of growth in light followed by dark , and frq is hypermethylated in the Δchd1 strain relative to WT grown under the same conditions ( Figure 4B and 4C , lanes 1–4 ) . To ensure that inhibition of Sau3AI digestion at frq was due to DNA methylation , we routinely stripped and reprobed blots for a region of the mitochondrial genome ( Figure S4 and data not shown ) . Mitochondrial DNA , which is not methylated , but is at much higher concentrations relative to nuclear DNA , was completely digested when compared with frq at similar time points ( Figure S4 ) . Interestingly , the DNA methylation at the frq promoter decreased with time in darkness , although not in an overtly circadian manner ( Figure 4B and 4C , lanes 5–30 ) . In addition , promoter methylation appeared greatly reduced in cultures grown in constant light for extended periods ( 48 hours in light ) . In a Δchd1 strain over the circadian time course frq promoter hypermethylation was seen at all time points examined ( Figure 4D , compare lanes 1 and 2 with lanes 3–24 ) . The unanticipated observation of DNA methylation at frq immediately suggested the possibility that other genetic loci were similarly modified and similarly affected by loss of chd1 . To examine this we performed methylated DNA immunoprecipitation ( MeDIP ) , followed by hybridization of differentially labeled input and MeDIP fractions to a high-density chromosome VII ( LGVII ) microarray ( MeDIP-chip ) [45] . MeDIP-chip experiments using genomic DNA from WT or Δchd1 cultures grown in constant light or in the dark for four hours revealed increased DNA methylation in the Δchd1 strain , consistent with Southern blot analyses . A peak of DNA methylation at the frq promoter was observed in the WT strain . In the Δchd1 strain , we observed an expanded domain of DNA methylation , which covered the entire frq promoter and open reading frame ( Figure 5A ) . We next examined DNA methylation across the entire chromosome and found a peak of DNA methylation in the promoter of wc-1 in Δchd1 that was absent in the WT strain ( Figure 5B ) . DNA methylation at wc-1 in Δchd1 was confirmed by a methyl-sensitive Southern blot , further supporting the validity of the MeDIP-Chip data ( Figure S6 ) . In contrast to frq and wc-1 where methylation is influenced by loss of CHD1 , DNA methylation at RIP'd regions was similar in WT and Δchd1 ( Figure 5B and data not shown ) , suggesting that CHD1 does not affect DNA methylation at constitutive heterochromatin . Because previous studies clearly showed a lack of methylation at the frq locus , it seems likely that particular growth conditions may be important to observe methylation and may indicate that it is dynamic [45] . Incidentally , when growth conditions consisting of elevated temperatures and extended light similar to previous work was used in this study , we failed to see significant methylation at frq ( Figure 4B and 4C , lanes 1 and 2 ) supporting the notion that light-dark transitions or perhaps circadian entrainment are needed for methylation at frq . We extended our analysis of the breadth of DNA methylation by examining more parts of the frq locus as well as other genes ( Figure S5 ) . Consistent with Figure 5 , Southern hybridization confirmed that DNA methylation within the frq coding region is only seen in Δchd1 , not WT . A third light and clock regulated gene , vvd [47] is similarly seen to be methylated in Δchd1 but not in WT ( Figure S5E and S5F ) but another light and clock controlled gene eas ( ccg-2 ) appeared never to be methylated , even in Δchd1 ( Figure S5C and S5D ) . Lastly , we examined the well-studied methylated Ψ63 region and found no discernable difference between WT and Δchd1 ( Figure S5G and S5H ) , consistent with the microarray data , which revealed that CHD1 does not effect the strong methylation seen at relics of RIP . In considering common elements among the genes whose methylation is influenced by CHD1 , it became clear that all are also regulated by light and by the components of the circadian clock . To examine this in more detail we followed DNA methylation in clock-defective strains ( Figure 6 , Figure S6 ) . frq9 is a loss-of-function allele in which a frame-shift mutation results in premature termination; therefore , frq9 encodes a protein that is incapable of establishing circadian negative feedback [22] . Cultures containing frq9 were grown at 25°C in constant light and then transferred to darkness , and DNA was isolated and digested using the restriction endonucleases Sau3AI and DpnII followed by Southern blot analysis . We found a marked reduction of the slower migrating DNA in the Sau3AI digest compared with WT , indicative of hypomethylated DNA in frq9 ( Figure 6A , compare lanes 1 and 2 with lanes 3–24 ) . We next assayed how loss of FRH affected DNA methylation by using a strain that expresses an inducible hairpin RNA ( dsfrh ) that causes silencing of the frh gene [12] . Under conditions in which FRH expression was reduced , we found a noticeable decrease in DNA methylation at frq that was similar to that in the frq9 strain ( Figure 6B ) . We conclude that normal levels of methylation require proper expression of core clock genes or at least FRQ-FRH dependent negative feedback , suggesting that both FRQ and FRH play an important , although perhaps indirect , role in regulating methylation at frq . An antisense frq transcript is also expressed from the frq locus [41] . Because antisense transcripts are known to be involved in X-chromosome inactivation ( Xist/Tsix ) [48] , and because RNA-dependent DNA methylation ( RdDM ) requires formation of double stranded RNAs [49] , we sought to examine the role of the frq antisense transcript in establishment of normal methylation . To test this , we used a strain frq10frqccg-2 [41] in which the 3′ region of frq , containing the promoter of this antisense transcript qrf , is replaced with the 3′ region of eas ( ccg-2 ) . This drastically reduces expression of the antisense transcript and results in a small phase lag in the oscillator [41] . In frq10frqccg-2 , methylation is still present but is significantly reduced compared to WT ( Figure 6C ) . To further quantify the level of DNA methylation in WT and to allow direct comparison among Δchd1 and the clock mutant strains , we examined methylation at a single time point , DD16 ( Figure 6D ) and then performed a densitometric analysis on the methylated region ( Figure 6E ) . We confirmed that the observed differences were not a result of the ras-1bd mutation that is present in frq9 , dsfrh , and frq10frqccg-2 , but not in WT or Δchd1 . Because there was no significant difference in the methylation patterns of WT and ras-1bd , we conclude that ras-1bd has no apparent effect on the promoter methylation at frq ( Figure S6B and S6C ) . The degree of methylation enhancement in Δchd1 and reduction in the strains bearing mutations in core clock components compared to WT is apparent . In the time course experiments , we routinely observed a reduction in the overall amount of methylated DNA at later time points ( DD36 and beyond ) , which may suggest that the level of DNA methylation is reduced after prolonged incubations in the absence of light/dark cycles . To better understand the possible role of DNA methylation in circadian regulated gene expression , we identified the methyltransferase responsible for this activity . Consistent with prior studies showing the DNA methyltransferase ( DNMT ) DIM-2 ( defective in methylation ) to be required for all observed DNA methylation in Neurospora [50] , we found that Δdim-2 lacked all detectable methylation at frq ( Figure 7A ) . This allowed us to examine the role of DNA methylation in circadian rhythmicity . Circadian clock-regulated conidiation is still observed in Δdim-2 , ras-1bd strains using race tubes , indicating that DNA methylation in not essential for rhythmicity per se ( Figure 7B , Figure S7 ) . There was , however , a slight phase-advance of approximately 2 hours in strains lacking DIM-2 ( Figure 7B , Figure S7A ) . To confirm the phase-advance , we generated a Phase Response Curve ( PRC ) comparing Δdim-2 to WT ( Figure 7C ) . Normally , a pulse of light will advance or delay the phase of the clock depending on the time when the pulse of light is given . In WT , the greatest shift in phase occurs around CT18; however , in Δdim-2 the largest shift was observed at CT15 , further supporting the 2 hour phase-advance . A representative set of race tubes from the CT16 time point is shown in Figure S7B to highlight the phase shift difference between WT and Δdim-2 . These results suggest that DNA methylation plays an ancillary but supportive role in clock regulation; namely , overt rhythms are not affected yet the onset of circadian clock-regulated expression is affected . Further supporting the correlation between altered DNA methylation and altered circadian phase , we found that the vvd deletion strain , with its well-documented defects in clock phasing , also has an altered DNA methylation profile ( Figure 7D ) . We note that the frq promoter is hypermethylated in Δvvd , further supporting the notion that normal DNA methylation is required for proper phasing . We also note that there is no DNA methylation in the Δwc-1 strain , consistent with a connection between normal expression of the frq/qrf locus and normal DNA methylation . The early onset of conidiation observed in Δdim-2 is consistent with the notion that DNA methylation is involved in transferring regulatory signals to pertinent clock genes thereby delaying the start of clock gene expression .
We set out to understand how chromatin is remodeled at frq , and to investigate how this might help establish permissive and non-permissive states for rhythmic transcription . Our work led us to discover a link between CHD1-catalyzed chromatin remodeling , DNA methylation , and phase determination . We demonstrate that CHD1 contributes to apparent changes in chromatin structure at frq and is needed for normal frq expression . Surprisingly , we discovered DNA methylation at frq , providing the first example of promoter methylation in Neurospora , and we found that frq and other loci are hypermethylated in Δchd1 strains . We showed that DIM-2 is the DNMT responsible for methylating frq and that deletion of dim-2 results in a small phase defect . Changes in DNA methylation at frq were also observed in Δvvd , and frq10frqccg-2 , and both these strains have phase defects , providing a link between DNA methylation and phasing of clock gene expression . The apparent minor phase advance of approximately two hours in Δdim-2 is significant considering the circadian timescale . Under normal circadian conditions , the onset of frq expression occurs approximately 8 hours after the transition to dark conditions; therefore , a 2-hour advance represents a 25% shift . Moreover , there is probably a FRQ-induced refractory period after the transition to dark conditions where the WCC is inactive due to negative feedback caused by light-expressed FRQ-FRH . Thus WC-mediated transcription cannot occur until light-expressed FRQ is cleared by the proteasome , which takes roughly 4–6 hours . Based on expression profiles of frq in frq9 [22] and frh10 [51] , we know expression of frq is suppressed upon transition to dark with a WT core-oscillator . Thus , under conditions where the negative elements are unaffected , the maximum observable phase advance should be approximately two hours , as found in the Δdim-2 strain . In this report , we present data indicating that proper expression of both frq and its antisense counterpart , qrf , are essential for normal DNA methylation in this region . In fact , WC-1 dependent expression of frq , and its antisense noncoding counterpart qrf , clearly influences methylation . Interestingly , non-coding RNAs affect the methylation status of corresponding genes in other systems ( i . e . mammals , plants ) [52] , [53] , raising the possibility that the novel methylation that we report also depends on RNA . Further work is needed to explore this possibility but it is noteworthy that the mammalian clock gene mPer2 has promoter methylation [54] , [55] . The hypermethylation phenotype observed in Δchd1 may result from improper expression of the sense/antisense pair or spurious transcripts , which would ultimately lead to defects in disiRNA ( Dicer-independent small interfering RNA ) production . A recent report indicates that in Neurospora sense/antisense transcripts produce disiRNA [56] and these may direct the DNA methylation through an unknown mechanism . Perhaps such RNAs could play a role in DNA methylation . An unrelated possibility is that CHD1 is involved in RNA processing of the qrf and/or 3′ end of the frq transcripts and this is coupled to a chromatin remodeling event whose misregulation leads in some way to the observed hypermethylation phenotype . A role for CHD1 in mRNA processing has been described in mammalian cell culture [57] . It is clear that CHD1 is needed for proper frq expression and does appear to remodel chromatin at frq . However , the role CHD1 plays in clock-regulated expression remains complicated by the residual remodeling that occurs in the absence of CHD1 , and additional work is also needed to fully understand how DNA methylation influences frq expression . Regardless of the eventual role discovered for promoter methylation though , the simple fact that transient , apparently facultative , and presumably regulated DNA methylation occurs in the fungal kingdom is noteworthy . That this phenomenon went undiscovered for so long , but is so apparent in cultures grown at room temperature in light dark cycles , may serve to highlight the value of studying organisms under conditions that approximate the conditions in which they live in nature .
The wild-type ( WT ) Oak Ridge strain FGSC2489 ( 74A ) strain or the clock WT strain , 328-4 ( ras-1bd , A ) were used as controls and parent strains for crosses . Strains generated in this study are shown in Table S1 . The Δchd1 strain was generated by the knockout consortium [40] , obtained from the Fungal Genetics Stock Center ( FGSC , University of Missouri , Kansas City ) , and crossed to 74A generating XB99-1 . Ascospores were germinated on complete media and genotyped by Southern blot for the hygromycin resistance cassette ( Figure S1 ) . The Δdim-2 , ras-1bd strain , XB98-3 , was generated by crossing FGSC8594 to 328-4 and spores were selected for hygromycin resistance and then screened on race tubes for the presence of the ras-1bd allele . For luciferase strains , the plasmid pVG110 [42] , containing the full-length frq promoter fused to luciferase , was first transformed into rid− strains , FGSC9014 and FGSC9015 , and targeted to the his-3 locus ( Larrondo et al . , in preparation ) . XB100-9 ( Δchd1::hph , rid− , A ) was crossed to FGSC9015 containing pVG110 and spores were selected on hygromycin , then screened for luminescence to obtain XB105-13 . Growth media on race tubes consisted of 1X Vogel's salts , 0 . 1% glucose , 0 . 17% arginine [58] . Luciferase readings were measure using 12 . 5 µM luciferin as described [42] . The method used to generate the phase-response curve is outlined elsewhere [47] . Briefly , the phase response curve was generated using standard race tubes grown in the light for 12 or 24 hours and then transferred to dark conditions . After the third day of growth , individual race tubes were subjected to a 15 minute light pulse at the indicated circadian time and then placed back into a dark incubator for an additional 5–6 days ( 8 days of total growth ) . The phase on day 1 and on day 5 was determined using Chrono software and plotted as the difference of the two days . The liquid culture assays were performed in media ( 2% LCM ) containing 1X Vogel's salts , 0 . 17% arginine with 2% glucose and grown at 25°C . Conidia were used to seed mycelial mats in 75 mm Petri dishes and 0 . 5 cm plugs were cut from these and grown in 100 ml cultures . Mats were harvested by filtration , frozen in liquid nitrogen and ground in a mortar and pestle . Time course experiments have been described previously [22] . The ChIP assays and oligonucleotides were identical to those previously described for WC-2 [18] . For MNaseI assays , nuclei were isolated in the dark as previously described [10] , [13] and then subjected to limited MNaseI digestion following established protocols [59] . Equal amounts of nuclei were resuspended in MNaseI buffer A , treated with 1 . 0 ml 0 . 1 M CaCl2 , incubated at 37°C for 1 . 5 minutes , and then varying amounts of MNaseI were added and the nuclei incubated for an additional 1 . 5 minutes . The enzyme was inactivated by the addition of Proteinase K ( 100 µg/ml ) in TENS buffer ( 20 mM Tris-HCl , pH 7 . 4 , 200 mM NaCl , 2 mM EDTA , 2% SDS ) , and the nuclei were incubated for a minimum of 2 hours to remove the chromatin . The DNA was purified by phenol chloroform extraction-EtOH precipitation and digested with NcoI as the secondary enzyme . DNA fragments were resolved on a 1 . 5% agarose gel , transferred to positively charged nitrocellulose , and probed with frqP4 specific probe . Remodeling at the antisense promoter was performed using the identical protocol except EcoRI was used as the restriction enzyme with frqP18 . MeDIP-chip experiments were performed as previously described [45] . Standard Northern and Southern blots were performed using digoxigenin label DNA probes following Roche guidelines . All of the oligonucleotides used for probes are contain in Table S2 . RNA was isolated from cells using a hot-phenol extraction or Trizol and 15 or 25 ug/ul of RNA were fractionated on a 1 . 3% agarose formaldehyde gel [22] . Quantitative RT-PCR was performed as described [60] . DNA was isolated using the Puragene Kit following the manufactures' protocol . Immunoblot analysis was as described [19] . | Circadian rhythms facilitate daily changes in gene expression via a transcriptional negative feedback loop . In eukaryotes , chromatin remodeling is an integral part of transcriptional regulation and is proving to be one of the major determinants for the proper timing and amplitude of clock-gene expression . We describe here the action of chromodomain helicase DNA–binding ( CHD1 ) , one of two ATP–dependent chromatin-remodeling enzymes required for normal circadian regulated gene expression of the central clock gene frequency ( frq ) . Molecular analysis of strains lacking chd1 indicates that CHD1 is required for remodeling chromatin structure at the frq locus as a part of the daily clock cycle . Moreover , we discovered DNA methylation in the promoter of frq that diminishes over time in the absence of light/dark cycles and determined that normal DNA methylation appears to require a functional clock . The DNA methyltransferase DIM-2 is responsible for this DNA methylation , and the DNA methylation is required for proper phasing of clock gene expression . Collectively , these data demonstrate a close connection among chromatin remodeling , DNA methylation , and clock gene expression . | [
"Abstract",
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] | [
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] | 2011 | CHD1 Remodels Chromatin and Influences Transient DNA Methylation at the Clock Gene frequency |
Maintenance of normal mineral ion homeostasis is crucial for many biological activities , including proper mineralization of the skeleton . Parathyroid hormone ( PTH ) , Klotho , and FGF23 have been shown to act as key regulators of serum calcium and phosphate homeostasis through a complex feedback mechanism . The phenotypes of Fgf23−/− and Klotho−/− ( Kl−/− ) mice are very similar and include hypercalcemia , hyperphosphatemia , hypervitaminosis D , suppressed PTH levels , and severe osteomalacia/osteoidosis . We recently reported that complete ablation of PTH from Fgf23−/− mice ameliorated the phenotype in Fgf23−/−/PTH−/− mice by suppressing serum vitamin D and calcium levels . The severe osteomalacia in Fgf23−/− mice , however , persisted , suggesting that a different mechanism is responsible for this mineralization defect . In the current study , we demonstrate that deletion of PTH from Kl−/− ( Kl−/−/PTH−/− or DKO ) mice corrects the abnormal skeletal phenotype . Bone turnover markers are restored to wild-type levels; and , more importantly , the skeletal mineralization defect is completely rescued in Kl−/−/PTH−/− mice . Interestingly , the correction of the osteomalacia is accompanied by a reduction in the high levels of osteopontin ( Opn ) in bone and serum . Such a reduction in Opn levels could not be observed in Fgf23−/−/PTH−/− mice , and these mice showed sustained osteomalacia . This significant in vivo finding is corroborated by in vitro studies using calvarial osteoblast cultures that show normalized Opn expression and rescued mineralization in Kl−/−/PTH−/− mice . Moreover , continuous PTH infusion of Kl−/− mice significantly increased Opn levels and osteoid volume , and decreased trabecular bone volume . In summary , our results demonstrate for the first time that PTH directly impacts the mineralization disorders and skeletal deformities of Kl−/− , but not of Fgf23−/− mice , possibly by regulating Opn expression . These are significant new perceptions into the role of PTH in skeletal and disease processes and suggest FGF23-independent interactions of PTH with Klotho .
Maintaining normal mineral ion homeostasis is crucial for essential biological activities that include but are not limited to energy metabolism , signaling activities , and normal skeletal growth , development and function . Blood calcium and phosphate levels are determined by counterbalance between absorption from the intestine , mobilization from bone and excretion from the kidney into urine [1] . This complex process is regulated by several endocrine factors , including parathyroid hormone ( PTH ) , FGF23 and active Vitamin D , which have been widely studied [2]–[5] . More recently , another protein , Klotho , has been suggested to have an important role in regulating calcium and phosphate homeostasis . Klotho is a type-I membrane protein mainly expressed in kidneys , parathyroid glands , and the choroid plexus [6] . It is also related to β-glucosidases and is found in a soluble form in blood and cerebrospinal fluid [7] , [8] . Klotho forms a complex with the FGF receptor 1c ( FGFR1c ) , thereby converting this canonical FGF receptor into a receptor specific for FGF23 [9] , a negative regulator of serum phosphate . FGF23 uses the FGFR1c/Klotho complex to directly target the kidney where it induces phosphate wasting by decreasing the expression of the sodium-dependent phosphate co-transporters NaPi2a and NaPi2c [10] , [11] . Klotho also regulates serum calcium by affecting both parathyroid gland and kidney independent of FGF23 . When serum calcium is low , Klotho hydrolyzes extracellular sugar residues on the renal transepithelial calcium channel TRPV5 , entrapping the channel in the plasma membrane [12] . This maintains continuous calcium channel activity and membrane calcium permeability , leading to an increase in tubular reabsorption of calcium in the kidneys and finally increased serum calcium . In the parathyroid gland , Klotho recruits Na+/K+-ATPase to the cell surface , which results in an increase in PTH production , which in turn elevates serum calcium level [13] , [14] . In addition to Klotho's independent effect on PTH induction , it can act together with FGF23 to decrease PTH levels [15] , [16] . An in vivo study has shown that injection of FGF23 protein into rats can lower PTH secretion and expression [15] . This was confirmed by in vitro experiments using parathyroid gland cultures [16] . The function of Klotho as a cofactor of FGF23 was confirmed in studies by us and others showing that genetic ablation of either Fgf23 or Klotho results in a similar phenotype [17]–[19] . Both Klotho knockout ( Kl−/− ) and Fgf23 knockout ( Fgf23−/− ) mice exhibit hypercalcemia , hyperphosphatemia with low to undetectable PTH levels [20]–[22] , and severe osteomalacia . We have previously described [23] that deletion of PTH in Fgf23−/− mice ameliorated the abnormal phenotype by normalizing serum Ca2+ and lowering serum vitamin D levels , however , the severe osteomalacia persisted in Fgf23−/−/PTH−/− mice . Because Klotho also has FGF23-independent functions , we thought it would be important to investigate the effects of deleting PTH from Kl−/− mice . We demonstrate that the skeletal mineralization defect in Kl−/−/PTH−/− mice was completely rescued and that this phenomenon was accompanied by a reduction in the high levels of osteopontin in bone and serum , a finding that could not be observed in Fgf23−/−/PTH−/− mice . We also present data showing that continuous infusion of Kl−/− mice with PTH results in an elevation in OPN levels and subsequently increased osteoid volume . Our finding demonstrates for the first time that the skeletal abnormalities and the bone mineralization defect in Kl−/− can be rescued by ablation of PTH actions . Our data suggest regulatory actions on osteopontin by PTH , an important observation with clinical significance . The identical levels in serum calcium , phosphate and vitamin D in Fgf23−/−/PTH−/− and Kl−/−/PTH−/− preclude any effects of these parameters on the regulation of skeletal mineralization and/or Opn levels in these mice . Additional studies are required to identify the mechanisms by which PTH affects osteopontin and mineralization in Kl−/− but not in Fgf23−/−mice .
We successfully generated Kl−/−/PTH−/− ( DKO ) mice by interbreeding heterozygous Kl+/− and PTH+/− mice . DKO mice were more active , healthier and larger in size than Kl−/− mice and more comparable to wild-type and PTH−/− single knock-out littermates ( Figure 1A ) . DKO mice did not show any obvious gross abnormalities with regard to movement and physical activities , whereas Kl−/− littermates were severely weakened , showing restricted movement as well as sluggish physical activities . DKO mouse body weight was significantly higher than that of Kl−/− mice ( Figure 1B ) . Compared to the Kl−/− mice , DKO mice also showed a clear improvement in life span as evidenced by a right shift of the survival curve ( Figure 1C ) . All mice , however , died before 16 weeks of age , probably due to the severe soft tissue calcifications such as found in kidney and lung of both Kl−/− and DKO mice ( Figure S1 ) . Six-week old Kl−/− and PTH−/− mice were severely hypercalcemic ( 10 . 86±0 . 52 mg/dL ) and hypocalcemic ( 6 . 85±1 . 17 mg/dL ) , respectively . However DKO animals were normocalcemic ( 8 . 78±0 . 42 mg/dL ) at 6 weeks , comparable to WT control animals ( 9 . 52±0 . 41 mg/dL ) , ( Figure 2A ) . Serum phosphate levels in both Kl−/− ( 14 . 61±0 . 48 mg/dL ) and PTH−/− ( 14 . 52±1 . 87 mg/dL ) mice were significantly higher compared to those in WT mice ( 9 . 75±1 . 34 mg/dL ) , ( Figure 2B ) . Interestingly , DKO exhibited a further increase in serum phosphate to levels ( 17 . 65±1 . 86 mg/dL ) far exceeding those in single Kl−/− or PTH−/− mice . We determined the total mineral content by calculating the calcium/phosphate product and found that Kl−/− and DKO mice exhibited similarly high levels ( Figure 2C ) . Measurements of serum 1 , 25 ( OH ) 2D levels showed increased amounts in Kl−/− single knockout mice compared to those in WT and PTH−/− mice . Serum 1 , 25 ( OH ) 2D levels in DKO mice were significantly reduced compared to those of Kl−/− single knockout mice , but were still significantly higher than in wild-type or PTH−/− mice ( Figure 2D ) . We also measured intact serum FGF23 levels and found that DKO mice had a 50% decrease in serum FGF23 compared to Kl−/− mice , however the levels were still significantly higher ( 1000 fold ) than those in wild-type or PTH−/− mice ( Figure 2E ) . We performed peripheral quantitative computerized tomography ( pQCT ) to analyze the bone density in the femurs of all genotypes . Kl−/− mice showed decreased total BMD in the distal femur metaphysis ( Figure 3A ) . Ablation of the PTH gene from these mice significantly increased the BMD , which was now comparable to that of WT and PTH−/− mice . Radiographs showed that the length and radiopacity of the tibiae from DKO mice were increased and comparable to those of WT and PTH−/− mice ( Figure 3B ) . We performed Alizarin red S and Alcian blue staining to determine the mineralization pattern of the bones . As shown in Figure 3C , Kl−/− mice exhibited abnormally widened ribs , but this abnormality could not be observed in the DKO mice , suggesting an improved skeletal architecture . MicroCT ( μCT ) analysis of the femurs was performed on all four genotypes . Representative images of distal femoral metaphyses and midshaft cortex are shown in Figure 3D and 3E . Quantification of trabecular bone volume fraction demonstrated that there is no significant difference between each genotype ( Figure 3F ) . As μCT can only detect mineralized bone , Kl−/− mice didn't show increased trabecular volume . However , the midshaft cortical thickness of the Kl−/− mice ( 0 . 132±0 . 013 mm ) was significantly reduced compared to the other groups ( Figure 3G ) . It was restored in DKO mice ( 0 . 170±0 . 015 mm ) to a volume comparable to that in WT ( 0 . 184±0 . 011 mm ) and PTH−/− ( 0 . 186±0 . 016 mm ) mice ( Figure 3G ) . We further analyzed the skeletal properties by generating undecalcified methylmethacrylate sections from the distal ends of femurs to confirm the observed improvement in bone mass compared to Kl−/− mice ( Figure 4A and 4B ) . Most importantly , the severe osteoidosis seen in the secondary spongiosa of Kl−/− mice was completely absent in DKO mice ( Figure 4B ) , indicating the mineralization defect of Kl−/− mice was rescued by PTH ablation . To quantify this observation , we performed histomorphometric analyses ( Figure 4C–4N ) . The increased trabecular bone volume observed in Kl−/− mice ( 18 . 1±4 . 4 ) was restored in DKO mice ( 13 . 5±1 . 9 ) to values close to those of PTH−/− mice ( 11 . 4±1 . 6 ) . Interestingly , deletion of PTH also rescued the severe mineralization defect of Kl−/− mice as evidenced by normalized osteoid volume ( OV/TV ) , osteoid surface ( OS/BS ) and thickness ( OTh ) in DKO mice ( Figure 4D–4F ) . DKO mice also showed normal trabecular thickness ( Tb . Th . ) , trabecular number ( Tb . N . ) and separation ( Tb . Sp . ) , as well as osteoclast surface ( Oc . S/BS ) and osteoclast numbers ( N . Oc/B . Pm ) ( Figure 4G–4K ) . We also found that the dynamic parameters , including mineral surface ( MS/BS ) , bone formation rate ( BFR/BS ) and mineral apposition rate ( MAR ) ( Figure 4L–4N ) , were normalized in DKO mice while the bone labeling in Kl−/− mice was unsuccessful due to the severe mineralization defect . We next analyzed the concentration of serum markers for bone turnover . Consistent with histomorphometric data , serum levels of the carboxyl-terminal telopeptide of type 1 collagen ( CTX ) , a biomarker of bone resorption activity , were comparable in DKO ( 31 . 5±17 . 2 ng/ml ) , WT ( 40 . 8 . 5±16 . 2 ng/ml ) and PTH−/− ( 31 . 5±3 . 6 ng/ml ) mice ( Figure 5A ) . Similarly , circulating levels of N-terminal propeptide of type I procollagen ( PINP ) , a reliable and sensitive marker of bone formation , were significantly elevated in Kl−/− mice ( 21 . 8±6 . 0 ng/ml ) ( Figure 5B ) . PINP levels were restored in DKO mice ( 14 . 5±5 . 0 ng/ml ) to levels observed in WT ( 14 . 2±4 . 6 ng/ml ) and PTH−/− ( 17 . 0±1 . 6 ng/ml ) . To explain the rescue in bone mineralization in DKO mice , we compared the expression of osteopontin and other factors associated with skeletal mineralization in all genotypes . As shown by in situ hybridization and immunohistochemical staining on decalcified paraffin sections ( Figure 6A and 6B ) , the expression of Opn , an inhibitor of osteogenic mineralization and member of the SIBLING protein family , is abnormally high in the bone . Interestingly , its expression was normalized in DKO mice to levels seen in WT and PTH−/− mice ( Figure 6A and 6B ) . We also measured serum Opn levels using an ELISA kit and were able to confirm significantly elevated serum Opn levels in Kl−/− mice , which were restored to normal levels in DKO mice ( Figure 6C ) . Since we previously reported increased Opn expression in Fgf23−/− bones [17] , we were interested in also examining their serum Opn levels and found that they were also significantly increased . In contrast , however , deletion of PTH from Fgf23−/− mice failed to normalize their serum Opn levels ( Figure S2 ) . To further confirm the in vivo observations in Kl−/− and DKO mice calvarial osteoblasts from 2-day-old littermates were isolated . Cells were cultured in osteogenic medium for 2 weeks and RNA was isolated . qPCR analyses showed normal expression of Opn in osteoblasts of DKO mice ( Figure 6D ) . These osteoblasts also exhibited normal mineralization as evaluated by Alizarin red staining while the mineralization of the osteoblasts from Kl−/− mice was markedly impaired ( Figure 6E ) . We also evaluated the expression of Dmp1 and Matrix gla protein ( Mgp ) by in situ hybridization and/or qPCR . Both were significantly increased in Kl−/− mice , but rescued in DKO mice ( Figure S3 ) . To further investigate the role of Opn in regulating the mineralization , we perfused PTH ( 1–34 ) peptides into the WT and Kl−/− mice using osmotic minipumps . After continuous infusion for 3 weeks , we observed that the serum Opn levels were significantly elevated in both WT and Kl−/− mice ( Figure 7A ) . As expected , serum phosphate levels were significantly decreased in both kinds of mice ( Figure S4 ) . PTH infusion , as shown in Figure 7B–7D , did not change the bone volume in WT mice , but significantly decreased it in Kl−/− mice . More importantly , OV/BV ( Figure 7E ) and OS/BS ( Figure 7F ) of Kl−/− mice were further increased by the PTH infusion , while the mineralized bone volume ( MdV/TV ) ( Figure 7G ) was significantly decreased , indicating that extraneous PTH induces Opn and thereby worsens the skeletal mineralization defect in the Kl−/− mice . Continuous PTH infusion also increased osteoblast surface ( Ob . S/BS ) , Oc . S/BS and N . Oc/B . Pm ( Figure 7K–7M ) in both WT and Kl−/− mice . Opn contains a RGD sequence , which is important for osteoclast attachment on the bone . Thus it is not surprising that infusion of PTH resulted in a significant elevation in serum CTX levels , indicating increased osteoclastic resorption ( Figure 7N ) . In addition , PTH infusion significantly elevated serum PINP levels in both WT and Kl−/− mice ( Figure 7O ) .
This is the first study using a genetic mouse model with dual ablation of the Klotho and PTH genes . The results show that deletion of PTH from Kl−/− mice resulted in healthier mice with normalization of serum calcium levels and complete rescue of the skeletal phenotype , suggesting that PTH is a crucial contributor to the skeletal abnormalities caused by loss of Klotho function . More importantly , we found that deletion of the PTH completely rescued the mineralization defect in Kl−/− mice . Kl−/− mice , as well as Fgf23−/− mice , exhibit a severe mineralization defect in their bones despite excesses in serum calcium and phosphate when compared to WT mice . The underlying reason for this is largely unknown but FGF23 is recognized as an inhibitor of mineralization . Serum Fgf23 levels in Kl−/− mice are two thousand fold higher than in wild-type littermates . Wang et al [24] show that adenoviral overexpression of FGF23 in rat calvarial cells inhibits bone mineralization independent of its systemic effects on phosphate homeostasis . Our previous report also demonstrated that FGF23 treatment of primary calvarial osteoblasts from wild-type mice leads to an inhibition of mineralization [18] . However , FGF23 requires Klotho for its actions [9] , [20] , [25] . In the absence of Klotho , FGF23 has very low affinity to the FGFR1 and cannot induce signal transduction ( phosphorylation ) [9] , [25] , [26] . Another intriguing possibility is that Klotho may have a specific function in osteoblasts associated with PTH . Although Klotho has been widely accepted as the cofactor of FGF23 signaling [9] , [26] , [27] , and Kl−/− and Fgf23−/− mice share very similar phenotypes [20] , we show here that deletion of PTH fully rescues the mineralization defect in Kl−/− mice , but could not improve this defect in Fgf23−/− animals [23] . More recently , Klotho has been reported to be expressed in the osteoblastic cell linage [28] . In addition , previous studies have shown that Klotho does exert endogenous actions in calcium homeostasis and control of PTH secretion [13] , [14] that are independent of FGF23 . Similarly , Klotho directly mediates secretion of PTH through recruitment of Na+/K+ ATPase to the plasma membrane [14] . Recent studies also showed that Kl−/− mice have increased trabecular bone [29] , [30] . In this study , we confirmed elevated bone volume in Kl−/− mice compared to the normal bone volume in Fgf23−/− mice . More importantly , cultured osteoblasts isolated from Kl−/− pups at the age of 2-days showed markedly impaired mineralization , suggesting that Klotho may play a specific role in osteoblasts . Moreover , we detected low Klotho expression by qPCR in both cultured osteoblasts and isolated cortical bone ( Figure S5 ) . An osteoblast-specific Klotho knockout mouse model may be required to dissect the role of Klotho during skeletogenesis . To examine the bone mineralization defect in more detail , we determined the expression of Opn by in situ hybridization , immunohistochemistry , ELISA and quantitative PCR . Opn is a well-known mineralization inhibitor , and mice deficient in Opn show soft tissue calcification and premature bone mineralization [31] , [32] . Our analyses showed that the increased amount of Opn detected in Kl−/− mice was normalized in DKO mice . PTH is known to be an important regulator of Opn and can induce the expression of Opn in MC3T3-E1 cells within 3 hours [33] , [34] . Therefore , the complete ablation of PTH might be responsible for the normalization of the increased Opn levels in Kl−/− mice and subsequently rescue the mineralization defect . Furthermore , infusion of exogenous PTH into Kl−/− mice resulted in a significant elevation in Opn levels with a worsened mineralization defect . This further strengthens our hypothesis that PTH could regulate skeletal mineralization in Kl−/− mice via Opn . Although studies suggest that phosphate could also regulate the expression of Opn [35]–[37] , our previous study using Fgf23−/−/NaPi2a−/− mice suggested that the elevated expression of Opn in bones of Fgf23−/− mice is at least partially independent of systemic phosphate levels [18] . This was further supported by our observation in this study that PTH−/− and DKO mice had normal expression of Opn despite very high serum phosphate levels . In addition , we also found in this study that PTH infusion could increase the serum Opn , even while the serum phosphate levels were decreased ( Figure S4 ) . Moreover , serum calcium , phosphate and vitamin D levels in Fgf23−/−/PTH−/− and Kl−/−/PTH−/− are identical and can therefore not contribute to the regulation of skeletal mineralization and/or Opn levels in these mice . In summary , the findings in this study demonstrate that genetic ablation of PTH resulted in healthier DKO mice . More importantly , deletion of PTH completely rescued the skeletal abnormalities , including the severe mineralization defect in Kl−/− mice , and this effect is very likely associated with normalized expression of Opn in DKO mice . Interestingly , we previously showed that deletion of PTH in Fgf23−/− mice could not rescue mineralization , implying an independent function of Klotho in bone . This study demonstrates that the activity of the low level of PTH remaining in Kl−/− mice contributes to the severe mineralization disorder and skeletal abnormalities caused by the loss of Klotho function . Moreover , we show that Klotho affects mineralization independently of its role as a co-factor for FGF23 . Further analyses are needed to determine the independent roles of PTH and Klotho in mineral ion homeostasis and skeletal mineralization and their detailed molecular interactions , including those involving OPN .
Heterozygous- Kl+/− and PTH+/− animals were interbred to attain wild-type ( WT ) , Kl−/− , Kl−/−/PTH−/− ( double knockout , DKO ) and PTH−/− animals for subsequent analyses . Routine PCR was used to genotype various mice as described previously [20] , [38] . The total body weight of each mouse was measured weekly starting at 3 weeks after birth . All studies performed were approved by the Institutional Animal Care and Use Committee at the Harvard Medical School . Blood was obtained by puncturing the cheek pouch of animals . Total serum calcium and phosphorus levels were determined using Stanbio LiquiColor ( Arsenazo III ) and LiquiUV kits ( Stanbio Laboratory , Boerne , TX ) , respectively . Serum concentrations of FGF23 and Opn were measured using commercial kits from Kainos Laboratories , Inc . , ( Tokyo , Japan ) , and R&D Systems , Inc . ( Minneapolis , MN ) , respectively . The ELISA kits for 1 , 25 ( OH ) 2D , PINP and CTX were purchased from IDS ( Fountain Hills , AZ ) . The mineralization pattern of the skeleton was analyzed by Alizarin red S and Alcian blue staining in 6- week-old mice , as described by McLeod [39] . Femurs of all genotypes at 6 weeks of age were flushed and exposed to X-ray ( 20 kV , 5 seconds ) . As described previously [40] , bone mineral density ( BMD ) and μCT analysis were performed by peripheral quantitative computerized tomography ( pQCT ) and by using a Scanco Medical μCT 35 system ( Scanco ) , respectively . Processing of undecalcified bone specimens and cancellous bone histomorphometry in the distal femoral metaphysis were performed as described [40] . The area within 0 . 25 mm from the growth plate was excluded from the measurements . Von Kossa staining was performed using 5 µm paraffin sections of kidneys and lungs isolated from 9-week-old animals . Complementary 35S-UTP-labeled riboprobe osteopontin ( Opn ) and dentin matrix protein 1 ( Dmp1 ) were used for performing in situ hybridization on paraffin sections , as described previously [41] . Immunohistochemistry was performed using mouse Opn antibody ( R&D , Minneapolis , MN ) with a working concentration of 0 . 5 µg/ml overnight at 4°C . Tissue was stained with anti-goat HRP substrate and DAB ( Vector , Burlingame , CA ) , and then counterstained with hematoxylin . Mouse calvarial cell culture was carried out as previously described [18] . Cells were treated with 50 mg/ml ascorbic acid and 10 mM β-glycerophosphate ( βGP ) to induce matrix mineralization . Total RNA isolation and Alizarin red S staining were performed 14 days after induction . Total RNA was from cultured osteoblasts using Trizol reagents ( Invitrogen ) according to the manufacturer's protocol . For qRT-PCR , cDNA was prepared using QuantiTec reverse transcription kit ( Qiagen ) and analyzed with SYBR GreenMaster Mix ( SABiosciences ) in the iCycler ( Bio-Rad ) using specific primers designed for each targeted gene . Relative expression was calculated using the 2−ΔΔCt method by normalizing with Gapdh housekeeping gene expression , and presented as fold increase relative to control . 50 µg per kilogram of body weight per day of human PTH 1–34 ( Polypeptide Group , France ) were delivered into 3-week-old animals for a 3-week period using implanted ALZET osmotic minipumps , Model-1004 ( DURECT Corporation , Cupertino , CA ) . Animals of vehicle group were infused with an equal volume of sterile saline . Statistically significant differences between groups were evaluated by Student's t-test for comparison between two groups or by one-way analysis of variance ( ANOVA ) followed by Tukey's test for multiple comparisons . And those between vehicle and PTH-infused groups were evaluated by Student's t-test . All values were expressed as mean ± SD . A p value of less than 0 . 05 was considered to be statistically significant . | Maintenance of normal mineral ion homeostasis is crucial for many biological activities , including proper mineralization of the skeleton . PTH , Klotho , and FGF23 are the key regulators of blood mineral ion homeostasis . Klotho is a type-I membrane protein and has been identified as cofactor required for FGF23 to bind and activate its receptor . Loss of either Klotho or Fgf23 activity results in a similar abnormal phenotype , including severe defects in skeletal mineralization and alterations in mineral ion balance . Here we describe a new mouse model in which we eliminated PTH from Kl−/− mice , and we can show that the skeletal mineralization defect was completely rescued in Kl−/−/PTH−/− mice and that this phenomenon was accompanied by a reduction in the high levels of osteopontin in bone and serum . We also present additional data showing that continuous infusion of Kl−/− mice with PTH results in an elevation in Opn levels and subsequently increased osteoid volume . Interestingly , this result differs from our previous report in which we describe that the osteomalacia and the high Opn levels in Fgf23−/−/PTH−/− mice persisted . Our finding suggests that PTH , possibly by regulating osteopontin , is responsible for the skeletal mineralization defect in Kl−/− mice , but not in Fgf23−/− mice . | [
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... | 2012 | Deletion of PTH Rescues Skeletal Abnormalities and High Osteopontin Levels in Klotho−/− Mice |
The emerging field of sociogenomics explores the relations between social behavior and genome structure and function . An important question is the extent to which associations between social behavior and gene expression are conserved among the Metazoa . Prior experimental work in an invertebrate model of social behavior , the honey bee , revealed distinct brain gene expression patterns in African and European honey bees , and within European honey bees with different behavioral phenotypes . The present work is a computational study of these previous findings in which we analyze , by orthology determination , the extent to which genes that are socially regulated in honey bees are conserved across the Metazoa . We found that the differentially expressed gene sets associated with alarm pheromone response , the difference between old and young bees , and the colony influence on soldier bees , are enriched in widely conserved genes , indicating that these differences have genomic bases shared with many other metazoans . By contrast , the sets of differentially expressed genes associated with the differences between African and European forager and guard bees are depleted in widely conserved genes , indicating that the genomic basis for this social behavior is relatively specific to honey bees . For the alarm pheromone response gene set , we found a particularly high degree of conservation with mammals , even though the alarm pheromone itself is bee-specific . Gene Ontology identification of human orthologs to the strongly conserved honey bee genes associated with the alarm pheromone response shows overrepresentation of protein metabolism , regulation of protein complex formation , and protein folding , perhaps associated with remodeling of critical neural circuits in response to alarm pheromone . We hypothesize that such remodeling may be an adaptation of social animals to process and respond appropriately to the complex patterns of conspecific communication essential for social organization .
Social behavior , like phenotypes of any level of complexity , is regulated by the activity of genomic networks and resulting gene expression . At the same time that specific examples of genes influencing behavior were being discovered empirically[1 , 2] , the field of systems biology was developing[3] . The essence of systems biology is to use computation and genomic technologies to enable detailed observation at the sequence level of the dynamics of cell , tissue , and organism responses to specific challenges . The power of systems biology is that it enables comprehensive dynamic patterns of transcription , translation , post-translational modification , and functioning of gene products to be observed and analyzed . These approaches provide fertile ground for the development of testable hypotheses and ultimately confident inferences about the relationship between the genome and phenome ( the sum total of the organism’s phenotypic traits ) , even when the phenome is based on complex patterns of gene interactions . The systems approach has catalyzed the development of the fields of evo-devo[4] and , more recently , sociogenomics [1] . Sociogenomics focuses on how genes influence social behavior [2] and how environmental attributes—especially those related to the social environment—influence genome activity [5] . Evo-devo has led to new insights into the molecular basis for the evolution of morphological novelties , molecular mechanisms underlying the development of morphology in the individual , and how development responds to the environment on a genomic level . Specifically , it has shown that the major ( but not only ) driver in evolution of form has been changes in expression patterns of functionally conserved genes [6] Synergistically , sociogenomics seeks to provide insights into the evolution of social behavior , the genomic mechanisms underlying social behavior in an individual and a species , and how social behavior is influenced by the environment at the genomic level [1] . Similar to the evolution of biological form , the evolution of a vertebrate social decision-making network has been shown to be largely ( but again not entirely ) by variations in conserved genes and networks [7] . One approach to sociogenomics is hypothesis-driven . In this approach , researchers begin with a hypothesis about the role of a gene or a group of genes in social behavior based on prior knowledge of the function or activity of those genes . As an example of this approach , O’Tuathaigh et al [8] observed that the knockout of the mouse ortholog of the human schizophrenia gene neuregulin 1 disrupted social novelty behavior , but left spatial learning and working memory processes intact . This gene has close homologs throughout the vertebrates , putative orthologs in arthropods , and significantly similar homologs annotated as coding for cell wall anchoring proteins in some bacteria . By contrast , systems biology studies often begin with no hypothesis ( except the fundamental one that social behavior has genomic bases ) and scan comprehensively to see what correlations emerge . As an example of this approach , Cummings et al [9] identified differential gene expression patterns in the response of female swordtail fish to different classes of conspecifics ( attractive males , unattractive males , other females ) . This broad systems approach was extended across multiple species in a study in which molecular orthology and comparative brain morphology were used to identify social behavior networks in vertebrates [10] . This work nicely illustrates the above-mentioned convergence of sociogenomics and evo-devo . The studies cited above highlight the fact that understanding the genomic correlates of human social behavior requires us to use a variety of model organisms , in part because of the invasive nature of many experimental protocols . Ebstein et al [2] observed that “Human beings are an incredibly social species and along with eusocial insects engage in the largest cooperative living groups in the planet’s history . ” This leads to the question: to what extent are there relevant genomic correlates between eusocial insects and humans , given that the last common ancestor of eusocial insects and humans lived approximately 670 million years ago [11] and almost certainly was very different in appearance from either an insect or a vertebrate . It may be that both eusocial insect and human social traits are elaborations and modifications of underlying patterns that were present in a common ancestor , even if the elaborations occurred independently[12] . As a corollary to this view , some species in the lineages leading to both insects and chordates would have lost or inhibited expression of these traits , while other species such as the eusocial insects and humans would have continued to express them and use them as a set of building blocks for social behavior . To the extent this is true , comparative genomics of eusocial insect social behavior and human social behavior may yield insights into some of the most fundamental aspects of the genomics of social behavior . This would be an example of the general principle that conserved elements between species separated by great evolutionary distance are likely to be universal building blocks of common aspects of the species’ phenomes [6] . Among the eusocial insects , the honey bee is a valuable model organism . Many experiments have linked brain gene expression patterns to social behavioral characteristics and environmental stimuli , and the honey bee genome has been sequenced [13] . In addition , individual members of a honey bee colony have well-defined social roles . It is known that the division of labor within the hive is based on both genetic differences between individual honey bees and also on environmental influences that include visual , tactile , and chemical signals that colony members send to each other , as well as environmental influences external to the colony [13] . However , the interplay between these factors is poorly defined with respect to variation in particular genes or regulatory domains in the genome . There are statistically detectable hereditary tendencies for particular honey bees to play particular social roles , but the individual bee’s social role is determined by the interactions between both social and environmental factors , as well as heredity . Understanding this complex interplay of internal and external factors is central to sociogenomics . One way to make a connection between honey bee and human sociogenomics is by inference of genetic orthology . Unfortunately , orthology is of necessity not verifiable in the same fashion as other techniques of bioinformatics , since it involves theoretical reconstruction of an evolutionary history that cannot be experimentally replicated . Thus , there is no reliable validation set on which to test a method . Different reasonable ways of creating orthologies may give significantly different results [14] . Whether one makes a liberal or conservative interpretation of orthological relationships produced by a particular method depends on the context , in particular whether one is concerned about contamination by false positive identifications of orthologs , or more concerned about loss of information by false negatives . In the present paper , we use a new application of orthology to test the hypothesis that the social behavior of honey bees and other metazoans , including humans , has common fundamental genomic building blocks . This paper seeks to explore the degree of relevant sequence conservation between honey bees and humans . Our starting point is the data set from Alaux et al [15] , who used microarrays to analyze differential brain gene expression patterns exhibited by individual honey bees of different genetic backgrounds , engaged in different social roles and in different colony environments . African and European honey bees are subspecies of the Western honey bee , Apis mellifera , and they differ from each other in their hive defense behavior in a number of ways that have been summarized as a social behavioral counterpart to variations of threshold and intensity of the “flight or fight” response seen in vertebrate organisms; African bees are much more aggressive than European bees [16] . In general , different phenotypes may arise from either differences in gene function or from different patterns of gene expression [17] . In the African and European honey bees it is presumed that the different phenotypes are largely the result of different patterns of gene expression , and differences in the expression of hundreds of genes in the brain have been reported [15] . Bees in Alaux et al were raised in a cross-fostered experimental design to examine the influences of genetic background and social environment on brain gene expression . We analyzed the above-cited [15] data sets to explore the following two questions: 1 ) to what extent are the differentially expressed genes associated with social behavior in the honey bee conserved across the Metazoa; and 2 ) through analysis of the highly conserved genes , is it possible to infer that there are likely to be gene co-expression patterns associated with social behavior that are common to a wide range of metazoans , including humans ?
Table 2 provides the overall summary of the results . At the 0 . 05 significance level ( based on Benjamini-corrected p-values ) , three of the sets were selectively enriched in genes conserved across the Metazoa: the Alarm_Pheromone set , the Old_vs_Young set , and the Soldier_CG ( colony genotype ) set . By the same standard of significance , the Guard_CG , Guard_WG ( worker genotype ) , and the Forager_WG sets were significantly depleted in highly conserved genes ( i . e . , the Benjamini-corrected p-value was over 0 . 95 ) . We examined the conservation pattern with each of the species used in the analysis , via a heat map , for the eight data sets ( Fig 3 ) . These analyses were based on the InParanoid orthology database ( Fig 3A ) and the OrthoMCL orthology database , which contained a smaller number of species ( Fig 3B ) . A relatively high degree of conservation was distributed across a wide range of metazoans for Old_vs_Young , Alarm_Pheromone , and Soldier_CG sets . For Soldier_CG and Old_vs_Young , the most significant conservation was within the insect group . For the Alarm_Pheromone set , on the other hand , Fig 3A and 3B indicate that the greatest degree of conservation was in mammals . Another way of visualizing the greater degree of conservation in mammals is in Fig 3C , which shows box-and-whisker plots of the distribution of p-values for orthology enrichment of the honeybee Alarm Pheromone set for the honeybee’s closest relatives ( arthropods ) and for human’s closest relatives , the highly social placental mammals . For both the InParanoid and the OrthoMCL databases , the degree of conservation clearly tends higher ( lower p-value ) for the mammals than for the arthropods . To test the statistical significance of the greater conservation of the Alarm Pheromone set in placental mammals we applied the Kolgomorov-Smirnov ( KS ) test , which is a standard method for assessing the significance of the difference between two unbinned distributions [19] . Fig 3D and 3E show the KS comparison cumulative fraction plots for the arthropod/placental mammal p-value distributions from the Alarm Pheromone gene set using the InParanoid and the OrthoMCL orthology databases , respectively . In these plots the horizontal axis represents the range of p-values for orthology enrichment and the vertical axis represents the fraction of species in each class whose p-values are below a particular level . The critical features of each graph are the quantity D , representing the maximum different between the plots for the two distributions , and a corresponding P ( The likelihood that the difference between the distributions arose by chance , which is a function of D and of the number of values in the two distributions; see Press et al , 1992 [19] , for exact expression for computing P ) . For the InParanoid set , the value of D is 0 . 75 , meaning that the lowest quartile of the p-values for the arthropods is within the range of the p-values for the placental mammals , while the upper 75% of the arthropod p-values is larger than any of the placental mammals . The value of P ( the likelihood that this discrepancy between the distributions arose by chance ) is . 001 . In Fig 3E , which shows the comparison cumulative fraction plots for the distributions as derived from the OrthoMCL data base , the value of D is 1 , because there is no overlap between the distributions . The largest p-value of any of the placental mammals is smaller than the smallest p-value for any of the arthropods . Therefore the value of P is vanishingly small . Based on these statistics , we confidently conclude that the genes differentially expressed in the honey bee in response to the alarm pheromone are systematically enriched in orthologs to genes in placental mammals . This finding suggests that , of all the gene sets analyzed , the set differentially expressed in response to the alarm pheromone stimulus was most likely to include genes from genomic networks common to honey bees and mammals . The analyzed gene expression data and the results of the orthology searches are provided in spreadsheet form in S3 Table . In order to be conservative in our assignment of orthologs ( minimize false positives , even at the expense of incurring false negatives ) we chose for detailed further analysis the set of 145 genes that were differentially expressed in the alarm pheromone response and conserved in all the Eutheria ( placental mammals ) species ( altogether 10 species in InParanoid , ranging from B . taurus to H . sapiens ) considered in this study . The p-value for over-representation of orthologs of placental mammals in this set was actually smaller than 1e-6 ( see Methods ) , which constitutes a correlation effectively impossible to have occurred by chance . Similarly , the most significantly conserved genes for all the insect species in the Old_vs_Young set were identified by a correlation effectively impossible to have occurred by chance ( also with a p-value smaller than 1e-6 ) . A larger set of genes ( conserved in mouse and human but not necessarily in all 10 eutherian species ) was also analyzed , as was a smaller set of genes conserved in all the vertebrates . Generally , the mouse-and-human set showed very similar GO enrichment patterns to the eutherian set , while the all-vertebrate set showed far fewer enriched ontology classes . Results of this analysis are provided in supplementary material . In each of the three classes of bees ( soldier , forager , guard ) where we have both a CG gene set ( differential gene expression between bees raised in predominantly African and European colonies ) and a WG gene set ( differential gene expression between genetically African and genetically European honey bee ) , we compared the degree of enrichment in orthologs with other metazoans . There was greater enrichment in orthologs in the CG set than in the WG set ( p = . 043 for guards , p < . 0005 for foragers , p < . 0005 for soldiers ) . The soldier cg-wg orthology is especially interesting for two reasons . Firstly the overall degree of orthology is much greater for the soldiers than for the foragers or guards . Secondly the most dramatic behavioral difference between the African and European bees is the behavior of the soldiers . The degree of difference between the soldier cg and wg orthologies is visualized in Fig 3F , which shows the cumulative fractional difference of the two distributions of p-values for pairwise orthology enrichment between the honey bee and the other 54 organisms represented in the analysis . It is important to note that the behavioral phenotype of the soldiers corresponds mainly to the phenotype of the colony in which they were raised . The cross-fostered soldiers are phenotypically much like the other soldiers in their colony , but differ in gene expression patterns . Our finding speaks to the general issue of the interaction between nature and nurture in defining social behavior , suggesting that if we wish to draw inferences for other metazoans from the different behavior of African and European honey bees , we must consider how the colony socializes individual bees . At the genomic level , this suggests that the overall genetic composition of African and European colonies ( which would presumably be reflected in the nurturing environment in the hive , but is beyond the scope of the current study ) is perhaps more important than the genetic differences of individual bees for understanding the broader comparative relevance of strain differences in behavior . Note also that the pattern of orthology enrichment across the metazoa is quite different for the soldier cg set than for the alarm pheromone set . Whereas the alarm pheromone set showed enriched orthology particularly for the highly social placental mammals , the orthology enrichment for the soldier cg set is higher for closer relatives to the honey bee , most notably the arthropods—most of whom are not eusocial . To summarize the orthology results: We used the DAVID suite of programs to identify Gene Ontology ( GO ) categories that were over-represented in the 145 alarm pheromone-responsive genes mentioned above , relative to their overall incidence in the human genome ( 131 of these 145 genes” human orthologs have Entrez annotations ) , at p-values of 0 . 01 and 0 . 05 ( Benjamini-corrected for multiple hypothesis assumption ) . For better comparison , we performed three separate GO analyses: 1 ) for all these 131 genes , 2 ) for the 73 up-regulated genes 3 ) for the 58 down-regulated genes . The results are summarized in Figs 4 and 5 and in Tables 3 and 4 . Tables 3 and 4 provide the names of the enriched GO categories , together with the p-value for their enrichment . The GO output analysis output , upon which Figs 4 and 5 and Tables 3 and 4 are based , is shown in spreadsheet form in S2 Table . The analysis below is based specifically on the gene set that was conserved among all the Eutheria . We also did the analysis on a larger set of genes conserved in the mouse and human but not necessarily in all the Eutheria . The results of that analysis was practically identical to the Eutheria-conserved set , so the verbal analysis below applies to that set as well . Several of the same GO categories appeared in the results of more than one of the three analyses ( up-regulated , down-regulated , all differentially expressed ) . Three GO categories were enriched in all three of the analyses , all three in the “Cellular Component” category ( Table 4 ) . They are: GO:0005737 ( cytoplasm—“All of the contents of a cell excluding the plasma membrane and nucleus , but including other subcellular structures” ) , GO:0005622 ( intracellular—“The living contents of a cell; the matter contained within ( but not including ) the plasma membrane , usually taken to exclude large vacuoles and masses of secretory or ingested material . In eukaryotes it includes the nucleus and cytoplasm . ” ) , and GO:0044424 ( intracellular part—essentially the same definition as GO:0005622 and with the same parent term , GO:0044464 ( cell part ) . ( Definitions in quotation marks are from EBI QuickGO[20] ) . The enrichment of these three terms in all of the three categories of differentially expressed genes means that few of the differentially expressed gene products reside in the plasma membrane , and both up-regulated and down-regulated genes were enriched for gene products found in other parts of the cell . The rest of the Cellular Component categories provided more specificity with respect to the locations of up-regulated and down-regulated genes . Examination of enrichment in “Biological Processes” categories revealed several insights ( Fig 4 ) . There was a strongly enriched GO category under “cellular component organization or biogenesis” [node 54]—“macromolecular complex subunit organization” [node38] ( Benjamini p-value = 0 . 0096 ) . This enrichment suggests that the human pattern orthologous to the expression pattern of the honey bee alarm pheromone response involves protein complex organization and biogenesis . This GO term was not significantly enriched for down-regulated genes . 2 ) “Cellular metabolic process” [node10] was also an enriched GO term ( Benjamini p-value = 0 . 016 ) . This suggests that the human pattern orthologous to the expression pattern of the honey bee alarm pheromone response involves modulation of metabolism . 3 ) More specialized categories within the “response to stimulus” GO term were “response to stress” [node 31] and “response to unfolded protein” [node 29] . Taken together , these enrichments suggest that the human response pattern that is orthologous to the honey bee alarm pheromone response also involves responses to chemical and possibly other stimuli . It is plausible that the response to unfolded protein seen in this section of the tree was related to protein metabolism and biogenesis , and the protein complex assembly that was simultaneously being up-regulated during the overall organism response as indicated in other parts of the tree . “Protein folding” [node 15] was also enriched . Gene Ontology analysis for molecular function revealed that that all the enriched GO terms fall under one general category—“binding” ( 22 ) . GO analysis for cellular component ( Fig 5 and Table 4 ) revealed the enrichment pattern included multiple cell components—cytoplasm , nucleus , mitochondria ( mostly significant for down-regulated genes ) and other organelles , protein , and possibly other macromolecular complexes . This was consistent with the biological processes and the molecular functions enriched in our analyses , which are localized in in a variety of cell components . Since the members of the gene set from which these inferences are derived were conserved across the eutherians , it is plausible that the inferences are valid for eutherians in general . However , it should be reiterated that the results described in this section do not refer to the totality of either the honey bee alarm pheromone response or of a complete network in humans and other vertebrates . Rather , they refer to components of the honey bee alarm pheromone response network that are widely conserved in eutherians and have a well-defined GO classification in humans . These components were presumably present and possibly part of an interacting network in the last common ancestor of the human and the honey bee about 670 million years ago . Both the honey bee alarm pheromone network and networks in eutherians that share these components will undoubtedly have other different non-shared components particular to their respective classes of organism . Tables 5 and 6 show genes in our analysis set annotated with enriched GO biological process terms that have been implicated in neural and behavioral disorders , and those biological process terms with which they are associated that are also included in the list of enriched terms for the complete alarm pheromone set . This list was constructed by manual inspection of literature and OMIM databases , so is not comprehensive . The results of a GO analysis for this set of 25 genes is given in S4 Table , showing all biological process terms enriched to a p-value of 0 . 05 or better . The overwhelming majority of the enriched biological processes relate to metabolism in a way that would pertain to many different types of cells in addition to brain cells . Protein folding and organization of macromolecular complexes also appear as enriched categories . These genes are selected for both a specific brain response in the honey bee and also for broad conservation in the placental mammals . The interesting feature of this analysis is the convergence of three factors: 1 ) implication in human mental disease , 2 ) differential expression in the honeybee in response to a conspecific language element ( the alarm pheromone ) and 3 ) broad conservation across the placental mammals . It appears at least in part that several varieties of mental illness are based on issues related to evolutionarily deeply rooted and broadly conserved genes , as opposed to being solely related to genes specific to human cognition and behavior , or even specific to brain or neural function .
This study was designed to examine the plausibility of the premise that the genomic networks underlying a response to a stimulus for social behavior ( alarm pheromone response in honey bees ) might have counterparts conserved in mammals , even though mammals do not use this particular alarm pheromone and the last common ancestor between honey bees and mammals lived approximately 670 million years ago [11] . Based on results from two different orthology databases , we found that the honey bee genes differentially expressed in response to alarm pheromone were more strongly conserved in orthologs to mammals than in orthologs to other metazoans , including those more closely related to the honey bee ( nonsocial insects ) . We hypothesize that these orthologous sets are conserved remnants of a network responding to conspecific signals that first emerged in a common ancestor of insects and vertebrates and has been selectively conserved in social metazoa . The reader will have noted that the experimental context of this paper was done on material from whole brains . For processing of conspecific signals such as spoken or written language in humans , many imaging studies show that several different regions of the brain are simultaneously activated . We therefore believe that whole brain studies such as ours are useful in revealing underlying commonalities of mechanism , but should be complemented by region-specific analyses . It should be noted that this particular study deals only with those parts of the putative conserved network that are differentially expressed in response to the external signal . There may be other genes that are part of the network , but are present at relatively steady levels . This may be the reason for the conspicuous lack of genes for plasma membrane proteins in the “cellular component” category of enriched GO classes found in this study ( Table 4 ) . Plasma membrane proteins must be involved in any response to external signals , but their role in mediating between extracellular stimuli and intracellular response does not necessarily require either up- or down-regulation in immediate response to the alarm pheromone stimulus . By contrast , genes in our study whose products reside in the nucleus were upregulated , genes in the mitochondria and other organelles were downregulated , and significant numbers of genes in the remainder of the cell were differentially regulated in both directions . Our results indicate that alarm pheromone exposure triggers significant physical remodeling of intracellular molecular signaling machinery . At the core of sociality is the ability to transmit and respond to complicated signals from conspecifics [21] . This is widely thought to involve the ability of nervous systems to rapidly increase the activity of some cellular networks and reduce the activity of others in response to these signals [22] . Our results suggest that there is another level of complexity enabled by the ability to remodel macromolecular interaction networks within cells in response to a transient signal from conspecifics , such as alarm pheromone . This remodeling would allow for changes in responses to subsequent signals , i . e . , for stimuli experienced presently to enable individuals to “predict” the future . Since our results are based on enrichment of orthologous genes between honey bees and mammals , this hypothesis implies the original development of this remodeling ability in an ancient common ancestor of mammals and insects . Based on these results we offer the following speculation about possible mechanisms for macromolecular remodeling within brain cells and organismic sociality . The time scales for protein folding , for binding reactions , and for assembly of macromolecular complexes from pre-existing elements , can be fractions of a second , so these processes can take place rapidly enough to be consistent with the time scale of the alarm pheromone response . However , transcription and translation of genes will take many seconds or minutes [23] . The necessarily faster time scale for the alarm pheromone response suggests involvement of a more rapid remodeling process , perhaps involving microRNAs , which have for several years been postulated to play a role in synaptic plasticity [24] . The recently developed CLIP-seq technology [25] permits comprehensive identification of microRNA binding sites in a variety of tissues , including the brain [26] . Thus it should be possible in the future to explore this speculation and experimentally characterize the roles of specific microRNA in brain remodeling in response to conspecific signals . Perhaps one aspect of the dichotomy between highly social and solitary animals is in the ability of the individual brain cells in social animals to remodel their interaction networks in response to signals from conspecifics . This ability would not come without a tradeoff , since maximal speed of response would be achieved by activating existing hard-wired networks . Thus evolutionary niches have persisted for both highly social and less social animals , with less social animals optimized for speed of response to all stimuli by activating hard-wired circuits , while highly social animals have developed the ability to remodel molecular circuits in response to signals from conspecifics—a process which results in necessarily slower response . This may also apply to the evolution of the most complex form of conspecific communication–human language . In this view the corresponding circuits underlying honey bee chemical language and human auditory language would be “phenologs”; that is , varying phenomes based on orthologous genes [27] .
The p-values in Table 2 for the average number of metazoan orthologs for each data set were computed as follows: For each experimental data set , random sets of matching size were sampled from the 7462 honey bee genes that were present in InParanoid database [18] and spotted on the array , and the average number of orthologs per gene was calculated for each random set . This random sampling was repeated one million times and the number of random sets with average ortholog number equal to or larger than the experimental set was counted . The count divided by 106 gave us the p-value for the average ortholog number of the test set . S1 and S2 Figs show how the p-values of the average ortholog number of Forager_CG and Alarm_Pheromone sets were calculated . The p-values for the total number of orthologs of each set for each species ( Fig 3 ) were computed similarly . For calculating the p-value for the CG-WG difference , the KS-test p-values for the CG-WG difference for Soldier , Forager and Guard ( 0 . 026 , . 122 , and . 612 respectively ) were combined using Fisher’s method [28] . For calculating the p-value for over-representation of orthologs of placental mammals in the Alarm_Pheromone set and over-representation of orthologs of insects in the Old_vs_Young set , p-values in each species ( S1 Table ) were also combined using Fisher’s method . In presenting and discussing the results , we use the term “conserved” to be measured by the number of orthologs that a particular sequence has; i . e . , the more orthologs a gene or protein has in other species , the more “conserved” the gene is . Enrichment of the conserved gene sets in particular Gene Ontology categories was determined using the functional annotation tool in the Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) [29] . All parameters are default except that we use GO_TERM_*_ALL instead of GO_*_FAT . Extra functional analyses ( of various qualities ) were also included: OMIM_Disease [30] , COG_Ontology [31] , SP_PIR_Keywords [32] , Up_Seq_Feature [33] , BBID [34] , BioCarta [35] , Kegg_Pathway [36] , Interpro Domains [37] , Pir_Superfamily [38] , and Smart [39] . The raw Gene Ontology results of “Eutheria-conserved” , Alarm_Pheromone genes are listed in S2 Table . Figures of Gene Ontology trees ( Figs 4 and 5 ) were generated by Python scripts and Cytoscape [40] . Benjamini-Hochberg corrected p-values provided by DAVID are used for indication of significance [29] . Scientific references about the relationship between behavior/neural functions and genes associated with significant GO terms were identified with GeneCard and manual search with Google Scholar , using keywords “behavior” , ”disease” , ”neural” , and”aggression” . First , honey bee genes that showed up on the microarray studied in Alaux et al [15] were selected . This was done based on the annotation file of the Honey Bee Oligonucleotide Microarray [15] . Out of many available methods [14] of defining orthologs , two were chosen , InParanoid [18] and OrthoMCL [41] . InParanoid has the most extensive coverage of the honey bee proteome and other proteomes of completed genomes in searchable ortholog databases . Out of all these “microarray-present” honey bee genes , we identified those that are also present in InParanoid . This was done by mapping the BeeBase IDs ( which are the IDs used in the data set from Alaux et al [15] ) to NCBI Refseq IDs ( which are the IDs used in InParanoid for honey bee ) . 7462 of these “microarray-present” honey bee genes are present in InParanoid . At the time of the analysis , there were 100 eukaryotic species in InParanoid with 54 of them ( including Apis mellifera ) being metazoan species . With S . cerevisiae added as a control , the data set used for our analysis had 55 species , which we interrogated for orthology with the 7462 InParanoid honey bee proteins . | Sociogenomics explores the relationship between social behavior and the genome . An important issue is the extent to which results from social insects can be used to understand social behavior in other animals . We address this question through computational studies of previously published experimental data on patterns of brain gene expression in honey bees in response to particular environmental conditions and stimuli . We found that for one particular stimulus , response to alarm pheromone , the set of honey bee genes differentially expressed in the brain contains disproportionately large numbers of genes also found in mammals , including humans . This enrichment in orthologous genes suggests surprisingly strong similarities in socially responsive genetic circuits common to honey bees and mammals . A large number of the human counterparts of these genes are important for regulating protein folding , protein metabolism , and regulation of protein complex formation , perhaps reflecting changes in macromolecular complexes involved in remodeling critical neural circuits in response to the alarm pheromone . Noting that alarm pheromone is a component of the honey bee’s communication system , we hypothesize that such rapid remodeling may be an adaptation in the brain cells of social animals to deal with the complex patterns of conspecific signaling essential for social organization . | [
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... | 2016 | Conservation in Mammals of Genes Associated with Aggression-Related Behavioral Phenotypes in Honey Bees |
Infection by large dsDNA viruses can lead to a profound alteration of host transcriptome and metabolome in order to provide essential building blocks to support the high metabolic demand for viral assembly and egress . Host response to viral infection can typically lead to diverse phenotypic outcome that include shift in host life cycle and activation of anti-viral defense response . Nevertheless , there is a major bottleneck to discern between viral hijacking strategies and host defense responses when averaging bulk population response . Here we study the interaction between Emiliania huxleyi , a bloom-forming alga , and its specific virus ( EhV ) , an ecologically important host-virus model system in the ocean . We quantified host and virus gene expression on a single-cell resolution during the course of infection , using automatic microfluidic setup that captures individual algal cells and multiplex quantitate PCR . We revealed high heterogeneity in viral gene expression among individual cells . Simultaneous measurements of expression profiles of host and virus genes at a single-cell level allowed mapping of infected cells into newly defined infection states and allowed detection specific host response in a subpopulation of infected cell which otherwise masked by the majority of the infected population . Intriguingly , resistant cells emerged during viral infection , showed unique expression profiles of metabolic genes which can provide the basis for discerning between viral resistant and susceptible cells within heterogeneous populations in the marine environment . We propose that resolving host-virus arms race at a single-cell level will provide important mechanistic insights into viral life cycles and will uncover host defense strategies .
To examine the variability within infected E . huxleyi cells , we measured the expression levels of selected host and viral genes over the course of infection at a single-cell resolution . Cells were isolated during infection of E . huxleyi CCMP2090 at different phases , at 0 , 2 , 4 , 24 hours post infection ( hpi ) ( Fig 1 , Fig A in S1 Text ) . We used the C1 single-cell Auto Prep System to sort and extract RNA from single E . huxleyi cells during viral infection by EhV201 . The presence of a single cell captured in an individual isolation chamber was confirmed by microscopic inspection of emitted chlorophyll auto-fluorescence ( Fig 2A ) . In order to detect variability in viral infection states , we conducted simultaneous measurements of expression profiles of host and virus genes at a single-cell level by using multiplexed qPCR . We selected viral genes encoding for sphingolipid biosynthesis as well as gene markers for early and late infection [18 , 62] . Selected genes involved in host metabolic pathways were targeted based on previous reports which demonstrated their functional role during infection , including primary metabolism ( glycolysis , fatty acid biosynthesis ) , sphingolipid and terpenoid metabolism , autophagy and antioxidant genes [18 , 27 , 33 , 34] . In addition , we examined the expression of host genes associated with life cycle [63] , meiosis and PCD [32] that exhibited induction during infection . ( In total expression of 107 host genes and 10 viral genes was measured , see S1 Table for primers list ) . To test for the sensitivity in detection of gene expression on a single cell level , we spiked-in , to each C1 well , a set of External RNA Controls Consortium ( ERCC ) molecules that span a wide range of RNA concentrations ( from ~0 . 5 to ~100 molecules per well ) . We subsequently quantified their concentration using similar qPCR amplification setup as used for the host and virus genes . Pairwise correlation between spike concentrations and Et ( Et = 30-Ct ) values obtained from the qPCR was >0 . 98 ( Pearson correlation coefficient , p-value = 4 . 2˙10−12 , Fig 2B ) . We found a highly sensitive level of detection with 40% probability to detect an RNA spike that is at a level of 1 molecule per sample ( Fig 2C ) , similar to the detection level reported for mammalian cells [64] . Mean expression of viral and host genes in all examined cells were found to be 11 . 8 ± 4 . 0 and 6 . 96 ± 2 . 5 ( Et values ± SD ) , respectively ( Fig 2D ) . We detected a high variability in viral expression profiles among individual cells within the same infected population . For example , heterogeneity in the expression levels of virus-encoded ceramide synthase ( vCerS , EPVG_00014 ) , a key enzyme in sphingolipid biosynthesis [18 , 30] was detected during early phase of infection ( 2 and 4 hpi of CCMP2090 , Fig 3A ) . Similar results were obtained for the average expression of 10 viral genes ( Fig 3B ) . At the onset of viral lytic phase ( 24 hpi ) , all of the examined cells showed high viral gene expression ( Fig 3A and 3B ) , suggesting that viruses eventually infected all of the examined host cells . Nevertheless , we cannot exclude the existence of a rare subpopulation that did not express viral genes . The observed heterogeneity in viral expression is probably not a result of infection with defective viruses since no viral expression was detected using UV-inactivated virions ( Fig B in S1 Text ) . A possible source of the observed heterogeneity is the asynchronous state of cells in the initial culture resulting in a difference in cell cycle phase and metabolic state between individual cells . Principal component analysis ( PCA ) of viral gene expression among individual host cells showed that infected cells are distributed across distinct states of viral expression levels ( Fig 3C ) . All viral genes had positive and similar coefficients for the PC1 component which captures >90% of the variability of viral gene expression and found to be highly correlated to the average viral infection level ( r = 0 . 99 , Pearson linear correlation ) . These results demonstrate that PC1 reflected the intensity of viral infection . Accordingly , we used the score value of PC1 as an index for the level of expression of viral genes in each individual cell and termed it “infection index” . We further realized that averaging host gene expression over the course of infection might hinder our ability to observe the initial response of the host to viral infection and that single-cell analysis could significantly increase the resolution for sensitive detection of host response at this early stage of infection . We therefore re-ordered infected cells based on their viral infection index ( PC1 ) , rather than the actual time of infection ( i . e . hpi ) , resulting in “pseudotemporal” hierarchy of single cells ( Fig 4 ) . Intriguingly , we unmasked a fraction of cells that were exposed to the virus but did not exhibit any detectable expression of viral genes . These cells had similar infection index values as control cells , with PC1 values < -10 ( Fig 4A ) . We found that 33/179 ( 17% ) of infected cells of CCMP2090 were at this distinct “lag phase” of viral infection . These individual cells were analyzed for their respective host gene expression levels based on a sliding window approach ( Fig 4B and 4C ) , as it is less sensitive to technical noise , often observed in single cell data . We also used a statistical model to test for genes that are differentially expressed at these early stages of viral infection . This model incorporates the two types of heterogeneity that usually appear in single cell data , namely , the percentage of cells expressing a gene in a given population ( e . g . Et value > 0 ) and the variability in expression levels in cells exhibiting positive expression values [65] . Up-regulation of several host genes in infected cells was detected in this subpopulation ( Fig 4C and S2 Table ) . An intriguing example is the metacaspase-2 gene ( p = 0 . 0000027 ) which was previously suggested to be induced and recruited during EhV lytic phase and activation of E . huxleyi PCD [32] . We also found early induction of triosephosphate isomerase ( TPI , p = 0 . 00063 ) and phospholipid:diacylglycerol acyltransferase ( PDAT , p = 0 . 0018 ) which are involved in glycolysis and TAG biosynthesis respectively . In addition , genes involve in autophagy [34] and de novo sphingolipid biosynthesis [18 , 30] were detected in this unique early phase of host response . Since major alteration in these specific metabolic pathways were recently shown to be essential for EhV infection [14 , 18 , 20 , 21 , 27 , 30 , 31 , 33 , 34] , early induction of these pathways may serve as an effective viral strategy to prime optimal infection . Alternatively , this phase of early host response prior to viral gene expression may represent a newly unrecognized phase of immediate host anti-viral defense response . At the late stages of infection ( infection index >10 ) , we observed induction of several meiosis-related genes , including HOP1 and MND , two SPO11 homologues and MYB in CCMP2090 ( Fig 4B ) . These results are in agreement with previous studies that suggested a phenotype switch of E . huxleyi to evade viral infection [38] and propose the induction of meiosis-related genes as part of transcriptomic reprogramming of during infection [63] . Further inspection of the PCA analysis showed the cells exhibiting low to moderate level of PC1 were highly variable in their PC2 level ( Fig 3C ) . To identify the viral genes that contribute to this variability , we further examined the correlation coefficients between the viral gene expression and principal components 2 ( PC2 ) . Interestingly , this analysis revealed a positive correlation ( r = 0 . 53 ) between PC2 and the expression level of viral RNA polymerase gene ( EPVG_00062 ) which was previously reported to be expressed at early-mid phases of infection [18 , 62] , while a negative correlation ( r = -0 . 44 ) was found for a viral gene ( EPVG_00010 ) that is known to be expressed at late phases of infection . Accordingly , cells with low PC2 levels expressed EPVG_00010 and not EPVG_00062 , while cells with high PC2 values exhibited the opposite trend ( as compared with Fig 5A and 5B ) . To further characterize host gene expression during different phases of infection , we manually clustered CCMP2090 cells according to their infection index ( PC1 ) and the expression of either early or late viral genes ( PC2 ) ( Fig 5C ) and examined the expression of host metabolic genes in these clusters ( Fig 5D ) . This analysis showed that induction of most of host metabolic genes occurred in cells that expressed predominantly late viral genes ( Fig 5D , CL5 , -10<PC1<10 , PC2>-5 ) and in cells with moderate expression of viral genes ( Fig 5D , CL6 , 10<PC1<36 ) . Down-regulation of many host genes was found in cells exhibiting high viral expression ( Fig 5D , CL7 , PC1>37 ) , suggesting that these cells were at the final stages of infection . In order to further explore the link between optimal host metabolic state and efficient viral infection , we infected CCMP2090 stationary culture and subjected single cells to dual gene expression analysis at 2 hpi ( Fig 6A ) . While most of the exponential growing cells exhibited viral expression , we detected only moderate viral expression in 3/27 ( 11% ) of the stationary phase cells ( Fig 6A ) , while the rest of the cells had viral expression patterns similar to uninfected cells ( control ) . In parallel , stationary phase cells ( either control or infected ) exhibited down-regulation of most of the examined host metabolic genes , in contrast to their general up-regulation in infected exponential phase cells ( Fig 6B ) . These data suggest that the cell-to-cell variability in host metabolic state may play important role in determining susceptibility to infection by large viruses with high metabolic demand . “Kill the Winner” is a key theory in microbial ecology which suggests that viruses shape diversity of microbial populations by infecting the most dominant proliferative host [66] . We propose that “Kill the Winner” may even act within isogenic populations based on the variability in the metabolic state , which will lead to differential susceptibility to viral infection , forming continuous host-virus co-existence [67] . It is possible that cell-to-cell heterogeneity in the metabolic activity is shaped by the tradeoff between complex abiotic stress conditions ( e . g . nutrient availability [68–70] and light regime [71] ) and biotic interactions ( e . g . bacterial pathogenicity [72] ) , and may result in differential susceptibility to viral infection in the marine environment . We further investigated whether uninfected susceptible and resistant E . huxleyi cells exhibited altered expression profiles in the host metabolic genes that showed variable expression during infection ( S3 Table ) . We exposed E . huxleyi cultures to viral infection and isolated cells that acquired resistance to subsequent viral infection of diverse EhV isolates ( Fig 7A , [63] ) . We compared the expression profiles of recovered resistant cells ( n = 18 ) to their mother cells that were highly susceptible to viral infection ( n = 76 ) . The tendency of resistant cells to aggregate make it difficult to isolate single cells , therefore for these analysis also included doublet cells . Intriguingly , resistant and susceptible cells tend to cluster distinctively along the PC2 dimension ( Fig 7B ) . Among the genes that drive the separation along the PC2 dimension and were differentially expressed in resistant and susceptible cells were TPI , diphosphomevalonate decarboxylase ( MVD1 ) and ceramidase-3 ( Fig 7C ) which are key enzymes in glycolysis , terpenoid and sphingolipid metabolism , respectively . Since de novo ceramide biosynthesis is uniquely encoded in the EhV genome , activation of ceramidase may serve as an anti-viral host response [18 , 30] . Interestingly resistant cells also exhibited high expression of metacaspase2 which was also highly expressed in cells with no viral expression in early phase of infection ( Fig 7C ) . This data suggests that susceptibility to viral infection has a clear signature in expression profiles of host genes detected on a single-cell level . Although the mechanism for resistance of E . huxleyi to viral infection requires further investigations , the differential regulation of host metabolic genes suggests a unique specialized metabolism that differs from that of susceptible cells [73–75] . Future single-cell RNA-seq transcriptomic studies will provide high throughput identification of gene markers that are specific for resistant strains as well as new mechanistic insights into the molecular basis for resistance mechanisms . Tracking host-virus dynamics at the single cell resolution provides a novel approach to characterize the continuum viral infection states and host responses which is commonly masked in whole population analysis [76] . By applying dual gene expression profiling during algal host-virus interactions , we uncovered an early host transcriptional responses . This newly defined phase can result in different scenarios including , resistant cells , cells infected by defective virions , cells exposed to chemical cues released during infection and cells at the very early stage of infection . The new ability to define distinct “infection states” on a pseudo-temporal manner can provide valuable information regarding the dynamics of active viral infection in “real time” in the natural environment . Clustering of individual cells based on their specific transcriptomic signatures will uncover the relationship between host metabolic states and specific phenotypes associated with differential levels of viral infection or modes of resistance in natural populations . In situ quantification of the fraction of infected cells , their infection and metabolic states and the fraction of resistant cells will provide important insights into the infection dynamics and may provide fundamental understating of host-virus co-existence strategies in the ocean . Resolving host-virus interaction on a single cell will promote discovering of novel sensitive biomarkers to assess the ecological impact of marine viruses and their role in regulating the fate of algal blooms in the ocean .
Cells of the non- calcified CCMP2090 E . huxleyi strain were cultured in K/2 medium [77] and incubated at 18°C with a 16:8 h light– dark illumination cycle . A light intensity of 100 μM photons·m-2·s-1 was provided by cool white LED lights . Experiments were performed with exponential phase ( 5·105–1·106 cells·ml-1 ) or stationary phase ( 5·106 cells·ml-1 ) cultures . E . huxleyi virus EhV201 ( lytic ) used for this study was isolated originally in [12] . E . huxleyi was infected with a 1:50 volumetric ratio of viral lysate to culture . By using plaque assay we counted the infectious particles of EhV lysate commonly use in our lab and found that the concentration of infectious particles is around 2 . 5*107–5*107 ·ml-1 ( which is around 20% of particles counted by flow-cytometry ) . Thus , at the time of infection , there is about one infectious particle per cell . For virus deactivation , 15 ml of 0 . 45 μm filtered viral lysate was placed in a Petri plate and radiated on uvitec ( Cambridge , UK ) ultra-violate light table , with 312 nm light for 15 min . To evaluate the infectivity of the deactivated viral lysate plaque assay was performed indicating a reduction of 8 fold in the number of infective particle per mL in the deactivated ( ~20 infective particles per mL ) in comparison to the active viral lysate ( 108 infective particles per mL ) . For single-cell analysis , E . huxleyi cells were concentrated to 2 . 5·106 cells·ml-1 by gentle centrifugation ( 3000 RPM , 3 min ) prior to single-cell isolation . To compare between viral infection in exponential and stationary phases , stationary phase cells were diluted to similar concentration of exponential phases cells using stationary conditioned medium ( 5·105–1·106 cells·ml-1 ) and then infected by EhV . The growth dynamics of E . huxleyi CCMP2090 were monitored in seawater-based K/2 medium in control conditions and in the presence of the lytic viral strain EhV201 . Resistant single cells were isolated after infection by mouth-pippetting over multiple passages through fresh medium under an inverted microscope as described in [63] . Single isolates were maintained in K/2 medium . Cells were monitored and quantified using a Multisizer 4 Coulter counter ( Beckman Coulter , Nyon , Switzerland ) . For extracellular viral production , samples were filtered using 0 . 45 μM PVDF filters ( Millex-HV , Millipore ) . Filtrate was fixed with a final concentration of 0 . 5% glutaraldehyde for 30 min at 4°C , then plunged into liquid nitrogen , and stored at -80°C until analysis . After thawing , 2:75 ratio of fixed sample was stained with SYBER gold ( Invitrogen ) prepared in Tris–EDTA buffer as instructed by the manufacturer ( 5 μl SYBER gold in 50 mL Tris–EDTA ) , then incubated for 20 min at 80°C and cooled down to room temperature . Flow cytometric analysis was performed with excitation at 488 nm and emission at 525 nm . Calculation of infectious particles during infection ( Fig C in S1 Text ) was done by using the most probable number ( MPN ) method as described in [20] . Briefly , medium was of infected culture was subjected to a series of fivefold dilutions for each sample . Each dilution ( 10 μl ) was then added , in six technical replicates , to 100 μl of exponentially growing E . huxleyi cultures in multiwell plates over four or five days . MPN was calculated using the MPNcalc software . Single cells were captured on a C1 STA microfluidic array ( 5–10 μm cells ) using the Fluidigm C1 and imaged on IX71S1F-3-5 motorized inverted Olympus microscope ( Tokyo , Japan ) to examine chlorophyll autofluorescence ( ex:500/20 nm , em:650 nm LP ) . Only wells that exhibited chlorophyll autofluorescence signal emitted from single cells were further analyzed . External RNA Controls Consortium ( ERCC ) spikes were added to each well in a final dilution of 1:40 , 000 . Cells were lysed and pre-amplified cDNA was generated from each cell using the Single Cells-to-CT Kit ( Life Technologies ) . Pooled qPCR primers and Fluidigm STA reagents were added according to manufacturer’s recommendations . Preamplified cDNA was then used for high-throughput qPCR measurement of each amplicon using a BioMark HD system . Briefly , a 2 . 7 μl aliquot of each amplified cDNA was mixed with 3 μl of 2X SsoFast EvaGreen Supermix with Low ROX ( Bio-Rad ) and 0 . 3 μl of 20X DNA Binding Dye Sample Loading Reagent ( Fluidigm ) , and 5 μl of each sample mix was then pipetted into one sample inlet in a 96 . 96 Dynamic Array IFC chip ( Fluidigm ) . Individual qPCR primer pairs ( 50 μM , S1 Table ) in a 1 . 08 μl volume were mixed with 3 μl Assay Loading Reagent ( Fluidigm ) and 1 . 92 μl Low TE , and 5 μl of each mix was pipetted into one assay inlet in the same Dynamic Array IFC chip . Subsequent sample/assay loading was performed with an IFC Controller HX ( Fluidigm ) and qPCR was performed on the BioMark HD real- time PCR reader ( Fluidigm ) following manufacturer’s instructions using standard fast cycling conditions and melt-curve analysis , generating an amplification curve for each gene of interest in each sample ( cell ) . Data was analyzed using Real-time PCR Analysis software ( Fluidugm ) with the following settings: 0 . 65 curve quality threshold , linear derivative baseline correction , automatic thresholding by assay ( gene ) , and manual melt curve exclusion . Cycle threshold ( Ct ) values for each reaction were exported . As seen in other applications of this technology [65] , the data had a bimodal distribution with some cells ranging from 2 . 5 Ct to 30 Ct , and another set of cells with Ct >40 . Similar bimodal distribution was also observed for the ERCC spikes . Accordingly , we set the minimal threshold level of detection to 30 Ct and calculated expression threshold values ( Et ) by linear transformation of the data so that minimal Et was zero ( 30 Ct ) . For heat map visualization , expression data was normalized by subtracting the mean of each gene and dividing it with its standard deviation across cells . Single-cell PCR data was analyzed and displayed using MATLAB ( MathWorks ) . Additional statistical analyses were performed using The SingleCellAssay R package [65] . Calculation of number of spike molecule per Fluidigm C1 well was performed according to [64] . | Almost all of our current understanding of the molecular mechanisms that govern host-pathogen interactions in the ocean is derived from experiments carried out at the population level , neglecting any heterogeneity . Here we used a single cell approach to unmask the phenotypic heterogeneity produced within infected populations of the cosmopolitan bloom-forming alga Emiliania huxleyi by its specific lytic virus . We found high variability in expression of viral genes among individual cells . This heterogeneity was used to map cells into their infection state and allowed to uncover a yet unrecognized host response . We also provide evidence that variability in host metabolic states provided a sensitive tool to decipher between susceptible and resistant cells . | [
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"mi... | 2019 | Unmasking cellular response of a bloom-forming alga to viral infection by resolving expression profiles at a single-cell level |
The generation of two non-identical membrane compartments via exchange of vesicles is considered to require two types of vesicles specified by distinct cytosolic coats that selectively recruit cargo , and two membrane-bound SNARE pairs that specify fusion and differ in their affinities for each type of vesicles . The mammalian Golgi complex is composed of 6–8 non-identical cisternae that undergo gradual maturation and replacement yet features only two SNARE pairs . We present a model that explains how distinct composition of Golgi cisternae can be generated with two and even a single SNARE pair and one vesicle coat . A decay of active SNARE concentration in aging cisternae provides the seed for a cistrans SNARE gradient that generates the predominantly retrograde vesicle flux which further enhances the gradient . This flux in turn yields the observed inhomogeneous steady-state distribution of Golgi enzymes , which compete with each other and with the SNAREs for incorporation into transport vesicles . We show analytically that the steady state SNARE concentration decays exponentially with the cisterna number . Numerical solutions of rate equations reproduce the experimentally observed SNARE gradients , overlapping enzyme peaks in cis , medial and trans and the reported change in vesicle nature across the Golgi: Vesicles originating from younger cisternae mostly contain Golgi enzymes and SNAREs enriched in these cisternae and extensively recycle through the Endoplasmic Reticulum ( ER ) , while the other subpopulation of vesicles contains Golgi proteins prevalent in older cisternae and hardly reaches the ER .
The Golgi apparatus is composed of multiple compartments , called cisternae , typically 6–8 in mammalian cells . The individual cisternae are enriched in glycosylation and other enzymes , which form distinct but overlapping gradients with peaks in the cis , medial or trans cisternae [1] . As anterograde cargo traverses the Golgi apparatus from cis to trans , it becomes modified by Golgi enzymes in an assembly-line fashion . Efficient and correct cargo processing depends on the distribution of glycosidases , glycosyltransferases and other enzymes within the different Golgi sub-compartments in their expected order of function [2] . Several mechanisms for cargo movement through the Golgi apparatus have been proposed . Of those , the cisternal maturation hypothesis is best supported by all available experimental data [3] , [4] . According to this concept , cargo enters the Golgi by fusion of Endoplasmic Reticulum ( ER ) -derived vesicles with each other that form a new cisterna at the cis face of the Golgi . The cargo exits the Golgi in transport carriers that emerge from the trans most cisterna when it disintegrates , thus maintaining the Golgi apparatus at a steady state . Individual cisternae mature by shedding their characteristic Golgi enzymes and at the same time acquiring Golgi resident proteins from the more trans cisterna [5] , [6] ( Fig . 1A ) . It has been shown that Golgi resident proteins shuttle between the cisternae in vesicles [7] , [8] , [9] . But how do individual cisternae acquire and maintain their specific and distinct enzyme compositions via vesicular transport while the Golgi apparatus undergoes maturation ? Glick et al . provided one piece of explanation with a simple model according to which competition of Golgi proteins for incorporation into retrograde-destined vesicles accounts for their sorting within the Golgi cisternae [10] . Proteins that are good competitors are efficiently removed from the maturing cisternae and accumulate in the cis Golgi while proteins that are poor competitors can only enter vesicles after the good competitors have been depleted , and thereby end up in more trans cisternae . While this model explains steady enzyme segregation , it is based on an unexplained premise , namely , that the Golgi-enzyme containing vesicles preferentially fuse with the younger rather than the older cisternae . Fusion of vesicles with acceptor membranes is specified by Soluble N-ethyl-maleimide-sensitive factor Attachment protein Receptors ( SNAREs ) , integral membrane proteins that reside in the vesicle and target membrane [11] , [12] . They function according to a key-lock principle: Cognate SNAREs form a four-helical bundle , with one chain contributed by a R-SNARE on one membrane and one heavy and two light chains provided by corresponding Q-SNAREs on the opposite membrane to pull donor and acceptor membranes close enough to fuse [13] . Theoretical work by Heinrich and Rapoport has shown that sets of compatible SNAREs with preference for incorporation into a specific type of coated vesicle can spontaneously generate and maintain non-identical compartments [14] when each compartment features a specific pair of compatible SNAREs and corresponding vesicle type . The Golgi however , maintains its 6–8 compartments with only 2 cognate SNARE pairs and one type of vesicle ( COPI ) [15] . How is this accomplished ? A higher concentration of SNARE complexes in younger compared to older cisternae could readily explain the preference for retrograde fusion of COPI vesicles , which in turn can yield the differential enzyme peaks as described by Glick et al . [10] . A cis-to- trans decrease is indeed observed for Golgi Q-SNAREs [15] ( Fig . 1C ) . But how are these SNARE gradients established in the first place ? To complicate matters , a R-SNARE implicated in intra-Golgi traffic forms a counter-current gradient with increasing levels from cis-to- trans , [16] [17] , ( Fig . 1C ) . How is this compatible with retrograde transport ? We present a model of inter-cisternal vesicular transport in which we do not assume any a priori asymmetry within the Golgi apparatus . The transport is mediated by 2 cognate SNARE pairs , which compete with each other and with other Golgi residents for incorporation into a single vesicle type . The retrograde directionality of vesicular flux is triggered by the temporal decrease of the concentration of cisternal SNAREs , which occurs via loss of SNARE-containing vesicles , including the recycling of COPI vesicles from the Golgi to the ER , decay , and inhibition of SNARE molecules . As a result , cisternal age becomes a distinguishing factor: trans cisternae are older than cis cisternae and thus contain fewer SNAREs . A small distinction in SNARE concentrations provides the seed for a cistrans gradient , which becomes self-enhanced by vesicular transport of the SNAREs . The steady SNARE gradient controls a predominantly retrograde vesicular flux in which Golgi enzymes with stronger affinities for the coated vesicles cycle predominantly between the cis cisternae and the ER , while weaker-binding enzymes only enter vesicles from later cisternae and exhibit less ER retrieval .
We assume that Functioning of the model hinges on two general principles: Establishment and maintenance of a directed retrograde vesicular flux and sorting of the vesicular cargo via competition for binding sites . To reveal the universality of the proposed self-establishing mechanism of vesicular traffic directionality we first consider the simplest possible setup , a single cognate SNARE pair and vesicle type . We assume that the rate of vesicular fusion is proportional to the product of the concentrations of the SNAREs present in vesicles and cisternae , respectively . The precise nature of SNARE molecules does not have to be specified here . We can even consider the SNAREs as mere proxy for fusion-specifying factors . The probability for a vesicle to fuse with a given cisterna depends solely on the cisternal concentration of compatible SNAREs , and cisternae with higher SNARE concentration have a higher probability to absorb vesicles . A retrograde vesicular flux thus requires a cistrans gradient in cisternal SNAREs . We propose that key to a robust cistrans SNARE gradient is the observation that all systems , living and otherwise , function with a loss . As Golgi cisternae mature they inevitably lose active SNARE molecules . Such a decay of active SNAREs breaks the symmetry between the otherwise identical cisternae in a systematic way: The older trans cisternae contain less SNAREs than the younger cis cisternae . The SNARE loss can occur by escape of SNARE-carrying vesicles that fuse with the ER thus recycling their content . However , some of the cisternal SNARE decay is likely due to irreversible loss that requires some new SNARE synthesis to replenish the system . The “seed” SNARE gradient generated in this manner sets a preference for vesicles to fuse with cis rather than trans cisternae , thus initiating the directed vesicular transport . As SNAREs are transported retrograde , their cistrans gradient is further enhanced . When the vesicular flux becomes balanced by the anterograde transport of SNAREs due to cisternal maturation , the system comes to a steady state . Indeed , we show both numerically and analytically that the seed gradient , created by the temporal decay of SNAREs , is self-enhancing ( Figs . 2a and 5 , which is presented in Methods , and Eqs . ( 11 , 12 , 13 ) ) . Importantly , while vesicular transport significantly increases the seed gradient produced by SNARE loss , without that loss vesicular transport by itself cannot produce or maintain any gradient , ( see Eq . ( 13 ) and subsequent illustrations in Methods ) . This is in accordance with the results of [14] that the single SNARE pair/single coat minimal system cannot spontaneously break the initial symmetry of compartments . The constant progression of cisternae is equally important for maintaing the steady state SNARE gradient and directional vesicular flux . Without the progression , the seed SNARE gradient would have been equilibrated via vesicular transport . We note that at steady state the vesicular flux does not depend on the concentration of SNAREs in the vesicles: Lower concentrations of vesicular SNAREs are compensated by a higher steady state number of vesicles . Naturally , a vesicle should contain a minimum number of vesicular SNARE molecules to ensure any fusion at all . The calculation of the steady state SNARE gradient and vesicular flow are presented in the Methods section . Next , we investigated how retrograde vesicular flow , created by the cisternal SNARE gradient , maintains the inhomogeneous steady state distribution of Golgi enzymes during cisternal maturation . To this end , we further developed the principle proposed by Glick et al . that attributes the different cisternal enzyme profiles to the competition of enzymes for the binding sites in vesicles [10] . For simplicity , we assume three categories of Golgi enzymes with peaks in cis , medial and trans cisternae , and with strong , intermediate and weak affinities for vesicular binding sites , respectively ( Schematically depicted in Fig . 1B ) . Unlike in earlier models [10] and [21] , the fraction of binding sites occupied by each type of enzyme is determined by mass action equilibrium . Also , in contrast to [10] and [21] where a number of ad hoc assumptions about vesicular flow were used , we “couple” the enzyme-carrying capacity to the self-established vesicular flow described above . Hence , while each vesicle competitively uploads enzymes according to their dissociation constants , its fusion probability is determined by the cisternal SNARE gradient shown by the black curve in Fig . 3A . To study the competition mechanism in its simplest form , we assume here that the SNARE distribution is unperturbed by enzyme uploading . We find that the distribution of enzymes radically depends on whether vesicles originating from the first cisterna can exit the Golgi and fuse with its cis neighbor , the ER . If we permit ER recycling , the desired cis-medial- trans 3-mode steady state localization can be reproduced ( Fig . 3B ) . Enzymes that have the highest affinity for the vesicular coat concentrate in the cis Golgi . A substantial fraction of these enzymes is loaded from the first cisterna into ER-bound vesicles and leaves the Golgi . In more central compartments , where cis enzymes are depleted , medial Golgi enzymes outcompete the weaker-binding trans enzymes for space in the vesicles . As a result , those enzymes advance with the maturing cisterna until the mid-Golgi where their concentration peaks , and then become effectively loaded into retrograde vesicles . Finally , the weak-binding enzymes can only incorporate into vesicles when all stronger-binding competitors are depleted . Their concentration peaks in the penultimate cisterna . The ultimate cisterna , equivalent to the disintegrating cisterna or trans Golgi network ( TGN ) , exhibits a somewhat lower enzyme concentration as it does not receive any incoming retrograde vesicular traffic . On the other hand , if none of the enzyme-carrying vesicles can escape to the ER , the cisternal distribution of all enzymes converges onto a single peak form ( Fig . 6 , presented in the Methods section ) . In this case the overall steady state abundance of enzymes increases with their affinity for vesicular binding: The stronger-binding enzymes are more efficiently retrieved to younger cisternae and thus better avoid being flushed out with the disintegrating trans cisterna than the weaker-binding enzymes . As a consequence of their higher concentration , the stronger-binding enzymes do not get sufficiently removed from younger cisternae to achieve their cis-Golgi peak and at the same time they do not give their weaker competitors any chance to enter the retrograde transport vesicles in the later cisternae . Hence , all enzymes peak at the trans face of the Golgi . Thus , recycling of enzymes to the ER is necessary for establishing the cis-medial- trans enzyme segregations . At the same time , we observe that the steady state SNARE distribution and resulting intra-Golgi vesicular flux is only weakly affected by the presence or absence of ER-recycling . This is because both scenarios feature an inherent loss mechanism , which breaks the intra Golgi conservation of SNAREs . We also observe that , as discussed in [21] , the competition-based enzyme segregation is rather sensitive to the variation of model parameters . Thus , it is possible that mechanisms have evolved to make the cisternal enzyme distribution more robust . One such mechanism , the change in enzyme affinity for vesicular binding sites with cisternal age , has been studied in [21] and could easily be incorporated into the a more detailed versions of our model . The quantitative details of the calculation of the steady state enzyme concentrations , including Fig . 6 , which illustrates enzyme distribution in the absence of ER-recycling , are presented in Methods . We now apply the general mechanisms of fusion asymmetry and competitive vesicle binding to explain the specific SNARE and enzyme distributions as they are actually observed in the mammalian Golgi . The important adjustment to our basic model is that the Golgi apparatus features not one , but two cognate SNARE pairs . The first pair , which we label , consists of the monomer SNARE rBet1 with its trimer SNARE partner Membrin/ERS24/Syntaxin5 . The second pair , labeled , consists of the monomer SNARE GS15 , compatible with the trimer SNARE complex of Gos28 , Ykt6 and Syntaxin5 . There is solid experimental evidence for both pairs to be incorporated in COPI vesicles [17] , [22] and to participate in vesicular traffic of Golgi resident proteins [23] , [24] , [25] . To reproduce three Golgi enzyme peaks in concurrence with the experimentally observed distributions of the and SNARE pairs we introduce an additional specification at this point , namely that the monomeric SNAREs rBet1 and GS15 mediate fusion only when present on the vesicle , and the trimeric-SNARE complexes only when present in the cisternae . In the following paragraph we provide a justification for the functional allocation of SNAREs as vesicular and cisternal . In the Golgi , only the SNARE proteins actually have a cistrans distribution [17] such as shown in Fig . 2A . The SNARE Gos28 also decreases from cis to trans [26]; however , its cognate monomeric SNARE partner GS15 accumulates in the trans-most cisternae instead [17] , and the GS15 yeast homologue Sft1p is also enriched in the late Golgi [16] , [23] , ( see Fig . 1C ) . If GS15 and the Gos28-Ykt6-Synt5 complex could both function as fusiogenic SNAREs in the cisternae , our model of vesicular flux would imply that Golgi enzymes known to depend on this SNARE pair for vesicular traffic undergo anterograde rather than retrograde transport . The anterograde vesicular enzyme transport does little to improve enzyme segregation as the cisternal maturation already moves enzymes in trans direction . More importantly , the anterograde vesicular transport makes the enzyme recycling impossible . Our allocation agree with in vivo observations: monomeric SNAREs act indeed most often as vesicle- or v-SNAREs and the trimeric SNAREs generally function at the target membrane ( and are therefore typically referred to as t-SNAREs ) , [27] . But we also have a mechanistic explanation for why trimeric Golgi SNAREs function in the cisternae rather than the vesicles: When we consider the monomeric and trimeric SNAREs of a cognate SNARE pair separately , the SNARE that is most abundant in the vesicle determines which of the cisternal SNAREs the vesicle engages with . If the monomeric SNARE is more abundant in a vesicle than the trimeric SNARE , it will specify that the vesicle fuses with the cisterna which has the highest amount of cognate trimeric SNAREs , regardless of its monomeric SNARE concentration . Thus , when monomeric and trimeric SNARE partners differ significantly in their affinity for vesicles , the one with higher affinity becomes the v-SNARE , leaving the other to function in the cisternae . This is the case especially for the SNARE pair as Syntaxin 5 , the limiting partner in both and trimeric SNARE complexes , is at least 4 times less abundant than the monomer GS15 in COPI vesicles ( See Fig . 7B in [17] ) . Syntaxin-5's apparent poor affinity for Golgi vesicles explains its observed homogenous distribution in Golgi cisternae . However , the other constituents in and trimers are more efficiently transported by vesicles , thus maintaining the cisternal SNARE gradient . It follows from these observations that the two Syntaxin 5 containing trimeric Golgi SNAREs function as t-SNAREs . In addition to the two Golgi SNAREs , we consider a third v-SNARE , which mediates the fusion of Golgi-derived vesicles with the ER . It is ERS24 , which thus has a dual function as part of a t-SNARE complex in intra Golgi transport and as v-SNARE in Golgi-to-ER transport . The corresponding ER t-SNARE does not leave the ER and is therefore not considered here [15] . Apart from the SNARE specifications , we implemented a similar set of minimal assumptions as for the single SNARE scenario: We found a good qualitative agreement between our results and the experimentally observed concentration profiles . With the proper choice of dissociation constants ( Table 1 ) , t-SNARE decay rate , and vesicular transport intensity , the model functions in the following way: The strong coat-binding affinities of and ER SNAREs effectively package them into vesicles that bud from the younger cis cisternae . These vesicles have a high probability to fuse with the ER due to a substantial concentration of the ER SNAREs . These vesicles also recycle a good fraction of strong-binding cis enzymes , and a part of medial enzymes to the ER . The recycling of t-SNAREs to the ER seeds a cisternal gradient , which is responsible for the mostly retrograde direction of vesicular transport in the early cisternae . The recycling of t-SNAREs to the ER is poor , yet when coupled with age-dependent decay , the t-SNAREs extends the cistrans t-SNARE gradient to the trans Golgi . In more mature cisternae where the and ER SNAREs and cis-enzymes are depleted , the vesicles incorporate the weaker-binding molecules , such as medial and , to a lesser extent , trans enzymes and SNAREs . These vesicles have a much lower probability to reach the ER and transport their cargo mostly to younger Golgi cisternae . Finally , the trans-most cisternae bud vesicles that contain predominantly trans enzymes and v-SNAREs . These cargoes are transported mostly retrograde , but hardly reach the ER . The total fraction of each protein that is retained in the Golgi ( as compared to that recycled to the ER ) can be appreciated by its concentration in the trans-most cisterna in Fig . 3B . Since the figures represent the situation before the last cisternal maturation step and removal of the last cisternae , the protein concentration that remains in the Golgi is equal to the initial concentration ( set equal to one for all molecules ) , minus the loss to the ER . Our prediction that cis-enzymes , and ER SNAREs recycle through the ER at a higher level than SNAREs and trans enzymes is indeed born out by numerous experimental observations in yeast and mammalian cells . Cis but not trans Golgi markers accumulated in the ER upon an acute ER-exit block [23] , [29] or in the ER-derived intermediate compartment ( ERGIC ) after a temperature-induced exit block from this compartment [30] , [24] . Based on the SNARE dissociation constants that yielded the experimentally observed protein gradients ( Table 1 ) we further predict that monomeric ERS24 , which functions as ER-v-SNARE , has the highest affinity of all SNAREs for the COPI coat , followed by the v- and t-SNAREs , ( rBet1p and the proteins Syntaxin5 and Membrin , which together with ERS24 make up the t-SNARE complex ) . Indeed , ERS24 is much higher concentrated in COPI vesicles than any of the other v-SNAREs ( Fig . 8B in [17] ) . Syntaxin 5 is translated as a long and short version in mammalian cells [31] . The longer form features a known ER-retrieval signal and we predict that it is this form that predominantly functions in the t-SNARE complex and is more efficiently incorporated into COPI vesicles then the short form that likely functions mostly as t-SNARE , which has a higher dissociation constant than the t-SNARE . So far we assumed that vesicles only fuse with the immediate neighbors of their progenitor cisternae . A stacked Golgi , however , is not a requirement for Golgi asymmetry and cisternal maturation , which are also observed in S . cerevisiae where individual Golgi cisternae are scattered throughout the cytoplasm [32] , [33] , [34] , [35] . Removing the local fusion restriction , and allowing vesicles to fuse with any cisterna and the ER depending on their SNARE concentrations , we achieve only poor enzyme segregation with all enzyme maxima shifted towards younger cisternae , ( Fig . 4 ) . We suggest therefore , that a realistic description of fusion probability in S . cerevisiae must include a factor that considers fusion preferences related to cisternal age although it might be less stringent than the nearest neighbor limitation of a Golgi stack . Golgi scattering occurs when novel cisternae emerge from multiple , short-lived transitional ER ( tER ) sites rather than from a single , stable tER [36] . If individual tER sites release multiple cisternae in short succession before ceasing their activity , the diffusion limits imposed by the ER-network could maintain sister cisternae that are close in age in proximity to each other , thus ad hoc generating a series of maturing Golgi cisternae that remain separate from those generated in parallel by other tER sites . Evidence from a recent study by Nakano et al in S . cerevisiae supports this prediction: When due to altered ER-morphology the motility of Golgi elements away from the ER-exit site ( s ) is impeded , cis and trans Golgi elements could be seen in close proximity to each other and to ER-exit sites [37] . A position-age correlation is also apparent from the more coarse-grain viewpoint: Consider the emission of Golgi elements from multiple scattered ER exit sites and their subsequent one-dimensional diffusion in the cytoplasmic half-space away from ER membrane . The average distance from the ER membrane of a Golgi element at time t after emission scales as . Thus , the older Golgi elements are on average further away from the ER than the younger ones . Real-time imaging maps of the spatial relationship between yeast Golgi cisternae that exchange cargo should provide the experimental framework to make our model applicable to Golgi systems with scattered compartments where we expect the enzyme distribution to be somewhere in between the examples shown in Fig . 3 and Fig . 4 .
We present a simple model that explains the establishment and maintenance of directed vesicular flow and concentration gradients in the Golgi apparatus , an organelle system that undergoes constant rejuvenation by adding a new cisterna at the cargo-entering cis side while dissolving the oldest cisterna as secretory and lysosomal cargo exit at the trans end . Age is indeed the distinguishing feature of individual Golgi cisternae that we identify as the key to symmetry breaking . As cisternae mature the concentration of their functional SNAREs decreases , thereby providing the seed for a cistrans cisternal gradient of fusion factors for transport vesicles . This SNARE gradient causes the predominantly retrograde direction of vesicular flux that retrieves Golgi resident proteins , such as the SNAREs themselves and enzymes , from older to younger cisternae and back to the ER . The vesicular transport of SNAREs further enhances their gradient until a steady state between the retrograde vesicular and anterograde cisternal progression is reached . Both the seed gradient and cisternal maturation are indispensable for this outcome . The “seeding” temporal decay of cisternal SNARE concentrations occurs via several mechanisms: i ) Retrieval to the ER alone can account for the loss of the SNAREs present mostly in the young cisternae . However , the retrieval to the ER of the Golgi SNAREs from the medial and trans cisternae is not sufficient to create a seed gradient . ii ) Experimental evidence for one such late Golgi SNARE , Gos28 , are compatible with the notion that its loss occurs through degradation: The levels of Gos28 can go up as much as 40% when the availability of its chaperone GATE-16 is increased , preventing Gos28's proteolytic degradation [38] , [39] . Gos28-levels also increase when components of the Golgi-tethering complex COG are overexpressed [40] . This adjustability means that a fraction of Gos28 is indeed wasted under the normal operational conditions . iii ) Loss of SNAREs may also involve mechanisms in which Golgi-SNAREs become diverted to extra-Golgi functions . In yeast , Golgi-derived vesicles were shown to serve as source for autophagic membranes , which are later retrieved back to the Golgi [41] , [42] . The Gos28 homologue Gos1p in particular , has been implicated in the retrieval of the autophagic membrane protein Atg9 to the Golgi [41] . iv ) The loss of function of t-SNARE in older cisternae may occur due to modification of the membrane properties . v ) A fraction of the decay of the late Golgi t-SNARE is due to its inactivation by the corresponding v-SNARE with its emerging counter-current gradient ( see Fig . 3 ) . Cognate SNARE complexes not only assemble when present on opposite membranes ( i . e . in trans ) but also when present at the same membrane ( i . e . in cis ) , where most of them are disrupted under energy expenditure by the NSF/SNAP machinery [43] , [24] . Nevertheless , in freshly isolated plasma membranes , where the v-SNARE concentration is low , about 10% of t-SNAREs are found in unproductive SNARE complexes [44] . As the v-SNARE concentration goes up from cis- to trans-Golgi ( blue line in Fig . 3A ) concomitant with the decreasing t-SNARE levels ( green line in Fig . 3A ) , binding of the t-SNARE into fusion-incompetent SNARE complexes will sharpen the cistrans gradient of its fusion-competent concentration . Once the retrograde vesicular flux is established , different affinities of Golgi enzymes for the vesicles explain the enzyme peaks in cis , medial and trans cisternae . One finding from our simulations is that the differential distribution of Golgi proteins can only be achieved when the vesicles are allowed to recycle back to the ER . This is in good agreement with experimental observations [45] , [23] , [29] . However , the importance of Golgi protein cycling through the ER for the enzyme segregation had not been appreciated in previous models that explained the Golgi enzyme peaks [10] , [21] because of the arbitrarily implementation of the directionality of vesicle transport . It should be possible to test this important conclusion from our model experimentally . In yeast , ER- recycling of Golgi-derived vesicles can be stopped and the consequences for the segregation of cis and trans Golgi enzymes can be monitored by dual color time-lapse microscopy [33] , [34] . This approach is feasible in strains harbouring temperature-sensitive mutations in ER-t-SNAREs [46] , [47] . Importantly , the switch to the non-permissive temperature does not lead to the accumulation of Golgi-derived transport vesicles in these strains , presumably because ER-destined vesicles also contain significant amounts of v-SNAREs , which allows them to efficiently fuse with the Golgi when fusion with the ER is thwarted . Such a scenario is indeed consistent with the SNARE dissociation constants of our model ( Table 1 ) . Our simulations are insensitive to a broad spectrum of initial conditions . Regardless of whether we started with a single cisterna and added new cisternae one by one as it would occur during Golgi de novo formation , or considered a complete stack of identical cisternae when turning on the vesicular flux and SNARE loss mechanism , in each case the same steady state was reached . An important question is the relevance and specificity of constants used for the modeling . Naturally , the range of admissible constants narrows as one reproduces more detailed and specific scenarios . Our first observation , that a temporal loss of SNAREs results in directed vesicular flux , is very general and holds for virtually any set of constants ( see Eqs . ( 11 , 12 ) ) . The selection of constants became more restrictive when the cis , medial , and trans peaks of Golgi enzymes and the actual 2 SNARE pair scenario were reproduced . The dissociation constants for binding of SNAREs and enzymes to vesicular sites had to be tuned within a 10% precision . The actual values of the dissociation constants are of the same order as protein concentrations , which is quite common for protein-protein interactions and appears to be evolutionally attainable [48] . Furthermore , to reproduce the shape of experimentally measured enzyme and SNARE peaks , the directionality of vesicular flux needed to be sufficiently strong , which we attempted to achieve while minimizing the decay term for SNAREs . The SNARE decay and vesicular transport constants did not have to be tuned as precisely as the dissociation constants and their admissible variation range is generally 20–30% . We observed that the distinct enzyme peaks can be achieved with just one cognate SNARE pair . Why then does the Golgi afford two SNARE pairs ? One proposal , put forward by Volchuk et al . , is that only the SNARE mediates retrograde transport of Golgi resident proteins while the SNARE is dedicated to anterograde transport of exocytic and lysosomal cargo [17] . We consider this unlikely , however , based on the collective experimental evidence . Immuno electron microscopy-based observations of anterograde cargo in COPI vesicles is controversial and more recent organelle proteomics readily identified Golgi resident proteins but no exocytic cargo in COPI vesicles ( reviewed in [49] ) . Moreover , functional data in yeast have provided unequivocal evidence for a role of the SNARE in Golgi enzyme trafficking . Thus , acute inhibition of the SNARE Sft1 leads to a rapid loss of trans and medial Golgi enzymes from Golgi cisternae and their dispersion into vesicles that are apparently unable to fuse [23] . Therefore , both SNARE pairs are likely to operate in tandem rather than in a countercurrent fashion . Although the concentration of vesicular SNAREs does not influence the directionality of fusion , it determines fusion efficiency . Thus , high concentrations of one of each v-SNAREs on either end of the Golgi can sustain efficient vesicular traffic throughout the Golgi stack . In addition , each SNARE pair could have distinct , additional roles at the Golgi boundaries . While this is well established only for the SNARE , which mediates fusion of ER-derived vesicles at the cis face ( reviewed in [15] ) , recent evidence suggests that SNAREs GS15 and Ykt6 can participate in the fusion of endosomes with the trans Golgi or TGN [50] . According to our model , the experimentally observed steep cistrans gradient of the SNARE results in an almost sequential action of the two SNAREs within the maturing Golgi stack . This in turn yields two de facto distinct COPI vesicle populations , one enriched in SNAREs and cis Golgi markers , the other in SNAREs and enzymes from the medial and trans Golgi . Plant Golgi stacks indeed feature morphologically distinct vesicles around the rim of the trans and cis cisternae , respectively [51] and in mammalian cells COPI vesicles enriched in either cis and trans Golgi proteins and the corresponding SNAREs have been distinguished [52] , [8] , [53] . In our model these two subpopulations of COPI vesicles are simply due to differences in the competitiveness of the SNAREs and enzymes for binding to a universal COPI-coat rather than to two vesicle types that differ in the composition of the COPI coat or , more generally , in the machinery for cargo selection . Even though vertebrates have been reported to possess several COPI isoforms [54] , we show that a single COPI species , as in fungi and plants , is sufficient generate the variance in vesicle content . In summary , we have presented an explanation for why the minimal requirement of one SNARE pair and one vesicle type for the generation and maintenance of each distinct organelle [14] is relaxed for organelles that evolve from each other through maturation . Apart from the Golgi apparatus this might also be relevant for the organelles along the plasma membrane-early endosome-late endosome axis .
Here we do not specify the nature of t- and v- SNAREs , simply calling fusiogenic molecules present in a vesicle and cisterna v-SNAREs and t-SNAREs . The chemical distinction between t- and v-SNAREs will be stated later . However , to keep the same notations throughout the paper , we use the specific and notations already here . Small letters denote the vesicular concentrations of a molecule with the subscript referring to the parental cisterna . So and are concentrations of v- and t-SNAREs , and is the concentration of the th Golgi enzyme in a vesicle that emerged from the th cisterna . Capital letters , , and denote the concentrations of these substances in cisterna number . We number the compartments in the cis to trans direction , so the youngest cisterna has number one . The number of vesicles that bud from the th compartment per unit time , , is assumed to be proportional to the area of the compartment , ( 1 ) where is the budding rate constant which depends on the concentration and activity of coat proteins . A vesicle emitted from the th cisterna fuses with the th cisterna with a probability proportional to the product of the concentrations of the SNARE in the vesicle and the SNARE in the cisterna . The number of vesicles that fuse with the cisterna per unit time is ( 2 ) with being the fusion rate constant . The assumption of local transport restricts a vesicle emitted by the th cisterna to fuse with the th , th , and th cisternae . The trans-most cisterna does not receive any retrograde vesicular input . The time evolution of the population of vesicles emitted by the th compartment is described by the rate equation which includes both the budding and fusion terms . ( 3 ) At steady state , the concentration of vesicles emitted by the th compartment becomes ( 4 ) Hence , an increment in SNARE concentration in the st cisterna due to the vesicular flux from the th cisternae is ( 5 ) A dimensionless factor describes how the cargo is “diluted” when a vesicle fuses with its target cisterna and is equal to the ratio of vesicle to compartment surface areas . Assuming mass-action equilibrium between the vesicular binding sites and its cargo ( t-SNARE ) and that budding of a single vesicle does not significantly alter the cisternal concentration of t-SNARE , the amount of t-SNARE in a vesicle isHere is the concentration of cargo binding sites in a vesicle and is the dissociation constant for binding between cargo and such sites . Eq . ( 5 ) indicates that the steady state flux of vesicles does not depend on the v-SNARE concentration and is only determined by the budding rate and t-SNARE distribution . In the following we assume that the volume and the budding area of the compartments remains constant , . Relaxing this assumption does not substantially change the results . The rate equation that describes the evolution of the t- SNARE concentration in the th compartment reads ( 6 ) The first term describes the loss of the t-SNARE with the per molecule rate . To keep it general , the loss term collects all mechanisms of t-SNARE decay approximately described by first-order kinetics , such as degradation , loss of mis-targeted vesicles , etc . Thus , here the lost vesicles are not accounted for in Eqs . ( 3 , 4 ) , but are only included in the first term in Eq . ( 6 ) . The second line describes the outgoing vesicular transport from the th cisterna to its th and th neighbors , and the last two lines represents the incoming flux from the same neighbors to the th cisterna . To complete the description of t-SNARE distribution , the vesicular transport equation ( 6 ) has to be complemented by the definition of cisternal dynamics: Every time units the running number of each cisterna is incremented by one , so that the th cisterna becomes st . A new first cisterna with a given initial concentration of t-SNARE is added to the cis end of the stack , while the trans-most cisterna is removed . We measure cisternal concentrations of SNAREs and other molecules in the units of their initial concentrations in the first cisterna and the natural unit of time is the period of cisternal maturation . This is equivalent to setting these quantities equal to one . Then the t-SNARE distribution is described by three parameters: decay rate , the vesicular transport coefficient , and the dissociation constant . This cisternal maturation scenario together with Eq . ( 6 ) are implemented numerically as a simple Euler update . For reasonable values of parameters the system quickly converges to a steady regime: In each cisterna concentrations of t- and v-SNAREs undergo periodic evolution with the period . Plots of the cisternal distributions of the t-SNARE are presented in Fig . 2A in the Results . Consider a hypothetical system where the number of cisternae is non-biologically large . For older cisternae , the concentrations of SNAREs are small , , so the uptake of a SNARE into a vesicle is proportional to the concentration of the SNARE in the progenitor cisterna , . In the asymptotic regime , i . e . , sufficiently far from the first and the last cisterna , we seek a solution of Eq . ( 6 ) in the form ( 7 ) After substitution into ( 6 ) it yields ( 8 ) where ( 9 ) We look for the periodic solution in a sense that each time units , after the addition of a new cisterna and dissolution of the most mature cisterna , the system returns to the same state . So the th cisterna at the time must be identical to the cisterna at the time , ( 10 ) This yields the following equation for , ( 11 ) which is solved numerically . We observe that in the asymptotic regime , the steady state t-SNARE concentration decays exponentially with the number of cisterna , ( 12 ) with the coefficient being the solution of Eq . ( 11 ) . Simulations confirm our theoretical prediction given by ( 7 , 11 ) , see Fig . 5 . The necessity of the loss term for establishing the gradient by breaking the initial symmetry between the cisternae is clearly revealed by the following analytic argument: For a small loss rate ( ) , the expansion of the steady state exponent reads ( 13 ) Hence for independent of the intensity of the vesicular transport characterized by . Indeed , without breaking the initial similarity between cisternae , a vesicle would fuse with any of its three target compartments with the same probability , so that vesicular flux into a compartment would be equal to the vesicular flux out of this compartment . In other words , the vesicular transport can only enhance the initial difference in concentrations between cisternae , created by some other mechanism , rather than create this difference de novo . The transport of Golgi enzymes with cisternal concentrations , where labels an enzyme class , is described by an equation analogous to Eq . ( 6 ) . The difference is that instead of a single vesicular cargo type ( t-SNARE ) , we now consider three classes of competitors for vesicular seats . Thus , for each , replaces the in the left-hand side and replaces in the right-hand side of Eq . ( 6 ) , ( 14 ) Here ( only in this subsection ) we assume that the enzyme transport does not affect the vesicular flow , which is established by the autonomously evolving t-SNARE distribution , described by ( 6 ) . The concentration of enzyme uploaded to a vesicle is determined by the mass action equilibrium ( 15 ) Here are vesicle-th enzyme dissociation constants , the last equation states that the total number of the vesicular binding sites is equal to the number of free sites plus the number of sites occupied by enzymes of all three classes . Solving ( 15 ) , one finds , ( 16 ) which are subsequently substituted into Eq . ( 14 ) , As with SNAREs , each newly formed ( first ) cisterna is assumed to be loaded with Golgi enzymes with given concentrations , We assume that there is no temporal decay of enzymes , so is put equal to zero in the transport equations . When the retrograde vesicular transport is counterbalanced by the anterograde cisternal progression , the enzyme distribution reaches its steady state . The nature of the steady state depends on the boundary conditions imposed on the cis side of the Golgi stack: An “open” boundary condition is implemented as a zeroth cisterna ( ER ) with a fixed concentration of t-SNAREs which can fuse vesicles ( see Fig . 2B ) , while under the “closed” boundary conditions vesicles do not escape the Golgi , ( see Fig . 6 ) . Putting together the two mechanisms considered above , we introduce a realistic model of Golgi transport . It describes the evolution of 8 distinct types of molecules: and sets of t- and v-SNAREs controlling intra-Golgi fusion , a v-SNARE for fusion with the ER , and cis , medial , and trans types of enzymes . For brevity of equations , we use the universal notations and for cisternal and vesicular concentrations of each of the eight molecules , . At the same time , in the fusion rate terms we retain the specific notations for t- and v-SNAREs with superscripts “” , “” and “ER” denoting the affiliation of particular SNAREs . The evolution of the cisternal concentration , of each type of molecule is described by the rate equation similar to ( 14 ) with two important distinctions . First , the rate of fusion of a vesicle with a cisterna , previously given by ( 2 ) , is now proportional to the sum of the products of the concentrations of cis and trans SNAREs [14] . ( 17 ) The increment in the vesicular cargo concentration in the th cisterna due to the vesicular flux from the th cisternae is ( compare to ( 5 ) ) , ( 18 ) Here is the vesicular concentration of molecule defined by mass-action equilibrium ( 16 ) between vesicular binding sites and all eight competing molecules . The last term in the denominator corresponds to the fusion of vesicles with the ER , which is the second distinction of the considered mechanism from the model case analyzed above . The ER t-SNARE concentrations is considered to remain constant and vesicles originating from any cisterna can fuse with the ER . Assembling together all gain and loss mechanisms for the cisternal concentration of , we write the complete system of kinetic equations that describe the vesicular transport . ( 19 ) The escape of a fraction of vesicles from the Golgi to the ER provides one part of a loss mechanism necessary for seeding the gradient of t-SNAREs . Yet we do not exclude the possibility of other mechanisms of t-SNARE decay , so the remains present in the rate equation . In the simulations , we set for the t-SNARE equal to a small value , while the decay coefficients for all other substances are put equal to zero . To model the vesicular transport in yeast , we used an equation similar to Eq . ( 19 ) where the restriction of nearest neighbor fusion was relaxed , ( 20 ) ( 21 ) A typical steady state distribution of enzymes produced by the unrestricted vesicular fusion is shown in Fig . 4 . | We have developed a quantitative model to address a fundamental question in cell biology: How does the Golgi apparatus , an organelle composed of multiple cisternae that exchange vesicles , steadily maintains its inhomogeneous protein composition in the face of ongoing cisternal aging and replacement , and cargo entry and exit . We do not assume any a priori polarity within the Golgi apparatus or directionality of vesicular traffic . The Golgi cisternae inevitably lose active proteins that specify vesicle fusion , the SNARE molecules , as they age , thus breaking the symmetry between compartments and establishing the “seed” for directional vesicular transport . This small decrease in SNARE concentration in older cisternae is then further self-enhanced by the progressively more directional vesicular transport of SNAREs . Competition of enzymes for incorporation into predominantly retrograde-fusing vesicles in turn generates overlapping but distinct stationary enzyme peaks . Applying these general mechanisms of fusion asymmetry and competitive vesicle loading to the actual situation in the stacked mammalian Golgi , we reproduced the experimentally observed distributions of the two SNARE pairs that operate in the Golgi , and enzyme peaks in cis , medial and trans cisternae . We believe that our study attempts the first self-consistent explanation for the polarity in the Golgi stack . | [
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] | 2013 | A Model for the Self-Organization of Vesicular Flux and Protein Distributions in the Golgi Apparatus |
Glucocorticoids ( GCs ) mediate physiological responses to environmental stress and are commonly used as pharmaceuticals . GCs act primarily through the GC receptor ( GR , a transcription factor ) . Despite their clear biomedical importance , little is known about the genetic architecture of variation in GC response . Here we provide an initial assessment of variability in the cellular response to GC treatment by profiling gene expression and protein secretion in 114 EBV-transformed B lymphocytes of African and European ancestry . We found that genetic variation affects the response of nearby genes and exhibits distinctive patterns of genotype-treatment interactions , with genotypic effects evident in either only GC-treated or only control-treated conditions . Using a novel statistical framework , we identified interactions that influence the expression of 26 genes known to play central roles in GC-related pathways ( e . g . NQO1 , AIRE , and SGK1 ) and that influence the secretion of IL6 .
Glucocorticoids ( GCs ) are steroid hormones that mediate homeostatic responses to environmental stressors through the regulation of critical physiological processes ( e . g . immune response , energy metabolism and blood pressure ( reviewed in [1] ) ) . Owing to early observations of the anti-inflammatory properties [2] of cortisol ( i . e . the endogenous GC in humans ) , synthetic GCs are widely used as pharmaceuticals for inflammatory and autoimmune diseases ( e . g . asthma [3] and rheumatoid arthritis [4] ) . GCs are also used in the treatment of several types of cancer [5] , most notably lymphoid malignancies [6] , due to their pro-apoptotic activities and for symptomatic relief . While there is evidence for a substantial genetic contribution [7]–[12] , and for inter-ethnic differences in drug response [13] , [14] , little is known about the genetic architecture of variation in GC response within and between human populations . Genetic effects on GC action could provide a mechanism for a vast array of gene-environment interactions , which could have major implications for human phenotypic variation . In fact , evidence of such interactions has been observed for numerous traits relevant to GCs including obesity [15] , cardiovascular disease [16] and asthma [17] . With few exceptions ( e . g . a regulatory polymorphism in the promoter of IL6 [18] ) , little is currently known about the mechanisms that underlie gene-environment interactions . If not properly accounted for , these interactions can complicate efforts to identify genetic and environmental factors associated with disease risk . Furthermore , identifying genetic variation that interacts with pharmaceutical treatments like GCs , which are a specific subset of environmental factors , is of particular interest from a clinical perspective and constitutes the primary goal of pharmacogenetics . As GCs act largely by inducing changes in the expression of target genes [19] , regulatory polymorphisms are likely to contribute to variation in response . The initial steps of the GC response pathway are mediated by the GC receptor ( GR ) and interacting transcription factors . GC binding allows the GR to translocate from the cytoplasm to the nucleus , where it regulates gene expression through at least two distinct mechanisms . The GR can either drive the assembly of novel transcriptional regulatory complexes at target genes , or inhibit regulatory complexes , such as NFκB [20] , that are already active at target genes . Some direct GR target genes are , in turn , transcription factors that regulate downstream target genes . Here , we provide an initial view of the genetic architecture of variation in the GC-mediated regulation of transcription and protein secretion . To accomplish this , we measured the expression of 13 , 232 genes and the secretion levels of 10 proteins in paired aliquots , one treated with the synthetic GC dexamethasone ( dex ) and one treated with the vehicle for dex ( EtOH ) as a control , in a panel of 114 densely genotyped HapMap B-lymphocytes transformed with Epstein-Barr Virus ( EBV ) , commonly known as lymphoblastoid cell lines ( LCLs ) . This panel included 57 Yoruba ( YRI ) from Nigeria and 57 Toscani ( TSI ) from Italy . EBV transformation proceeds , in part , by mimicking CD40 activation and ultimately leads to cellular proliferation through a variety of mechanisms , including the activation of the NFκB signaling pathway [21] . Given their activated state , LCLs are a suitable system for studying the immunorepressive effects of GCs . Additionally , some regulatory variants that affect GC response in LCLs may be shared with other cell types , as observed for baseline expression [22]–[24] .
We found that 4 , 568 genes were differentially expressed , at a FDR<0 . 01 ( p<0 . 003 ) , following treatment with GCs ( 8 h , 1 uM dexamethasone ) , corresponding to ∼32% of the expressed genes . This number is similar to that observed in a recent study of equivalent sample size in osteoblasts treated with GCs [25] , but larger than previous studies that used much smaller samples ( often a single cell line; e . g . [26] ) . This suggests that large sample sizes are necessary to identify many GC target genes . Accordingly , we found that sub-sampling data from our full panel of LCLs dramatically reduced the number of differentially expressed genes , especially at genes with inter-individual variation in transcriptional response ( Figure S1 ) . It should be noted that tests of differential expression rely on magnitude of transcriptional response and its consistency across individuals . Because our main goal is to identify the genetic basis of variation in response , we did not limit our mapping analyses ( see below ) to the differentially expressed genes . Among the differentially expressed genes in LCLs , we found roughly equal numbers of up and down-regulated genes . Up-regulated genes were enriched for GC-related biological processes including cellular response to stimulus ( p = 4 . 1×10−6 , FDR = 7 . 5×10−5 ) and cell cycle ( p = 1 . 4×10−5 , FDR = 2 . 6×10−4 ) , consistent with GC regulation of lymphocyte proliferation . Down-regulated genes were enriched for immune response genes ( p = 1 . 1×10−10 , FDR = 4 . 6×10−9 ) and for genes involved in the positive regulation of I-kappaB kinase/NF-kappaB cascade ( p = 3 . 3×10−5 , FDR = 3 . 3×10−4 ) , consistent with the immunorepressive role of GCs . To explore the extent of tissue-specificity in the transcriptional response to GCs , we compared our data to the results in osteoblasts [25] . We found a significant overlap between the genes differentially expressed in LCLs and in osteoblasts ( p = 4 . 8×10−13 ) , but only 28% of genes differentially expressed in our study are differentially expressed ( p<0 . 05 ) in osteoblasts . This likely reflects some amount of tissue specificity , although other factors are likely to contribute ( e . g . incomplete power [23] , differences in duration of treatment ) . We measured and corrected for multiple factors related to EBV-transformation that have been previously shown to be associated with gene expression patterns at baseline [27] , [28] ( e . g . EBV copy number ) . Unlike baseline expression , these factors showed hardly any evidence for an effect on transcriptional response ( see Table S1 ) ; nonetheless , we corrected for them in all subsequent analyses . Many of the proteins involved in the GC-mediated regulation of transcription are well characterized ( i . e . GR and interacting transcription factors ) . Genetic variants that impact the function of these regulatory proteins are likely to influence transcriptional response at several , and potentially many , downstream genes . Consequently , the genes that encode these proteins are candidate expression quantitative trait loci ( eQTLs ) acting in trans to modulate the transcriptional response to GCs . However , genome-wide tests for trans eQTLs suffer from a tremendous multiple testing burden . Therefore , to reduce the number of tests being performed , we first examined only these candidate genes for response eQTLs . We used simple linear regression to test for an association between log fold change in expression at each expressed gene in the genome and genotype at all HapMap SNPs within 100 kb of the gene that encodes the GR ( NR3C1 ) , and found no significant evidence of association at a FDR<0 . 2 ( Figure 1a , Figure S4a-S4b ) . Similarly , as the GR interacts with other transcription factors in the regulation of target gene transcription , we also tested all HapMap SNPs within 100 kb of 34 genes that encode transcription factors known to interact with the GR ( listed in Materials and Methods [29] ) . Here again , we found no evidence for an effect of genetic variation at these loci on the transcriptional response to GCs at a FDR<0 . 2 ( Figure 1b , Figure S4c-S4d ) . We then performed an unbiased , genome-wide scan for genetic variation associated with GC response . Specifically , we tested for an association between every HapMap SNP and log fold change at every gene . While this analysis did not reveal any significant associations at a FDR<0 . 2 , we found that the top association was between log fold change at C1orf106 and genotype at an intronic SNP ( rs4915463 , p = 8 . 4×10−11 , FDR<0 . 67 ) . Given the proximity of the associated SNP to the C1orf106 locus and work by others highlighting the impact of cis-acting regulatory polymorphisms on baseline expression [30] , [31] , we then focused our analyses on HapMap SNPs near each of the 12 , 619 expressed , autosomal genes . We found the strongest signal when we tested for an association between log fold change at each gene and all SNPs within 100 kb ( compared to either a genome-wide scan or testing SNPs within 500 kb of each gene , Figure 1c ) . This analysis revealed local response eQTLs for 8 genes at a FDR<0 . 1 ( Figure 2 ) . These included genes previously shown to play important roles in GC-related biological processes , including regulation of immune response ( MT1X [32] and MFGE8 [33] ) and cell cycle progression ( e . g . BIRC3 [34] ) . These also included NQO1 , a gene previously shown to affect variation in response to GC pharmaceutical treatment [35] . Visual examination of the genes in Figure 2 indicates that different genes show qualitatively different patterns . For some genes , a genotypic effect is evident either in only the GC-treated condition ( C1orf106 , NQO1 , C9orf5 , MFGE8 , and BIRC3 ) or only the control-treated condition ( MT1X ) . For others , an effect is evident in both , but differs between the two conditions ( DNAJC5G and MS4A7 ) . These different patterns may have different mechanistic and phenotypic interpretations , but are not distinguished by the test of log fold change , and so researchers have previously been forced to identify such patterns post hoc ( e . g . [36] ) . To address this , we developed a novel statistical framework that explicitly compares and identifies these different patterns of interaction . In brief , our method explicitly compares five different models relating each SNP to phenotypic measurements in the two treatment conditions ( GC and control ) : For each SNP , we computed a likelihood ratio , or Bayes Factor ( BF ) that measures the relative support in the data for each model 1–4 . These BFs take account of the paired nature of the data , and the correlations between measurements in the same LCL in different conditions . We used a hierarchical model [37] to combine information both across SNPs in each gene region , and across genes , ultimately computing a posterior probability for each gene that it follows each of the models 1–4 , i . e . that it is affected by a polymorphism that follows that model . We used these posterior probabilities both to identify high-confidence eQTLs of each type , and to estimate false discovery rates among eQTLs exceeding any given posterior probability threshold . This method is broadly applicable to the study of any gene-environment interactions with paired phenotype measurements . Using this novel framework we identified 26 genes with high-confidence interactions ( posterior probability of interaction>0 . 7 , FDR = 0 . 10 ) between GC treatment and eQTLs . These interaction eQTLs included 7 of the 8 response eQTLs identified by mapping log fold change . The remainder generally showed strong , but not genome-wide significant ( FDR<0 . 10 ) , association with log fold change ( see Table S2 and Figure S2 ) . The larger number of interactions identified compared with mapping log fold change ( 26 versus 8 ) , therefore , reflects an increase in power that comes from explicitly considering different plausible interaction scenarios . Of these , the majority ( 18 of 26 ) showed strongest support for GC-only interactions , with the remainder ( 8 of 26 ) showing strongest support for control-only eQTLs . Only one interaction between treatment and genotype identified through mapping the log fold change was not identified by the Bayesian hierarchical model ( DNAJC5G ) . This interaction was the least significant of the 8 identified by mapping log fold change , and although it also shows some signal in the Bayesian analysis ( BF for general interaction versus null = 4 . 9×102 , BF for general interaction versus no-interaction = 6 . 4×103 ) , the signal was insufficient to outweigh the low prior probability of a general interaction estimated by the hierarchical model ( prior = 0 . 001 , see Table S4 ) . The Bayesian hierarchical model revealed eQTLs at genes with clear biological relevance to GC-related biological processes that were not identified through mapping the log fold change . These include additional genes involved in the regulation of immune response ( e . g . CST7 [38] or NLRP2 [39] ) and cell cycle progression ( e . g . PDGFRL [40] ) , well-established GC targets , such as serum and glucocorticoid regulated kinase 1 ( SGK1 [41] ) , and previously unknown GC target genes . For example , we found a control-only eQTL for multiple coagulation factor deficiency 2 ( MDCF2 ) , which is involved in the production of pro-coagulation factors [42] . The effect of GCs on coagulation is controversial [43] , but has been suggested to play a role in their therapeutic effects on diseases such as asthma [44] . Given that cortisol regulates a variety of physiological processes relevant to numerous diseases , we compared our eQTL results to those from genome-wide association studies collected as a part of the GWAS catalog [45] . We found that a GC-only eQTL for AIRE ( rs762421 ) was associated with risk of the Crohn's disease [46] . AIRE encodes a potent repressor of autoimmunity and can cause severe autoimmune disease when mutated [47] . In addition to its role in removing autoreactive T cells in the thymus , AIRE also plays a role in B-cell mediated immune response [48] . We found that the putative risk allele ( rs762421-G ) is associated with the down-regulation of AIRE expression by GCs . This allele may confer increased susceptibility to this autoimmune disease by allowing GCs to decrease AIRE expression . In addition to these interacting polymorphisms , our analysis identified a much larger number of genes ( 6 , 813 genes ) affected by no-interaction eQTLs ( posterior probability>0 . 7; FDR = 0 . 16 ) . In other words , transcript levels at these genes depend on the eQTL genotype , but the magnitude of transcriptional response does not ( i . e . model 2 , see Figure S10 ) . Our observation that the vast majority of cis-acting regulatory polymorphisms with identical genotypic effects across treatment conditions is consistent with findings in osteoblasts treated with GCs [25] and in yeast [49] , suggesting that this may reflect a general biological trend , rather than a feature specific to our treatment and experimental system . We compared the distribution of minor allele frequencies between the eQTLs following these three models and did not observe any significant differences ( Figure S9 ) . The results reported above come from using a hierarchical model , which combines information across SNPs within each gene . One limitation of this hierarchical model is that it allows at most one eQTL per gene . This may cause it to miss interacting SNPs in genes that contain both interacting and non-interacting eQTLs , and for this reason the probabilities on interacting models may be underestimated . ( More generally this feature could cause apparent discrepancies between the results from the hierarchical model and the log fold change analysis , although this does not seem to be the case in the results above . ) To assess whether this limitation might have led us to miss some strong interaction signals we also performed a SNP-level analysis using the BF ( for interaction models 2–4 vs non-interaction models 0–1 ) computed for each SNP . This analysis identified 247 SNPs , in 120 distinct genes , with BF exceeding 103 , although none exceeding 105 , that are candidates for being interacting eQTLs ( Table S5 ) . To determine whether GC-only and control-only eQTLs represented regulatory polymorphisms with treatment-specific genotypic effects , we assayed treatment-dependent allelic imbalance using quantitative real time PCR in heterozygotes . This assay also asks whether local eQTLs act in cis , as alleles at cis-regulatory polymorphisms , by definition , affect target gene transcription only on the chromosome on which they reside . Among the 26 interaction eQTLs , we chose five at random among those for which a common coding SNP could be reliably genotyped . We assayed three genes with evidence of GC-only eQTLs ( C9orf5 , LSG1 , and MFGE8 ) . We found significant allelic imbalance , with allelic effects in the same direction as predicted by the eQTL mapping results , in GC-treated samples , but not in control-treated samples , for all of them ( Table 1 ) . We also performed allelic imbalance assays on 2 of the 8 control-only eQTLs ( SRD5A2 , C12orf45 ) . We found significant allelic imbalance at C12orf45 only in the control-treated samples . While not significant at p<0 . 05 , we observed a pattern consistent with a control-only eQTL at SRD5A2 . Our failure to fully validate all 5 assayed eQTLs by allelic imbalance could reflect some level of false positive identifications of eQTL interactions , but may also reflect incomplete power of the allelic imbalance assay . We compared our results with those from an independent GC response eQTL mapping study in LCLs derived from asthma patients ( W . Qui and K . Tantisira , personal communication ) . We found that 4 of the 9 interaction eQTLs that we identified , and that were tested in both studies , showed significant associations with log fold change in this independent dataset ( p<0 . 05 , C1orf106 , LSG1 , CST7 , and MS4A7 ) , and an additional 2 showed suggestive associations ( p<0 . 1 , SYT17 and BIRC3 ) . This overlap is highly significant ( p = 8 . 5×10−4 ) . Importantly , the overlap for single-treatment eQTLs is much greater than that for response eQTLs: all of the top 10 eQTLs identified by Qiu et al ( 2011 ) in each treatment condition were replicated in our data ( p<0 . 05 ) , while only 1 of the top 10 eQTLs for log fold change was replicated . This contrast highlights the known statistical challenge of mapping gene-environment interactions . We also tested 15 of our interaction eQTLs ( i . e . all eQTLs tested in both studies ) for an association with response to GC therapy in 172 asthma patients ( W . Qui and K . Tantisira , personal communication ) . We found that a GC-only eQTL for TNIP1 was significantly associated with patient response ( rs6870205 , p = 2 . 5×10−3 , Bonferroni-corrected p = 0 . 037 ) . TNIP1 has an established role in the immune response , as it encodes a protein that inhibits NFκB [50] and contains polymorphisms that have been associated with risk of systemic lupus erythematosus [46] . We observed substantial allele frequency differences between populations at many of the putative interaction expression quantitative trait nucleotides ( eQTNs ) , defined as the most strongly associated SNP for each gene . Furthermore , differences in allele frequency at these eQTNs were predictive of differences in average transcriptional response between populations ( r2 = 0 . 33 , p = 5 . 3×10−3 , Figure 3a ) . This demonstrates that these eQTNs contribute to differences in response between populations , and so may also contribute to inter-ethnic disparities in GC-related diseases and in drug response . It also provides independent supporting evidence that these eQTNs interact with GC treatment . In some cases , allele frequency differences may explain why genes respond to GC treatment only in individuals of one population . For example , we observed that the GC-only eQTL allele associated with up-regulation of the detoxification enzyme NAD ( P ) H:quinone oxidoreductase 1 ( NQO1 ) was extremely rare outside equatorial African populations ( Figure 3b ) , likely causing the observed lack of NQO1 response in TSI LCLs , and the strong up-regulation in many YRI LCLs ( Figure 3c ) . This result may be of particular relevance to ethnic disparities in leukemia patient response to GCs , as alleles that reduce NQO1 enzymatic activity have been associated with decreased response to a chemotherapy regime that included GCs in patients with acute lymphoblastic [51] , [52] and acute myeloid leukemia [53] . In an effort to identify additional genes with differences in average transcriptional response between populations , we applied the same statistical framework described above to test for interactions between population ( rather than genotype ) and GC treatment . Using this approach , we identified 258 genes with differences in transcriptional response ( posterior>0 . 7 , FDR = 0 . 128 ) between populations; of these , 130 were up-regulated by GC treatment while 128 were down-regulated . We found a consistent pattern across genes , with a tendency for stronger up-regulation in YRI LCLs at 78% of up-regulated genes with population differences in response ( Figure S3 ) . Interacting eQTLs are enriched among genes with population differences in response compared to all expressed genes ( odds ratio = 6 . 0 , p = 5 . 4×10−3 ) while no-interaction eQTLs are not enriched ( odds ratio = 0 . 99 ) . The attenuation of the immune response by GCs is partially mediated by decreased secretion of pro-inflammatory molecules . We measured the secreted levels of 9 pro-inflammatory proteins ( IL1α , IL6 , IL8 , IP10 , MDC , Rantes , TNFα , TNFβ ) and 1 anti-inflammatory protein ( IL10 ) . Five pro-inflammatory proteins showed significant differential secretion in response to GCs in LCLs ( TNFα , TNFβ , Rantes , IP10 and IL1α –Table S3 ) ; all five showed lower secretion levels in the presence of GC , consistent with the immune-repressive role of GCs . To identify genetic variation that influences GC-mediated regulation of protein secretion , we tested HapMap SNPs for association with log fold change in secretion at each protein . Similar to our eQTL results , we found significant associations ( at a FDR<0 . 2 ) only when we limited our search to SNPs near the genes that encode each protein ( i . e . we found no significant associations in genome-wide or a candidate gene analysis ) . Testing SNPs within 100 kb of each cytokine , we found a significant association between secretion response at IL6 and genotype at a SNP ∼56 kb downstream ( rs10225286 , p = 1 . 9×10−4 , FDR = 0 . 1 , Figure 4 ) . Because this SNP did not show strong evidence of an effect on IL6 transcriptional response , we propose that it affects secretion through a mechanism independent of mRNA levels or that it affects transcriptional response at a different treatment time point .
Here , we report a genome-wide scan for genetic variation that influences the GC-mediated regulation of transcription and protein secretion . The cellular response to GCs depends on a well-characterized set of regulatory proteins ( i . e . the GR and interacting proteins ) . This provided us with a set of strong candidate loci to perform trans-eQTL mapping tests . Despite this , we found no evidence for trans-acting factors . In contrast , the strongest signal from an unbiased genome-wide scan was a SNP associated with transcriptional response at a nearby gene , and even more eQTLs were revealed when we limited our analysis to SNPs within 100 kb of each gene . Numerous studies have tested genetic variation within or near the GR and interacting transcription factors for association with patient response to GC treatment . These studies have found mostly rare functional polymorphisms that are unlikely to explain most heritable variation in GC response ( reviewed in [54] ) . Furthermore , rare polymorphisms in the GR have dramatic phenotypic effects ( e . g . extreme hypoglycemia and hypertension [55] ) , as expected for a master regulator that influences all downstream processes . Instead of genetic variants in master regulators , our results suggest that cis-regulatory polymorphisms that interact with GC treatment at target genes could play an important role in GC response , as first suggested based on observations at the SGK1 gene [56] . These findings suggest that future attempts to identify genetic variation associated with clinical response to GCs may benefit from focusing on likely cis-regulatory polymorphisms that impact response at individual GC target genes , instead of testing master regulators of the GC response pathway . We found that associations between genotype and transcriptional response could be discriminated into distinct categories based on the configuration of genotypic effects across treatment conditions . These categories likely correspond to specific genetic mechanisms . GC-only eQTLs may reflect polymorphisms that influence the binding of transcription factors that are only active in the presence of GC treatment ( e . g . the GR and interacting transcription factors ) . In support of this hypothesis , we found that GC-only eQTLs tended to affect up-regulated genes ( 13 of 18 ) . Although the causative polymorphism may not be among the genotyped SNPs , we found examples of GC-only eQTLs where most of the signal centered on a SNP that disrupts a predicted GR binding site , such as the eQTN for C9orf5 ( rs10816772 , p for motif match = 6 . 8×10−3 ) . Control-only eQTLs are compatible with a variety of mechanisms . For example , they may reflect polymorphisms that disrupt the binding of regulatory complexes , like NFκB , that are directly inhibited by the GR ( e . g . through protein-protein interaction ) . Consistent with this , we found examples of control-only eQTLs where most of the signal centered on a SNP that disrupts a predicted binding site for a transcription factor directly inhibited by GR , such as the eQTN for FBXL6 ( rs10448143 , matrix and core similarity for NFkB>0 . 9 ) . Direct inhibition of transcription factors by GR generally leads to down-regulation of target genes . However , we found equal numbers of control-only eQTLs affecting up-regulated and down-regulated genes ( 4 of each ) , so additional mechanisms must explain some fraction of control-only eQTLs . These may include genetic effects on regulatory elements that are indirectly inhibited by GC treatment ( e . g . through GR competition for access to DNA by another transcription factor ) or polymorphisms that affect transcriptional response at secondary targets . The different categories of interactions identified by our method may also have distinct phenotypic interpretations . Polymorphisms with GC-only effects on expression are likely to directly affect the action of the GC-activated regulatory machinery . In contrast , polymorphisms with control-only effects have no impact on the cellular processes in the presence of GCs , but may still affect phenotype by influencing variation in a ‘pre-treatment’ state . For example , genetic effects on pro-inflammatory cytokine levels prior to GC exposure could affect the amount of time cells take to reach the optimal , lower levels required to effectively suppress inflammation . In summary , control-only QTLs may contribute more to variation in underlying disease mechanisms , while GC-only QTLs may contribute to variation in GC pharmacodynamics . However , we also note that , given their lower rates of validation and replication , there may be a higher false positive rate for control-only eQTLs . Inter-ethnic differences in GC response have been observed clinically [13] , [14] , and the prevalence of many GC-regulated physiological traits differs across human populations [57] . By combining association mapping with comparisons between populations , our study allowed a direct assessment of the genetic basis of population differences in the cellular response to GCs . We found that ancestry had substantial and systematic effects on the transcriptional response to GCs , with a tendency for stronger up-regulation after GC treatment in YRI LCLs . Possible causes of such patterns include: non-genetic ‘confounders’ ( e . g . differences in immortalization procedure [58] ) , trans-acting alleles that increase response and are at higher frequency in YRI , or multiple , independent cis-acting alleles that increase response in YRI at up-regulated genes . Our data favor the last explanation . It seems unlikely that non-genetic ‘confounders’ explain all or most of the population differences , as we found that the measured ‘confounders’ showed limited evidence of effects on transcriptional response or differences between populations ( Figure S5 ) . Although we cannot exclude the possibility that population differences reflect a trans-acting eQTL with differences in allele frequency , we found little support for this explanation . Instead , we found evidence suggesting that population differences may reflect differences in allele frequency at cis-regulatory polymorphisms , as genes with population differences in response were more likely to have local interaction eQTLs . The possibility that a stronger response in YRI reflects differences in allele frequency at cis-regulatory polymorphisms is particularly interesting from an evolutionary perspective , as differences in allele frequency acting in a consistent direction ( i . e . increasing GC responsiveness ) across multiple independent QTLs are usually interpreted as evidence of polygenic adaptation [59]–[61] . In addition to these biological insights , we contribute novel statistical methodology for mapping response phenotypes and identifying gene-environment interactions . These methods are applicable for any setting contrasting genotypic effects between two conditions ( with paired measurements ) , including pharmacogenetic studies of clinical response to drug therapy ( e . g . [62] ) and , especially , functional genomic studies of genetic effects on treatment response similar to the one presented here . These methods provide a more powerful alternative to mapping a measure of response ( e . g . log fold change ) , which fails to distinguish among different types of interactions , or comparing results from mapping separately in each condition , which ignores the paired nature of the data . In summary , this study provides an initial characterization of the genetic basis of variation within and between human populations for a key physiological regulator and commonly administered pharmaceutical . The biological insights and statistical tools presented here extend our current understanding of the genetic basis of variation in response to GCs , and will aid future efforts to characterize the genetics of response to this and other treatments .
All cellular experiments described were conducted in lymphoblastoid cell lines ( LCLs ) , B lymphocytes transformed with Epstein-Barr virus , that were collected as a part of the International HapMap project . LCLs were thawed and passed once in RPMI media supplemented with 15% fetal bovine serum , then washed twice with phosphate-buffered saline and moved to RPMI media supplemented with 15% charcoal-stripped fetal bovine serum . After one passage in media with charcoal-stripped fetal bovine serum ( corresponding to a minimum culturing time of 5 days ) , LCLs were seeded in the evening at a density of 5×105 cells/ml . After an overnight incubation , LCLs were treated with 10−6 M dexamethasone , and an equal amount of vehicle solution ( solution composed of 1% ethanol and 99% cell culture media ) as a negative control for treatment . For each LCL , one set of dex and control aliquots was treated for 8 hours ( to quantify mRNA abundance ) and the other for 24 hours ( to assay inflammatory protein secretion ) . The study design is depicted in Figure S6 . LCLs were thawed , cultured and treated in batches completely balanced by treatment , population , technician and time of day . For quality control purposes , biological replicates were performed for one batch of four cell lines and both expression and treatment response were highly replicable ( Figure S7 ) . Collection of all samples took 4 months . For each expression study described in the preliminary data , total RNA was extracted from each cell culture sample using the QIAgen RNeasy Plus mini kit , and was found to be of high quality . RNA was extracted from all 240 samples over the course of 5 days . Total RNA was then reverse transcribed into cDNA , labeled , hybridized to Illumina HumanHT-12 v3 Expression BeadChips and scanned at the Southern California Genotyping Consortium ( SCGC: http://scgc . genetics . ucla . edu/ ) at the University of California at Los Angeles . Each RNA sample was hybridized to two separate arrays ( i . e . in two technical replicates ) . To avoid batch effects on RNA measurements , all 480 microarrays were hybridized within 7 days . Summary data ( e . g . mean intensity of each probe across within-array replicates ) were obtained using the BeadStudio software ( Illumina ) at the SCGC . The microarray data has been deposited in the Gene Expression Omnibus ( GEO ) , www . ncbi . nlm . nih . gov/geo , under accession number GSE29342 . Low-level microarray analysis was performed using the Bioconductor software package LUMI [63] in R ( http://www . r-project . org ) . We used applied variance stabilizing transformation [64] to all arrays , removed probes with intensities indistinguishable from background fluorescence levels in all samples ( leaving 23 , 700 expressed probes ) , and performed quantile normalization across all arrays . Probes were annotated by mapping to the RNA sequences from RefSeq using BLAT . To avoid ambiguity in the source of a signal due to cross-hybridization of similar RNA species , probes that mapped to multiple genes were excluded from further analyses . Probes that contained one or more HapMap SNPs were also removed from further analyses to avoid spurious associations between expression measurements and SNPs in linkage disequilibrium . To avoid spurious results and to reduce noise due to potential confounders , we measured several covariates relevant to LCL biology including: EBV genome copy number , growth rate and mitochondrial genome copy number . EBV and mitochondrial genome copy number were assessed using Taqman Gene Expression Assays ( Assay # Hs02596867_s1 for mitochondria and Pa03453399_s1 for EBV ) . RNaseP was used as an endogenous control for both assays . We then used linear regression to remove the effects of these potential confounders at each gene and confounder-corrected data were used in all subsequent analyses . In order to identify genes that , on average across individuals , changed expression levels upon treatment with GCs , we performed multiple linear regression at each gene with treatment as the covariate of interest while taking other measured covariates into account . To reduce the effects of outliers , microarray intensity values were quantile normalized to a N ( 0 , 1 ) distribution across all samples ( treated and untreated ) . We used the distribution of p-values observed when sample labels are permuted ( ten permutations were used ) , an empirical estimate of the p-value distribution under the null , to estimate the false discovery rate ( FDR ) . We used the online tool DAVID [65] , [66] to identify biological categories enriched among differentially expressed genes , using all genes expressed in LCLs ( based on microarray data ) as a background . We used all HapMap SNPs for all mapping experiments described . As TSI LCLs were only typed for phase III SNPs , we used the CEU population sample to impute genotypes at all HapMap phase I and II SNPs . Similarly , we imputed SNPs for phase III YRI LCLs based on the YRI LCLs included in phase I and II . Imputation was performed using BIMBAM [67] , which infers missing genotypes based on correlations between missing and typed genotypes observed in samples where all genotypes are typed . QTL mapping results were not qualitatively different if using imputed or genotyped SNPs . We tested for association between all HapMap SNPs and transcriptional response at each gene , using log fold change in expression ( GC-treated over control-treated expression ) as a measure of response . For our candidate gene-based scan for trans-acting eQTLs that influenced response , we tested all HapMap SNPs within 500 kb and 100 kb ( in two separate sets of analyses ) of genes encoding the GR and transcription factors that interact with the GR . Interacting transcription factors include the genes that encode the components of the NFkB complex , AP1 , Oct1 , Oct2 , CREB , ETS1 , STAT3 , STAT5 , STAT6 , C/EBP , TFIID , T-bet , PU . 1/Spi-1 , Smad3 , Smad4 , Smad6 , COUP-TFII , IRF3 , STIP1 , Hic5/Ara55 , and nTrip6 [29] . P-values calculated with permutated genotype labels were used as an empirical null distribution . In order to maintain the correlation structure across genes , the same permutation seed was used for all genes in both candidate gene tests and the genome-wide scan . Ten permutations were performed for the test of variation within 500 kb , 100 permutations were used for the test of variation within 100 kb and 3 permutations were used for the genome-wide scan . For mapping log fold change at SNPs within 500 kb or 100 kb of each gene , permutation seeds were set separately at each gene . Association tests were performed using a combination of Python , the R statistical package and the genetic association mapping program PLINK . We developed a novel Bayesian statistical framework for genetic association analysis in settings where measurements are available on the same individuals in two different conditions ( in our case , GC-treated and control-treated ) . Our methods extend and improve the methods from Barber et al . ( 2009 ) to explicitly consider “qualitative interaction” models where genetic variants are associated with measurements in only one of the two conditions . Our method takes into account both sample pairing and the intra-individual correlation of measurements under the two conditions . We describe our method in greater detail in Text S1 . These methods are implemented in software called BRIdGE ( Bayesian Regression for Identifying Gene-Environment interactions ) , which is available on the Stephens and the Di Rienzo laboratories' web pages ( http://stephenslab . uchicago . edu/software . html , http://genapps . uchicago . edu/labweb/index . html ) . We used TaqMan quantitative genotyping assays to test for allelic imbalance at coding SNPs in LD with eQTLs that interacted with GC treatment . Imbalanced expression of the two coding alleles is an independent line of evidence for a cis-acting regulatory polymorphism and for the configuration of the effect in the two treatment conditions ( i . e . the interaction model ) . Total RNA from an aliquot of the same culture samples used to hybridize microarrays ( this was a separate RNA extraction as that used to hybridize microarrays ) was synthesized into cDNA using the High-Capacity cDNA Reverse Transcription Kit ( Applied Biosystems , Foster City , CA ) according to the manufacturer's protocol . Taqman SNP Genotyping Assays were used to quantify relative mRNA abundance of each allele on an ABI PRISM 7900HT Sequence Detection System . To account for differences between the two fluorochromes , a standard curve was built for each of the two alleles using serial dilutions of a genomic DNA from an individual that was heterozygous at the coding SNP . For each assay , we calculated the natural log-ratio between the two different alleles . The numerator of this ratio was always the allele associated with increased expression in the corresponding treatment condition . Within each treatment , we quantile normalized allelic log-ratios and used a one-tailed t-test to identify significant differences in average allelic log-ratios between heterozygotes and homozygotes ( as an empirical null distribution of allelic log-ratios ) at the eQTL . We compared our eQTL results to multiple genetic association studies including Qiu et al . ( 2011 ) and those in the GWAS catalog . For each interaction eQTL , we compared evidence at the most associated SNP in our data when it was tested in both studies . When the most associated SNP was not tested in the comparison dataset , we identified the best proxy SNP for each eQTL among those tested in both studies . To ensure that the best proxy SNP captured the pattern at the original eQTL , we required the proxy SNP to show strong evidence of association for the same eQTL model as the original eQTL ( BF for association>500 and posterior probability for model>0 . 5 ) . We contrasted the transcriptional response to GCs between YRI and TSI LCLs . Differences in transcriptional response between populations will result in differences in average expression levels that differ depending on treatment , as opposed to GC-independent population differences that will be identical in both treatments . As this is analogous to gene-environment interactions , we used the same statistical framework to identify genes with differences in transcriptional response between populations ( see Bayesian regression for identifying genetic associations and interaction with treatment in Text S1 ) . Covariate-corrected expression levels were quantile normalized across individuals ( both YRI and TSI ) for each gene to reduce the effect of outliers . As population differences at the phenotypic level may reflect population differences in response following a consistent pattern across many genes , we identified the direction of population differences at each gene in terms of log-fold change . A multianalyte ELISA assay ( Millipore ) was performed on the culture medium of the cell aliquots treated for 24 hours . The assay was performed at the Flow Cytometry Facility at the University of Chicago , according to the manufacturer instructions . Two technical replicates were run for each sample . Samples were assayed in batches balanced by treatment and population . For each analyte , the average quantity across technical replicates was calculated and used for all subsequent analyses . The correlation structure between paired aliquots for each sample ( GC and control ) was visually inspected ( Figure S8 ) . A small subset of samples with low quantity detected showed no correlation between GC and control aliquots because of noise in the measurement at low concentrations . Consequently , these samples were excluded from downstream analyses . Secretion levels were highly correlated across proteins , likely representing a latent factor that generally affects secretion levels . To remove the effect of this latent factor , we used linear regression to correct secretion levels at each protein by secretion levels at all other measured proteins . | Glucocorticoids ( GCs ) are steroid hormones produced by the human body in response to environmental stressors . Despite their key role as physiological regulators and widely administered pharmaceuticals , little is known about the genetic basis of inter-individual and inter-ethnic variation in GC response . As GC action is mediated by the regulation of gene expression , we profiled transcript abundance and protein secretion in EBV-transformed B lymphocytes from a panel of 114 individuals , including those of both African and European ancestry . Combining these molecular traits with genome-wide genetic data , we found that genotype-treatment interactions at polymorphisms near genes affected GC regulation of expression for 26 genes and of secretion for IL6 . A novel statistical approach revealed that these interactions could be distinguished into distinct types , with some showing genotypic effects only in GC-treated samples and others showing genotypic effects only in control-treated samples , with differing phenotypic and molecular interpretations . The insights into the genetic basis of variation in GC response and the statistical tools for identifying gene-treatment interactions that we provide will aid future efforts to identify genetic predictors of response to this and other treatments . | [
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"pharmacogenetics",
"endocrinol... | 2011 | Interactions between Glucocorticoid Treatment and Cis-Regulatory Polymorphisms Contribute to Cellular Response Phenotypes |
Dual colour total internal reflection fluorescence microscopy is a powerful tool for decoding the molecular dynamics of clathrin-mediated endocytosis ( CME ) . Typically , the recruitment of a fluorescent protein–tagged endocytic protein was referenced to the disappearance of spot-like clathrin-coated structure ( CCS ) , but the precision of spot-like CCS disappearance as a marker for canonical CME remained unknown . Here we have used an imaging assay based on total internal reflection fluorescence microscopy to detect scission events with a resolution of ∼2 s . We found that scission events engulfed comparable amounts of transferrin receptor cargo at CCSs of different sizes and CCS did not always disappear following scission . We measured the recruitment dynamics of 34 types of endocytic protein to scission events: Abp1 , ACK1 , amphiphysin1 , APPL1 , Arp3 , BIN1 , CALM , CIP4 , clathrin light chain ( Clc ) , cofilin , coronin1B , cortactin , dynamin1/2 , endophilin2 , Eps15 , Eps8 , epsin2 , FBP17 , FCHo1/2 , GAK , Hip1R , lifeAct , mu2 subunit of the AP2 complex , myosin1E , myosin6 , NECAP , N-WASP , OCRL1 , Rab5 , SNX9 , synaptojanin2β1 , and syndapin2 . For each protein we aligned ∼1 , 000 recruitment profiles to their respective scission events and constructed characteristic “recruitment signatures” that were grouped , as for yeast , to reveal the modular organization of mammalian CME . A detailed analysis revealed the unanticipated recruitment dynamics of SNX9 , FBP17 , and CIP4 and showed that the same set of proteins was recruited , in the same order , to scission events at CCSs of different sizes and lifetimes . Collectively these data reveal the fine-grained temporal structure of CME and suggest a simplified canonical model of mammalian CME in which the same core mechanism of CME , involving actin , operates at CCSs of diverse sizes and lifetimes .
Clathrin-mediated endocytosis ( CME ) is the principal means by which mammalian cells internalize cell surface receptors ( reviewed in [1] ) . Some 40 years of electron microscopy ( EM ) , genetic , and biochemical studies are distilled in the canonical model of CME [2] ( reviewed in Figure S1 ) . Here , interaction of receptors with adaptor proteins stabilise nascent clathrin-coated pits ( CCPs ) at random sites on the plasma membrane [3] . Growing CCPs acquire cargo and invaginate via clathrin polymerization [4] and the coordinated action of curvature-inducing/sensing BAR [5] and F-BAR domain proteins [6] , [7] , ENTH domain proteins [8] , and possibly actin [9]–[11] . The neck of the deeply invaginated CCP is severed in a mechanism involving the large GTPase dynamin [12] , [13] , and possibly a phosphoinositide ( PI ) phosphatase [14] , to release a clathrin-coated vesicle ( CCV ) , which uncoats through the action of GAK/auxilin [15] , [16] . Understanding how the multiple components of CME are spatially and temporally organized is a challenging problem that has been tackled using live-cell fluorescence microscopy ( reviewed in [2] , [17] ) . In a typical experiment using dual colour total internal reflection fluorescence microscopy ( TIR-FM ) , the recruitment dynamics of fluorescent protein ( FP ) –tagged endocytic proteins were measured relative to the disappearance of spot-like CCPs , which was used as a fiducial marker to indicate internalization [6] , [18] , [19] . Using this strategy the recruitment dynamics of endocytic proteins were coarsely grouped into “early” and “late” relative to CCP disappearance [20] ( Figure S1 ) , but finer temporal resolution was not possible because the moment of scission , the endpoint of the invagination process , was unknown . In addition to spot-like CCPs , larger clathrin patches were also observed at the substrate proximal surface of many cell types , where they were variously thought to participate in the canonical pathway of CME [4] , [21] or cell adhesion [22] , [23] , or were thought to represent endocytic intermediates in an actin-dependent mode of endocytosis distinct from the canonical pathway of CME [23] . To circumvent the subjective classification of endocytically active clathrin-coated structures ( CCSs ) , a TIR-FM assay was invented to detect single scission events directly by monitoring the accessibility of pH-sensitive fluorescent CCP cargo to rhythmically imposed changes in extracellular pH ( the “pulsed pH” [ppH] assay [10] , reviewed in Figure S2 ) . Surprisingly , it was discovered that scission events were hosted by spot-like CCPs , as predicted from the canonical model , and also by larger clathrin patches ( collectively referred to as CCSs [10] ) , thus raising questions about what characterises endocytically active CCS at optical resolution . The following study was designed to explore the fine-grained temporal structure of late stages of the mammalian CME machinery using TIR-FM and the ppH assay . First , scission events were mapped to their host CCSs to determine what dynamic characteristics defined endocytically active CCSs . It was found that CCSs of diverse size and lifetimes hosted scission events that engulfed comparable amounts of receptor cargo , and CCSs could either disappear ( “terminal events” ) or persist ( “non-terminal events” ) following scission . Second , we assessed the accuracy of CCS disappearance as a fiducial marker for internalization and showed it introduced an error comparable to the time course of CCS invagination and CCV formation . It was thus necessary to use the ppH assay to obtain a precise measurement of recruitment dynamics . Third , we surveyed the recruitment dynamics of a representative set of 34 mammalian endocytic proteins to sites of scission and derived , for each protein , a characteristic “recruitment signature” by aligning and averaging ∼1 , 000 recruitment traces per protein . A cluster analysis of recruitment signatures revealed the modular organization of the CME machinery , similar to yeast [24] , while closer inspection revealed unanticipated features of some signatures . Finally , scaling relationships between CCS size and lifetime and the cohort of endocytic proteins recruited to scission events were explored . It was found that the same set of proteins was recruited in the same order to scission events at diverse dynamic classes of CCSs , although subtle scaling relationships between CCS size and protein recruitment were identified . Collectively these data provide , to our knowledge , the highest resolution temporal map of the late stages of mammalian CME to date . This map ( 1 ) suggests a simplified model of mammalian CME in which the same core mechanism can operate at both spot-like CCSs and larger clathrin patches observed with fluorescence microscopy , ( 2 ) illustrates the similar modular organization of mammalian and yeast endocytosis , and ( 3 ) proves that recruitment dynamics of endocytic proteins such as the F-BAR protein FBP17 and BAR domain protein SNX9 cannot always be predicted from biochemical or structural properties .
To detect CME scission events at CCSs , NIH-3T3 cells were transiently transfected with Clc-mCherry and TfR-phl and assayed using the ppH assay , as described previously [10] . A large-diameter perfusion tip was brought close to the target cell , and perfusate was cycled between buffer of pH 7 . 4 and pH 5 . 5 in synchrony with image acquisition at 0 . 5 Hz ( see [10] and Figure S2 ) . In an image acquired at arbitrary time point t , at pH 7 . 4 , TfR-phl concentrated in spots and patches of Clc-mCherry and free in the plasma membrane fluoresced brightly ( Figure 1A ) . When the perfusate was switched to pH 5 . 5 and an image was acquired 2 s later ( at t+2 s ) , TfR-phl fluorescence at the plasma membrane was quenched and revealed bright punctae of pH-insulated TfR-phl sequestered in internal vesicles , while Clc-mCherry fluorescence remained unchanged ( Figure 1A ) . The cycle of pH switching and image acquisition was repeated to generate an image series acquired at alternating high and low pH . Scission events manifested as the abrupt appearance of TfR-phl spots in images acquired at pH 5 . 5 , colocalized with Clc-mCherry-labelled CCSs ( Figure 1B; Video S1 ) . Although it took 4 s to complete a cycle of pH change , the precision with which scission events were detected was ∼2 s because , for an event to be detected , scission had to occur in a ∼2-s time window at pH 7 . 4 prior to detection at pH 5 . 5 ( see [10] and Figure S2 ) . We could therefore align the red fluorescence traces , acquired at 0 . 5 Hz , with an accuracy of 2 s . Visual inspection revealed that scission events were associated with both punctate CCSs and also larger , pleiomorphic clathrin patches ( Figure 1C; Video S1 ) , and events could occur repeatedly at larger CCSs , as shown previously [10] ( Figure 1D ) . Larger CCSs may represent flat clathrin lattices , with peripheral invaginations , or clusters of smaller CCSs too close to resolve by optical microscopy [25] , [26] . Inspection of kymographs revealed that Clc-mCherry and TfR-phl patches waxed and waned in synchrony at both small and large CCSs , demonstrating the similarity of these two signals and suggesting that TfR7 fluorescence could be used as a surrogate signal to report the relative size or lifetime of CCSs ( Figure 1E ) . Scission events were not always associated with the disappearance of the host CCS , and , similar to previous findings , events were either terminal ( where the spot-like CCS disappeared following scission , red arrows in Figure 1E ) or non-terminal ( where CCS persisted following scission , yellow arrows in Figure 1E ) [10] . To analyse large numbers of scission events we developed a semi-automated analysis pipeline to identify candidate events , screen for bona fide events , and quantify the fluorescence changes associated with these events in both the green and red channels . The purpose of this screening strategy was not to detect all scission events in an image series but to impose stringent selection criteria and automatically sample a large proportion of genuine scission events . The criteria for selection of bona fide scission events included persistence of the TfR5 spot , association with a “host” CCS , adequate signal-to-noise ratio ( SNR ) , and slope of the TfR5 signal following appearance ( Figure 1B , see Materials and Methods for details ) . To quantitatively investigate the characteristics of endocytically active CCSs we detected scission events in seven cells expressing Clc-mCherry and TfR-phl and identified a set of 851 bona fide events . First we analysed the relationship between the relative amount of TfR-phl localized at a CCS ( TfR7 fluorescence ) , the relative amount of clathrin ( Clc fluorescence ) , and the relative amount of TfR-phl internalized by a scission event ( TfR5 fluorescence ) . As expected , there was a significant correlation between TfR7 fluorescence and Clc7 fluorescence ( Spearman's rho = 0 . 85 , p<0 . 05; Figure S3 ) , showing that larger CCSs contained more TfR-phl cargo , and indicating that CCS size could be estimated using TfR7 fluorescence . However , there was no significant correlation between Clc7 and TfR5 fluorescence ( Spearman's rho = −0 . 0024 , p>0 . 05 ) or between TfR7 and TfR5 fluorescence ( Spearman's rho = −0 . 0022 , p>0 . 05; 3 ) . Therefore , and consistent with both visual inspection of the current data and previous results [10] , the amount of cargo internalized by scission events was independent of the size of the host CCS , and endocytically active CCSs could be either spot-like structures or larger , pleiomorphic clathrin patches . In mechanistic terms , this is consistent with the relatively constant dimensions of coated invaginations viewed by EM whether they occurred in isolation , as part of a cluster , or as a peripheral invagination at a flat patch of clathrin [25] , [26] . To check that extracellular acidification did not affect the size of clathrin-coated invaginations , we fixed cells under control conditions and after exposure to acidic buffer for 1 min or 10 min , and imaged them using thin section EM ( Figure S3 ) . Under both control and acidified conditions the clathrin-coated invaginations were of relatively uniform size , with a maximum dimension of ∼100 nm ( Figure S3F–S3I ) . Next we explored what dynamic characteristics defined endocytically active CCSs . All CCSs present in the Clc-mCherry dataset ( seven cells ) were tracked using a multi-particle tracking algorithm , similar to previous studies [27] ( see Materials and Methods ) , yielding a set of 11 , 447 track histories . For each CCS track history the fluorescence of Clc-mCherry was quantified , and the CCS track histories were classified according to the presence or absence of scission events , wherein a track history was defined as scission detected if a bona fide scission event fell within five pixels , or 500 nm . The median normalised Clc-mCherry fluorescence of scission detected CCSs was significantly greater than for scission undetected CCSs ( 0 . 190 versus 0 . 078 , p<0 . 05; Figure 1F and 1G ) , and the median lifetime of scission-detected CCSs was longer than the lifetime of scission-undetected CCSs ( 189 s versus 38 s , p<0 . 05; Figure 1H and 1I ) . Therefore , scission events defined a class of larger , longer-lived CCSs . The shorter-lived scission-undetected CCSs most likely correspond to the “abortive” CCSs described previously [3] , [27] , [28] , although some of these structures may have represented endosomal clathrin . For NIH-3T3 fibroblasts the average time between de novo appearance of a spot-like CCS and the first detected scission event was previously found to be ∼100 s [10] . This was similar to previous estimates in BSC1 cells , wherein productive CCSs were defined as spot-like CCSs having lifetimes anywhere from tens to hundreds of seconds ( average 87 s ) [3] , [27] , [28] . Because the size and lifetimes of scission-detected CCSs were so variable ( Figure 1H and 1I ) , in our subsequent investigation of late events in CME we made measurements over a time window of ±80 s , centred on scission . In previous analysis of the molecular dynamics of CME , the disappearance of spot-like CCSs was used as a fiducial marker to indicate endocytic events [6] , [18] , [19] . However , we discovered that CCS disappearance gave an inaccurate and imprecise estimate of scission , with a temporal uncertainty comparable to the time course of CCS invagination and CCV formation [10] ( −7±22 s; n = 107; six cells ) ( Figure 1J and 1K ) . CCS disappearance most likely corresponded to CCV uncoating and/or movement , and if CCS disappearance was used as a fiducial marker for CME the waveform of aligned and averaged recruitment signatures would be significantly smeared . We hypothesized that measuring the recruitment of endocytic proteins with improved temporal accuracy might reveal otherwise hidden temporal structure in the CME mechanism , and so we measured the recruitment signatures of a representative set of 34 mammalian endocytic proteins relative to scission . First , and to illustrate the experimental strategy and details of the analysis , we determined the kinetics of dynamin1 recruitment relative to scission . The Dyn1-mCherry signals acquired at pH 5 . 5 and pH 7 . 4 were corrected for bleed through and interlaced , and confidence intervals were calculated on the fluorescence recruitment signature using a randomization procedure ( –S4D ) . As a negative control for protein recruitment we assayed caveolin1-mCherry , which forms spot-like structures at the plasma membrane but which is not enriched at sites of CME [29] ( Figure S4E–S4H ) . Dynamin is essential for scission [30] , and it is thought to be recruited in the last steps of vesicle formation [18] , [19] . Cells co-transfected with TfR-phl and Dyn1-mCherry and imaged with TIR-FM microscopy at pH 7 . 4 showed punctuate patterns that were partially colocalized ( Figure 2A ) , and scission events , localized to patches of TfR-phl marking CCSs ( Figure 2B ) , were frequently ( 75% ) preceded by a transient burst of Dyn1-mCherry ( Figure 2B and 2C ) . Examination of the average fluorescence traces revealed that the TfR7 signal dropped before scission , which might indicate progressive polarization of receptor cargo in the invaginating CCS similar to AP2 [31] ( Figure 2D ) . The average recruitment signatures of Dyn1-mCherry showed a peak 2 to 4 s before vesicle detection ( Figure 2D–2F ) , which corresponded to the time of vesicle creation . Before this transient burst Dyn1-mCherry was , on average , present at low levels on the CCS , as seen in the average and in individual examples , consistent with previous observations [32] ( Figure 2B and 2D ) . Visual inspection revealed that pre-scission recruitment of Dyn1-mCherry manifested as low-amplitude “flickering” , which persisted following scission in non-terminal events , consistent with continued recruitment of Dyn1-mCherry to the remaining portion of CCSs at the plasma membrane ( Figure 2E ) . Strikingly , the temporal spread of Dyn1-mCherry average fluorescence ( ∼8 s ) and peak recruitment around scission ( Figure 2D ) was much narrower than when CCS disappearance was used as a reference for CCV creation ( ∼20 s ) [18] , [19] . Finally , the recruitment kinetics of Dyn2-mCherry was very similar to that of Dyn1-mCherry ( Figure 2G ) . Visual inspection revealed heterogeneity among individual Dyn1-mCherry fluorescence traces ( Figure 3A and 3B ) . To explore whether there was any evidence for natural sub-classes of recruitment signature , the full set of Dyn1-mCherry recruitment traces was normalised and overlaid to generate a cloud plot ( Figure 3C ) . The average fluorescence recruitment trace followed the highest data density , and there was no obvious evidence of bifurcations or the presence of “natural” sub-classes of Dyn1-mCherry recruitment traces ( Figure 3C ) . Therefore , the heterogeneity apparent among individual traces was largely unstructured and most likely represented natural noise rather than mechanistic differences between scission events . To further test the reproducibility of the Dyn1-mCherry average recruitment signature two datasets were generated using either human or mouse Dyn1-mCherry . For human Dyn1-mCherry seven cells were analysed ( 1 , 276 events ) , and for mouse Dyn1-mCherry 21 cells were analysed , arbitrarily divided into two pools of 10 cells ( Pool 1 , 2 , 126 events ) and 11 cells ( Pool 2 , 2 , 622 events ) . The average recruitment signatures for human Dyn1-mCherry-transfected cells and either pool of mouse Dyn1-mCherry-transfected cells were very similar ( correlation coefficient >0 . 95 ) , with only minor differences in the pre-scission offset ( Figure 3D ) . Therefore , although individual Dyn1-mCherry fluorescence recruitment traces were variable , the average Dyn1-mCherry recruitment signatures were reproducible and remarkably stable . Next , we applied the ppH protocol and analysis to an additional set of 33 mammalian endocytic proteins fused to mCherry ( Figure S5 ) . To generate an overview of the molecular dynamics of CME we chose a range of proteins that included well-established players ( e . g . , dynamin and GAK ) , proteins with tentative or poorly understood links to CME ( e . g . , Eps8 [33] ) , and proteins with established links to endocytosis in yeast and which we hypothesized should be recruited to sites of endocytosis in mammalian cells ( e . g . , cofilin and coronin [34] , [35] ) . Of the 34 endocytic proteins analysed , only the recruitment signature of cortactin had been previously measured with a temporal resolution of 2 s , and the recruitment dynamics of the other 33 proteins remained uncharacterised at this resolution . A reverse transcription PCR ( RT-PCR ) analysis revealed that all proteins except ACK1 , amphiphysin1 , CIP4 , and FCHo1 were expressed in fibroblasts ( Figure S6 ) . It remains possible that the expression of such a diverse set of endocytic proteins is peculiar to cultured cells and would not normally be seen in native tissue . For example , dynamin1 is thought to be expressed predominantly in neurons [36] , although low levels of dynamin1 expression have been detected in primary mouse fibroblasts , and the expression level in fibroblast cell lines was found to increase upon immortalization [9] . However , and as described previously [15] , [23] , [27] , [37] , [38] , we expected that proteins expressed in fibroblasts and heterologously expressed proteins would still incorporate into the CME machinery and could thus reveal useful information . For each protein we generated red FP ( RFP ) fusion constructs and assayed 5–7 cells per construct using the ppH protocol , yielding a dataset of ∼1 , 000 bona fide scission events per protein type ( Table S1 ) . Overexpression of mCherry-tagged proteins may perturb the recruitment dynamics of endocytic proteins or have other deleterious effects on the endocytic machinery . Therefore , to ameliorate the possible effects of overexpression cells were transiently co-transfected with TfR-phl and the relevant RFP chimera ∼48 h prior to the experiment , and cells with only the lowest 10%–20% levels of expression used for imaging experiments . In our experience this procedure gave the most consistent results , and target cells showed no overt changes in morphology . Although the incidence rate of scission events varied up to 5-fold between constructs ( Table S1 ) , variability between cells expressing the same construct was also high and cells expressing low levels of a selection of RFP fusion proteins still internalized Tfn-A647 ( Figure S7 ) . Moreover , and by definition , the ppH assay measured the dynamics of protein recruitment only to successful scission events . The recruitment signatures of each protein were assessed , and the full set of traces compared pair-wise and organized in a dendrogram by hierarchical clustering ( Figure 4; the full set of fluorescence recruitment signatures is shown in Figure S8 and peaks histograms in Figure S9 ) . This analysis revealed , similar to previous results in yeast [24] , that natural groups or clusters were formed based on the similarity of recruitment signatures . In each of the seven groups or modules there were proteins expected to show similar recruitment signatures on the basis of previous knowledge ( i . e . , previous imaging studies , known binding affinities , and known biochemical properties ) , while some patterns of recruitment were unexpected . A brief comparison of key predictions , based on a priori models , and actual observations follows below . The clathrin recruitment signature , reported by Clc-mCherry , showed a slow build up that peaked at scission and dropped sharply thereafter , presumably as the newly formed vesicle uncoated ( Figure 4A and 4B; although note the different signatures of terminal and non-terminal events , Figure S8 ) . Most similar to clathrin were the PI ( 4 , 5 ) P2-binding epsin N-terminal homology domain ( ENTH ) /AP180 N-terminal homology domain ( ANTH ) adaptor proteins epsin and CALM [8] , [39] , both of which directly bind clathrin . Surprisingly , NECAP , which has a high affinity for the AP2α-ear [40] , displayed a similar recruitment profile to clathrin rather than AP2 . Other adaptor proteins formed a distinct subgroup within the clathrin/adaptor protein module . Based on previous work it was predicted that AP2 fluorescence ( marked by mu2-mCherry ) should markedly decrease before scission , indicating the polarized segregation of AP2 in the nascent bud [31] and/or loss from developing buds before clathrin [41] ( though see [42] ) . This was indeed the case , and , in addition , the adaptor protein Eps15 , TfR7 ( i . e . , the receptor cargo ) , and the F-BAR domain proteins FCHo1 and FCHo2 showed similar signatures , suggesting that these proteins were also polarized and/or lost from the developing bud before clathrin ( Figure 4A , 4B , and 4F ) . This latter observation may be consistent with a recently proposed role for FCHo proteins in CCP nucleation and the generation of curvature early in bud formation [43] . Dynamin was present at low levels on CCSs at all times , and a burst of recruitment preceded scission ( Figures 2 , 4A , and 4C ) . Other proteins showed a similar pattern of biphasic recruitment and thus defined a dynamin module . These included actin-binding proteins such as the actin- and clathrin-binding protein Hip1R [44] as well as the motor protein myosin6 , which binds actin and the adaptor protein Dab2 [45] . Other proteins involved in actin dynamics and grouped in the dynamin module included the Arp2/3 activator N-WASP [46] , [47] , Eps8 , an actin capping protein that forms a complex with Abi1 and binds N-WASP [33] and the motor protein myosin1E [48] . The PI ( 4 , 5 ) P2 phosphatase synaptojanin2β1 , which binds to the NBAR domain protein amphiphysin1 [49] , had recruitment kinetics similar to those of dynamin and peaked at scission but showed little recruitment at time points before −20 s ( Figure 4A ) . Finally , the F-BAR protein syndapin2 [50] , [51] , which binds dynamin and N-WASP , was recruited early , peaking at −4 s before being quickly discarded following scission ( Figure 4A and 4E ) . The rapid loss of syndapin2 signal may be due to collapse of the highly curved membrane neck at the moment of scission . The improved temporal accuracy of the ppH assay allowed us to re-evaluate the temporal relationships between dynamin recruitment and actin dynamics . Earlier work suggested that dynamin and actin were recruited sequentially to sites of scission [18] . Here , a more accurate comparison of dynamin and actin recruitment revealed that dynamin and actin recruitment both peaked at scission and that the final burst of dynamin recruitment lagged the onset of actin polymerization by ∼20 s ( Figure 4C ) . It is generally accepted that actin polymerization plays a role in some ( but not all , see [52] , [53] ) forms of CME [9] , [11] , [18] , [44] , [46] , [47] , [50] , [54] . Here , a more accurate measurement of actin dynamics using the ppH assay revealed an ordered sequence of proteins involved in actin dynamics . After the Arp2/3 complex activator N-WASP , which peaked before all the other actin module proteins and groups with the dynamin module , the F-actin-binding proteins Arp3 , Abp1 , cortactin , and lifeAct were recruited ( Figure 4D ) . Unique among tested proteins , the average lifeAct signal was significantly below random prior to scission ( Figure 4A ) , probably because bright stress fibres adjacent to sites of scission artificially lowered the background subtracted fluorescence value ( e . g . , see Figure S4 ) . Peak recruitment of the actin-severing protein cofilin [55] and the Arp2/3 suppressor coronin [56] were both significantly skewed post-scission , suggesting an ordered shut down of the actin polymerization machinery and disassembly of scission-associated actin ( Figure 4D ) . Based on contemporary models of CME [57] we predicted that recruitment of BAR and F-BAR domain proteins should follow patterns consistent with the differing curvatures of their respective membrane-binding domains , since purified proteins induce different degrees of curvature in membrane tubulation assays in vitro and membrane curvature increases as CCSs invaginate [6] , [7] . The sequential recruitment of the F-BAR domain protein syndapin2 and a group of NBAR domain proteins ( endophilin2 , BIN1 , and amphiphysin1 ) followed by scission matched this prediction ( Figure 4E ) . Similar to syndapin2 , NBAR proteins were also rapidly discarded following scission , presumably because of the collapse of the highly curved membrane neck at the moment of scission . However , the recruitment of the BAR domain protein SNX9 differed from prediction . SNX9 recruitment began before scission , peaking ∼12 s after scission , similar to coronin and cofilin rather than to its binding partner , dynamin ( Figure 4E ) . Similarly , the recruitment of the F-BAR domain proteins CIP4 and FBP17 also differed from prediction [6] ( Figure 4F ) . Both proteins showed complex recruitment dynamics , with components of recruitment both before and after scission and , strikingly , FBP17 recruitment peaked markedly post-scission , at a time similar to that of GAK ( Figure 4F ) . It was shown previously that the kinase GAK , which is necessary for CCV uncoating , was recruited shortly after the large GTPase dynamin to sites of CME [15] , [16] . Here , we found that GAK recruitment commenced at scission and peaked on average ∼8 s thereafter , as predicted . The recruitment profile of GAK was the same for both terminal and non-terminal scission events ( Figure 4G ) . In the canonical model of CME , bona fide endocytic structures were represented as spot-like CCSs that formed de novo [23] . We therefore analysed a subset of 100 scission events associated with spot-like CCSs that formed de novo and found that the first detected scission event occurred , on average , 93 s following CCS inception ( minimum = 20 s ) and similar to the 100 s calculated in a previous study [10] . The GAK recruitment signature was again similar ( Figure 4G , inset ) , and therefore , irrespective of the behaviour of the host CCS , the dynamics of the uncoating reaction associated with scission events were comparable . The recruitment signature of GAK defined a module including ACK1 , a serine threonine kinase implicated in tumorigenesis [58] and OCRL1 , a 5′ phosphatase and Rab5a effector [59] . Interestingly , GAK and OCRL1 were recruited only after scission , whereas ACK1 was gradually recruited as CCSs matured ( Figure 4A ) . The last module to be recruited consisted of the Rab5a effector APPL1 [60] and Rab5a itself ( Figure 4A ) . The Rab5a signal was small and temporally spread , but significantly raised above baseline . Most likely this marks the outer limits of recruitment detection using the ppH protocol . Having accurately measured the recruitment signatures of a representative set of endocytic proteins we next asked whether the same set of proteins was recruited to scission events at different dynamic classes of CCSs . Previous studies defined different populations of CCSs on the basis of size ( i . e . , spot-like CCSs versus larger CCSs ) and lifetime or whether CCSs disappeared following scission ( terminal events ) or persisted ( non-terminal events ) [10] , [23] , [28] , [61] . Detailed mechanistic inferences have been based on these types of dynamic classification [23] . Therefore , we explored whether the set of endocytic proteins recruited differed between terminal and non-terminal scission events or between scission events at CCSs of different size or lifetime . First we analysed whether the same set of proteins was recruited to scission events at terminal and non-terminal scission events . Terminal and non terminal events were sorted by computing the ratio of average FTfR7 before and after scission ( see Materials and Methods ) . For all constructs tested , there was approximately the same number of events in each category ( Table S1 ) . For all proteins tested the average fluorescence profiles were strikingly similar between terminal and non-terminal events before scission , with occasional shifts towards higher values for non-terminal events ( Figure S8 ) . This strongly suggests that the mechanisms of protein recruitment were the same for both classes of events . By contrast , the recruitment signatures after scission differed markedly for proteins that were significantly recruited at time points well removed from scission such as clathrin module proteins or some dynamin/myosin module proteins . Interestingly , in many recruitment signatures ( e . g . , Eps15 , mu2 , myosin6 , or CALM ) , the average fluorescence trace of non-terminal events increased steadily after scission to a maximum around 40 s post-scission , suggesting a characteristic time course of CCS maturation between successive scission events , and similar to findings in a previous study [10] . We established that there was a good correlation between Clc-mCherry fluorescence and TfR7 fluorescence and that , by inference , TfR7 fluorescence could be used to confidently predict the relative size or lifetime of CCSs ( Figures 1E , S3 , 5A , and 5B ) . Therefore , to investigate the relationship between CCS size and patterns of protein recruitment , TfR7 patch fluorescence was normalised by cell , and , for each trace , the average fluorescence ( FTfR7 ) was calculated over the time interval −18 s to −10 s relative to scission ( Figure 5C ) . For each cell the FTfR7 values formed a continuous distribution ( Figure 5C ) that was divided into three equally populated groups representing “small” CCSs ( blue fluorescence traces ) , “medium” CCSs ( green fluorescence traces ) , and “large” CCSs ( red fluorescence traces , Figure 5 ) . As expected , when the normalised fluorescence recruitment traces for Clc-mCherry were assigned to CCS size groups 1–3 , the group average recruitment signatures were well separated ( Figure 5Di ) . This simply reflected the fact that larger CCSs had more clathrin and confirmed that TfR7 fluorescence could be used to predict CCS size ( see also Figure S3 ) . However , and as a control , when Clc-mCherry recruitment traces were randomly assigned to three groups , the average traces for groups 1–3 were almost identical ( Figure 5Dii ) . Thus , we can be confident that Clc-mCherry fluorescence scaled strongly with TfR7 fluorescence , as expected . Similarly , when TfR7 fluorescence traces were assigned to CCS size groups 1–3 , the fluorescence signatures were ( by definition ) well separated ( Figure 5Ei ) , but when TfR5 fluorescence traces were assigned to CCS size groups 1–3 and averaged , the TfR5 class averages were virtually identical ( Figure 5Eii ) . Therefore ( and similar to Figure S3 ) , the amount of TfR internalized did not scale strongly with the size of the host CCS , consistent with the idea that quantized scission events occurred at CCSs of apparently different sizes . The analysis was repeated for the 34 endocytic proteins of this study to assess how different recruitment signatures scaled with CCS size . Sample classified recruitment signatures are shown in Figure 5F , and , for each protein , the relative strength of the scaling relationship between CCS size and protein recruitment was visualised by calculating the summed absolute difference between the group averages and overall average ( Figure 5G , 95% bootstrapped confidence interval in grey ) . Thus , for example , the average FCHo1 fluorescence traces for the small and large groups of CCSs ( Figure 5G , blue and red , respectively ) were well separated from the pooled FCHo1 average fluorescence trace , indicating a strong scaling relationship between CCS size and the amount of FCHo1 at the CCS . The scaling relationship was significant because it exceeded the boundaries of the confidence interval in grey ( Figure 5G ) . In general , the group averages for structural proteins such as clathrin and the adaptor proteins ( mu2 , Eps15 , and FCHo1/2 ) scaled strongly with CCS size . The relationship between Dyn1-mCherry recruitment and TfR7 cluster size was more complex . As noted earlier , low amplitude flickering of Dyn1-mCherry was noted at CCSs before the final recruitment burst that marked scission ( Figures 2B , 2C , 3A , and 3B ) . The overall amplitude of Dyn1-mCherry recruitment did scale with CCS size , but this could be explained by the difference in offset of the “pre-scission” signal , consistent with two components to the Dyn1-mCherry signal: pre-scission recruitment scaled with CCS size , suggesting a link with clathrin in the host CCS , but the burst of dynamin associated with scission was of relatively constant amplitude , consistent with recruitment to budding structure of constant dimensions . Other transiently recruited proteins , such as endophilin2 , showed similar behaviour ( Figure 5F ) . A notable exception was synaptojanin2β1 , which showed robust recruitment to large CCSs but lower amplitude recruitment to smaller CCSs ( Figure 5F ) . Finally , the amplitudes of Arp3 and lifeAct recruitment signatures were independent of CCS size ( Figure 5F and 5G ) . In general , proteins of the actin module were among the proteins least dependent on the size of the host CCS . A second characteristic that has been used to define dynamic groups of CCSs is lifetime [27] , [28] . To test whether TfR7 patches could be used as indicators of CCS lifetime , TfR7 patches from cells expressing Clc-mCherry were tracked , and the set of 11 , 091 track histories was classified according to the presence or absence of scission events . The estimate of scission-undetected TfR7 patch lifetime was 33 . 8 s , which was ∼11% lower than the 38 s estimated using Clc-mCherry as a marker for CCSs . This slightly lower lifetime is because the TfR-phl signal tended to drop slightly before the Clc-mCherry signal in the run-up to scission ( Figures 2D and 4B ) . The estimate of scission-detected TfR7 patch lifetime was found to be 178 s , which is within 6% of the 189 s estimated using Clc-mRFP as a CCS marker . Therefore , TfR7 patch lifetime could be used to estimate CCS lifetime . Similar to CCS size , scaling relationships were found between CCS lifetime and the relative amount of protein recruited ( Figure 6A–6E ) . Longer lived CCSs tended to have more clathrin and adaptor proteins while , by contrast , GAK and lifeAct showed the weakest dependence on CCS lifetime ( Figure 6E and 6F ) . This is trivially explained if larger CCSs tended to have longer lifetimes , and indeed TfR7 patch fluorescence and lifetime had a positive ( though modest ) correlation of 0 . 29 ( p<0 . 05 , full set of events used ) , similar to previous observations [62] . Collectively , these analyses demonstrate that the same set of proteins was recruited to scission events at different dynamic groups of CCSs , with subtle scaling relationships between CCS size , lifetime , and the relative amount of different proteins recruited . However , this analysis did not reveal whether the same set of proteins was recruited to each scission event . The physical properties of CCSs were not predictive of which endocytic proteins were recruited to scission events ( Figures 5 and 6 ) . However , there is evidence that CCSs with different complements of adaptor proteins and receptor cargo coexist in the same cell [63] , and it has been shown that the dependence of CME on actin differs between the apical and basolateral domains in epithelial cells [53] . Therefore , there may be differences in the set of proteins recruited to individual scission events , even though they internalized similar amounts of the same cargo ( TfnR-phl ) . First , we checked whether the automated selection criteria were biased towards a mechanistically distinct subtype of CME . For five example cells expressing mCherry chimeras of Clc , Hip1R , N-WASP , dynamin1 , or GAK we visually inspected the set of events rejected by our selection criteria and “recalled” events judged to be bona fide by a human operator ( see Materials and Methods; Figure S10 ) . There was no significant difference in the kinetics of protein recruitment to the subset of “recalled” events when compared to events automatically selected ( Figure S10 ) . Therefore , no measurable bias was introduced by the parameters set for automatic detection . Second , we determined how many scission events scored positive for recruitment of any given protein ( Figure 7 ) . The probability of detecting protein recruitment is dependent on multiple physical factors including signal and detector limitations , the kinetics of protein recruitment , and the magnitude and texture of background fluorescence ( see Materials and Methods ) . Two strategies were used to detect recruitment ( Figure 7 ) . The first strategy was biased towards the detection of proteins recruited with slower kinetics and used image segmentation to determine the maximum probability of detection relative to scission ( Figure 7A–7C ) . The second strategy was biased towards the detection of more transient signals and identified significant peaks in the quantified fluorescence traces ( Figure 7D and 7E ) . Of the 34 proteins analysed 25 proteins from six modules ( clathrin , actin , dynamin , GAK , FBP17 , and Rab5 modules ) were detected at more than 50% of scission events using either detection strategy ( Figure 7 ) . It seems unlikely that these 25 proteins were recruited to distinct and mutually exclusive variants of CME , and there was most probably some overlap between any given pair . Of these proteins high-abundance structural proteins such as clathrin , adaptor proteins , and other members of the clathrin module were most readily detected ( gold bars , Figure 7C and 7E ) . Proteins of the dynamin module were the next most frequently detected ( pale blue bars , Figure 7C and 7E ) . Proteins of the actin module ( red bars , Figure 7C and 7E ) were detected less frequently , with the notable exception of Abp1 ( maximum probability of detection = 0 . 97 , Figure 7C ) . The clathrin- and F-actin-binding protein Hip1R was also detected with high frequency ( maximum probability of detection = 0 . 99 , Figure 7C ) . Detection of the F-actin-binding protein Abp1 was facilitated by the proteins' punctate distribution and low background fluorescence ( Figure 7F ) . By contrast , an alternative F-actin marker , lifeAct , was recruited promiscuously to all F-actin structures at the cell cortex , which gave a bright and highly textured background , and most likely contributed to the lower probability of detecting lifeAct at scission events ( Figure 7G ) . The set of proteins that were detected at ∼50% of scission events or fewer using either detection strategy included the NBAR module ( BIN1 , Endo2 , and Amph1 , pink bars , Figure 7C and 7E ) . However , because NBAR proteins are thought to be essential components of the CME machinery [9] , [54] , this most likely represents limitations of detection , as found previously [37] , rather than core mechanistic differences between scission events . The low incidence of detection of other proteins is less easy to interpret . For instance , CIP4 and Rab5 were detected with low incidence , but the significance of this currently remains unclear ( Figure 7 ) .
Early EM studies revealed clathrin-coated invaginations at the substrate proximal surface of adherent cells as discrete entities , in clusters or at the edges of large , flat lattices of clathrin [25] , [26] . Subsequent live-cell imaging studies using TIR-FM described , for a variety of cell types , corresponding heterogeneity among CCSs labelled with clathrin-FP at the substrate proximal surface of adherent cells [3] , [10] , [23] , [28] , [31] , [62] . It was shown that both transient spot-like CCSs ( average lifetime = ∼40–60 s ) and larger , longer lived CCSs ( average lifetime = ∼60 s to 10 min [or more] ) coexisted in NIH-3T3 fibroblasts , HeLa , and COS cells [10] , [23] , while transient spot-like CCSs ( average lifetime = ∼40 s ) predominated in freshly plated BSC1 cells [3] , [28] , [31] , [62] . Larger and longer lived CCSs were triggered by specific receptor/adaptor combinations [62] , and cell adhesion could also play a role [22] . Faced with such natural ultrastructural and dynamic heterogeneity , it was important to establish which CCS characteristics , measured in live-cell TIR-FM experiments , defined CCS intermediates in CME . The detection of individual scission events presented here and previously [10] helps achieve this by quantifying the relationships between scission and CCS dynamics and size in an unbiased manner . We can make four main conclusions from our study of CCS characteristics relative to scission . First , the lifetimes of scission-detected CCSs followed a left-skewed distribution ranging from a few tens of seconds through to hundreds of seconds , as predicted by earlier studies [3] , [28] . The shorter lived population of scission-undetected CCSs identified most likely corresponded to abortive CCSs described previously [3] , [28] , although intracellular CCSs may have contributed . The average time between CCS inception and the first detected scission event was ∼100 s ( minimum lifetime of 20 s ) , which reflected the time required to construct a productive CCS . However , CCS lifetimes should be interpreted with caution since CCSs can host multiple scission events ( see also [10] and the third point below ) . Second , the size of scission-detected CCSs followed a left-skewed distribution without obvious quantization . No correlation was detected between overall CCS size and the amount of TfR-phl cargo internalized by scission events , consistent with an earlier study [10] . Third , the disappearance of spot-like CCSs , which has been widely used as a fiducial marker for CME [18] , [19] , coincided with scission events with the predicted frequency but it was found to be an imprecise marker for scission ( Δt between scission and spot-like CCS disappearance = 7±22 s; Figures 1 and S2 ) . Moreover , CCS disappearance did not report all scission events , and approximately ∼50% of scission events were classified as non-terminal because the host CCS did not completely disappear following scission . Indeed , CCSs could host multiple scission events before disappearing ( see also [10] ) . Fourth , evidence that the scission events detected at different dynamic groups of CCSs proceeded through to completion ( i . e . , CCV uncoating ) was provided by the remarkable invariance of the GAK recruitment signature . The kinase GAK is an established marker for CCV uncoating [15] , [16] , and the GAK recruitment signature was the same for terminal and non-terminal scission events , for scission events at spot-like CCSs that formed de novo , and for scission events at different size and lifetime classes of CCSs . The most parsimonious explanation for these findings is that CCVs , of similar size , could either bud in isolation or from larger , heterogeneous CCSs ( Figure 8 ) . This is consistent with the relatively constant dimensions of clathrin-coated invaginations previously observed by EM , irrespective of whether the invaginations were isolated or part of larger CCSs [25] , [26] . Based on these results we conclude that the classification of endocytically active CCSs , observed at optical resolution using TIR-FM , should be broad to encompass the heterogeneity of scission-competent CCS sizes and lifetimes . As a practical guide , any CCS that colocalizes with acid-accessible TfR-phl and that exists for more than 20 s could be considered scission competent [10] and potentially capable of hosting multiple scission events . In a further exploration of the organization of CME in fibroblasts we analysed the recruitment of 34 types of endocytic protein to scission events , 30 of which were native to NIH-3T3 fibroblasts . To appreciate this analysis properly it is important to consider what physical factors contribute to the observed dynamics of protein recruitment and the resulting shapes of ensemble recruitment signatures . First , the fluorescence signals measured at single scission events using TIR-FM occur in a volume of ∼1 al , illuminated by an evanescent field in which the intensity of the electromagnetic field decreases exponentially as a function of distance in the z-axis [64] . Due to the small depth constant of the illuminating evanescent field ( ∼100 nm ) and the comparable dimension of an invaginating CCP ( ∼100 nm diameter ) , for two proteins to show a similar average recruitment signature they must be recruited to the detection volume over a similar time course and must share a similar spatial distribution at the developing CCP as it projects into the evanescent field along the z-axis [10] , [31] . Second , a recruitment signature reflects the average concentration of an FP-labelled protein at the site of endocytosis relative to the cytoplasm . Labelled protein , expressed at low levels , must compete with endogenous proteins for recruitment , and this , combined with detector limitations and the relatively low quantum efficiency of mCherry [65] , most likely contributes to noise among individual recruitment profiles and influences the probability of detecting protein recruitment . We established that for one example protein , dynamin1 , the noise appeared to be unstructured and that the trajectories of the averaged recruitment signatures for dynamin1 in NIH-3T3 cells were remarkably stable . A detailed analysis suggested involvement of the core clathrin , actin , and dynamin modules in the majority of scission events since all coat components ( clathrin , AP2 , epsin2 , FCHo , CALM , and NECAP ) and both Hip1R ( which binds clathrin and F-actin [44] ) and Abp1 ( which binds dynamin , F-actin , and Arp2/3 [66] ) were detected at >90% of scission events , and dynamin1/2 , synaptojanin2β1 , myosin6 , and Eps15 were detected at >75% of events . These findings agree with the widely accepted view that TfR internalizes via a clathrin- and dynamin-dependent pathway [67] , [68] and are in agreement with earlier studies that demonstrated an important , though nonessential , role for actin in CME in fibroblasts ( [9]–[11] , but see [23] ) . The fact that other proteins such as the BAR domain proteins endophilin2 or BIN1 were detected at only a subset of scission events suggests that there were inherent limitations of recruitment detection , since these proteins are thought to be essential for scission [9] , [54] . However , it remains possible that there were genuine molecular differences between scission events , perhaps through the influence of other types of ( unlabelled ) receptor cargo [63] , in response to changes in physical parameters , such as membrane tension [69] or because of genuine underlying variability in the core mechanism of CME [53] . Nonetheless , and based on the data presented here , at optical resolution potential molecular differences between scission events in NIH-3T3 cells did not correlate with obvious differences in CCS behaviour . Next we explored scaling relationships between CCS size and lifetime and the set of proteins recruited to scission events . As shown previously [62] , CCS lifetime and size were moderately correlated , with longer lifetimes for larger CCSs , and , as predicted , the recruitment signatures of some proteins such as the coat protein clathrin and adaptor proteins scaled with overall CCS size . A set of core components ( e . g . , dynamin and endophilin2 ) showed more complex scaling relationships with CCS size , perhaps reflecting variable degrees of recruitment to the budding and non-budding portions of larger CCSs . However , the recruitment signatures of a core set of proteins including GAK ( a kinase essential for the uncoating reaction [16] , [70] ) , and most notably actin and actin-binding proteins , were independent of CCS size . This is consistent with our central thesis that CCVs of relatively constant size budded at host CCSs of diverse size and lifetime via a common core mechanism , and supports a role for actin in CME in NIH-3T3 fibroblasts [10] , [11] . Seminal imaging studies from the Drubin lab and other groups revealed the modular organization of yeast endocytosis [24] . Here it was shown that at least four modules or groups of proteins showed similar recruitment dynamics to sites of endocytosis at yeast actin patches [24] . More recently , comparisons were drawn between the modular organization of yeast and mammalian endocytosis , with an emphasis on the conserved role of actin [9] , [71] . However , earlier TIR-FM studies of the late stages of mammalian CME used the disappearance of spot-like CCSs as a fiducial marker , which could not sample endocytic events from all dynamic classes of CCSs nor yield a temporally precise estimate of scission . Consequently , the recruitment dynamics of endocytic proteins could only be broadly classified as “early” and “late” ( Figure S1 ) . The data presented here , based on the comparison of accurately measured recruitment signatures derived from large datasets ( ∼1 , 000 events ) , give a more detailed overview of the modular organization of mammalian CME . The modules identified here comprise the following ( Figure 8 ) : ( 1 ) the coat module , divided into ( i ) a clathrin sub-module ( epsin2 , CALM , clathrin light chain , and NECAP ) and ( ii ) an adaptor/F-BAR sub-module ( FCHo1/2 , Eps15 , AP2 ) ; ( 2 ) the NBAR domain module ( endophilin2 , amphiphysin2 , and BIN1 ) ; ( 3 ) the actin module , divided into ( i ) actin polymerization sub-module ( Abp1 , cortactin , and Arp3 ) and ( ii ) actin depolymerization/suppression ( cofilin , coronin1B , and SNX9 ) ; ( 4 ) the dynamin/myosin/N-WASP module ( dynamin1 , dynamin2 , synaptojanin2β1 , myosin1E , N-WASP , Eps8 , Hip1R , myosin6 , and syndapin2 ) ; ( 5 ) the GAK/post-scission module ( GAK , ACK1 , and OCRL1 ) ; ( 6 ) the Rab5a module ( Rab5a and APPL1 ) ; and ( 7 ) the FBP17/CIP4 module , based on the unique recruitment signatures of these two proteins and dissimilarity to any other recruitment signatures . The shapes and relative timing of many of the recruitment signatures are broadly consistent with measurements made in previous imaging studies in yeast [24] and in mammalian cells [6] , [18] , [19] . In addition , many recruitment signatures provided new information as a consequence of improved accuracy . First , and as predicted from a previous study [31] , the recruitment signatures of members of the adaptor sub-module decreased before scission because of polarization in the developing invagination . In addition , the F-BAR domain proteins FCHo1 and FCHo2 showed similar recruitment signatures , suggesting these curvature-inducing proteins were also polarized and consistent with a proposed role for FCHo proteins in the early stages of the invagination process [43] . Second , it was predicted that actin recruitment should begin before dynamin recruitment at sites of scission , although time-locked measurements with the required accuracy to test this hypothesis had not previously been made [9] , [18] . Here , we showed that the onset of actin polymerization did indeed precede the final burst of dynamin recruitment by ∼20 s , consistent with a role for actin polymerization early in the invagination stage of CME and the later recruitment of dynamin to the deeply invaginated CCP , where it executed scission [9] ( Figure 8 ) . We also discovered that coronin1B and cofilin , proteins involved in the down-regulation of actin polymerization and F-actin severing , respectively , were recruited at later time points , again similar to yeast endocytosis [24] , [72] , [73] . Third , it was proposed that scission of endocytic invaginations in yeast is triggered by a PI-phosphatase that dephosphorylates PiP2 and thus induces a line tension in the membrane neck [74] . In mammalian cells the large GTPase dynamin is thought to execute scission [9] , [30] , but , intriguingly , the recruitment of the PI-phosphatase synaptojaninβ1 showed a recruitment trajectory similar to that of dynamin ( and proteins of the NBAR module ) and also peaked at scission ( Figure 4 ) . Therefore , it is plausible that induction of a line tension also contributes to the mechanochemistry of scission in mammalian cells [74] . Finally , it was predicted that recruitment of F-BAR and BAR domain proteins should follow an ordered sequence dictated by their preference for different-curvature membrane tubules in vitro [75] and that recruitment should occur over a trajectory similar to that of actin polymerization [6] , [9] , [76] . The ordered recruitment of syndapin2 and the NBAR module ( endophilin2 , BIN1 , and amphiphysin1 ) did indeed match this prediction . However the post-scission peak recruitment of SNX9 and the complex , biphasic recruitment of FBP17 and CIP4 did not . These findings illustrate that the recruitment sequence of these BAR and F-BAR domain proteins could not be predicted purely on the basis of either structural information or biochemical properties . The possible function ( s ) of SNX9 and FBP17/CIP4 post-scission remain to be elucidated , although it is possible that these proteins may act as relays to recruit additional binding partners to the newly formed endosome ( Figure 8 ) . The study presented here employed the detection of scission events to construct what is to our knowledge the highest resolution temporal map of mammalian CME to date . The map ( 1 ) suggests a simplified canonical model of mammalian CME in which the same core mechanism operates at both spot-like CCSs and larger CCSs observed with fluorescence microscopy , ( 2 ) illustrates the similar modular organization of mammalian and yeast endocytosis , and ( 3 ) proves that recruitment dynamics of endocytic proteins such as the F-BAR protein FBP17 and BAR domain protein SNX9 cannot always be predicted from biochemical or structural properties .
NIH-3T3 cells were cultured as described previously [10] . Cells were co-transfected using Lipofectamine 2000 ( Invitrogen ) with human transferrin receptor fused to super-ecliptic phluorin ( hTfnR-phl [10] ) and the relevant endocytic protein open reading frame ( ORF ) fused to a RFP . Freshly transfected cells were replated onto pre-cleaned number 1 borosilicate glass coverslips ( VWR International ) and imaged 24–48 h later as described previously [10] . ORFs of endocytic proteins were amplified by PCR ( Phusion PCR kit; Finnzyme ) from IMAGE clones ( Geneservice ) , or directly amplified from cDNA libraries ( see Table S2 for details of primers and cDNA sources for the expression constructs used ) . Each pair of PCR primers was engineered with the appropriate 3′ and 5′ restriction sites for cloning and sequence for either a 9- , 12- , or 13-amino-acid linker between the target protein and FP , as described previously [65] . The amplified cDNAs were cloned into mammalian expression vectors in frame with a RFP ( in the case of Hip1R , tDimer [65]; in the case of myosin1E , mApple [77]; and for all other proteins mCherry [78]; see Table S2 ) to generate either N- or C-terminal fusion proteins upon expression . Primers were designed to PCR a ∼700-bp fragment that was specific to the protein isoforms used in this study . Total cell RNA was purified from NIH-3T3 cells using the RNAeasy Mini Kit ( Qiagen ) . RT-PCR reactions were run using the OneStep RT-PCR kit from ( Qiagen ) using the manufacturer's protocol . The QIAxcel capillary gel electrophoresis system ( Qiagen ) was used to visualise RT-PCR products . Samples were run using the DNA screening cartridge using the AM420 run settings ( 5 kV sample injection voltage for 10 s , 5 kV separation voltage for 420 s; suitable for DNA concentrations of 10–100 ng/µl ) . A photomultiplier detector converted the emission signal into a gel image and an electropherogram that allows visualisation and quantification , respectively , of each PCR product . The Biocalculator software package ( Qiagen ) was used to analyse the peaks for each sample . Aligment marker of 50 bp to 1 . 5 kb was used to align run samples . The TIR-FM and ppH perfusion system have been described previously [10] . Cells were transfected with plasmid encoding TfR-phl and RFP-tagged endocytic protein and plated onto coverslips 24 h before imaging . Transfected cells were located and imaged using a spinning disk UltraVIEW ERS confocal ( PerkinElmer ) using a ×40/1 . 4 NA oil immersion PlanApo objective ( Olympus ) . After acquiring an initial image ( denoted t = 0 min ) transferrin conjugated to Alexa 647 ( Tfn-A647; Invitrogen ) was added to the chamber at 10 mg/ml in 10 mM HEPES buffer saline solution ( pH 7 . 4 ) . After 30 min at room temperature the cells were washed twice in 10 mM MES ( pH 4 . 0 ) to strip away surface-bound Tfn-A647 and returned to HEPES buffer saline ( pH 7 . 4 ) . The cells were then imaged to determine uptake of transferrin ( image denoted as t = 30 min ) . Movies of cells during the alternate pH protocol were divided in four parts , TfR-phl at pH 5 . 5 ( TfR5 movie ) and at pH 7 . 4 ( TfR7 movie ) , and the RFP fusion protein at the two pH values ( movies RFP5 and RFP7 ) . To detect protein clusters ( CCSs or TfR7 clusters as in Figure S4 , CCVs in the TfR5 movies ) images were subjected to segmentation based on wavelet transform ( Multidimensional Image Analysis [MIA] add-on to Metamorph 6 , written by V . Racine and J . -B . Sibarita , Curie Institute , Paris , France ) . The objects detected were then tracked using a simulated annealing algorithm [79] to identify endocytic events . The output of this tracking was a series of coordinates corresponding to the centre of mass of the objects , with unique identifiers ( event numbers ) . To determine the lifetimes of CCSs using either Clc7 or TfR7 , a different tracking algorithm was used to account for transient breaks in track histories of 1–2 frames ( i . e . , gap closing was incorporated ) . The coordinate lists generated by MIA were reassigned in Matlab using a nearest-neighbour algorithm ( “track . pro” , John C . Crocker and Eric R . Weeks , http://www . physics . emory . edu/~weeks/idl/index . html ) . For Clc data , independent track histories generated by MIA from Clc7 and Clc5 data were combined and reassigned , while for TfR only the TfR7 data were used . To verify tracking fidelity the reassigned tracks were overlaid on the original image series in Matlab and inspected visually . Although the tracking was perhaps not as robust as more recently published techniques [27] , it was sufficiently robust to differentiate between long-lived CCSs and shorter lived CCSs ( Figures 1 and 6 ) . All the tracked objects in the TfR5 movies were screened to identify genuine endocytic events using routines programmed in Matlab 7 . 4 ( Mathworks ) . To qualify as bona fide events each candidate event required a TfR5 vesicle ( i ) that persisted for at least three frames ( i . e . , 8 s ) following appearance , ( ii ) that appeared at least 20 frames after the start of the movie , or 20 frames before its end , to ensure quantification of signals for 80 s before and after the vesicle's appearance , ( iii ) that appeared and remained at more than seven pixels ( 0 . 7 µm ) from the edge of the image , to ensure proper quantification ( see below ) , ( iv ) that appeared de novo , and was not produced by the fusion of two objects or the dissociation of an object into two , ( v ) that overlapped , on appearance , with a pre-existing cluster detected in the segmented TfR7 movie , ( vi ) whose fluorescence was bigger than a defined SNR of 5 wherein SNR = ( F0−av ) /std , where F0 is the fluorescence at time 0 , and av and std are the average fluorescence and standard deviation , respectively , in the five frames before vesicle appearance , and ( vii ) that was close to maximal fluorescence at the time of appearance . We calculated the slope of the fluorescence change in the first three frames of vesicle appearance ( Figure 1 ) and discarded the events where this slope was greater than 0 . 1 , which corresponds to a 10% increase in fluorescence . The purpose of this screening was not to detect all events in a recording , but to have stringent criteria to select automatically a large proportion of events that were genuine scission events , to test a large number of candidate proteins in a manageable analysis time . Among the events that occurred at suitable times and locations ( criteria i–iv ) , only 18 . 5%±0 . 8% of events ( n = 191 cells ) passed the last three criteria ( v–vii ) , for a total of 239±11 candidate events per cell . Nevertheless , some false-positive events remained , so we reviewed our dataset visually by watching each event individually ( the portion of the TfR5 and RFP5 movies around the 0 frame , and an average of five frames of movie TfR7 before the event ) to assess if there were tracking errors , poor signal , simultaneous events nearby , or other problems . On average , 82 . 3%±0 . 9% ( n = 191 cells ) of events were confirmed by this second , manual screen , for a total of 191±8 confirmed scission events per cell . To check for bias in the screening procedure we performed a visual screen on all tracked objects for five cells , each transfected with different mCherry-tagged proteins ( 1 , 400±360 tracked objects per cell ) . Of the events rejected by the automated screen ( 1 , 100±318 objects ) , a total of 10 . 3%±2 . 2% were visually identified as genuine scission events ( 104±24 events ) . Importantly , when the fluorescence from these “recalled” events was quantified and averaged , the RFP recruitment signatures were the same as the signatures obtained from “semi-automatically selected” events ( Figure S10 ) . The sum of absolute differences between average fluorescence traces of semi-automatically selected and recalled events was not statistically significant . This shows that our semi-automated procedure did not select a particular category of scission events . Images in the green fluorescent protein ( GFP ) and RFP channels were acquired simultaneously with a Dual View ( Optical Insights ) beam-splitter that was adjusted with an image of beads that fluoresce in the two channels ( yellow fluorescein carboxylate beads , 0 . 2-µm diameter , Invitrogen ) to minimize distortion from one channel to another . However , small ( 0–5 pixels ) shifts remained in the two channels that needed to be corrected digitally for optimal colocalization . We used a third-order polynomial spatial transform that interpolates between ten bead pairs to make the correction . When we quantified experimental data we did not transform the raw images ( i . e . , interpolate and reassign pixel fluorescence values ) but instead used the spatial transform to recalculate the vesicle centre coordinates in the RFP channel . This works well , since the difference between the coordinates of a pixel ( x , y ) in the green channel and its transformed coordinates ( u , v ) in the red channel is only ever a fraction of a pixel . We quantified the fluorescence 20 frames before and 20 frames after the appearance of a vesicle for all four movies in a three-pixel-radius circle centred on the object coordinates at the time of appearance ( frame 0 ) for this frame and the 20 preceding frames , then centred at the tracked vesicle coordinates during tracking , and then on the last known coordinates after tracking was lost . Local background was estimated in an annulus ( three pixels inner radius , six pixels outer radius ) by taking an average of pixel values between the 20th and 80th percentiles to avoid contributions from neighbouring brightly fluorescent patches . This quantification is similar up to this point to other quantifications performed by us in previous studies [10] , [18] . To correct for bleed through from the GFP to RFP channels we introduced a bleed-through coefficient ( BT ) for each cell to correct the fluorescence values with the formula FRFPx , corr = FRFPx−BT·FTfRx , where x is 5 or 7 . Such corrections are acceptable as they involve only linear combinations of fluorescence values . BT was determined for each cell by minimizing the summed squared difference for values of BT taken between 0 and 0 . 05 in 0 . 001 increments ( Figure S4 ) . Values of BT were on average 3 . 00%±0 . 07% ( n = 191 ) . Differences in BT values could arise from small differences in background fluorescence , non-linearity in the camera , or changes over months of the optical properties of the various parts of the system ( filters , mirrors , or camera ) . With this correction , fluorescence values from RFP5 and RFP7 could be combined to achieve a time resolution of 2 s ( Figure S4 ) . To determine when the recruitment of a labelled protein became significant , we generated randomized datasets by shifting the event coordinates in a random manner within the cell footprint ( Figure S4 ) , and calculated fluorescence for all four movies as described above . We generated 200 randomized datasets for each cell , and then combined the average fluorescence measures to determine , for each data point , 95% upper and lower intervals ( Figure S4 ) . To sort events into terminal and non-terminal events , we measured the average FTfR7 for four frames before scission and nine frames ( 36 s ) after scission . The ratio between these two values was used to determine whether the event was terminal ( ratio <0 . 4 ) or non-terminal ( ratio >0 . 6 ) . Events with ratios close to 0 . 5 were not sorted . To determine the time of peak RFP recruitment , we estimated a noise level with standard deviation of the last six FRFP values ( 12 s ) of the recording . If the maximum is bigger than a threshold ( six times noise above average ) , the time of the maximum FRFP value is taken as the maximum RFP recruitment time and used to construct the histograms in Figure 1F and others . The proportion of events with significant peak recruitment is given in Table S2 for each tested protein . The goal was to visualise the overall structure of the Dyn1-mCherry set of fluorescence recruitment traces and determine whether there were “natural” ( as opposed to analyst-imposed ) classes . First the amplitudes of fluorescence traces were normalised by cell over the range [0 , 1] , and the mean of each fluorescence trace was subtracted to reduce dispersion in the y-axis . Each normalised , offset fluorescence trace was projected into an image matrix , and at those points where the fluorescence trace overlaid a pixel a “1” was added to the pixel value , “0” otherwise . The resulting density plot was log-transformed to visualise both high- and low-density features . We compared the average recruitment signatures by computing the correlation coefficients for each pair of curves corr ( RFPa , RFPb ) . Correlation coefficients were 0 . 45±0 . 43 ( average ± standard deviation , n = 561 ) . We then used the correlation distance , dist ( RFPa , RFPb ) = 1−corr ( RFPa , RFPb ) , to perform a hierarchical clustering using an average linkage algorithm that generated the dendrogram in Figure 4 . This hierarchical cluster tree reflected the actual correlations between RFP curves , with a correlation coefficient between the cophenetic distances ( the distances represented as horizontal bars in the tree ) and the correlation distances of 0 . 81 . Other linkage algorithms yielded lower correlation coefficients . To perform these comparisons , we used the full range of measurements , from −82 s to +76 s relative to the time of vesicle detection . Away from time 0 , the differences between the curves would be less significant than close to the moment of vesicle formation and so similarity measurements could be affected by the choice of time interval around vesicle creation . We performed the same clustering procedure using RFP measures only between −44 and +36 s relative to vesicle formation . The cluster tree generated was very similar to the one shown in Figure 4 . There were only three minor differences between these two trees: ( i ) N-WASP grouped first with syndapin instead of with Eps8 , ( ii ) dynamin2 grouped first with dynamin1 instead of with Hip1R , and ( iii ) coronin grouped first with Arp3 and cortactin instead of with SNX9 and cofilin . Finally , for many proteins the non-terminal fluorescence traces showed little variation before and after scission ( Figure S8 , see definition below of these two types of events ) . The clustering could be different in an analysis using only the terminal fluorescence traces , wherein most proteins reach random values 80 s after scission . Therefore , we performed the clustering on non-terminal events only . Again , the resulting dendrogram was very similar to the one shown in Figure 4 , with the same number of modules defined by a distance threshold of 0 . 2 , and only minor differences: ( i ) ACK1 leaves the GAK cluster to be weakly ( distance >0 . 2 ) attached to the dynamin cluster , ( ii ) endophilin groups first with syndapin within the dynamin cluster , and ( iii ) four other different groupings occur between proteins within the same cluster . Overall , these tests suggest that the clusters defined in Figure 2 correspond to genuine similarities between the different RFP recruitment signatures that would correspond to functional units involved in CCV formation . To explore the relationship between scission and CCS disappearance NIH-3T3 cells were transfected with Clc-mCherry and TfR-phl and assayed using the ppH protocol . All CCSs were tracked as described above . The end of each track history was extended by 20 frames ( 40 s ) by padding with the last detected CCS location , and the Clc-mCherry fluorescence and TfR5 fluorescence were quantified for each candidate CCS . To identify CCS disappearance , abrupt drops in Clc-mCherry fluorescence were detected by convolving each Clc-mCherry fluorescence trace with a one-dimensional kernel appropriately tuned for negative edge detection ( a negative step function kernel , convolved with a Gaussian , σ = 36 s ) . Step decreases in Clc-mCherry fluorescence manifest as spikes in the convolved signal , and the maximum response was used to define a t0 for each CCP fluorescence trace . By definition , this algorithm aligns the Clc-mCherry fluorescence traces to their respective maximal negative derivatives ( i . e . , maximal rate of fluorescence decrease ) . Although this differs slightly to the algorithm used previously [18] , the temporal alignment is more robust . The resulting candidate CCS disappearance events were screened by comparing the average fluorescence of the first nine time points ( t = −80 s to t = −44 s ) and the last nine time points ( t = 44 s to t = 80 s ) of the fluorescence trace . Only those traces showing a decrease in average fluorescence with a magnitude at least 2 . 5-fold greater than the standard deviation of the first nine values were deemed bona fide . This removed false-positive disappearance events ( i . e . , abrupt but incomplete drops in Clc-mCherry fluorescence ) . To detect scission events associated with disappearing CCSs the TfR5 trace associated with each candidate CCP was screened for step increases in fluorescence of at least 25 fluorescence units between a given time point t and the average fluorescence over of the previous four time points . This is a less stringent criterion for detecting scission events than used in the main analysis but it was less prone to discarding dim or noisy scission events . Of 197 disappearing CCSs , 107 ( 54% ) were associated with a scission event ( Figure 1 ) , close to the prediction that50% of events would be detected when the cell is bathed in pH 7 . 4 solution , the other 50% being invisible as they occur when the cell is under pH 5 . 5 solution . NIH-3T3 cells expressing hTfR-SEpHl were isolated by FACS 24 h post-transfection , replated , and allowed to adhere overnight . To examine potential effects of acidic buffer on CCS morphology NIH-3T3 cells were incubated with MES buffered saline ( pH 5 . 5 ) for 1 or 10 min before being washed briefly in PBS and fixed at room temperature in a solution of paraformaldehyde ( 2% ) and glutaraldehyde ( 2 . 5% ) in sodium cacodylate ( 0 . 1 M at pH 7 . 2 ) . Fixed cells were harvested by scraping and centrifuged in a horizontal rotor ( 1 , 000 g , 5 min ) . The resulting cell pellet was placed in fresh fixative and stored at 4°C . In preparation for EM , fixed samples were washed thoroughly in sodium cacodylate buffer ( 0 . 1 M ) , post-fixed in OsO4 ( 1% in 0 . 1 M sodium cacodylate ) for 1 h , and then washed with distilled water . Samples were stained en bloc with uranyl acetate ( 2% ) in ethanol ( 30% ) before dehydration in a graded ethanol series followed by 1 , 2-epoxypropane ( propylene oxide ) and then infiltrated and embedded in CY212 resin ( Agar Scientific ) . Ultrathin ( 50–70 nm ) sections were cut on a Reichert Ultracut E microtome and collected on uncoated 200-mesh grids . Sections were post-stained with saturated uranyl acetate before staining with Reynolds lead citrate . Images were acquired using a Philips EM208 microscope , with an operating voltage of 80 kV , and a CCD camera . Graphics for protein structures were downloaded from the Research Collaboratory for Structural Bioinformatics ( RCSB ) consortium Protein Data Bank ( PDB ) website , where the original citations are also listed . | The molecular machinery of clathrin-mediated endocytosis concentrates receptors at the cell surface in a patch of membrane that curves into a vesicle , pinches off , and internalizes membrane cargo and a tiny volume of extracellular fluid . We know that dozens of proteins are involved in this process , but precisely when and where they act remains poorly understood . Here we used a fluorescence imaging assay to detect the moment of scission in living cells and used this as a reference point from which to measure the characteristic recruitment signatures of 34 fluorescently tagged endocytic proteins . Pair-wise comparison of these recruitment signatures allowed us to identify seven modules of proteins that were recruited with similar kinetics . For the most part the recruitment signatures were consistent with what was previously known about the proteins' structure and their binding affinities; however , the recruitment signatures for some components ( such as some BAR and F-BAR domain proteins ) could not have been predicted from existing structural or biochemical data . This study provides a paradigm for mapping molecular dynamics in living cells and provides new insights into the mechanism of clathrin-mediated endocytosis . | [
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"biology/syste... | 2011 | A High Precision Survey of the Molecular Dynamics of Mammalian Clathrin-Mediated Endocytosis |
Deciphering the effects of nonsynonymous mutations on protein structure is central to many areas of biomedical research and is of fundamental importance to the study of molecular evolution . Much of the investigation of protein evolution has focused on mutations that leave a protein’s folded structure essentially unchanged . However , to evolve novel folds of proteins , mutations that lead to large conformational modifications have to be involved . Unraveling the basic biophysics of such mutations is a challenge to theory , especially when only one or two amino acid substitutions cause a large-scale conformational switch . Among the few such mutational switches identified experimentally , the one between the GA all-α and GB α+β folds is extensively characterized; but all-atom simulations using fully transferrable potentials have not been able to account for this striking switching behavior . Here we introduce an explicit-chain model that combines structure-based native biases for multiple alternative structures with a general physical atomic force field , and apply this construct to twelve mutants spanning the sequence variation between GA and GB . In agreement with experiment , we observe conformational switching from GA to GB upon a single L45Y substitution in the GA98 mutant . In line with the latent evolutionary potential concept , our model shows a gradual sequence-dependent change in fold preference in the mutants before this switch . Our analysis also indicates that a sharp GA/GB switch may arise from the orientation dependence of aromatic π-interactions . These findings provide physical insights toward rationalizing , predicting and designing evolutionary conformational switches .
The role of protein biophysics is increasingly recognized in the study of evolution , and the study of protein biophysics has also benefitted from evolutionary information [1–4] . Emerging from a more physical perspective of molecular evolution is the realization that natural selection can act on a nonsynonymous mutation as long as it modifies the conformational distribution , even if it leaves the folded structure of a protein unchanged and maintains the original biological function . For instance , if the mutation stabilizes a nonnative “excited” conformational state which is structurally distinct from native , this state can potentially serve an additional “promiscuous” biological function which is then subject to natural selection [5] . This effect , demonstrated experimentally [6] , is a direct consequence of the ensemble nature of protein conformations and follows simply from the principle of Boltzmann distribution [7 , 8] . Similarly , even if the most stable structure of a protein is robust against a mutation , the protein’s functional structural dynamics can be modulated by the mutation , which should then also be subjected to natural selection [5 , 9] . In this way , positive selection of an excited conformational state favors mutations that gradually increase the stability of the excited state , so that it finally becomes the new dominant native structure or one of two ( or more ) native structures with comparable stabilities in a “bi-stable” ( or “multi-stable” ) protein . Protein sequences interconnected by mutations and encoding for the same folded structure form neutral networks [10] . Bi-stability was predicted to occur at the intersection of neutral networks [8 , 10] . Consistent with theory [7 , 8 , 11–14] , some phylogenetically reconstructed ancestral proteins are bi-stable [15] . Although there is no direct measurement to date of a gradually shifting conformational equilibrium for a set of naturally occurring amino acid sequences traversing two neutral networks , recent advances in NMR spectroscopy allow mutational changes in the stability of nonnative excited states to be detected [16] . A handful of conformational switches and bi-stable sequences have now been designed in the laboratory [17–19] . Among them , the one that is most extensively characterized is the set of designed mutant sequences that span the human serum albumin-binding and IgG-binding domains of Streptococcus protein G [19 , 20] . The wildtype sequences of these proteins , termed GAwt and GBwt respectively , are of equal length ( 56 residues ) in the experimental system . GAwt and GBwt have only 16% sequence identity with very different folded structures . GAwt folds to a three-helix bundle ( 3α ) , whereas folded GBwt is a helix packing against a four-stranded β-sheet ( 4β+α ) . By carefully selecting amino acid substitutions , Alexander et al . created mutant sequence pairs with 30% , 77% , 88% , 95% , and 98% identity while still maintaining the original different folds . A single L45Y substitution separates the pair of mutants GA98 and GB98 with 98% identity . L45Y switches the dominant fold of GA98 from that of GA ( 3α ) to that of GB ( 4β+α ) for GB98 [19 , 20] . As suggested by theory [7 , 8] and by molecular dynamics simulations of the unfolded states of the GA88/GB88 pair [21] , appreciable excited-state populations for the alternative fold should be present in the GA/GB mutants with 95% , 88% , or even 77% identity . Ligand binding data provide evidence that GA98 and another mutant GB98-T25I that also adopts the 3α GA fold have excited-state populations of the alternative GB fold . However , GB98-T25I is the only mutant for which the alternative fold is directly observable by NMR [22] , as nonnative populations lower than ~1% are currently difficult to detect experimentally . By simulating the folding energy landscapes of the mutants , the goal of the present computational analysis is to gain physical insights into the mechanism of the GA/GB conformational switch , including how it might evolve via a gradual increase in stability of the alternate fold as the mutants approach the switch . The most direct method of molecular simulation is to use a completely general physics-based potential . Such an approach has succeeded recently in showing that it is computationally possible for a series of mutants of a 16-residue peptide to undergo an α to β switch [14] . Owing perhaps to the limitations of molecular dynamics forcefields [23 , 24] , folding simulations with fully transferrable potentials have not reproduced much of the switching behavior of the larger GA/GB system [25 , 26] , although complementary theoretical methods have made useful progress . For instance , some of the GA/GB mutants can be assigned to their correct native folds by various threading approaches [8 , 27] or a “confine-and-release” simulation algorithm applied to the GA88/GB88 and GA95/GB95 pairs [28] , suggesting that the forcefields used in these techniques may be quite adequate . But the conformations sampled by these techniques are limited only to those very similar to the GA and GB folded structures [8 , 27] , or at best include also a highly confined set of conformations between them [28] . As such , the rather restricted conformational sampling in these techniques can mask possible shortcomings of the forcefields , e . g . , by missing low-energy conformations that the techniques fail to sample . Therefore , to address fundamental physics of the GA/GB system , as for any protein folding study , it is necessary to employ self-contained explicit-chain models that extensively sample both the folded and unfolded conformations [29] . One class of self-contained models proven useful in biomolecular studies is the Gō-like explicit-chain structure-based models ( SBMs ) . These models are native-centric in that the only contacts favored by the potential are those that exist in the known native structures [29–32] . Most SBMs studied to date are single-basin in that they target a single native structure; but dual- and multi-basin SBMs can be constructed to fold to two or more native structures . The latter approach has been employed to analyze the conformational switches between different functional states of a protein [33–36] . A prime example is the large-scale allosteric conformational transition between the open and close forms of adenylate kinase [34 , 37] . Recent applications of dual-basin all-atom SBMs to the GA/GB system suggest that the conformational preferences of some of the mutants can be rationalized to an extent by their differences in steric packing [38 , 39] . However , the effects of nonnative interactions that are not present in either the GA or GB folds are not considered in these SBMs; but nonnative interactions are important for protein evolution because they may lead to detrimental aggregation [40–42] . In any event , the extent to which these dual-basin SBMs are generalizable is not clear . They have only been applied to a small number of mutants , viz . , GA95/GB95 and GA98/GB98 in ref . [38] and GA98/GB98 in ref . [39] . Moreover , in some cases , it appears necessary to single out contacts involving the mutated residues for ad hoc treatment [38] . To delineate the utility and limitation of common physical notions in accounting for experimental GA/GB observations , we introduce a model that combines a SBM potential with a physics-based transferrable all-atom potential . Going beyond prior efforts that considered only two or four sequences , our model is applied coherently to an extensive set of twelve GA/GB sequence variants covering the 3α and 4β+α folds . Favorable nonnative contacts are possible in our formulation because of the transferrable terms . This “hybrid” modeling approach recognizes that current knowledge of protein energetics is not sufficiently adequate—thus the need for a native-centric bias—yet at the same time posits that physical nonnative effects should manifest at least as a perturbation [43] . Within this conceptual framework , the transferrable component represents what we believe we know physically , whereas the SBM component represents the extent of our ignorance , which we should aim to eliminate in the future . To tackle the GA/GB system , we generalize the well-studied hybrid approach for a single-basin SBM [43–50] to one based upon a dual-basin SBM [33–36 , 51] . The formalism is general , however , and thus should be applicable also to conformational switches other than GA/GB . As detailed below , the GA/GB switching predicted by our model agrees with experiment . Moreover , the robustness and physicality of our predictions are buttressed by control simulations indicating a lack of folding of decoy protein sequences with folded structures very different from that of either GA or GB . Interestingly , refinements of the transferrable component in our potential to better account for the π-interactions of aromatic residues [52] leads to a sharper conformational switch , suggesting that incorporation of more accurate descriptions of the physical interactions can lead to tangible improvement of the model under the present framework .
As noted above , SBMs are valuable conceptual tools; but SBMs and hybrid models are admittedly interim measures . Ultimately , one wishes to simulate biomolecular processes using a completely transferrable physical potential . With this in mind , to maximize the physical content , our hybrid model was constructed with a native-centric , structure-based component as nonspecific and as unimposing as we found technically possible . For example , in contrast to previous all-atom SBMs for GA/GB [38 , 39] that enforce detailed native biases on dihedral angles and inter-atom distances [32] , the SBM component of our hybrid model constrains only the Cα-Cα distances between residues that are at least three sequence positions apart . The rest of the interactions—including local backbone preferences and sidechain excluded volume—are provided entirely by the transferable component . The SBM component of our model is sequence independent , in that the same native-centric potential applies to all GA/GB variants ( Fig 1 ) . In this way , the spatially coarse-grained SBM component serves merely to enable folding to the GA or GB native structures in an unbiased manner , all the while reducing as much as possible any artefactual memory of the sequence-structure relationship of any particular sequence . Accordingly , the differences in population in the two alternate folds for different sequences are determined solely by the physical transferable potential that admits nonnative as well as native interactions . As described in Methods , the present sequence-independent SBM component is based on the consensus Cα-Cα native contact maps for GA and GB . Each consensus map was constructed using the four PDB structures for GA or GB variants for which experimental folded structures are available ( Fig 2a and 2b ) . The consensus map contains only the native contacts common to all four PDB structures . Two residues of a given PDB structure are defined to form a native contact if the closest distance between any two non-hydrogen sidechain atoms , one from each residue , does not exceed 6 Å . Here the SBM energy for each consensus residue-residue native contact is constructed as a multi-Gaussian well potential [53] , wherein the position of the minimum for each of the wells is determined by the four defining PDB structures . In most cases , the individual minima fuse into a single wider well because they are in close proximity ( Fig 2c ) , although in some cases they retain their distinct minima when there are larger variations in contact distances among the PDB structures ( Fig 2d ) . The potentials for all contacts in the two consensus native contact maps ( Fig 2e ) are provided in S1 Fig and S2 Fig . Summing the energy terms for individual consensus native GA contacts gives the overall native-centric potential EA for GA and EB for GB , the strengths of which are given , respectively , by εA and εB ( Methods ) . A bi-stable SBM potential , ESBM , is then obtained by combining EA and EB . The multi-Gaussian contact potentials here ensure that all native conformers used as input for the SBM potential are at an energy minimum of the same depth ( εA or εB ) for a given fold . This approach captures the salient features of the two folds while allowing sufficient flexibility to accommodate variations in backbone and sidechain configurations among different GA/GB sequences . To achieve an unbiased baseline sampling of the GA and GB folds , the SBM energy scales εA and εB are expected to be somewhat different and thus a calibration is necessary . Indeed , it has long been known from the study of single-basin SBMs that imposing a single SBM energy scale for different native structures would result in a spurious correlation between folding temperature and native contact density that is not observed experimentally [54] . For our system , the GA fold was found to be only slightly more dominant in test simulations using min ( EA ) = min ( EB ) and the GB fold was only slightly more dominant for εA = εB . ( Supporting Information S1 Text and S3a and S3b Fig and S3c and S3d Fig , respectively ) , whereas εA = 0 . 96εB allows for unbiased baseline sampling to produce results consistent with experiment . To minimize native-centricity as much as possible , we have examined the effect of different overall SBM interaction strengths and arrived at a workable value of εB = −0 . 37 ( S1 Text , S4 Fig and S5 Fig ) . This strength corresponds to a weak native bias relative to the transferrable component , yet strong enough to guide folding . Under εB = −0 . 37 , on average only less than one third ( 18 . 9/60 . 2 = 0 . 31 ) of the stabilization of GB98 is contributed by the SBM component ESBM ( S6 Fig ) . The rest ( 69% ) is contributed by the transferrable Etrans . Further analyses in S1 Text and S7 Fig–S11 Fig , including Hamiltonian replica exchange simulations ( S9 Fig and S10 Fig ) , indicate that the GA98/GB98 switching behavior is robust over values of εB ranging from −0 . 30 to −0 . 50 ( S7 Fig and S8 Fig ) , and that folding and switching are observed only when neither ESBM nor Etrans vanishes ( S11 Fig ) . We adopt for Etrans the implicit-solvent all-atom potential developed at Lund University ( available as PROFASI ) , which accounts for backbone , non-bonded excluded-volume , hydrogen-bonding , charged and hydrophobic side chain interactions in a physical manner [55 , 56] . With a SBM component providing minimally necessary restriction on the accessible conformational space , the transferable component of our hybrid model modulates the stability of the native and unfolded populations . Using the progress variables QA and QB and the simulation procedure described in Methods , the present modeling setup correctly identifies the native basin of 12 sequence variants of the GA/GB system ( Fig 3a ) . The variables QA≡EA/95εA and QB≡EB/137εB are continuum versions of the discrete native contact fraction Q commonly used in protein folding studies [57 , 58] . For the energy landscapes in Fig 3a , the GA and GB native basins are situated , respectively , at QA≈0 . 9 , QB≈0 . 15 and QA≈0 . 3 , QB≈0 . 85; whereas the basin for the common unfolded state is centered at QA≈0 . 4 , QB≈0 . 15 . The dual native-bias of the SBM notwithstanding , Fig 3a shows that the transferable component is sufficiently strong to capture the physical mutational effects , resulting in significant shifts in populations and , in the case of GAwt , GBwt and GA30/GB30 , virtual depopulation of the entire alternate native basin . We computed a free energy difference ΔF ( GA-GB ) ≡ −ln ( PA/PB ) between the GA and GB folds for all the sequence variants ( Fig 3b ) , where PA and PB are the populations of the two native basins defined , respectively , by QA ≥ 0 . 6 , QB < 0 . 6 and QB ≥ 0 . 6 , QA < 0 . 6 . Thus , a negative ΔF ( GA-GB ) favors GA whereas a positive ΔF ( GA-GB ) favors GB . The replica-exchange simulation results in Fig 3b show that the single L45Y mutation from GA98 to GB98 entails a small yet appreciable shift in favor of GB , a robust finding corroborated by constant-temperature simulations ( S12 Fig ) . The aromatic Y45 partakes in a hydrophobic cluster in GB but apparently contributes little to stability in GA [22] . In the present transferrable potential , the hydrophobicity-based non-bonded energy term is mostly responsible for favoring this Y45-containing hydrophobic GB cluster because the strength of the term scales with the number of contacting nonpolar atoms , and aromatics provide large contact areas [55] . The three mutations separating GA95 and GB95 result in a more notable population shift . In addition to L45Y , the other two amino acid substitutions are I30F leading from GA95 to GA98 and L20A leading from GB98 to GB95 . Notably , the phenylalanine substitution of I30F fits into the hydrophobic core of both GA ( partially buried ) and GB ( almost fully buried ) . As the sequence separation between the pair is further widened ( GA88/GB88 , GA77/GB77 , GA30/GB30 , and GAwt/GBwt differ by 7 , 13 , 39 , and 47 mutations respectively , Fig 1 ) , the GA/GB free energy difference increases . The value of ΔF ( GA-GB ) increases rather smoothly from GAwt to GBwt as expected . The only exception is the step from GA88 to GA95 , for which there is a decrease in GB propensity instead of the expected increase ( Fig 3b ) . As mentioned above , for the GA30/GB30 pair , and the GAwt/GBwt pair that shares only 16% of their amino acids , the preference for the dominant native structure is so strong that only the fringe but not the bottom of the alternate native basin was sampled ( Fig 3a ) . These free energy shifts are echoed by the balance between transition frequencies to and from the native basins along Monte Carlo simulation trajectories . Using a three-state division of the QA/QB energy landscape into unfolded ( U ) , GA , and GB regions , a gradual shift from U↔GA to U↔GB transitions is concomitant with the sequence variation from GAwt to GBwt ( S1 Text and S13 Fig ) . We also compared the experimental and simulated melting temperatures of the GA/GB variants ( Fig 3c and 3d ) . Because the model potential in the present hybrid GA/GB model lacks cooperativity-enhancing desolvation barriers [59 , 60] and neglects temperature dependence in the solvent-mediated interactions [29 , 61 , 62] , simulated and experimental Tms are not directly comparable . For example , as suggested by related kinetic trends in other protein folding models [29] , insufficient folding cooperativity in the present hybrid model likely caused the simulated Tm range to be narrower than that observed experimentally ( the Tm ratios of GB98 over GA77 is 0 . 99 for simulation and 0 . 88 for experiment; see Fig 3d ) . Nonetheless , for the sequence variants from GA77 to GB77 , the correlation between simulated and experimental Tm is reasonably good . The consistency in Tm trend for seven of these eight variants is apparent in the comparison using a normalized non-absolute temperature scale ( Fig 3c ) as well as in the scatter plot for absolute temperatures ( Fig 3d ) . The steady drop in experimental Tm from GA77 to GB98 was captured very well by simulation ( Fig 3c ) . The outlier GB88 is known to be very unstable experimentally ( Tm ≈ 44°C ) . Curiously , this effect is also reflected in our model , albeit to an exaggerated degree . Combined structure-based clustering of the simulated GA98 and GB98 conformations allows for an analysis of likely kinetic events during bi-stable folding ( Methods ) . The centroid positions of 50 conformational clusters on the QA/QB landscapes are shown in Fig 4 together with the outlines of the bi-stable GA98 free energy landscape , which is quite similar to that of GB98 ( Fig 3a ) . The size of a cluster is the number of sampled conformations that are within a certain degree of structural similarity among themselves . Each centroid conformation is a representative of all the conformations in a given cluster . Fig 4 shows that the centroid positions cover most accessible regions of the free energy landscape . Naturally , the unfolded state harbors the majority of clusters because unfolded conformations are structurally most diverse . The most extended conformations are positioned in the bottom-left region with small QA and QB values as expected ( cluster no . 7 ) . Under our model potential , there is a significant bias in favor of helical structures instead of unstructured coils in the unfolded ensemble . As has been demonstrated , kinetic information can be gleaned from features on low-dimensional free energy landscapes determined solely by equilibrium sampling of one or two progress variables [63–65] . In using QA/QB landscapes for kinetic inference , we are following this tradition . It should be noted , however , that not all kinetic properties , especially those related to kinetic trapping , are deducible from low-dimensional landscapes [45 , 50] . For instance , not all structurally similar conformations based on the superposition-map measure and indicated by connecting lines in Fig 4 are readily accessible to one another kinetically . Therefore , here we qualify the “transition state” and “intermediate states” suggested by free energy landscape features as “putative” . With this caveat in view , we identify the conformations around the 0 . 66 < QA < 0 . 74 , 0 . 12 < QB < 0 . 22 bottleneck region as the putative transition state for GA folding . Likewise , we identify the conformations around the two bottleneck regions around 0 . 3 < QA < 0 . 55 , 0 . 35 < QB < 0 . 43 and 0 . 28 < QA < 0 . 4 , 0 . 58 < QB < 0 . 66 as two putative transition states for GB folding ( yellow boxes in Fig 4 ) , and the local-minima region between the latter two transition states as a putative GB intermediate state . Along the QA direction at QB ≈ 0 . 15 , a simple folding transition via a compact transition state TS-GA is apparent in Fig 4 . This putative process starts from an extended , mostly disordered state ( cluster no . 7 ) . Subsequently , more helices form and the chain first collapses into a loose arrangement of three helices around TS-GA and then proceeds to form the ordered native GA structure , with cluster no . 43 and adjacent clusters differing only by their disordered termini . Folding along QB at QA ≈ 0 . 35 is more complex . Fig 4 suggests that the second ( C-terminal ) β-hairpin is formed upon reaching the first GB transition state TS1-GB , but at this stage the rest of the protein chain is still relatively open . The GB intermediate state that follows consists mainly of a variety of conformations with the second β-hairpin aligned with the N-terminal β-strand . TS1-GB encompasses more conformational diversity than the single centroid conformation might convey . When we partition the conformational ensemble in this region into two or more clusters ( S14 Fig ) , alternative pathways across this transition region appear possible . One of the alternate pathways may entail a “mirrored” version of the second β-hairpin collapsing and accumulating as an “off-pathway” intermediate ( see , e . g . , the centroid conformation of cluster no . 12 in S15 Fig ) . As such , conformations with this topology likely constitute a kinetic trap that requires significant unfolding before folding to the GB native state can proceed . Direct transition from an “on-pathway” intermediate to native GB is expected for those conformations with native-like orientation of the terminal secondary structure elements . To reach the second putative GB transition state TS2-GB , excess helical structure needs to be converted into the fourth β-strand . The chain then proceeds to sample different near-native orientations of the central helix relative to the β-sheet , and attempt packing of the hydrophobic core before finally arriving at the GB native state ( cluster no . 4 ) . A detailed analysis of the population shift caused by the L45Y mutation in the conformational clusters in Fig 4 indicates that L45Y can start biasing in favor of the GB structure even when the folding is in its early stage ( S1 Text and S15 Fig ) . In this process , the aromatic-aromatic Y45-F52 interaction , which is more frequent in GB98 than in GA98 , is seen as playing a significant role in the GB-favoring effect of L45Y ( S16 Fig ) . As a test of the robustness of our hybrid model , we challenged it by several other sequences from the PDB that have the same 56-residue chain length as the GA/GB sequences but with native folds different from either GA or GB . The same GA/GB SBM was applied with each sequence’s Lund potential used as the transferrable component . The goal is to ascertain whether these decoy sequences would mistakenly adopt the GA or GB fold . Seven of the decoy sequences tested behaved reassuringly . Despite the GA/GB SBM , they did not populate either of the GA/GB native basin , even though some of their native conformations have secondary structures similar to those of GA or GB ( Fig 5a–5g ) . This result shows that Etrans can override ESBM , underscoring that the transferrable physical potential plays a highly significant , if not dominant , role in our model . Among the decoys tested , serine protease inhibitor infestin 4 is an interesting exception because its native structure is not similar to GA but it populates the GA basin ( Fig 5h ) ; but the bulk of its conformations remain unfolded . In this regard , depopulation of both native basins is remarkable for the double helical Ral binding domain because its helical secondary structures are similar though not identical to that of GA ( Fig 5i ) . Finally , to test whether our model can fold a non-GA/GB sequence if its native fold is essentially identical to either GA or GB , we considered a modified 56-residue version of Protein L ( Methods ) . Protein L has only ~ 16% sequence identity with GBwt but adopts the overall GB fold experimentally . Reassuringly , our simulation shows that the modified Protein L sequence is compatible with the GB basin but not the GA basin ( Fig 5j ) . Apart from decoys , we also challenged our formulation with an alternative structure switch in the GA/GB system discovered more recently . Experiments indicate that the T25I mutant of GB98 reverts back to the helical structure of the GA folds , but with an additional L20A mutation can be restored to the GB fold [22] . Our simulations show a high degree of bi-stability for these two sequences as for GA98 and GB98 . Nonetheless , we also found a small free energy difference that is consistent with the experimentally observed native structures of these two variants ( Fig 6a and 6b ) . Another pair of possible GA/GB switch sequences that came to our attention was proposed recently [66] , but the predicted switching behavior has not been confirmed by experiment or investigated by explicit-chain modeling . Our simulations here are in agreement with the predictions in finding that sequence “S2” prefers the GA fold while “S1” prefers the GB fold ( Fig 6c and 6d ) . The free energy differences for these two alternative switch mutations are provided in S17 Fig . Our results suggest that GB98-T25I , L20A and S1 favor GB via different mechanisms . GB98-T25I , L20A predominantly stabilizes the entire unfolded state and parts of the GB native state yet leaving the native GA basin appreciably populated ( Fig 6b ) , whereas the S2 to S1 mutation P54V clearly destabilizes the GA fold ( Fig 6c ) . The analysis of the L45Y mutation in S15 Fig reveals that a major part of its stabilizing effect on the GB fold is through enabling the aromatic-aromatic Y45-F52 interaction in GB98 . In view of this observation and the general importance of π-related interactions in biomolecular processes [49 , 67] , we constructed a rudimentary π-π interaction potential for F and Y residues ( Methods ) . Our goal here is to explore how an orientation-dependent interaction between aromatics that goes beyond simple hydrophobic effects may affect the behavior of the GA/GB conformational switch , although a comprehensive study of aromatic interactions is beyond the scope of this work . By using three geometric variables for two neighboring aromatic rings ( Fig 7a ) , we derived an empirical π-π potential [68] for F and Y from PDB statistics ( Fig 7b ) . When this π-π potential replaces the simpler hydrophobic interactions among F and Y residues in the original Lund potential , the effect of L45Y is affected appreciably ( Fig 7c ) . We define a difference landscape for the original Lund potential ( Fig 7c , left ) as the difference between the GA98 and GB98 panels in Fig 3a . The difference landscape for the modified transferrable potential ( Fig 7c , right ) is similarly defined using the QA/QB landscapes of GA98 and GB98 in S18 Fig that incorporates our π-π potential . In the Lund potential ( Fig 7c , left ) , L45Y stabilizes the unfolded and GB intermediate states rather homogeneously ( stabilization indicated by blue coloring ) . The GA native basin is destabilized ( red coloring ) , but so are parts of the GB native basin . In contrast , with the π-π potential ( Fig 7c , right ) , L45Y has a stronger impact . It now destabilizes most of the unfolded state and parts of the GA native basin whereas the stabilization focuses more on the intermediate and native basins of GB . Although the present π-π potential is rudimentary , this comparison suggests that orientation-dependent π-π interactions likely play a significant role in the experimental sharpness of the GA/GB conformational switch .
Beside this overall success , two findings from our investigation are of experimental relevance: ( i ) existence of an equilibrium intermediate for GB folding ( Fig 3a , GB panels ) ; and ( ii ) a critical role of the second β-hairpin in the GB folding pathway ( Fig 4 and S15 Fig ) . On both counts , our model results are in general agreement with experimental findings ( see below ) , lending additional credence to our contention that the present hybrid model is capable of capturing essential physics of GA/GB bi-stability and the GA98/GB98 conformational switch . Firstly , our prediction of a GBwt ( also called protein G or GB1 ) intermediate is in line with several [69–72] though not all [73] simulation studies . Experimental evidence for a GB folding intermediate was presented , but there is no clear consensus yet regarding the existence and/or nature of a GB intermediate–unlike the generally recognized two-state nature of GA folding . Two early experiments oncluded that GBwt folding is two-state [74 , 75] . In contrast , another early continuous-flow ultrarapid mixing experiments on GBwt suggested a native-like intermediate [76] , but this conclusion was disputed [77] . A later FRET study also found an intermediate near the urea denaturation midpoint of GBwt [78] . A subsequent equilibrium GBwt unfolding experiment showed two-state behavior; but the kinetic chevron rollover was indicative of an intermediate [79] . The latter finding is in line with a recent experimental and molecular dynamics study showing that GBwt folding is three-state [80] . As for GB variants , one study found that GB88 and GA88 are two-state folders [21] . However , an investigation on a different set of variants GA30/GB30 , GA77/GB77 , and GA88/GB88 supported three- and two-state folding , respectively , for all GB and GA variants [81] . Taken together , recent evidence appears to be somewhat more preponderant on the existence , rather than non-existence , of a GB folding intermediate; and is unequivocally affirmative of the two-state nature of GA folding . This trend is reflected by our simulated free energy landscapes in Fig 3a . Secondly , Fig 4 and S15 Fig suggest that the second β-hairpin is critical and more important than the first β-hairpin in GB folding . Although this finding was deduced from an analysis of GA98 and GB98 clusters , it is likely applicable to other GB variants , including GBwt , because of the similarity among their free energy landscapes ( Fig 3a ) . Indeed , NMR experiments on peptides from GBwt found that , in isolation , the second β-hairpin is much more stable than both the helix and the first β-hairpin . It forms a stable , native-like β-hairpin with its three aromatic residues W43 , Y45 , and F52 forming a cluster stabilized by both hydrophobic and ( probably π-related ) polar interactions [82] . In contrast , the first hairpin was found to be mostly flexible in isolation and not native-like [83] . Hydrogen exchange experiments on the entire GBwt protein also revealed an early folding state with the second β-hairpin having the highest protection factors , whereas the helix has a lower and the first hairpin has the lowest [77 , 84] . Based on Φ-value analysis for a single transition state , another study also pointed to the presence of the second β-hairpin in the GBwt transition state [74] . Taken together , the experimental data summarized above provide support for a critical role of Y45-F52 in favoring early formation of the second β-hairpin and its partial collapse together with the helix , as suggested by our simulation ( compare TS1-GB in Fig 4 and S14 Fig ) . In this regard , some differences between the folding transition states of GB variants and that of GBwt were reported . In particular , Φ-value analysis [85] has found that the first transition state in GB30 is more sensitive to mutations in the second β-hairpin whereas GB88 is more sensitive in the first hairpin [81] . Nonetheless , the same set of data for GB88 is suggestive of native-like transition-state contacts , such as I6-T53 , that are between strands at the two termini because some of their residues have high Φ-values ( e . g . , 0 . 48 for I6 and 0 . 42 for T53 ) . If this is indeed the case , the experimental data is not inconsistent with our simulation result suggesting that the anti-parallel alignment of the termini is an early rate-limiting event for GB folding ( Fig 4 and S15 Fig ) . Taking all the evidence presented together , the performance of our model suggests that the remarkable GA/GB bi-stability phenomenon can be rationalized to a significant extent by specific hydrophobic interactions , though our physical understanding is still far from complete . As discussed above , future improvement in matching theory with experiment should be sought by enhancing folding cooperativity and increasing sharpness of the conformational switch in our model . One possible direction is to incorporate desolvation barriers in the transferrable potential because this is a robust physical feature of solvent-mediated interactions that have a significant impact on folding cooperativity [29] . Another direction , which was initiated with some success here , is to devise a more accurate description of aromatic interactions [67] . In this respect , a natural next step is to extend our model π-π interactions to encompass Trp and to adopt a more comprehensive account of the relative position and orientation of interacting aromatic sidechains that goes beyond the three variables in Fig 7 . Despite the simplicity of the Lund potential , it has succeeded in folding several smaller proteins [55 , 86] and the 92-residue Top7 [87] . However , in long unbiased folding simulations using only the Lund potential with no SBM , we were unable to observe stable native-like conformations of GA/GB variants , indicating that as-yet-unknown energetic contributions , in addition to those in the Lund potential , are needed for a complete physical account . The GA/GB system is a useful benchmark for testing forcefields and simulation techniques . Recent success in using all-atom explicit-water molecular dynamics to simulate folding of a number of small proteins is remarkable [88–90] . However , despite the notable advance and ongoing force-field improvement [23 , 91] , no ab initio forcefield to date has been able to fold the GA/GB variants correctly [25] . In this context , hybrid modeling is a highly useful interim approach to gain physical insight into protein folding energetics , effects of mutations , and to assist in protein design . Owing to its reliance on SBMs , this approach is limited to proteins with known structures . Nonetheless , for many globular proteins , the native structure is either known or can be inferred through homology or sequence-based statistical models [92 , 93] , and are therefore amenable to hybrid modeling . Common approaches to estimate mutational ΔΔG [94] only consider the known native structure with little or no regard to the unfolded state and folding dynamics . Hybrid models can address this shortcoming by providing testable predictions about the mutational effects on the entire free energy landscape . Indeed , because of its computational tractability , hybrid models can facilitate efficient development and testing of physically more accurate transferrable potentials , and thus can contribute to an ultimate elimination of the current necessity for SBMs .
As described above in Results , we derived for the native-centric SBM component of our hybrid model two consensus native contact maps that capture the general features of the GA and GB folds by using PDB structures for four GA sequence variants and four GB sequence variants ( Fig 2a and 2b ) . The sequences and their corresponding structures ( in parentheses ) are GAwt ( 2FS1 ) , GA88 ( 2JWS ) , GA95 ( 2KDL ) , GB98 ( 2LHC ) , GBwt ( 1PGA ) , GB88 ( 2JWU ) , GB95 ( 2KDM ) , and GB98 ( 2LHD ) . All of these PDB structures except the x-ray structure for GBwt were determined using NMR and contain multiple model structures . For simplicity , we used only the first model in each NMR PDB file in our analysis . Assuming that these consensus contact maps provide a reasonable coverage of the structural variations in the GA/GB system , we apply these maps to sequence variants GA30 , GB30 , GA77 , and GB77 as well , since no detailed structural data were available for the latter four sequences [20] . We introduce EA and EB as the individual native-centric potential energy functions for the GA and GB folds , respectively . EA and EB depend on the Cα-Cα distances rij for all residue pairs i , j that belong to the given consensus native contact map via the following Gaussian form [53]: EA=εA∑i , jnA[∏sns ( 1−e− ( rij−dij ( s ) ) 2/2w2 ) −1] , and a similar equation for EB with all instances of “A” replaced by “B” . Here the summation over i , j for EA and EB runs over , respectively , all nA = 95 and nB = 137 contacts in the consensus contact maps for GA and GB . The product over s takes into account the multiple native distances dij ( s ) for residue pair i , j in the ns = 4 PDB structures contributing to the consensus map . The strength of EA or EB is given , respectively , by εA or εB , which corresponds to the well depth for a single native contact . The w parameter that controls well width is set at 0 . 5 Å . In the present study , this formulation leads to a wide potential well for an overwhelming majority of consensus contacts . Because in most cases the native Gaussian wells for individual structures overlap considerably , we observe only minor barriers between individual Gaussian minima among all the consensus native potentials shown in S1 Fig and S2 Fig . In Fig 2c and 2d , examples of the consensus potential Eij=∏sns{1−exp[− ( rij−dij ( s ) ) 2/2w2]} for an individual contact ( black curves ) are provided together with the corresponding energy term 1−exp[− ( rij−dij ( s ) ) 2/2w2] for one of the four contributing PDB structures ( color curves ) . The above Gaussian form of the native-centric energy function is more suitable than the Lennard-Jones ( LJ ) form for our present purpose . As has been noted , it is difficult to produce a viable combined energy function from multiple native-centric LJ functions for multiple structures unless the conformational diversity is approximated by a single centroid structure [95] . LJ potentials are inflexible in their well shape ( width ) . Each inter-residue contact comes with a built-in repulsion term determined by the minimum-energy contact distance in LJ . As a result , multiple instances of the same contact at varying distances can lead to occlusion of the shorter-range contact by the repulsion of the longer-range contact if the LJ form is used instead of the Gaussian form to construct a combined energy function in accordance with the equation above ( S1 Fig and S2 Fig ) . As outlined above , the total potential energy Etotal is the sum of a native-centric component and a transferrable component , viz . , . Etotal = ESBM + Etrans . The dual-basin native-centric SBM component ESBM is constructed simply as ESBM = EA + EB . Aiming to increase the weight of the transferrable component in our model potential , we did not employ the more native-specific prescription of logarithmic mixing in ref . [35] for ESBM . For the transferrable component Etrans , we adopt the Lund potential: Etrans = Elocal + EEV + EHB + ESC + EHP , where the energy terms on the right are for local backbone interactions ( Elocal ) , non-bonded excluded volume ( EEV ) , hydrogen bonds ( EHB ) , charged ( ESC ) and hydrophobic ( EHP ) sidechain interactions . Bond lengths and bond angles are kept constant , as described by the original authors [55] . Dimensionless energy units are used in our simulations with Boltzmann constant kB effectively set to unity . We use a Monte Carlo ( MC ) [96] package [56] from Lund University to conduct parallel tempering ( temperature replica exchange ) MC simulations [97] . MC chain moves included backbone and side chain rotations as well as biased Gaussian steps [98] . All simulations were initialized from random chain conformations and time propagated in units of MC cycles . Each cycle consisted of a number of elementary conformational MC updates scaled to the number of rotational degrees of freedom of the simulated protein chain so that on average all degrees of freedom were perturbed once per cycle . For example , for GA98 and GB98 these numbers of degrees of freedom were 283 and 282 , respectively . Initially , parallel tempering simulations were performed over 32 replicas per simulation over a wide temperature range . This is then followed by a second simulation using a finer temperature grid around the melting ( unfolding ) temperature , Tm , determined as the temperature at which the heat capacity function CV ( T ) =1kBT2 ( ⟨Etotal2⟩T−⟨Etotal⟩T2 ) computed from the first set of simulations attains its maximum . Here T is absolute temperature of the simulation , Etotal is the total energy defined above , and <…>T denotes conformational averaging at T . The refined temperature grid was tuned to ascertain sufficient replica exchange acceptance probability around Tm ( ~99% ) . Replica exchange was attempted every 5 , 000 MC cycles , the first 30% ( 3 . 0×106 MC cycles ) of every trajectory was excluded from analysis . Populations simulated at different temperatures were reweighted to Tm using WHAM [99] . In select instances , 128 constant-T simulations at Tm with increased sampling were conducted to corroborate parallel tempering results ( S5 Fig and S12 Fig ) . In view of the need for a high computational throughput for varying input parameters and sequences , most simulations were terminated after 107 MC cycles ( ~2 . 8×109 elementary MC updates ) . We verified that the resulting simulated ΔF ( GA‒GB ) for GA98 and GB98 is reasonably robust in longer simulations . From the replica exchange simulations around Tm for GA98 and GB98 , for each sequence we randomly sampled 20 , 000 conformations obtained at the two sequences’ respective Tms . These conformations were combined into a single pool of 40 , 000 conformations for clustering analysis . Each conformation in the pool was represented as a ( 4×56 ) -dimensional vector . The first 56 and second 56 components of this vector are the distances between the Cα atoms in the given conformation and the corresponding Cα atoms , respectively , of an optimally superposed GA98 PDB structure ( 2LHC ) and an optimally superposed GB98 PDB structure ( 2LHD ) . Similarly , the third 56 and fourth 56 components of the ( 4×56 ) -dimensional vector are the distances between the Cβ atoms in the given conformations and the corresponding Cβ atoms in the optimally superposed PDB structures , respectively , for GA98 and GB98 . Structural superpositions were optimized using the MDtraj [100] implementation of Theobald’s algorithm for RMSD calculations [101] . The ( 4×56 ) -dimensional distance vectors were then clustered by the k-means algorithm [102] with k = 50 chosen as the number of clusters . Cluster centroids are defined as actual conformations situated closest to the cluster centers in the ( 4×56 ) -dimensional space . We define a distance measure between the centroids of two conformational clusters as the Cartesian distance between the centroids’ ( 4×56 ) -dimensional vectors normalized by ( 4×56 ) 1/2 . We refer to this distance measure as RMSDsm because it is the root mean square difference of the centroids’ superposition maps , RMSDsm . The latter is defined for any pair of conformations Cμ and Cν as RMSDsm ( Cμ , Cν ) =14×56∑i=14×56 ( di ( μ ) −di ( ν ) ) 2 where di ( μ ) and di ( ν ) are the components of the ( 4×56 ) -dimensional vectors representing , respectively , conformations Cμ and Cν . RMSDsm was first used in the general clustering for all conformations . For Fig 4 , the general definition was applied to pairs of cluster centroids , wherein only pairs with RMSDsm ≤ 5 . 75 Å are shown by connecting lines . This threshold was chosen solely for the presentational purpose of not obstructing the visualization in Fig 4 yet providing as much information as possible about the structural relationships between clusters that share a reasonable degree of geometric similarity . The sequence of the modified version of protein L in Fig 5 was obtained by first structurally aligning its PDB structure ( 2PTL ) with that of GB1 ( 1PGA ) and then removing the unaligned N- and C-terminal tails . Internal loop residues 12 , 40 , 41 , and 42 were also removed and a glycine was inserted between residues 23 and 24 . This procedure led to the following sequence used in Fig 5: VTIKANLIFANSTQTAEFKGTFAEKATSEAYAYADTLKKEYTVDVADKGYTLNIKF . Interactions between aromatic residues in the Lund potential are treated only by its hydrophobic side chain potential [55] . To explore possible π-interactions that are not hydrophobic in nature but are nonetheless known to play significant structural roles in biomolecules [49 , 67 , 103 , 104] , we modified the Lund potential for Phe and Tyr , replacing their contact-area-dependent hydrophobic interactions by an orientation-dependent potential . This rudimentary π-π potential is parametrized by three geometric variables r , θ , φ characterizing the relative position and orientation of two aromatic rings ( Fig 7a ) . There is one Trp in the GA/GB sequences ( W43 ) ; but for simplicity we restrict our exploration to Phe and Tyr , leaving the treatment of the geometrically more complex Trp to future studies . The present π-π interaction is derived as a statistical potential from a PDB data set obtained through the PDB-SELECT [105] repository at http://swift . cmbi . ru . nl/gv/select/index . html . The sequence similarity cut-off was 30% , R-factor cutoff was 0 . 21 , and resolution cut-off was 2 . 0 Å . The dataset contained 9 , 796 protein crystal structures ( created on January 26 , 2013 ) . For all the observed F-F , Y-Y , and F-Y contact pairs in this data set , the number of occurrences P ( r , θ , φ ) of r , θ , φ were distributed into bins of size 0 . 3 Å for r between r = 3 Å and 12 Å and bins of size 3° for θ , φ between θ , φ = 0° and 90° . Based on this statistics and following Procacci and coworkers [68] , we define a rudimentary π-π interaction energy Eππ ( r , θ , φ ) = −εππ{1+ln[P ( r , θ , φ ) /Pmax]/|ln ( Pmin/Pmax ) |} for each of the three residue type pair F-F , Y-Y , or F-Y , where Pmax and Pmin are , respectively , the maximum and minimum non-zero values of P ( r , θ , φ ) among all the bins for a given pair . We further set Eππ = 0 for all r , θ , φ bins that received zero entry from the PDB data set . In this way , for εππ > 0 , the present π-π potential is an attractive interaction that varies between Eππ = −εππ and 0 ( Fig 7b ) . Here we use εππ = 1 . 5 for all three residue type pairs . | The biological functions of globular proteins are intimately related to their folded structures and their associated conformational fluctuations . Evolution of new structures is an important avenue to new functions . Although many mutations do not change the folded state , experiments indicate that a single amino acid substitution can lead to a drastic change in the folded structure . The physics of this switch-like behavior remains to be elucidated . Here we develop a computational model for the relevant physical forces , showing that mutations can lead to new folds by passing through intermediate sequences where the old and new folds occur with varying probabilities . Our approach helps provide a general physical account of conformational switching in evolution and mutational effects on conformational dynamics . | [
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"mathemati... | 2016 | Theoretical Insights into the Biophysics of Protein Bi-stability and Evolutionary Switches |
For samples of admixed individuals , it is possible to test for both ancestry effects via admixture mapping and genotype effects via association mapping . Here , we describe a joint test called BMIX that combines admixture and association statistics at single markers . We first perform high-density admixture mapping using local ancestry . We then perform association mapping using stratified regression , wherein for each marker genotypes are stratified by local ancestry . In both stages , we use generalized linear models , providing the advantage that the joint test can be used with any phenotype distribution with an appropriate link function . To define the alternative densities for admixture mapping and association mapping , we describe a method based on autocorrelation to empirically estimate the testing burdens of admixture mapping and association mapping . We then describe a joint test that uses the posterior probabilities from admixture mapping as prior probabilities for association mapping , capitalizing on the reduced testing burden of admixture mapping relative to association mapping . By simulation , we show that BMIX is potentially orders-of-magnitude more powerful than the MIX score , which is currently the most powerful frequentist joint test . We illustrate the gain in power through analysis of fasting plasma glucose among 922 unrelated , non-diabetic , admixed African Americans from the Howard University Family Study . We detected loci at 1q24 and 6q26 as genome-wide significant via admixture mapping; both loci have been independently reported from linkage analysis . Using the association data , we resolved the 1q24 signal into two regions . One region , upstream of the gene FAM78B , contains three binding sites for the transcription factor PPARG and two binding sites for HNF1A , both previously implicated in the pathology of type 2 diabetes . The fact that both loci showed ancestry effects may provide novel insight into the genetic architecture of fasting plasma glucose in individuals of African ancestry .
Genome-wide association studies are conventionally performed with an implicit assumption that the prior probability of association is uniform across loci [1] . This assumption can be useful in discovery or hypothesis-generating analysis because the entire genome is scanned rather than limiting the scan to regions selected according to preconceptions of where disease susceptibility loci or trait loci ought to be . However , for admixed samples , this assumption means that any prior evidence from admixture mapping of ancestry effects is completely ignored . Thus , the main motivation of this study is to develop an approach that integrates heterogeneous data types that operate at different scales , i . e . , ancestry and genotype effects , in order to maximize statistical power in mapping disease susceptibility loci or trait loci in admixed samples . Three approaches to combine admixture mapping and association mapping have been described . Tang et al . [2] derived a joint test for case-control data under a family-based design based on the transmission-disequilibrium test . Lettre et al . [3] described a combined test for samples of unrelated individuals . They performed association mapping by linear regression , modeling local ancestry as an additive covariate [3] . They estimated separate summary statistics for association and local ancestry effects , summed the two statistics , and converted the sum into a combined p-value , assuming that the sum was -distributed with two degrees of freedom [3] . Two limitations of this approach are that local ancestry and genotype are not independent and the test costs a second degree of freedom . Pasaniuc et al . [4] described a combined test that does not suffer from these two limitations . Notably , none of the three tests takes advantage of the reduced testing burden of admixture mapping relative to association mapping . Here , we describe a joint test called BMIX for admixture mapping and association mapping in unrelated individuals that addresses all three issues . We illustrate application of the joint test by analyzing fasting plasma glucose among 922 non-diabetic , admixed African Americans from the Howard University Family Study ( HUFS ) conducted in the Washington , D . C metropolitan area . The prevalence of type 2 diabetes ( diagnosed mainly on the basis of elevated fasting plasma glucose levels ) among adults in the USA is currently 11 . 3% , ranging from 10 . 2% among European Americans to 18 . 7% among African Americans [5] . It is unknown how much genetics contribute to this difference in prevalence . If genetics does contribute , then admixture mapping is an appropriate and efficient approach to use to identify relevant loci [6] and association mapping can be used for fine-mapping .
We first describe the characterization of local ancestry for the 922 admixed African Americans using 797 , 831 autosomal SNPs . The mean proportion of African ancestry was 0 . 797 ( 95% confidence interval 0 . 770 to 0 . 819 , Supplementary Figure S1 ) . The mean number of ancestry switches per person was 186 . 0 , leading to an estimated 8 . 1 generations since admixture began [7] . To empirically estimate the testing burdens of admixture mapping and association mapping , we fit autoregressive models and estimated the effective number of tests based on autocorrelation . For example , for the first individual in our sample , there were five ancestry switches along chromosome 22 ( Figure 1 ) and the effective number of tests was 5 . 5 , based on fitting an AR ( 1 ) model ( see The Bayesian Model subsection of Materials and Methods for the definition of this model ) . Summed across autosomes for each individual and averaged across individuals , the effective number of tests for admixture mapping was 368 . 8 . Thus , the genome-wide significance level for admixture mapping was and the noncentrality parameter for the alternative density for admixture mapping was 21 . 7 . Similarly , the average , genome-wide effective number of tests for association mapping was 345 , 450 . 3 . Thus , the genome-wide significance level for association mapping was and the noncentrality parameter for the alternative density for association mapping was 37 . 2 . We stress that both testing burden estimates are sample-based ( i . e . , based only on observed markers rather than all possible markers ) and account for correlation for all markers chromosome-wide . Adjusting for global ancestry will not completely control confounding due to local ancestry in association mapping [8] , [9] . Wang et al . [10] concluded that adjusting for local ancestry is sufficient to control confounding due to either local or global ancestry . However , their conclusion was based on conflating two definitions of local ancestry . The conventional definition of local ancestry is the number of copies of chromosomes inherited from a parental population at a given marker . In the Appendix , Wang et al . [10] unconventionally defined local ancestry as either “local ancestry at one locus ( referred to as stratification due to local ancestry difference ) or the combinations and possibly interactions of ancestries at multiple loci ( referred to as stratification due to global ancestry difference ) ” . An indicator of ancestry defined in the latter way is not equivalent to an indicator of ancestry defined solely by local ancestry . By simulation , we show that adjusting for global ancestry controls confounding due to global ancestry whereas adjusting for local ancestry is insufficient to control confounding due to global ancestry , evident by an inflated type I error rate for association ( Supplementary Table S1 ) . Thus , adjusting for local ancestry is necessary to control confounding due to local ancestry and adjusting for global ancestry is necessary to control confounding due to global ancestry . If the posterior probability of a local ancestry effect is smaller than the prior probability of association in the absence of performing admixture mapping , i . e . , , then more compelling evidence of association is needed to achieve genome-wide significance by our joint test . Conversely , if the posterior probability of a local ancestry effect exceeds , then less compelling evidence of association is needed to achieve genome-wide significance by our joint test . To quantify such behavior , we calculated the change in sample size corresponding to different p-values from admixture mapping while maintaining power and the genome-wide significance level for association . As expected , a large p-value from admixture mapping implies that the locus is less likely to affect the phenotype , thereby increasing the sample size necessary for association to reach genome-wide significance ( Figure 2 ) . The complete absence of local ancestry effects costs the equivalent of a 26 . 5% increase in the association sample size . Conversely , a small p-value from admixture mapping implies that the locus is more likely to affect the phenotype , thereby decreasing the sample size necessary for association to reach genome-wide significance ( Figure 2 ) . The break-even point occurs at admixture mapping p-values of 0 . 31 , i . e . , all admixture mapping p-values<0 . 31 increase the power of subsequent association mapping in our joint test . This break-even point is larger than the point-wise significance level of 0 . 05 , indicating that weak ancestry effects or weakly differentiated markers are capable of improving the power of association mapping . A genome-wide significant p-value from admixture mapping equates to a 63 . 7% reduction in association sample size . For our data , the average prior probability for association mapping conditional on local ancestry was , more than two orders of magnitude larger than the prior probability for association mapping in the absence of performing admixture mapping , indicating a substantial gain in average power . We also compared the average power of our joint test to the MIX score [4] . The MIX score is based on the ancestry odds ratio defined as , in which and are the allele frequencies among controls in the two parental populations and is the allelic odds ratio [4] . We simulated 10 , 000 independent data sets consisting of one marker for 1 , 500 controls and 1 , 500 cases , assigning biologically realistic local ancestry and genotype effect sizes and marginalizing over local ancestry and allele frequencies . To mimic the size of chromosome 22 , we set the testing burden of admixture mapping to be 8 . 067 and the testing burden of association mapping to be 6 , 039 , as estimated from our real data . Correspondingly , the significance level for MIX was set at . We first note that the MIX test is valid [4] , and that the false positive error rate of our joint test is not different from that of MIX ( , Fisher's exact test , Table 1 ) , indicating that the joint posterior probability of 0 . 5 is properly calibrated with respect to the admixture mapping and association mapping type I and type II error rates . Our joint test was generally one to two orders of magnitude more powerful than MIX ( Table 1 ) . Notably , MIX is less powerful than our joint test when the ancestry and genotype effects oppose each other ( i . e . , one effect increases risk and the other effect decreases risk ) . Given that the ratio of the testing burdens for association mapping to admixture mapping for chromosome 22 is smaller than the ratio genome-wide , the gain in power demonstrated by these simulations underestimates the gain in power of BMIX over MIX at the genome-wide scale . We performed admixture mapping for fasting plasma glucose by linearly regressing fasting plasma glucose on local ancestry , adjusted for age , global ancestry , and sex . We detected two genome-wide significant loci ( Figure 3 ) , one at chromosome 1q24 ( ) and the other at chromosome 6q26 ( ) . The signal at the 1q24 locus consisted of 93 consecutive genome-wide significant SNPs ( posterior probabilities ranging from 0 . 637 to 0 . 711 ) at which increased African ancestry correlated with increased fasting plasma glucose . This locus explained 1 . 8% of the variance in fasting plasma glucose . The signal at the 6q26 locus consisted of nine consecutive genome-wide significant SNPs at which increased African ancestry correlated with increased fasting plasma glucose . This locus explained 1 . 7% of the variance in fasting plasma glucose . We performed association mapping for fasting plasma glucose by linearly regressing fasting plasma glucose on genotype stratified by local ancestry , assuming an additive genotype model , adjusted for age , global ancestry , and sex . The genomic control inflation factor was 1 . 009 ( Supplementary Figure S2 ) . We used the posterior probabilities from admixture mapping as the prior probabilities for association mapping . For comparison , using a uniform prior probability of , there were no genome-wide significant findings ( Figure 4A ) . In contrast , using the joint test , we detected two genome-wide significant SNPs , rs7523538 and rs1932355 , both at the 1q24 locus detected by admixture mapping ( Figure 4B and Supplementary Table S2 ) . To functionally annotate these two SNPs , we first identified the intervals based on linkage disequilibrium surrounding these two SNPs containing all SNPs with pairwise . For the top SNP , rs7523538 , we identified a 248 . 6 kb interval from 166 , 110 , 586 bp to 166 , 359 , 212 bp that lies upstream of the gene FAM78B . The FAM78B protein has no known function . However , within the promoter for FAM78B , three binding sites for the transcription factor PPARG ( from 166 , 140 , 317 bp to 166 , 140 , 340 bp; from 166 , 148 , 656 bp to 166 , 148 , 677 bp; and from 166 , 134 , 895 bp to 166 , 134 , 911 bp ) and two binding sites for the transcription factor HNF1A ( from 166 , 153 , 088 bp to 166 , 153 , 103 bp and from 166 , 153 , 241 bp to 166 , 153 , 256 bp ) have been identified ( http://www . sabiosciences . com and [11] ) . Both PPARG and HNF1A are known susceptibility genes for type 2 diabetes [12] . For the second SNP , rs1932355 , we identified a 180 . 6 kb interval from 163 , 581 , 663 bp to 163 , 762 , 232 bp . This interval does not overlap any known genes or promoters [11] .
We present a joint test of ancestry and association applicable to mapping disease susceptibility loci or trait loci in admixed individuals . Although we proceed through the calculations sequentially by performing admixture mapping first followed by association mapping , equivalence to a joint test can be seen by recognizing that the joint probability of ancestry and association effects equals the product of the probability of an ancestry effect and the probability of association conditional on ancestry . Conditional independence of association given ancestry is necessary for validity of the joint test . For any given marker , admixture mapping is based on the “between” component of local ancestry strata and association mapping is based on the “within” component of local ancestry strata , so that even though both admixture mapping and association mapping are fundamentally based on observed genotypes the data are not used twice . Our joint test is based on generalized linear models and so can be performed with standard statistical software . The admixture mapping step can also accommodate a case-only test [4] . Our joint test of ancestry and association are both genome-wide at equivalent high marker density . Every marker in a sample is tested by both admixture mapping and association mapping , i . e . , every marker is tested for genotypic association regardless of the significance of the admixture mapping . Consequently , there is no “winner's curse” [13] in our procedure , because we do not test for association conditional on significance from admixture mapping . As another consequence , our joint test has power to detect loci which do not achieve significance in admixture mapping if the association signal is sufficiently strong . This is in direct contrast to conditional two-stage approaches in which only a subset of markers based on stage one analysis are carried forward to stage two [14] , [15] . By design , such conditional approaches have zero power to detect loci that are not selected for analysis in stage two . Compared to previous approaches , our joint test has several favorable characteristics . The approach of Deo et al . [16] is based on sparse panels of ancestry informative markers , whereas high density panels of random markers capture more of the information content regarding ancestry [9] . Lettre et al . [3] perform association mapping by linear regression , modeling local ancestry as an additive covariate . However , this approach is not recommended because local ancestry and genotype are correlated . We recommend stratifying genotype by local ancestry because association cannot be confounded by local ancestry within a homogeneous stratum of local ancestry [9] . Perhaps most importantly , our approach fully capitalizes on the reduced testing burden of admixture mapping relative to association mapping while generating a test statistic with only one degree of freedom . For example , using our approach , a p-value from admixture mapping of combined with a p-value from association mapping of achieves a posterior probability of 0 . 5 . However , using the approach of Lettre et al . [3] , the posterior probability would be 0 . 105 . The MIX score [4] also fails to capitalize on the reduced testing burden of admixture mapping , resulting in a combined test not as powerful as our joint test . The main limitation of BMIX is that if the local ancestry effect is so strong that the posterior probability after admixture mapping is 1 , then the posterior probability will not be updateable with the association data . By sequentially updating the probability that a locus is a trait locus based on ancestry with the probability that the locus is a trait locus based on genotypic association conditional on ancestry , our procedure estimates the joint probability that a locus has ancestry and association effects . At chromosome 1q24 , association mapping resolved the admixture signal into two regions , i . e . , association mapping effectively fine-mapped the admixture signal . Chromosome 1q21–q25 is one of the three most often replicated loci from genome-wide linkage analysis for type 2 diabetes , having been replicated in samples of European ancestry ( Amish , French , UK , Utah ) , East Asian ancestry ( Chinese , Hong Kong ) , and Native American ancestry ( Pima Indians ) [17] . However , candidate gene analyses and dense genotyping have failed to identify common causal variants explaining linkage [17] , [18] . Our index SNP rs7523538 is not located in a known functional element but may be in linkage disequilibrium with genetic variation altering transcription factor binding sites , thereby providing a new lead to investigate in terms of locating functional variation as well as determining the functional mechanism . At chromosome 6q26 , association mapping eliminated the significance of the admixture signal . One possible interpretation is that the original admixture signal was a false positive finding and the association data appropriately decreased the posterior probability that the 6q26 locus is a trait locus . Alternatively , if the original admixture signal is truly positive , then the association data may be indicating that there is at least one untyped and untagged marker within the interval driving the admixture signal . Given that chromosome 6q26 has been previously linked to insulin sensitivity in a sample of obese African Americans [19] , the latter explanation seems more likely . In summary , we describe a joint test of ancestry and association for mapping disease susceptibility loci and trait loci in admixed individuals . Key properties of our test are that it maintains conditional independence of genotype and local ancestry and that it fully capitalizes on the reduced testing burden of admixture mapping relative to association mapping , making it more powerful than all existing joint tests . Upon application to fasting plasma glucose in African Americans , we identified two loci at genome-wide significance levels , whereas conventional association mapping yielded no new discoveries . Both loci have been identified previously by genome-wide linkage analysis , providing evidence of replication and indicating that linkage analysis , admixture mapping , and association mapping are all converging on the same loci . By taking advantage of fine-mapping afforded by association mapping and background linkage disequilibrium , we resolved one locus into two separate intervals . One of these intervals contains a promoter with multiple binding sites for transcription factors previously implicated in type 2 diabetes . The fact that both loci were discovered via admixture mapping directly implies that the genetic architecture of fasting plasma glucose is different in individuals of European ancestry vs . individuals of African ancestry .
First , we briefly review Bayes' Theorem [20] . Let represent a probability and let represent a conditional probability . For a given locus , let be the hypothesis that the locus does not affect the phenotype and let be the hypothesis that the locus does affect the phenotype , subject to the constraint that . According to Bayes' Theorem , conditional on data , the posterior probability that the locus affects the phenotype is . The quantity is the marginal likelihood ratio , also known as the Bayes factor , and indicates the strength of evidence for either hypothesis . Let the likelihood function be the distribution with degrees of freedom and noncentrality parameter and let the likelihood function be the distribution with degrees of freedom and noncentrality parameter . Thus , we can analyze statistics or p-values that can be transformed using quantile functions . Given a type I error rate and a type II error rate , for a one-tailed test , and for a two-tailed test , , in which is the standard normal cumulative distribution function and is the standard normal quantile function [21] . As is conventional , we specify power to be . To complete the specification of the alternative densities , we need the type I error rates for admixture mapping and association mapping . We assign the type I error rates to be 0 . 05 divided by the effective number of tests ( i . e . , both type I error rates are partially Bonferroni-corrected ) . We therefore need estimates of the effective number of tests for both admixture mapping and association mapping , which we obtain based on autocorrelation . For admixture mapping , we first estimate the effective number of tests for each chromosome for each individual by fitting an autoregressive model to the vector of local ancestries ( 0 , 1 , or 2 chromosomes of African ancestry ) and evaluating the spectral density at frequency zero [22] . The notation for an autoregressive model of order is and the model is defined as , in which is a constant , are the parameters , and is white noise . The order of the fitted autoregressive model is chosen by minimizing the Akaike information criterion [22] . We sum the effective number of tests for the chromosomes for each individual and then average across individuals . For association mapping , we use the vector of genotypes ( recoded as 0 , 1 , or 2 copies of the minor allele ) instead of the local ancestries . Two main quantities in Bayesian inference are Bayes factors and posterior probabilities . One advantage of Bayes factors over p-values is that the latter accounts only for the density under the null hypothesis whereas the former also accounts for the density under the alternative hypothesis . On the other hand , a disadvantage of Bayes factors is that they , like p-values , reflect the probability of the data rather than the probability of a hypothesis . In contrast , posterior probabilities directly measure the probability of a hypothesis . A natural , objective threshold of posterior probabilities is 0 . 5 , which is the point at which the hypothesis favored by the posterior odds switches . The algorithm consists of six steps . All calculations were performed in R [23] . Code is provided in Supplementary Text S1 . The procedure to simulate admixed data under a vicariance model has been detailed previously [24] , [25] . Briefly , two isolated parental populations were generated with an average value of FST of 0 . 12 , mimicking the amount of population differentiation between the African and European ancestors of African Americans . A sample of admixed individuals was generated with an average of 80% of the genome inherited from the first parental population , mimicking the amount of African ancestry in African Americans . For each marker and individual , the genotype was coded as 0 , 1 , or 2 copies of the derived allele and local ancestry was coded as 0 , 1 , or 2 copies inherited from the first parental population . To investigate whether adjusting for local ancestry is sufficient to control confounding due to global ancestry , we simulated two independent SNPs for a sample of 1 , 000 admixed individuals . The first SNP was the test SNP and the second SNP was untested . We estimated global ancestry by averaging local ancestries . Ethical approval was obtained from the Howard University Institutional Review Board and written informed consent was obtained from each participant . We used BMIX to analyze fasting plasma glucose among 922 non-diabetic , unrelated African Americans from the HUFS ( Supplementary Table S3 ) . Fasting plasma glucose was measured from blood samples obtained from participants after an overnight fast using the COBAS INTEGRA Glucose HK Gen . 3 test ( Roche Diagnostics , Indianapolis , IN ) . Non-diabetics had fasting plasma glucose levels <126 mg/dL ( 7 . 0 mmol/L ) . Genotyping was performed using the Affymetrix Genome-Wide Human SNP Array 6 . 0 , with quality control as described previously [26] , [27] . Local ancestry estimates ( 0 , 1 , or 2 chromosomes of African ancestry ) were obtained for 797 , 831 autosomal single nucleotide polymorphisms ( SNPs ) using LAMPANC version 2 . 3 [28] and HapMap Phase II+III CEU and YRI reference allele frequencies ( http://hapmap . ncbi . nlm . nih . gov/downloads/frequencies/2010-08_phaseIIIII/ ) . We note in passing that we did not include imputation in our study because there is no agreed-upon standard approach to perform imputation in admixed samples at this time . Admixture mapping was performed by linearly regressing fasting plasma glucose on local ancestry , adjusted for age , global ancestry ( equal to the individual admixture proportion ) , and sex . Association mapping was performed assuming an additive genetic model by linearly regressing fasting plasma glucose on genotype stratified by local ancestry , adjusted for age , global ancestry , and sex . | Most genome-wide association studies performed to date have focused on individuals with European ancestry . Admixed African Americans tend to have disproportionately higher risk for many common , complex diseases . Disease or trait mapping in admixed individuals can benefit from joint analysis of ancestry and genotype effects . We developed a joint test that is more powerful than either admixture mapping of ancestry effects or association mapping of genotype effects performed separately . Our joint test fully capitalizes on the reduced testing burden of admixture mapping relative to association mapping . The test is based on generalized linear models and can be performed using standard statistical software . We illustrate the increased power of the joint test by detecting two loci for fasting plasma glucose in a sample of unrelated African American individuals , neither of which loci was detected as significant by traditional association analysis . | [
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] | 2011 | Joint Ancestry and Association Testing in Admixed Individuals |
Genome maintenance in germ cells is critical for fertility and the stable propagation of species . While mechanisms of meiotic DNA repair and chromosome behavior are well-characterized , the same is not true for primordial germ cells ( PGCs ) , which arise and propagate during very early stages of mammalian development . Fanconi anemia ( FA ) , a genomic instability syndrome that includes hypogonadism and testicular failure phenotypes , is caused by mutations in genes encoding a complex of proteins involved in repair of DNA lesions associated with DNA replication . The signaling mechanisms underlying hypogonadism and testicular failure in FA patients or mouse models are unknown . We conducted genetic studies to show that hypogonadism of Fancm mutant mice is a result of reduced proliferation , but not apoptosis , of PGCs , resulting in reduced germ cells in neonates of both sexes . Progressive loss of germ cells in adult males also occurs , overlaid with an elevated level of meiotic DNA damage . Genetic studies indicated that ATM-p53-p21 signaling is partially responsible for the germ cell deficiency .
Fanconi anemia ( FA ) is a genomic instability ( GIN ) syndrome characterized by developmental abnormalities affecting the renal , gastrointestinal and reproductive systems , the skeleton , skin pigmentation , and heart . It also causes progressive bone marrow failure and increased incidence of cancer [1] , [2] . It can be caused by germline mutations in any of at least 17 genes ( FANCA , FANCB , FANCC , FANCD1 ( BRCA2 ) , FANCD2 , FANCE , FANCF , FANCG , FANCI , FANCJ , FANCL , FANCM , FANCN ( PALB2 ) , FANCO ( RAD51C ) , FANCP ( SLX4 ) , FANCQ ( ERCC1 or 4 ) ) [3] , [4] ) . The products of these genes coordinately function in the repair of DNA interstrand crosslinks ( ICL ) during DNA replication [5] . A key event in FA pathway activation is the monoubiquitination of FANCI-FANCD2 ( ID ) heterodimers by the FA “core complex” ( FANCA/B/C/E/F/G/L/M ) [6]–[8] . The monoubiquitinated ID complex is recruited to DNA ICLs , and coordinates ICL repair together with downstream FA proteins ( D1/J/N/O/P ) and other ( BRCA1 , ATR ) DNA repair proteins [1] , [9] , [10] . FANCM complexed with FAAP24 initiates FA pathway activity by recognizing DNA damage and loading the FA core complex . FAAP24 is particularly important in activating ATR in response to ICLs [11] . FANCM also has translocase activity that promotes branch migration of Holliday junctions and replication forks independent of FAAP24 [12] . FA deficient cells are hypersensitive to agents that induce ICLs , such as mitomycin C [MMC] or cisplatin . Most FA patients manifest anemia and bone marrow failure during childhood and are predisposed to cancer . Reduced fertility , hypogonadism and testicular failure , which is a consequence of impaired gametogenesis , are also common [13] , [14] , and this is reflected in most mouse models for FA , including knockouts for Fanca , Fancc , Fancd2 , Fancf , Fancg , Fancl , Fancm , and Fancp , though Fancd1 is an exception [15]–[22] . While the severity varies amongst mutants , males generally present a partial Sertoli Cell Only-like phenotype whereby a subset of seminiferous tubule sections are depleted of germ cells . In mutant females , the number of ovarian follicles is typically reduced . Although most of these mutants have been characterized only as adults , the germ cell defects in three have been investigated perinatally or earlier . Germ cell depletion in Fancd2−/− is evident in newborn mice [22] , and defects in the proliferation of PGCs were reported in Fancc and Fancl mutants [15] , [23] . While defects in DNA repair presumably underlie these germ cell phenotypes , the downstream DNA damage signaling pathway ( s ) that respond to these defects , ultimately leading to germ cell depletion , have not been identified . The FA pathway appears to function in all cell types , including germ cells . However , experimental difficulties in studying the mammalian germline – particularly those stages occurring during embryonic development – have limited investigations into the roles of the FA and other DNA damage response ( DDR ) pathways in these cells . Importantly , the germline mutation rate is significant lower than that in somatic cells [24] , [25] , indicating a fundamental difference in genome maintenance that appears to reflect the biological importance of minimizing the germline mutation rate . While specific DDRs in the C . elegans germline have been identified [26] , the DDRs operative in mammalian PGCs have not . Here we investigate a Fancm mouse model ( FancmChaos4 ) that was recovered in a forward genetic screen for GIN mutants . Mutant mice exhibit GIN and PGC depletion during embryogenesis . Using a genetic approach , we found that the ATM-p53-p21 axis contributes to the PGC depletion in this model , underscoring the critical importance of genome maintenance in these cells that undergo rapid cellular proliferation during a short period of time during development .
We previously conducted an N-ethyl-N-nitrosourea ( ENU ) mutagenesis screen in mice for mutants showing chromosome instability , as assessed by micronucleus levels in erythrocytes [27] . Chaos4 ( chromosome aberrations occurring spontaneously 4 ) was one mutation identified in this screen . Homozygous mutants show a mildly elevated ( 3 fold ) frequency of erythrocytes with micronuclei ( Figure 1A ) . Using combined SNP- [28] and microsatellite-based mapping , Chaos4 was genetically localized to a 9-Mb region between RS13481482 and D12Mit71 containing 9 RefSeq genes , including Fancm ( Figure 1B ) . Sequencing of Fancm cDNA from mutants and controls identified a de novo T to C transition at nucleotide 524 of the coding region ( Figure 1C ) . This point mutation changes a highly conserved cysteine residue to arginine ( C142A ) that is located within the DEXDc domain of this DEAD-like helicase superfamily region of FANCM ( Figure 1D ) . To confirm that the point mutation in Chaos4 underlies the GIN phenotype , we performed complementation analysis with a Fancm gene-trap allele , FancmGt ( XH297 ) Byg , abbreviated hereafter as FancmXH . The gene-trap vector resides in exon 14 , between the helicase and endonuclease domains ( Figure 1D ) . FancmXH homozygotes also had elevated erythrocyte micronuclei ( Figure 1A ) as did FancmC4/XH mice , providing strong evidence that the FancmChaos4 allele ( hereafter abbreviated FancmC4 ) is responsible for the GIN phenotype . We further assessed the chromosomal instability phenotype of our alleles via the sister chromatid exchange ( SCE ) assay . Consistent with results from a FancmΔ2 knockout mouse model [18] , untreated FancmC4/C4 and FancmXH/XH MEFs both had elevated DNA breaks and radial chromosomes ( Figure 1E; Figure S1 ) , further confirming that the Chaos4 phenotype is attributable to the mutation in Fancm . Both FancmC4/C4 and FancmXH/XH mice were born at a Mendelian ratios , indicating that the mutations do not compromise embryonic viability ( Table S1 ) . The proliferation of untreated FancmC4/C4 primary MEFs during early passages was diminished compared to wild-type ( Figure 2A , B ) . However , they recovered from senescent crisis and became immortalized much earlier ( by passage 7 ) than wild-type ( passage 10 or later ) ( Figure 2A , B ) . Cancer predisposition is a defining feature of Fanconi Anemia . To determine if the early immortalization was an indicator of cancer susceptibility , FancmC4 mutants were aged for up to 1 . 5 years . Fancm+/C4 and FancmC4/C4 females congenic in the C3HeB/FeJ background had significantly elevated cancer/neoplasia susceptibility ( Table S2 ) , developing multiple tumor types ( Table S3 ) . Thirty-three percent ( 33% ) of heterozygotes ( 9/27 ) and 58% of homozygous females ( 15/26 ) developed tumors by ∼1 year of age , compared to none of the 28 WT controls ( p = 0 . 004 and p = 0 . 0002 , respectively ) . The most common tumor types were ovarian , mammary and uterine . Heterozygous and homozygous FancmC4 males also were significantly tumor prone ( 42% , p = 0 . 001 and 47% , p = 0 . 002 , respectively , vs . 9% of WT males; Tables S2 , S3 ) . Fancm null mice were reported to have a similar degree of tumor susceptibility [18] . In a limited gross and histological study , adult Fancm null mice were reported to have smaller gonads , germ cell loss in a subset of seminiferous tubule sections , and a reduced number of ovarian follicles [18] . Similar to those findings , we found that although FancmC4/C4 males appear grossly normal and were fertile , they had markedly smaller testes and about 60% the amount of sperm as wild-type littermates at 12 weeks of age ( Figure 3A , B ) . Testis histology of young mice ( ≤16 weeks of age ) revealed subtle seminiferous tubule abnormalities , namely the presence of occasional giant multinucleated cells that are not present in WT ( Figure 3C , D ) . Prior to inbreeding onto strain C3HeB/FeJ , young FancmC4/C4 also exhibited germ-cell depleted individual tubules ( not shown ) . Spermatogenesis defects in FancmC4/C4 mice ( but not WT controls ) became more severe over time , such that most seminiferous tubules in mice over 1 year of age were highly disrupted ( Figure 3E , F ) . Gonadal defects in FancmC4/C4 mutants were sex independent; females manifested a significant depletion of primordial follicles compared to WT animals ( Figure 3G ) . The presence of multinucleate cells in younger animals was suggestive of abnormal meiotic or premeiotic cell divisions . To investigate potential meiosis defects , we immunolabeled meiotic chromosomes from 12-week FancmC4/C4 males with markers of DSB signaling ( γH2AX , the phosphorylated form of H2AX ) , DSB repair ( RAD51 ) , and meiotic chromosome structure ( SYCP3 , which detects axial elements of the synaptonemal complex ) . H2AX phosphorylation is also a marker of , and is involved in , transcriptional Meiotic Silencing of Unsynapsed Chromatin ( MSUC ) during meiosis [29] . As in WT ( Figure 4A , E ) , most mutant pachytene spermatocytes had a normal XY body ( marked by an intense γH2AX domain ) and no RAD51 foci or autosomal γH2AX staining ( Figure 4B , F ) , indicative of proper chromosome synapsis and recombinational repair of programmed ( SPO11-induced ) meiotic DSBs . However , 42% of the pachytene nuclei showed abnormal γH2AX staining , either spreading as a cloud into autosomes ( Figure 4C ) or as punctate foci on chromosome axes ( Figure 4D ) , reflective of unsynapsed chromosomes and unrepaired DSBs , respectively . Consistent with the γH2AX results , twenty-seven percent of the spreads showed persistent RAD51 foci ( Figure 4G , H ) . The data suggest that FancmC4/C4 spermatocytes have a defect in meiotic DSB repair , which in turn may affect synapsis of chromosomes in a subset of spermatocytes . The incomplete , sex-independent germ cell depletion in young adults , characterized by primordial follicle reduction , reduced testis size , and germ cell losses in some seminiferous tubules was suggestive of premeiotic germ cell defects . To explore this , newborn gonads were serially sectioned and probed with the germ cell-specific marker MVH ( mouse vasa homolog ) to quantify the number of germ cells at birth . In FancmC4/C4 males and females , there were markedly fewer germ cells ( 55% and 30% , respectively ) compared to wild-type littermates ( Figure 5 ) . This indicates that the germ cell depletion is initiated during embryogenesis . To identify the stage at which germ cell depletion starts , we examined the PGC population at various times of gestation . PGCs are first specified extra-embryonically at embryonic day 7 . 5 ( E7 . 5 ) . Between E8 . 5 and E10 . 5 , this pool of alkaline phosphatase-positive PGCs then migrates along the epithelia of the hindgut towards the urogenital ridge , undergoing a modest degree of proliferation along the way . From there , they traverse the dorsal mesentery and populate the primitive gonad . They then undergo a dramatic proliferation after which male PGCs enter mitotic arrest until 3–4 dpp , while female PGCs enter meiosis at ∼E13 . 5 and arrest in meiotic prophase I until puberty ( reviewed in [30] ) . We quantified PGCs at E11 . 5 , E12 . 5 and E13 . 5 . The numbers were not significantly decreased in either male or female FancmC4/C4 embryos at E11 . 5 ( Figure 6 ) . However , a significant reduction was evident by E12 . 5 and E13 . 5 ( Figure 6 ) . These combined data suggest that FANCM deficiency does not significantly impair PGC specification or migration , but rather that mutant PGCs either proliferate more slowly or undergo elevated apoptosis . To distinguish between these possibilities , we assessed PGC proliferation and apoptosis using BrdU incorporation and TUNEL assays , respectively . The BrdU incorporation assays indicated that PGC proliferation is reduced in both male and female FancmC4/C4 gonads at E12 . 5 and E13 . 5 ( Figure 6B; Figure S2 ) . Furthermore , apoptosis was not evident in either wild type or FancmC4/C4 gonads at E12 . 5 ( Figure S3 ) . Previous studies estimated the number and the doubling time of PGCs between E11 . 5 and E13 . 5 [31] , [32] . The doubling time of wild type PGCs is 15 . 8 h in males , and 16 . 1 h in females ( see Methods ) . Based on our PGC quantification , the doubling time of FancmC4/C4 PGC increased to 17 . 9±0 . 2 h in males , and 18 . 9±0 . 3 h in females . Although hypogonadism and testicular failure is characteristic of FA , a possible link between this and FA-related GIN has not been established . We hypothesized that if activation of a particular DDR pathway triggers PGC growth arrest or attenuation , then genetic disruption of that pathway would relieve the PGC depletion . Accordingly , we crossed FancmC4/C4 with various checkpoint mutants , including alleles of Atm , Chk2 ( Chek2 ) , p53 ( Trp53 ) , p21 ( Cdkn1a ) , and Hus1 to obtain double mutants . All mutations were congenic or near congenic ( at least 7 backcross generations ) on the C3H strain background . The numbers of MVH-positive germ cells in newborn gonads were then quantified . We first analyzed the role of p53 and its downstream effector p21 [33] , [34] . Deletion of one or both p53 alleles partially but significantly rescued germ cell loss in FancmC4/C4 male newborns ( Figure 7A ) . This partial rescue implies that some but not all germ cell depletion is due to p53 activation . Similar partial rescue was observed in FancmC4/C4 p21−/− males ( Figure 7B ) . The involvement of p21 , a CDK inhibitor and downstream effector of p53 [35] , [36] , is consistent with our previous finding that PGC depletion in FancmC4/C4 is a result of reduced proliferation . Surprisingly , the partial rescue was sexually dimorphic; neither p53 nor p21 knockout ameliorated the germ cell deficiency in newborn FancmC4/C4 females . Next , we focused on the upstream kinases of two major DDR pathways , ATM and ATR [37] . These two proteins primarily respond to DSBs and sites of replication errors ( RPA-coated ssDNA ) , respectively . Intercrosses of FancmC4/C4 Atm+/− mice produced 49 pups , none of which were homozygous for both mutations ( p<0 . 001; expected = 12 . 25 ) . Whereas doubly deficient mice were not born , FancmC4/C4 mice heterozygous for Atm were viable , and the genetic reduction of ATM partially rescued the germ cell loss in males but not females ( Figure 7C ) . Therefore , Atm may respond to increased DNA damage in FancmC4/C4 PGCs , ultimately activating p53-p21 signaling to protect the fidelity of genetic information in the PGC pool . In contrast , a hypomorphic viable allele ( Hus1neo ) of the ATR-pathway gene Hus1 [38] had no apparent impact on the depletion of FancmC4/C4 PGCs ( Figure 7D ) . Given the partial phenotypic rescue of FancmC4/C4 PGCs by Atm haploinsufficiency and p53 nullizygosity , we hypothesized that the ATM target CHK2 served as the intermediate transducer kinase . However , Chk2 deficiency did not rescue germ cells loss in FancmC4/C4 males , but significantly rescued the germ cell population in FancmC4/C4 females ( Figure 7E ) . Interestingly , Chk2−/− newborn females had more germ cells than WT controls ( Figure 7E ) . Therefore , the rescue effect of Chk2 mutation is probably independent of FancmC4 mutation . As previously reported [39] , we observed that Chk2−/− adults had histologically normal gonads . Chk2−/− males did not have more gonocytes at birth than WT siblings ( Figure 7E ) . Since female but not male PGCs enter meiosis before birth , and Chk2 was recently found to play a crucial DNA damage checkpoint role in female meiosis [40] , this may account for the elevated number of oocytes in double mutants .
FANCM is a key component of the FA signaling pathway . Numerous in vitro studies have suggested that FANCM is a sensor of DNA damage at replication forks and helps anchor the FA core complex to chromatin [8] , [41]–[44] . Fancm was also reported to have the non-canonical function of regulating meiotic crossovers in Arabidopsis thaliana and Saccharomyces pombe , specifically by catalyzing interference-independent recombination intermediates to undergo noncrossover rather than crossover resolution [45]–[47] . It was recently shown that FANCM , via its translocase activity , interacts with MHF to allow replication to “traverse” ICLs without repair , and that this activity is independent of other FA members [48] . Despite the substantial biochemical and mechanistic information on Fancm function , the physiological roles of Fancm in vertebrates are incompletely characterized . A previous study found that Fancm null mice not only phenocopied other FA mouse models in causing hypogonadism and hypersensitivity to cross-linking agents ( in MEFs ) , but also had decreased longevity and tumor-free survival [18] . As with the null mutant , FancmC4/C4 mice had elevated SCE and tumor susceptibility , and FancmC4/C4 MEFs underwent senescence prematurely . The general similarity in phenotypes between the null and FancmC4 alleles indicates that the single amino acid change in the DEAH helicase domain disrupts the crucial function of this protein in mice . This domain has no detectable helicase activity , but does encode the translocase activity of FANCM that is important for promoting the recovery of stalled replication forks [49] , [50] . Given that mutating the translocase function of FANCM alone disrupts replication traverse of ICLs in the same manner as null cells [48] , we speculate that the FancmC4 mutation disrupts translocase function to yield phenotypes that are essentially indistinguishable from nulls . Future studies to test this and other possibilities , such as protein stability , would be of interest . We traced the cause of germ cell depletion in newborn FANCM-deficient mice to defects in PGC proliferation , which was not reported for the knockout , but which has been noted for knockouts of other FA genes ( discussed earlier ) . Specifically , we found that the ATM-p53-p21 DDR pathway is operative in regulating PGC proliferation in males . Mutations of each partially restored germ cell numbers in newborns . However , the results with compound Atm mutants suggest a complex relationship with FANCM in PGCs . It has been reported that FANCM is actually regulated in part by ATR and ATM in response to damaged DNA in a Xenopus extract system [51] , but the synthetic lethality between Fancm and complete ATM deficiency ( Atm−/− ) suggests that ATM and FANCM also have parallel , non-epistatic roles in DDRs during development . The Fancg−/− Atm−/− genotype also causes embryonic lethality [52] , and inhibition of the FA pathway selectively kills ATM-deficient cells [53] , [54] , supporting the idea that the DNA damage to which the ATM and the FA pathway responds overlap . The viability of , and partial rescue of PGC loss in , FancmC4/C4 Atm+/− males suggests that the parallel DNA repair role of reduced ATM is sufficient to overcome the lack of functional FA pathway repair , but compromises checkpoint-mediated cell cycle delay in PGCs , presumably via reduced signaling to p53 . p53 is a key transcription factor that regulates several signaling pathways involved in the response to cellular stress , DNA damage , oncogene activation and other physiological signals [55] . Genetic experiments in mice have shown that p53 plays a role in FA signaling . p53 deficiency partially rescues the embryonic lethality in Fancn ( Palb2 ) and Fanco ( Rad51c ) mutants [56] , [57] and bone marrow failure in Fancd2 mutants [58] . Our studies provide the first evidence that p53 is involved in genome surveillance of PGCs during their expansion phase in development , at least in males . In the context of Fancm deficiency and the presumed increase of DNA lesions this causes , p53 appears to slow cell cycle progression rather than causing apoptosis ( see model in Figure 8 ) . Mutations in Fancl and Fancc also cause germ cell reduction traced to reduced PGC proliferation and not apoptosis [15] , [23] , suggesting that the level of endogenous DNA damage induced by FA pathway defects is not sufficient to stimulate p53-mediated apoptotic signaling . In contrast , p53 was reported to mediate germ cell apoptosis in Zebrafish fancl mutants [59] , implying either that germ cells in this organism are more sensitive to DNA replication defects , the p53 pathway is more active in zebrafish germ cells , and/or zebrafish lack a redundant repair pathway ( s ) . The activity of p53 alone doesn't fully account for germ cell depletion in Fancm mutants . Aside from only partial rescue in FancmC4/C4 males by p53 deletion , which suggests that an additional or parallel DDR pathway might still be operative such as one involving paralogs p63 and p73 , p53 deficiency did not rescue loss of oocytes in newborn females . One possible explanation for this sexual dimorphism may relate to the direct entry of female PGCs into meiosis at ∼E13 . 5 , unlike the mitotic arrest that male PGCs undergo . Since quantification of germ cell number in compound mutants was conducted in newborns , the number of oocytes at birth reflects events that occur both during PGC proliferation and during meiotic prophase I . Considering that male FancmC4/C4 meiocytes had substantially elevated DSBs , and mouse oocytes have a stringent meiotic DNA damage checkpoint that causes apoptotic elimination perinatally [60] , it is possible that any rescue of PGC proliferation in FancmC4/C4 p53−/− females was counteracted by subsequent meiotic losses of those oocytes derived from damage-bearing “rescued” PGCs . Importantly , the oocyte DNA damage checkpoint involves signaling of CHK2 to both p53 and TAp63 , and that in the absence of p53 , DSB-bearing oocytes are still efficiently eliminated by CHK2-activated TAp63 [40] . As mentioned earlier , our observation that perinatal FancmC4/C4 germ cell numbers were rescued in CHK2-deficient females but not males likely reflects this oocyte-specific meiotic DNA damage pathway , not a PGC DDR . Few DNA repair gene mutations are known to impact PGC growth or maintenance . Beyond FA mutants , Pin1 , Mcm9 , Rev7 and Helq are four other genes that have been correlated with both a function in genome maintenance and a PGC depletion phenotype [61]–[66] . Pin1 is a prolyl isomerase which directly regulates cell cycle genes . Pin1 deletion depletes PGCs by delaying their proliferation [64] . Mcm9 and Helq appear to be involved in homologous recombination repair ( HRR ) of ICLs . HELQ interacts with the RAD51 paralog complex , but appears to function in a pathway in parallel to FA [61] , [62] , [67]–[70] . MCM9 is required for normal homologous recombination , promoting recruitment of RAD51 to DNA damage sites and repair of ICLs [68]–[70] It also appears to act downstream of the FA pathway [70] . Interestingly , FANCM was reported to be required for HR-independent ICL repair [11] . Despite these indications of multiple pathways for DNA repair in PGCs , that these cells remain highly sensitive to perturbations of any of them . FancmC4/C4 males also exhibited progressive germ cell depletion with age . The reason for this is unclear , since histological analysis revealed only subtle seminiferous tubule abnormalities in young mice . The progression to a near Sertoli Cell Only-like phenotype in many tubules suggests a defect in spermatogonial proliferation or renewal . The lack of more dramatic testicular pathology in young mice is also curious in light of evidence for DNA repair and XY-body defects in a substantial fraction of spermatocytes . Aside from the occasional appearance of abnormal multinucleated cells near the lumen of seminiferous tubules , coordinated arrest of pachytene stage spermatocytes was not observed as is typical for mutants that are recombination-defective and which disrupt XY silencing , an event proposed to underlie meiotic arrest [71] . One possible explanation is that the level of defects is below the threshold that would trigger a checkpoint , or that the unrepaired DNA damage is eventually repaired before checkpoint-mediated elimination . It may be relevant in this regard that we have not noticed visual abnormalities in offspring of Fancm mutants . Another possibility is that the DNA damage in FancmC4/C4 spermatocytes , inferred as such by the presence of γH2AX and RAD51 foci , may be of a nature that does not trigger elimination . For example , it is possible that these foci correspond to sites of damage incurred during premeiotic DNA replication , as opposed to SPO11-induced DSBs . Another example of apparently tolerated meiotic damage is the case of Rad54−/− spermatocytes , which are not eliminated despite bearing extensive RAD51 foci in late pachynema [72] . Finally , it is possible that Fancm has a hitherto unknown role in meiotic checkpoint activation in addition to DNA repair . This study contributes to an emerging picture that the FA pathway is particularly important in stem cell biology [2] . Reprogramming of fibroblasts into induced pluripotent stem cells requires FA pathway function [73] , [74] . Furthermore , not only is bone marrow failure a hallmark of FA , but this failure depends upon p53/p21 signaling [58] . The involvement of p53/p21 activation in both hematopoietic and germline stem cells bearing FA mutations , and the particular sensitivity of these lineage , emphasizes the importance of expanding studies of the FA pathway into diverse cell types including additional stem cell lineages .
These were performed as described [75] . The Chaos4 mutation was ENU-induced on the C57BL/6J ( “B6” ) background [27] . To identify the causative mutation , the mutation was outcrossed to strain C3HeB/FeJ ( “C3H” ) , then intercrossed to produce potential homozygotes . F2 offspring were screened for micronucleus levels and a genome scan with a collection of microsatellite markers polymorphic between C3H and the parental strain B6 was performed [28] . This localized Chaos4 to a 44-Mb interval on chromosome 12 , between D12Mit285 and D12Mit71 . Subsequently , we conducted an inter-subspecific mapping cross with Mus castaneus ( CAST/Ei ) . The F1s were either intercrossed or backcrossed to CAST/Ei and scored for micronuclei . A total of 956 informative meioses were examined , defining a 9-Mb critical region ( Figure 1B ) . The XH297 ES cell line ( derived from the 129/Ola strain; BayGenomics ) [76] bearing a gene trap insertion of Fancm ( abbreviated FancmXH ) were cultured in DMEM ( Gibco ) supplemented with 15% FBS ( HyClone ) , 0 . 1 mM MEM nonessential amino acids , 1 mM sodium pyruvate , penicillin-streptomycin ( 100 units/ml ) , 100 µM beta-mercaptoethanol ( Sigma ) and recombinant leukemia inhibitory factor ( produced in-house ) . Cells were microinjected into C57BL/6J blastocysts by standard methods . Fancm+/XH mice were then backcrossed into the C3HeB/FeJ background . Genotyping of FancmC4 mice was performed by PCR amplification of a 240 bp mutated segment with two primers: Chaos4L ( CTTCTGGCAAGGTGGTTTTC ) and Chaos4R ( TTTGCTACCCACAGACGATG ) . PCR products were then digested by restriction enzyme AciI , which is present in the Chaos4 allele only . The Chaos4 allele is cut into 180 bp and 60 bp fragments . Genotyping of FancmXH mice was performed indirectly using microsatellite markers D12Mit69 and D12Mit71 that flank Fancm , and which are polymorphic between strain C3H and B6 ( B6 alleles at D12Mit69 and D12Mit71 are indicative of the Chaos4 allele ) . The use of mice in this study was approved by Cornell's Institutional Animal Care and Use Committee . Mice bearing alleles of other mutations were: Atm ( Atmtm1Led , abbreviated as Atm− ) , Chk2 ( Chek2tm1Mak , abbreviated as Chk2− ) , p53 ( Trp53tm1Tyj , abbreviated as p53− ) , p21 ( Cdkn1atm1Tyj , abbreviated as p21− ) , and Hus1 ( Hus1tm2Rsw , abbreviated as Hus1neo ) [39] , [77]–[80] . The stocks of mice bearing the p53 , p21 and Hus1 alleles were all congenic in the C3H background ( N10 or greater ) . The Atm , Chk2 stocks were at the N7 backcross generation . Euthanasia was performed by CO2 administration . MEFs were generated from 12 . 5- to 14 . 5-dpc embryos . Cells were cultured in DMEM supplemented with 15% FBS ( fetal bovine serum ) , 0 . 1 mM MEM nonessential amino acids , 1 mM sodium pyruvate , penicillin-streptomycin ( 100 units/ml ) , and beta-mercaptoethanol . For cell proliferation assays , 0 . 5×106 cells were seeded per 100-mm plate and then cultured and harvested to count cell numbers at various time points . For the cell senescence assay , 0 . 5×106 cells were seeded per 100-mm plate and then cultured and passaged every 3 days until they became immortalized . MEF metaphase spreads and the sister chromatid exchange assay were performed as previously described [18] , [81] . For basic histology , tissues were fixed in 4% paraformaldehyde ( PFA ) overnight , paraffin-embedded , sectioned at 5 µm , and stained with H&E ( hematoxylin and eosin ) . Statistical differences in tumor types were assessed via Fisher's exact test . For germ-cell counts on embryonic or newborn gonads , 5 µm sections were immunostained as previously described [82] . Germ cells in postnatal gonads were counted in three sections from the midportion of each gonad and averaged . Antibodies: Rabbit anti-DDX4/MVH ( Abcam ab13840; 1∶250 ) ; rabbit anti-Stella ( Abcam ab19878; 1∶250 ) ; goat anti-mouse Alexa 594 conjugate ( Molecular Probes A11005; 1∶1 , 000 ) ; goat anti-rabbit Alexa 488 conjugate ( Molecular Probes A11008; 1∶1 , 000 ) . The data were analyzed using one-way ANOVA with Bonferroni correction ( Prism software package ) . The resulting P values were used to determine significance ( P<0 . 05 ) . Pregnant females received a single BrdU intraperitoneal injection ( 50 mg/kg ) at 11 , 12 , or 13 days after vaginal plug detection ( their corresponding embryos were E11 . 5 , E12 . 5 and E13 . 5 ) . Injected mice were sacrificed two hours later , and embryos were collected . Embryonic gonads together with mesonephric tubules ( for E12 . 5 and E13 . 5 embryos ) or the dorsal part of the trunk without other internal organs ( for E11 . 5 embryos ) were fixed in 4% PFA . Tissues were embedded in paraffin and sectioned . BrdU was detected by the Invitrogen BrdU Staining Kit ( Cat . No . 93-3944 ) , and PGCs were detected with rabbit anti-Stella ( Abcam ab19878; 1∶250 ) . At least three sagittal sections across the central part of the gonads were used for PGC quantification and BrdU scoring . Since no cell apoptosis was obvious and no cell migration occurs between E11 . 5 and E13 . 5 , PGC doubling time was calculated based on an exponential growth model: NE13 . 5 and NE11 . 5 are the absolute number of PGCs in the whole gonad , which was estimated based on the previous studies and the relative ratio between wild type and mutants . Embryonic gonads were stained as described [83] . Briefly , fixed gonads were washed with dH2O and stained with freshly made staining solution ( 0 . 1 mg/ml Sodium α-naphthyl phosphate , 5 mg/ml Borax , 0 . 6 mg/ml MgCl2 , and 0 . 5 mg/ml Fast Red TR salt ) for 15–30 min . Tissues were then washed in dH2O and cleared with 70% glycerol . Five µm paraffin sections of embryonic gonads were TUNEL stained using the In Situ Cell Death Detection Kit ( Roche 11684817910 ) . Atm−/− adult testes were used as a positive control [84] . This was performed as described [75] . Primary antibodies used in this study: rabbit anti-SYCP3 ( 1∶500 , Abcam ) ; mouse anti-γH2AX ( 1∶500 , JBW301 Upstate Biotechnology ) ; rabbit anti-RAD51 ( 1∶250 , this polyclonal antibody recognizes both RAD51 and DMC1; Oncogene Research Products ) . The use of mice in this study was approved by Cornell's Institutional Animal Care and Use Committee , under the approved protocol of JCS ( 2004-0038 ) . Euthanasia was performed by CO2 administration . | The precursors to sperm and eggs begin are a group of <100 cells in the embryo , called primordial germ cells ( PGCs ) . They migrate in the primitive embryo to the location of the future gonads , then undergo a rapid proliferation over the next few days to a population of many thousands . Because these cells contain the precious genetic information for our offspring , and the DNA replication associated with rapid PGC proliferation is subject to spontaneous errors , mechanisms exist to avoid propagation of mutations . A manifestation of this is the high sensitivity of PGCs to genetic perturbations affecting DNA repair . We studied mice defective for a gene called Fanconi anemia M ( Fancm ) that is important for repair of DNA damage that occurs during replication . Although it is expressed in all tissues , only the PGCs are affected in mutants , and are reduced in number . We find that PGCs lacking Fancm respond by slowing cell division , and identified the genetic pathway responsible for this protective response . | [
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] | 2014 | Hypersensitivity of Primordial Germ Cells to Compromised Replication-Associated DNA Repair Involves ATM-p53-p21 Signaling |
To complete cell division with high fidelity , cytokinesis must be coordinated with chromosome segregation . Mammalian Polo-like kinase 1 , Plk1 , may function as a critical link because it is required for chromosome segregation and establishment of the cleavage plane following anaphase onset . A central spindle–localized pool of the RhoGEF Ect2 promotes activation of the small GTPase RhoA , which drives contractile ring assembly at the equatorial cortex . Here , we have investigated how Plk1 promotes the central spindle recruitment of Ect2 . Plk1 phosphorylates the noncatalytic N terminus of the RhoGAP HsCyk-4 at the central spindle , creating a phospho-epitope recognized by the BRCA1 C-terminal ( BRCT ) repeats of Ect2 . Failure to phosphorylate HsCyk-4 blocks Ect2 recruitment to the central spindle and the subsequent induction of furrowing . Microtubules , as well as the microtubule-associated protein ( MAP ) Prc1 , facilitate Plk1 phosphorylation of HsCyk-4 . Characterization of a phosphomimetic version of HsCyk-4 indicates that Plk1 promotes Ect2 recruitment through multiple targets . Collectively , our data reveal that formation of the HsCyk-4-Ect2 complex is subject to multiple layers of regulation to ensure that RhoA activation occurs between the segregated sister chromatids during anaphase .
Cell division requires crosstalk between various cell cycle regulatory proteins and the actomyosin and microtubule cytoskeletons . The small GTPase RhoA lies at the interface between these cytoskeletal systems , and its activation at the equatorial cortex following chromosome segregation is a critical step in the specification of the division plane [1] . RhoA activation leads to a dramatic reorganization of the actomyosin cytoskeleton underneath the plasma membrane to form a contractile network necessary for cell cleavage . The spatial regulation of RhoA activation is largely dictated by the microtubule cytoskeleton , through the combined action of microtubule asters and a set of interpolar microtubule bundles termed the central spindle [2] . The central spindle forms between the divided sister chromatids and acts as a signaling hub , integrating positional and temporal cues to facilitate activation of RhoA at the equatorial cortex [3] . The centralspindlin complex , a heterotetramer consisting of the kinesin-like protein Mklp1 ( UniProt Q02241 ) and the RhoGAP HsCyk-4/MgcRacGAP ( hereafter referred to as HsCyk-4 [UniProt Q9H0H5] ) , is required for assembly of the central spindle [4] , [5] . In addition to its role in assembly , HsCyk-4 recruits the RhoGEF Ect2 ( UniProt Q9H8V3 ) to the central spindle [6]–[9] . HsCyk-4 binds directly to the noncatalytic N terminus of Ect2 in a phosphorylation-dependent manner [6] , via a region possessing BRCA1 C-terminal ( BRCT ) repeats . Because the Ect2 N terminus has been proposed to associate with and inhibit the activity of its C-terminal GEF domain [10] , HsCyk-4 binding may facilitate both targeting and activation of the exchange factor for RhoA . Consistent with this model , depletion of either Ect2 or HsCyk-4 prevents RhoA-dependent cortical contractility [6]–[9] . Hence , formation of the Ect2–HsCyk-4 complex represents a critical step in cleavage plane specification , linking positional information from microtubules with cortical actomyosin contractility through RhoA activation . Because elevated Cdk1–cyclin B activity prevents HsCyk-4 and Ect2 binding [6] , [7] , this association is tightly controlled with respect to the cell cycle such that it occurs only during late mitosis . The mammalian Polo-like kinase Plk1 ( UniProt P53350 ) was shown recently through a complementary series of chemical genetic and small molecule inhibitor-based studies to be an essential activator of RhoA [11]–[14] . Inhibition of Plk1 prevents Ect2 association with HsCyk-4 and blocks its recruitment to the central spindle [11]–[14] , suggesting that Plk1 might serve as a stimulatory kinase . Consistent with this , Plk1 phosphorylates Ect2 in vitro , and this phosphorylation may affect its exchange activity [15] . The functions of Polo-like kinase family members ( Plks ) are modulated through their subcellular distribution . Plks are recruited to various cellular sites through recognition of a phosphorylated , or primed , substrate [16] . Specifically , a C-terminal Polo-box domain ( PBD ) binds to a priming phosphorylation site , which serves to localize and locally activate the Plk [17] , [18] . The PBD of Plk1 interacts with hundreds of mitotic proteins , each with varying affinities , to control different aspects of cell division [19] . PBD binding to the MAP Prc1 ( UniProt O43663 ) is reported to be the primary anchor for Plk1 at the central spindle [20] . However , additional PBD binding sites independent of Prc1 are present to ensure the anaphase concentration of Plk1 at the central spindle [15] , [19] , [21] , [22] . Here , we have examined the mechanism by which Plk1 stimulates association between Ect2 and HsCyk-4 during anaphase to trigger the onset of cytokinesis . Our data indicate that Plk1 has multiple functions in RhoA activation , part of which can be explained through its phosphorylation of HsCyk-4 , and that these functions are enhanced through its targeting to the central spindle .
Chemical genetic and small-molecule–based inhibition of Plk1 kinase activity prevents both Ect2 association with HsCyk-4 and its recruitment to the central spindle [11]–[14] . Therefore , we sought to understand the mechanism whereby Plk1 regulates formation of this complex . We have shown previously that the Ect2 N terminus ( Ect2-BRCT ) is sufficient to localize to the central spindle [6] . Using the localization of this fragment as an assay , we tested the possibility that Plk1 may relieve the autoinhibition of Ect2 , freeing the N terminus to associate with HsCyk-4 . This model predicts that the central spindle localization of Ect2-BRCT would be independent of Plk1 activity . However , the Plk1 inhibitor BI-2536 [23] disrupted Ect2-BRCT localization to the central spindle in 98% of anaphase HeLa cells compared with 8% of DMSO-treated control cells ( n = 50 for each ) ( Figure 1A ) , suggesting that relieving Ect2 autoinhibition is not the sole function of Plk1 in controlling HsCyk-4–Ect2 complex formation . In addition to localizing to the central spindle when expressed in cells , a recombinant form of Ect2-BRCT associates with endogenous HsCyk-4 from mitotic lysates in a phosphorylation-dependent manner [6] . We therefore used this assay to ask if Plk1 activity is required for this association . First , HsCyk-4 association with Ect2-BRCT was analyzed in lysates from HeLa cells synchronously released from a metaphase block . Although only weak association was detected in metaphase , HsCyk-4 , as well as its binding partner , Mklp1 , were precipitated in abundance when the vast majority of control cells were in anaphase or telophase ( Figure 1B ) . In contrast , BI-2536 potently inhibited the ability of recombinant Ect2-BRCT to precipitate either HsCyk-4 or Mklp1 ( Figure 1B ) , suggesting that Plk1 regulates complex formation via HsCyk-4 . To confirm this result , nocodazole-arrested cells were forced to exit mitosis through application of the Cdk inhibitor purvalanol A . Treatment of prometaphase cells with purvalanol A induces assembly of ectopic cleavage furrows in the absence of chromosome segregation in a manner dependent upon known cytokinesis regulators [24] . Cdk1 inhibition led to a dramatic increase in the amount of HsCyk-4 precipitated by Ect2-BRCT ( Figure 1C ) . Concurrent application of BI-2536 and purvalanol A potently blocked HsCyk-4 precipitation by Ect2-BRCT and abrogated ectopic cleavage furrowing ( Figure 1C ) [12] . Dephosphorylation of mitotic lysates prevents HsCyk-4 precipitation by Ect2–BRCT [6] . Hence , we examined whether the BRCT domains of Ect2 act as a bona fide phosphobinding module . Mutation of residues within the BRCT domains of Ect2 analogous to those that coordinate phosphate in BRCA1 and MDC1 [25] abolished precipitation of HsCyk-4 from mitotic lysates ( Figure 1D ) , confirming that Ect2's BRCT domains function as a phosphobinding module akin to those previously analyzed . Consistent with these biochemical findings , mutation of these two conserved phosphopeptide coordinating residues abolished targeting of the Ect2-BRCT domain to the central spindle in anaphase ( Figure 1E ) . Our previous findings suggested that the N terminus ( Nt ) of HsCyk-4 could associate with Ect2-BRCT in the absence of other proteins , albeit with relatively low affinity under nonstringent conditions [6] . In vitro , Plk1 has the ability to phosphorylate both HsCyk-4-Nt and Ect2-BRCT ( Figure 2A and 2B ) [15] . Using purified recombinant proteins and more stringent buffer conditions , we tested whether Plk1 phosphorylation of HsCyk-4-Nt or Ect2-BRCT could stimulate association with its respective binding partner . Although Plk1 phosphorylated both proteins to high stoichiometry as evidenced by a gel mobility shift , only phosphorylation of HsCyk-4-Nt enhanced the association with Ect2-BRCT ( Figures 2A and S1A ) . HsCyk-4-Nt contains seven consensus motifs for Plk1 phosphorylation ( E/D-X-S/T-φ ) [26] . To determine which site ( s ) Plk1 phosphorylates , we utilized two complementary in vitro approaches . First , truncations of HsCyk-4-Nt fused to glutathione-S-transferase ( GST ) were used as substrates for Plk1 . Of the various truncations tested , Plk1 preferentially phosphorylated the fragment encompassing amino acids 111–188 ( Figure 2B ) . An array of HsCyk-4 N-terminal peptides was used to confirm this result and more precisely define the region of Plk1 phosphorylation . Only peptides containing amino acids 139–174 of HsCyk-4 were strongly phosphorylated by Plk1 ( Figures 2C and S2A ) . Within this region , four putative Plk1 phosphorylation sites ( Ser149 , Ser157 , Ser164 , and Ser170 ) are well conserved across species and are clustered in a region downstream of the coiled-coil domain and upstream of the C1 domain ( Figure 2D ) . Two of these residues were previously identified in a proteomic screen for phosphorylated mitotic spindle proteins ( Ser164 and Ser170 ) [27] . Mutation of any of these four residues alone was insufficient to prevent Plk1-stimulated binding ( Figure S1B ) . Therefore , these residues were mutated , in combination , to nonphosphorylatable Ala residues and assayed for Plk1-stimulated association with Ect2 . In vitro phosphorylation of HsCyk-4-4A ( amino acids 111–188 ) by Plk1 was dramatically reduced , to below 5% of the wild-type fragment ( Figure S2B ) . Importantly , mutation of these four residues effectively disrupted the Plk1-mediated stimulation of HsCyk-4–Ect2-BRCT complex formation ( 14 . 9%±8 . 3% of wild-type association ) ( Figure 2E ) . Conversely , mutation of these four serine residues to Asp ( HsCyk-4-4D ) to mimic the phosphorylated state generated a form of HsCyk-4 capable of associating with Ect2-BRCT even in the absence of Plk1 phosphorylation ( Figure 2E ) . Whereas Plk1 phosphorylation of HsCyk-4-wt stimulated association with Ect2-BRCT 20 . 2±2 . 8-fold , HsCyk-4-4A and HsCyk-4-4D produced fold increases of only 3 . 1±0 . 9 and 1 . 7±0 . 5 , respectively , suggesting that the identified residues account for the vast majority of Plk1-mediated stimulation . Yeast two hybrid analysis confirmed that HsCyk-4-4D possessed an increased affinity for Ect2-BRCT ( Figure S3 ) . We conclude that Plk1 phosphorylates multiple serine residues in the N terminus of HsCyk-4 in vitro , thereby stimulating its association with Ect2 . To confirm that Plk1 phosphorylates these sites in vivo as well as to determine their spatial and temporal regulation , we obtained phosphospecific antibodies directed at Ser170 in HsCyk-4 . Phospho-Ser170 antibodies specifically stained the central spindle of control , but not HsCyk-4–depleted , anaphase cells ( Figure 2F ) . Application of BI-2536 abolished phospho-Ser170 central spindle staining , providing further evidence that Plk1 is the major kinase responsible for generating phosphorylated Ser170 in vivo . We next tested whether Plk1 phosphorylation of HsCyk-4 is essential for cleavage furrow formation . To address this , we created stable HeLa cell lines expressing RNA interference ( RNAi ) -resistant derivatives of HsCyk-4 fused to EGFP at its C terminus . Although variability in expression level existed within and between lines ( Figure 3A ) , each line expressed HsCyk-4–EGFP in at least 70% of cells ( Figure S4A ) . All HsCyk-4–EGFP derivatives , despite being expressed above endogenous levels , localized to the central spindle in cells depleted of endogenous HsCyk-4 ( Figures 3D , 4A , 4C , S4A , S4B , and S4C ) , indicating that they retain basic functionality . Additionally , central spindle assembly remained unperturbed as indicated by proper Prc1 localization in each of the cell lines ( Figure S4B ) . Phospho-Ser170 antibodies were used to confirm the absence of Plk1 phosphorylation in HsCyk-4-4A–EGFP stable cells ( Figure S4C ) . We analyzed the ability of the HsCyk-4–EGFP derivatives to rescue the cytokinesis defect associated with depletion of the endogenous protein . Cytokinesis failure was scored both in fixed cell populations ( Figure 3B ) and by live cell imaging of individual cells ( Figure 3C and 3D ) . In both cases ( and all subsequent experiments ) , cells were transfected with small interfering RNA ( siRNA ) to deplete endogenous HsCyk-4 for between 28 and 32 h ( knockdown = 21 . 3%±7 . 4% of endogenous levels , Figure S4D ) . In bulk populations , a wild-type copy of HsCyk-4–EGFP was able to largely rescue the cytokinesis failure of endogenous HsCyk-4 depletion ( 20 . 7%±1 . 1% versus 66 . 7%±1 . 5% cytokinesis failure ) , whereas expression of EGFP alone failed to rescue ( 65 . 2%±0 . 2% cytokinesis failure ) ( Figure 3B ) . The Plk1 phospho-site mutants differed markedly in their abilities to restore efficient cytokinesis: HsCyk-4-4A–EGFP failed to rescue ( 59 . 4%±3 . 2% cytokinesis failure ) , whereas HsCyk-4-4D–EGFP rescued to a similar extent as wild type ( 16 . 7%±0 . 7% cytokinesis failure ) ( Figure 3B ) . In individual live cells depleted for endogenous HsCyk-4 , expression of EGFP alone or HsCyk-4-4A–EGFP failed to restore cleavage furrow formation in 67% ( n = 9 ) and 47% ( n = 32 ) of cells , respectively ( Figure 3C and 3D ) . In contrast , 100% of HsCyk-4-wt–EGFP ( n = 15 ) and HsCyk-4-4D–EGFP ( n = 23 ) expressing cells formed proper cleavage furrows and successfully completed cytokinesis . Similar results were obtained from cells cotransfected with HsCyk4 siRNA and HsCyk-4-EGFP-wt/-4A/-4D expression plasmids ( Figure S5A ) . We conclude that phospho-site mutants of HsCyk-4 that fail to associate with Ect2 in vitro block cleavage furrow formation in vivo . To more fully characterize the cytokinetic defect caused by the nonphosphorylatable allele of HsCyk-4 , we examined the localization of critical cytokinetic regulators during anaphase in individual fixed cells . As was the case with all single-cell experiments , only cells expressing the transgenes at comparable levels were scored for phenotypic analysis ( Figure S6B and S6D ) . First , we examined Ect2 localization in cells expressing HsCyk-4–EGFP derivatives . Although Ect2 was recruited to the central spindle by HsCyk-4-wt–EGFP and HsCyk-4-4D–EGFP during anaphase ( 97 . 8% and 95 . 4% , respectively , see Figure S6A for method of assessing localization ) , a positive Ect2 central spindle signal was not detected in nearly 90% ( n = 96 ) of anaphase cells expressing HsCyk-4-4A–EGFP ( Figures 4A , S6A , and S6B ) . Similarly , while recombinant Ect2-BRCT precipitated HsCyk-4-wt–EGFP and HsCyk-4-4D–EGFP from purvalanol A–treated cells , Ect2-BRCT failed to precipitate either EGFP alone or detectable amounts of HsCyk-4-4A–EGFP when stably expressed ( Figure 4B ) . To exclude the possibility that the reduced level of HsCyk-4-4A–EGFP expression precluded our ability to detect its association with Ect2-BRCT , we transiently transfected HsCyk-4–EGFP derivatives to approximately equal levels and confirmed the absence of detectable association of HsCyk-4-4A–EGFP with recombinant Ect2-BRCT in purvalanol A-treated cells ( Figure S5B ) . Consistent with its central role in cleavage furrow formation , RhoA localization to the equatorial cortex was perturbed in cells stably expressing HsCyk-4-4A–EGFP ( Figure 4C ) . To quantitatively examine a requirement for Plk1 phosphorylation of HsCyk-4 in RhoA activation , we examined the localization of Anillin , a factor that requires active RhoA signaling for its localization to the equatorial cortex [7] , [28] . Cortical accumulation of Anillin was disrupted in nearly half of all anaphase cells stably expressing HsCyk-4-4A–EGFP ( Figure S6C and S6D ) . We conclude that Plk1 phosphorylation of the N terminus of HsCyk-4 is necessary to mediate association with Ect2 and promote RhoA activation in vivo . The phenotype associated with HsCyk-4-4A–EGFP expression is highly reminiscent of Plk1 inhibition [11]–[14] , suggesting that , with respect to cleavage furrow formation , HsCyk-4 is a relevant target of Plk1's kinase activity . If the primary function of Plk1 in generating the HsCyk-4-Ect2 complex is to phosphorylate HsCyk-4 , then HsCyk-4-4D–EGFP should be able to associate with Ect2 in the absence of Plk1 kinase activity . To test this possibility , wild type and HsCyk-4-4D–EGFP stable cells were induced to exit mitosis with addition of purvalanol A in the presence and absence of BI-2536 . HsCyk-4-4D–EGFP retained the ability to be precipitated by Ect2-BRCT despite the presence of BI-2536 , whereas the association of wild-type HsCyk-4–EGFP with Ect2–BRCT was sensitive to the inhibitor ( Figure 5A ) . Surprisingly , although HsCyk-4-4D–EGFP coprecipitated with Ect2-BRCT in the presence of BI-2536 , endogenous Ect2 was not recruited to the central spindle during anaphase of BI-2536 treated HsCyk-4-4D–EGFP cells ( Figure 5B , upper images ) . As a likely consequence , HsCyk-4-4D–EGFP expression did not prevent BI-2536-induced cytokinesis failure ( Figure 5B , lower images ) . These data suggest that Plk1 likely phosphorylates multiple targets essential for cytokinesis , and that the regulated association between HsCyk-4 and Ect2 at the central spindle requires other Plk1-dependent steps in addition to HsCyk-4 phosphorylation . Because Plk1 is known to phosphorylate the C terminus of Ect2 [15] , we asked whether the Ect2-BRCT fragment could localize independently of Plk1 activity in HsCyk-4-4D–EGFP cells . Whereas less than 2% of cells expressing Myc-tagged Ect2-BRCT localized the truncated protein to the central spindle in BI-2536-treated HsCyk-4-wt–EGFP expressing cells , those expressing HsCyk-4-4D–EGFP retained Ect2-BRCT localization in 16 . 2%±7 . 1% of BI-2536 treated anaphase cells ( Figure 5C ) . We conclude that while Plk1 phosphorylation of the N terminus of HsCyk-4 is necessary and to a certain extent sufficient to mediate association with Ect2-BRCT , other Plk1-dependent events , occurring at least partially through the C terminus of Ect2 , are necessary for its recruitment to the central spindle . Having established that HsCyk-4 phosphorylation by Plk1 is critical for cleavage furrow formation , we next investigated how this phosphorylation is regulated . Plk1 substrate binding is often mediated through binding of the PBD to a phosphorylation site on the target protein . However , we did not find appreciable amounts of HsCyk-4 associated with either endogenous Plk1 or with recombinant PBD during forced mitotic exit ( Figure S7A and S7B ) , a time when HsCyk-4 serves as a substrate for Plk1 . In contrast , the MAP Prc1 associated with endogenous Plk1 as well as recombinant PBD during forced mitotic exit ( Figure S6A and S6B ) and , together with Mklp2 , promotes Plk1 recruitment to central spindle microtubules during anaphase [20] , [21] . In addition , Prc1 colocalizes with centralspindlin and direct association with HsCyk-4 has been reported [29]–[31] , raising the possibility that it might serve as an intermediary in the phosphorylation of HsCyk-4 . Because Ect2-BRCT precipitates HsCyk-4 in lysates from purvalanol A–treated cells , a reaction requiring Plk1 activity ( Figure 1C ) , we were able to ask whether Prc1 was required for efficient Ect2-BRCT precipitation of HsCyk-4 . The amount of HsCyk-4 precipitated in Prc1-depleted cells ( knockdown = 21 . 5%±5 . 0% of endogenous Prc1 levels ) was significantly decreased relative to control cells ( Figure 6A ) . The inability of Prc1-depleted cells to generate phosphorylated HsCyk-4 provided a molecular explanation for the failure of HsCyk-4 to associate with Ect2-BRCT ( Figure 6B ) . Consistent with the interpretation that Prc1 functions by facilitating the phosphorylation of HsCyk-4 by Plk1 , expression of HsCyk-4-4D–EGFP bypassed the requirement for Prc1 to permit HsCyk-4 precipitation by Ect2-BRCT ( Figure 6C ) . A direct association between Prc1 and HsCyk-4 may functionally link Plk1 kinase activity with the N terminus of HsCyk-4 . To test this possibility , a mutant of Prc1 ( Prc1-ST601/2AA ) incapable of Plk1 recruitment to the central spindle was expressed in cells depleted for endogenous Prc1 [20] . As previously reported , Prc1-ST601/2AA did not detectably interact with Plk1 ( Figure S8B ) . Despite the disruption in Plk1–Prc1 association , Plk1 central spindle localization was only moderately attenuated ( Figure S8A ) , and phosphorylated HsCyk-4 on Ser170 persisted at the central spindle ( Figure S8C ) . Although this reduced level of Plk1 at the central spindle could suffice for HsCyk-4 phosphorylation , these data suggest that Plk1 need not associate directly with Prc1 in order to phosphorylate HsCyk-4 . Alternatively , as Prc1 , Plk1 , and centralspindlin all concentrate on a microtubule-based scaffold , Prc1-mediated bundling of microtubules may facilitate phosphorylation of HsCyk-4 by Plk1 . To test this possibility , we asked whether microtubule disruption would influence the ability of Ect2-BRCT to precipitate endogenous HsCyk-4 . Low-dose nocodazole treatment ( 0 . 04 µg/ml ) , which only weakly disrupted the microtubule cytoskeleton and retained HsCyk-4 microtubule association , permitted robust precipitation of HsCyk-4 by Ect2-BRCT upon purvalanol A addition ( Figure 6D ) . However , 5 µg/ml nocodazole caused full microtubule destabilization , delocalized HsCyk-4 , and rendered HsCyk-4 association with Ect2-BRCT undetectable ( Figure 6D ) , underscoring the importance of a microtubule platform for this association . Although these data implicate both Prc1 and a microtubule scaffold as critical regulators of RhoA activation by modulating Plk1 phosphorylation of HsCyk-4 , cells depleted for Prc1 can form ingressing cleavage furrows [30] , [32] and those depleted of microtubules retain contractility [33] , indicating that Prc1 and microtubules are not strictly essential for RhoA activation upon mitotic exit . Both prometaphase cells and cells containing monopolar spindles that are forced out of mitosis have more stringent requirements for the formation of cleavage furrows [24] , [34] , suggesting that the process is less robust under these circumstances . We therefore induced prometaphase cells to exit mitosis with purvalanol A and asked whether Prc1 was required for cortical RhoA localization and ectopic cleavage furrow formation . Indeed , in this context , Prc1 depletion severely compromised RhoA-induced contractility ( Figure 7A ) . We conclude that Prc1 facilitates Plk1 phosphorylation of HsCyk-4 to allow recruitment of Ect2 to the central spindle where it can stimulate the local activation of RhoA . These data suggest that other mechanisms can compensate for the absence of Prc1 during bipolar cytokinesis .
The central spindle serves as a platform for the coordinated recruitment of numerous signaling proteins that regulate cytokinesis [35] . The Plk1-mediated recruitment of the RhoGEF Ect2 to the central spindle by the RhoGAP HsCyk-4 component of centralspindlin appears to be a critical step in the generation of a localized band of cortical RhoA to a region just overlying the central spindle [11]–[14] . Here , we provide a molecular mechanism whereby the HsCyk-4-Ect2 complex is formed at the central spindle following anaphase onset . By combining a reconstituted system with cell-based analyses , we have demonstrated that Plk1 promotes the phosphorylation of the N terminus of HsCyk-4 , thus generating a phospho-epitope recognized by the BRCT domains of Ect2 . BRCT binding may relieve Ect2 autoinhibition and facilitate activation of its intrinsic exchange activity toward RhoA . Indeed , stimulation of the HsCyk-4-Ect2 complex formation by Plk1 phosphorylation is critical for RhoA cortical localization and cleavage furrow formation . Our data also demonstrate that the role of Plk1 in stimulating HsCyk-4–Ect2 association is not limited to phosphorylation of HsCyk-4 , but rather may also involve relief of Ect2 autoinhibition . Furthermore , we have identified the microtubule-associated protein ( MAP ) Prc1 and microtubules as critical factors facilitating Plk1 phosphorylation of HsCyk-4 . Collectively , our work has elucidated a molecular basis for Plk1-regulated cleavage furrow formation and has provided further evidence that central spindle microtubules act as a crucial signaling center for cytokinesis ( Figure 7B ) . In order to stimulate association with the BRCT domains of Ect2 , Plk1 targets multiple serine residues within the N terminus of HsCyk-4 . While BRCT–phosphopeptide interactions have only been modeled with a single phosphorylated residue [36] , it is possible that the tandem BRCT domains of Ect2 contact multiple phosphorylated residues . However , because mutation of the equivalent residues in Ect2 that coordinate the phosphate in MDC1 and BRCA1 [25] abrogates binding to HsCyk-4 , we do not favor this possibility . Alternatively , phosphorylation of multiple residues within HsCyk-4 may lead to a conformational change that favors recognition of one particular phosphoserine by the BRCT domains of Ect2 . Consistent with this , mutation of Ser157 alone within HsCyk-4 abolished association with Ect2-BRCT in mitotic lysates ( unpublished data ) . Still , as mutation of any one of the serine residues was insufficient to prevent Plk1 stimulation of the HsCyk-4-Ect2 association in vitro , these data are not in complete accordance with one another . Instead , the most plausible explanation of these results is that each of the four phosphoserine residues within the cluster contributes to the overall binding affinity , perhaps by facilitating rebinding upon dissociation . We have shown that the BRCT repeats of Ect2 can bind a phosphomimetic allele of HsCyk-4 ( 4D ) from Plk1-inhibited mitotic lysates , suggesting a rather simple pathway for regulation of HsCyk-4 by Plk1 . However , despite premature Ect2 association with HsCyk-4-4D in early mitotic cells ( unpublished data ) , RhoA-induced cortical contractility was not observed . These data suggest that further steps are necessary to form a complex competent for triggering contractile events . One such step may be the removal of inhibitory Cdk1-cyclin B phosphorylations [6] , [7] . Additionally , Plk1 phosphorylation of HsCyk-4 is necessary , but not sufficient , for Ect2 central spindle recruitment . As appreciable central spindle localization of isolated BRCT domains can occur in Plk1-inhibited cells expressing phosphomimetic HsCyk-4 , Plk1 may also function to relieve Ect2 autoinhibition [15] . However , the recruitment of this N-terminal fragment of Ect2 is far more efficient in the presence than in the absence of Plk1 activity , suggesting the involvement of additional Plk1 substrates and additional modes of regulation . Indeed , a number of other Plk1 substrates are involved in cytokinesis , including Rock2 [19] , Anillin [19] , and an additional exchange factor for RhoA , MyoGEF [37] . Further work is required to define the full spectrum of Plk1 substrates required for complete activation of RhoA during cytokinesis . Our biochemical results , obtained with synchronized cells forced out of mitosis , reveal that Prc1 and a microtubule scaffold are critical for robust generation of phosphorylated HsCyk-4 . Because Prc1 recruits Plk1 , and because Prc1 and HsCyk-4 colocalize at the central spindle [20] , [29] , [30] , [32] , [38] , [39] , it is possible that docking of Plk1 on Prc1 allows the kinase to phosphorylate HsCyk-4 while bound to Prc1 . While we cannot exclude this possibility , HsCyk-4 phosphorylation persisted in cells expressing a Prc1 derivative lacking the previously defined Plk1 docking sites [20] . One important caveat is that these mutations did not completely abrogate Plk1 recruitment to the central spindle . This diminished Plk1 localization may arise through an alternative pathway , perhaps through association with Mklp1 , Mklp2 , or HsCyk-4 itself ( Figure 7B ) [19] , [21] , [22] . These modes of Plk1 recruitment may provide a sufficient amount of localized kinase activity to generate a biologically relevant pool of phosphorylated HsCyk-4 . Because these mutations still allow microtubule bundling , Prc1 may create a scaffold of appropriately bundled microtubules on which Plk1 can target HsCyk-4 for phosphorylation . In a manner analogous to the phosphorylation of Mklp2 [21] , microtubules may also enhance Plk1-mediated phosphorylation of HsCyk-4 . While in vivo analyses of bipolar cytokinesis events indicate that depletion of either Ect2 , RhoA , or HsCyk4 , as well as Plk1 inhibition and prevention of HsCyk-4 phosphorylation , all result in complete abrogation of cortical contractility [6]–[14] , [28] , depletion of Prc1 does not cause such a severe phenotype [20] , [39] . In contrast , our biochemical analyses using prometaphase cells forced out of mitosis indicate that Prc1 is critical for generating significant amounts of phosphorylated HsCyk-4 . Prc1 depletion in normally dividing cells results in a dramatically disorganized central spindle and a delocalization of cytokinetic factors such as Plk1 and HsCyk-4 [20] , [30] , [32] , rendering an examination into the localization of the phosphorylated form of HsCyk-4 equivocal . Additionally , while Prc1 depletion does not block furrowing in bipolar cells , it does greatly attenuate ectopic cleavage furrowing induced by purvalanol A treatment of prometaphase cells . A similar scenario occurs in cells depleted for Mklp1 . Whereas its depletion causes a furrowing defect in only 40%–50% of anaphase cells and allows generation of active RhoA [6] , [8] , loss of Mklp1 during forced mitotic exit almost completely abrogates the formation of ectopic cleavage furrows and the recruitment of RhoA to the cortex ( unpublished data ) . Thus , the phenotypes resulting from a reduction in RhoA activation are likely to be context dependent , with the ectopic cleavage furrowing induced by purvalanol A perhaps being particularly dependent on events that occur on bundled microtubules [34] , [40] , [41] . In summary , our data reveal that many levels of protein phosphorylation regulate the activation of RhoA upon anaphase onset . The recruitment of critical central spindle components such as Prc1 and centralspindlin , as well as the formation of the HsCyk-4-Ect2 complex , are negatively regulated by Cdk1–cyclin B–mediated phosphorylation [6] , [20] , [39] , [42] , [43] . Following a decline in Cdk1–cyclin B activity at anaphase onset , microtubules are bundled and Plk1 is recruited to the central spindle through association of its PBD with Prc1 [20] , Mklp2 [21] , and possibly other factors [19] , [22] . At the central spindle , Plk1 performs several functions critical for cleavage furrow formation , including HsCyk-4 phosphorylation and subsequent Ect2 recruitment , as well as activation of RhoA . These multiple layers of regulation conspire to ensure that activation of the cytokinetic machinery occurs at the appropriate time and place .
Kyoto HeLa S3 cells were grown in Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with 10% FBS , 2 mM L-glutamine , 100 U penicillin , and 0 . 1 mg/ml streptomycin . Stable cell lines expressing siRNA-resistant HsCyk-4–EGFP and derivatives were constructed in HeLa cells grown in DMEM containing 200 µg/ml hygromycin B . Previously described dsRNAs were used in this study to deplete endogenous HsCyk-4 [6] , Ect2 [6] , and Prc1 [20] . Oligofectamine ( Invitrogen ) and Lipofectamine-2000 ( Invitrogen ) were used for transfection of siRNA and plasmid DNA , respectively . Purvalanol A ( Axxora ) was used at 22 . 5 µM . Nocodazole ( Sigma ) was used at 0 . 04 µg/ml to mildly perturb the microtubule cytoskeleton and arrest cells in prometaphase , and at 5 µg/ml to fully deplete the microtubule cytoskeleton . BI-2536 ( generously provided by Norbert Kraut , Boehringer Ingelheim ) was used at 100 nM [23] . MG132 ( Sigma ) was used at 10 µM . To perform live cell imaging analysis , cells were grown in the Delta T4 open dish system ( BiOptechs ) and controlled at 37°C during the filming process . Cells were visualized using a 40×/ . 75 NA objective on a Zeiss Axiovert 200M equipped with a Yokogawa CSU-10 spinning-disk unit ( McBain ) , illuminated with a 50 mW 473 nm DPSS laser ( Cobolt ) . Single-plane multipoint acquisitions were captured every 20 s on a Cascade 512B EM-CCD camera ( Photometrics ) using MetaMorph ( Molecular Devices ) software . All images were acquired under identical conditions and scaled and processed identically ( with the exception of EGFP stable cells , which were acquired using a reduced exposure ) . Cells were considered to be “positive” for transgene expression if the maximum intensity of cellular fluorescence was >2 , 000 above the background intensity . For immunofluorescence analysis , cells were grown on coverslips and fixed with methanol at −20°C for either 15 min or overnight ( for Anillin , Ect2 , GFP , Myc , phospho-Ser170 , Plk1 , and Prc1 staining ) or with 10% trichloroacetic acid on ice for 15 min ( for RhoA , GFP , HsCyk-4 , and α-tubulin staining ) . Fluorescently conjugated secondary anti-mouse or anti-rabbit ( Alexa 488 or 568 , Molecular Probes ) antibodies were used at 1∶500 dilutions . Images were collected with a Zeiss AxioImager M1 microscope using a 40×/0 . 75 objective . All images were acquired under identical conditions using Metamorph ( Molecular Devices ) software . The 16-bit images were then opened in Image J and scaled to 8-bit with a single scale for all images in a given experiment . For analysis of Ect2 localization to the central spindle , quantification was performed as follows: using raw image data acquired under identical conditions , the fluorescence intensity of Ect2 in a region of interest ( ROI ) containing the central spindle was quantified . From that value , we subtracted the average of ROIs immediately adjacent to the central spindle . Should the resulting value be ≥1 , 000 , then Ect2 central spindle localization was deemed “positive . ” A similar scheme was used to determine “positive” Anillin localization to the equatorial cortex . Specifically , using raw image data acquired under identical conditions , the fluorescence intensity of Anillin in a ROI containing the equatorial cortex as well as a ROI in the cytoplasm that was immediately adjacent to the equatorial cortex were quantified . When the ratio of the equatorial ROI to cytoplasmic ROI was greater than 1 , then Anillin localization to the equatorial cortex was deemed “positive . ” HeLa cells and stable cell lines were synchronized as described [6] , [12] . Cells were either released from a nocodazole ( prometaphase ) block or forced to exit mitosis by addition of purvalanol A ( Axxora ) for 30 min . For all synchronization experiments involving RNAi , siRNA transfections took place approximately 1 h following release from the first overnight thymidine block so that the duration of RNAi was between 28 and 32 h . Lysates were prepared as described [6] . For the Ect2-BRCT and PBD precipitation experiments , approximately 650 µg of total protein was incubated with 10 µg of immobilized chitin-binding domain ( CBD ) –Ect2-BRCT ( 1–421 ) and 10 µg GST–PBD+ ( construct generously provided by D . Lim and M . Yaffe , Massachusetts Institute of Technology [MIT] ) , respectively , for 12–15 h at 4°C with mixing . For immunoprecipitation experiments , approximately 1 . 5–2 mg of total protein was incubated with either 2 µg of anti-Plk1 or anti-Ect2 antibodies for 12–15 h at 4°C with mixing . Protein A-sepharose was added and mixing at 4°C was continued for an additional 45 min . All recombinant beads and immunocomplexes were washed extensively in lysis buffer and boiled in sample buffer prior to separation on SDS-PAGE . The following antibodies were used for Western blot analysis: mouse anti-HsCyk-4 ( Abnova RacGAP1 1∶1 , 000 ) , mouse anti-α-tubulin ( Sigma DM1α 1∶10 , 000 ) , mouse anti-Plk1 ( Santa Cruz 1∶500 ) , mouse anti-c-Myc ( Boehringer Mannheim 9E10 1∶5 , 000 ) , rabbit anti-GFP ( Santa Cruz 1∶500 ) , rabbit anti-Ect2 ( [6] 1∶1 , 000 ) , rabbit anti-Mklp1 ( [4] 1∶1 , 000 ) , rabbit anti-Prc1 ( Santa Cruz 1∶500 ) , rabbit anti-cyclin B1 ( Santa Cruz 1∶500 ) , rabbit anti-phospho-Ser170 ( generously provided by P . Jallepalli , MS-KCC , 1∶1 , 000 ) goat-anti-mouse IR-680 ( Invitrogen 1∶5 , 000 ) , and goat-anti-rabbit IR-800 ( Jackson Labs 1∶5 , 000 ) . Membranes were imaged using an Odyssey scanner ( Li-Cor ) and bands quantified using Odyssey v2 . 1 software ( Li-Cor ) . One hundred forty-two 18-mer peptides corresponding to HsCyk-4 amino acids 1–300 and a positive control Plk1 substrate peptide ( SFNDTLDFD ) [44] were synthesized and spotted on cellulose membranes . The sequence of each 18-mer peptide is shifted by two residues resulting in a 16 amino acid sequence overlap . Membranes were equilibrated in kinase buffer ( 50 mM Tris-HCl [pH 7 . 5] , 10 mM MgCl2 , 1 mM EGTA , and 2 mM DTT ) and incubated with 100 nM full-length recombinant Plk1 T210D or Plk1 K82M/T210D in kinase buffer with 1 µM ATP and 25 µCi [γ-32P] ATP at 30°C for 60 min . Reaction was terminated with 100 mM phosphoric acid , and membranes were washed extensively in 1 M NaCl and 0 . 5% phosphoric acid . Membranes were soaked into methanol before drying , then exposed to phosphor screen ( GE Healthcare ) . The incorporation of 32P was analyzed by phosphor imager ( Typhoon , GE Healthcare ) , and spot signal intensities were quantified using ImageQuant ( GE Healthcare ) . 50 nM of purified recombinant Plk1-T210D 1–306 ( generously provided by D . Lim and M . Yaffe , MIT ) was used to phosphorylate 100 nM bead-bound CBD tagged HsCyk-4-Nt or Ect2-BRCT in kinase buffer ( 50 mM Tris-HCl [pH 7 . 5] , 10 mM MgCl2 , 1 mM EGTA , and 2 mM DTT ) for 60 min at 30°C with rigorous mixing . Beads were washed extensively in binding buffer ( 20 mM HEPES [pH 7 . 2] , 150 mM NaCl , 5 mM MgCl2 , 1 mM DTT , 0 . 1% Triton X-100 ) , and the appropriate binding partner was added in soluble form at equimolar amounts . Binding reactions were incubated for 90 min at 4°C with mixing . Beads were washed extensively in binding buffer and boiled in sample buffer prior to separation on SDS-PAGE . Bands were visualized either by Coomassie staining or by Western blot analysis . Ect2-BRCT ( 1–421 ) and HsCyk-4-Nt ( 1–288 ) and associated derivatives were fused to the CBD and expressed in BL21 ( DE3 ) RIL cells . Expression was induced by the addition of 0 . 4 mM IPTG at 25°C for 4–5 h . Bacteria were resuspended in 10 mM HEPES ( pH 7 . 7 ) , 250 mM NaCl , 1 mM EGTA , 1 mM MgCl2 , 0 . 1% Triton-X 100 , 1 mM DTT , 0 . 1 mM ATP , 10 µg/ml leupeptin/pepstatin , and 1 mM phenylmethylsufonylflouride containing 0 . 5 mg/ml lysozyme prior to sonication . Lysates were cleared at 18 , 000 rpm at 4°C for 20 min in a JA . 20 Beckman rotor . Prewashed chitin beads ( New England Biolabs ) were added to the cleared lysates and incubated for 2 h at 4°C with mixing . Following washes , CBD fusions were either maintained on beads , aliquoted , and stored at −80°C , or the fusion tag was removed by addition of recombinant Tobacco Etch Virus ( TEV ) overnight at 4°C with mixing , and the resulting eluate was stored in aliquots at −80°C . Mutations Thr210 to Asp and Lys82 to Met were introduced in the cDNA of human Plk1 using site-directed mutagenesis ( Quickchange II , Stratagene ) to generate full-length activated ( kinase active [KA] ) Plk1 ( T210D ) and enzymatically inactive ( kinase dead [KD] ) Plk1 ( K82M/T210D ) . The resulting Plk1 variants were cloned into pFastBac1 ( Invitrogen ) containing GST and a PreScission protease recognition sequence upstream of the cloning site . GST-Plk1 T210D and GST-Plk1 K82M/T210D were expressed in Sf-9 cells using the baculovirus system . Infected cells were incubated for 72 h , treated with 0 . 1 µM okadaic acid for 2 h , and then harvested . Cells were resuspended in buffer A ( 50 mM HEPES [pH 7 . 5] , 150 mM NaCl , 1 mM EDTA , 2 . 5 mM EGTA , 0 . 1% NP-40 , 10% glycerol , 1 mM DTT , 1 µM microcystin LR , and protease inhibitor cocktail [Roche] ) . After incubation in buffer A for 30 min at 4°C , cells were lysed by sonication . Lysates were centrifuged at 28 , 000g for 30 min at 4°C . GST fusion proteins purified using glutathione sepharose 4B ( GE Healthcare ) . GST-Plk1 proteins were eluted in buffer containing 20 mM glutathione and subsequently incubated with PreScission protease ( GE Healthcare ) for 3 h at 4°C . Following cleavage , the reaction mixture was dialyzed three times against buffer C ( 50 mM Tris-HCl [pH 7 . 5] , 150 mM NaCl , 1 mM EDTA , 10% glycerol , and 1 mM DTT ) . To remove GST and PreScission protease , the dialyzed fraction was incubated with glutathione sepharose 4B . The flow-through containing Plk1 was frozen in liquid nitrogen and stored at −80°C . Amount , purity , and activity of Plk1 T210D and Plk1 K82M/T210D were determined by SDS-PAGE , Coomassie brilliant blue staining , and in vitro kinase assays using casein as a model substrate ( unpublished data ) . For labeled kinase reactions , GST and a series of HsCyk-4 fragments fused to the C terminus of GST were expressed in E . coli and purified using glutathione sepharose 4B ( GE Healthcare ) . Immobilized GST or GST-HsCyk-4 protein was incubated with full-length recombinant Plk1 T210D or Plk1 K82M/T210D in kinase buffer with 50 µM ATP and 1 µCi [γ-32P] ATP at 30°C for 30 min . Beads were washed in PBS containing 1% Triton X-100 and boiled in SDS sample buffer . Samples were resolved by SDS-PAGE , visualized by Coomassie brilliant blue staining , and finally analyzed using a phosphor imager ( typhoon , GE Healthcare ) . S . cerevisiae strain PJ69-4A ( generously provided by K . Gould , Vanderbilt ) was cotransformed by lithium acetate method with bait ( pGBT9 , Trp+ ) and prey ( pGAD424 , Leu+ ) plasmids according to standard techniques [45] . Leu+/Trp+ transformants were scored for positive interactions by serial dilution on synthetic dextrose medium lacking adenine and histidine . | The plane of cell division in animal cells is determined by the position of the mitotic spindle during early anaphase , but the molecular signaling that leads to proper formation of the division plane is not fully understood . The actin- and myosin-rich contractile ring , which physically divides a cell in two , localizes to the presumptive division plane through the local activation of a molecular switch protein , RhoA . RhoA is activated by Ect2 , which binds to the protein complex centralspindlin found on microtubules in the vicinity of the division plane ( the midzone microtubules ) . One critical component of centralspindlin is Cyk-4 , a putative negative regulator of RhoA . Here , we have analyzed the mechanisms that are responsible for targeting the RhoA activator Ect2 to the midzone microtubules . We show that Polo-like kinase 1 ( Plk1 ) , in part through the microtubule-associated protein Prc1 , phosphorylates Cyk-4 . Ect2 binds to phosphorylated Cyk-4 and is then able to activate RhoA and induce proper formation of the contractile ring . Our study therefore has elucidated important details of the signaling cascade in animal cells that ensures proper division-plane formation . | [
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"biology/cytoskeleton"
] | 2009 | Polo-Like Kinase 1 Directs Assembly of the HsCyk-4 RhoGAP/Ect2 RhoGEF Complex to Initiate Cleavage Furrow Formation |
Bacterial genes that confer crucial phenotypes , such as antibiotic resistance , can spread horizontally by residing on mobile genetic elements ( MGEs ) . Although many mobile genes provide strong benefits to their hosts , the fitness consequences of the process of transfer itself are less clear . In previous studies , transfer has been interpreted as a parasitic trait of the MGEs because of its costs to the host but also as a trait benefiting host populations through the sharing of a common gene pool . Here , we show that costly donation is an altruistic act when it spreads beneficial MGEs favoured when it increases the inclusive fitness of donor ability alleles . We show mathematically that donor ability can be selected when relatedness at the locus modulating transfer is sufficiently high between donor and recipients , ensuring high frequency of transfer between cells sharing donor alleles . We further experimentally demonstrate that either population structure or discrimination in transfer can increase relatedness to a level selecting for chromosomal transfer alleles . Both mechanisms are likely to occur in natural environments . The simple process of strong dilution can create sufficient population structure to select for donor ability . Another mechanism observed in natural isolates , discrimination in transfer , can emerge through coselection of transfer and discrimination alleles . Our work shows that horizontal gene transfer in bacteria can be promoted by bacterial hosts themselves and not only by MGEs . In the longer term , the success of cells bearing beneficial MGEs combined with biased transfer leads to an association between high donor ability , discrimination , and mobile beneficial genes . However , in conditions that do not select for altruism , host bacteria promoting transfer are outcompeted by hosts with lower transfer rate , an aspect that could be relevant in the fight against the spread of antibiotic resistance .
MGEs such as plasmids or phages are defined by their ability to undergo horizontal gene transfer ( HGT ) between bacterial hosts [1] , and are widespread in nature . Genes present on MGEs often affect their hosts’ fitness in a specific environment [2] . Particularly , many mobile genes increase virulence or antibiotic resistance and thus have harmful consequences on human health . Antibiotic resistance genes are enriched on plasmids [3] , leading to their fast spread among bacterial species via horizontal transfer [4] . Genes coding for secreted proteins , often involved in virulence , are also enriched on MGEs promoting cooperative secretion [5 , 6] . In order to better combat the medical issues arising from horizontal transfer , we must understand the selective pressures acting on gene mobility . The population dynamics and evolution of transfer have mostly been studied by focusing on MGEs themselves [7]; however , transfer is influenced not only by MGE genes , but also by genes of the bacterial host chromosome . Both donor [8] and recipient cells [9 , 10] can regulate transfer , with different donor and recipient genetic backgrounds resulting in as much as eight orders of magnitude variance in the transfer rates for the same plasmid [11 , 12] . Thus , to fully understand the evolution of horizontal gene spread and the natural variation in transfer rates among hosts , we must consider the selective pressures acting on hosts . On one side , horizontal transfer confers varied and often extreme costs onto the bacterial host . Phage mobility usually requires host cell lysis that leads to death , while plasmid transfer through conjugation renders host cells sensitive to male-specific phages [13] and decreases the host's growth rate and fitness [14 , 15] . Because of these costs , horizontal transfer has classically been considered as a selfish trait of parasitic MGEs , selected as it favours their spread [7] . Direct support for transfer being a purely costly trait to the host came from studies of plasmid–host coevolution , where host genes that decrease transfer were selected [16] . On the other side , it has also been suggested that HGT could benefit the host because of the transfer of accessory genes not directly involved in MGE maintenance and transfer . Indeed , MGEs are often thought to constitute a communal pool of genes [2] , a flexible genome [17] that can be quickly shuffled by HGT in response to environmental changes , making host populations more robust [18] . In this view , HGT is beneficial to the host population because it allows cells to share beneficial traits and provides diversity at the population level . However , it is not clear that these proposed benefits are sufficient for HGT to be favoured by the host . Traits advantageous at the group level—here the maintenance of a communal pool of genes—are not necessarily selected for at the individual level , especially when individuals can benefit from others that invest in the trait while not paying the cost of investing themselves [19] . Indirect , population-wide benefits alone are not necessarily sufficient to explain the selection of host genes promoting costly transfer [20] . The ability to receive genes can clearly be directly selected for when these genes enhance individual fitness: for instance , CRISPR immunity against antibiotic resistance plasmids , a form of HGT resistance , was rapidly lost in the presence of antibiotics when receiving plasmids was beneficial to the host [21] . On the contrary , the ability to donate genes need not be selected , as the donor cell does not directly benefit from transferring genes to neighbouring recipients . To quantitatively understand HGT , the selection acting on donor ability must be analysed in a social context , taking into account both the costs and benefits transfer bestows onto donor and recipient hosts . Here , we theoretically and experimentally analyse the evolution of host genes controlling plasmid transfer . We show that from the host side , transfer represents a form of altruism: actors pay a cost of investing in transmission and deliver a benefit to recipients of beneficial mobile elements . Altruistic donation of MGEs can be maintained when transfer is sufficiently biased towards cells sharing donation alleles , increasing the donor allele inclusive fitness . This bias can arise in structured populations or by an association between transfer and discrimination alleles . Fitness gains due to the transfer of mutualistic plasmids further select for genotypes where donor ability alleles , discrimination alleles , and mutualistic plasmids are associated .
We first perform a qualitative analysis to identify if and in which conditions a strain with high donor ability can be selected . We model the fitness of nonmobile host genes controlling donor ability for a given plasmid using a neighbour-modulated fitness approach that partitions fitness into the effects of an individual’s own genotype and those of social neighbours [22 , 23] . We consider a population of bacteria structured in an infinite number of patches [24] and model a simplified life cycle with nonoverlapping patch generations , in which the following processes occur successively [25 , 26]: founding , reproduction , transfer , selection , and dispersal ( see S1 Text for details ) . A cell i in patch j is characterized by three traits: plasmid carriage pij ( pij = 1 for plasmid-bearing cells and 0 for plasmid-free cells ) , donor ability qij and recipient ability sij . Successful transfer is controlled by three factors: the probability of contact between plasmid-bearing and plasmid-free cells , the donor ability of plasmid-bearing cells , and the recipient ability of plasmid-free cells . We assume that plasmid and host traits are distributed independently in the starting population so that the cell's donor ability qij is independent from its initial plasmid content pij . Initially uninfected cells become infected with a probability proportional to the patch level frequency of plasmid-bearing cells modulated by their own recipient ability and by the average patch donor ability . A cell i in patch j will thus be modified by transfer with the probability ( 1 − pij ) pj qjsij . Plasmid presence has an effect ep on the host cell , and we can express the plasmid effect on host fitness as ep pij . The cost of donor ability is cq leading to an effect of transfer on host fitness that is proportional to donor ability , experienced only by cells bearing plasmids before transfer , and equal to −pij cq qij . Donor ability is costly independently of actual transfer efficiency , modelling the effect of expressing the transfer machinery ( which happens even in the absence of successful transfer ) . The fitness of an individual founding cell i in patch j , measured over the patch life cycle , is noted by Wij . With W0 being the basal host fitness , we obtain ( see S1 Text ) : Wij=W0+ep[pij+ ( 1−pij ) pjqjsij]−pijcqqij ( 1 ) To understand selection acting on donor ability q , we apply the Price equation [27 , 28] to Eq ( 1 ) . We obtain the regression coefficient between fitness and donor ability , β ( Wij , qij ) , that describes the effect of donor ability on fitness ( Eq 2 ) . We provide the derivation of Eq ( 2 ) and a detailed analysis in S1 Text . The Ej[pj ( 1 − pj ) ] term describes the effect of patch composition on the efficiency of plasmid transfer: transfer events are more likely when both plasmid-bearing and plasmid-free cells are abundant within each patch . β ( qjsij , qij ) is a regression coefficient between individual donor ability qij and the product of individual recipient ability with patch-level donor ability . It corresponds to the relatedness between plasmid donor and recipient cells , noted by Rq , at the locus determining donor ability ( see S1 Text for a detailed analysis ) : Rq is higher when donor cells preferentially encounter recipients that share their donation allele and when transfer is more successful towards those cells . Rq thus determines how much a donor cell transfers plasmids to individuals bearing the same donation allele because of population structure and specificity in transfer . Finally , p cq is the average cost of transfer for the donor genotype: high donor ability is costly to the proportion of cells that bear plasmids and express their transfer machinery . An increase in donor ability is selected for when it is correlated with increase in fitness , namely when β ( Wij , qij ) > 0 , which combined with Eq ( 2 ) leads to the following condition: epEj[pj ( 1−pj ) ]Rq>pcq ( 3 ) Eq 3 is a form of Hamilton’s rule [29] , which postulates that a cooperative allele is selected for when its indirect benefits , weighted by relatedness among actors and recipients , outweigh its direct cost , maximizing its inclusive fitness ( fitness inclusive of alleles present in other individuals ) . Applied here to donor ability , the indirect benefits are the benefits of plasmids to the recipient cells after transfer ep Ej[pj ( 1 − pj ) ] , and the direct cost is the cost of donor ability for cells bearing plasmids p cq . Rq is the relatedness at the donor ability locus among donor and recipient cells of plasmid transfer . High , positive Rq implies that most of transfer events from cells with high donor ability will be directed towards recipients sharing their donation allele . On the contrary , negative Rq means that transfer will be biased towards cells with a different allele than the one carried by the donor . Thus , a high donor ability allele can be selected even when individually costly , when transfer maximizes its inclusive fitness through plasmid effects on recipient cells . We note that relatedness in bacteria can vary across loci [30] , as it can be modified in a locus-specific way by mutation [31] or HGT [5 , 6] . Thus , unlike relatedness arising from genealogical kinship in sexually reproducing organisms , it will not necessarily tend to be the same across the genome . To underline this and avoid any potential semantic confusion , we follow nomenclature defined already in [30] and consider that cells that specifically share alleles at the locus of interest ( plasmid donation ) are cells of the same kind but not necessarily kin . Rq will be positive when donors preferentially transfer plasmids to recipients of their kind . Positive relatedness generally arises through the combination of two processes: limited dispersal and discrimination mechanisms [29 , 32] . Here , limited dispersal is due to patch structure: the correlation between qj and qij is governed by the initial repartition of genotypes among patches , with no migration before transfer occurs . Positive relatedness can arise from strong population bottlenecks leading to stochastic variations in founding cell frequencies among patches , followed by clonal reproduction [33] . Effective discrimination in transfer also leading to positive relatedness arises if sij and qij are positively correlated , with genotypes with high donor ability having higher recipient ability than average , or if donors have a way to direct transfer specifically to their kind ( see S1 Text for discussion ) . Alternatively , negative relatedness can arise if sij and qij are negatively correlated , leading to preferential transfer to cells bearing a different donation allele . We can distinguish two scenarios for the effect and selection of transfer depending on the plasmid effects on the host cell . In the first case , the transferred plasmid is mutualistic with its host ( ep > 0 ) , for instance conferring antibiotic resistance: transfer is therefore an altruistic behaviour [29] with a direct cost of performing transfer and indirect benefits through the plasmid benefits in recipient cells . Transfer is selected if Rq is positive and sufficiently high: Rq > p cq / ( ep Ej[pj ( 1 − pj ) ] ) . In the second case , the transferred plasmid is parasitic ( ep < 0 ) : donor ability for parasitic plasmids , decreasing the fitness of recipient cells , is selected if Rq is negative and sufficiently low: Rq < p cq / ( ep Ej[pj ( 1 − pj ) ] ) . This would be a case of spiteful behaviour [32 , 34] . Specific population structure or discrimination processes are required to produce negative relatedness , and spite is thus thought to be less common than altruism [34] . We focus here on the transfer of mutualistic MGEs and more specifically on antibiotic resistance plasmids that allow their hosts to grow when antibiotics are present . The main prediction arising from our model is that donor ability for these mutualistic plasmids is an altruistic trait , counterselected if transfer occurs indiscriminately towards any cell , but selected for when plasmid donors and recipients share donation alleles . We present the model graphically in Fig 1 , focusing on the three relevant scenarios affecting relatedness: random interactions between individuals ( Fig 1A ) , discrimination in transfer ( Fig 1B ) , and structured populations ( Fig 1C ) . We next test the model’s predictions with both simulations and experiments , performing competition assays between strains with varying donor ability in order to investigate quantitatively if and how much selection favours donor ability in biologically realistic settings . Discrimination in transfer occurs if during the encounters between a donor and potential recipients the plasmids are transferred to cells of the donor’s kind more often than would be expected based on its frequency in the population . Discrimination of plasmid recipients could be based on differences in the initial recognition between cells or differences in plasmid establishment in recipient cells . To search for evidence of discrimination , we analyse two available datasets [11 , 12] that quantify plasmid conjugation rates among different pairs of natural isolates . Both studies measured conjugation rates for the multiresistant R1 plasmid , among 10 strains from the ECOR collection [11] or 9 other natural Escherichia coli strains [12] . In each dataset , we compute normalized donor ability for each pair of donor and recipient strains ( see Materials and Methods ) , which corrects for basal differences in donor ability between strains . We find that transfer to self occurs at rates higher than average in 18 out of 19 cases ( Fig 2A ) . Additionally , in 8 out of 19 cases , the highest rate of transfer is from a strain to itself . Overall , transfer to self is 7 . 3 times higher than average donor ability over all tested isolates ( two tailed t test for difference from 0 for normalized donor ability to self , p = 0 . 0003 ) . In a mixed population with many different strains , the high rates of transfer to self we describe here would translate into a biased transfer between cells sharing donation alleles . This apparent discrimination does not imply that the same locus is responsible for high donor ability and for discrimination , as multiple genes could be involved in discrimination . However , the signal we observe in Fig 2A suggests that alleles for high donor ability and for discrimination in transfer are linked in natural isolates sufficiently to lead to an effective discrimination at the donor ability locus . We next experimentally investigate if discrimination may allow for the selection of host transfer genes . We use two widely studied E . coli strains , the K12 strain MG1655 [35] and the B strain REL606 [36] , and the multiresistant R1-19 plasmid [37] . K12 and B strains bear different restriction-modification systems [9] , potentially leading to discrimination in plasmid transfer [38 , 39] . We first measure the conjugation rate in a well-mixed environment between all four combinations of K12 and B as donor and recipient strains ( Fig 2B , red bars ) . We find that K12 is generally a better donor , but also that K12 transfers R1-19 plasmid to itself at a 5-times higher rate than to B ( Mann-Whitney Wilcoxon test , p = 0 . 003 ) . Overall , the K12 strain is an example of good donor strain displaying discrimination for transfer , in comparison to the lower donor B strain . Moreover , R1-19 carriage leads to a 54% reduction in exponential growth rate for K12 strain , compared to a 1 . 6% reduction only for B strain ( S1 Fig ) . To test if part of the costly effect R1-19 has on K12 is due to donor ability , we use a K12-derived strain with a deletion in the arcA gene , a gene known to affect transfer [40] , and the repressed R1 plasmid , which transfers approximately 1 , 000-fold less than R1-19 [37 , 15] . The K12ΔarcA mutant transfers R1-19 plasmid at a strongly reduced rate to both itself and B ( Fig 2B , grey bars ) , and R1-19 cost is reduced as well ( 6 . 4% , S1 Fig ) . Similarly , R1 plasmid imposes almost no cost to K12 growth ( 1 . 8% , S1 Fig ) . Both results suggest that most of R1-19 cost to K12 is due to its high transfer rate . We then test whether K12 discrimination in transfer can lead to biased transfer towards other K12 cells in a well-mixed population and subsequent selection of the better donor strain . We compete the K12 and B strains by mixing them equally in a well-mixed population , with a common proportion of cells from each strain initially bearing R1-19 plasmid . In the absence of antibiotic selection , the better donor K12 decreases in frequency ( Fig 2C , dark blue ) , showing a lower basal fitness than B in those culture conditions . When antibiotic selection is applied at the end of the competition by plating the population on kanamycin ( Kn ) -containing medium , only Kn-resistant , plasmid-bearing cells grow . When all cells initially bear plasmids , selection does not favour the K12 strain ( Fig 2C orange , 19% decrease in K12 frequency , two sided t test for difference from 0 , p = 0 . 003 ) . However , when only a fraction ( 2 . 5% ) of both K12 and B cells initially bear R1-19 plasmid , providing opportunity for plasmid transfer , K12 is selected ( 19% increase in K12 frequency , two sided t test for difference from 0 , p = 0 . 009 ) . Finally , to confirm that this specific selection of donors is due to R1-19 transfer to K12 cells , we analyse the outcome of competition when the arcA gene is deleted from K12 and transfer is impaired . In the absence of antibiotic selection ( Fig 2C , light blue ) , the arcA deletion does not affect K12 fitness when plasmids are absent or rare and increases K12 fitness when all cells bear plasmids ( 11% decrease for K12ΔarcA versus 25% decrease for K12 , two-sided t test , p = 0 . 043 ) , possibly because of the reduced plasmid cost for K12ΔarcA . With antibiotic selection , the specific selection of K12 when a fraction of cells bear plasmids disappears for K12ΔarcA ( Fig 2C middle , yellow bars , two-sided t test , p = 2 . 10−5 ) , demonstrating that K12 selection was due to plasmid transfer . Discrimination effectively biases antibiotic resistance transfer strongly enough so that the better donor K12 strain is selected for in the presence of antibiotics . Thus , when transferred plasmids are needed for growth , discrimination in transfer towards kind , at naturally appearing levels , can be sufficient to select for the better donor . A second possible reason for transfer bias is bacterial growth in structured populations , where donors interact preferentially with their kind . Next , we examine whether , in the absence of discrimination , structured populations can provide a sufficient bias in transfer to select for good donors . To analyse the effect of biased transfer in structured populations , we use a synthetic system with fluorescently tagged plasmids in which we can identify plasmid transfer between two strains . We adapted the system from the one we designed for an earlier study on interaction between conjugation and cooperation [6] . A helper plasmid FHR , that is nonmobile and thus behaves like a chromosomal allele , governs the host cell donor ability for a mobile plasmid C , which confers chloramphenicol ( Cm ) resistance . We compete ( Fig 3A ) two strains differing in their donor ability: the good donor D+ strain bears FHR that transfers C plasmids , and the nondonor D− strain does not ( S2A Fig ) . After a transfer phase ( t0 to t1 ) , populations are grown with or without Cm during the selection phase ( t1 to t2 ) . We compare a single , well-mixed population ( m ) , where D+ and D− are mixed in equal proportions , to a structured metapopulation ( s ) , consisting of two subpopulations that grow separately during the transfer phase , s1 and s2 , founded respectively with a 10% and 90% proportion of D+ ( leading to equal proportions of D+ and D− at the metapopulation level ) . In this setup , the changes in the good donor frequency can be followed both within and among populations to evaluate the effect of population structure on donor selection . D+ strain frequency does not change significantly in m or s populations during the transfer phase ( Fig 3B left ) . D+ frequency then increases at t2 only for the structured s population grown in the presence of Cm ( Fig 3B right , 26% increase from t0 , Mann-Whitney Wilcoxon test , p = 0 . 004 ) . It decreases for m population with Cm ( 19% decrease , two-sided t test for difference from 0 , p = 3 . 10−9 ) and stays constant in the absence of Cm . The dynamics generally follows our predictions: D+ selection requires both population structure and plasmid selection . However , the expected cost of D+ during the transfer phase is not present at the population level . We next investigate in more detail both this cost and the selection of the good donor strain . By looking at the dynamics of individual subpopulations during the transfer phase , we observed that D+ increases in frequency when prevalent ( S3A Fig ) . We confirmed with an independent experiment that D+ fitness in competition with D− linearly increases with D+ frequency ( S3B Fig ) . This positive frequency-dependence for donor fitness could be due to lethal zygosis , a phenomenon known to damage recipients at high donor cell frequencies [41] , which could be aggravated by the absence of entry exclusion in our strains [42] . In natural systems , entry exclusion may protect new transconjugants but would also make initial plasmid-bearing donors immune to lethal zygosis , probably leading to a similar frequency-dependence of fitness when most donor cells initially bear plasmids . In our system , frequency-dependence leads to no observable cost for D+ at the metapopulation level . At low frequencies , donor ability still has a cost , which is also observed as a decrease in the strain’s growth rate when growing in isolation ( D+ versus D− , S2C Fig ) . Interestingly , donor cells grow significantly more slowly when they bear C plasmids , which is not the case for nondonor cells ( D+C versus D−C , S2C Fig ) , suggesting that donor ability cost is enhanced by the presence of transferable plasmids in the cell . During the selection phase , good donors are selected only in the structured s population and only in the presence of Cm , meaning that donor selection requires both population structure during the transfer phase and subsequent antibiotic selection . We see that , as predicted by our model , biased transfer due to population structure promotes indirect selection of the donor strain . To better understand the factors affecting D+ selection , we proceed to analyse the dynamics of C plasmids ( Fig 3C ) . During the transfer phase , plasmid frequency changes depend on the proportion of cells able to transfer . In the s1 population where D+ cells are few , plasmid frequency declines slightly . It increases mostly in the s2 population enriched in D+ strain . Increases are due to transfer , as the increase in plasmids present in D− strain is due to plasmids that originate from D+ ( as identified by fluorescence markers , see Materials and Methods and S4 Fig ) . We then follow the proportion of C plasmids that are present in D+ cells , as plasmid localization controls survival in the presence of antibiotics . During the transfer phase , the proportion of C plasmids present in D+ cells compared to D− cells decreases in the well-mixed m population ( 13% decrease , Mann-Whitney Wilcoxon test , p = 0 . 004 ) but increases in the structured s population ( 28% increase , Mann-Whitney Wilcoxon test , p = 0 . 004 ) ( Fig 3D ) . In the well-mixed population , the decrease is probably due to the strong fitness cost D+ cells incur specifically when they bear C plasmids ( S2 Fig ) . The same cost also explains the subsequent decrease in D+ strain frequency under Cm selection in both populations . The enrichment of C plasmids in D+ cells depends on the population structure of the s population: total plasmid transfer is more prevalent in the s2 subpopulation , effectively biasing transfer towards D+ at the metapopulation level . These results experimentally confirm our models prediction: in the absence of discrimination mechanisms , donor ability for antibiotic resistance plasmids can be selected when population structure ensures preferential transfer to cells sharing donation alleles . So far , we have shown that both discrimination and population structuring can select for donor ability . However , we have always assumed and ensured that discrimination and population structure are present in the system . Here , we study how both phenomena can themselves emerge . When analysing effects of population structure , we imposed starting proportions of both the strains and the plasmids in order to dissect the dynamics of transfer . We continue by studying the outcome of competitions arising in more natural population structures using simulations . In the absence of a mechanism for discrimination of recipients , our model suggests that population structure may influence the selection of donor ability in two opposing ways ( Eq 3 ) . Efficient transfer is favoured by the coexistence of plasmid-bearing and plasmid-free cells within patches , while biased transfer towards kind is favoured by high relatedness at the donation locus . The selection of transfer thus requires preferential interactions between cells sharing donor ability alleles but also sufficient cell mixing that would favour the encounter between plasmid-bearing and plasmid-free cells . To study the effects of a natural population structure and the possibility that both conditions are met simultaneously , we simulate strong population dilution , which leads to stochastic founder cell numbers and genotype frequencies [6 , 33] . We follow the frequency of the donor D+ strain in a simulated metapopulation initiated from a strongly diluted mix of equal proportions of D+ and D− . With growth parameters based on our experimental results , we vary both the dilution factor applied to founding populations and the proportion of plasmid-bearing cells ( Fig 4 ) . The results exhibit a clear pattern: the donor strain is selected under the combination of strong initial dilution and low initial plasmid frequency . D+ selection in the presence of antibiotics is controlled primarily by the enrichment of plasmids in D+ at t1 ( S5A Fig ) , which occurs when there is biased transfer towards D+ during the transfer phase . As predicted by Eq 3 , biased transfer requires high relatedness at the donation locus but also effective transfer , which will be affected by dilution and initial plasmid prevalence . First , diluting down to low founding cell numbers provides sufficient variation in D+ frequencies among populations to ensure high relatedness ( S5B Fig ) . Second , the contribution of transfer to plasmid abundance declines with increasing dilution and increasing plasmid initial abundance ( S5C Fig ) . Transfer promotes plasmid invasion primarily when plasmid-bearing cells are initially scarce , because in those conditions , the majority of plasmid-bearing cells actually arise from transfer . Increasing dilution leads to the frequent absence of one of the cell types from each population , which in turn decreases the number of possible encounters between plasmid-bearing and plasmid-free cells . Selection for donor ability is strongest at high dilution because of high relatedness; however , dilution simultaneously limits transfer , which decreases the strength of selection compared to the optimal population structure studied in Fig 3 . Overall , our simulations suggest that the conditions for selection of donor ability can be met in natural environments through limited dispersal alone , despite the trade-off between relatedness and transfer efficiency that arises from population structure . We now focus on the association between discrimination mechanisms and plasmid transfer , which was apparent in natural isolates data ( Fig 2A ) and ask how this association itself could emerge . The effect on donor ability ( Fig 2B and Fig 2C ) suggests that a genotype bearing both discrimination and high donor ability alleles could be favoured by selection . We consider as an example the case of a strain inactivated at a chromosomal restriction-modification locus: such a mutant appearing in a wild type population will transfer plasmids preferentially to clonemates bearing the same allele , as unmodified plasmids transferred from this mutant will be degraded in a wild-type recipient cell [38 , 39] . The mutant allele with no modification is denoted by M− , and the wild type allele by M+ , D+ , and D− stand for high and low donor ability , as before . We use simulations to follow the dynamics of modification and donation alleles , in populations of cells with no initial association between M− and D+ . In a well-mixed population ( analogous to the one studied in Fig 2C ) , the M− allele is selected for , but the D+ is not ( Fig 5A , left ) . Discrimination in transfer by M− cells leads to a reduced total transfer to M+ cells , and direct selection of M− with comparatively higher recipient ability . D+ cells are outcompeted , as they do not receive more plasmids than D− cells in the absence of association to M− alleles . On the contrary , in a population where D+ cell frequency differs sufficiently among subpopulations , both M− and D+ alleles are selected for ( Fig 5A , right ) : similarly to the dynamics presented in Fig 3 , population structure biases transfer towards D+ cells , allowing for D+ selection in the presence of antibiotics . Linkage between M− and D+ alleles is also controlled by population structure ( Fig 5B ) : with antibiotic selection , positive linkage appears when D+ cell frequency varies among subpopulations . This does not occur in the absence of antibiotics , suggesting that linkage is due to the specific selection of plasmid-bearing cells . With increasing D+ population structure , most plasmids end up in D+ M− cells ( Fig 5C ) due to the combined effect of higher recipient ability and biased transfer by D+ cells . We conclude that the association between discrimination and transfer alleles can emerge simply through selection of plasmid-bearing cells , when population structure ensures that cells with high donor ability are favoured . Selective pressures acting on donor ability depend on the fitness effects of genes carried by the transmitted plasmids . Here , we focus on mutualistic antibiotic resistance plasmids , but parasitic and mutualistic plasmids can coexist in host populations , and hosts may not be able to evolve differential control of transfer based only on the accessory genes plasmids carry . Next , we consider the presence of parasitic plasmids N , which are similar to mutualistic plasmids C but do not confer benefits during the selection phase ( see S2B Fig and Materials and Methods ) . In simulations , increase in the initial proportion of parasitic plasmids lead to a decrease in final frequency of the good donor strain ( S6 Fig ) . We can conclude that the strength of selection for donor ability decreases when donors encounter a mix of mutualistic and parasitic plasmids , as they cannot distinguish between the two plasmid types . However , the benefits conferred by mutualistic plasmids could indirectly favour their association to the host . To study the association between plasmids and hosts , we measure experimentally and with simulations the linkage disequilibrium between plasmids and the good donor allele in a metapopulation similar to the previous one ( Fig 3 ) but now with a mix of C and N plasmids in equal proportions instead of only C plasmids , and with no initial association between plasmids and a specific strain ( each plasmid is equally present in D+ and D- strains ) . After the transfer phase , both C and N plasmids become significantly linked to the good donor D+ strain in the structured population , but not in the well-mixed population ( Fig 6A , left ) . Moreover , after Cm selection , linkage to D+ slightly increases for C plasmids but decreases to zero for N plasmids in the structured population and remains at zero in the well-mixed population ( Fig 6A , right , plain lines ) . Finally , this pattern relies on the specific selection of C-bearing cells: when selecting with Kn ( an antibiotic to which both plasmids confer resistance ) instead of Cm , linkage decreases to zero for both plasmids ( dashed lines ) . Our experiments show that linkage between plasmids and good donor cells arises only when two specific conditions are met: ( 1 ) the population is structured , and ( 2 ) plasmids are beneficial . To better understand the factors controlling the association , we independently vary D+ donor ability and C plasmid beneficial effect on the host strain fitness in our simulations . The linkage that appears at t1 between both plasmids and D+ strain increases with D+ donor ability , independently of subsequent plasmid benefits ( S7A Fig ) . This linkage arises because transfer is biased towards D+ cells at the metapopulation level ( as seen in Fig 3 ) . In our experiments , the observed linkage is stronger for N plasmids because they are preferentially transferred . At t2 , however , C plasmid benefits modify the linkage patterns . With increasing C benefits , C linkage to D+ increases ( Fig 6B ) , and N linkage to D+ decreases ( S7B Fig ) , when donor ability is sufficiently high . The specific association between C plasmids and the donor strain thus arises from the benefits provided by C plasmids to the host: antibiotics promote the selection of cells bearing C plasmids , which are mainly good donor cells because of previous transfer . Overall , this mechanism selects for cells simultaneously bearing both D+ and C alleles . Linkage between the good donor strain and beneficial plasmids arises without directly enforcing any association between the two , due to the combination of two effects: population structure biasing transfer towards good donor cells and the plasmids benefiting the host .
Our work investigates the evolution of host genes controlling the transfer of mutualistic MGEs such as those conferring antibiotic resistance . We focus on genes modulating plasmid donation , a property that , unlike plasmid reception , does not directly benefit the host . Earlier interpretations have described MGEs as a communal pool of genes conferring benefits at the population level [2 , 18] . We demonstrate here that donor ability , when it is costly to the host , is not selected directly . However , we do not need to invoke population-level benefits to explain why the host may promote MGE transfer . Instead , we show that host donor ability alleles can be selected indirectly when transfer increases their inclusive fitness ( Fig 1 ) . We then investigate further this qualitative result by measuring selection direction and strength in simulations as well as experiments using both natural and synthetic microbes , in situations close to ones that could be observed in nature . In the absence of discrimination , population structure is a simple mechanism ensuring that cells encounter preferentially neighbours of the same kind . Here , we demonstrate that in a synthetic biological system devoid of any mechanism for discrimination in transfer , population structure enables the selection of donor ability , biasing plasmid transfer prior to the selection of plasmid-bearing cells ( Fig 3 ) . Donor ability is not selected within well-mixed populations where donors do not interact preferentially with their kind , and good donors decline in frequency due to donor ability costs . However , donor ability is selected at a metapopulation scale , where population structure provides sufficient relatedness at the locus controlling donor ability . With simulations , we then show that populations with sufficient relatedness can arise simply through strong population dilution , despite the reduction in transfer due to fewer interactions between plasmid-bearing and plasmid-free cells ( Fig 4 ) . Further , we experimentally demonstrate that differences in transfer rates between isolates , leading to effective discrimination in transfer , can also be sufficiently high to favour a strain with high donor ability ( Fig 2 ) . In natural isolates , we observe discrimination for the transfer of an antibiotic resistance plasmid . These results motivate future studies that would quantify the generality of discrimination by examining other plasmids and strains , as well as determine the underlying mechanisms . Discrimination can result from specific recognition during cell–cell contact [30] or even direct spread through the cytoplasm of clonemates in the case of bacterial chains [43] . Alternatively , discrimination can arise during the establishment of plasmids in recipient cells . In particular , plasmid transfer rate is greatly diminished when restriction-modification systems present in recipients differ from those in donor cells [9 , 38 , 39] . At a larger phylogenetic scale , a plasmid host range can be limited by its mechanisms of replication or transfer [44] . Even when plasmids are successfully transferred , they need not confer any fitness benefit , because genes beneficial in the initial donor may be suboptimal in a novel , unfamiliar host [45] , favouring a donor strain over distant competitors in which the transferred accessory genes are not fully beneficial . Finally , discrimination may rely on quorum-sensing mechanisms regulating transfer [46] , which can provide an indication of the local abundance of related cells . Any of these mechanisms could lead to discrimination among transfer recipients , but they may not all be controlled by the same locus as donor ability . Discrimination by plasmid donors towards their kind necessitates genetic linkage between donation and discrimination alleles . The pattern observed for natural isolates ( Fig 2A ) suggests that a sufficient level of association does exist in nature , at least for the R1 plasmid . Moreover , this observation may be explained and maintained by the dynamics we describe in Fig 5 , where linkage between discrimination and donor alleles emerges from their coselection in structured populations . Biased transfer to kind can thus happen in host cells that differ from others at a single locus modulating donor ability in structured populations; the benefits of transfer then promote the emergence of discriminating genotypes through linkage with a second locus determining the specificity of transfer . Population structure plays a central role , allowing both the spread of donor alleles in the absence of discrimination mechanisms and the emergence of discrimination . As the selective pressures we describe here are indirect , they may be too weak to have a significant effect on the evolution of transfer rates . To examine this , we calculate selection coefficients acting on the donor allele in our experiments and simulations . The strength of selection observed for the discriminating strain K12 in competition with B is high ( s = 0 . 35 , S8 Fig ) . As the degree of discrimination displayed by K12 is close to the average one measured across natural isolates ( Fig 2A ) , this result suggests that selection of donor strains that transfer preferentially to their kind may occur widely in nature , even in unstructured populations . In the structured populations we studied , the strength of selection depends on the details of population structure: when relatedness at the donor ability locus is high but plasmids are present in each strain in equal abundance , donors are again efficiently selected for in our experiments ( s = 0 . 10 , S9 Fig ) . When both parameters are controlled purely by initial dilution , they behave in opposing ways and selection is lower ( S10 Fig , s = 0 . 0025 in the optimal case ) . In natural populations , selection arising through population structure might thus be weaker than the one due to discrimination in transfer and vary depending on the details of host and plasmid population dynamics . Still , bacteria are characterized by large population sizes , leading to estimates of effective population sizes around 107 [47 , 48] , which implies that mutations with selection coefficients larger than 10−7 can be selected for [49] . Thus , even in the presence of a trade-off between relatedness and transfer efficiency , selection acting on hosts can result in biologically significant changes in transfer rates . In the long-term , continued selection for transfer requires that plasmids do not spread to fixation . As plasmid transfer itself increases plasmid prevalence in host populations , selection for donors will be progressively decreased with plasmid spread . However , many factors may contribute to maintaining plasmids at intermediate frequencies in bacterial populations . Accessory genes on plasmids are often beneficial in transient or local conditions [50 , 51] and could be repeatedly lost when they are not selected for . Plasmid-free segregants occur regularly and will rapidly invade populations when plasmids are costly . Other factors like the presence of bacteriophages can also lead to unstable dynamics , increasing plasmid loss [52] . Moreover , transfer is strongly regulated as a function of environmental conditions [8] and could be induced specifically in the conditions where plasmid-bearing cells are favoured . A striking case of such a scenario are mobile elements providing tetracycline resistance , whose transfer is induced by subinhibitory concentrations of tetracycline [53]: transfer occurs in conditions where mobile elements are likely to increase host fitness in the near future , as indicated by antibiotic gradients . Regulation of plasmid transfer will also modify the cost of transfer to the host . In our model , we assumed that plasmid-bearing cells experience a constitutive cost proportional to donor ability , leading to a higher cost to donor genotypes when plasmids are abundant . Transfer can be repressed when plasmid-free cells are likely to be rare [46] , leading to the expression of transfer genes only when transfer efficiency is maximised . Finally , on a wider scale , cell migration between populations that experience different selection pressures for plasmid traits strongly increases the potential for horizontal transfer , as the immigration of plasmid-free cells in populations where plasmid traits are beneficial prevents plasmid fixation and allows sustained transfer [54] . Transferring plasmids increases the donor allele inclusive fitness because it enriches cells of the same kind with beneficial alleles . This phenomenon can be compared to the evolution of teaching in animals: teaching of adaptive information can be selected when teachers and pupils are related [55] . The difference between genetic information transfer in bacteria and cultural transmission is that beneficial genes are by default also transmitted vertically ( together with the donor allele ) , making transfer ineffective if they are already prevalent in the population . Thus in order to be selected , horizontal transfer needs to improve transmission to kind compared to vertical transmission . Indeed , horizontal transfer is selected mostly when initially only few cells bear plasmids ( Fig 4 and S5C Fig ) , as in these conditions it allows a more rapid and efficient spread than vertical transfer . Interestingly , the phenomenon of lethal zygosis suggested by the positive frequency-dependence observed in our synthetic system ( S3 Fig ) [41 , 42] could act on the selection of donor ability in a complementary way , by selecting for donor genotypes when plasmids are prevalent . Transfer would then be a spiteful behaviour , in this case , not because of the indirect effects of transferred genes but due to the direct damage to recipient cells . Bacteria frequently encounter parasitic MGE decreasing fitness . Eq 3 suggests that the transfer of parasitic elements could be selected if it can be preferentially directed towards cells of another kind . The spread of parasites has been suggested to be a typical case of spiteful behaviour , since the donors may be immune to the negative effects of their parasites [56] . Bacteriophages are a well-known example , where phage lysogeny ensures that most cells of the initial strain are protected from lysis and phages preferentially lyse the cells of a competing strain , at the same time ensuring phage spread [57] . Similar mechanisms are not yet known for plasmids . However , transfer to unrelated cells is well described in the case of the Ti plasmid of Agrobacterium tumefasciens , where the T-DNA is transferred to plant cells [58] , and specific transfer and gene expression ensure that another species produces resources . Even with no specific targeting , suboptimal effects of transferred genes could render the plasmid harmful , damaging specifically unrelated recipients and effectively leading to spite . We conclude that the inclusive fitness benefits conferred by transferred plasmids can lead to indirect selection for host donor ability . Plasmid transfer rates thus can be shaped not only by their direct effects on plasmid fitness [7] but also by their indirect effects on host fitness . The direction and strength of selection acting on donor ability will depend on the potential for plasmid transfer , its bias towards kind , the fitness effects of plasmids present in the host population , and the costs of transfer . Thus , all these factors might , at least in part , determine both the strikingly large variability of transfer rates observed among bacterial isolates [11 , 12] and the existence of high donor ability strains . Our findings have consequences in the context of the fight against the spread of antibiotic resistance , as the indirect selection of donor strains could promote widespread dissemination of antibiotic resistance . Treatments that decrease the spread of MGEs have already been considered , like male-specific phages that inhibit plasmid transfer but also kill preferentially the cells that actively transfer plasmids [59] . Our work suggests that , the same as for other cooperative behaviours [60] , bacteria resistant to such treatments may evolve , but relatively slowly [61] , which should be taken into account when aiming to diminish horizontal transfer [62] . More generally , our results underline the active role hosts may play in the evolution of transfer rates and the necessity to take bacterial social interactions into account when studying plasmid transfer . Plasmids themselves often bear public good genes involved in host sociality and interaction with neighbouring cells [5 , 20] , and plasmid transfer promotes host public good production by modifying relatedness in structured populations [6] . The indirect benefits of added public good production may in turn further favour the hosts that are investing in transfer . Finally , we show that biased transfer in structured populations combined with selection of plasmid-bearing cells promotes association between hosts with high donor ability , discrimination mechanisms ( Fig 5 ) , and beneficial plasmids ( Fig 6 ) . Donor ability can be selected in the absence of initial linkage with discrimination alleles or mutualistic plasmids , but selection itself creates linkage at the population level . This dynamic will alleviate the cost of parasitic plasmids and lead to a prevalence of donor strains associated with mobile , transiently beneficial plasmids . In the long term , the phenomenon could promote mutualistic coevolution between beneficial plasmids and strains that transmit them at high rates to their kind , in a way analogous to the evolution of mutualism between species . The benefits generated by mutualism can create an association between mutualistic partners [63] , while the association itself favours further mutualism [64 , 65] . Plasmids would be a special case of mutualism , with a complex and important role of horizontal transmission , a mechanism that is generally expected to inhibit mutualism [66] but here actually benefits both partners . Social selection promotes host investment in plasmid transfer , increasing plasmid fitness but simultaneously promoting host association to mutualist plasmids . This will likely lead to complex social selective pressures acting on plasmids themselves and shape the mobile gene pool .
To test for discrimination , the better plasmid donor was the Escherichia coli K12 strain MG1655red , which is MG1655 [35] marked with the td−Cherry gene . The worse donor was the E . coli B strain REL606 [36] . To measure conjugation rates , two spontaneous mutants resistant to rifampicin ( RifR ) for each strain MG1655 and REL606 were used as recipients . The plasmid used was the multiresistant R1-19 plasmid ( that provides resistance to Cm , sulfonamides , ampicillin , Kn , streptomycin , and spectinomycin ) [37] . The K12ΔarcA strain was MG1655red transduced with the Keio collection arcA deletion mutant [67] . To test for transfer selection in structured populations , we used two synthetic strains , D− and D+ , and two associated plasmids , C and N . D− strain is E . coli K12 MG1655 . D+ strain is a derivative of MG1655 marked with the td−Cherry gene and bearing the helper plasmid FHR . FHR is a variant of the F plasmid with low self-transfer and entry exclusion [6] , which provides efficient mobilization of plasmids carrying F oriT sequence . N plasmids bear F oriT and an aph gene providing Kn resistance , while C plasmids additionally carry a cat gene providing Cm resistance . N and C plasmids express either YFP or GFP under control of the strong promoter PR . For selection experiments ( Fig 3 ) , D− strain initially bears C-GFP plasmid , and D+ strain initially bears C-YFP plasmid , in order to identify the origin of C plasmid ( see S2A Fig ) . For linkage experiments ( Fig 6 ) , C-YFP and N-GFP plasmids were used respectively as C and N plasmids ( see S2B Fig ) . FHR and N-GFP plasmids and their construction are described in detail in [6] ( where N-GFP was called the T+P− plasmid ) . N-YFP was constructed by amplification of YFP sequence with primers AGCGACTCGAGGATAAATATCTAACACCGTGCGTGTTGAC and AGCACAAGCTTTTCCCGGGTCATTATTTGTATAG , then ligation of N-GFP plasmid and the PCR product after digestion with XhoI and HindIII . To construct C plasmids , the cat gene was amplified from pKD3 plasmid [68] with primers TACTAAGACGTCAGGAACTTCATTTAAATGGCG and TACTAGCTCGAGAAGAGGTTCCAACTTTCACC . The PCR product was ligated into the corresponding ( GFP or YFP ) N plasmid after digestion with AatII and XhoI . The D+ and MG1655red strains were constructed by integration of the pRNA1-tdCherry gene construction on pNDL32 plasmid obtained from Nathan Lord ( Paulsson laboratory , Harvard Medical School ) . pNDL32 was transformed into MG1655 with selection on 100 μg/mL ampicillin , then streaked twice at 30°C on LB-agar ( Luria-Bertani , BD Difco ) . Colonies were streaked overnight on LB-agar at 42°C , and plasmid loss was confirmed by checking that clones were ampicillin-sensitive . FHR was finally added to D+ strain by conjugation . To construct K12ΔarcA , the Keio collection arcA deletion mutant was used for P1 transduction of MG1655 red , then the kan resistance cassette was removed with pCP20 plasmid [68] . Spontaneous RifR mutants of MG1655red and REL606 were obtained by plating overnight cultures on LB-agar with Rif ( Sigma-Aldrich ) at 100 μg/mL . Experiments were conducted under well-mixed conditions with 5 mL medium in 50 mL tubes ( Sarstedt ) . Exponential growth rates ( S1 Fig and S2C Fig ) were measured in a Tecan Infinite M200 reader on 100 μL cultures with 50 μL mineral oil ( Sigma ) in 96-well plates , after 100-fold dilution from stationary phase cultures . Our simulations mimic the experimental conditions of strain growth and plasmid transfer in the same way as described in our previous work [6] . Plasmid transfer follows a mass-action law: the number of transfer events is proportional to both donor and recipient cell densities in the local population . The probability coefficient is the transfer rate constant γ ( mL . cell-1 . h-1 ) . Strains are characterized by their donor ability q that modulates effective transfer and leads to a proportional cost of donor ability for the donor cell cq . Similarly to our experiments , we model two steps: a transfer phase ( from t0 to t1 ) , then a selection phase , in conditions where the plasmid genes affect growth ( from t1 to t2 ) . The length of the transfer phase is set to 12 h after 100-fold initial dilution from carrying capacity , and growth for the selection phase is allowed for 36 h after a second 100-fold dilution . Equations governing changes in cell densities , presented below , are common to the two steps . Ntot is the total cell density . | In bacteria , genes can move between cells , sometimes with the donor host cell actively involved in the gene transfer mechanisms . This movement of genes is called horizontal gene transfer , and it increases the prevalence of mobile genes in bacterial populations . However , it is not clear if donor host cells benefit from gene spread , or are simply exploited by selfish genes . Here , we show with both modelling and experiments that for the donor host , investing in the transfer of beneficial genes—such as those conferring antibiotic resistance—can be understood as an altruistic behaviour . This behaviour is costly to the donor but beneficial to recipients and can be selected for if a sufficient proportion of recipient cells share the donors’ transfer allele . Preferential transfer from donors towards recipients that share this allele occurs when dispersal is limited or if discrimination mechanisms are present . Our work suggests that both processes are likely to be widespread in nature , promoting horizontal gene spread by host donor cells . As many antimicrobial resistance and virulence genes are mobile , our work further implies that the spread of harmful traits among human pathogens may be modulated by host bacteria in a direction that depends on the bacterial ability to transfer the traits specifically to their kind . | [
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... | 2016 | Indirect Fitness Benefits Enable the Spread of Host Genes Promoting Costly Transfer of Beneficial Plasmids |
Caenorhabditis elegans has traditionally been used as a model for studying nematode biology , but its small size limits the ability for researchers to perform some experiments such as high-throughput tissue-specific gene expression studies . However , the dissection of individual tissues is possible in the parasitic nematode Ascaris suum due to its relatively large size . Here , we take advantage of the recent genome sequencing of Ascaris suum and the ability to physically dissect its separate tissues to produce a wide-scale tissue-specific nematode RNA-seq datasets , including data on three non-reproductive tissues ( head , pharynx , and intestine ) in both male and female worms , as well as four reproductive tissues ( testis , seminal vesicle , ovary , and uterus ) . We obtained fundamental information about the biology of diverse cell types and potential interactions among tissues within this multicellular organism . Overexpression and functional enrichment analyses identified many putative biological functions enriched in each tissue studied , including functions which have not been previously studied in detail in nematodes . Putative tissue-specific transcriptional factors and corresponding binding motifs that regulate expression in each tissue were identified , including the intestine-enriched ELT-2 motif/transcription factor previously described in nematode intestines . Constitutively expressed and novel genes were also characterized , with the largest number of novel genes found to be overexpressed in the testis . Finally , a putative acetylcholine-mediated transcriptional network connecting biological activity in the head to the male reproductive system is described using co-expression networks , along with a similar ecdysone-mediated system in the female . The expression profiles , co-expression networks and co-expression regulation of the 10 tissues studied and the tissue-specific analysis presented here are a valuable resource for studying tissue-specific biological functions in nematodes .
Gene expression profiling is fundamental to understanding organismal biology , development and underlying functions at a specific time or under specific conditions . Tissue-specific gene expression provides fundamental information about the biology of diverse cell types within an organism and interactions among tissues within multicellular organisms . Molecular knowledge based on stage- and/or tissue-specific gene expression profiles in model organisms is explored to understand many aspects of complex diseases , and in parasitic helminths is explored to identify the properties/functions of tissues that may serve as targets for treatments and control measures . However , such studies ( especially high-throughput tissue-specific gene expression studies ) are experimentally challenging in smaller organisms , such as many nematodes species [1] . The phylum Nematoda is composed of the most abundant and diverse species of all animal phyla , with an estimated million species that are found in almost every environment including extremes such as hot springs and polar ice [2] . Members of this phylum are free-living or parasitic , and include one of the most well-studied model organisms , Caenorhabditis elegans . Of the ∼28 , 000 described nematode species , ∼16 , 000 are parasitic [3] . Infections by parasitic nematodes cause extensive suffering in humans , animals , and plants , as well as major losses in agricultural production due to disease and the cost of implementing control programs [4] . Calculations of the aggregate burden of human nematode diseases in Disability Adjusted Life Years ( DALYs ) indicate a tremendous global impact of these pathogens [5] . Research progress on anthelmintic discovery and immunological control of parasitic nematode infections has been impeded by the biological complexity of nematodes and their interactions with the host . Extensive and high-quality genomic databases are available for C . elegans [6] and are also emerging for parasitic nematodes [7] , providing a welcome infusion of information that opens valuable new avenues for progress in nematode research . This information has been used to produce stage-specific high-throughput gene expression experiments for nematode species using conventional expressed sequence tags ( ESTs ) , next-generation sequencing ( 454/Roche , Illumina ) or microarrays ( e . g . [8] , [9] , [10] , [11] ) , providing many novel insights into nematode biology . The elucidation of gene repertoires expressed by specific tissues of nematodes can further facilitate the development of broader and deeper insights into individual tissue functions , with applications to parasitic and non-parasitic nematodes alike . At the comparative level , information of this kind will aid in understanding of both conserved and divergent aspects of nematode biology , while also enhancing the value of model organisms , such as C . elegans , in biomedical research . However , due to the small size of nematodes , it is not possible to accurately dissect enough tissue in most of these species to run high-throughput gene expression , proteomics or cellular biochemical experiments at the individual , isolated tissue level . Bioinformatic-based predictions of tissue-specific high-throughput gene expression have been inferred based on whole-organism , stage-specific C . elegans microarray data [1] , but these computational tissue-specific expression predictions still await experimental confirmation and are not useful in identifying genes relevant to host-parasite interactions , since C . elegans is a free-living species [12] . In this context , Ascaris suum ( the large roundworm of swine ) is of particular interest as a model parasitic nematode . The A . suum genome has recently been sequenced [13] , and this parasite serves as a research model for its close relative , A . lumbricoides , which is responsible for widespread disease infecting over one billion people worldwide [14] . The large size of A . suum relative to other nematodes ( adults can reach up to 40 cm in length ) allows for accurate dissection of individual tissues and organs that is not possible in smaller nematodes . Previous studies have analyzed expression from a single tissue in A . suum using conventional ESTs [15] and then multiple tissues using microarrays [16] . The multiple tissue study [16] , while providing insight on many biological functions of tissues investigated , was based on ∼40 , 000 60-mer ( 40-k array ) elements derived from genes predicted from low coverage of the A . suum genome , resulting in multiple elements representing a single gene or genes not being represented in the partial genome . Indeed , when the A . suum genome became available [13] the 40-k array elements was shown to cover just 58% of the predicted A . suum genes [16] . This limitation has made it challenging to identify exact genes contributing to the expression patterns , and did not take advantage of the wide range of functional information that can be annotated using full-length gene sequences , or the high expression-level accuracy that can be provided with RNA-seq analysis . Here , we build significantly on the previous tissue-specific gene expression research in A . suum , by producing the first nematode RNA-seq dataset that spans multiple specific tissues , including three non-reproductive and two reproductive tissues in both male and female A . suum worms ( Fig . 1A ) . The analysis presented here provides additional annotation to the A . suum genome , through i ) tissue-specific gene expression profiling , ii ) detailed Gene Ontology-based functional enrichment for each tissue , iii ) delineation of putative cis and trans regulatory elements involved in regulating expression in the specific tissues investigated and iv ) co-expression networks , which identified putative genes that link molecular pathways across different tissues . The 10-tissue specific expression profiles ( Supp . Table S1; Also available on www . nematode . net [7] ) and the analysis presented here provide valuable resources for studying basic functional relationships in nematodes , including both non-parasitic and parasitic species .
Adult worms were collected from infected pigs at an abattoir when being processed as part of the normal work of the abattoir . The fresh worm tissues , including three non-reproductive ( head , pharynx , and intestine ) tissues in both male and female worms , as well as two reproductive tissues per sex ( testis , seminal vesicle , ovary and uterus ) were dissected ( Fig . 1 ) and snap frozen in liquid nitrogen for subsequent storage at −80°C . Note that while the term “tissue” is used to describe all of these samples for simplicity , they may be more accurately described as “organs” or “regions of the body” rather than pure tissues . For the non-reproductive “tissues” , the “head” is defined as the terminal anterior region of the worm anterior to the beginning of the muscular pharynx , the “pharynx” is defined as the anterior body region that extends from the anterior-most to posterior-most margins of the muscular pharynx , and the “intestine” is defined as posterior to the posterior-most margin of the muscular pharynx to the posterior-most limit of the intestine near the anus ( female ) or cloaca ( male ) . In the male reproductive samples , the “testis” sample contained the entire male reproductive system distal to the seminal vesicle . The “seminal vesicle” itself is likely to contain sperm , spermatids and vesicle wall . For the female reproductive system , some eggs were present in the “uterus” tissue , and while they are relatively resistant to Trizol , some contribution of mRNA from eggs in the uterine preparation cannot be excluded . The “ovary” samples contained the female reproductive system distal to the oviduct . Tissue homogenization and RNA extraction were performed using TRIzol ( Invitrogen; according to the manufacturer's instruction ) and a rotor/stator probe ( Tissue Tearor Model 985270-395 , BioSpec Products Inc ) , which was used to mix the samples for 15 second intervals until the samples were completely homogenized . The integrity and yield of the RNA was verified by the Bioanalyzer 2100 ( Agilent Technologies , Cedar Creek , Texas ) . Total RNA was treated with Ambion Turbo DNase ( Ambion/Applied Biosystems , Austin , TX ) , and 1 ug of the DNAse-treated total RNA went through polyA selection via the MicroPoly ( A ) Purist Kit according to the manufacturer's recommendations ( Ambion/Applied Biosystems , Austin , TX ) . 1 ng of the mRNA isolated was used as the template for cDNA library construction using the Ovation RNA-Seq ( version 2 ) kit according to the manufacturer's recommendations ( NuGEN Technologies , Inc . , San Carlos , CA ) . Whole-worm male and female samples were prepared using the same protocol . Non-normalized cDNA was used to construct Multiplexed Illumina paired end small fragment libraries according to the manufacturer's recommendations ( Illumina Inc , San Diego , CA ) , with the following exceptions: 1 ) 1 ug of cDNA was sheared using a Covaris S220 DNA Sonicator ( Covaris , INC . Woburn , MA ) to a size range between 200–400 bp . 2 ) Four rounds of PCR amplifications were performed to enrich for proper adapter ligated fragments and properly index the libraries . 3 ) The final size selection of the library was achieved by an AMPure paramagnetic bead cleanup ( Agencourt , Beckman Coulter Genomics , Beverly , MA ) , targeting 300–500 bp . The concentration of the library was accurately determined through qPCR according to the manufacturer's protocol ( Kapa Biosystems , Inc , Woburn , MA ) to produce cluster counts appropriate for the Illumina GAIIx platform . Multiple libraries were pooled together and loaded into one lane of a HiSeq2000 version 3 flow cell . 2×101 bp read pairs ( later clipped to 100 bp using Consensus Assessment of Sequence and Variation [CASAVA , version 1 . 8] ) were generated for each sample , generating ∼2 Gb per sample . Whole-worm male and female samples were sequenced using the same protocol ( SRA Accession numbers SRR851237 , SRR851252 , SRR851258 and SRR869505 ) . Analytical processing of the Illumina 100 bp reads was performed using in-house scripts . DUST was used to filter out regions of low compositional complexity and to convert them into Ns [17] . An in-house script was used to remove Ns , which discards reads without at least 60 bases on non-N sequence . Sequences from host ( pig genome; Sscrofa9 . 2 , GCA_000003025 . 2 from GenBank [18] ) , bacteria ( GBBCT from GenBank [18] ) , and an A . suum mitochondrial database were screened using the A . suum Illumina short-reads . The number of RNA-seq reads identified and mapped per tissue sample is listed in Table 1 . Processed and raw paired-end RNA-seq datasets are deposited at SRA ( Accession Numbers SRR85166 , SRR85167 , SRR851186-SRR851203 , SRR851213 , SRR851223-SRR851225 , SRR851254-SRR851257 , SRR851632-SRR851637 , SRR851639-SRR851641 , SRR851855-SRR851857 , and SRR869476; http://www . ncbi . nlm . nih . gov/sra ) . Whole-worm male and female samples were processed using the same protocol ( Supp . Fig . S1 ) . Gene expression for each sample was calculated by mapping the screened RNA-seq reads to the recently released A . suum genome [13] using Tophat [19] ( version 1 . 3 . 1 ) , and calculating depth and breadth of coverage using Refcov ( version 0 . 3 , http://gmt . genome . wustl . edu/genome . shipit/gmt-refcov/current ) . Gene expression values were normalized using the depth of coverage per million reads ( DCPM ) per sample [20] . Stage-specific over-expression and under-expression for each gene with at least 50% breadth of coverage across all of the tissues was tested using SAMSeq ( v4 . 0 , released 2011 [21] ) . Genes with less than 50% breadth of read coverage of the gene sequence across all samples were excluded from the analysis . This algorithm was chosen because ( i ) it has been designed for multi-class testing among RNA-seq datasets ( i . e . allows for more than pair-wise comparisons simultaneously , and can identify over-expression in multiple tissues ) , ( ii ) it has been shown to have low bias and false discovery rates relative to other differential expression algorithms for other RNA-seq datasets [22] , [23] , [24] , and ( iii ) it has demonstrated effectiveness in other studies [21] , [25] , [26] , [27] , [28] . This algorithm identified approximately 69% of the expressed genes as being over-expressed in at least one of the tissues ( with p≤0 . 05 confidence and a false discovery rate of 0 . 8% ) . Tissue-overexpression profiles for every gene were generated based on these results ( Supp . Table S1; Also available on www . nematode . net [7] ) . In this context , the term “overexpression” is used to denote significantly higher expression for a gene in any given tissue , relative to the other tissues according to the test described above . Gene expression levels ( DCPM ) for the two replicates in every tissue were averaged , and the samples were clustered based on their expression across all genes using hierarchical agglomerative clustering ( with “unweighted pair group method with arithmetic mean” , and “Spearman correlation coefficient similarities” settings in XLSTAT-Pro version 2012 . 6 . 02 , Addinsoft , Inc . , Brooklyn , NY , USA; Fig . 1B ) . Interproscan [29] , [30] was used to determine associations of genes to Gene Ontology ( GO ) terms [31] . Interproscan also identified predicted Interpro domains found in each gene . In addition , predicted proteins were searched against the KEGG database [32] using KAAS [33] . Proteins with signal peptides and transmembrane were identified using the Phobius [34] web server , and non-classical secretion was predicted using SecretomeP 1 . 0 [35] . FUNC [36] ( which considers the hierarchical structure of GO ) was used to determine significant functional enrichment among the genes overexpressed in each tissue , with a p≤0 . 01 significance threshold ( after FDR population correction; Figs . 2 and 3 , Supp . Table S2 ) . For the non-reproductive tissues , overexpressed genes from both the male and female organs were pooled for the enrichment analysis . Interpro domain enrichment was determined using a non-parametric binomial distribution test with a p≤10−5 significance threshold ( after FDR population correction ) . Only Interpro domains found in at least five predicted proteins were considered for enrichment testing ( Supp . Figs . S2 and S3 ) . Reciprocal best hits between predicted A . suum proteins based on the current version of the genome [13] and predicted C . elegans proteins from WormBase WS230 [37] were identified using WU-BLAST with a minimum bit score of 60 for each identified pair ( using the parameters “hitdist = 40 wordmask = seg postsw” ) ( Table 2 ) . All available isoforms of the proteins were used as input in this comparison . Within the sets of A . suum genes overexpressed in each tissue , enrichment of genes with reciprocal best hits to C . elegans was tested using a non-parametric binomial distribution test with p≤0 . 05 significance cutoff ( after FDR population correction for the total number of tissues ) . The identification of genes that are overexpressed in individual A . suum tissues facilitated the analysis of potential cis and trans regulatory elements responsible for this differential expression . 2000 bp upstream untranslated regions ( UTRs ) were extracted for each gene based on the A . suum genome annotation [13] . The 5′ end of 725 gene sequences ( 4% of the gene set ) was less than 2000 bp from the end of a contig; these genes were not included the in motif enrichment testing . Motif enrichment was performed using a discriminative motif analysis algorithm ( DREME [38] , using an 8-nucleotide sequence search ) , where the 5′ UTRs of the genes overexpressed in a tissue were compared to the 5′ UTRs of the expressed genes not overexpressed in that tissue , in order to determine over-represented enriched motifs . FIMO [39] was used to calculate the coordinates of motifs similar to the enriched motifs among all genes , and potential transcription factors binding the discovered motifs were identified using Tomtom [40] ( where transcription factors from the JASPAR CORE nematoda and vertebrata motif databases [41] , as well as the UniProbe motif database [42] were considered for annotation ) . It should be noted that the Tomtom transcription factor binding site [7] , [40] databases used to annotate the motifs described below ( including the JASPAR vertebrate and nematode database [41] as well as the UniProbe [42] ) contained only five nematode sequences , and hundreds of vertebrate sequences ( primarily from Mus musculus ) , so many of the best-hit motif annotations described below are based on transcription factor data from mice due to bias in the best databases available . BLASTP [43] was used to identify potential orthologs of these transcription factors in the A . suum genome . The top five BLAST hits were considered for selection as the probable tissue-specific transcription factor , and the optimal target was chosen from these five based on gene annotation as well as on the tissue-specific gene expression profile for each potential TF ( Fig . 4; Supp . Table S3 ) . Supp . Table S4 contains a key for the base ambiguity among the motifs shown in Fig . 4 [44] . Constitutively expressed genes were identified based on the criteria that they were not significantly differentially expressed among tissues , and the minimum expression level in every tissue was greater than the median expression level for the entire dataset ( 0 . 26 DCPM ) , and novel genes were identified based on the the criteria that there was no annotation from best hit in the NCBI's NR database ( provided in the original genome publication [13] ) , and no Interpro , GO or KEGG annotations ( Figure 5 ) . Male and female gene co-expression networks were constructed to further explore the tissue-specific gene expression data . For the male network ( Fig . 6A ) , all genes overexpressed in at least one male tissue were considered for the network . For these genes , the Pearson correlation ( based on the expression values across both replicates in all 10 tissues ) between all gene pairs was calculated , and every gene pair with a correlation ≥0 . 90 was connected with an edge using Cytoscape software ( version 3 . 0 ) [45] . It should be noted that a previous study showed that a Pearson correlation-based gene co-expression network of over 22 , 000 genes constructed with only 14 samples was sufficient to identify the same functional modules as a much larger dataset , so the 20 samples used here are thought to be sufficient to find biologically meaningful modules and subnetworks [46] . The male network contained 4 , 784 genes with 1 , 387 , 028 edges . The default “prefuse force-directed” layout was used with the “spring length” variable set to 100 in order to avoid overlapping of unconnected subnetworks . The positions of four nodes in the long vertical bridges of this network were manually repositioned in order to better display the connectivity without overlaps . Genes were colored according to the tissue in which they were overexpressed; if a gene was overexpressed in more than one tissue , the tissue with the highest expression level was chosen for the color coding . The same approach and settings were used to construct the female gene co-expression network ( Fig . 7A ) , and this network contained 7 , 741 genes with 1 , 188 , 989 edges . High-resolution images of these networks as well as the Cytoscape network files are available on www . nematode . net [7] .
RNA-seq analysis was performed in duplicate on three non-reproductive ( head , pharynx , and intestine ) tissues in both male and female worms , as well as two reproductive tissues per sex ( testis , seminal vesicle , ovary and uterus ) ( Fig . 1A; Methods ) . Across the 20 RNA-seq samples ( 2 replicates from 10 different tissues ) , 348 million reads were generated , and 199 million reads mapped to the 18 , 542 genes of the A . suum genome [13] . The number of reads mapped in individual tissues ranged from 6 million ( in the second uterine replicate ) to 16 million ( in the second male intestinal replicate; Table 1 ) . The average Pearson correlation for expression values of all expressed genes between replicates was 0 . 90 , while the average correlation between samples from different tissues was 0 . 24 ( based on all pair-wise comparisons between tissues ) . The lowest correlation among replicates was between the replicates of the female pharynx ( 0 . 61 ) , and may reflect the relative difficulty of the dissection procedure for this particular tissue ( see Methods ) . A total of 16 , 854 genes ( 91% of the complete A . suum geneset ) had ≥50% breadth of coverage ( ie , ≥50% of the gene sequence was covered with at least one read from any of the samples ) , and this final set of expressed genes was used in the subsequent differential expression analysis , which identified 11 , 690 genes ( 63% of all expressed genes ) as being significantly overexpressed in at least one tissue . The male and female samples had relatively consistent gene expression profiles in the head , pharynx and intestinal tissues ( Fig . 1B ) . The tissue with the most distinct gene expression profiles was the testis , which had a low similarity ( Spearman correlation of 0 . 26 ) compared to the other tissues . However , the similarity between the two testis replicates based on the same statistics was over 0 . 90 ( data not shown ) , indicating that this large difference is not due to inter-replicate variability . Unlike the previous microarray-based study of A . suum tissues [16] , genes with high similarity to C . elegans genes were found to be more enriched among overexpressed genes in all tissues except for the testis and pharynx ( Table 2 ) . In the previous study , similarity was measured by the identification of PANTHER domains among the genes , which may have biased the identification towards genes with known functions rather than genes with similarity to other species . Also , unlike the previous microarray-based study of A . suum tissue-specific expression which found substantial differences between genders in terms of expression profiles in non-reproductive tissues ( particularly the intestine ) [16] , we observed strong agreement between the gene expression profiles for the male and female intestine and pharynx ( Spearman r = 0 . 93 and 0 . 94 , respectively ) , but observed a higher disparity for the head ( r = 0 . 79 ) . While this difference may be biological in nature , it may be accounted for by the higher accuracy of the expression data here , provided both by RNA-seq ( as opposed to microarray ) and the higher-quality gene set provided by the recent genome publication ( which was not available when the microarray study was performed [16] ) . The current study focuses primarily on tissue-specific differences among these tissues rather than on the gender differences . Separate whole-worm male and female A . suum RNA-seq samples were also generated as a comparison to these tissue-specific samples . In the whole-worm male sample ( Supp . Fig . S1A ) , 15 , 604 genes were detected ( see methods for criteria ) , and in the combined tissue-specific male samples , 15 , 941 genes were detected , including 863 which were not detected in the whole-worm samples . Among those 863 , more than half ( 52% ) had their highest expression in either the head or pharynx ( compared to 34% in the entire tissue dataset ) . Likewise , in the female comparison ( Supp . Fig . S1B ) , 45% of the 1 , 655 genes detected only in the tissue-specific dataset were most highly expressed in the head or pharynx , compared to 31% across all of the genes expressed in the tissue samples ( Supp . Table S1 ) . Thus , the whole-worm samples more often failed to capture the expression of genes which are most actively expressed in the head and pharynx , which highlights the importance of the production of these tissue-specific datasets . Interproscan [29] , [30] was used to determine associations of genes to Gene Ontology ( GO ) terms [31] , and FUNC [36] ( which considers the hierarchical structure of GO ) was used to determine significant functional enrichment among the genes overexpressed in each tissue , with a p≤0 . 01 significance threshold ( after FDR population correction; Figs . 2 and 3 , Supp . Table S2 ) . In the context of this study , “overexpression” denotes significantly higher expression in a given tissue relative to the other tissues , and genes may be overexpressed in more than one tissue or no tissues ( see Methods , “Analytical processing of the reads and differential expression” ) . The most enriched term in the head of A . suum ( including the circular three-lipped mouth , the outer cuticle layer , amphids , some muscle tissue , and internal structures consisting primarily of neurons ) was “structural constituent of cuticle” ( GO:0042302 ) . This term was identified as being enriched in the head of A . suum in the previous microarray study [16] , and was enriched only in the head-overexpressed genes in this study , which could be expected since most of the other internal tissues are not cuticle-lined , or are only partially lined with cuticle ( e . g . pharynx or the distal posterior part of the intestine-anus ) . Additionally , many of the GO terms exclusive to the head ( Fig . 2 ) are linked to neuronal activity , including ten terms related to ion channel/transport activity ( related to synaptic transmission and action potential depolarization in neurons [47] , [48] ) , which were not previously identified [16] . “Arylesterase activity” ( GO:0004064 ) was also enriched , which is of interest because arylesterase is negatively correlated with inflammation in mammals [49] , and was found to be significantly decreased in the serum of rats infected with the nematode N . brasiliensis [50] . This presents a possible mechanism by which A . suum achieves its anti-inflammatory properties inside the host , and genes annotated with this term may be of interest for future immunological studies [51] . Most research performed on the nematode pharynx has focused on the anatomy , development and neuronal connectivity of the pharynx , primarily because it is an excellent model for organogenesis [52] , [53] . In C . elegans , pharyngeal secretions are thought to be involved in digestion , but the nature of those secretions is largely unknown [52] . The previous microarray-based study found no significant functional enrichment for the pharynx [16] , but here we have identified twelve enriched terms . This difference is likely due to an improved dataset resulting in more comprehensive coverage of the genome , as well as the recent improvement of the genome itself . Five child terms of “catalytic activity” ( GO:0003824 ) were found to be significantly enriched in the pharynx ( including two which were also enriched in the intestine ) . Necepsins have nematode-specific characteristics and are able to hydrolyse host proteins including hemoglobin and serum proteins [54] , and eight out of the nine genes annotated as necepsins ( according to NCBI RefSeq database search results provided in the genome publication [13] , [55] ) were also annotated with “aspartic-type endopeptidase activity” ( GO:0004190 , enriched in the pharynx; p = 8×10−4 ) . Nearly half ( 46% ) of the A . suum intestinal transcripts conserved with H . contortus and C . elegans in a previous study [56] were identified in our A . suum intestinal genes , representing 395 unique intestinal genes ( enriched for overexpression in the intestine , p<10−10 ) . Here , a total of 31 GO terms were significantly enriched in the intestinal tissue , including nine ‘Molecular Function’ child terms of “hydrolase activity” ( GO:0016787 ) ( including “cysteine-type endopeptidase activity” , a category previously identified in the A . suum intestine [16] ) and eleven terms related to transport of protons , lipids and amino acids . The testis in A . suum is the best characterized of the four reproductive tissues analyzed in this study . A previous study focused on the functional activity in the A . suum testis identified genes with phosphatase and kinase activity as being particularly overrepresented in this tissue [57] . It is speculated that this catalytic activity relates to the discarding of protein synthesis-related machinery in sperm and an upregulation of genes required for pseudopod extension and sperm cell motility [57] , and proteins expressed specifically in the testis that are lost during chromosome diminution were found to be enriched for these functions [57] , [58] . Although not found in the previous microarray-based study [16] , the presence of high phosphatase activity in the testis is supported here by the enrichment of the MF GO term “protein tyrosine phosphatase activity” ( GO:0004725 ) , and the presence of high kinase activity is supported by the enrichment of six different terms describing kinase activity . Five A . suum major-sperm protein ( MSP ) domain-containing proteins were previously found to be active in the testis [57]; The A . suum genes with the highest sequence similarity to each of these MSP genes was found via a BLAST search [43] , and all five were over-expressed in the testis in this analysis ( Supp . Table S5 ) . Also , previously , a serine protease inhibitor expressed in the A . suum testis ( As_SRP-1 ) was found to be critical for cytoskeleton assembly and motility [59]; The A . suum gene with the highest similarity to As_SRP-1 ( GS_04617 ) was very highly overexpressed in the testis ( with the 6th highest average expression value of all the genes in the testis ) . Unlike the testis , the broad molecular activity in the ovary , seminal vesicle and uterus of A . suum have not been previously studied outside of the previous microarray study [16] . However , a number of studies have identified many genes responsible for different stages of embryo and oocyte development in the ovary of C . elegans [60] , [61] , including a study which estimated that more than 2 , 600 genes are responsible for these processes alone [62] . Here , more genes were overexpressed in the A . suum ovary than in any other tissue ( 5 , 446; Fig . 1B ) . The top five enriched GO terms ( and fourteen total terms ) were directly related to DNA binding and replication , including “mitosis” ( GO:0007067; Fig . 3 ) , consistent with an RNA-seq dataset produced from the A . suum genome publication [13] , and demonstrating that our approach is identifying the expected biological functions in the ovary . Two BP GO terms related to phosphatidylinositol signalling were also enriched in the ovary , which supports previous literature suggesting that at least one of these signalling pathways ( the ppk-1 pathway ) is necessary for ovulation in C . elegans [63] . Also , two terms related to chitin binding were found to be enriched among ovary-overexpressed genes , consistent with findings in the previous microarray study [16] . Like with the ovary , very little is known about the specific molecular functional activity of the seminal vesicle in A . suum [16] , [64] , and genes overexpressed in this tissue ( and the uterus ) were enriched for sharing high sequence similarity ( based on reciprocal BLAST hits ) to C . elegans ( p = 1×10−5 ) . In C . elegans , seminal fluid has been shown to modulate sperm function , promote sperm viability and initiate physiological changes in the female uterus [65] . Actin and cytoskeleton activity have been shown to be critically important for nematode sperm motility and activation [66] , consequently it is possible that the high enrichment of the MF GO terms “protein binding” ( GO:0005515 ) and “actin binding” ( GO:0003779 ) in the seminal vesicle is due to the overexpression of several genes responsible for binding spermatids ( Fig . 3 ) . In addition , “fucosyltransferase activity” ( GO:0008417 ) was found to be enriched in the seminal vesicle , a function which has also been found in the seminal fluid of mammals and implicated in fertility via the removal of fertility-inhibiting fucose-containing molecules on the sperm surface [67] , but this observation has not been previously reported in the literature for nematodes . The A . suum uterus is the site of fertilization and egg development , and as with the ovary and seminal vesicle , A . suum-specific studies of the uterus have focused on morphology rather than detailed functional analysis [68] , [69] . Genes overexpressed in the A . suum uterus were enriched for sharing high sequence similarity with C . elegans ( 1×10−12 ) , but only limited knowledge of the biological pathways in the mature C . elegans uterus is available , as most research has focused on uterine developmental pathways rather than functional activity in the adult uterus [70] , [71] . Here , we have identified a range of molecular functions associated with the A . suum uterus ( Fig . 3 ) , including four child terms of “protein binding” ( GO:0005515 ) and four child terms of “catalytic activity” ( GO:0003824 ) . Highly and significantly enriched ( p≤10−5 ) Interpro domains among genes in each of the tissues studied are shown in Supporting Figures S2 and S3 . These domains are consistent with the GO term enrichment results , since they were both based on Interproscan identifications [72] . The identification of genes that are preferentially or exclusively expressed in individual A . suum tissues facilitated the analysis of potential cis and trans regulatory elements responsible for this differential expression . The sequences upstream of the first base of the gene models ( up to 2000 bp ) were examined for potential transcription factor binding site enrichment using a discriminative motif analysis ( DREME [38]; Fig . 4 ) . The binding motif “ADTTCGC” was the most significantly enriched out of three motifs enriched among genes overexpressed in the A . suum head , and matched MAB-3-like ( “Male Abnormal 3” ) , which has been previously described in C . elegans [40] . In C . elegans , MAB-3 is required for expression of male-specific genes in sensory neurons of the head , and acts synergistically with LIN-32 , a neurogenic bHLH transcription factor [73] . The A . suum protein GS_21204 had significant amino acid sequence similarity to the C . elegans MAB-3 ( E = 2×10−11 ) , was annotated with the GO term “sequence-specific DNA binding transcription factor activity” ( GO:0003700 ) , and expression for its gene was detected only in the head and pharynx , Only one binding motif ( CATACAYA ) was found to be significantly enriched among genes overexpressed in the A . suum pharynx . This motif matched the SOX-17 ( “SRY-related HMG-box” ) transcription factor binding motif previously described in M . musculus [40] . While SOX-17 activity has not been studied specifically in nematodes , another SOX protein in C . elegans ( SOX-1 ) was found to be one of a small group of transcription factors activated during pharyngeal development [52] . Here , the M . musculus SOX-17 protein had high sequence similarity to an A . suum protein ( GS_07983; E = 4×10−28 ) . GS_07983 was found to be most highly expressed in the pharynx and the head , and was annotated with the KEGG term “SOX1/2/3/14/21 ( SOX group B ) ” ( K09267 ) . The most significant binding motif found among genes overexpressed in the A . suum intestine ( CTTATCAR ) matches the reverse complement binding sequence of ELT-2 ( TGATAA ) , the predominant transcription factor controlling differentiation and function of the C . elegans intestine [74] as well as the GATA-like intestine-enriched motif previously reported in C . elegans ( TCTTATC ) [1] . A protein with high sequence similarity to ELT-2 ( GS_05212 , E = 1×10−22 ) was annotated with the Interpro domain “Zinc finger , NHR/GATA-type” ( IPR013088 ) , and its gene was found to be highly expressed in the intestine ( as well as in the pharynx ) . A M . musculus POU2F3 ( “pituitary-ocular-Unc-2 family 3” ) transcription factor matched the binding motif ( TATGCARA ) that was the most significantly enriched among genes overexpressed in the A . suum testis . This transcription factor is a putative ortholog to the C . elegans gene CEH-18 ( ZC64 . 3 ) [75] , which has been found to be responsible for cell division in gonadal sheath cells [76] . GS_16028 in A . suum was primarily expressed in the testis , shared high protein sequence similarity to CEH-18 ( E = 3×10−34 ) and was annotated with the KEGG term “POU domain transcription factor , class 4” ( K09366 ) . A total of 12 predicted binding motifs were enriched among genes overexpressed in the A . suum ovary , which may be due to the expression of early-stage developmental genes which are not present in other tissues . This idea is supported by the annotation of the most highly enriched binding motif ( GGGGGDK ) , which matches the ZFP281 ( “zinc finger protein 281” ) transcription factor binding site in M . musculus . The closest ortholog to the ZFP281 gene in C . elegans is BLMP1 [77] , which has very-early embryo developmental activity , but specific genetic targets for this gene have not been previously characterized [78] . In the A . suum genome , GS_10180 was overexpressed only in the ovary , had high protein sequence similarity to ZFP281 ( E = 3×10−16 ) , and was annotated with a “Zinc finger , C2H2-type” Interpro domain ( IPR007087 ) . In the A . suum seminal vesicle , the binding motif ( TCGTTMA ) matching the M . musculus GMEB-1 ( Glucocorticoid Modulatory Element Binding protein-1 ) transcription factor binding motif was the only one that was significantly enriched . There is a known ortholog of GMEB-1 in C . elegans ( C01B12 . 2 ) [75] but its function has not been studied specifically . The A . suum protein GS_22365 shares high sequence similarity to GMEB-1 ( E = 1×10−22 ) . GS_22365 was highly expressed in the seminal vesicle , and contained a SAND Interpro domain ( IPR000770 ) , which is a transcription factor domain also found in GMEB proteins [79] . Finally , the motif “CSCCACW” ( which matches the M . musculus SMAD3 binding motif ) was one of two significantly enriched in the A . suum uterus . Although no direct orthologs of this protein have been identified in nematodes , other SMAD transcription factors are known to be involved in a wide range of complex tissue interactions in C . elegans , including in many reproductive tissues [80] . GS_00234 in A . suum shares high protein sequence similarity with SMAD3 ( E = 4×10−129 ) , contained a SMAD Interpro domain ( Dwarfin-type; IPR001132 ) , and its gene was overexpressed in the uterus . These results on binding motif enrichment suggests existence of tissue-specific co-expressed genes that are under similar transcriptional control , and identifies their putative transcription factors in A . suum , most of which have putative orthologues that have been previously described in the literature . Several examples provide very promising targets for further study to identify specific mechanisms governing tissue-specific gene expression in adult A . suum . The similarities to C . elegans , a distant nematode relative to A . suum , indicate that findings reported here should have broad applicability to species across the phylum Nematoda . As the annotation of the A . suum genome is improved , binding motif enrichment analyses may be improved through the identification of promoters and more accurate sequencing of intergenic regions . A total of 1 , 255 genes were constitutively expressed across the tissues . Nineteen GO terms were significantly enriched among these constitutively expressed genes , nearly all of which were related to translational activity , as is expected for eukaryotic housekeeping genes [81] ( Supp . Table S2 ) . A total of 4 , 886 genes were identified as being “novel” based on a lack of annotation from any source . Only 7% of constitutively expressed genes were characterized as novel in this analysis , compared to 23% of all expressed genes ( Fig . 5 ) , which is expected since constitutively expressed genes are often conserved and have well-studied biological functions in eukaryotes . Likewise , there were a smaller proportion of novel genes among the gene sets overexpressed in all of the tissues except for the testis ( Fig . 5 ) . The significant over-representation of novel genes in the testis ( compared to expressed genes not overexpressed in the testis; P<10−10 ) indicates the potential for important and previously undescribed biological functions occurring in the testis of A . suum . Gene co-expression networks , in which genes are represented as nodes and are connected by edges corresponding to their co-expression across a number of samples of gene expression , are a powerful approach for developing hypotheses regarding the functions of both annotated and unannotated genes [82] , [83] , [84] ( including identifying genes related to functions not specifically tested in the source datasets [1] , as well as for identifying putative functional modules related to transcriptional activity [46] ) . Here , sex-independent co-expression networks were constructed ( using Cytoscape software V3 . 0 [45]; Methods ) for 4 , 784 genes overexpressed in male tissues ( with 1 , 387 , 028 edges; Fig . 6 ) and for 7 , 741 genes overexpressed in female tissues ( 1 , 188 , 989 edges; Fig . 7; Methods ) . In both male and female networks , there are far fewer reproductive to non-reproductive connections in the networks than expected based on the total number of inter-tissue connections . If the network was random , then 60% of the inter-tissue connections should be between reproductive and non-reproductive tissues , but only 4% and 18% of the between-tissue edges were found to connect these tissue types ( p<10−15 for both networks , binomial distribution test ) , making the existing reproductive to non-reproductive connections in the network particularly interesting for further study . The male gene co-expression network automatically arranged in a pattern similar to the body plan of the male A . suum worm ( Fig . 6A ) , with the pharynx serving as a bridge between the head and the intestine , and very few connections between the non-reproductive and reproductive tissues . However , a subnetwork of male head , pharynx and intestine genes closely associated with seminal vesicle genes ( Fig . 6B ) may present a functional link between these tissues . This gene cluster was most significantly enriched for “regulation of transcription , DNA-dependent” ( GO:0006355 ) , “sequence-specific DNA binding transcription factor activity” ( GO:0003700 ) and “acetyl-CoA carboxylase activity” ( GO:0003989; p = 8×10−4 , 1×10−3 and 4×10−3 , respectively ) . At the top-left of this subnetwork is a head-overexpressed gene ( GS_05069 , in red ) , one of only two head-overexpressed genes not directly connected to the main head-network hub ( Fig . 6A ) . The predicted protein for this gene was matched to the “nicotinic acetylcholine receptor , invertebrate” KEGG category ( K05312 ) . Acetylcholine functions as a modulatory neurohormone in Ascaris lumbricoides [85] , and here GS_05069 was found to share very high sequence similarity to the H . contortus protein Hco-monepantel-1 ( E = 4e−85 ) , which has been identified as a target for the recently developed anthelmintic drug monepantel ( an amino-acetonitrile derivative ) [86] , [87] , [88] . This head-overexpressed gene is only highly correlated with one other gene ( GS_05881 ) which was also head-overexpressed and was annotated with a “Nematode fatty acid retinoid binding” Interpro domain ( IPR008632 ) , and which shared high protein sequence similarity to FAR-1 in Onchocerca volvulus ( E = 4e−72 ) [13] . FAR-1 belongs to a family of orthlogous proteins which play important roles in development and reproduction in nematodes [13] , [89] . GS_05881 connects to a subnetwork of intestine-overexpressed genes which are highly correlated with the expression patterns of many seminal vesicle-overexpressed genes , and which are rich with annotations related to transcriptional activity ( Fig . 6B ) . These observations are consistent with the predicted role of FAR-1 in reproduction . Other FAR-1 homologs are a focus of interest in terms of their crucial role in parasitism [90] , [91] , and have been suggested to be potential targets for new anthelmintics due to their expression on the epidermis , their lack of similarity to any host proteins and their critical function in host environment detection [92] , [93] . Here , we present the first evidence that the A . suum homologs to Hco-monepantel-1 and FAR-1 ( both previously described as anthelminthic drug targets ) are co-expressed in A . suum , and the networks of genes with similar expression patterns may be used in future research to develop hypotheses about members and functions of the network , or to identify other potential downstream drug targets . Like in the male network , the female co-expression network ( Fig . 7A ) arranged in a pattern similar to its body plan layout , with the pharynx bridging the head and intestine and the reproductive tissues largely separated . However , in the female , the subnetwork connecting the head and ovary networks is very dense ( Fig . 7B ) , involving a large set of co-expressed genes . One of the head-overexpressed genes central to this head-ovary bridge network ( GS_05636 ) was annotated as an “ecdysone receptor” ( K14034 ) . This was the only gene in the current A . suum genome annotated to this KEGG category , and is important because in the parasitic nematode Brugia malayi , ecdysteroid signalling has been found to play a role in molting and fertility , but the mechanism behind these relationships is unknown [94] . Similar to the story for the male subnetwork , this gene bridges a gap between reproductive and non-reproductive networks ( as evidenced by the its first-neighbor co-expression pairs which include both head-overexpressed and ovary-overexpressed genes ) , and may be an interesting target for further study in order to elucidate signal transduction pathways and design drug targets for eliminating A . suum fertility . Although the co-expression networks were segregated by gender here , the pathways described are not necessarily restricted to only one gender . The functional enrichment results across different A . suum tissues present many confirmations of existing knowledge in nematode tissues , as well as many suggestions of novel functions which are interesting subjects for further study . This analysis indicates that the A . suum pharynx may be actively involved in digestive processes and it provided functional descriptions of the A . suum seminal vesicle , ovary and uterus , which have not been previously studied in this detail . Constitutively expressed and novel genes were also characterized , and putative tissue-specific transcriptional factors and corresponding binding motifs were deduced stemming from results of the tissue expression analysis , which included the intestine-enriched ELT-2 motif/transcription factor previously described in nematode intestines . Also , the gene co-expression networks constructed here present several possible novel molecular signalling pathways between non-reproductive and reproductive tissues , and provide a resource for quickly identifying genes co-expressed between different tissues . As the A . suum genome is better annotated and specific pathways are more carefully identified , additional subnetworks of interest could be identified in these networks . The analyses in this paper present several approaches for mining data from this rich RNA-seq analysis of 10 different A . suum tissues . Hence , the dataset , co-expression relationship and transcriptional regulation that were derived from it provide a valuable resource for studying tissue-specific biological activity in nematodes . In addition , the annotation data , gene expression data and overexpressed gene lists in each tissue ( Supp . Table 1; also deposited into www . nematode . net , enabling readers to perform advanced searches ) provide valuable resources for building future tissue-specific analyses for helping with drug and vaccine design directed against parasitic nematodes .
Processed and raw paired-end RNA-seq datasets are deposited at the sequence reads archive ( SRA ) on the NCBI website ( http://www . ncbi . nlm . nih . gov/sra; Accession Numbers SRR85166 , SRR85167 , SRR851186-SRR851203 , SRR851213 , SRR851223-SRR851225 , SRR851254-SRR851257 , SRR851632-SRR851637 , SRR851639-SRR851641 , SRR851855-SRR851857 , SRR869476 , SRR851237 , SRR851252 , SRR851258 and SRR869505 ) . Reads were mapped to the A . suum genome assembly produced and described by Jex et al . ( Nature , 2011 ) , and all gene names used in this manuscript are consistent with the gene names in that publication . | Tissue-specific gene expression provides fundamental information about the biology of diverse cell types within an organism and interactions among tissues within multicellular organisms . However , such studies are experimentally challenging in smaller organisms such as many nematodes species , including the species ( Caenorhabditis elegans ) that is widely used in biomedical research . Ascaris suum ( the large roundworm of swine ) , however , is of particular interest as a model nematode because it is large enough to allow for the dissection of individual tissues , and equally important because it is closely related to A . lumbricoides , which infects ∼1 billion people worldwide . Here , we build significantly on the previous tissue-specific gene expression research in A . suum by producing the first nematode RNA-seq dataset that spans multiple specific tissues , including three non-reproductive and two reproductive tissues in both male and female A . suum worms . This analysis provides significant details on the biological functions occurring within each of these tissues , which has not been previously explored . It also provides insight into specific gene regulation pathways active in each of the tissues , which have broad applicability across other nematodes , including both non-parasitic and parasitic species . | [
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] | 2014 | Genome-Wide Tissue-Specific Gene Expression, Co-expression and Regulation of Co-expressed Genes in Adult Nematode Ascaris suum |
Current Aedes aegypti larval control methods are often insufficient for preventing dengue epidemics . To improve control efficiency and cost-effectiveness , some advocate eliminating or treating only highly productive containers . The population-level outcome of this strategy , however , will depend on details of Ae . aegypti oviposition behavior . We simultaneously monitored female oviposition and juvenile development in 80 experimental containers located across 20 houses in Iquitos , Peru , to test the hypothesis that Ae . aegypti oviposit preferentially in sites with the greatest potential for maximizing offspring fitness . Females consistently laid more eggs in large vs . small containers ( β = 9 . 18 , p<0 . 001 ) , and in unmanaged vs . manually filled containers ( β = 5 . 33 , p<0 . 001 ) . Using microsatellites to track the development of immature Ae . aegypti , we found a negative correlation between oviposition preference and pupation probability ( β = −3 . 37 , p<0 . 001 ) . Body size of emerging adults was also negatively associated with the preferred oviposition site characteristics of large size ( females: β = −0 . 19 , p<0 . 001; males: β = −0 . 11 , p = 0 . 002 ) and non-management ( females: β = −0 . 17 , p<0 . 001; males: β = −0 . 11 , p<0 . 001 ) . Inside a semi-field enclosure , we simulated a container elimination campaign targeting the most productive oviposition sites . Compared to the two post-intervention trials , egg batches were more clumped during the first pre-intervention trial ( β = −0 . 17 , P<0 . 001 ) , but not the second ( β = 0 . 01 , p = 0 . 900 ) . Overall , when preferred containers were unavailable , the probability that any given container received eggs increased ( β = 1 . 36 , p<0 . 001 ) . Ae . aegypti oviposition site choice can contribute to population regulation by limiting the production and size of adults . Targeted larval control strategies may unintentionally lead to dispersion of eggs among suitable , but previously unoccupied or under-utilized containers . We recommend integrating targeted larval control measures with other strategies that leverage selective oviposition behavior , such as luring ovipositing females to gravid traps or egg sinks .
At present , dengue virus transmission can be controlled or prevented only through suppressing mosquito vector populations [1] . Even with the advent of a licensed dengue vaccine , which is anticipated by 2015 [2] , vector control will remain a necessary component of any sustainable program to eliminate dengue transmission in endemic areas or prevent virus introduction into new areas [3] . Unfortunately , few contemporary dengue control programs have achieved the high thresholds of vector population suppression ( estimated to be >90% at some locations [4] , [5] ) needed to prevent epidemics [6] . Controlling Aedes aegypti , the primary dengue vector worldwide , is challenging because it is well-adapted to the domestic environment [7] , [8] . Adult mosquitoes rest indoors on clothing and underneath furniture , where they are difficult to reach using traditional aerosol or residual insecticides [7] , [9] . Furthermore , females deposit their eggs in a wide assortment of man-made containers , ranging from water storage drums to discarded bottles and cans , making exhaustive larval control impractical in most cases [4] , [10] , [11] . Ae . aegypti productivity tends to be clustered at most field locations , with the majority of the adult population emerging from a small subset of water-holding containers [10]–[12] . Thus , targeting larviciding and container elimination efforts to these most productive containers may substantially improve the efficiency and cost-effectiveness of dengue control [13] . Proponents of targeted larval control predict that elimination of containers producing , for example , 80% of pupae will lead to a sustained linear reduction in the total adult density [10] . This expectation is based , however , upon two key assumptions: ( 1 ) all available Ae . aegypti larval development sites are already at carrying capacity and ( 2 ) oviposition behavior has little impact on population dynamics [10] . Field evaluations of targeted larval control programs have yielded mixed outcomes . Investigators in Myanmar and the Philippines reported nearly linear reductions ( 73–77% ) in the Ae . aegypti Pupae per Person Index ( PPI ) after 5 months [12] . In Thailand , however , only a 15% reduction in PPI was observed after implementing a targeted control campaign designed to eliminate 80% of pupal production . In Iquitos , Peru , a 236% increase in PPI was noted after an intervention designed to eliminate 92% of pupal production [12] . Thus , the efficacy of targeted larval control varies substantially between settings and likely depends upon details of Ae . aegypti ecology and population dynamics at the local scale . Selection of an oviposition site by a female mosquito directly affects offspring survival and growth [14]–[16] , and has consequences for population dynamics [17] . Because evolutionary theory predicts that animals should act to maximize their reproductive success , egg-laying females are expected to select the most suitable sites for their offspring based on reliable cues of habitat quality [18]–[20] . Whether and how female Ae . aegypti select oviposition sites , the impact of oviposition decisions on offspring fitness , and how females adjust to changes in oviposition site availability will affect the validity of the two key assumptions underlying targeted larval control . Previously , we demonstrated that free-ranging Ae . aegypti in Iquitos actively select egg-laying sites [21] . In particular , females exhibited a preference for containers holding conspecific larvae and pupae . Container characteristics of secondary importance included large size , abundant organic material , and exposure to sunlight [21] . In the present study , we assessed whether Ae . aegypti oviposition site choice is correlated with offspring performance . We tested the prediction that females will lay more eggs in containers in which more juveniles successfully complete development and grow to large adult size , two important components of mosquito fitness [22]–[24] . We also investigated how individual females partition their egg batch among available containers . We predicted that , prior to targeted container elimination , individual females would cluster their egg batch in a preferred container , but switch to spreading their eggs widely among more remaining , available containers if preferred sites were eliminated . By examining whether Ae . aegypti females adjust their egg-laying strategies in response to environmental change as well as the implications of oviposition site choice for population dynamics , we hope to better understand why targeted larval control measures may not achieve the desired level of population reduction in some settings . Ultimately , we expect our detailed findings on Ae . aegypti behavior to provide insight for the development of improved strategies for vector population suppression .
Households included in our field experiment were selected based on the home owners' willingness to participate . After explanation of study objectives and procedures , verbal consent was obtained from the head of each household . We did not collect information on household residents . Our study was approved by the local Ministry of Health , Dirección Regional de Salud-Loreto . Institutional Review Boards ( IRBs ) from the University of California , Davis and the United States Naval Medical Research Center ( Project #: PJT-NMRCD . 032 ) determined that our study did not meet the definition of human subjects research and IRB approval was , therefore , not required . A waiver of IRB approval was granted by the UC Davis IRB for feeding laboratory-reared mosquitoes on humans . Our study was conducted in Iquitos ( 73 . 2°W , 3 . 7°S , 120 m above sea level ) , a city of approximately 380 , 000 people in northeast Peru . Iquitos is located at the confluence of the Amazon , Nanay , and Itaya Rivers in the Department of Loreto and has been described in detail previously [25]–[27] . Daily air temperature , relative humidity , and rainfall data collected from a National Oceanic and Atmospheric Administration meteorological station located at the airport ( ∼6 km from the city center ) demonstrated that the climate of Iquitos is relatively consistent year round , with rain falling during all months and small fluctuations occurring in temperature and relative humidity [28] , [29] . Our experiments took place during August to November 2008 . During these months , mean temperature ( ± SD ) was 26 . 2±1 . 3°C , mean relative humidity ( ± SD ) was 81 . 2±5 . 1% , and mean daily rainfall ( ± SD ) was 6 . 0±12 . 0 mm [28] . Both experiments conducted during this study ( described below ) required genotyping mosquitoes to match them to parents . We established 18 Ae . aegypti family lines in the field laboratory by collecting Ae . aegypti eggs ( F0 generation ) from 36 households across 18 neighborhoods in Iquitos . Because our goal was to make these families easily distinguishable , each family originated from a different neighborhood ( males and females collected >100 m apart to avoid inbreeding ) to maximize the number of alleles shared within a family and minimize alleles shared between families . Field-collected eggs were hatched by immersion in hay infusion overnight and larvae reared according to the standardized protocol described by Wong et al . [29] . Throughout the rearing process , mosquitoes were kept separated by collection house and date . Paired matings were set up as detailed by Wong et al . [30] and all F0 mosquitoes were assigned unique identifying numbers . Females were offered an opportunity to imbibe blood from a human daily , but were not fed sugar ( see [30] ) . F1 eggs were collected daily , labeled by the mother's identifying number , allowed to embryonate in a moist chamber for 48 hrs , dried for storage , and later hatched for experiments . Upon completion of three gonotrophic cycles or death , F0 parents were transferred to 1 . 5 mL plastic vials filled with 96% ethanol and stored at −20°C for subsequent genotyping . Data loggers were used to record weather variables once per hour . During the field experiment , Hobo ProV2 data loggers ( U23-001 ) were deployed in 14 of the 20 houses ( attached to the side of a container ) to monitor ambient temperature and relative humidity ( Onset Computer Corporation , Pocasset , MA ) . In the same houses , Hobo Pendant loggers ( UA-002-64 ) were placed inside containers to monitor water temperature . We did not have enough data loggers to monitor weather at all 20 houses , but based on previous experience we expected that temperatures would be consistent across the city . Within the semi-field enclosure , loggers were used to record air temperature , relative humidity , and water temperature indoors and outdoors once per hour . All specimens from this study were transported to the University of California , Davis ( UCD ) for DNA extraction and genetic analysis . DNA from adults used in paired laboratory matings ( to establish families ) was purified by potassium acetate/ethanol precipitation [37] . DNA from legs of released females was isolated using the same method , with the exception that reagents were used at 50% volume . Due to the large number of experimentally collected mosquitoes ( from the field or semi-field enclosure ) , DNA from these individuals was purified using the automated BioSprint 96 DNA extractor and reagents from the BioSprint 96 Kit ( Qiagen , Valencia , CA ) . Individuals were genotyped at ten microsatellite loci using fluorescent-labeled forward primers as described in Wong et al . [30] . Polymerase chain reaction ( PCR ) products were diluted 1∶60 in ddH2O and submitted to the College of Agriculture and Environmental Sciences Genomics Facility at UCD ( http://cgf . ucdavis . edu/home/ ) for fragment analysis on an ABI 3730 XL capillary sequencer ( Life Technology Corp . , Carlsbad , CA ) . Resulting chromatograms were analyzed using ABI Peak Scanner™ software ( Applera Corp . , Norwalk , CT ) . Exclusion-based parentage analysis was performed using PROBMAX version 1 . 2 [38] to identify offspring of parental pairs [30] .
During the field study , mean air temperature , water temperature , and relative humidity were consistent across houses and between the two trial periods ( Table S1 ) . Mean air temperature ranged from 26 . 6±1 . 9°C to 28 . 1±2 . 7°C . Water temperature was similar to air temperature , but exhibited less fluctuation throughout the day . Mean relative humidity ranged from 76 . 3±6 . 1% to 82 . 8±6 . 1% . Within the semi-field enclosure , air temperature , water temperature , and relative humidity were also similar between trials ( Table S2 ) . Mean air temperature ranged from 27 . 3±0 . 8°C to 29 . 3±1 . 0°C indoors and from 26 . 4±1 . 2°C to 29 . 7±1 . 3°C outdoors . In general , temperatures fluctuated less in water compared to air , and less indoors compared to outdoors . Mean relative humidity ranged from 70 . 8±4 . 8% to 83 . 6±4 . 9% . Data on the mean number of eggs deposited per container per week are shown in Figure 4 . The optimal model included a random effect due to house and fixed effects due to container size , fill method , larval density , and week ( Table 1 ) . In general , more eggs were laid in large vs . small containers , in unmanaged vs . manually filled containers , with increasing larval density , and to a lesser extent , with week . There was no significant effect of trial ( likelihood ratio = 0 . 57 , p = 0 . 45 ) or container size by fill interaction ( likelihood ratio = 1 . 29 , p = 0 . 256 ) . A total of 3 , 263 pupae were collected from all containers located in the 20 households ( mean [± SE] = 40 . 7±7 . 3 pupae per container; range = 0 to 384 ) . More pupae were collected from large unmanaged containers ( n = 1 , 933 pupae ) than any other container treatment ( Figure S1 ) . Due to the time-intensive nature of genotyping all mosquitoes in order to identify those introduced from established families ( 25 F1 larvae introduced per container ) , standardized pupation probability was calculated for Ae . aegypti from containers in eight houses from the first trial ( Figure 5 ) . In these eight households , mean ( ± SE ) larval density just prior to F1 introduction was 2 . 56 ( ±0 . 83 ) larvae per L in large unmanaged containers , 0 . 76 ( ±0 . 37 ) larvae per L in large manually filled containers , 0 . 75 ( ±0 . 32 ) larvae per L in small unmanaged containers , and 0 larvae per L in small manually filled containers . Of the 996 pupae genotyped , we matched 231 individuals to parental pairs from established families ( mean [± SE] = 7 . 2±1 . 3 matched individuals per container; range = 0 to 23 ) . Pupation probability was significantly influenced by treatment ( container size by fill method interaction ) and house . First instar larvae introduced into small unmanaged containers exhibited significantly higher probability of pupation ( β = 3 . 37 , p<0 . 001 ) compared to individuals in the three other container types . No differences in pupation probability were observed among individuals developing in small manually filled containers compared to large unmanaged ( β = −2 . 37 , p = 0 . 214 ) or large manually filled containers ( β = −1 . 79 , p = 0 . 479 ) . There was also no difference in pupation probability between individuals from large containers , regardless of fill method ( β = 0 . 58 , p = 0 . 811 ) . Within each container treatment , we found no significant effect of larval density on pupation rates ( likelihood ratio = 0 . 67 , p = 0 . 414 ) . Mean wing length of female mosquitoes collected from all 20 houses are shown in Figure 6 . Wing lengths of males followed a similar pattern ( Figure S2 ) . Female wing lengths ranged from 1 . 85 to 3 . 23 mm ( median = 2 . 51 mm ) and wing lengths of males ranged from 1 . 55 to 2 . 56 mm ( median = 2 . 00 mm ) . The optimal mixed effects models included a random effect due to house and fixed effects due to container size , fill method , and larval density ( Table 2 ) . Female wing length decreased significantly among Ae . aegypti developing in large vs . small containers , in unmanaged vs . manually filled containers , and with increasing larval density . Similar patterns were observed for males , with wing length decreasing in large containers , in unmanaged containers , and with increasing larval density . For both sexes , there was no significant effect of trial ( females: likelihood ratio = 1 . 72 , p = 0 . 190; males: likelihood ratio = 1 . 38 , p = 0 . 24 ) or container size by fill interaction ( females: likelihood ratio = 0 . 25 , p = 0 . 803; males: likelihood ratio = 0 . 61 , p = 0 . 435 ) . The numbers of females released , eggs collected , and offspring genotyped during each trial within the semi-field enclosure are shown in Table 3 . The total number of eggs collected decreased steadily during each successive trial . Detailed results regarding on which days and in which containers individual females laid their eggs ( those that could be genotyped ) are displayed in Figure S3 . Based on genotyped offspring , we calculated the largest proportion of each egg batch that was concentrated within a single container ( Figure 7 ) . During the first trial ( pre-intervention ) , six egg batches were each aggregated within a single container ( always in a large unmanaged container ) . Among all subsequent trials ( pre- and post-intervention ) , there was only a single batch in which all eggs were deposited within a single container ( trial 2 , concentrated in a small manually filled container ) . In general , egg distribution was more clumped during the first trial compared to the later three trials . Values for the Shannon equitability indices for each trial are shown in Figure 8 . In our model , Shannon indices were affected by trial , but not by gonotrophic cycle number or female ( data not shown ) . Shannon equitability indices were significantly different between the two pre-intervention trials ( trial 1 vs . trial 3 , β = 0 . 18 , p = 0 . 014 ) , but not between the two post-intervention trials ( trial 2 vs . trial 4 , β = 0 . 02 , p = 0 . 991 ) . Individual females' egg batches were more clumped during trial 1 ( pre-intervention ) compared to the two post-interventions trials ( β = −0 . 17 , p<0 . 001 ) . There was no difference , however , in Shannon equitability indices for trial 3 ( also pre-intervention ) compared to the two post-intervention trials ( β = 0 . 01 , p = 0 . 900 ) . When containers were examined daily for whether or not they received eggs ( all eggs included , genotyped or not ) , the random effect of trial was not significant ( intercept variance = 0 ) . The probability that a container received eggs increased when containers were located indoors ( β = 1 . 36 , p<0 . 001 ) and if containers were large and unmanaged ( β = 1 . 16 , p = 0 . 012 ) . The overall probability that any container received eggs increased during the post-intervention scenario ( only small manually filled containers present in enclosure: β = 1 . 36 , p<0 . 001 ) .
When presented with a choice of four container types varying in size and organic content , wild female Ae . aegypti consistently deposited more eggs in large containers with abundant organic material . This behavior is expected to be adaptive , with females choosing sites based on cues of habitat quality . After monitoring the development of juvenile Ae . aegypti , however , we did not find a positive association between female egg-laying choice and juvenile growth or survival . The container type most preferred by ovipositing females ( large unmanaged ) produced individuals with low pupation probability and small adult body size . Pupation probability was highest among Ae . aegypti in small unmanaged containers , which received ample food and relatively few eggs , creating an environment consistent with low competitive pressure for food . In large unmanaged containers , we suspect that high food content was offset by high larval density . Large unmanaged containers may have quickly reached carrying capacity , so that F1 pupation rates were no better than in sites receiving little total food ( manually filled containers ) . Prior to F1 introduction , mean larval density was 3 . 4 times greater in large unmanaged containers ( 2 . 56 larvae per L ) compared to small unmanaged containers ( 0 . 75 larvae per L ) . To avoid colinearity with container size and fill method , we did not directly assess larval density as a predictor in our models . We instead examined relative larval density within each container treatment , but found no significant effect of larval density on pupation rates . The negative impact of high larval density was evident , however , in our analysis of Ae . aegypti body size . Large unmanaged containers yielded the smallest adult mosquitoes . Furthermore , within each of the four container treatments , body size clearly decreased with increasing larval density . Our result is consistent with previous field studies in Iquitos [26] , Puerto Rico [44] , and Thailand [5] that demonstrated negative relationships between the density of larvae in aquatic habitats and the size of emerging adults . Wing lengths of females collected during our study ( range = 1 . 85 to 3 . 23 mm , median = 2 . 51 mm ) were comparable to those reported by Schneider et al . [26] in Iquitos ( range = 1 . 67 to 3 . 83 mm , median = 2 . 60 mm ) . Mismatches between female oviposition preference and offspring performance have been reported for several insect species ( e . g . , [45] , [46] ) , including mosquitoes [47] . Sub-optimal oviposition site selection may result from females' inability to predict stochastic events , sense determinants of site quality , or obtain complete knowledge of the environment [47] . Alternatively , apparent mismatches are sometimes attributed to experimental design and/or failure to examine important variables [46] . We attempted to simulate Ae . aegypti container colonization and water-use patterns typical of Iquitos , but our study was limited in some respects . During the re-introduction of larvae into containers to imitate colonization , eggs were hatched synchronously rather than gradually in installments , as is typical for Ae . aegypti [35] . The faster rate of larval introduction may have disproportionately increased levels of density-dependent competition in the most preferred containers ( large unmanaged ) . Containers occurring naturally in the field are likely to experience different rates of water evaporation and filling . This may result in dramatic fluctuations in larval densities , as well as variable cycles of desiccation and/or overflowing . To make our study design and analysis tractable , we artificially maintained stable water levels in our experimental containers . For species whose larvae develop in small containers and must mature before the habitat desiccates , maternal ability to assess water permanence would be favored [48] . It is possible that female Ae . aegypti evolved to detect cues associated with water permanence , and thus acted to trade off risks between desiccation and food competition for their progeny . Due to our experimental design , we were unable to assess the importance of container desiccation as a selective force in oviposition site choice . Such an investigation would require a detailed study on water dynamics of naturally-occurring ( i . e . , non-experimental ) containers . Previously , we observed that the majority of Ae . aegypti eggs tend to be aggregated within a small subset of containers . In addition , females were most likely to oviposit in sites that contained , or had recently contained , conspecific larvae and/or pupae [21] . These findings are consistent with other studies demonstrating that semiochemicals produced by conspecifics [49] , [50] and conspecific-associated bacteria [51] act as oviposition attractants for Ae . aegypti ( reviewed in [52] ) . During the present study , we did not attempt to isolate or identify these chemical mediators . Instead , our intention was to complement chemical ecologists' studies by investigating the consequences of conspecific attraction for Ae . aegypti offspring fitness and population dynamics . In our study , large aggregations of larvae in preferred containers led to the production of numerous small adults . For mosquitoes , adult body size can have important impacts on the rate of pupation growth and patterns of virus transmission . Small body size has been correlated with reduced life span and decreased fecundity for females and decreased mating success for males ( e . g . , [24] , [53]–[56] ) . Female body size also exhibits a complex relationship with several components of vectorial capacity . A population dominated by small females , which are less susceptible to oral dengue infection [57] and less persistent in seeking blood meals [58] , may serve to attenuate dengue transmission . On the other hand , small females must feed more frequently [59] , [60] , which could lead to increased rates of human-vector contact and enhance virus transmission . Our results indicate that Ae . aegypti oviposition site choices that lead to crowding of larvae may play a role in population regulation by limiting the production and size of adults . In this situation , removal of the most productive containers would reduce adult abundance in the short term , but the long term population-level outcome would depend on the availability of alternative suitable oviposition sites in the area . If all water-filled containers are infested to their carrying capacity , targeted larval control is expected to result in a sustained , linear reduction in adult mosquito density [10] . On the other hand , if suitable unoccupied or under-utilized containers are available , targeted larval control could merely shift production to new containers over the next few generations . Results from our companion study indicated that , in Iquitos , containers suitable for Ae . aegypti development are frequently unoccupied ( STS , unpublished ) . We predict that colonization of previously unoccupied sites could release large numbers of larvae from density-dependent food competition , eventually attenuating or undermining the immediate gains of targeted larval control . Results from a Brazilian field study support this idea . Maciel-de-Freitas and Lourenço-de-Oliveira [61] documented that elimination of the most productive container type ( water tanks accounting for 72% of pupae ) led to increased productivity from almost all other container classes , most notably in metal drums , which shifted from producing 3 . 5% to 30 . 7% of all pupae . Accompanied by this shift in productivity was a rebound in the adult densities to pre-intervention levels within 4–5 weeks . Only after eliminating both water tanks and metal drums ( which were considered unimportant prior to the intervention ) did investigators observe a long term drop in adult densities . The authors speculate that sustained reductions in Ae . aegypti densities were possible because of the similarity between water tanks and metal drums; both are large , typically shaded , perennial water storage containers . Even then , interventions that were designed to eliminate 75 . 9% of pupal production resulted in a 45 . 7% reduction in adult densities [61] . We suspect that in Iquitos and other locations where rain falls year round , large numbers of alternative containers and plasticity in Ae . aegypti oviposition behavior will render the long term results of targeted larval control less effective than anticipated . The degree and speed of population recovery will also depend on whether females' egg distribution strategies are influenced by the characteristics of available containers . Inside the semi-field enclosure , egg distribution patterns were more aggregated for females during the first pre-intervention trial ( trial 1 ) , but not the second ( trial 3 ) , compared to the two post-interventions trials ( trials 2 and 4 ) . During trial 1 , females frequently shared preferred oviposition containers , clustering the overall majority of eggs in these two sites . When only least preferred containers were available ( post-intervention , trials 2 and 4 ) , females were less likely to concentrate a large portion of their egg batch in any particular site , leading to a more even overall dispersion of eggs among containers . This pattern of spreading eggs evenly among sites , however , was also observed during our second pre-intervention trial ( trial 3 ) . Our mixed results suggest that egg distribution strategies are somewhat plastic and context-dependent . Differences between trials 1 and 3 may be the result of behavioral variation among individuals . Even individuals within a population are expected to vary in oviposition site selection strategies [62] . It is thus conceivable that individuals faced with similar environments could vary in their egg distribution strategies as well . Nonetheless , when we examined all eggs laid within the enclosure ( genotyped or not ) , the overall probability that a container received eggs did increase during the post-intervention trials . The possibility that females may spread eggs more widely after elimination of the most productive containers is consistent with evidence from the field [61] and deserves further investigation . A major shortcoming of this experiment was our inability to genotype offspring from eggs that failed to hatch . Overall , we were able to assign parentage to 74% of all offspring from the semi-field enclosure . This provides an informative , albeit incomplete , picture of oviposition patterns among the released females . If all unhatched eggs could be attributed to a few uninseminated females , we would expect our conclusions to be unbiased . Alternatively , if a proportion of every female's egg batch failed to hatch , this could lead us to underestimate the number of containers used by ovipositing females . We suspect that the true explanation lies somewhere between these two extremes . Another limitation of our study was that we substituted the two large containers with two small containers under the post-intervention scenario , which is unrealistic for a dengue control campaign . We took this step to prevent confounding between the effects of targeting specific containers as opposed to reducing container abundance in general . Targeted larval control campaigns are specifically directed at the small subset of most productive containers , so we would not expect overall container abundance to change dramatically . For this reason , we were more interested in how females responded to the non-availability of large containers rather than a reduction in container numbers . Had we been able to conduct more trials inside the enclosure , we would have examined effects of container removal without substitution , as well as effects of varying Ae . aegypti female density in the household . Finally , all females in this experiment were confined to the one household within the semi-field enclosure . This design precluded us from testing whether female oviposition choices would be different if they had access to multiple houses and different container types , as occurs naturally in the field . We had originally planned to address that question during a field validation in which we would release females into the field and search for their progeny in the release house as well as neighboring houses . Due to a dengue-4 epidemic in Iquitos during fall 2008 [63] , [64] , however , we were unable to release mosquitoes to conduct this field validation . We do not dispute that larval Ae . aegypti control should be practiced or that interventions such as container elimination , larviciding , and biological control are more cost effective when targeted to the most productive containers [12] . We suggest , however , that targeted larval control alone should not be relied upon as the predominant strategy to prevent dengue transmission . Due to the complexity of Ae . aegypti ecology and the low population threshold densities required for dengue transmission [4] , [5] , a combination of multiple control measures ( e . g . , container elimination , egg sinks , autodissemination of insect growth regulators , lethal ovitraps , etc . ) will likely be necessary to produce an epidemiologically significant change in vector abundance . For example , elimination of the most productive containers could be coupled with deployment of gravid traps or egg sinks [21] , [65] . Such a combined strategy may encourage females to lay eggs in traps , either for themselves ( gravid traps ) or for their offspring ( egg sinks ) , as well as minimize shifts in productivity to under-utilized containers . Regardless of the specific combination of tools used , successful integrated control strategies should be based on sound understanding of Ae . aegypti behavior and population dynamics . | Controlling the mosquito Aedes aegypti , the predominant dengue vector , requires understanding the ecological and behavioral factors that influence population abundance . Females of several mosquito species are able to identify high-quality egg-laying sites , resulting in enhanced offspring development and survival , and ultimately promoting population growth . Here , the authors investigated egg-laying decisions of Ae . aegypti . Paradoxically , they found that larval survival and development were poorest in the containers females most often selected for egg deposition . Thus , egg-laying decisions may contribute to crowding of larvae and play a role in regulating mosquito populations . The authors also tested whether removal of the containers producing the most adult mosquitoes , a World Health Organization-recommended dengue prevention strategy , changes the pattern of how females allocate their eggs . Elimination of the most productive containers led to a more even distribution of eggs in one trial , but not another . These results suggest that behavioral adjustments by egg-laying females may lessen the effectiveness of a common mosquito control tactic . The authors advocate incorporating control strategies that take advantage of the natural egg-laying preferences of this vector species , such as luring egg-laying females to traps or places where their eggs will accumulate , but not develop . | [
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"heal... | 2012 | Linking Oviposition Site Choice to Offspring Fitness in Aedes aegypti: Consequences for Targeted Larval Control of Dengue Vectors |
Abasic ( AP ) sites in DNA arise through both endogenous and exogenous mechanisms . Since AP sites can prevent replication and transcription , the cell contains systems for their identification and repair . AP endonuclease ( APEX1 ) cleaves the phosphodiester backbone 5′ to the AP site . The cleavage , a key step in the base excision repair pathway , is followed by nucleotide insertion and removal of the downstream deoxyribose moiety , performed most often by DNA polymerase beta ( pol-β ) . While yeast two-hybrid studies and electrophoretic mobility shift assays provide evidence for interaction of APEX1 and pol-β , the specifics remain obscure . We describe a theoretical study designed to predict detailed interacting surfaces between APEX1 and pol-β based on published co-crystal structures of each enzyme bound to DNA . Several potentially interacting complexes were identified by sliding the protein molecules along DNA: two with pol-β located downstream of APEX1 ( 3′ to the damaged site ) and three with pol-β located upstream of APEX1 ( 5′ to the damaged site ) . Molecular dynamics ( MD ) simulations , ensuring geometrical complementarity of interfaces , enabled us to predict interacting residues and calculate binding energies , which in two cases were sufficient ( ∼−10 . 0 kcal/mol ) to form a stable complex and in one case a weakly interacting complex . Analysis of interface behavior during MD simulation and visual inspection of interfaces allowed us to conclude that complexes with pol-β at the 3′-side of APEX1 are those most likely to occur in vivo . Additional multiple sequence analyses of APEX1 and pol-β in related organisms identified a set of correlated mutations of specific residues at the predicted interfaces . Based on these results , we propose that pol-β in the open or closed conformation interacts and makes a stable interface with APEX1 bound to a cleaved abasic site on the 3′ side . The method described here can be used for analysis in any DNA-metabolizing pathway where weak interactions are the principal mode of cross-talk among participants and co-crystal structures of the individual components are available .
Loss of a nucleobase without cleavage of the DNA backbone results in formation of an abasic ( AP ) site . AP sites arise frequently in normal DNA from a variety of causes: spontaneous hydrolysis of nucleobases , DNA damaging agents or DNA glycosylases that remove specific abnormal bases , such as uracil , N3-methyladenine , or 8-oxoguanine . Since AP sites are pre-mutagenic lesions that can prevent normal DNA replication and transcription , the cell contains systems to identify and repair such sites , specifically the base excision repair ( BER ) pathway [1] . Apurinic/apyrimidinic endonuclease 1 ( APEX1 ) cleaves the phosphodiester backbone 5′ to the AP site [2]–[4] . The cleavage , which is a key step in the BER pathway , is followed by nucleotide insertion and removal of the downstream deoxyribose moiety , performed most often by DNA polymerase beta ( pol-β ) [5] . The fact that nucleotide insertion requires cleavage of the AP site suggests interaction of the two enzymes . Biological experiments to examine whether APEX1 and pol-β interact have been carried out using several different methodologies [6]–[11] . A complex of the two proteins was detected not only by yeast-two hybrid studies , but also by electrophoretic mobility shift assay ( EMSA ) and EMSA supershift followed by immunoblotting [6] , [11] . In the latter studies the complex was detected only when DNA containing an uncleaved AP site was present . Furthermore , in kinetic studies the presence of APEX1 . pol-β . DNA ternary complex stimulates pol-β gap filling activity [11] . The current model for a substrate containing a single nucleotide gap in double stranded DNA suggests that DNA binding specificity of APEX1 and pol-β determines the orchestrated coordination of the sequential steps , although a multiprotein–DNA complex facilitates coordination . The fact that the evidence for coordination of both enzymes requires the presence of substrate , i . e . , AP-site containing DNA , suggests that the two proteins must be seated on the DNA in proximity to each other or that binding by APEX1 to the AP-site recruits the second protein . We present detailed theoretical analysis of possible complexes between APEX1 and pol-β , at which EMSA analysis or the yeast two-hybrid system can only hint . The analysis predicts a communicative interaction between the two proteins under the assumption that the initial interaction occurs when both proteins are seated on the DNA helix . Crystal structures of the individual proteins bound to DNA are known [12] , [13] . In this study the two proteins are positioned on a DNA helix to examine possible placements for interactions between them . Subsequent Molecular Dynamic ( MD ) simulation is applied to the complexes in order to ensure optimal atom packing on the protein-protein interface and identify interacting reisdues . Having identified critical amino acid residues at the interface , we propose a mechanism by which pol-β might displace APEX1 as the former enzyme seats itself at the cleaved abasic site .
For this orientation ( see schematic diagram in Figure 1A ) , initial complexes for pol-β in the closed conformation ( PDB-file 2fmq ) and APEX1 ( PDB-file 1de8 ) were constructed by aligning the 3′-side of damaged DNA from the APEX1 co-crystal with the 5′-side of the DNA lesion in the pol-β co-crystal . Three complexes termed c1 , c2 , and c3 satisfied the described requirements ( see corresponding alignment in Figure 2A ) . In the first complex ( c1 ) , steric overlaps between APEX1 and pol-β involved more than 10 residues comprising more than 100 atoms in each protein . Polypeptide chains of the two proteins interlaced with each other to produce an unrealistic complex . In the third complex ( c3 ) the interface area was ∼200 Å2 but the ratio of gap volume to area ( ∼140 ) was unacceptably large compared to other values in Table 1 , indicating very weak interaction , if any . The remaining complex ( c2 ) represented an optimal prediction with only several atoms in steric overlap , which were resolved during MD ( see below ) . The complex is shown in Figure 2B . In order to resolve steric overlaps and ensure optimal atom packing at the protein-protein interface , an MD simulation was applied to the complex . The MD simulation continued for 1 ns ( see Methods ) . The interface between APEX1 and pol-β was analyzed after each 0 . 1 ns of simulation ( see Table 1 ) . During the entire simulation a stable complex was observed with interface area reaching 644 Å2 and binding energy as low as −10 kcal/mol . The interface area fluctuated by ∼30% while the shape of interface stayed essentially the same ( see column ‘Length&Breadth’ ) . Similarly , the interface atomic content did not change and consisted of slightly less than 50% of polar and slightly more than 50% non-polar atoms . On average the interface had 4 short-lived hydrogen bonds . Out of 21 different hydrogen bonds observed during the simulation at most 6 could be observed at any time . The most stable hydrogen bonds observed throughout the simulation contained the sidechain atoms of Asn222 and mainchain oxygen of Gln31 and mainchain nitrogen of His34 . Estimation of binding energies with FOLD-X server revealed that the major contribution to the free energy of binding comes from hydrophobic desolvation and Van der Waals interaction with 1/3 contribution from hydrogen bonding . Thus , we concluded that the major interactions stabilizing the interface were hydrophobic . Analysis of the complex allowed identification of potential interacting residues of APEX1 and pol-β ( see Table 2 ) . The interface of APEX1 contained 16 residues with six , Arg221 , Asn222 , Lys224 , Gln235 , Ser275 and Lys276 , representing the largest interface surface . The interface of pol-β contained 13 residues with seven , Gln31 , Ile33 , His34 , Ser109 , Lys113 , Gly305 and Val306 , contributing the largest interface area . Overall the interface consisted of three distinct spatial segments ( Figure 3 ) . In pol-β the interfaces of each segment were composed of residues from different subdomains: in segment #1 from the thumb subdomain , in segment #2 from the 8-kD subdomain and in segment #3 from the finger subdomain . Segments #1 and #3 were smaller then segment #2 . The segments behave differently during the simulation ( see Table 1 ) . The areas of segments #2 and #3 were essentially stable while the area of segment #1 fluctuated ( see Table 2 ) . Not all of the amino acid residues in the segment #1 participated in interaction at all times . The 3′-complex with pol-β in open conformation ( PDB-file 9ici ) was constructed in the same fashion as the 3′-complex with pol-β in the closed conformation ( see schematic diagram on Figure 1B ) . The MD simulation of the complex revealed critical differences in behavior of the interface ( see Table 3 ) . Namely the interface area increased steadily during the simulation and was on average 10% larger than that for the complex with pol-β in closed conformation , suggesting stronger binding , even though the estimated free energies of binding were similar . The fact that the interface was larger was remarkable since the protein-protein interface in the complex with pol-β in open conformation lacked segment #1 . For most of the simulation time the interface consisted of a single surface patch formed from segments #2 and #3 of the complex with closed conformation of pol-β plus several peripheral residues: Leu44 , Glu217 , Ile218 , Asn259 , Pro261 , Tyr262 , Tyr264 in APEX1 and Ala32 , Arg40 , Thr93 , Val115 , Glu117 in pol-β . In the last 0 . 1 ns of simulation another patch appeared between residues 177–183 in APEX1 and 231–233 in pol-β . Since its area was less than 50 Å2 , we neglected it in subsequent analysis . The interface atomic content was similar to the one observed for complex with pol-β in closed conformation , i . e , it consisted of slightly less than 50% polar and slightly more than 50% non-polar atoms . At the same time there were fewer hydrogen bonds at the interface suggesting that hydrophobic interaction was even more important for interface stabilization than in the complex with pol-β in closed conformation . For this orientation ( see schematic diagram on Figure 1C ) , three possible complexes using pol-β in the closed conformation and APEX1 were initially constructed by aligning the 5′-side of the damaged DNA from the APEX1 co-crystal with the 3′-side of the DNA with lesion in the pol-β co-crystal . Three complexes termed c4 , c5 , and c6 satisfied the described requirements ( see corresponding alignment in Figure 4A ) . In the first complex ( c4 ) , steric overlaps of APEX1 and pol-β were large , involving more than 15 residues with more that 150 atoms in each protein . Moreover , polypeptide chains from the two proteins interlaced , producing an unrealistic complex . In the third complex ( c6 ) the proteins hardly touched each other so that the corresponding interface was small with large water filled space between proteins . The remaining complex ( c5 ) represented an optimal prediction with several atoms in steric overlaps , which were resolved during MD ( see below ) . The complex is shown in Figure 4B . MD was applied to the 5′-complex in order to resolve steric overlaps and ensure optimal atom packing at the protein-protein interface . The MD simulation continued for 1 ns ( see Methods ) and revealed an unstable , short-lived complex . Already after 0 . 1 ns of simulation the interface area was only 228 Å2 and after 0 . 4 ns the complex dissociated completely . Therefore , we concluded that the 5′-complex with pol-β in closed conformation was not likely to exist . The 5′-complex with pol-β in open conformation was constructed by replacing the structure of pol-β in the c4 complex ( see Figure 4A ) with pol-β in the open conformation ( see Methods ) . Because replacement of pol-β in the c5 complex resulted in a complex lacking an interface , we used the c4 complex instead . In order to resolve steric overlaps and ensure optimal atom packing at the protein-protein interface , an MD simulation was applied to the c4 complex ( see Table 4 ) . During the entire simulation a complex with negative ( favorable ) free energies of binding and large interface area was observed . Despite the favorable energy of binding and the apparently large interface area , the interdigitated configuration of the interface suggests that the physical measurements were misleading . The interface of APEX1 ( see Table 5 ) contained 17 residues with Ser123 , Asp124 , Lys125 , and Gln153 , representing the largest interface area . The majority of interface ( 60–70% ) in pol-β was composed of the loop consisting of residues 299–306 from the thumb domain . Val303 , Thr304 and Val306 of pol-β were buried into the APEX1 molecule hooked around APEX1 residues Tyr144 and Asp152 . Furthermore , the interface dynamics ( see Table 4 ) indicate that the interface is unstable . Thus , based on analysis of interface dynamics and visual inspection we concluded that the complex is not likely to exist . The 90 degree bend of DNA found in the co-crystals of pol-β reflects the state when pol-β is seated on the cleaved AP-site . Since details of its binding to DNA are unknown we considered the possibility that pol-β can bind to a straight DNA , displace APEX1 and occupy the site of the lesion . In order to explore this possibility we , extended the DNA in the pol-β co-crystal with a straight 12-mer template DNA taken from the PDB . We then constructed complexes in the same way as we did for the complexes described above , i . e . , by aligning DNAs from pol-β and APEX1 co-crystals . No new complexes at the 3′-side of APEX1 could be constructed , since the extended DNA intruded into pol-β . For the same reason no complex at the 5′-side of APEX1 with pol-β in closed conformation could be constructed ( see Figure 1E ) . The only meaningful complex with straight DNA strand included pol-β in the open conformation located at the 5′-side of APEX1 ( see Figure 1F ) . The DNA alignment and the complex are shown in Figure 5 . MD of the complex revealed weak interaction of pol-β and APEX1 ( see Table 6 ) . Over the time of simulation the interface area increased and binding energies improved ( average of −4 . 3 kcal/mol ) but was still less than the typical binding energy ( <−10 kcal/mol ) for crystallized protein-protein complexes [15] . The interface atom content consisted of more non-polar atoms than polar atoms . While 15 different hydrogen bonds were observed during the simulation , almost all of them were short lived , with at most 7 observed at a time . Only one hydrogen bond was preserved through out the simulation , between Lys125 of APEX1 and Asp17 pol-β . Estimation of binding energies with FOLD-X server revealed that the major contribution to the free energy of binding came from hydrophobic desolvation and Van der Waals interaction , while contribution of hydrogen bonding and electrostatic interactions ( salt bridge ) were two to three times smaller . Thus we concluded that the major interactions stabilizing the interface were hydrophobic . Overall the interface consisted of one large and one small segment ( Table 7 ) . The residues from pol-β in the small segment were from the thumb domain , while residues in the large segment were from the fingers domain . The interface differed from that in the 3′-complex by being less planar and more elongated , despite similar surface areas ( Tables 1 and 6 ) . Otherwise this complex was substantially weaker in binding . If APEX1 and pol-β evolved to form a molecular complex so that the specificity of their interaction optimized the function of the BER pathway , then one would expect that the network of inter-residue contacts constrains the protein sequence . In other words , the changes accumulated in the evolution of one of the interacting proteins would be compensated by changes in the other one [16] . Therefore , we explored whether correlated mutations in predicted interface regions between the two proteins were observed across a variety of species where the sequences of both proteins were available in the PDB . In fact , multiple sequence analyses of APEX1 and pol-β revealed correlated mutation at the interface of the two proteins in the 3′-complex ( Figures 3 and 6 ) . In particular Arg221 of APEX1 and Gln31 of pol-β that interacted in the 3′-complex with pol-β in the closed conformation were changed in five organisms to Lys and Arg respectively . In four of these organisms there was also correlated variation of Ser275 in APEX1 and Ser109 in pol-β , but these residues did not interact in the predicted complex . In addition , in S . purpuratus there was one more coordinated change in interacting residues , Gly225 of APEX1 was mutated to Ser and Ile33 of pol-β was mutated to Met . Altogether these observations of correlated mutations provide additional support for the interactions proposed in this study .
In the present work we have made detailed predictions about possible interacting complexes of apurinic/apyrimidinic endonuclease ( APEX1 ) and DNA polymerase beta ( pol-β ) . Although it is possible that the two proteins function entirely independently of each other , our predictions were based on the assumption that at concentrations found in the nucleus the proteins interact with each other when handing off the product of APEX1 to pol-β . Experimental data indicate that for interaction to occur the two proteins have to be associated with DNA . Aligning the DNAs in the co-crystallized complexes of APEX1 and pol-β effectively positioned proteins on a DNA . Similarly , shifting the co-crystallized DNAs in either direction enabled us to orient pol-β downstream or upstream of an abasic ( AP ) site . Five optimal complexes were identified: two with pol-β located downstream of APEX1 ( 3′ to the lesion ) and three with pol-β located upstream of the APEX1 ( 5′ to the lesion ) . The complexes are schematically displayed on Figure 1 . Additional multiple sequence analysis of APEX1 and pol-β sequences reveals correlated mutations of predicted interacting residues in the 3′-complex , supporting the prediction . The same analysis reveals no correlated mutation in the 5′-complex . Both 3′-complexes were energetically favorable while only one 5′-complex was stable . In particular , interacting surfaces of both proteins in the 3′-complexes open or closed conformation of pol-β repacked during MD simulation analysis to permit sufficient binding to account for complex formation . During the 1 ns of the MD simulation each complex was stable with relatively constant quantitative values of the interfaces and favorable corresponding estimated binding energies ( −10 kcal/mol in each complex ) . On the contrary , MD for the 5′-complex with pol-β in closed conformation revealed an unstable complex that dissociated completely after 0 . 4 ns . Although , similar MD simulation for the 5′-complex with pol-β in open conformation revealed interactions , interface dynamics and visual inspection led us to conclude that the complex was not realistic and physical measurements were misleading . For this complex a steric trap formed in APEX and entangled a loop of pol-β ( residues 299–308 ) . Comparison of the 3′-complexes and the weak 5′-complex with straight DNA revealed several important differences . The 3′-complexes had on average large interface areas and significantly stronger binding energies than the 5′-complex . Also the interfaces in the 3′-complexes required almost no repacking since the binding energies were low already at the beginning of the MD simulations ( see Tables 1 and 3 ) and , therefore , the interfaces could be characterized as complementary and “ready-to-interact” . On the contrary binding energy for 5′-complex with straight DNA was very weak from the beginning and only moderately strong at the end of simulation ( see Table 6 ) . Both APEX1 and pol-β are truncated at the N-terminus by 42 and 9 residues respectively in their crystal structures . The truncated residues would be unlikely to interfere with the predicted interacting protein surfaces in the 3′-complex . The 42 N-terminal residues of APEX1 , if present , would be located at the side of the predicted interface where there is enough space to accommodate them ( see Figure 2 ) and the missing residues in pol-β face away from the interface . In contrast , the nine missing N-terminal residues of pol-β would likely destabilize the 5′-complex as the N-terminus of pol-β is located at the interface in contact with DNA in this complex in such a tight environment ( see Figure 5 ) . This comparison provides further evidence that the 3′-complex is likely to predominate . Pol-β binds a cleaved AP site in open conformation [14] inserts a correct nucleotide in closed conformation and returns to the open conformation before it dissociates from the AP site . It is likely that APEX1 performs its 3′-5′ proofreading function for pol-β at this stage [17] . Pol-β then returns to the site to perform the lyase function to remove the dRP residue [18] , [19] . We propose the following mechanism for APEX1 and pol-β interaction in the 3′-complex ( see Figure 7 ) . After APEX1 has cleaved the AP-site , pol-β in open conformation binds to APEX1 , making a single interface comprising segments #2 and #3 and several adjacent residues including those in-between the two segments . The formed complex displaces APEX1 laterally from the cleaved site although both pol-β and APEX1 remain associated with DNA . Transition of pol-β into the closed conformation ( precatalytic state ) shifts the interface as movement of the 8-kDa domain splits the interface into two distinct segments #2 and #3 and weakens the interaction , while movement of the thumb introduces the new interface segment #1 . Once insertion has occurred , the open conformation , still in communication with APEX1 , is re-established , allowing a shift for APEX1 3′-exonuclease activity . Pol-β then returns to the site to perform its lyase function . Although we do not see complex dissociation in our simulations , we propose two possible scenarios . In the first scenario segment #1 serves as a springboard to displace the APEX1 , which eventually leads to dissociation of the complex . Since APEX1 is processive [20] it could move away from pol-β , but remain associated with DNA or alternatively it could dissociate completely from DNA . Although the complex could dissociate while pol-β is still in open conformation , such dissociation is less likely because of the larger interface area , compare to the complex when pol-β is in closed conformation . This scenario might be required when pol-β cannot perform the lyase activity and moves into long patch repair . In the second scenario , the complex does not dissociate . Instead , the proteins continue to work as a pair: APEX1 recognizes and cleaves AP sites , while pol-β inserts the incoming nucleoside and performs its lyase function . APEX1 then drags pol-β along the DNA strand to the next AP site , which pol-β is less likely to find by itself due to its transient processivity . Therefore , both scenarios imply enhanced catalytic efficiency through interaction of APEX1 and pol-β . Of course , APEX1 and pol-β could bind and dissociate from the DNA independently from one another . However Sokhansanj et al . [21] point out that experimental data for the BER pathway indicate greater overall efficiency then can be accounted for by the individual kinetic constants of the participating enzymes . We have just provided a theoretical basis for interaction between components in one of the known subcomplexes involved in the pathway . The method described here can be used for analysis in any DNA-metabolizing pathway where weak interactions are the principal mode of cross-talk among participants and co-crystal structures of the individual proteins with DNA are available .
The structure of APEX1 was taken from PDB-file 1de8 [12] , which contains APEX1 bound to abasic DNA . The structure of pol-β in the closed conformation was taken from PDB-file 2fmq [13] , which contains pol-β bound to DNA in the precatalytic state . The structure of pol-β in open conformation was taken from PDB-file 9ici [22] , which contains pol-β bound to DNA . The structure of template DNA has been taken from PDB-file 2ezd [23] , which contains 12-mer of double stranded DNA , the longest straight piece of DNA available in the PDB . Chain U of 1de8 representing the AP-site containing DNA was used to position the structure of APEX1 . Chains C and D of 2fmq representing DNA with lesion were used to position the structure of pol-β . The DNA strands from crystal structures of APEX1 and pol-β identified above were aligned in order to construct possible interacting complexes . Superposition of the proteins was calculated from the alignment by minimizing RMSD between mainchain atoms of aligned nucleotides . The Kabsch algorithm [24] implemented in software Friend [25] was utilized for minimization . Chain B of 2ezd was aligned to the DNAs in pol-β co-crystal in order to extend that DNA when constructing 5′-complex with straight DNA . The length of DNA in co-crystal of pol-β in open conformation was not enough to align to the DNA in APEX1 co-crystal to construct 5′-complex . That is why the 5′-complex was constructed by replacing the structure of pol-β in the 5′-complex with pol-β in the closed conformation . Optimal position of pol-β in open conformation was calculated by aligning its structure to the structure of pol-β in the complex by using protein structure alignment method TOPOFIT [26] with RMSD of 1 Å and 198 aligned residues . Of the aligned residues , 176 were from fingers and palm subdomains , which are rigid parts of pol-β . Therefore , positioning of pol-β in open conformation was not biased by alignment of movable 8-kD and thumb subdomains . Molecular dynamics simulation was performed with the aid of the Gromacs software package [27] . The total size of the system was more than 90 thousand atoms . Atomic charges have been set by using OPLSAA force field . For abasic site atoms the charges of atom from nucleic bases have been used . The simulation was executed at 305oK with a time step of 1 fs . Each simulated complex included APEX1 , pol-β , the DNA and explicit solvent . All parameters of protein-protein interfaces except that for free energy of binding were calculated using Protein-Protein interaction server [28] . The free energies of binding for protein complexes were calculated using the FOLD-X server [29] in the fashion of calculating the energies for the complex and each protein and then evaluating the free energy of binding as: ddG ( complex ) −ddG ( APEX1 ) −ddG ( pol-β ) . BLAST [30] searches against non-redundant protein sequence database were performed by using human APEX1 ( accession number NP_001632 ) and pol-β ( accession number NP_002681 ) as query sequences . Fourteen eukaryotic organisms were identified where both proteins were available in each organism . The selected sequences were aligned using CLUSTALW program [31] . | Oxidative damage to DNA happens in every cell as a consequence of the life process . Such damage can inhibit DNA replication and RNA transcription; if not repaired , it can lead to cancer . Consequently , all cells contain an important mechanism for identification and repair of oxidative lesions . Two proteins figure prominently: AP endonuclease 1 , which cleaves the damaged site , and DNA polymerase beta , which inserts a new nucleotide to replace the damaged one . While several biochemical studies indicate interaction between the two proteins , the details of the interaction remain unknown . Here , we develop and apply a new methodology to predict the most likely protein-protein interface between the two proteins . The methodology relies on the assumption , which is validated by experimental evidence , that both proteins must bind to DNA in order to interact . Analysis of the simulated protein behavior in water allowed us to suggest how protein interaction might be coupled to conformational changes in DNA polymerase beta . Further comparative analysis identified coordinated mutations of specific residues at the predicted interfaces . This method can be applied to predict interaction details for any protein pair as long as the proteins in the pair are associated with DNA during the interaction . | [
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] | 2008 | An AP Endonuclease 1–DNA Polymerase β Complex: Theoretical Prediction of Interacting Surfaces |
Triatoma infestans is the main vector of Chagas disease in South America . As in all hematophagous arthropods , its saliva contains a complex cocktail that assists blood feeding by preventing platelet aggregation and blood clotting and promoting vasodilation . These salivary components can be immunologically recognized by their vector's hosts and targeted with antibodies that might disrupt blood feeding . These antibodies can be used to detect vector exposure using immunoassays . Antibodies may also contribute to the fast evolution of the salivary cocktail . Salivary gland cDNA libraries from nymphal and adult T . infestans of breeding colonies originating from different locations ( Argentina , Chile , Peru and Bolivia ) , and cDNA libraries originating from F1 populations of Bolivia , were sequenced using Illumina technology . Coding sequences ( CDS ) were extracted from the assembled reads , the numbers of reads mapped to these CDS , sequences were functionally annotated and polymorphisms determined . Over five thousand CDS , mostly full length or near full length , were publicly deposited on GenBank . Transcripts that were over 10-fold overexpressed from different geographical regions , or from different developmental stages were identified . Polymorphisms were mapped to derived coding sequences , and found to vary between developmental instars and geographic origin of the biological material . This expanded sialome database from T . infestans should be of assistance in future proteomic work attempting to identify salivary proteins that might be used as epidemiological markers of vector exposure , or proteins of pharmacological interest .
Chagas disease is endemic to Latin America [1] , [2] and is caused by the protozoan parasite Trypanosoma cruzi , which is transmitted to humans by triatomine vectors [3] . Although there are 140 extant species of triatomine bugs , a relatively small number are implicated as human vectors , related to their adaptation to colonize human dwellings . Among these limited numbers of species , Triatoma infestans is recognized as an important vector in South America , being responsible for half of the disease transmission to humans . It historically covered a large geographical range , including Argentina , Chile , Brazil , Paraguay , Bolivia and Peru [4] . When attempting to feed , blood sucking animals inject saliva into their vertebrate hosts' skin to counteract their hemostasis and inflammatory reactions that might otherwise stop blood flow . In particular , anti-platelet and anti-clotting inhibitors , vasodilators and anesthetics are known to occur in these animals saliva as well as in T . infestans [5] , [6] . Probably because of their hosts' immune pressure against salivary proteins , genes coding for salivary polypeptides in blood sucking arthropods are at a very fast pace of evolution , demonstrated for a set of salivary coding genes from the mosquito , Anopheles gambiae , which showed indications of positive selection [7] . Related to the fast pace of salivary protein evolution , host immune response to vectors can be quite specific and serve as an epidemiological marker of vector exposure [8]–[14] . This has also been considered for a T . infestans salivary antigen that might serve as an epidemiological marker of chicken exposure to this insect [15] , [16] . Its recombinant form , rTiSP14 . 6 , was very effective in detecting differences in infestation levels of T . infestans in Bolivian households by analyzing IgG levels against the corresponding salivary protein using chicken sera [17] . IgM antibodies of chicken sera also reacted with rTiSP14 . 6 , but compared to IgG immune responses of chickens , no differences were detectable in the overall antibody reactions to either crude saliva or rTiSP14 . 6 from sera originating from animals at low or high T . infestans infested households [15] . The saliva composition of hematophagous arthropods does not only differ between populations of the same species as analyzed for sand flies [18] and triatomines [19] , [20] , but also between developmental stages [21] , [22] . Furthermore , the immune response of T . infestans-exposed guinea pigs varies according to the developmental stage ( nymphs or adults ) and the geographical origin of the colonies [23] . In order to develop an appropriate T . infestans exposure marker , in particular a salivary antigen that will be recognized by sera of triatomine host species exposed to any developmental stage or strain of T . infestans , we aim in this study to use an RNAseq strategy to determine developmental stage and geographical variations in the sialome ( from the Greek sialo = saliva ) of T . infestans that could eventually be used to design specific immunological markers of vector exposure . Additionally we aim to extend the sialome database of T . infestans that could be used for further functional determination of the identified salivary proteins .
All experimental exposures of animals to triatomines carried out in the Czech Republic were in accordance with the Animal Protection Law of the Czech Republic ( §17 , Act No . 246/1992 Sb ) and with the approval of the Academy of Science of the Czech Republic ( protocol approval no . 172/2010 ) which complies with the regulations of the European Directive 2010/63/EU on the protection of animals used for scientific purposes in Europe . All T . infestans strains ( n = 22 ) originating from Argentina , Bolivia , Chile and Peru were reared in the insectary at an air temperature of 28±1°C , a relative humidity of 60–70% and with a 12 h/12 h light/dark cycle . Supplemental Table S1 summarizes the origin and the collection date of the different T . infestans strains from the natural settings in South America . For colony maintenance triatomines were regularly fed on guinea pigs or rabbits . Complete sequences of ITS-1 , 5 . 8S and ITS-2 comprising the entire rDNA intergenic region of the different T . infestans strains were analysed and for each T . infestans strain an adult and either a 4th or 5th nymphal stage were examined . One or two legs of each bug were used for DNA extraction using methods as previously described [24]–[26] and the primers designed for the flanking regions of the intergenic region derived from [27] and [4] . Amplification procedures and thermal cycler conditions were carried out in a Mastercycle ep Gradient ( Eppendorf , Germany ) using 30 cycles of 30 s at 94°C , 30 s at 50–55°C and 1 min at 72°C following 30 s at 94°C and 7 min at 72°C . Primers and nucleotides were removed from PCR products by purification using the Ultra Clean PCR Clean-up DNA Purification System ( MoBio , USA ) according to the manufacturer's protocol . Amplified DNA was resuspended in 50 µl of 10 mM TE buffer ( pH 7 . 6 ) and the final DNA concentration was determined by measuring the absorbance at 260 nm and 280 nm . Sequencing of the complete intergenic region was performed on both DNA strands by the dideoxy chain-termination method using the Taq dye-terminator chemistry kit for ABI 3130 and ABI 3700 ( Applied Biosystems , USA ) and PCR primers . Given the importance of the recent discovery of a pseudogenic 5 . 8S+ITS-2 sequence , named as “ps ( 5 . 8S+ITS-2 ) ” , that is widely distributed in triatomines of North , Central and South America [28] , it was assured that no double sequencing signal was present in the chromatograms in order to confirm that variable positions in the intergenic region were not due to an underlying paralogous sequence . The haplotype ( H ) terminology used for both ITS haplotypes followed the nomenclature for composite haplotyping ( CH ) as previously proposed [4] , [29] , [30] . Accordingly , ITS-2 haplotypes are labelled by numbers and ITS-1 haplotypes by capital letters . Sequences were aligned with CLUSTAL W2 [31] and molecular evolutionary analyses were conducted using MEGA , version 6 [32] and default parameters , including gap penalties in pairwise and multiple alignments . NCBI BLAST ( Basic Local Alignment Search Tool , http://blast . ncbi . nlm . nih . gov/Blast . cgi ) was used for sequence comparison against the GenBank database . Length and GC-content of each marker sequence including the complete intergenic rDNA region were determined by PAUP 4 . 0b10 [33] and MEGA 6 programs . Triatominae sequence comparisons were performed using complete or almost complete sequences of the same molecular markers available in GenBank-EMBL . The following sequences have been used: A ) rDNA ITS1-5 . 8S-ITS2 , complete region: T . infestans GT1A , GT2A , GT3A , GT4A , GT5A , GT1B , GT1C ( AJ576051- AJ576055; AJ582024 , AJ582025 ) ; Triatoma platensis GT1A , GT1B ( AJ576061 , AJ576062 ) ; Triatoma delpontei GT1A , GT2A , GT3A , GT3B ( AJ576057- AJ576060 ) ; Triatoma melanosoma GT1A ( AJ576056 ) [4]; B ) rDNA ITS-1 , complete or almost complete region: T . infestans haplotypes D and E ( HQ437705 , HQ437706 ) [34]; T . infestans clone TiITS10037 ( DQ118960 ) ( unpublished ) ; C ) rDNA ITS-2 , complete or almost complete region: T . infestans Hap1 , Hap2 , Hap3 , Hap4 ( HQ333211–HQ333214 ) [35]; T . infestans isolate ITInf72 and ITInf74 ( AY860387 , AY860388 ) [36] . All exposure experiments were performed with starved triatomines of 14 out of 22 T . infestans strains . These triatomines had starved for two weeks following moulting . Guinea pigs were obtained from our in-house breeding of the animal facility for the triatomine feeding experiments and rabbits from Velaz , Únětice , Czech Republic . As summarized in Supplemental Table S1 seven out of the 14 T . infestans strains were fed on guinea pigs and the other 7 strains were fed on rabbits . A total of 1876 triatomines were fed once until a full blood meal was completed ( Supplemental Table S2 ) ; feeding times depended on the developmental stage and were about 45–60 min using guinea pigs and 15–30 min using rabbits . Triatomines were dissected in sterile DEPC/PBS buffer and the salivary glands ( SG ) transferred into lysis buffer RA1 of the NucleoSpin RNA and RNA XS kits ( Macherey-Nagel , Germany ) . The salivary glands of 2 individuals of each developmental stage and strain of T . infestans were dissected after 6 h , 12 h , and 24 h after feeding followed by additional dissections every 2 days after the triatomine blood meal until each developmental stage moulted into the next stage ( see Supplemental Table S2 for development times ) . Adults ( 1 female and 1 male per dissection time point ) were dissected until female T . infestans started to lay eggs ( about 12 days after a blood meal ) and SGs of starved T . infestans of all instars and adults were additionally prepared . A total of 1 , 624 nymphs and 252 adults were dissected and the glands were stored at −70°C . No normalization of the RNA was made , since the pools had equal representation of tissues from their instar developmental time . From these insects , a total of 10 pooled total RNA samples were prepared for cDNA library constructions and next generation sequencing following the NucleoSpin RNA and RNA XS kits ( Macherey-Nagel ) manufacturer's instructions ( Supplemental Table S1 and Table 1 ) . In particular , 4 total RNA samples contained SG RNA of adults from colony T . infestans strains of Chile ( Chile-A , 18 bugs dissected ) , Peru ( Peru-A , 36 bugs dissected ) , Argentina ( Arg-A , 18 bugs dissected ) and Bolivia ( BolCol-A , 108 bugs dissected ) and 4 other RNA samples were prepared from nymphal SG RNA of also colony strains from Chile ( Chile-N , 116 bugs dissected ) , Peru ( Peru-N , 232 bugs dissected ) , Argentina ( Arg-N , 116 bugs dissected ) and Bolivia ( BolCol-N , 696 bugs dissected ) . The further 2 RNA samples were set up of either adult SG RNA from the Bolivian T . infestans strain collected in 2012 ( BolNat-A , 72 bugs dissected ) or of nymphal SG RNA from field collected T . infestans ( BolNat-N , 464 bugs dissected ) . Each RNA sample contained total RNA from all SGs of all developmental stages of the different T . infestans strains dissected 6 h after a blood meal until the triatomines moulted ( covering the entire life cycle ) and/or started to lay eggs as in the case of female T . infestans . The total RNA concentration of all 10 samples was measured using the NanoDrop spectrophotometer ( Thermo Fisher Scientific , USA ) , and the RNA of each sample was precipitated with ethanol using a final concentration of 0 . 3 M Na-acetate , pH 5 . 2 . Precipitated RNA was overlaid with 80% ethanol and stored at −70°C . All 10 SG RNA samples were sent to the Genomic Sciences Laboratory of the North Carolina State University , Raleigh , NC , USA , for Illumina cDNA library constructions and sequencing . Prior to cDNA library constructions , the RNA quality and concentration were checked with the Agilent Bioanalyzer 2100 using an Agilent RNA 6000 Nano Chip ( Agilent Technologies , USA ) and 0 . 5 µg of each cDNA library was used to prepare Illumina cDNA sequencing libraries . Poly-A mRNA was purified using the oligo-dT beads provided in the NEBNExt Poly ( A ) mRNA Magnetic Isolation Module ( New England Biolabs ( NEB ) , USA ) . Libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit ( NEB ) for Illumina and indexed with the NEBNext Mulitplex Oligos for Illumina ( NEB ) . The Poly-A mRNA was chemically fragmented and primed with random oligos for first strand cDNA synthesis with a heating step of 94°C for 5 min . First strand cDNA synthesis was performed with an incubation time of 10 min at 25°C , 50 min at 42°C , and 15 min at 70°C . Second strand cDNA synthesis was carried out with dUTPs to preserve strand orientation information . The sample was purified , end repaired and dA-tailed prior to adaptor ligation . Illumina Multiplex Adaptors were ligated , the ligation reaction was purified according to the protocol for a 500–700 bp insert , and a PCR amplification of 15 cycles was performed . The PCR product was purified and the cDNA libraries were checked for quality and concentration with the Agilent Bioanalyzer 2100 using a High Sensitivity DNA Chip ( Agilent Technologies , USA ) . The 10 prepared Illumina cDNA libraries were pooled in equal molar amounts and the resulted , final pooled cDNA library was run on a Illumina MiSeq instrument using the MiSeq Reagent Kit v3 ( Illumina , USA ) . Ten independent 2×300 bp paired-end runs were carried out and clustered at a concentration of 8pM . The software packages Real Time Analysis ( RTA ) , version 1 . 18 . 42 , MiSeq Control Software ( MCS ) , version 2 . 3 . 0 , and MiSeq Reporter , version 2 . 3 . 32 , were used for sequencing and to generate fastq files . Bioinformatic analyses were conducted following methods described previously [37] , [38] . Briefly , the fastq files were trimmed of low quality reads ( <10 , rejecting those that have an average <20 ) and concatenated for single-ended assembly using the Abyss [39] and Soapdenovo Trans ( http://arxiv . org/ftp/arxiv/papers/1305/1305 . 6760 . pdf ) assemblers using k parameters from 21–91 in 10 fold increments . The combined fasta files were further assembled using a iterative blast and cap3 pipeline as previously described [40] . Coding sequences were extracted based on the existence of a signal peptide in the longer open reading frame ( ORF ) and by similarities to other proteins found in the Refseq invertebrate database from the National Center for Biotechnology Information ( NCBI ) as well as proteins from Hemiptera deposited at NCBI's GenBank and from Swissprot . CDS were automatically annotated based on a program written by JMCR that searched a vocabulary of ∼300 words on the matches of the Swissp and Refseq databases , as well as the CDD , KOG and PFAM databases . This automatic annotation was further refined by manual annotation when needed . Transposable elements were discovered by RPSBlast of the transcripts against a PSIBLAST-made database derived from the clusterization at 90% identity on 50% of the larger sequence length of all larger open reading frames available in the Repbase database [41] . An e value of 1e-15 was considered as a threshold call . Reads for each library were mapped on the deducted coding sequences using blastn with a word size of 25 , 1 gap allowed and 96% identity or better required . Up to five matches were allowed if the scores were the same as the largest score . A Χ2 test was performed for each CDS to detect statistically significant differences between the number of reads in paired comparisons . The results of these tests are mapped to the hyperlinked excel sheet presented as a supplemental file S1 . The normalized ratio of the reads were calculated as r1×R2/[R1× ( r2+1 ) ] and r2×R1/[R2× ( r1+1 ) ] where r1 and r2 are reads for libraries 1 and 2 , and R1 and R2 are total number of reads from libraries 1 and 2 mapped to all CDS . One was added to the number of reads in the denominator to avoid division by zero . Sequence alignments were done with the ClustalX software package [42] . Phylogenetic analysis and statistical neighbor-joining bootstrap tests of the phylogenies were done with the Mega 6 . 0 package [32] . For visualization of synonymous and non-synonymous sites within coding sequences , the tool BWA aln [43] was used to map the reads to the coding sequences ( CDS ) , producing SAI files that were joined by BWA samse which was converted to BAM format and sorted . The samtools package [44] was used to do the mpileup of the reads ( samtools mpileup ) and the bcftools program from the same package was used to make the final variant call format ( VCF ) file containing the single nucleotide polymorphic sites ( SNP ) , which were only taken if the CDS coverage depth was at least 20 and the quality was 13 or better ( default ) . Determination of whether the SNP lead to a synonymous or non-synonymous codon change was achieved by a program written in Visual Basic by JMCR , the results of which are mapped into the excel spreadsheet shown as supplemental file S1 . Heat maps were made with the gplots and heatmap . 2 programs [45] from the R package [46] using average normalized data ( row values were divided by the row average ) . The raw RNAseq data were submitted to the Sequence Read Archive ( SRA ) of the NCBI under bioproject PRJNA238208 . The raw data file accession numbers can be found in Table 1 . Extracted coding sequences were submitted to the Transcriptome Shotgun Annotation ( TSA ) portal of the NCBI under accessions GBBI01000001-GBBI01005114 .
To detect the most genetic variations in the transcriptome of different T . infestans populations from South America , especially between the Bolivian T . infestans strains either kept for several years in our laboratory or collected recently in the field ( F1 generation ) , the haplotypes of 22 T . infestans strains ( Supplemental table S1 ) were identified . T . infestans strains with different haplotypes were selected for Illumina sequencing . A total of 44 sequences of the ITS-1 , 5 . 8S and ITS-2 were obtained from sylvatic , peridomestic and domestic T . infestans specimens . The sequence alignment revealed the existence of only two combined haplotypes ( CH ) named T . inf-CH1A and T . inf-CH2A ( Table 2 ) . Their length and AT content were of 1304 bp and 1310 bp and 67 . 41% and 67 . 56% , respectively . The sequences of the haplotypes T . inf-CH1A and T . inf-CH2A were identical to those found in two different ITS1-5 . 8-ITS2 CH regions previously reported for T . infestans [4] . The 5 . 8S gene had a length of 155 bp and an AT content of 41 . 94% and was identical in all specimens analysed . The relation between geographic origins and genetic characteristics of the populations , whether sylvatic , peridomestic or domestic , suggests that CH1A is present only in Bolivia and Peru and well-established among peridomestic , domestic and sylvatic samples , while CH2A is only found in domestic and peridomestic habitats of Chile and Argentina and in one locality from Bolivia ( Table 3 ) . In the 481 bp-long alignment , including the two ITS-2 haplotypes , representing the samples of the T . infestans analysed and the eleven ITS-2 sequences from GenBank , 13 variable positions appeared ( 2 . 70% ) of which , 3 were substitutions ( 0 . 62% ) , including 2 parsimony-informative positions ( P-info ) and 1 singleton site , and 10 ( 2 . 05% ) gapped positions . The analysis of variable positions among all ITS-2 sequences , revealed the existence of only 8 different haplotypes for T . infestans , although there is only the complete sequence of five of them ( Table 3 ) . In the 703 bp-long alignment including the only one ITS-1 haplotype representing the samples of the T . infestans analysed and the six ITS-1 sequences from GenBank , 36 variable positions appeared ( 5 . 12% ) of which , 2 were substitutions ( 0 . 28% ) , including 1 P-info position and 1 singleton site , and 34 ( 4 . 84% ) gapped positions . The main genetic variation was observed in the minisatellite region between positions 179 and 217 in the alignment ( Table 3 ) . As in the case of the ITS-2 , six different ITS-1 haplotypes have been described for T . infestans , although three of them are partial or incomplete sequences . Following the above analysis , and having in consideration the determination of developmental stage and strain specific variations in the T . infestans salivary gland transcriptome , we selected 14 of the 22 available strains of T . infestans ( Supplemental Table S1 ) as follows ( strain numbers refer to strain named in the first column of supplemental table S1 ) : All Peruvian , domestic and peridomestic ( strains 4 and 5 ) , Chilean , 1 domestic strain ( 6 ) , and Argentinian , 1 peridomestic strain ( 30 ) ; from the more numerous Bolivian strains the two T . inf-CH2A haplotype strains covering the two different regions in the Department of Santa Cruz in Bolivia ( Cabezas and Boyuibe , F1 generation , domestic and peridomestic strains 50 and 54 ) were chosen plus two sylvatic ( colony strains 43 and 47 ) , four peridomestic ( colony strains 28 , 40 and F1 generation strains 24 and 56 ) and two domestic T . infestans strains with the T . inf-CH1A haplotypes ( colony strains 21 and 37 ) , as detailed in supplemental table S1 and table 1 . From the fourteen T . infestans strains selected for next generation sequencing , ten Illumina libraries were prepared and sequenced discriminating nymphal ( 5 libraries ) and adult T . infestans transcripts ( 5 libraries ) of different triatomine populations from Chile , Peru , Argentina and Bolivia , including two Bolivian libraries , one from colony strains and another from insects recently collected in the field ( F1 generation ) ( Supplemental table S1 and Table 1 ) . A total of over 395 million sequences , summing over 109 billion nucleotides , were recovered from the 10 sequenced libraries , ranging from 38 to 48 million sequences , or reads , per library ( Table 1 ) . The reads had an average length of 277 bases and a median of 300 bases after clipping low quality ( <10 ) bases . The assembly allowed the extraction of 11 , 188 coding sequences ( CDS ) having an average deducted protein length of 398 amino acids . These CDS mapped a total of 213 , 756 , 622 reads from all 10 libraries , or about half of the total reads ( Table 4 ) . Following an automated classification , the CDS were classified into putative Secreted , Housekeeping , Transposable element , Viral and Unknown ( Table 4 ) . Although the 2 , 228 CDS of the Secreted class were only 20% of the total 11 , 188 , they accrued 49% of the reads . On average these CDS were assembled each from 46 thousand reads . On the other hand , the Housekeeping class represented 65% of all CDS , collected 49% of the reads , and was assembled with an average of 14 thousand reads per CDS . Transposable element-coding sequences were represented by 5% of the CDS , a larger value than found on previous sialotranscriptomes , which is near 1% . Among the viral sequences identified , the assembly recovered the 5 , 388 nucleotides of the full length CDS for the nonstructural protein precursor of the Triatoma virus [47] , which was assembled from over 171 thousand reads . Other viral fragments were additionally found , some of which may derive from retrotransposable elements . A description of the sialome of T . infestans was disclosed in 2008 based on 1 , 534 EST sequences sequenced by Sanger methodology [6] . From this work , 167 protein sequences have been deposited to GenBank , including many lipocalin sequences , which are abundant in triatomine sialomes [48] , salivary enzymes such as apyrase , and other triatomine-specific proteins . The current update has identified over 1 order of magnitude additional sequences that appear as full length , or near full length , 5 , 114 of which have been deposited to GenBank . This submission expands the T . infestans sialome database and should help in the identification of pharmacologically active proteins as well as markers of vector exposure . Presently we will describe highlights of this expanded transcriptome , identify and describe stage specific and geographic specific protein variants , and attempt a measure of the polymorphism of the T . infestans sialome . This work has no biological replicates in the comparisons described below , a fact considered in this discussion . Compared to classical Sanger-derived transcriptomes with a few thousand sequenced EST's , next-generation Illumina libraries derived from hundreds of millions of sequences provides for an unprecedented depth of transcript coverage allowing detection of poorly expressed mRNA as well as their quantitation as hundreds , or millions of reads accrue to each assembled contig . For example , the most expressed CDS in this sialome is for the peptide trialysin , which accrued over 13 million reads and deriving an RPKM = 77 , 477 when all reads from all libraries are considered . Supplemental file S2 presents a classified version of supplemental file S1 that has the different functional classes sorted by their overall RPKM , allowing for fast identification of well-expressed CDS , including putative secreted proteins . In the following , we present some remarkable findings in this sialotranscriptome . To obtain a general view of the differential transcriptome expression among the 10 libraries , we produced a hierarchical clustering-based heat map of average normalized RPKM values for the contigs with overall RPKM equal or larger than 20 , totaling 4 , 207 CDS ( Fig . 1 ) . Notice that following normalization , the average row value is equal to one , with transcripts expressing above average having values above one and vice versa . The resulting clusterization is complex and shows that only a minority of the CDS have 4 or more units above average ( represented by white or red color in the graph ) . Some of these transcript differences will be further highlighted in the following sections . For these analyses , nymphal and adult reads from each geographical area were combined and compared with the sum of reads from the other libraries , to identify differentially expressed CDS specific to each region . We report here only those CDS that are significantly expressed 10× or more according to these comparisons . Single nucleotide polymorphisms were identified from each of the 10 libraries and mapped into the derived CDS . A rich text format ( RTF ) file is provided for each CDS showing the polymorphisms , hyperlinked to the Excel supplemental file S1 . Of the 11 , 188 deducted CDS , one or more polymorphic sites were identified in 5 , 391 when all data was combined ( Table 12 ) . The classes Viral , Transposable element , Unknown and secreted had the highest percentages of both synonymous ( S ) and non-synonymous ( NS ) polymorphisms , and also displayed high NS/S ratios , although the highest ratio derived from the protein synthesis category ( Table 12 ) . Sixty three members of the lipocalin family had on average 2 . 5 NS polymorphisms per 100 codons while the secreted class rate was 0 . 83 S/100 codons ( results not shown ) . This high rate of NS to S rate suggests that these genes are under positive selection , as has been suggested for a subset of salivary genes of the mosquito Anopheles gambiae [7] . The degree of polymorphism was also compared among the ten sequenced libraries using the 5 , 391 polymorphic CDS mentioned above ( Fig . 2 ) . Results indicate that polymorphisms are lower in the Peruvian and Bolivian colony libraries , but somewhat surprisingly the nymphal Argentinian library has significant less polymorphism than the adult counterpart , as is the case of the Bolivian F1 library . The Chilean strain that was collected in 1979 has higher polymorphism than the Peruvian strains collected 30 years later , or the recently collected ( 2012 ) Bolivian strains . The observed differences do not appear to derive from CDS coverage depth differences between libraries , because the average RPKM for each library were not significantly different , the minimum being 49 . 1±4 . 7 ( average ± SE ) for the Bolivian adult colony and the maximum being 60 . 5±8 . 51 for the Peruvian adult library , considering the 5 , 391 CDS . The differences here observed between nymphs and adults as well as from different geographical areas are congruent to previous T . infestans salivary immunological studies [23] . The polymorphism differences between long colonized and more recently colonized insects indicate that colonization time not necessarily leads to reduced polymorphism , which may be maintained by the immune pressure created by using and reusing live animals for colony maintenance . Discovery-driven research , as opposed to conventional hypothesis-driven work , should provide or enlarge the knowledge platform to allow further development of hypothesis-driven research . This study enlarges the sialome database for T . infestans and at the same time provides information on differentially expressed transcripts based on developmental stage and geographical origin . As a result , 5 , 114 sequences are now publicly available from GenBank and should be of value in assisting proteomic experiments attempting to determine proteins that might be used to immunologically identify T . infestans exposure , as proposed in [23] , as well as identification of pharmacologically active salivary proteins of T . infestans . Polymorphisms were mapped to individual coding sequences and its differential distribution was identified among geographical strains and developmental stages . This should be of help in developing specific markers of exposure , as well as to avoid extreme polymorphic proteins . Several novel viral and transposon sequences were discovered , which may stimulate virologists or scientists interested in transposable elements to describe new insect viruses or transposable elements . The awkward finding of a second cytochrome oxidase IV gene product in the Argentian population may represent a second horizontal transfer of this gene from a bacterial or mitochondrial genome and remains to be investigated . Despite the lack of biological replicates in our study , the observed increased sequences associated to cuticle proteins and JH metabolism in nymphs , as well as the increased vitellogenin and doublesex transcription factor in adults which are expected from the known insect physiology , serve to validate the broader differences in transcript abundance found among the different libraries despite lack of replicates , but these should be verified in future follow up work . | Triatoma infestans is the main vector of Chagas disease in South America . As in all hematophagous arthropods , its saliva contains a complex cocktail that assists blood feeding by preventing platelet aggregation and blood clotting and promoting vasodilation . These salivary components can be immunologically recognized by their hosts and targeted with antibodies that might disrupt blood feeding . The respective antibodies can be used to detect vector exposure using immunoassays . On the other hand , antibodies may also contribute to the fast evolution of the salivary cocktail . In this work , we attempted to identify variations in the salivary proteins of T . infestans using Illumina technology that allowed identification of over five thousand proteins based on over 300 million sequences obtained from ten salivary gland libraries . This expanded sialome database from T . infestans should be of assistance in future work attempting to identify salivary proteins that might be used as epidemiological markers of vector exposure , or proteins of pharmacological interest . | [
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] | 2014 | An Updated Insight into the Sialotranscriptome of Triatoma infestans: Developmental Stage and Geographic Variations |
A systematic review and meta-analysis of all available case-control studies on the relationship between onchocerciasis and epilepsy . Because age and level of onchocerciasis endemicity in the area of residence are major determinants for infection , an additional analysis was performed , restricted to studies achieving control of these confounding factors . Medical databases , the “African Neurology Database , Institute of Neuroepidemiology and Tropical Neurology , Limoges , ” reference lists of relevant articles , commercial search engines , up to May 2012 . We searched for studies examining infection status with Onchocerca volvulus in persons with epilepsy ( PWE ) and without epilepsy ( PWOE ) providing data suitable for the calculation of pooled odds ratios ( ORp ) and/or standardized mean differences ( SMD ) using random-effects models . Eleven studies providing data of qualitative skin biopsies for diagnosis of onchocerciasis were identified . Combined analysis on the total sample of 876 PWE and 4712 PWOE resulted in an ORp of 2 . 49 ( 95% confidence interval ( 95%CI ) : 1 . 61–3 . 86 , p<0 . 001 ) . When this analysis was restricted to those studies achieving control for age , residence and sex ( 367 PWE , 624 PWOE ) , an ORp of 1 . 29 ( 95% CI: 0 . 93–1 . 79; p = 0 . 139 ) was found . Presence of nodules for diagnosis of onchocerciasis was analyzed in four studies ( 225 PWE , 189 PWOE; ORp 1 . 74; 95%CI: 0 . 94–3 . 20; p<0 . 076 ) , including two studies of the restricted analysis ( 106 PWE , 106 PWOE; ORp 2 . 81; 95%CI: 1 . 57–5 . 00; p<0 . 001 ) . One study examined quantitative microfilariae counts in patients without preceding microfilaricidal treatment and demonstrated significantly higher counts in PWE than in PWOE . Our results strengthen the hypothesis that , in onchocerciasis foci , epilepsy and infection with O . volvulus are associated . Analysis of indicators giving information on infection intensity , namely nodule palpation and quantitative microfilaria count in untreated patients , support the hypothesis that intensity of infection with O . volvulus is involved in the etiology of epilepsy .
Onchocerciasis is a parasitic disease caused by infection with the nematode worm Onchocerca volvulus . Infective larvae are transmitted through bites of flies of the genus Simulium which breed along fast-flowing rivers throughout tropical Africa and some areas of Latin America and Yemen [1] . Whereas children in the first years of life are only exceptionally infected , the prevalence rises to virtually 100% in adults living in highly endemic areas , with usually a higher proportion in the male population [2]–[4] . Within one endemic area , the risk of infection may vary significantly over short distances even between neighbouring villages . Thus , the main determinants of infection status with O . volvulus are: Following infection of the human host the parasite develops into adult worms of considerable size ( length up to 50 cm for female worms ) and aggregates within nodules which can be located in the sub-cutaneous layer of the skin but also in internal parts of the body [6] . Subcutaneous nodules can be used for diagnosis of onchocerciasis although this is of limited sensitivity because deeply seated nodules cannot be located by palpation [7] . Adult parasites can produce an estimate of 1300–1900 larvae ( microfilaria , mf ) per day [6] which invade the dermis of the host to be newly incorporated with the blood meal of the vector fly . The detection of mf in skin biopsies ( skin snips ) allows a more sensitive and specific diagnosis of O . volvulus infection compared to nodule palpation . Beyond that , the count of mf per mg of skin or per skin snip of standardized weight is also a measure of infection intensity [8] , [9] . Mf were identified as the pathogenic stage of the parasite , and mf density was shown to be linked with the known disease manifestations of onchocerciasis in the skin and the eye . An association between onchocerciasis and epilepsy was first suspected in the 1930's and 1940's in Latin American endemic areas [10]–[12] and there were sporadic anecdotal accounts mentioning a clustering of epilepsy in several African onchocerciasis foci as well [13]–[15] . Only with the implementation of community-wide treatment campaigns with the microfilaricidal drug ivermectin , which has been made available at no cost by its manufacturer since 1987 ( Merck & Co . , Whitehouse Station , NJ , U . S . A . ) , could this issue be investigated more systematically . Ecological studies carried out in onchocerciasis foci throughout West , Central and East Africa found a strong positive exponential relationship between the prevalence of onchocerciasis and that of epilepsy [16] . Differing from this association demonstrated at the community level , case-controlled studies on the issue gave equivocal results . A meta-analysis comparing onchocerciasis status in persons with and without epilepsy yielded widely deviating findings ranging from a relative risk ( RR ) of 6 . 80 ( 95% CL 3 . 00–15 . 20 ) suggesting a strong positive association to a RR of 0 . 84 ( 95% CL 0 . 74–0 . 95 ) suggesting a negative association [17] . When looked at more closely , the most extreme results included in this review originated from two studies carried out in the same endemic area in Western Uganda with a time interval of only one year [18] , [19] . The discrepancy between the results of these studies was most likely due to shortcomings in study design and the selection of comparison groups [20] . Keeping in mind the strong dependency of onchocerciasis infection on age , O . volvulus endemicity in the area of residence , and sex , it is essential that comparative studies ensure that these major confounders are controlled if valid conclusions on a relationship between the parasite and disease manifestations are to be made . The present article provides a review of all case-control studies investigating the relationship between onchocerciasis and epilepsy carried out to date , with a focus as to whether age , residence and sex as the major determinants for infection status were controlled .
Data for this article were entirely assessed from previously published work and no information that could identify individual patients is provided . Therefore , written consent and institutional ethical review are not required for this review . The PRISMA checklist and flow diagram [21] are available as supporting information ( Diagram S1 , Checklist S1 ) . Several medical databases ( MEDLINE , ScienceDirect , Scopus ) and the “African Neurology Database of the Institute of Neuroepidemiology and Tropical Neurology of the University of Limoges” ( http://www-ient . unilim . fr/ ) were screened by use of the key words “onchocerciasis” AND “epilepsy” . Other sources , such as commercial search engines or unpublished congress proceedings , were searched with no specified limits , and reference lists of retrieved articles and reviews were screened for further records of relevance ( Latest search: May 23 , 2012 ) . Full text of articles , theses and abstracts thus identified were independently examined by two of the authors ( CK and MB ) and eligibility for inclusion into the review was agreed upon by consensus . A study was considered eligible if it made a comparison of O . volvulus infection status in a group of people with epilepsy ( PWE ) to that in a comparison group of people without epilepsy ( PWOE ) , and if it reported data allowing for the calculation of an odds ratio ( OR ) with O . volvulus infection status considered as exposure and epilepsy as outcome and its 95% confidence intervals ( 95% CI ) . The pooled OR was calculated as an overall measure of a connection between onchocerciasis and epilepsy in all studies providing compatible data . In a second step , the analysis was restricted to those studies fulfilling defined criteria concerning their composition of age ( time of exposure ) , residence ( intensity of exposure ) and sex ( Age: Matching on age with a maximum interval of 5 years , or Standardization on age with a maximum interval of 5 years; Residence: Matching by village , or Stratification by endemicity level; Sex: Matching on sex , or Results reported separately for males and females ) . These criteria were based on the known relationship between the respective parameters and infection status with O . volvulus and were also guided by the suggestions made for epidemiological studies on the association between cysticercosis and epilepsy [22] . Those studies which met the pre-defined selection criteria were further classified according to the methods used for the ascertainment of O . volvulus infection: ( i ) Qualitative and/or quantitative detection of mf of O . volvulus in the skin , or ( ii ) Presence of subcutaneous nodules and/or number of nodules detected by palpation . Pooled ORs and 95% CI were calculated for studies presenting qualitative indicators of onchocerciasis infection whereas pooled standardized mean differences ( SMD ) and Cohen's d statistics were computed for studies presenting quantitative assessment of onchocerciasis infection [23] . All pooled calculations included a test of homogeneity of means . Pooled ORs were estimated using random-effects models ( DerSimonian-Laird method ) and heterogeneity of studies was assessed using Cochrane's Q chi-squared tests and the I2 statistics [24] , [25] . The latter describes the percentage of total variation across studies that is due to heterogeneity rather than chance . Analyses were performed using the metan procedure [26] available in the Stata software ( StataCorp , TX ) . In order to discuss factors that might have influenced the results , information on the characteristics of each study site was also collected , and in some cases , the authors were directly contacted to get data not available in the publications . The levels of endemicity were defined using either the prevalence of microfilaridermia [27] or the prevalence of nodules [28] , following whichever data were available .
An overall number of 290 entries were found in the medical databases by use of the keywords “onchocerciasis” AND “epilepsy” , corresponding to a number of 210 distinct records after duplicates were removed . Out of these records , 16 articles , medical theses and abstracts representing 11 studies were found to include a control group for the investigation of a relationship between onchocerciasis and epilepsy [18] , [20] , [29]–[42] . Two records , each representing an additional eligible study [43] , [44] , and one unpublished congress abstract [45] containing relevant information about one of the above-mentioned studies were identified in other sources . Information about all identified studies , including their design , the levels of endemicity for onchocerciasis in each site , and the onchocerciasis control activities conducted before the study is given in Tables 1 and 2 . The article by König et al . [36] and the abstract by Schmutzhard et al . [45] from the Morogoro focus in Tanzania reported information on O . volvulus infection status of PWE and controls only as pooled data , based on skin snip microscopy combined with the results of a polymerase chain reaction ( PCR ) method searching for O . volvulus specific DNA in the skin . However , data on the prevalence of skin mf alone were provided in an abstract about the same study [37] . In their survey reported from Burundi , Newell et al . [38] used a skin scarification technique for the detection of O . volvulus mf which is considered equivalent to the standard skin snip procedure established by the Onchocerciasis Control Programme in West Africa ( OCP ) . This was complemented by a serology assay in those with a negative scarification result and figures are given in the article on the proportions of subjects found positive either with the scarification method alone , or with both techniques [38] . In the study conducted by Kipp et al . [18] , infection with O . volvulus was defined by the presence of skin mf or the presence of nodules . As the proportion of individuals harboring nodules but no mf in the skin is usually very low [46] , we have considered that the prevalence given in this publication corresponded to the prevalence of skin mf . Thus , with the exception of two studies where onchocerciasis status has been evaluated by searching the presence of nodules only [40] , or by the detection of circulating antigens only [43] , all studies communicated data of qualitative skin biopsies allowing for the calculation of a pooled OR of 2 . 49 ( 95%CI: 1 . 61–3 . 86 , p<0 . 001 ) ( Table 2; Figure S1 ) . Among these studies , heterogeneity in ORs was significant ( Cochrane's Q test: p<0 . 001 ) and yielded an I2 value of 65 . 7% . Data on the prevalence of nodules in PWE and PWOE was provided in four studies [20] , [39] , [40] , [44] from which we calculated a pooled OR of 1 . 74 ( 95%CI: 0 . 94–3 . 20 , p = 0 . 076 ) ( Table 2 ) . Between-studies heterogeneity was not significant ( p = 0 . 093 ) and I2 was assessed as 53 . 3% . When the results of the above mentioned study from the Morogoro focus in Tanzania were analyzed based on the results of both methods used for detection of O . volvulus ( microscopy and PCR ) instead of microscopy alone , the proportion of participants with a positive O . volvulus infection status was found to increase more in PWE than in PWOE , resulting in an OR of 4 . 36 ( 95%CI: 2 . 63–7 . 24 ) [36] instead of 3 . 77 ( 95%CI: 2 . 18–6 . 51 ) ( Table 3 ) [37] . In the study conducted by Newell et al . from Burundi [38] the OR was found at 2 . 49 ( 95%CI: 1 . 38–4 . 50 ) when only the results of skin scarification were considered , instead of 2 . 09 ( 95%CI: 1 . 07–4 . 09 ) when findings of skin scarification and serology were combined for analysis ( Table 3 ) . The study of Tume et al . [43] from Cameroon , which was exclusively based on detection of circulating antigens of O . volvulus , found no significant difference between the seroprevalence in 441 PWE ( 17 . 7% ) and 98 PWOE ( 20 . 4% ) ( Table 3 ) . Four studies from Cameroon [29] , [39] , Central African Republic ( CAR ) [30] , Mali [31] and Uganda [20] were found to meet with the criteria for sufficient control of age , residence and sex ( Table 2 ) . All these studies applied a pair-matching protocol for age and residence , and all but one were also fully matched for sex . In one study from western Uganda an equal distribution of male and female participants was documented although 5 out of 38 pairs were of deviating sex [20] . The endemicity level of the different areas at the time of the realization of the studies varied considerably and was apparently related to the duration of treatment campaigns with ivermectin and , in one case , to previous vector control activities ( Table 2 ) . All studies included in the restricted analysis used comparable methods for the assessment of epilepsy diagnosis . Qualitative analysis of skin biopsies ( prevalence of infection ) yielded a non-significant result for the individual studies as well as for all studies in combination ( pooled OR: 1 . 29 , 95% CI: 0 . 93–1 . 79; p = 0 . 139 ) ( Table 4 ) . No statistically significant heterogeneity was found between studies ( p = 0 . 59; I2 = 0% ) . Two studies also reported the result of quantitative mf counts . When this was done in a cohort of patients in the CAR who had been receiving microfilaricidal treatment for five years , almost no difference was found between patients with epilepsy and controls [30] . In contrast , a highly significant result was obtained in a study from Cameroon performing quantitative mf counts in patients who had not yet received ivermectin at the time of the skin biopsy [29] . Pooling the two studies yielded a non significant standardized mean difference ( SMD ) ( p = 0 . 407 ) and revealed a substantial heterogeneity between them ( p<0 . 001; I2 = 92 . 1% ) ( Table 4 ) . Nodule palpation was analyzed in the studies conducted in Uganda [20] and in Cameroon [39] and produced congruent results , with a significantly higher prevalence of nodule carriers ( pooled OR: 2 . 80 , 95%CI: 1 . 57–5 . 00; p<0 . 001 ) ( Table 4 ) , as well as higher nodule counts in patients with epilepsy ( SMD: 0 . 384 , 95%CI: 0 . 02–0 . 75; p = 0 . 037 ) . No significant heterogeneity was found between the Ugandan and Cameroonian studies either for the prevalence or the number of nodules ( p = 0 . 981 and p = 0 . 64 , respectively; and I2 = 0% for both indicators of O . volvulus infection ) .
In a search for comparative studies on the relationship between onchocerciasis and epilepsy published until to date , we identified a total number of 13 studies of which 11 studies presented data of qualitative skin biopsies for the diagnosis of onchocerciasis amenable to combined analysis . In this sample of 876 epilepsy patients and 4712 control subjects we found a highly significant ( 2 . 5-fold , p<0 . 001 ) increase in the risk of having a positive skin biopsy for PWE if compared to PWOE . Some of these studies were probably subject to bias because of limitations in the study design or because they did not make sufficient use of measures to control for confounding factors . In one study [18] , a substantial overrepresentation of control subjects with residence in low endemic areas probably led to an over-estimate of the reported positive association . Another study [33]–[35] was carried out in an area in Burkina Faso which at the time of the survey had been subject to effective vector control measures as part of the Onchocerciasis Control Programme in West Africa ( OCP ) for more than 15 years [47] . A possible effect of O . volvulus infection on epilepsy would not be expected at the resulting low level of endemicity . It was mentioned that the mean microfilarial density in the 2040 persons aged ≥15 years examined as part of this study was only 7 . 5 mf per snip [34] , which is low in this historical onchocerciasis focus . Similarly , the study from Mali [31] was conducted in an area which had benefitted from 11 rounds of annual distributions of ivermectin , with drug coverage as high as 66 . 8% in 1997 while the study was conducted in 1998 [42] , plus ground vector control activities during four years [47] . However , if either one of these or all three studies were excluded in the calculation of the combined OR , still a significant result was found for the remaining studies ( pooled OR = 2 . 41 ( 95%CI: 1 . 55–3 . 76 ) , p<0 . 001; Cochrane's Q test: p = 0 . 016; I2 = 59 . 4% ) . To overcome the apparent weakness of design of some studies , we performed a second step of analysis restricted to those studies giving evidence of control for age , residence and sex as confounding factors of major relevance [20] , [29]–[31] , [39] . When an analysis was made of the qualitative skin snip data of these four studies , all found an OR above 1 indicating a trend for a positive correlation , but the clearly significant result found for all studies was not confirmed . This may be explained with the limited sample of 367 PWE and 624 PWOE included in the restricted analysis . Alternatively , the highly significant result of the non-restricted analysis might be due to bias from those studies achieving limited control . Actually , unless larger samples are examined with an appropriate protocol , it seems that the qualitative investigation of mf in the skin is of limited use in analyzing the question of an association between onchocerciasis and epilepsy . The first reason is that in highly endemic areas where almost all inhabitants will be infected with O . volvulus , such as that studied by Boussinesq et al . in Cameroon [29] , the difference in prevalence of infection between cases and controls will be small . On the other hand , in areas of low endemicity a higher fraction of patients with epilepsy due to other causes will be found and this will diminish the strength of a possible association . In savanna regions , the prevalence of onchocercal eye disease is known to increase exponentially with that of onchocerciasis in a similar way to that observed for epilepsy in O . volvulus endemic areas [16] , [27] . Eye manifestations are also known to be closely connected with high intensity of infection [3] , [27] and it should appear plausible if this is also found with epilepsy . So far , the immediate relation between epilepsy and intensity of infection with O . volvulus has been adequately investigated at only one occasion [29] . This study found a significantly higher mf count in dermal biopsies of patients with epilepsy if compared to pair-matched controls without epilepsy when examined prior to ivermectin treatment . Another study reported quantitative results from an area where annual mass treatment had been implemented prior to the investigation [30] . However , after five treatment rounds in this area of the CAR , the strong microfilaricidal effect of ivermectin had , as expected , considerably reduced the microfilarial densities in the population [48] . This effect has probably levelled out a possible difference of infection intensity between PWE and PWOE , which would explain why no significant result was found . In addition to skin biopsies , two of the studies considered to achieve sufficient control for age , residence and sex also communicated results of nodule palpation [20] , [39] . When the results of both these studies were looked at together , a significant result was found for presence of palpable nodules and for the total number of nodules found in the examined sample . As has been mentioned , the sensitivity of nodule palpation is low in patients harbouring only one or few nodules because these may not be accessible . However , the probability that at least one nodule will be palpable increases with nodule load , and it has been demonstrated that , when taking skin snip diagnosis as reference , the sensitivity of nodule palpation exceeds 80% for patients with a total nodule load of 5 or more [7] . Because of the close correlation between nodule load and infection intensity [6] , [9] , in an onchocerciasis endemic area persons with one or more palpable nodules will be representing the fraction of the population with more intense infection if compared to those persons without a palpable nodule . Therefore , the significant result found for nodule palpation in the present analysis supports the assumption that infection intensity plays a role in the induction of epilepsy in O . volvulus endemic areas . The observation of an unexplained clustering of epilepsy in an endemic focus has been the starting point of most investigations on a possible association between onchocerciasis and epilepsy . As far as detailed information on age-specific epilepsy incidence and prevalence is available from onchocerciasis endemic areas , a consistent distribution has been found with a peak incidence between 10 and 15 years [38] , [40] , [41] , [49] , [50] , and a peak prevalence in adolescents and young adults [38] , [41] , [50] , [51] . In contrast , studies on epilepsy prevalence from African areas not endemic for onchocerciasis reported minor differences across age groups [52] or highest rates in those younger than 10 years [53] . This unusual age distribution of epilepsy cases in onchocerciasis endemic areas is well compatible with the build-up of O . volvulus infection in the population . Two clinical observations indicate a link between onchocerciasis and epilepsy: ( 1 ) A so far poorly understood form of growth failure , which had when first described been named “Nakalanga syndrome” [54] has only been observed in O . volvulus endemic areas and an overlap with epilepsy has been described [19] , [38] , [55] , [56] . ( 2 ) A specific type of epileptic seizure which has been designated as “head nodding seizures” ( HN ) , again associated with stunted growth , has also been found exclusively in onchocerciasis endemic areas [40] , [57]–[61] . Recently , a case-control study conducted in South Sudan reported a significantly higher proportion of infection with O . volvulus in patients with HN than in controls matched for age and location of residence [62] . However , it was mentioned that many participants examined with this study were internally displaced persons and the actual duration of exposure to onchocerciasis may have been substantially different between cases and controls depending on their individual history of migration . Further studies using carefully selected controls are needed to confirm - or disprove - the intriguing positive association found in this preliminary investigation [62] . A general problem of cross-sectional studies – as were analyzed with the present review – is that they cannot contribute much to our understanding of exact mechanisms of pathogenesis and also the temporal relationship between the condition assumed as exposure and the outcome cannot be easily studied . One approach to find an answer to the crucial question whether infection with O . volvulus is factually preceding the onset of epileptic seizures would be to longitudinally investigate O . volvulus infection status of newly incident epilepsy cases and appropriate controls . The so far only prospective assessment of epilepsy incidence data from an onchocerciasis endemic area was carried out on the occasion of an epilepsy treatment programme with repetitive follow up visits [49] , [63] in western Uganda . Although with this programme , being based on limited means and having patient care as its focus , individual onchocerciasis infection status was not assessed , this could be done with appropriate and simple protocols in the setting of health facilities or humanitarian programmes engaged in treating epilepsy patients in endemic areas . Numerous possible infective causes of epilepsy have been described in sub-Saharan Africa [64] and in particular cysticercosis [22] and malaria [65] are considered to be established etiologies . It might be conceivable that epilepsy in the onchocerciasis endemic areas is factually due to some of these factors but an association between the two entities is erroneously found because PWE for various reasons may be more susceptible to O . volvulus infection than PWOE . In this case it should be expected that other causes or etiologic factors for epilepsy can be found in patients living in onchocerciasis endemic areas . For instance , it was suggested that neurocysticercosis could be the cause of epileptic seizures in patients living in areas co-endemic for onchocerciasis and cysticercosis [66] . However , although it may happen in some individual patients that nodules due to sub-cutaneous cysticercosis ( SCC ) are mistaken as O . volvulus nodules , this is not expected to play a major role in African endemic areas . The prevalence of SCC there is found to be very low and nodules are usually located on the upper limbs and on the head [67] , [68] , whereas , at least in Africa , onchocercal nodules are usually localized on the lower part of the body . When the evidence on the inter-relation between onchocerciasis , cysticercosis and epilepsy was reviewed in more depth , the above mentioned presumption was not confirmed [69] . In two larger case series searching for etiologies for epilepsy in patients living in onchocerciasis endemic areas in Tanzania ( n = 196 ) [36] , and Uganda ( n = 91 ) [58] no evidence was found for malaria or other infections of the central nervous system as a possible cause . When other suspected pathogens such as infection with arboviruses [70] , [71] or Paragonimus sp . [72] , [29] were investigated , also no connection with epilepsy was found . Thus , as long as no prove is demonstrated of one or several alternative causative factors in PWE to explain the extremely elevated epilepsy incidence [49] and prevalence [16] , [51] found in onchocerciasis endemic areas , O . volvulus will remain the first suspect . The pathomechanism by which O . volvulus could damage the brain and lead to epileptic seizures is not clarified . O . volvulus mf were sporadically revealed in the cerebrospinal ( CSF ) fluid of patients before and , to an even greater extent , following treatment with diethylcarbamazine [73]–[75] , but the examined patients were not affected by epilepsy . When CSF was analysed in patients with epilepsy living in a Tanzanian onchocerciasis endemic area , no mf were found even in patients with documented skin infestation of O . volvulus [36] , [60] . However , as the activities of the African Programme for Onchocerciasis Control ( APOC ) started in the Mahenge focus in 1997 , i . e . 8 years before the study [37] , most of these patients had probably received microfilaricidal treatment with ivermectin prior to their examination and this may have removed mf from the CSF [76] . Magnetic resonance imaging in these patients revealed a number of non-specific pathologic changes which were slightly more frequent in patients with proof of dermal mf [60] . Another route of entry of O . volvulus mf into the brain might be the optic nerve , and indeed the parasite has been found in this location on several occasions [77]–[81] . Apart from a direct involvement of one of the developmental stages of O . volvulus it has been suggested that an immunological response of the human host , and possibly auto-immune mechanisms such as those found in the development of onchocercal chorioretinitis , may be involved in the pathogenesis of epilepsy [36] , [82]–[84] . In view of the scarcity of investigational facilities and neurological expertise in the endemic areas [85] , studies on the pathophysiology of onchocerciasis-related epilepsy will remain difficult . We found a significant association between epilepsy and a positive infection status with O . volvulus when all studies identified with an extensive literature search were analysed in combination . This relationship was less pronounced when the analysis was restricted to those studies giving evidence of sufficient control for age , residence and sex . When within this restricted sample indicators giving information on infection intensity , namely nodule palpation and microfilarial density in untreated patients , were taken into consideration , again a significant result was found . Although the overall data base is still of limited size , in their overall constellation these findings corroborate the hypothesis that epilepsy in endemic areas is caused by a disease process induced by infection with O . volvulus , especially in patients with severe infection . As already suggested by Balanzario in 1942 [11] , infection with O . volvulus could be a necessary but not sufficient factor leading to onchocerciasis-associated epilepsy . A better understanding of the dimension and the nature of onchocercal brain disease is needed and will contribute to motivate sustained efforts aiming at the reduction of the disease burden and finally elimination of onchocerciasis . | Onchocerciasis is known as a cause of skin and eye disease infecting a great number of people , mainly in rural Africa . In the endemic areas , infection status and severity is essentially determined by the duration and intensity of exposure to the parasite and to host sex . A link between onchocerciasis and epilepsy has been suggested over a long time but the existence of a definite association is still controversial . Based on a comprehensive literature review of case-control studies on this issue , the authors found a significant association between epilepsy and a positive infection status with Onchocerca volvulus . This relationship was less pronounced when the analysis was restricted to those studies giving evidence of sufficient control for age , endemicity level of the area of residence and sex . When within this restricted sample indicators giving information on infection intensity , namely nodule palpation and microfilarial density in untreated patients , were taken into consideration , again a significant result was found . In their overall constellation these results corroborate the hypothesis that epilepsy in endemic areas is caused by a disease process induced by infection with O . volvulus , especially in patients with severe infection . | [
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"tropica... | 2013 | Case-control Studies on the Relationship between Onchocerciasis and Epilepsy: Systematic Review and Meta-analysis |
To maintain a stable intracellular environment , cells utilize complex and specialized defense systems against a variety of external perturbations , such as electrophilic stress , heat shock , and hypoxia , etc . Irrespective of the type of stress , many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops . In general , the degree of deviation of the controlled variables , such as electrophiles , misfolded proteins , and O2 , is first detected by specialized sensor molecules , then the signal is transduced to specific transcription factors . Transcription factors can regulate the expression of a suite of anti-stress genes , many of which encode enzymes functioning to counteract the perturbed variables . The objective of this study was to explore , using control theory and computational approaches , the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species ( controlled variables , transcription factors , and gene products ) in these gene regulatory networks . Our work indicated that the shape of dose response curves ( linear , superlinear , or sublinear ) depends on changes in the specific values of local response coefficients ( gains ) distributed in the feedback loop . Multimerization of anti-stress enzymes and transcription factors into homodimers , homotrimers , or even higher-order multimers , play a significant role in maintaining robust homeostasis . Moreover , our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control , superlinear more highly controlled , linear uncontrolled , and sublinear catastrophic . Each phase relies on specific gain-changing events that come into play as stressor level increases . The low-dose region is intrinsically nonlinear , and depending on the level of local gains , presence of gain-changing events , and degree of feedforward gene activation , this region can appear as superlinear , sublinear , or even J-shaped . The general dose response transition proposed here was further examined in a complex anti-electrophilic stress pathway , which involves multiple genes , enzymes , and metabolic reactions . This work would help biologists and especially toxicologists to better assess and predict the cellular impact brought about by biological stressors .
Cells in vivo must maintain a relatively stable intracellular milieu in an extracellular environment that is constantly changing and is potentially unpredictable . Notably , many intracellular biomolecules need to be held within closely regulated ranges of concentrations for normal cell functions . Examples of these biochemical species , which could be detrimental and/or beneficial to cellular health , are electrophiles , reactive oxygen species ( ROS ) , DNA adducts , misfolded proteins , O2 , and glucose . When external stressors cause these molecules to deviate from their basal operating concentrations for an extended period of time , normal cell functions become disrupted , and cell cycle arrest and apoptosis may ensue [1] . Homeostatic regulation of vital intracellular biochemical species appears to operate primarily via gene regulatory networks that respond specifically to particular types of physical/chemical insults , such as electrophilic chemicals , heat shock , hypoxia , and hyperosmolarity [2–5] . As with many manmade control devices , such as thermostats and automobile cruise controls , these homeostatic gene regulatory networks are usually organized into negative feedback circuits that can be generalized into a common control scheme ( Figure 1 ) . The output of the system , referred to as controlled variable , is the biochemical species that is perturbed by external stressors and therefore needs to be tightly controlled . The system contains specific transcription factors that serve as transducers to either directly or indirectly sense the level of the controlled variable . In this fashion , alterations in the concentration of the controlled variable affect the activity or abundance of the transcription factor . Activated transcription factors then upregulate expression of individual or suites of anti-stress genes , many of which encode enzymes that participate in an array of interconnected biochemical reactions to counteract the perturbation of the controlled variable . Control and dynamic system theory has benefited applied fields such as electronic and mechanical engineering for many decades , and in recent years increasing efforts have been made to apply similar concepts to biological systems including adaptive responses [6–14] . Our goal is to understand nature's design principle for anti-stress cellular homeostasis and to improve prediction of the disrupting effects of biological stressors . Of practical importance for risk assessment at the cellular level is the steady-state dose response relationship between stressor levels and various measurable biochemical endpoints including the controlled variables , transcription factors , and gene expression . Cell responses in the low-dose region are particularly relevant to human health risk assessment , and it is traditionally difficult to explain and predict dose response behaviors in this region due to uncertainty and subtlety of the curvature . To accurately describe and fully understand complex dose response behaviors , the underlying biochemical networks will have to be examined through quantitative models . With respect to the mathematical approaches involved , theoretical development in quantitative analysis of controls in biochemical networks , including metabolic control analysis ( MCA ) and biochemical systems theory ( BST ) , has proven to be of great value [15–18] . Using numerical simulation and concepts from MCA , BST , and classical control theory , the present study focused on understanding the quantitative basis for the steady-state dose response in an anti-stress gene regulatory network . While some of the conclusions presented in this paper may seem implicitly familiar , or even obvious , to engineers , they nonetheless provide an important framework by which biologists and especially toxicologists can improve the accuracy with which they evaluate the influence of biological stressors on intracellular control processes under different exposure conditions .
If a signal molecule X controls a target molecule Y , either directly or indirectly , then the steady-state transfer function from X to Y can be quantitatively described as the ratio of the fractional change in Y over the fractional change in X , i . e . , , known as the response coefficient in MCA [16 , 19] , and logarithmic gain in BST [17 , 20] , is analogous to the gain of an amplifier or a transducer in electronics ( in the rest of the text , we use the terms gain and response coefficient interchangeably ) . Assuming that remains constant within a range of X of interest , then for some constant k , which is a linear function on a logarithmic scale , with being the slope and lnk the intercept ( Figure S1A ) . When transformed to a linear scale , Equation 2 becomes Hence , response Y is a single-term polynomial function of dose X of degree . The value of , relative to unity , determines the curvature of the Y versus X dose response curve ( Figure S1B ) . Specifically , for = 1 , Y = kX , the Y versus X dose response is linear; for > 1 , the dose response is sublinear ( concave upward ) , denoting an ultrasensitive response; for 0 < < 1 , the dose response is superlinear ( concave downward ) , denoting a subsensitive response . In situations where X negatively regulates Y , hence < 0 , 1/Y versus X relationship observes the same curvature rule as above , which depends on | | . This logarithmic to linear transformation and the shape of dose response curve with respect to response coefficient has been previously described with the S-system in BST [17 , 21] . In this section , we set out to investigate the steady-state dose response relationships for the generalized negative feedback control scheme ( Figure 2A; for model details see Figure S2 and Tables S1–S3 ) . We defined that stress signal S increases the production rate of controlled variable Y with a local gain r0 . Y then activates transcription factor T with a gain r1 . T induces gene expression of enzyme G with a gain r2 . Finally , G catalyzes the clearance of Y with a gain r3 . Since G negatively regulates Y , the local gain r3 has a negative value . The total stress S is composed of S0 and Se ( S = S0 + Se ) where S0 is the background stress level at the basal condition , and Se is the stress level introduced by external stressors . The total stress level S is expressed as multiples of S0 . Levels of Y , T , and G are also normalized to their respective levels at the basal condition where S = S0 . For simplicity , we first considered the circumstance where all local gains are independent of each other and remain constant as the value of S is varied . According to signal transfer/modular response analysis [22–24] , the systems-level gain for Y , T , and G over S can be mathematically derived ( see Text S1 for derivation ) and expressed as follows: After rearranging the feedback control graph in Figure 2A by taking S as the input , and Y , T , and G as the respective output ( Figure 2C ) , Equations 4–6 can be uniformly expressed in the format of where and are , respectively , the systems-level ( closed-loop ) gain and open-loop gain for C over S , and Rloop = |r1r2r3| is the loop gain . This formalism , conforming to that originally derived for intracellular signal propagations with feedback [17 , 20 , 22 , 24] , is analogous to the closed-loop gain of a proportional feedback control system such as an electronic amplifier ( Figure 2B ) . The shapes of dose response curves , i . e . , linear , superlinear , or sublinear , for the control circuit in Figure 2A depend on the systems-level gains . Assuming the production rate of Y is proportional to stress level S , and Y is cleared at a first-order rate by G ( i . e . , level of Y is far from saturating G ) , then r0 = 1 , i . e . , the controlled variable Y increases linearly with S in the absence of the feedback . Equations 4–6 can be simplified to: According to Equation 8 , for the controlled variable Y , is always less than or at best equal to unity since the loop gain Rloop ≥ 0 ( zero is equivalent to open loop ) . Therefore , the Y versus S dose response curve is superlinear or at best linear . The smaller is , the more superlinear the dose response curve becomes , and Y is more insensitive to changes in S . Since the goal of the feedback gene regulation is to maintain homeostasis for Y ( which could be ROS , DNA adduct , misfolded protein , etc . ) , it is desirable to have Rloop as large , hence as small as possible , to effectively resist perturbations . Augmentation of loop gain Rloop can be achieved by increasing local gain r1 , r2 , and r3 , either alone or in combination . Along the feedback loop—from activation of transcription factor , to gene induction , to enzyme formation , to enzymatic reaction—a variety of local interactions can operate in an ultrasensitive manner to provide high local gains ( ultrasensitivity is generally defined as a response that has a Hill coefficient greater than unity ) . For instance , in response to stress , some transcription factors involved form homodimers or homotrimers to become transcriptionally active [25 , 26] . Ideally , homodimerization and homotrimerization can give r1 a characteristic value of 2 and 3 , respectively . Simulation results indicated that dimerization or trimerization of T increasingly suppresses the Y versus S dose response curve , consistently matching the analytic results ( Figure 3A , top panel ) . For local gain r2 , it can be enhanced at least by the following two mechanisms . For one , a transcription factor may interact with promoters of inducible genes cooperatively if multiple copies of its response element exist . This cooperativity in DNA binding results in a more repressed Y versus S dose response curve in our control scheme ( Figure 3B , top panel , blue line ) . For another , many enzymes induced in response to cellular stresses need to form homodimers or even tetramers from their initial translated products to become fully active . For instance , enzyme glutathione peroxidase ( GPx ) , induced by oxidative stress and responsible for removing H2O2 and lipid peroxide , is a tetramer [27] . Similar to dimerization of transcription factors , these multimerization processes enhance local gain r2 , leading to a more robust homeostatic control of Y ( Figure 3B , top panel , green line ) . As far as r3 is concerned , which is the local response coefficient of Y controlled by G , no obvious interactions appear to be able to specifically enhance it . And since an enzyme generally has a linear control over its reaction rate ( i . e . , the elasticity is unity [15] ) , a characteristic value of −1 would be expected for r3 under conditions where G is far from being saturated by Y ( how S and Y level influences r3 will be considered in later sections ) . Lastly , cells are likely to use combinations of ultrasensitive steps described above to achieve a large loop gain for robust homeostasis . For instance , dimerization of T , coupled with cooperative binding and dimerization of G , gives rise to a very insensitive , almost horizontal Y versus S dose response curve ( Figure 3C , top panel , blue line ) . In contrast , when the feedback loop is broken , by opening up at either of the local steps , the Y versus S dose response curve is linearized ( Figure 3 , top panels , orange lines ) . The shape of dose response curves for gene expression of G is determined by the systems-level gain according to Equation 10 . In contrast to the Y versus S dose response , which is superlinearized by increases in local gain r1 and r2 , similar increases in r1 and r2 tend to linearize the G versus S dose response curve ( Figure 3 , bottom panels ) . This is because when the loop gain Rloop ≫1 , Equation 10 can be simplified to . As previously discussed , for relatively small Y , hence , indicating a linear relationship . This sole dependency of on feedback gain r3 , and linearization of the output , as suggested by Savageau with BST [17 , 20] , is similar to that of an operational amplifier with a very high open-loop gain . On a similar basis , it has been shown that mitogen-activated protein kinase ( MAPK ) signaling cascade can potentially ultrasensitize or linearize its output by adjusting the negative feedback strength [28 , 29] . The shape of dose response curves for transcription factor T is determined by the systems-level gain according to Equation 9 . Local gain r1 and r2 alter the curvature of the dose response curve in opposite directions—increases in r1 reduce the superlinearity , whereas increases in r2 further superlinearize the T versus S dose response curve ( Figure 3 , middle panels ) . In the above section , we explored in principle the shape of dose response curves in a homeostatic gene regulatory system with negative feedback , and how the curvature is altered by local gains distributed in the feedback loop . In these analyses , local gains were independent of each other and remained at their respective characteristic values as S varied . In an actual gene regulatory network , local gains at different steps and the total loop gain are unlikely to remain constant in response to a wide range of S . The resulting dose response curves are typically more complex than a simple linear , superlinear , or sublinear function can describe . Despite such complexity , it is possible to decompose a dose response curve into distinct phases each associated with a specific profile of changes in local/loop gains . In this section we set out first to consider the shape of each individual phase in isolation , and then to reconstruct the full-range dose response curve by linking individual phases in the order they are expected to become active as S increases . The anti-stress gene regulatory network illustrated in Figure 2A is a generalized control scheme for cellular homeostasis . With respect to risk assessment , it is practically important to determine whether the dose response transition derived from this general scheme would hold in more complex and realistic anti-stress biological systems , which often involve multiple genes , enzymes , and biochemical reactions . Extensive validation of the proposed transition would require experimental studies systematically characterizing the full-range dose responses , especially at low doses with detailed and reliable measurements . Unfortunately , such studies are rare so far , and , in most cases , the inability to obtain low-dose data reliably and efficiently has been the primary motivation behind dose response extrapolation . Nevertheless , in keeping with the current idea , available experimental data in rat livers have shown that the level of DNA adducts in response to carcinogen dimethylnitrosamine ( DMN ) [30] , and that of protein conjugates in response to electrophile-generating agent vinylidene chloride ( VDC ) [31] , indeed followed the proposed dose response transition ( Text S3 ) . To further solidify our conclusions , in the remaining section we focused specifically on the mammalian anti-electrophilic stress system . By formulating a detailed model of this system ( Figure 10 ) , we studied its dynamic and dose response behaviors and how the complexity of the underlying gene regulatory network contributes to the system's controllability ( for model details , see Text S4 and Tables S4 and S5 ) . Electrophiles are electron-attracting chemical agents/metabolites that are cyto- or genotoxic via reactivity with proteins and DNAs . They are primarily detoxified in the cell by conjugation with reduced glutathione ( GSH ) enzymatically or in some cases non-enzymatically . The anti-electrophilic stress gene regulatory network in mammalian cells can be decomposed , from an engineer's perspective , into three functional units as seen in a classical control system , i . e . , transducer , controller , and biochemical plant ( Figure 10 ) . The transducer contains Kelch-like ECH-associating protein 1 ( Keap1 ) and nuclear factor erythroid 2-related factor 2 ( Nrf2 ) , which sense the levels of intracellular electrophile ( X ) and ROS . Specifically , Keap1 is a cytosolic cysteine-rich protein that facilitates the degradation of transcription factor Nrf2 through ubiquitination [32 , 33] . An increase in the level of intracellular electrophiles causes conjugation and/or oxidization of certain key cysteine residues in Keap1 , rendering Keap1 incapable of mediating Nrf2 ubiquitination and degradation [34 , 35] . The ensuing stabilization of Nrf2 results in elevated cytosolic Nrf2 levels through de novo protein synthesis and subsequently its nuclear translocation . The controller receives the input from Nrf2 through the electrophile response element ( EpRE ) and integrates it with other transcriptional signals to regulate gene expression of a set of anti-electrophilic enzymes , including glutamate cysteine ligase catalytic subunit ( GCLC ) , glutamate cysteine ligase modifier subunit ( GCLM ) , glutathione synthetase ( GS ) , glutathione S-transferase ( GST ) , and multidrug resistance-associated protein ( MRP ) [36–40] . These enzymes function in cohort to control the level of electrophiles by catalyzing a set of interconnected metabolic reactions in the biochemical plant . Specifically , GCLC , holoenzyme GCL , ( heterodimer of GCLC and GCLM ) , and GS collectively contribute to the de novo synthesis of GSH in two sequential reactions . GST then transforms , using GSH as a co-substrate , the electrophile into less toxic and more water-soluble glutathione conjugates ( GSX ) . GSX is then extruded by MRP out of the cell . Our simulation indicated that the anti-electrophilic defense system launches a typical adaptive response when challenged continuously with electrophilic stresses ( Figure 11A ) . The simulated dynamics is similar to experimental observations in a variety of cells exposed to many electrophiles , such as 4-hydroxy-2-nonenal ( 4-HNE ) and 15-deoxy-delta ( 12 , 14 ) -prostaglandin J2 ( 15d-PGJ2 ) [41 , 42] . The electrophile X initially rises sharply , but after a few hours it settles at much lower steady-state levels . GSX follows a similar dynamic change , albeit it declines to steady states more slowly . In comparison , Nrf2 , GCL , GS ( unpublished data ) , and GST levels peak in a more delayed manner before leveling off . MRP , due to its long half-life ( 27 h in the current model ) , does not reach a steady state until a much later time ( unpublished data ) . The level of intracellular GSH initially decreases as a result of consumption by electrophiles . But the downtrend is soon reversed as the expression level of GCL and GS increases . After a few hours , GSH surpasses its basal level and then peaks before settling down on elevated steady-state levels . The steady-state dose response curve for electrophile X , which is a controlled variable here , transitions from an initial superlinear controlled phase , through a minimal linear phase , to a sublinear catastrophic phase ( Figure 11B ) . This transition is clearly consistent with the dose response profile derived from the generalized control scheme . Conjugation product GSX , a minor controlled variable here due to its much lower toxicity , also experiences an initial superlinear phase , albeit of much less extent , before moving upward sublinearly . Compared with X , the smaller superlinear controlled phase of GSX is likely due , at least in part , to the fact that MRP is the only gene controlling GSX . The steady-state GSH dose response has a biphasic appearance—it first increases at small doses then decreases as the dose increases further . This biphasic profile has been observed in human epithelial cells treated with lipid electrophile 15-deoxy-delta ( 12 , 14 ) -prostaglandin J2 ( 15d-PGJ2 ) [43] . Interestingly , nuclear Nrf2 appears to have a dose response profile similar to X , suggesting the transducer containing Keap1 may have a close-to-linear signal transfer property . Levels of enzymes such as GCL and GST increase dose dependently and tend to plateau at high doses . As far as homeostatic feedback control is concerned , the mammalian anti-electrophilic system , as at least in the current model , is complex in the following aspects . First , control of detoxification of electrophile X is a concerted action by multiple genes through a series of metabolic reactions , including co-substrate GSH synthesis by GCL and GS , conjugation of X by GST , and extrusion of GSX by MRP . Second , there are several places in the feedback loop where local gains can be enhanced for effective homeostatic control . These include formation of GS , GST , and MRP homodimers by their respective monomeric subunits [44–46] , formation of holoenzyme GCL heterodimers by GCLC and GCLM [36] , and positive auto-regulation of Nrf2 [47 , 48] . Third , the core electrophile-detoxifying reaction catalyzed by GST is , in many cases , subjected to product inhibition by GSX [49–51] . This inhibition suggests that extrusion of GSX and regulation of MRP , though downstream of X , may also play a role in its homeostatic control . In the following section , we investigated how individual elements of these complexities affect the homeostatic performance of the control system . According to the two-gene control system analyzed in Text S5 , inclusion of each additional feedback gene regulation adds to the loop gain ( Equation S8 ) . To examine the role of each individual gene regulation in the controllability of electrophile X , simulations were performed by clamping their expression levels at values seen at the basal condition . The results revealed that deregulation of genes responsible for GSH synthesis , i . e . , Gclc , Gclm , and Gs , does not affect the controllability at low stressor levels significantly , as X level follows very closely that in the complete system ( Figure 12A ) . However , the response diverges at intermediate stressor levels , swinging upward sharply with clamped Gs expression , but more mildly with clamped Gclc or Gclm expression . Regardless , the general superlinear-to-sublinear dose response profile seems to hold in these cases . In contrast , deregulation of Gst results in a sharp early rise in X level such that it enters the catastrophic phase at very low doses ( Figure 12A , red line ) . Clamping Mrp gene expression resulted in an initial superlinear response that overlaps with that for the complete system , but it soon shoots upward almost vertically as the dose increases further ( Figure 12C , red line ) . In summary , it appears that to keep the electrophile level contained , it is more crucial to adaptively upregulate Gst and Mrp than to enhance GSH replenishment by upregulating Gcl and Gs . Moreover , the simulation supports the concept that transcriptional feedback regulation of multiple anti-stress enzymes enhances the loop gain and improves the resistance to cellular stressors , albeit each individual gene regulation may exert its effect at different dose levels . We next investigated the homeostatic role played by processes potentially enhancing local gains . Simulations indicated that in the absence of certain gain-enhancing processes , such as GST homodimerization , Nrf2 autoregulation , or both , the dose response curve for X diverges upward at low doses with an earlier onset of the catastrophic sublinear phase ( Figure 12B ) . In the case where MRP can function without forming a dimer , the dose response curve for X mostly overlaps with that for the complete system until it diverges upward at intermediate doses ( Figure 12C , green line ) . While exclusion of these gain-enhancing processes notably renders the system more sensitive to external perturbations of different levels , the superlinear-to-sublinear dose response profile remains intact in most cases . Simulating the system without GS dimerization does not alter the dose response curve for X when compared with the complete system ( unpublished data ) , suggesting a lesser role of this process in the homeostatic control . The GSH conjugates of many electrophiles can exert an inhibitory action on the catalytic activity of GST [49–51] . With product inhibition , the homeostatic control of X will be affected by the level of GSX and MRP that controls GSX extrusion . This effect was clearly demonstrated by altering the inhibition constant Ki of GSX over GST . An increase in Ki delays the occurrence of the catastrophic phase , whereas a decrease in Ki sensitizes the response , advancing the occurrence of the catastrophic phase ( Figure 12C ) . As mentioned above , deregulation of Mrp gene expression ( red line ) and lack of MRP functioning as a homodimer ( green line ) both unfavorably affect the homeostatic performance , with the former being more damaging . Thus , through product inhibition , MRP , the enzyme located last in the detoxification chain , plays a significant role in the homeostatic control of X . In supporting this role , it has been experimentally demonstrated that MRP2 can potentiate GST A1–1 mediated cellular resistance to electrophilic agents [52 , 53] . All in all , the superlinear-to-sublinear dose response profile remains intact regardless of the alterations made to the system with respect to product inhibition . The intracellular GSH level has been experimentally manipulated via both genetic and pharmacological means to study the role of GSH in anti-oxidant/electrophilic response [54–56] . Here we examined , in silico , how altered GSH levels via disruption of Gclc and Gclm genes , as well as inhibition of GCL activity , affect the homeostatic control in anti-electrophilic defense . Because mouse homozygous Gclc ( −/− ) knockout is embryonically lethal , it is not discussed here . While basal GSH level is 5 mM in the complete model system , it decreases to 4 , 3 . 6 , and 1 . 2 mM in our models , equivalent to Gclc ( +/− ) , Gclm ( +/− ) , and Gclm ( −/− ) knockouts , respectively . These values are in line with or close to changes in GSH levels reported experimentally for respective knockout mice , which are 80% , 43%~83% , and 9%~16% of that in the wild-type [54 , 55] . Our simulation revealed that both the Gclc ( +/− ) and Gclm ( +/− ) models produce a dose response curve for X that is almost identical to that with the complete system ( Figure 12D ) . This unaltered anti-electrophilic control capability predicted with our model resonates with the fact that the heterozygous animals have nearly normal phenotypes and viability [54 , 55] . In contrast , more severe disruption of basal GSH levels , as observed with Gclm ( −/− ) homozygous knockout , results in a slightly elevated superlinear controlled phase followed by an earlier onset of the catastrophic phase ( Figure 12D , green line ) . A similar dose response curve with a more advanced catastrophic phase was obtained by inhibiting GCL activity , which produces a basal GSH level of 0 . 5 mM in this case ( Figure 12D , red line ) . Overall , these results are consistent with experimental findings that Gclm ( −/− ) knockout mice and cells depleted of GSH with GCL-inhibiting agent BSO are more sensitive to electrophilic/oxidative damage [54 , 56] . While most electrophilic compounds are detoxified through conjugating with GSH enzymatically , certain chemicals , such as the electrophilic intermediates of vinylidene chloride ( VDC ) , can efficiently react with GSH to form conjugates without GST [57] . Thus , it is necessary to ascertain whether the dose response transition observed for electrophile X , which is conjugated enzymatically , will recur in nonenzymatic situations . Simulations indicated that although the dynamic responses are similar to GST-catalyzed situations ( Figure S3A ) , lack of GST participation results in a less robust homeostatic control of X—the same stressor level produces a higher X level ( Figure S3B ) . Nevertheless , the superlinear-to-sublinear profile seems retained , albeit less prominently . Interestingly , compared with GST-catalyzed situations , the homeostatic control of the minor controlled variable GSX becomes more robust and exhibits a more pronounced superlinear-to-sublinear appearance . In summary , we have demonstrated that the dose response transition derived from the generalized control scheme holds well in an anti-stress gene regulatory network as complex as the system defending electrophilic stresses , which involves multiple genes , enzymes , and metabolic reactions . The transition profile is even retained in highly impaired circumstances , such as gene knockout and deregulation . The feedback regulation of multiple genes not only enhances the system's controllability , but also makes the system less vulnerable to functional disruptions of individual genes .
Cells do not remain passive when confronted with environmental challenges . To maintain a relatively stable intracellular milieu , they are equipped with a suite of specialized defense programs that are launched in response to various external stressors [2–5] . These defense mechanisms often comprise gene regulatory networks organized into negative feedback circuits , which can be decomposed into basic functional units seen in a classical control system . Our ultimate goal is to assess quantitatively the impact of external stressors , such as environmental toxicants , on such a complex control system and on consequent higher-level functions such as cell survival and death . To this end , it would be helpful to first reduce the complex systems to a basic control scheme and to study its behavior . The present study demonstrated that local gains , distributed in the feedback loop of a homeostatic gene network , shape the steady-state dose response curves , leading to linear , superlinear , or sublinear relationships under different conditions . The dose response relationship for intracellular controlled variables is multiphasic as stressor level increases—initial superlinear with lesser control , superlinear more highly controlled , linear uncontrolled , and sublinear catastrophic . The appearance of each phase depends on specific gain-changing events that come into play as the stressor level increases . Our work also indicated that responses in the low-dose region could vary from superlinear to sublinear , and even to J-shaped curvatures , depending on the strength of homeostatic regulation . Overall , these changing responses are consistent with the so-called dose-dependent transition proposed for many chemical compounds [58 , 59] . Analogous to the I/O relationship in a manmade control system implemented via proportional negative feedback , the steady-state systems-level gain in a gene regulatory network functioning to resist perturbations abides by a similar transfer principle . The systems-level gain for the controlled variable Y , transcription factor T , and gene product G can be generically described by their respective open-loop gain and the loop gain ( Equation 7 ) , a formalism that resembles the closed-loop gain of an electronic amplifier and conforms to that originally derived for intracellular signal propagations with feedback [17 , 20 , 22 , 24] . Under small S where r0 ≈ 1 , the systems-level gain , or sensitivity of Y to S , , is less than unity ( Equation 8 ) . This results in a superlinear Y versus S dose response curve . Since Y is the controlled variable , to minimize alterations in Y in response to changes in stress level S , it is desirable to keep as small , and thus the loop gain Rloop as large , as possible . The larger Rloop is , the more superlinear the Y versus S dose response curve becomes , and cells are more resistant to perturbations . Since Rloop is the product of individual local gains sequentially distributed along the feedback loop , increases in any individual local gain will augment Rloop , leading to more robust homeostasis . Delving into the molecular details of many anti-stress gene regulatory networks readily reveals that local gain enhancement appears to be a common strategy cells utilize for robust homeostasis . In this regard , cells are furnished with many biochemical reactions/interactions or functional modules that can transfer signals in an ultrasensitive , or even switch-like manner , and thereby enhance local gains . It is not uncommon that many transcription factors specifically involved in stress responses must homodimerize or homotrimerize to become transcriptionally active . For instance , in response to heat shock , heat shock transcription factor 1 ( HSF1 ) monomers multimerize into homotrimers to gain affinity to bind the heat shock element ( HSE ) [25] . Ideally homodimerization and homotrimerization can enhance signal transfer sensitivity , thus the local gain , by a factor of 2 and 3 , respectively . Binding of transcription factors to specific DNA response elements is the next step where the stress-triggered signal can be amplified . Existence of multiple copies of a response element in a gene promoter provides the possibility for cooperative binding , which is a classical interaction that can give rise to ultrasensitivity . In yeast , where the Hsp82 promoter contains three copies of HSE in adjacency , HSF binds the promoter in a profound cooperative fashion to induce Hsp82 gene expression [60] . Similar homeostatic roles played by transcription factor multimerization and cooperative binding have been suggested in maintaining intracellular protein concentrations [61 , 62] . Subsequent steps in the feedback loop , including RNA transcription and protein translation , are largely linear processes , adding little , if any , to local gains . But in many cases , the initial translation products need to form high-order multimers to become fully active enzymes . This process provides a gain-enhancing mechanism similar to transcription factor dimerization or trimerization . For instance , among the antioxidant enzymes that are activated in oxidative stress response , glutathione reductase and superoxide dismutase are homodimers [63 , 64] . More intriguingly , glutathione peroxidase ( GPx ) and catalase ( CAT ) , the two major enzymes responsible for removing intracellular H2O2 and lipid peroxide , exist largely as homotetramers [27 , 65] . It is thus highly likely that apart from potentially stabilizing the enzymes , a primary function of dimer or tetramer formation is to augment the loop gain for robust redox homeostasis . To further improve signal transfer sensitivity , cells can also use localized positive feedback which is known to enhance response coefficient . A common positive feedback motif in the cell is gene autoregulation , in which a transcription factor upregulates gene expression of itself or its cofactors . In Nrf2-mediated gene regulatory network against electrophilic stress , the electrophile response element ( EpRE ) is found in Nrf2 gene promoters , and Nrf2 can transcriptionally upregulate its own gene expression [47 , 48] . Lastly , a very common ultrasensitive signaling motif is the MAPK cascade , which can produce a switch-like response due to the combination of zero-order ultrasensitivity , distributive dual-phosphorylation , and layered arrangement [66] . Specifically , c-Jun N-terminal kinase ( JNK ) , a member of the MAPK family , mediates a series of stress responses [67] and was shown to transfer signals in an ultrasensitive fashion [68] . Cells are likely to use combinations of these ultrasensitive mechanisms to enhance the loop gain as well as to compensate for gain losses from individual mechanisms operating at less ideal conditions , such as significant degradation of high-order multimers and substrate sequestration in zero-order covalent modification cycles [69] . Local gains do not remain characteristically constant as the feedback network is increasingly activated by external stressors . This stress level–dependent variation suggests that a dose response curve could undergo multiple phases that cannot be represented by a simple function . The slow recovery of local gains from repression [70] , owing to constitutive activity of transcription factors or anti-stress genes , results in a sluggish response in gene expression , leaving the perturbation less countered under low-level stresses . Although such inadequacy in effectively mounting a protective response is seemingly undesirable , in certain situations it may be an energy-saving design . For cells living in an environment featuring frequent but minor fluctuations , they may have purposely evolved to tolerate perturbations of small magnitude to avoid otherwise expensive and frequent activation of anti-stress genes . Another situation where the less-regulated phase may be preferred is in cells where the controlled variable is also used for signaling purposes . For instance , in adipocytes where H2O2 is used to mediate intracellular insulin signaling , it would be less desirable for an insulin-induced H2O2 signal to frequently trigger anti-oxidant gene expression , which will otherwise dampen H2O2 as a second messenger [71] . The second phase of the Y versus S dose response curve is superlinear in appearance and characterized with the highest loop gain . In this phase the homeostatic mechanism operates at full capacity so that cells are best able to effectively resist external disturbance . With a large loop gain , the controlled variable could change very little in response to a wide range of stress levels . But ultimately , activation of gene expression would approach saturation , and the superlinear controlled phase will transition either into the linear phase or directly into the catastrophic sublinear phase , depending on the degree of enzyme saturation by that point . In the two latter phases , the system loses active controls , and persistent elevation of the controlled variable may lead to cell death . Since the feedback regulation in a homeostatic gene network is transcriptionally mediated , it can take hours or longer for the system to settle at a new steady state , as demonstrated in the electrophilic stress response . Before reaching the steady state , the controlled variable may be at very high levels . If cells cannot tolerate such a short-term spike of the controlled variable internally , programs such as apoptosis may be initiated , and the steady-state response for high doses would not be achieved . The homeostatic benefit of a high loop gain is obvious—it increases the resistance of the cell to external perturbations at low doses , and extends the resistance to higher doses by delaying catastrophic rises in the levels of controlled variables . A high loop gain can be obtained either by concentrating it in one or two local steps or by distributing it more evenly throughout the feedback loop . Overly concentrated loop gains may be less preferred since they may impose special biochemical or energy challenges to the specific reactions/interactions involved . Even if allocating the loop gain more evenly is a better design , some locations within the feedback loop may be preferred over others for gain placement . Among three of the ultrasensitive steps discussed here where local gains can be enhanced—transcription factor multimerization , cooperative binding of transcription factor to response elements , and enzyme multimerization—the latter is probably preferred over the former two when a choice has to be made about gain placement . Although a high loop gain achieved at the two pre-transcriptional locations can definitely enhance the local superlinearity of the controlled phase , thus boosting resistance to perturbations by relatively low-dose stressors , this increased resistance eventually has to succumb to the limiting effect of gene saturation , with the response converging invariably into a similar linear and eventually catastrophic phase ( Figure S4A ) . In contrast , a loop gain enhanced post-transcriptionally through enzyme multimerization cannot only superlinearize the controlled phase further but also extend it , delaying the arrival of the uncontrolled linear and particularly the catastrophic phase ( Figure S4B ) . Since metabolic enzymes usually exist in much higher abundance than transcription factors , the homeostatic benefit of post-transcriptional gain enhancement is nonetheless at the cost of higher energy consumption for synthesizing more enzyme molecules . Additional considerations for gain placement may include avoidance of persistent oscillation , which has been observed in gene regulatory networks with delayed negative feedback [72 , 73] . Although the present study is concerned with the steady-state behaviors of anti-stress gene regulatory networks , it is important to note that gene expression is intrinsically stochastic and may fluctuate to a great extent around the steady state in both simple and complex gene networks [74–86] . Given the homeostatic objective of an anti-stress gene regulatory network , it is important to understand how cells can cope with this noisy nature of gene expression that may undermine the stability of the intracellular environment . A recent genome-wide study indicated that compared with other genes , those essential to the fitness of organisms are expressed at higher transcription but lower translation rates [87] . This is a strategy believed to lower protein expression noise [74] . Given the fact that most anti-stress genes are indispensable for normal cell functions ( the deletion of which is often embryonically lethal or results in severely impaired viability [55 , 88 , 89] ) , a similar expression strategy , i . e . , high transcription low translation , may have been adopted by genes responsible for cellular homeostasis to reduce fluctuation . Moreover , the negative feedback nature of anti-stress gene regulatory networks may also help alleviate noise in gene expression . Despite a few studies suggesting that negative autoregulation may increase protein expression noise under certain conditions [90–92] , the majority of the literature , including experimental evidence , regards negative feedback as a design that effectively attenuates intrinsic noise in gene expression [6 , 11 , 74 , 90 , 91 , 93–98] . Additionally , it has also been shown that dimerization of transcription factors can further reduce protein expression noise in gene feedback loops [93 , 96] . This noise-reducing interaction resonates with the fact that transcription factors involved in anti-stress regulation often dimerize or trimerize to become active [25 , 26] . Recently , El-Samad and Khammash suggested that regulated degradation of heat shock factor σ32 is a mechanism for suppressing stochastic fluctuation in the heat shock gene regulatory network [98] . In this regard , it is worth mentioning that Nrf2 and HIF , the key transcription factors mediating electrophilic and hypoxic stress responses , respectively , are also regulated primarily through protein degradation [34 , 99 , 100] . Taken together , it is highly likely that fluctuations in protein expression and thus controlled variables in anti-stress gene regulatory networks may well be at a minimum through , at least , the above noise-attenuating mechanisms . A critically important issue in toxicological research and risk assessment is how to estimate low-dose effect from experimental data obtained for high doses . Although linear extrapolation from the high-dose region to the basal point has been a popular practice , in many situations the assumption that dose response relationships in the low-dose region behave linearly does not have theoretical basis . The present study revealed that for cells capable of anti-stress homeostatic regulation , the low-dose region has various nonlinear characteristics . The nature of negative feedback regulation determines that the low-dose region for the controlled variable is basically superlinear , and the stronger the feedback is , the more superlinear it becomes . However , in the presence of diminishing gains for gene activation owing to saturation , the superlinear phase gradually reverses its curvature to become sublinear during its course to join the subsequent linear phase . Therefore , the primary curvature in the low-dose region depends , by and large , on the relative influence from the superlinear controlled phase and the sublinear segment that immediately follows . In conditions where the effect of constitutive activation is insignificant , pre-transcriptional gain is high , and saturation of gene activation occurs early , the superlinear controlled phase appears only transiently , leaving the low-dose region dominated by the sublinear segment . In the presence of feedforward gene activation , the sublinear appearance is even more prominent , and eventually a J-shaped curve could arise . It has long been hypothesized that low-dose effects including hormesis are of homeostatic and adaptive nature [101 , 102]; our results are consistent with such a concept . Given the diversity and complexity of the dose response curve in the low-dose region , it thus appears inappropriate to extrapolate from high- to low-dose regions with any simple function . Regardless of the curvature within the low-dose region , if linear extrapolation starts from high-dose points in the catastrophic sublinear phase , the low-dose effect is likely to be consistently overestimated , albeit to various degrees . However , if the extrapolation starts from the linear phase or even below , the cellular impact from low doses would be either underestimated or overestimated , depending on the curvature in the low-dose region ( Figure 9 ) . For government regulatory purposes , different curvatures in the low-dose region , relative to linear extrapolation , may give rise to significantly different cutoff “safe” exposure levels for a biological stressor of interest . As a result , the difference in the economic cost associated with preventive measures taken to keep exposures below the regulated safe level can be significant . The present study represents our initial effort to achieve a quantitative understanding of the adaptive cellular response for homeostasis . We realize that the generalized control scheme we studied is a simplification of realistic biological networks that are more complex . But as we showed for the anti-electrophilic defense system , complex feedback networks involving multiple genes , enzymes , and biochemical reactions may observe similar transitions in their controllability . Although the dose response transition we proposed remains to be extensively examined , experimental studies by others on the formation of DNA adducts and protein conjugates [30 , 31] have provided preliminary evidence indicating the proposed transition may indeed operate in realistic biological systems ( Text S5 ) . Clearly , low-dose extrapolations for risk assessment need to acknowledge the complexity of adaptive responses in order to be consistent with biological signaling dynamics and the inherent ability of organisms to detect environmental stressors and maintain homeostatic functions .
Details of the model structures , reactions , and parameter values are provided under Supporting Information , which contains references and rationale for the choice of parameter values . For the generalized control scheme , the parameter values were kept dimensionless and chosen to assist in visualizing the analytical results ( Tables S1–S3 ) . For the anti-electrophilic stress model , the parameter values were obtained from the literature if available , or estimated to meet the constraints imposed by experimental observations of our own or others ( see Tables S4 and S5 for details ) . All models were first constructed and parameterized in PathwayLab ( InNetics , http://www . innetics . com ) and then exported into MatLab ( MathWorks , http://www . mathworks . com ) . All the dose response simulation results were obtained by running the models to steady state in MatLab . Models in the format of MatLab are available for downloading in File Collection S1 . | To maintain a stable intracellular environment , cells are equipped with multiple specialized defense programs that are launched in response to various external chemical and physical stressors . These anti-stress mechanisms comprise primarily gene regulatory networks , and like many manmade control devices , such as thermostats and automobile cruise controls , they are often organized into negative feedback circuits . A quantitative understanding of how these control circuits operate in the cell can help us to assess and predict more accurately the cellular impacts brought about by perturbing stressors , such as environmental toxicants . Using control theory and computer simulations , we explored nature's design principle for anti-stress gene regulatory networks , and the manner in which cells respond and adapt to perturbations . We showed that cells can exploit multiple mechanisms , such as protein homodimerization , cooperative binding , and auto-regulation , to enhance the feedback loop gain , which , according to control theory , is a basic principle for effective perturbation resistance . We also illustrated that the steady-state dose response curve is likely to transition through multiple phases as stressor level increases , and that the low-dose region is inherently nonlinear . Our results challenge the common practice of linear extrapolation for evaluating the low-dose effect , and would lead to improved human health risk assessment for exposures to environmental toxicants . | [
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] | 2007 | Dose Response Relationship in Anti-Stress Gene Regulatory Networks |
Neural circuits are dynamic , with activity-dependent changes in synapse density and connectivity peaking during different phases of animal development . In C . elegans , young larvae form mature motor circuits through a dramatic switch in GABAergic neuron connectivity , by concomitant elimination of existing synapses and formation of new synapses that are maintained throughout adulthood . We have previously shown that an increase in microtubule dynamics during motor circuit rewiring facilitates new synapse formation . Here , we further investigate cellular control of circuit rewiring through the analysis of mutants obtained in a forward genetic screen . Using live imaging , we characterize novel mutations that alter cargo binding in the dynein motor complex and enhance anterograde synaptic vesicle movement during remodeling , providing in vivo evidence for the tug-of-war between kinesin and dynein in fast axonal transport . We also find that a casein kinase homolog , TTBK-3 , inhibits stabilization of nascent synapses in their new locations , a previously unexplored facet of structural plasticity of synapses . Our study delineates temporally distinct signaling pathways that are required for effective neural circuit refinement .
Neurons communicate through synapses , necessitating a system of checks and balances to achieve precise patterns of synaptic connectivity that execute neural circuit function . Large scale axonal growth and pruning mediate synapse formation with appropriate targets during development , shaping neuronal circuits during critical periods of plasticity [1] . Hyper- and hypo- connectivity in different brain regions is a widely observed phenomenon in children with autism spectrum disorders ( ASDs ) and related comorbid conditions [2] . Brain development defects during critical postnatal periods of plasticity are also thought to contribute to schizophrenia , which has a varying age of onset [3] . Structural synaptic plasticity is not purely a developmental phenomenon-synapse remodeling occurs in both normal and diseased adult brains in various contexts [4 , 5] . The mechanisms underlying synapse assembly and elimination have thus been the subject of intense study for several decades , although a majority of experimental models focused on synaptic plasticity that is coupled to neurite outgrowth and retraction [6 , 7] . With recent advances in in vivo imaging techniques , instances of synaptic rewiring that are independent of large scale neurite rearrangement have been identified in the mammalian central nervous system [4 , 8] . Elucidating the mechanisms underlying the cellular dynamics of such refinement , particularly in pre-synaptic terminals , is of general significance . In the C . elegans locomotor circuit , a subset of type-D GABAergic motor neurons exhibit critical period synapse plasticity . Upon birth and in young larvae , the Dorsal D ( DD ) neurons initially form synapses with ventral body wall muscles . During an early developmental molt , these early synapses are disassembled , and new synapses are formed with dorsal body wall muscles , without overt changes in neuronal morphology ( Fig 1A ) [9] . DD synapse remodeling is developmentally stereotyped , activity dependent , and uncoupled from neurite outgrowth , providing a tractable genetic framework to study the molecular mechanisms underlying structural synaptic plasticity . Numerous studies have provided insights into the conserved transcriptional programs that regulate the initiation of DD synapse remodeling and that maintain the temporal precision of synapse remodeling ( reviewed in [10] ) . Once circuit connectivity changes have been initiated in the DD neurons , the cellular execution of synapse assembly and disassembly takes place . Pre-synaptic terminals are eliminated from the DD ventral neurite , following which synaptic vesicles are transported to the DD dorsal neurite where they assemble to form new synapses that are stable for the lifetime of the animal . Previous work from our lab and others has found that dynamic microtubules ( MTs ) are required for synaptic vesicle transport to DD dorsal neurite during remodeling [11] and the patterning of new pre-synaptic terminals is achieved by the sequential action of anterograde and retrograde motors Kinesin-3/UNC-104 and dynein , respectively [12] . Synapse elimination from the DD ventral neurite is mediated in part by the cyclin Y homolog CYY-1 and the apoptotic cell death pathway [12 , 13] . In this study , we characterized multiple mutants isolated from a genetic screen for genes involved in DD synapse remodeling . We performed this screen on a mutant strain containing a gain-of-function ( gf ) mutation of alpha-tubulin tba-1 , and a loss-of-function ( 0 ) mutation of the conserved MAPKKK dlk-1 . This tba-1 ( gf ) dlk-1 ( 0 ) double mutant combination results in defective DD synapse remodeling due to a reduction in MT dynamics [11] . We identified mutations in the C . elegans α- and β-tubulin genes tba-1 and tbb-2 that reversed defects in MT architecture . We also show that novel mutations in the minus end directed motor dynein and its adaptor protein dynactin ameliorate defects in kinesin-mediated synaptic vesicle transport to the DD dorsal neurite during remodeling , highlighting the interdependence of the two motors even in cases of polarized cargo movement . We further find that a member of the casein kinase superfamily , TTBK-3 , specifically acts after remodeling is complete to modulate nascent synapse stability on the dorsal neurite , a previously uncharacterized aspect of synaptic plasticity . Our observations indicate that the dynein motor complex and TTBK-3 act at distinct temporal windows to differentially regulate synapse rewiring .
A missense mutation in C . elegans α-tubulin , tba-1 ( ju89 ) ( henceforth tba-1 ( gf ) ) results in a mild reduction in the synapse number of GABAergic motor neurons and a dampening of the amplitude of sinusoidal locomotion ( Fig 1B ) [11 , 14] . In animals carrying both tba-1 ( gf ) and a loss of function of the conserved MAPKKK DLK-1 ( dlk-1 ( 0 ) ) , DD remodeling is completely blocked such that the dorsal neurites contain almost no synapses , when visualized by the synaptic marker juIs1 ( Punc-25-SNB-1-GFP ) ( Fig 1B ) [11] . Consistent with a lack of GABAergic innervation , tba-1 ( gf ) dlk-1 ( 0 ) double mutant animals are uncoordinated and coil dorsally when touched in the head ( Fig 1C ) [11] . We performed a suppressor screen on tba-1 ( gf ) dlk-1 ( 0 ) ; juIs1 animals , first based on behavioral improvement , then by visual examination of synapses in DD neurons . We mapped the suppressor mutations to various genetic loci using whole genome sequencing and subsequent recombinant mapping ( see methods ) . Below , we report the characterization of these mutations . tba-1 ( gf ) alters a conserved glycine residue to arginine ( G414R ) in the C-terminal H11-12 loop of α-tubulin ( S1 Fig ) [14] . This C-terminal domain of α-tubulin is implicated in microtubule associated protein ( MAP ) binding [15] , and the G414R mutation resulted in MTs that are misoriented at both synaptic and asynaptic sites along the axonal processes of DD neurons [11] . tba-1 ( 0 ) null mutants appeared wild-type , presumably due to functional redundancy among the nine α-tubulin homologs in C . elegans [11 , 14] . Double mutant animals of tba-1 ( 0 ) and dlk-1 ( 0 ) are also superficially normal , indicating that tba-1 ( gf ) acts synergistically with dlk-1 ( 0 ) to produce defective synapse remodeling [11] . We found eight mutants that fully suppressed the uncoordinated behavior of tba-1 ( gf ) dlk-1 ( 0 ) and were completely normal with regards to synapse formation and DD synapse remodeling . DNA sequence analyses of tba-1 revealed that these suppressors contained additional mutations besides the ju89 nucleotide change , and therefore were classified as intragenic revertants of tba-1 ( gf ) ( Fig 1C ) . Three suppressors ( ju964 , ju966 and ju975 ) caused nonsense mutations at different amino acids of tba-1 ( S1 Fig and S1 Table ) . This suggests that truncated versions of TBA-1 produced in these mutants were likely not functional . One missense mutation at the start codon ATG ( ju980 ) also behaved similar to tba-1 ( 0 ) , with synapse formation and locomotion restored to normal in tba-1 ( ju980 ju89 ) dlk-1 ( 0 ) animals ( Fig 1B , S1 Fig ) . The other four suppressors caused missense mutations at highly conserved regions of α-tubulin ( S1 Fig and S1 Table ) . Two missense mutations ( ju973 ( L426F ) and ju987 ( A419T ) ) were in H12 of the C-terminal domain , and could possibly have reversed the MAP binding defects caused by tba-1 ( gf ) ( S1 Fig ) . Another missense mutation , ju965 ( S138L ) , was close to the GTP binding pocket of α-tubulin [15] ( S1 Fig ) . The last missense mutation , ju962 ( S285F ) , was in the intermediate domain of α-tubulin , reported to be necessary for binding of the MT stabilizing drug , taxol ( Fig 1D and S1 Fig ) [15] . Mutations in the intermediate domain or the GTP binding pocket possibly prevented the incorporation of tba-1 into MT polymers . While we cannot exclude the possibility that these mutations might also reduce TBA-1 protein levels , our results highlight the in vivo importance of specific residues within functional domains of α-tubulin . The C . elegans genome encodes six β-tubulin genes that function partially redundantly in various tissues of the organism and at different developmental stages [16–19] . We mapped the suppressor ju1535 to β-tubulin tbb-2 , causing a conserved Proline305 to Serine change ( P305S ) in the intermediate domain , which lies in the internal surface of the MT polymer ( S1 Fig ) . tbb-2 ( ju1535 ) partially suppressed the behavioral and synapse remodeling defects of tba-1 ( gf ) dlk-1 ( 0 ) ( S1 Table and Fig 1D ) , and such suppression was rescued by transgenic expression of wild type TBB-2 in tba-1 ( gf ) dlk-1 ( 0 ) ; tbb-2 ( ju1535 ) animals ( Fig 1F ) . Interestingly , tbb-2 ( ju1535 ) single mutant animals were smaller than wild type animals , and displayed a significant reduction in DD neuron synapse number , similar to tba-1 ( gf ) animals ( Fig 1D and 1E ) . Since tba-1 and tbb-2 form heterodimers in the C . elegans embryo , and have overlapping neuronal expression patterns [18–20] , we hypothesized that tbb-2 ( ju1535 ) might suppress tba-1 ( gf ) alone . Indeed , tba-1 ( gf ) ; tbb-2 ( ju1535 ) double mutant animals displayed increased DD neuron synapse numbers compared to either single mutant , albeit fewer than those seen in wild type animals ( Fig 1D and 1E ) . tbb-2 ( ju1535 ) behaved as a neomorphic allele , as gk129 , a null ( 0 ) mutation of tbb-2 did not cause overt defects in locomotion or synapse formation and also did not suppress the synapse remodeling defects of tba-1 ( gf ) dlk-1 ( 0 ) animals ( S1 Fig , Fig 1D and 1E ) . tba-1 ( gf ) ; tbb-2 ( 0 ) animals were viable and displayed similar synapse formation defects to tba-1 ( gf ) single mutant animals , whereas tba-1 ( 0 ) ; tbb-2 ( 0 ) animals were lethal due to their requirement in early embryonic development [14] . Taken together , these results suggest that while heterodimers of TBA-1 and TBB-2 were essential for embryonic development , in the absence of tbb-2 , tba-1 ( gf ) could form heterodimers with other β-tubulins that were then incorporated into MTs . tba-1 ( gf ) resulted in a change in MT architecture [11] and is a mutation altering the external surface of MTs , separate from domains responsible for GTP binding and heterodimer formation ( S1 Fig ) [14 , 15] . Thus , the suppression of tba-1 ( gf ) by tbb-1 ( ju2535 ) may likely be through either modifying or reducing the incorporation of tba-1 ( gf ) ; tbb-2 ( ju1535 ) heterodimers in MTs , in turn reducing the number of abnormal MTs . We mapped one of the suppressors , ju1279 , to the C . elegans cytoplasmic dynein heavy chain , dhc-1 . Cytoplasmic dynein is a large multi-subunit molecular motor that comprises of two catalytic heavy chains , as well as numerous light and intermediate chains . Dynein moves towards MT minus ends and is the primary motor involved in retrograde axonal transport , with mutations in dynein and its adaptor proteins implicated in several neurodegenerative disorders [21] . ju1279 converts a conserved proline residue to leucine ( P262L ) in the N-terminal region 1 of the tail domain of DHC-1 ( Fig 2A and S2 Fig ) , which is responsible for dynein homodimerization and acts as a scaffold for subunit assembly [22] . dhc-1 ( ju1279 ) acted as a weak suppressor of tba-1 ( gf ) dlk-1 ( 0 ) , since synapse remodeling was only partially restored in dhc-1 ( ju1279 ) tba-1 ( gf ) dlk-1 ( 0 ) triple mutant animals , with a significant reduction in dorsal neurite synapse number compared to tba-1 ( gf ) animals ( Fig 2B and 2C ) . We confirmed that dhc-1 ( ju1279 ) was causative by rescuing the suppression of tba-1 ( gf ) dlk-1 ( 0 ) using extra-chromosomal copies of a fosmid containing the full genomic region of DHC-1 ( Fig 2B and 2C ) . To understand how the ju1279 allele affects dhc-1 function , we examined two well characterized alleles of dhc-1 , or195 [23] and js319 [24] . or195 is a conserved serine to leucine change in the MT binding stalk region of DHC-1 , resulting in temperature sensitive lethality ( Fig 2A ) , while js319 is a splice site mutation in the C-terminal conserved motor domain of DHC-1 [25] , producing viable but visibly dumpy animals at 25°C ( Fig 2A , S2 Fig ) . In contrast , dhc-1 ( ju1279 ) animals appeared superficially wild type at 25°C , as did dhc-1 ( ju1279 ) /dhc-1 ( js319 ) and dhc-1 ( ju1279 ) /dhc-1 ( or195 ) heterozygous animals ( S2 Fig ) . dhc-1 ( js319 ) animals also had reduced DD synapse numbers ( Fig 2D ) while overexpression of wild type DHC-1 or dhc-1 ( ju1279 ) did not alter synapse number in adult animals ( Fig 2C and 2D ) . Additionally , triple mutants of dhc-1 ( js319 ) or dhc-1 ( or195 ) with tba-1 ( gf ) dlk-1 ( 0 ) were embryonic lethal even at temperatures lower than 25°C . Taken together , these results suggest that ju1279 is a novel allele of dhc-1 , uniquely altering dynein function in the context of synapse remodeling . Another suppressor , ju993 , changed valine 229 to isoleucine in the C . elegans p62 subunit of dynactin DNC-4 ( Fig 2D ) . Dynactin is a large multi-subunit protein complex that is essential for most cellular functions of cytoplasmic dynein , including MT binding and linking dynein to its cargo during fast axonal transport [26–29] . DNC-4 , together with p25 and p27 subunits , interacts with actin-related proteins Arp1 and Arp11 to form the pointed end of the dynactin complex , which is positioned diametrically opposite dynein motor domains to primarily influence cargo binding [28–30] . In dnc-4 ( ju993 ) adult animals DD neurons formed synapses in the dorsal neurites , and the pattern and number of synapses were comparable to wild type . In tba-1 ( gf ) dlk-1 ( 0 ) animals , dnc-4 ( ju993 ) significantly increased the number of synapses in DD dorsal neurites ( Fig 2E ) . Expression of wild type copies of DNC-4 ( + ) rescued the suppression of tba-1 ( gf ) dlk-1 ( 0 ) by dnc-4 ( ju933 ) ( Fig 2E ) . dnc-4 ( ju933 ) complemented the temperature-sensitive embryonic lethality of dnc-4 ( or633 ) , which altered a conserved glutamic acid to lysine in the N-terminal region ( S2 Fig ) [31] . Additionally , dnc-4 ( or633 ) did not suppress the synapse remodeling defects of tba-1 ( gf ) dlk-1 ( 0 ) at the permissive temperature ( 20°C ) , or when shifted to the restrictive temperature ( 25°C ) after embryonic development ( Fig 2F ) , indicating that ju993 is a novel mutation of dnc-4 . In conjunction with the effects on SV transport seen in dhc-1 ( ju1279 ) animals , and the similar levels of suppression seen in both dhc-1 and dnc-4 alleles ( Fig 2C and 2E ) , we propose that altering dynein-dynactin complex interactions has a profound effect synapse remodeling . We next sought to understand the mechanism by which dhc-1 ( ju1279 ) suppressed the synapse remodeling defects of tba-1 ( gf ) dlk-1 ( 0 ) . The remodeling defects in tba-1 ( gf ) dlk-1 ( 0 ) animals were primarily brought about by an increase in MT stability , resulting in reduced synaptic vesicle ( SV ) transport in the DD neurons during remodeling [11] . We first considered the possibility that dhc-1 ( ju1279 ) modifies MT dynamics in tba-1 ( gf ) dlk-1 ( 0 ) , since an established role of cytoplasmic dynein is to stabilize dynamic MT plus ends by tethering them to the cell cortex [32] . However , dhc-1 ( ju1279 ) had no significant effect on the number of dynamic MTs or their direction of growth in both wild type and tba-1 ( gf ) dlk-1 ( 0 ) adults ( S2 Fig ) , leading us to conclude that the suppression of synapse remodeling defects by dhc-1 ( ju1279 ) did not result from a change in MT dynamics . Since the ju1279 allele affects the tail domain of DHC-1 , which is structurally adjacent to the cargo binding domain of the dynein motor complex [30] , we wondered whether SV transport was altered in the mutant animals . We assayed SV transport along the commissures of DD neurons during synapse remodeling in wild type and mutant animals using 4-dimensional ( 4-D ) imaging ( S1 Movie , Fig 3A ) . In wild type animals , most SVs moved in the anterograde direction , i . e . , away from the cell body and towards their new location in the dorsal neurite ( Fig 3C ) . This proportion of anterogradely moving SVs was maintained in tba-1 ( gf ) dlk-1 ( 0 ) animals ( Fig 3C ) , albeit with a strong reduction in the total number of mobile SVs ( Fig 3B ) [11] . Addition of dhc-1 ( ju1279 ) did not change mobile SV numbers; instead we observed a significant increase in the proportion of SVs moving anterogradely in both dhc-1 ( ju1279 ) single and dhc-1 ( ju1279 ) tba-1 ( gf ) dlk-1 ( 0 ) triple mutant animals ( Fig 3B and 3C ) . We also imaged SV transport in dhc-1 ( js319 ) animals during DD remodeling , and did not find any increase in anterogradely moving SVs . Together , these data indicate that an increase in anterogradely moving SVs in dhc-1 ( ju1279 ) tba-1 ( gf ) dlk-1 ( 0 ) triple mutant animals ameliorates a reduction in the total number of SVs reaching the dorsal neurite to promote synapse formation during remodeling . We previously reported two suppressors ( ju972 and ju977 ) to be novel alleles of the anterograde motor , Kinesin-1/UNC-116 [11] . unc-116 ( ju972 ) strongly suppressed defective remodeling in tba-1 ( gf ) dlk-1 ( 0 ) by increasing total SV transport during remodeling , without altering the proportion of anterogradely moving SVs ( Fig 3B–3D ) [11] . Kinesins and dynein are classically thought of being in a “tug-of-war” during bi-directional cargo transport , highlighting the interdependence of the two motors for axonal transport in either direction ( Fig 3D ) [33–35] . We then wanted to see whether modifying dynein function using dhc-1 ( ju1279 ) would have any effect on SV transport in unc-116 ( ju972 ) animals . In both wild type and tba-1 ( gf ) dlk-1 ( 0 ) backgrounds , dhc-1 ( ju1279 ) ; unc-116 ( ju972 ) double mutant animals displayed a significant increase in both total number of mobile SVs and the proportion of anterogradely moving SVs , resulting in a strong anterograde bias in SV transport ( Fig 3B and 3C ) . These observations led us to hypothesize that dhc-1 ( ju1279 ) likely weakens the interaction between the dynein complex and SVs , shifting the balance of bidirectional cargo transport in the anterograde direction ( Fig 3D ) . We mapped another suppressor mutation of tba-1 ( gf ) dlk-1 ( 0 ) , ju978 , to the kinase ttbk-3 ( F32B6 . 10 ) ( S1 Table and Fig 4A ) . ttbk-3 ( tm4006 ) , a deletion allele that removes the N-terminus and part of the kinase domain , also suppressed the synapse remodeling defects of tba-1 ( gf ) dlk-1 ( 0 ) ( S3 Fig ) . Single mutants of ju978 or tm4006 were superficially wild type with no synapse formation or remodeling defects ( Fig 4A–4C and S3 Fig ) . We verified that ttbk-3 ( ju978 ) was causative for suppression of tba-1 ( gf ) dlk-1 ( 0 ) by overexpressing wild type TTBK-3 in tba-1 ( gf ) dlk-1 ( 0 ) ; ttbk-3 ( ju978 ) animals , and observed a block in synapse remodeling in the transgenic animals ( Fig 4B and 4C ) . These results indicate that loss of ttbk-3 specifically restores synapse remodeling in tba-1 ( gf ) dlk-1 ( 0 ) animals . TTBK-3 belongs to a large expansion of the Casein-kinase ( CK1 ) superfamily in C . elegans , sharing 32% identity in the kinase domain to human tau-tubulin kinases ( TTBK1 and TTBK2 ) [36] . Tau-tubulin kinases were first identified by their ability to phosphorylate the MT-associated protein tau and tubulin; mammalian TTBK1 is highly enriched in the nervous system , while TTBK2 is more broadly expressed [37 , 38] . ju978 generated a STOP codon in the C-terminal end of TTBK-3 , which could result in a truncated protein with an intact kinase domain that lacked a coiled-coil domain further downstream ( Fig 4A and 4D ) . Since ttbk-3 ( ju978 ) suppressed tba-1 ( gf ) dlk-1 ( 0 ) to a similar extent as ttbk-3 ( tm4006 ) , we asked whether the catalytic activity of ttbk-3 was required for synapse remodeling . Overexpressing kinase-dead versions of TTBK-3 ( K115A or D209A ) [39] failed to rescue tba-1 ( gf ) dlk-1 ( 0 ) ; ttbk-3 ( ju978 ) ( Fig 4D ) . We also obtained similar results using a mutant TTBK-3 lacking the C-terminal coiled-coil domain ( Fig 4D ) . These results suggest that both the catalytic activity of the kinase domain and the coiled-coil domain are likely required for TTBK-3 function in synapse remodeling . As reported by a recent study , expression of ttbk-3 was weak and extremely variable [40] and we were not able to reliably examine its neuronal expression pattern using extrachromosomal arrays of GFP driven by the endogenous ttbk-3 promoter . Expression of GFP tagged TTBK-3 in D motor neurons ( Punc-25-TTBK-3-GFP ) showed both diffuse and punctate GFP accumulation in the cell body and neurites during the L3 and L4 developmental stages , with a more diffuse distribution of GFP observed in adult animals ( S3 Fig ) . GFP expression was almost undetectable outside the cell body during the period of DD synapse remodeling in L1 and L2 animals . To test if TTBK-3 acted cell autonomously in the DD neurons to regulate remodeling , we overexpressed TTBK-3 under a DD neuron specific promoter ( Pflp-13 ) , which rescued the suppression of defective remodeling by ttbk-3 ( ju978 ) to a similar degree as full-length ttbk-3 . As a control , expression of TTBK-3 from a muscle specific promoter ( myo-3 ) failed to do so ( Fig 4B and 4C ) , supporting the conclusion that TTBK-3 is required in DD neurons . Next , we focused on how ttbk-3 could regulate synapse remodeling . Tau is a MAP that plays important roles in axonal transport , MT dynamics and neurite outgrowth during development , and is misregulated in neurodegenerative disease [41] . We tested a null mutation of the C . elegans homolog of tau , ptl-1 , and found that ptl-1 ( 0 ) did not have any effect on synapse formation or remodeling in wild type animals , and also failed to suppress the synapse remodeling defects of tba-1 ( gf ) dlk-1 ( 0 ) animals ( S3 Fig ) . We also tested a null allele of ttbk-7 ( R90 . 1 ) , the closest homolog of mammalian TTBK1/2 ( 65% sequence identity ) , which also failed to suppress tba-1 ( gf ) dlk-1 ( 0 ) ( S3 Fig ) . These observations suggest ttbk-3 likely acts through mechanisms independent of regulation of Tau . We next imaged MT dynamics and SV transport and found that neither changed in tba-1 ( gf ) dlk-1 ( 0 ) ; ttbk-3 ( tm4006 ) animals when compared to tba-1 ( gf ) dlk-1 ( 0 ) animals ( S3 Fig ) . Taken together with the expression pattern in DD neurons , this data suggested that ttbk-3 was not involved in the early stages of synapse formation during remodeling . We then assayed the temporal requirement of ttbk-3 in synapse remodeling using GFP tagged TTBK-3 expressed under a heat-shock inducible promoter ( Phsp-16 . 2-TTBK-3-GFP ) . Following heat-shock in young adult animals TTBK-3-GFP formed punctate aggregates in neurons , the pharynx , the intestinal lumen and posterior intestinal cells ( S3 Fig ) . To assay ttbk-3 requirement , we induced TTBK-3 expression at various larval stages , and observed behavioral and synapse remodeling phenotypes in the induced animals at adulthood ( Fig 5A ) . Heat-shock treated wild type animals ( with or without TTBK-3-GFP ) did not coil dorsally and had normal dorsal neurite synapse formation ( Fig 5B and 5C ) . On the other hand , heat shock treatment of L4 stage tba-1 ( gf ) dlk-1 ( 0 ) ; ttbk-3 ( ju978 ) animals expressing TTBK-3-GFP significantly reduced the suppression of both behavioral and synapse remodeling defects by ttbk-3 ( ju978 ) , when compared to non-transgenic animals undergoing the same heat shock treatment ( Fig 5D and 5E ) . We observed no difference in the extent of suppression between transgenic and non- transgenic animals that were heat-shocked at any other developmental stage , suggesting that ttbk-3 specifically played a role in synapse remodeling at the L4 stage , and was not required for the induction of synapse remodeling at the L1-L2 stage . We previously reported the presence of transient dorsal neurite synaptic puncta in tba-1 ( gf ) dlk-1 ( 0 ) animals from the L2-L4 stage , which were then eliminated as the animal achieved adulthood [11] . Indeed , the intensity of SNB-1-GFP puncta observed along the dorsal neurite at the L4 stage was higher in ttbk-3 ( tm4006 ) animals , both in the wild type and tba-1 ( gf ) dlk-1 ( 0 ) backgrounds ( S4 Fig ) . This suggested the existence of a mechanism to regulate the stabilization and maintenance of new synaptic sites formed on the dorsal neurite during remodeling . Since adding back wild type TTBK-3 at the L4 stage removed dorsal neurite synapses in tba-1 ( gf ) dlk-1 ( 0 ) ; ttbk-3 ( ju978 ) animals , we concluded that TTBK-3 antagonizes the stability of nascent synapses that are formed during synapse remodeling .
Modifications to synapse architecture occur throughout the lifetime of an animal , either dependent or independent of large scale neurite rewiring [4–9] . In this study , we dissect the mechanisms underlying a C . elegans model of structural synaptic plasticity , where synapses are eliminated and re-assembled along different neurites of the same neuron during a developmentally defined time scale [9–12] . We had previously shown that tba-1 ( gf ) dlk-1 ( 0 ) animals failed to complete synapse remodeling because of enhanced MT stability in DD neurons [11] . To identify additional regulators of the remodeling process , we screened for mutants that reversed the synaptic and behavioral defects of tba-1 ( gf ) dlk-1 ( 0 ) animals following EMS mutagenesis . Since we performed a non-clonal screen for homozygous viable adult animals , we were not able to isolate suppressors that might cause either lethality or sterility . Of the viable suppressors that we characterized , more than half were missense or nonsense alleles of tba-1 that either reverted tba-1 ( gf ) function back to wild type or resulted in null mutation of tba-1 . Multiple mutations in human α-tubulin have been implicated in neuronal disorders like lissencephaly and ALS , and their effects on MTs have only been studied using in vitro overexpression models [42–44] . All the missense tba-1 alleles that we identified in this screen alter invariant residues adjacent to disease-linked mutations in the C-terminal H12 , the GTP binding domain and the loop between H8 and S7 ( annotated in S1 Fig ) [42–44] , highlighting the importance of these three regions for α-tubulin functionality in vivo . The non-tubulin suppressor mutations displayed incomplete suppression of the synapse remodeling defects of tba-1 ( gf ) dlk-1 ( 0 ) . On their own , none of these alleles had any effect on synapse formation or animal behavior . This might result from multiple factors . First , as was the case with motor proteins and their adaptors ( unc-116 , dhc-1 and dnc-4 alleles ) , the screen identified novel mutations that subtly altered protein function in such a way that embryonic development was completed and the animals were viable . Next , loss of function alleles in ttbk-3 ( ju978 ) behaved superficially wild type , likely because of the presence of other redundant kinases , which might also explain the incomplete suppression on tba-1 ( gf ) dlk-1 ( 0 ) . Finally , any suppressors with strong behavioral deficits were likely missed since the design of our screen aimed to identify animals with wild type behavior first , and then assayed for restoration of DD synapse remodeling . A bottleneck to successful DD synapse remodeling is the motor dependent transport of SVs from the ventral to the dorsal neurite . Consistently , a majority of the suppressors altered either anterograde or retrograde motor function to compensate for a reduction in SV transport in tba-1 ( gf ) dlk-1 ( 0 ) . We previously identified kinesin-1 mutations that increased motor motility , and here , we characterized dynein-dynactin mutations that could alter cargo binding , with both strategies increasing anterograde SV transport along the DD neuron commissure during remodeling . However , while there was an overall increase in bidirectional SV transport in unc-116 ( ju972 ) animals , dhc-1 ( ju1279 ) selectively increased anterograde SV transport . This likely reflects the distinct motor domains targeted by ju972 and ju1279 on kinesin and dynein , respectively , with MT-motor interactions affecting bidirectional transport and variations in motor-cargo binding biasing transport in the direction with more engaged motors . Our results thus highlight both the interdependence and the competition between anterograde and retrograde motors during bidirectional cargo transport in vivo . The combination of unc-116 ( ju972 ) and dhc-1 ( ju1279 ) had an additive effect on SV transport , with an increase in both the total and anterogradely moving vesicle pools . Interestingly , dhc-1 ( ju1279 ) ; unc-116 ( ju972 ) did not enhance the number of dorsal neurite synapses in tba-1 ( gf ) dlk-1 ( 0 ) compared to unc-116 ( ju972 ) alone ( S4 Fig ) , indicating that the synapse formation defects caused by tba-1 ( gf ) alone [14] could not be suppressed simply by an increase in the number of available SVs . Studies from various experimental models and cultured neurons indicate that nascent synapse stability is dependent on the transport of sufficient numbers of SVs to the site of new synapse formation [45–47] . In tba-1 ( gf ) dlk-1 ( 0 ) animals , the lack of sufficient SVs at sites of new synapse formation on the dorsal neurite resulted in their destabilization and subsequent elimination at the L4 stage [11] . Here , we found that TTBK-3 , a C . elegans member of the casein kinase superfamily , promotes synapse destabilization along the dorsal neurite , and loss of ttbk-3 was sufficient to maintain newly formed synapses in tba-1 ( gf ) dlk-1 ( 0 ) . Synapse destabilization by TTBK-3 is likely phosphorylation dependent , and future studies will be required to identify potential substrates . The coiled-coil domain was also required for TTBK-3 function , leading us to speculate a possible regulatory role for the domain on kinase function . We previously showed that the initiation of dorsal neurite synapse formation was mediated by dlk-1 at the L2 stage; however ttbk-3 does not appear to play a role at this stage [11] . ttbk-3 is also not involved in ventral neurite synapse elimination during DD neuron remodeling , which is complete by L3 stage [11–13] . These results demonstrate that developmental synapse remodeling is marked by distinct phases of synapse formation , elimination and maintenance , each under tight spatio-temporal control to achieve precise neuronal connectivity .
Strains were maintained at 20°C on NGM plates unless noted otherwise [48] . Information on alleles and genotypes of strains is summarized in S2 Table . Plasmids were generated using Gateway technology ( Invitrogen ) . DNA for dnc-4 and ttbk-3 constructs was amplified from purified genomic or cDNA by PCR using Phusion HF DNA polymerase ( Finnzyme ) ( S4 Table ) , and subcloned into PCR8 entry vectors . Transgenic animals were generated by microinjection , following standard procedures [49] , using plasmids of interest at various concentrations ( listed in S3 Table ) and Pgcy-8-GFP ( 80–90 ng/μl ) or Pmyo-2-mCherry ( 2 . 5 ng/μl ) as co-injection markers . A minimum of 2–3 transgenes were generated for each construct described in this study . For rescue experiments using dhc-1 , dnc-4 and ttbk-3 constructs , the data from 3 transgenes was pooled in statistical analyses . A list of the plasmids used in this study and the transgenic arrays generated from them is listed in S3 Table . Primer information for the DNA clones generated in this study is listed in S4 Table . L4 animals were cultured at 20°C overnight , and day 1 adults were imaged using a Zeiss LSM 710 confocal microscope . Animals were anaesthetized in 0 . 6 mM levamisole on 2% agar pads for image acquisition . Z-stacks were generated from slices of 0 . 6 μm thickness . Images were processed using Zen lite software . Synaptic puncta number was quantified manually using a Zeiss Axioplan 2 microscope equipped with Chroma HQ filters . L2 stage animals ( 14–18 hrs post hatching when maintained at 20°C ) were collected for analysis , and anesthetized using 30 mM muscimol on 10% agarose pads . 4-D imaging was performed using a Yokogawa CSU-W1 spinning disc confocal head placed on a Leica DMi8 confocal microscope equipped with a piezo Z stage for fast Z- acquisition controlled using MetaMorph ( Molecular Devices ) . The entire DD commissure was visualized in 5–6 slices and images were collected for a total of 20 frames . The resulting movies were analyzed using Metamorph to generate kymographs for analysis of number and direction of movement of synaptic vesicles . Animals were anaesthetized in 30mM muscimol on 10% agarose pads for image acquisition . Live imaging for monitoring EBP-2 dynamics was done using a Yokogawa CSU-W1 spinning disc confocal head placed on a Leica DMi8 confocal microscope controlled by MetaMorph . 200 single plane images were taken serially at an exposure time of 113ms with an interval of 230ms between each frame , and analyzed using Metamorph software ( Molecular Devices ) to generate kymographs for analysis . tba-1 ( gf ) dlk-1 ( 0 ) animals were mutagenized using Ethyl Methane Sulphonate ( EMS ) following standard procedures [48] . F2 animals with improved locomotion were selected as putative suppressors in a non-clonal screen . Several suppressors were determined to be intragenic loss of function mutations in tba-1 ( gf ) by Sanger sequencing the genic region of tba-1 for any additional mutations . Two of the extragenic suppressors , ju972 and ju977 , were determined to be extragenic and mapped to the gene unc-116 following whole genome sequence analysis by MAQGene [50] . The results of whole genome sequencing of the remaining suppressors were analyzed using a Galaxy ( https://usegalaxy . org/ ) workflow , and the causative mutations were identified by linkage analysis of the suppression to the SNPs identified in the whole sequence analysis . Transgenic animals expressing Phsp-16 . 2TTBK-3-GFP; Pmyo-2 mCherry in the wild type and tba-1 ( gf ) dlk-1 ( 0 ) ; ttbk-3 ( 0 ) backgrounds were selected by positive pharyngeal mCherry expression . L1 , L2 , L3 , L4 and young adult animals were heat shocked at 34°C for 2 hours in an incubator . Heat shocked animals were maintained at 20°C after heat shock until they reached day 1 adulthood , when they were imaged using a Zeiss LSM 710 confocal microscope . Statistical analysis was performed using GraphPad Prism 5 . Normal distribution of samples was tested using D'Agostino & Pearson omnibus normality test . Significance was determined using One way ANOVA followed by Tukey’s multiple comparison tests and two way ANOVA followed by Bonferroni posttests for multiple samples . | In this study , we identify pathways that regulate the formation and maintenance of synapses , the functional connections between neurons , in the nervous system of the nematode C . elegans . Our work characterizes the interaction between molecular motors kinesin and dynein , which carry cargo and move towards opposite ends of microtubules during synapse formation . We also address the role of a protein kinase gene TTBK-3 in maintaining synapse structure once synaptic components have reached the sites of new synapses . Our findings shed mechanistic insight into the coordination of molecular motors and the cytoskeleton in neural circuit function . | [
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... | 2017 | Differential regulation of polarized synaptic vesicle trafficking and synapse stability in neural circuit rewiring in Caenorhabditis elegans |
The Philippines is one of the developing countries highly affected by rabies . Dog vaccination campaigns implemented through collaborative effort between the government and NGOs have played an important role in successfully reducing the burden of disease within the country . Nevertheless , rabies vaccination of the domestic animal population requires continuous commitment not only from governments and NGOs , but also from local communities that are directly affected by such efforts . To create such long-term sustained programs , the introduction of affordable dog vaccination and registration fees is essential and has been shown to be an important strategy in Bohol , Philippines . The aim of this study , therefore , was to estimate the average amount of money that individuals were willing to pay for dog vaccination and registration in Ilocos Norte , Philippines . This study also investigated some of the determinants of individuals’ willingness to pay ( WTP ) . A cross-sectional questionnaire was administered to 300 households in 17 municipalities ( out of a total of 21 ) selected through a multi-stage cluster survey technique . At the time of the survey , Ilocos Norte had a population of approximately 568 , 017 and was predominantly rural . The Contingent Valuation Method was used to elicit WTP for dog rabies vaccination and registration . A ‘bidding game’ elicitation strategy that aims to find the maximum amount of money individuals were willing to pay was also employed . Data were collected using paper-based questionnaires . Linear regression was used to examine factors influencing participants’ WTP for dog rabies vaccination and registration . On average , Ilocos Norte residents were willing to pay 69 . 65 Philippine Pesos ( PHP ) ( equivalent to 1 . 67 USD in 2012 ) for dog vaccination and 29 . 13PHP ( 0 . 70 USD ) for dog registration . Eighty-six per cent of respondents were willing to pay the stated amount to vaccinate each of their dogs , annually . This study also found that WTP was influenced by demographic and knowledge factors . Among these , we found that age , income , participants’ willingness to commit to pay each year , municipality of residency , knowledge of the signs of rabies in dogs , and number of dogs owed significantly predicted WTP .
Rabies is an acute , viral zoonosis globally responsible for more than 59 , 000 deaths annually [1] . Once clinical symptoms develop , the disease has one of the highest case fatality ratios of any infectious disease . The majority of all human deaths from rabies occur in Africa ( 36 . 4% ) and Asia ( 59 . 6% ) where canine rabies virus variants are predominant [1] . Transmission of rabies virus from dogs accounts for more than 90% of human cases [2] . Another important aspect of the impact of rabies is its economic burden that arise from disease prevention efforts and mortality cost in humans , livestock , and other domestic animals . In Asia alone , the human mortality cost of rabies is estimated to be approximately 67 . 87 billion US dollars annually [3] . Although dog rabies vaccination is considered as the most cost-effective solution to prevent rabies deaths in humans , the vaccination coverage of dogs in some Asian countries remain as low as 33% which is well below the suggested necessary coverage limit of 70% to obtain herd immunity [4–6] . The Philippines is one of the developing countries highly affected by rabies where , annually , an estimated 200–300 human deaths are attributed to rabies [7] . Rabies prevention and control policies and dog vaccination campaigns are currently the cornerstone of rabies elimination strategy in this country . The “Anti-rabies Act of 2007” with the objective of eliminating rabies throughout the Philippines by 2020 was enacted by the government of Philippines in 2007 [8] . Dog vaccination campaigns have so far been shown to be an effective rabies prevention and control strategy in reducing the burden of disease in parts the country [9] . In particular , the Bohol Rabies Prevention and Elimination Program ( BRPEP ) , with the support of the local government and international Non-Government Organizations ( NGOs ) , was able to considerably reduce human rabies in the province of Bohol . This was made possible through effectively utilizing social awareness campaign , dog population control measures , dog registration and mass dog vaccination campaigns , in addition to improved dog bite management and veterinary quarantine services [10] . Similar models of rabies elimination have also been initiated in other provinces of the Philippines . The Communities Against Rabies Exposure ( CARE ) Project , with the aim of creating another rabies free zone using similar rabies elimination strategies implemented in Bohol , was launched in Sorsogon Peninsula and Ilocos Norte in 2012 [11] . In 2014 , with yet another support from the local government and international NGOs , this program have successfully incorporated rabies prevention messages into the elementary school curriculum and vaccinated 35 per cent of some 76 , 000 dog population in the province of Ilocos Norte [12 , 13] . Through this program , the province of Ilocos Norte has been rabies-free since 2013 [13] . Nevertheless , long-term rabies elimination from an area requires recurrent implementation of mass dog vaccination campaigns that can help maintain the herd immunity in a given population . To achieve this , a multi-year commitment is required not only from governments and NGOs , but also from the local community that directly benefit from such efforts . An introduction of affordable dog vaccination and registration fees to the public is therefore essential and has been shown to be an important strategy in Bohol [10] .
A cross-sectional study was conducted in Ilocos Norte located in the northernmost province on the western side of Luzon , Philippines ( Fig 1 ) . Ilocos Norte had an estimated population of 568 , 017 in 2010 and is predominantly rural ( 2010 census data ) [14] . The annual average family income is 204 , 000 Philippine Pesos ( PHP ) ( 4 , 334 USD ) and the annual average family expenditure is 159 , 000 PHP ( 3 , 378 USD ) ( 2012 census data ) [15] . The survey was conducted over a period of two weeks in August 2012 during the peak of the rainy season . To identify participants , combinations of random sampling , cluster sampling , and convenience sampling methods were employed in three sequential steps . Cluster sampling with probability proportionate to size ( PPS ) was used to identify villages ( locally known as barangays ) . A random sampling method was used to identify roads ( locally known as puroks ) and the first participating household in the cluster . Convenience sampling was subsequently used to identify the remaining nine households as well as actual participants within households . This methodology was particularly employed because complete randomization of households was not feasible due to the vastness of the study area and population . The identification of the village was carried out by creating a cumulative list of community populations and selecting a systematic sample from a random start . A list of all the villages in Ilocos Norte and their corresponding population size was first obtained from a census data and then the villages were arranged in an alphabetical order . A sampling interval ( SI ) was calculated by taking the ratio of the cumulative population of Ilocos Norte to the total number of clusters ( thirty ) . A random start number was then generated to obtain the first participating village . This village had a population closest to but not greater than the random number . Subsequently , the SI was added to the previously identified random number in order to obtain the second village . The third village was again identified by adding the SI to the preceding random number . This process was repeated again and again until the 30th village ( cluster# 30 ) was identified . Through this process , a total of 30 villages in 17 municipalities ( out of a total of 21 municipalities ) were identified and included . A road in a particular village was randomly selected based on a list obtained from the respective village officials . The same list also included households within the selected roads and served as the sampling frame for randomly selecting the first participating household around which a cluster of households was further identified . A total of 10 households in each road were included in the survey . Participants were then selected by convenience sampling according to the inclusion criteria ( respondents must be equal to or greater than 18 years of age ) . Only the head of the household or the next representative household member were interviewed . These participants were purposively selected to be interviewed because it was believed that they play an important role in the decision making process of the household . To determine the appropriate sample size for this survey , an estimated population proportion of 20% , with ±5% confidence interval and 95% and coefficient was used . In addition , a cluster size of 10 with rate of homogeneity at 0 . 2 was employed . Based on a priori evidence , we anticipated a design effect of 1 . 18 [9] . Human subjects’ clearance for this study was obtained from the Centers for Disease Control and Prevention ( CDC ) Human Research Protection Office in Atlanta , United States , under CDC protocol #6337 as well as the Mariano Marcos Memorial Hospital and Medical Center Ethics Review Committee in Batac City , Ilocos Norte under the protocol number 2012-07-014 , and was determined exempt from full Institutional Review Board ( IRB ) review . Written informed consent was obtained from all participant prior to commencement of the study . If the participant was unable to read and write , the consent form was read to the participant and a thumbprint was obtained in place of a signature . The age of consent in the Philippines is 18 years . Therefore only participants 18 years old and over were allowed to participate in this study . A paper based questionnaire was administered in Ilocano , the local language of the majority of Ilocos Norte’s population ( 2002 census data ) . Interviews were conducted by representatives from CDC Atlanta , in collaboration with the Provincial Veterinary Office in Ilocos Norte . A total of 23 interviewers participated in conducting the survey . Interviewers were pre-trained on the survey methodology used and the questionnaire was pre-tested in the field in order to evaluate its workability , and appropriate modifications were made . Responses obtained from the interview were subsequently translated into English for analysis . The questionnaire covered four major categories relevant to this analysis . The first category consisted of questions regarding household dog ( s ) . The second category of questions , the WTP section , included an introductory statement explaining the purpose and importance of dog vaccination and registration campaigns and collected information on WTP for dog rabies vaccination and registration accordingly . In this section , the contingent valuation method ( CVM ) was used to elicit WTP for dog rabies vaccination and registration . In the context of health care , the CVM is a survey-based , hypothetical and direct approach to elicit monetary value to improve goods and services . Contingent valuation questions are used to estimate the willingness to pay distribution of consumers towards specific goods/services . It is a stated preference model that can measure the value consumers place on certain aspects of health care services[16 , 17] . In this survey , a particular elicitation method , a bidding game [18] with a series of yes/no questions that aim to find the maximum WTP was employed . This method was chosen because it was expected to have criterion validity ( which here refers to whether the instrument of measurement adequately represents the object of measurement ) in the setting of Ilocos Norte where there was an established culture of price negotiation for most goods [19] . Furthermore , empirical studies have found this method to be very reliable [20 , 21] . During the process of the bidding game , the participants were first offered an initial maximum WTP price . If the respondent accepted the initial price , a series of higher prices were offered until the respondent rejected the price . Alternatively , if the respondent refused the initially offered price , then the prices were repeatedly decreased until the respondent accepted or reached zero ( Fig 2 ) . For a more accurate estimation of the maximum WTP , the bid was presented in Philippines Pesos ( PHP ) . During the bidding process , a uniform distribution of 12 bid levels was presented for the WTP for vaccination section , and 10 bid levels were presented for the WTP for registration section . Each increasing and decreasing level had a difference of 20PHP ( ~ 0 . 50 US dollars ) [22] . To minimize potential starting point bias , when the offered start bid influences the direction of the WTP , the initial biding value offered was selected randomly using random number generation at the interview site . The interviewers were pre-trained in applying the randomization processes as well as in conducting the bidding game . The third and the fourth category comprised of sets of questions that aimed to explore determinants of WTP . Specifically , these sections looked at the demographic characteristics of survey respondents ( gender , age , employment status , household income , and dog ownership status ) and explored participants’ awareness of rabies transmission , exposure , and outcome . Paper surveys were digitized and a database was built in Microsoft Access . Accuracy of the data was subsequently checked to minimize data entry error . Descriptive statistics of the demographics of the study population were calculated . Linear regression was used to examine factors influencing participants’ WTP for dog rabies vaccination and registration . Linear regression diagnostics were first performed to check how well the data met the assumptions of the linear regression . Normality of the data was tested using graphical methods of residuals versus fitted ( predicted ) plot . Cook-Weisberg test was used for detecting heteroscedasticity [23 , 24] . Since the data violated the linear regression assumptions of both normality and heteroskedasticity , an attempt to attain the validity of that assumption was made using power transformation and robust standard errors , respectively . The appropriate power transformation of the response variable was determined using Tukey’s ladder of power [25] . Accordingly , a power transformation of 0 . 5 was used for the approximation of the residuals of the response variable to normality . Kernel density plots were then generated against each continuous predictor variables to assess if the predictor variable satisfies the linearity assumption [26] . Predictor variables that violated this assumption were further transformed using the appropriate power transformation . The analysis also accounted for the sampling methodology used ( cluster sampling ) and standard errors were adjusted during the model building . Accordingly , cluster robust standard errors that put into consideration the clustering effect were used during the analysis . All predictor variables that remained significantly associated ( P≤0 . 1 ) in the univariate model were retained ( variables tested are presented in Tables 4 and 5 ) . Multicollinearity among predictor variables was assessed for using Variance Inflation Factor ( VIF ) . As a rule of thumb , a tolerance value ( 1/VIF ) of lower than 0 . 1 ( or VIF greater than 10 ) was used as a cutoff point to check if some level of multicollinearity existed . All of the VIF scores were less than 1 . 5 and therefore no signs of multicollinearity was observed between predictor variables . The predictor variables were then fitted into a full model and further reduced through backward selection at a 5% significance level to obtain the final model . Coefficients and direction of the linear association between variables were determined from the final model and back transformed to the original scale . Mean and range values of WTP for vaccination and registration were calculated using the transformed data . The sample mean of the transformed data together with its 95% confidence interval was then back transformed to obtain the population median and its corresponding 95% confidence interval of the original data [27] . Statistical analyses were performed using STATA version 13 ( Statacorp , Texas , USA ) .
A total of 300 respondents were included in the study ( Table 1 ) . The majority of the respondents were female ( 65% ) and the mean age was 48 ( SD 16 ) . Eighty six percent of the respondents reported an annual income of less than 120 , 000PHP ( 2 , 876USD ) . In addition , the majority of respondents owned dogs ( n = 64% ) . The average number of dogs owned by a household was 1 . 3 ( SD 1 . 6 ) and the dog to human ratio was 1:3 . 5 . Therefore , the estimated owned dog population of Ilocos Norte is expected to be 162 , 290 utilizing the predicted dog to human ratio . Forty three percent of the dog owners stated that they have vaccinated at least one of their dogs once within the past two years . Out of these , 8% stated that they have previously paid for their dog ( s ) to be vaccinated . In regards to rabies awareness , almost all ( 99% ) of the respondents have heard of rabies and the majority ( 69% ) knew how to recognize the clinical signs of rabies in dogs . Signs and outcomes of rabies in humans were adequately identified by 50% and 63% of the respondents , respectively ( Table 2 ) . This results varied by gender . Females were significantly more aware of the signs and outcome of rabies in humans than males . However , only 46% of respondents knew how rabies was transmitted to humans . There were no significant discrepancies in awareness of rabies between those who own dog ( s ) and those who did not ( Table 2 ) . Bidding games were completed by almost all ( 298 of the 300 ) respondents to elicit their maximum WTP for dog vaccination and registration ( Table 3 ) . Two people withdrew from the interview due to the inconvenience associated with the length of the interview . Eighty eight percent of the WTP for dog vaccination and 86% of WTP for dog registration responses were above zero . The odds of participants stating they were willing to pay for dog registration was almost 30 times ( CI; 13 to 70 ) higher among those who were willing to pay for dog vaccination compared to those who were not willing to pay for vaccination . The population medians for the WTP for dog vaccination and registration were estimated to be 69 . 65 PHP ( 1 . 67 USD ) and 29 . 13 PHP ( 0 . 70 USD ) , respectively ( Table 3 ) . Looking at the distribution of the WTP medians across the selected municipalities in Ilocos Norte , the lowest median WTP for vaccination value was observed in the municipality of Pasuquin while the highest was in the municipality of Bacarra . For dog registration , the lowest and the highest medians were observed in the municipality of Banna and Burgos , respectively ( Fig 3A and 3B ) . A good majority of those who owned dog ( s ) were willing to pay the stated amount for dog vaccination and/or registration . Only 10% and 14% of dog owners had a stated maximum WTP of zero PHP for vaccination and for registration , respectively . In general , the majority of respondents ( 86% ) indicated they were willing to pay the stated amount to vaccinate each of their dogs annually , while the remaining proportion were either not willing to accept this commitment ( 12% ) or didn’t know ( 2% ) if they wanted to commit . Eighty-six percent of dog owners were willing to commit . The same percentage of those who did not own dog/s where also willing to commit to pay for each of their dogs , annually . Fig 4A and 4B displays the proportion of the population willing to pay a given price or more for dog vaccination and registration . The hypothetical demand for dog vaccination falls gradually as price increases while it falls rapidly for dog registration . In the univariate analysis , WTP for vaccination was negatively associated with age and positively associated with income . Willingness to pay decreased as age increased and people in the relatively higher income group ( above 120 , 001 PHP and above ) were , on average , willing to pay significantly more than people of the relatively lower income group categories ( 120 , 000 PHP and below ) ( Table 4 ) . Also , we observed similar variables and direction of association for WTP for registration as we did for vaccination . In addition to age , gender , income , and municipality of residency , dog ownership status and number of household dogs predicted WTP for dog registration ( Table 4 ) . Some of the knowledge parameters were also found to be independently associated with participants’ WTP in the univariate analysis ( Table 5 ) . Adequate recognition of the outcome of rabies in humans was positively associated with participants’ WTP for dog registration and vaccination , while adequate recognition of rabies signs in dogs and in humans were only associated with participants’ WTP for registration . Participants’ willingness to commit to pay for each of their dogs , annually was also found to be an important determinant of WTP for vaccination and registration in this study . Those who were not willing to commit to pay each year were , on average , willing to pay significantly less for dog vaccination and registration ( Table 5 ) . In assessing the characteristics of participants willing to commit to pay each year in this survey , participants aged 20 to 39 years were 3 . 94 ( 1 . 11–13 . 98 ) times as likely to be willing to commit to pay each year as those who were over the age of 65 years . Moreover , we also found that people who stated they strongly liked dogs were 4 . 22 ( 1 . 52–11 . 75 ) times as likely to be willing to commit to pay as people who strongly disliked dogs . This indicates that participants of younger age group or participants with a more favorable attitude towards dogs may be willing to commit to pay for each of their dogs annually . Similar direction and magnitude of association seen in the univariate linear regression were also observed in the multivariable analysis . A number of factors ( such as age , income , number of dogs , participants willingness to commit to pay for each of their dogs annually , and participants’ knowledge regarding the signs of rabies in dogs ) that were significantly associated with WTP in the univariate analysis remained independently associated with the WTP for dog vaccination and/or registration in the multivariable model . Similarly , age was a significant predictor of the amount individuals were willing to pay for vaccination and registration in the multivariable model as it was in the univariate analysis . We observed that even after adjusting for the other significant determinants of WTP for vaccination/registration , the higher the age , the lower the amount individuals were willing to pay ( Table 6 ) . No direct relationship was observed between WTP and employment status . However , we found a strong association between age and employment status . Specifically , those who were between the ages of 20–39 years , and 40–64 years were 11 . 20 ( CI , 4 . 74 to 26 . 47 ) and 11 . 37 ( CI , 5 . 25 to 24 . 61 ) times more likely to be employed , respectively , as those who were over the age of 65 . Further stratification of data by employment status revealed that there was no statistically significant relationship between age and WTP for vaccination in the employed group while in the unemployed group , the relationship between age and WTP for vaccination was still significant . In the case of dog registration , however , the association between age and WTP still persisted regardless of employment status . Therefore employment status may have modified the relationship between age and individuals’ WTP for vaccination , but may have had no effect on individuals’ WTP for registration .
This study provided evidence on the perceived monetary value of dog vaccination and registration in Ilocos Norte , Philippines by assessing the maximum amount of money individuals are willing to pay . It found that the majority of Ilocos Norte residents stated they were willing to pay an average of 1 . 67 USD for dog vaccination and 0 . 70 USD for dog registration . Socio-economic and demographic factors such as age , income , and number of dogs owned , municipality of residency , and participants willingness to pay for each of their dogs annually were found to influenced stated WTP . This factors , therefore , may need to be considered prior to the introduction of such fees to the public . Creating rabies awareness and promoting favorable attitude towards dogs may also aid in the effective delivery of such programs . | Rabies is one of the most fatal viral diseases mostly transmitted through a bite of an infected mammal . In many parts of Africa and Asia , rabid dogs are the main transmitters of the disease to humans . In the Philippines , government enforced dog registration laws and government/NGO sponsored mass dog vaccination campaigns have so far been the cornerstone in successfully reducing the burden of disease in parts of the country . To further enhance the continuation of such programs , however , an introduction of affordable dog vaccination and registration fees to the public is vital and have shown to be an important strategy in other parts of the country . Our main objective here was to systematically assess how much money individuals were willing to pay for dog vaccination and registration in Ilocos Norte , Philippines . We also assessed whether the amount individuals were willing to pay was influenced by their demographic and knowledge characteristics . We conducted a cross-sectional study using a combination of cluster sampling , random sampling , and convenience sampling methods to identify study participants . We employed a specific elicitation strategy ( bidding game ) to elicit how much individual were willing to pay for such services . Our results indicate that Ilocos Norte residents , on average , were willing to pay 69 . 65 PHP ( 1 . 67 USD ) for dog vaccination and 29 . 13 PHP ( 0 . 70 USD ) for dog registration . The majority ( 86% ) of respondents were willing to pay the stated amount to vaccinate each of their dogs , annually . We also found that willingness to pay was influences by age , income , municipality of residency , people’s willingness to commit to pay each year , number of dogs owned , and knowledge regarding rabies signs in dogs . This findings give policy makers some indication of how much people were willing to contribute financially towards dog vaccination and registration in particular and towards rabies elimination from Ilocos Norte , in general . Socio-economic and demographic factors , however , may need to be considered prior to the introduction of such fees to the public . | [
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"medi... | 2016 | Willingness to Pay for Dog Rabies Vaccine and Registration in Ilocos Norte, Philippines (2012) |
X-linked lymphoproliferative disease ( XLP ) is a primary immunodeficiency caused by mutations in SH2D1A which encodes SAP . SAP functions in signalling pathways elicited by the SLAM family of leukocyte receptors . A defining feature of XLP is exquisite sensitivity to infection with EBV , a B-lymphotropic virus , but not other viruses . Although previous studies have identified defects in lymphocytes from XLP patients , the unique role of SAP in controlling EBV infection remains unresolved . We describe a novel approach to this question using female XLP carriers who , due to random X-inactivation , contain both SAP+ and SAP− cells . This represents the human equivalent of a mixed bone marrow chimera in mice . While memory CD8+ T cells specific for CMV and influenza were distributed across SAP+ and SAP− populations , EBV-specific cells were exclusively SAP+ . The preferential recruitment of SAP+ cells by EBV reflected the tropism of EBV for B cells , and the requirement for SAP expression in CD8+ T cells for them to respond to Ag-presentation by B cells , but not other cell types . The inability of SAP− clones to respond to Ag-presenting B cells was overcome by blocking the SLAM receptors NTB-A and 2B4 , while ectopic expression of NTB-A on fibroblasts inhibited cytotoxicity of SAP− CD8+ T cells , thereby demonstrating that SLAM receptors acquire inhibitory function in the absence of SAP . The innovative XLP carrier model allowed us to unravel the mechanisms underlying the unique susceptibility of XLP patients to EBV infection in the absence of a relevant animal model . We found that this reflected the nature of the Ag-presenting cell , rather than EBV itself . Our data also identified a pathological signalling pathway that could be targeted to treat patients with severe EBV infection . This system may allow the study of other human diseases where heterozygous gene expression from random X-chromosome inactivation can be exploited .
X-linked lymphoproliferative disease ( XLP ) is an inherited primary immunodeficiency caused by mutations in SH2D1A , which encodes the cytoplasmic adaptor protein SLAM-associated protein ( SAP ) [1]–[3] . SAP functions as an adaptor protein by associating with members of the SLAM family of surface receptors—SLAM ( CD150 ) , 2B4 , NTBA , CD84 , CD229 , and possibly CRACC [4]–[7]—that are expressed on a variety of hemopoietic cells . A defining characteristic of XLP is extreme sensitivity to infection with EBV ( reviewed in [7]–[9] ) . Thus , in contrast to infection of healthy individuals , which is self-limiting , exposure of XLP patients to EBV induces a vigorous and uncontrolled immune response involving polyclonally activated leukocytes . Despite such immune activation , XLP patients fail to control EBV infection , which results in severe and often-fatal fulminant infectious mononucleosis [7]–[9] . XLP patients who survive primary EBV infection can develop hypogammaglobulinemia and B-cell lymphoma , although exposure to EBV is not a prerequisite for these clinical manifestations [8] , [9] . Strikingly , XLP patients do not display the same degree of vulnerability towards other herpes viruses—herpes simplex virus , cytomegalovirus ( CMV ) , varicella zoster—which can cause life-threatening infections in individuals with other immunodeficiencies [10] . This highlights the unique role of EBV in the pathogenesis of XLP , and the critical—albeit undefined—role of SAP in anti-EBV immunity . XLP is associated with a diverse range of lymphocyte defects including abolished NKT cell development [11] , [12] , compromised humoral immunity [13]–[15] , and impaired functions of CD4+ T cells [13] , [16]–[18] , CD8+ T cells [19] , [20] , and NK cells [21]–[27] . This reflects the involvement of SAP in multiple signalling pathways . Given the complexity of the immunological abnormalities in XLP patients , it is unclear which of them underlies their unique susceptibility to EBV . While the defective response of NK cells following engagement of 2B4 or NTB-A may contribute to the susceptibility to EBV in XLP [22] , [24] , [26] , [27] , it is unlikely to be the predominant cause since a deficiency in either the absolute number of NK cells or NK cell cytotoxicity in the presence of intact T cell development and function in humans is associated with more generalised susceptibility to multiple viruses ( reviewed in [28] ) . Similarly , while NKT cells may have a role in anti-viral immunity , the impact of an NKT cell deficiency on EBV sensitivity in XLP is unclear because patients with other immunodeficiencies have also been reported to lack NKT cells , yet they do not develop fulminant infectious mononucleosis [29]–[31] . Lastly , while several previous studies have investigated the function of CD8+ T cells in XLP [19] , [20] , [32] , it is difficult to separate direct effects of SAP deficiency in these cells from indirect effects that may result from lack of “help” from either functionally impaired SAP-deficient CD4+ T cells or NK cells , or the absence of NKT cells , all of which can promote CD8+ T cell responses [33]–[36] . Furthermore , these studies of SAP-deficient CD8+ T cells have not provided an explanation as to why XLP patients are so vulnerable to infection with EBV , but not with other pathogens . In addition to these issues , delineating the EBV-specific defect in XLP has been hindered by the lack of an appropriate experimental model . Thus , while SAP-deficient mice have proved key to elucidating mechanisms underlying some of the immunological defects in XLP [4] , [7] , [9] , they cannot directly address the question of EBV susceptibility because neither EBV nor its close relatives in other primates infect mice , and no mouse virus can reproduce EBV's biology or its strictly B-lymphotropic means of persistence [37] . The question of EBV pathogenesis therefore can only be answered using a human model in which SAP-deficient immune cells develop in an otherwise intact immune system . Fortuitously , female carriers of XLP are healthy [38] and harbour both SAP-positive and SAP-negative T cells through random inactivation of the X-chromosome [11] . Here we demonstrate that such XLP carriers provide an ideal model for elucidating the role of SAP in anti-viral immune responses in humans . XLP carriers were shown to contain both SAP+ and SAP− T cells , which allowed us to determined which virus-specific responses were dependent on SAP . While both SAP+ and SAP− CMV or influenza-specific memory CD8+ T cells were able to respond to their cognate peptides , EBV-specific memory CD8+ T cells were exclusively restricted to the SAP+ population , revealing a specific requirement for SAP in anti-EBV immunity . Further analysis of the response of SAP− CD8+ T cells to different Ag-presenting cells ( APC ) showed that SAP is required for B cell-mediated CD8+ T cell responses but not for responses induced by other APCs . Our studies further demonstrated that an important function of SAP was to prevent the delivery of inhibitory signals downstream of SLAM family receptors on CD8+ T cells following interaction with their ligands on target B-cells . These data provide compelling evidence that the unique susceptibility to EBV infection in XLP patients is due to the inability of SAP− CD8+ T cells to respond to Ag-presenting B cells due to inhibitory signalling mediated by SLAM family receptors , rather than an inability to recognise and respond to EBV Ags .
We analysed seven female carriers of XLP , each of whom was confirmed as heterozygous at the SH2D1A locus by sequencing genomic DNA ( Figure 1A , B ) . Analysis of lymphocyte subsets revealed that these carriers , unlike XLP patients [11] , [15] , [16] , had normal frequencies of total and isotype switched memory B cells ( Figure 1C , D , F ) and NKT cells ( Figure 1E , G ) . The proportions of memory CD8+ and CD4+ T cells were also within the range of healthy controls ( unpublished data ) . This is consistent with XLP carriers being asymptomatic and lacking evidence of any obvious deficiency in anti-viral immune responses , including against EBV [38] , [39] . Intracellular flow cytometric analysis using a SAP-specific monoclonal antibody ( mAb ) enabled us to identify SAP expression in different cell populations . SAP was expressed in CD4+ T cells , CD8+ T cells , and NK cells from normal donors ( Figure 2A ) , but not in the same lymphocyte populations obtained from XLP patients ( Figure 2B ) . Using this approach we confirmed heterozygous SAP expression ( i . e . , 40%–60% of the cells being SAP+/− ) within the T and NK cell compartments of XLP carriers ( Figure 2C , D ) . There was no significant difference in the frequency of CD8+ central memory ( CD45RA−CCR7+ ) T cells ( Figure 2C ) or NK cells ( Figure 2D ) that were SAP− or SAP+ . However , significantly more naïve CD8+ T cells were SAP− ( p = 0 . 045 ) , whereas more effector memory ( CD45RA−CCR7− ) and TEMRA ( effector memory cells expressing CD45RA ) cells were SAP+ ( Figure 2C ) . The greater frequency of SAP− cells in the naïve compartment would be consistent with proposed functions for SAP in negatively regulating T cell responses in mice in vivo [40] , [41] and in promoting apoptosis of human cells in vitro [42] , [43] . In contrast to T and NK cells , >90% of NKT cells in XLP carriers were SAP+ ( Figure 2E ) , consistent with the absolute requirement of SAP for their development [11] , [12] . SAP was not detected in human B cells ( Figure 2A , F ) [15] , supporting the concept that intrinsic defects in T cells , NK cells , and NKT cells , rather than B cells , are responsible for the XLP phenotype . To determine the contribution of SAP+ and SAP− CD8+ T cells to antiviral immunity , we analysed SAP expression in populations of memory CD8+ T cells that were specific for EBV , CMV , and influenza ( Flu ) , as detected by soluble peptide:MHC class I complexes ( i . e . , tetramers ) . Five of the XLP carriers had MHC class I types that allowed epitope-specific cells to be visualised by this approach . The frequency of CMV and Flu-specific CD8+ T cells within the SAP+ population ( CMV: range 21%–72%; mean ± sem: 46 . 3%±12 . 3% , n = 4; Flu: 8% and 46%; mean: 27 . 0%±19% ) was not significantly different from that within the SAP− population ( CMV: 55 . 7%±12 . 3% , n = 4 [p = 0 . 78]; Flu: 73 . 0%±19% , n = 2 ) ( Figure 3A , B ) . In stark contrast , almost all EBV-specific CD8+ T cells expressed SAP ( 95 . 0%±2 . 9% versus 5 . 0%±2 . 9% in SAP− cells , n = 4; p = 0 . 004; Figure 3A , B ) . The same clear-cut distinction was seen when the functional response of virus-specific CD8+ T cells to various antigenic peptide challenges was assessed in vitro . Following stimulation of PBMCs from XLP carriers with CMV or Flu Ags , both SAP+ and SAP− cells produced IFN-γ ( Figure 3C , E ) and expressed surface CD107a ( Figure 3D , E ) , an indicator of the ability of cells to degranulate [44] , [45] . However , when PBMCs were stimulated with various EBV peptides , including those from both lytic and latent Ags , only SAP+ CD8+ T cells responded ( Figure 3C–E ) . Consistent with the recognition of EBV tetramers , the differences in the responses of SAP+ and SAP− CD8+ T cells to in vitro stimulation with EBV peptides were highly significant ( p = 0 . 0001; Figure 3E ) . Taken together these data demonstrated that the CD8+ T cell response to EBV infection in healthy XLP carriers had been preferentially recruited from SAP+ T cells , whereas the CD8+ T cell response to other viruses showed no preference for SAP-expressing cells . One explanation for the disparate responses of SAP− and SAP+ CD8+ T cells to EBV , but not to other viruses , may result from differential expression of co-stimulatory or inhibitory molecules in the absence of SAP . Thus , we determined the phenotype of SAP− and SAP+ cells with respect to expression of a suite of molecules known to regulate CD8+ T cell function . Expression of the co-stimulatory/activation/effector molecules CD27 , CD28 , CD38 , OX40 , ICOS , perforin , and granzyme B did not differ between SAP− and SAP+ CD8+ T cells , irrespective of whether the cells were of a naïve or memory phenotype . Similarly molecules known to inhibit lymphocyte function—PD-1 , BTLA—were comparably expressed on SAP− and SAP+ naïve and memory CD8+ T cells ( unpublished data ) . We also analysed the TCR repertoire of SAP− and SAP+ cells by determining expression of distinct TCR Vβ chains by flow cytometry to deduce whether the TCR usage was significantly different between these cells . Although this approach may not be sufficiently sensitive to detect restricted diversity , the TCR repertories of SAP− and SAP+ cells appeared to be generally similar ( Table 1 ) . The few biased TCR Vβ chains used in two carriers ( #1 , #3; Table 1 ) probably reflects the responses of different subsets of effector/memory cells to different viruses and their unique antigenic epitopes . Thus , lack of SAP expression does not appear to alter thymic selection of CD8+ T cells , or their ability to acquire expression of receptors involved in regulating lymphocyte function . Consequently , it is unlikely that perturbed selection or activation of SAP− CD8+ T cells through co-stimulatory and regulatory receptors underlies their poor responsiveness to stimulation with EBV . Rather , this is likely a direct effect of SAP deficiency . The selective dependence of EBV-specific CD8+ T-cell-mediated immunity on SAP raised the question of which T-cell extrinsic mechanisms might explain the differences between the responses to EBV versus CMV and Flu . Since Ag presentation was a logical place to start , we developed an approach that would allow us to analyse the ability of SAP− T cells to respond to distinct types of APCs . Thus , multiple SAP− and SAP+ clonal pairs were established from different XLP carriers ( Figure S1 ) and then tested for their ability to recognise cognate peptides presented on different APC targets , namely autologous EBV-transformed lymphoblastoid cell lines ( B-LCLs ) , or HLA class I-matched monocytes or fibroblasts . SAP+ CD8+ T cell clones responded to their specific peptide regardless of the nature of the APC , as evidenced by enhanced IFN- γ production ( Figure 4A , upper panels ) , acquisition of expression of CD107a ( Figure 4B–E , Figure S2A upper panel ) and lysis of Ag-presenting target cells ( Figure 4F , G ) . In contrast , SAP− CD8+ T cell clones responded poorly upon stimulation with peptide-pulsed B-LCLs compared to SAP+ clones , irrespective of whether the clones were specific for CMV ( Figure 4A , B , Figure S2A lower panels ) or Flu ( Figure 4C lower panel , Figure 4D , F ) . Importantly the defective responses of SAP− clones to specific Ag presented on B-LCLs did not reflect a generalised activation defect because these cells responded as well as SAP+ cells following PMA/ionomycin stimulation ( Figure 4A–C , Figure S2A ) . Strikingly , the impairment was restricted to Ag presented in a B cell context . Thus , the same SAP− CMV-specific or Flu-specific clones responded as well as their SAP+ counterparts to peptides presented on HLA-matched monocytes ( Figure 4B , Figure S2 ) , or fibroblasts ( Figure 4C , E , G ) . We extended these studies by assessing induction of CD107a expression by SAP− and SAP+ CD8+ T cells within a CMV-specific T cell line in response to presentation of specific Ag by in vitro–derived dendritic cells ( DCs ) compared to B-LCLs . Although the frequency of total CD8+ T cells responding to CMV peptides was similar irrespective of whether B-LCLs or DCs were the APC ( ∼5%–6% ) , the SAP+ CD8+ T cells predominated the response when CMV-derived peptides were presented by B-LCLs ( >90% of responding cells; Figure S2B ) . In contrast , both SAP− and SAP+ CD8+ T cells responded to Ag-presenting DCs ( 35% and 65% of responding cells , respectively; Figure S2B ) . These findings are entirely consistent with the data for Ag-specific paired SAP− and SAP+ clones ( Figure 4 , Figure S2A ) , and together provide compelling evidence for an important role for SAP in mediating CD8+ T cell recognition of B cell targets . It would be ideal to also demonstrate that EBV-specific SAP-deficient CD8+ T cells are unable to respond to Ag endogenously presented by B cells . This could not be investigated using XLP carriers due to the extreme paucity of EBV-specific cells within the SAP− subset of CD8+ T cells in these individuals ( see Figure 3 ) . To address this , we generated EBV-specific CD8+ T cell lines from an XLP patient with a well-characterised loss-of-expression mutation in SH2D1A ( [F87S] , XLP#3 in [46] ) . This was achieved by repeatedly expanding their purified CD8+ T cells on autologous EBV-transformed B-LCLs , as performed previously for other SAP-deficient patients [19] . As expected , EBV-specific CD8+ T cells from normal donors efficiently lysed autologous B-LCL target cells . In contrast , there was a profound defect in the ability of XLP CD8+ T cells to lyse autologous B-LCLs ( Figure S2C , panel [i] ) . For these experiments , the donor and XLP patient were HLA matched . This allowed assessment of the ability of EBV-specific CD8+ T cells to lyse B-LCL derived from a SAP-sufficient donor or SAP-deficient XLP patient , and thereby to determine whether the cytotoxic defect of XLP CD8+ T cells resulted from impaired presentation of EBV Ag by SAP-deficient B-LCL . When this experiment was performed , XLP CD8+ T cells proved to be equally defective in killing allogeneic B-LCLs , which contrasted the behaviour of EBV-specific CD8+ T cell lines from normal donors ( Figure S2C panel [ii] ) . Importantly , the inability of XLP CD8+ T cells to lyse B-LCL target cells did not appear to result from altered expression of lytic effector molecules since acquisition of perforin and granzyme B by XLP CD8+ T cells was comparable to that of normal CD8+ T cells ( Figure S2C panel [iii] ) . This is consistent with the reduced cytotoxicity of SAP-deficient cells resulting from impaired recognition of B-LCL targets , which subsequently compromises immune synapse formation between effector and target cells , and polarisation of lytic mediators [19] , [47] . To begin to elucidate the mechanism underlying compromised SAP− CD8+ T cell recognition of peptide-pulsed B cell targets and explore ways in which function might be restored , we examined the expression of SAP-associating receptors on subsets of SAP− and SAP+ T cells . SAP associates with the cytoplasmic domains of SLAM , 2B4 , CD84 , NTB-A , CD229 , and possibly CRACC [4] , [7] . When expression of these molecules was assessed on lymphocytes from XLP carriers , we found no significant differences in their expression on SAP− and SAP+ CD8+ T cells within the naïve and TEMRA subsets ( p>0 . 05; Figure 5A; Figure S3 ) . Most of these molecules were also expressed comparably on SAP− and SAP+ central memory and effector memory CD8+ T cells . However , there were significant differences in the expression levels of 2B4 and NTB-A on SAP− and SAP+ central memory CD8+ T cells , and of 2B4 and CRACC on SAP− and SAP+ effector memory CD8+ T cells , with them being lower on SAP− , relative to SAP+ , cells . While these differences were statistically significant , the net differences in expression were <2-fold . Thus , it is unknown whether this would translate to a biological effect; furthermore , it is important to highlight that CRACC has been reported to function independently of SAP , at least in the context of human NK cells [48] . Thus , the lower level of CRACC on SAP− cells will be inconsequential at least with respect to SLAM-receptor/SAP-dependent signalling and lymphocyte activation . These data generally imply that , at the cell surface , SAP− and SAP+ CD8+ T cells are similarly capable of interacting with relevant ligands of the SLAM family . The next step was to examine expression of ligands of the SLAM family receptors on different APCs because expression of these molecules on APCs could also influence the outcome of CD8+ T cell-mediated recognition of target cells . While 2B4 interacts with CD48 , the other SLAM family receptors are self-ligands [4] , [7] . In contrast to SAP+ and SAP− CD8+ T cells , there were substantial differences in expression of SLAM family ligands by B-cell and non-B-cell APCs . NTB-A expression was highest on B cells and B-LCLs , while CD48 was highest on monocytes and B-LCLs ( Figure 6A , B ) . B-LCLs also expressed higher levels of CD229 , CRACC , and SLAM than resting B cells and monocytes ( Figure 6A , B ) . Interestingly , NTB-A , CD48 , and CD229 were all absent from in vitro–derived DCs; however , DCs did express CRACC , SLAM , and CD84 ( Figure 6A , B ) . The relative levels of these molecules on DCs were similar to monocytes , with CRACC and SLAM being less , and CD84 being greater , than on B-LCLs ( Figure 6A , B ) . Unlike APCs of hematopoietic origin , fibroblasts did not express any SLAM family ligands ( Figure 6A , B ) . Thus , APCs exhibit substantial differences in their pattern of expression of SLAM family ligands . The above findings implied that engagement of distinct arrays of co-stimulatory receptors on SAP− and SAP+ CD8+ T cells by ligands expressed on different APCs would modulate the acquisition of effector function of the responding CD8+ T cells . This would be consistent with the ability of SLAM family receptors to switch their function from activating or inhibitory depending on the presence of SAP [22] , [24] , [32] . We therefore explored the possibility that defined interactions between specific SLAM receptors on SAP+ or SAP− CD8+ T cells and their ligands on APCs differentially regulated cytotoxicity . We first examined the ability of SAP+ and SAP− CD8+ T cells to respond to the Hodgkin's lymphoma cell line HDLM2 . This line was chosen as a target cell because ( a ) it lacked expression of all SLAM family ligands with the exception of SLAM/CD150 itself ( Figure 7A ) , ( b ) SLAM has been reported to enhance the cytotoxicity of human CD8+ T cells [49] , and ( c ) SLAM was expressed at the highest levels on B cells relative to other APCs ( Figure 6 ) , revealing it as a candidate molecule to regulate CD8+ T cell function . Thus , if expression of SLAM on B cells , but not fibroblasts , controls the effector function of CD8+ T cells , then it would be predicted that SAP− CD8+ T cells would exhibit reduced cytotoxicity against HDLM2 cells than their SAP+ counterparts . When this was tested experimentally by pulsing either autologous B-LCLs or MHC class I–matched HDLM2 cells with CMV peptides and assessing the response of CMV-specific CD8+ T cells , both SAP− and SAP+ cells were equally capable of responding to HDLM2 , as evidenced by acquisition of CD107a expression by a comparable proportion of cells ( Figure 7B , lower panel ) , but not to B-LCLs , as expected ( Figure 7B , upper panel ) . This dichotomy in recognising and responding to B-LCLs versus HDLM2 was not due to differences in expression of MHC class I by the target APCs ( Figure 7A ) . This finding suggested that SLAM was unlikely to be the predominant receptor mediating the effector function of CD8+ T cells in the absence of SAP . This led us to focus on NTB-A and 2B4 because their ligands ( i . e . , NTB-A , CD48 ) are highly expressed on B cells ( Figure 6; [22] , [50] ) and they can deliver activating and inhibitory signals in the presence and absence , respectively , of SAP to human NK and CD8+ T cells [22] , [24] , [26] , [27] , [32] . Although CRACC was also more highly expressed on human B-LCLs than on monocytes ( Figure 6 ) , its role in regulating CD8+ T cell function was not explored because it functions independently of SAP [48] , [51] . When interactions between NTB-A/NTB-A and/or 2B4/CD48 were blocked with specific mAbs [22] , [52]–[54] , activation of SAP+ CD8+ T cells by B cell targets was not significantly affected ( %CD107a+ cells—no mAb: 51 . 3%±3 . 8%; + anti-NTB-A mAb: 56%±6 . 5%; + anti-2B4 mAb: 55 . 7%±5 . 6%; + anti-NTB-A/2B4 mAbs: 55 . 7%±7 . 3%; n = 4 , p = 0 . 48 [27] , [32] ) . By contrast , blocking interactions between NTB-A/NTB-A or 2B4/CD48 substantially improved the effector function of SAP− CD8+ T cells compared to when these cells were examined in the absence of added mAbs ( Figure 7C , D ) . Importantly , combined blockade of both pathways could restore effector function of SAP− T cells to a level comparable to SAP+ clones ( Figure 7C ) . These observations suggest that signalling through NTB-A and 2B4 impedes the effector function of SAP-deficient , but not SAP-sufficient , CD8+ T cell in response to Ag-presenting B cell targets . To provide additional data that homotypic NTB-A interactions can suppress the function of SAP-deficient CD8+ T cells , we transfected fibroblasts to express NTB-A ( Figure 7E ) and compared the ability of SAP+ and SAP− clones to lyse the parental ( i . e . , NTB-A− ) or transduced NTB-A+ cells in a 51Cr release assay . Consistent with the data presented in Figure 4 , there was no difference in lysis of either parental fibroblasts by SAP+ and SAP− CD8+ T cell clones ( compare Figure 7F and G; red lines ) , or lysis of NTB-A− and NTB-A+ fibroblasts by SAP+ CD8+ T cells clones ( Figure 7F ) . However , the cytotoxic activity of the same SAP− CD8+ T cell clone was significantly reduced when NTB-A was ectopically expressed on fibroblasts ( Figure 7G , p<0 . 05 ) . Thus , these data provide evidence that in the absence of SAP , SLAM family receptors acquire inhibitory function which compromises the ability of CD8+ T cells to be activated by Ag-presenting B cells .
Primary immune deficiencies are characterised by increased susceptibility to infection by a range of pathogens [10] . The molecular mechanism underlying this heightened vulnerability is often explained by the nature of the genetic defect responsible for a particular immune deficient condition . Thus , a lack of B cells in X-linked agammaglobulinemia ( XLA ) a lack of T and NK cells in X-linked several-combined immunodeficiency ( X-SCID ) and impaired B-cell responses in X-linked hyper-IgM syndrome due to mutations in BTK , IL2RG , and CD40LG , respectively , predispose affected individuals to severe , recurrent , and often life-threatening infections [10] , [55] . In contrast to these conditions , the explanation for why loss-of-function mutations in SH2D1A , resulting in SAP-deficiency , render XLP patients exquisitely sensitive to infection with EBV , but not other viruses , is enigmatic . Indeed , while previous studies that examined lymphocytes from XLP patients or Sap-deficient mice have clearly shed light on the role of SAP in different immune cells and allowed us to understand the complex nature of some of the clinical manifestations of XLP [4] , [7] , the question of why XLP patients are uniquely susceptible to EBV infection remains unanswered . Efforts to address this have also been hampered by the absence of appropriate animal models due to the specificity of EBV infection for humans . For these reasons , we developed a novel approach to answer this basic question relating to XLP . Female carriers of several X-linked diseases , such as X-SCID , XLA , and Wiskott-Aldrich syndrome , display skewed X-chromosome inactivation with preferential expression of the wild-type ( WT ) allele in some lymphocyte lineages [56]–[58] . This occurs because expression of the WT allele in specific hematopoietic cells confers a survival advantage over cells expressing the mutant allele , which therefore fail to develop in the female carriers . In contrast to these X-linked diseases , normal numbers of T and NK cells are detected in XLP patients [11] , [16] , and lymphocytes from female carriers of XLP exhibit random inactivation of the X-chromosome [11] . These observations demonstrate that SAP is not required for lymphocyte development ( with the exception of NKT cells [11]; Figures 1 , 2 ) . Consequently , female carriers of XLP represent an ideal model to assess the role of SAP in CD8+ T cell-mediated anti-viral immune responses because both SAP+ and SAP− cells with the same genetic background are generated at similar frequencies ( Figure 2 ) . This is essentially the human equivalent of a mixed bone marrow chimera in mice , and therefore eliminates any variability that may arise from comparisons of SAP-deficient CD8+ T cells from XLP patients with SAP-sufficient cells from unrelated normal donors , as has been performed in earlier studies [19] , [20] , [32] . Another feature of female XLP carriers is that they have an intact immune system and are not susceptible to any known infections [38] , [39] . Thus , any secondary defects in the function of CD8+ T cells from XLP patients due to a lack of NKT cells or impaired NK cell function—which can all contribute to fine-tuning CD8+ T cell responses [33]–[36]—are circumvented by studying XLP carriers . These attributes of XLP carriers allowed us to perform a detailed analysis of the responses of SAP− and SAP+ CD8+ T cells from the one individual to not only EBV but other common viruses including CMV and Flu in the setting of a normal host immune response . Previous studies using tetramers have demonstrated that EBV-specific CD8+ T cells could be detected in XLP patients ( n = 2; [59] ) . These cells , however , exhibit poor in vitro responses to EBV Ags [19] , [32] . Our phenotypic and functional analysis of Ag-specific CD8+ T cells from XLP carriers demonstrated that CMV or Flu-specific CD8+ T cells are distributed within both SAP+ and SAP− memory populations , however there was a dramatic , and highly significant , skewing of EBV-specific CD8+ T cells such that >95% of these cells were detected within the SAP+ compartment ( Figure 3 ) . By using peptides derived from both lytic and latent EBV Ag , we established that the exclusive SAP+ effector CD8+ T cells generated following EBV infection were not restricted to a single dominant antigenic epitope ( Figure 3 ) . This demonstrates that there is a selective advantage for SAP+ CD8+ T cells in anti-EBV immunity , but not in either anti-CMV or anti-Flu immunity . Thus , although SAP− cells are abundant within the pool of naïve CD8+ T cells , the SAP+ cells expressing a TCR with specificity for EBV vigorously outcompete their SAP− counterparts and subsequently become the predominant cell type that expands and is maintained following exposure to EBV . Thus , our studies reveal a strong requirement for SAP expression not only in mediating the effector function of CD8+ T cells in response to EBV infection but also in the expansion and survival of these cells . These findings underscore the obligate requirement for SAP , and by extension SLAM family receptors , at multiple stages in CD8+ T cells in mediating protection against EBV infection . The ability to examine competition between WT and gene-deficient cells ex vivo is another powerful feature of the carrier model , and a human equivalent of the studies performed in mice using mixed bone marrow chimeras to determine the intrinsic responses of WT versus mutant cells in a competitive environment . The mechanism underlying this fundamental requirement for SAP expression during the generation of EBV-specific CD8+ T cells was revealed by investigating the ability of SAP− and SAP+ CD8+ T cells specific for the same CMV or Flu epitopes to respond to their cognate peptide when presented on B-cell or non-B-cell target APCs ( monocytes , DCs , fibroblasts ) . The rationale for these experiments was 2-fold: first , one of the key differences between the three viruses studied here is the identity of the APC responsible for activating the CD8+ T cell response . CMV persists in immature myeloid cells and , on reactivation , is likely to be presented by infected monocytes/DCs [60] , whereas influenza infects respiratory epithelial cells and can be cross-presented by DCs [61] . By contrast , EBV is a predominantly B-lymphotrophic virus and there is strong evidence to suggest that the CD8+ T cell response is driven by epitopes displayed on infected B cells themselves [37] , [62] . Second , although the response of XLP CD8+ T cells to B cells is impaired , they can respond relatively normally to other types of target cells [19] , [32] . Thus , it was possible that SAP-deficient CD8+ T cells failed to be activated when Ag was specifically presented by B cells . Indeed , SAP-deficient CD8+ T cell clones from XLP carriers were specifically defective in responding to their cognate epitopes when presented by B-cell , but not non-B-cell , targets irrespective of the viral origin of the specific Ag ( Figure 4 ) . Similarly , EBV-specific SAP-deficient CD8+ T cells expanded from XLP patients were severely compromised in their capacity to lyse B cells presenting endogenously processed EBV peptide Ags ( Figure S2C ) . Our findings have several important implications . First , although EBV can presumably be presented by numerous non-B-cell types of APCs ( e . g . , tonsillar epitheilium , cross-primed DCs ) [63] , [64] , and this may contribute to the initial generation of detectable EBV-specific CD8+ T cells in XLP patients [19] , [59] , the predominant APC involved in maintaining a robust anti-EBV CD8+ T cell–mediated immune response appears to be B cells . Second , the inability to control EBV infection in XLP is likely to result from a direct defect in CD8+ T cells . Defects in CD4+ T cells may contribute to impaired anti-EBV immunity in XLP because analysis of the CD4+ T cell compartment from XLP carriers revealed a predominant response by SAP+ CD4+ T cells to EBV lysate in vitro ( Figure S4 ) . Third , and most importantly , the exquisite sensitivity of XLP patients to EBV infection results from the ability of the virus to sequester itself in infected B cells which can only induce a cytotoxic T cell response in SAP-sufficient cells . In other words , the functional defect in SAP− CD8+ T cells does not relate to a specific virus but rather to the nature of the target cell presenting viral epitopes . The finding of a requirement for SAP in CD8+ T cell–mediated lysis of Ag-presenting B cells , but not monocytes , DCs , or fibroblasts , predicted that expression of ligands of the SLAM family would differ between these populations of APCs . This was confirmed by demonstrating that while fibroblasts lacked expression of all SLAM family ligands , B cells , monocytes , and DCs expressed differing levels of some of these ligands ( Figure 6 ) . Signalling downstream of SLAM family receptors is regulated by SAP via several mechanisms . SAP can deliver activation signals via Fyn-dependent or Fyn-independent processes [6] . Alternatively , SLAM family receptors can alter their function to become inhibitory receptors in the absence of SAP [5] , [6] . This appears to be mediated by the recruitment and/or activation of inhibitory phosphatases [22] , [24] , [65] , [66] . We therefore reasoned that engagement of SLAM receptors delivered either activating signals to SAP-expressing CD8+ T cells or inhibitory signals to SAP-deficient CD8+ T cells . Our finding that ( 1 ) impeding NTB-A/NTB-A and 2B4/CD48 interactions with blocking mAbs [22] , [52] , [54] could improve the function of SAP− CD8+ T cells in the context of responses to Ag-presenting B cell targets and ( 2 ) ectopic expression of NTB-A on fibroblasts protected these cells from cytotoxicity induced by SAP-deficient Ag-specific CD8+ T cells favoured an inhibitory function for these receptors in the absence of SAP ( Figure 7 ) . This is reminiscent of early descriptions of inhibitory function of these receptors on SAP-deficient human NK cells [22] , [24] , [67] , [68] , and the recent demonstration of such a phenomenon for CD8+ T cell clones from XLP patients [32] . This conclusion is also consistent with the reported ability of NTB-A to associate with SHP-1 in the absence of SAP in human NK cells and T cells [22] , [42] , thereby suggesting a mechanism of how NTB-A exerts its inhibitory effect . Veillette and colleagues proposed that the SAP homolog EAT-2 mediates inhibitory signalling downstream of some SLAM family receptors in the absence of SAP [69] . Interestingly , EAT-2 associates with NTB-A in human lymphocytes [70] , and SH2D1B ( encoding EAT-2 ) was expressed at increased levels in memory CD8+ T cells from XLP patients compared to healthy donors ( Figure S5 ) . Thus , it is possible that in XLP heightened expression of EAT-2 mediates an alternative pathway downstream of NTB-A for inhibitory signalling in SAP-deficient CD8+ T cells following engagement of SLAM family receptors . Irrespective of these possibilities , it is clear that expression of SAP significantly alters the function of SLAM family receptors on human NK and CD8+ T cells such that these receptors inhibit cytotoxicity in the absence of SAP . Previous studies established defects in SAP-deficient CD8+ T cells [19] , [20] , [32] . However , there have been major limitations to all of these inasmuch as they only examined responses of XLP CD8+ T cells to polyclonal ( i . e . , Ag non-specific ) stimulation [19] , [20] , or only studied responses to EBV and not additional viruses [19] , [32] . Thus , none of these earlier studies offered an explanation for the selective inability of XLP patients to respond to infection with EBV but not other viruses . We have now significantly extended these observations by providing mechanistic insight into the dysfunctional behaviour of SAP− CD8+ T cells by ( 1 ) revealing that the defect in anti-EBV immunity in XLP reflects the nature of the APC , rather than EBV itself , ( 2 ) proving that NTB-A is inhibitory for the function of SAP-deficient CD8+ T cells , and ( 3 ) excluding a role for SLAM itself in regulating the function of human Ag-specific CD8+ T cells , a scenario proposed by a previous study [49] . Our findings that SAP-deficient CD8+ T cells respond poorly to EBV-infected B cells , but not to monocyte , DC , or fibroblast APCs , parallel those reported recently for CD4+ T cells from Sap−/− mice . In that system no difference was found in the quality of interactions between DCs and either SAP-deficient or SAP-sufficient CD4+ T cells [17] . However , SAP-deficient CD4+ T cells exhibited greatly reduced interactions with cognate B cells , resulting in impaired help for T-dependent B cell responses [17] . Interestingly , mouse Ly108 ( i . e . , human NTB-A ) is involved in the formation of stable conjugates between normal CD4+ T cells and B cells , while interactions with DCs were predominantly mediated by integrins [71] . The absence of NTB-A and CD48 from DCs potentially explains why DC-mediated Ag-presentation to CD8+ T cells is unaffected by SAP deficiency . While SAP was required in murine CD4+ T cells for NTB-A-mediated interactions with B cells [71] , it appears that SAP functions in human CD8+ T cells to prevent the delivery of inhibitory signals downstream of NTB-A that probably involve the recruitment and/or activation of phosphatases or EAT-2 [22] , [42] , [70] . This apparent disparate function of NTB-A on murine CD4+ and human CD8+ T cells may be explained by the pattern of expression of EAT-2 , inasmuch as it is detected in human CD8+ T cells ( Figure S5 ) [72] , but not murine CD4+ T cells [69] . Despite these potential differences , an emerging theme is that loss of SAP in T cells leads to altered interactions with B cells , while interactions with other APCs remain intact . This specific defect not only explains the molecular pathogenesis of the unique susceptibility to EBV infection in XLP patients but potentially explains their high incidence of B-lymphomas . Interestingly , EBV is the only known human pathogen that selectively infects B cells , which results in expression of high levels of SLAM family ligands to facilitate the T-B cell cross-talk necessary for immunity . Thus , our studies have identified a unique pathological signalling pathway that may be targeted to treat patients with severe EBV infection . Furthermore , the innovative XLP carrier model has allowed us to unravel the mechanisms of disease in the absence of a relevant animal model . This system may also allow the study of other human diseases , for instance XIAP deficiency , which also predisposes to EBV infection [8] , [73] , where heterozygous gene expression from random X-chromosome inactivation could be exploited .
Blood samples were collected from seven different XLP carriers and an XLP patient . PBMC were isolated and either used fresh or cryopreserved in liquid nitrogen . Genomic DNA was sequenced to confirm the heterozygous state of the carriers . Primers used for amplification of the four exons of SH2D1A are: Exon 1 sense: CAA CAT CCT GTT GTT GGG G , Exon 1 antisense: CCA GGG AAT GAA ATC CCC; Exon 2 sense: GCA ATG ACA CCA TAT ACG , Exon 2 antisense: GAA CAA TTT TGG ATT GGA GC; Exon 3 sense: GTA AGC TCT TCT GGA ATG , Exon 3 antisense: CAT CTA CTT TCT CAC TGC; Exon 4 sense: CTG TGT TGT GTC ATT GTG , Exon 4 antisense: GCT TCC ATT ACA GGA CTA C . All participants gave written informed consent and the experiments were approved by the Human Research Ethic committees of the Sydney South West Area Health Service ( Royal Prince Alfred and Concord Zones ) and St . Vincent's Hospital . PBMC , CD8 T cell clones , B-LCLs , and fibroblasts were stained with fluorochrome-conjugated mAbs specific for cell surface receptors . The following mAbs were used to identify different lymphocyte populations: anti-CD3 , CD4 , CD8 ( T cells ) , CD56 ( NK cells ) , CD20 ( B cells ) , CD14 ( monocytes ) , CD1a , CD11c ( DC ) ( BD Biosciences ) , and TCR Vα24/Vβ11 ( NKT cells ) ( Immunotech , France ) mAbs . CCR7 ( R&D Systems ) , CD45RA ( BD Biosciences ) , and CD27 ( BD Biosciences ) were used to identify subsets of naïve and memory T and B cells . CD83 ( eBioscience ) , CD86 , MHC class II , and MHC class I mAbs ( BD Biosciences ) were used to phenotype LPS-matured DCs . Expression of the SLAM family of receptors and ligands was determined using mAbs against CD84 ( BD Biosciences ) , CD229 , NTBA , CRACC ( R&D Systems ) , 2B4 ( Beckman Coulter ) , CD48 ( Immunotech , France ) , and SLAM/CD150 ( eBiosciences ) . TCR Vβ repertoire analysis was performed according to the manufacturer's instructions ( Beckman Coulter ) . For degranulation assays mAb against CD107a ( BD Biosciences ) was used as previously described [44] , [45] and for intracellular cytokine stains anti-IFN-γ ( BD Biosciences ) mAb was used . Stained cells were analyzed on either FACSCanto I or II flow cytometers ( BD Biosciences ) and the data processed using FlowJo software ( Treestar , Ashland , USA ) . MHC class I tetramers were prepared in-house , where the appropriate MHC class I heavy chain molecule was refolded with β2 microglobulin and the peptide and complexed with streptavidin-PE as described [74] . CMV epitopes used were the HLA-A*0201-restricted peptides NLVPMVATV from pp65 ( UL83 ) protein , and VLEETSVML from IE-1 ( UL122 ) protein; HLA-A*0101 restricted peptide , VTEHDTLLY from pp50 ( UL44 ) protein . EBV epitopes used were HLA-A*0201-restricted GLCTLVAML from the lytic Ag BMLF-1 , CLGGLLTMV from LMP2 , HLA-B*4402-restricted peptides VEITPYKPTW from EBNA3B latent protein , and EENLLDFVRF from EBNA3C . The influenza A epitope was the HLA-A*0201-restricted peptide GILGFVFTL from matrix protein . Cells were first stained for surface markers and then fixed with 2% paraformaldehyde , permeabilized with 0 . 5% saponin , and incubated with Alexa Fluor 647 ( Invitrogen ) -conjugated isotype control or anti-SAP mAb ( Abnova , clone 1C9 ) . Cells were washed and resuspended in PBS/1% FCS and analysed on a FACSCanto I or II flow cytometer ( BD Biosciences ) . 1–2×106 PBMCs were stimulated with either an irrelevant peptide , specific MHC class I restricted synthetic peptide , or PMA/ionomycin as a positive control for 4–6 h in the presence of Brefeldin A ( for IFN-γ production ) or monensin ( for CD107a expression ) . The capacity to respond to these peptides was tested by harvesting the cells and determining expression of IFN-γ or CD107a by SAP+ and SAP− CD8+ T cells . DCs were generated from peripheral blood monocytes by culturing sort-purified CD14+ cells ( 5×105/ml ) in human lymphocyte media [15] , [16] supplemented with 500 U/ml of IL-4 ( provided by Dr . Rene de Waal Malefyt ) and 50 ng/ml GMCSF ( Peprotech ) . After 5 d , monocyte-derived DCs were harvested , washed , and cultured ( 5×105/ml ) in the presence of 1 µg/ml of LPS ( Sigma ) for a further 18 h . Monocyte-derived DCs were CD1a+ CD11c+ CD14− . Upon maturation with LPS , they upregulated expression of CD83 , CD86 , and MHC class I and MHC class II . Virus-specific CD8+ T cell clones were established from PBMCs by sort-purifying tetramer positive cells and limiting dilution cloning as described [75] . Clones were established by seeding sort-purified tetramer+ CD8+ T cells at 0 . 3–3 cells/well into media containing 104 autologous B-LCLs and 105 feeder cells per well . CMV-specific clones were selected based on their recognition of the pp50 ( UL44 ) epitope VTEHDTLLY ( HLA-A1 restricted ) , while influenza-specific clones recognised the matrix protein epitope GILGFVFTL ( HLA-A2 restricted ) . All clones were expanded and tested for specificity by staining with the appropriate tetramer and for SAP expression ( see Figure S1 ) . EBV-specific CD8+ T cell lines used in DC assays were generated by sort purifying tetramer-positive cells and expanding them in vitro on peptide-pulsed autologous B-LCLs and feeder cells . EBV-specific CD8+ T cell lines from XLP patients and normal donors were established by repeated stimulation of purified CD8+ T cells on autologous B-LCLs [19] . The ability of CD8+ T cell clones to respond to various target cells was measured either by intracellular IFN-γ staining or by staining for CD107a . Autologous B-LCLs were used as B cell targets . HLA-matched monocytes were sort-purified from buffy coats on the basis of CD14 ( Immunotech ) expression and used as APCs . DCs were generated as described above . HLA-matched human fibroblasts used were JuSt ( HLA-A1 & A2 ) and MeWo cells ( HLA A2 ) ( ATCC ) . All APCs were pulsed with appropriate peptides ( 1 µg/ml ) and used to stimulate CD8+ T cell clones . Where cytotoxicity was measured , APCs were sensitised with cognate peptide at a concentration of 1 µg/ml while loading with 51Cr . After washing , T cells were incubated at different APC∶T cell ratios and incubated for 5 h in standard cytotoxicity assay [75] . In some experiments , blocking mAbs against NTB-A ( MA127 ) [22] and 2B4 ( C1 . 7 [52] , [53] ) were used to prevent NTB-A/NTB-A and 2B4/CD48 interactions , respectively . B-LCLs were incubated with the relevant mAb at a final concentration of 20 µg/ml for 30–45 min prior to mixing with CTL clones . Cultures were incubated for 4–6 h in the presence of blocking mAbs and mAb to CD107a . Cells were then appropriately stained and analysed by flow cytometry . Fibroblasts were transfected using Lipofectamine with the pcdef3 plasmid containing cDNA encoding human NTB-A . Positive cells were initially selected in the presence of G418 and then isolated by sorting NTB-A+ cells . NTB-A+ transfected and untransfected parental fibroblasts were then used as targets in 51Cr release assay as described above . | X-linked lymphoproliferative disease ( XLP ) is an immunodeficiency caused by mutations in the SH2D1A gene , which encodes a cytoplasmic component , SAP involved in a signalling pathway in certain populations of immune cells . The Achilles' heel in XLP is extreme sensitivity to Epstein-Barr virus ( EBV ) infection . Although EBV infection in normal individuals is generally innocuous , in XLP it can be fatal . Strikingly , individuals with XLP do not display this same vulnerability to other viruses , and here we investigate what immune defects underlie this specific susceptibility . We developed a system to examine the behaviour of immune cells that are identical with the exception of whether or not they have a functional SH2D1A gene . This approach uses human female carriers of XLP ( one of their X chromosomes carries the mutation ) . Following the process of X-chromosome inactivation in female cells , which is random , individuals harbour T cells that express the normal SH2D1A gene as well as cells that express the mutated version . We found that SAP-deficient CD8+ T cells fail to be activated by antigen-presenting B cells , but are activated by other antigen-presenting cell types . Since EBV selectively infects B cells , the exquisite sensitivity in XLP to EBV infection results from the ability of the virus to sequester itself in B cells , which can only induce a cytotoxic T cell response in SAP-sufficient cells . Thus , the functional defect in SAP-deficient CD8+ T cells does not relate to a specific virus but rather to the nature of the target cell presenting viral epitopes . | [
"Abstract",
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] | 2011 | Molecular Pathogenesis of EBV Susceptibility in XLP as Revealed by Analysis of Female Carriers with Heterozygous Expression of SAP |
Recent experiments have demonstrated that visual cortex engages in spatio-temporal sequence learning and prediction . The cellular basis of this learning remains unclear , however . Here we present a spiking neural network model that explains a recent study on sequence learning in the primary visual cortex of rats . The model posits that the sequence learning and prediction abilities of cortical circuits result from the interaction of spike-timing dependent plasticity ( STDP ) and homeostatic plasticity mechanisms . It also reproduces changes in stimulus-evoked multi-unit activity during learning . Furthermore , it makes precise predictions regarding how training shapes network connectivity to establish its prediction ability . Finally , it predicts that the adapted connectivity gives rise to systematic changes in spontaneous network activity . Taken together , our model establishes a new conceptual bridge between the structure and function of cortical circuits in the context of sequence learning and prediction .
The ability to predict the future is a fundamental challenge for cortical circuits . At the heart of prediction is the capacity to learn sequential patterns , i . e . , the ability for sequence learning . Recent experiments have shown that even early sensory cortices such as rat primary visual cortex are capable of sequence learning [1] . Specifically , Xu et al . [1] have shown that the visual cortex can learn a repeated spatio-temporal stimulation pattern in the form of a light spot moving across a portion of the visual field . Intriguingly , when only the start location of the sequence is stimulated by the light spot after learning , the network will anticipate the continuation of the sequence as revealed by its spiking activity . While these results are remarkable , the cellular basis of this ability has remained elusive . A natural candidate for a cellular mechanism of sequence learning is spike-timing dependent plasticity ( STDP ) [2 , 3] . Theoretical work has suggested that the inherent temporal asymmetry of STDP seems ideally suited for learning temporal sequences and storing them into the structure of cortical circuits [4–8] . At present , it is still unknown , however , exactly how such sequence memories are stored in real cortical circuits and how they become reflected in the structure of these circuits . Yet , a number of generic structural features of cortical circuits have been established in recent years . Among them is the lognormal-like distribution of synaptic efficacies between excitatory neurons [9–13] , the distance-dependence of synaptic connection probabilities [12 , 14] , and an abundance of bidirectional connections between excitatory neurons [12 , 15] . While recent theoretical studies have successfully modeled the origins of these generic structural features of cortical circuits , there is currently no unified model that explains both the emergence of structural features of cortical circuits and their sequence learning abilities . Here we present such a model and therefore establish a new conceptual bridge between the structure and function of cortical circuits . Our model is a recurrently connected network of excitatory and inhibitory spiking neurons endowed with STDP , combined with a form of structural plasticity that creates new synapses at a low rate and destroys synapses whose efficacies have fallen below a threshold , as well as several homeostatic plasticity mechanisms . We used this network to model recent experiments on sequence learning in rat primary visual cortex [1] . The model successfully captured how multi-unit activity is changing during learning and explained these changes on the basis of STDP and the other plasticity mechanisms adapting the circuit during learning . It additionally demonstrated how homeostatic mechanisms prevent the runaway connection growth and unstable overlearning [16–19] that otherwise tends to occur from STDP alone . Furthermore , the model predicted that the changes to the network during learning also alter spontaneous activity patterns in systematic ways leading to an increased probability of spontaneous sequential activation . Finally , the model also captured the experimental finding that the training effect is only short-lasting . In sum , we present the first spiking neural network model that explains recent sequence learning data from rat primary visual cortex while also reproducing key structural features of cortical connectivity .
The network model we used is a member of the class of self-organizing recurrent neural network ( SORN ) models ( see e . g . [6 , 20–23] ) . Specifically , we used the leaky integrate-and-fire SORN ( LIF-SORN ) introduced by Miner and Triesch [22] . The LIF-SORN is a model of a small section of L5 of the rodent cortex . Here , we used a version of it consisting of NE = 1000 excitatory and NI = 0 . 2 × NE = 200 inhibitory leaky integrate-and-fire neurons with conductance based synapses and Gaussian membrane noise . The neurons are placed randomly on a 2500 μm × 1000 μm grid ( Fig 1A ) and their connectivity is distance-dependent , meaning a neuron is more likely to form a connection with a neuron nearby than with a remote neuron . While the weights of connections involving inhibitory neurons are fixed , recurrent excitatory connections are subject to a set of biologically motivated plasticity mechanisms . Exponential STDP with an asymmetric time window endows the network with the ability to learn correlations in external input . It is complemented by a form of structural plasticity ( SP ) , which creates new and prunes weak connections . The network dynamics are stabilized by three additional plasticity mechanisms . First , synaptic normalization ( SN ) keeps the total incoming weight for each excitatory neuron constant . Second , intrinsic plasticity ( IP ) regulates the threshold potential of each excitatory neuron to counteract overly high or low firing rates . Third , short-term plasticity ( STP ) facilitates or impedes signal transmission along a specific connection based on the firing history of the presynaptic neuron . Using this network model , we replicated the study by Xu et al . [1] . In this experiment , a multielectrode array was inserted in the primary visual cortex of rats and the receptive field of each channel was determined . The rats were then presented with a bright light spot , which was moved from a start point S ˜ to an end , or goal , point G ˜ along the distribution of receptive fields . The effect of this conditioning was assessed by measuring the responses to different kinds of cues of the full motion sequence . Xu et al . [1] investigated both awake and anesthetized rats , but unless noted otherwise , we compared our results to the results from awake animals . In the LIF-SORN , we modeled the movement of the light spot by sweeping a spot from x S ˜ = ( 375 μ m , 500 μ m ) T to x G ˜ = ( 2125 μ m , 500 μ m ) T . The amplitude of this spot at the position x n e of an excitatory neuron ne represents the rate r spot ( x n e , t ) of external Poissonian spike trains , which this neuron receives ( Fig 1B ) . Furthermore , we approximated recording with a multielectrode array by introducing clusters of excitatory neurons . These clusters are located between x S ˜ and x G ˜ and are named A to H according to their distance to x S ˜ ( Fig 1C ) . For the analysis of the sequence learning task , we only considered the activity of these clusters , which we defined to be the pooled spikes of the neurons part of a cluster . See the Materials and methods section for a more detailed description of our model . To get an impression of the behavior of the LIF-SORN , we simulated the network for 500 s solely under the influence of the background noise , i . e . , without external input . Thereby we also showed that it exhibits some basic properties of both the activity and connectivity in biological neural networks . We began by analyzing the spiking activity after an initial equilibration phase ( Fig 2 ) . Both excitatory and inhibitory neurons seemed to exhibit unstructured firing ( Fig 2A and 2B ) with the excitatory neurons firing with frequencies distributed closely around 3 Hz due to the IP ( Fig 2C ) and inhibitory neurons firing with roughly twice this frequency ( Fig 2D ) . The activity of the cortex in the absence of external stimuli is often assumed to lie in a regime of asynchronous irregular spiking . Synchrony refers , in this context , to the joint spiking of a set of neurons . Biological data shows that population level activity in the cortex is highly asynchronous [24 , 25] . Mostly , the pairwise correlation coefficient is used to quantify synchrony ( see e . g . [26] ) . The pairwise correlation coefficient between a neuron m and n is defined as c m n = cov ( C m , C n ) Var ( C m ) Var ( C n ) , ( 1 ) where Cm is the time series of spike counts of neuron m within successive time bins . Fig 2E shows the distribution of pairwise correlation coefficients of all disjoint pairs of excitatory neurons in the LIF-SORN for time bins of 20 ms duration . The pairwise correlation coefficients were closely distributed around zero , implying a low level of synchrony in the LIF-SORN . When varying the duration of the time bins , the mean of the distribution of pairwise correlation coefficients stayed close to zero while its width increased ( decreased ) with increasing ( decreasing ) duration of the time bins . Regularity refers to the variability of the spiking of individual neurons . In the cortex , this spiking is highly irregular and can , apart from the refractory period , often be quite accurately described by a Poisson process [27] , in which the interspike intervals follow an exponential distribution with a coefficient of variation equal to unity . We found that interspike intervals of excitatory neurons in the LIF-SORN were approximately exponentially distributed with a distortion caused by the refractory period ( Fig 2F ) and that the coefficients of variation were generally close to one ( Fig 2G ) , indicating irregular spiking . Next , we considered the structural properties of the LIF-SORN . The LIF-SORN was initialized without recurrent excitatory connections , but due to SP , these connections grew for about 200 s , as can be seen in Fig 3A . Afterwards the pruning rate of existing synapses approached the growth rate of new synapses and the network entered a stable phase in which the connection fraction of excitatory connections did not change anymore . The values of individual weights were , on the other hand , still fluctuating ( Fig 3B ) . This constant change was also found in biological networks [13] . Additionally , the excitatory weights assumed an approximately lognormal-like distribution ( Fig 3C ) as observed in cortical circuits [9–13] . We also converted the connection weights in approximate amplitudes of the corresponding postsynaptic potentials ( PSP ) . In excitatory neurons , the mean excitatory PSP ( EPSP ) amplitude was 0 . 72 mV and the mean inhibitory PSP ( IPSP ) amplitude was 0 . 96 mV and in inhibitory neurons , the mean EPSP amplitude was 0 . 74 mV and the mean IPSP amplitude was 0 . 94 mV . These values lie within the experimentally observed range [11 , 12 , 28] . See S1 Appendix for a description of how this conversion was done and figures of the distribution of PSP amplitudes and their ratios . Taken together , the LIF-SORN displayed key features of both the activity and connectivity in cortical circuits . Besides the here mentioned structural properties , the LIF-SORN has also already been shown to reproduce more complex properties of cortical wiring , namely the overrepresentation of bidirectional connections and certain triangular graph motifs compared to a random network and various aspects of synaptic dynamics [22] . To investigate the sequence learning ability of the LIF-SORN , we employed a similar test paradigm as Xu et al . [1] . That means we trained the network with the moving spot as described above and tested it by stimulating the network with brief flashes of the spot at the start point x S ˜ = ( 375 μ m , 500 μ m ) T , the mid point x M ˜ = ( 1250 μ m , 500 μ m ) T and the end point x G ˜ = ( 2125 μ m , 500 μ m ) T . This testing was performed before and after training and the responses to each of the stimuli after training were compared to their counterpart before training . Specifically , our simulation protocol started with a growth phase of 400 s duration , to initialize a network that exhibits key features of cortical circuits . It followed a test phase , during which one of the cues , i . e . a brief flash of the spot at the start point , mid point or end point , was presented once every two seconds . This first test phase lasted 100 s , leading to a total of 50 repetitions . After this test phase , the network was given a short relaxation phase of 10 s such that its activity got back to base level . Afterwards , the full motion sequence was shown to the network in the training phase , which lasted 200 s . The sequence was also presented once every two seconds leading to a total of 100 repetitions . After another relaxation phase of 10 s , the simulation ended with another test phase , during which the same cue as in the first test phase was shown to the network . This second test phase lasted 100 s leading to a total of 50 repetitions . The purpose of the presentation of the test cue at the start point was to examine if the network learned the sequential structure of the motion sequence . Fig 4A shows the spike trains of the neurons that are part of one of the clusters A to H during training in response to one presentation of the full motion sequence as well as before and after training in response to one trial of cue presentation at the start point . While the spiking was clearly sequential during training , such sequential spiking was much less pronounced in response to the test cue before and after learning . Additionally , the spiking was quite variable over trials and different networks . Similar results were found by Xu et al . [1] . A common method to assess the sequential spiking in animal studies of sequence replay is to compute the cross-correlation between pairs of spike trains [1 , 29] . We performed such an analysis in a similar way as Xu et al . [1] by pooling the spikes for each cluster and than computing the correlation between these spike trains for each trial and for all cluster combinations . Therefor , we only considered spikes within the window 0 ms–500 ms relative to stimulus onset to minimize the impact of spontaneous activity . Next , we pooled the cross-correlations according to the corresponding difference in cluster position . This was done for the test phases before and after training . We then also took the difference between the resulting cross-correlograms and finally normalized each of the three cross-correlograms to the range between 0 and 1 . This was done independently for each of the sets of cross-correlations corresponding to a specific difference in cluster position . Fig 4B shows the thereby obtained cross-correlograms , which qualitatively resembled the cross-correlograms obtained from rats [1] in that the correlation function took on higher values for positive time delays compared to negative time delays even before learning—an observation that can be linked to the spread of activity from S ˜ towards G ˜—and in that this rightward slant enhanced due to training . This increase of the correlation function for positive time delays indicated that the network indeed learned about the sequential structure of the motion sequence . To quantify the cue-triggered sequence recall , we again adapted the analysis used by Xu et al . [1] . That is to say we pooled the spikes of all neurons for each cluster and calculated their rate by convolving them with a Gaussian filter with width τrate = 50 ms . We only considered spikes within the window 0 ms–500 ms relative to stimulus onset to minimize the impact of spontaneous activity and defined the firing time of a cluster as the first peak of its rate curve . Then we computed for each test trial the Spearman correlation coefficient between the firing times of the clusters and their location on the S ˜ → G ˜ axis . The Spearman correlation coefficient between two variables is defined as the Pearson correlation coefficient between the rank values of those variables . Hence , it measured how much the replay order resembled the training order of clusters . For the test phases with a cue at S ˜ , we found a significant rightward shift of the correlation coefficient distribution after learning with a change in mean from 0 . 26 to 0 . 30 ( P = 7 . 9 × 10−3; Kolmogorov-Smirnov test ) as shown in Fig 5A . Thus , there was enhanced sequential spiking after training compared to before training as found by Xu et al . [1] , who observed a change in mean from 0 . 21 to 0 . 29 ( P = 1 . 5 × 10−3; Kolmogorov-Smirnov test; Fig 5A ) . The purpose of the presentation of the test cue at the mid point was to avoid having a rightward bias even before training . For that case , Xu et al . [1] also found a significant rightward shift of the correlation coefficient distribution with a change in mean from −0 . 08 to −0 . 02 ( P = 1 . 3 × 10−4; Kolmogorov-Smirnov test; Fig 5B ) . In the LIF-SORN , we observed only a very small rightward shift , which wasn’t significant , however ( change in mean from 0 . 0 to 0 . 02; P = 0 . 31; Kolmogorov-Smirnov test; Fig 5B ) . The purpose of the presentation of the test cue at the end point was to examine if the cue-triggered replay was specific to the direction of the motion sequence . Thereby , Xu et al . [1] computed the Spearman correlation coefficients between the firing times of the clusters and their location on the G ˜ → S ˜ axis and found no significant shift in the correlation coefficient distribution ( change in mean from 0 . 20 to 0 . 20 , P = 0 . 59; Kolmogorov-Smirnov test; Fig 5C ) , indicating that the direction of replay was indeed specific to the direction of the motion sequence used during training . In the LIF-SORN , we found a small but not significant leftward shift ( change in mean from 0 . 22 to 0 . 20 , P = 0 . 53; Kolmogorov-Smirnov test; Fig 5C ) . The small leftward shift can be explained by the weakening of connections between the clusters pointing in the opposite direction of the motion sequence due to STDP , since this decreased the correlation between the activity of one cluster and a cluster located further along the G ˜ → S ˜ axis . Although Xu et al . [1] did not observe even a small leftward shift , it may still be that STDP was also responsible for the training effect in rats as the weakening of connections pointing in the opposite direction of the motion sequence could have been too small to have an observable effect . Furthermore , Xu et al . [1] showed that blocking NMDA receptors lead to the disappearance of the training effect indicating that some form of NMDA-dependent plasticity was indeed responsible for the training effect . To test whether the training effect was restricted to a small region of V1 or if it was also apparent elsewhere in V1 , Xu et al . [1] performed an experiment where , during training , the motion sequence was shifted orthogonal to the long axis of the recorded distribution of the receptive fields , i . e . orthogonal to the S ˜ → G ˜ axis . They neither found a significant shift in the correlation coefficient distribution for S ˜-evoked nor for G ˜-evoked responses and concluded that the effect of learning was indeed specific to the location of the motion sequence . As in the animal study , the LIF-SORN exhibited for this scenario no significant shift in the correlation coefficient distribution for both S ˜-evoked and G ˜-evoked responses ( S1 Fig ) . To examine if training with a dynamic stimulus is actually necessary to achieve a more distinctive sequence replay , two different experiments using a static stimulus during training were performed by Xu et al . [1] . In the first one , this stimulus was a flashed bar spanning the region between S ˜ and G ˜ . In the second one , the stimulus used during training was just a briefly flashed spot at S ˜ . A significant shift in the correlation coefficient distribution was neither found for S ˜-evoked nor for G ˜-evoked responses . Again , the LIF-SORN also didn’t show significant training effects for these scenarios ( S1 Fig ) . The activity of the LIF-SORN is determined by the input and its connectivity . In this section , we show how the training with a moving spot modulated the connectivity through the plasticity mechanisms . Therefore , we analyzed the weight matrix of the recurrent excitatory connections before and after training with the full motion sequence from S ˜ to G ˜ . This allowed us to connect a large part of the results of the previous sections with the change in connectivity . We start by considering the weights between neurons part of one of the clusters A–H for one network instance . Fig 6A–6C show the connection weight matrix before and after training and their difference . Before training , we observed a structure with stronger weights distributed symmetrically around the diagonal . This reflected the distance dependency of the connectivity . After training , the symmetry was broken and the connections running in the direction of the moving spot used during training were strengthened while the connections in the opposite direction were weakened . To get a clearer picture of the weight change , we also determined the average connection weights between the different clusters A–H ( Fig 6D ) . The connection weights between adjacent clusters in the forward direction were increasing due to training , while the opposite was true for the backward direction . The increase in connection weight was strongest for the A → B connections as these connections didn’t have as much SN-induced competition as the connections between adjacent clusters further along the S ˜ → G ˜ axis , since they had to compete with connections starting from other clusters located closer to S ˜ . This stripe-like connectivity was a result of STDP and caused the increase in sequential spiking when triggering the sequence with a cue at S ˜ and the decrease when triggering the sequence with a cue at G ˜ . Next , we examined if the training with a sequence had an impact on the spontaneous activity of the LIF-SORN . Therefor , we utilized the simulation protocol as described above with the difference that during testing no external input was used . Thus , only the noise drove the network during testing . As before , we determined the rate of each cluster by pooling the spikes of all neurons which were part of that cluster and convolving them with a Gaussian . However , we considered all spikes during the test phases and not only spikes within a window of 500 ms after stimulus presentation . Next we computed the times of all relative maxima of the firing rate for each cluster and ordered them . In the resulting sequence of firing times , we replaced each firing time with the corresponding cluster name A–H . Finally , we computed the transition probabilities between all clusters from this sequence . The transition probability from A to D , for example , was computed by dividing the number of times D was the cluster that fired directly after A by the total number of times A appeared . This was done for all combinations of clusters A–H and for the test phases before and after training . Fig 7 shows the resulting transition probabilities before and after learning and their differences . Before and after learning , the transition probabilities from a cluster to itself were negligible and the transition probabilities between adjacent clusters as for example A to B or D to C were higher compared to the other transitions . This is not surprising as neurons close to one another strongly influenced each other due to the distance dependency of the connectivity . When considering the change in transition probabilities caused by the training , we observed that transitions between clusters in the S ˜ → G ˜ direction , which were separated by at most one other cluster , tended to be more likely while transitions between clusters in the opposite direction , which were separated by at most one other cluster , tended to be less likely . This finding was consistent with the weight change caused by the training ( Fig 6D ) . Thus , the characteristics of the sequence used during training were imprinted in the spontaneous activity of the LIF-SORN . So far , we were only concerned with the order of the replayed sequence and did not pay attention to its speed . In this section , we examine the recall speed vrc after presentation of the test cue at S ˜ with varying velocities vspot of the motion sequence during training . We adopted the analysis of Xu et al . [1] . That is to say we considered only the test trials after learning for which the Spearman correlation coefficient was greater than 0 . 9 . Then , we determined the recall speed for each of those trials by linear regression of the positions of the centers of each cluster as a function of their firing times . We found that the mean recall speed was independent of the training speed ( Fig 8 ) . It rather seemed to be determined by the network’s parameters . Similar results were found by Xu et al . [1] , i . e . they also observed no dependence of the recall speed on the speed used during training for anesthetized rats . Furthermore , spontaneous replay in cortex and hippocampus was found to be accelerated compared to training [29 , 31] . All of these results suggest that only sequence order is learned and that the recall speed is primarily determined by the network’s dynamics and not the speed of the trained sequence . This observation on the level of local circuitry matches with the trivial fact that the recall of memories doesn’t happen with the speed with which they were experienced . Xu et al . [1] also tested the persistence of the increase in sequential spiking caused by the training . To test this persistence in the LIF-SORN , we used a similar simulation protocol as before , i . e . , training consisted of a moving spot shown along the S ˜ → G ˜ axis and testing of a briefly flashed spot at S ˜ . The duration of the test phase after training was tripled . We adapted the analysis of Xu et al . [1] in that we defined a match as a test trial whose Spearman correlation coefficient was above a threshold of 0 . 6 and computed the change in percentage of matches during the test phase after training compared to the test phase before training . This was done for different times after training . We found that the effect of training as measured by the change in percentage of matches decayed within approximately 5 min ( Fig 9A ) . The training effect decayed within a similar time , namely within around 7 min , in rats ( Fig 9A ) . Hence , the training effect was short-lasting in both rats and the LIF-SORN . Again , we can link the results obtained from the LIF-SORN’s activity with its connectivity . During training , the forward weights between adjacent clusters were increasing for approximately 100 s and then stayed roughly constant until the training ended as a consequence of the synaptic normalization . In the following test phase , the values of the weights from one cluster to the next were decaying back to their initial values , resulting in the simultaneous decay of the training effect on the network’s activity ( Fig 9B ) . This decay was caused by the interplay of STDP with the asynchronous , irregular network activity .
Establishing the relationship between structure and function of cortical circuits remains a major challenge . Here we have presented a spiking neural network model of sequence learning in primary visual cortex that establishes a new conceptual bridge between structural and functional changes in cortical circuits . The model posits that STDP is the cellular basis for the sequence learning abilities of visual cortex . The temporally asymmetric shape of the STDP window ( pre-before-post firing leads to potentiation , post-before-pre firing leads to depression ) allows the circuit to detect the spatio-temporal structure of the stimulation sequence and lay it down in the circuit structure . The homeostatic mechanisms prevent runaway weight growth , among other functions . Importantly , while doing so the model also explains the origin of key structural features of the connectivity between the population of excitatory neurons . Among them are the lognormal-like distribution of synaptic efficacies , the distance-dependence of synaptic connection probabilities and the abundance of bidirectional connections between excitatory neurons . There are many studies that have addressed the functioning of STDP in feed-forward models ( e . g . [32 , 33] ) . In addition , several previous studies have successfully modeled elements of sequence learning with STDP in recurrent networks [5 , 6 , 34–37] , and another set of studies has attempted to account for the development of structural features of cortical wiring [20 , 22 , 38 , 39] . However , our model is the first to combine both . Thereby it offers the most advanced unified account of the relation between structure and function of cortical circuits in the context of sequence learning . Furthermore , it does so from a self-organizing , bottom-up perspective , a critical component missing in most other examples of artificial sequence learning in recurrent neural networks [40–42] . Equally important , however , the model makes precise and testable predictions regarding how the excitatory-to-excitatory connectivity changes during learning . Specifically , it predicts that synaptic connections that project “in the direction of” the stimulation sequence are strengthened , while the reverse connections are weakened . Furthermore , it makes the testable prediction that spontaneous activity after learning should reflect the altered connectivity such that it leads to an increased probability of sequential activation . Similarly , replay of activity patterns is a well-known and widely studied phenomenon in hippocampus [43] , but has also been observed in the neocortex [29 , 31] . Despite these contributions , our model also has several limitations . First , while our network reproduced most of the experimentally observed results by Xu et al . [1] , namely enhanced sequential spiking in response to a cue at the start point of the sequence ( Figs 4B and 5A ) , independence of the recall speed on the training speed ( Fig 8 ) and a short persistence of the training effect ( Fig 9A ) , it did not show the experimentally observed significant rightward shift of the Spearman correlation coefficient distribution in response to a cue at the midpoint ( Fig 5B ) and it exhibited an , experimentally not observed , small leftward shift of the Spearman correlation coefficient distribution in response to a cue at the goal point ( Fig 5C ) . Second , most of the chosen neuron and network parameters were taken from studies on layer 5 of rodent cortex [11 , 12 , 14] , while Xu et al . [1] recorded from both deep and superficial layers . Third , synaptic plasticity in our model was restricted to the connections among excitatory neurons . As a consequence , inhibition is unspecific in our network . From a functional perspective , this shouldn’t make much of a difference for the simple sequence learning task we considered . For more complex situations , such as multiple disparate assemblies , multiple sequences or branching sequences , this may be different , however . Adding plasticity mechanisms to the other connection types would also make the model more realistic and may allow it to establish additional links between the structure and function of cortical circuits in the context of sequence learning . This will be an interesting topic for future work . Additional limitations exist in the model as a function of computational practicality . These include network size and related subsampling effects , as well as more complex input structures , noise correlations , etc . Overcoming these limitations would also be an interesting topic for future investigation . Finally , in both the experiments of Xu et al . [1] and our model the training effect persists for only a short time . Xu et al . [1] astutely noted that even such short term storage can be quite useful for perceptual inference [44 , 45] , as repeated experiences in the recent past are often a good predictor of similar experiences in the near future . It is also clear , however , from perceptual learning experiments that visual cortex can store information for long periods of time [46] . So how are new memories protected from being quickly forgotten ? How can they be stabilized for weeks , months , and years ? This is an important question for future work .
The LIF-SORN is a recurrent neural network model of a small section of L5 of the rodent cortex . It consists of noisy leaky integrate-and-fire neurons and utilizes several biologically motivated plasticity mechanisms to self-organize its structure and activity . It was introduced by Miner and Triesch [22] with the plasticity mechanisms being short-term plasticity ( STP ) , spike-timing dependent plasticity ( STDP ) , synaptic normalization ( SN ) , structural plasticity ( SP ) , and intrinsic plasticity ( IP ) . Here , we employed a modified version in comparison to this study . The most major changes were the use of a conductance based model instead of an additive model of synaptic transmission to make the synaptic signaling more realistic , the adjustment of the SN mechanism to account for boundary effects and the enlargement of the network to a size that is similar to size of the cortical region considered by Xu et al . [1] . The parameter values in the LIF-SORN are set in accordance to experimental data from L5 of the cortex , although the timescales of SN , SP and IP are accelerated compared to biological findings in order to decrease the necessary simulation time . See [22] for a more detailed explanation of the selection of the individual values than the explanations given below . The network was simulated with the help of the Brian spiking neural network simulator [47] using a simulation timestep of Δtsim = 0 . 1 ms . | A central goal of Neuroscience is to understand the relationship between the structure and function of brain networks . Of particular interest are the circuits of the neocortex , the seat of our highest cognitive abilities . Here we provide a new link between the structure and function of neocortical circuits in the context of sequence learning . We study a spiking neural network model that self-organizes its connectivity and activity via a combination of different plasticity mechanisms known to operate in cortical circuits . We use this model to explain various findings from a recent experimental study on sequence learning and prediction in rat visual cortex . Our model reproduces the changes in activity patterns as the animal learns the sequential pattern of visual stimulation . In addition , the model predicts what stimulation-induced structural changes underlie this sequence learning ability . Finally , the model also predicts how the adapted network structure influences spontaneous network activity when there is no visual stimulation . Hence , our model provides new insights about the relationship between structure and function of cortical circuits . | [
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"c... | 2018 | Bridging structure and function: A model of sequence learning and prediction in primary visual cortex |
Experiments have generated a plethora of data about the genes , molecules , and cells involved in thymocyte development . Here , we use a computer-driven simulation that uses data about thymocyte development to generate an integrated dynamic representation—a novel technology we have termed reactive animation ( RA ) . RA reveals emergent properties in complex dynamic biological systems . We apply RA to thymocyte development by reproducing and extending the effects of known gene knockouts: CXCR4 and CCR9 . RA simulation revealed a previously unidentified role of thymocyte competition for major histocompatability complex presentation . We now report that such competition is required for normal anatomical compartmentalization , can influence the rate of thymocyte velocities within chemokine gradients , and can account for the disproportion between single-positive CD4 and CD8 lineages developing from double-positive precursors .
The mammalian thymus receives stem cells from the bone marrow . These cells—thymocytes—go through a series of anatomical subcompartments in a process termed T cell education [1 , 2] . About 97% of candidate T cells die , while the remaining 3% are essential to the continuing development of the adaptive immune system [3] . For a recent review of thymic architecture and cell traffic , see [4] . For a brief schematic animation of thymic maturation , see Video S1 . Extensive research in disparate disciplines has uncovered a mass of data regarding thymocyte development . Subfields of thymus research include genes , gene expression and differentiation; molecules ( integrins , chemokines , cytokines , receptors , antigens , and other ligands ) ; cells ( stem cells , thymocytes , epithelial cells , dendritic cells , and macrophages ) ; cell behavior ( adhesion , migration , and anatomic localization ) ; cell states ( differentiation states , cell cycle , proliferation , and apoptosis ) ; and physiology ( antigen expression , positive and negative selection , lineage choice , and antigen-receptor repertoires ) . The technologies used to study thymopoeisis include genetics , transgenes and gene knockouts , protein chemistry , microscopy and immunohistochemistry , in vitro cell cultures and interactions , in vivo phenotypes , cell and organ transfers , immunizations , and more . A systematic integration of these data into an accurate and comprehensive representation is much needed . We address this need using reactive animation ( RA ) to reveal multiscalar emergent properties and to guide experimentation in thymocyte development . RA is a computational approach to simulating complex dynamic systems . The technology of RA has been described elsewhere [5–7] . Briefly , the RA simulation is built in two tiers . The first tier is built , bottom-up , from the actual cellular and molecular data , and incorporates the program , the logic , and the dynamics of the simulation . The second tier is a front-end visualization of the simulation capable of real-time interactive manipulation of the action . RA allows the experimenter to extract statistical and local information from the running simulation . Moreover , the experimenter can intervene in the simulation and observe in silico the effects of thought experiments . Our simulation was built primarily using for the first tier the language of Statecharts [8] , with its enhanced legibility and organizational structure . We added to Statecharts a second tier of animation . Between the two tiers , we built a set of tools to facilitate data-mining options , such as tracking cells , manipulation of surface receptors , inducement of apoptosis , tracking cell ancestry , data displays , visualization of chemokine gradients , zooming in and out across scales , streaming reports , statistical data , and more ( see Figure 1B ) . Demonstrations of these tools are in Videos S2–S4 and online . The references to papers we used for the database are listed in Text S1 . RA differs fundamentally from other approaches directed to network modeling [9–11] or to simulating the microarchitecture of the immunological synapse or membrane [12–14] . We did not simulate membrane data in the present work due to the lack of quantitative data . Another spatially based system has been developed [15] , and thus far it has been applied to molecular signaling [16] .
Data at the level of single cells and their microenvironment culled from hundreds of papers were coded to the simulation . Anatomic localization is critical to thymus development; thymocytes at different developmental stages migrate to specific thymic compartments [2] . Validation of a bottom-up simulation such as RA ( and of the database itself ) requires that the microscale molecular data put into the model suffice to generate realistic macroscale thymocyte migration and anatomical location . Cell migration depends on thymocyte receptor profiles , chemokine gradients , epithelial cells , cell proliferation , cell survival , cell velocity , and other factors that enter the simulation . For example , a thymocyte at the double-negative ( DN ) 1 stage expresses a profile of surface markers CD4−CD8−CD25−CD44+LselectinlowCD69− [17] . Experimentally , we know that thymocytes at the DN1 stage migrate towards the chemokine CCL25 [18] . The thymus stroma , too , influences migratory decisions; a thymocyte's path to a chemokine may be blocked by another cell . Furthermore , the chemokine itself is involved in two dynamic processes: first , specific regions of the thymus continuously secrete the chemokine , and second , the chemokine diffuses over time and space . Thus , the migration of a thymocyte continuously changes as a function of secretion and diffusion of chemokines and the current locations of other migrating thymocytes and stationary stromal cells . This and much additional information is included in the simulation . Figure 1 demonstrates that the simulated thymic lobule faithfully produces the fine anatomical relationships of real thymic structure; thousands of thymocytes , individually computed , localize , as seen in histological sections , to particular anatomical sites according to twelve distinct developmental stages—color-coded in the legend box . Figure 2 shows the migratory path of a single thymocyte . Both the emerging anatomy and the emerging path are faithful to experimental results [2] . Video S1 and Video S3 show dynamic versions of RA figures . These results demonstrate that the molecular data in hand suffice to generate the macroscale thymus , and that RA can reveal this cross-scalar emergence . The effects on thymus fine anatomy of chemokine receptors CXCR4 [19] and CCR9 [20] have been studied experimentally , so we could compare an RA simulation of knocking out these receptors to the experimental results . Targeted gene deletion of CXCR4 [19] resulted in failed cortical localization and developmental arrest . Figure 3A ( left ) shows the thymic lobule as it was captured under the microscope [19] and as it is captured during simulation ( right ) . In both cases , the thymocytes do not respond to CXCR4 stimulation; both Figure 3 panels show that thymocyte development gets hung up close to the cortico–medulary junction ( CMJ ) in the DN1 stage ( labeled red ) . Figure 3B shows the same time frame and anatomical section in a wild-type thymus . Note that double-positive ( DP ) cells ( blue cells in the simulation ) have spread into the cortex . Unlike the static histology of the experimental model , RA provides a dynamic representation . In contrast to the CXCR4 knockout , deleting CCR9 had no major effect experimentally on intrathymic T cell development [20] . However , competitive transplantation experiments revealed that bone marrow cells from CCR9−/− mice were less efficient in repopulating the thymus of lethally irradiated Rag-1−/− mice than were bone marrow cells from littermate CCR9+/+ mice [18 , 20] . The RA simulation results , presented in Figure 4 and in Videos S1–S4 , show both the influence of the lack of response to CCL25 , the chemokine ligand of CCR9−/− , and the outcome of competition between CCR9−/− and wild-type cells . Figure 4A shows the normal thymus at the same time point as the altered thymus that appears in Figure 4B; the abnormal cells are coded gray here . Figure 4 is animated in Video S3 , where the upper panel shows the wild-type phenotype and the lower panel shows the CCR9−/− phenotype: the CCR9−/− cells congregate around the subcortical zone ( SCZ ) . Thymocytes at the transition from DN to DP would normally migrate towards the chemokine CCL25 and enter the cortex , but the CCR9−/− cells cannot respond to this chemokine and are blocked . The blocked thymocytes , however , can still move randomly , and population pressure pushes them away from the SCZ , so that some of them reach their next developmental checkpoint—the cortical epithelial cells—passively . These fortunate cells can then mature into their next developmental stage and migrate towards the medulla ( via a different chemokine ) , where they can further mature ( depending on further selection events ) into fully functional single-positive ( SP ) cells . RA discloses these dynamics , surmised from static experimental histology alone . RA also made it possible to observe the dynamics in silico of a competitive experiment , in which equal numbers of CCR9−/− and wild-type cells are seeded into the thymus: Video S2 shows that the wild-type cells survive and mature in much higher numbers . RA makes it possible to quantify the ratios between mature wild-type cells and mature CCR9−/− cells ( Figure 5 ) : we can see an initial peak of maturing wild-type cells , followed by a decrease and an eventual asymptotic ratio , as the buildup of random pressure of CCR9−/− cells eventually generates homeostasis . This competition has not yet been performed experimentally , but RA simulation predicts the outcome shown in Figure 5 . Figure 5 shows an asymptotic value of four wild-type thymocytes to every CCR9−/− thymocyte . The dynamics of the asymptote and the final value are our predictions if such an experiment was to be performed . A critical point evident from Figure 5 is the overwhelming advantage that wild-type cells have immediately after seeding the thymus . Such a marked effect should be easy to witness experimentally . Thymocytes need to traverse developmental niches; thus , when the number of thymocytes exceeds the space available for antigen presentation sites on epithelial cells , the thymocytes pile up and those waiting their turn for stimulation may undergo apoptosis from the lack of interaction [21 , 22] . RA makes it possible to study the function of competition by modifying interaction time constants , as shown in Video S3 . The results indicate that competition is essential to normal thymic development . Figure 6A shows the normal pattern of apoptosis that occurs in the cortex in the competition-enabled thymus . Figure 6B , in contrast , shows that an abnormal pattern of apoptosis develops in a thymus free of cell competition; here , most thymocytes die of negative selection in the medulla , rather than in the cortex . The RA simulation suggests that the waiting times for interactions with cortical epithelial cells constitute a bottleneck that is a factor in normal thymus development . RA in silico experimentation suggests that competition also selects for differential speeds of trafficking in response to chemokine gradients . Figure 7 shows that faster thymocytes enjoy greater chances of survival , at least up to a point . Nevertheless , some thymocytes that are relatively slower may avoid the negative selection suffered by some of their more speedy brothers . Thus , competition selects for a range of cell velocities , and not only for a uniformly high velocity . How selection for a range of T cell velocities might enhance defense against invaders [23] as well as for body maintenance [24] needs to be investigated . Another prediction emerging from cell competition relates to lineage commitment . A developing thymocyte must choose whether to become an SP CD4 T cell ( helper ) or an SP CD8 T cell ( cytotoxic ) . The decision-making process is obscure because mature SP CD4 and CD8 T cells evolve from precursors that are DP for both CD4 and CD8 , yet CD4 cells predominate at a 2:1 ratio . Current theories of lineage commitment deal with the molecular details of the choice . The two most significant themes in the theories distinguish between an “instruction” approach and a “stochastic” approach [25] . The “instruction” approach proposes that the productive interaction of a T cell receptor with a particular major histocompatability complex ( MHC ) molecule , class I ( for CD8 ) or class II ( for CD4 ) as the case may be , rewards the thymocyte and induces a genetic choice to differentiate to the CD8 or CD4 phenotype . The more fitting T cell receptor–MHC interaction instructs the T cell . The “stochastic” approach proposes that SP CD4 or CD8 thymocytes are “randomly” generated , and are later selected according to their functional performance with the MHC . These theories attempt to found lineage choice on its molecular components aimed at showing where exactly , during development , the cell chooses its lineage [25–27] . However , the emergence of competition between thymocytes for interaction space provides a novel solution to the CD4:CD8 2:1 paradox . If the dissociation rates of CD8 cells from epithelial cells are lower than those of CD4 cells , then the CD8 cells will remain longer at their epithelial-cell interaction stations ( peptide-MHC I sites ) . As long as a CD8 thymocyte lingers at a peptide-MHC 1 niche , this niche is unavailable for other , competing CD8 thymocytes . CD8 thymocytes , we propose , do not compete with the CD4 thymocytes , because CD4 thymocytes compete among themselves for stimulation by interacting with peptide–MHC II stations on epithelial cells . We tested the outcome on lineage frequency of simulating different dissociation rates for interactions between epithelial cells and CD4 and CD8 thymocytes . The results are shown in Figure 8 . It can be seen that about two-thirds of thymocytes will mature into CD4 T cells and one-third into CD8 T cells ( the de facto ratio ) when the dissociation rate of CD8 thymocytes is 1 . 7 to 3 . 3 times slower than the dissociation rate of CD4 thymocytes . A relatively greater avidity of CD8 cells for epithelial cell niches ( by 1 . 7–3 . 3 ) would generate the observed lineage predominance of CD4 T cells .
RA analysis of thymocyte development sheds new light on the dynamic relationship between molecules and cells in generating the structure and function of the thymus organ . First , we can see that the existing body of data , however discrete and piecemeal , can be integrated by RA simulation into a representation of the functional anatomy of the thymus seen in histologic sections . What we know about cells and molecules can indeed account for what we see; the macroscale organ emerges from the microscale mass of data in hand . In this regard , RA can be said to validate the database . Note , however , that classical histologic sections are two-dimensional slices of a three-dimensional organ frozen in time; RA simulation adds the dimension of time—dynamics—and so shows us the formative power of the dynamic flux of cells , molecules , and interactions that give rise to the higher-scale organ . In another project involving a different organ , we are currently extending RA simulations to accommodate the third dimension in space; hopefully , the added complexity of the representation will enhance our understanding of the biology . Second , RA simulation offers novel explanations for the observed outcomes of experimental intervention . In our case , for example , RA simulation suggested that the lack of phenotype observed in mice with CCR9 knocked out ( CCR9−/− ) might be explained by dynamic compensation through population pressure . RA simulation also explains the competitive growth advantage enjoyed by wild-type cells over CCR9−/− cells . Indeed , overexpression of CCR9 on thymocytes leads to an in vivo phenotype that can be explained by RA as an untimely attraction of the thymocytes by cortical epithelial cells . RA simulation also suggests that the absence of thymic output resulting from CXCR4 inhibition can be attributed to the nonmigratory behavior of cells entering the thymus . Third , the visualization of cell dynamics through RA provides a view of emergent physiology . Although the thymus is packed full of cells , the existence of competition among thymocytes for space and stimulation has not been a subject for experimentation or even discussion; competition is simply not seen in static histologic sections . Since competition was not recorded in the database , we did not explicitly program competition into our model . Nevertheless , cell competition emerged before our very eyes as we witnessed , via RA , the animated struggle between individual thymocytes for productive interactions with thymic epithelial cells . In silico manipulation of various parameters suggested that thymocyte competition might function as an important factor in three emergent properties of T cell maturation: the functional anatomy of the thymus , the selection of thymocytes with a range of migratory velocities , and the relative preponderance of SP CD4 T cells . Obviously , these suggestions require experimental validation . Irrespective of the outcome , however , the animation arm of RA , in providing a higher-scale view of complex emergent properties [28] , can alert us to new questions for experimentation . Ultimately , we would like to model a complete biological system—an entire cell , organ , or organism—in a way that is sufficiently realistic so as to be able to test the role of any known fact about the system . This goal has been formulated as the ability of a model to pass a sort of Turing test , and can be viewed as taking to the utmost limit the notion of prediction , confirmation , and verification of emergent properties; see [29] .
The RA simulation was written in C++ using the Rhapsody tool , and so RA code was generated by Rhapsody's code-generation engine , initiated by the language of Statecharts . To this automatically generated code , manually encoded objects and function were added . RA is the bridge made between the running simulation and the animation . Communication is made over a TCP/IP connection between a server implementing the Statecharts simulation and built-in animation functions in Flash . We used Matlab to analyze populations and population-level behavior . See [6] for further details . | Biological systems are the embodiment of complexity that defies intuitive understanding . Biologists have accumulated masses of data about the molecules , cells , and discrete interactions that compose living systems , but the list of facts alone cannot explain how such systems work dynamically . We have developed a hybrid , computational approach to the simulation of complex systems called reactive animation ( RA ) . RA uses a bottom-up integration of diverse experimental data to create an integrated and dynamic representation of the system's interacting cells and molecules . RA is faithful to experimental fact , while it plays out the action in animated formats directly accessible to the eye and mind . Most importantly , RA is analytical , interactive , and allows experimentation in silico . Here , we use RA to reveal unexpected emergent properties of thymocyte development . In particular , we now report that competition between thymocytes for sites of stimulation could be important in generating the fine anatomy of the thymus , in selecting for thymocytes with a range of migration velocities , and in explaining the paradox of CD4 to CD8 T cell lineage ratios . This study highlights the explanatory power and the potential aid to experimentation offered by an animated , interactive simulation of complex sets of data . | [
"Abstract",
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] | [
"immunology",
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] | 2007 | Emergent Dynamics of Thymocyte Development and Lineage Determination |
Myxococcus xanthus , a model organism for studies of multicellular behavior in bacteria , moves exclusively on solid surfaces using two distinct but coordinated motility mechanisms . One of these , social ( S ) motility is powered by the extension and retraction of type IV pili and requires the presence of exopolysaccharides ( EPS ) produced by neighboring cells . As a result , S motility requires close cell-to-cell proximity and isolated cells do not translocate . Previous studies measuring S motility by observing the colony expansion of cells deposited on agar have shown that the expansion rate increases with initial cell density , but the biophysical mechanisms involved remain largely unknown . To understand the dynamics of S motility-driven colony expansion , we developed a reaction-diffusion model describing the effects of cell density , EPS deposition and nutrient exposure on the expansion rate . Our results show that at steady state the population expands as a traveling wave with a speed determined by the interplay of cell motility and growth , a well-known characteristic of Fisher’s equation . The model explains the density-dependence of the colony expansion by demonstrating the presence of a lag phase–a transient period of very slow expansion with a duration dependent on the initial cell density . We propose that at a low initial density , more time is required for the cells to accumulate enough EPS to activate S-motility resulting in a longer lag period . Furthermore , our model makes the novel prediction that following the lag phase the population expands at a constant rate independent of the cell density . These predictions were confirmed by S motility experiments capturing long-term expansion dynamics .
New interest in the study of microbial collective behaviors has been ignited by recent discoveries that are critical to bacterial pathogenesis and multicellular developmental processes in these single-cell organisms , including quorum sensing [1 , 2] , phenotypic heterogeneity [3] , and biofilm formation [4] . The soil bacterium , Myxococcus xanthus is the premiere bacterial model organism for investigations of self-organization and multicellular development [5] . Different M . xanthus multicellular behaviors emerge depending on their environmental conditions . In nutrient-rich conditions , M . xanthus cells spread in a coordinated manner forming organized groups [5] . When spreading over prey microbes , M . xanthus cells self-organize into bands of traveling waves termed ripples [6–8] . When nutrients are scarce , M . xanthus executes a multicellular developmental program in which roughly 100 , 000 cells aggregate into a hay stack-shaped fruiting body within which many of the cells sporulate [9 , 10] . M . xanthus cells move exclusively on solid surfaces and this movement is essential for all their multicellular behaviors . M . xanthus possesses two genetically distinct types of motility: gliding or adventurous ( A ) motility and twitching or social ( S ) motility [5 , 11 , 12] . Single cell movement is facilitated by A-motility , which is most efficient at high agar concentrations . In contrast , group movement is facilitated by S-motility , which is most efficient at low agar concentrations . S-motile cells only move when they are within a cell length of a neighbor [11] . Wild-type cells exhibit these two motility systems simultaneously . This most likely has been a selective advantage enabling M . xanthus to adapt to a variety of physiological and ecological environments . Both motility systems enable these rod-shaped cells to move along their long axis and periodically reverse direction by switching polarity , i . e . the leading cell pole becomes a lagging pole and vice versa [13–15] . The molecular basis of A and S motility has been studied extensively [5 , 11 , 12 , 16–19] . Recent studies have proposed a ‘focal adhesion complex’ model for A motility in which intracellular motors interact with adhesion complexes on the membrane that are bound to substrate and power movement by pushing against the substrate [19] . S motility has been determined by genetic and behavioral analysis to require interaction between type IV pili ( TFP ) that powers movement and extracellular matrix polysaccharide ( EPS ) [16 , 20–23] . The lipopolysaccharide ( LPS ) O-antigen is also required for social motility , yet its contribution is currently unclear [23] . The TFP are filaments 5–7 nm in diameter and 3–10 μm in length composed of PilA monomers encoded by the pilA gene . Each step of S movement , involves TFP extension and retraction , which is achieved by polymerization and depolymerization of PilA monomers . Secreted EPS is the anchor and/or trigger for TFP retraction [24] . Consequently , M . xanthus mutants lacking TFP or EPS fail to display S motility [16 , 18 , 22 , 25] . Although it is clear that TFP and EPS are essential for social motility , many aspects of S motility-driven colony expansion remain unexplained [26] . For example: why , despite of cell reversals , does the colony radius increase [27 , 28] and why does the observed colony expansion rate depend on the initial cell density [11 , 27 , 29] ? To explain the S motility-driven colony expansion dynamics of M . xanthus cells we have developed a mathematical model that accounts for the interaction between TFP and EPS . This model makes two novel predictions that are confirmed experimentally in this report .
To study social motility-driven colony expansion of M . xanthus cells we developed a reaction-diffusion model . The major assumptions and ingredients of the model are summarized and justified in this section and the technical details are included in the Methods section . Experimentally , social motility in M . xanthus is usually studied by placing a specific number of liquid-grown cells on an agar plate and measuring the increase in the colony diameter over time . Notably , these colony expansion experiments start with over 105 cells and the cell population further increases over time [27 , 29] . The colony dimensions ( ∼10–30 mm ) are orders of magnitude larger than the single cell length ( 4–5 μm ) . These conditions make it impractical to simulate the expansion using agent-based modeling [30] . Therefore , we focused on continuous approaches formulating the equation for ρ ( r , t ) –cell density at a given location and time . Furthermore , the experimental studies are conducted over a long observation period ( ∼10–100 hr ) , which is much longer than the single-cell reversal period ( ∼5–10 min ) [27–29] . Under these conditions , we can approximate the cell movement over a time-scale of multiple reversals as diffusion [31] . An effective diffusion coefficient can be estimated based on the single-cell speed and reversal period ( see Methods section ) [31] . For S-motile cells , TFP adhesion and/or retraction is stimulated by the presence of EPS [24 , 25 , 32 , 33] and therefore , effective diffusion should increase with increasing EPS . To incorporate this into the model , we used the following expression for the effective diffusion coefficient , D ( e ) =D0+Dpϕ ( e ) . ( 1 ) Here D0 is a S motility-independent diffusion coefficient . For strains lacking A-motility ( A−S+ strains ) or wild-type cells under conditions in which A motility is ineffective ( low agar concentrations ) this term is small and can arise from the mechanical cell-cell repulsion during growth [34 , 35] . It can be estimated from the expansion of mutants lacking both A and S motility [27] . In the second term , Dp , the maximal diffusion coefficient due to S-motility is multiplied by a dimensionless factor 0≤ϕ ( e ) ≤1 . This factor is a function of the local EPS concentration ( e ) and can be interpreted as a probability of pilus retraction . We assume that in the absence of EPS retractions fail ( ϕ ( 0 ) = 0 ) and at high EPS concentrations retractions always succeed ( ϕ ( ∞ ) →1 ) . For our model we have chosen a phenomenological Hill-function of ϕ ( e ) : ϕ ( e ) =eme0m+em where m is the Hill coefficient and e0 is half-saturation concentration . To compute the local EPS concentration , e ( r , t ) we assume that each cell produces EPS at a constant rate ( α ) with a resulting production flux being a linear function of cell density . We assume that EPS is not diffusible as it consists of large macromolecules that bind to the agar surface and that it degrades/dries with a constant rate ( β ) . If cell diffusion ( random motion ) was the only factor contributing to cell expansion , we would expect that the colony radius would increase as a square root of the expansion time [36] and correspondingly the expansion rate would gradually decrease . However , this is not experimentally observed [27 , 28]; instead , an approximately constant expansion rate is seen . This apparent contradiction can be resolved by our observations that during long-term expansion experiments the cells continue to grow ( the M . xanthus generation time of ~4–5 hr [37–39] is shorter than the typical spreading experiment time-scale ) . Thus , since cell growth can substantially change the expansion dynamics [31 , 39] , it must to be accounted for in the model . The growth rate is modeled using the Monod equation g ( N ) =gmaxNN0+N ( 2 ) where gmax is the maximum growth rate , N is a local density of growth-limiting nutrients and N0 is half-saturation coefficient [40] . The nutrients will also diffuse through the agar [41] ( the corresponding diffusion coefficient is denoted as DN ) . When the assumptions described above are combined together , the following set of three coupled partial differential equations describe S motility-driven colony expansion , ∂∂tρ ( r , t ) =1r∂∂r ( rD ( e ) ∂∂rρ ) ⏟cellmotility+g ( N ) ρ⏟cellgrowth ( 3 ) ∂∂tN ( r , t ) =1r∂∂r ( rDN∂∂rN ) ⏟nutrientdiffusion−g ( N ) ρ⏟nutrientconsumption ( 4 ) ∂∂te ( r , t ) =αρ⏟EPSproduction−βe⏟EPSdrying ( 5 ) where D ( e ) and g ( N ) are given by Eqs ( 1 ) and ( 2 ) , respectively . To reduce the number of unknown parameters we can without loss of generality set the half-saturation EPS level e0 = 1 . This is done by rescaling the EPS level and production rate to e → e/e0 and α → α/e0 , respectively . M . xanthus motility parameters were estimated in a modeling study that showed existence of traveling waves during colony expansion [31] . In this model , a cell density-dependent diffusion rate for cell movement was assumed irrespective of their motility ( A or S ) type . Whereas our model is based on the experimental observation that the TFP motility ( or the diffusion rate ) is regulated by the self-produced cellular EPS . We numerically solved the set of equations described above with the appropriate initial and boundary conditions ( details are provided in the Methods section ) . Fig 1A shows the numerical solution of the population density and nutrients at different times . In our simulation , cells enter from the outer edge of the initial colony ( at distance r = 0 in Fig 1A ) into an empty region and grow by consuming the available nutrients ( Fig 1A ) . As the cell density increases , the level of EPS rises , which in turn increases the diffusion rate of the cells and causes the population to spread outward . The population profile shows a sharp increase in density at the colony front ( defined as the advancing part of the population profile ) , which is a consequence of the sharp increase in diffusion rate with an increasing EPS density . The existence of a sharp profile is consistent with the colony patterns observed during social motility [5 , 11 , 27] , where there are no single cells at the colony edge ( defined as the low density region leading the advancing colony front ) . At longer incubation times , the shape of colony front becomes fixed and the colony expansion rate becomes constant , i . e . there is a traveling wave solution . Such properties of the reaction diffusion model with population growth are traditionally observed in Fisher’s equation ( also known as Fisher-Kolmogorov equation ) , which is widely used in theoretical ecology [42] . The equation was first formulated by Fisher to describe the spread of advantageous genes in spatial populations and assumed logistic growth and constant diffusion [43 , 44] , ∂∂tρ ( x , t ) =D∂2ρ∂x2+gρ ( 1−ρ ) . This equation admits a traveling wave solution of the form ρ ( x , t ) = ρ ( x − c t ) , where the wave speed is given by [43 , 44] c=2Dg An extended form of this equation in which the growth rate depends on the nutrient concentration ( via Eq ( 2 ) ) , also displays a traveling wave solution [45] with a speed that can be shown ( for non-diffusing nutrients , i . e . DN = 0 ) to be c=2Dgmax ( NinN0+Nin ) [45] ( see S1 Text ) where Nin is the initial nutrient concentration . In these examples , the expansion rate is determined by the maximum growth rate at the tip of the wave [42] where the population density is low ( ρ~0 ) and nutrients are high N~Nin . In contrast to these cases , in our model , the diffusion rate is non-linear and it increases from a low value ( D0 ) at the outer edge to a higher value ( D0+Dp ) in the interior as EPS levels increase from the outer edge to the interior of the colony ( Fig 1B ) . Therefore , the colony interior determines the expansion rate in our model . For Fisher’s equation with non-linear diffusion , an analytical expression of the expansion rate is often not straightforward . However using simple scaling ( see S2 Text ) , we show that the expansion rate is proportional to Dpgmax ( as shown in Fig 1D and 1E ) , therefore four-fold changes in effective diffusion or growth leads to two-fold changes in the wave speed . The proportionality coefficient may differ from the value of 2 for the Fisher equation and depends on model parameters D0 , N0 and Nin . The dependence of the expansion rate on the cell diffusion rate and growth rate is a common feature displayed in colony expansion models for different bacteria [35 , 42 , 46] including M . xanthus [31] . By applying traveling wave solutions to our model , we can numerically calculate the exact wave speed using a method of phase-space analysis ( see Methods section ) . The expansion rates determined by the phase-space analysis are in agreement ( solid and dashed lines in Fig 1D and 1E ) with the expansion rates calculated by measuring the advance of colony front over time with variations in the model parameters ( circles in Fig 1D and 1E ) . For instance , the expansion increases from a minimum value cmin to a maximum cmax as the initial level of nutrients is increased ( as shown in Fig 1F ) . In biologically relevant conditions , colony expansion is observed on nutrient-rich agar and therefore the nutrients are sufficient for the cells to produce EPS at least to its half saturation value to enable S motility-driven movement . Therefore , for S motility-driven colony expansion , the expansion rate will be between c' ( dotted line in Fig 1F ) and cmax , which are the expansion rates for constant EPS at half saturation levels and for large nutrients level . Given that the effective diffusion coefficient increases with increasing EPS density , which in turn increases as more cells produce EPS , we hypothesized that these effects could be responsible for the increase in the expansion rate at higher cell densities . To test this hypothesis , we used experimental data from two papers which measured M . xanthus S motility-driven colony expansion [27 , 29] . Briefly , in these experiments M . xanthus cells at different densities are placed on an agar substrate and as the cells move and divide , the colony expands and its radius increases . The expansion rate is quantified by measuring the difference between the initial and final radius of the colony . The final time corresponded to 8 hr ( for expansion rate estimation ) in the Kaiser et al . experiments [27] and 24 hr in the Berleman et al . experiments [29] . Using our model equations , we simulated each set of experiments by adjusting our model parameters . The results reveal that our model reasonably matches the data from Berleman et al . ( Fig 2A ) and Kaiser et al . ( Fig 2B ) which represent a > 100-fold variation in the initial cell density . Notably , the best fit to each data set was achieved using a set of parameters that was identical , except for the maximum diffusion coefficient , Dp , and EPS production rate , α . The difference in the effective diffusion coefficients can be easily attributed to the differences in experimental conditions . The Berleman et al . experiments [29] were performed on soft agar ( 0 . 5% agar ) and led to a high effective diffusion coefficient ( Dp = 220 μm2min-1 ) , whereas the Kaiser et al . experiments [27] were performed on harder agar ( 1 . 5% agar; Dp = 16 μm2min-1 ) . This is consistent with the fact that S-motile cells perform better on soft agar surfaces . This difference can be achieved with about 3 . 5-fold differences in the cell speed ( see Methods section ) . Furthermore , our model predicts differences in EPS-related parameters for the two experiments . To match the data we needed to include an approximately 8-fold difference in the EPS production rate . The same effect can be achieved by changes of the effective EPS drying/degradation rate or the EPS threshold to enable social motility . These results indicate that the production rate or degradation or threshold of EPS could be different for different agar conditions or for the different bacteria strains ( A-S+ [27] and A+S+ [29] ) used in these two experiments . We also noted that the fit was best when the effective cell diffusion D ( e ) sharply changes with the EPS level , i . e . for a high value of Hill’s coefficient ( m≥4 ) . At lower values of Hill’s coefficient , the model does not fit the experimental data in Fig 2A as it lacks the sharp increase in expansion radius above a threshold initial density ( see S3 Text and S1 Fig ) . This result indicates the existence of a sharp threshold in the EPS level above which TFP are able to attach and/or retract . This sharp threshold is another model prediction that can be tested in the future . To further explore the effects of the different initial cell densities on the colony expansion , we used our model to compute how the expansion rate ( defined as the time derivative of the position of the leading edge , where ρ is very low ~0 . 01 ) depends on time and on the initial cell density for the parameters estimated to match the experimental data used in Fig 2 . The results of our simulation ( Fig 3 ) show that the colony expansion rate has a transient slow expansion phase with a duration that depends on the initial cell density , followed by a constant expansion phase at longer incubation times . Our results indicate that a population with a low initial cell density will lag behind higher density populations due to its slower transition to a steady-state expansion rate . This effect is mediated through the production of EPS , which is low for low initial densities . This leads to reduced motility and thereby a slower expansion driven only by the basal diffusion rate D0 . The cells start moving with an effective diffusion rate close to Dp , only when the EPS density reaches a threshold value . At steady state , the expansion rates for populations that had different initial cell densities are similar because the initial cell density does not affect the cell density of the advancing colony edge . Previous experiments found the rate of expansion to be different for different initial cell densities and these data suggested that it would remain constant over time [27 , 29] . Our model , in contrast , shows that the expansion rate eventually becomes independent of the initial cell density and the density dependence is observed only during a transition period before the constant expansion phase . This contradiction could be due to the fact that previous experiments measured expansion rates for short periods [27] , during which some populations were still in their slower expansion phase . Thus , our model , which quantitatively reproduces the previous results , generated a new prediction that could not be confirmed with the previous experimental data . Therefore , new experiments were needed to determine how long the effect of the initial cell density persists during colony expansion . The prediction of a transient density-dependent lag phase followed by a density-independent expansion rate motivated us to conduct systematic long-term colony expansion experiments . Previous M . xanthus S motility-expansion experiments at different initial cell densities only reported net expansion after 8 hr [27] or 24 hr [29] , without reporting any later time-points . To test the prediction we decided to extend these experiments to > 4 days , which according to our model is sufficient to reach the steady-state expansion rate . The expansion assays were performed on 0 . 5% agar CTT plates with 0 . 2% yeast extract and initial cell numbers ranging from about 6 x 104 to 1 . 2 x 107 cells per initial spot ( initial spot radius ~1 . 7mm ) . To this end , we inoculated cultures of A−S+ cells ( strain DK1218 ) and incubated them until an exponentially growing density of ~4 x 108 cells/ml was reached . The cultures were 10-fold concentrated and then diluted to achieve densities ranging from 2 x 107 to 4 x 109 cells/ml . Three μl drops of cells at each density were spotted onto the agar plates and incubated at 32°C . To quantify the colony expansion images of the colonies were collected for at least 96 hr using a stereo microscope and digital camera . The increase in colony diameter commenced at different times for different initial cell densities indicating the presence of a density-dependent lag phase . The populations with lower initial cell densities began expansion later than the populations with higher densities , shown in Fig 4A . We performed three replicates of this experiment and quantified the colony expansion by computing the net increase in the colony radius as a function of time for different initial cell densities . We observed that the model fits our data using the same set of parameters as in Fig 2B with the diffusion rates Dp = 200 μm2min-1 due to the use of 0 . 5% agar . As predicted by the model , the colony expansion during the longer incubation times occurred at a constant rate in our experiments regardless of the initial density ( all lines in Fig 4B have equal slopes ) . Furthermore , we observe that the expansion curve for high initial cell numbers ( 6 x109 cells & 12 x109 cells in Fig 4B ) nearly overlap indicating there is either no lag phase or a very short lag phase at high initial cell densities . These data indicate that cell motility rapidly becomes active due to high EPS production . This scenario directly corresponds to the saturation in the Hill-function ( ϕ ( e≫e0 ) ~1 ) at high EPS concentrations and justifies our choice of function ϕ ( e ) to represent TFP activity . Similarly , at low initial cell numbers ( 0 . 6 x108 cells & 1 . 2 x108 in Fig 4B ) the differences in the lag phase duration are small , resulting in near overlap of the expansion curves , which suggests that the TFP activity below half saturation ( ϕ ( e≪e0 ) ≈ ( e/e0 ) m ) is low until a threshold EPS level is reached . Therefore , our long-term experiments validate the assumptions of our model and confirm its predictions . Moreover , these data reveal the regulation of cell motility by EPS as a mechanistic basis of M . xanthus social motility .
Reaction-diffusion models have been widely used in many biological systems to study various spatial and temporal patterns [47 , 48] , including the expansion of microorganisms on surfaces [46 , 49 , 50] . In this paper , we formulated a deterministic reaction-diffusion model with a key characteristic that cell motility depends on EPS deposition . Our model successfully explains several salient features of S motility-driven colony expansion in M . xanthus . Specifically , it predicts that M . xanthus colonies expand as a traveling wave with a sharp front . The speed of expansion scales with Dpgmax and its absolute value depends on the EPS half-saturation ( e0 ) and the diffusion rate ratio ( D0/Dp ) . Using a calibrated set of parameters the model recapitulates the experimental trends showing density-dependent colony expansion . Our model suggests that , in order to achieve agreement between the modeling results and the experimental trends , a sharp increase in cell diffusion rate at a threshold EPS level is critical . As a consequence the model predicts that populations starting at low initial densities have a lag phase until sufficient EPS accumulates . Our model further predicts that this lag phase ends after longer incubation times and then the population advances at a constant rate irrespective of the initial cell density . To validate these predictions we performed long-term colony expansion experiments with S-motile cells . Our results confirm the presence of a lag phase that depends on the initial density followed by density-independent steady-state expansion . To explain the experimental data the model assumes that the activity of TFP motility is triggered by the EPS level . To date no definitive evidence proves this relationship . However , experimental studies have reported the loss of social motility in mutants lacking EPS production [25 , 29] and the gain of social motility when EPS is complemented externally [29 , 33] or when cells are subjected to specific conditions which overcome the EPS requirement ( e . g . , polystyrene substrate submerged in 1% methylcellulose ) [51] . However , it would be useful to show a direct relationship between S motility-driven colony expansion and EPS production . A systematic study could be performed in which EPS mutants are placed on various concentrations of EPS purified from M . xanthus wild-type cells or mixed with strains producing different amounts of EPS . According to our model , expanding colonies will achieve different expansion rates depending on the EPS level . Using such expansion rate data , the EPS-dependent diffusion rate can be extracted using the relation D ( e* ) = c2/2g for each known EPS level ( e* ) . Furthermore , examining cell behavior at the colony edge might provide additional insights into this relationship . We expect the cells at the very edge of the colony move less compared to the cells in the interior until the sufficient EPS accumulates . Future quantitative experiments will provide stronger evidence for the role of EPS in the regulation of cell motility . Although our model is used here to explain social motility-driven expansion in M . xanthus , the model can also be applied to other bacterial species . For example , a similar density-dependent lag phase is observed during swarming motility in undomesticated strains of Bacillus subtilis [52] . Swarming motility is multicellular movement on solid surfaces ( soft agar plates in a narrow concentration range: 0 . 3%-0 . 5% agar ) powered by rotating flagella . B . subtilis swarming depends on surfactin , which is a surfactant produced by the cells that acts as a lubricant to reduce the surface tension between the cells and substrate , and thereby promotes surface spreading . The surfactant production depends on the local cell-density and appears to regulate colony expansion in a similar fashion as EPS does during social in M . xanthus . Moreover , the long-term colony expansion rate of B . subtilis cells lacking surfactant production is shown to be dependent on the amount of the externally provided surfactant levels [52] . Despite these similarities the underlying biophysical mechanism behind the extracellular-component-dependent expansion may be somewhat different . For instance , the surfactin dependency could arise from the fluidic properties of the colony itself . It has been observed that as non-flagellated B . subtilis colonies expand on hard agar , the colony height ( thickness ) increases . This transiently increases the osmotic pressure , which eventually decreases as the colony expands . In this case , a non-linear dependence of diffusion rate on the cell density originates from a fluid dynamic model [53] .
We considered most model parameters from the literature [31] . The gliding speed of Myxococcus xanthus is reported to be in the range vg = 4–7 μm min-1 [54] . The reversal period of a single cell varies between 5–10 min [13] , giving a reversal frequency of f = 0 . 1–0 . 2 min-1 . Single cell movement with periodic reversals can be considered as a velocity-jump process , in which a cell moves along its length at a velocity vg and reverses direction according to a Poisson process with a constant rate ( 1/f ) . A diffusion equation can be obtained for such a velocity-jump process and the corresponding diffusion rate can be approximated as Dp≈ ( vg ) 2/2f~80–245 μm2 min-1 [31 , 55] . The nutrient diffusion rate in agar media is faster than the cell diffusion rate and is taken to be DN = 104 μm2min-1[31 , 56] . The doubling time is 4 hr ( DZ2 strain ) giving the growth rate g = 0 . 173 hr-1 . Other parameters are reported in Fig 1A . We solve the model equations starting from the edge of the initial spot , which is taken to r = r0 ( the initial radius of the colony ~1300–1700 μm ) to a boundary ( rb = r0+30 mm ) . The initial conditions are set as ρ ( r , 0 ) = e ( r , 0 ) = 0 and N ( r , 0 ) = Nin . The boundary conditions are ( ∂ρ∂x ) r=r0=c0ρ0 , ( ∂ρ∂x ) r=rb=0 , ( ∂N∂x ) r=r0 , r=rb=0 , ( ∂e∂x ) r=r0 , r=rb=0 where c0 = 0 . 003 μm-1 ( 10% of vg/Dp = 2f/vg , the net flux of cells in the presence of EPS ) is the initial flux at which the cells disperse from the edge of the colony and grow by consuming the available nutrients . The initial nutrient profile is set to a uniform value of Nin = 3 a . u . per μm2 and the nutrient half-saturation is chosen to be N0 = 0 . 1 a . u per μm2 ( so that cells stop growing in low nutrient conditions ) . We numerically solved the partial differential equations ( Eqs ( 3–5 ) ) using the Crank-Nicolson method [57] . A phase-space analysis method was used for calculating the steady expansion rate of the traveling waves formed in our model . To simplify the analysis we neglected the radial part of the diffusion term as it decays inversely with expansion distance . In addition we assumed that the EPS concentration quickly reaches its steady state level e* = αρ/β . As a result the model can be reduced to two equations , ∂ρ∂t=∂∂x ( D ( ρ ) ∂ρ∂x ) +gmaxρ ( NN0+N ) ( 6 ) ∂N∂t=DN∂2N∂x2−gmaxρ ( NN0+N ) ( 7 ) where D ( ρ ) ≡D ( e* ) =D0+Dp ( ( αρ/β ) m1+ ( αρ/β ) m ) is a density-dependent diffusion rate . In steady state the two model equations display traveling wave solutions . Thus , the following property for ρ ( z ) = ρ ( x − c t ) and N ( z ) = N ( x − c t ) can be considered , where c is the speed of the traveling wave . Using these properties , the equations become −cdρdz=ddz ( D ( ρ ) dρdz ) +gmaxρ ( NN0+N ) −cdNdz=DNd2Ndz2−gmaxρ ( NN0+N ) Adding the above equations , results in the following −cdρdz−cdNdz=ddz ( D ( ρ ) dρdz ) +DNd2Ndz2 Integrating once the equations become −cρ ( z ) −cN ( z ) =D ( ρ ) dρdz+DNdNdz+IC where IC is an integration constant that can be set by substituting the values ρ ( z ) = 0 and N ( z ) = Nin at z → +∞ ( in the unpopulated region ) . Note that their derivative also vanishes at z ± ∞ , i . e . , ρ ( ±∞ ) = 0 and N ( ±∞ ) = 0 . As a result IC = −cNin . Therefore , we arrive at the following set of equations N″ ( z ) =ρgmax ( NN0+N ) −cN′DN ( 8 ) ρ′ ( z ) =−c ( ρ+N−Nin ) +DNN′ ( z ) D ( ρ ) ( 9 ) In the moving z-frame , the traveling wave starts from a fixed point ( ρ ( z ) = Nin , N ( z ) = 0 ) at z → −∞ and approaches another fixed point ( ρ ( z ) = 0 , N ( z ) = Nin ) at z → +∞ . To determine the wave speed numerically , we cast the equations above into the following first order autonomous equations , Ω′ ( z ) =ρgmax ( NN0+N ) −cΩDN N′=Ω ρ′ ( z ) =−c ( ρ+N−Nin ) +DNΩD ( ρ ) The fixed points of the equations listed above are ( ρ , N , Ω ) : ( 0 , Nin , 0 ) ( S2 Fig stable node , closed circle ) and ( Nin , 0 , 0 ) ( S2 Fig saddle node , open circle ) . The traveling wave solution connects the saddle node or initial state at z = −∞ to the stable node or the final state at z = +∞ in the phase plane ( ρ , N ) . Numerical analysis shows that the solution behavior transitions from oscillatory to negative and then to non-negative values in cell density ( ρ ) as the wave speed c increases . As a result , for physically realistic solutions , we need to identify the minimal c value for which the solution becomes non-negative ( rather than non-oscillatory ) . This determines the expansion rate for our model’s equations . M . xanthus strain DK1218 ( A-S+ ) was grown overnight in CTT broth ( 1% Difco Casitone , 10 mM Tris-HCl pH 8 . 0 , 8 mM MgSO4 and 1 mM KHPO4 pH 7 . 6 ) at 32°C with shaking [58] . When the M . xanthus culture reached mid-log phase ( 4x108 cells/ml , 100 Klett units ) , the cells were harvested in 1 . 5 ml micro-centrifuge tubes at 13 , 000 rpm and ten-fold concentrated to Klett 1000 by resuspension in CTT broth . The cells were then diluted in CTT broth to Klett 5 , 10 , 25 , 50 , 125 , 250 , and 500 . For microscopy a 3-μL drop of each dilution of M . xanthus cells was placed onto one 10 cm CTT 0 . 5% agar plate that also contained 0 . 2% yeast extract . The plates were incubated at 32°C in an in-house-designed humidity-controlled chamber ( a plastic shoe box with a lid in which the bottom was covered by wet paper towels ) for more than 96 hrs . Each spot was imaged using an Olympus SZH10 stereo microscope and OptixCam Pinnacle series digital camera with OCView7 software . The distance moved by each colony edge was measured at 2 , 4 , 6 , 8 hrs , and at least twice a day for at least 4 days . Each image was quantified using imaging software GIMP . Specifically , a circle was fitted to the colony in the image to obtain the radius . The expansion distance was computed as a difference between the colony radius at any given time t to the initial colony radius . The average and the standard deviation of the expansion distance from three experimental repeats were calculated in an Excel spreadsheet ( plotted in Fig 4B ) . The cell initial density used in the simulation was determined by calculating the number of cells in the drop used for inoculation divided by the average initial colony radius ( ∼1700 μm ) . | Collective motility is a key mechanism bacteria use to self-organize into multicellular structures and to adapt to various environments . An important example of such behavior is social ( S ) motility in the gram-negative bacterium Myxococcus xanthus . S-motile cells are restricted to movement in groups and do not move as individual cells . S-motility is powered by type IV pili ( TFP ) –multi-subunit filaments , which extrude from the cell poles , adhere to the substrate and retract , pulling the cell forward . TFP retraction or adhesion is suggested to be triggered by extracellular exopolysaccharides ( EPS ) deposited by cells on the substrate . As individual cells synthesize both pili and EPS , it is unclear why S-motile cells only exhibit group movement . Moreover , the experimentally observed initial cell-density dependence of S-motility remains unexplained . To understand these phenomena , we developed a mathematical model for the colony expansion of S-motile cells . Our model hypothesizes that the EPS level regulates the TFP activity that initiates collective cell movements . With this assumption , the model quantitatively matches the density-dependent expansion rate . Moreover , the model predicts two phases during colony expansion: an initial density-dependent lag phase with a slow expansion rate , followed by a faster expansion phase with a density-independent rate . These model predictions were confirmed by long-term colony expansion experiments . | [
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"bacteria",... | 2016 | Colony Expansion of Socially Motile Myxococcus xanthus Cells Is Driven by Growth, Motility, and Exopolysaccharide Production |
Several channels , ranging from TRP receptors to Gap junctions , allow the exchange of small organic solute across cell membrane . However , very little is known about the molecular mechanism of their permeation . Cyclic Nucleotide Gated ( CNG ) channels , despite their homology with K+ channels and in contrast with them , allow the passage of larger methylated and ethylated ammonium ions like dimethylammonium ( DMA ) and ethylammonium ( EA ) . We combined electrophysiology and molecular dynamics simulations to examine how DMA interacts with the pore and permeates through it . Due to the presence of hydrophobic groups , DMA enters easily in the channel and , unlike the alkali cations , does not need to cross any barrier . We also show that while the crystal structure is consistent with the presence of a single DMA ion at full occupancy , the channel is able to conduct a sizable current of DMA ions only when two ions are present inside the channel . Moreover , the second DMA ion dramatically changes the free energy landscape , destabilizing the crystallographic binding site and lowering by almost 25 kJ/mol the binding affinity between DMA and the channel . Based on the results of the simulation the experimental electron density maps can be re-interpreted with the presence of a second ion at lower occupancy . In this mechanism the flexibility of the channel plays a key role , extending the classical multi-ion permeation paradigm in which conductance is enhanced by the plain interaction between the ions .
Cyclic Nucleotide Gated ( CNG ) channels are nonselective cation channels opened by the direct binding of cyclic nucleotides , cAMP and cGMP . They play a key role in olfactory and visual signal transduction , generating the electrical responses to lights in photoreceptors and to odorants in olfactory receptors [1] . CNG channels are members of the voltage-gated ion channel ( VGIC ) superfamily that includes voltage-gated potassium ( Kv ) , sodium ( Nav ) and calcium ( Cav ) and the transient receptor potential ( TRP ) channels [1 , 2] . They are heterotetramers composed of a combination of A subunits ( CNGA1-CNGA5 ) and B subunits ( CNGB1 and CNGB3 ) and , like all other members of the VGIC superfamily , each subunit contains six transmembrane α-helices ( S1-S6 ) including a pore loop between S5 and S6 that forms the ion selectivity filter . Despite a significant homology with the highly selective K+ channels , CNG channels from both rod and cone photoreceptors do not discriminate among monovalent alkali cations and are permeable also to larger methylated and ethylated ammonium ions including dimethylammonium ( DMA ) and ethylammonium ( EA ) [3 , 4] . We have previously demonstrated that the filter of a CNG-like channel , named “NaK2CNG” channel , is rather flexible and dynamic [5] . However , an important—and at the moment unanswered—question is whether the permeation of large organic cations ( i . e . the DMA ) follows the same physical mechanisms of the alkali cations’ ( i . e . K+ or Na+ ) permeation . Indeed , thermodynamic considerations and the results of Molecular Dynamics ( MD ) simulations have elucidated the mechanism of permeation of K+ and Na+ ions through ionic channels [6–12] , demonstrating that the crossing of one or a few free energy barriers is the key limiting factor . In particular , at the selectivity filter a permeating ion , strongly hydrated in the bulk solution , has to lose some water from its hydration shell [8 , 13 , 14] . The free energy cost for dehydration is only partially compensated by the interactions gained in the binding site . Selectivity for K+ over Na+ arises when the difference in free energies of those ions in the pore departs from the corresponding difference in bulk solution [15] . In the multi-ion models , ions influence each other leading to the well-known anomalous mole fraction effect where the higher affinity ions effectively block the conduction of lower-affinity ions [6 , 16–18] . Indeed , Hodgkin and Keynes in their seminal paper [19] showed that the K+ channels can be occupied by more than one ion at a time , and ions hop in single file into vacant file with rate constants which depend on barrier heights , membrane potential and ion-ion repulsion . Recently , it has been proposed that destroying the multi-ion mechanism could lead to the nonselective ion conduction observed in the CNG channels [20 , 21] suggesting that the nonselective channels have a broken multi-ion mechanism [22 , 23] . While the permeation of Na+ and K+ ions and the mechanism for K+ selectivity has been widely studied , little is known about the permeation of larger molecules in ion channel . To address this point , we considered a NaK2CNG chimera channel , where the CNG selectivity filter ( ETPP ) was engineered onto a bacterial NaK channel . The NaK2CNG chimera , which extensive electrophysiological and crystallographic experiments have demonstrated to be a good model for the channel core [5 , 24 , 25] , was crystallized in complex with DMA ( PDB ID: 4R7C ) ( Fig 1 ) [5] . By combining electrophysiology and MD simulation within the Bias Exchange-Metadynamics ( BE-META ) scheme [5] , we demonstrate that the permeation of DMA through the CNGA1 channels takes place by a different mechanism from the one governing the permeation of alkali cations [8 , 11 , 13 , 14 , 26] . Since DMA has two hydrophobic groups ( CH3 ) which interact favorably with the hydrophobic groups present inside the channel pore , despite its large size , the organic cation enters into the channel core more easily than K+ or Na+ ions , forming a stable complex with a binding affinity of almost 50 kj/mol . We further show that the simultaneous presence of two DMA ions inside the channel significantly changes the pore structure , destabilizing the binding site that is observed with only one DMA . Importantly , we show that the rather significant conformational change induced by the presence of a second DMA is the key factor for the permeation . We thus propose that some organic molecules might permeate through channels by a mechanism in which the flexibility of the channel plays a key role , extending the single file hopping paradigm previously proposed ( 19–22 ) .
We started our analysis considering the crystal structure of the NaK2CNG chimera ( 23 ) in the presence of DMA [5 , 24] , which include two crystallographically-independent tetramers ( AABB and CCDD ) ; we have therefore trapped in the crystal two slightly different configurations of the tetramer corresponding to two distinct subpopulations , possibly providing two snapshots of the permeation process . The electron density is characterized by one strong peak at site 3 ( S3c ) in both AABB and CCDD tetramers , which has been modelled as a DMA ion ( PDB ID: 4R7C ) ( Fig 1 ) [5] . Moreover , additional weaker electron density peaks in the cavity are observed just below ( S4c ) in the AABB tetramer and in the inner layer right above the site 1 ( S0c ) in the CCDD tetramer . These peaks could be interpreted as either water molecules or a partially occupied DMA cation ( Fig 1 ) . To investigate the exact nature of the binding sites for DMA we performed Molecular Dynamics simulations of the CNG mimic embedded in a lipid membrane and solvated by water molecules [5] . As crystallography shows a clear occupancy inside the pore only for one DMA , we initially considered a single DMA . We first verified that , due to its hydrophobic nature , DMA in S3c appears to be less hydrated than monovalent cations [15] . Indeed , the number of waters around the DMA in a sphere of 3Å is 1–2 during the entire long-term MD simulation ( S1 Fig ) . DMA is stabilized in the S3c site by both hydrogen bonding with Val64 carboxyl oxygen and hydrophobic interactions with the CH3 moiety of Thr63 . To study in details DMA permeation through the pore , we then used the Bias Exchange-Metadynamics ( BE-META ) scheme [27] that allows computing the multidimensional free energy landscape of the system as a function of a set of Collective Variables ( CVs ) ( see Materials and methods ) . The projection of the free energy along the vertical distance of the DMA from the Val64 residue corresponding to site S3c in Fig 1—in the selectivity filter provided a description of the DMA progression along the channel ( Fig 2 and S2 Fig ) . Interestingly , the first free energy minimum ( S4MD in Fig 2A and 2B ) corresponds to the weak electron density peak above site 4 observed in the crystal structure of the AABB tetramer . Molecular Dynamics indicates that a DMA ion in this position is stabilized by a bifurcated hydrogen bond with the hydroxyl group of two Thr63 and by hydrophobic interactions with the side chain of Val64 and the methyl groups of Thr63 ( Fig 2C ) . As a consequence , the S4MD site is asymmetrically disposed along the pore direction . The next site along the pore axis , S3MD is the deepest free energy minimum and corresponds to the main DMA site identified in the crystal structure ( S3c in Fig 1 ) ( Fig 2A and 2B ) . Due to the two-fold symmetry axis of the crystallographic tetramer , in the crystal structure the DMA ion was modeled in a “horizontal” configuration , whereas MD simulation , having no symmetry constrains , shows a more “vertical” configuration , aligned with the selectivity filter . In this position , the DMA is engaged in a bifurcated hydrogen bond with both the carboxyl oxygen of Val64 and the Thr63 hydroxyl group , while one of its methyl groups still points towards the CH3 moieties of two of the Thr63 residues in the filter ( Fig 2C ) . A site “off-axis” , which does not coincide with any of the canonical binding sites along the pore , is also visualized ( SXMD in Fig 2A and 2B ) , where the DMA forms a hydrogen bond with the Gly65 of a single subunit; in this free energy minimum the DMA is stabilized by a bifurcated hydrogen bond with the Gly65 and Glu66 carboxylates . As previously observed , Glu66 has an important role in CNG channels , being engaged in an intrasubunit interaction with Tyr55 residue in the P-helix [5] . Glu66 side chain significantly shifts from the crystallographic position , moving away from the pore axis and bringing the DMA in the last free energy minimum ( S0MD in Fig 2A–2C ) . This last site corresponds to the weaker electron density peak in the inner layer right above the site S1 ( S0c ) in the CCDD tetramer of the crystallographic molecule ( Fig 1 ) . The MD results resolve the ambiguities of the crystallographic data concerning the interpretation of the electron density peaks in the selectivity filter shown in Fig 1 . These peaks correlate very well with S4MD , S3MD and S0MD ( Fig 2D ) . The global Free Energy minimum ( S3MD ) corresponds to a density peak that can be unambiguously assigned to DMA . The site S4MD is disfavored by 3 kJ/mol with respect to S3MD and is therefore occupied only occasionally by DMA . Previous studies have suggested that the ring of Thr360 ( equivalent to Thr63 in the CNG mimic ) forms a binding site for intracellular cations [28] and that Na+ inward current is reduced by the presence of intracellular ammonium derivatives [3] . To assess the contribution of Thr360 in DMA permeation we studied the DMA current flowing through Thr360A mutants at different voltages . These records clearly show that this mutation strongly affects the DMA current-voltage relationship ( S3 Fig ) , suggesting an important role of these Thr in DMA coordination . Finally , site SXMD corresponds to a small free energy minimum , significantly less stable than the other minima . To sum up , BE-META , combined with crystallography , clearly identifies S3MD—equivalent to S3c —as the main binding site for DMA . However , it is also points to S4MD—equivalent to S4c—as an important additional binding site with a lower , but significant , occupancy than S3MD . The presence of this second free energy minimum prompted us to investigate the behavior of the channel in the presence of a second DMA . To verify whether two DMA could simultaneously fit inside the CNG pore with a full occupancy , we performed several unbiased MD simulations with one DMA in S3—corresponding to S3MD—and the other in S2 ( Fig 3 ) . In this new configuration , the DMA in S3 is pushed towards the intracellular side , due to their electrostatic repulsion . The DMA reaches a different position to the one observed in the crystallographic structures and in the global free energy minimum ( Fig 3 and S4 Fig ) . Indeed , the number of contacts between the N of the DMA and the hydroxyl oxygens of the Thr63 is significantly higher in the case of the DMA-DMA configuration then for the single DMA ( S3 Fig ) . To gain insight into the effect that a second DMA has on the DMA in S3 , we estimated the free energy landscape experienced by the system during the DMA-DMA configuration using the BE-META scheme [27] ( S5 Fig ) ( see Materials and methods ) . Remarkably , the projection of the free energy along the vertical distance of the DMA from the Val64 residue—corresponding to both S3MD and S3c—in the selectivity filter revealed that the presence of a second DMA completely changes the free energy landscape . Indeed , the presence of the second DMA decreases the depth of the well by 25 kJ/mol ( Fig 3B-right panel ) compared to the single DMA system ( Fig 3A ) . When a single DMA is in the channel , it needs to move to a distance of 2 nm from Val64 to become unbound from the selectivity filter and to become free to diffuse in the cytoplasm; in the presence of two ions , the DMA becomes unbound at a distance of 1 nm from Val64 ( Right panels in Fig 3A and 3B ) . Moreover , when two DMA are present inside the channel , there is a complete rearrangement of the position of the binding sites with one global minimum corresponding to a DMA in a site “off-axis” where , similarly to the SXMD site described in the Fig 2C , the DMA forms a hydrogen bond with the Gly65 of a single subunit ( i panel in Fig 3C ) ; and the second DMA stabilized by a bifurcated hydrogen bond with the hydroxyl group of two Thr63 ( ii panel in Fig 3C ) . Taken together these data highlight that the presence of a second DMA destabilizes the first one and strongly affects the structure of the channel , completely changing the free energy landscape . In order to further validate the picture emerging from MD simulations we then performed electrophysiological measurements aimed at estimating the affinity of the CNGA1 channel for DMA . We prepared a patch with 110 mM DMA inside the patch pipette and we varied the concentration of DMA in the bathing medium—corresponding to the intracellular side of the membrane—from 0 to 250 mM ( Fig 4 ) . Fig 4A shows representative currents observed at 200 mV when the concentration of DMA in the bath was 20 , 50 , 110 and 250 mM , respectively . The cGMP activated current was measured as the difference between the current recorded in the presence of cGMP and its absence . Fig 4B shows the values of the observed normalized conductance at different voltages between +140 and +200 mV as a function of the DMA activity . At these high membrane potentials , the outward current is carried by DMA ions moving from the bath toward the patch pipette , and backward crossing is assumed to be negligible . We simultaneously fitted these data with the Michaelis-Menten equation [7] , which is derived assuming that the channel is occupied by at most one ion at a time . As illustrated in Fig 4B , the match between the experimental data and this model is almost perfect . However , the binding affinity estimated by this fit is of 52 mM . Clearly this number is not consistent with the free energy profile reported in Fig 2 , which , in the case of a single DMA ion , is characterized by the presence of a free energy minimum whose depth would imply a binding affinity in the low nanomolar range . In order to understand the reason for this discrepancy , we considered a generalization of the Michaelis Menten theory , in which a channel can be simultaneously occupied by two ions . We will show that this model , in the presence of a significant interaction between the ions , predicts that the relation between the current J and the concentration of the permeating ion Xin has the same functional form of the Michaelis Menten equation , but with a different half-activation constant K1/2 . In the standard Michaelis Menten the channel is characterized by two states: empty ( pE ) and occupied ( pO ) , with pE + pO = 1 . Under the assumption that backward crossing can be neglected , i . e . that ions can move only from the intracellular to the extracellular medium , the model is therefore fully defined by two rates: the rate kL for the transition in which the ion enters into the channel , and the rate kR for the transition in which the ion leaves the channel crossing the barrier towards the right ( Fig 5 ) . At the stationary state , we have: J=kL[Xin]pe 0=kL[Xin]pe−kRpO ( 1 ) where [Xin] is the concentration of the ion Xin in the intracellular medium ( Fig 5 ) . From these equations , we obtain the usual Michaelis-Menten equation [7]: J=kL[Xin][Xin]kLkR+1 ( 2 ) We now consider the case in which the channel can be occupied by two ions , in two binding sites at the left ( L ) and the right ( R ) . The channel can now exist in four states: ( i ) empty ( with probability pE ) ; ( ii ) occupied by one ion in the right site ( probability pR ) ; ( iii ) occupied by one ion in the left site ( probability pL ) and ( iv ) occupied by two ions ( probability pD ) . The allowed transitions between these states are as in Fig 5 . After some algebra reported in S1 Appendix we obtain that the flux J of ions of concentration Xin from the intracellular to the extracellular medium is given by J=[Xin]kLk+kR~ ( [Xin]kL+kR ) ( k++kR~ ) [Xin]2kL2+kR~ ( kR+k−+k+ ) [Xin]kL+kRk+kR~ ( 3 ) here k+ is the transition rate from the binding site at the left to that at the right and k- is the corresponding reverse rate . The rate kR~ is associated to a transition between the state with double occupancies ( D ) and the state with a single ion occupying the left side ( L ) . The rates k+ , k- and kR can be estimated from the free energy profile reported in Fig 2 . Indeed , assuming that the rate satisfies Arrenius law , and assuming that the kinetic prefactor is the same for all the rates , we have k+ ≈exp ( -0 . 5 ) , k- ≈exp ( -1 . 5 ) and kR ≈ exp ( -15 ) ( all in the units of the kinetic prefactor ) . We have seen that the rate kR~ is approximately equal to exp ( -7 . 5 ) significantly smaller than k+ and k- , but much higher than kR , due to the repulsion between the two ions . If we neglect the terms proportional to kR , Eq 3 becomes J=kLk+ ( k−+k+ ) [Xin] ( k++kR~ ) kL ( k−+k+ ) kR~[Xin]+1 ( 4 ) Remarkably , the functional form of the dependence of the current on the concentration is exactly the same for this model and for the model with a single site ( see Eq 2 ) . Therefore , the two scenarios cannot be distinguished based on the dependence of the current of the concentration . From Eq ( 4 ) the term ( k++kR~ ) kL ( k−+k+ ) kR~ ( 5 ) plays the role of the inverse of the of the concentration of ions causing half of the maximal current . Inserting in this equation the estimates of the rates obtained from the metadynamic profiles ( see legend of Fig 5 ) , we find that this concentration has the value of 0 . 6 mM . This value is close to what seen experimentally in Fig 4 , where a global fitting of the data obtained at different voltages yielded an apparent Kd of 52 mM . Summarizing , when two DMA ions are simultaneously present inside the pore , the interaction between the ions lowers the exit rate by several orders of magnitude bringing in qualitative agreement the experimentally measured half activation value with that obtained from simulation . The residual discrepancy can be ascribed to the several approximations that we have done to estimate the rates , and to the inaccuracy of the force field used for simulating the channel presumably due to the assumption that the charges of the atoms are fixed and do not depend on the environment . In summary , by combining electrophysiology and Molecular Dynamics ( MD ) simulations , we uncover an unusual molecular mechanism underlying the permeation of large organic cations through the CNGA1 channels . We find that the free energy landscape associated to the translocation of a single dimethylammonium ( DMA ) through the CNGA1 pore is characterized by the presence of a few small local minima in a single very deep free energy well . This contrasts sharply with the free energy landscape for monovalent alkali cations , which are normally characterized by the presence of at least one free energy barrier [19] . We identified the molecular mechanisms leading to the DMA permeation through the CNGA1 channel: the presence of a second DMA changes completely the free energy landscape , leading to a destabilization of the DMA-channel complex . In agreement with experimental measurements , the free energy well depth is reduced by about half when two DMA occupy simultaneously the selectivity filter . Like in other multi-ion channels , including the Ca2+ channels [16 , 17] and CorA channels at high Mg2+ concentration [30] , the permeation of DMA is due to the interaction between the ions . However , in the CNGA1 this interaction induces a significant change in the free energy landscape: the binding sites observed in the presence of a single DMA ion are not observed when two ions are present . This is ultimately a consequence of the significant flexibility of the CNGA1 . This effect is accurately described by molecular simulations . Indeed , a model in which the ions move in a single file on a pre-sculptured free energy landscape with a sequence of free energy minima is clearly not adequate for describing the permeation process of DMA through the CNGA1 . This finding has a remarkable physiological relevance since it discloses an unexpected mechanism for the permeation of large cations through the CNGA1 channel and , possibly , through other voltage-gated channels .
Protein expression and purification for the NaK2CNG-E plasmid was performed as previously described [5] . Briefly , the plasmid coding for the NaK2CNG-E chimera fused to a C-terminal hexahistidine tag was transformed into Escherichia coli XL1B , and following expression at 25°C the proteins were extracted in 50 mM Tris•HCl pH 8 , 100 mM DMA•HCl , 40 mM n-decyl-β-D-maltoside ( DM ) ( Anagrade ) and purified by affinity chromatography on a Talon resin , followed by size esclusion chromatography on a Superdex-200 gel filtration column ( GE Healthcare ) in 20 mM Tris • HCl pH 8 . 0 , 5 mM DM and 100 DMA• HCl . Purified NaK2CNG-E was concentrated to 20 mg/mL and crystallized using the sitting drop vapor diffusion method at 20°C by mixing equal volumes of protein and reservoir solution containing 40–44% ( ± ) -2-methyl-2 , 4-pentanediol ( MPD ) , 100 mM of MES pH 6 . 5 and 25mM Glycine . Crystals belong to space group P2221 with cell dimension a = 67 . 62 , b = 67 . 68Å and c = 89 . 90Å . All crystals were flash-frozen in liquid nitrogen and X-ray diffraction data were collected at 100K at Eettra XRD1 beamline at 1 Å . Data reduction was performed as previously described [5] . Briefly , iMOSFLM [31] , XDS [32 , 33] and the CCP4i suite [34] were used . Resolution cut-off was chosen following I/σI criterion [35] . The dataset exhibited merohedral twinning [36] . The structure was determined by molecular replacement using the published K+ complex structure ( PDB: 3K0D ) with selectivity filter region omitted as an initial search model . Repeated cycles of refinement using REFMAC5 [37] and model building using Coot [38] was carried out . Figs 1 , 2 and 3 were prepared using PyMOL [39] . One-dimensional electron density profiles were obtained by sampling the electron density maps along the central axis . The starting point of this study was the structure of the NaK2CNG-E obtained from our previous study [5] . The model of the chimera NaK2CNG-E was built using the chain B ( residues from 19 to 113 ) of the 2 Å resolution crystal structure soaked with Na+ ions ( PDB accession code 3K0G ) [24] . The protein was embedded in a pure , pre-equilibrated 1-palmitoyl-2-oleilphosphatidylcholine ( POPC ) lipid model ( kindly supplied by T . A . Martinek ) [40] using the g_membed4 tool of Gromacs and then it was oriented following OPM5 database model . Afterward the system was neutralized and solvated with TIP3P model6 water molecules ( 76305 total atoms in a box size of 92 . 8 91 . 9 87 . 5 Å3 ) . The system was prepared with a single DMA in the strongest binding site identified by the crystal structure . The simulations were performed in periodic boundary conditions at 300 K using the Nose-Hoover thermostat7 and Parrinello-Rahman barostat with a semisotropic pressure coupling type and a time step of 2 fs . Position restraints of atoms were fixed with a force constant ( K ) equal to 1000 kJ mol-1 nm-2 . We used GROMACS410 package with Amber0311 force field for protein and GAFF12 for the membrane . The equilibration was performed in three stages: ( 1 ) the system was heated for 2 . 5 ns with protein backbone and DMA fixed , while sidechains were left free to move; ( 2 ) 5 . 2 ns were run using position restraints only for the selectivity filter and the DMA . In the first stage we used the NPT ensemble , while in the second one a surface tension equal to 600 . 0 bar*nm2 was added . ( 3 ) For the next 1 ns the membrane area was kept constant . A configuration taken from this step was used as a starting point for a molecular dynamics ( MD ) simulation of 98 ns . The same procedure was followed with the 2 DMA system . In order to better explore the free energy surface associated to the DMA permeation pathway , we performed a Bias-Exchange Metadynamics ( BE-META ) simulation of 450 ns ( 50 ns x 9 walkers ) , using the Plumed package [41] . The Collective Variables used are: 1 ) the distance of the DMA from its binding site , represented by center of mass of Val64; 2 ) the distance between Cα of E66 and C ( of carboxylic group ) of Glu66 in the opposite monomer; 3 ) the coordination number of the ions with the two oxygens of the carboxylic group of E66s; 4 ) the distance between Cα of Gly65 in the opposite monomer; 5 ) the radius of gyration of the Gly65 residues; 6 ) the distance between Cα of Pro68 in the opposite monomer; 7 ) the radius of gyration of the Pro68 residues . In the case of the 2 DMA system , we considered also the distance of the second DMA ( DMA in S1 site in Fig 4A ) from the center of mass of Val64 as Collective Variable . We have divided the whole BE-META run in 4 separate runs , each corresponding to the different colors shown in Fig 2 , in order to enhance the convergence of the system following a standard procedure of weighted-histogram procedure [42] . The 4 Plumed input files containing the exact definition of these collective variables have been provided as S2 Appendix All structural and free energy analyses were performed using METAGUI , a VMD interface for analyzing metadynamics and MD simulations [43] . The structures generated during such a simulation are clustered together into a set of microstates ( i . e . structures with similar values of the collective variables ) and their relative free energies are then computed by a weighted-histogram procedure , METAGUI returns configurations which are representative of ensemble averages of the corresponding microstates [43] . All studies were approved by the SISSA’s Ethics Committee according to the Italian and European guidelines for animal care ( d . l . 26 , March 4th 2014 related to 2010/63/UE and d . l . 116/92; 86/609/C . E . ) . Oocytes were harvested from female Xenopus laevis frogs ( ‘Xenopus express’ Ancienne Ecole de Vernassal , Le Bourg 43270 , Vernassal , Haute-Loire , France ) using an aseptic technique or , if necessary , purchased from Ecocyte Bioscience ( Am Förderturm , 44575 , Castrop-Rauxel , Germany ) . Xenopus laevis were kept in tanks—usually 6–8 animals per tank—and were exposed to a 12/12 hours dark/light cycle . All Xenopus laevis surgeries were performed under general anesthesia , obtained by immersion in a 0 . 2% solution of tricaine methane sulfonate ( MS-222 ) adjusted to pH 7 . 4 for 15–20 min . Depth of anesthesia was assessed by loss of the righting reflex and loss of withdrawal reflex to a toe pinch . After surgery , animals were singly housed for 48 h . Frogs were monitored daily for 1 week post-operatively to ensure the absence of any surgery-related stress . Post-operative analgesics were not routinely used . Considering the simplicity of the procedure , the lack of complications , the effectiveness of anesthetic regimen and the reduction in the number of animals likely to be used compared to the number that would be required if only one surgery were permitted , multiple surgeries on a single animal were performed . Individual donors were used up to five times , conditional upon the health of an individual animal . Recovery time between oocyte collections from the same animal was maximized by rotation of the frogs being used . A minimum recovery period of 1 month was ensured between ovarian lobe resection from the same animal to avoid distress . Evidence of surgery-related stress resulted in an extended rest period based on recommendations from the veterinary staff . After the fifth terminal surgery frogs were humanely killed through anesthesia overdose via 2 h of immersion in a 5 g/l MS-222 solution adjusted to pH 7 . 4 . Bovine CNGA1 cRNA were injected into Xenopus laevis oocytes . Oocytes were prepared as described [44] . Injected eggs were maintained at 18°C in a Barth solution supplemented with 50 μg/ml gentamycin sulfate and containing ( in mM ) : 88 NaCl , 1 KCl , 0 . 82 MgSO4 , 0 . 33 Ca ( NO3 ) 2 , 0 . 41 CaCl2 , 2 . 4 NaHCO3 and 5 Tris-HCl , pH 7 . 4 ( buffered with NaOH ) . During the experiments , oocytes were kept in a Ringer solution containing ( in mM ) : 110 NaCl , 2 . 5 KCl , 1 CaCl2 , 1 . 6MgCl2 and 10 Hepes , pH 7 . 4 ( buffered with NaOH ) . Usual salts and reagents were purchased from Sigma Chemicals ( St Louis , MO , USA ) . cGMP-gated currents from excised patches were recorded with a patch-clamp amplifier ( Axopatch 200; Axon Instruments Inc . , Foster City , CA , USA ) , 2–6 days after RNA injection , at room temperature ( 20–24°C ) [45] . The perfusion system allowed a complete solution change in less than 0 . 1 seconds [44 , 45] . Macroscopic current recordings were obtained with borosilicate glass pipettes which had resistances of 2–5 MOhm in symmetrical standard solution . The standard solution on both sides of the membrane consisted of ( in mM ) 110 DMACl , 10 Hepes and 0 . 2 EDTA buffered with tetramethylammonium hydroxide ( pH 7 . 4 ) . The ion composition of the bath solutions was similar except 110 mM-DMACl was substituted as specified in the figure legends . We used Clampex version 10 . 0 for data acquisition . Recordings were low-pass filtered at 5 kHz and sampled at 20 kHz kHz . The activity coefficients γi were calculated according to Debye-Huckel equation ( for DMACl concentrations below 100 mM ) logγi=−0 . 509zi2Ic1+ ( 3 . 28diIc ) or Davies equation ( for higher DMACl concentrations ) logγi=−0 . 509 ( Ic1+Ic−0 . 3Ic ) where Ic is the ionic strength of the solution , zi is the charge ( +1 ) and di is the ion size parameter ( 0 , 35 nm ) for DMA [46] . No statistical methods were used to predetermine sample sizes that are similar to those reported in previous publications [47–49] . We normally excluded data when we lost the patch during the experiments , when the level of expression was too low and we could not distinguish the noise from random channel openings from electrical noise due to an unstable patch and /or from spurious electrical noise . We kept only data obtained during experiments in which the amplitude of the seal ( i . e . the current evoked by voltage pulses in the absence of cGMP ) was stable . Representative electrical recordings are shown as well as descriptive statistics where data are presented as mean +/- SD . n indicates the number of excised patches . | Cyclic Nucleotide Gated ( CNG ) channels are nonselective cation channels with a key role in sensory transduction . Despite sharing a high homology with K+ channels , they allow the passage of large compounds like dimethylammonium ( DMA ) which are not permeable through K+ channels . We demonstrate that the conduction mechanism of this compound is radically different from the textbook scenario , in which an ion , in order to diffuse through the channel , must cross a series of barriers , whose height is possibly perturbed by the presence of other ions in the channel . We show that permeation of large cations in CNG is due to the destabilization of the pore induced by the simultaneous presence of two ions in the channel . | [
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"thermodynamic... | 2018 | The permeation mechanism of organic cations through a CNG mimic channel |
Endoplasmic reticulum ( ER ) stress activates the Unfolded Protein Response , a compensatory signaling response that is mediated by the IRE-1 , PERK/PEK-1 , and ATF-6 pathways in metazoans . Genetic studies have implicated roles for UPR signaling in animal development and disease , but the function of the UPR under physiological conditions , in the absence of chemical agents administered to induce ER stress , is not well understood . Here , we show that in Caenorhabditis elegans XBP-1 deficiency results in constitutive ER stress , reflected by increased basal levels of IRE-1 and PEK-1 activity under physiological conditions . We define a dynamic , temperature-dependent requirement for XBP-1 and PEK-1 activities that increases with immune activation and at elevated physiological temperatures in C . elegans . Our data suggest that the negative feedback loops involving the activation of IRE-1-XBP-1 and PEK-1 pathways serve essential roles , not only at the extremes of ER stress , but also in the maintenance of ER homeostasis under physiological conditions .
The accumulation of misfolded proteins in the endoplasmic reticulum ( ER ) , also known as ER stress , activates the Unfolded Protein Response ( UPR ) , which upregulates the synthesis of chaperones such as BiP and components of ER-associated degradation ( ERAD ) , promotes ER expansion , and attenuates translation [1]–[3] . The UPR is conserved from yeast to humans and in metazoans is comprised of three branches , mediated by the transmembrane ER luminal sensors IRE-1 , PERK/PEK-1 , and ATF-6 [1]–[3] . In response to ER stress , IRE-1 oligomerizes , activating an endoribonuclease domain that splices the mRNA of xbp-1 to enable the generation of the activated form of the XBP-1 transcription factor [4]–[7] . PERK phosphorylates the translation initiation factor eIF-2α , causing global translational attenuation that diminishes the secretory load to the ER [8] . In addition , phosphorylation of eIF-2α selectively increases the translation of ATF4 , a transcription factor that regulates stress responses [9] . ATF-6 undergoes proteolysis , releasing the cytosolic domain of ATF-6 , which functions as a transcription factor that translocates to the nucleus and activates transcription of UPR genes [10] . Genetic studies suggest essential roles for UPR signaling in animal development . In mice , genetic studies focused on either the IRE-1-XBP-1 or the PERK pathway have shown that each functions in the development of specialized cell types , including plasma cells , pancreatic ß-cells , hepatocytes , and intestinal epithelial cells [3] , [11]–[15] . In Caenorhabditis elegans , mutants deficient in any one of the three branches of the UPR are viable , but combining a deficiency in the IRE-1-XBP-1 pathway with loss-of-function mutations in either the ATF-6 or PEK-1 branch has been reported to result in larval lethality [6] , [16] . These studies suggest that the UPR is required for animal development , but the specific essential role has not been defined . For example , UPR signaling may be required for a particular stage of development , or alternatively , constitutive UPR activity may be required . The experimental analysis of UPR signaling both in yeast and in mammalian cells has been greatly facilitated by the use of chemical agents that induce ER stress , such as the N-linked glycosylation inhibitor tunicamycin , the calcium pump inhibitor thapsigargin , and the reducing agent dithiothreitol ( DTT ) . However , the activation of the UPR under physiological conditions is less well understood [17] . Constitutive IRE-1 activity has been observed in diverse types of mammalian cells , particularly with high secretory activity or in the setting of increased inflammatory signaling [11] , [14] , [15] , [18] . These studies suggest critical roles for IRE-1-XBP-1 signaling in physiology and development , some of which have been proposed to be independent of its role in maintaining protein folding homeostasis in the ER [11] , [19] , [20] . Recently , we showed that XBP-1 is required for C . elegans larval development on pathogenic Pseudomonas aeruginosa , conferring protection to the C . elegans host against the ER stress caused by its own secretory innate immune response to infection [21] . Our study established that the innate immune response to microbial pathogens represents a physiologically relevant source of ER stress that necessitates XBP-1 function . We sought to better understand the consequences of UPR deficiency under physiological conditions during C . elegans larval development . We describe our studies which suggest that even in the absence of ER stress induced by exogenously administered chemical agents , the IRE-1-XBP-1 pathway , in concert with the PEK-1 pathway , functions in a homeostatic loop that is under constitutive activation during C . elegans larval development . Our data implicate an essential role for the UPR in ER homeostasis , not only in the response to toxin-induced ER stress , but also under basal physiological conditions .
The detection of IRE-1 activity provides a sensitive and responsive measure of ER stress . Most methods used to measure IRE-1 activity require functional IRE-1-XBP-1 output , relying on either detection of the activated spliced form of the xbp-1 mRNA or the transcriptional activity of the resulting XBP-1 protein . In order to follow IRE-1 activity in the absence of a functional XBP-1 protein , we utilized the C . elegans xbp-1 ( zc12 ) mutant , which has a C→T mutation that results in an early premature stop codon [4] . We reasoned that we could detect IRE-1-mediated splicing of xbp-1 ( zc12 ) mRNA by quantitative RT-PCR ( qPCR ) , as we have done previously for wild type xbp-1 mRNA , as a measure of IRE-1 activity [21] . We anticipated , however , that the xbp-1 ( zc12 ) mRNA might be degraded by nonsense-mediated decay ( NMD ) [22] , which would reduce the abundance of xbp-1 ( zc12 ) mRNA ( Figure 1A ) . Thus , we constructed a strain carrying xbp-1 ( zc12 ) and a null allele of smg-2 , the C . elegans homolog of the NMD component Upf1 [23] . Indeed , we observed that the level of xbp-1 mRNA in the xbp-1 ( zc12 ) mutant was markedly diminished compared with the level of xbp-1 ( zc12 ) mRNA in the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) mutant ( Figure 1B ) . These data confirmed that xbp-1 ( zc12 ) mRNA is a substrate for the NMD pathway , but that inhibition of NMD permits detection of xbp-1 ( zc12 ) mRNA . As expected from the predicted truncated protein product made from translation of the xbp-1 ( zc12 ) mRNA ( Figure 1A ) , loss of NMD had no effect on the null phenotype of the xbp-1 ( zc12 ) allele , as assessed by the effect of the smg-2 ( qd101 ) mutation on expression of an xbp-1-regulated gene , the C . elegans BiP homolog hsp-4 ( Figure 1B ) . We next examined the level of WT xbp-1 mRNA in the smg-2 ( qd101 ) mutant , and we observed that NMD inhibition increased the level of xbp-1 mRNA 2-fold relative to WT C . elegans ( Figure 1C ) , which suggests that the NMD complex may function to decrease the level of WT xbp-1 mRNA . This observation is consistent with a prior report suggesting that stress-induced genes may be NMD targets [24] , although we hypothesize that the relatively early termination codon present in the xbp-1 mRNA prior to IRE-1-mediated splicing may also contribute to recognition and degradation by the NMD pathway . Consistent with this explanation , after exposing both the WT and smg-2 ( qd101 ) strains to tunicamycin for 4 h , the level of IRE-1-spliced xbp-1 mRNA was similar between the two strains ( Figure 1C ) . Furthermore , the loss of NMD did not increase the lethality of either the WT strain or xbp-1 ( zc12 ) mutant when grown in the presence of tunicamycin ( Figure 1D ) . Comparing levels of IRE-1 activity between smg-2 ( qd101 ) and smg-2 ( qd101 ) ; xbp-1 ( zc12 ) animals , we observed a dramatic elevation in the level of spliced xbp-1 mRNA in the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) strain ( Figure 1E ) . To provide a measure for comparison , the basal elevation of spliced xbp-1 mRNA in the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) mutant far exceeded the level of spliced xbp-1 mRNA in the smg-2 ( qd101 ) mutant even after administration of tunicamycin . Treating the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) strain with tunicamycin resulted in only a minor additional increase in spliced xbp-1 mRNA compared with the magnitude of elevation in spliced xbp-1 in that strain under basal conditions ( Figure 1E ) . These data show that XBP-1 deficiency results in a dramatic increase in IRE-1 activity , even in the absence of exogenously administered agents such as tunicamycin . If the elevated level of IRE-1 activity observed in the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) mutant were indicative of increased ER stress due to loss of xbp-1 , we might anticipate compensatory activation of the PEK-1 and/or ATF-6 pathways in the absence of XBP-1 . We therefore sought to determine levels of PEK-1 activity in an xbp-1 mutant through the detection of eIF-2α phosphorylation . In particular , these measurements would provide an additional measure of ER stress in the xbp-1 mutant that is not dependent on the inactivation of the NMD pathway and its aforementioned experimental caveats . Antibodies raised against mammalian eIF-2α and specifically phosphorylated eIF-2α ( P-eIF-2α ) cross-react with the highly homologous C . elegans protein [25] , [26] . We detected a single band in immunoblots using these antibodies with lysates from WT C . elegans ( Figure 2A ) . We observed that eIF-2α phosphorylation was induced by a 4 h exposure to a high dose of tunicamycin in a PEK-1-dependent manner ( Figure 2A and Figure S1A ) . Of note , eIF-2α phosphorylation appears to be a less sensitive measure of ER stress than IRE-1-mediated xbp-1 mRNA splicing , as we did not observe a significant increase in eIF-2α phosphorylation in response to standard doses of tunicamycin sufficient to induce xbp-1 mRNA splicing . We next determined PEK-1 activity under basal physiological conditions , specifically in the xbp-1 mutant . We saw induction of PEK-1-mediated eIF-2α phosphorylation relative to WT in the absence of exogenously administered agents to induce ER stress at 16°C ( Figure 2B and Figure S1B ) . The magnitude of the effect of XBP-1 deficiency on PEK-1 activity was comparable to the induction of PEK-1 in WT by treatment with high-dose tunicamycin . We observe no increase in eIF-2α phosphorylation in the xbp-1; pek-1 mutant relative to the pek-1 mutant , confirming that the increase in eIF-2α phosphorylation in the xbp-1 mutant relative to WT is due to activation of PEK-1 . In fact , we noticed a slight decrease in eIF-2α phosphorylation in the xbp-1; pek-1 mutant relative to the pek-1 mutant , but the mechanisms underlying this difference are unclear . These data corroborate our observations of increased xbp-1 mRNA splicing in the xbp-1 mutant . Taken together , the increase in levels of IRE-1 and PEK-1 activity in the xbp-1 mutant suggests that XBP-1 deficiency is accompanied by a marked increase in constitutive ER stress under basal physiological conditions . Previously , we reported that the activation of innate immunity by infection with pathogenic P . aeruginosa induces ER stress , and that XBP-1 serves an essential role in protecting the host against the detrimental effects of immune activation [21] . Our prior ultrastructural analysis of the ER in xbp-1 mutants suggested that disruption of ER homeostasis contributes to this phenotype . One explanation for these observations is that ER homeostasis in the xbp-1 mutant might be minimally perturbed under basal physiological conditions but have a pronounced sensitivity to ER stress from endogenous ( e . g . immune activation ) or exogenous ( e . g . tunicamycin ) sources . However , the data in Figure 1 and Figure 2 suggest that even during physiological growth and development , XBP-1 deficiency results in a marked elevation in levels of basal ER stress . We hypothesized , therefore , that under these circumstances , the activation of innate immunity might further increase ER stress levels . The smg-2 ( qd101 ) ; xbp-1 ( zc12 ) strain provided the opportunity to assess levels of ER stress caused by immune activation in the setting of XBP-1 deficiency . Whereas a 4 h exposure of the WT strain to P . aeruginosa PA14 causes a two-fold increase in spliced xbp-1 mRNA relative to exposure to the relatively non-pathogenic bacterial food Escherichia coli OP50 ( Figure 3A and [21] ) , we observe a blunted response to P . aeruginosa infection in the smg-2 ( qd101 ) mutant ( Figure 3A ) . This observation is likely due to the 5-fold elevation in spliced xbp-1 mRNA levels in the smg-2 ( qd101 ) mutant ( Figure 1C ) , which may buffer the ER from the stress caused by pathogen-induced immune activation . Nevertheless , we observed that the level of spliced xbp-1 mRNA in the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) mutant was increased by a 4 h exposure to P . aeruginosa relative to the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) mutant treated in parallel with E . coli ( Figure 3A ) . Specifically , under these treatment conditions , the level of spliced xbp-1 mRNA in the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) mutant was 20-fold greater than that of the smg-2 ( qd101 ) mutant in the absence of additional stress , whereas exposure to P . aeruginosa increased the level of spliced xbp-1 mRNA to over 25-fold that of the smg-2 ( qd101 ) mutant ( Figure 3A ) . The total amount of xbp-1 mRNA was unchanged between smg-2 ( qd101 ) and smg-2 ( qd101 ) ; xbp-1 ( zc12 ) strains , indicating that the increase in spliced xbp-1 mRNA is due to increased IRE-1 activation . We observed persistent elevation of spliced xbp-1 mRNA after an 11 h exposure to P . aeruginosa , above the elevated basal levels of spliced xbp-1 mRNA in the xbp-1 mutant , suggesting that IRE-1 activity is not attenuated under conditions of physiological ER stress ( Figure 3B ) . Previously , we established that the ER stress induced by exposure to P . aeruginosa , as well as the lethality of the xbp-1 mutant during infection by P . aeruginosa , are suppressed by a loss-of-function mutation in pmk-1 , which encodes a conserved p38 mitogen-activated protein kinase ( MAPK ) that regulates innate immunity in C . elegans [27] . Our interpretation of these data was that loss of PMK-1 activity diminished the secretory load on the ER by attenuating the innate immune response . In support of this interpretation , we found that the pathogen-induced increase in spliced xbp-1 mRNA in smg-2 ( qd101 ) ; xbp-1 ( zc12 ) was suppressed in the smg-2 ( qd101 ) ; xbp-1 ( zc12 ) ; pmk-1 ( km25 ) mutant , although the basal levels on E . coli OP50 nevertheless remained markedly elevated ( Figure 3A ) . These data provide quantitative support for a model in which the activation of PMK-1-mediated innate immunity is a physiologically relevant source of ER stress , which in XBP-1-deficient animals exacerbates an already elevated level of ER stress to cause larval lethality . What are the functional consequences of the elevated ER stress present in the xbp-1 mutant under standard growth conditions , in the absence of infection ? The xbp-1 mutant , while viable , exhibits increased sensitivity to exogenously administered ER stress as well as physiological ER stress from immune activation [21] , [28] . Inactivation of both xbp-1 and pek-1 was previously reported to result in larval arrest when propagated at 20°C [16] . Our observations of constitutive ER stress in the xbp-1 mutant and increased PEK-1 activity suggest a compensatory functional role for pek-1 , and thus we sought to further characterize the larval arrest phenotype of the xbp-1; pek-1 mutant . Surprisingly , we observed that the xbp-1 ( tm2482 ) ; pek-1 ( ok275 ) double mutant exhibited temperature-dependent viability over the physiological temperature range of C . elegans ( Figure 4A ) . The larval development of the xbp-1 ( tm2482 ) ; pek-1 ( ok275 ) mutant was similar to that of WT at 16°C . At 20°C , however , approximately half of xbp-1 ( tm2482 ) ; pek-1 ( ok275 ) eggs developed to become gravid adults , while the remainder arrested during larval development in the L2 and L3 stages . These arrested larvae died over the course of several days with intestinal degeneration as previously described ( Shen et al . , 2005 ) . At temperatures greater than 23°C , larval lethality was 100% . At 25°C , 100% of the population died in the L1 and L2 stages after just 2 days . The physiological temperature range for propagation of C . elegans in the laboratory is generally 15°C to 25°C , with optimal reproduction at 20°C . Thus , the observed temperature dependence is observed not at “heat shock” temperatures , but rather , well within the range of physiological temperatures for C . elegans . The temperature dependence of xbp-1 ( tm2482 ) ; pek-1 ( ok275 ) lethality permitted the investigation of whether the larval lethality of the xbp-1; pek-1 mutant is due to a requirement for XBP-1 and PEK-1 at a specific stage of development , or whether the activities of XBP-1 and PEK-1 are required constitutively for viability at other life stages . Specifically , we propagated two xbp-1; pek-1 mutants comprised of different mutant alleles at 16°C until the animals reached the L4 larval stage , then either maintained the mutants at 16°C or shifted them to 25°C to monitor survival . When shifted to 25°C , the xbp-1; pek-1 double mutants exhibited a sharp decrease in survival as compared with the strains maintained at 16°C ( Figure 4B ) . These observations suggest that the activity of either XBP-1 or PEK-1 is not specifically required at a particular developmental stage; instead , the constitutive activities of XBP-1 and PEK-1 are required for survival at physiological temperatures . Although we previously observed that pek-1 and atf-6 single mutants did not exhibit larval lethality in the presence of pathogenic bacteria [21] , our data presented in this paper suggest that PEK-1 functions in parallel to XBP-1 under physiological conditions in C . elegans to maintain ER homeostasis . Because the xbp-1; pek-1 mutant is viable through larval development at 16°C , we were able to ask whether PEK-1 contributes to protection against immune activation in the absence of XBP-1 . Populations of synchronized eggs were grown at 16°C with P . aeruginosa as the only food source and development was monitored over time . P . aeruginosa has been shown to exhibit markedly diminished pathogenicity to C . elegans adults at 16°C relative to 25°C [29] , and we found this to also be the case during larval development . Specifically , the pmk-1 mutant was able to complete larval development on P . aeruginosa at 16°C ( Figure 5A ) , whereas only half of the pmk-1 eggs grown on P . aeruginosa develop to the L4 stage at 25°C [21] , indicating that immune activation is less important for development in the presence of P . aeruginosa grown at 16°C than it is at 25°C . Likewise , the larval development of the xbp-1 mutant , which is severely compromised on P . aeruginosa at 25°C [21] , was equivalent to that of WT at 16°C ( Figure 5A ) . Both the diminished pathogenicity of P . aeruginosa at 16°C and the aforementioned temperature-sensitive requirement for UPR function may contribute the survival of the xbp-1 mutant at 16°C . Nevertheless , even under these conditions , the xbp-1 ( tm2482 ) ; pek-1 ( ok275 ) mutant exhibited complete larval lethality on P . aeruginosa at 16°C , reminiscent of the larval lethality of xbp-1 on P . aeruginosa grown at 25°C . Eliminating PMK-1-mediated immunity completely rescued this larval lethality ( Figure 5A ) , demonstrating that PEK-1 functions with XBP-1 to protect against PMK-1-mediated immune activation during larval development . We next asked whether the UPR is required for survival in the presence of pathogen during adulthood . In parallel with our observation that the xbp-1; pek-1 mutant exhibits temperature-sensitive lethality both during larval development and when shifted to a higher temperature from the L4 larval stage , we found that the xbp-1; pek-1 mutant exhibits enhanced lethality relative to the WT strain or either of the single mutants when shifted at the L4 stage to P . aeruginosa at 16°C ( Figure S2 ) . These data suggest that the UPR is required for survival during immune activation both in larval development and in adulthood . Larval arrest of xbp-1; pek-1 mutants has been reported to be accompanied by evidence of intestinal degeneration , including the appearance of vacuoles and light-reflective aggregates in intestinal cells , degradation of intestinal tissues , and distention of the intestinal lumen [16] . We observe similar morphology not only in xbp-1; pek-1 larvae at 23°C on E . coli OP50 , but also at 16°C on P . aeruginosa . The similar appearance between xbp-1; pek-1 larvae dying either at 16°C on pathogenic bacteria or at 23°C on E . coli OP50 led us to consider whether ER stress arising from intestinal innate immune activation might contribute in a similar manner to both conditions . We have previously characterized PMK-1-mediated innate immunity and observed both basal and induced components to immunity regulated by PMK-1 [30] . We therefore hypothesized that basal immune activity under standard , non-pathogenic growth conditions could present a low level of ER stress that is severely exacerbated in the absence of intact physiological UPR function , leading to larval lethality of the xbp-1; pek-1 mutants . Consistent with this hypothesis , we observed that pmk-1 loss-of-function was able to partially suppress the larval lethality of the xbp-1; pek-1 double mutant at 23°C and 25°C ( Figure 5B ) . One explanation for the temperature-sensitive lethality of the xbp-1; pek-1 mutant is that increased temperature leads to increased PMK-1 pathway activation , perhaps as the “non-pathogenic” E . coli OP50 becomes slightly pathogenic . However , the temperature-sensitive lethality is not abrogated by loss of PMK-1; instead , the xbp-1; pmk-1; pek-1 mutant exhibits larval lethality at a temperature several degrees higher than the xbp-1; pek-1 mutant ( Figure 5B ) . Furthermore , the temperature-sensitive larval lethality of the xbp-1; pek-1 mutant on E . coli OP50 was not suppressed by the presence of the bacteriostatic drug ampicillin ( Figure S3A ) . These data indicate that basal immune activation and temperature are distinct contributors to ER stress that function in parallel during growth on E . coli OP50 . The temperature-dependent larval lethality of the xbp-1; pek-1 mutant over a physiological temperature range suggested that UPR signaling might be required for survival in response to thermal stress . Indeed , we observed that the xbp-1 mutant exhibited larval lethality when grown at 27°C , an elevated temperature at which WT N2 C . elegans exhibits a reduced brood size and increased dauer formation ( Figure 5C ) . Similar to our observation that depletion of basal immunity rescued the development of the xbp-1; pek-1 mutant when propagated on E . coli OP50 , the temperature-sensitive lethality in the xbp-1 mutant was suppressed in the xbp-1; pmk-1 double mutant ( Figure 5C ) , but not by the presence of ampicillin ( Figure S3B ) . Unlike the xbp-1 mutant , the development of the pek-1 mutant at 27°C was similar to WT . This is reminiscent of our previous observation that the pek-1 mutant did not exhibit the larval lethality found in xbp-1 when grown on P . aeruginosa at 25°C [21] . However , we next grew the pek-1 mutant on P . aeruginosa at 27°C , reasoning that the elevated temperature would not only increase the ER stress caused by basal growth , but also enhance the pathogenicity of the P . aeruginosa and thereby increase the immune response relative to that at 25°C . Indeed , the pmk-1 mutant exhibited 100% larval lethality on P . aeruginosa at 27°C ( Figure 5D ) , as compared with the 50% lethality we have previously reported for the pmk-1 mutant on P . aeruginosa at 25°C ( Richardson et al . , 2010 ) . The increased susceptibility of this immune-deficient mutant to P . aeruginosa at 27°C relative to 25°C indicates that the increased temperature causes an increase in P . aeruginosa pathogenicity . On P . aeruginosa at 27°C , the pek-1 mutant exhibited larval lethality relative to the WT strain grown 27°C ( Figure 5D ) . These data further suggest that PEK-1 functions in parallel with XBP-1 to protect C . elegans against the ER stress caused by immune activation . We showed in Figure 5B and 5C that loss of PMK-1 improves larval development of the xbp-1; pek-1 mutant and the xbp-1 mutant , respectively , in the absence of infection . We suggested that the mechanism behind this phenomenon is that the previously described basal immune activity through the PMK-1 pathway [31] contributes to ER stress . However , we also considered the possibility that the PMK-1 pathway might play an immunity-independent role in exacerbating ER stress in the setting of UPR deficiency . To test this possibility , we examined the ability of WT and UPR mutants to develop in the presence of tunicamycin with or without functional pmk-1 . We found that the pmk-1 mutant actually exhibited increased sensitivity to tunicamycin during development . In fact , the pmk-1 mutant exhibited greater lethality at a lower dose of tunicamycin than either the xbp-1 or pek-1 single mutants ( Figure 6 ) . These data suggest that the PMK-1 pathway influences ER stress in two ways . First , during infection or under standard growth conditions in the setting of UPR depletion , activation of the PMK-1 pathway generates an increased secretory load that contributes to ER stress . However , when ER stress is induced exogenously with tunicamycin , the PMK-1 pathway activity serves a protective function .
We have shown that the IRE-1-XBP-1 and PEK-1 pathways function together to maintain ER homeostasis in C . elegans under physiological conditions . We found that XBP-1 deficiency results in marked activation of both IRE-1 and PEK-1 , reflecting constitutive ER stress . Activation of innate immunity mediated by PMK-1 p38 MAPK further exacerbated the constitutive ER stress in the xbp-1 mutant . To investigate the physiological roles of UPR signaling as well as the compensatory activity between distinct UPR pathways , we examined both the individual and the combined effects of XBP-1 and PEK-1 deficiency in vivo . We found that the xbp-1; pek-1 double mutant exhibited temperature-sensitive lethality that was independent of developmental stage . Compared with the xbp-1; pek-1 mutant , the xbp-1; pmk-1; pek-1 mutant had moderately increased survival during larval development on non-pathogenic bacteria , when there is a low level of PMK-1-mediated basal immune activity , and dramatically increased survival on pathogenic P . aeruginosa , when the PMK-1-mediated immune response is induced . We further showed that both XBP-1 and PEK-1 are required for full protection against the combined stress of immune activation and that of growth at elevated physiological temperatures , confirming that these two branches of the UPR function together to protect against physiological ER stress . Our observation of dramatically elevated levels of IRE-1 and PEK-1 activity in the setting of XBP-1 deficiency , under standard growth conditions in the absence of exogenous agents to induce ER stress , provides strong evidence for homeostatic activity of the IRE-1-XBP-1 signaling pathway under physiological conditions ( Figure 7A ) , and not merely at the extremes of ER stress induced by pharmacological treatment or in specialized secretory cell types . Our data also reveal a dynamic requirement for UPR signaling in survival that increases with both temperature and increased secretory activity as is induced by immune activation ( Figure 7B ) . Interestingly , the temperature-dependent role for the IRE-1 and PEK-1 pathways is manifest at physiological temperatures optimal for C . elegans development and fecundity , far from commonly utilized “heat shock” conditions ( Figure 7A ) . We speculate that this temperature dependence may be due to altered secretory load at higher temperature or increased tendency for proteins to aggregate in the ER in the absence of intact chaperone production . Importantly , our data suggest that PMK-1-mediated immune activation is one of many sources of the requirement for the UPR during larval development in the absence of infection . We found that , although loss of basal PMK-1 pathway activation partially suppressed the temperature-sensitive larval lethality of the xbp-1; pek-1 mutant , the xbp-1; pmk-1; pek-1 mutant nevertheless exhibited almost complete larval lethality at 25°C . Further , using our smg-2 ( qd101 ) ; xbp-1 ( zc12 ) strains , we observed high constitutive IRE-1-mediated xbp-1 splicing in the xbp-1; pmk-1 mutant that was similar under these experimental conditions to that of the xbp-1 mutant ( Figure 3A ) . These results indicate that the UPR has an essential role during development in protection against immune activation as well as additional processes . Identification of these processes will likely lead to increased understanding of conserved physiological roles of the UPR . We found that the PMK-1 pathway not only contributes to basal ER stress but also protects against exogenous ER stress induced by exposure to tunicamycin ( Figure 6 ) . We speculate that the mechanism underlying this dual function of the PMK-1 pathway may be differences in the PMK-1-activated transcriptional output under different circumstances . The importance of the PMK-1 pathway in protection against exogenous ER stress makes the role of the PMK-1 pathway in contributing to endogenous ER stress even more striking . In mice , Xbp1 deficiency in intestinal epithelial cells ( IEC ) resulted in marked intestinal inflammation that may contribute to the observed activation of not only IRE1 but also of PERK , as measured by expression of one of its downstream effectors , CHOP [12] . In mammals , the transcription factor CHOP promotes apoptosis of mammalian cells that experience prolonged ER stress [32] , and indeed , the majority of Paneth cells underwent apoptosis in the Xbp1−/− IECs . Our observations are consistent with the idea that Xbp1−/− IECs may be predisposed to detrimental consequences of additional ER stress caused by intestinal inflammation because of deregulation of basal ER homeostasis due to XBP-1 deficiency . In pancreatic ß-cells , another cell type that is specialized for high-level secretory activity , XBP1 deficiency has been observed to result in IRE1α hyperactivation , with increased degradation of mRNAs that encode insulin processing enzymes [33] . Our observations that PEK-1 , in concert with XBP-1 , functions to protect against ER stress from immune activation differ from observations in mouse macrophages , in which TLR stimulation was shown to activate IRE1 , but PERK activation was reported to be suppressed rather than elevated [34] , [20] . This difference may be due to roles for XBP-1 in macrophages that extend beyond its function in maintaining ER homeostasis . Indeed , when stimulated by TLRs in macrophages , the IRE1-XBP1 pathway was shown to induce expression of immune effectors rather than typical UPR genes , suggestive that the IRE1-XBP1 pathway may have been co-opted in macrophages to promote macrophage-specific function independent of the UPR [20] . Our data support the idea that UPR signaling does not function simply in response to the extremes of ER stress , as when induced by tunicamycin or by the elevated secretory load of specialized cells such as plasma cells , but instead , as a critical pathway in the maintenance of ER homeostasis during normal growth and development in C . elegans . The diverse and dramatic consequences of XBP-1 deficiency on development and disease , taken together with our observations on the effect of XBP-1 deficiency on basal ER stress levels , underscore the critical role of homeostatic UPR signaling in both normal physiology and disease .
C . elegans strains were constructed and propagated according to standard methods on E . coli OP50 at 16°C [35] . The smg-2 ( qd101 ) allele was isolated by K . Reddy and contains a C→T nonsense mutation at nucleotide 1189 of the spliced transcript . The following strains were used in the study: N2 Bristol , ZD627 smg-2 ( qd101 ) , ZD607 smg-2 ( qd101 ) ;xbp-1 ( zc12 ) , ZD605 smg-2 ( qd101 ) ;xbp-1 ( zc12 ) ;pmk-1 ( km25 ) , KU25 pmk-1 ( km25 ) , RB545 pek-1 ( ok275 ) , ZD510 xbp-1 ( tm2482 ) ;pek-1 ( ok275 ) , ZD524 xbp-1 ( zc12 ) ;pek-1 ( tm629 ) , ZD496 xbp-1 ( tm2482 ) ;pmk-1 ( km25 ) ;pek-1 ( ok275 ) . All of the alleles used are predicted to be null alleles . Specifically , xbp-1 ( tm2482 ) is a 202 bp deletion from nt 231 that causes a frame-shift . The xbp-1 ( zc12 ) allele is a nonsense mutation that changes Q34 to an ochre stop . The two alleles exhibit an equivalent phenotype in every assay tested ( [21]; this work , and our unpublished data ) . The pek-1 ( ok275 ) allele is a 2013 bp deletion and the pek-1 ( tm629 ) allele is a 1473 bp deletion , both of which remove the PEK-1 transmembrane domain and are therefore likely null alleles [6] . Double mutants were made between xbp-1 and pek-1 by crossing strains marked with GFP: xbp-1 ( III ) ;pT24B8 . 5::GFP ( agIs220 ) ( X ) and pT24B8 . 5::GFP ( agIs219 ) ( III ) ;pek-1 ( X ) . GFP-negative F2s were singled , propagated , and genotyped by PCR . For the experiment in Figure 1B , 1C , and 1E , L1 larvae were synchronized by hypochlorite treatment , washed onto E . coli OP50 plates , and grown for 40 h at 16°C to the L3 stage , when they were washed in M9 to plates containing E . coli OP50 or E . coli OP50 with 5 µg/ml tunicamycin . For the experiments in Figure 3 , strains were grown and treated as previously described [21] . Specifically , L1 larvae were synchronized by hypochlorite treatment , washed onto E . coli OP50 plates and grown at 20°C for 23 h , then washed in M9 onto treatment plates . After incubation at 25°C for indicated times , worms were washed off plates and frozen in liquid nitrogen . For P . aeruginosa treatment , P . aeruginosa strain PA14 was grown in Luria Broth ( LB ) , and 25 µl overnight culture was seeded onto 10 cm NGM plates . Plates were incubated first at 37°C for 1 d , then at room temperature for 1 d . All RNA extraction , cDNA preparation , qRT-PCR methods and specific primers to detect xbp-1 mRNA were as described previously [21] . For all immunoblots , strains were synchronized by hypochlorite treatment and washed onto E . coli OP50 plates for growth until the L4 stage . For the experiment in Figure 2A , strains were grown at 20°C and L4 worms were then washed in M9 onto treatment plates for incubation at 25°C for 4 hours . For the experiment in Figure 2B , strains were grown at 16°C until the L4 stage and harvested without treatment . All strains were collected and rinsed 2 times in M9 . Worm pellets were resuspended in an equal volume of 2× lysis buffer containing 4% SDS , 1oomM Tris Cl , pH 6 . 8 , and 20% Glycerol . After boiling for 15 minutes with occasional vortexing to aid in dissolution , lysates were clarified by centrifugation . Protein samples ( 50 µg of total lysate loaded per lane ) were separated by SDS-PAGE and transferred to a nitrocellulose membrane ( Bio-rad ) . Western blots were blocked in 5% milk in PBST and probed with ( 1∶10 , 000 ) anti-eIF2α [26] , ( 1∶1 , 000 ) anti-phospho-eIFα ( Cell Signaling Technology ) , or ( 1∶10 , 000 ) anti-tubulin ( E7 Developmental Hybridoma Bank , Iowa City ) . All primary antibodies were diluted in 5% milk in PBST . Following incubation with anti-rabbit or anti-mouse IgG antibodies conjugated with horseradish peroxidase ( HRP ) ( Cell Signaling Technology ) , signals were visualized with chemiluminescent HRP substrate ( Amersham ) . Quantification of immunoblots was preformed with ImageJ [36] . For all development assays , strains were egg laid on 4–5 prepared plates for no more than 3 h ( at least 110 eggs for each strain and treatment ) . Development was monitored daily for 4 d for experiments conducted at 16°C and 3 d for experiments conducted at all other temperatures . Experiments monitoring development on E . coli OP50 were performed on 6 cm NGM plates . P . aeruginosa PA14 plates were prepared as described [27] . For the data presented in Figure S2 , plates were prepared as described [37] , except that ampicillin was used instead of carbenicillin . To monitor L4 survival on E . coli OP50 or P . aeruginosa PA14 , strains were incubated at 16°C to the L4 stage , when they were transferred to plates containing FUDR and incubated at either 16°C or 25°C . For each strain , 30 worms were transferred to each of 3–4 plates . Alive vs . dead worms were counted , and worms that died by exploding through the vulva or desiccating on the side of plates were censored . | Proteins destined for secretion outside of eukaryotic cells are trafficked through the endoplasmic reticulum ( ER ) . Protein folding in the ER involves the activity of chaperones , as well as catalysis of post-translational modifications such as disulfide bond formation and glycosylation . When the folding capacity of the ER is exceeded , the resulting accumulation of misfolded proteins activates the Unfolded Protein Response ( UPR ) , a conserved signaling response that functions to restore protein folding homeostasis in the ER . Genetic studies have established that the UPR is required for the development of specific cell types in mammals , such as antibody-secreting plasma cells , and recent studies implicate a critical role for UPR signaling in the pathogenesis of metabolic and inflammatory diseases . In this paper we show that innate immunity and elevated physiological temperatures each necessitate UPR activity for C . elegans survival . Furthermore , we show that , under physiological conditions of larval development , basal activity of the UPR is required for the maintenance of ER homeostasis . Our data support the idea not only that the UPR functions as a “stress response” pathway , protecting against the extremes of unfolded protein accumulation , but also that the UPR plays a more general role in animal physiology and development . | [
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"ce... | 2011 | Physiological IRE-1-XBP-1 and PEK-1 Signaling in Caenorhabditis elegans Larval Development and Immunity |
Plant autophagy plays an important role in delaying senescence , nutrient recycling , and stress responses . Functional analysis of plant autophagy has almost exclusively focused on the proteins required for the core process of autophagosome assembly , but little is known about the proteins involved in other important processes of autophagy , including autophagy cargo recognition and sequestration . In this study , we report functional genetic analysis of Arabidopsis NBR1 , a homolog of mammalian autophagy cargo adaptors P62 and NBR1 . We isolated two nbr1 knockout mutants and discovered that they displayed some but not all of the phenotypes of autophagy-deficient atg5 and atg7 mutants . Like ATG5 and ATG7 , NBR1 is important for plant tolerance to heat , oxidative , salt , and drought stresses . The role of NBR1 in plant tolerance to these abiotic stresses is dependent on its interaction with ATG8 . Unlike ATG5 and ATG7 , however , NBR1 is dispensable in age- and darkness-induced senescence and in resistance to a necrotrophic pathogen . A selective role of NBR1 in plant responses to specific abiotic stresses suggest that plant autophagy in diverse biological processes operates through multiple cargo recognition and delivery systems . The compromised heat tolerance of atg5 , atg7 , and nbr1 mutants was associated with increased accumulation of insoluble , detergent-resistant proteins that were highly ubiquitinated under heat stress . NBR1 , which contains an ubiquitin-binding domain , also accumulated to high levels with an increasing enrichment in the insoluble protein fraction in the autophagy-deficient mutants under heat stress . These results suggest that NBR1-mediated autophagy targets ubiquitinated protein aggregates most likely derived from denatured or otherwise damaged nonnative proteins generated under stress conditions .
Autophagy is an evolutionary conserved mechanism for degradation of cytoplasmic constituents including proteins and organelle materials [1] , [2] , [3] . During autophagy , an isolation membrane forms , elongates and sequesters cytoplasmic constituents including organelles . The edges of the membrane then fuse to form a double-membrane vesicle termed autophagosome , which can fuse with the lysosomes or vacuoles to deliver the content for degradation [4] . In the budding yeast , Saccharomuces cerevisiae , where autophagy has been well studied , there are more than 30 autophagy-related ( ATG ) proteins identified [5] . The products of these ATG genes are involved in the induction of autophagy , autophagosome nucleation , elongation , maturation and fusion with vacuoles . Most of the ATG genes initially discovered in yeast have been detected and analyzed in other eukaryotes , suggesting the highly conserved nature of the core autophagy process . One of the best-characterized and probably most important cellular roles of autophagy is to provide an internal source of nutrients under starvation through nonselective , bulk degradation of cytoplasmic constituents including proteins and organelles [6] . However , autophagy also functions as a quality control mechanism that selectively targets damaged organelles and toxic macromolecules [6] . Selective autophagy is mediated by autophagy adaptors that recognize specific autophagy substrates , on the one hand , and interact with autophagosomal marker protein ATG8 , on the other hand , thereby facilitating delivery of captured autophagy cargos to autophagosomes for degradation [6] . In mammalian organisms , for example , autophagic clearance of cytosolic ubiquitinated substrates or aggregate-prone proteins is mediated by autophagy cargo adaptors P62 and NBR1 , which bind ubiquitinated proteins via their C-terminal ubiquitin-associated ( UBA ) domains and the mammalian ATG8 homolog , LC3 ( microtubule-associated protein 1 light chain 3 ) via the LIR ( LC3-interacting region ) motifs [6] . In addition , Nix acts as an adaptor for autophagy of mitochondria ( mitophagy ) during erythrocyte differentiation [7] . There are also adaptors for selective autophagy of bacteria and viruses ( xenophagy ) [8] , [9] . Over the past twenty years or so , more than 30 ATG genes have been identified in Arabidopsis [10] . Similar ATG genes from other plants including tobacco , rice and maize have also been reported and functionally analyzed [11] , [12] , [13] , [14] . These studies have shown that autophagy plays an important role in nutrient recycling and utilization in plants . Under nitrogen- or carbon-limiting conditions , both the formation of the autophagosome and expression of some of the ATG genes are induced [15] , [16] . Furthermore , Arabidopsis mutants defective in autophagy are hypersensitive to nitrogen- or carbon-limiting conditions [15] , [16] , [17] , [18] , [19] . Apparently , during nutrient deprivation , cells rely on autophagy for degradation of cellular structures or macromolecules for free nutrients and energy in order to survive nutrient starvation . Other studies have revealed that autophagy is also involved in the regulation of plant senescence [19] , [20] , [21] . Plant senescence can be considered a process of nutrient redistribution . In the parts of plants undergoing senescence such as old leaves , autophagy participates in the degradation of cellular structures and molecules including chloroplasts and chloroplast proteins for efficient nutrient relocalization and utilization by young tissues and developing fruits and seeds . Autophagy is involved in plant response to biotic stresses . One of the most effective mechanisms in plant immune responses to biotrophic pathogens is immunity-related programmed cell death ( PCD ) ( also known as hypersensitive responses or HR ) . In Tobacco mosaic virus ( TMV ) -inoculated Nicotinana benthamiana expressing the N resistance gene , virus-induced silencing of ATG6 and ATG7 genes resulted in expansion of N-mediated HR to uninfected tissue in inoculated leaves and uninfected distant leaves [12] . Likewise , antisence suppression of Arabidopsis ATG6 limited HR PCD triggered by the RPM1 R gene in response to the avirulent Pseudomonas syringae pv . tomato DC3000 expressing the avirulent gene AvrRpm1 [22] . These studies indicate that autophagy negatively regulates HR PCD in plant immune responses to biotrophic pathogens . We recently reported that Arabidopsis WRKY33 , a transcription factor important for plant resistance to necrotrophic pathogens [23] , interacts with an autophagy protein , ATG18a , in the nucleus , suggesting possible involvement of autophagy in plant responses to necrotrophic pathogens [24] . Indeed , autophagy is induced by infection of the necrotrophic fungal pathogen Botrytis cinerea and Arabidopsis autophagy mutants exhibited enhanced susceptibility to the necrotrophic pathogens B . cinerea and Alternaria brassicicola [24] , [25] . Thus , autophagy plays an important role in plant resistance to necrotrophic fungal pathogens . Autophagy is also induced in plants during abiotic stresses including oxidative , high salt and osmotic stress conditions [26] , [27] . In addition , transgenic RNAi-AtATG18a lines defective in autophagy are hypersensitive to ROS , salt and drought conditions [17] , [18] , [26] . Likewise , rice mutant for OsATG10b was hypersensitive to methyl viologen ( MV ) -induced oxidative stress [13] . Thus , autophagy is involved in in plant responses to a variety of abiotic stresses . Although high temperature is one of the most common abiotic stresses , to our knowledge , there is no reported study that examines the role of autophagy in plant heat tolerance . Although the roles of autophagy in a wide spectrum of biological processes including stress responses in plants have been well established , our understanding of the mechanistic basis for the important roles of plant autophagy in different biological processes is very limited . Functional analysis of plant autophagy has almost exclusively focused on the genes required for the highly conserved core process of autophagosome formation and it is unclear whether other processes of autophagy such as cargo recognition and delivery operates through the same or different mechanisms in diverse biological processes in plants . To gain knowledge about the mechanistic basis of the roles of autophagy and its regulation in plant defense and stress responses , we were interested in ATG8-interacting autophagy cargo adaptors from plants . Using yeast two-hybrid screens , we isolated several ATG8-interacting proteins from Arabidopsis including NBR1 , the homolog of mammalian P62 and NBR1 proteins [28] . In the present report , we isolated two independent knockout mutants for Arabidopsis NBR1 and found that the mutants were compromised in plant tolerance to specific abiotic stresses but were normal in the other biological processes in which autophagy is involved . These results provided genetic evidence that the broad roles of autophagy in diverse biological processes in plants are mediated by multiple , distinct mechanisms . Through a comprehensive molecular , cellular and biochemical analysis of the autophagy mutants , we provided strong evidence that NBR1-mediated autophagy targets ubiquitinated protein aggregates most likely derived from denatured and otherwise damaged nonnative proteins generated under stress conditions .
In Arabidopsis , nine ATG8 genes ( ATG8a to ATG8i ) have been identified [20] . Preliminary qRT-PCR analysis indicated that ATG8a , ATG8e , ATG8f and ATG8i were the most abundantly expressed members of the gene family . To identify ATG8-interacting autophagy cargo adaptors using yeast two-hybrid screens , we first fused full-length coding sequences of Arabidopsis ATG8a and ATG8f with the DNA-binding domain ( BD ) of Gal4 . Using the fused ATG8 proteins as baits , we screened 4×106 independent transformants of an Arabidopsis cDNA prey library and identify more than twenty clones by prototrophy for His and by LacZ reporter gene expression through assays of β-galactosidase activity . The proteins encoded by these positive clones include ATG4 ( Ag2g44140 ) , ATI1 ( At2g45980 ) and ATI2 ( At4g00355 ) . ATG4 , a cysteine protease and known ATG8-interacting protein , cleaves off the C-terminal amino acid of ATG8 to expose a C-terminal glycine residue so it can be conjugated to the lipid phosphatidylethanolamine , a key step in autophagosome formation at the phagophore assembly site [29] . ATI1 and ATI2 are two closed related , plant specific ATG8-interacting proteins that are partially associated with endoplasmic reticulum under normal growth conditions but become mainly associated with newly identified spherical compartments under carbon starvation [30] . Another protein encoded by some of the identified positive clones is NBR1 ( At4g24690 ) , the Arabidopsis homolog of mammalian P62 and NBR1 . In yeast , NBR1 interacted strongly with ATG8f and , to a less extent , with ATG8a , ATG8e and ATG8i . In human , P62 , also known as Sequestosome-1 ( SQSTM1 ) , acts as an autophagy cargo adaptor by interacting with both ubiquitinated cargo proteins and ATG8 , thereby facilitating docking of autophagy substrates to the autophagosomes [6] . There is also growing evidence that human NBR1 cooperates with P62 in the autophagic degradation of ubiquitinated cargo proteins [6] . Mammalian P62 and NBR1 share a number of domains including the N-terminal PB1 domain , a zinc finger domain , a LIR ( LC3 or ATG8-interacting ) motif and one or two C-terminal UBAs [6] . These conserved domains are also present in Arabidopsis NBR1 . When our studies on Arabidopsis NBR1 were in progress , another group reported the identification and characterization of Arabidopsis NBR1 [28] . It was shown that Arabidopsis NBR1 homo-polymerized via the PB1 domain and bound ATG8 through its conserved LIR motif [28] . Pull-down assays showed that NBR1 interacted with six of the eight Arabidopsis ATG8 proteins ( ATG8e was not tested ) [28] . NBR1 did not interact with ATG8h and interacted very weakly with ATG8g [28] . In addition , although Arabidopsis NBR1 contains two UBA domains at its C-terminus , only the C-terminal UBA bound ubiquitin [28] . Further analysis using fused fluorescent proteins demonstrated that Arabidopsis NBR1 is an autophagy substrate degraded in the vacuole in an autophagy-dependent manner [28] . Identification and characterization of a similar protein from tobacco , Joka2 , have also been recently reported [28] , [31] . While these results suggest that Arabidopsis NBR1 may act as an autophagy cargo adaptor , these authors failed to isolate mutants for NBR1 and no genetic analysis was performed to determine its biological functions . To determine whether ATG8 and NBR1 interact in vivo , we performed bimolecular fluorescence complementation ( BiFC ) in Agrobacterium tumefaciens-infiltrated tobacco ( Nicotiana benthamiana ) . We fused Arabidopsis ATG8a to the N-terminal yellow fluorescent protein ( YFP ) fragment ( ATG8a-N-YFP ) and NBR1 to the C-terminal YFP fragment ( NBR1-C-YFP ) . When fused ATG8a-N-YFP was co-expressed with BNR1-C-YFP in tobacco leaves , BiFC signals were detected in transformed cells , including punctate fluorescent structures likely representing pre-autophagosome or autophagosome structures that were induced most likely by bacterial infiltration ( Figure 1 ) . Control experiments in which ATG8a-N-YFP was coexpressed with unfused C-YFP or unfused N-YFP was coexpressed with NBR1-C-YFP did not show fluorescence ( Figure 1 ) . Furthermore , we generated a mutant NBR1 in which the Trp and Ile residues in the conserved WxxI LIR motif between the two UBA domains were changed to Ala residue ( W661A/I664A ) . Previously it has been shown that the double-point mutant is unable to bind ATG8 in vitro [28] . When the mutant NBR1W661A/I664A protein was fused to the C-terminal YFP fragment ( mNBR1-C-YFP ) and coexpressed with ATG8a-N-YFP , we observed no fluorescence ( Figure 1 ) . Autophagy is known to play an important role in plant responses to a range of abiotic stresses including salt , drought and oxidative stress . Heat stress due to high temperature is one of the most common abiotic stresses but there is no reported study on the role of autophagy in plant heat tolerance . Heat stress causes accumulation of denatured proteins that are prone to aggregate [32] . In mammalians , P62 and NBR1 are known to target ubiquitinated protein aggregates for selective autophagy [6] . Therefore , we reasoned that NBR1-mediated autophagy could target , among other damaged proteins , heat-denatured proteins and might be necessary for plant heat tolerance . To analyze possible involvement of NBR1-mediated autophagy in plant heat tolerance , we first examined the expression patterns of seven Arabidopsis autophagy genes ( ATG5 , ATG6 , ATG7 , ATG8a , ATG9 , ATG10 , ATG18a ) and NBR1 in response to high temperature . Arabidopsis wild-type ( Col-0 ) plants were placed in the 22°C and 45°C chambers and total RNA was isolated from rosette leaves for detection of ATG gene transcripts using qRT-PCR . As shown in Figure 2 , the transcript levels of the ATG and NBR1 genes remained largely constant throughout the 10-hour period of the experiments at 22°C . At 45°C , however , the transcript levels of the ATG genes were elevated with varying kinetics . For some of the ATG genes including ATG7 and ATG9 , the increased levels of transcripts were detected as early as 2 hours after initiation of the heat stress . Other ATG genes including ATG8a and ATG18a exhibited increased transcript levels after 6-hour exposure to the high temperature ( Figure 2 ) . Of note , these ATG and NBR1 genes displayed largest increases in their transcript levels after 8–10 h under heat stress ( 45°C ) , when the plants started to show symptoms of dehydration ( Figure 2 ) . We also compared induction of the nine members of the ATG8 gene family and found that there was 4–5 fold induction for ATG8a , ATG8e and ATG8h and 2–3 fold induction for the other members of the gene family following 10-hour heat stress at 45°C ( see Figure S1 ) . To further assess induction of autophagy by heat stress , we examined the effect of heat stress on induction of autophagosome formation using green fluorescent protein ( GFP ) -tagged ATG8a , which is associated with autophagosomes and therefore can be used as a marker of autophagosomes in Arabidopsis [15] , [33] , [34] . Transgenic plants expressing GFP-ATG8a were exposed to 45°C for 3 h , recovered for 0 . 5 h at room temperature and then observed by confocal fluorescence microscopy . In the wild-type Col-0 background , we observed a low number of punctate GFP signals in the plants grown at 22°C ( Figure 3 ) . In heat-treated Col-0 plants , there was a 3-fold increase in punctate GFP-ATG8a fluorescent structures likely representing pre-autophagosome or autophagosome structures ( Figure 3 ) . In the atg7 mutant background , GFP-ATG8a signal was observed but there were few punctate structures ( Figure 3 ) . We also generated transgenic NBR1-GFP plants and observed an increase in punctate NBR1-GFP signals in wild-type and nbr1 mutant plants but not in the atg7 mutant background under heat stress ( Figure 4 ) . Expression of NBR1-GFP complemented the nbr1 heat sensitive mutant phenotypes ( data not shown ) . Thus , both expression of ATG genes and formation of autophagosomes were induced under heat stress . To provide genetic analysis of the role of Arabidopsis NBR1 , we screened five independent T-DNA insertion stocks and isolated a T-DNA insertion mutant for Arabidopsis NBR1 ( nbr1-1 ) from one of them . The nbr1-1 mutant contains a T-DNA insertion in the fourth exon and had only about 3% of the wild-type level of NBR1 transcript , indicating that it is likely to be a knockout mutant ( see Figure S2 ) . Like other autophagy mutants , nbr1-1 mutant plants were normal in growth and development and displayed no detectable morphological phenotypes . We , therefore , used the mutant to analyze the role of NBR1 in the biological processes in which autophagy is involved . First , to analyze directly the role of NBR1-mediated autophagy in heat tolerance , we compared Col-0 wild type , atg5-1 , atg7-2 and nbr1-1 to heat stress . The wild type and mutants were placed in a 45°C growth chamber for 10 hours followed by 3–5 days of recovery at the room temperature . For heat-treated wild-type plants , only some patches of old leaves displayed symptoms of dehydration while a majority of the leaves remained green and viable after the recovery ( Figure 5A ) . On the other hand , a majority of leaves from the atg and nbr1 mutant plants exhibited extensive wilting and bleaching after the recovery ( Figure 5A ) . Thus , disruption of the ATG or NBR1 gene caused increased sensitivity to heat stress . At biochemical levels , heat stress causes increased membrane permeability , aggregation of the light-harvesting complex of photosystem II ( PSII ) and inhibition of PSII [35] , [36] , [37] . Therefore , we also compared the mutants with the wild type for difference in the electrolyte leakage ( EL ) and maximum quantum yield of PSII ( Fv/Fm ) of fully expanded leaves immediately after heat treatment . As shown in Figure 6 , both the EL and Fv/Fm values of these mutants were similar to those of wild-type plants when they were grown at 22°C . After 10-h heat stress at 45°C , the EL values for atg5-1 , atg7-2 and nbr1 mutants were 26 , 41 and 40% higher than that of wild type , respectively ( Figure 6A ) . Likewise , heat stress caused ∼30% more reduction in Fv/Fm in the atg5 , atg7 and nbr1 mutants than in the wild-type plants ( Figure 6B and 6C ) . Thus , membrane integrity and the capacity of PSII photochemistry were more compromised in the atg and nbr1 mutants than in wild type . We subsequently isolated a second mutant for NBR1 ( nbr1-2 ) that contains a T-DNA insertion in the third exon and had only about 5% of the wild-type level of NBR1 transcript ( Figure S2 ) . The nbr1-2 mutant was equally sensitive to heat stress as nbr1-1 mutant ( data not shown ) . In addition , as described below , transformation of the wild-type NBR1 gene into the nbr1-1 mutant restored the heat tolerance in the mutant . These results indicated that the mutant phenotype of compromised heat tolerance of nbr1-1 is due to disruption of NBR1 . Autophagy is known to play an important role in plant responses to oxidative , salt and osmotic stress . When five-weeks old wild type , atg5 , atg7 and nbr1 mutants were sprayed with 20 µM MV , a ROS-generating herbicide , and kept under light for two days , old leaves were bleached but more than 80% of leaf areas remained green in wild-type plants ( Figure 5B ) . By contrast , 80–100% of leaf areas of the atg5 , atg7 and nbr1 mutant plants were bleached after MV treatment ( Figure 5B ) . Thus , like autophagy mutants , nbr1 mutant plants were hypersensitive to oxidative stress . For testing drought tolerance , we transferred five-weeks old Arabidopsis plants into a growth chamber with ∼50% humidity . The plants were unwatered and observed for drought stress symptoms . As shown in Figure 7A , wild-type plants were still largely green and exhibited relatively minor wilting 10 days after watering was stopped . The atg and nbr1 mutants , on the other hand , showed extensive wilting and drought stress symptoms ( Figure 7A ) . For testing salt tolerance , seven days-old seedlings grown on solid MS medium were transferred to the same medium with or without addition of 0 . 16 M NaCl and the survived seedlings were scored 5 days after the transfer . As shown in Figure 7B , ∼90% of wild-type seedling survived in the medium containing 0 . 16 M NaCl . On the other hand , only about 10–15% of atg5 , atg7 and 35% of nbr1 mutant seedlings survived in the salt medium ( Figure 7B ) . Thus , like autophagy-deficient atg and atg7 , nbr1 was sensitive to oxidative , drought and salt stresses . Autophagy is also involved in the regulation of plant senescence , response to nutrient deprivation and resistance to necrotrophic pathogens . As an autophagy receptor , NBR1 might play a broad role in autophagy that recognizes and facilitates docking of a wide range of cellular structures and proteins or target damaged proteins generated by specific stress conditions to the autophagosomes for autophagic clearance . To distinguish between these two possibilities , we also compared Col-0 wild type , nbr1 and autophagy atg5 and atg7 mutants for phenotypes in senescence , response to nutrient deprivation and disease resistance . As reported previously [38] , atg5 and atg7 mutants were indistinguishable from wild type during the first 4–5 weeks after germination when grown under normal conditions but displayed enhanced senescence afterwards as indicated from increased chlorosis of old , fully-expanded leaves ( Figure 8A ) . The nbr1 mutant plants , on the other hand , exhibited no significant difference from wild-type plants in the timing or extent of age-related senescence ( Figure 8A ) . Thus , unlike atg5 and atg7 , nbr1 is normal in age-induced senescence . To determine whether the nbr1 is compromised in response to carbon-deprivation , we placed the nbr1 mutant plants in the dark along with wild type and atg5 and atg7 mutants . As shown in Figure 8B , after 5 days in dark , the atg5 and atg7 mutants started to display severe chlorosis but the nbr1 mutant plants were as green as wild-type plants ( Figure 8B ) . When plants were kept in dark for 6 days and then returned to light , both the atg5 and atg7 mutants had a drastic reduction in survival rates while all of the wild type and nbr1 mutant plants recovered ( Figure 8C ) . These results indicated that the nbr1 mutant was not compromised in response to carbon-starvation . We have recently reported that atg mutants were susceptible to necrotrophic fungal pathogens [24] . To assess the involvement of NBR1 in plant responses to necrotrophic fungal pathogens , we compared the nbr1 mutant with Col-0 wild type , atg5 and atg7 mutants for resistance to the necrotrophic fungal pathogen B . cinerea . In the atg5 and atg7 mutants , the necrotic spots and chlorosis spread rapidly , and the majority of leaves displayed chlorosis by 4 dpi , and macerated by 5 dpi ( Figure 9A ) . By contrast , the majority of leaves from wild type and nbr1 remained green at 4 dpi and 5 dpi ( Figure 9A ) . Quantitative real-time PCR ( qRT-PCR ) indicated that B . cinerea ActA gene transcript levels in the atg5 and atg7 mutants were 6–8 times higher than those in the wild type and nbr1 mutant plants ( Figure 9B ) . Thus , both disease symptoms and fungal growth indicated that the nbr1 mutant was not compromised in resistance to the necrotrophic pathogen . To determine whether the role of NBR1 in plant stress tolerance is due to its action as an autophagy cargo adaptor that interacts with ATG8 , we performed genetic complementation of nbr1-1 with NBR1 genes that differ in the LIR motif for interaction with ATG8 . We generated a mutant NBR1 in which the Trp and Ile residues in the conserved WxxI LIR motif between the two UBA domains were changed to Ala residue ( W661A/I664A ) ( Figure 10A ) . The double-point mutant is unable to bind ATG8 in vitro [28] or in vivo ( Figure 1 ) . Both the wild-type and W661A/I664A mutant NBR1 genes , tagged with a myc epitope , were placed into a plant transformation vector under the control of the CaMV 35S promoter and transformed into nbr1-1 . Transgenic plants were first analyzed by western blotting using anti-myc antibody ( see Figure S3 ) and those plants with similar levels of tagged NBR1 proteins were identified and tested for heat stress tolerance ( Figure S3 ) . As expected , the nbr1-1 mutant was compromised in heat tolerance as indicated from enhanced symptoms developed after heat stress ( Figure 10B ) . Transformation of nbr1-1 with the wild-type NBR1 gene completely restored the heat tolerance of the mutant ( Figure 10B ) . In contrast , in the transgenic nbr1 mutant plants expressing the gene for mutant NBR1 W661A/I664A , there was no restoration of heat tolerance as indicated from the severe symptoms developed after heat treatment ( Figure 10B ) . Likewise , transformation of the nbr1 mutant with the wild-type NBR1 gene but not the mutant NBR1 W661A/I664A gene restored the tolerance of the mutant to PQ-induced oxidative stress ( data not shown ) . These results indicated that interaction with ATG8 is necessary for the important role of NBR1 in plant stress tolerance . As an autophagy adaptor , NBR1's role in plant tolerance to specific abiotic stresses is likely to be mediated by its ability in capturing and delivering specific autophagy cargos to autophagosomes for degradation . To identify NBR1-recognized autophagy cargos , we constructed transgenic lines in both Col-0 and atg7 mutant backgrounds that harbor tandem-affinity purification ( TAP ) -tagged NBR1 under control of the constitutive CaMV 35S promoter . Transgenic lines containing similar levels of NBR1-TAP transgene transcripts were identified by RNA blotting ( Figure S4 ) and used for purification of NBR1-interacting proteins . After heat stress , total soluble proteins were isolated from the transgenic plants and subjected to the TAP procedure [39] . However , even in the heat-stressed atg7 mutant background , in which the autophagy cargo proteins are expected to accumulate , several attempts of affinity purification of NBR1-containing protein complexes from the soluble protein fraction failed to isolate NBR1-interacting proteins that could be readily and reproducibly detected by Coomassie blue staining following sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS/PAGE ) . The failure to isolate cargo proteins of NBR1 from the soluble protein fraction prompted us to examine NBR1-TAP and associated proteins in the insoluble protein faction . After homogenization in a detergent-containing buffer and filtering through nylon membranes , insoluble detergent-resistant protein aggregates were separated from soluble proteins by low-speed centrifugation [40] . Both soluble proteins in the supernatant and insoluble proteins in pellets were separated by SDS/PAGE and NBR1-TAP was detected by western blotting . As shown in Figure 11 , in transgenic wild-type plants grown at 22°C , NBR1-TAP was detected almost exclusively in the supernatant . Under heat stress at 45°C , some NBR1-TAP was also detected in the insoluble pellet but there was only marginal change in the total levels of NBR1-TAP in the transgenic wild-type plants ( Figure 11 ) . In the transgenic atg7 mutant background , a majority of NBR1-TAP was also present in the supernatant when grown under 22°C ( Figure 11 ) . Interestingly , the levels of NBR1-TAP in the transgenic atg7 mutant plants increased substantially after 3–6 hours at 45°C and there was an increased enrichment of NBR1-TAP in the insoluble detergent-resistant pellets in heat-stressed atg7 mutant ( Figure 11 ) . After 9 hours under 45°C , a majority of NBR1-TAP in the transgenic atg7 plants was detected in the pellets ( Figure 11 ) . Thus , in the autophagy-deficient mutant , heat stress resulted in increased accumulation of NBR1 as insoluble protein aggregates . The increased accumulation of NBR1-TAP in the insoluble fraction and the failure to isolate NBR1-interacting proteins from the soluble fraction in heat-stress atg7 mutant plants raised the possibility that the cargo proteins of NBR1 might be predominantly present in the pellets as insoluble protein aggregates . To test this possibility , we directly compared wild type , atg7 and nbr1 mutants for heat-induced accumulation of insoluble detergent-resistant protein aggregates . The plants were first subjected to various periods of 45°C heat stress and soluble and insoluble proteins were quantitated after separation by low-speed centrifugation . As shown in Figure 12A , the percentages of insoluble to total proteins in the wild type did not display significant change over the period of 9 hours under heat stress ( Figure 12A ) . By contrast , in both the atg7 and nbr1 mutants , insoluble proteins increased significantly after 3-hour exposure to 45°C ( Figure 12A ) . After 9 hours at 45°C , the levels of insoluble protein aggregates in the atg7 and nbr1 mutants were about three times of those in the wild type ( Figure 12A ) . To compare the profiles of the insoluble proteins with those of soluble ones , we separated the proteins on SDS/PAGE . As shown in Figure 12B , increased levels of insoluble proteins in heat-stressed atg7 and nbr1 mutants were associated with the detection of major protein bands on SDS/PAGE gel that appeared to be the same proteins as the the most abundant proteins in the soluble fraction based on the migration patterns ( indicated by arrows in Figure 12B ) . Thus , the most abundant insoluble proteins accumulated in the heat-stressed autophagy mutants were likely to be derived from the most abundant soluble proteins . Arabidopsis NBR1 contains two UBA domains with the C-terminal one capable of binding ubiquitin [28] and may play a critical role in stress tolerance by targeting ubiquitinated protein aggregates that are formed under stress conditions . To test this , we compared the soluble and insoluble proteins for the levels of ubiquitination prior to and after heat stress . We isolated both soluble and insoluble proteins from the leaves of the wild type , atg7 and nbr1 mutants after 0 , 3 and 9 hours of heat treatment . The proteins were fractionated on a SDS gel and analyzed for ubiquitinated proteins using an anti-ubiquitin monoclonal antibody . As shown in Figure 13 , we observed similar levels of ubiquitinated proteins in the soluble fractions in these plants with or without heat stress . In the insoluble fractions , we observed only slighly higher levels of ubiquitinated proteins in the atg7 and nbr1 mutants than in wild type when grown at 22°C ( Figure 13 ) . However , after 3 hours at 45°C , we observed a drastic increase in the levels of ubiquitinated proteins in the atg7 and nbr1 mutants but not in the wild-type plants ( Figure 13 ) . The levels of ubiquitinated proteins were further increased in the atg7 and nbr1 mutants after 9 hours at 45°C ( Figure 13 ) . Thus , proteins in the insoluble fraction from heat-stressed atg7 and nbr1 mutants are highly ubiquitinated .
Autophagy plays a broad role in diverse biological processes in plants including senescence , nutrient recycling and plant immune responses . Autophagy also plays a critical role in plant responses to oxidative , drought and salt stresses . In the present study , we showed that autophagy is also involved in plant response to heat stress . First , both the formation of autophagosomes and expression of ATG genes were induced in heat-stressed Arabidopsis plants ( Figure 2 , Figure 3 , Figure 4 ) . The number of autophagosomes started to increase as early as 0 . 5 hour and tripled after 3 hours of heat stress at 45°C in wild-type plants ( Figure 3 and Figure 4 ) . Thus , induction of autophagosome formation in response to heat stress was quite rapid . On the other hand , transcript levels of the ATG genes were elevated in heat-stressed plants with relatively slow and varying kinetics . Although increased levels of transcripts for some of the ATG genes including ATG7 and ATG9 were detected as early as two hours after initiation of the heat stress , other ATG genes exhibited increased transcript levels after 6 hours and displayed large increases after 8–10 hours of heat stress . The large increase in ATG gene expression may be necessary for sustained autophagosome formation under prolonged heat stress . Second , the heat tolerance of autophagy-deficient atg5 and atg7 mutants was compromised based on their increased morphological symptoms associated with enhanced biochemical defects and reduced recovery after heat stress ( Figure 5 and Figure 6 ) . As a common abiotic stress with increasing importance in agriculture in many parts of the world , high temperature can be manipulated relatively easily , impacts the cells of an exposed plant not only rapidly but also uniformly and , therefore , can be a useful environmental condition for studying autophagy . In plants , studies of the roles of autophagy in diverse biological processes have been mostly through functional analysis of the genes required for the core process of autophagosome assembly . On the other hand , little is known about the genes involved in other important processes of autophagy such as autophagy cargo recognition , sequestration and transport . As a result , our knowledge about the mechanistic basis for the broad roles of plant autophagy in diverse biological processes is very limited . In the present study , we isolated and characterized Arabidopsis NBR1 , a homolog of mammalian autophagic adaptors P62 and NBR1 . Identification and characterization of NBR1 and a similar protein from tobacco , Joka2 , have also been recently reported [28] , [31] . These studies suggest that plant NBR1 proteins are autophagy cargo adaptors based on the structures , interaction with ATG8 , subcellular localization and autophagic degradation . To determine the biological functions of plant NBR1 , we isolated two independent knockout mutants for NBR1 and conducted a comprehensive comparison of their phenotypes with those of autophagy-deficient atg5 and atg7 mutants in the biological processes in which autophagy is necessary . Like autophagy-deficient atg5 and atg7 mutants , the nbr1 mutants were compromised in plant tolerance to heat , salt , drought and oxidative stress ( Figure 5 , Figure 6 , Figure 7 ) . Mutant NBR1 with a mutated LIR motif unable to interact with ATG8 is not functional in conferring plant stress tolerance ( Figure 10 ) , indicating that the role of NBR1 in plant stress tolerance is mediated by autophagy . Unlike atg5 and atg7 mutants , nbr1 mutants were normal in age- and dark-induced senescence and in response to necrotrophic pathogen infection ( Figure 8 and Figure 9 ) . Thus NBR1-mediated autophagy is necessary only in plant responses to specific abiotic stresses including heat , ROS , salt and drought but is dispensable in plant senescence , nutrient recycling and defense responses . The lack of phenotypes of nbr1 mutants in some of the biological processes involving autophagy suggests that the broad roles of autophagy in diverse biological processes are mediated by multiple cargo recognition and delivery systems in plants . As an autophagy adaptors , the selective roles of NBR1 in plant response to specific abiotic stresses is most likely mediated by its ability in recognizing specific cargo proteins generated under abiotic stresses and facilitating their delivery to autophagosomes for degradation . Arabidopsis NBR1 contains a C-terminal UBA domain capable of binding ubiquitin [28] ( Figure 10A ) , suggesting that NBR1 recognizes ubiquitinated protein substrates during plant stress responses . To identify NBR1-captured cargo proteins , we used the TAP procedure but failed to isolate sufficient amounts of NBR1-interacting proteins from the soluble fraction of heat-stressed atg7 mutant , in which cargo proteins are not subjected to autophagic degradation and , therefore , should accumulate to high levels . Further analyses revealed that under heat stress , NBR1 was increasingly enriched in the insoluble fraction in the atg7 mutant ( Figure 11 ) and the compromised heat tolerance of the atg5 , atg7 and nbr1 mutants were also associated with increased accumulation of insoluble detergent-resistant protein aggregates under heat stress ( Figure 12 ) . Importantly , insoluble protein aggregates accumulated in heat-stressed atg7 and nbr1 mutants were highly ubiquitinated ( Figure 13 ) . Abiotic stresses such as high temperature causes damages to a variety of cellular structures and macromolecules including protein denaturation and aggregation [41] . Apparently , under heat stress , denatured or otherwise damaged cellular proteins are ubiquitinated by plant cellular protein quality control machinery and targeted by NBR1 for autophagic degradation . In autophagy-deficient mutants , these denatured or otherwise damaged proteins are still ubiquitinated but not degraded and therefore accumulate at high levels as insoluble detergent-resistant protein aggregates . Protein ubiquitination , catalyzed by a cascade of reactions involving a ubiquitin-activating enzyme ( E1 ) , a ubiquitin-conjugating enzyme ( E2 ) and a ubiquitin ligase ( E3 ) , plays diverse roles in regulating cellular activities including selection of proteins to be degraded [42] . The majority of the cytosolic proteins destined for degradation in the eukaryotic cells are first polyubiquitinated and then targeted for degradation by the 26S proteasomes , a large protease complex consisting of a barrel-shaped 20S proteolytic core in association with two 19S regulatory caps [42] . For degradation by the proteasomes , proteins must be unfolded to enter the 13-Å wide central cavity since the steric conditions of a folded globular protein would not fit through the narrow entrance channel [43] . Under certain stress or pathological conditions , protein substrates to be degraded may form non-dissociable aggregates and , therefore , cannot be processed by proteasomes . In fact , accumulation of protein aggregates such as the polyglutamine-expanded huntingtin , which is associated with the neurodegenerative Huntington disease , can inhibit proteasome activities by clogging the proteasomes [44] . The increased accumulation of ubiquitinated proteins in heat-stressed autophagy mutants indicates that autophagy is also a major route for degradation of ubiquitinated proteins under stress conditions in plants . Conceivably , under heat and other related stress conditions , some of the cellular proteins are irreversibly denatured or damaged and , as a result , form non-dissociable protein aggregates that can be efficiently degraded only by NBR1-mediated selective autophagy but not by size-limited proteasomes . Direct protein quantification revealed approximately a 3-fold increase in insoluble detergent resistant proteins in the atg7 and nbr1 mutants over the wild-type plants after 9 hours of heat stress ( Figure 12A ) . However , western blotting showed much higher levels of ubiquitinated proteins in the insoluble fraction of the atg7 and nbr1 mutants than in the wild-type plants ( Figure 13 ) . This discrepancy indicated that the insoluble proteins from the wild-type plants were much less ubiquitinated than those from the atg7 and nbr1 mutants . It is possible that in heat-stressed wild-type plants , denatured or otherwise damaged proteins would form insoluble aggregates and then gradually become ubiquitinated . Those ubiquitinated protein aggregates would be recognized by NBR1 and preferentially degraded , while those un-ubiquitinated or under-ubiquitinated proteins aggregates would accumulate in the insoluble fraction . In addition , some of the un-ubiquitinated proteins in the insoluble fraction might come from contamination from the soluble fraction or from proteins that were denatured or damaged during the isolation process . Comparison of protein profiles by SDS/PAGE reveled that the most abundant soluble proteins were also present abundantly in the insoluble fraction in heat-stressed atg mutant ( Figure 12B ) . Since these abundant insoluble proteins were present at much lower levels in heat-stressed wild-type plants , it is unlikely that they were resulted from contamination of soluble proteins or from protein denaturation or aggregation during the isolation process . More likely , they were derived from denatured or otherwise damaged soluble proteins during heat stress that were rapidly degraded in wild type but accumulated as insoluble protein aggregates in the autophagy-deficient mutants . Thus , it seems that under heat stress many if not all abundant soluble proteins can potentially become substrates of NBR1-mediated autophagy , presumably because a majority of plant cellular proteins are not very heat-resistant and are prone to denaturation and aggregation after a prolonged period at 45°C . Under milder heat or other stress conditions , however , plant cellular proteins may display differential proneness in aggregation and therefore may be differentially targeted by NBR1-mediated autophagy . Studies from yeast to mammalians have shown that misfolded or damaged proteins generated during protein synthesis or from post-synthetic modifications such as oxidation are recognized and ubiquitinated by cellular protein quality control machinery [45] , [46] , [47] . Our results strongly suggest that NBR1-mediated selective autophagy does not appear to target specific proteins; more likely it targets insoluble , aggregated forms of many if not all of plant cellular proteins . As protein ubiquitination is an important and extensive mechanism in protein quality control , it is intriguing that the nbr1 mutants shared only some but not all of the phenotypes of the autophagy-deficient atg5 and atg7 mutants . Arabidopsis has no additional genes encoding proteins structurally similar to NBR1 and , therefore , mechanisms other than functional redundancy are likely to be responsible for the lack of phenotypes of nbr1 in some of the biological processes involving autophagy . Under carbon-deprivation conditions in dark , plant autophagy may participate in non-selective , bulk degradation of cellular contents , which may not necessarily require NBR1 . During age-induced senescence , autophagy may also be engaged in the non-selective degradation of cellular structures and macromolecules of older leaves so that the nutrients can be re-distributed and utilized by young tissues and developing fruits and seeds . From the phenotype of increased senescence of autophagy mutants , however , the role of autophagy in plant age-induced senescence is not only about nutrient redistribution to young/growing tissues but also about increasing lifespan of older leaves . Indeed , the analysis of Arabidopsis autophagy mutants has revealed that salicylic acid biosynthesis and signaling is accelerated and fed into an amplification loop through reactive oxygen species to promote cell death during senescence [38] . Autophagy is also induced by this senescence-induced salicylic acid and ROS to operate a negative feedback loop modulating salicylic acid and ROS production most likely by selectively removing damaged organelles or macromolecules generated during senescence [38] . Likewise , necrotrophic pathogens kill host cells during early infection stages through a combined action of ROS , toxins , hydrolytic enzymes and other virulent factors [48] , [49] and autophagy promotes plant resistance to necrotrophic pathogens by promoting host cell survival most likely through removal of irreversibly damaged , inactivated and other toxic cytoplasmic constituents in infected plant cells [24] . The normal phenotypes of nbr1 in age-induced senescence and plant resistance to necrotrophic pathogens , however , might suggest that ubiquitination is not a major route of recognition of damaged proteins during senescence and innate immune responses . Alternatively irreversibly damaged and inactivated proteins are first recognized through ubiquitination during senescence and innate immune responses but are removed by other non-autophagy pathways such as 26S proteasomes or by autophagy through a different ubiquitin-recognizing adaptor . Proteomic profiling of ubiquitinated proteins under different types of stress conditions and identification of additional autophagy adaptors and their respective cargos will be very fruitful in addressing these important issues . In summary , we isolated two nbr1 knockout mutants and discovered that disruption of Arabidopsis NBR1 caused increased sensitivity to a spectrum of abiotic stresses but had no significant effect on plant senescence , responses to carbon starvation or resistance to a necrotrophic pathogen . A selective role of NBR1 in plant responses to specific abiotic stresses suggest that plant autophagy in diverse biological processes operates through multiple cargo recognition and delivery systems . We have further discovered that under heat stress NBR1 was increasingly enriched in the insoluble fraction in association with increased accumulation of insoluble detergent-resistant protein aggregates that are highly ubiquitinated . These results strongly suggest that NBR1-mediated autophagy targets ubiquitinated protein aggregates most likely derived from denatured and otherwise damaged nonnative proteins generated under stress conditions .
The Arabidopsis mutants and wild-type plants used in the study are all in the Co-0 background . The atg5 and atg7 mutants have been previously described [23] , [24] . Homozygous nbr1-1 ( Salk_135513 ) and nbr1-2 ( GABI_246H08 ) mutants were identified by PCR using primers flanking the T-DNA/transposon insertions ( 5′-AGCATCCTCGTCGTGTTTGT-3′ and 5′-CAACCTAACTCAAGCCATCG-3′ ) ( Figure S2 ) . Quantitative RT-PCR of the NBR1 transcripts were reduced more than 20 fold in the nbr1-1 and nbr1-2 mutants , indicating that they are both knockout mutants ( Figure S2 ) . Arabidopsis plants were grown in growth chambers at 22°C , 120 µE m−2 light on a photoperiod of 12 h light and 12 h dark . The ATG8a coding sequence was fused with N-YFP to generate N-terminal in-frame fusions with N-YFP , and DNA sequences for NBR1 and NBR1 W661A/I664A were fused with C-YFP to generate C-terminal in-frame fusions with C-YFP as previously described [50] . The resulting clones were verified by sequencing . The plasmids were introduced into Agrobacterium tumefaciens ( strain GV3101 ) , and infiltration of N . benthamiana was performed as described previously [50] . Infected tissues were analyzed at approximately 24 hours after infiltration . Fluorescence staining were visualized using a Zeiss LSM710 confocal microscope and images were superimposed using ZEISS LSM710 software . Total RNA was isolated from 4-week-old plants using Trizol reagent ( Sangon , China ) , according to the manufacturer's recommendations . Genomic DNA was removed with the RNeasy Mini Kit ( Qiagen , Germany ) . Total RNA ( 1 µg ) was reverse-transcribed using ReverTra Ace qPCR RT Kit ( Toyobo , Japan ) , following the manufacturer's instructions . Gene-specific RT-PCR primers were designed based on their cDNA sequences ( Table S1 ) . Quantitative real-time PCR was performed using the iCycler iQTM real-time PCR detection system ( Bio-Rad , Hercules , CA , USA ) . Each reaction ( 25 µL ) consisted of 12 . 5 µL SYBR Green PCR Master Mix ( Takara , Japan ) , 1 µL of diluted cDNA and 0 . 1 µmol of forward and reserve primers . PCR cycling conditions were as follows: 95°C for 3 min , and 40 cycles of 95°C for 10 seconds ( s ) and 58°C for 45 s . The relative gene expression was calculated as previously described [51] . The Arabidopsis ACTIN2 gene was used as internal control as previously described [52] . Transgenic wild-type Col-0 plants expressing a GFP–ATG8a fusion construct were previously described [24] . To generate transgenic atg7-2 mutant plants expressing GFP–ATG8a , the fusion construct was transformed into atg7-2 using the floral-dip method [53] and transgenic plants were identified on the basis of kanamycin resistance and confirmed by RNA blotting using the GFP DNA fragment as a probe . For generating transgenic NBR1-GFP plants , Arabidopsis NBR1 full-length cDNA was PCR-amplified using gene-specific primers ( 5′-CGATGGAGTCTACTGCTAACGCA-3′ and 5′- GAAGAAGAGAGGTGCTGCCATGGCAGCCTCCTTCTCCCCTGTGAG-3′ ) and fused to a GFP gene . The NBR1–GFP fusion gene was sub-cloned under the control of the CaMV 35S promoter in the PFGC5941 binary vector . Transgenic plants were identified on the basis of Basta resistance , and confirmed by RNA blotting using a GFP DNA fragment as probe . For visualization of induction of autophagy , 4-weeks old transgenic plants expressing the GFP-ATG8a and GFP-NBR1 fusion gene were treated with or without heat shock for 3 h and recovered for 0 . 5 hour . The leaves of transgenic plants were observed using LSM710 confocal microscope with excitation at 488 nm , and imageswere superimposed using ZEISS LSM710 software . For testing heat tolerance , five weeks-old Arabidopsis Col-0 wild type ( WT ) and mutant plants were placed in 22°C and 45°C growth for 10 hours and then immediately analyzed for electrolyte leakage ( EL ) or Fv/Fm , or moved to room temperature for 3–5 day recovery for observation of heat stress symptoms . For testing tolerance to oxidative stress , five weeks-old Arabidopsis plants were sprayed with 20 µM methyl viologen ( MV ) and kept under light for two days before the picture of representative plants was taken . For testing drought tolerance , five weeks-old Arabidopsis plants were placed into a growth chamber with approximately 50% humidity . The plants were unwatered and observed for drought stress symptom development . For testing salt tolerance , seven days-old seedlings grown on solid MS medium were transferred to the same medium with or without added NaCl ( 0 . 16 M ) and the survived seedlings were scored 5 days after the transfer . For determination of electrolyte leakage caused by high temperature , the leaves of 4 to 5-week-old plants were measured after different treatments as previously described [54] . Chlorophyll ( chl ) fluorescence was measured using an Imaging-PAM Chlorophyll Fluorometer equipped with a computer-operated PAM-control unit ( IMAG-MAXI; Heinz Walz , Effeltrich , Germany ) . The seedlings were kept in the dark for approximately 30 min before the measurements were taken . The intensities of the actinic light and saturating light settings were 280 µmol mol−2 s−1 and 2500 µmol mol−2 s−1 PAR , respectively . The maximum quantum yield of PSII ( Fv/Fm ) were measured and calculated as previously described [52] . For generating transgenic NBR1 over-expression lines , the full-length coding sequences for NBR1 genes was first PCR-amplified using gene-specific primers ( 5′- GCACAAGAAGGTCCATGGAGTCTACTGCTAACGCA-3′ and 5′-AGCTTAATTAAAGCCTCCTTCTCCCCTGTGAG-3′ ) and then inserted behind the CAMV 35S promoter in the plant transformation vector PFGC5941-myc . The NBR1W661A/I664A mutant gene was generated using the QuikChange kit from Agilent with the following primers: 5′-GAGTTAGCGAGGCGGATCCAGCCCTAGAGGAGCT-3′ and 5′-AGCTCCTCTAGGGCTGGATCCGCCTCGCTAACTC-3′ . The resulted plasmids were transformed into nbr1-1 mutant plants and transformants were identified for resistance to Basta . Transgenic plants overexpressing the NBR1 transgene were identified by western blot using an anti-myc monoclonal antibody ( Figure S2 ) . Homozygous T2 transformants were used in the study . Culture and inoculation of B . cinerea were performed as previously described [24] , [55] . Biomas of the fungal pathogen was quantified by qRT-PCR of total RNA isolated from inoculated plants for the B . cinerea ActA gene transcript levels as described previously [55] . Full-length NBR1 cDNA was PCR-amplified using gene-specific primers ( 5′- GCACAAGAAGGTCCATGGAGTCTACTGCTAACGCA -3′ and 5′- GAAGAAGAGAGGTGCTGCCATGGCAGCCTCCTTCTCCCCTGTGAG - 3′ ) and then inserted behind theCAMV 35S promoter in the plant transformation/TAP vector [56] . The resulted plasmid were transformed into Col-0 and atg7-2 mutants . Transformants were identified for resistance to Gentamycin . Transgenic plants expressing similar levels of the NBR1 transgene were identified by northern blotting . Arabidopsis leaves were collected before and after heat treatment , ground in liquid nitrogen and homogenized in an detergemt-containing extraction buffer ( 100 mMTris/HCl , pH 8 . 0 , 10 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 2% ß-mercaptoethanol ) . The homogenates were filtered through a 300 µm and 100 µm nylon mesh and clarified by centrifugation at 2 , 200xg for 5 minutes . Supernatants were kept for further analysis . The pellets were resuspended in the same buffer and subjected to the low speed centrifugation . The process was repeated twice and after the last centrifugation the pellets were resuspended in the extraction buffer . The concentrations of proteins in the homogenates ( total proteins ) , the first supernatants ( soluble proteins ) and last pellets ( insoluble proteins ) were determined using Bio-Rad protein assay kit . The first supernatant fractions and last pellets were separated by SDS–PAGE . For western blotting , fractionated proteins on SDS/PAGE gel were transferred to nitrocellulose membrane . NBR1-TAP was detected by a peroxidase-conjugated anti-peroxidase antibody . Ubiquitinated proteins were detected by protein blotting using an anti-ubiquitin monoclonal antibody ( Sigma , USA ) . The antigen-antibody complexes were detected by enhanced chemiluminescence using luminal as substrate as previously described [39] . Sequence data for the genes described in this study can be found in the GenBank/EMBL data libraries under the accession numbers shown in parentheses: ACTIN2 ( AT3G18780 ) , ATG5 ( At5g17290 ) , ATG6 ( At3g61710 ) , ATG7 ( At5g45900 ) , ATG8a ( AT4G21980 ) , ATG9 ( At2g31260 ) , ATG10 ( At3g07525 ) , ATG18a ( At3g62770 ) , NBR1 ( AT4G24690 ) . | Autophagy is an evolutionarily conserved process that sequestrates and delivers cytoplasmic macromolecules and organelles to the vacuoles or lysosomes for degradation . In plants , autophagy is involved in supplying internal nutrients during starvation and in promoting cell survival during senescence and during biotic and abiotic stresses . Arabidopsis NBR1 is a homolog of mammalian autophagy cargo adaptors P62 and NBR1 . Disruption of Arabidopsis NBR1 caused increased sensitivity to a spectrum of abiotic stresses but had no significant effect on plant senescence , responses to carbon starvation , or resistance to a necrotrophic pathogen . NBR1 contains an ubiquitin-binding domain , and the compromised stress tolerance of autophagy mutants was associated with increased accumulation of NBR1 and ubiquitin-positive cellular protein aggregates in the insoluble protein fraction under stress conditions . Based on these results , we propose that NBR1 targets ubiquitinated protein aggregates most likely derived from denatured and otherwise damaged nonnative proteins for autophagic clearance under stress conditions . | [
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] | 2013 | NBR1-Mediated Selective Autophagy Targets Insoluble Ubiquitinated Protein Aggregates in Plant Stress Responses |
PMEL is a pigment cell-specific protein that forms physiological amyloid fibrils upon which melanins ultimately deposit in the lumen of the pigment organelle , the melanosome . Whereas hypomorphic PMEL mutations in several species result in a mild pigment dilution that is inherited in a recessive manner , PMEL alleles found in the Dominant white ( DW ) chicken and Silver horse ( HoSi ) —which bear mutations that alter the PMEL transmembrane domain ( TMD ) and that are thus outside the amyloid core—are associated with a striking loss of pigmentation that is inherited in a dominant fashion . Here we show that the DW and HoSi mutations alter PMEL TMD oligomerization and/or association with membranes , with consequent formation of aberrantly packed fibrils . The aberrant fibrils are associated with a loss of pigmentation in cultured melanocytes , suggesting that they inhibit melanin production and/or melanosome integrity . A secondary mutation in the Smoky chicken , which reverts the dominant DW phenotype , prevents the accumulation of PMEL in fibrillogenic compartments and thus averts DW–associated pigment loss; a secondary mutation found in the Dun chicken likely dampens a HoSi–like dominant mutation in a similar manner . We propose that the DW and HoSi mutations alter the normally benign amyloid to a pathogenic form that antagonizes melanosome function , and that the secondary mutations found in the Smoky and Dun chickens revert or dampen pathogenicity by functioning as null alleles , thus preventing the formation of aberrant fibrils . We speculate that PMEL mutations can model the conversion between physiological and pathological amyloid .
Amyloid fibrils are polymers of single proteins that oligomerize and assemble into a characteristic fibrillar structure with a cross-beta sheet backbone [1] , [2] . Amyloid formation is typically associated with pathologies , such as the Aβ aggregates in Alzheimer Disease or prion aggregates in inherited or acquired spongiform encephalopathies . However , the amyloid fold has also been exploited for functional means in prokaryotes , lower eukaryotes and even in mammals [2]–[6] . The structural and biogenetic features that distinguish functional from pathological amyloid are not well understood . Discerning these features might lead to novel therapies for amyloid diseases . A potential model for distinguishing functional from pathological amyloids is the pigment cell-specific integral membrane glycoprotein , PMEL ( also called gp100 , Pmel17 , or Silver ) [7] , [8] . Functional amyloid fibrillar sheets , composed largely of lumenal proteolytic fragments of PMEL , form the structural foundation of eumelanosomes , which are membrane-bound , pigment cell-specific lysosome-related organelles within which black and brown melanin pigments are synthesized and stored [9] , [10] . The fibrils begin to form in association with intralumenal membrane vesicles ( ILVs ) within multivesicular melanosome precursors [11] , [12] , to which PMEL is selectively delivered during biosynthetic transport [13] , [14] . Either in the trans Golgi network [15] or in association with the ILVs or with membrane domains destined for the ILVs [16] , PMEL is cleaved by two site-specific proteases to liberate a lumenal fragment called Mα [13] , [14] , [16]–[18] ( see Figure 1 ) . Mα then undergoes ordered oligomerization into protofibrils that are detergent insoluble [17] , detectable by electron microscopy ( EM ) [11] , [13] , [14] , [17] , and reactive with amyloidogenic dyes [19] . The Mα fibrils are further matured by proteolytic processing [20]–[23] and assemble into sheets [11] , [24] , upon which melanins deposit as they are synthesized during melanosome maturation [10] , [24] . In vitro , denatured recombinant Mα fragments that are diluted into non-denaturing buffers rapidly assemble into fibrils that are classified as amyloid by a number of biophysical measures [19] , [23] , [25] . The physiological function of the fibrils is not entirely clear , but they likely serve to condense melanin intermediates to facilitate their detoxification , polymerization , and/or intercellular or intracellular transfer [8] . This function seems to be important for optimal pigmentation , as animal models with apparently hypomorphic mutations in PMEL show varying levels of hypopigmentation , ranging from modest in the silver mouse [26] to more pronounced in Merle dogs [27] and fading vision zebrafish [28] . In the accompanying paper by Hellström et al . [29] , we show that a complete loss of PMEL expression in the Pmel -/- mouse also presents with a modest pigment dilution . Compared to the more modest effects of the PMEL mutations described above , genetic models in the chicken and horse show that in some cases , PMEL mutations can result in severe hypopigmentation . In the chicken , a nine bp in-frame insertion within the coding region for the PMEL transmembrane domain ( TMD; TMinsWAP ) is associated with the nearly complete loss of feather eumelanin in Dominant White ( DW ) chickens [30] . The pigment loss in DW chickens is associated with poor melanocyte survival in culture and with melanocyte depletion along the feather shaft in vivo [31] , both exaggerations of the increased cell cycle length [32] and slow , progressive melanocyte loss [33] observed in homozygous mice bearing the recessive , hypomorphic PMEL silver mutation . However , unlike mouse silver , the DW allele is associated with impaired melanosome maturation and with melanosome loss within epidermal melanocytes [34] , [35] . Moreover , the DW allele differs from mouse silver and other PMEL hypomorphs in that it confers hypopigmentation in a dominant fashion , suggesting that the DW PMEL product must either inhibit endogenous PMEL function in a dominant-negative manner or confer some gain-of-function that is detrimental to melanocyte function and/or health . Interestingly , a concomitant deletion within the conserved lumenal Polycystic Kidney Disease-1 homology ( PKD ) domain in the Smoky chicken ( PKDΔLVVT ) reverts the dominant phenotype of the TMinsWAP DW mutation , resulting in a much more modest hypopigmentation that is inherited in a recessive manner [30] . In Silver horses ( HoSi ) , a dominant missense mutation in PMEL results in a transversion that substitutes a cysteine for the second of three consecutive arginine residues immediately following the PMEL TMD ( TMR625C ) , causing a dilution of black pigment that is most noticeable in the mane and tail of the animal [36] , [37] . An orthologously identical TMR625C mutation in the Dun chicken PMEL allele is accompanied by an additional gene deletion ( TMΔ5 ) that eliminates five residues from the PMEL TMD [30] . However , while Dun is likewise dominant , it confers a more modest hypopigmentation , suggesting either species-specific variation in the TMR625C PMEL phenotype or that the TMΔ5 secondary mutation might partially dampen the pigmentation defect that the TMR625C mutation confers individually . Why the mutations in the DW chicken and Silver horse present a dominant phenotype and how they might be reverted by the additional deletions in the Smoky and ( perhaps ) Dun chickens is not understood . Given the severity of their pigmentation phenotype , the association with loss of melanosome integrity and melanocyte viability in vivo and in cell culture , and the dominant nature of the mutations , we hypothesized that the TMinsWAP and TMR625C mutations of PMEL in the DW chicken and the Silver horse , respectively , alter the cellular or biophysical properties of PMEL to ultimately convert functional amyloid into a pathological form . Here , by recapitulating these mutations in the context of human PMEL ( hPMEL ) , we provide evidence to support this hypothesis . Moreover , we show that the secondary PKDΔLVVT and TMΔ5 mutations in the Smoky and Dun chicken PMEL orthologues represent null or partial null alleles that revert the pathological effects of the TMinsWAP and TMR625C mutations on pigmentation . Finally , we show that changes in or near the TMD of an integral membrane amyloidogenic protein can influence the oligomerization of a distal lumenal fragment into functional amyloid . We discuss these findings with regard to their potential implications for the formation of functional vs . pathological amyloid .
Human PMEL ( hPMEL ) forms pre-amyloid oligomers that are stabilized by disulfide bonds [13] , and a recent study found that hPMEL formed fewer disulfide-bonded oligomers when coexpressed with chicken PMEL bearing the DW-associated TMinsWAP mutation [38] . However , it is not known whether the result in that study reflected a direct effect of the TMinsWAP mutation or an inability of hPMEL to form the appropriate disulfide bonds with chicken PMEL . To specifically test whether and how mutations in or near the PMEL TMD influence PMEL oligomerization , we introduced mutations analogous to those found in the DW chicken ( TMinsWAP ) and Silver horse ( TMR625C ) in the context of hPMEL ( Figure 2A ) . Upon expression of these mutants or wild-type full-length hPMEL in HeLa cells , we found that the TMD mutants were equally effective as wild-type hPMEL at forming disulfide bonded oligomers , as detected by non-reducing SDS-PAGE and immunoblotting ( Figure S1A ) . These data suggest that the PMEL mutations associated with the DW chicken and HoSi do not affect the formation of disulfide-bonded PMEL oligomers when presented within the context of hPMEL . The previously observed reduction in disulfide-bonded oligomers by hPMEL upon coexpression of chicken DW PMEL [38] thus likely reflects the inability of these cross-species PMEL isoforms to form oligomers , which is supported by our inability to co-immunoprecipitate wild-type mouse PMEL with wild-type hPMEL ( data not shown ) . While oligomerization of the hPMEL lumenal domain is stabilized by disulfide bonds , it is not known whether the TMD itself has oligomeric properties that might otherwise influence lumenal domain interactions . To determine whether the hPMEL TMD can oligomerize on its own and whether the TMinsWAP and TMR625C mutations influence this property , we turned to the widely used TOXCAT assay [39] . In this assay , a chimeric protein consisting of the transcription factor ToxR , the TMD of interest , and maltose binding protein ( MBP ) is expressed in MBP-deficient bacteria . Proper insertion of the chimeric protein into the plasma membrane confers growth in maltose as the only carbon source , and oligomerization mediated by the TMD activates ToxR and stimulates ToxR-dependent transcription of the chloramphenicol acetyl transferase ( CAT ) gene . Upon expression of ToxR-TMD-MBP chimeras containing the PMEL TMD with or without the natural border residues at either end ( Figure 2B ) , only chimeras lacking the C-terminal border residues , RRR ( SS , LS ) , conferred growth in maltose — regardless of the presence of the N-terminal border residues , QE — despite equivalent expression of all chimeras as determined by immunoblotting ( Figure S1B ) . This suggested that the C-terminal residues interfered with insertion of the chimera into the plasma membrane with the proper orientation . Interestingly , altering the second arginine in the C-terminal border sequence to either Cys ( R/C; as in the TMR625C mutant ) , Ser ( R/S ) , or Lys ( R/K ) allowed the chimeric protein to insert properly into the membrane , as evidenced by maltose complementation ( Figure 2B ) . This indicates that the TMR625C substitution associated with HoSi PMEL alters the properties of the PMEL TMD . For those chimeras that confer growth in maltose , we then tested their ability to dimerize by measuring CAT activity using a spectrophotometric assay ( Figure 2C ) [40] . As a positive control we used a chimera containing the strongly dimerizing TMD of Glycophorin A ( GA wt ) , and as a negative control we used either the vector without a TMD ( no TM ) or a chimera containing the non-dimerizing Glycophorin A G83I mutant TMD ( GA mut ) [39] , [41] . The wild-type PMEL TMD , lacking ( SS ) or containing ( LS ) the N-terminal border residues , conferred similar CAT activity as the negative controls . In contrast , the TMinsWAP ( InsWAP ) and TMR625C ( R/C ) mutations conferred substantial CAT activity . These data indicate that whereas the wild-type hPMEL TMD is not capable of oligomerization , both the DW-associated TMD insertion and the HoSi-associated R/C transversion facilitate TMD dimerization . Insertion of three leucines ( InsLLL ) in place of the WAP insertion conferred an even slightly higher CAT activity , suggesting that the increased dimerization mediated by the TMinsWAP mutation is likely due to the increase in TMD length . To test whether the R/C mutation reflected a specific property of the cysteine ( such as disulfide bond formation ) , we tested whether replacement of the same arginine residue by serine ( R/S ) or lysine ( R/K ) affected CAT activity . Interestingly , whereas both of these mutants conferred proper insertion into the plasma membrane as indicated by growth in maltose , the R/S mutant , but not R/K , conferred CAT activity similar to that by the R/C mutant . This suggests that the dimerization conferred by the R/C mutant reflects decreased repulsion between adjacent basic RRR ( or RKR ) motifs . We next tested whether the altered TMD properties associated with the TMinsWAP and TMR625C mutations influenced PMEL trafficking or processing . hPMEL without ( wild-type ) or with the TMinsWAP or TMR625C mutations was expressed ectopically in non-melanocytic HeLa cells by transient transfection . As previously shown , wild-type hPMEL expressed in these cells is enriched at steady state within late endosomes and lysosomes [13] ( Figure 3Aa–c ) . Neither the TMinsWAP nor the TMR625C mutation affected this steady state localization , as shown by the predominant labeling for these mutants by immunofluorescence microscopy ( IFM ) on the interior of structures labeled by the late endosome/lysosome marker , LAMP1 ( Figure 3Ad–i ) . These data suggest that the TMD mutations in dominant PMEL mutants do not affect hPMEL trafficking , supporting a similar earlier conclusion for chicken DW PMEL [38] . In order to liberate the amyloidogenic Mα fragment , PMEL undergoes regulated proteolytic cleavage in the lumenal domain by a proprotein convertase ( PC ) and an as yet unidentified site 2 protease ( S2P ) [17] , [18] ( Figure 1 ) . Mα is then further processed by as yet unidentified proteases during fibril maturation [12] , [21]–[23] . The products of these cleavages are detected and semi-quantified by immunoblot analysis of detergent-soluble lysates from HeLa cells transfected with wild-type hPMEL; the characteristic Mα and Mβ fragments result from PC cleavage of full-length hPMEL [17] , the C-terminal fragment ( CTF ) results from S2P cleavage of Mβ [18] , and the MαC fragments that are enriched in detergent-insoluble fibrils result from further proteolysis of Mα [21] ( Figure 1 ) . Similar levels of all fragments are present in lysates from cells expressing TMinsWAP or TMR625C hPMEL , with the exception of a slight increase in the levels of the CTF relative to Mβ in cells expressing the TMR625C mutant ( Figure 3B ) . Moreover , whereas wild-type and TMinsWAP hPMEL often form two species of CTF ( see doublet in Figure 3B ) , TMR625C hPMEL forms predominantly a single species . These data suggest that TMinsWAP and TMR625C hPMEL are effectively cleaved to fibrillogenic fragments by the PC and by proteases within late endosomal compartments but that S2P cleavage is favored at one of two sites in TMR625C hPMEL and the ensuing CTF is likely more stable than the wild-type CTF . To test whether processing kinetics are altered by the TMD mutations , we analyzed PMEL maturation in transfected HeLa cells by metabolic pulse/ chase analysis of wild-type , TMinsWAP , or TMR625C hPMEL immunoprecipitated from detergent-soluble lysates . As shown in Figure 3C , both the TMinsWAP and TMR625C mutant hPMEL were matured to the Golgi-processed P2 form , cleaved to the Mα/ Mβ forms , and disappeared from detergent-soluble lysates with roughly wild-type kinetics ( Figure 3D ) . Moreover , for all hPMEL variants , Mα was secreted into the medium with similar kinetics and efficiency , and CTF was generated with similar kinetics ( data not shown ) . Altogether , these results indicate that the TMD mutations found in the DW chicken and the Silver horse affect neither the delivery of PMEL to late endocytic compartments nor its ability to be processed to amyloidogenic Mα and MαC fragments . PMEL fibril formation is often inferred from the detection of detergent-insoluble MαC fragments by immunoblotting , but non-fibrillar mutants of PMEL that form disorganized aggregates — such as hPMEL lacking a PC cleavage site — can also generate similar fragments [12] , [17] . To test whether the TMinsWAP and TMR625C variants are capable of supporting fibril formation , HeLa cells transiently expressing these variants or wild-type hPMEL were analyzed by standard electron microscopy ( EM ) . As shown in Figure 4 , stage II-melanosome-like compartments with fibrillar arrays were detected in cells expressing any of the hPMEL variants , but not in cells expressing empty vector ( Figure S2 ) . As for cells expressing wild-type hPMEL , fibrils in cells expressing TMinsWAP or TMR625C were detected within organelles that often contained internal membrane vesicles or sheets , but not in secretory or early endocytic compartments . Immunoelectron microscopy ( IEM ) analysis using immunogold labeling of ultrathin cryosections further showed that all of the hPMEL variants were incorporated into the fibrils ( data not shown , but see below for incorporation of variants into melanosome fibrils in melanocytic cells ) . These data indicate that the TMinsWAP and TMR625C mutations do not impair the intrinsic ability of hPMEL to form fibrils within endosomal compartments . Unlike in non-pigment cells , in which fibril maturation is inefficient and occurs within late endosomes [13] , PMEL protofibrils in pigment cells mature efficiently into sheets [11] that accumulate in stage II melanosomes and that then serve as sites of melanin deposition during melanosome maturation [24] . To determine whether the TMinsWAP and TMR625C mutations influence fibril maturation or downstream pigmentation , we analyzed the behavior of hPMEL with or without these mutations expressed transiently or stably in pigment cells . In order to distinguish the transgene from the endogenous mouse PMEL , we expressed the hPMEL variants in “wild-type” mouse melanocyte cell lines , and exploited antibodies ( NKI-beteb and HMB-50 ) that only detect hPMEL . By IFM , wild-type , TMinsWAP and TMR625C hPMEL each localized to punctate structures that partially overlapped with late endocytic compartments marked by LAMP2 and that did not overlap with mature pigmented eumelanosomes ( Figure 5 and Figure S3 ) . These data suggest that the wild-type , TMinsWAP and TMR625C hPMEL variants localize similarly in melanocytes . Endogenous mouse PMEL does not significantly co-localize with late endocytic markers ( not shown ) and thus the partial co-localization of ectopic hPMEL with LAMP2 might reflect less efficient delivery of this isoform to early stage melanosomes , likely due to the low expression levels attained by infection ( see below ) and the inability of hPMEL to interact with the mouse isoform as detected by coimmunoprecipitation ( data not shown ) . We next tested whether expression of the TMinsWAP and TMR625C hPMEL variants affect the morphology or degree of pigmentation of individual melanosomes . In mouse melan-Ink4a melanocyte stable transfectants that expressed very low levels of the dominant TMinsWAP and TMR625C hPMEL variants ( ∼10–20% of endogenous mouse PMEL as assessed by immunoblotting ) , no changes in overall pigmentation were observed relative to cells expressing wild type hPMEL ( bright field images in Figure S3 , with melanosomes pseudocolored blue in the insets ) , but this is likely due to the low expression levels ( see below ) . Nevertheless , by IEM of ultrathin cryosections from each of the stable cell lines , thin and disorganized immature protofibrils ( arrowheads ) that were densely immunogold labeled with anti-hPMEL antibodies were observed within Stage I melanosomes ( Figure 6a–c ) . However , upon maturation of the protofibrils into elongated fibrillar sheets , wild-type and mutant hPMEL showed strikingly different characteristics ( compare Figure 6d to 6e–f ) . In cells expressing wild-type hPMEL , only very sparse immunogold labeling was observed on the parallel sheets of organized fibrils , and most of the organelles with thickened fibrillar sheets , corresponding to Stage III or IV melanosomes , were densely pigmented ( Figure 6d ) as has been previously reported [24] . This is consistent with the notion that although a fraction of hPMEL is delivered to the late endocytic pathway in mouse melanocytes , sufficient protein is properly trafficked to melanosomes to form fibrils . The lack of labeling on the pigmented fibrils likely reflects epitope sequestration as pigmentation proceeds [13] , [14] , [42] . In contrast to cells expressing wild-type hPMEL , compartments with tightly packed fibrils that were densely immunogold labeled for hPMEL were easily observed in cells expressing the TMR625C and especially the TMinsWAP variants ( asterisks , Figure 6e–f; more images can be found in Figure S4 ) . The sheets seemed unusually tightly packed with no space between the fibrils ( note the spacing between fibrils in organelles that lack labeling for the transgene , indicated by brackets , and the loss of spacing in organelles that are labeled for the TMinsWAP and TMR625C transgenes ) . The organelles that were immunogold labeled largely showed no overt pigmentation , suggesting that either the fibrils were no longer capable of binding to melanins or that they inhibited melanin production . In some cases , particularly in cells expressing the TMR625C variant , labeling could be observed on the non-pigmented periphery of melanized melanosomes ( Figure S4h and i ) . To better test whether the variant hPMEL isoforms affected overall pigmentation , we transiently expressed them in melan-mu:MuHA , a highly pigmented mouse melanocyte cell line that is less likely than melan-Ink4a to de-differentiate ( our unpublished observations ) , using recombinant retroviruses that coexpress EGFP . The highest hPMEL expressers were enriched by cell sorting for high EGFP expression . By immunoblotting , transgene expression was substantially higher than in the stable transfectants but still less than endogenous mouse PMEL expression ( data not shown ) . Cells were then processed for standard EM analysis . Whereas cells expressing wild-type hPMEL were normally pigmented as compared to cells expressing empty vector ( not shown ) and harbored few stage II ( unpigmented ) melanosomes , cells expressing the TMR625C and especially the TMinsWAP variants were hypopigmented , harbored fewer pigmented melanosomes and were enriched in early stage melanosomes ( Figure 7A–7B ) . Quantification showed that cells expressing TMinsWAP hPMEL showed an increase in non-pigmented Stage II ( p = 0 . 006 ) with a concomitant loss of pigmented Stage III melanosomes ( p = 0 . 014 ) as compared to wild-type hPMEL ( Figure 7B , 7C ) ; cells expressing TMR625C hPMEL likewise showed a decrease in Stage IV melanosomes ( p = 0 . 027 ) with a concomitant increase in Stage II ( p = 0 . 018 ) and Stage I melanosomes ( p = 0 . 014 ) . Importantly , mutant PMEL-expressing cells with the highest increase in early stage melanosomes consistently showed the most marked decrease in pigmented organelles , suggesting a defect in melanosome maturation or pigment production that might be associated with higher expression levels of mutant PMEL . Moreover , quantification of the number of pigmented ( Stage III and IV ) organelles per unit area was decreased most strikingly in cells expressing TMinsWAP ( p = 0 . 002; Figure 7D ) . In cells expressing TMR625C there was also a tendency towards a decrease in the number of pigmented organelles ( see Figure 7A–7C ) , but it was not significantly different from cells expressing wild-type PMEL ( Figure 7D ) , likely due to the high variability encountered in these cells ( see Figure 7B ) . These results suggest that even after short periods of time and with modest expression levels , the fibrils formed by the TMinsWAP—and to a lesser degree the TMR625C variant hPMEL—impair pigmentation . Together , these results suggest that the mutations found in the dominant DW chicken PMEL and Silver horse influence the assembly of the fibrils into sheets , creating a tightly packed structure that may be inaccessible to pigment and/or that inhibits melanin biosynthesis . The Smoky chicken is a recessive revertant of the DW allele , reflecting a second site mutation that results in deletion of four residues from the PKD domain ( PKDΔLVVT ) ( Figure 8A ) [30] . In homozygous form , the Smoky allele imparts modest pigment dilution in the feathers—similar to the modest pigment dilution observed in the silver mouse [26] , the Pmel -/- mouse [29] and the fading vision zebrafish [28]—as compared to the dramatic loss of pigment imparted by the dominant DW allele . To investigate how the PKDΔLVVT mutation might reverse the DW phenotype , we created hPMEL variants with either the PKDΔLVVT deletion alone or together with the WAP insertion ( PKDΔLVVT-TMinsWAP ) as found in the Smoky chicken PMEL allele . The variants were expressed in HeLa cells , and their maturation , proteolytic processing , and trafficking were assessed by metabolic labeling/pulse chase and immunoprecipitation analysis and by IFM . As shown in Figure 8B , introduction of the PKDΔLVVT deletion , either by itself or in combination with the TMinsWAP insertion , impaired PMEL exit from the endoplasmic reticulum ( ER ) as shown by a decreased maturation into the fully glycosylated , post-Golgi P2 form . Furthermore , the PKDΔLVVT and PKDΔLVVT-TMinsWAP hPMEL forms were not efficiently processed by proprotein convertase cleavage into Mα and Mβ fragments . These data are reminiscent of the effects of deletion of the entire PKD domain [16] , [22] and suggest that the PKDΔLVVT deletion impairs PMEL maturation . Although the pulse chase data suggested that the PKDΔLVVT mutation impairs PMEL exit from the ER , a fraction of the Golgi-modified P2 form did accumulate over time . To determine whether this fraction of “mature” PKDΔLVVT PMEL is properly trafficked to late endosomal compartments , we analyzed its localization in HeLa cells by IFM using an antibody ( HMB45 ) that only recognizes Golgi-modified PMEL [12] , [20] . Unlike wild-type hPMEL , this “mature” form of PKDΔLVVT or PKDΔLVVT-TMinsWAP hPMEL did not localize significantly to late endosomal compartments marked by LAMP1 ( Figure 8C , central panels ) . Rather , it partially overlapped with endocytic recycling compartments labeled by internalized transferrin ( Figure 8C , right panels ) , much like PMEL lacking the entire PKD domain [16] . This indicates that the PKDΔLVVT deletion impairs not only PMEL maturation in the early biosynthetic pathway but also its selective incorporation into ILVs for trafficking to late endosomal compartments , thus precluding access to proprotein convertase cleavage [16] . Consistent with the requirement for sorting to ILVs and proprotein convertase cleavage for fibril formation , MαC fragments are not detected in detergent-insoluble fractions of cells expressing the PKDΔLVVT or PKDΔLVVT-TMinsWAP mutants ( Figure S5 ) . We conclude that the PKDΔLVVT mutation reverts the dominant phenotype of the TMinsWAP mutation by impairing access of PMEL to fibrillogenic compartments and blocking aberrant amyloid fibril formation . The results described above suggest that primary PMEL mutations that inhibit pigmentation in a dominant fashion can be reverted by second site mutations that prevent the aberrant PMEL variants from accumulating in fibrillogenic compartments . The dominant but milder Dun chicken PMEL allele contains both the orthologous TMR625C mutation found in the HoSi allele and an additional deletion of 5 amino acids within the TMD ( TMΔ5; Figure S6A ) . We therefore predicted that the TMΔ5 mutation , like the Smoky-associated PKDΔLVVT mutation , would impair PMEL accumulation within fibrillogenic endosomal compartments . Consistent with this prediction , when expressed in HeLa cells , introduction of the TMΔ5 mutation in the context of hPMEL decreases the expression level of all mature ( post-ER ) PMEL species , including the fibrillar MαC forms ( Figure S6B ) , even when corrected for mRNA expression ( data not shown ) . This likely reflects enhanced ER-associated degradation due to poor folding or membrane incorporation , as the material that does exit the ER appears to be processed with normal kinetics ( Figure S6C-S6D ) . These data suggest that while the TMΔ5 mutation does not prevent delivery of PMEL to fibrillogenic compartments ( Figure S6E ) , it can impair the efficiency with which aberrant PMEL fibrils accumulate within such compartments . Given that the primary effect of the TMΔ5 mutation is on ER exit , it is highly likely to have similar effects in the context of the TMR625C mutation , as in Dun chickens , which does not affect ER exit ( Figure 3 ) .
Unlike most commonly known forms of amyloid , which are thought to provoke pathologic processes , PMEL is an example of a benign and functional amyloid . Here we show how mutations in the PMEL TMD are associated with an aberrant amyloid fibril biogenetic pathway , altering the normally physiological amyloid to produce a pathological form that impairs pigmentation within melanocytes . Epidermal melanocytes from animals harboring these mutations are depleted of melanosomes [34] and have decreased viability in vitro and perhaps in vivo [31] , suggesting that the formation of these aberrant fibrils impairs melanosome integrity and may be toxic to the pigment cell . Although the TMD does not form part of the amyloid core , mutations in this domain influence TMD oligomeric properties that reverberate distally on the association between the amyloidogenic domains of PMEL , as evidenced by an abnormal packing of the mutant PMEL fibrils . We also show that secondary mutations found in animals in which the pigment dilution associated with the primary pathogenic TMD mutations are dampened or reverted prevent the accumulation of these PMEL isoforms in fibrillogenic compartments , thus mimicking a PMEL knockout . This finding indicates that it is less detrimental to express no fibrils at all than to express aberrant fibrils that inhibit pigmentation and might be toxic to the melanocyte . Interactions among TMDs are known to influence multisubunit complex assembly and function in vivo [43]–[48] . Here , we show that whereas the TMD of hPMEL normally does not promote oligomerization , introduction of either the TMinsWAP or the TMR625C mutations found in the DW chicken or Silver horse PMEL orthologues results in substantial oligomerization potential . Oligomerization by the PMEL TMD was similarly enhanced by insertion of three leucine residues in place of the DW-associated WAP insertion , suggesting that the effect reflected increased TMD length rather than specific amino acid side chain interactions . Although the observed increase in dimerization by the TMR625C could not be directly compared to the wild-type TMD with the extended cytosolic domain because the latter did not insert properly into the E . coli plasma membrane , a similar degree of oligomerization was observed upon alteration of R625 to serine as with the HoSi-associated cysteine , but not to lysine , all of which supported proper membrane insertion . This suggests that increased oligomerization mediated by the TMR625C mutation reflects removal of a positive charge from the TMD boundary , decreasing the electrostatic repulsion between neighboring PMEL molecules by the membrane proximal arginine triplet . Interestingly , the TMR625C mutation was associated with greater CTF stability . In addition , whereas we often observed a CTF doublet for both wild-type and TMinsWAP PMEL , reflecting the two possible S2P sites [18] , we always detected a single TMR625C CTF species . The altered TMD mediated oligomerization of this mutant might thus result in either a greater accessibility of one site over the other or aberrant partitioning of PMEL to membrane subdomains that preferentially harbor a site-specific enzyme , akin to what has been proposed to occur between α- and β-secretases in the cleavage of APP to produce pathologic Aβ [49] . How increased TMD-mediated dimerization might influence PMEL folding , assembly , and fibril formation is not yet clear . A previous study found that the DW chicken PMEL associated with membrane microdomains to a similar degree as wild-type hPMEL [38] , suggesting that the TMinsWAP mutation does not alter membrane partitioning . The same study suggested that maturation and proteolytic processing of DW chicken PMEL was not substantially different from that of wild-type hPMEL . Consistently , we find that neither the TMinsWAP nor the TMR625C mutation affect hPMEL biosynthetic trafficking , proteolytic maturation , delivery to ILVs within endosomes , or the initial stages of protofibril formation . While we could not detect a previously described effect of the TMinsWAP mutation ( or of the TMR625C mutation ) in reducing disulfide bond-mediated dimerization of the PMEL lumenal domain [38]—which likely reflected more a lack of heteromeric interactions between chicken and human PMEL than an effect of the TMD mutation itself—it is highly likely that the induced TMD interactions impact the orientation and proximity of PMEL dimers that form early in PMEL biosynthesis [13] , [38] . Although the induced conformational changes are likely subtle and do not impact recognition by the ER quality control system , biosynthetic trafficking , or the ability to form fibrils , they do appear to have downstream effects on the assembly of fibrils into sheets and/or in the packing of the sheets . One potential explanation for these effects is that non-amyloidogenic domains of PMEL dimers that protrude from the fibrils and regulate the packing of fibrils into higher order assemblies might be positioned differently . An alternative explanation is that oligomerization via the PMEL TMD might increase the kinetics of higher order fibril assembly . Either effect might result in more tightly packed fibrils within early stage melanosomes . How might the TMinsWAP or TMR625C PMEL variants impair melanogenesis and melanosome integrity ? If indeed increased TMD oligomerization translates a conformational change to the lumenal domain to alter either the mode or kinetics of fibril polymerization into sheets , several mechanisms could be envisioned . Both altered conformation or kinetics—either by physical blockade through tighter packing or by overly rapid kinetics of sheet assembly—would potentially preclude the delivery of melanogenic enzymes , such as tyrosinase , to the lumen of the maturing melanosome [50] . This would in turn have the effect of concentrating the formation of oxidative melanin intermediates at the limiting membrane of the maturing melanosome and subjecting the limiting membrane to oxidative attack , potentially damaging the integrity of the organelle . This would explain both the loss of melanization ( this study and refs . [31] , [34] ) , despite the presence of a potentially active tyrosinase [34] , [35] , and the decrease in melanosome numbers [34] , [35] . Release of melanosomal contents might then impact cell viability [31] . Alternatively , it is known that melanosomes are highly enriched in divalent cations [51]–[53] , and PMEL has been suggested to sequester calcium [54]; alterations to PMEL fibril packing might reduce its ability to sequester divalent cations , with potential harmful effects on melanosomes by further oxidative damage . Potential negative effects on copper-dependent tyrosinase activity within melanosomes might result from a similar loss of copper sequestration [55] . A third possibility is that the altered conformation of the fibrils—which very likely are a variant form of amyloid—makes them inherently toxic . For example , Aβ amyloid has been shown to insert into and disrupt lipid bilayers [56]; a similar property of the TMinsWAP and TMR625C PMEL amyloid fibrils could potentially disrupt the melanosome membrane directly , leading to a loss of melanosome integrity and consequent loss of pigmentation . Finally , it is possible that pigmentation and melanosome viability are disrupted by an intermediate in fibril or sheet assembly that might persist due to a decrease in kinetics or that might be produced only by the variants . None of these possibilities are mutually exclusive , and it is possible that a combination of effects inhibits pigmentation . It has not been understood why the phenotype of the Smoky chicken , with a PKDΔLVVT mutation in addition to the DW-associated TMinsWAP mutation , restores substantial pigmentation relative to the parental DW chicken [30] . We show here that the secondary PKDΔLVVT mutation prevents the accumulation of PMEL in fibrillogenic compartments , likely explaining the decreased pigment dilution observed in Smoky vs . DW chickens . The PKDΔLVVT mutation largely impairs PMEL maturation through the early biosynthetic pathway , causing retention in the ER . Moreover , the small fraction of PMEL that exits the ER is not selectively targeted to multivesicular endosomes and rather accumulates in early endosomal recycling compartments . This correlates with a lack of proprotein convertase cleavage into Mα and Mβ fragments and decreased accumulation of PMEL fragments in detergent insoluble , fibril-enriched fractions . The behavior of this mutant form of hPMEL is similar to that of hPMEL in which the entire PKD domain is deleted [16] , supporting a critical role for the PKD domain in targeting PMEL to fibrillogenic compartments and perhaps directly in fibrillogenesis [23] . The results indicate that the Smoky allele is functionally a PMEL null allele that counteracts the DW mutant's pathogenic effects on pigmentation in a recessive manner by preventing the formation of aberrant fibrils . Thus , Smoky chickens show a slight pigment dilution similar to that observed in PMEL knockout mice [29] or in the hypomorphic silver mouse [26] , [33] rather than a dramatic loss of eumelanin pigment as observed in the DW chicken and Silver horse . Since melanocytes in the DW chicken show decreased viability [31] and melanosome integrity [34] , our studies further suggest that the formation of tightly packed TMinsWAP fibrils may be toxic to pigment cells and that it is therefore less detrimental to the cell to have no PMEL fibrils at all . The reversion of the dominant DW phenotype by the PKDΔLVVT mutation in Smoky chickens suggests that a general mechanism for averting PMEL amyloid pathology is to prevent access of the aberrant amyloidogenic protein to compartments within which amyloid formation occurs . The Dun chicken appears to be another example of such a mechanism . Whereas the Dun chicken PMEL allele contains a mutation orthologous to that of the dominant TMR625C mutation in the toxic Silver horse PMEL , it also has a secondary deletion of 5 amino acids in the TMD ( TMΔ5 ) [30] . Introduction of this secondary mutation into hPMEL impairs ER exit , trafficking through the plasma membrane ( data not shown ) , and accumulation of all mature PMEL species at steady state , suggesting inefficient PMEL folding and a greater propensity for degradation . Thus , while the mechanism is different from that of the secondary PKDΔLVVT mutation in Smoky chickens , the overall effect of the TMΔ5 mutation in Dun chickens might be similar — a reduction of aberrant PMEL accumulation in fibrillogenic compartments . We therefore liken this secondary mutation to a revertant of the Silver horse phenotype . We speculate that mutations in other toxic/ pathological forms of amyloidogenic proteins that prevent appropriate accumulation of the amyloidogenic species within amyloidogenic compartments will be associated with protection from disease .
The mouse monoclonal antibodies used , their targets and sources were as follows: HMB45 and NKI-Beteb to PMEL were from Lab Vision ( Freemont , CA ) ; TA99 to TYRP1 was from American Type Culture Collection ( Manassas , VA ) ; H4A3 to LAMP1 was from Developmental Studies Hybridoma Bank ( University of Iowa , Iowa City , IA ) . The rabbit polyclonal antibodies used , their targets and sources were αPep13h to the C-terminal peptide of hPMEL [13] , αPmel-N to the N-terminal peptide of hPMEL [17] , α-LAMP1 from Affinity BioReagents ( Golden , CO ) , and α-MBP from New England Biolabs ( Beverly , MA ) . Rat α-LAMP2 was from Developmental Studies Hybridoma Bank . Unless otherwise specified , chemicals were obtained from Sigma-Aldrich ( St . Lois , MO ) . Tissue culture reagents were from Invitrogen ( Carlsbad , CA ) . FuGENE-6 and hygromycin were from Roche Diagnostics ( Indianapolis , IN ) . Wild-type hPMEL ( long form ) in pCI has been described [13] and was used as a template for site directed mutagenesis via PCR to generate the TMinsWAP , TMR625C , PKDΔLVVT , PKDΔLVVT-TMinsWAP , TMΔ5 mutations; the primers used are indicated in Table S1 . Wild-type and mutant hPMEL XhoI-NotI inserts from pCI were subcloned into pBMN-IRES-Hygro ( a gift from R . Scheller , Genentech , San Francisco , CA ) or pBMN-IRES-EGFP retroviral vectors for stable or transient infection , respectively . For the TOXCAT assays , the pccKan expression vector was used . The pccGpA WT and pccGpA G83I mutant derivatives of pccKan , encoding wild-type or mutant glycophorin A , as well as the MBP deficient Escherichia coli ( malE- ) have been described [39] . PMEL wild-type or mutant TMDs were PCR-amplified from their pCI templates and cloned into the NheI-BamHI sites of pccKan . All plasmid inserts were verified by DNA sequencing . malE- bacteria were transformed with pccKan or its derivatives encoding the ToxR-TMD-MBP chimeras . Ampicillin resistant colonies were selected , grown to mid-log phase in Luria Broth , streaked over amp-M9 agar plates with either glucose ( positive control ) or maltose as the only source of carbon [39] , and incubated for 2–4 days at 37°C . CAT activity of clones that grew in maltose was measured using a spectrophotometric assay [40] . Briefly , the pellet from 1 ml of OD600 = 0 . 75 culture was resuspended in 300 µl of sonication buffer ( 25 mM Tris pH 7 . 8 2 mM EDTA ) , sonicated , and centrifuged 30 min at 4°C , 13000 x g to obtain cell free extracts . 10 µl of lysate was combined with 230 µl of CAT reaction buffer ( 100 mM Tris pH 7 . 8 , 0 . 4 mg/ml DTNB , 0 . 1 M AcetylCoA ) and the A412 nm was recorded every minute for 5 min to obtain the background . Then , 10 µl of 2 . 5 mM chloramphenicol was added to initiate the CAT reaction and the A412 nm was recorded every minute for 30 min to determine the CAT activity according to the method by Shaw [40] . The change in A412 nm was linear throughout the experiment . HeLa cells were grown as described previously [13] and transiently transfected with 0 . 1 µg DNA/ 3-cm dish for low or 7 . 5 µg DNA/ 10-cm dish for high transgene expression using FuGENE-6 according to the manufacturer's instructions . HeLa cells were analyzed either 48 hrs ( immunofluorescence microscopy , metabolic labeling/pulse chase and immunoblotting ) or 72–96 hrs ( electron microscopy ) post-transfection . The immortalized mouse melanocyte cell lines melan-Ink4a [57] and melan-mu:MuHA rescued cells ( MuHA; [58] ) were grown as described previously [57] and stably or transiently infected with the retroviral vectors described above . Stable melan-Ink4a transductants were selected with 200–400 µg hygromycin B and processed for immunofluorescence ( IFM ) or immunoelectron microscopy ( IEM ) . Transiently infected MuHA cells were sorted ( University of Pennsylvania Cell Sorting Core Facility , Philadelphia , PA ) for high EGFP expression 96 hrs post-infection and processed for conventional electron microscopy ( EM ) . Cells were fixed with 2% formaldehyde , incubated with primary and fluorochrome-conjugated secondary antibodies as described previously [13] and analyzed on a DM IRBE microscope ( Leica Microsystems , Wetzlar , Germany ) . Digital images were captured with an Orca camera ( Hamamatsu , Bridgewater , NJ ) and deconvolved and manipulated with OpenLab software ( Improvision , Lexington , MA ) . Insets were magnified using Adobe Photoshop ( Adobe Systems , Mountain View , CA ) . For recycling endosome labeling by continuous transferrin ( Tf ) uptake , HeLa cells were starved in serum-free media containing 0 . 5% BSA 15 mM HEPES for 30 min at 37°C , incubated with 7 . 5 µg/ml Alexafluor488-conjugated human Tf ( Molecular Probes , Eugene , OR ) diluted in starvation media for an additional 30 min at 37°C , fixed , and processed for IFM as indicated above . For conventional electron microscopy , cells were fixed in 2% glutaraldehyde 4% paraformaldehyde , dehydrated and embedded in epon resin . Ultrathin sections were contrasted with 2% uranyl acetate and analyzed by transmission electron microscopy . For immunoelectron microscopy ( IEM ) , cells were fixed with 2% paraformaldehyde 0 . 5% glutaraldehyde and ultrathin frozen sections were single- or double-immunogold labeled as described previously [14] , [59] using Protein A gold conjugates . For quantification of numbers of Stage I-IV melanosomes , at least 50 random fields of each cell type ( >400 compartments ) were analyzed . For immunoblotting , cells were harvested with 5 mM EDTA in PBS , washed with 30 mM NEM in PBS , and frozen . Thawed cells were lysed with TX-100 lysis buffer as described previously [60] in the presence of protease inhibitors and NEM and fractionated into detergent soluble and insoluble fractions by centrifugation . For metabolic labeling , cells were harvested with trypsin-EDTA , starved for 30 min in methionine/cysteine free media , labeled with 35S methionine-cysteine for 30 min and chased for the indicated periods of time . Cells were then lysed as indicated above and detergent soluble fractions were immunoprecipitated with antibodies directed to the C-terminus of hPMEL , followed by SDS-PAGE fractionation and analysis with a STORM PhosphorImager and ImageQuant software ( GE Healthcare , Buckinghamshire , United Kingdom ) . | Amyloid is a protein fold that is normally associated with pathology , such as neurodegeneration in Alzheimer , Parkinson , and Creutzfeldt–Jakob diseases . The amyloid fold has also been exploited by nature for functional purposes; for example , proteolytic fragments of the pigment cell-specific integral membrane protein , PMEL , form amyloid fibrils upon which melanin pigments polymerize within subcellular organelles called melanosomes . Whereas animal models that entirely lack PMEL expression have modest pigment loss , chickens or horses with small in-frame mutations that alter a non-amyloidogenic region of PMEL have severe pigment loss that is thought to be associated with pigment cell death . We show here that these mutations alter the capacity of this region to self-associate , likely changing the conformation of full-length PMEL oligomers . While these changes do not affect the intrinsic ability of PMEL to form amyloid fibrils , they alter either the fibrils themselves or the kinetics of fibril formation such that they form more compact structures and inhibit melanin formation when expressed in melanocytes in culture . Based on our results , we speculate that minor alterations in pre-amyloid assembly of an amyloidogenic protein influences entry into either a benign or a pathogenic amyloid pathway . | [
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] | 2011 | Mutations in or near the Transmembrane Domain Alter PMEL Amyloid Formation from Functional to Pathogenic |
Understanding how stochastic molecular fluctuations affect cell behavior requires the quantification of both behavior and protein numbers in the same cells . Here , we combine automated microscopy with in situ hydrogel polymerization to measure single-cell protein expression after tracking swimming behavior . We characterized the distribution of non-genetic phenotypic diversity in Escherichia coli motility , which affects single-cell exploration . By expressing fluorescently tagged chemotaxis proteins ( CheR and CheB ) at different levels , we quantitatively mapped motile phenotype ( tumble bias ) to protein numbers using thousands of single-cell measurements . Our results disagreed with established models until we incorporated the role of CheB in receptor deamidation and the slow fluctuations in receptor methylation . Beyond refining models , our central finding is that changes in numbers of CheR and CheB affect the population mean tumble bias and its variance independently . Therefore , it is possible to adjust the degree of phenotypic diversity of a population by adjusting the global level of expression of CheR and CheB while keeping their ratio constant , which , as shown in previous studies , confers functional robustness to the system . Since genetic control of protein expression is heritable , our results suggest that non-genetic diversity in motile behavior is selectable , supporting earlier hypotheses that such diversity confers a selective advantage .
Cell behavior is controlled by biochemical signaling networks . Because network dynamics depend on the number of each protein involved , the dynamical and functional properties of signaling pathways—and ultimately cell survival—are sensitive to random fluctuations in protein numbers . Such fluctuations are expected because the molecular components of signaling pathways are typically present in small numbers [1] . Various mechanisms that cause molecular fluctuations have been identified , including stochastic protein expression or unequal partitioning of components between daughter cells [2 , 3] . Selective pressure to maintain robust performance against inherent molecular fluctuations is likely to have played a significant role in the evolution of biological networks [4–7] . In fluctuating environments , however , non-genetic phenotypic variability can be an advantageous strategy for clonal populations raising the possibility that the distribution of different phenotypes within the population has a functional role beyond the mean phenotype [2 , 8–13] . Investigating how variability in protein number controls the distribution of cell performances requires experiments that can measure both behavior and protein numbers in individual cells . At the same time , accurate characterization of the distribution of protein numbers , phenotypes , and performances in a population requires high-throughput experiments to obtain sufficient statistics . Fluorescent reporters paired with video-microscopy or flow cytometry have enabled the detailed characterization of phenotypic variability and cellular response dynamics in many systems [1 , 14–19] . Microfluidics have given substantial control over experimental conditions , allowing researchers to probe single-cell responses to perturbations over extended periods of time [20–26] . However , due to the relatively long exposure times required to measure fluorescence signal from a small number of molecules , current experimental techniques are fundamentally limited by the requirement that cells to be stationary , making it difficult to correlate fluorescent reporters directly with cell behaviors in the same cells . We developed a high-throughput experimental method , FAST ( Fluorescence Analysis with Single-cell Tracking ) , to correlate directly the individual behaviors of freely swimming cells and the numbers of proteins in the signaling pathway of each cell . FAST overcomes the conflicting requirements of tracking cell motile behavior over a large field of view with fluorescence imaging of stationary cells at high magnification . FAST uses in situ hydrogel polymerization to ‘freeze’ cells in place after the tracking period to allow automated single-cell fluorescence imaging . Therefore , the navigational performance of individual cells , such as exploration of an environment , was directly measured as a function of long-term motile behavior and intracellular protein numbers . Here , we demonstrate FAST on the chemotaxis pathway of Escherichia coli , a model system for the study of cellular behavior . Motile E . coli cells explore their environment by alternating periods of relatively straight swimming ( runs ) with brief changes of direction ( tumbles ) . Tumbles are caused by a reversal of the flagellar motor rotation from counter-clockwise to clockwise direction . The activity of the chemotaxis receptor cluster controls the frequency at which the cell tumbles by controlling the kinase activity of CheA , which phosphorylates the response regulator CheY . The probability to tumble—the tumble bias—increases when the phosphorylated form of CheY ( CheY-P ) binds the motor . The kinase activity decreases when the receptors bind attractant . As a result , the CheY-P concentration is decreased by the constitutive phosphatase CheZ and the tumble bias is reduced . If attractant concentrations remain steady , tumble bias returns to pre-stimulus level due to the activity of CheR and CheB . CheR methylates inactive receptors , which increases the activity of the kinase , whereas CheB demethylates active receptors , reducing kinase activity [27] . Theoretical studies predict that this resting tumble bias is an important determinant of the chemotactic performance of E . coli and that the fitness of a population of cells might depend not only on its mean tumble bias but also on the population variability in tumble bias [12 , 13] . Early studies revealed a substantial amount of cell-to-cell variability in the motility behavior of clonal cells adapted to a uniform environment [28 , 29] . However , the molecular origin and functional consequences of this variability remain unclear . Because CheR and CheB are engaged in a futile cycle of methylation and demethylation of the chemoreceptors , the balance between the actions of CheR and CheB is an important factor in determining the resting activity of the receptor cluster [27] . Consequently , variations in the numbers of CheR and CheB are expected to affect the tumble bias of single cells . How changes in the levels of expression of CheR and CheB affect the distribution of the tumble bias in a clonal population of cells is unknown . It also remains unclear if the mean and the variance of the tumble bias distribution can be controlled independently from each other . Here , we examine these questions experimentally by quantifying in vivo how variations in the numbers of CheR and CheB in single cells shape the distributions of swimming phenotypes and exploration capabilities E . coli .
We recorded trajectories of freely swimming cells to determine the distribution of swimming phenotypes in an isogenic population . Following established protocols , E . coli RP437 cells were grown to mid-exponential phase in minimal medium then suspended in motility buffer in which the motile behavior remains constant for more than one hour and identical to the behavior observed in the growth medium ( S1 Fig ) [30] . Therefore , the characterization of single-cell motile behavior was done without confounding effects from cell growth or protein turnover ( Methods ) . Cells were imaged at low-density using phase contrast microscopy at 10X magnification in an isotropic liquid environment . Individual trajectories were reconstructed over a large field of view using a multiple-particle tracking algorithm [31] to enable the quantitative characterization of individual cell behavior ( Fig 1A , S2 Fig ) . We constructed a probabilistic model using the instantaneous velocity , acceleration , and angular acceleration to describe the behavior of each cell along their trajectories and identify tumbling events ( Fig 1B , S3A and S3B Fig ) . We then calculated the tumble bias ( defined as the time spent tumbling over the total trajectory time ) of each cell . The average cell behavior obtained from more than 6 , 000 cell trajectories was consistent with previously published , population-averaged results from E . coli RP437 cells . Specifically , the average tumble bias was 0 . 24 ( Fig 1C ) with an average swimming speed of 33 μm/s ( S3D Fig ) [29 , 32 , 33] . Single-cell trajectories revealed the variability in behavior within the isogenic population . The distribution of tumble biases was unimodal with mode at 0 . 2 and a standard deviation of 0 . 093 ( Fig 1C ) . These values are consistent with the previously reported distribution of single flagellar motor biases from tethered cells [29] and taking into account the effect of multiple flagella that increases the cell tumble bias relative to the clockwise bias of single motors [34 , 35] . As expected , we observed very few cells with tumble biases outside the 0 . 1 to 0 . 4 range because the robust architecture of the chemotaxis pathway ensures that the population tumble bias is maintained within a functional range [7 , 36–40] . However , our experiment shows that there is still substantial phenotypic diversity among cells consistent with prior observations [28] . Phenotypic variability in tumble bias and swimming speed can result in large differences in how efficiently cells explore their environment . We calculated the diffusion coefficient of each cell by simultaneously fitting the mean square displacement and the velocity autocorrelation ( S3E , S3F and S3G Fig ) of the trajectories to the Green-Kubo relations [41 , 42] . This analysis revealed that the effective diffusion coefficients of individual cells vary over more than one order of magnitude within the isogenic population in spite of the pathway robustness ( Fig 1D ) . The tumble bias of individual cells is determined in part by the numbers of the two receptor modification enzymes: CheR and CheB [12 , 29 , 38 , 43] . It has been previously observed that ectopic expression of CheR increases the cell tumble bias on average [29 , 38] . Transcriptional and translational coupling in the expression of CheR and CheB provides robustness to the system against uncorrelated stochastic fluctuations in protein numbers to maintain good chemotactic performance [7 , 44] . In addition , because of the slow exchange of the modification proteins between the cytoplasm and the cluster of receptors , the numbers of CheR and CheB affect signaling noise , which results in slow fluctuations of the cell tumble bias [29 , 45 , 46] . To understand the consequences of variations in the numbers of CheR and CheB on the swimming phenotype of E . coli , we sought to make a detailed quantitative map of motile behavior as a function of protein numbers . We genetically constructed the fluorescent protein fusions CheB-mYFP and mCherry-CheR , which have been previously shown to be functional proteins [47] . To explore a large dynamic range of absolute protein expression and CheR to CheB ratio , the gene constructs were placed under the control of two independent inducible promoters on the chromosome in a strain lacking both the native cheR and cheB genes ( Fig 2A ) . The separate transcriptional regulation of the two genes allowed for the control of both the absolute numbers and the ratio of CheB-mYFP and mCherry-CheR ( Fig 2B and 2C , S4 Fig ) . The fluorescence intensities per molecule of CheB-mYFP and mCherry-CheR was calibrated using quantitative immunoblotting and single-cell fluorescence microscopy [48] , following a previously described method [49] . The fluorescence intensity per fluorescent protein was calculated using a Bayesian regression analysis ( Methods and S5 Fig ) . We used the mutant strains to create different populations with widely different distributions of tumble bias ( Fig 2D , S6 Fig and S7 Fig ) . Consistent with previous work that measured average population behavior , the tumble bias distribution of cells increased with higher expression of mCherry-CheR and decreased with higher expression of CheB-mYFP , which has antagonistic activity to CheR [29 , 38 , 43 , 50] . The observed variability in protein expression and motile behavior within the population for each induction level is substantial despite the fact that each gene is expressed from a single chromosomal locus ( Fig 2D , S6 Fig and S7 Fig ) . Although CheR and CheB expression in the mutant strains is different from the wild type regulation , the results illustrate in general how phenotypic diversity can be altered through changes in the activity of single promoters . The FAST protocol relies on the immobilization of cells in a hydrogel , which is polymerized in situ , after tracking motile cells . We first mixed cells washed with motility buffer with a hydrogel precursor solution that contained polyethylene glycol diacrylate ( PEGDA ) and a photoinitiator ( LAP ) [51] . The presence of PEGDA and LAP did not affect motile behavior beyond a small reduction in swimming speed ( S1 Fig ) . Swimming was recorded at 10X as described above for 5 minutes ( Fig 3A ) . A flash of violet light was delivered through the microscope objective to activate the photo-initiator and trigger rapid hydrogel polymerization over the entire field of view . Cells were immobilized within 1 second and their coordinates were registered by processing the real-time image from the camera . After switching the microscope configuration for fluorescence imaging at 100X magnification , each cell was automatically located by a motorized stage and imaged in phase contrast and three fluorescence channels ( Fig 3B ) . An average of 200 cells was imaged in less than 40 minutes for each experimental trial . The fluorescence signal from each cell did not change significantly as a function of time during the single-cell imaging phase indicating that the fluorescent protein fusions are stable when the cells are trapped in the hydrogel ( S8 Fig ) . Therefore , the measured single-cell fluorescence corresponds to the number of proteins present while the cells were swimming . Finally , cells trajectories were matched to fluorescence images in order to map motile behavior to proteins numbers in single cells . We combined measurements from several experiments in which different combinations of inducer concentrations were used with the two fluorescent strains to produce a wide range of phenotypes , with cells expressing the labeled proteins over three orders of magnitude and tumble bias ranging from 0 to 0 . 6 . We mapped tumble bias to the number of mCherry-CheR and CheB-mYFP for more than 5 , 000 individual cells ( Fig 3C ) . Tumble bias increases with higher numbers of mCherry-CheR and decreases with lower numbers of CheB-mYFP , consistent with population measurements . We verified that the phenotypes showed no correlation with the constitutively expressed mCFP protein that was not fused to any of the native E . coli proteins ( S9 Fig ) . The uneven sampling of CheR and CheB ( Fig 3C ) is due to bistability of the activity of the rhamnose promoter that was used to control protein expression . We also mapped the individual diffusion coefficients to mCherry-CheR and CheB-mYFP numbers ( Fig 3D ) . Because of the large range of protein expression in this experiment , the individual cell diffusion coefficients span four orders of magnitude . This variability is larger than what was observed in the wild-type population ( Fig 1C ) , indicating that the natural cell-to-cell variability in CheR and CheB numbers is constrained to a range smaller than the artificially induced conditions . As expected , the relationship between the cell diffusion coefficients and protein numbers ( Fig 3D ) mirrors the relationship obtained from characterizing the cell tumble biases ( Fig 3C ) . Because the value of diffusion coefficient calculated from a trajectory does not rely on tumble detection , this analysis provides additional support for the observed effect of variations in protein numbers on behavior . Together , the single-cell data provide a direct mapping of protein numbers to swimming phenotype with unprecedented detail over a large range of protein numbers . We first examined how CheB-mYFP and mCherry-CheR control the mean tumble bias by performing a local linear regression fit of the single-cell tumble bias ( Fig 4A ) . The relationship between mean tumble bias and the logarithm of the CheB-mYFP and mCherry-CheR numbers has the characteristic feature of diagonal contour lines of increasing tumble bias , qualitatively indicating that tumble bias depends more on the ratio than the absolute number of these proteins . This trend was expected since CheR and CheB have antagonistic effects on kinase activity by participating in a futile cycle of methylation and demethylation of the chemoreceptors . However , the tumble bias appears to be more sensitive to changes in CheB-mYFP numbers than mCherry-CheR numbers . For example , changing the tumble bias by 0 . 1 requires approximately a 10-fold change in CheB-mYFP ( Fig 4B ) but a 40-fold change in mCherry-CheR ( Fig 4C ) . We analyze this unexpected asymmetry in more detail in the next section . With a quantitative relationship between tumble bias and the numbers of CheB-mYFP and mCherry-CheR , we asked how much of the variability in tumble bias measured in the wild-type population ( Fig 1E ) could be attributed to the natural cell-to-cell variability in the numbers of CheR and CheB . In wild type cells , cheR and cheB are transcriptionally and translationally coupled , hence fluctuations in protein numbers have correlated lognormal distributions with extrinsic noise ( Ƞext ) and a smaller intrinsic noise ( Ƞint ) . This genetic organization prevents large variations in tumble bias as a result of fluctuations in protein numbers [7 , 44] . Using previously reported estimates of the noise intensity in the expression of the chemotactic proteins , we generated the expected distributions for CheR and CheB numbers across individual cells in the wild type population ( Fig 4A ) . Then , we used the quantitative relationship between tumble bias and protein numbers to estimate the contribution of the natural cell-to-cell variability in CheR and CheB numbers to the observed cell-to-cell variability in tumble bias in the wild-type population . Using Ƞext = 0 . 26 and Ƞint = 0 . 125 [12 , 44 , 52] , CheR and CheB fluctuations explain 11% of the variance in tumble bias . Therefore , additional factors must contribute to a large portion of the wild type variability in tumble bias . Likely candidates are variations in the numbers of CheA , CheY , CheZ , the number of flagellar motors ( cell-to-cell variations ) , or slow stochastic fluctuations in the methylation levels of the receptors ( single-cell variations ) [12 , 45] . Further examination of the single-cell data indicates that there is large residual variability around the mean tumble bias for any given level of mCherry-CheR and CheB-mYFP . Calculating the standard deviation of the residual tumble bias as a function of mCherry-CheR and CheB-mYFP ( Fig 4D ) reveals that the residual variability depends strongly on CheB-mYFP ( Fig 4E ) and weakly on mCherry-CheR ( Fig 4F ) . Remarkably , the dependency of the residual variability on mCherry-CheR and CheB-mYFP numbers is not aligned with the dependency of the mean tumble bias ( the contours in Fig 4A and 4D are not aligned in the same direction ) . This observation suggests that the mean and the variance of the tumble bias distribution can be adjusted independently from each other by controlling chemotaxis protein expressions . Importantly , focusing on the data along the diagonal in Fig 4A and 4D reveals that the amount of phenotypic diversity in the population can be adjusted by changing the global level of expression of CheR and CheB while simultaneously maintaining their ratio nearly constant , therefore maintaining the robustness of chemotaxis pathway conferred by the co-expression of CheR and CheB from one operon [44 , 53] . These findings support the hypothesis that phenotypic diversity in E . coli chemotaxis is a selectable trait [12 , 54] . Current standard models of the bacterial chemotaxis system do not explain two aspects of our experimental results [12 , 54] . First , the observed mean tumble bias is not equally sensitive to changes in CheB-mYFP and mCherry-CheR numbers . A fit of the tumble bias to the logarithm of the protein numbers gives the relationship: TB=0 . 274+0 . 0627log10 ( NR/NB1 . 60 ) , where NR and NB are the numbers of mCherry-CheR and CheB-mYFP proteins in a single cell ( the 95% confidence intervals for the parameters are , in order , [0 . 258 , 0 . 291] , [0 . 0586 , 0 . 0668] , and [1 . 49 , 1 . 72] ) . We investigated whether the secondary feedback loop in chemotaxis system ( created by the phosphorylation of CheB by CheA ) could increase the sensitivity of the tumble bias to CheB numbers . We found that the tumble bias would still be determined by a simple ratio of CheR to CheB numbers consistent with previous models [12 , 43 , 54 , 55] ( S10 Fig ) . Second , the variance in tumble bias does not depend on mCherry-CheR and CheB-mYFP numbers the same way the mean tumble bias does ( Fig 4C and 4F ) . This observation was unexpected because it is not explained by models that assume that cell-to-cell variability in tumble bias results solely from cell-to-cell variability in chemotaxis protein numbers . Published models predict that the mean and the residual variance of the tumble bias have parallel dependencies on variations in CheR and CheB numbers [12 , 56] ( S10 Fig illustrates the theoretical predictions from these models , wherein the contours of mean and variance in tumble bias are aligned in the same direction ) . To explain our experimental findings , we propose a model that takes into account the dual role that CheB plays in both demethylating and deamidating the receptor proteins [57] and the slow fluctuations of the methylation level of the receptor cluster [45 , 46] . When the most abundant receptors , Tar and Tsr , are synthesized , they are translated with two glutamines ( Q ) instead of glutamates ( E ) at the methylation sites in a QEQE configuration . Glutamate residues can be reversibly methylated and demethylated to adjust the basal activity , or free energy , of the receptors . Non-mature receptors have a semi-active conformation in the absence of stimuli and cause higher-than-expected tumble bias in a cheRcheB mutant [58] because glutamine acts similarly to a methylated glutamate but with half the change in receptor free energy [43 , 59 , 60] . CheB irreversibly deamidates the glutamines to glutamates so that the residues can then be used for adaptation [57] . Therefore , cells need to synthesize and deamidate a full set of receptors during each cell division to ensure that all modification sites are available for reversible methylation . We hypothesized that high tumble bias arising from low levels of CheB-mYFP is caused by the incomplete deamidation of the receptors . We introduce a deamidation rate equation to take into account the maturation of receptors by CheB: d[Q]dt=−akQ[CheBP][Q]KB+[Q]+2r[TTot]−r[Q] , ( 1 ) where a is the activity of the receptor cluster , [CheBP] , [TTot] , and [Q] are the concentrations of phosphorylated CheB , total receptors , and glutamine residues , kQ is the deamidation rate , KB is the Michaelis-Menten constant characterizing the CheB-receptor binding , and r is the cell growth rate . The first term in Eq ( 1 ) corresponds to the rate of CheB-dependent deamidation , and the last two terms correspond to generation and dilution of glutamine residues within a cell as new receptors proteins are synthesized and cell divides . We modified the ( de ) methylation rate reaction [12] accordingly to take into account the presence of glutamine residues that cannot be ( de ) methylated and the dilution of methylated receptor by cell growth: d[M]dt= ( 1−a ) kR[CheR]4[TTot]−[M]−[Q]KR+4[TTot]−[M]−[Q]−akB[CheBP][M]KB+[M]−r[M] , ( 2 ) where [CheR] and [M] are the concentrations of CheR and methylated glutamate residues , kR and kB are the methylation and demethylation rates , KR is the Michaelis-Menten constant characterizing the CheR-receptor binding . The first term in Eq ( 2 ) corresponds to the rate of CheR-dependent methylation of the available glutamate residues , the second term corresponds to the CheB-dependent demethylation of the methylated glutamate residues , and the last term corresponds to the dilution of the methylated residues from cell growth . The remaining equations describing the dynamics of phospho-relay reactions remain unchanged [12] because the kinetics rates remain much faster than the cell growth rate even when CheR and CheB numbers are low ( see Methods ) . Finally , the activity of the receptor in the absence of chemical stimuli as a function of glutamine and methylated glutamate residues is described by: a= ( 1+Exp[ε0+ε12N[M][TTot]+ε22N[Q][TTot]] ) −1 , ( 3 ) where ε0 , ε1 , and ε2 are free energy constants , N is the size of the MWC complexes [54] . In the absence of chemotactic signals , the system can be solved at equilibrium to determine the steady-state tumble bias . To simulate cell-to-cell variability in protein expression we sampled protein numbers for each cell from lognormal distributions with intrinsic and extrinsic noise generators [12] . Introducing the effects from the synthesis of non-mature receptors and their deamidation by CheB in the model was sufficient to reproduce the higher sensitivity of the cell tumble bias to CheB than CheR ( Fig 5A and S10C Fig , the orientation of the contours produced by our model matches the data in Fig 4A ) . The model predicts that when the number of CheB molecules becomes limiting , glutamine residues accumulate in the receptor cluster during cell growth resulting in an increase of the average tumble bias . We verified this hypothesis by following the population tumble bias immediately after transfer from growth medium to chemotaxis buffer . When the number of CheB is lower than the wild-type population mean of 240 molecules per cell [52] , the population average tumble bias starts high and slowly decreases over the course of an hour ( S11 Fig ) . From this experiment , we estimated that the CheB-dependent deamidation rate is half of the demethylation rate . To explain the relationship of the residual variance of the tumble bias as a function of CheR and CheB , we took into account the slow fluctuations of the methylation level of the receptor cluster within the timescale of our experiments . When the numbers of CheR and CheB are small compared to the number of receptors , the average methylation level becomes hyper-sensitive to the ratio of the two modification proteins [45 , 46 , 61] . As a result , the tumble bias of one cell can fluctuate significantly over time scales similar to the duration of the tracks that we used to quantify tumble bias in our experiment [29 , 46] . When the spontaneous fluctuations of the receptor activity are taken into account , the residual variance in tumble bias becomes more dependent on CheB rather than the mean tumble bias ( Fig 5B , the orientation of the contours produced by our model align with changes in CheB numbers similar to what is observed in the data in Fig 4D ) . Overall , the dependency of the observed residual variance supports the hypothesis that behavioral variability in a clonal population is a result of both signaling noise caused by the receptor adaptation dynamics and cell-to-cell variability in protein numbers .
Understanding the functional role of variability in clonal populations of cells will require understanding how molecular variations map onto phenotypic variations , which in turn translate into performance differentials between individual cells . While molecules , phenotype , and performance of individual cells can all be measured separately [17 , 28 , 29 , 33 , 46 , 62 , 63] , making all these measurements in the same cells has not been possible due to the large differences in length scales and time scales involved . By combining fast in situ hydrogel polymerization with automated fluorescence microscopy , we were able to bridge scales and directly correlate for the first time individual motile behaviors of freely swimming cells to intracellular protein numbers . We mapped single-cell tumble bias and exploratory capability as a function of the numbers of the two adaptation proteins of the chemotaxis pathway , CheR and CheB , with unprecedented details . We found that CheR and CheB numbers affect both the mean and the variance of the tumble bias but in different ways . Therefore , the shape of the phenotypic distribution in an isogenic population could be adjusted through genetically encoded factors such as the levels of protein expression . This suggests that the variability in tumble bias can evolve in an isogeneic population while the mean tumble bias remains constant and vice versa solely through mutations that change the relative expression levels of CheR and CheB ( and possibly other chemotaxis proteins such as CheY and CheZ ) . These experimental observations support previous theoretical predictions that the degree of phenotypic diversity in swimming behavior could be a selectable trait in E . coli [12 , 13] . Previous studies demonstrated that translational and transcriptional coupling of CheR and CheB confer robustness to the chemotactic system [44 , 53] and that even when phenotypic diversity is advantageous it is important to maintain specific ratios in the numbers of proteins [12] . Our results show that a clonal E . coli population can adjust phenotypic diversity by adjusting the total expression CheR and CheB without disrupting their coupling . We also found that single-cell behavioral variability caused by the dynamics of receptor methylation , as previously described [45 , 46] , contributes significantly to the observed population phenotypic diversity in addition to cell-to-cell variability in protein expression . The contributions of additional molecular and morphological factors , such as the number of flagella , cell shape , or the location of the receptor clusters , to individual cell motile behavior remain to be characterized . By enabling the quantitative measurement of multivariate distributions , FAST will facilitate the characterization of phenotypic variability as a function of protein numbers , signaling pathway architecture , or other cell components . Taking advantage of the large field of view and high-resolution offered by modern scientific cameras , we were able to track and quantify the tumble of thousands of wild type E . coli cells . We found that tumble bias and diffusion coefficient were widely distributed . This variability is expected to have a significant impact on the spatial organization and fitness of cells when competing for resources [12 , 64–66] . Few cells had tumble biases above 0 . 4 , consistent with predictions that high tumble biases perform poorly [13] . To explain the unpredicted finding that tumble bias is more sensitive to CheB than CheR , we propose that the deamidation of the newly synthesized receptor proteins becomes incomplete when the number of CheB falls below approximately one hundred molecules , which is within reason since the mean expression level is ~240 [52] . With incomplete deamidation , the basal activity of the receptor is increased , causing elevated tumble bias not explained by previous models . From the analysis of our model , we found that the activation of CheB through phosphorylation by CheA was not sufficient to explain our experimental observations because this feedback does not introduce an asymmetry in the relationship between the mean and the variance of the tumble bias as a function of CheR and CheB numbers . The biological significance of the CheB-dependent maturation of the dominant receptor proteins via the deamidation of specific Q residues is still not understood . However , our results suggest that wild-type cells express on average just enough CheB to keep up with the synthesis and maturation of new receptors during growth . One possibility is that the QEQE configuration may place the ( de ) methylation dynamics of the receptor cluster closer to equilibrium , saving a significant amount of time and energy to methylate new receptor proteins since they outnumber CheR and CheB by about two orders of magnitude . Another possibility is that synthesizing receptor proteins with a QEQE configuration is a bet-hedging strategy . Because of cell-to-cell variability in the expression levels of CheR and CheB some cells will express few ( de ) methylation enzymes . Previous work has shown that when CheR and CheB are limiting the tumble bias become hyper-sensitive to the ratio of the numbers of CheR and CheB [45 , 46 , 61] . Upon expression of the chemotaxis pathway cells should initially have higher tumble bias and therefore stay close to their sisters due to the higher activity of the QEQE configuration . However , as the chemotaxis receptors become fully deamidated , individual cells will start leaving the colony and explore their surroundings . Cell-to-cell variability in the expression of CheR and CheB may result in a slow trickling of explorers from the colony . This slow transition from tumbler to explorer may be a bet-hedging strategy when turning the chemotaxis pathway on . An important aspect of signal transduction is that changes in behavior affect how cells interact with environmental signals . This is especially true for navigation where behavior feeds back onto the statistics of input signals and vice versa [13] . This important feedback loop is lost when cell are attached on surfaces or immobilized with optical tweezers [35 , 40 , 59] . FAST alleviates these constraints , by allowing behavioral tracking and fluorescence imaging of freely swimming cells . The combination of FAST with the use of nano-fabricated landscapes to create chemical gradients should facilitate the investigation of the molecular , cellular , and population level mechanisms that underlie the emergent behaviors of cells in complex environments .
E . coli RP437 was used as the wild-type strain for chemotaxis and as the parental strain for all the mutants generated in this study . Cells were cultured in M9 minimal medium supplemented with 10 g/L glycerol , 1g/L tryptone , 2 mM magnesium sulfate , 0 . 1 mM calcium chloride , 10 mg/L thiamine hydrochloride , and 50 mg/L streptomycin . Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) and rhamnose were added to the growth medium when indicated to induce protein expression . Cells were grown in aerobic conditions at 30°C in an Erlenmeyer flask on an orbital shaker at 200 rpm for aeration . Starting from single colonies isolated on agar plates , cells were grown to saturation overnight in broth cultures and sub-cultured using 1:100 dilution ratio in fresh medium and grown to an optical density at 600 nm ( OD600 ) of 0 . 25 . Under these growth conditions , virtually all cells were highly motile . Before performing the behavioral experiments , cells were washed twice at room temperature with motility buffer ( M9 salts supplemented with 0 . 1 mM ethylenediaminetetraacetic acid ( EDTA ) , 0 . 01 mM L-methionine , 10 mM sodium lactate , and 0 . 05% weight/volume polyvinylpyrrolidone ( M . W . ~40 , 000 Da ) ) by centrifuging cells at 2 , 000 g for 5 minutes and diluted to a low cell density ( OD600 ~ 0 . 01 ) . The buffer exchange and centrifugation did not appear to affect the cell behavior when compared to cells sampled from the growth medium ( S1 Fig ) . To record motile behavior , 5 μL of cells in motility buffer was sealed between a glass microscope slide and a 22 mm2 #1 . 5 coverslip using VALAP ( equal amount of petrolatum , lanolin , and paraffin wax ) . Cells were free to swim in a pseudo two-dimensional environment ~10 μm deep . Cell motion was recorded at 10 frames per second with a digital scientific CMOS camera ( Hamamatsu ORCA-Flash4 . 0 V2 , 2x2 pixel binning , 50 ms exposure , rolling shutter , full frame ) mounted on an inverted microscope ( Nikon Eclipse TI-U ) with a 10X phase contrast objective ( Nikon CFI Plan Fluor , N . A . 0 . 30 , W . D 16 . 0mm ) and LED white light diascopic illumination ( Thorburn Illumination Systems ) . The field of view was ~1 . 3 mm square containing on average 200 cells . To reconstruct the cell trajectories each image sequence was processed using custom MATLAB ( Mathworks ) code . First , the mean pixel intensities of the frames over the entire image sequence was calculated to obtain an image of the background and subtracted from each image . The subpixel resolution coordinates of each cell in each frame were detected using a previously described method using radial symmetry [67] with an intensity detection threshold set to 6 standard deviations over the background . Coordinates were linked to obtain cell trajectories using a previously described self-adaptive particle tracking method , u-track 2 . 1 [31] , with the linear motion model linkage cost matrices , an expected particle velocity of 30 μm/s , and otherwise default parameters . The cell velocity at each time point was calculated according to vi= ( xi+1−xi ) 2+ ( yi+1−yi ) 2/ ( ti+1−ti ) . The acceleration was calculated according to ai = ( vi+1−vi ) / ( ti+1−ti ) . The angular acceleration was calculated according to αi = ( ( θi+1−θi ) − ( θi−θi−1 ) ) / ( ti+1−ti ) 2 . θi is the angle between consecutive velocity vectors . The velocity auto-correlation and mean square displacement of each trajectory were analyzed to extract the average mean run time and diffusion coefficient of each cell . The velocity autocorrelation , Cv , and the mean square displacement , MSD , were calculated according to: Cv ( Δt ) =1N∑i=1N ( v→ ( ti ) . v→ ( ti+Δt ) ) and MSD ( Δt ) =1N∑i=1N ( x ( ti+Δt ) −x ( ti ) ) 2+ ( y ( ti+Δt ) −y ( ti ) ) 2 , where ti represents the relative time for each frame of the image sequence , and ∆t the time interval between time points . The data was fitted using a non-linear least-square method with the functions: Cv ( t ) =v02e−tτcos ( ωt ) , and MSD ( t ) =2t∫0tCv ( u ) du−2∫0tsCv ( u ) du ( or MSD ( t ) =2v02τ ( τcos ( ωt ) –τetτ+tetτ−2τ2ωsin ( ωt ) −τ3ω2cos ( ωt ) +τ3ω2etτ+τ2ω2tetτ ) / ( etτ ( τ2ω2+1 ) 2 ) ) , where t is time , v0 is the average cell speed , τ is the time scale of the cell directional persistence ( a function of the cell tumble bias , mean tumble angle , and rotational diffusion ) , and ω is the angular frequency of the circular trajectory resulting from the interaction when cells swim near the glass surface ( S12 Fig ) . The mean square displacement function was calculated by taking the integral of the velocity autocorrelation function in two dimensions according to the Green-Kubo relations [41 , 42] . The effective diffusion coefficient , D , was calculated according to Deff=v02τd to remove the effect of the glass–surface interaction , where d is the number of dimensions ( two in our experiment ) . The effective diffusion coefficient is in good agreement with a previously derived approximation of the diffusion coefficient derived for swimming E . coli cells as a function of the mean run time between tumbles [68] defined as: Dapx=v2Td ( 1−θ ) , where T is the average run time between tumbles calculated for each cell using our tumble detection analysis , and θ is the mean cosine of the tumble angles ( θ = 0 . 18 in our dataset , S3H Fig ) . The posterior probabilities for a cell to be swimming ( S ) , tumbling ( T ) , and an intermediate state recovering from tumbling ( I ) given the instantaneous velocity ( v ) , acceleration ( a ) , and angular acceleration ( α ) ( P ( S|v , a , α ) , P ( T|v , a , α ) , and P ( I|v , a , α ) ) were constructed from a reference dataset containing more than 6 , 000 trajectories from wild-type cells . The parameters of each distribution were estimated by fitting a mixture of three tri-variate Gaussian distributions to the pooled distributions of instantaneous velocity ( v ) , accelerations ( a ) , and angular acceleration ( α ) ( S3A and S3B Fig ) . Therefore , each behavioral state is represented by a tri-variate Gaussian distribution . The mixture model was fitted to the reference dataset using an iterative approach . First , the swimming speed of each cell was normalized by their average speed when in the swimming state ( the first iteration was initialized using the 95th percentile of their instantaneous speeds ) . The relative acceleration and the angular acceleration between consecutive velocity vectors were computed . Then , all the relative speed , acceleration , and angular acceleration , were fitted with the mixture model . Each time point of the cell trajectories was assigned to the state with the largest posterior probability . The normalization , fitting , and state assignment were done iteratively until changes in state assignment between consecutive iterations converged below a tolerance of 1% ( 10 iterations on average ) . The resulting posterior probabilities were used to analyze the trajectories of all the cells in this work . Based on our validation of the tumble detection on simulated trajectories ( see next section ) , we discarded all trajectories that were shorter than 10 seconds because short trajectories resulted in inaccurate tumble bias calculations ( the code is available at https://github . com/dufourya/SwimTracker ) . To validate the tumble detection model we simulated the swimming trajectories of cells with defined phenotypes in the absence of signal gradients . Simulations were run following a previously described method [34] , with a constant swimming speed of 20 μm/s and rotational diffusion of 0 . 062 rad2/s [33] . Cells are stationary during tumbles and their orientations are uniformly randomized . The simulated cell tumble bias was changed by varying the internal CheY-P , Yp , concentration , which controls the transitions rates k+ and k_ to clockwise and counter clockwise of the flagellar motor according to: k±=εexp{±[g2 ( 12− ( YpYp+KD ) ]} , with ε = 1 . 3 s-1 , g = 40 , and KD = 3 . 06 μM , according to previously published experimental data [40] . The simulated environment was bounded in the z-dimension by reflecting boundaries 10 μm apart . Cell positions were sampled every 100 ms and projected in two dimensions to reproduce the experimental conditions . The accuracy and precision of the trajectory analysis was determined using 1 , 000 simulated trajectories for each tumble bias ( S13 Fig ) . The simulations showed that the tumble detection and tumble bias calculations were accurate for trajectories as short as 10 seconds . Plasmids and mutant strains were constructed following standard cloning protocols ( see S1 Table , S2 Table and S3 Table for the lists of plasmids , strains , and oligonucleotides ) . The deletion of cheR and cheB in E . coli RP437 was done using the λ Red disruption system [69] . Approximately 300 base pairs at the end of cheB were kept in the genome to maintain the proper regulation of the downstream expression of cheY and cheZ ( Victor Sourjik , personal communication ) . The sequences homologous to the targets ( cheR and cheB ) in the genome were added to the oligonucleotide primers . PCR reactions were performed with these primers to amplify the sequences containing a tetracycline resistant cassette flanked by flippase recognition target ( FRT ) sites from pCP16 [70] . Cells were first transformed with the plasmid pKD46 , and then transformed with the purified PCR product after induction of the recombinase protein from pKD46 . After successful recombination , the portion of the genome containing the deletion and the tetracycline cassette was transduced to a new E . coli RP437 background using the phage P1vir . Finally , the tetracycline resistant cassette was excised from the genome with flippase ( Flp ) by transforming the mutant strain with pCP20 leaving a single FRT sequence scar to obtain the strain NWF121 ( ΔcheRcheB ) . Gene fusions of cheR and cheB with the genes encoding for the fluorescent reporters sfYFP , [71] , or mCherry [72] , which have been codon-optimized for E . coli expression , and a cassette containing a kanamycin marker flanked by FRT sites ( from pCP15 [70] ) were constructed using the Gibson assembly method [73] from PCR fragments and a pUC19 vector backbone . The constructs were PCR amplified with sequences homologous to the targets added to the oligo primers and recombined separately into the wild-type MG1655 strain following the same protocol described above . Constructs were recombined into either the native lactose ( lac ) or rhamnose ( rha ) operon loci in the chromosome to take advantage of the host inducible transcription regulation . Each construct was transduced sequentially into the mutant RP437 strain lacking cheR and cheB ( NWF121 ) using the phage P1vir and excision of the kanamycin resistant cassette with the flippase after each successful transduction . A gene coding for the fluorescent protein sfCFP under the control of the constitutive promoter pBla was also recombined into the genome of the mutant strains to provide an independent fluorescence control . Two strains , which are almost identical except that the inducible promoters are swapped , were obtained: YSD2072 ( ΔcheRcheB-FRT , pRha-mCherry-cheR-FRT , pLac-cheB-mYFP-FRT , pBla-CFP-FRT ) and YSD2073 ( ΔcheRcheB-FRT , pLac-mCherry-cheR-FRT , pRha-cheB-mYFP-FRT , pBla-CFP-FRT ) . An identical approach was used to clone pLac-cheB-mYFP and pBla-CFP into RP4972 ( ΔcheB ) to create YSD2044 ( ΔcheB , pLac-cheB-mYFP-FRT , pBla-CFP-FRT ) . Deletions and insertions in the final mutant strains were verified by PCR and DNA sequencing . Fluorescence microscopy images were acquired using an inverted microscope fitted with a 100x oil immersion objective ( Nikon CFI Plan Fluor , N . A . 1 . 30 , W . D 0 . 2 mm ) , a solid state white light source ( SOLA II SE , Lumencor ) , a digital scientific CMOS camera ( Hamamatsu ORCA-Flash4 . 0 V2 , 1x1 pixel binning , rolling shutter , full frame , 16 bits ) . Cells were spotted on agarose pads ( 1% wt/vol agarose with M9 salts ) after being washed twice in M9 salts and mounted between a glass slide and a #1 . 5 glass coverslips . Five different frames containing on average 200 cells were acquired in phase contrast and three fluorescence channels for each sample ( CFP filters ex436/20 , 455LP , em480/40 , YFP filters ex500/20 , 515LP , em535/30 , mCherry filters ex560/40 , 585LP , em630/75 ) . The camera dark current was subtracted from each images and the uneven illumination was corrected using a flat-field image acquired using uniform fluorescent slides . Cell outlines were determined using MicrobeTracker [48] on the phase contrast images . Cells with sizes deviating from the population by more than three standard deviations were discarded from the analysis . Single-cell fluorescence intensities were calculated by summing the fluorescence signal over each cell area and subtracting the background fluorescence intensity . The autofluorescence of wild-type cells ( RP437 ) in each channel was determined and subtracted from the fluorescence intensities of cell expressing the fluorescent reporters . The small amount of cross talk between the fluorescent proteins was determined using cells expressing single fluorescent labels and corrected in cells expressing multiple labels using linear unmixing [74] . The calibration of cell culture optical density ( OD600 ) to colony forming units ( CFU ) was done using serial dilution and plating . Cells expressing different concentrations of the fluorescently labeled proteins were suspended in Laemmli buffer , then boiled for 5 minutes and homogenized in an ultrasonic water bath for 1 minute . Known concentrations of purified fluorescent protein standards ( GFP: Rockland 000-001-215 lot 23193 , and RFP: abCam ab51993 lot GR25411-12 ) were added to wild-type cell lysate and treated with the same conditions as the samples to generate standard curves . The lysate of 108 cells in 20 μL were loaded in each lane of pre-casted polyacrylamide gels ( BioRad cat . #456–9035 ) and run in Tris/glycine/SDS buffer at 100 Volts for 90 minutes at 4°C . The proteins were transferred to a low fluorescence 0 . 45 μm PVDF membrane ( BioRad , cat . #162–0261 ) using wet transfer in Tris/glycine/20% Methanol buffer at 100V for 60 minutes at 4°C . The membranes were blocked to prevent non-specific antibody binding using blocking buffer ( EMD Millipore , cat . # WBAVDFL01 ) for 60 minutes at room temperature . To detect CheB-mYFP , mCFP , and standard GFP , the membranes were hybridized with 1:5 , 000 dilutions of anti-GFP antibodies conjugated to DyLight488 ( Rockland cat . #600-141-215 lot 23518 ) in Tris buffer saline with 0 . 05% Tween 20 pH 7 . 5 for 12 hours with gentle agitation at 4°C . To detect mCherry-CheR and standard RFP , the membranes were hybridized first with 1:2 , 500 dilutions of anti-RFP antibodies ( abCam cat . # ab183628 lot GR170176-1 ) in Tris buffer saline with 0 . 05% Tween 20 pH 7 . 5 for 12 hours with gentle agitation at 4°C , then with 1:10 , 000 dilutions of anti-Rabbit antibodies conjugated with Dylight488 ( Rockland #611-141-002 lot 23521 ) for 1 hour at room temperature . The membranes were washed three times for 15 minutes with Tris buffer saline with 0 . 05% Tween 20 pH 7 . 5 after each incubation . The membranes were dried and scanned with a laser scanner ( GE Typhoon 9400 ) at 488 nm . The images were processed with ImageJ [75] to quantify the signal intensities . The calibration of fluorescence intensities to protein numbers was calculated using Bayesian linear regression of the quantitative immunoblotting data with the average cell fluorescence signals to obtain the posterior probability distributions of the fluorescence signal per protein . The regression model was setup in the R statistical computing environment [76] with the RStan package [77] . The number of each fluorescent protein per cell was determined as the maximum a posteriori estimate . Trapping cells with fast in situ hydrogel polymerization was done by supplementing the motility buffer with 5% wt/vol polyethylene glycol diacrylate ( PEGDA ) ( M . W ~2 , 000 , JenKem Technology cat . #A4047-5 ) and 0 . 05% wt/vol of the photoiniator lithium phenyl-2 , 4 , 6- trimethylbenzoylphosphinate [51] . To remove traces of reactive contaminants in the PEGDA , a 20% wt/vol solution was incubated for 10 minutes with a high concentration of washed E . coli cells . Cells were removed by centrifugation and filtration through a 0 . 22 μm filter . The hydrogel polymerization was triggered by exposing the sample for 5 seconds with violet light using a solid state light source ( SOLA II SE , Lumencor ) at full intensity through a band pass excitation filter ( 395/25 ) and the microscope 10X objective ( Nikon CFI Plan Fluor , N . A . 0 . 30 , W . D 16 . 0mm ) . The automated imaging of immobilized cells was done using custom Matlab scripts controlling the microscope and a motorized stage ( Prior Scientific , cat . # H117 ) through the Micro-Manager core library [78] ( the code is available at https://github . com/dufourya/FAST ) . After immobilization , the cell coordinates were registered using image analysis in Matlab and the microscope was configured for epifluorescence imaging at 100X . The computer-controlled stage moved sequentially to each cell location . The z-focus was automatically adjusted for each cell before imaging in phase contrast and the three fluorescence channels ( CFP filters ex436/20 , 455LP , em480/40 , YFP filters ex500/20 , 515LP , em535/30 , mCherry filters ex560/40 , 585LP , em630/75 ) . Cells that were not properly aligned with the focal plane , which were determined by detecting non-closed edges of the outlines of cells in the analysis of phase contrast images , were skipped . Cells with sizes deviating from the population by more than three standard deviations were discarded from the analysis . About 200 cells were imaged in less than 40 minutes for each experiment trial . The fluorescence signal from each cell did not change significantly as a function of time during the single-cell imaging phase indicating that the fluorescent protein fusions are stable when the cells are trapped in the hydrogel ( S8 Fig ) . The model used to calculate tumble bias as a function of protein numbers is based on a previously published model [12] . The concentration of phosphorylated CheA [CheAP] changes according to d[CheAP]dt=aaP ( [CheATot]−[CheAP] ) −aB[CheAP] ( [CheBTot]−[CheBP] ) −aY[CheAP] ( [CheYTot]−[CheYP] ) , ( 4 ) in which a is the receptor cluster activity , [CheATot] , [CheBTot] , [CheYTot] , and [CheYP] are the concentrations of all CheA , all CheB , all CheY , phosphorylated CheY , aP , aB , and aY are the phosphorylation rate constants . The concentrations of phosphorylated CheB and CheY changes according to d[CheBP]dt=aB[CheAP] ( [CheBTot]−[CheBP] ) −dB[CheBP] , ( 5 ) and d[CheYP]dt=aY[CheAP] ( [CheYTot]−[CheYP] ) −dZ[CheZ][CheYP] , ( 6 ) in which dB and dZ are the dephosphorylation rates and [CheZ] is the concentration of CheZ . The biochemical rate and binding parameters are kept the same for all cells ( the values used are summarized in S4 Table ) . To simulate cell-to-cell variability in protein expression , the protein numbers were sampled for each cell from log-normal distributions according to a noisy gene expression model as previously described [12] using experimentally determined average concentrations [52] . To match as well as possible our experimental results , the intrinsic and extrinsic noise levels in the expression of all the chemotaxis proteins ( except CheR and CheB which were measured directly ) were reduced to be a tenth of what was previously proposed [12] . The concentration of CheZ was reduced slightly to match the observed range of tumble bias in our experiments . In the absence of stimuli , Eqs 1–6 can be solved at equilibrium to calculate the steady-state concentration of each protein . The variance of [CheYP] resulting from the spontaneous fluctuations of the cluster activity for each single cell was calculated using the linear noise approximation of the Master equation as previously described for the chemotaxis system [45] . Briefly , taking the stoichiometry matrix of the system , S , and the propensity vector , v , the diffusion matrix of the system , B , was calculated using the linear noise approximation by solving BTB = S diag ( v ) ST [79 , 80] . The correlation matrix , C , which contains the variance and covariance for the fluctuations of all the components in the model , was calculated from the linearized rate equations near the equilibrium solution given by the Jacobian matrix of the system , A , by solving numerically the Lyapunov equation AC + CAT + BTB = 0 [81 , 82] . To simulate the effect of the slow fluctuations in cluster activity on the concentration of phosphorylated CheY , [CheYP] was sampled randomly from a Gaussian distribution centered at the steady-state [CheYP] with variance var ( [CheYP] ) form the correlation matrix , C . Because the cluster activity fluctuates according to the adaptation time scale , the effective [CheYP] was calculated from the average of neff samples according to neff=‖Tτ‖ , in which T is the average length of the recorded trajectory in seconds ( 100 seconds ) and τ is the slowest relaxation time-scale of the Jacobian matrix A evaluated at the equilibrium solution given by the largest of the eigenvalues λ of A , τ = −1/max ( λ ) . The tumble bias was calculated as a function of the effective [CheYP] using the steady-state function of the adaptive flagellar motor as previously described [13] and the coordination of multiple flagella as previously described [34] . | Cell-to-cell variations in protein numbers due to random fluctuations at the molecular level lead to cell-to-cell variations in behavior . To maintain predictable responses , signaling networks have evolved robustness against noise , but in some situations phenotypic diversity in a clonal population can be beneficial as a bet hedging or division of labor strategy . Investigating of how random molecular fluctuations affect cell behavior requires to measure biological parameters at different scales . Here , we report a new experiment that allows the measure of both protein numbers and behavior in cells that are free to move in their environment . Using Escherichia coli , a model system for the study of cellular behavior , we investigated the effects variations in the numbers of the chemo-receptor modification enzymes on single-cell swimming behavior . We found that the mean and variance of the behavior can be adjusted independently in the population by adjusting protein expression . This mechanism allows for the genetic selection of phenotypic diversity without disrupting correlations in protein expression that are important for the overall robustness of the chemotaxis system . | [
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"... | 2016 | Direct Correlation between Motile Behavior and Protein Abundance in Single Cells |
A temperature independent period and temperature entrainment are two defining features of circadian oscillators . A default model of distributed temperature compensation satisfies these basic facts yet is not easily reconciled with other properties of circadian clocks , such as many mutants with altered but temperature compensated periods . The default model also suggests that the shape of the circadian limit cycle and the associated phase response curves ( PRC ) will vary since the average concentrations of clock proteins change with temperature . We propose an alternative class of models where the twin properties of a fixed period and entrainment are structural and arise from an underlying adaptive system that buffers temperature changes . These models are distinguished by a PRC whose shape is temperature independent and orbits whose extrema are temperature independent . They are readily evolved by local , hill climbing , optimization of gene networks for a common quality measure of biological clocks , phase anticipation . Interestingly a standard realization of the Goodwin model for temperature compensation displays properties of adaptive rather than distributed temperature compensation .
The most prevalent model of temperature compensation is also the most parsimonious in that it makes no structural assumptions about how temperature enters the network equations , and was proposed by Ruoff and Rensing [10] . The period , , depends in an unknown way on all the constants in the model e . g . , the rates and equilibrium constants in a Michaelis-Menten description . Their temperature , T , dependence can be expressed in Arrhenius form , . Then temperature compensation is expressed as ( 1 ) and becomes a linear constraint on the since the are supposed constant over a physiological temperature range . Since Eq 1 imposes a global constraint on all parameters , we describe it as “distributed” temperature compensation . This model though parsimonious is not intuitively satisfactory in all respects , though it does not directly contradict any experiment . As noticed by Tyson and coworkers , several mutants do not appear fully consistent with distributed compensation [18]: in particular , more than of mutants in fly and Neurospora whose period is different from 24 hrs retain compensation ( see [18] Table 1 and references within and [19] , [20] ) . So we have to assume there are genes that affect the period , yet are not temperature dependent i . e . , are compensated locally , or do not change their temperature dependence when mutated . Also model parameters can subsume multiple biochemical events that collectively appear temperature independent [21] . Of course there has to be temperature dependence somewhere to allow entrainment . Other fly mutants like have a temperature compensated period but fail to entrain [4] . This is unexpected if several terms are temperature dependent; from our understanding of typical non linear oscillating systems any coupling to temperature will lead to entrainment . Finally if multiple Michaelis-Menten constants vary with temperature one would expect the shape of the orbit to vary in an arbitrary way with temperature . If the orbits change , the phase response curves to either light or temperature should too , since they just measure the isochrons ( surfaces of constant phase ) around the orbit .
The dual properties of a temperature independent period and strong entrainment by an oscillating temperature are tantamount to asserting that the time rate of change of the phase ( angular velocity ) around the orbit is adaptive , Fig . 1 . Adaption means that subject to a temperature step , the system responds with a pulse but then returns to the same steady value it had before the step ( see for instance [22] ) . A minimal expression of this idea , now for the phase , is given by an idealization of the situation envisioned in [14] ( 2 ) where is the angular frequency of the oscillator ( period ) and the temperature is . The angular velocity only deviates from its temperature independent value when the temperature changes . A temperature step applied when the oscillator has phase induces a phase shift . If we compute the cumulative effect of a rapid step-up step-down in the temperature one finds a total phase change: ( 3 ) which is the definition of the PRC with respect to temperature changes and where depends on details of the temperature pulse ( e . g . , duration and intensity ) . Thus the PRC at different temperatures can all be superimposed by scaling the overall amplitude ; they have the same shape [14] . The magnitude of and its dependence both control how well the oscillator is entrained by a periodic temperature signal [23] . Whereas is proportional to the temperature pulse no matter how large , in more realistic models the phase response to a light pulse occurs through the degradation of one or more clock components ( like TIM in fly [24] ) and clearly saturates . Since there is no basis in our models for designating a light sensitive variable , we define a with respect to any parameter by making a rapid excursion in the parameter from its nominal value and back to baseline . While Eq . 2 may seem very artificial , we show next that its principal features are recovered in a widely used model for temperature compensation in the Neurospora clock . Ruoff and coworkers have used the Goodwin model [10] as a generic negative feedback oscillator with which to model the circadian clock in Neurospora . ( 4 ) ( 5 ) ( 6 ) Following [11] X would roughly correspond to FRQ RNA , Y for cytoplasmic FRQ and Z to nuclear FRQ . For simplicity , in the following , we will call production rates and degradation rates . If we assume that variable is larger than ( as it actually is in ref [10] ) , we can neglect relative to in the first term of Eq . 4 . One can then rewrite the equations for rescaled variables , , so as to reduce Eqs . 4–6 to ( 7 ) ( 8 ) ( 9 ) using . So the production rate parameters have been completely absorbed in the rescaling and the degradation terms are not affected . This has several consequences ( Fig . 1 ) : These remarks then explain the results of Ruoff and coworkers [10] , [11] on temperature compensation in the Goodwin model , since they chose small activation energies for degradation rates , and large ones for the production rates . Thus the amplitude of the clock changes substantially with temperature , while the period is fixed since the degradation rates were not changed . The Goodwin model for their parameters is not an example of distributed temperature compensation as sometimes claimed , but rather is effectively temperature independent ! Thus the PRC shape is also temperature independent Fig . 2D . The linear transformation on the orbits induced by temperature and the temperature invariant PRC we derived from Eq . 7–9 seems very specific to the Goodwin model , and we would like to demonstrate that the same properties are found in a wider class of models . As explained above , temperature compensation looks formally very similar to biochemical adaptation . Thus it is natural to ask if we can build temperature compensation upon an adaptive network for temperature . To be consistent with mutants such as , we are looking for models where temperature explicitly changes only very few parameters : temperature compensation in this limit is expected to rely on structural properties rather than the distributed compensation mechanism . Given the complexity of these constraints , we use in silico evolution as a mathematical tool to generate temperature compensated models . Our simulations evolve both the gene network and the parameters as we have done previously [22] , [25]–[27] , and we allow just transcription and protein-protein interactions , PPI , ( see Supplementary Text S1 for more details ) . Temperature is introduced through a so called input variable , , which typically couples to just one or a few other variables . The input will vary over a range of 2 or more to represent a substantial temperature dependence as defined by a typical parameter . To emphasize the connection to adaptation we initialize our simulations with a simple two gene adaptive network , shown in 3A , that we evolved previously [22] and is standard [28]: ( 10 ) ( 11 ) ( We hence forth generically use lower case variables in all equations with no implication that they are rescaled in some manner . ) The identical temperature dependence is implied wherever the parameter occurs in the equations . Thus in Eqs . 10 , 11 , controls the rate of an interaction that consumes and makes , so there is really only one instance of . Adaption is realized by the output , that responds to a temperature step with a pulse ( as in Fig . 1 C ) but ultimately returns to the value . The absolute level of temperature is reflected in . In contrast with the model of Zimmerman et al . [14] or Eq . 2 , where temperature was filtered through an adaptive system and only the output , essentially , was coupled to the clock variables , this adaptive initial system forms the core clock components . This ensures that the mean levels of some variables analogous to are required to vary substantially with temperature as is observed in natural systems . When we evolve a temperature compensated circadian oscillator , the objective function has to overcome the tendency for all features of the system to vary with Input , our surrogate for temperature . The evolution optimizes the fitness , F , defined here as a sum of two functions . The first part of the fitness , is average correlation between the output , ( the model variable that evolves from in the adaptive system ) , and an Input . Let brackets denote the average over the time window for the fitness evaluation , typically 12 periods of the input and the subtracted normalized correlator i . e . , . Then: ( 12 ) We take , where , defines the period . There are one or more random jumps in the phase defined by for : the phase jumps favor entrainment since the fitness forces the output to follow the jump . The second part of the fitness is the average correlation between output - entrained by a different Input signal - and output computed in the first part of the fitness ( 13 ) For the first third of the integration period , , then tapers down a constant for the remainder of the integration , as shown in Fig . 3B . The constant input for encourages an autonomous oscillator ( rather than a system that merely follows the initial input oscillations ) and its variable level directly enforces temperature compensation . As can be seen , Fig . 3B , the mean of continues to register the final input level , as it does in Eq . 10 , even when oscillating , while the amplitude of the other variables is nearly independent of input level ( i . e . , our temperature surrogate ) . The correlation function between Outputs computed for different constant Input values ensures that the shape of the limit cycle for the different terminal Input values is the same . is computed for different terminal values of the Input between and , then averaged . The final fitness is , which when minimized ensures that the Output is fully correlated with an oscillating Input , and that Output for different Inputs constant values behave in similar way . One of the simplest models found by numerical evolution is presented in Fig . 3A . The first step in evolution adds variable 3 which creates a delayed negative feedback from the output back to itself by repressing and creates an oscillator . The PPI between 1 and 3 is added next and actually improves the temperature compensation illustrated in Fig . 3B . Schematically , compensation in this model works in a way very reminiscent to biochemical adaptation in the network used to initialize evolution: variable buffers most variation by essentially scaling as ( Figs . 3 and 4 , Supplementary Table S1 ) while other variables vary much less in comparison . The reason is that the effective reaction rate controlling is and is therefore roughly Input invariant , and consequently so is the shape of the limit cycle . The properties of the oscillations defined by the network in Fig . 3 as a function of the input level are shown in Fig . 4 . Oscillations arise as a Hopf bifurcation and persist for input values in the interval . There is no change in period for two inputs that cause a 2× change in the mean of and only a change in period when changes by its maximum possible range of 5× . ( Fig . 4B and C , Supplementary Table S1 ) . These values are perfectly compatible with the typical variation in clock period with temperature : for instance , in zebrafish , the oscillation amplitude and average value of changes roughly by and period decreases by for a temperature change from 20 to 30 C [5] . In Neurospora , the period decreases by between and for the control strain KAJ10 [3] , [29] . In a pure WT background , the average concentrations of FRQ protein are roughly multiplied by over the same range while the period decreases of around [11] , [30] , and at higher temperature , a of is observed [30] . Clocks built from an adaptive system , share a feature of the Goodwin model that the orbits for different inputs , as well as the location of the unstable Hopf fixed point , can be superimposed by a linear rescaling , Fig . 4D . Thus the phase corresponding to the limits of the orbits is temperature invariant . A stronger form of this property is seen in the PRC . If we simulate a PRC by zeroing the output at a defined phase , Fig . 4E , we see that they too collapse for all inputs even though we are administering a strong perturbation . We verified numerically that the PRC are shape invariant whether derived from a strong localized decay rate applied to any of the adapted variables in Fig . 3 ( i . e . , variables other than ) , or by momentarily jumping up the production rates of adapted genes ( see Supplementary Fig . S1 ) . ( The PRC defined for the decay of the buffer variable is less well conserved since its more mixed in with a change in input level ) Since the fitness is linear correlation with a sinusoidal reference phase , it is maximum when the solution is itself sinusoidal and optimally remains so when the temperature is shifted , thus explaining the linear covariance of the orbits with temperature . In general the evolved models behave as if they were near the Hopf bifurcation , yet do so over a parameter range that causes a 10× change in the orbits . We have also verified that a two-fold variation in parameters does not appreciably degrade the period compensation shown in Fig . 4B ( Supplementary Fig . S2 ) , i . e . , the period changes but it remains temperature independent . However doubling the PPI between the input and is equivalent to doubling the input range and thus shows more period variation since its like doubling the temperature range . Thus parameters are not tuned , and their general magnitudes are easy to find by a simple local hill climbing algorithm ( a . k . a . gradient search ) . Two other evolved networks with similar properties are presented in Supplementary Figs . S3 and S4 . Network of Fig . 4 is adaptive via a feedback mechanism , however feed-forward networks form the other main class of adaptive networks and their evolution into oscillators gives rise to the Mixed Feedback Loop ( MFL ) that is common in circadian clocks [22] , [28] and has been proposed by one of us as a core model for the Neurospora circadian clock [31] , [32] . The MFL is an oscillator in which a transcriptional activator A activates a gene B and then A and B dimerize . Examples include WCC and FRQ in Neurospora , Clock and PER/TIM in fly , Clock/BMAL and PER/CRY in mammals . If the production rates of A and B ( via transcription or translation ) depend in a similar way of the input , it can be shown that the fixed point for A is adaptive as we evolved in [22] . Strikingly , for this adaptive MFL , the limit cycle shape and period are then automatically independent of the input value and still entrain , as seen in Fig . 4 and shown analytically in the Supplementary Text S1 . This is again a structural property of the network which can be understood mathematically : the strong PPI makes the system function as a relaxational oscillator between two states either A or B high . The fact the production rates of both genes have similar dependence on the input then implies that the input dependence can be scaled out of the equations , in analogy to the Goodwin model , and the period is input independent . We further wondered if computational evolution is able to select for different categories of compensated clocks , where the limit cycle and PRCs depend much more significantly on the input . We modified the fitness so it continued to favor entrainment to temperature , but we dropped the linear correlation so as not to constrain the shape of the output for the free running clock . Instead , we computed the number of peaks of the Outputs for different constant Input values and forced it to be equal to the number of peaks of : this ensures that only the period of the Output is constrained , but not the shape of the limit cycle . Properties of a network evolved under this scheme is described in Fig . 5 . This network obviously is much more complex , with two interconnected transcriptional negative feedbacks explaining oscillations ( via species 4 and 7 ) . The Input enters in various places , not only in the imposed original core network ( species 1 and 2 ) but it also acts independently on species within the two negative feedback loops ( species 5 and 4 ) . This system is therefore closer to the traditional picture of distributed temperature compensation , with the input entering the equations at various places . This network displays autonomous oscillations for input values higher than 0 . 1 . Remarkably , while the input is changing from to7 , variables and vary themselves over more than one order of magnitude ( Fig . 6 C ) , while the period only varies by about ( Fig . 6B ) . Clearly , neither limit cycle shapes nor PRCs are conserved over this input interval ( Fig . 6 C and D ) . This illustrates our contention in the Introduction that distributed temperature compensation is incompatible with a shape invariant PRC . Unexpectedly , both PRCs and limit cycle shapes cluster onto two different regimes , namely inputs above and below 0 . 7 , and in each regime the scaling has all the characteristics of the adaptive compensation in Fig . 4 .
We have exhibited a sequence of adaptive models for temperature compensation and entrainment in circadian oscillators . They arise naturally when a system that adapts to temperature steps evolves to become an oscillator . These models can be generated by a gradient search or hill climbing optimization , thus there are no subtle correlated changes that have to be made to generate these models . Prior analysis of temperature compensation e . g . , [12] , [13] , has focused on distributive models . An exception is [18] , which however does not temperature entrain since the period is given by model parameters . Properties of these models ( beyond the temperature compensation and entrainment that we imposed on the evolution ) are: Experiments from a variety of organisms are better explained by adaptive rather than distributive temperature compensation . In fly , mutations in nocte abolish temperature entrainment but not compensation [4] . Since periodic modulation of any parameter should generically ( i . e . , other than for special choices of parameters ) entrain a nonlinear oscillator , the nocte mutant is very suggestive of item ( 1 ) . The numerous mutants with altered periods that continue to temperature compensate [18] suggest that the net result of the biochemistry that defines the transitions between the principal phases of the clock is temperature invariant . There is contradictory data about the loss-of- function mutation cryb and temperature . Reference [4] notes cryb flies still temperature entrain , but [33] show the temperature PRC is almost flat . In saturating light the fly PRC are temperature invariant [14] and the activity peak in light-dark synchronized flies is temperature independent as first observed by Pittendrigh [8] which support items ( 3–4 ) . Adaptive compensation in fly would also suggest that clock phases defining the interval of tim expression and its maximum are temperature invariant [34] ( item 3 ) In cyanobacteria circadian clock temperature compensation occurs through the KaiC component alone and temperature compensation persists in mutants with periods substantially different from 24 hrs [35] , suggesting again localized temperature compensation . Importantly , KaiC ATPase rate is temperature independent and obviously does not follow an Arrhenius law [35] . Evidence for an adaptive mechanism of temperature response as in Eq . 2 where the temperature jump generated the phase shift , was provided in [2] ( their ‘nonparameteric’ model ) . In Neurospora the mean of the oscillating FRQ protein varies substantially with temperature and provides a mechanism for how a step up in temperature resets the phase [29] . The majority of circadian temperature effects seem to be mediated by FRQ [29] , [36] , supporting item ( 1 ) . We consider FRQ analogous to our buffer variable 1 in Fig . 4 . The mean of frq transcripts appears much less temperature dependent , supporting item ( 2 ) . Data from [29] are consistent with the idea that phases of FRQ peaks do not vary much with temperature ( item 3 ) . The VIVID protein [37] is implicated in the temperature invariance of the PRC . The situation appears less clear to us in plants , perhaps because there are many more duplicated genes in Arabidopsis . It has been suggested that two cycles could co-exist , one sensitive to temperature , the other sensitive to light [38] which is consistent with ( 1–2 ) . For all models presented here , properties 1–4 , when they apply , are structural : for the Goodwin model this is due to the specific forms of the equation that allowed rescaling , in the MFL model the properties derive from the specifics of the coupling to inputs , and for Fig . 4 we verified 1–4 survive parameter variation . These properties would be difficult to understand unless temperature appeared in only a few terms of the equations . Experiments that would most readily substantiate an adaptive model for temperature would be comprehensive data on the zeitgeber time of the maxima and minima of the clock components as a function of temperature . We predict their invariance , while a generic model of distributed compensation would predict that they move with temperature but of course continue collectively add up to the invariant period length . Temperature invariance of the extrema in the clock gene orbits , would suggest some degree of shape invariance in the PRC , but the later is in principal a separate prediction . The linear rescaling of orbits at different temperatures that we found in our models could be probed by time lapse imaging two out of phase clock genes . However the effect might not occur for all choices of genes if there was some saturation . In that situation the phases of extrema will be invariant and thus provide a more robust prediction . The primary 24 h periodic pacemaker in nature is light . It is worth stressing that adaptation for light inputs themselves has been suggested in Neurospora , a phenomenon called photoadaptation [39] , [40] . In , a computational study showed that phase shifts happen only when luminosity strongly changes [41] , and this observation has been related to robust entrainment for all species [23] . These examples actually suggest a generic adaptive model for light sensing , just like described in Fig . 1C . Temperature variations are certainly correlated to sunlight [34] , as well as other metabolic properties ( such as the ADP/ATP ratio zeitgeber for cyanobacteria [42] ) and could have been used as the original pacemaker . However , intrinsic day-to-day variations in the level of any zeitgebers would favor evolution of mechanisms to buffer these changes , and hence adaptation . We have no definitive proposal for how almost all the temperature dependent biochemical rates disappear from the schematic or phenomenological models we are proposing for the circadian clock . We speculate that the shape of the PRC is under strong selection to remain temperature independent along with the period , and thus forces local compensation to render most model parameters temperature invariant , but leaving behind adaptive temperature dependence to allow temperature entrainment . The experimental implications of phase orbits that linearly rescale with temperature are sufficiently dramatic that their observation would render adaptive circadian models plausible though still surprising from the biochemical vantage point .
For evolutionary simulations we follow [22] and use only transcriptional interactions and protein-protein interactions . Regulation of transcription of a protein B is modelled as a combination of Hill functions . Assuming that transcription factors and activate expression of gene and that repressor represses it , equation for would then be: ( 14 ) and are threshold concentrations in Hill functions , are Hill coefficients accounting for cooperativity . Parameters are chosen and evolved randomly . Equation 14 expresses that we assume an “OR” combinatorial between activators ( i . e . one single activator is enough to activate trannscription ) while repressors act multiplicatively . Protein-protein interaction ( PPI ) are explicitly modelled using standard mass-action laws . For instance , if proteins A and B form a dimer C , the equations are: The fitness is computed for a population of networks , typically 40 in number . The most fit half of the population is retained , and a copy of each network is mutated and added back to the population to maintain its number . Parameter changing mutations are typically ten times as likely as topology changing events . Mutations are sampled according to their intrinsic rates and the generation time is chosen such that approximately one mutation occurs per network . | Circadian clocks are biological oscillators which evolved to couple the internal rhythm of animals , plants and even some bacteria to the alternation of light and day . Circadian oscillators are temperature compensated , i . e . they keep a 24-h period irrespective of the temperature of the organism . This is surprising , since many biochemical parameters , including average concentration of clock proteins , vary with temperature . From dynamical system theory , we therefore expect changes in both period and relative lengths of features in the phase response curve which are not seen . We couple mathematical modelling and computational evolution of gene networks to formulate a novel explanation for temperature compensation that accords better with experimental facts than alternatives . Our model has deep mathematical connections with the process of biochemical adaptation , by which cells respond to temporal gradients of signals rather than their absolute value . | [
"Abstract",
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] | 2012 | Adaptive Temperature Compensation in Circadian Oscillations |
The liver performs many essential metabolic functions , which can be studied using computational models of hepatocytes . Here we present HepatoDyn , a highly detailed dynamic model of hepatocyte metabolism . HepatoDyn includes a large metabolic network , highly detailed kinetic laws , and is capable of dynamically simulating the redox and energy metabolism of hepatocytes . Furthermore , the model was coupled to the module for isotopic label propagation of the software package IsoDyn , allowing HepatoDyn to integrate data derived from 13C based experiments . As an example of dynamical simulations applied to hepatocytes , we studied the effects of high fructose concentrations on hepatocyte metabolism by integrating data from experiments in which rat hepatocytes were incubated with 20 mM glucose supplemented with either 3 mM or 20 mM fructose . These experiments showed that glycogen accumulation was significantly lower in hepatocytes incubated with medium supplemented with 20 mM fructose than in hepatocytes incubated with medium supplemented with 3 mM fructose . Through the integration of extracellular fluxes and 13C enrichment measurements , HepatoDyn predicted that this phenomenon can be attributed to a depletion of cytosolic ATP and phosphate induced by high fructose concentrations in the medium .
No other organ performs as many physiological functions as the liver . The liver is responsible for detoxification , bile acid and blood proteins synthesis , plays a key role in the inflammatory response and , above all , it is a key regulator of glucose and lipid homeostasis in blood . Most of its functions and properties can be linked to hepatocytes , the most abundant cell type in liver , and therefore hepatocytes are often used as a model to study liver function and pathologies [1] . Accordingly , computational modelling of hepatocyte metabolism has received a great deal of interest . Recently , genome scale metabolic reconstructions based on stoichiometric modelling techniques have been successfully used to model hepatocyte metabolism [2–4] . However , stoichiometric models provide a static picture of metabolism based on mass balance equations and the assumption that the system is under a strict steady state . In these models each reaction step is described by only one parameter , its steady state flux [5] . The alternative is to use dynamic metabolic models , usually referred to as kinetic models . They are based on building a system of ordinary differential equations ( ODEs ) , with kinetic laws describing transport and chemical transformations for each reaction-step and parameters describing biochemical and biophysical constraints . Kinetic modelling has two main advantages over stoichiometric based modelling; firstly , it is capable of performing dynamic simulations , that is to say , it can predict the variation in metabolite concentrations and fluxes over time outside of the steady state . Secondly , it can follow the global effects of constraints emerging from the specific kinetic properties of enzymes , post-translational modifications and regulatory circuits , thus revealing the complex regulation of the system . Over the years , multiple kinetics models of hepatocyte metabolism have been developed [6–11] . The main limitation of kinetic models is that they are complex to build and parametrize . Due to this complexity , kinetic models of hepatocyte metabolism available in the literature contain only a small number of reactions and , with some exceptions [11] , are often limited to a single pathway . Furthermore , with the exception of some models focused on mitochondria [8 , 9] , most of them assume a constant redox and energy state , which limits their application . In fact , despite the huge interest in hepatocyte metabolism , there are no models capable of adequately modelling the effects of the energy and redox dynamics on hepatocyte core metabolism . Additionally , while 13C experiments have proven their usefulness in studying the metabolism of hepatocyte under metabolic steady state [12–24] , there was only one kinetic model of hepatocyte capable of integrating 13C data [10] . In this work , we present HepatoDyn ( Hepatocyte Dynamics ) a model of hepatocyte core metabolism capable of simulating the redox ( NAD/NADH , NADP/NADPH , etc . ) and energy ( ATP/ADP/AMP , etc . ) dynamics . The model includes glycolysis , gluconeogenesis , glycogen metabolism , the pentose phosphate pathway , the Krebs cycle and fatty acid metabolism as well as reactions associated with energy and redox metabolism ( respiratory chain , malate/aspartate shuttle , glycerol phosphate shuttle , etc . ) . To our knowledge , no model of such size capable of dynamic redox and energy metabolism simulations exists in the literature . Furthermore , the model was coupled to the module for isotopic label propagation of the software package IsoDyn [25 , 26] . This enables HepatoDyn to integrate data from 13C based experiments to assist in the parametrization process , regardless of whether experimental measurements correspond to an isotopic steady state . The latter is a key feature because the levels of isotopic label enrichment are often a non-steady phenomenon with long transition times [27] . Therefore , HepatoDyn is a very powerful tool capable of taking advantage of both the constraints derived from a detailed tissue-specific kinetic model and data derived from 13C based experiments to simulate hepatocytes . In the last decades there has been a significant increase in fructose in our diets [28] and accordingly there is great interest in studying the potential effects of fructose in the metabolism [29–32] . To date , fructose-rich diets have been associated with many adverse metabolic conditions , such as nonalcoholic fatty liver disease , insulin resistance and obesity [28 , 33 , 34] , most of which are directly or indirectly related to abnormal hepatocyte function . Therefore , we used HepatoDyn to study the short-term response of hepatocyte metabolism to different concentrations of fructose .
A metabolic network , including those pathways deemed necessary to accurately and dynamically simulate the core metabolism of rat hepatocytes in the study conditions , was constructed based on pathways that have been reported in the literature to be active in hepatocytes [42 , 43] . Each reaction in the metabolic network was assigned a kinetic law . Kinetic laws describe the dependence of each reaction flux on metabolite concentrations . They take into account the affinity of substrates and products , the reaction mechanism and the effect of activators and inhibitors on reaction fluxes . The kinetic laws used were mostly derived from existing kinetic laws described in the literature [6 , 11 , 44] . The exceptions were the kinetic laws for aldolase activity , which catalyses eight related elementary reactions , which were built as described in the Supplementary Material ( S1 Text ) . Kinetic laws are integrated with the metabolic network topology , described by the stoichiometric matrix ( N ) , to build a system of ordinary differential equations ( ODEs ) that predict the evolution of metabolite concentrations , and by extension the evolution of reaction fluxes , over time . Because fluxes are provided in units of mmol per cell per minute , but ODEs are solved in units of mmol per litre per minute , in order to build the ODEs , the cell number and the volume of the compartment at which each metabolite is located must also be taken into account . Therefore , the system of ODEs can be written as: dc[t]dt=N·j ( c[t] , p ) ·ncellvol ( 1 ) Where j is a vector of reaction fluxes , which is a function of the vector of metabolite concentrations ( c[t] ) in mM , and a vector model parameter ( p ) as defined by the kinetic laws used in the model , ncell is the cell number and vol is a vector containing the volumes of the compartment at which each metabolite is localized in litres . In reversible reactions , forward and reverse reaction rates are computed separately with different kinetic laws , albeit sharing most of the parameters . Additionally , the fluxes of invisible reactions , that is to say , reactions that can propagate labelled carbons even though they do not change the overall concentrations of metabolites , are also computed [10] . This is necessary in order to fully simulate the propagation of 13C . To simulate the propagation of 13C through the metabolic network , fluxes are decomposed into isotopomer fluxes . Then , an ODE system is built using the algorithms from IsoDyn [25 , 26] . The resulting ODE accounts for concentrations of all isotopomers , isomers with 13C substitution in specific carbon positions [24] . To avoid unnecessary complexity , isotopomers are not simulated for those metabolites where , according to the defined metabolic network , 13C from labelled substrates cannot be propagated . The process is briefly summarized in Fig 1 . The system of differential equations for metabolite and isotopomer concentrations is solved to predict metabolic fluxes , metabolic concentrations and isotopomer concentrations from the initial time to the defined end time . Model predictions are for isotopomers but experimental measurements refer to isotopologues ( or mass isotopomers ) , isomers with a specific number of 13C substitutions [24] . Thus , the resulting concentrations of isotopomers are converted into fractions of isotopologues , by adding up all isotopomers that correspond to each isotopologue and dividing by the total concentration of each metabolite ( S1 Fig ) . The fractions of such isotopologues can then be compared with the experimental measurements obtained with GC coupled to MS . Kinetic parameters representing enzyme activity ( Vmax or equivalent ) were fitted to the experimental data . For this process Vmax from the reverse reaction rate in reversible reactions are assumed to be a function of the Vmax of the forward reaction and of the equilibrium constant as described by the Haldane relationship [44] . To further reduce the number of parameters fitted , enzyme activities catalysing sequential reactions with no ramifications ( the so called reactions chains ) were fitted as a group . This is because in reactions chains the flux through the whole chain could be determined by any of the enzyme activities involved and consequently most of the activities of enzymes constituting the chain would be unidentifiable . Furthermore , other activities known to be unidentifiable are not fitted , such as the activities of reactions that are known to operate in rapid equilibrium in physiological conditions ( glucose phosphate isomerase , triose phosphate isomerase , enolase , etc . ) . The remaining parameters of the kinetic model were assigned based on an extensive literature search , completed with data from Brenda [45] and UniProt [46] databases . The fitting algorithm , a variant of the basic simulated annealing algorithm [47] , seeks the set of m parameters ( Ez ) that minimizes the objective function . The objective function ( X2 ) is the square deviation between the n experimentally measured values ( Yi ) and simulated values ( Zi ) for both isotopologue fractions and total metabolite concentrations , normalized by the experimental standard deviation ( σi ) . To prevent a bias generated by very low standard deviations , a minimum threshold of 0 . 01 was used . Additionally , parameter sets where any metabolite reached concentrations greater than 50 mM were discarded . Consequently , the fitting algorithm seeks the set of enzyme activities that minimize the difference between experimentally measured and simulated isotopologue fractions and metabolite concentrations in the experimental conditions considered . The fitting procedure provides one set of fitted parameters , which minimizes the objective function , and is referred to as the best fit parameter set . However , other sets of parameter values might result in similar or equal objective function values and are therefore as valid as the best fit . The range of acceptable variation in parameters was evaluated through an identifiability analysis . Identifiability is a property that indicates whether unknown model parameters can be determined from the available experimental data . It depends both on the structure of the model and the quality and amount of experimental data . A parameter is defined as identifiable if the confidence interval for its estimated value at a given significance level is finite [48 , 49] . If we define X2 ( θi ) as the optimized square deviation if parameter i is fixed to a value of θi and the remaining parameters being fitted θj are readjusted to minimize the square deviation X2 ( θi ) =minθj≠i[x2 ( θj ) ] ( 3 ) then if experimental errors are assumed to follow a normal distribution , for a parameter i , the confidence interval can be defined as: {θi|X2 ( θi ) −Xbf2<Δα} with Δα=X2 ( α , 1 ) ( 4 ) where X2bf is the best fit square deviation ( optimized with no fixed parameters ) and Δα is the significance threshold associated with a given significance level ( α ) with a Chi Square distribution with one degree of freedom . Accordingly , the upper and lower limit of the confidence intervals for a given parameter are estimated by respectively increasing and decreasing the value of the parameter until the square deviation difference obtained when optimizing the remaining parameters exceeds the threshold ( Δα ) [48] . Additionally , intervals for system dependent variables ( fluxes , metabolite concentrations and isotopologue fractions at different time points ) are estimated from the maximum and minimum parameter values of confidence intervals generated during the identifiability analysis .
We present HepatoDyn , the first detailed model of hepatocyte core metabolism capable of dynamically simulating energy and redox metabolism . It consists of 88 reactions and 81 metabolites distributed into three compartments ( extracellular , cytosolic and mitochondrial ) . A schematic representation of the model can be found in Fig 2 and a complete list of metabolites , reactions and compartments can be found in S1 , S2 and S3 Tables , respectively . Each reaction has an associated kinetic law and the model has a total of 470 parameters associated to kinetic laws ( S4 Table ) . 55 of these parameters correspond to enzyme activities that were fitted to experimental data , taking parameter groups ( S5 Table ) into account this results in 29 independent parameters that were fitted to experimental data . To the greatest extent possible , the kinetic laws and their parameters were specific to the enzyme isoforms active in the liver . It is worth noting , that while most of the reactions included in HepatoDyn are also present in genome scale reconstructions of hepatocyte metabolism [2–4] , HepatoDyn includes complete kinetic laws and regulatory loops , which allow for dynamic and regulatory studies . Nevertheless , HepatoDyn also has 2 reactions that are absent in genome scale reconstructions of hepatocyte . Specifically , the reactions aldolase 3 ( Fru16bP + Gra ↔ Fru1P + GraP ) and transketolase 3 ( Fru6Pa + Rib5P ↔ E4P + Sed7P ) . Those reactions emerge because the enzymes aldolase and transketolase allow multiple combinations of substrates and products . Additionally , HepatoDyn also incorporates the channelling of hexose phosphates to glycogen in the form of two separate pools of hexose phosphates , a and b , as previously described in the literature [10] . The kinetic model , fully parametrized , can be found in SBML format in the Supplementary Material ( S1 XML and S2 XML ) . In addition , HepatoDyn is capable of simulating the propagation of 13C from isotopically labelled substrates to metabolic intermediaries and products . This allows HepatoDyn to integrate isotopologue enrichment measurements from 13C based experiments greatly enhancing the predictive capabilities of the model . HepatoDyn is provided in the Supplementary Material as a C++ program ( S1 Software ) . The liver has a high capacity to metabolize fructose , it is estimated that up to 50% of fructose ingested is metabolized by hepatocytes [50] . Fructose metabolism in hepatocytes consists of phosphorylation of fructose to fructose 1-phosphate by fructokinase and the split of this metabolite by the liver aldolase isoform ( aldolase B ) into dihydroxyacetone-phosphate and glyceraldehyde , with the latter metabolite being phosphorylated by triokinase into glyceraldehyde 3-phosphate . Because fructose enters at the level of triose phosphate , bypassing the highly regulated glucokinase and phosphofructokinase steps of glycolysis , fructose uptake is largely unregulated . Consequently , the limiting step in fructose metabolism is assumed to be fructose uptake by hepatocytes , which is heavily dependent on the extracellular concentration of fructose due to the low affinity of the proteins mediating fructose transport into hepatocytes , GLUT2 and other carriers like GLUT8 [51–53] . As a proof of concept of the capabilities of HepatoDyn , we applied it to study the short term response of hepatocytes to incubation with 20mM glucose supplemented by either 3mM fructose or 20mM fructose . These concentrations were chosen because our experimental data showed that hepatocytes responded quite differently to them . While incubation with 20mM glucose supplemented with 3mM fructose resulted on a rapid glycogen accumulation , incubation with 20mM glucose supplemented with 20mM fructose resulted on almost no glycogen accumulation ( Fig 3 . A ) . While it has been reported that supplementation with low concentrations of fructose favours glycogen accumulation [19 , 29 , 54] , the fact that supplementation with high fructose concentrations inhibits glycogen accumulation was not known . Furthermore , isotopologue analysis indicated that in the second condition , unlike the first condition , almost no 13C from labelled glucose was propagated to lactate ( Fig 3 . B ) . In both conditions lactate and glucose were produced from fructose at a similar rate . Hence it was an interesting case of study . Specifically , HepatoDyn was used to integrate experimental measurements derived from rat hepatocytes incubated for 2 h with the following media: 20 mM glucose 50% enriched in [1 , 2-13C2]-glucose and 3 mM fructose ( condition A1 ) , 20 mM glucose and 3 mM fructose 50% enriched in [U-13C6]-fructose ( condition A2 ) and 20 mM glucose 50% enriched in [1 , 2-13C2]-glucose and 20 mM fructose ( condition B ) . The experimental data for condition A1 had been published previously [19] . This integration was achieved using the experimental measurements of extracellular concentrations and isotopologue fractions as input to fit the 29 independent parameters associated to enzyme activities in the model assuming that the enzyme activities , normalized by cell number ( S2 Fig ) , were equivalent in the three conditions . Consequently , the fitting algorithm identifies a single set of parameters that allows reproduction of the three experimental conditions . It is worth noting that because conditions A1 and A2 only differ in the labelling pattern of substrates , the predicted fluxes and concentrations values will be the same in both conditions . The resulting values of the fitted parameters can be found in S6 Table . The resulting metabolites concentrations for condition A1/A2 and condition B can be found on S3 and S4 Figs respectively . The resulting fluxes for condition A1/A2 and condition B can be found on S5 and S6 Figs respectively . The resulting isotopologue fractions for key metabolites in condition A1 , A2 and B can be found on S7 , S8 and S9 Figs respectively . A comparison between the experimentally measured metabolite concentrations and isotopologue fractions and those simulated by the model with the best fit parameter set can be found in Fig 3 . High concentrations of fructose have been shown in vivo and in vitro to result in the depletion of ATP and phosphate in hepatocytes [52 , 55] . This occurs due to an accumulation of fructose 1-phosphate caused by the elevated fructokinase activity [52 , 55] . This phenomenon was predicted by HepatoDyn . The model predicted that a persistent cytosolic ATP and phosphate depletion would occur with an extracellular concentration of 20 mM fructose ( Fig 4 ) . This is mainly caused by an accumulation of fructose 1-phosphate , although the depletion can also be partially attributed to the accumulation of some other phosphorylated metabolites . In this context , the low glycogen synthesis observed at 20mM glucose supplemented with 20 mM fructose can be attributed to the depletion of cytosolic ATP and phosphate . Likewise , the almost non-existent propagation of 13C from glucose to lactate under this condition can mainly be attributed to the low glucokinase and phosphofructokinase activities caused by ATP depletion . Conversely , at 20mM glucose supplemented with 3 mM fructose , a persistent accumulation of fructose 1-phosphate does not occur . Accordingly , under this condition , ATP and phosphate are not persistently depleted ( Fig 4 ) . Overall , 25 of the 29 independent parameters were identifiable with at least 95% confidence . This remarkable degree of identifiability can be attributed to the numerous feedback regulations through the redox and energy balances ( ATP/ADP , NADH/NAD , etc . ) , the use 13C data and the integration of data from multiple metabolic conditions . Concerning the non-identifiable parameters , the non-identifiability of the aldolase activity and the activities involved in the lactate production and malate aspartate shuttle reaction chains can be attributed to the fact that the reactions associated to those pathways are predicted to be close to the equilibrium in experimental conditions , hence the system is fairly insensitive to the value of the enzyme activities associated to them . On the other hand , the non-identifiability of the citrate synthase activity arises because in our model the flux through the citrate synthase reaction can depend solely on the two activities upstream , pyruvate dehydrogenase and β-oxidation , which catalyse the production of acetyl-CoA , the substrate of citrate synthase . Compared to parameters , fluxes and to a lesser extent concentrations , show a much narrower range of variation ( S3 , S4 , S5 and S6 Figs ) . This can serve as an indication of robustness , the capacity of the system to maintain its functional properties in the face of external and internal perturbations and uncertainty [56] . Interestingly , fluxes associated with the pentose phosphate pathway and fatty acid synthesis have fairly low upper bounds in both conditions ( incubation with 3 mM fructose and 20 mM glucose and incubation with 20 mM fructose and 20 mM glucose ) . This is consistent with hepatocytes extracted from fasted rats , as they can be expected to have low activity in fatty acid synthesis , and thus only need to generate a small amount of reductive potential ( NADPH ) to maintain cell functions . However , with longer incubation times , an increase in the fatty acid synthesis and pentose phosphate pathway activities and fluxes should be observed as fructose is known to increase the expression of key lipogenic enzymes in hepatocytes[28 , 57 , 58] . It is also worth noting that the identifiability analysis further reinforces the notion that hexose phosphate metabolism in hepatocytes is compartmentalized into two different pools as previously reported [10] . This is because most of enzyme activities present in both hexose pools have a lower bound above 0 in the confidence interval , suggesting that the separation of hexose phosphates into two separate pools must be taken into account to adequately simulate the experimental conditions . If there was no compartmentalization , all activities present in both pools would have a lower bound of 0 because they would be made redundant by the activities in the other pool .
Metabolic modelling is based on applying constraints to limit the space of feasible solutions for system variables , such as reaction fluxes and metabolite concentrations . Constraints can arise from different components of the model including reaction stoichiometry and kinetic laws , and from the experimental measurements integrated by the model . Consequently , the use of a highly complete metabolic network , including the fundamental balances affecting redox and energy metabolism ( ATP/ADP , NAD/NADH , etc . ) , serve as an important set of constraints . Furthermore , the inclusion of highly detailed kinetic laws and parameters derived from the literature further constrains the solution space . For instance , important constraints that emerge from kinetic laws are regulatory circuits , such as fructose 6-phosphate inhibiting glucokinase or fructose-1-phosphate disrupting such inhibition [59–61] . Other important constraints that emerge from the kinetic laws are thermodynamics constraints , which are in the form of equilibrium constants . Finally , integrating 13C based data provides additional constraints such as labelling enrichments which provide information on ratios among fluxes through alternative metabolic pathways . While numerous kinetic models of hepatocytes exist in the literature [6–11] , HepatoDyn is the first that is capable of integrating all the aforementioned constraints in a single model . As a proof of concept of the capabilities of the model , we applied HepatoDyn to study the metabolic effects of high fructose concentrations on rat hepatocytes . Experimental data showed that hepatocytes behaved quite differently depending on whether they were incubated with 20mM Glucose supplemented with either 3 mM fructose or 20 mM fructose . Using HepatoDyn , we managed to find a physiological explanation for this behaviour , which involved the rapid and persistent depletion of cytosolic ATP and phosphate at 20 mM fructose , which was in accordance with information reported in the literature [52 , 55] . This phenomenon has a strong dynamic component , is dependent on the kinetic properties of enzymes and on the balances involved in energy metabolism . Additionally , it may be relevant for understanding the potential adverse effects of fructose-rich diets . This is because ATP depletion impairs protein synthesis and induces inflammatory and prooxidative changes and thus , in a fructose-rich diet , this depletion might result in increased susceptibility of hepatocytes to injury leading to adverse hepatic conditions such as nonalcoholic fatty liver disease [62] . Furthermore , HepatoDyn has countless applications that go beyond studying the effects of fructose . For instance , HepatoDyn can be used to study liver centric metabolic diseases such as diabetes . Given that HepatoDyn is capable of dynamically simulating the redox and energetic state of hepatocytes , it can be used to better understand the mechanism of action of anti-diabetic drugs like metformin which target the energetic and redox metabolism [63] as well as identifying new drug targets . HepatoDyn can also be used to study the relative contribution of different reactions to redox and energy balances in different conditions . Therefore , potential applications of HepatoDyn can be to analyse the ATP consumption or production associated to different pathways or the relative contribution of the glycerol phosphate shuttle and the malate aspartate shuttle to the transfer of reducing equivalents between the cytosol and the mitochondrial matrix . Last , but not least , new reactions can easily be added to HepatoDyn provided kinetic mechanisms and kinetic information such as affinity constants or inhibition constants are known for the enzymes catalysing those reactions . Likewise , through the modification of reactions and kinetic laws specific to hepatocytes , HepatoDyn can be adapted to other cell types . | Despite the key role of hepatocytes in carbohydrate and lipid homeostasis , available dynamic models of hepatocyte metabolism tend to be limited to a single pathway and/or are based on assumptions of constant concentrations of key metabolites involved in redox and energy metabolism ( ATP , NAD , NADPH etc . ) . Furthermore , most dynamic models are unable to integrate information from 13C based experiments . 13C based experiments allow us to infer the relative activity of alternative pathways and hence are highly useful for indicating flux distributions . To overcome these limitations , we developed HepatoDyn , a dynamic model of hepatic metabolism . HepatoDyn uses a large metabolic network including key pathways such as glycolysis , the Krebs cycle , the pentose phosphate pathway and fatty acid metabolism , and dynamically models the concentrations of metabolites involved in the redox and energy metabolism of hepatocytes . In addition , the model was coupled to the label propagation module of the package IsoDyn , allowing it to integrate data from 13C based experiments to assist in the parametrization process . These features make HepatoDyn a powerful tool for studying the dynamics of hepatocyte metabolism . | [
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"and"... | 2016 | HepatoDyn: A Dynamic Model of Hepatocyte Metabolism That Integrates 13C Isotopomer Data |
Leprosy is a disease caused by Mycobacterium leprae where the clinical spectrum correlates with the patient immune response . Erythema Nodosum Leprosum ( ENL ) is an immune-mediated inflammatory complication , which causes significant morbidity in affected leprosy patients . The underlying cause of ENL is not conclusively known . However , immune-complexes and cell-mediated immunity have been suggested in the pathogenesis of ENL . The aim of this study was to investigate the regulatory T-cells in patients with ENL . Forty-six untreated patients with ENL and 31 non-reactional lepromatous leprosy ( LL ) patient controls visiting ALERT Hospital , Ethiopia were enrolled to the study . Blood samples were obtained before , during and after prednisolone treatment of ENL cases . Peripheral blood mononuclear cells ( PBMCs ) were isolated and used for immunophenotyping of regulatory T-cells by flow cytometry . Five markers: CD3 , CD4 or CD8 , CD25 , CD27 and FoxP3 were used to define CD4+ and CD8+ regulatory T-cells . Clinical and histopathological data were obtained as supplementary information . All patients had been followed for 28 weeks . Patients with ENL reactions had a lower percentage of CD4+ regulatory T-cells ( 1 . 7% ) than LL patient controls ( 3 . 8% ) at diagnosis of ENL before treatment . After treatment , the percentage of CD4+regulatory T-cells was not significantly different between the two groups . The percentage of CD8+ regulatory T-cells was not significantly different in ENL and LL controls before and after treatment . Furthermore , patients with ENL had higher percentage of CD4+ T-ells and CD4+/CD8+ T-cells ratio than LL patient controls before treatment . The expression of CD25 on CD4+ and CD8+ T-cells was not significantly different in ENL and LL controls suggesting that CD25 expression is not associated with ENL reactions while FoxP3 expression on CD4+ T-cells was significantly lower in patients with ENL than in LL controls . We also found that prednisolone treatment of patients with ENL reactions suppresses CD4+ T-cell but not CD8+ T-cell frequencies . Hence , ENL is associated with lower levels of T regulatory cells and higher CD4+/CD8+ T-cell ratio . We suggest that this loss of regulation is one of the causes of ENL .
Leprosy is a disease caused by Mycobacterium leprae , an intracellular acid-fast bacillus . It mainly infects the skin and peripheral nerves . Leprosy is a spectrum of disease with a five-district forms with the localized tuberculoid leprosy ( TT ) and the generalized lepromatous leprosy ( LL ) forming the two poles of the spectrum . The clinical spectrum of leprosy correlates with the host immune response [1] . Leprosy reactions ( Reversal reactions and Erythema Nodosum Leprosum ) are immune-mediated inflammatory complications of the disease which can occur before , during or after successful completion of multi-drug treatment ( MDT ) [2] . They are a major cause of morbidity in a significant proportion of leprosy patients [3] . Erythema Nodosum Leprosum ( ENL ) is an inflammatory complication of leprosy , manifesting as tender erythematous skin lesions and systemic features of disease including fever , neuritis and bone pain [4] . ENL reactions were initially thought to be due to immune-complex deposition in the blood vessels suggestive of Arthus reaction [5] . However , immune-complex deposition is not consistently demonstrable and typical features of immune-complex diseases are absent in ENL . Histologically , neutrophils are considered the signature cell in ENL lesions [6] . However , not all clinically confirmed ENL cases have neutrophilic infiltration in lesions [7] . Hence , there is little direct evidence as to the actual role of neutrophils in the pathogenesis of ENL . Recent data suggest that cell-mediated immune responses may also play an important role in the pathogenesis of ENL [8 , 9] . An increased percentage of CD4+ T-cells and reduced CD8+ T-cells in ENL lesions and in the periphery were reported previously [10 , 11] . Increased mitogen induced lymphoproliferation and inhibition of strong antigen-induced leukocyte migration during ENL reaction had been reported in ENL patients [12] . However , others reported that reduced percentage of CD4+ and increased percentage of CD8+ T-cells in these patients [8] Hence , strong evidence is required to explain the role of T- cells in the pathophysiology of ENL . Regulatory T-cells ( Tregs ) formerly called suppressor T-cells are subpopulations which modulate the immune system and maintain tolerance to self-antigens [13] . It has previously been described that Tregs inhibit naïve CD4+ T-cell proliferation and differentiation , prevent cytotoxic activity of CD8+ T-cells , suppress the activation and antibody production of B-cells , and limit the stimulatory capacity of antigen presenting cells by down regulating the surface expression of costimulatory molecules such as CD80 and CD86 [14] . A reduced percentage of Tregs has been associated with immune-complex mediated autoimmune diseases such as Wegener’s granulomatosis ( WG ) [15] and anti-neutrophil cytoplasmic antibody ( ANCA ) -associated vasculitis [16] . In patients with these diseases , the percentage of Tregs is inversely related to disease progression or relapse and a relatively increased proportion of Tregs is associated with rapid disease remission . However , the role of Tregs in leprosy has only been addressed by few studies [17–20] and a clear picture has not yet emerged . Although there are few studies on the association of Treg phenotypes with leprosy , M . leprae specific suppression of effector responses had been described prior to the definition and characterisation of Tregs [21] . Mehra et al . made the first report when they described suppression of proliferative responses to concanavalin A in the presence of lepromin in LL and BL patients [22] . Quantification of Tregs in PBMCs stimulated with M . leprae antigenic preparations and phytohemagglutinin ( PHA ) by flow cytometry and in the skin lesions by immunohistochemistry showed that M . leprae antigens induced low lymphoproliferative responses ( low mean cell counts per minute ) but higher number of Tregs in lepromatous patients than in tuberculoid patients ( TT ) [23] . A cell subset analysis and confocal microscopy of skin biopsies in Ethiopian leprosy patients showed increased frequencies of Tregs in the blood as well as in the lesions of LL patients compared to TT and borderline leprosy lesions [18] . Similar results have been reported in Indian [24 , 25] . The analysis of the frequency of circulating Tregs in PBMCs of 6 ENL patients by flow cytometry showed that both the absolute count and percentage of Tregs were significantly lower in patients with ENL reaction ( 1 . 2% ) compared to patients with LL 2 . 8% [17 , 26] . However , the authors also reported that patients with ENL had significantly higher percentage of Tregs-FoxP3 expression along with higher percentages of effector T-cells than patients with the other types of leprosy . A recent study performed flow cytometry on PBMCs isolated from 6 patients with ENL in comparison to 8 LL patient controls , after stimulation with M . leprae sonicated antigen ( MLSA ) described a significant reduction in percentage of CD4+CD25+FoxP3+Tregs and Mean Fluorescence Intensity of FoxP3 in PBMC of ENL patients [20] . However , the same study also reported an increased expression of FoxP3 in the PBMCs of patients with ENL compared to LL controls when measured by qPCR . Th17 cells have been identified as a new subset of the T- helper cells and as potential mediators of inflammation associated with various autoimmune and mycobacterial diseases [27] . Th17 cells are the least studied T-cells in leprosy and only three studies have indicated the involvement of Th-17 in immunopathogenesis of ENL [20 , 26 , 28] . Th-17 produces a group of cytokines called IL-17 of which IL-17A is one of the groups . IL-17A is an immunoregulatory cytokine capable of promoting the generation of pro-inflammatory cytokines and chemokines , which leads to the attraction of neutrophils and macrophages to the inflammation site ( Jin and Dong , 2013 ) . Recently a cross-sectional study has reported that increased IL-17A production to M . leprae stimulation in ENL patients compared to non-reactional LL patients [20] . The conflicting reports could be due to use of small sample size , lack of strict case definitions , use of inappropriate controls and variations in assay methods . Nonetheless , these studies of immune regulation draw our attention to the potential importance of regulatory T-cells in the evolution and subsequent course of ENL reactions , though a consensus conclusion remains elusive . Therefore , in the current study we investigated the frequency of Tregs in a relatively large cohort of patients with ENL reactions compared to non-reactional LL matched controls before , during and after prednisolone treatment of ENL cases .
Informed written consent for blood and skin biopsies were obtained from patients following approval of the study by the Institutional Ethical Committee of London School of Hygiene and Tropical Medicine , UK , ( #6391 ) , AHRI/ALERT Ethics Review Committee , Ethiopia ( P032/12 ) and the National Research Ethics Review Committee , Ethiopia ( #310/450/06 ) . All patient data analysed and reported anonymously . A case-control study with follow-up for 28 weeks after the initiation of prednisolone treatment was used to recruit 46 patients with ENL reaction and 31 non-reactional LL patient controls between December 2013 and October 2015 at ALERT Hospital , Ethiopia . The clinical assessment of the patient was used as main diagnostic criterion for ENL cases and LL controls [4] A structured questionnaire was used to obtain clinical data for each participant . The ENL International Study ( ENLIST ) format was modified and used for clinical data recording . The data collection sheet included the demographic , clinical and diagnostic information set following the standard guideline at each time point . The clinical information included core points such as the clinical feature , skin lesion , nerve functions and systemic involvement . Blood samples were obtained from each patient at three time points: at recruitment before prednisolone administration , after 12 and 24 weeks of prednisolone treatment for ENL cases . The 12th week was chosen as second sampling time point because the steady decrease in prednisolone dose reaches less than half of the start dose by week 12 and after the 24thweek prednisolone is normally off unless the patient experiences a recurrence or chronic condition . The third time-point ( 24th week ) sample was obtained when an ENL patient completed prednisolone treatment and the treatment free period has lasted 15 days or more . This means the third sample was obtained 15 days after the patient stopped prednisolone treatment to avoid the effect of steroids on immunological assays . Twenty milliliter of venous blood was collected in sterile BD heparinised vacutainer tubes ( BD , Franklin , Lakes , NJ , USA ) . PBMC were separated by density gradient centrifugation at 800g for 25 min on Ficoll-Hypaque ( Histopaque , Sigma Aldrich , UK ) as described earlier[18] . Cells were washed three times in sterile 1x phosphate buffered saline ( PBS , Sigma Aldrich , UK ) and re-suspended with 1mL of Roswell Park Memorial Institute ( RPMI medium 1640 ( 1x ) + GlutaMAX + Pen-Strip GBICO , Life technologies , UK ) . Cell viability was determined by 0 . 4% sterile Trypan Blue solution ( Sigma Aldrich , UK ) ranged from 94–98% . PBMC freezing was performed using a cold freshly prepared freezing mediumcomposed of 20% Foetal Bovine Serum ( FBS , heat inactivated , endotoxin tested ≤5 EU/ml , GIBCO Life technologies , UK ) , 20% dimethyl sulphoxide ( DMSO ) in RPMI medium 1640 ( 1x ) . Cells were kept at -80°C for 2–3 days and transferred to liquid nitrogen until use . Cell thawing was done as described [29] . The procedure is briefly described as: cells were transported in liquid nitrogen to a water bath ( 37°C ) for 30 to 40 seconds until thawed half way and resuspended in 10% FBS in RPMI medium 1640 ( 1x ) ( 37°C ) containing 1/10 , 000 benzonase until completely thawed , washed2 times ( 5–7 minutes each ) and counted . The percentage viability obtained was above 90% . Cell concentration was adjusted to 106 cells/mL in RPMI , 1 ml of the cell suspension was added to wells of a 24 well polystyrene cell culture plate ( Corning Costar cell culture plates ) , and the plates incubated at 37°C in a 5% carbon dioxide incubator . After overnight rest [30] , cells were stained for flow cytometry with fluorochromes conjugated antibodies as described below . The cells were harvested , transferred to round bottomed FACS tubes ( Falcon , BD , UK ) and washed twice at 400g for 5 minutes at room temperature . The cells were resuspended in 50μl of PBS and incubated in 1ml of 10% human AB serum ( Sigma Aldrich , UK ) for 10 minutes in the dark at room temperature to block nonspecific Fc-mediated interactions , and centrifuged at 400g for 5 minutes . After resuspending cells in 50μL PBS buffer , Life/dead staining was performed at a concentration of 1μl /1mL live/dead stain ( V500 Aqua , Invitrogen , Life technologies , UK ) for 15 minutes at 4°C in the dark . Cells were washed once and stained for surface markers directed against CD3-Pacific blue , CD4-APC-eFluor 780 , CD8-PerCp-Cy5 . 5 , CD127-APC , CD161-PE ( all eBioscience , UK ) and CD25-PE-Cy7 ( BD , Biosciences , UK ) . After staining at 30 minutes at 4°C , the cells were washed with FACS buffer . One mL of 1x FoxP3 Fixation/Permeabilization buffer ( eBioscience , UK ) was added to each tube , mixed thoroughly and incubated at 4°C for 60 minutes and washed with permeabilization buffer at 400g for 5 minutes . Cells were resuspended in 50μl of buffer followed by staining the permeablized cells with anti-human FoxP3 ( FITC , eBioscience ) for 30 minutes at 4°C . After two additional washes , the cells were re-suspended in 400μl FACS buffer for acquisition . Compensation beads were stained in parallel with samples under the same environment . A single-stained OneComb eBeads ( affymetrix , eBioscience , UK ) for all fluorescence compensation except for the live dead stain were used . For the viability dye , cells rather than beads were stained and used for fluorescence compensation . Forward scatter height ( FSC-H ) versus Forward scatter area ( FSC-A ) plots were used to select singlets , and FSC-A versus dead cell marker plots identified viable cells . Side scatter area ( SSC-A ) versus FSC-A plots were used to discriminate lymphocytes from monocytes and residual granulocytes . The threshold for FSC was set to 5 , 000 . For each sample , 500 , 000–1 , 000 , 000 cells were acquired . S1 Fig ) Flow cytometry analysis was performed with FlowJo version 10 ( Tree Star , USA ) using logicle ( bi-exponential ) transformations as recommended [31 , 32] . CD4+ and CD8+ Tregs were defined as CD3+CD4+CD25+FoxP3+CD127lo/- ( S1 Fig ) and CD3+CD8+CD25+FoxP3+CD127lo/- ( S2 Fig ) cells , respectively [33] . CD25+ and FoxP3+ cells were also gated on CD4 and CD8 T-cells ( S3 Fig ) . The percentage of each subpopulation was defined relative to the parent population and data exported to Excel for each sample , compiled and exported to statistical software for further analysis . Differences in percentage of T-cell subsets were analyzed with either the two-tailed Mann-Whitney U test or the Wilcoxon signed rank non-parametric tests using STATA 14 version 2 ( San Diego California USA ) . Graphs were produced by GraphPad Prism version 5 . 01 for Windows ( GraphPad Software , San Diego California USA ) . The median and Hodges–Lehmann estimator were used for result presentation . Hodges–Lehmann is used to measure the effect size for non-parametric data [34] . P-values were corrected for multiple comparisons . The statistical significance level was set at p≤0 . 05 .
Forty-six LL patients with ENL reaction and 31 LL patient controls without ENL reaction were recruited between December 2013 and October 2015 . The male to female ratio was 2:1 with a median age of 27 . 5 [range: 18–56] years in patients with ENL and 3:1 with a median age of 25 . 0 [range: 18–60] years in patients with non-reactional LL controls . All ENL patients were untreated with corticosteroid before recruitment . At time of recruitment , 20 ENL patients were previously untreated with MDT , 21 were on MDT and 5 were completed MDT treatment . Twenty non-reactional LL patients were about to start MDT , 7 were on MDT and 4 were completed MDT at recruitment . By using CD4+CD25+FoxP3+ CD127-/lo and CD8+CD25+FoxP3+ CD127-/lo as CD4+ and CD8+Treg markers respectively , we investigated the median percentage of CD4+ and CD8+Tregs in patients with ENL reaction compared to matched non-reactional LL patient controls before , during and after prednisolone treatment of ENL patients . We also described the kinetics of these cells within ENL group before , during and after treatment . The median percentage of CD4+ regulatory T- cells was significantly lower ( 1 . 67% ) in the PBMCs of patients with ENL compared to LL patient controls ( 3 . 79% ) before treatment ( P≤0 . 0001; ΔHL = 1 . 93% ) . During treatment , the percentage of CD4+ Tregs in the PBMCs of patients with ENL almost doubled ( from 1 . 67% to 3 . 21% ) while it significantly dropped from 3 . 79% to 2 . 43% in LL patient controls , though these differences did not reach statistical significance . After treatment , 3 . 2% of CD4+ T-cells were positive for these Tregs markers in patients with ENL while only 2 . 5% of CD4+ T-cells were positive in LL patient control ( P≤0 . 005 ) . The median percentage of CD8+Tregs was lower in the PBMCs of untreated patients with ENL ( 0 . 37% ) compared to LL patient controls ( 0 . 54% ) but the difference was not statistically significant ( P ≥ 0 . 05 ) . During treatment , the median percentage of CD8+Tregs decreased in both groups ( 0 . 23% in patients with ENL and 0 . 42% in LL patient controls ) ( P ≥ 0 . 05 ) . The median percentage of CD8+Tregs after treatment in patients with ENL and LL controls was 0 . 34% and 0 . 47% respectively ( P ≥ 0 . 05 ) . Thus , it appears that CD4+Tregs are associated with ENL reaction but not CD8+Tregs ( Fig 1A ) . Comparison within ENL group has shown that the median percentage of CD4+ regulatory T- cells was significantly lower ( 1 . 67% ) in the PBMCs of patients with ENL reactions before treatment than during treatment ( 2 . 5% ) ( P<0 . 0001; ΔHL = 1 . 0% ) . After treatment , the median percentage of CD4+Tregs increased to 3 . 20% and it was significantly higher than before treatment ( 1 . 67% ) ( P<0 . 0001; ΔHL = 1 . 6% ) suggesting that ENL reaction is associated with decreased percentage of CD4+Treg cells . In contrast to CD4+ Tregs , the percentage of CD8+ Tregs was not significantly different before and after prednisolone treatment of patients with ENL reactions ( Fig 1B ) . Untreated patients with ENL reactions had a significantly higher median percentage of CD4+T-cells ( 61 . 3% ) compared to LL patient controls ( 49 . 1% ) at enrolment ( P < 0 . 0001; ΔHL = 12 . 8% ) . During prednisolone treatment of patients with ENL reactions , the median percentage of CD4+ T cells decreased to 54 . 2% while that of LL patient controls increased to 61 . 4% and the difference was statistically significant ( P≤0 . 05 ) . On the other hand , patients with ENL had a significantly lower median percentage of CD8+ T-cells ( 27 . 0% ) before treatment compared to LL patient controls ( 35 . 7% ) and the difference was statistically significant ( P<0 . 0001; ΔHL = 8 . 2% ) . Interestingly , while patients with ENL were on treatment , unlike the CD4+ T-cells , the median percentage of CD8+ T-cells increased to 34 . 4% and it was higher than that of LL patient controls ( 28 . 3% ) ( P<0 . 001 ) . After treatment , the corresponding values did not significantly changed in patients with ENL ( 33 . 5% ) and LL controls ( 27 . 2% ) ( Fig 2A ) . Comparison within ENL group revealed that the median percentage of CD4+ T-cells was significantly higher ( 61 . 3% ) before treatment than during treatment ( 54 . 2% ) ( P≤0 . 001; ΔHL = 6 . 8% ) and after treatment ( P≤ 0 . 05; ΔHL = 4 . 6% ) ( P>0 . 05 ) . On the other hand , the median percentage CD8+ T-cells in untreated ENL patients was significantly lower ( 27 . 0% ) than during treatment ( 34 . 4% ) ( P≤0 . 0001; ΔHL = 6 . 8% ) and after treatment ( P≤0 . 005 ) ( Fig 2B ) . The CD4+/CD8+ T-cell ratio was higher ( 2 . 3: 1 ) in patients with ENL compared to LL patient controls ( 1 . 4:1 ) before treatment ( P<0 . 001; ΔHL = 0 . 09 ) . However , a significantly lower CD4+/CD8+ T-cell ratio was seen in patients with ENL ( 1 . 7: 1 ) compared to LL patient controls ( 2 . 25:1 ) during treatment ( P≤ 0 . 001 ) . After treatment , the ratio of CD4+/CD8+ T- cell was 2 . 14: 1 and 1 . 8:1 in patients with ENL and LL controls respectively and the difference was not statistically significant ( P>0 . 05 ) ( Fig 3A ) . Hence , this result indicates that patients with ENL reactions and non-reactional LL controls not only showed significant differences in the percentage of CD4+ and CD8+ T-cells but also in their CD4+ / CD8+ T-cell ratio . Patients with ENL had higher median percentages ratio of CD4+ and CD4+/CD8+ T-cells than LL patient controls before treatment . Analysis within ENL group has shown that the median percentage of CD4+ to CD8+ T-cell ratio before treatment was significantly higher ( 2 . 23:1 ) than during treatment ( 1 . 8: 1 ) ( P<0 . 0001; ΔHL = 0 . 6 ) and after prednisolone treatment ( 1 . 7:1 ) ( P≤0 . 005; ΔH = 0 . 5 ) ( Fig 3B ) . This finding shows that prednisolone treatment was associated with a decreased median percentage of CD4+ T-cells and CD4+ to CD8+ T-cell ratio but with increased median percentage of CD8+ T-cells in ENL reactions . Patients with ENL had a significantly higher median percentage of IL-17 producing lymphocytes ( 26 . 45% ) than LL patient controls ( 20 . 6% ) before treatment ( P≤ 0 . 05; ΔHL = 5 . 7 ) . Similarly , the proportion of IL-17 producing T-cells in the PBMCs of patients with ENL was considerably higher ( 23 . 1% ) than in LL patient controls ( 18 . 4% ) before treatment and the difference was statistically significant ( P≤ 0 . 05; ΔHL = 4 . 5 ) . Patients with ENL had a higher percentage of IL-17 producing CD4+ T-cells than LL patient controls before treatment . The percentage of IL-17 producing CD8+ T-cells was not significantly different in both patient groups ( Fig 4A ) . The median percentage of IL-17 producing cells was also compared within patients with ENL reactions before , during and after prednisolone treatment to see the trend of these cells during the follow-up period . The median percentage of IL-17 producing lymphocytes in the PBMCs of untreated patients with ENL reactions was significantly higher ( 26 . 45% ) than during treatment ( 15 . 4% ) ( P<0 . 0001 , ΔHL = 9 . 5% ) . After treatment , the median percentage of these IL-17 producing lymphocytes was further decreased to 12 . 7% and it was significantly lower than before treatment ( P<0 . 0001 , ΔHL = 11 . 855% ) . Similarly , the proportion of IL-17 producing T-cells was substantially higher ( 23 . 1% ) before treatment than during treatment ( 16 . 4% ) and after treatment ( 13 . 2% ) ( P<0 . 0001 ) ( Fig 4B ) . It was found that the proportion of IL-17 producing CD4+ T-cells was significantly decreased during treatment ( P< 0 . 0001 ) but again increased after treatment . Unlike IL-17 producing lymphocytes , the median percentage of IL-17 producing CD4+ T-cells was not significantly different before and after treatment showing that CD4+ T-cells may not be the main source of IL-17 or IL-17 producing CD4+ -cells may not respond to prednisolone treatment in these patients . IL-17 producing CD8+ T-cells did not show any significant difference before , during and after treatment of patients with ENL reactions ( Fig 4B ) . Prednisolone treatment did not seem to affect the proportion of Il-17 producing CD8+ T-cells in ENL but it could affect transiently IL-17 producing CD4+T-cells . The median percentage of CD25 positive T-cells in CD4+ T-cells in the PBMCs of untreated patients with ENL and LL patient controls were found to be 8 . 9% and 8 . 8% respectively ( P>0 . 05 ) . About 2 . 6% of CD8+ T-cells had expressed CD25 in the PBMCs from patients with ENL and a slightly higher proportion of CD8+ T-cells ( 3 . 2% ) expressed CD25 in the PBMCs from LL patient controls before treatment ( P≤0 . 05 ) . The expression of CD25 on both CD4+ and CD8+ T- cells during and after treatment did not change in both patient groups ( Fig 5A ) . Hence , these results indicate that the expression of CD25 by CD4+ and CD8+T-cells is similar in patients with ENL and LL controls and it does not discriminate active ENL from non-reactional LL . The analysis of differential expression of CD25 by CD4+ T-cells within ENL group has revealed that the median percentage expression of CD25 in CD4+ T-cells before and during treatment was 8 . 9% and 8 . 2% respectively . After treatment with prednisolone , about 9 . 5% of CD4+ T-cells expressed CD25 . The expression of CD25 by CD4+ T-cells before , during and after treatment was not statistically significantly different ( P> 0 . 05 ) implying that prednisolone may not affect the expression of CD25 in CD4+ T-cells . Similarly , the expression of CD25 in CD8+ T-cells during and after prednisolone treatment of patients with ENL did not change ( P> 0 . 05 ) . About 2 . 6% of CD8+ T-cells were positive for CD25 staining before treatment . The median percentage of CD8+CD25+ T- cells during and after treatment was 3 . 2% and 2 . 2% respectively ( P> 0 . 05 ) ( Fig 5B ) . The median percentage of FoxP3-expressing CD4+ T-cells in the PBMCs of patients with ENL was lower ( 2 . 1% ) compared to the median percentage expressed in the PBMCs of LL patient controls ( 5 . 1% ) before treatment ( P<0 . 0001 ) . During treatment , the frequency of FoxP3-expressing CD4+ T-cells slightly increased to 3 . 5% in patients with ENL and decreased by half from 5 . 1% to 2 . 6% in LL controls and the difference between the two groups was statistically significant ( p≤0 . 05 ) . However , the frequency of FoxP3-expressing CD4+ T-cells in the PBMCs from patients with ENL and LL controls did not change significantly after treatment implying the possible association of reduced percentage of CD4+ FoxP3+ T-cells in patients with active ENL reaction . Although the median percentage of FoxP3-expressing CD8+ T-cells in the PBMCs of patients with ENL was slightly lower ( 0 . 57% ) compared to LL controls ( 0 . 71% ) before treatment , the difference was not statistically significant ( P>0 . 05 ) . During treatment , patients with ENL had a lower frequency of CD8+FoxP3+ T-cells ( 0 . 49% ) compared to LL patient controls ( 1 . 17% ) ( P≤0 . 05 ) . After treatment , the percentage of FoxP3 expression in CD8+ T-cells in patients with ENL and LL controls was not statistically significantly different suggesting that CD8+FoxP3+ T-cells may not associated with ENL reaction ( P ≥0 . 05 ) ( Fig 6A ) . Comparison within ENL group has shown that the median percentage of CD4+FoxP3+ T-cells was significantly lower ( 2 . 1% ) before treatment than during ( 3 . 5% ) ( P<0 . 0001; ΔHL = 1 . 4% ) and after prednisolone treatment ( 4 . 4% ) ( P≤0 . 0001; ΔHL = 2 . 2 ) . The expression of FoxP3 in CD4+ T-cells doubled after prednisolone treatment of patients with ENL suggesting an association of prednisolone and FoxP3 expression in CD4+ T-cells unlike the CD25 expression in these patients . Interestingly , the expression of FoxP3 in CD8+ T-cells was not significantly different before ( 0 . 59% ) , during ( 0 . 49% ) and after ( 0 . 50% ) prednisolone treatment of these patients ( Fig 6B ) . Hence , it appears that prednisolone treatment does not affect the expression of FoxP3 in CD8+ T-cells unlike in CD4+T-cells . The frequency of CD25+FoxP3+ expression in CD4+ and CD8+ T-lymphocytes was measured in the PBMCs of patients with ENL and LL controls before , during and after treatment . About 1 . 8% of CD4+ T-cells expressed CD25+FoxP3+ in the PBMCs of untreated patients with ENL which was significantly lower than the proportion of CD25+FoxP3+ cells expressed in CD4+ T-cells ( 3 . 8% ) in the PBMCs of LL patient controls ( P<0 . 0001 ) . During treatment , the median percentage of CD4+CD25+FoxP3+ T-cells in the PBMCs of patients with ENL increased to 2 . 6% while it decreased from 3 . 8% to 2 . 5% in LL patient controls . After treatment , the percentage of CD4+CD25+FoxP3+ T-cells in the PBMCs of patients with ENL had further increased to 3 . 3% while it dropped to 2 . 2% in patients with LL controls and the difference was statistically significant ( P≤0 . 001 ) ( Fig 7A ) . A small proportion of CD8+ T- cells in the PBMCs of patients with ENL and LL controls expressed CD25+FoxP3+ . The percentage of CD8+CD25+FoxP3+ T- cells in the PBMCs of patients with ENL and LL controls was 0 . 4% and 0 . 6% respectively before treatment and slightly decreased in both groups ( 0 . 28% in patients with ENL and 0 . 52% in LL controls ) during treatment . After treatment , these figures were slightly increased in both groups . The frequency of CD8+CD25+FoxP3+ T- cell expression in the PBMCs of patients with ENL and LL controls was not significantly different before , during and after treatment ( Fig 7A ) . Hence , it appears that CD4+CD25+FoxP3+ T- cells are more closely associated with ENL reaction than do the CD8+CD25+FoxP3+ T- cells . Analysis within ENL group has shown that the median percentage of CD4+CD25+FoxP3+ T-cells in the PBMCs of patients with ENL was significantly lower before treatment than during treatment ( 2 . 6%; P<0 . 0001; ΔHL = 1 . 04% ) and after treatment ( 3 . 3%; P<0 . 0001; ΔHL = 1 . 55% ) . On the other hand , the median percentage of CD8+CD25+FoxP3+ T-cells was not significantly different before , during and after treatment ( Fig 7B ) .
The results indicated that patients with ENL had a significantly higher median percentage of CD4+ T-cells and lower CD8+ T-cells before treatment compared to LL controls . Patients with ENL had also a higher CD4/CD8 ratio ( 2 . 3:1 ) compared to LL controls ( 1 . 4:1 ) . Prednisolone treatment significantly reduced the percentage of CD4+ T-cells in patients with ENL implying that prednisolone could suppress CD4+ T-cells to resolve the inflammation . Other studies elsewhere have shown that prednisolone treatment promotes an immunological state that favours immune regulation rather than inflammation through downregulation of CD4+ T-cell proliferation [39] . The CD4+/CD8+ ratio obtained in our study for patients with ENL was higher than the reference value for apparently healthy Ethiopian adults ( 1 . 5:1 ) [40] . However , the CD4+/CD8+ ratio of LL patient controls was slightly lower than the normal value . Hence , it is logical to conclude that rather than the actual percentage of CD4+ and CD8+ T cells , the balance between the two T-cell subtypes is indeed associated with ENL reactions . Therefore , the higher the CD4+/CD8+ ratio the greater is the risk of developing ENL reactions . Increased CD4+ counts and CD4+/CD8+ T-cells ratio in 11 Indian ENL patients has been reported [41] which is in agreement with the present finding . However , they did not look at the proportion of CD4+ T-cells and CD4+/CD8+ ratio during and after treatment unlike the present study . Similar results have been reported by several studies [10–12 , 42 , 43] . Lymphocyte imbalance ( CD4+and CD8+ ) has also been indicated in the progression of many other diseases elsewhere [44 , 45] . However , in addition to the CD4+/CD8+ T-cell ratio , other factors such as the expression pattern of costimulatory molecules on T-cells ( CD28 ) and on antigen presenting cells ( CD80/86 ) in patients with ENL need to be explored . Abnormal T-cell co-simulation and T-cell senescence have been implicated in the expansion of effector memory T-cells and are thought to facilitate the breakdown of tolerance in inflammatory diseases such as anti-neutrophil cytoplasmic autoantibodies ( ANCA ) -associated vasculitis [37] . In ANCA-associated vasculitis , CD28 was found to be downregulated on circulating and lesional CD4+ T- cells [46] and CD4+CD28- T-cells have been described as the major source of pro-inflammatory cytokines ( particularly IFN-γ and TNF-α ) in Wegener’s Granulomatosis [47] . These authors have also shown that the severity of the disease was positively correlated with the increased proportion of CD4+CD28- T-cells . Therefore , the proportion of CD4+CD28- T-cells in untreated ENL patients needs to be investigated . The analysis within ENL group has revealed that the median percentage of CD4+ T-cells was significantly decreased during and after prednisolone treatment in each ENL patient . On the other hand , the median percentage of CD8+ T-cells was significantly increased following prednisolone treatment unlike CD4+ T-cells . Interestingly , the CD4+/CD8+ T-cell ratio was significantly deceased after prednisolone treatment implying the role of prednisolone in re-establishing the immune homeostasis by downregulating excessive CD4+ T-cell activation . Studies have shown that prednisolone reduces CD4+ T-cells [14 , 48] . A reduction of CD4+ to CD8+ T cells ratio in patients with rheumatoid arthritis when as little as 2 . 5mg of prednisolone was administered every 6hrs has been reported [49] . Mshana et al . were the first to hypothesize that ENL is precipitated by an imbalance of T-lymphocyte subpopulations [50] . According to this hypothesis , ENL has two phases: initiation , due to an imbalance in T-cell subpopulations with decreased suppressor cells ( now called Tregs ) and perpetuation . Hence , the finding of the imbalance of T-lymphocyte subpopulations ( CD4+/ CD8+ ratio ) in this study would support the initiation of ENL reaction in patients with lepromatous leprosy . The initiation of ENL reaction by hyper activation of T-cells further explained by the association of viable bacterial load with the release of soluble antigenic material at the site of bacterial degranulation in ENL patients compared to the intact bacteria observed in non-reactional LL patients [51 , 52] . Hence , it seems that unlike in the LL patients , macrophages in ENL patients may be activated and processes the bacteria and hence fragmented and granular bacterial deposits seen in these patients . The activation of macrophages by T-cells ( most likely through IFN-γ secretion ) may produce inflammatory cytokines such as TNF-α and IL-1β [53] which could amplify the immune hyperactivation and hence tissue damage in ENL patients . We confirmed that untreated patients with ENL had a significantly higher median percentage of IL-17 producing T-cells than LL patient controls . T-cell subset analysis has shown that patients with ENL had a higher percentage of IL-17 producing CD4+ T-cells than LL patient controls before treatment . On the other hand , the percentage of IL-17 producing CD8+ T-cells was not significantly different in both patient groups . After prednisolone treatment , none of these cells show significant difference . Th17 cells have been identified as a new subset of the T- helper cells and as potential mediators of inflammation associated with various autoimmune and mycobacterial diseases [27] . Th-17 produces IL-17 and it has been reported that IL-17 plays a key role for activation and recruitment of neutrophils to the site of infection in inflammatory diseases [54] . IL-17 is considered as a pro-inflammatory cytokine because it increases IL-6 , IL-8 , nitric oxide , TNF-α and IL- 1β production by various cell types . Th17 cells are the least studied T-cells in leprosy and only few studies have indicated the involvement of Th-17 in the immunopathogenesis of ENL [20 , 26 , 28] . The involvement of IL-17 as pro-inflammatory cytokine in human inflammatory diseases such as rheumatoid arthritis , psoriasis , crohn’s diseases , systemic lupus erythematosus , inflammatory bowel diseases and multiple sclerosis has been reviewed by Miossec [55] . It has been described that Th17 and Tregs have reciprocal functions . We identified increased IL-17 producing T-cells in untreated ENL patients compared to LL patient controls . Analysis of IL-17 producing T-cell subsets has shown that IL-17 producing CD4 T-cells are significantly increased in active untreated ENL patients and diminished after prednisolone treatment which signifies the importance of CD4 T-cells in the pathogenesis of ENL . Hence , understanding the exact role of IL-17 in ENL reaction will benefit the development of novel immune modulators that reduce inflammation and thereby protect tissue damage in patients with ENL . This study has shown that the level of CD25 expression on CD4+ T-cells was not significantly different in patients with ENL and LL controls before treatment but the expression of CD8+CD25+ was lower in patients with ENL than in LL patient controls . This could be explained by the fact that CD25 is not only expressed on Tregs but also on activated T-cells as previously described [56] . Hence , it is not appropriate to compare the level of phenotypic expression of CD25 only on either CD4+ or CD8+ T-cells in patients with ENL and LL controls since the expression of CD25 could have different implications in these groups: activation in patients with ENL reaction and regulation in non-reactional LL patients . Although transient FoxP3 expression in ex-vivo activated human T-cells has been reported [57] , it remains as a good marker for Tregs . In this study , for immunophenotyping of T-cell subtypes , unstimulated PBMCs were used and hence our FoxP3 result is less likely to be over represented or activated . The stimulation of CD4+CD25– human T-cells have shown to generate CD4+CD25+ T cells which can also express FoxP3 [58] . The present result shows that the percentage of CD4+FoxP3+ T-cells in the PBMCs from patients with LL controls was more than twice the percentage of CD4+FoxP3+ T-cells in the PBMCs from patients with ENL before treatment . However , a similar percentage of CD4+FoxP3+was obtained in both groups after treatment . On the other hand , the expression of FoxP3 in CD8+ T-cells was not significantly different in both groups before and after treatment . To obtain a more characterized FoxP3 population that has a regulatory property , CD127 was used in conjunction with CD25 as an additional marker as previously described [59] . Therefore , the Tregs described here are better characterized than previous reports and thus , our present finding of Tregs is more refined than previous studies . Similarly , the expression of CD25 by CD4+ and CD8+ T-cells was not significantly different before and after prednisolone treatment within ENL group . This can be explained by the fact that CD25 can be expressed by activated as well as regulatory T-cells as previously described [58] . Therefore , although the CD25 expression in CD4+ or CD8+ T-cells before and after treatment is comparable , it may not have the same role i . e . it could play with activation role before treatment and a regulatory role after treatment in the same patient . However , this needs to be verified by functional assay of these cells . Interestingly , the expression of FoxP3 in CD4+ T-cells was significantly increased after prednisolone treatment within ENL group . Thus , unlike the expression of CD25 in CD4+ T-cells , prednisolone upregulates the expression of FoxP3 in CD4+ T-cells and hence increases tolerance through immune suppression [14 , 39] . On the other hand , the expression of FoxP3 in CD8+ T-cells was not significantly different before and after prednisolone treatment . Hence , it appears that prednisolone does not affect the expression of FoxP3 in CD8+ T-cells in these patients . It has shown that Tregs may protect from non-specific memory T-cell activation and potential tissue damage [60] . Hence , the reduced frequency of CD4+Tregs and the increased CD4+/CD8+ T-cells ratio in untreated patients with ENL may explain the possibility of induction of excessive immune activation owning to the pre-existing high load of bacterial antigens in patients with lepromatous leprosy . We found that a significant reduction of the percentage of CD4+ regulatory T-cells and an increased percentage of CD4+/CD8+ T-cell ratio and IL-17 producing T-cells in untreated patients with ENL compared to the non-reactional LL patient controls . These findings suggest that ENL is associated with a reduced percentage of regulatory T-cells and increased CD4+/CD8+ T-cell ratio as well as IL-17 producing T-cells . This immune imbalance could lead to the initiation of ENL reactions either by permitting increased production of antibodies critical to immune-complex formation or as a cell-mediated immune response in patients with lepromatous leprosy . | Leprosy reactions ( Type 1 and 2 ) are important causes of nerve damage and illness . Erythema Nodosum Leprosum ( ENL ) also called type 2 reaction is a severe systemic immune-mediated complication of borderline and lepromatous leprosy . ENL causes high morbidity and thus requires immediate medical attention . We recruited 77 untreated patients with lepromatous leprosy ( 46 patients with ENL reactions and 31 patients without ENL reactions ) in Ethiopia to better define the immune regulation process in patients with ENL reactions . We took blood samples at 3 time points ( before , during and after prednisolone treatment ) and measured regulatory T-cells at each time point . Patients with ENL reactions had a lower percentage of CD4+ regulatory T-cells than in non-reactional LL patient controls before treatment . Patients with ENL reactions had higher percentage of CD4+ T- cells and CD4+/CD8+ ratio than LL patient controls before treatment . These experiments indicate the need to explore ways of restoring regulatory T-cells in patients with ENL reactions to control the undesired outcome of the reaction . | [
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"diseas... | 2017 | T-cell regulation in Erythema Nodosum Leprosum |
Stem cell maintenance is established by neighboring niche cells that promote stem cell self-renewal . However , it is poorly understood how stem cell activity is regulated by systemic , tissue-extrinsic signals in response to environmental cues and changes in physiological status . Here , we show that neuropeptide F ( NPF ) signaling plays an important role in the pathway regulating mating-induced germline stem cell ( GSC ) proliferation in the fruit fly Drosophila melanogaster . NPF expressed in enteroendocrine cells ( EECs ) of the midgut is released in response to the seminal-fluid protein sex peptide ( SP ) upon mating . This midgut-derived NPF controls mating-induced GSC proliferation via ovarian NPF receptor ( NPFR ) activity , which modulates bone morphogenetic protein ( BMP ) signaling levels in GSCs . Our study provides a molecular mechanism that describes how a gut-derived systemic factor couples stem cell behavior to physiological status , such as mating , through interorgan communication .
Maintenance and regeneration of adult tissues requires a robust stem cell system that balances self-renewal with differentiation [1] . Because abnormalities in stem cell regulation may result in loss of tissue integrity or tumorigenesis , this robust stem cell system is precisely modulated by local and systemic signals [2] . Stem cells reside in a specialized microenvironment , or niche , where they are exposed to local signals required for stem cell function and identity [3 , 4] . A number of studies have demonstrated the importance of local niche signals in regulating stem cell identity . Less is known , however , regarding how stem cell activity is regulated by systemic , tissue-extrinsic signals in response to environmental cues and changes in physiological status . The Drosophila ovary is one of the most powerful models for studying adult stem cell behavior in vivo [3 , 5] . This tissue is composed of many chains of developing egg chambers called ovarioles [6] . The most anterior region of each ovariole , the germarium , contains germline stem cells ( GSCs ) that give rise to the eggs ( Fig 1A ) . GSCs can divide symmetrically to produce a generative cell population or asymmetrically to produce daughter cells called cystoblasts . Each cystoblast undergoes differentiation into 15 nurse cells and 1 oocyte in each egg chamber , which is surrounded by somatic follicle cells . Therefore , the balance between self-renewal and differentiation of GSCs plays a pivotal role in regulating oogenesis because disruption of this balance may cause germ cell depletion , infertility , or tumorigenesis [1] . Drosophila female GSCs are anchored to the somatic niche , which comprises cap cells , escort cells , and terminal filaments . The niche produces local signals such as the bone morphogenetic protein ( BMP ) ligand Decapentaplegic ( Dpp ) , which activates its receptors Saxophone ( Sax ) , Punt ( Put ) , and Thickveins ( Tkv ) expressed in GSCs to induce GSC division and maintenance [7] . Local signals from the niche have a crucial role in regulating reproduction because impairment of niche signals can cause germ cell depletion , infertility , and tumorigenesis . On the other hand , it is well known that animal reproduction is also coordinated by signals from the external environment [8 , 9] . One such example involves nutrients , which are important materials for producing eggs . On a protein-poor diet , egg production is restricted through blocking vitellogenesis [8] . A protein-poor diet also results in a reduction of GSC division , which is mediated by neural-derived Drosophila insulin-like peptides ( DILPs ) [10] . Moreover , in response to nutrients , GSC maintenance is controlled by the adipocyte metabolic pathway [11 , 12] . Another example of an environmental cue that affects reproduction is mating . Mated females show a dramatic increase in egg production , which is induced by a male-derived peptide from seminal fluid termed sex peptide ( SP ) [13] . We have previously reported that neural SP signaling also promotes GSC proliferation through its effects on the biosynthesis of ecdysteroids ( insect steroid hormones ) in the ovary [14 , 15] . Taken together , these findings suggest that GSC proliferation and maintenance are modulated by tissue-extrinsic signals in response to environmental cues . Here , we present a series of new findings that reveal a novel and fundamental interorgan communication mechanism controlling GSC proliferation in response to mating . We demonstrate that Drosophila neuropeptide F ( NPF ) , a homolog of mammalian neuropeptide Y ( NPY ) , acts as a key regulator of mating-induced GSC proliferation in Drosophila females . Although NPF is expressed in both the brain and the midgut , we found that only the enteroendocrine-derived peptide—not neuronal NPF—is required for activation of GSCs after mating . The NPF protein is highly accumulated in enteroendocrine cells ( EECs ) of the middle midgut of virgin female flies and is released in response to SP-dependent signaling upon mating . Through fly injection and ex vivo ovary cultures with synthetic peptide , we show that NPF signaling is sufficient for increasing GSC number in virgin female flies . Notably , after mating , midgut-derived NPF acting on the ovaries through the NPF receptor ( NPFR ) up-regulates BMP signaling levels in GSCs to induce their proliferation . Our findings describe a mechanism of gut-to-ovary communication that couples stem cell behavior to physiological status by sensing external cues such as mating . Considering NPY’s role in regulating reproduction in many animal species , our study also provides new insights into the role of interorgan communication during animal germline development .
We employed a genetic screen using Drosophila fly lines carrying Clustered Regularly Interspaced Short Palindromic Repeats ( CRISPR ) /CRISPR-associated protein 9 ( Cas9 ) -generated mutations in neuropeptide-encoding genes [16] and identified stem cell phenotypes in mutants of NPF ( Fig 1B and 1C ) . In control flies , mated female flies had more GSCs than virgin female flies ( Fig 1D ) , as we reported previously [14] . In contrast , the mating-induced increase in GSC number was suppressed in genetic null mutants for NPF itself or for the gene encoding the NPFR ( Fig 1B–1D ) . We also found that genetic null mutant flies of short neuropeptide F precursor ( sNPF ) , encoding an RxRFamide neuropeptide related to NPF , showed a normal increase in GSC number after mating ( Fig 1D ) , suggesting that the GSC-suppression phenotype is specific to NPF signaling . Our immunostaining analysis confirmed the presence of anti-NPF signals in the brain [17 , 18] and EECs of the middle midgut [19 , 20] in control flies , but not in NPF mutants ( S1A Fig ) . A number of previous studies have already reported that neuronal NPF regulates multiple aspects of physiology and behavior in adult flies , such as circadian rhythm , alcohol sensitivity , male courtship behavior , and food intake [17 , 18 , 21–24] , whereas the function of EEC-derived NPF remains unclear [20 , 25] . We first investigated whether neuronal NPF function is required for the normal increase in GSC number induced by mating . However , although RNA interference ( RNAi ) -mediated knockdown of NPF either pan-neuronally ( using nSyb-GAL4 ) or in neuroendocrine cells ( using 386Y-GAL4 ) resulted in a drastic decrease in NPF protein levels in the brain ( S1B Fig ) , neither manipulation had any effect on post-mating GSC number ( S1C Fig ) . We also confirmed that RNAi driven by neither the nSyb-GAL4 nor 386Y-GAL4 driver resulted in reduced NPF levels in midgut EECs ( S1B Fig ) . Therefore , we next examined whether the mating-induced increase in GSC number is controlled by midgut-expressed NPF , the other potential source of NPF protein . For this purpose , we utilized the Tk-gut-GAL4 ( Tkg-GAL4 ) driver because this GAL4 driver is known to be active in a restricted population of midgut cells , including NPF-positive EECs [20] , but not in the ovary ( S2A Fig ) . Immunostaining analysis with anti-NPF antibody revealed that Tkg-GAL4-mediated transgenic RNAi against NPF ( hereafter Tkg>NPFRNAi ) dramatically reduced the number of NPF-positive cells in the middle midgut compared with controls ( Fig 2A ) . It should be noted that Tkg-GAL4 is also active in some neuronal cells in the brain and the ventral nerve cord ( VNC; S2A and S2B Fig ) ; however , Tkg>NPFRNAi animals did not show a significant reduction in NPF levels in these neuronal cells ( S2C Fig ) . In Tkg>NPFRNAi females , we found that the mating-induced increase in GSC number was severely impaired ( Fig 2B ) . In addition , we performed RNAi-mediated knockdown of NPF driven by several other midgut-GAL4 drivers . Suppression of the increase in GSC number after mating was observed with 4 of these drivers ( Fig 2C ) , which are active in middle midgut EECs [26] but not in ovaries or NPF-positive neurons ( S3 Fig ) . On the other hand , NPF RNAi in enterocytes ( using Myo1A-GAL4 ) or intestinal stem cells and enteroblasts ( esg-GAL4 ) had no effect on the mating-induced increase in GSC number ( Fig 2C ) . We found that the GSC proliferation defect in NPF genetic null mutants was rescued by overexpression of the NPF transgene under the control of the Tkg-GAL4 driver ( Fig 2D ) . Tkg-GAL4-positive cells also express other gut peptide hormone genes , including Tachykinin ( Tk ) and diuretic hormone 31 ( Dh31 ) [20] . However , transgenic RNAi against either Tk or Dh31 driven by Tkg-GAL4 had no effect on post-mating increase in GSC number ( Fig 2B ) . On the other hand , ablating the Tkg-GAL4-positive cells by expressing the cell death–inducing factor reaper ( rpr ) and head involution defective ( hid ) led to suppression of the increase in GSC number after mating ( Fig 2B ) . Taken together , these results suggest that EECs play an important role in regulating the mating-induced increase in GSC number , mainly through the function of NPF . We next examined whether midgut-derived NPF controls GSC division . For this purpose , we counted the number of GSCs in M phase and S phase by staining with anti-phospho-histone H3 ( pH3 ) and bromodeoxyuridine ( BrdU ) , respectively , in control and Tkg>NPFRNAi adult females . In control female flies , we found that mating increased the frequency of GSCs in both M and S phases ( Fig 2E ) . We also monitored GSC fusome morphology as an indicator of cell cycle phase [27] and did not observe any difference in the frequency of GSCs in G2/M and G1/S phases ( Fig 2E ) . In Tkg>NPFRNAi animals , the increase in the fraction of GSCs in M and S phases was suppressed ( Fig 2E ) , suggesting that midgut-derived NPF promotes GSC progression through both DNA replication and mitosis . We also monitored the fraction of apoptotic cells in the germarium by staining with anti-cleaved Death caspase-1 ( Dcp-1 ) , a marker for apoptotic cells [28] . The number of apoptotic cells did not change in Tkg>NPFRNAi female flies compared with controls ( Fig 2F ) , suggesting that the lack of post-mating GSC-number increase seen with NPF RNAi was not caused by increased cell death but mainly by a lack of NPF-induced cell proliferation . Consistent with their GSC proliferation phenotype , we found that mated Tkg>NPFRNAi female flies laid fewer eggs than mated control females ( Fig 2G ) . Taken together , these data indicate that midgut-derived NPF has a positive impact not only on GSC proliferation but on reproductive fitness after mating as well . To rule out the possibility that the GSC phenotype was due to the absence of mating , we confirmed that the copulation rate of Tkg>NPFRNAi female flies did not change compared with that of control female flies by using males expressing GFP in their sperm ( Fig 2H ) . These findings are all consistent with the idea that NPF from EECs modulates GSC division after mating . In Drosophila females , mating induces midgut epithelium remodeling , including increases in gut size and in the number of mitotic cells , which is essential for enhancing reproductive output [29 , 30] . Thus , there may be a possibility that the suppression of mating-induced GSC proliferation in Tkg>NPFRNAi animals is indirectly caused by the dysfunction of midgut remodeling after mating . We therefore examined whether midgut-derived NPF affects gut size or the number of mitotic cells in the midgut after mating; however , Tkg>NPFRNAi females displayed normal increases in mitosis in the midgut epithelium and posterior midgut diameter ( S4A and S4B Fig ) . This suggests that midgut NPF is not involved in tissue remodeling after mating and that the GSC phenotypes in Tkg>NPFRNAi female flies are not due to an indirect effect of defective mating-induced remodeling in the midgut epithelium . To investigate the relationship between mating and NPF in EECs , we performed immunostaining with anti-NPF antibody on the midguts of virgin and mated female flies . Anti-NPF signal was stronger in the EECs of virgin females compared with mated females ( Fig 3A ) . In contrast , mating did not alter NPF mRNA abundance in the middle midgut ( Fig 3B ) , indicating that the observed change in NPF protein levels was not due to transcriptional regulation . This situation was reminiscent of the case of Drosophila insulin-like peptide 2 ( Dilp2 ) because it is well known that increased Dilp2 protein level in insulin-producing cells reflects decreased Dilp2 release into the hemolymph when dilp2 transcription is constant [31] . Similarly , although immunostaining alone cannot completely rule out the contribution of post-transcriptional regulation of NPF , these results do imply that mating promotes NPF release from EECs . Our previous study [14] revealed that mating-induced GSC proliferation is mediated by the male seminal-fluid component SP , which plays a central role in triggering dramatic changes in female physiology and behavior after mating [13 , 32] . SP is received by female neurons expressing the sex peptide receptor ( SPR ) , resulting in the silencing of these neurons [33 , 34] . We found that female flies mated with male flies lacking SP showed NPF accumulation in middle midgut EECs without significant changes in NPF mRNA levels ( Fig 3A and 3B ) , suggesting that male-derived SP regulates NPF release from EECs upon mating . The silencing of SPR-positive neurons located on the oviduct , which also express pickpocket ( ppk ) [35] , is particularly important for inducing female GSC proliferation after mating [14] . We found that the expression of a transgene encoding membrane-tethered SP ( mSP ) in ppk-positive female neurons decreased NPF protein levels in EECs , even in virgin female flies ( Fig 3C ) , while NPF mRNA levels in the middle midgut were not significantly altered ( Fig 3D ) . In contrast , NPF protein levels did not change after expression of mSP with the Tkg-GAL4 driver ( S5A and S5B Fig ) . These results suggest that neuronal—but not midgut—SP signaling is both necessary and sufficient for post-mating NPF release from EECs . We also examined whether neuronal inactivation of SPR-positive neurons was sufficient to reduce NPF accumulation in EECs in virgin female flies , as the binding of SP to SPR silences SPR-positive neurons [36] . We utilized a mutant of shibire ( shits1 ) that blocks synaptic vesicle release in a temperature-dependent manner [37] . Normal NPF accumulation was observed at the permissive temperature in virgin female flies overexpressing shits1 ( S5C Fig ) . On the other hand , when SPR-positive neurons were silenced in virgin females at the restrictive temperature to mimic mating , NPF protein levels in middle midgut EECs were reduced without any significant changes in NPF mRNA levels ( S5C and S5D Fig ) . These results suggest that NPF release from EECs is induced by silencing the transmitter-release activity of some neurons within the SPR-positive population . To further examine the necessity and sufficiency of circulating NPF in inducing the increase in GSC number , we manually delivered synthetic NPF peptide by injecting it using glass needles into adult females . NPF injection into wild-type virgin females resulted in a significant increase in GSC number compared with injection of phosphate-buffered saline ( PBS ) vehicle ( Fig 3E ) . No such increase in GSC number was observed after injection of synthetic sNPF peptide ( Fig 3E ) . Moreover , we observed that the impairment in GSC increase seen in mated Tkg>NPFRNAi animals was restored by NPF injection but not by control PBS injection ( Fig 3F ) . These results suggest that elevation in circulating NPF levels is both necessary and sufficient to trigger mating-induced GSC proliferation . The next question to be addressed is whether the NPF signal is directly received by the ovary or is transmitted via other tissues to control mating-induced GSC proliferation . We therefore performed ex vivo ovary culture experiments with synthetic NPF peptide . In this experiment , we dissected ovaries from virgin female flies and cultured them in Schneider’s Drosophila cell culture medium with or without synthetic NPF peptide for 1 day . We found that dissected virgin ovaries cultured with the NPF peptide possessed more GSCs than controls ( Fig 4A ) , indicating that NPF directly affects the ovary in controlling GSC number . This increase was not observed when the ovaries were cultured with synthetic sNPF peptide ( Fig 4B ) , suggesting that the observed response is specific to NPF . To further understand the role of NPF signaling in the ovary , we focused on the NPFR ( CG1147 ) in Drosophila [38] . We confirmed that NPFR is expressed in the ovary ( Fig 4C ) ; however , expression levels in this tissue were much lower than in the head or midgut , the tissues previously reported to express NPFR [17 , 38] . We also found that the increase in GSC number after mating was suppressed by transgenic NPFR RNAi driven by tj-GAL4 , e22c-GAL4 , or c587-GAL4 drivers ( Fig 4D ) , which are known to be active in the somatic cells of ovarian germaria , including escort and follicle cells [39–42] . Conversely , we found that overexpression of either NPFR or NPF driven by tj-GAL4 was sufficient to increase GSC number in virgin females ( Fig 4E ) . On the other hand , no suppression of the increase in GSC number was observed when we knocked down NPFR function in germ cells ( using nos-GAL4 ) , cap cells ( bab1-GAL4 ) , or posterior follicle cells ( c306-GAL4; Fig 4D ) . Although both tj-GAL4 and c587-GAL4 drive expression in the brain and VNC ( S6A Fig ) , pan-neuronal RNAi knockdown of NPFR function did not affect the mating-induced increase in GSC number ( S6B Fig ) . We also confirmed that intestinal RNAi knockdown of NPFR function did not disrupt the mating-induced increase in GSC number ( S6C Fig ) . We found that the GSC proliferation defect in NPFR genetic null mutants was rescued by overexpression of the NPFR transgene under the control of tj-GAL4 ( Fig 4E ) . We next examined cell cycle progression of GSCs in ovarian NPFR-knockdown females . This manipulation led to phenotypes similar to those seen in Tkg>NPFRNAi female flies—namely , a decrease in GSC frequency in M and S phases after mating without any effects on G1/S or G2/M phase transitions ( Fig 4F ) . Similar to the case of Tkg>NPFRNAi , mated tj>NPFRRNAi female flies laid fewer eggs than mated control females ( Fig 4G ) . We also found that the copulation rate of tj>NPFRRNAi animals did not change in comparison with control female flies by using males expressing GFP in their sperm ( Fig 4H ) , ruling out the possibility that the GSC phenotype was due to the absence of mating . Taken together , these data suggest that NPFR in ovarian somatic cells is necessary and sufficient for positively controlling mating-induced GSC proliferation and reproductive fitness . Remarkably , we found that the increase in GSC number induced by in vivo injection or ex vivo ovary culture with synthetic NPF peptide was completely suppressed in NPFR-knockdown animals ( Fig 5A and 5B ) , suggesting that NPFR expressed in the ovary is epistatic to NPF for controlling GSC proliferation . Because the midgut and ovaries are distinct and separate organs , midgut-derived NPF must remotely act on the ovary to control GSC proliferation in response to mating . Therefore , based on our data described above , we hypothesized that a mating stimulus triggers the release of NPF from EECs into the hemolymph , after which the circulating NPF signal can be received by the ovary . GSC maintenance and proliferation are controlled by signals from the GSC niche , in particular Dpp—the fly counterpart to BMPs [1] . We therefore examined whether down-regulation of NPF signaling affects Dpp signaling by measuring the level of phosphorylated Mad ( pMad ) , a readout of Dpp signaling activation in cells including GSCs [43] . We found that mating increased pMad levels in GSCs of control flies ( Fig 6A ) . On the other hand , Tkg>NPFRNAi led to a reduction in pMad levels in GSCs of mated female flies ( Fig 6A ) . The same phenotype was also observed in ovarian NPFR RNAi female flies and genetic null alleles of NPFR ( Fig 6A ) . Conversely , overexpression of NPF or NPFR in ovarian somatic cells resulted in elevated pMad levels in the GSCs of virgin female flies ( Fig 6A ) . We also tested genetic interactions between NPF and Dpp signaling pathways in controlling the mating-induced increase in GSC number . We counted GSCs in double heterozygous mutant flies carrying NPFRsk8 and one of the Dpp pathway mutations , dpphr56 , sax5 , tkv1 , or put135 . The mating-induced increase in GSC number was disrupted in the double heterozygous mutant flies carrying NPFRsk8/dpphr56 and NPFRsk8/sax5 ( Fig 6B ) . On the other hand , the opposite phenotype of increased GSC number in virgin females was observed in NPFRsk8/tkv1 and NPFRsk8/put135 flies ( Fig 6B; see Discussion ) . These results suggest that midgut-derived NPF signaling in ovarian somatic cells affects Dpp signaling in GSCs to control their proliferation after mating . We also analyzed cap cells , which are critical components of the GSC niche . However , Tkg>NPFRNAi did not change the number of cap cells in virgin or mated female flies ( Fig 6C ) , suggesting that midgut-derived NPF does not affect the overall architecture of the niche . This is consistent with observations that found no mating-induced effects on cap cell numbers [14] . Thus , these findings indicate that the NPF-dependent post-mating increase in GSC proliferation is not dependent on the physical size of the GSC niche but rather is regulated through modulation of the Dpp signaling pathway . Previous studies have revealed that biosynthesis of ecdysone , the major insect steroid hormone , is induced by mating stimuli and that ovarian ecdysteroid transmits its signal directly through the ecdysone receptor ( EcR ) expressed in the ovarian niche to increase GSC number [14 , 15 , 42 , 44 , 45] . We therefore examined the relationship between NPF and ecdysteroids in the ovary . Ovarian ecdysteroid levels after mating were not different between control and Tkg>NPFRNAi animals ( S7A Fig ) , indicating that NPF is not essential for mating-induced ecdysteroid biosynthesis . Consistent with this , exogenous application of 20-hydroxyecdysone—the active ecdysteroid—did not rescue the GSC phenotype of Tkg>NPFRNAi animals ( S7B Fig ) . However , ex vivo culture experiments revealed that virgin ovaries dissected from animals with knocked down neverland ( nvd ) , encoding an ecdysteroid biosynthesis enzyme , did not exhibit an increase in GSC number in the presence of synthetic NPF peptide ( S7C Fig ) . In addition , synthetic NPF peptide in ex vivo cultures only induced a minor increase in GSC number in ovaries dissected from EcR RNAi animals ( S7C Fig ) . These results suggest that NPF signaling in this context requires ecdysteroid signaling , which possibly interacts with EcR and/or downstream signaling components ( S7D Fig ) .
Stem cells are maintained by a specialized microenvironment , or niche , that produces local signals . While the importance of these signals is unquestionable , systemic signals from other tissues are also required for stem cell regulation . However , it remains unclear whether and how crucial systemic signals influence stem cell behavior in response to environmental factors . Our present study demonstrated that midgut-derived NPF modulates GSC proliferation in response to mating stimulus . In mated female flies , SP signaling regulates NPF accumulation levels in EECs in the middle midgut . We showed that NPF acts on the ovary to control GSC proliferation via its receptor NPFR . Furthermore , NPF–NPFR signaling positively modulates Dpp signaling in GSCs to support symmetric GSC divisions . Our results reveal a mechanism of interorgan communication between the gut and the ovary that promotes mating-induced activation of gametogenesis . This is the first study to show that a gut-derived factor modulates GSC activity ( Fig 7 ) . In many animals , reproduction involves significant behavioral and physiological shifts in response to mating . In female Drosophila melanogaster , post-mating responses result from signals , especially SP , delivered by male seminal fluid during mating . Beyond the previously described SP-dependent post-mating changes [13 , 33 , 36 , 46] , our data imply that SP induces the post-mating release of NPF from EECs by silencing SPR-positive neurons . However , the molecular and cellular mechanisms by which SP–SPR signaling influences EECs remain unclear . One possibility is that juvenile hormones ( JHs ) transmit SP-dependent mating signals to the midgut . It has been reported that mating induces JH biosynthesis in an endocrine organ , the corpus allatum , in vivo [29] and that SP stimulates JH biosynthesis in the corpus allatum in vitro [47] . After mating , elevated circulating JH signals are received by the intestinal epithelium , leading to gut remodeling and an increase in gut size , which is required for reproductive success [29] . However , our data indicate that midgut RNAi ( in EECs and NPF/Tk/Dh31-positive EECs ) against the 2 known genes encoding JH receptors in Drosophila ( Methoprene-tolerant and germ cell-expressed bHLH-PAS ) does not affect mating-induced GSC proliferation ( S4C Fig ) . Therefore , NPF-dependent control of GSC proliferation appears to be independent of gut JH signaling . It is also unlikely that the midgut directly receives SP because overexpressing SP in EECs did not affect NPF accumulation in EECs ( S5A and S5B Fig ) . Consistent with these results , we did not detect anti-SPR immunostaining signals in EECs . It would be interesting to identify the specific humoral factor that conveys the SP-dependent neuronal signal to EECs . Another possibility is that nutrient intake after mating is the key in releasing NPF from EECs . Because SP signaling is important for the drastic increase in food intake by females after mating [48] , it is possible that SP promotes gut NPF secretion indirectly through some nutrient ( s ) from food . Notably , the presence of amino acids activates calcium signaling in some EECs in the posterior midgut [49] . In addition , sugar is known to affect a subpopulation of EECs , as activin-β in EECs is up-regulated by chronic high-sugar diets and acts on the fat body [50] . Alternatively , metal ions , such as copper ions , would also be interesting candidates because many NPF-positive cells are located in the stomach-like copper cell region that accumulates copper ions [51 , 52] . Moreover , NPF has already been identified as a regulatory neuropeptide in discriminating nutritional and food-related conditions [53 , 54] . Future studies examining whether nutrients can affect NPF release from midgut EECs to control mating-induced GSC proliferation will be worthwhile . Our findings support the notion that ovarian NPFR enhances BMP signaling in GSCs to promote their self-renewal . Transgenic RNAi and overexpression of NPFR driven by tj-GAL4 revealed that NPFR is necessary and sufficient for induction of female GSC proliferation . More remarkably , ex vivo cultures demonstrated that synthetic NPF peptide is sufficient to induce GSC proliferation in dissected ovaries from virgin females . However , we were unable to address which ovarian cell type expresses NPFR for controlling GSC proliferation . Even though we showed that NPFR transcripts are detected in dissected ovaries ( Fig 4C ) , we failed to observe any clear signals of digoxigenin-labeled NPFR RNA probes by in situ hybridization , whereas the same method was successfully applied for detecting NPFR mRNA in the head and midgut [38] . We speculate that this may be due to lower amounts of transcript in the ovary than in the head and midgut ( Fig 4C ) . Nevertheless , our analysis using several cell type–specific GAL4 drivers suggests that GSC proliferation requires NPFR acting in some combination of escort or follicle cells but not with germ and cap cells . Although cap cells act as the main niche component by producing the short-range Dpp ligand that ensures GSC self-renewal , escort cells also function in the GSC niche by producing Dpp to repress cystoblast differentiation [55 , 56] . In escort cells , the Hedgehog ( Hh ) and Janus Kinase ( JAK ) -Signal Transducer and Activator of Transcription ( STAT ) signaling pathways are important for GSC maintenance through activating expression of genes encoding BMP ligands [57–59] . Of these 2 pathways , Hh signaling appears particularly relevant in this context . It is well known that activation of Hh signaling results in stabilization and nuclear localization of the transcription factor Cubitus interruptus ( Ci ) [60] . Conversely , Hh signaling is negatively regulated by proteolysis of Ci following its phosphorylation by several protein kinases , including cAMP-dependent protein kinase ( PKA ) [61] . Notably , NPF–NPFR signaling negatively regulates adenylyl cyclase activity , leading to down-regulation of cAMP production [38] . Therefore , it is feasible to hypothesize that NPF–NPFR signaling may result in lower cAMP levels and reduced PKA activity , allowing Ci to persist within the nucleus , leading to enhanced Hh signaling in escort cells . Examining genetic interactions between NPF–NPFR signaling and Hh signaling in ovarian somatic cells is currently underway . It is also of interest to detect in vivo cAMP fluctuation by imaging with fluorescence resonance energy transfer ( FRET ) probes , such as Epac-based FRET sensors [62] , to examine which cells actually respond to NPF and observe whether Hh signaling is involved in NPF–NPFR–dependent GSC proliferation . Although NPF–NPFR signaling positively affects pMad levels in GSCs , it should be noted that there are both positive and negative roles for BMP receptors in NPF–NPFR–dependent regulation of mating-induced GSC proliferation . Our genetic data suggest that the BMP type-I receptor Sax positively regulates mating-induced GSC proliferation , while the other type-I receptor Tkv and the type-II receptor Put negatively regulate it . This complex situation is reminiscent of a recent finding that Tkv plays both positive and negative roles in female GSC proliferation in Drosophila [63] . In the latter case , Tkv proteins in escort cells sequester excess cap cell–produced Dpp , thereby reducing Dpp activity . Thus , it is possible that each subtype of BMP receptor may have a unique function in mediating NPF–NPFR signaling in different cell types of the germarium . In addition to ecdysteroids [44 , 64] and insulin [10 , 65] , we have identified NPF as a new essential humoral regulator for GSC proliferation and self-renewal . It will be important to investigate whether and how these endocrine signals reciprocally work in GSCs and the GSC niche . While previous studies demonstrated parallel regulation by ecdysone and insulin for GSC proliferation [14 , 44] , our study suggests that NPF–NPFR signaling requires ecdysteroid signaling to control GSC proliferation ( S7D Fig ) . Ecdysteroid signaling is known to be crucial for GSC maintenance that is dependent on intrinsic epigenetic machinery [64 , 66] . Thus , NPF–NPFR signaling may affect chromatin remodeling in GSCs to control mating-induced GSC proliferation . The question as to the effective dose of synthetic NPF peptide in adult female flies must be addressed . As shown in Fig 3E , the threshold concentration to induce GSC increase by injection is 3 pM of synthetic NPF peptide , and we estimate a single-fly injection amount of 100 nL . Assuming that the total amount of hemolymph per single female fly is approximately 1 μL , the injected NPF peptide should increase hemolymph NPF titers by approximately 0 . 3 pM . On the other hand , binding assays with isotope-labeled NPF in mammalian cells shows a half maximal inhibitory concentration ( IC50 ) of 65 nM NPF on NPFR [38] . Based on the pharmacological profiles of NPFR , in conjunction with the assumption that the peptide is rapidly degraded in the hemolymph [67] , the minimal effective amount of injected synthetic NPF peptide ( 0 . 3 pM ) seems to be very low . Coupled with a lack of published evidence on the actual chemical characteristics of NPF peptide in hemolymph , the uncertainty as to the exact fate of injected NPF and why our results were reproducible even at concentrations well below the theoretical threshold require further examination . Future studies must address how exogenous NPF peptide behaves in the animal after injection ( e . g . , which tissues NPF accumulates in or how long it is stable in the hemolymph ) , whether and how NPF in vivo is biochemically modified or complexed before interaction with the ovary , and the measurement of the actual amount of circulating NPF in virgin and mated females . In many animal species , NPY has a role in regulating reproduction . In planarians , neuronal neuropeptide Y-8 ( NPY-8 ) and neuropeptide Y receptor Y1 ( NPYR-1 ) signaling regulate germline development , including GSC differentiation [68] . In mammals , administration of NPY results in various effects on luteinizing hormone ( LH ) and gonadotropin-releasing hormone ( GnRH ) secretion , either stimulatory or inhibitory . Injection of NPY into ovariectomized and sex steroid-treated rats stimulates secretion of LH and GnRH [69 , 70] . Conversely , NPY suppresses the gonadotropic axis and delays sexual maturation in intact rats [71–73] . NPY also has a role in coordinating mammalian reproductive function and energy balance [74] . Although several studies have described the role of neuronal NPY signaling on reproduction , the role of intestinal NPY is poorly understood . Therefore , it would be of interest to explore the role of intestinal NPY on reproduction in the context of mammalian nutritional status .
Flies were raised on cornmeal-yeast-agar medium at 25°C; temperature-sensitive mutants were cultured at 29°C for 1 day prior to performing the assays . yw was used as the control strain . The mutant alleles NPFsk1 , NPFsk2 , sNPFsk4 , sNPFsk8 , and NPFRsk8 were created in a yw background using CRISPR/Cas9 as previously described [16] . The following guide RNA ( gRNA ) sequences were used: NPF , 5ʹ-GCCCTTGCCCTCCTAGCCGC-3ʹ; sNPF , 5ʹ-GTTGGGAAAGACGACGGGTC-3ʹ; NPFR , 5ʹ-GCGTGCGCTATCTGGACGAC-3ʹ . Breakpoint details of NPFsk1 , NPFsk2 , and NPFRsk8 are described in Fig 1 . The following transgenic and mutant stocks were used: nSyb-GAL4 ( Bloomington 51941 ) , elav-GAL4 ( Bloomington 8765 ) , 386Y-GAL4 ( Bloomington 25410 ) , Tk-gut-GAL4 [20] ( gift from Masayuki Miura , the University of Tokyo , Japan ) , Myo1A-GAL4 [75] ( gift from Kazutaka Akagi , National Center for Geriatrics and Gerontology , Japan ) , esg-GAL4 ( Bloomington 26816 ) , SPR-GAL4::VP16 ( see the section “Establishing the SPR-GAL4::VP16 strain” below in Materials and methods for details ) , tj-GAL4 ( Kyoto 104055 ) , e22c-GAL4 ( Kyoto 106609 ) , c587-GAL4 [76] ( gift from Hiroko Sano , Kurume University , Japan ) , bab1-GAL4 [77] ( gift from Satoru Kobayashi , University of Tsukuba , Japan ) , c306-GAL4 ( Bloomington 3743 ) , nos-GAL4 ( Kyoto 107748 ) , UAS-NPF [17] ( gift from Ping Shen , University of Georgia , USA ) , UAS-NPFR ( gift from Ping Shen ) , UAS-rpr; UAS-hid ( gift from Katja Brückner , University of California San Francisco ) , UAS-shits1 ( Bloomington 44222 ) , dj-GFP/CyO [78] ( Bloomington 5417 ) , Df ( 3R ) ED10642 ( Kyoto 150266 ) , Df ( 3R ) BSC464 ( Bloomington 24968 ) , dpphr56 [7] ( Bloomington 36528 ) , sax5 ( Bloomington 8785 ) , tkv1 ( Bloomington 427 ) , put135 [7] ( Bloomington 3100 ) , and SP0 [79] and SPΔ130 [79] ( gifts from Nobuaki Tanaka , Hokkaido University , Japan ) . Janelia GAL4 stocks [80] were obtained from the Bloomington Drosophila Stock Center: R41D08-GAL4 ( 45279 ) , R42C03-GAL4 ( 50148 ) , R46G06-GAL4 ( 41271 ) , and R50F10-GAL4 ( 45998 ) . RNAi constructs targeting NPFR ( KK107663 ) , gce ( KK101814 ) , and Met ( KK100638 ) were obtained from the Vienna Drosophila Resource Center ( VDRC ) [81] and lines targeting NPF ( 27237 ) , NPFR ( NPFR RNAi-2; 25939 ) , Tk ( 25800 ) , and Dh31 ( 41957 ) were obtained from the Bloomington TRiP collection [82] . Flies were reared at 25°C and aged for 5 to 7 days . Virgin female flies were mated overnight to yw male flies at 25°C ( 10 males and 5–10 females per vial ) . For egg-laying assays , individual female flies were transferred to a fresh chamber with cornmeal-yeast-agar medium , after which the eggs were counted manually . Ovaries and midguts were dissected in Grace’s supplemented insect medium ( Gibco ) and fixed in 4% paraformaldehyde in Grace’s medium for 30 to 60 minutes at room temperature ( RT ) . Fixed samples were washed 3 times in PBS supplemented with 0 . 1% Triton X-100 . After washing , the samples were blocked in blocking solution ( PBS with 0 . 1% Triton X-100 and 0 . 2% bovine serum albumin [BSA] ) for 1 hour at RT and then incubated with a primary antibody in blocking solution at 4°C overnight . Primary antibodies used in this study were mouse anti-Hts 1B1 [83] ( 1:50; Developmental Studies Hybridoma Bank [DSHB] ) , rat anti-DE-cadherin DCAD2 [84] ( 1:50; DSHB ) , rabbit anti-pH3 ( 1:1000; Merck Millipore ) , rabbit monoclonal anti-pMad ( 1:1000; Abcam ) , mouse anti-Lamin-C LC28 . 26 [85] ( 1:10; DSHB ) , rat anti-BrdU ( 1:50; Abcam ) , rabbit cleaved Dcp-1 ( 1:100; Cell Signaling Technology ) , rabbit anti-NPF ( 1:2000; provided by Ping Shen ) , rat anti-Vasa ( 1:1000; DSHB ) , and Alexa Fluor 546 phalloidin ( 1:200; Invitrogen ) . When anti-pMad antibody was used , the immunofluorescent signals were enhanced by Can Get Signal Solution B ( ToYoBo ) . After washing , fluorophore ( Alexa Fluor 488 or 546 ) -conjugated secondary antibodies ( Invitrogen ) were used at a 1:200 dilution , and the samples were incubated for 2 hours at RT in blocking solution . After another washing step , all samples were mounted in FluorSave reagent ( Merck Millipore ) . For BrdU incorporation , dissected ovaries were incubated in Grace’s medium containing 10 μM BrdU ( Sigma-Aldrich ) for 1 hour at RT , washed , and then fixed with 4% paraformaldehyde in Grace’s medium for 1 hour . Ovaries were denatured in 2 N HCl for 30 minutes , neutralized in 100 mM borax for 2 minutes , and then immunostained using mouse anti-BrdU ( 1:50; Abcam ) . GSC number was determined based on morphology and positioning of their anteriorly anchored spherical spectrosome [44] . Samples were visualized using a Zeiss LSM 700 confocal microscope or Zeiss Axioplan 2 . Images were processed using ImageJ software ( NIH ) . To quantify mating-induced changes in gene expression , the middle midguts from 5 to 10 adult female flies were dissected . Total RNA was extracted using RNAiso Plus reagent ( TaKaRa ) . cDNA was prepared with ReverTra Ace qPCR RT Master Mix with gDNA Remover ( ToYoBo ) . Quantitative reverse transcription PCR ( qRT-PCR ) was performed using the Universal SYBR Select Master Mix ( Applied Biosystems ) with a Thermal Cycler Dice TP800 system ( TaKaRa ) . Serial dilutions of a plasmid containing the open reading frame of each gene were used as standard . The amount of target RNA was normalized to ribosomal protein 49 ( rp49 ) and then relative fold changes were calculated . The following primer pairs were used to measure transcript level: rp49 forward , 5ʹ-CGGATCGATATGCTAAGCTGT-3ʹ and reverse , 5ʹ-GCGCTTGTTCGATCCGTA-3ʹ; NPF forward , 5ʹ-CTCCGCGAAAGAACGATGTCAACAC-3ʹ and reverse , 5ʹ-CCTCAGGATATCCATCAGCGATCCG-3ʹ; NPFR forward , 5´-GATCCTGTCCAAGTACTGGCCCTAC-3´ and reverse , 5´-ACGATCACCTGATATCTGTCGAAGGC-3´ . All qRT-PCR runs were performed in triplicate . To prepare the SPR-GAL4::VP16 line , we used a recombineering approach based on previously described methods [86] . To prepare a landing-site cassette , 5′ and 3′ homology arms were amplified from the GAL4/terminator gene of pBPGUw [87] and were used to flank the positive/negative selectable marker RpsL-kana [88] , conferring kanamycin resistance and streptomycin sensitivity . SPR-specific arms were added to this landing-site cassette by PCR using the following primers: SPR-F , 5ʹ-gaattaaggcagcgccaggggaatccgctcgagaaacccacgtccacgagATGAAGCTACTGTCTTCTATCGAACAAGC-3ʹ and SPR-R , 5ʹ-ttggtgtgcacactaaattatcgatataaacaacaagccatttaacttacGATCTAAACGAGTTTTTAAGCAAACTCACTCCC-3ʹ . These primers contained 50 bases matching the SPR locus ( in lower case ) and sequences corresponding to the GAL4 and terminator arms that were previously added ( in upper case ) ; note the underlined ATG of both SPR and GAL4 . This cassette was recombined into the bacterial artificial chromosome CH321-69P02 [89] ( obtained from the Children’s Hospital Oakland Research Institute ) , containing the SPR locus within 88 kb of genomic DNA sequence; recombinants were then selected on kanamycin medium . In a second round of recombination , the landing pad was replaced with full-length GAL4::VP16+terminators amplified from pBPGAL4 . 2::VP16Uw [90]; recombinants were identified by streptomycin resistance . Potential regulatory elements in flanking regions , upstream and downstream untranslated regions , and introns remained intact , although regions downstream of the target region are presumably no longer transcribed . The recombined regions were sequenced , and the finished bacterial artificial chromosome was integrated into the attP40 site by Rainbow Transgenic Flies ( Camarillo , CA ) . Fly injection was performed using a previously described technique [91] . NPF peptide amidated at its C-terminus ( SNSRPPRKNDVNTMADAYKFLQDLDTYYGDRARVRF-NH2 ) was synthesized by Eurofins Genomics . The synthetic NPF peptide was diluted in PBS to 3 pM . The NPF peptide solution was injected into the thoraces of virgin female flies chilled on ice . Although we could not control the exact amount of peptide solution for fly injection because of limitations imposed by our injection apparatus , we can roughly estimate the amount , which should be 100 nL . Injected flies were transferred into vials with standard fly food . Flies were cultured at RT in vials with or without males for 16 hours . Afterwards , female flies were dissected and immunostained to count GSC number . Synthetic Bm-sNPF peptide ( SPSRRLRF-NH2 ) [92] was a gift from Yoshiaki Tanaka ( National Agricultural and Food Research Organization , Japan ) . Adult females were cultured on standard medium and dissected in Schneider’s insect medium ( Gibco ) . Approximately 6 ovaries were transferred to a microcentrifuge tube containing 20 μL Schneider’s medium supplemented with 15% fetal calf serum and 0 . 6% penicillin-streptomycin with the addition of NPF peptide , sNPF peptide , or PBS . Cultures were incubated at RT for 16 hours , and then samples were immunostained to count GSC number . All experiments were performed independently at least twice . Fluorescence intensity in confocal sections was measured via ImageJ . For NPF quantification , an average of 10 cells were examined for each midgut . For pMad quantification , signal intensity was calculated by measuring the fluorescence intensity in GSCs and CBs , which were costained with anti-Vasa antibody to visualize their cell boundaries . Size of the posterior midgut in confocal sections was measured via ImageJ . Sample sizes were chosen based on the number of independent experiments required for statistical significance and technical feasibility . The experiments were not randomized , and the investigators were not blinded . All statistical analyses were carried out in the “R” software environment [93] . The P value is provided in comparison with control and indicated as *P ≤ 0 . 05 , **P ≤ 0 . 01 , ***P ≤ 0 . 001 , and “NS” for nonsignificant ( P > 0 . 05 ) . | Communication between different organs is essential to respond quickly to environmental cues or changes in the physiological status of an organism . Recent studies have shown the existence of humoral factors or hormones , which are transported by the circulatory system to different organs and achieve coordination between them . Here , we have analyzed the communication mechanism between organs that regulates proliferation of germline stem cells ( GSCs ) in the ovary of the fruit fly Drosophila melanogaster . We show that a peptide hormone called neuropeptide F ( NPF ) is a key player in this process . This peptide is produced in both the brain and the midgut , and , remarkably , we find that only NPF released from the midgut is crucial for controlling post-mating GSC proliferation . Our data suggest that mating stimulates the release of NPF from the endocrine cells of the midgut stimulated by the presence of a seminal peptide . Midgut-derived NPF is then transduced through an NPF-specific G-protein–coupled receptor expressed in the ovary , and this triggers GSC proliferation . Our study identifies an essential interaction between the digestive system and the ovary that regulates the size of stem cell populations in flies depending on mating . | [
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"epigeneti... | 2018 | Midgut-derived neuropeptide F controls germline stem cell proliferation in a mating-dependent manner |
Fatty acid ( FA ) metabolism is deregulated in several human diseases including metabolic syndrome , type 2 diabetes and cancers . Therefore , FA-metabolic enzymes are potential targets for drug therapy , although the consequence of these treatments must be precisely evaluated at the organismal and cellular levels . In healthy organism , synthesis of triacylglycerols ( TAGs ) —composed of three FA units esterified to a glycerol backbone—is increased in response to dietary sugar . Saturation in the storage and synthesis capacity of TAGs is associated with type 2 diabetes progression . Sugar toxicity likely depends on advanced-glycation-end-products ( AGEs ) that form through covalent bounding between amine groups and carbonyl groups of sugar or their derivatives α-oxoaldehydes . Methylglyoxal ( MG ) is a highly reactive α-oxoaldehyde that is derived from glycolysis through a non-enzymatic reaction . Glyoxalase 1 ( Glo1 ) works to neutralize MG , reducing its deleterious effects . Here , we have used the power of Drosophila genetics to generate Fatty acid synthase ( FASN ) mutants , allowing us to investigate the consequence of this deficiency upon sugar-supplemented diets . We found that FASN mutants are lethal but can be rescued by an appropriate lipid diet . Rescued animals do not exhibit insulin resistance , are dramatically sensitive to dietary sugar and accumulate AGEs . We show that FASN and Glo1 cooperate at systemic and cell-autonomous levels to protect against sugar toxicity . We observed that the size of FASN mutant cells decreases as dietary sucrose increases . Genetic interactions at the cell-autonomous level , where glycolytic enzymes or Glo1 were manipulated in FASN mutant cells , revealed that this sugar-dependent size reduction is a direct consequence of MG-derived-AGE accumulation . In summary , our findings indicate that FASN is dispensable for cell growth if extracellular lipids are available . In contrast , FA-synthesis appears to be required to limit a cell-autonomous accumulation of MG-derived-AGEs , supporting the notion that MG is the most deleterious α-oxoaldehyde at the intracellular level .
Deregulation of metabolism occurs in several pandemic human diseases whose incidence has dramatically increased due to changes in lifestyle and extended lifespan . These disorders include metabolic syndrome and type 2 diabetes ( T2D ) that are typified by insulin resistance and elevated levels of glucose and triacylglycerols ( TAGs ) in the plasma [1 , 2] . However , insulin resistance does not directly depend on an increase in TAG levels , but is rather a consequence of diacylglycerol and/or ceramides accumulation [1 , 3 , 4] , whose levels increase as adipose tissue reaches a saturating point [5 , 6] . Cancer cells also exhibit metabolic perturbations characterized in part by a dramatic increase in glycolysis and fatty acid ( FA ) synthesis [7 , 8] . These changes emphasize direct links between sugar catabolism and FA synthesis . Recent studies support the notion that glycation of proteins , DNA and/or phospholipids is likely to be responsible for the toxic effects induced by excess sugar [9 , 10] . The resulting compounds , advanced-glycation-end-products ( AGEs ) , maybe responsible for vascular complication , nephropathy and retinal degeneration in T2D patients [11 , 12] . Glycation is a spontaneous reaction that occurs between an amine group and a carbonyl group of sugars or α-oxoaldehydes [13] . The latter include methylglyoxal ( MG ) that largely derives from spontaneous oxidation of the glycolytic intermediates dihydroxyacetone-phosphate ( DHAP ) and glyceraldhehyde-3-phosphate ( G3P ) [14] . The glyoxalase system [15] , an enzymatic system composed of glyoxalase 1 ( Glo1 ) and glyoxalase 2 , maintains tolerable levels of MG . In healthy organisms , circulating glucose is taken up by cells and is used to produce energy through glycolysis and the citric acid cycle . In postprandial condition , dietary glucose is used to synthesize glycogen in the liver and muscles . Excess glucose is also used for FA synthesis in hepatocytes and adipocytes . Synthesis of FA first requires carboxylation of acetyl-CoA to malonyl-CoA by the enzyme ACC ( Acetyl-CoA carboxylase ) [16] . Next , the Fatty acid synthase ( FASN according to the current mammalian nomenclature ) sequentially incorporates several malonyl-CoA molecules onto an acetyl-CoA primer to form a long chain FA ( LCFA ) [17] . Drosophila genetics has proven a powerful model system to investigate metabolic regulation at the level of the organism [18 , 19 , 20] . We previously demonstrated that in larvae , ACC is cell-autonomously required for the synthesis and storage of TAGs in the fat body ( FB ) [21] , an insect organ with hepatic and adipose functions . We also provided evidence that within the oenocytes—abdominal cells with a hepatic-like function [22]—ACC is required to maintain the watertightness of the tracheal system [21] . Here , we have focused on the Drosophila FASN orthologs , of which only one ( FASNCG3523 ) is ubiquitously expressed . By directing inducible RNA-interfering ( RNAi ) to FASNCG3523 and glycogen synthase ( GlyS ) , we observed that the larval FB synthesizes both TAGs and glycogen . Next , we observed that expression of FASNCG3523 is induced by dietary sugar and that FASNCG3523 deficient animals are extremely sensitive to moderate increases in dietary sugar . Furthermore , we provide evidence that the activity of FASN and Glo1 cooperate both at the organismal and cellular level to protect against sugar toxicity .
To investigate the physiological consequences of FA synthesis defect in Drosophila , we focused on the ortholog of the anabolic enzyme FASN , encoded by three distinct genes ( FASNCG3523 , FASNCG3524 , FASNCG17374 ) [21] . Previous reports show that in larval tissues , FASNCG3523 is ubiquitously expressed , while FASNCG3524 and FASNCG17374 are mostly expressed in the carcass , which is comprised of epidermal cells , oenocytes and skeletal muscles [23] . To corroborate these findings , transcript levels of the three FASN genes were monitored using quantitative-PCR ( RT-Q-PCR ) in third stage larvae ( L3 ) separated in two fractions , the internal organs , which can be easily removed and the leftover carcass . Consistently , FASNCG3523 transcripts were detected at high levels in both fractions , whereas FASNCG3524 and FASNCG17374 transcripts were detected at high levels in the carcass , but minimally in the internal organs ( S1A Fig . ) . To determine whether these enzymes are essential , we made use of the binary Gal4/UAS system to direct specific RNAi to each FASN gene and to the ACC orthologue [21] . Ubiquitous knockdown of these genes caused lethality at late embryogenesis for ACC , at L1 stage for FASNCG3523 , at the L2 stage for FASNCG17374 , while no phenotype was observed for FASNCG3524 ( Table 1 ) . The lethality at the L2 stage observed in FASNCG17374-RNAi knockdown resembled the phenotype previously observed when inducing this RNAi using an oenocyte specific driver [21] , typified by a defect in the watertightness of the tracheal system ( S1B–S1C Fig . ) . Therefore , we have used a svp-gal80 transgene to inhibit Gal4 in the oenocytes [22] . Driving FASNCG17374-RNAi in the entire animal , except in the oenocytes , resulted in a total rescue of the lethal phenotype ( Table 1 and S1D Fig . ) , indicating that FASNCG17374 does not serve an essential function in other tissues . To get further insights into the organ-specific function of these enzymes , we used the Cg-gal4 and Mef2-gal4 drivers , which are specific to the FB and the muscles , respectively . When induced in either tissue , knockdown to any of either gene did not affect viability ( Table 1 ) . Nonetheless , muscle knockdown of ACC or FASNCG3523 , but not of FASNCG3524 or FASNCG17374 , led to a motility defect in adult flies ( Table 1 ) . Taken together , these findings indicate that the synthesis of LCFA is not essential in either the FB or the muscles . However , consistent with previous studies reporting that muscle-specific knockdown of ACC affects body homeostasis and motility of adult flies [24 , 25] , our findings indicate that FA synthesis plays an important role in muscle development and/or activity . We previously reported that FB-knockdown of ACC results in a decrease in total TAG levels [21] . To determine which of the three FASN members is necessary for LCFA synthesis in the FB , RNAi to each FASN gene was induced using the FB-specific driver . Consistent with the finding that FASNCG3523 is the only FASN gene expressed in internal organs ( S1A Fig . ) , total TAG levels were dramatically reduced in FASNCG3523 but not in FASNCG17374 and FASNCG3524 knockdowns ( S2A Fig . ) . We previously observed that in Cg>ACC-RNAi ( Cg-gal4 directing ACC-RNAi ) animals the drop in whole larvae TAG levels was accompanied by an increase in glycogen storage [21] . Thus , to investigate the physiological relationship between TAG and glycogen storage , RNAi transgenes to either ACC or FASNCG3523 was combined with an RNAi transgene to the gene encoding the unique Drosophila GlyS . Single or dual knockdowns were induced in either the FB , the muscles or in both tissues . Total amounts of TAG , glycogen , trehalose , glucose and protein were measured in 0–5h prepupae , as this is a convenient phase to stage the animals after the feeding period . Prepupal weighing revealed that animals expressing GlyS-RNAi in combination with either ACC-RNAi or FASNCG3523-RNAi in both the muscles and the FB exhibited the most prominent reduction in body weight ( S2B Fig . and S1 Table ) . Total TAG levels decreased dramatically when either ACC-RNAi or FASNCG3523-RNAi were induced in the FB but not in the muscles ( S2C Fig . ) ; this decrease was roughly similar when either RNAi were induced in both tissues ( S2C Fig . ) . Furthermore , TAG levels measured in control , ACC-RNAi- or FASNCG3523-RNAi-expressing animals were not markedly modified by the expression of the GlyS-RNAi ( S2C Fig . ) . Total glycogen levels decreased when GlyS-RNAi was expressed in either the FB or the muscles , and decreased further when GlyS-RNAi was expressed in both tissues ( S2D Fig . ) , indicating that in prepupae both organs contribute to glycogen storage . This finding contrasts with a previous study reporting that in larvae , glycogen can be detected in skeletal muscles only [26] . Therefore , since glycogen is unlikely to be transported between organs , it is conceivable that FB glycogen synthesis mostly occurs at late larval stage . Unexpectedly , driving either ACC-RNAi or FASNCG3523-RNAi in the muscles provoked a moderate decrease in glycogen levels ( S2D Fig . ) . This may be a consequence of muscle dysfunction linked to the above mentioned motility defect ( Table 1 ) . Importantly , FB-knockdown to ACC or FASNCG3523 provoked a very strong increase in total glycogen levels that is not observed when co-expressing GlyS-RNAi ( S2D Fig . ) , indicating that this extra-glycogen is synthesized inside the FB . Furthermore , we observed that trehalose levels ( S2E Fig . ) in part mirrored the variations observed with in glycogen ( S2D Fig . ) , as shown by a strong correlation ( S2F Fig . ) . Since energy stores are mobilized during metamorphosis , the decrease in trehalose levels might be a direct consequence of reduced glycogen breakdown . However , when ACC-RNAi or FASNCG3523-RNAi was expressed in the FB , glycogen but not trehalose levels increased dramatically ( S2D–S2F Fig . ) , suggesting that trehalose levels cannot be increased in the prepupae . Finally , neither glucose ( S2G Fig . ) nor protein ( S2H Fig . ) levels exhibited severe perturbation in any of the tested genotypes . Taken together these results indicate that at the end of larval life , glycogen accumulates in both the muscles and the FB , whereas TAGs accumulate mainly in the FB . Further , a reduction in TAG storage can , in part , be compensated for by an increase in glycogen storage . Considering that the synthesis of glycogen and TAG constitutes a metabolic mechanism to safely store high quantities of glucose , we hypothesized that the anabolic enzymes FASN , ACC and GlyS , are induced by dietary sugar . Therefore larvae were fed a low carbohydrate diet ( LCD ) or a sucrose-supplemented diet ( SSD ) . Using RT-Q-PCR , the expression of ACC , GlyS and the three FASN genes was monitored in larvae fed on 0% ( LCD ) , 5%- , 10%- and 20%-SSDs ( S2 Table ) . FASNCG17374 expression was insensitive to increases in dietary sugar , while the expression of all the other genes was enhanced by sucrose ( Fig . 1A ) . This response was observed following a 5%-SSD but was not further enhanced by 10%- and 20%-SSD , indicating that a moderate increase in dietary sugar elicits an adaptive metabolic response . Next , we wondered whether the synthesis of FA may protect against excess dietary sugar . Considering that the FB is the main storage organ , RNAi to ACC , FASNCG3523 , or GlyS were induced with the Cg-gal4 driver and the duration of larval development was monitored by following the onset of metamorphosis . When fed LCD , no developmental delay was observed in control , Cg>ACC-RNAi , Cg>FASNCG3523-RNAi or Cg>GlyS-RNAi larvae ( Fig . 1B ) . In contrast , when fed a 10%-SSD , the onset of metamorphosis was delayed by roughly two days in Cg>ACC-RNAi and Cg>FASNCG3523-RNAi larvae , while Cg>GlyS-RNAi larvae were only slightly delayed ( Fig . 1C and lines 1–3 , S3 Table ) . The effect was enhanced for larvae fed a 20%-SSD . Control larvae exhibited a 3-day delay , Cg>GlyS-RNAi larvae exhibited a 4-day delay , whereas Cg>ACC-RNAi and Cg>FASNCG3523-RNAi larvae exhibited approximately an 8-day developmental delay ( Fig . 1D and lines 4–6 , S3 Table ) . Together , these findings indicate that FA synthesis is a crucial metabolic pathway , which buffers the developmental defects induced by excess dietary sugar . To gain further insights into the physiological requirements of LCFA synthesis we generated FASN mutants . As shown above , FASNCG3523 is an essential gene ubiquitously expressed , whereas FASNCG17374 sustains the synthesis of an essential FA only within the oenocytes ( Table 1 and S1C–S1D Fig . ) . FASNCG3524 is not essential ( Table 1 ) and may be redundant with FASNCG3523 , as these two genes are in tandem on the second chromosome ( Fig . 2A ) and both are induced by dietary sugar ( Fig . 1A ) . Therefore , we took advantage of two FRT-containing P-elements , located within the FASNCG3524 and the FASNCG3523 genes ( Fig . 2A ) . Flipase recombination between the FRT sequences of these two P-elements resulted in a complete deletion of FASNCG3524 , hereafter referred to as FASNΔ24 . The resulting chimeric P-element links the 5’ region of FASNCG3524 to most of the FASNCG3523 genomic sequences ( Fig . 2A ) . To generate a null FASNCG3523 mutant , we performed a remobilization of the chimeric P-element and looked for imprecise excisions that remove part of the FASNCG3523 gene . 22 excisions were recovered , one of them ( hereafter referred to as FASNΔ24-23 ) removed 1200-bp of the FASNCG3523 gene ( Fig . 2A ) , including the first methionine codon and the sequence coding half of the β-ketoacyl synthase ( KS ) domain [17] . Both FASNΔ24 and FASNΔ24-23 are lethal at the L1 larval stage . RT-Q-PCR analysis showed that FASNCG3524 expression could not be detected in either mutants fed a lipid-supplemented diet ( Fig . 2B and see below ) . In addition , FASNCG3523 transcript levels were severely reduced in homozygous FASNΔ24 larvae and barely detectable in homozygous FASNΔ24-23 larvae ( Fig . 2B ) . Therefore , both mutations delete FASNCG3524 , however , FASNΔ24 appears to be a hypomorphic mutant and FASNΔ24-23 a null mutant for FASNCG3523 . To ascertain that the L1 lethality observed in both mutants was solely due to FASN deficiency , rescue experiments were performed , using UAS lines expressing either FASNCG3524 or FASNCG3523 cDNA . Ubiquitous overexpression revealed that FASNCG3524 cDNA could partially rescue the lethality of FASNΔ24-23 mutants to the pupal stage , although none emerged as adults ( S4 Table ) . In contrast , ubiquitous overexpression of FASNCG3523 cDNA did not rescue the lethal phenotype in either FASN mutants and induced embryonic lethality when driven with any of the ubiquitous gal4-lines tested ( S4 Table ) . However , one of the UAS-FASNCG3523 lines was able to partially rescue the lethal phenotype to pupal or adult stages in both mutants in the absence of gal4 drivers ( S4 Table ) . Consistently , RT-Q-PCR analysis revealed that FASNCG3523 but not FASNCG3524 transcripts were detected at high levels in both FASN mutant rescued animals ( Fig . 2B ) , indicating that an endogenous promoter could drive the expression of this UAS-FASNCG3523 transgene . These findings show that both FASNΔ24 and FASNΔ24-23 are bona fide mutants and suggest that FASNCG3523 protein levels should be maintained within a precise window of expression . To determine whether the lethal phenotype could be rescued by dietary lipids , a LCD was supplemented with lipids ( S2 and S5 Tables ) . Interestingly , supplementing a LCD with soy lipids could in part rescue the lethality of the hypomorph FASNΔ24 mutant to pupal or adult stages ( S5 and see below ) but not the lethality of the null FASNΔ24-23 mutant ( S5 Table ) . We therefore , supplemented a LCD with various dietary lipids , including oils , margarine , butter and egg yolk alone or in combination . In isolation , none of the dietary lipids could rescue lethality of FASNΔ24-23 mutants , although a few larvae grew and developed to the L2 or L3 stages ( S5 Table ) . In contrast , a LCD supplemented with butter and egg yolk ( beySD ) ( S2 Table ) could partially rescue lethality of both FASNΔ24 and FASNΔ24-23 mutants ( S5 Table ) . To evaluate the metabolic consequences of the FASN deletion , TAG , glycogen , trehalose and glucose levels were measured in the FASNΔ24-23 mutant and control prepupae fed a beySD . FASNΔ24-23 prepupae exhibited a net decrease in TAG levels ( Fig . 2C ) associated with a moderate increase in glycogen and trehalose levels ( Fig . 2D-E ) , whereas glucose levels were not significantly modified ( Fig . 2F ) . Then , we performed a detailed analysis of FA composition of the TAGs , the sterol esters , and the various phospholipid classes . This analysis revealed that the relative FA content of the various phospholipids was not significantly modified ( S3A–S3F Fig . ) . In each phospholipid class , palmitic acid ( 16:00 ) was always the most abundant FA component , although palmitoleic ( 16:01 ) , stearic ( 18:00 ) oleic ( 18:01 ) and linoleic ( 18:02 ) acids were also highly represented . In contrast , the relative FA content in the sterol ester and TAG classes significantly varied in FASNΔ24-23 mutants versus controls ( Fig . 2G-H ) . For the sterol ester class , oleic acid was less abundant in the mutants than in the control; however , this deficit was compensated for with higher levels of myristic ( 14:00 ) , myristoleic ( 14:01 ) , palmitoleic , linoleic ( 18:02 ) and arachidonic ( 20:04 ) acids ( Fig . 2G ) . For the TAG class , control prepupae contained a higher proportion of saturated lauric ( 12:00 ) , myrictic and palmitic acids , whereas mutants contained a higher proportion of unsaturated myristoleic , palmitoleic , oleic , linoleic and arachidonic acids ( Fig . 2H ) . Together , these findings suggest that dietary lipids provide phospholipid precursors in sufficient amounts to compensate for the loss of FASN . Further , the difference in the FA composition of the TAG class in mutant versus control animals suggests that the structure of the TAGs is not critical . Next , we investigated sucrose sensitivity in FASN mutant . Importantly , about 40% of the FASNΔ24 mutants fed a soy-lipid supplemented diet and of the FASNΔ24-23 mutant fed a beySD underwent metamorphosis onset ( S4A Fig . and Fig . 3A ) . As shown by standard deviation values the percentages of rescue was highly variable . Nonetheless , addition of 10% sucrose to either lipid supplemented diet , resulted in a total lethality at L1 stage for both FASN mutants ( S4A Fig . and Fig . 3A ) . These findings indicate that individuals that are unable to synthesized FAs are extremely sensitive to moderate increases in dietary sucrose . Moreover , less than half of the control larvae were able to pupariate when fed a lipid supplemented diet ( S4A Fig . and Fig . 3A ) . The lipotoxicity was markedly suppressed when beySD was supplemented with 10% sucrose ( Fig . 3A ) , possibly due to a reduction in the feeding rate ( see below ) . Since metabolic analysis is easier to perform on late rather than early larvae—which are very small— , a diet-shift protocol was established . FASNΔ24-23 mutant and control larvae were fed a beySD until the L2/L3 transition , transferred onto the same feeding media with or without 10% sucrose supplementation and left to develop 24h or 40h . First , to evaluate the feeding rate , larvae were transferred onto fresh media stained with brilliant blue FCF dye , and absorption of stained food was evaluated from whole larval extracts after one hour . Colorimetric measurement revealed that FASNΔ24-23 mutants contained much less food in their gut than control animals ( Fig . 3B ) , and that sucrose supplementation also reduced the stained food content in both FASNΔ24-23 and control larvae ( Fig . 3B ) . The lower gut content suggests that food uptake was reduced , although we could not exclude an increase in stool elimination . Next , levels of circulating sugars in larval hemolymph were measured . Interestingly , neither glucose nor trehalose levels increased in control larvae fed a 10%-sucrose supplemented beySD ( Fig . 3C-D ) , suggesting that this feeding protocol does not induce a diabetic-like phenotype . Nonetheless , FASNΔ24-23 mutants fed a beySD exhibited a moderate increase in trehalose levels ( Fig . 3D ) , while glucose levels remained unchanged ( Fig . 3C ) . In contrast , after 24h of feeding on a 10%-sucrose supplemented beySD , both glucose and trehalose levels were strongly increased ( Fig . 3C-D ) . Considering that increases in levels of circulating sugar is a hallmark of diabetes [27] , the insulin response was evaluated in the FB of larvae expressing a tGPH reporter [28] . FBs were dissected from larvae fed a beySD with or without a 10%-sucrose supplement , and membrane translocation of tGPH was analyzed after 20 mn incubation with or without insulin . When grown on either feeding media , both control and mutant FBs were highly responsive to insulin stimulation ( Fig . 3E-L and S4B Fig . ) indicating that neither the FASNΔ24-23 mutant nor control larvae exhibit a T2D-like phenotype when fed a 10%-sucrose supplemented beySD . Importantly , the membrane-GFP fluorescence induced by insulin stimulation was much higher in FASNΔ24-23 mutant than in control larvae ( Fig . 3F , H , J , L and S4B Fig . ) , suggesting that the former were hypersensitive to insulin . Together , our findings indicate that FASNΔ24-23 mutant animals are highly sensitive to dietary sugar but do not exhibit a T2D-like phenotype . Since an increase in AGEs is linked to high levels of circulating sugar in T2D patients [12] , we compared the amounts of AGEs in whole control or FASN mutant larvae . In L3 larvae transferred onto fresh beySD for 24h , the amounts of AGEs were higher in FASNΔ24-23 mutants than in controls . This was the case regardless or whether the beySD was supplemented with 10% sucrose ( Fig . 4A ) . In older L3 larvae transferred on fresh beySD for 40h , the amounts of AGEs were strongly increased in FASNΔ24-23 mutants compare to controls ( Fig . 4B ) . In addition , exposure to 10%-sucrose supplemented beySD further increased AGE levels in FASNΔ24-23 mutants ( Fig . 4B ) , suggesting that FA synthesis constitutes a metabolic pathway to restrict AGE accumulation . To further investigate the effects of dietary sucrose , we performed RNAi knockdown to two glycolytic enzymes encoded by single genes , Phosphofructokinase 1 ( PFK1 ) and Pyruvate kinase ( PK ) that catalyze an early and the last glycolytic steps , respectively ( Fig . 4C ) . FB-targeted knockdown to either PFK1 or PK did not result in a phenotypic defect in larvae fed a LCD , as developmental times did not differ markedly from controls ( S5A Fig . ) . However these larvae were very sensitive to sucrose . When fed a 10%-SSD , both RNAi-knockdown larvae exhibited a significant developmental delay ( S5B Fig . and lines 7–8 , S3 Table ) . Moreover , when fed a 20%-SSD , the developmental delay was further increased for Cg>PFK1-RNAi larvae , whereas most of the Cg>PK-RNAi animals died during larval life ( S5C Fig . and lines 9–10 , S3 Table ) . The difference in sucrose sensitivity suggests either that PK-RNAi induces a more efficient knockdown than PFK1-RNAi , or that some glycolytic intermediates produced downstream of the enzymatic step catalyzed by PFK1 are extremely toxic . Following the glycolytic step catalyzed by PFK1 , an Aldolase cleaves fructose 1 , 6 bisphophate ( Fru-1 , 6-BP ) in the trioses phosphate , DHAP or G3P . Either metabolite leads to pyruvate , or to the highly reactive glycating α-oxoaldehyde MG via a non enzymatic reaction ( Fig . 4C ) . We therefore used UAS-RNAi to the single glo1 ortholog that encodes an MG neutralizing enzyme . FASNCG3523-RNAi and glo1-RNAi were induced independently or together in the FB and the duration of larval development was monitored . When fed a LCD , glo1-RNAi larvae exhibited a moderate developmental delay ( Fig . 4D and line 11 , S3 Table ) . This developmental delay was slightly prolonged when fed a SSD , although not to the same extent as FASNCG3523-RNAi larvae , which were much more sensitive to dietary sucrose ( Fig . 4D-D” and lines 11 , 14 , 17 , S3 Table ) . Furthermore , animals dually expressing FASNCG3523-RNAi and glo1-RNAi in their FB exhibited a high rate of larval lethality and a developmental delay that dramatically increased concurrently with sucrose concentration ( Fig . 4D-D” and lines 12–13 , 15–16 , 18–19 , S3 Table ) . Conversely , FB-overexpression of Glo1 was able to partially compensate for the developmental delay induced by an increase dietary sugar ( Fig . 4E-E” and lines 20 , 22 , S3 Table ) . FB-overexpression of Glo1 was also able to partially suppress the strong developmental delay of FASNCG3523-RNAi larvae grown on SSD ( Fig . 4E-E” and lines 21 , 23 , S3 Table ) . In each assay , the percentage of pupae is relative to the number of their SM5-TM6B siblings ( see material and methods ) . Intriguingly , when fed a 20%-SSD , the ratio of UAS-glo1 larvae relative to the number of their SM5-TM6B siblings was higher than the control ratio , reaching a maximum at roughly 120% ( Fig . 4E” ) . Furthermore , we also observed that when testing homozygous w- control flies , the rate of larval lethality was significantly higher in 20%-SSD than in LCD or in 10%-SSD ( S5D Fig . ) . This observation suggests that in the Glo1-overexpressing assay , a significant number of the SM5-TM6B siblings underwent lethality when fed a 20%-SSD and that Glo1 overexpression suppresses this lethality . In contrast in the control assay all the larvae underwent the same rate of lethality irrespective of the SM5-TM6B balancers . Together , these findings indicate that sucrose toxicity can be alleviated by overexpression of Glo1 and conversely , the deleterious effects are exacerbated when both FA synthesis and Glyoxalase activity are simultaneously dampened . To determine whether a lack of FA synthesis induces cell-autonomous defects , we generated flip-out recombination during embryogenesis and analyzed the resulting clones in the FB of feeding larvae at the end of the L3 stage . Interestingly , the size of FASNCG3523-RNAi cells was almost normal in larvae fed a LCD , but drastically reduced in larvae fed a 20%-SSD ( S6A–S6B , S6M Fig . ) . A similar phenotype was observed for PK-RNAi flip-out cells ( S6C–S6D , S6M Fig . ) , although the size reduction observed in larvae fed a 20%-SSD , varied a lot depending on the experiment , possibly because of variability in RNAi efficiency . In contrast , PFK1-RNAi flip-out cells were insensitive to dietary sucrose since cell size remained unchanged irrespective of sucrose supplementation ( S6E–S6F , S6M Fig . ) . To perform genetic interactions at the cellular level , we generated MARCM clones either mutant ( FASNΔ24-23 ) or wild-type ( FASN+ ) . Firstly , the sucrose sensitivity of FASNΔ24-23 cells was evaluated in the FB of larvae raised on media containing increasing quantities of sucrose . For larvae fed a LCD , the size of FASNΔ24-23 cells was slightly reduced compare to neighboring control cells ( Fig . 5A , M ) . However , as the sucrose content in the diet increased , a concomitant reduction in the size of FASNΔ24-23 cells was observed ( Fig . 5B-D , M ) . This cell size reduction was not correlated with lipid content , as Nile red staining revealed that FASNΔ24-23 cells were severely depleted in LDs , irrespective of sugar supplementation ( S6G–S6I Fig . ) . Next , we generated FASNΔ24-23 MARCM clones expressing PFK1-RNAi or PK-RNAi . Under these conditions , the size of FASNΔ24-23 cells , expressing either RNAi was hardly reduced in larvae fed a LCD ( Fig . 5E , G , N ) . However , in larvae fed a 20%-SSD , the size of FASNΔ24-23 cells remained unaffected when expressing PFK1-RNAi ( Fig . 5F , N ) , but were dramatically reduced when expressing PK-RNAi ( Fig . 5H , N ) . The phenotypic suppression produced by PFK1-RNAi , suggests that an intermediate metabolite , downstream of PFK1 ( Fig . 4C ) , is responsible for the size reduction of FASNΔ24-23 cells observed in SSD-fed larvae . Therefore , MARCM clones , expressing glo1-RNAi were analyzed . Interestingly , MARCM FASN+ clones expressing only the glo1-RNAi were insensitive to sucrose supplementation ( Fig . 5I-J , O ) . Nonetheless , FASNΔ24-23 cells expressing glo1-RNAi exhibited an extreme size reduction in larvae fed LCD ( Fig . 5K , O ) accompanied by a severe decrease in nucleus size ( see below ) . Furthermore , these clonal cells could not be observed when larvae were fed 20%-SSD , suggesting that these cells were eliminated during development . Conversely , FASNΔ24-23 MARCM cells overexpressing glo1 were of normal size in larvae fed a 20%-SSD ( Fig . 5L , P ) . However , neither FASNΔ24-23 MARCM cells in LCD-fed larvae , nor FASN+ MARCM cells were affected in size by Glo1 overexpression ( S6J–S6L , S6M Fig . ) . Together , these findings indicate that Glo1 can compensate for cell size reduction due to a sugar-dependent FA-synthesis defect , but is unlikely to promote cellular growth . Finally , an antibody to MG-derived AGEs ( MG-AGEs ) was used for immunostaining . In FASNΔ24-23 clonal cells the amounts of MG-AGEs were barely detectable in larvae fed a LCD ( Fig . 6A-C ) but were dramatically increased in larvae fed a 20%-SSD ( Fig . 6D-F ) . Importantly , increased MG-AGE levels induced by 20%-SSD were abolished in FASNΔ24-23 MARCM clones expressing either PFK1-RNAi ( Fig . 6G-I ) or UAS-glo1 ( Fig . 6J-L ) . Furthermore , in larvae fed a LCD , FASNΔ24-23 clones expressing glo1-RNAi exhibited a strong accumulation of MG-AGEs ( Fig . 6M-O ) . Nucleus size in these clones , was also dramatically reduced ( Fig . 6O , O’ ) . Taken together , these findings indicate that FA synthesis and Glyoxalase activity cooperate in a cell-autonomous manner to neutralize the toxicity of dietary sugar , which may result in cellular growth defects or putative cell elimination .
In this study , we investigated the role of FA synthesis in regulating homeostasis in response to dietary sugar . To maintain tolerable levels of circulating sugars , organisms synthesize and store macromolecules in appropriate organs . In contrast to previous studies in insects , which report that the majority of TAGs stored in the FB are of dietary origin [29 , 30] , we observed that in Drosophila , the larval FB is a lipogenic organ . However , in FASNΔ24-23 mutant fed a beySD , TAG levels were decreased but not abolished . This indicates that as in mammalian hepatocytes and adipocytes [2 , 31 , 32] , TAGs stored in the Drosophila larval FB originate from either food assimilation or de novo synthesis . Together , our findings confirm that metabolic pathways act within an integrative network to maintain homeostasis and support the notion that in term of post-feeding macromolecules storage ( TAGs and glycogen ) , the Drosophila larval FB constitutes an alternative model for mammalian liver and adipose tissue ( Fig . 7A ) . Our FASN mutants are lethal at L1 stage , and this lethality can be recued by a beySD . Rescue of FASNΔ24 but not of FASNΔ24-23 mutants by soy lipid extracts likely reflects the strength of the mutation since FASNCG3523 is still weakly expressed in the hypomorphic mutant . Consistently a SREBP mutant that down-regulates but does not abolished the expression of several FA anabolic enzymes including FASNCG3523 , could also be rescued by soy lipid extracts [33] . Rescue of the lethal phenotype by dietary lipids , as well as the minor phenotype observed in FASNΔ24-23 clonal cells , suggests that neighboring cells or organs can provide FAs to those that are deficient . This may be achieved through lipophorin activity [34] . Intriguingly , we found that in contrast to other lipid-supplemented media , a mix of butter and egg yolk could rescue the FASNΔ24-23 lethal phenotype . TAGs cannot be directly assimilated by enterocytes; first they require digestive lipases to cleave TAGs to di-acyl-glycerol ( DAG ) , mono-acyl-2-glycerol ( MAG ) and free FAs ( FFAs ) [35 , 36] . In several mammalian species , lipids interact with bile acids to form micelles prior to enzyme cleavage and enterocyte absorption . However , it has been reported that in rats and human infants , FFAs may interact with calcium or magnesium ions to form soaps that are hardly assimilated [37 , 38 , 39] . In insects , lipid emulsifiers are poorly characterized although glycolipid or amino acid complexes are likely to be involved in lipid assimilation [40 , 41] . As egg yolk lipoproteins are highly efficient emulsifiers [42] , they may help solubilize lipids , thereby favoring their absorption . The composition in FAs and their positions on the glycerol backbone vary depending on the origin of the TAGs . Regarding FA synthesis , FASNΔ24-23 mutants are expected to lack palmitic acid . Analysis of various oils and fats , revealed that TAGs found in butter contain high quantities of palmitic acid in position sn-2 of the glycerol [36] . Thus , assimilation of MAGs resulting from butter digestion , are high in palmitic acid . Hence , it is possible that MAGs are better assimilated than FFAs in our FASNΔ24-23 mutants . In order to fully understand the process of lipid absorption in FASNΔ24-23 mutants , extensive analysis , including the precise measurement of ingested and excreted FAs , will be required . Rescue of lethality of FASN mutant by a lipid-supplemented diet indicates that FA synthesis deficiency can be compensated for by an appropriate lipid diet . Previous studies in Drosophila have reported that the FA composition of the various lipid classes varies depending on the diet [43 , 44] . Here , we show that the relative FA composition of phospholipids is not significantly different in FASNΔ24-23 rescued mutants and control animals fed a beySD . These findings not only confirm that diet contributes to phospholipid composition , but reveal that in the presence of an exogenous lipid supply , the essential FASN enzyme becomes dispensable for phospholipid synthesis . In contrast , sterol esters and TAGs exhibit variation in their FA composition . Compared to controls , TAGs from mutants contain less saturated FAs and more long chain unsaturated FA , suggesting that expression of desaturases and elongases [45] may be increased in FASN mutants . The high variability in TAG composition suggests that TAG structure is not a crucial parameter , which strengthens the notion that TAG synthesis constitutes a metabolic strategy to neutralize the potential toxicity of nutrients . Previous studies suggested that the fat tissue fulfills a protective role against excess sugar . In agreement with this , it has been shown that fat transplantation in lipoatrophic mice reverses T2D [46 , 47] and that in genetically induced obese mice , a decrease in adipose FASN expression is linked to T2D progression [48] . In addition , mice and flies with defects in ChREBP—a transcriptional activator of lipogenic enzyme expression—do not survive increases in dietary sugar levels [44 , 49 , 50] . However , it was unknown that FASN activity also protects against sugar toxicity . This finding is in contrast to a previous report which showed that in flies , lethality induced by ubiquitous expression of FASNCG3523-RNAi can be partially rescued by dietary sugar [50] . Here , we demonstrate that FA synthesis protects against dietary sugar at both a systemic and cell-autonomous level . The media used in our study contained low concentrations of digestible sugar , 64 , 164 and 264 mg/ml for the LCD , 10%-SSD and 20%-SSD , respectively . In other studies , which used Drosophila larvae as a model for sugar tolerance , the concentration of digestible sugar was 86 , 140 or 80 mg/ml for the low carbohydrate media and 377 , 380 or 230 mg/ml for the sugar enriched media [27 , 50 , 51] . Importantly , while circulating sugar levels increase in FASNΔ24-23 animals , these mutants do not exhibit a T2D-like phenotype and become insulin hypersensitive . Therefore , as previously suggested [44 , 49 , 50] , disrupting FA synthesis provides a convenient model to investigate the effect of glucotoxicity independent of lipotoxicity . Here , we provide evidence to propose that FASN and Glo1 cooperate both in a systemic and in a cell-autonomous manner to protect against the deleterious effect of dietary sucrose . Our findings indicate that when FA synthesis is very active , as in the FB of Drosophila larvae , Glo1 activity is dispensable in term of neutralizing the few toxic metabolites produced through sugar catabolism ( Fig . 7A ) . Conversely , the detoxifying activity of Glo1 becomes critical when FASN activity is disrupted in the larval FB ( Fig . 7B ) . Thus , the observed decrease in lipogenic enzyme expression in the adipose tissue of a diabetic mouse model [48] , may require an increase in Glo1 activity . If lipogenic enzyme expression is also decreased in T2D patients , the increase in glycating agents [52] may result not only from an increase in circulating sugar but also from a decrease in FA synthesis . For a few decades , pathological damage induced by excess sugar was thought to be a consequence of AGE formation [53] , a paradigm substantiated by recent studies on experimental diabetic nephropathy [9 , 10 , 54] . Consistent with a study in Caenorhabditis elegans , reporting that Glo1 overexpression protects against glucose toxicity [55] , we show that manipulating Glo1 levels in the larval FB modulate a sugar-induced developmental delay . Studies in diabetic models and patients mostly focused on AGE levels in body fluids [56 , 57 , 58 , 59] , although alterations to intracellular products have also been reported [60 , 61 , 62] . At the cellular level , glo1 knockdown in FB cells induces a cell-autonomous phenotype , only when clones are also FASN deficient . This phenotype results in either an extreme reduction in cell size or elimination of cells , when larvae are fed LCD or SSD , respectively . The number of FB cells is determined during a proliferative phase at embryogenesis . During larval life , FB cells do not divide , but undergo a rapid cell growth phase [63 , 64] . The lack of a visible phenotype in glo1-deficient cells indicates that even when larvae are fed SSD , Glo1 does not affect the growth process of FB cells . In contrast , increasing quantities of sucrose in the food , even to moderate levels , induces a size reduction of FASN mutant cells . This phenotype is unlikely to depend directly on sugar since addition of moderate amounts of sucrose to food media does not markedly increase circulating sugar levels . In contrast , it is likely to directly depend on an increase of intracellular MG , since the cell size reduction is suppressed if FASN mutant cells are either deficient in PFK1 or overexpressing glo1 cDNA . In summary , our findings suggest that FASN activity is dispensable in sustaining cell growth but plays a key role in protecting against the potentially toxicity of MG produced through glycolysis . In conclusion , we have demonstrated that FA synthesis constitutes a metabolic strategy to restrict the production of intermediate toxic molecules , suggesting that obesity is not a harmful process , as long as storage capacity is not overwhelmed . Furthermore , our study highlights the need for caution when using FA synthesis inhibitors to treat cancers and metabolic diseases , as they might provoke negative side effects .
Fly strains: P[tGPH] [28] , daughterless ( da ) -gal4 , Mef2-gal4 , actin5C>CD2>gal4 , UAS-GFP , P[w[+mC] = tubP-GAL80]LL10 , P[ry[+t7 . 2] = neoFRT]40A , UAS-Dcr-2 ( Bloomington Stock Center ) ; Inducible RNA-interfering ( UAS-RNAi ) lines to ACC ( VDRC 108631 ) , FASNCG3523 ( VDRC 29349 ) , FASNCG3524 ( VDRC 4290 ) , GlyS ( VDRC 35136 ) , glo1 ( remobilized on chromosome III from VDRC 26832 ) , PFK1 ( VDRC 3017 ) , PK ( remobilized on chromosome III from VDRC 49533 ) ; FASCG17374-RNAi , svp-gal80 , Cg-gal4 [21] . The P-element insertions ( Exelixis collection ) P[XP]v ( 2 ) k05816d04154 and P[XP]CG3523d06961 were used to generate deficiency as described [65] . All the fly lines were isogenized from single males in a white1118 mutant ( w- ) background . For clonal analysis FASNΔ24-23 was over SM5-TM6B , Tb- balancers [21]; For survival and metabolic analyses , FASN mutants were balanced by a CyO GFP-labelled chromosome . The results presented for ubiquitous or tissue-targeted UAS-RNAi lines—including the corresponding controls—were obtained with a UAS-Dcr-2 that strengthens the RNAi effect . Developmental delays were evaluated from overnight egg collection and the number of prepupae formed was counted every morning . For each assay , several tubes were collected , overcrowded tubes were discarded and the numbers of prepupae were pooled . As some of the transgenes used in the genetic combinations were homozygous lethal , all the lines ( driver , RNAi , w- control ) were balanced with co-segregating SM5-TM6B , Tb- balancers that lead to non-mendelian offspring distribution . Therefore , for each assay , the number of RNAi-expressing Tb+ larvae was divided by the final number of Tb- larvae and all assays were normalized to the control ratio . For controls , a similar calculation was done from the offspring of driver females crossed with w-;SM5-TM6B males and this control ratio was adjusted to reach 100% . To generate the overexpressing lines , the locus of FASCG3523 , and FASCG3524 were recovered by gap repair and the endogenous promoter replaced by a UAST [66] . glo1 cDNA was amplified from GH24818 ( DGRC ) and cloned into the pUAST vector . Plasmid constructs were injected by BestGene . RT-Q-PCR were performed as previously described [21] using the following primers: FASNCG3523 ( 5’-F CTTCTTCATTTCCCCGA-3’ and 5’-CGAAGGAGTATCCGGC-3’ ) FASNCG3524 ( 5’-CTTTGACAATATGCTCTAC-3’ and 5’-AAGTCCGGAGTGTCCAG-3’ ) FASNCG17374 ( 5’-F ATCAGCTCCAACCTCTAC-3’ and 5’-GGGCTACATGCAAGTCT-3’ ) ACC ( 5’-TTGGGAAACTCATTCGTG-3’ and 5’-CCAGGACCTTGGCATTA-3’ ) GlyS ( 5’-CCCCTCATACTACGAGC-3’ and 5’-CGATATAGCGGCGATCC-3’ ) Flip-out clones were performed as described [21] . MARCM clones [67] were heat-shock induced at 4–6h embryogenesis and the larvae were allowed to grow on various sucrose-supplemented media until mid/late L3 stage . FB were dissected as described [21] but fixed with 3 . 7% formaldehyde in PBT ( PBS 0 . 1% Tween20 ) . FB were stained with Phalloidin-Rhodamine B ( sigma ) at 625 ηg/ml for 2h at RT , extensively washed and mounted in DABCO ( sigma ) . Relative cell size was expressed as a ratio , clonal:neighboring control cells , which was estimated using the image-j software . The insulin responsive assay was performed as described [27] . tGPH quantification was measured in squares ( 10X10 pixels ) positioned either at the membrane or at the nuclei . Measurements for each assay were recorded from 8 cells taken from 2 different FBs . For each cell , the maximum fluorescence intensity at the membrane was divided by the maximum fluorescence intensity at the nucleus and the mean ratio was plotted ( S4B Fig . ) . Nile Red staining was performed as previously described [21] . For MG-AGE Immunostaining , dissected FB were fixed as described above , but blocked for 20 mn in PBS containing 0 . 1% Triton X100 and 2% bovine serum albumin . Samples were incubated overnight at 4°C with diluted ( 1:400 ) MG-AGE antibody ( Cell Biolabs ) , extensively washed and incubated for 2h at room temperature with secondary antibody and DAPI in the blocking solution . Samples were finally washed in PBT and mounted in DABCO . Image acquisitions were obtained using a Nikon TE2000-U or a Leica SP8 confocal laser-scanning microscopes . TAGs , protein , glucose , trehalose and glycogen measurements were performed as previously described [21] . To measure circulating sugar , 6μl samples of hemolymph were collected from 20 to 30 bled L3 larvae . For AGE measurement , 5 samples of 10 L3 larvae were washed in PBS and crushed at 4°C in a Precellys 24; extracts were cleared 10 mn at 4° C in a microfuge at maximum speed . Extracts were diluted 100X in PBS and 100 μL of this diluted extract were treated with an ELISA kit ( Cell Biolabs STA-317 ) . AGEs estimation evaluated from spectrophotometric dosage at 450nm , was normalized to the protein concentration of each sample . To measure feeding rates , food media was tinted with 0 , 1% brilliant blue FCF . 3 samples of 10 L3 larvae were collected , frozen , extracted in 200μl water and centrifuged for 7min at maximum speed . The final volume was adjusted to 800μl and measured at 629ηm . Lipidomics were performed in triplicates of 100 mg 0–5h prepupae . Lipids were extracted and analyzed by GC-MS as described [68] . Statistical analyses were performed with R version 3 . 0 . 2 [69] . Error bars in figures stand for empirical standard deviations measured independently from the replicates in each category . Significance for the statistical tests was coded in the following way based on the p-values: ***: 0 < p < 0 . 001; **: 0 . 001 < p < 0 . 01; *: 0 . 01 < p < 0 . 05 . In all the graphs , the error bars represent the standard deviations . For S3 Table ( corresponding to Fig . 1C , 1D , 4D , 4E , and S5A–S5C Fig . ) , the effect of the genotype was tested with one-way ANOVAs on developmental rates . Developmental rates ( in units of days-1 ) were computed as the inverse of developmental duration to pupation . Lethality ( evaluated for each developmental curve and corrected with the lethality rates for control measured in S5D Fig . ) was accounted for by including a corresponding number of ( unobserved ) lethal events ( individuals with a developmental rate of 0 ) . Since all nine ANOVAs detected a significant effect of the genotype , pairwise comparisons between genotypes were tested with a post-hoc Tukey "Honest significant difference" test [70] for each sub-figure , and the biologically-relevant comparisons are reported . | Consumption of sugar and lipid ( fat ) enriched food increases the risk of developing metabolic diseases and cancers . However , lipids are essential molecules for life , as they are the major components of cell membranes . Metabolism refers to biochemical reactions that transform nutrients into molecules required by an organism , although toxic by-products can also formed . Sugars or their derivatives are likely to induce toxic effects by forming stable conjugates with proteins . To neutralize their toxic potential , sugars are metabolized and stored as fat . Here , we have used the fruitfly model to investigate the consequences of lipogenesis deficiency upon ingestion of sugar-enriched diets . We show that lipogenesis deficient animals are dramatically sensitive to dietary sugar . Further , we have identified the sugar by-product responsible for intracellular toxicity , in the context of lipogenesis inhibition . Our study reveals that inhibiting lipogenesis does not disrupt cellular growth if extracellular lipids are available . In contrast lipogenesis inhibition may have deleterious consequences due to accumulation of toxic by-products . The efficacy of lipogenic inhibitors in fighting cancers and metabolic diseases is currently under investigation . Therefore , to evaluate the clinical benefit of these inhibitors , accumulation of the toxic molecules should be monitored in both sick and healthy cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Fatty Acid Synthase Cooperates with Glyoxalase 1 to Protect against Sugar Toxicity |
The polymerase chain reaction ( PCR ) and nucleic acid sequence-based amplification ( NASBA ) have been recently modified by coupling to oligochromatography ( OC ) for easy and fast visualisation of products . In this study we evaluate the sensitivity and specificity of the PCR-OC and NASBA-OC for diagnosis of Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense human African trypanosomiasis ( HAT ) . Both tests were evaluated in a case-control design on 143 HAT patients and 187 endemic controls from the Democratic Republic of Congo ( DRC ) and Uganda . The overall sensitivity of PCR-OC was 81 . 8% and the specificity was 96 . 8% . The PCR-OC showed a sensitivity and specificity of 82 . 4% and 99 . 2% on the specimens from DRC and 81 . 3% and 92 . 3% on those from Uganda . NASBA-OC yielded an overall sensitivity of 90 . 2% , and a specificity of 98 . 9% . The sensitivity and specificity of NASBA-OC on the specimens from DRC was 97 . 1% and 99 . 2% , respectively . On the specimens from Uganda we observed a sensitivity of 84 . 0% and a specificity of 98 . 5% . The tests showed good sensitivity and specificity for the T . b . gambiense HAT in DRC but rather a low sensitivity for T . b . rhodesiense HAT in Uganda .
Human African trypanosomiasis ( HAT ) is an important public health problem that affects rural populations of sub-Saharan Africa . Previous estimates indicated an annual incidence of about 70 , 000 cases [1] , [2] . The number of cases in 2006 in the Democratic Republic of the Congo ( DRC ) and Uganda was recently reported to be 11382 and 486 , respectively [3] , but given the lack of accurate reporting the actual number of cases is probably higher . The disease in DRC is exclusively linked with Trypanosoma brucei gambiense that causes the chronic form of HAT . Uganda represents a unique case as it is the only country reporting both the chronic and the acute form of HAT in hitherto non-overlapping foci . The acute form is caused by T . b . rhodesiense and may claim the patients in just a few weeks . Following infection , trypanosomes multiply mainly in the lymph and/or blood ( haemolymphatic stage ) . Over time , the parasites cross the blood-brain barrier and invade the central nervous system ( neurological stage ) . HAT is almost invariably fatal if left untreated and major efforts to control the disease rely on vector and reservoir suppression ( both human and animal ) . For the latter , chemotherapy is the mainstay but unfortunately relies on few drugs with unacceptable toxicity and high relapse rates in some foci [4] , [5] . In addition to the search for new trypanocidal compounds , current efforts to overcome the problem of drug resistance involve rational use of existing drugs , such as the recently reported nifurtimox-eflornithine combination therapy ( NECT ) [6] . Control of HAT is challenged by unsatisfactory diagnostics , although they play a central role in the decision to treat affected individuals and in disease control . The card agglutination test for trypanosomiasis ( CATT ) [7] is extensively used in screening for Trypanosoma brucei gambiense although it may miss cases where the infecting trypanosomes do not express the LiTat 1 . 3 variable antigen type on which it is based [8] , [9] . For a similar reason , most T . b . rhodesiense infections can not be detected by the CATT . To date , a field applicable screening test for T . b . rhodesiense HAT has remained elusive , despite attempts using procyclic trypanosomes [10] , [11] . Hence , definite diagnosis is based on microscopic demonstration of trypanosomes in the blood , lymph or cerebrospinal fluid . However , conventional microscopy exhibits a low sensitivity and is therefore often combined with prior parasite concentration such as the haematocrit centrifugation technique ( HCT ) [12] and the miniature anion exchange centrifugation technique ( mAECT ) [13] . Despite these innovations , up to 30% of cases are still missed [14] leaving an undetected human reservoir . Molecular methods for diagnosis of HAT are increasingly gaining attention as possible ways to overcome the problem of low sensitivity of the current parasite detection methods . Recently , two innovative tests for T . brucei detection have been developed , the PCR-Oligochromatography ( OC ) [15] and the NASBA-OC [16] . Both tests are based on nucleic acid amplification followed by simple and rapid detection of the amplified products by dipstick . While PCR-OC amplifies a short sequence within the 18S ribosomal RNA ( rRNA ) gene by thermal cycling , NASBA-OC is based on isothermal amplification of the 18S rRNA itself . Both tests showed promising diagnostic accuracy during the phase I evaluation studies [15] , [16] as well as satisfactory repeatability and reproducibility in a multi center evaluation study ( Mugasa et al . , submitted ) . The aim of the presented study was to evaluate the sensitivity and specificity of the two tests in a case control study in Uganda and DRC .
Participant recruitment and specimen collection was coordinated by the Institut National de la Recherche Biomédicale ( INRB , Kinshasa ) in DRC and by Makerere University ( Kampala ) in Uganda . Ethical clearance for the study was obtained from the relevant institutional ethical committees in DRC ( Ministry of Health ) , Uganda ( Ministry of Health ) and Belgium ( University of Antwerp ) . Written consent was obtained from the study participants or their parents/guardians in presence of independent witnesses . During this prospective study carried out between 2006 and 2008 , T . brucei gambiense HAT patients were recruited from Dipumba hospital in Mbuji-Mayi ( Kasai-Oriental , DRC ) . Healthy endemic control persons were recruited from the same region and from volunteers at the University of Kinshasa ( DRC ) . In Uganda , T . b . rhodesiense HAT patients and healthy endemic control persons were recruited at Namungalwe health centre ( Iganga district , Eastern Uganda ) and Serere health centre ( Soroti district , Northeastern Uganda ) . Individuals were included in the study if 12 years old or more , not in critical condition and if the informed consent was given . A patient was classified as HAT if parasites were observed in the blood , lymph or cerebrospinal fluid ( CSF ) . Both patients in the early ( haemolymphatic ) and late ( neurological ) stage were included . Individuals were classified as healthy endemic controls if they had no history of HAT , no clinical signs suggestive for HAT , were negative by CATT on whole blood and if no trypanosomes were observed in the blood . The CATT was executed on all participants in DRC and on the endemic controls in Uganda [7] . When a positive CATT result on whole blood was observed , the CATT was repeated with plasma diluted 1/8 as described by Simarro and co-workers [17] . When positive , the individual was subjected to parasitological detection by direct examination of wet smears from lymph node aspirates , Giemsa stained blood smears ( only for T . b . rhodesiense ) , the HCT [12] and/or mAECT [13] . Staging of the disease was done by detecting parasites in the CSF by modified single centrifugation [18] and/or by white blood cell ( WBC ) counting using the Fuchs-Rosenthal chamber or disposable counting chambers ( Uriglass , Menarini ) . Patients with more than 5 cells per µl CSF and/or with parasites in the CSF were considered late stage HAT . Data collection , analysis and reporting were done in consideration of the “Strengthening the Reporting of Observational Studies in Epidemiology” ( STROBE ) statement [20] . The sensitivity and specificity of PCR- and NASBA-OC were calculated from data entered into contingency tables . Sensitivity was defined as the proportion of HAT cases that are positive by the index test and specificity as the proportion of controls that are negative by the index test . Differences in sensitivity and specificity between the two tests were estimated by the Mc Nemar test and differences among centers were estimated with the Fisher exact test . Agreement between the two tests was determined using the kappa index . A kappa index ranges from 0 to 1 and the higher the value the stronger the agreement . All calculations were estimated at a 95% confidence interval ( 95% CI ) .
In the study 68 T . b . gambiense , 75 T . b . rhodesiense HAT patients and 187 healthy endemic controls were recruited ( table 1 ) . Out of the 68 T . b . gambiense cases , 17 showed parasites in the blood , 28 in the lymph ( of which 13 were blood negative ) , 60 in the CSF ( of which 34 were blood and lymph negative ) . Out of the 75 T . b . rhodesiense cases , 73 showed parasites in the blood and 53 in the CSF ( of which 2 were blood negative ) . No lymph node aspirates were examined in T . b . rhodesiense cases . An overview of the sensitivity and specificities of both index tests on the blood specimens of the participants recruited in the study is presented in Table 2 . Trypanosomes were observed in the blood of 90 out of 143 stage I and II patients . The blood of 117 of the 143 was positive by PCR-OC indicating a sensitivity of 81 . 8% ( 95% CI of 74 . 7–87 . 3% ) , while we observed an overall sensitivity of 90 . 2% ( 95%CI: 84 . 2–94 . 1% ) for NASBA-OC . Of the 187 healthy endemic controls , 6 showed a positive PCR-OC result and 2 a positive NASBA-OC result yielding an overall specificity of 96 . 8% ( 95% CI: 93 . 2–98 . 5% ) and 98 . 9% ( 95% CI: 96 . 2–99 . 7% ) , respectively . While the difference in specificity of both tests was not significant , the Mc Nemar test indicated a significant difference in sensitivity ( P<0 . 05 ) . Out of the 68 patients from DRC , 56 were positive by PCR-OC on blood indicating a sensitivity of 82 . 4% ( 95% CI: 71 . 6–89 . 6% ) . NASBA-OC was positive on blood from 66 of the 68 patients yielding a sensitivity of 97 . 1% ( 90 . 0–99 . 2% ) which is significantly higher than the sensitivity of PCR-OC ( P<0 . 05 ) . In only 17 of the 68 patients had parasites been observed in the blood by the reference standard tests . Sixteen out of these 17 patients were positive by PCR-OC ( 94% , 95% CI: 73%–99% ) while all 17 were positive in NASBA-OC ( 100% , 95% CI: 82%–100% ) . Of the 47 patients that were parasitologically negative in blood but positive in lymph or CSF , 36 were positive by PCR-OC and 45 with NASBA-OC . Four patients did not undergo microscopic examination of the blood ( trypanosomes observed in other tissues ) but were all positive by PCR-OC and NASBA-OC . Of the 75 patients from Uganda , 73 had parasites in the blood and 2 only in the CSF as determined by the reference tests . Sixty one of the 75 patients were positive by PCR-OC ( 81 . 3% 95% CI: 71 . 1–88 . 5% ) and 63 by NASBA-OC ( 84% , 95% CI: 74 . 1–90 . 6% ) , which is not significantly higher ( P>0 . 05 ) . Of the 2 stage II patients with negative parasite detection results in blood , 1 was positive by PCR-OC and both by NASBA-OC . One of the 122 endemic controls from DRC and 5 of the 65 endemic controls from Uganda were positive by PCR-OC . Hence , the specificity of the test was 99 . 2% ( 95% CI: 95 . 5–99 . 9% ) and 92 . 3% ( 95% CI: 83 . 2–96 . 7% ) , on the specimens from DRC and Uganda respectively . The NASBA-OC was positive on 1 endemic control from DRC and 1 from Uganda indicating a specificity of 99 . 2% ( 95% CI: 95 . 5–99 . 9% ) and 98 . 5% ( 91 . 8–99 . 7% ) for DRC and Uganda respectively . These 2 endemic controls were also positive in PCR-OC . There was no significant difference in specificities between PCR-OC and NASBA-OC ( P>0 . 05 ) . Considering all 330 specimens analysed in this study ( 143 cases and 187 endemic controls ) , the two tests exhibited a kappa value of 0 . 85 ( 95% CI: 0 . 74–0 . 95 ) . In DRC , the two tests showed a kappa value of 0 . 88 ( 95% CI: 0 . 74–1 . 02 ) and in Uganda a kappa value of 0 . 8 ( 95% CI: 0 . 63–0 . 96 ) . The Fisher exact test indicated a significant difference ( P<0 . 05 ) between the sensitivity of NASBA-OC on specimens from DRC and from Uganda , and between the specificities of the PCR-OC for the two countries . When we compared results on the specimens collected in the two health centers in Uganda , we observed no significant difference in the specificity of both tests but a significant difference in sensitivity .
This paper reports on the phase II evaluation of T . brucei PCR-OC and NASBA-OC , two innovative molecular tests for the diagnosis of HAT [15] , [16] . The PCR-OC showed a sensitivity of 82 . 4% on blood from 68 T . brucei gambiense HAT patients from DRC , while the sensitivity on blood from 75 T . brucei rhodesiense HAT patients from Uganda was 81 . 3% . The sensitivity of the test on blood from the Congolese HAT patients is promising , since most patients were in the neurological stage and parasites were observed in the blood of only 17 patients by the reference tests . One of these 17 patients showed a negative PCR-OC result although parasites were detected in the blood by the HCT [12] on 2 capillaries . The detection threshold of the HCT and PCR-OC were estimated at 500 trypanosomes [21] and 5 trypanosomes per ml of blood [15] respectively . As conventional parasite detection is usually highly specific , the negative PCR-OC test result might be due to loss of DNA quality during specimen transport , storage or nucleic acids extraction . Nevertheless , the observation that 36 out of 47 patients whose blood was parasitologically negative were positive by the PCR-OC indicates higher sensitivity of the test compared to conventional parasite detection on blood . The 81 . 3% sensitivity of the PCR-OC on the 75 T . brucei rhodesiense HAT patients was unexpectedly low , given the fact that parasites were observed in the blood of 73 of the 75 patients . Furthermore , T . brucei rhodesiense infections are generally linked with acute HAT and higher parasite load in patient blood . The hypothesis that the PCR-OC is less sensitive on T . brucei rhodesiense than on T . brucei gambiense is unlikely since we expect the DNA target copy numbers in the two subspecies to be in the same range . The observed low sensitivity on the HAT cases from Uganda could have been loss of DNA or DNA quality due to unsuccessful sample storage or DNA extraction . These samples were collected over a period of two years , nucleic acids being extracted and frozen as they were delivered from the rural treatment centers to the central laboratories . DNA quality could have been checked by amplifying a part of the human ß-globin gene [22] but this could be biased by the much higher number of human cells in the specimen . Batch to batch variation of the PCR-OC is implausible since extensive quality control was performed on the test kits prior to dispatch to trial centers . This highlights a weak point of our study , namely the lack of external quality control on a subset of specimens at a central reference laboratory . In addition , test executors were not blinded to the participant classification and we did not apply subspecies-specific PCRs to confirm the presence of T . b . gambiense and T . b . rhodesiense in the clinical specimens of the HAT cases . Although the HAT patients in Uganda were recruited in T . b . rhodesiense areas , infection with T . b . gambiense can not be fully excluded since both subspecies are present in this country . Another limitation of the study was that each specimen was tested only once with each index test . However , both assays have proven to be repeatable and reproducible in a multicentre evaluation study comprising 9 different laboratories ( Mugasa et al . submitted ) . The PCR-OC was positive for one of the 122 endemic controls from DRC and 5 of the 66 endemic controls from Uganda . These might be true HAT cases since the sensitivity of the CATT is not 100%; confirmed T . brucei gambiense HAT patients with negative CATT have been reported [8] , [9] , [23] , while an accurate serological test for T . b . rhodesiense HAT remains elusive . However , neither DNA contamination during nucleic acid extraction or PCR , nor cross-reaction of the test with DNA from other organisms can be excluded , although such cross-reactions were not observed during the phase I evaluation [15] . The observed higher sensitivity of the NASBA-OC is not unexpected , given that this assay targets the 18S ribosomal RNA ( rRNA ) while the PCR-OC targets the 18S rRNA gene . It has been documented that the 18S rRNA is present in approximately 10 , 000 copies , at least 100 times more that the 18S rRNA gene copy number [24] . Indeed , in a study to compare quantitative assays , van der Meide et al . [25] observed that the RNA amplifying assays such as NASBA and real time reverse transcriptase PCR detect lower parasite loads compared to real-time PCR . However , in line with the PCR-OC results , the sensitivity of the NASBA-OC on the T . b . rhodesiense HAT specimens is surprisingly low and significantly lower than the sensitivity on the T . b . gambiense HAT specimens . Given the general high parasite load in the blood of T . b . rhodesiense HAT patients , defects in specimen processing , storage and/or transportation are more likely to have contributed to the observed sensitivity than a lower diagnostic performance of the assay for T . b . rhodesiense . Although comparisons between foci should be critically made since specimen collection and DNA extraction was done by different persons and in different laboratories , further evaluation studies may clarify the observed discrepancy in sensitivity on both subspecies . The NASBA-OC showed a higher specificity on the endemic controls compared to PCR-OC . This might indicate that DNA contamination during specimen processing is more likely the cause of the low specificity of PCR-OC than the presence of true HAT cases among the endemic controls . Recently , Lutumba and colleagues estimated the effectiveness of the best current diagnostic algorithm for T . b . gambiense HAT at 80% ( quantified in terms of the number of lives saved ) [26] . Hence , the observed sensitivities of PCR-OC and NASBA-OC are probably higher than each of the current parasite detection tests used alone and could improve this effectiveness . However , one should bear in mind that the PCR-OC and NASBA-OC are not yet an option for routine diagnosis at the primary care level as they require basic molecular biology laboratory facilities [27] . HAT typically affects rural populations in sub-Saharan Africa where health centers most often suffer from infrastructural limitations and thus only apply less sophisticated diagnostic methods . Yet , these standardized molecular test formats can be valuable tools in disease surveillance and epidemiological studies in which specimens are analysed at central reference laboratories . In recent years , the loop-mediated isothermal amplification ( LAMP ) has emerged as another isothermal amplification technique for the detection of T . brucei nucleic acids [28] , [29] . The LAMP , being isothermal , is similar to NASBA but amplifies DNA instead of RNA and might thus be less prone to effects of specimen degradation during transport and storage . Although the diagnostic accuracy of LAMP on clinical specimens still has to be proven , the technique is promising and comparative evaluation of NASBA and LAMP on the same patients and controls would be useful . In this study , we could not evaluate the PCR-OC and NASBA-OC for disease staging as CSF specimens were not included in the analysis with the index tests . Hence , further evaluations of the tests for disease diagnosis and staging are required . In conclusion , the sensitivity and specificity of the PCR-OC and NASBA-OC were successfully evaluated in a case-control study in DRC and Uganda . The tests showed good sensitivity and specificity for T . b . gambiense HAT but a rather low sensitivity for T . b . rhodesiense HAT in Uganda . | Diagnosis plays a central role in the control of human African trypanosomiasis ( HAT ) whose mainstay in disease control is chemotherapy . However , accurate diagnosis is hampered by the absence of sensitive techniques for parasite detection . Without concentrating the blood , detection thresholds can be as high as 10 , 000 trypanosomes per milliliter of blood . The polymerase chain reaction ( PCR ) and nucleic acid sequence-based amplification ( NASBA ) are promising molecular diagnostics that generally yield high sensitivity and could improve case detection . Recently , these two tests were coupled to oligochromatography ( OC ) for simplified and standardized detection of amplified products , eliminating the need for electrophoresis . In this study , we evaluated the diagnostic accuracy of these two novel tests on blood specimens from HAT patients and healthy endemic controls from D . R . Congo and Uganda . Both tests exhibited good sensitivity and specificity compared to the current diagnostic tests and may be valuable tools for sensitive and specific parasite detection in clinical specimens . These standardized molecular test formats open avenues for improved case detection , particularly in epidemiological studies and in disease diagnosis at reference centres . | [
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] | 2010 | Phase II Evaluation of Sensitivity and Specificity of PCR and NASBA Followed by Oligochromatography for Diagnosis of Human African Trypanosomiasis in Clinical Samples from D.R. Congo and Uganda |
Vaccination is highly effective in preventing various infectious diseases , whereas the constant threat of new emerging pathogens necessitates the development of innovative vaccination principles that also confer rapid protection in a case of emergency . Although increasing evidence points to T cell immunity playing a critical role in vaccination against viral diseases , vaccine efficacy is mostly associated with the induction of antibody responses . Here we analyze the immunological mechanism ( s ) of rapidly protective vaccinia virus immunization using mousepox as surrogate model for human smallpox . We found that fast protection against lethal systemic poxvirus disease solely depended on CD4 and CD8 T cell responses induced by vaccination with highly attenuated modified vaccinia virus Ankara ( MVA ) or conventional vaccinia virus . Of note , CD4 T cells were critically required to allow for MVA induced CD8 T cell expansion and perforin-mediated cytotoxicity was a key mechanism of MVA induced protection . In contrast , selected components of the innate immune system and B cell-mediated responses were fully dispensable for prevention of fatal disease by immunization given two days before challenge . In conclusion , our data clearly demonstrate that perforin-dependent CD8 T cell immunity plays a key role in MVA conferred short term protection against lethal mousepox . Rapid induction of T cell immunity might serve as a new paradigm for treatments that need to fit into a scenario of protective emergency vaccination .
The most effective approach to prevent infectious diseases caused by viruses is vaccination . During the period of rational vaccine development , immunogenicity and the efficacy of vaccines were evaluated in terms of their ability to induce virus-specific antibodies . More recently however , the focus has shifted to considering the importance of cellular immune responses . In fact , vaccine-induced T cell immunity might be crucial to overcome some viral diseases . Viruses such as influenza virus or HIV are highly versatile in changing their envelope antigens to escape the host antibody response . Thus , induction of robust T cell immunity is believed to be the key to achieving successful immunization against AIDS , or enabling cross-protective capacities in next generation influenza vaccines [1]–[3] . Moreover , T cells are being recognized as playing an important role in the control of certain viral infections such as human cytomegalovirus diseases [4] , [5] . Surprisingly however , there is very limited data about the contribution of T cell immunity to protection provided by any licensed viral vaccine . Even today , as in the case of influenza vaccines , most applications for marketing approval only assess the potency and efficacy of candidate vaccines using antibody correlates [6]–[8] . Moreover , regarding the immunological requirements for protective vaccination at times close to viral infection our knowledge is very limited , perhaps with exception of rabies where antibodies induced by post exposure vaccination are well known to prevent the disease and death [9] , [10] . Vaccinia virus ( VACV ) is one of the most successful vaccines in human medicine . Vaccination of live VACV provided efficient protection against human smallpox , resulting in worldwide eradication of this devastating infectious disease [11] . Today , the development of new VACV vaccines is important due to the increasing emergence of zoonotic diseases caused by orthopoxviruses [12] , and the potential misuse of these viruses as agents of bioterrorism [13] . One promising VACV vaccine candidate is based on the highly attenuated virus strain modified VACV Ankara ( MVA ) [14]–[16] . MVA has also been developed as a non-replicating viral vector to construct experimental recombinant vaccines against various infectious diseases [17]–[22] . Immunizations with MVA in animal models proved highly efficacious when compared to conventional VACV vaccines , and elicited antigen-specific humoral and cellular immune responses [19] , [23]–[30] . Mass immunization with VACV during the smallpox eradication program indicated a critical role for cellular immune responses , since severe complications could occur in patients with T cell deficiencies [31]–[33] . However very little information is available on the role of cell-mediated immunity in protective VACV vaccination , although more recent analyses suggest that humans maintain VACV specific T cells for decades after vaccination [34]–[36] . There is more historical evidence correlating protection against smallpox with VACV neutralizing antibodies [37] , [38] . Indeed , recent studies with orthopoxvirus challenge infections in animal models support the supposed protective role of antibody responses elicited by VACV immunization [39] , [40] . Interestingly , very comparable levels of VACV neutralizing antibodies are found after MVA or conventional VACV ( Dryvax ) immunization [27] . In addition , MVA immunization can induce VACV specific antibody responses slightly earlier than conventional VACV ( Elstree/Lister or Dryvax ) in mice or non-human primates [39] , [41] . Moreover , VACV vaccination of mice and macaques can result in full protection from lethal disease if administered shortly before or even after infection with virulent orthopoxviruses [39] , [41]–[43] . In such cases rapid protection was associated with relatively high doses of vaccine , which may elicit earlier induction of antigen-specific immunity [39] , [41] , [43] . Here , we used ectromelia virus ( ECTV ) infections of mice , probably the best surrogate animal model for human smallpox . Our aim was to analyze the immunological mechanism ( s ) of the early protective capacity conferred by MVA immunization . Surprisingly , we found that rapid protection against lethal systemic poxvirus disease , as mediated by vaccination with MVA or conventional VACV , is solely dependent on the cellular adaptive immune response with an important role of CD4 and CD8 T cells , and perforin mediated cellular cytotoxicity . In contrast , the humoral response seems to be fully dispensable in providing early protection . Our data clearly demonstrate that T cell immunity plays a key role in the protective capacity of vaccination with a gold standard live viral vaccine . In general , the rapid induction of robust T cell responses might be of great importance for developing vaccines that need to meet the demands of protective emergency vaccination .
Intranasal ( i . n . ) application of MVA vaccine can rapidly induce robust protection against lethal respiratory orthopoxvirus infections [41] , [43] . However , the mechanisms of protective immunity still need to be elucidated . In particular , only limited information about local immune responses in the respiratory tract is available . Recent data suggest that in the vaccinated host MVA is recognized via multiple sensor pathways , which results in the activation of innate immunity , including the synthesis of type I interferons ( IFN ) and chemokines . These innate responses will trigger attraction of immune cells to the site of immunization , which might be responsible for the rapid development of protective adaptive immunity [44]–[47] . In agreement with these data , our histopathological analysis of C57BL/6 mice two days after i . n . inoculation with MVA revealed a marked peribronchiolar and perivascular infiltrate of leukocytes in the infected lung sections compared to lung sections of mock inoculations with PBS ( Figure S1 ) . Histopathological inspection at higher magnification clearly showed neutrophil granulocytes and macrophages among the infiltrate ( Figure S2 ) . We further characterized the infiltrated leukocytes by bronchoalveolar lavages ( BAL ) at different time-points after i . n . administration of MVA . During the first 72 hours post infection ( h p . i . ) the infiltrates mainly consisted of monocytes , dendritic cells ( DC ) , neutrophils , and NK cells ( Figure S3 ) , suggesting that the lung environment is highly favorable for antigen presentation and induction of adaptive responses . Indeed , we were able to observe increasing amounts of T and B cells in the BAL fluids of MVA exposed animals at later time points of infection ( Figure 1 ) . We detected CD4 and CD8 positive ( + ) T cells starting 48 h p . i . , which increased in numbers to 20 . 5% ( CD4+ ) and 41 . 5% ( CD8+ ) of total BAL cells on day 6 p . i . ( Figure 1A ) . To monitor VACV-specific CD8+ T cell responses we used the Kb-restricted immunodominant determinant TSYKFESV from the VACV B8 protein being referred to as B8R20–27 [48] The immunodominance of B8R20–27 has been shown to be conserved for various orthopoxviruses including ECTV and VACV , and even in mice lacking IFN-γ or perforin [48] , [49] . In MVA , the B8R open reading frame lacks some nucleotides compared to the B8R gene sequence of conventional VACV strain Lister/Elstree and encodes for a truncated B8 polypeptide . Importantly , MVA is expected to produce a fully conserved N-terminal part of the B8 protein containing the peptide epitope B8R20–27 and very similar expression levels of this specific B8R product were found for MVA and conventional VACV strain Elstree ( Figure S4 ) . When performing intracellular cytokine staining for interferon gamma ( IFN-γ ) we found proportionally high numbers of activated VACV ( B8R20–27 epitope ) -specific CD8+ T cells in BAL liquids by day 5 p . i . , but comparatively lower numbers of VACV-specific CD8+ T cells in the spleen . This pattern was also observed by day 7 p . i . , with about three times higher numbers of specific CD8+ T cells in BAL than in the spleen ( Figure 1B ) . When determining B220+ CD3- B cells in the BAL cell population we could detect the presence of substantial numbers of B cells at day 6 p . i . ( Figure 1C ) . Additionally , we also monitored for the presence of MVA-specific antibodies in BAL fluids at days 3 , 6 and 8 after i . n . inoculation with MVA . We initially detected low levels of MVA-specific IgG by day 6 , but antibody levels increased by day 8 p . i . ( Figure 1D ) . Altogether , these results indicate that early local immune responses induced after in vivo MVA inoculation are characterized by powerful innate responses , including the migration of massive amounts of innate immune cells to the site of infection . Moreover , antigen-specific cellular and humoral immune responses were also rapidly induced within the respiratory tract , suggesting that the rapid protection provided by MVA may be due to a close interplay between innate and adaptive immunity . To elucidate the role of selected components of the innate and adaptive immune system on the rapid protection , we performed MVA immunization experiments in the C57BL/6 mouse/ECTV challenge model [41] . Briefly , we i . n . inoculated mice with 108 PFU MVA two days before a lethal respiratory infection with ECTV ( 200 PFU corresponding to 3×LD50 ) ( Figure 2A ) . This immunization fully protected the animals from disease and death after ECTV infection ( P = 0 . 0001 ) . In contrast , unvaccinated control mice started to show signs of morbidity ( body weight loss ) at about 6 days after challenge infection , and all died within 11 days ( Figure 2A ) . Moreover , analysis of the viral loads in liver and lungs of vaccinated animals demonstrated full viral clearance at day 21 post infection ( data not shown ) . Recognition of invading pathogens by host cells is considered essential for activating innate and adaptive immune responses . Indeed as mentioned above , we confirmed a strong early activation of local innate immunity upon intranasal immunization ( Figure S3 ) . Also in previous experiments with IFNAR−/− mice lacking the type I IFN receptor , we had found a somewhat lesser protective capacity of MVA vaccination [41] , indicating a role of sensing pathways that mediate recognition signals . This sensing is mainly achieved via Toll-like receptors ( TLRs ) and retinoic acid-inducible gene-I ( RIG-I ) -like receptors ( RLRs ) [44] . Here , upon MVA i . n . inoculation of C57BL/6 mice we did indeed detect interleukin-6 ( IL-6 ) and interferon-alpha ( IFN-α ) in BAL fluids within 6 hours and 24 hours p . i . ( Figure S5 ) suggesting TLR and RLR dependent immune activation . We therefore performed the MVA immunization/ECTV challenge experiments in MyD88/Trif−/− mice lacking TLR signaling ( Figure 2B ) , and IPS−/− mice lacking RLR signaling ( Figure 2C ) . Clearly , the absence of either TLR or RLR signaling had no influence on the protective capacity of immunization with MVA ( P = 1 ) , confirming that several innate signaling cascades and innate immune cells can respond to MVA immunization . These likely compensate for each other in mediating sufficient immune activation to provide rapid protection . NK cells are known to play a major role in mediating resistance to ECTV infections in mice [50]–[52] and indeed , we found NK cells infiltrating the lungs of mice early after i . n . MVA inoculation ( Figure S3D ) . To examine the role of NK cells in rapid protection we removed NK cells by antibody-mediated depletion . Absence of NK cells on the day of MVA immunization ( day -2 ) was confirmed by FACS analysis ( Figure S6A ) . We observed no difference in the protective capacity acquired by NK cell-depleted mice compared to controls ( Figure 2D; P = 1 ) , indicating that the protective effect of short-term immunization with MVA is fully maintained even in the absence of NK cells . Previous work had already indicated that adaptive immunity contributes to MVA-induced protection against lethal ECTV infection [41] . Furthermore , passive immunization with vaccinia immune globulin around the day of infection fully protects mice against lethal mousepox [53] , indicating that antibodies are the important players in adaptive immunity . Indeed , we showed above that specific antibodies are found in the respiratory tract early after intranasal inoculation with MVA ( Figure 1D ) . To further analyze the role of antibody responses we performed experiments to assess the rapid protective capacity of MVA vaccination in RAG-1−/− mice lacking mature T and B cells , and in B cell-deficient μMT mice [54] . Confirming previous data , vaccinated RAG-1−/− mice ( Figure 3A ) all succumbed to the ECTV challenge infection , confirming the importance of adaptive responses in rapid protection . However , MVA immunization robustly protected B cell-deficient mice from disease and death ( Figure 3B; P<0 . 0001 ) . For further confirmation , we tested an alternative strain of B cell-deficient mice [55] , JHT , where we also found full protective capacity of MVA immunization ( Figure S7 ) . Thus surprisingly , B cells and antibodies seem to be dispensable for this rapidly induced protective immunity . The data so far indicated that T cells might play a crucial role in short-term protective immunity . Analysis of VACV specific CD8+ T cell responses elicited by MVA immunization in JHT mice showed levels of VACV specific CD8+ CD62low T cells comparable to those induced in C57BL/6 wt mice ( Figure S8 ) . Thus , B cell-deficient mice are still able to mount specific T cell responses , further corroborating that T cell responses might be important for short-term protection . Thus , we depleted C57BL/6 mice of CD4+ T cells , CD8+ T cells or both T cell subsets by i . p . injection of specific antibodies , and confirmed successful depletion by FACS analysis at the time point of MVA immunization ( day 0 ) and at day 7 after vaccination ( Figure S6B , C; data not shown ) . Control C57BL/6 mice were again fully protected by MVA immunization two days prior to the lethal respiratory challenge infection with ECTV ( Figure 4A ) . In contrast , vaccinated mice depleted of CD8+ T cells , or both T cell subsets ( Figure 4B , C ) succumbed to ECTV infection , with similar disease pattern and time to death as compared to unvaccinated animals . On the other hand , depletion of CD4+ T cells ( Figure 4D ) prior to MVA immunization resulted in delayed onset of disease , since the start of striking body weight loss occurred about six days after the onset of symptoms in unvaccinated controls . Nevertheless , CD4+ T cell depletion in immunized animals also resulted in 100% mortality within 21 days post challenge . These data clearly suggested that both CD4+ and CD8+ T cells are required to rapidly protect mice by MVA vaccination . To assess the need for CD4+ T cells in some more detail , we analyzed CD4-depleted C57BL/6 mice for defects in mounting VACV-specific antibodies or CD8+ T cells following MVA immunization . Lack of CD4+ T cells resulted only in a minor reduction of IgG antibody responses as revealed by ELISA testing of sera at day 21 post vaccination with 108 PFU MVA ( i . n . ) . The CD4 cell depleted mice were clearly able to mount levels of VACV-specific antibodies ( mean titer of pooled sera 1280 ) that were just about two fold lower than responses obtained in control mice ( data not shown ) . To study the impact of CD4+ T cell depletion on MVA induced CD8+ T cell responses we monitored for the expansion of endogenous CD8+ T cells specifically recognizing the B8R20–27 epitope ( TSYKFESV ) by FACS analysis using a TSYKFESV-Kb pentamer ( ProImmune ) ( Figure 5 ) . Upon inoculation of C57BL/6 mice with 2×105 PFU MVA , B8R-specific T cells massively expanded and reached a maximum of approximately 20% of total CD8+ T cells at day 6 after infection . On the contrary , MVA-vaccinated CD4+ depleted mice showed a significantly reduced CD8+ T cell expansion reaching less than 10% of total CD8+ T cells ( Figure 5A ) . This data suggested that CD4+ T cells seem to play an important role in regulating the strength of the MVA induced CD8- T cell response . To further confirm the relevance of T cell immunity in rapid protection by MVA immunization we adoptively transferred naïve CD3+ splenocytes , which comprise mainly T cells , into RAG-1−/− mice . After two days these mice were vaccinated with 108 PFU MVA and challenged two days later with ECTV . Indeed , the transfer of T cells prior to vaccination was fully sufficient to protect RAG-1−/− mice against lethal infection , whereas control RAG-1−/− mice all died despite MVA immunization ( Figure 6; P = 0 . 0003 ) . Interestingly , control RAG-1−/− showed a slight delay in onset of disease upon MVA immunization ( Figure 3A , Figure 6 ) which cannot be observed in mice depleted of CD4+ and CD8+ T cells ( Figure 4 ) . This effect may be due to elevated NK cell numbers in non-lymphoid tissue of RAG-1−/− mice [56] that might transiently compensate for the lack of T and B cells . T cells seemed to play a dominant role in rapid protection mediated by MVA . CD4+ and CD8+ T cells exert different effector functions to control infections . CD4+ T cells primarily activate other immune cells like B cells and macrophages through expression of cytokines [57]–[59] and they are also recognized as being crucially involved in the activation of antigen-specific CD8+ T cells [60] . In contrast , CD8+ T cells can directly kill infected cells which is mediated by the release of cytotoxic granules containing notably perforin and granzymes . Furthermore , studies showed that cell-mediated cytotoxicity and especially perforin is important for recovery from ECTV infection [61] , [62] . To analyze the role of perforin mediated cellular cytotoxicity in rapid protection , we vaccinated perforin deficient mice ( Prf−/− ) i . n . with 108 PFU MVA two days before a lethal challenge infection with ECTV . In contrast to wt mice , Prf−/− mice were not protected , developed severe disease and all mice succumbed to ECTV infection until day 18 post infection ( Figure 7A ) . Correspondingly , we detected high levels of virus in lung and liver of MVA vaccinated Prf−/− mice at the time point of death , while vaccinated wt mice had cleared the virus at the end of the experiment ( 21 dpi ) ( Figure 7B ) . Nevertheless , livers of MVA immunized Prf−/− mice contained reduced virus loads in comparison to mock vaccinated Prf−/− controls . This observation appeared to correlate with the somewhat prolonged course of disease in vaccinated animals ( not statistically significant ) and might be the consequence of innate or humoral adaptive immune responses including the possible contribution of type I and/or type II interferons . Histopathologic examination revealed multiple randomly located foci of necrosis and inflammation in the liver of vaccinated and ECTV challenged Prf−/− mice ( Figure 7C , upper panel ) . These lesions were characterized by hepatocytic necrosis and infiltration by macrophages and lymphocytes . However , MVA immunized wt mice had no necrotic and inflammatory lesions in liver tissues ( Figure 7C , lower panel ) . Thus , the availability of the cytotoxic effector protein perforin was essential to maintain the protective capacity of MVA immunization suggesting the induction of T cell mediated cytotoxicity as key mechanism of protective immunity . Intranasal immunizations with VACV are being increasingly investigated as a means to induce immunity associated with the respiratory tract or mucosal tissues . Such applications might be useful in swift mass vaccinations to help overcome major challenges in public health interventions . However , almost all immunizations with VACV in humans , e . g . with the widely used smallpox vaccine strains Lister/Elstree and New York City Board of Health , involved intradermal ( i . d . ) application through scarification [11] . Clinical uses of MVA-based vaccines routinely choose intramuscular ( i . m . ) or subcutaneous applications . To elucidate the role of T cells in the rapid protective capacity induced by more conventional vaccination , we again depleted C57BL/6 mice of CD4+ and CD8+ T cells by specific antibodies at the time point of immunization ( Figure S6B , C ) . The rapid protective capacity of i . n . MVA immunization was compared with i . m . MVA immunization using 108 PFU ( Figure 8A , B ) , as well as with i . d . vaccination with 106 PFU of VACV strain Elstree ( Figure 8C ) . Previous studies had confirmed the protective capacity of this lower-dose immunization using the fully replication competent VACV Elstree [41] . As expected , control C57BL/6 wt mice were fully protected by vaccination two days prior to the lethal respiratory challenge infection with ECTV ( p = 0 . 006 for MVA i . n . ; p = 0 . 0011 for MVA i . m . ; p = 0 . 005 for VACV Elstree ) . In sharp contrast , all vaccinated , T cell-depleted animals succumbed to ECTV infection irrespective of vaccination by MVA or VACV Elstree strains or the different routes of immunization . Additionally , the morbidity profiles of T cell-depleted , vaccinated animals were comparable to mock-vaccinated controls ( p = 1 for MVA i . n . ; p = 0 . 3489 for MVA i . m . ; p = 1 for VACV Elstree i . d . ) . Furthermore , the protective capacity of MVA vaccination was lost when i . m . inoculating heat-inactivated MVA doses ( corresponding to 108 PFU ) ( Figure S9 ) suggesting the need to immunize live MVA vaccine . These data clearly suggest an essential general requirement for both CD8+ and CD4+ T cells in rapidly protective immunization against fatal mousepox .
Vaccination is still the most successful approach to prevent viral diseases . The recent threats of suddenly emerging severe infectious diseases , e . g . caused by severe acute respiratory syndrome coronavirus , West Nile virus , or avian influenza virus , demonstrate the need for new vaccines ready to use in an immediate public health response . Previous studies in animal models for preventing fatal orthopoxvirus disease had shown that immunizations with MVA or conventional VACV could provide protection in a time window close to lethal infection [39] , [41] , [43] . The purpose of the present study was to determine the immunological mechanisms mediating this rapid protective capacity of MVA vaccination in an orthopoxvirus infection model . Previous studies in the mousepox model had shown that pre- and post-exposure protection can be achieved , and suggested that the induction of adaptive immune responses was essential [41] , [42] . Post-exposure immunizations , in particular when given at later times ( e . g . 2 days post ECTV infection ) , can not prevent the onset of severe mousepox disease [41] . However , this feature clearly hampers the definition of immune correlates for MVA vaccine mediated rapid protection ( our unpublished data ) [63] . In contrast , MVA vaccination two days prior to lethal ECTV challenge allows for solid protection also against the onset of morbidity . Therefore , we chose the pre-exposure immunization model for this study . The present work was carried out in C57BL/6 mice deficient in various components of the innate or adaptive immune system , exposed to a lethal respiratory infection with ECTV administered two days after immunization . Immunization and challenge of normal C57BL/6 mice served as controls to determine the degree of protection , as monitored by disease symptoms , body weight loss and survival . Moreover , intranasal vaccination of fully immune competent mice allowed us to assess the quality and kinetics of immune responses elicited at the primary site of immunization . Previous studies had suggested that VACV and MVA are recognized via multiple host-sensing pathways , including TLRs , RLRs and NOD-like receptors ( NLRs ) [44] , [64] . Furthermore , MVA infection induces pro-inflammatory cytokines such as TNF-α [44] , [65] , type I interferons [14] , [47] , [66] and chemokines like CCL2 that attract leucocytes to the site of inoculation [46] . At 24 to 48 hours after i . n . MVA inoculation we found prominent infiltrations of immune cells in the lungs of immunized mice . We also detected significantly increased amounts of the pro-inflammatory cytokines IL-6 and IFN-α in BAL fluids , which correlate with efficient activation of innate responses in the respiratory tract . Previous analysis of systemic adaptive immune responses had shown that MVA vaccination elicits strong CD8+ and CD4+ T cell responses with a T-helper type 1 ( Th1 ) -dominant profile [25] , [30] , [67] , [68] as well as orthopoxvirus-specific antibodies [25] , [68] , [69] . Here we demonstrate that antigen-specific immune responses can be detected in the respiratory tract at early times after immunization . Of note , we found two to three-times higher numbers of IFN-y producing VACV ( B8R20–27 ) specific CD8+ T cells in lungs than in spleens . This supports the hypothesis that early local priming of antigen-specific T cells may occur , which is possibly associated with MVA-induced formation of bronchus-associated lymphoid tissue [45] . The rapid expansion of virus-specific CD8+ T cells is further supported by the recent finding of Chaudhri and coworkers that antigen-specific T cell receptors can be transferred and shared among CD8+ T cells to enhance the anti-viral response upon orthopoxvirus infection [70] . Previous work in IFNAR−/− mice had indicated a partial influence of type I IFN in rapid protection mediated by MVA vaccination [41] . Here we show that the protective capacity of MVA immunization was fully maintained in MyD88/Trif−/− and IPS−/− mice that are deficient in TLR and RLR signaling pathways controlling the expression of type I IFN . Nevertheless , it cannot be excluded that other innate recognition pathways are involved in mediating a type I IFN response to MVA . Thus it seems likely that due to versatile recognition of MVA , signaling pathways can compensate for each other to provide the innate responses essential for developing protective adaptive immunity . In particular , further study of the possible contribution of type I IFN in rapidly protective MVA immunization seems promising because concomitant type I interferon receptor triggering on T cells and DC has been recently shown to allow for optimal expansion of MVA induced CD8 T cell responses [71] . NK cells are part of the cellular innate immune response and recent work established their key role in host specific control of a primary ECTV infection [51] , [52] . Interestingly , we found here that depletion of NK cells did not influence the rapid protective capacity of MVA vaccination . This observation already indicated differences in the modes of immune defense when comparing rapidly protective primary immunization , protection against secondary infection , and overcoming a primary ECTV infection . This latter scenario is well characterized in C57BL/6 mice that resist foot-pad infections known to be lethal in more susceptible mouse strains . This primary resistance was shown to mainly depend on the presence of NK cells and T cells . Nevertheless , B cells are essential for complete virus clearance and recovery [50] , [52] , [72] , [73] . In contrast , protective immunity against secondary infections , elicited by primary infection or conventional vaccination , is dominated by antibody responses [23] , [27] , [40] , [74]–[76] . Recovery from secondary ECTV infection was demonstrated to rely on a more rapid recall antibody response than in primary infection , whereas secondary recall CTL responses were not altered compared to primary CTL responses [77] . Nevertheless , memory CD8+ T cells are known to prevent viral spread by killing viral targets in the draining lymph nodes and thus are also important in controlling secondary ECTV infections [78] . Thus , the antibody memory response seems to be mandatory for long-term protective immunity against orthopoxvirus infections especially in controlling virus persistence while T cell responses might be more important to prevent viral spread . Previous immunization experiments in T and B cell deficient RAG-1−/− mice had already indicated that adaptive responses are indispensible to achieving rapidly protective immunity [41] , [42] . We also suspected a key role of humoral immunity in rapidly protective immunization , since MVA can induce antibody responses much faster than conventional VACV [39] , [41] . Moreover vaccinia immune globulin is effective in post-exposure treatment of lethal orthopoxvirus infections [28] , [53] , [79] . However surprisingly , we found that vaccinated B cell-deficient mice were still fully protected . MVA immunization prevented the onset of any detectable disease in B cell deficient animals for at least four weeks following respiratory challenge infection . This is remarkable because in this intranasal infection model ( at low dosage of 200 PFU ECTV ) normal C57BL/6 mice ( not vaccinated ) suffer from severe systemic mousepox and succumb within 10 to 14 days after challenge [80] . It is worth noting , however , that ECTV can persist for several months without any signs of disease in naïve C57BL/6 mice following footpad inoculation and , in infected B cell deficient animals , the onset of symptoms must not occur until very late in infection [72] , [73] . Yet , on the contrary , depletion of CD4+ or/and CD8+ T cells in C57BL/6 mice completely abrogated the protective capacity of immunization against the respiratory ECTV challenge . Moreover , the need for T cell-mediated immunity was underlined by the transfer of naïve CD3+ T cells into RAG-1−/− mice , which supported protective vaccination of these immunocompromised animals against lethal ECTV challenge . Furthermore , depletion of CD4+ T cells was sufficient to inhibit the protective effect of MVA immunization , although we observed clearly delayed onset of morbidity . This observation may be best explained by an essential role of T helper cells in mediating efficient clearance of virus by CD8+ T cell activity . Importantly , this possibility is clearly supported by our demonstration that depletion of CD4+ T cells significantly reduced the in vivo expansion of endogenous VACV-specific CD8+ T cells . Similarly , CD4+ T cells have been found essential for maintaining a robust or protective cytotoxic T cell memory response upon vaccination with recombinant VACV expressing lymphocytic choriomeningitis virus glycoprotein , or upon infection of mice with Listeria monocytogenes bacteria [81] , [82] , [83] The possibility that clearance of ECTV cannot be accomplished because of the absence of T helper cell dependent antibody responses appears unlikely in the view of the fact that CD4-depleted animals still mounted substantial levels of VACV-specific antibodies . Moreover , we clearly demonstrated the essential need for the direct cytotoxic effector function of CD8+ T cells to mediate rapid protection as the absence of perforin completely abrogated the protective capacity of immunization . Nonetheless , we observed reduced levels of ECTV also in the livers of vaccinated Prf−/− mice indicating that MVA induced innate responses might have modulated the course of infection . This hypothesis is in agreement with previous findings of an early enhanced production of chemokines and cytokines after in vivo inoculation of MVA but not other strains of VACV [46] , [84] . Importantly , we confirmed the necessity of T cells in rapid protection not only with MVA immunizations via the intramuscular route , but also conventional scarification using VACV strain Elstree vaccine . These data suggests that the need for T cell-mediated immunity is independent of the vaccination route or vaccine strain used . Moreover , this indicates a general requirement of T cells for rapidly protective immunizations against orthopoxvirus infections , and maybe also against other infectious diseases that necessarily fit a scenario of emergency vaccination . In addition , we present here an outstanding experimental model for immunizations with a live viral vaccine suitable for use in humans , where protective vaccination strictly depends on T cell responses . Future work with this model should help in the development of new vaccines eliciting more effective T cell mediated immunity .
This study was carried out in strict accordance with German regulations for animal experimentation ( German Animal Welfare Act ) . All experimentations were approved by the Government of the State of Hesse ( Paul-Ehrlich-Institut , Permit Numbers 107/65 , 107/67 , 107/82 ) , the Government of Upper Bavaria ( University of Munich LMU , Permit Number 59 . 10 ) and the Niedersächsische Landesamt für Verbraucherschutz und Lebensmittelsicherheit ( LAVES ) . All intranasal inoculations were performed under ketamine/xylazine anesthesia , and all efforts were made to minimize suffering of infected animals . Monkey BS-C-1 ( ATCC CCL-26 ) , murine NIH3T3 ( ATCC CRL-1658 ) , human monocytic THP-1 ( German Collection of Cell Culture DSMZ , Braunschweig , Germany ) and chicken embryo fibroblast ( CEF ) cells were used and routinely maintained as previously described [25] . [46] . Plaque purified Ectromelia virus ( ECTV ) strain Moscow ( ATCC VR-1374 , kindly provided by Mark L . Buller , St . Louis University School of Medicine , St . Louis , Missouri , USA ) was propagated on BS-C-1 cells . Modified vaccinia virus Ankara ( MVA ) ( clonal isolate F6 ) [16] , [22] was propagated on CEF cells . Viral titers were determined by plaque assay and titrated in plaque forming units ( PFU ) as previously described [25] , [85] . Female C57BL/6N mice ( 6–10 weeks old ) were purchased from Charles River Laboratories ( Sulzfeld , Germany ) . C57BL/6J-Igh-6tm1Cgn mice ( μMT , immunoglobulin heavy chain 6 deficient [heavy chain of IgM] ) [54] and C57BL/6-Prf1tm1Sdz/J mice ( Prf−/− , perforin deficient ) [81] . were purchased from The Jackson Laboratory . C57BL/6J-Rag1tm1Mom mice ( RAG-1−/− , mice homozygous for the Rag1tm1Mom mutation produce no mature T cells or B cells ) [86] , C57BL/6 MyD88−/− TRIF−/− mice [47] , [87] , C57BL/6 IPS-1−/− mice [88] and C57BL/6-Igh-Jtm1Cgn/J mice ( JHT , mice homozygous for the Igh-Jtm1Cgn targeted mutation fail to produce functional B cells ) [55] , [89] were bred under specific pathogen-free conditions at the central animal facility of the Paul-Ehrlich Institute . For experimental work , mice were housed in an ISOcage unit ( Tecniplast , Germany ) and had free access to food and water . All animal experiments were handled in compliance with the German regulations for animal experimentation ( Animal Welfare Act ) . Intradermal ( i . d . ) vaccination was performed by tail scarification as described elsewhere [41] . Briefly , 10 µl of virus suspension containing 106 PFU VACV Elstree was deposited on the mouse skin at the tail base . The skin was then scratched through the droplet with the tip of a 26-gauge needle ( Braun , Melsungen , Germany ) to allow virus uptake . For intramuscular ( i . m . ) vaccination , 50 µl of virus suspension containing 108 PFU of MVA or PBS as a mock control were injected into the right hind leg . Intranasal ( i . n . ) immunization was performed as described elsewhere [24] , [43] . Briefly , mice were anesthetized by intraperitoneal ( i . p . ) injection with 1 mg ketamine and 0 . 04 mg xylazine per 10 g body weight and instilled i . n . with 30 µl of virus suspension containing 108 PFU of MVA . In all experiments inoculations of corresponding amounts of PBS were used as controls ( mock vaccine ) . Mice were anesthetized by intraperitoneal ( i . p . ) injection with 1 mg ketamine and 0 . 04 mg xylazine per 10 g body weight and infected by i . n . inoculation with 200 PFU ( ∼3×LD50 ) ECTV virus suspension as described previously [43] . Signs of illness , weight loss and survival were monitored daily for at least three weeks . Mice were depleted of CD4+ T cells , CD8+ T cells , or NK cells by i . p . administration of mouse monoclonal antibodies purchased from Harlan Bioproducts , Indianapolis , USA . CD4+ T cells were depleted by applying 500 µg of anti-CD4 clone GK1 . 5 antibody [90] on days −8 , −6 , −3 , −2 , and −1 prior to immunization on day 0 . CD8+ T cell depletion was performed by administration of 100 µg anti-CD8 clone 2 . 43 antibody [91] on days −2 and −1 prior to immunization on day 0 . Depletion of both CD4+ and CD8+ T cells was achieved by combining the two described applications of GK1 . 5 and 2 . 43 antibodies . NK cells were depleted with an anti-NK1 . 1 clone PK136 antibody [92] applying 300 µg of antibody on days −2 and −1 prior to immunization on day 0 . Successful depletion of immune cells was confirmed by flow cytometric analysis of spleen cells from antibody treated animals on days 0 and 7 post immunization . Spleens were isolated from euthanized C57BL/6 mice in pre-warmed RPMI medium enriched with 10% fetal calf serum . Single cell suspensions were obtained by passing cells through a nylon mesh ( Nybolt PA-150/38 , Franz Eckert GmBH , Germany ) and erythrocytes were lysed by treatment with Red blood cell lysis buffer ( Sigma Aldrich , Taufkirchen , Germany ) . Subsequently , cells were washed with medium and passed through a 70 µm filter ( Filcon , BD Biosciences , Heidelberg , Germany ) and again washed with medium . For isolation of untouched T cells the spleen cell suspension was magnetically labeled using the Pan T Cell Isolation Kit ( Miltenyi , Bergisch Gladbach , Germany ) and isolated using an autoMacs™ separator according to the manufacturer's protocol . Purity of the isolated T cells was confirmed by flow cytometry . Rag-1−/− mice were injected intravenously with 2×107 CD3+ cells per mouse two days before immunization and the engraftment was confirmed by FACS analysis of blood samples on days 4 , 9 , 11 , 15 , 21 and 37 after administration . Lungs from sacrificed mice were fixed by instilling formaldehyde solution ( 4% , pH 7 . 2 ) through the trachea . Inflated lungs and livers were removed and fixed in phosphate buffered formalin . Tissues were embedded in paraffin and sections ( 4 µm ) were stained with hematoxylin and eosin before being evaluated by light microscopy . Lungs from sacrificed mice were inflated three times with 0 . 7 ml PBS using a stainless steel buttoned cannula ( ACUFIRM , Ernst Kratz KG Nadelfabrik , Germany 1428 LL ) . Pooled fluids ( n = 3 ) were collected and cells were harvested by low-spin centrifugation for antibody staining and FACS analysis . The cell-free fluids of the first instillation were collected and stored at −80°C for further analysis to detect cytokines and antibodies by ELISA . Approximately 105 cells were stained in 50 µl PBS supplemented with 3% FCS using monoclonal antibodies obtained from BD Biosciences ( Heidelberg , Germany ) . Monocytes , neutrophils , dendritic cells , NK cells , B cells , and T cells were detected using APC-labeled CD11c , PerCP-Cy5 . 5-labeled CD11b , PE-Cy7-labeled Gr-1 , PE-labeled CD49b , PE-Cy7-labeled NK1 . 1 , APC-Cy7-labeled B220 , PerCp-Cy5 . 5-labeled CD3 , PE-labeled CD4 and FITC-labeled CD8 antibodies . To ensure specificity of staining , all staining tests contained an isotype-matched control antibody . Stained cells were fixed with PBS supplemented with 0 . 5% paraformaldehyde and analyzed with BD LSRII and BD FACSDiva 6 . 0 software ( BD Biosciences , Heidelberg , Germany ) . Splenocytes or BAL cells from vaccinated C57BL/6 mice were stimulated for 5 h with VACV-specific peptide B8R20–27 ( TSYKFESV ) [48] or control peptide LacZ876 ( TPHPARIGL ) [93] purchased from Thermo Fisher Scientific GmbH ( Ulm , Germany ) in the presence of GolgiStop™ ( BD Biosciences , Heidelberg , Germany ) . Cells were blocked with anti-CD16/CD32-Fc-Block ( BD Biosciences ) and surface markers were stained with PacBlue-conjugated anti-CD8 ( BD Biosciences ) and APC-conjugated anti-CD62L ( BD Biosciences , Heidelberg , Germany ) in the presence of Fc-Block ( BD biosciences , Heidelberg , Germany ) and washed twice with PBS containing 3% FCS . Intracellular cytokine staining for IFN-γ production was performed with FITC anti–IFN-γ ( BD Biosciences , Heidelberg , Germany ) using the Cytofix/Cytoperm kit ( BD Biosciences , Heidelberg , Germany ) according to the manufacturer's recommendations . Data were acquired in a BD LSRII flow cytometer and analyzed with BD FACSDiva 6 . 0 software ( BD Biosciences , Heidelberg , Germany ) . Supernatants of the first BAL instillations were collected and pooled from 3 mice . Detection of different cytokines used undiluted BAL fluids for ELISA in triplicates . Measurement of interleukin-6 ( IL-6 ) used a Quantikine ELISA Kit purchased from R&D Systems ( Wiesbaden-Nordenstadt , Germany ) . ELISA for detecting interferon-α ( IFN-α ) was purchased from PBL InterferonSource ( distributed by tebu-bio GmbH , Offenbach , Germany ) . Assays were performed according to manufacturer's instructions and repeated at least three times . ELISA plates ( MaxiSorp 96-well flat-bottom , Nunc , Wiesbaden , Germany ) were coated with sucrose gradient-purified MVA ( at a protein concentration of 1 µg/ml ) for 3 h at 37°C and overnight at 4°C . The plates were blocked with PBS containing 0 . 05% Tween 20 and 10% fetal calf serum for 60 min at 37°C . BAL fluids were incubated for 60 min at 37°C , washed five times with PBS , and then incubated for 30 min with a goat anti-mouse IgG conjugated to horseradish peroxidase ( HRP ) ( Kirkegaard & Perry Laboratories , Gaithersburg USA ) ( diluted 1∶2000 in PBS ) . After five washes , the plates were incubated with OPD substrate ( Sigma , Taufkirchen , Germany ) at room temperature for 5–10 min . The optical density was measured immediately after addition of stop solution ( 0 . 5 M sulfuric acid ) at a wavelength of 490 nm [94] . RNA isolation and amplification of human GAPDH cDNA were performed as described [95] using 26 amplification cycles . Similarly , amplification of murine GAPDH cDNA was performed with the sense primer 5′-GAC AAC TCA CTC AAG ATT GTC AG-3′ and the antisense primer 5′-GTA GCC GTA TTC ATT GTC ATA CC-3′ , resulting in a product size of 540 bp . Amplification of VACV B8R gene ( GenBank accession no . AY603355 ) was undertaken using the sense primer 5′-TAA AAA TTA TGG CAT CAA GAC G-3′ and the antisense primer 5′-ACA TCT TCT TTG GAT CTA ATT GC-3′ , resulting in a product size of 495 bp for MVA , and 540 bp for VACV strain Elstree . The MVA E3L gene orthologue has been amplified using the sense primer 5′-TTA CTA GGC CCC ACT GAT TC-3′ and the antisense primer 5′-GTT CTG ACG CAG AGA TTG TG-3′ , resulting in a product size of 406 bp . Primer pairs were designed using Primer3 software [96] . All oligonucleotides were synthesized by Eurofins MWG Operon GmbH ( Ebersberg , Germany ) . PCR products were run on a 1 . 5% agarose gel and stained with GelRed purchased from MoBiTec ( Göttingen , Germany ) . Gel pictures acquired by a CCD camera were analyzed using the Photo-Capt 12 . 4 software ( Vilber Lourmat , Eberhardzell , Germany ) . Statistical comparison of different groups of mice was performed as means of the area under the weight curve ( AUC ) in percent of individual weight at baseline . The AUC was additionally weighted with the length of the observation period ( usually day of challenge ( day 0 ) until day 22 , or the day the animal died ) . The differences between vaccination groups were analyzed with a one-factorial analysis of variance model . For multiple comparisons p-values were adjusted with the Bonferroni method . The statistical evaluation was performed with SAS/STAT software , version 9 . 2 , SAS System for Windows . For statistical significant results the following convention was used: * – p-value<0 . 05 , ** – p-value<0 . 01 and *** – p-value<0 . 001 . | Prophylactic use of vaccinia virus allowed eradication of human smallpox , one of the greatest successes in medicine . However there are concerns that variola virus , the infectious agent of smallpox , may be used as bioterroristic weapon and zoonotic monkeypox or cowpox remain threatening infections in humans . Thus , new developments of safe and rapidly protecting orthopoxvirus-specific vaccines have been initiated . The candidate vaccine modified vaccinia virus Ankara ( MVA ) was recently shown to protect against lethal systemic poxvirus disease even when applied shortly before or after infection of mice with ectromelia virus , the probably best animal model for human smallpox . Surprisingly , little is known about the protective mechanism of early immune responses elicited against orthopoxvirus infections . Here , we used the mousepox model to analyze the immunological basis of rapidly protective MVA vaccination . In contrast to common understanding of orthopoxvirus vaccine efficacy relying mainly on antibody mediated immunity , we observed unimpaired protection also in absence of B cells . Surprisingly , rapid protection by vaccination with MVA or conventional vaccinia virus was solely dependent on T cells , irrespective of the route of injection . Thus , our study suggests a key role for T cell immunity in rapidly protective immunization against orthopoxviruses and potentially other infectious agents . | [
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] | 2012 | Critical Role of Perforin-dependent CD8+ T Cell Immunity for Rapid Protective Vaccination in a Murine Model for Human Smallpox |
Mutational neighbourhoods in genotype-phenotype ( GP ) maps are widely believed to be more likely to share characteristics than expected from random chance . Such genetic correlations should strongly influence evolutionary dynamics . We explore and quantify these intuitions by comparing three GP maps—a model for RNA secondary structure , the HP model for protein tertiary structure , and the Polyomino model for protein quaternary structure—to a simple random null model that maintains the number of genotypes mapping to each phenotype , but assigns genotypes randomly . The mutational neighbourhood of a genotype in these GP maps is much more likely to contain genotypes mapping to the same phenotype than in the random null model . Such neutral correlations can be quantified by the robustness to mutations , which can be many orders of magnitude larger than that of the null model , and crucially , above the critical threshold for the formation of large neutral networks of mutationally connected genotypes which enhance the capacity for the exploration of phenotypic novelty . Thus neutral correlations increase evolvability . We also study non-neutral correlations: Compared to the null model , i ) If a particular ( non-neutral ) phenotype is found once in the 1-mutation neighbourhood of a genotype , then the chance of finding that phenotype multiple times in this neighbourhood is larger than expected; ii ) If two genotypes are connected by a single neutral mutation , then their respective non-neutral 1-mutation neighbourhoods are more likely to be similar; iii ) If a genotype maps to a folding or self-assembling phenotype , then its non-neutral neighbours are less likely to be a potentially deleterious non-folding or non-assembling phenotype . Non-neutral correlations of type i ) and ii ) reduce the rate at which new phenotypes can be found by neutral exploration , and so may diminish evolvability , while non-neutral correlations of type iii ) may instead facilitate evolutionary exploration and so increase evolvability .
In a classic paper [1] , published in 1970 , John Maynard Smith introduced several key ideas for describing the structure of genotype-phenotype ( GP ) maps . He first outlined the concept of a protein space , the set of all possible sets of amino acid chains , and suggested that for evolution to smoothly proceed , these should be connected as networks of functional protein phenotypes that can be interconverted by ( point ) mutations . He then argued that one criterion for such networks to exist is for a protein X to have at least one mutationally accessible neighbour which is “meaningful , in the sense of being as good or better than X in some environment” . In other words , if X has N mutational neighbours , then the frequency f of “meaningful” proteins should satisfy f > 1/N . He pointed out that this was likely to be true in part due to the ubiquity of neutral mutations , which had been famously proposed by Kimura [2] and King and Jukes [3] just a few years prior to his paper . But he also gave a second reason for expecting connected networks , namely that , “There is almost certainly a higher probability that a sequence will be meaningful if it is a neighbour of an existing functional protein than if it is selected at random . ” This idea that mutational neighbours differ from the random expectation is what we will call genetic correlations . Following Maynard Smith , many authors have explored the role of networks of genotypes connected by single point mutations . Lipman and Wilbur [4] first showed that large networks of mutationally connected genotypes mapping to the same phenotype are found in the Hydrophobic-Polar ( HP ) model for protein folding , introduced by Dill [5 , 6] . They also pointed out that neutral mutations allow a population to traverse these networks , facilitating access to a larger variety of alternate phenotypes . Schuster and colleagues [7] developed these themes further using detailed models for the secondary structure of RNA [8] . They coined the term “neutral network” to describe sets of mutationally connected genotypes that map to the same secondary structure phenotype . As RNA secondary structure is fairly easy to calculate and thermodynamics based models such as the Vienna package are thought to provide an accurate prediction of real RNA secondary structure [9 , 10] , the nature of neutral networks in these models has been extensively studied [7 , 8 , 11–16] . Since these pioneering works , neutral networks have been considered in GP maps of other biological processes , including models for gene networks [17 , 18] metabolic networks [19] and the Polyomino model for self-assembling protein quaternary structure [20] . From these studies of model systems a number of basic principles have emerged , much of which has been reviewed in important books by Wagner [15 , 16] . Firstly , for neutral networks to exist , the GP map should exhibit redundancy , where multiple genotypes map onto the same phenotype . This many-to-one nature of the mappings is illustrated in Fig 1A . Redundancy is of course closely linked to the existence of neutral mutations [2 , 3] , although the relationship between these concepts is not entirely unambiguous . In the theory of neutral evolution , a mutation may lead to a slightly different phenotype , but as long as the change in fitness is small enough not to be visible to selection , it is considered to be effectively neutral [21] . Whether selection can act depends on the degree of phenotypic change , the environment , and other factors such as the population size and mutation rate . Therefore , identifying whether or not a mutation is neutral can be complex , and the answer may vary as parameters external to the GP map change with time . So while redundancy only couples identical phenotypes , and so is a more restrictive concept than neutral mutations , it has the advantage of sidestepping the subtle issues listed above and is more easily applicable to the study of a static GP map . The second basic principle to emerge is that the number of genotypes per phenotype ( the redundancy ) can vary , leading to phenotype bias , as depicted in Fig 1B . Thirdly , it is generally the case that the larger the redundancy , the greater the mean mutational robustness of genotypes mapping to that phenotype . Fourthly , the larger the neutral network , the greater the variety of alternative phenotypes within one ( non-neutral ) point mutation of the whole neutral network , leading to robustness and measures of evolvability that count the number of different phenotypes that are potentially accessible being positively correlated [22] . Finally , a key principle emphasised by Maynard Smith [1] , but which has earlier roots in concepts such as the shifting balance theory of Sewall Wright [23] , is that neutral mutations allow a population to access , over time , a wider variety of potential alternative phenotypes than would be available around a single genotype [4 , 11 , 16] . Evidence for the key role of these networks in promoting evolutionary innovation has been found , for example in experiments on RNA structures [24 , 25] and transcription factors [26] . The main focus of this paper is genetic correlations . To explore and quantify how they affect concepts such as neutral networks , robustness and evolvability , we study genetic correlations in three of the GP maps mentioned above . These are the sequence to RNA secondary structure map and HP model for protein folding ( tertiary structure ) , which have been extensively studied , as well as the more recently introduced Polyomino model for self-assembling protein quaternary structure . Several properties of these three GP maps have recently been compared [20 , 27] and we summarise some of the similarities and differences between them in the methods section . However , a detailed investigation of their genetic correlations has not yet been considered . A key question we consider is how to define genetic correlations in a quantitative way . For that one needs some kind of uncorrelated null model for how genotypes are distributed over phenotypes with which to compare the full biophysical systems . We employ a model we call the random GP map . It has the same number of genotypes mapping to each phenotype as the biophysical GP map to which it is being compared , as well as the same basic type of genotype space ( alphabet size and genome length ) , and nodes ( genomes ) that are linked by single mutations if they differ by one locus . The difference is that the genotypes are randomly distributed across the genotype space . Of course one does not expect biological systems to have such a random distribution , but it is not at all straightforward to think of a better null expectation . The great advantage of having such a null model , even if one knows that it is has limitations , is that it allows us to quantitatively contrast how the biophysical genotypes are organised across the genetic space , which should shed light on nature of the correlations that Maynard Smith introduced . The paper is organised as follows . We first define our models in the methods section . Next we examine neutral correlations , schematically illustrated in Fig 1C , through considering various measures of robustness that quantify the relative likelihood that mutationally neighbouring genotypes possess the same phenotype . We then perform a similar analysis comparing the biological GP maps to the random map for non-neutral correlations . Since these different kinds of correlations all modulate the way that novel variation arises through random mutations , we finish by commenting on how correlations affect subtle interplay of robustness and evolvability [15 , 28] , and also briefly suggest a few other forms of correlation that could be studied in GP maps .
We consider three separate GP maps for low-level self-assembling biological systems . Firstly , a model for RNA secondary structure [8] that determines which bases in an RNA sequence form bonded pairs . Secondly , the HP lattice model for protein tertiary structure [5 , 6] , that determines the three-dimensional shape of a folded amino acid chain . And thirdly , the Polyomino model for protein quaternary structures [20 , 29 , 30] , where quaternary structure of proteins is the topological arrangement of separate folded amino acid chains . All three of these models have been previously compared in ref . [20] , and below we briefly outline the three different systems . As discussed in the Introduction , in order to quantify genetic correlations we must first define an uncorrelated null model to which the biophysical GP maps can be compared . Here we employ a random GP map that was recently explicitly introduced for analysing whole GP map properties in ref . [14] , but has also been used implicitly in many earlier works see e . g . [16 , 35 , 37] , although we believe this is the first time this random model has been used to define correlations . The random GP map shares the following properties with the biological GP map to which it is being compared: the same alphabet size K , genome length L , number of 1-mutation neighbours ( K−1 ) L , number of genotypes NG = KL , number of phenotypes NP , and frequencies fp , defined as the fraction of all genotypes that possess phenotype p . It also has the same basic underlying connectivity . We summarise the GP map nomenclature used in this paper in Table 1 , which compares what is shared and what is different between the biological and the random GP maps . With these key global GP map properties fixed , the only difference between a biological GP map and its associated random GP map is that the Fp = fp × NG genotypes for each phenotype p are each randomly assigned to the set of NG possible genotypes . As phenotypes are randomly assigned , departures in properties between the two versions of the GP map may be considered to be due to correlations , that is that the mutational neighbourhood of a genotype is affected by what phenotype it maps to . We note that these correlations can be very complex , and depend not only on the identity of the phenotype , but also on higher order features such as the identity of one or two or more phenotypes in the direct neighbourhood ( higher order correlations ) . It is almost certainly also true that , depending on the phenotype , the correlations depend on which of the L genomic positions is mutated ( see e . g . Maynard Smith’s word game described in the discussion ) . However , in this paper we mainly focus on the simplest kinds of correlations; for example for neutral correlations we mainly look at effects that are captured by the concept of robustness . Other null models are conceivable . For example , in ref . [13] the authors used an approach based on network theory [13] , comparing the topology of neutral components to Erdős-Rényi networks and scale-free networks . While this type of network is helpful for understanding the topology of neutral networks themselves , a focus of this work here is not just how genotypes of the same phenotype connect to each other , but how phenotypes are arranged in relation to each other . Besides their simplicity , an advantage of using the random GP maps for this purpose is that the overall connectivity of the genotype space is left intact , along with several global properties of the map , allowing the way phenotypes are arranged to be directly considered . Moreover , this random map has been used ( implicitly and explicitly ) throughout the literature , see e . g . [16 , 35 , 37] , and so it is of general interest to carefully analyse some of its properties .
The concept of robustness to mutations is well established in the literature [15 , 16] . It is intimately tied to neutral correlations , in fact robustness helps quantify the amount of neutral correlation present . Before we study the more novel topic of non-neutral correlations , it is therefore interesting to compare various measures of robustness between the biological GP maps and the random uncorrelated GP map . The 1-robustness of a single genotype g that maps to phenotype p is straightforwardly defined as the number of genotypes np , g that map to p that are accessible within one point mutation of g . The phenotypic robustness ρp of a phenotype p is defined the average of the 1-robustness over the entire neutral set G p [22] . This can be expressed algebraically as ρ p = 1 F p ∑ g ∈ G p n p , g ( K - 1 ) L ( 1 ) In a random GP map , phenotypes are arranged randomly over genotypes so the probability that a genotype leads to phenotype p is given by its frequency fp , independently of the identity of its neighbours . The phenotypic robustness therefore is simply ρ p = f p and the mean number of neutral neighbours is 〈 n g , p 〉 = ( K - 1 ) L f p which is the expectation value for a binomial distribution with ( K−1 ) L trials and probability of a given neighbour being fp . It is independent of the identity of the genotype g . We define neutral correlations as the difference in how genotypes mapping to the same phenotype are distributed in a biologically relevant GP map as compared to the associated random GP map null model . One way of characterising these neutral correlations is by comparing the phenotype robustness ρp to the random expectation ρp = fp . The violation of this equality is a sufficient ( though not necessary ) condition for the existence of neutral correlations . Moreover , we define a phenotype p to have positive neutral correlations if ρp > fp is satisfied . This is intuitive—when robustness is greater than fp then phenotypes are closer to each other in the genotype network than would be expected by random chance . Using the above definitions around neutral correlations , we explicitly consider the robustness in the various GP maps . In Fig 2A , we compare the phenotypic robustness across our three biological GP maps to the robustness of the associated random GP map . The figure confirms both the analytical result derived above for the random model that ρp = fp ( we only show one schematic random map in the figure , but the others have the same behaviour ) . In sharp contrast , for the biological GP maps we find that , very roughly , ρp ∝ log fp , so that the robustness is much larger than would be expected for the null model , in fact by several orders of magnitude for smaller fp . Since ρp ≫ fp , this indicates the presence of extremely strong neutral correlations in these biological GP maps . Of course the fact that ρp > fp is not a new finding , but it is instructive to show this trend displayed explicitly for entire mappings in the three kinds of biological systems . We next extend phenotype robustness to n-mutations . Generalised robustness or n-robustness ρ p ( n ) , measures phenotypic robustness for a greater number of mutations . It is defined as the robustness of a genotype with phenotype p to n independent mutations to its genotype , rather than just the single mutation discussed above . This can be expressed algebraically as ρ p ( n ) = 1 F p ∑ g ∈ G p n p , g ( n ) 1 ( L n ) ( K − 1 ) n ( 2 ) where n p , g ( n ) is the number of n-mutant neighbours of g with phenotype p and the normalisation on the right-hand of the sum is the total number of n-mutants . In the same way as for the phenotype robustness , the n-robustness is averaged across the neutral set G p of all genotypes that map to phenotype p . A further quantity we define is the average n-robustness 〈ρ ( n ) 〉 which is the average of the n-robustness over all phenotypes in a given GP map: 〈 ρ ( n ) 〉 = 1 N P ∑ j ∈ P ρ j ( n ) ( 3 ) where P is the set of all NP phenotypes in the GP map . In contrast to the two previous definitions that measure robustness for a single phenotype , it is a general property of the whole GP map . One could imagine generalising this further to a subset of the phenotypes , for example those whose frequencies fp are greater than the average NP/NG . To establish the n-robustness and average n-robustness in the random GP map , the same logic can be applied as in the previous section . Since the probability of finding a phenotype is uniformly distributed over the genotype space , the n-robustness is given by ρ p ( n ) = f p with the n-robustness the same for all n , leading to an average n-robustness: 〈 ρ ( n ) 〉 = 1 N P ∑ j ∈ P f j = 1 N P ( 4 ) since the phenotype frequencies in a GP map sum to unity . The inequality 〈ρ ( 1 ) 〉 ≠ 1/NP can be used to define whether a biological GP map possesses neutral correlations as a whole . We consider the average n-robustness against the radius n for the three GP maps S2 , 8 , RNA12 and HP24 . A sample of 100 genotypes for each phenotype in the respective systems is taken ( apart from HP24 where a sample of 100 randomly chosen phenotypes is made due to the large number of phenotypes ) and the n-robustness is measured and averaged over phenotypes . In Fig 2B , we plot the average n-robustness at each radius along with the flat expectation lines from Eq 4 for the null models . In all three cases we observe a decay from greater than the null values for small radii to slightly less than the null expectation at larger radii . The reason for this drop below the random expectation can be understood intuitively: given that positive neutral correlations are present , the over-representation for small radii must be balanced at larger radii by under-representation in order for the number of genotypes to balance . We also define a neutral correlation length n* which measures the mutational hamming distance over which neutral correlations extend . We define n* for a phenotype to be equal to the smallest value of n where ρ p ( n ) < f p and for the GP map when 〈 ρ p ( n ) 〉 < 1 / N P . We find that n* = 7 for the RNA12 model , n* = 6 for the Polyomino S2 , 8 model and n* = 5 for the HP24 model . The neutral correlation length is smaller for the HP model than the other two systems . As discussed in the methods section , and illustrated in Fig 2A , the HP model typically has phenotypes with smaller frequencies/robustness than the other two systems suggesting neutral networks that do not expand to the same diameter which would reduce the expected neutral correlation length . All three models are of fairly small genome length L , so one should be careful of reading too much into the numerical values of these correlation lengths . However , it may very well be that this ordering of models will persist for larger L . Having illustrated the concept of positive neutral correlations—measured by ( generalised ) robustness greater than that of the random null model—we next show how other properties of neutral networks are affected by their presence . The neutral set G p is the set of all genotypes mapping to phenotype p . A component is the subset of the neutral set G p that is connected by single point mutations . We use this term because it is commonly used in graph theory to denote a set that is connected . Although the literature can be somewhat ambiguous , with the term neutral networks sometimes referring to the neutral set , and sometimes to a neutral component , we take a neutral network to be synonymous to a neutral component in this paper because if we have only point mutations then a population can only explore a neutral component and may not be able access the whole neutral set . There are several reasons why a neutral set may not be fully connected by neutral point mutations . If the genotypes are too diffusely spread out over the full genotype space , then they may be disconnected . But in some cases basic biophysical constraints , such as the neutral reciprocal sign epistasis described in the Methods section , also lead to fragmentation . We begin by comparing the size of neutral components in the random null model to those found in our biological GP maps . In the random model , there are two important threshold values: firstly , the giant component onset , when a phenotype’s components change from being largely isolated to forming larger connected clusters , and secondly , the single component onset where virtually all genotypes are taken up by a single giant connected component . As each genotype has many neighbours , a simple mean-field-like approach from percolation theory for random graphs [38] should be fairly accurate . This suggests that the giant component onset begins when the average number of neighbours of a given genotype with the same phenotype is approximately unity , which was also the criterion used by John Maynard Smith [1] . For the null model , where phenotypes are assigned to genotypes completely randomly , this reduces to an explicit threshold frequency δ = 1 ( K - 1 ) L ( 5 ) such that we expect the giant components for phenotypes with f p ≳ δ . It can be shown analytically in the limit L → ∞ [35] that there is another transition at λ = 1 - 1 K 1 K - 1 ( 6 ) where , for f p ≳ λ , all the components coalesce into one single giant component , so that the neutral set should be ( nearly ) fully connected . While the giant component threshold δ scales as 1/L , so that it decreases for larger maps , the single component threshold λ from Eq 6 is independent of genome length L , and only varies with alphabet size . For example , λ = 0 . 5 for K = 2 and λ ≈ 0 . 37 for K = 4 . These are large frequencies that are unlikely to be reached for more than a single phenotype in any realistic GP maps . In Fig 3 , we plot how the largest component size ( left ) and number of components ( right ) varies with frequency in both a null model ( K = 4 , L = 12 ) and three GP maps S2 , 8 , RNA12 and HP24 . We first focus on the simple schematic null model . Data is calculated by averaging over 100 independent realisations of the random mapping of genotypes to phenotypes in a way that preserves the frequencies . The largest component size , and the number of components formed by the phenotype , are then measured . These values are shown in Fig 3 for an array of frequencies in the schematic null GP map . Below the giant component onset δ ≈ 1/36 , most genotypes are completely isolated—the total number of neutral components scales with fp . Around the giant component threshold δ , this scaling changes markedly , and instead the size of the largest neutral components scales linearly with fp and takes up the majority of the genotypes in the neutral set . The number of components continues to decline until fp exceeds the single component connectivity threshold λ ≈ 0 . 37 , at which point there is just one component and the neutral set is completely connected . We next consider the biological GP maps relative to the behaviour exhibited by the null model . Firstly , all three GP maps have much larger maximum neutral set sizes than the random model . This is not surprising , as Fig 2A shows that , due to positive neutral correlations , ρp > δ for most phenotypes in each system ( ρ = δ for K = 4 , L = 12 is shown as a dotted red line in the plot ) . Once the probability of having a neutral neighbour is above the δ threshold , we expect large networks . For HP24 and RNA12 , the largest neutral component size clearly grows linearly with frequency , and so scales linearly with the size of the neutral set . For the Polyomino space S2 , 8 this scaling is less evident , but the components are still much larger than their random counterparts would be . Secondly , for all three models , the number of components does not vary much with fp , in contrast to the random model where this number scales , as expected , with the neutral set size if f p ≲ δ . Since these components typically have robustness above δ or even λ , the reason there are still multiple components must be due to biophysical constraints which are not present in the random model , such as the neutral non-reciprocal sign epistasis discussed earlier for RNA . These effects are to first order independent of fp which explains why the number of components does not correlate with fp . In each of these three models the largest “phenotype” of all is the deleterious non-folding or non-assembling one . Its frequency exceeds the threshold λ and its neutral set is fully connected . Differences between the three biophysical GP maps observed in Fig 2 can be fairly easily explained by some of the differences highlighted in the methods section . For example , the number of phenotypes per genotype is largest in the HP model , and smallest in the Polyomino model , which explains why they group at different frequencies . While the number of components in Fig 2B ) is generally much lower in the biological models than it is in the random model , the HP model has significantly fewer components than the RNA and Polyomino models do . This may be due to the fact that the HP model does not exhibit effects such as neutral reciprocal sign epistasis , which fragments neutral sets in the other two systems . We conclude that due to positive neutral correlations and the concomitant higher robustness , the biophysical models considered here have large neutral networks even for frequencies fp that are several orders of magnitude lower than the random model large component threshold δ . The abject failure of the random model to predict the robustness and the neutral network size highlights the importance of neutral correlations in these systems . We next consider non-neutral mutations . The first question is: Are two different phenotypes , on average , more or less likely to be connected to each other than one would expect by chance ? To address this question , we employ a generalisation of robustness , namely the phenotype mutation probability ϕqp of q with respect to p , defined as the fraction of 1-point mutations of genotypes in the neutral set for phenotype p that map to phenotype q . This can be written as: ϕ q p = 1 F p ( K - 1 ) L ∑ g ∈ G p n q , g . Thus ϕqp averages a local property , nq , g—the number of genotypes that map to phenotype q found the 1-mutation neighbourhood of a genotype that maps to phenotype p—over the entire neutral set G p . Note that this phenotype mutation probability is not symmetric ( ϕqp ≠ ϕpq ) and that , if p = q , it reduces to the phenotype robustness ϕpp = ρp . It has recently been shown [14] that ϕqp is a key quantity for incorporating the structure of a GP map into population genetic calculations . In the null model we expect ϕqp = fq to be an excellent approximation [14] , with the caveat that it must be possible for enough genotypes to be sampled . What do we mean by enough genotypes ? Given a phenotype p with redundancy Fp , there are at most Fp ( K−1 ) L unique neighbours available . This number provides an upper bound—in reality , several neighbours of one genotype will also be neighbours of another genotype with the same phenotype , resulting in a reduction in the number of unique neighbours . However , this allows us to define a minimum threshold γ = 1 F p ( K - 1 ) L ( 7 ) If f q ≲ γ , then the expected number of genotypes with phenotype q found around phenotype p is less than one , and the probability that ϕqp = 0 due to statistical fluctuations becomes appreciable . Further detail on how ϕqp and Fp relate when the threshold is not satisfied , which is mainly relevant for smaller GP maps and for lower Fp , is provided in S1 Text . Here we focus on phenotypes with larger Fp , in the larger GP maps of the previous section that do effectively sample the space of phenotypes . In Fig 4 , we plot the relationship between the phenotype mutation probability ϕqp and global frequency fq around the RNA20 phenotype with the second largest neutral set , the assembling phenotype for S3 , 8 with the largest neutral set , and the HP5x5 folding phenotype with the largest neutral set . For phenotypes in S3 , 8 and HP5x5 , with such large numbers of genotypes , every phenotype q will be effectively sampled , as all phenotypes have fq values that are significantly above fq = γ ( vertical dotted lines ) , which is the approximate threshold at which at least one genotype of phenotype q would be expected to be found . A small fraction of phenotypes lie close to the fq = γ threshold for RNA20 , but by far the majority may be expected to be effectively sampled . For RNA20 and S3 , 8 , we observe a very strong and highly significant positive correlation with the random null model expectation ϕqp = fq . In HP5x5 , there is also a strong positive correlation , though less strong than in the RNA and Polyomino cases , with a greater number of phenotypes falling below the one-to-one expectation . We did not plot the non-compact model HP24 because most of its frequencies are below the threshold γ ( see S1 Text ) . To summarise , in contrast to the robustness ρp = ϕpp where neutral mutations lead to strong deviations from the null model , the non-neutral phenotype mutation probabilities follow the random model expectation that ϕqp ≈ fq remarkably well . There are still important deviations , especially for those phenotypes that can not be reached due to biophysical constraints so that ϕqp = 0 [36] . Moreover , it may be an interesting exercise to look more closely at phenotypes for which ϕqp is significantly greater or less than fq as such deviations could signal similarities or differences between phenotypes . For example , two RNA phenotypes with similar hairpin topology , but perhaps a difference of one bond in a stem may have a larger probability of interconverting than topologically more dissimilar RNA phenotypes . The difference between ϕqp and fq could then be used to quantify the difference between phenotypes p and q . These more subtle types of correlation are beyond the scope of this paper . At any rate , compared to the result in the previous sections showing the strength of neutral correlations , the dominant agreement with the random model is apparent . However , given that ϕqp is averaged over a neutral set , it may be that there are local non-neutral correlations that are obscured by the averaging . With this in mind , we next investigate such local correlations . We first describe non-neutral local over-representation correlations which mean that , given phenotype q is found in the 1-mutation neighbourhood of a genotype g ( which maps to phenotype p ≠ q ) , then phenotype q will appear a greater number of times in total than predicted by fq or ϕqp in this 1-mutation neighbourhood , as pointed out in ref . [22] . These correlations are illustrated in Fig 5A . To measure 1-mutation neighbourhoods , we sample randomly chosen genotypes g from the neutral set G p , with a genotype of phenotype q in its neighbourhood . We then measure the phenotype of all other neighbours of g . From this sample , we obtain the probability P ( q , p , m ) of q occurring m times in the 1-mutation neighbourhood of a genotype mapping to phenotype p , given that q occurs at least once . Two control null expectations may also be derived for P ( q , p , m ) . In the random model where phenotypes are randomly assigned , given q is in the 1-mutation neighbourhood of a genotype g ( at a specific genotype g′ ) , the probability may be calculated as a binomial probability based upon the overall frequency of q , leading to P 1 ( q , p , m ) = ( L ( K − 1 ) − 1 m − 1 ) f q m − 1 ( 1 − f q ) L ( K − 1 ) − m ( 8 ) A second null expectation calculates the binomial probability by replacing fq in Eq 8 above by using the phenotype mutation probability ϕqp for the GP map instead: P 2 ( q , p , m ) = ( L ( K − 1 ) − 1 m − 1 ) ϕ q p m − 1 ( 1 − ϕ q p ) L ( K − 1 ) − m ( 9 ) In contrast to P1 ( q , p , m ) , this form accounts for any overall phenotypic heterogeneity known to be present in the GP map . We compare the actual local prevalence against these two null expectations in Fig 6 . For RNA20 , S3 , 8 and HP5x5 we chose the same three phenotypes for phenotype q as we did in the previous section , while for phenotype p we choose one-by-one the next n = 10 largest ( non-deleterious ) phenotypes available in the GP map . By sampling 10 , 000 neighbourhoods for each of the n = 10 phenotypes for p , we calculate an average for P ( q , p , m ) across the phenotypes ( P ¯ ( q , p , m ) ) and compare this in Fig 6 to the averages for the null expectations P ¯ 1 ( q , p , m ) and P ¯ 2 ( q , p , m ) . For each biological GP map , q is more likely to be over-represented , that is to appear multiple times if it appears at least once when compared to the null expectations , leading to a skewed distribution compared to the control case . The most striking result is seen in RNA20 , where there is a substantial tail to the distribution . We use average measures here to provide the general profile , smoothing out particular features that may occur between individual pairs of phenotypes q and p , but the local over-representation is seen for any of the phenotype pairs considered . One consequence of these local over-representation correlations is that the probability that a genotype with phenotype p has phenotype q at least once in its 1-mutant neighbourhood is less than expected from ϕqp . This is because those genotypes that have phenotype p in their 1-mutant neighbourhood typically do so a greater number of times than expected . This must therefore be compensated with fewer genotypes around which phenotype p actually appears at all , which we confirm numerically . In the RNA20 GP map , with the most frequent of the set of phenotypes used for p and the next most frequent used as q , the probability of finding q at least once is 0 . 12 versus a null expectation of 1 − ( 1 − ϕqp ) ( K−1 ) L = 0 . 20 . Thus these correlations lead to heterogeneity in the connections between phenotypes . How these correlations affect evolutionary dynamics will depend on the regime being explored [14] . If the population is neutrally exploring genotypes that map to phenotype p , then in the monomorphic regime of evolutionary dynamics , where NLμ ≪ 1 , this heterogeneity will lead to a significant drop in the rate at which q is first discovered by neutral exploration . In the polymorphic regime where NLμ ≫ 1 , and different individuals in the population have different genotypes , the rate at which novel variation with phenotype q occurs may not be that different from the expectation given by ϕqp , at least if the population is spread across a large enough number of different genotypes to average over local heterogeneity [14] . We next examine non-neutral local mutational neighbourhood correlations which are illustrated schematically in Fig 5B . They show that the 1-mutation neighbourhoods of two genotypes connected by a neutral point mutation are more likely to have similar phenotypic compositions than would be expected by two randomly chosen neutral non-neighbouring genotypes of the same phenotype . This type of correlation has already been demonstrated to exist for RNA [39] . To measure the similarity of neighbouring genotypes’ mutational neighbourhoods , we consider the local quantity ϕ q , g ( local ) = n q , g / ( K - 1 ) L , which becomes ϕqp when averaged over the whole neutral set G p . We compare the ϕ q , g ( local ) for neighbouring genotypes with non-neighbouring genotypes in both the null model and biological GP maps . The similarity or difference could be measured in several different ways . The statistical measure we employ here is the Bhattacharyya coefficient [40] , which for two discrete probability distributions xi and yi may be expressed as B C ( x i , y i ) = ∑ i x i y i ( 10 ) varying between 0 and 1 for maximally dissimilar and identical discrete probability distributions respectively . To quantify whether neutral neighbours g and h have more similar phenotype distributions in comparison to non-neighbouring neutral genotype pairs g and g2 , we compared the similarity ratio of the Bhattacharyya coefficients , BC ( g , h ) /BC ( g , g2 ) , using the ϕ q , g ( local ) to define the distributions . A ratio greater than unity indicates that the phenotype distributions around neutral neighbours are more similar than the randomly selected neutral pair , and vice versa . We remove the K−2 mutual neighbours of g and h from the distributions as these will automatically contribute to similarity between the neighbourhoods in a trivial manner which we wish to exclude . In Fig 7 we plot histograms of the similarity ratio for 10 , 000 samples of g , h and g2 in RNA20 , S3 , 8 and HP5x5 , where the phenotype sampled has the second largest frequency in RNA20 , and the largest frequency in S3 , 8 and HP5x5 ( excluding the del phenotype ) . For 10 , 000 samples the means are 1 . 357 ± 0 . 003 for RNA20 , 1 . 063 ± 0 . 001 for S3 , 8 and 1 . 025±0 . 001 for HP5x5 , where the error is the standard error on the mean . For RNA20 and S3 , 8 , a clear skew in the overall distribution may be visually observed , demonstrating that neutral neighbours , on average , have more similar mutational neighbourhoods . HP5x5 also has the mean of its distribution at a value slightly larger than unity but it is much more marginal in this case , and the skew is harder to detect . We note that in general , the non-neutral correlations are weakest for the HP5x5 model . Finally , just as is the case for the non-neutral local over-representation correlations of the previous section , these local mutational neighbourhood correlations also reduce the rate at which novel phenotypes would be discovered by neutral exploration since a neutral neighbour is more likely to have some of the same phenotypes in its mutational neighbourhood , and so fewer alternatives . The final , and perhaps most important , type of non-neutral correlation we consider is the accessibility of the deleterious phenotype from folding or self-assembling phenotypes , which we call non-neutral deleterious phenotype correlations . This type of non-neutral correlation is closest to the type of correlation suggested by Maynard Smith [1] . In Fig 8 we plot histograms of the ratio ϕdel , p/fdel for all phenotypes p in S3 , 8 and HP5x5 , and the top 20 most frequent ( largest fp ) in RNA20 ( limited due to computational expense of this larger system ) . In all cases , we see that the deleterious phenotype is significantly less frequent around the non-deleterious phenotypes . This behaviour contrasts to non-deleterious phenotypes , for which ϕqp ≈ fq . As a corollary of this effect , we also find ρdel/fdel equal to 1 . 10 , 1 . 16 and 1 . 19 for RNA12 , S3 , 8 and HP5x5 respectively , illustrating a corresponding local over-representation of the deleterious phenotype in its own mutational neighbourhoods . Moreover , for L = 20 we find that ρdel/fdel = 2 . 34 . suggesting that these positive neutral correlations may become stronger for larger L . We find that the del phenotype forms only a single component in RNA12 , S2 , 8 , HP24 and HP5x5 . This result is unsurprising because the large size of the del phenotype in each GP map ( 85% , 54% , 98% and 82% respectively ) means that the frequencies are all well above the single component threshold λ of Eq 6 , which would lead to the expectation of a single component even in the random null model .
We first explored the phenotype robustness for all three GP maps , showing that ρp > fp for all phenotypes , a result which is not unexpected in the literature , but to our knowledge has not been compared for a set of whole GP maps before . Since ρp = fp for the random model , the extent to which ρp is greater than fp can be viewed as a measure of the extent of the neutral correlations . We also introduced the concept of n-robustness , which measures robustness over n mutations . From this we derived another criterion that measures the presence of neutral correlations by averaging this measure over all phenotypes and comparing to the null expectation: If 〈ρ ( n ) 〉 > 1/Np then there are positive neutral correlations . We find that the enhanced probability of encountering a genotype mapping to the same phenotype can extend to multiple mutations n away from genotypes . The extent of the correlations in sequence space can be quantified by the number of mutations n* at which the criterion is violated , a measure we call the correlation length of the neutral mutations . We find that that n* is largest for the RNA12 model , and smallest for the HP24 model . How n* or even the relative ordering of the correlation lengths between the different systems will scale with increasing genome length L remains an open question . It should also be emphasised that the full complexity of neutral correlations for a phenotype are only partly be captured with the measures we introduced here , which average over the entire neutral set . As can be seen in Fig 3A of ref . [14] , a single phenotype can have significant local heterogeneity in its internal connections . Since neutral sets can be so vast that they frequently cannot be fully explored by populations on evolutionary time-scales , such local heterogeneities may also have implications for evolutionary dynamics . Thus local measures of robustness , measures of heterogeneity , or measures that take into account identities of multiple neighbours , or the position of a mutation along a genome , may also be important to develop in future work . We found for three biological maps that the dominant relationship of robustness with frequency is ρp ∼ log fp , a scaling that has already been pointed out earlier for RNA [13 , 41] . In an interesting paper that applies concepts from network theory [38] to neutral sets , Aguirre et . al . [13] rationalise this scaling for RNA by separating out the mutational behaviour of bound and unbound bases . It would be interesting to see if a more general argument could be developed to explain the logarithmic scaling across all the systems we studied . Moreover , these results also pose fascinating questions relating to why or how the constraints in a GP map lead to the kinds of neutral correlations they do . Some clues to the underlying causes of neutral correlations and robustness can be gleaned from Maynard Smith’s original paper , where he illustrated the concept of a neutral network with the parlour game of transitioning between connecting two words in the English language by changing one letter at a time , with each change also generating a valid word . He used the example of changing “WORD” to “GENE” in four steps as illustrated in Fig 9 . There are 264 = 456 , 976 different possible 4-letter words , but , according to the Merriam-Webster Official Scrabble Players Dictionary 2014 , only 4 , 175 , or just over 0 . 9% , are valid English words . If we consider the set of valid words to be a phenotype , then it has frequency of only fp = 0 . 009 , just under the giant component threshold δ = 1/ ( K−1 ) L = 0 . 01 , and well below the single component threshold λ ≈ 0 . 12 . On average the probability that a 4-letter word has a valid neighbour is just below one . However , if we measure the phenotype robustness , that is the mean probability that a valid 4-letter word has valid words in its ( K−1 ) L = 100 neighbours , we find that ρp ≈ 0 . 11 , or on average each word has 11 neighbours , which makes the game much simpler than if the random expectation held . The reasons this game exhibits such a large enhancement of the robustness over fp clearly arise from neutral correlations in English words . For example , vowels are more likely to appear in specific places in words than would be expected by chance . The second letter of 4-letter English words has a 74% chance of being a vowel compared to the 5/26 = 19% overall average probability at locus . So if a word has a vowel placed at the second letter , it is much more likely to have neighbouring words using the same vowel , as can be seen in Fig 9 . This example illustrates how basic properties ( in this case of language properties , in the case of our models , biophysical properties ) can generate correlations , which can result in high robustness . A question often debated in the literature is the extent to which mutational robustness is selected for . Here we argue that it is important to keep in mind that a major enhancement of robustness , often by many orders of magnitude over the random expectation , is not caused by selection , but rather emerges from the internal constraints of a GP map—the way that genotypes map to phenotypes—which naturally lead to positive neutral correlations . It may still be the case that more robust genotypes can be selected for within a neutral set , or that these genotypes are favoured in certain dynamic regimes [42] . It may also be true that in some cases a particular phenotype is preferred by selection because it is more robust than an alternative one . But even if this is so , natural selection is still acting on variation that is already naturally quite robust due to correlations caused by biophysical constraints . The relationship between robustness and evolvability has been the subject of much discussion in the literature [12 , 22 , 43 , 44] . Here we show , as already anticipated by Maynard Smith [1] , that if the phenotype robustness is roughly larger than δ = 1/ ( K−1 ) L , so that the expected number of neutral neighbours is greater than one , then the phenotype will exhibit large neutral networks . In the random model , large networks will generally be very rare , but neutral correlations mean that robustness above the δ threshold is common for the biophysical GP maps . The effect can be very large . For example , for L = 55 RNA , a recent study [33] suggests that there are about NP ≈ 8 × 1012 phenotypes , so that the mean frequency is f p ¯ ≈ 10 - 13 . In fact all phenotype frequencies are well below the threshold δ = 1/ ( 3 × 55 ) = 0 . 00606 above which we expect extended neutral networks . On the other hand , the mean robustness of all phenotypes was estimated to be ρ p ¯ ≈ 0 . 14 > δ ≫ f ¯ p . Neutral correlations increase the probability of a nearest neighbour generating the same phenotype by on average about 12 orders of magnitude over the mean expectation of the null model , lifting robustness well above the threshold δ . Thus the most important way that neutral correlations contribute to evolvability is by naturally creating robustness greater than the threshold needed to generate percolating networks which provide access to phenotypic novelty . In fact it may very well be that without neutral correlations and its attendant robustness , evolution as we know it would not be possible Non-neutral mutations are important for the generation of novel variation . For all three GP maps , the probability ϕqp that a phenotype q is found by a point mutation from genotypes mapping to phenotype p is , to first order , given simply by the global frequency: ϕqp ∼ fq , which is independent of p . Since fq can span many orders of magnitude , the rate at which variation appears ( which scales as τq ∼ 1/ϕqp ∼ 1/fq if a population is neutrally exploring phenotype p [14] ) can also range over many orders of magnitude in these systems . These large differences can lead , both in the monomorphic and polymorphic regimes , to effects such as the arrival of the frequent[14] , where frequent phenotypes ( with larger fq ) fix in a population even when alternate phenotypes that are much more fit , but much less frequent , are accessible in principle . The reason these fitter phenotypes are not fixed is because they are unlikely to be found on evolutionary time-scales . Natural selection can only work on variation that actually arises . In the alternative case where the system is effectively in steady state , so that a less frequent phenotype has a realistic probability to arise in a population , it can still be the case , especially at larger mutation rates , that a phenotype with lower fitness but larger frequency ( and robustness ) will fix , an effect known as the survival of the flattest [45] . Finally , we note that ϕqp can be viewed as a non-neutral generalisation of the phenotypic robustness , but that ϕpp = ρp scales very differently with fp than ϕqp does when p ≠ q . In the latter case local correlations more or less cancel out when averaged over the whole neutral set , so that ϕqp ∼ fq , while in the former case the local correlations do not cancel out at all because robustness is fundamentally a local quantity . It is quite striking that in all three models , a very large number of phenotypes are indeed connected to one another . The HP model merits further discussion in this regard . In a recent review [46] , RNA space was compared to “a bowl of spaghetti” , because the neutral spaces were connected to most other phenotypes , while proteins were compared to a“plum pudding” , where the neutral networks were more likely to be isolated from one another . We indeed find that the neutral networks in the HP24 model are not well connected , but locate the origin of this effect in the large NP/NG ratio for HP24 , which means that many networks are below the threshold of Eq ( 7 ) for connections . By contrast , the compact HP5x5 model with many fewer phenotypes but a similar sized genotype space is well connected , more like “spaghetti” than like a “plum pudding” . What happens for real proteins , without the simplifying assumptions and small system sizes typically studied in the HP model [34] , remains an open question . Another type of heterogeneity in the mapping of genotypes to phenotypes can be quantified as local non-neutral correlations , which occur when the local neighbourhood of genotypes are different from the global expectation given by ϕqp or fq . We investigated two types of correlation ( although one could imagine many more ) : i ) non-neutral local over-representation correlations which result in phenotypes being more likely to be found multiple times around genotypes , and ii ) non-neutral local mutational neighbourhood correlations , which mean that two genotypes connected by a neutral point mutation have mutational neighbourhoods that are more similar than do two randomly selected genotypes in a neutral set . These two types of correlation mean that the diversity of phenotypes in the direct neighbourhood of a genotype is lower than expected from the random model or even from the averaged phenotype mutation coefficients ϕqp . Thus the rate at which a neutrally exploring population encounters novel variation will be reduced due to these correlations . How this effect influences evolvability is complex , because the term is used in many different ways in the literature [15 , 47–51] . One type of evolvability simply measures the number of different phenotypes that are connected by single mutations to a neutral set [22] . While non-neutral correlations may not affect this number very much , they will affect the rate at which neutral exploration finds these new phenotypes . This lowering of the rate at which novelty appears may have a larger impact on other measures of evolvability . Each of the three models has a deleterious phenotype which either does not fold ( for RNA and the HP protein model ) or does not properly assemble ( in the Polyomino model for protein clusters ) . The third type of non-neutral correlations we considered were iii ) non-neutral deleterious phenotype correlations . For all three GP maps , the folding or assembling phenotypes have fewer mutational connections to the deleterious phenotypes than would be expected by the global frequency fdel . This last result is perhaps the most interesting type of non-neutral correlation . It was already predicted by John Maynard Smith in his classic 1970 paper [1] , where he argued that “meaningful” proteins were more likely to be neighbours of other “meaningful” proteins , and by extension , that the probability of finding a deleterious phenotype in the mutational neighbourhood of a “meaningful” protein would be less than by random chance . Such an effect can enhance evolutionary dynamics , because non-deleterious phenotypes are more strongly connected by mutations than expected by random chance , and so the population can more easily access potentially meaningful novel variation . Of course in practice , whether or not even the folding or self-assembling phenotypes are in fact “meaningful” will depend on the environment and other factors , but to first order a reduced propensity to mutate to manifestly deleterious phenotypes should be an advantage . While the effect of neutral correlations on robustness is straightforward , how correlations affect evolvability is more complex , not just because the concept itself is more diffuse , but also because the relationships between correlations and evolvability are more varied . Nevertheless , we can summarise how different correlations affect evolvability as follows: While we have built upon and introduced a wide variety of metrics for genetic correlations in GP maps , the framework of correlations has other avenues for future computational work . For example , the central focus here has been on the way phenotypes are neutrally connected and non-neutrally connected without any broader concern for the properties of the phenotypes themselves . Phenotypes themselves have measurable properties such as symmetry , size and modularity and one could take the analysis further by considering whether there is a relationship between the distance between two phenotypes and their similarity based on such properties . Making use of the null model again , where there is no predisposition for which phenotypes are mutationally close together , such phenotype similarity correlations could be studied in the biological GP maps . Computational studies on theoretical systems ultimately need to be backed up with empirical evidence in real biological systems . Robustness to mutations in protein tertiary structure has been a well-studied area in this regard , with both mutagenesis and phylogenetic experiments being used to illustrate robustness [15] . It may be that non-neutral local correlations could be verified using mutagenesis experiments . For example , for mutational neighbourhood correlations , two neighbouring genotypes both with a chosen structure could have their neighbourhoods examined for the range of phenotypes and compared to the neighbourhood of a more distant genotype with the same methodology used here computationally , potentially replicating the findings Fig 7 but for real molecules . It may also be possible to measure the neutral correlation length n* by doing multiple mutation experiments . However , because it is hard in practice to extract full GP map properties such as fp for a given phenotype , the most challenging aspect of such an experiment would be in generating an appropriate effectively random genotype mapping to the corresponding phenotype . This same challenge holds for the other kinds of correlations we investigate in this paper . A few final caveats are in order . In these models it is natural to use a restricted definition of a neutral mutation leading to exactly the same phenotype , whereas a more complete theory would count all mutations that are not visible to selection as effectively neutral . Thus the full picture of how these correlation affect evolutionary dynamics is complex , and depends not just on the GP map itself , but more generally on the genotype to phenotype to fitness map , for which the environment plays a key role . Moreover , population genetic parameters such as the population size and the mutation rate must be taken into account . But notwithstanding these complications , the important influence that structure in the GP map , in this case measured through the lens of genetic correlations , has on the manner in which variation arises ( the “arrival of the fittest” [53] ) , and so on evolutionary dynamics , should be evident , confirming Maynard Smith’s suggestions from many years ago . It may even be that without these correlations , Darwinian evolution , and therefore life itself , may not have been possible . | Evolutionary dynamics arise from the interplay of mutations acting on genotypes and natural selection acting on phenotypes . Understanding the structure of the genotype-phenotype ( GP ) map is therefore critical for understanding evolutionary processes . We address a simple question about structure: Are the genotypes positively correlated ? That is , will the mutational neighbours of a genotype be more likely to map to similar phenotypes than expected from random chance ? John Maynard Smith and others have argued that the intuitive answer is yes . Here we quantify these intuitions by comparing model GP maps for RNA secondary structure , protein tertiary structure , and protein quaternary structure to a random GP map . We find strong neutral correlations: Point mutations are orders of magnitude more likely than expected by random chance to link genotypes that map to the same phenotype , which vitally increases the potential for evolutionary innovation by generating neutral networks . If GP maps were uncorrelated like the random map , evolution may not even be possible . We also find correlations for non-neutral mutations: Mutational neighbourhoods are less diverse than expected by random chance . Such local heterogeneity slows down the rate at which new phenotypic variation can be found . But non-neutral correlations also enhance evolvability by lowering the probability of mutating to a deleterious non-folding or non-assembling phenotype . | [
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"com... | 2016 | Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability |
Clonal expansion of HIV infected cells plays an important role in the formation and persistence of the reservoir that allows the virus to persist , in DNA form , despite effective antiretroviral therapy . We used integration site analysis to ask if there is a similar clonal expansion of SIV infected cells in macaques . We show that the distribution of HIV and SIV integration sites in vitro is similar and that both viruses preferentially integrate in many of the same genes . We obtained approximately 8000 integration sites from blood samples taken from SIV-infected macaques prior to the initiation of ART , and from blood , spleen , and lymph node samples taken at necropsy . Seven clones were identified in the pre-ART samples; one persisted for a year on ART . An additional 100 clones were found only in on-ART samples; a number of these clones were found in more than one tissue . The timing and extent of clonal expansion of SIV-infected cells in macaques and HIV-infected cells in humans is quite similar . This suggests that SIV-infected macaques represent a useful model of the clonal expansion of HIV infected cells in humans that can be used to evaluate strategies intended to control or eradicate the viral reservoir .
T cells clonally expand in response to homeostatic/cytokine and antigen-specific stimuli as part of their normal physiology . Not surprisingly , some HIV-infected CD4+ T-cells also clonally expand [1 , 2] , and expanded clones can persist in infected individuals for more than 10 years . Analysis of clonal expansion of HIV infected cells has been done primarily using PBMCs obtained from the blood of patients who had been on suppressive combination antiviral therapy ( ART ) for at least several years when the samples were taken . Effective ART either greatly reduces , or entirely prevents , the de novo infection of additional T cells in HIV infected humans and RT-SHIV infected macaques [3–5] . Because ART blocks new rounds of HIV infection , and because most infected T cells die quickly after infection , clones are more easily detected in individuals who are on ART . Although clones of infected cells are present in untreated individuals , the confounding background of large numbers of proviruses in recently infected cells that have not clonally expanded makes it more difficult to use integration site analysis to identify the proviruses that are in clonally expanded cells . Our results suggest that , in HIV-infected individuals on effective ART , expanded clones make up at least 40% of the infected cells [1] , although , due to sampling limitations , this fraction is probably a considerable underestimate . In individuals on ART , a large majority of infected cells carry defective proviruses [6 , 7] . Despite claims that viral expression would necessarily lead to cytopathic effects caused by the toxicity of viral proteins and/or immune clearance of the infected cells , which would preclude clonal expansion [8] , it is now clear that some clonally expanded cells carry replication-competent proviruses and can release infectious virus into the blood [9] . There is accumulating indirect evidence that expanded clones that carry replication competent infectious proviruses are common [10–13] . Clones of infected cells appear to survive and expand , despite the toxicity of some of the viral proteins and immunological surveillance by the host , because , at any time , only a fraction of the cells that comprise the clones are producing viral RNA , and , by extension , viral proteins [9 , 14] . As long as the overall proliferation of the cells in an infected clone equals or exceeds the clearance of the cells that express viral proteins , HIV infected clones can persist and can even expand over time . Thus , clonal expansion of cells that carry replication-competent proviruses makes an important contribution to the generation and maintenance of the viral reservoir of infected cell , that persists despite long term ART . HIV infected cells that have clonally expanded can be detected by integration site analysis using DNA obtained from blood samples . However , only a small fraction ( estimated to be about 2% ) of the total T cell population is in the blood at any one time [15] . Lymph node biopsies have occasionally been obtained from HIV infected individuals , as have rare autopsy samples . There are , however , serious limitations on the samples that can be obtained from HIV infected people , and there are appropriate restrictions on experiments that can be done on humans . For those reasons , animal models of HIV infection have been developed . All the available animal models have limitations , which must be addressed to validate their relevance for asking specific research questions [16] . To evaluate the suitability of experimentally SIV-infected rhesus macaques as a model to investigate aspects of the clonal expansion of HIV-infected cells in humans , we first performed in vitro infections and compared the distributions of integration sites in HIV infected human PBMCs and SIV infected macaque PBMCs . Because this comparison is complicated by differences in the human and macaque genomes , and to ask whether there are differences in the overall distribution of the integration sites due to differences in the host cells ( human vs . macaque ) , we also prepared a library of SIV integration sites from human PBMCs infected in vitro with SIV and compared the integration site distributions in these libraries to the distribution of HIV integration sites in human PBMCs ( https://rid . ncifcrf . gov ) . Comparing the overall distributions of the integration sites , and the genes in which integration preferentially occurred , in the three libraries shows that the integration sites preferences of HIV and SIV are quite similar . These results confirm the conclusion , reached earlier based on a much smaller integration site dataset from a human cell line infected in culture with SIV [17] . Given the importance of clonal expansion of infected cells in the persistence of HIV in people on ART , we asked whether there was a similar clonal expansion of SIV infected cells in macaques . Samples were taken from 4 macaques that were infected for 4 weeks with SIVmac239 , and then treated with a suppressive ART regimen for at least one year . The data show that the clonal expansion of SIV-infected cells in macaques is quite similar to the clonal expansion of HIV-infected cells in humans , that the timing of clonal expansion for infected cells is similar in HIV infected humans and SIV infected macaques , and that most of the largest SIV infected clones were found in more than one tissue in macaques .
To assess the degree of similarity in the distributions of HIV and SIV integration sites in human PBMCs and SIV integration sites in macaque PBMCs , we stimulated human and macaque PBMCs , infected the cells with SIVmac239 , and prepared integration site libraries from the infected cells using the methods of Maldarelli et al . [1] ( see also Materials and Methods ) . The library prepared from SIV infected human PBMC had 107 , 000 independent integration sites , and the library prepared from SIV infected macaque PBMC had 74 , 000 independent sites . The distribution of the SIV integration sites in these two libraries was compared to the distribution of 385 , 000 independent HIV integration sites in a library we previously prepared from infected human PBMC ( https://rid . ncifcrf . gov ) . We did not attempt to prepare a library from HIV infected rhesus macaque PBMCs because species-specific viral restriction factors limit the efficiency of HIV infection of macaque PBMCs [18] . We began by comparing the overall SIV and HIV integration site preferences in human cells . This approach obviates the problem of trying to compare the integration sites in the genomes of different host species . A comparison of the fractions of integration sites present in genes , in association with CpG islands ( +/- 1kB ) , and in association with transcription start sites ( +/- 1kB ) ( Table 1 ) , showed that the integration site preferences of SIV and HIV in the human genome were similar , confirming an earlier interpretation based on a much smaller number of SIV integration sites [17] . We also compared the integration site preferences of HIV and SIV in human PBMCs with the SIV integration site preferences in macaque PBMCs . We initially mapped the SIV integration sites onto the macaque genome ( rheMac8 ) . However , it is difficult to compare the relative distribution of integration sites in the two host genomes , particularly because the genes in the macaque genome are not as well annotated as the genes in the human genome . To address this problem , the SIV integration sites were mapped onto the human genome using the interval in the SIV genome +/- 50 bp on either side of the integration site . There is sufficient similarity in the sequences of the human and macaque genomes so that more than 70% of the >74 , 000 SIV/macaque integration sites ( >52 , 000 ) could be mapped onto the human genome ( see Materials and Methods ) . SIV had similar overall integration site preferences in human and macaque PBMCs ( Fig 1 and Table 1 ) . It has been known for some time that HIV preferentially integrates into highly expressed genes [19] . To determine whether SIV has a similar preference , we used RNA-Seq to measure RNA levels in both human PBMCs and macaque PBMCs and showed that both viruses have a similar preference for integrating their DNA in highly expressed genes ( Fig 2 ) . Because our integration site datasets were large , we could also ask whether SIV and HIV preferentially integrated into the same subset of genes . We prepared lists of the 500 genes in which there were the greatest number of HIV and SIV integrations ( Table 2 ) . If we assume that there are approximately 25 , 000 genes in both species , and prepare two lists of 500 genes chosen at random , then the number of genes that would appear on both lists would be about 10 , or 2% ( after the first list of 500 genes has been chosen at random , there is a 1/50 chance that any gene chosen at random for a second list will be on the first list ) . However , when we did a comparison of the top 500 genes in the two libraries made from the infected human PBMCs , the number of genes in common in the two libraries was 300 ( 60% ) . As expected , the fraction of genes that were on both lists increased when lists of top 1000 , 2000 , or 5000 genes were compiled and compared ( Table 2 ) . To provide interpretive context for these observations , we made use of the fact that the 385 , 000 member HIV human PBMC library was created by infecting stimulated PBMCs from two different human donors ( Donor 1 , 225 , 000 integration sites; Donor 2 , 160 , 000 integration sites ) . When the lists of the top 500 genes in the two HIV/human PBMC libraries were compared , there were 422 ( 84% ) in common in the two libraries . We also prepared a list of the top 500 genes into which SIV preferentially integrated in the macaque genome ( mapped to the human genome ) and compared it to the lists of the top 500 genes into which SIV and HIV preferentially integrated in human cells ( Table 2 ) . The number of top 500 genes in common between the SIV infected macaque and SIV infected human PBMC libraries was 323 , or 65% . The overlap between the top 500 genes in the SIV infected macaque and HIV infected human PBMCs was 239 , or 48% . As was true for the overlap in the top genes in the SIV infected human PBMC and HIV infected human PBMC libraries , the fraction of overlapping genes went up when the number of genes on the lists was increased to 1000 , 2000 , or 5000 genes ( Table 2 ) . We also determined the fraction of genes that were in the top 500 , 1000 , 2000 , and 5000 in all three libraries . Again , the fraction that was present in all three libraries went up as the number of genes on the lists increased , from 41% for the top 500 genes to 67% when the top 5000 genes were compared . The lists of overlapping genes in the human and macaque libraries can also be used to connect the syntenic regions of the macaque and human genomes that comprise the gene-rich regions that are preferred targets for both HIV and SIV integration ( Fig 3 ) . To ask whether the similarities in the overall distribution of the integration sites were also evident on a smaller scale , we performed selected comparisons of the integration site distributions in specific regions of the genome , for example in the region around the genes for the Neat1 and Malat1 long non-coding RNAs , which are on human chr 11 , a region that is highly favored for both HIV and SIV integration . The corresponding/syntenic region of the macaque genome , on macaque chr 14 , is also highly favored for SIV integration ( see Figs 1 , 3 and 4 ) . These data confirm and extend the analysis that was done using large-scale comparisons and shows that the local distribution of integration sites is , like the overall distribution of integration sites , quite similar in the three libraries . The data also show that the genes that are expressed at high levels in macaque and human PBMCs are similar , and that both SIV and HIV preferentially integrate into the bodies of expressed genes . We also calculated the similarities in the overall distribution of the integration sites for the three libraries ( Tables 3 and 4 ) . All of the comparisons show that the distributions of SIV and HIV integration sites are quite similar . The substantial similarities among the in vitro integration sites in the three libraries make it possible to ask whether the limited differences seen in the distribution of the integration sites are due to differences in the target cell species ( human vs . macaque ) , in the viruses ( HIV vs . SIV ) , or both . Our analysis suggests that , although the overall patterns were quite similar , both the host and the virus make contributions to the modest differences in the integration site distributions ( for example , in all of the comparisons of the top genes , the human/HIV and macaque/SIV libraries have the fewest genes in common ) ( Table 2 ) . Having demonstrated that SIV and HIV have similar integration site preferences in cells infected in vitro , not only in terms of the distribution of integration sites near landmarks in the genome , but also in terms of which genes are the preferred targets , we determined the distribution of integration sites in PBMCs and tissue samples taken from SIV infected macaques . Samples were taken from 4 animals that were infected with SIVmac239 for 4 weeks , then placed on a suppressive ART regimen . Three of the animals received ART for approximately one year and were then euthanized; the fourth animal was maintained on ART for 90 weeks before being euthanized ( see Materials and Methods ) . Blood was taken from all four animals at two and four weeks post-infection , prior to initiation of ART . Samples of blood , spleen , and axillary and mesenteric lymph nodes were taken from two of the animals after one year of treatment . A spleen sample and an axillary lymph node were taken from the third animal at one year and blood was obtained from the fourth animal at one year and at 90 weeks . The samples were analyzed for viral DNA loads ( Table 5 ) . Blood samples were taken and analyzed to determine the viral RNA load ( Table 6 ) . We obtained a total of approximately 8000 integration sites from the in vivo samples ( Table 7 ) . As expected , based on the number of infected cells present in each of the samples , we obtained the greatest number of integration sites from the blood samples taken after two weeks of infection ( ~5000 ) , before the initiation of ART and at approximately the time of peak plasma viremia during primary infection ( Table 6 ) . Our ability to detect clones of infected cells is limited by sampling . Any of the samples we analyze contain only a small fraction of the infected cells present in the animals , and the methods we use can detect , at most , approximately 10% of the integration sites that are present in the samples [1] . We estimate that , in the animals on ART , clones must comprise >105 cells to be detected using our standard assay ( see Materials and Methods ) . Thus , integration site analysis underestimates the fraction of the infected cells that have clonally expanded . A total of 107 clones were identified in samples obtained from the animals ( S1 Table ) . 101 of those clones were found in the on-ART samples . Almost 20% of the total integration sites in the on-ART samples ( 19 . 4% , 299 of 1545 ) were shown to be in clonally expanded cells . Because after ART has been initiated , few if any additional cells are newly infected , the cells that gave rise to the expanded clones found in the on-ART sample were likely to have been infected before therapy was initiated . Cells from 5 of the 7 largest clones ( those in which the integration site was isolated 6 or more times ) were present in more than one of the tissues ( Table 8 ) . However , there was one clone for which we isolated the same integration site 10 times; all were in a sample taken from a single lymph node . To better understand the origin of the clones of SIV-infected cells that were identified in animals on effective ART for one year , we analyzed the integration sites in blood samples that were taken from the four animals two weeks and four weeks after the initial infection , prior to the initiation of ART ( Table 7 ) . Note that there are differences in the interpretation of pre-ART and on-ART integration site data . It is possible , in some cases , that integration sites were obtained from single cells that had recently replicated their DNA , but not yet divided . When , in an on-therapy sample , we obtain the same end of a provirus two or more times , we identify that site as being in a clone of expanded cells . If there are infected cells that have replicated their DNA , but not yet divided , in samples taken after a year or more on-ART , it is quite likely that the cell is dividing , and is part of a clone . However , in untreated SIV infected macaques , as in untreated HIV-infected humans , most newly infected cells die shortly after infected individuals are put on ART [16] . This large background of newly infected cells makes it much more difficult to identify clones of infected cells in pre-ART samples . In addition , any clones that are present in a pre-ART sample taken shortly after the initial infection might not have had time to expand to include a sufficiently large number of cells to be detected as clones . Moreover , in samples taken from untreated macaques ( or people ) , some of the recently infected cells could be in the S or G2 phases of the cell cycle . In experiments done with cells infected in culture , expression of either HIV-1 or SIV Vpr arrested cell division in G2 [20–22] . It is not clear whether there is an equivalent G2 arrest of infected cells in vivo , and , if there is , how long the arrested cells survive , but it is unlikely that they will divide and grow into clones . Nevertheless , such cells would contain two copies of the same provirus , which could allow us to isolate the same end of the provirus with two host DNA breakpoints in the integration site assay . For that reason , it is unclear , when we have obtained the same end of a provirus with two different breakpoints from a pre-therapy sample , whether that integration site was obtained from two infected cells or a single cell which carried two copies of the same provirus . Because the pre-therapy and on-therapy integration site data are somewhat different , and the ways in which clones are identified are different in the two datasets , the data are not strictly comparable , and are reported separately in Tables 9 and 10 ( see Discussion , and Materials and Methods ) . We identified one clone of SIV infected cells in a sample taken after the animal had been infected for only 2 weeks . This integration site was identified as being in a clone based on finding the same integration site in two different types of samples , obtained at different times . In this case , the integration site identified in a PBMC sample at 2 weeks post infection , prior to ART , was also found in a tissue sample taken one year later while on ART ( S1 Table ) . We found 6 clones in samples taken at 4 weeks , although there were many fewer overall integration sites in the 4-week samples ( about 1500 vs . about 5000; plasma viremia was declining by 4 weeks , compared to 2 weeks , Table 6 ) . In addition to the one site that could be verified as belonging to a clone , samples taken from all 4 macaques at 2 weeks and samples taken from 3 out of 4 macaques at 4 weeks contained sites with exactly 2 breakpoints . The frequency of such sites increased from about 0 . 4% at week 2 to 1 . 4% at week 4 ( Table 9 ) , in contrast to the 7-fold overall decline in SIV DNA and greater than 3-fold decline in integration sites recovered . At this time , we cannot determine which of the two possibilities discussed above explains how two breakpoints were obtained for these integration sites , and although we cannot rule out their possible origin from cells that had replicated their provirus containing DNA but not yet divided , the increase in their frequency between the two time points is consistent with growth of some infected cells into clones large enough for us to detect by two weeks after the initial infection . Because preferential growth or survival of a fraction of HIV infected cells in humans has been linked to the integration of HIV proviruses in specific introns of the BACH2 , MKL2 , and STAT5B genes [1 , 2 , 23] , we looked for SIV proviruses integrated in these genes . We found a total of 6 proviruses in BACH2 , 3 in MKL2 , and 9 in STAT5B . Table 11 describes the integration sites in these three genes in the four infected macaques , and compares the macaque data to the integration sites found in these three genes in an HIV-1-infected patient on suppressive ART [1] . Most of the in vivo SIV integration sites ( ca . 6500 out of about 8000 ) were obtained from the 2- and 4-week samples , which were taken from untreated animals . Based on the very small number of clones we detected in the pre-ART samples , we did not expect to find any clones whose replication was driven by proviruses integrated into BACH2 , MKL2 or STAT5B in the 2- and 4-week pre-ART samples . Of the 9 SIV integration sites in BACH2 and MKL2 in the macaque samples , only 3 were in the on-ART samples . Only one site ( in BACH2 , on-ART ) , was in the same region ( BACH2 intron 5 ) and the same orientation ( same as the gene ) as the integration sites that were selected in Patient 1 . None of the 9 integration sites was in a detectable clone of expanded cells . HIV integration in the first intron of the STAT5B gene , in the same orientation as the gene , has also been linked to clonal expansion of the infected cells [23] . However , in contrast to BACH2 and MKL2 , which are not preferential targets for HIV integration , STAT5B is a preferred target for HIV integration , which complicates the analysis . We found 5 SIV proviruses integrated in STAT5B in samples taken from infected animals before ART was initiated , and 4 additional proviruses in samples taken after a year of ART treatment . In the on-ART samples we analyzed , there were too few integration sites in STAT5B to determine if there was a selection for integration sites in STAT5B on-ART . We did find one expanded clone in which an SIV provirus was integrated in the first intron of the STAT5B gene , in the same orientation as the gene , which is the region of the gene and the orientation of the provirus that is associated with clonal growth of HIV infected T cells in humans . We did a similar comparison of the fractions of integration sites in the pre-ART PBMC dataset and the on-ART dataset for all of the genes to look for evidence of selection for proviruses integrated into any other genes in the macaques on ART . Because the current on-ART dataset of SIV integration sites is relatively small , there were only a few integration sites for most of the genes , and we were not able to find evidence of selection for integration sites in any gene that was statistically significant . The largest SIV infected clone ( judged by the number of times the integration site was recovered ) had an SIV provirus inserted in the RB1 ( retinoblastoma ) gene in the opposite orientation relative to the gene . RB1 is a frequent target for SIV integration ex vivo . 43 SIV proviruses were found in the RB1 gene in the rhesus PBMC infected in vitro . We identified only one clone in which there was an SIV provirus integrated in RB1 in any of the on-ART samples and there were 3 proviruses found in RB1 in the pre-therapy samples . Thus , the data we have do not provide any evidence that SIV integration into the RB1 gene is connected to the clonal expansion and persistence of the infected cells . We also compared the overall distribution of integration sites in the in vitro SIV infected PBMC , the ( combined ) pretherapy samples , and the ( combined ) on-ART samples ( Figs 5 and 6 ) . These analyses show that , as expected , the overall distribution of integration sites was quite similar in vivo and in vitro . The data also show that the initial distribution of integration sites changed only modestly during a year on ART . This result supports the conclusion that there is , at most , only a limited selection for proviruses that are integrated into specific genes . We looked in samples taken from SIV infected macaques for evidence of a selection for/against proviruses in either orientation relative to the host gene in which they are integrated . In human cells infected with HIV in culture , there is no initial bias in the orientation of the proviruses relative to the gene . However , in samples taken from HIV patients on therapy , there were significantly fewer proviruses integrated in the same orientation as the gene , relative to those in the opposite orientation [1] . We analyzed the orientations of SIV proviruses relative to the orientation of the host genes in macaque PBMCs infected in vitro and in samples from infected animals taken both pre-therapy and on-therapy . As was seen in the HIV/human data , there was no bias in the orientation of SIV proviruses in recently infected cells in tissue culture . There was a modest bias against the proviruses in the same orientation as the host gene in the pre-therapy samples , and there were significantly fewer SIV proviruses in the same orientation as the gene in the on-therapy samples ( Table 12 ) . Proviruses in the same orientation as the gene are more likely to interfere with normal gene expression than are proviruses integrated in the opposite orientation , which would explain the selection against proviruses that are integrated in the same orientation as the gene ( see Discussion ) .
The HIV and SIV viral RNA and DNA load , typically measured in plasma and cells obtained from the peripheral blood , usually reaches a peak within the first 2–3 weeks after the initial infection ( Tables 5 and 6 ) . The viral loads decline as target cells are depleted and the host’s immune system begins to recognize infected cells that express viral proteins [28 , 29] . If infected humans ( and macaques ) are put on ART , there is an additional decline in the viral DNA load because infected cells continue to die but are not replaced by new infections [28] . Both immune surveillance and the toxicity of viral proteins favor the accumulation of infected cells ( and clones ) that either carry extensively defective proviruses or intact proviruses that are either not expressed or are expressed at a very low level . Nevertheless , HIV-infected humans can have large clones that carry infectious proviruses and these clones can release detectable levels of infectious virus into the blood [9] . On ART , the viral DNA load eventually stabilizes although it may continue to decline very slowly [30 , 31] , presumably because cells infected with defective and/or latent proviruses are not subject to immune clearance or viral cytopathology , and because there is sufficient growth and expansion of some of the clones of infected cells to balance ( or nearly balance ) any additional loss of infected cells . In the current study , we recovered fewer integration sites from the pre-ART SIV infected macaque blood samples taken 4 weeks after the animals were infected than from pre-ART samples taken after 2 weeks of infection . However , because there was much less viral DNA present at 4 weeks , relative to 2 weeks , the fraction of the sites we recovered was greater . We obtained even fewer sites ( but a still larger ) fraction of the sites that were present ) from the samples taken after the animals were receiving ART for one year relative to the 4-week samples . These data reflect the increasing proportion of long-lived cells that contain proviral DNA in the 4 week and on-ART samples . ( Tables 9 and 10 ) . Although we obtained a considerably higher total number of integration sites from the pre-therapy samples , the majority of the pre-therapy integration sites were likely to have been derived from recently infected cells . Using stringent criteria to identify clones in the pre-ART samples , we identified only one confirmed clone in the 2-week samples ( 0 . 02% of total sites ) and only 6 confirmed clones in the 4-week samples ( 0 . 4% ) . The clone identified in the 2-week sample was identified because we found a provirus with the same integration site in a sample isolated at one year on-ART from the spleen of the same animal , showing that it had grown into at least a small clone by 2 weeks and that clones that arise early can persist . These data suggest that the earliest infected clones can grow large enough to be readily detected between 2 and 4 weeks after infection , in good agreement with our data for the time it takes HIV infected clones to grow to a detectable size in humans . We compared the time at which clones of SIV infected could first be detected in samples taken from untreated macaques with data from humans that were recently infected with HIV for approximately the same length of time , based on Fiebig staging [32 , 33] . We also compared the fraction of SIV infected cells that we could show had clonally expanded in the 4-week sample to the fraction of clonally expanded HIV-1 infected cells we detected in untreated humans who had been infected for a similar length of time . Both the times at which the clones grew to a detectable size , and fraction of integration sites that were in clones , were similar . In addition to the sites we found in the untreated macaques that could be confirmed as belonging to clonally expanded cells , there were about 20 integration sites for which we obtained 2 breakpoints at both 2- and 4-weeks post infection ( 0 . 4% and 1 . 4% , respectively ) . As noted in the Results , We were cautious in interpreting the data in part because the fraction of infected cells that carry two proviruses integrated at the same site may be enhanced by Vpr-mediated G2 arrest [20–22] . The >3-fold increase in frequency with which we detected integration sites with two breakpoints increased between 2 and 4 weeks , taken together with the demonstrated presence of clonally expanded cells at 4 weeks , suggests that we may have underestimated the number of clones in the pretherapy samples . We saw a similar increase in detectable clones in samples taken from humans sampled at similar times after infection [34] . In both SIV infected rhesus macaques and in HIV infected humans , the reservoir is known to be established within a few days of the initial infection [28 , 35 , 36] . Thus , if clonal expansion of infected cells carrying replication competent proviruses is an important part of the establishment and persistence of this reservoir , and ART blocks viral replication , it is important to show that the clones that comprise the reservoir are derived from cells that are infected before ART was initiated . However , even if all of the cells that gave rise to the expanded clones identified in samples taken after initiation of ART were infected early , many of those clones might not have grown large enough to be detected as early as 4 weeks after infection . We estimate , if we recover approximately 1000 integration sites from an on-ART sample , that clones must have expanded to >105 cells for us to have a good chance to detect them in our standard assay . If we get fewer integration sites , the clones must be larger for them to be readily detected; with more integration sites we can expect to detect smaller clones . The doubling time of infected activated CD4+ T cells in vivo is not known . If we assume it to be 1 day , it would take more than 2 weeks a for single infected cell to grow into a clone of 105 cells . However , a shorter doubling time has been estimated for activated CD4+ T cells in mice that were responding to a viral antigen [37] , a situation that resembles early HIV and SIV infection . If we assume a doubling time of one day , given the low levels of virus detected in the blood of infected macaques about a week after infection , and the rapid rise in the viral load in the blood in the second week of infection ( Table 6 ) , we would not have expected that there would be clones large enough for us to detect in the samples taken two weeks after the animals were infected , particularly given the large background of newly infected cells . If , on the other hand , the doubling time of human and macaque CD4+ T cells is closer to 11 hours ( the time estimated from the mouse data ) , then it would only take about 1 week for a single infected cell to become a clone large enough for us to detect . The data we present make it clear that there are infected cells that have grown into large clones by 4 weeks of infection . The data also suggest that there may be large clones that arise earlier; however , additional data will be needed to properly test that possibility . 100 additional clones were found in samples taken after one year of ART . Although some controversy remains , it is well-established that effective ART greatly reduces , or more likely eliminates , new rounds of productive HIV infection in humans and SIV infection in macaques [3–5] . Thus , the integrated proviruses we recovered from the samples taken from macaques after one year of ART were likely to be in cells that were infected before ART was initiated , or in their clonal descendants . Because the number of integration sites we recover also affects the fraction we can assign to clones , it is likely that the fraction of integration sites shown to be in clones in the SIV/macaque data ( about 4–10%; Table 10 ) is lower than the fraction in the HIV/human on-therapy sample from patient 1 of Maldarelli et al . [1] ( approximately 40% ) because the number of integration sites we recovered was lower for the SIV/macaque on-ART samples , although the much longer time on ART ( ca . 11 years ) for patient 1 may have contributed to the difference . What causes HIV infected cells to clonally expand and persist ? In humans , there is compelling evidence that integration of an HIV provirus in specific regions ( 1 or 2 specific introns ) of the MKL2 , BACH2 , and STAT5B genes , and in the same orientation as the gene , is associated with clonal expansion , although the exact underlying mechanism ( s ) remains to be elucidated [1 , 2] . The available data suggest that such proviruses affect the expression of these genes , and possibly the structure of their protein products , in a way that confers some proliferative or survival advantage to the cells . Proviruses integrated in MKL2 , BACH2 , and STAT5B genes represent only a small fraction ( <3% ) of the total proviruses in patients on long term ART , and there are other mechanisms , such as antigenic stimulation and homeostatic cytokine signaling that can contribute to the clonal proliferation and long-term survival of HIV ( and SIV ) infected CD4+ T cells . The relative contribution of these two mechanisms to the overall clonal expansion can , in theory , be answered by comparing the distribution of integrated proviruses in a sample taken from an acutely infected individual ( before there is a significant opportunity for selection ) with one taken after long-term therapy . However , as noted earlier , we cannot obtain a sufficient number of integration sites from in vivo samples to accurately determine the initial distribution of the integration sites in acute infection relative to the ~25 , 000 genes in the genome . Comparison of a large library ( 385 , 000 integration sites ) made from PBMCs from normal human donors that were infected in vitro with HIV with the distribution of a more limited number of integration sites obtained from acutely infected people showed that the overall distributions of the integration sites are quite similar [34] . This result supports the use of in vitro libraries as surrogates for the initial distribution of integration sites in vivo . We also found that the initial distribution of HIV integration sites was largely preserved even after years of successful ART , despite the selection for HIV proviruses integrated in MKL2 , BACH2 , or STAT5B . Based on what is known about the biology of T cells , it is likely that the majority of the infected T cells clonally expand in response to antigenic and/or homeostatic/cytokine stimulation , rather than due to the integration of proviruses in particular genes . We did a similar analysis of the data from SIV infected macaques to look for evidence for selection of cells with proviruses integrated into the BACH2 , MKL2 , and STAT5B genes in SIV infected rhesus macaques . In the on-ART data set , we found only a single provirus that was integrated in the region of BACH2 and in the same orientation relative to the gene as the HIV proviruses that were selected in infected humans . There were 3 proviruses in the region of STAT5B in the orientation that has been associated with the clonal expansion of HIV infected T cells in humans . One of these proviruses was shown to be in a clone of expanded cells . While tantalizing , this single SIV provirus is not sufficient to reach any conclusion . As was the case with BACH2 , the data for STAT5B and MKL2 were not sufficient to show a relationship between SIV integration into these genes and clonal expansion of cells in macaques . Nor did we find evidence linking SIV integration in any other gene with clonal expansion and/or persistence of the infected cells in macaques . However , thus far we have characterized relatively few integration sites from SIV infected macaques on ART . It is possible that the clones that expanded due to HIV integration only grow large enough for us to detect after several years of therapy [see , for example , Table 1 in Maldarelli et al . [1]] . Evidence connecting SIV integration to clonal expansion and persistence of infected cells in macaques may yet emerge as we extend our analyses to include more sites and longer times on ART . However , if , as the data for HIV infected cells in humans strongly suggest , the primary mechanism that causes the clonal expansion in SIV infected cells in macaques is not integration into specific genes , but instead involves antigen driven or homeostatic proliferation of infected cells , then the overall distribution of SIV integration sites should be minimally affected by long-term ART . We first showed that the distribution of integration sites in the in vitro library made by infecting macaque PBMCs with SIV and the integration sites in the samples taken from the macaques before ART was initiated were similar . We then asked if the distribution of the SIV integration sites seen in macaque PBMC infected in vitro was also seen in the samples taken after one year of ART . The results were quite similar to those obtained from a similar analysis comparing the distribution of HIV integration sites in PBMC infected in vitro to the integration sites in samples taken from humans on ART . In both systems , within the limits of the sampling and analysis , the initial distribution of integration sites was well preserved on ART . The SIV/macaque results support the conclusion that much of the clonal expansion of infected cells is not driven by integration into specific genes but is rather due to homeostatic/cytokine and/or antigen driven proliferation . Thus , the unresolved question of whether SIV integration into specific regions of BACH2 , MKL2 , STAT5B , or some other gene does ( or does not ) drive the expansion and/or persistence of a small fraction of the SIV infected clones in macaques is an interesting but relatively minor point in determining the relevance and utility of the SIV/macaque model . Although we did not find any strong evidence of selection for proviruses in specific host genes , we did see evidence of selection favoring proviruses integrated in the opposite orientation relative to the targeted host gene ( Table 12 ) , compared to proviruses integrated in the same orientation , a bias we also saw in HIV infected cells in patients on ART [1] . As noted earlier , it is likely that the selection against the proviruses integrated in the same orientation relative to the host gene are the result of sequences in the proviruses , such as splice sites and polyadenylation sites , interfering with the expression of the host gene . Because many of the proviruses in HIV infected humans and SIV infected macaques are defective , and their defects vary considerably , the specific consequences of inserting a particular defective provirus in any given intron of a host gene are difficult to predict . The breadth of selection against proviruses in the same orientation as the gene must mean that the presence of at least some types of defective proviruses in a number of host genes negatively affect growth or survival of the infected cell , despite the provirus being present in only one of the two copies of the host gene . Both positive and negative selections involving proviruses in a specific orientation relative to the host gene are well documented for other retroviruses . Examples involve tumor induction and the long-term survival of endogenous proviruses [38–40] . Overall , the results obtained in our studies of the clonal expansion of SIV infected cells in rhesus macaques suggest that the clonal expansion of HIV infected cells in humans can be effectively modeled in this nonhuman primate system . For example , in both untreated HIV infected humans and untreated SIV infected macaques , the vast majority of the infected cells are newly infected , and have not clonally expanded , which makes it difficult to find clones of infected cells using integration site analysis . This observation contrasts with the results presented by Haworth et al [41] , who studied the expansion of clones of HIV infected cells using humanized immunodeficient mice . In the humanized mice , which were infected for 12-14 weeks and were not given ART , it would appear that clones of HIV infected cells were larger , and made up a larger fraction of the infected cells , than what we found either in untreated humans or in untreated SIV infected macaques . We suggest that there are at least two possible explanations for the observed differences in the data that we obtained for clones of SIV infected cells in untreated macaques , HIV infected cells in untreated humans , and the data Haworth et al obtained for HIV infected cells in untreated humanized mice . First , the total number of T cells that are targets for infection with HIV or SIV is much smaller in the humanized mice than in it is humans or macaques . Second , in the immunodeficient mice , the human T cells must grow to create a hybrid immune system , which could create bottlenecks and/or impose selective forces on the population of infected cells that does not occur in HIV infected humans or SIV infected macaques . Both of these factors could contribute to having , in the mice , a relatively small number of large clones of HIV infected cells . These effects would give , in the untreated humanized mice , a ratio of newly infected T cells to previously infected T cells that have clonally expanded that is different from what it is found either in untreated humans infected with HIV , or in untreated macaques infected with SIV . Clonal expansion of HIV infected cells is an important mechanism in the generation and maintenance of the viral reservoir that has made it impossible to cure either HIV-infected humans or SIV-infected macaques with available ART . The results we present here suggest that the greater experimental latitude afforded by nonhuman primate models , including the opportunity to use defined challenge viruses , such as molecularly barcoded viruses that can be used to track distinct viral lineages [42] , flexibility in the timing of the initiation , duration , and intensification of ART , as well as intervention with cytokines , immunizations , modulation of cell populations with monoclonal antibodies , and increased opportunities to sample key tissues , should make it possible to address the mechanisms underlying clonal expansion of infected cells , including the roles of homeostatic and antigen driven proliferation . This model shows great promise for facilitating our understanding of the biology of clonally expanded infected cells and for the evaluation of experimental approaches designed to target the expanded clones , which represent a formidable barrier to treatments intended to cure HIV infection .
Rhesus macaque PBMCs from two donors that were not infected with SIV were stimulated with concanavalin A ( ConA; 2 μg/ml ) and recombinant human IL-2 ( 100 U/ml ) and cultured for three days . Human PBMCs from two donors were stimulated with phytohemagglutinin ( PHA; 2 . 5 μg/ml ) and recombinant human IL-2 ( 30 U/ml ) and cultured for four days . Stimulated cells were then washed and inoculated at a nominal MOI of 0 . 1–0 . 01 with a stock of SIVmac239 that was generated in transfected 293T cells ( purchased from American Type Culture Collection ( ATCC ) , Manassas , VA ) and treated with recombinant DNase I ( Roche ) to eliminate residual plasmid DNA . After 6 hours , input virus was washed out and infected cells were cultured in the presence of IL-2 for 7 days . Rhesus macaque PBMCs from two donors were stimulated and cultured as described above in the protocols used to prepare cells for infection with SIV in vitro , but the cells were not inoculated with infectious SIV . PBMC were stimulated with ConA ( 2 μg/ml ) and recombinant human IL-2 ( 100 U/ml ) and cultured for three days . Stimulated cells were then washed and cultured for an additional 7 days in the presence of IL-2 for 7 days . RNAseq was done using an Illumina TruSeq Stranded total RNA Prep Kit RS-122-2201 following the manufacturer’s protocol . Sequencing was performed on Illumina HiSeq 2500 with TruSeq V4 chemistry with 2x125bp paired end reads . Reads were trimmed for adapters and low-quality bases using Trimmomatic software and aligned with reference Rhesus Macaque Mmul 8 . 0 . 1 genome and RefSeq gtf file ( UCSC genome downloads ) using Star software . Quantification was carried out with RSEM using a transcriptome bam file created by Star . PBMC and single-cell suspensions of lymph node- and spleen-derived mononuclear cells were prepared as described [43] from samples collected from four Indian origin rhesus macaques ( animals DCCN , DCHV , DCT3 , and DCJB ) . The animals were infected and treated as described previously [43] . Briefly , the animals were intravenously inoculated with SIVmac239 and then , at 4 weeks post-infection , started on a combination antiretroviral therapy regimen comprising a co-formulated three drug cocktail of the reverse transcriptase inhibitors tenofovir ( TFV ) and emtricitabine ( FTC ) and the integrase strand transfer inhibitor dolutegravir ( DTG ) , plus the protease inhibitor darunavir ( DRV ) . Animals DCCN and DCT3 also received 7–8 infusions of the histone deacetylase inhibitor romidepsin ( Istodax ) . All four animals were housed at the National Institutes of Health ( NIH ) and cared for in accordance with the Association for the Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) standards in an AAALAC-accredited facility and all procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee of the National Cancer Institute ( Assurance #A4149-01 ) , as described previously [43] . Samples of approximately 100mg or less were homogenized in 1 ml of TriReagent ( Molecular Research Center , Inc . ) in 2 ml extraction tubes of Lysing Matrix D using FastPrep instrumentation ( MP Biomedicals ) according to the manufacturer's recommendations . Total RNA and DNA were prepared from the homogenates following manufacturer's recommendations; the alternative , back-extraction method was used for DNA preparation . Integration site analysis was performed as previously described [1] . Briefly , DNA was isolated from human or macaque PBMC grown in culture , PBMC isolated from the blood of infected macaques , or macaque necropsy tissue samples . Genomic DNA was fragmented by random shearing into 300–500 bp fragments . Linker-mediated nested PCR was performed to amplify the human or macaque genomic regions linked to the SIV sequences from both the 5’ and 3’ LTRs . Paired end-sequencing was carried out using the MiSeq 2x150bp paired end kit ( Illumina , San Diego , CA ) . The sequences of the host-viral junctions and the host DNA breakpoints were determined . The host DNA sequences were then mapped to human genome ( hg19 ) or the Rhesus macaque genome ( rheMac8 ) using BLAT . A stringent filter was used to select the integration sites . Most of the previous analysis of the clonal expansion involved DNA from HIV infected cells in samples taken from individuals who had been on successful ART for years . When exactly the same integration site was found twice ( i . e . , the identical integration site with exactly two different breakpoints ) in samples taken from donors on long-term ART , it was taken as evidence that the cells had clonally expanded . This conclusion was based on the absence of newly infected cells in the donors we were studying . It is formally possible some of the samples from individuals on ART contained infected cells in S , G2 , or M that contained two copies of a provirus present only once when the cells were in G1 . However , if there are any infected cells that are still dividing after years of successful ART , it is quite likely that they are part of a clone . A similar logic applies to the analysis of samples taken from SIV-infected macaques on ART . The situation is different in an untreated HIV infected human , or an untreated SIV infected macaque , in which there are large numbers of newly infected cells that will live only for a day or two . Both HIV and SIV preferentially infect activated T cells . It is likely that some of the newly infected cells will replicate their DNA but die before they can divide , much less form a clone . As such , finding the same integration site twice in a single sample from an untreated human or macaque is not sufficient to determine that the infected cell was part of an expanded clone . We used two criteria to identify and confirm clones in samples taken from untreated humans and macaques 1 ) The integration site was isolated three or more times in a single integration site assay . 2 ) the integration site was seen in two ( or more ) independent assays , either done with cells taken at the same time , or at different times . For samples taken from humans and macaques on ART , the isolation of the same integration site twice is still considered to be evidence that there is an infected clone of cells in the sample . In samples collected pre-ART , integration sites found with exactly two breakpoints are reported separately from those that could be confirmed as belonging to clones ( Table 9 ) . On average , an HIV-infected individual on therapy has about 1 provirus per 103 CD4+ T cells . Given a total of about 1010 CD4+ T cells in blood , and an estimated nearly 1012 CD4+ T cells in the whole body , these values imply that there are roughly 108−109 infected cells . Although macaques are smaller , the proviral burden , ( the fraction of T cells that are infected ) is somewhat larger , so the overall number of infected cells is similar [15 , 44] . Some of the infected cells will have clonally expanded and the cells in each infected clone will have a provirus with an identical integration site . We want to know the probability , if we sample the population N times , that we can detect a clone of a particular size . We performed a calculation based on the assumption that we had obtained a total of 100 integration sites , 500 sites , 1000 sites , etc . , from an infected individual . Given a population of 108 total infected cells , if we start with the case in which we obtained 1000 integration sites , and if we have a clone that comprises 105 cells , we would expect , on average , to isolate the integration site for the clone once . To detect a clone in a patient or a macaque on ART , we need to see the integration site for the clone of interest at least twice . We can use the Poisson distribution to get the probability of obtaining the same integration site two ( or more ) times . The probability is the sum of the Poisson values ( based on an average of 1 ) for which the number of positives was greater than or equal to 2 , or , more simply , 1 minus the sum of the Poisson expectations for 0 and 1 . Peripheral blood mononuclear cells were obtained from healthy donors on an IRB-approved NIH protocol ( 99-CC-0168 ) . Research blood donors provided written informed consent and blood samples were de-identified prior to distribution ( Clinical Trials Number: NCT00001846 ) . Four Indian-origin rhesus macaques were housed at the National Institutes of Health ( NIH ) and cared for in accordance with the Association for the Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) standards in an AAALAC-accredited facility and all procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee of the National Cancer Institute ( Protocol AVP-038; Assurance #A4149-01 ) , as described previously [43] . Work involving animals adhered to the standards of the Guide for the Care and Use of Laboratory Animals ( National Research Council . 2011 . Guide for the care and use of laboratory animals , 8th ed . National Academies Press , Washington , DC ) in accordance with the Animal Welfare Act . Animals were maintained in Animal Biosafety Level 2 housing with a 12:12-hour light:dark cycle , relative humidity 30% to 70% , temperature of 23 to 26°C and all animals were observed twice daily by the veterinary staff . Filtered drinking water was available ad libitum , and a standard commercially formulated nonhuman primate diet was provided thrice daily and supplemented 3–5 times weekly with fresh fruit and/or forage material as part of the environmental enrichment program . Environmental enrichment: Each cage contained a perch , two portable enrichment toys , one hanging toy , and a rotation of additional items ( including stainless steel rattles , mirrors , and challenger balls ) . Additionally , the animals were able to listen to radios during the light phase of their day and were provided with the opportunity to watch full-length movies at least three times weekly . Whole blood was collected from animals sedated with ketamine or via intravenous catheters in conscious animals in restraint chairs . For surgical lymph node extraction , animals were sedated with ketamine and isoflurane inhalation anesthesia , with perioperative administration of a local anesthetic . Pain and distress were relieved by appropriate measures . Animals experiencing more than momentary pain as diagnosed by the veterinarian were treated with appropriate analgesia , including non-steroidal anti-inflammatory drugs , such as ketoprofen , aspirin , and others , and opioids , such as buprenorophine , at the veterinarians discretion . Euthanasia , when appropriate or necessary , was performed in sedated animals using an overdose of sodium pentobarbital . | Although antiretroviral therapy ( ART ) effectively blocks HIV replication , infected people are not cured . As a part of its normal replication cycle , HIV inserts ( integrates ) a DNA copy of its genome into the genome of infected host cells , which allows the virus to persist as long as the infected cells survive . Not only can these infected cells survive , they can grow and divide , increasing the numbers of infected cells without viral replication . The ability of the infected cells to proliferate plays an important role in maintaining the numbers of infected cells ( and the infection ) in people on successful therapy . However , there are some important experiments that cannot easily be done with samples that can be obtained from HIV infected people . SIV infected macaques are often used as a model to do experiments that cannot be done in HIV infected people . We show here that the distribution of HIV and SIV integration sites is similar , and that , in infected macaques , the timing and extent of the proliferation of SIV infected cells is also quite similar to HIV infected cells in humans . This shows that the SIV/macaque system can be used to model the clonal expansion of HIV infected cells . | [
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"retroviruses... | 2019 | Clonal expansion of SIV-infected cells in macaques on antiretroviral therapy is similar to that of HIV-infected cells in humans |
Pandemics of vector-borne human and plant diseases often depend on the behaviors of their arthropod vectors . Arboviruses , including many bunyaviruses , manipulate vector behavior to accelerate their own transmission to vertebrates , birds , insects , and plants . However , the molecular mechanism underlying this manipulation remains elusive . Here , we report that the non-structural protein NSs of Tomato spotted wilt orthotospovirus , a prototype of the Tospoviridae family and the Orthotospovirus genus , is a key viral factor that indirectly modifies vector preference and increases vector performance . NSs suppresses the biosynthesis of plant volatile monoterpenes , which serve as repellents of the vector western flower thrips ( WFT , Frankliniella occidentalis ) . NSs directly interacts with MYC2 , the jasmonate ( JA ) signaling master regulator and its two close homologs MYC3 and MYC4 , to disable JA-mediated activation of terpene synthase genes . The dysfunction of the MYCs subsequently attenuates host defenses , increases the attraction of thrips , and improves thrips fitness . Moreover , MYC2 associated with NSs of Tomato zonate spot orthotospovirus , another Euro/Asian-type orthotospovirus , suggesting that MYC2 is an evolutionarily conserved target of Orthotospovirus species for suppression of terpene-based resistance to promote vector performance . These findings elucidate the molecular mechanism through which an orthotospovirus indirectly manipulates vector behaviors and therefore facilitates pathogen transmission . Our results provide insights into the molecular mechanisms by which Orthotospovirus NSs counteracts plant immunity for pathogen transmission .
Arthropod-borne viruses ( arboviruses ) are virulent causal agents of diseases in humans , animals , and plants . Vector behaviors have critical ecological and evolutionary consequences for arboviruses , which rely exclusively on their arthropod vectors for dispersal to new hosts . Therefore , it is of evolutionary significance for an arbovirus to alter its vector’s behavior to facilitate its own transmission . For plant viruses , such influence of vectors by viruses can include plant-mediated indirect effects or direct manipulation within the vector after acquisition . Among the indirect effects , infected plants tend to be more attractive to vectors [1] . For example , Geminiviridae and Luteoviridae viruses almost universally induce preferred settling of the vectors onto infected plants [2–5] , and this phenomenon also exists among the Potyviridae and Bunyaviridae [6–9] . Moreover , viruses can positively or negatively affect the performance or fitness of arthropod vectors on the host . Persistently transmitted viruses , which need a sustained feeding of insect vectors to be acquired or transmitted , in particular , have positive effects on vector performance . For example , insect vectors perform better on Geminiviridae- and Tospoviridae-infected plants [9–12] . For nonpersistently transmitted viruses , vectors acquire or transmit the viruses in seconds through probing or feeding , such as Potyviridae , Caulimoviridae and Bromoviridae , also can positively or negatively affect their vectors for efficient virus spread [1 , 6 , 13–15] . Bunyavirales encompasses nine families of viruses with single-stranded negative-sense RNA genomes . As a prototype of the plant-infected Tospoviridae family , Tomato spotted wilt orthotospovirus ( TSWV ) is transmitted mainly by Frankliniella occidentalis Pergande ( Western flower thrips , WFT ) in a persistent and propagative manner [16 , 17] . Plant infection with TSWV influences several vector behaviors , such as biting and host choice to increase virus transmission , similar to the animal-infecting members of Bunyavirales [18–20] . For instance , non-viruliferous F . occidentalis prefers to settle on TSWV-infected pepper ( Capsicum annuum L . ) and Datura stramonium plants over noninfected controls [9] . However , the underlying molecular mechanism of this conserved indirect manipulation of vector behaviors by Orthotospovirus and Bunyavirales species is still unclear , although this plant immunity suppression is thought to occur in TSWV-infected Arabidopsis thaliana [21] . The bunyavirus families are divided based on their different coding strategies for the additional non-structural proteins , NSm and NSs , which are often involved in host-pathogen interactions . Orthotospovirus NSm protein facilitates the movement of viral ribonucleoproteins from cell to cell within the plant host . NSm of TSWV has recently been identified as the avirulence factor recognized by the product of resistance gene Sw-5b from tomato ( Solanum lycopersicum L . ) [22] . The NSs proteins of many bunyaviruses modulate host innate immune responses , and NSs in Orthotospovirus functions as a silencing suppressor in both plants and insects [23 , 24] . These proteins are responsible for establishing systemic infection in plants and for virus transmission by insect vectors [25 , 26] . Many plant species emit herbivore-induced plant volatiles ( HIPVs ) , as an indirect anti-herbivore defense strategy [27–30] . HIPVs can repel insects such as aphids and caterpillars or deter lepidopteran oviposition [31–33] , and are a common induced defense mechanism among plants including cotton and tomato [34 , 35] . Phytohormones such as jasmonate ( JA ) play vital roles in regulating HIPV production upon insect attack [36 , 37] . Several viruses have been shown to modify this JA-regulated volatile biosynthesis to affect the communication between plant and insect vector . For instance , begomoviruses inhibit the JA pathway and modify volatile terpene-mediated defense responses against whitefly [38] . The JA-mediated biosynthesis of secondary metabolites is believed to be associated with thrips resistance [39] . However , whether and how TSWV influence JA signaling remains elusive , although this virus is thought to hijack the antagonistic relation between JA and salicylic acid signaling [40] . In this study , we showed that TSWV benefits to thrips vector by suppressing a JA-regulated defense pathway of plants against herbivores . We identified the NSs protein from thrip-borne TSWV as a viral genetic factor induced attraction of its insect vector . Various NSs from orthotospovirus suppress the JA signaling pathway in the host plant by directly interacting with MYCs , key regulators of the JA signaling pathway , to reduce host defense responses against thrips . Our results establish a molecular mechanism underlying how TSWV attracts and benefits to its thrips vector by targeting plant MYC proteins .
We first investigated the indirect effect of TSWV infection on the behavioral responses of the vector Frankliniella occidentalis Pergande ( Western flower thrips , WFT ) . We conducted a two-choice assay between infected and non-infected plants . Pepper ( Capsicum annuum L . ) , a natural host of TSWV and an important crop worldwide , was first tested in the tripartite thrip–orthotospovirus–plant interaction . A group of 50 non-viruliferous WFT was released from the center of the two-choice arena between two types of pepper plants . Consistent with previous results from Maris et al . [9] , ~68% of thrips approached TSWV-infected plants , whereas the remaining approached non-infected plants ( Fig 1A ) , suggesting that TSWV infection indirectly increases the attractiveness of peppers to the thrips vector . The attraction of insect vectors induced by the infection of other viruses is dependent on plant volatiles [38 , 41] . We therefore measured the expression levels of terpene synthase ( TPS ) genes in pepper leaves based on our previous functional analysis of TPS genes [38] . Reverse-transcription quantitative PCR ( RT-qPCR ) analysis showed that the expression of four pepper monoterpene synthase genes ( CaMTS1 , CaMTS2 , CaMTS3 , and CaMTS4 ) , which are related to monoterpene synthesis , were upregulated after thrips infestation ( Fig 1B ) . However , the terpene biosynthesis gene expression activated by thrips was significantly lower in TSWV-infected plants compared with the control ( Fig 1B ) . Another model ( host ) plant for tripartite interaction research , Nicotiana benthamiana , was also tested . Similar to the observations in pepper , TSWV-infected N . benthamiana leaves also were more attractive to thrips than non-infected leaves ( Fig 1C ) . Moreover , RT-qPCR analysis indicated that the terpene synthase genes NbTPS5 and NbTPS38 responded to thrips infestation in N . benthamiana ( S1A Fig ) . Consistent with the above results , NbTPS5 and NbTPS38 expression was notably induced by methyl jasmonate ( MeJA ) treatment , reflecting the same trends as during thrips infestation ( S1B Fig ) . JA signaling is normally rapidly activated by thrips feeding [40] . Considering that N . benthamiana is not a good host for thrips as indicated by their poor survival rate , and the finding that MeJA induces similar expression of TPS genes in N . benthamiana as thrips infestation ( S1 Fig ) , we used MeJA to mimic WFT infestation in further tripartite interaction experiments . The expression of NbTPS3 , NbTPS4 , NbTPS5 , and NbTPS38 was less changed in TSWV-infected plants compared to the control plants when induced by methyl jasmonate ( MeJA ) ( Fig 1D ) . To explore the metabolic consequences of the altered TPS gene expression , we investigated changes in the emission of plant volatile compounds after TSWV infection . Plants have evolved a blend of HIPVs that are emitted in response to , and directly repel , herbivores [27–33] . We measured the volatile emission collected in the headspace of peppers with or without thrips infestation . When infested by thrips , damaged plants emitted more volatiles than control plants ( S2 Fig ) . It is noteworthy that TSWV-infected plants emitted significantly less linalool , which is the main monoterpene collected from peppers after herbivory , compared to non-infected plants , consistent with their lower expression of TPS genes . In addition , there was no significant difference in the emissions of the monoterpene D-limonene ( Fig 2A ) . We also monitored the emission of volatile compounds in the headspace of N . benthamiana , after applying MeJA to mimic WFT infestation; this plant hormone is known to elicit the production of various terpenes [42] . Among the five detected terpenes , the levels of three volatile monoterpenes , linalool , α-pinene , and β-pinene , were significantly lower in TSWV-infected plants compared to non-infected plants ( Fig 2B ) . To examine whether linalool , α-pinene and β-pinene play a role in plant–WFT interactions , we performed a two-choice assay in which non-viruliferous WFT had the choice between the changed monoterpenes and the solvent control hexane . The α-pinene and β-pinene directly repelled thrips similarly to linalool ( Fig 2C ) . These consistent results on pepper and N . benthamiana revealed that TSWV infection induces a terpene-dependent preference in the thrips vector and that this feature is common among various TSWV hosts . Our data demonstrated that the orthotospovirus TSWV increases the attraction of insect vector WFT to its host plant by inhibiting terpene synthase expression in the host . Next , to explore which viral protein ( s ) in TSWV manipulate vector host choice , we selected three of the five viral proteins in TSWV , including a structural protein nucleocapsid protein ( Ncp ) and two non-structural proteins , NSm and NSs [24] . We used the heterologous Potato virus X ( PVX ) model system for systemic ectopic expression of individual genes for TSWV NSs , NSm or Ncp [43] . PVX-GFP , used to express green fluorescent protein ( GFP ) in the plant , was served as the control . There were no obvious morphological differences between these recombinant PVX vector-infected peppers ( Fig 3A ) . We performed a WFT two-choice assay to determine whether the expression of a single viral protein is sufficient to attract WFT . PVX-NSs-infected plants but not PVX-NSm- or PVX-Ncp-infected plants were significantly more attractive to WFT than PVX-GFP-infected plants ( Fig 3B ) , indicating the expression of NSs alone is sufficient to attract WFT in peppers . To explore the host protein targets of NSs , we screened an Arabidopsis cDNA library by yeast two-hybrid analysis and identified AtMYC2 , a key components of the JA signaling pathway [44–46] . Based on the importance of the JA signaling pathway to plant–herbivore interactions , we further confirmed the interaction between AtMYC2 and NSs . In a yeast two-hybrid assay , the yeast transformants harboring AD-AtMYC2 and BD-NSs could grow on SD-Leu-Trp-His medium with 0 . 04 mg/mL X-α-gal and turned blue , while the negative control transformants did not ( Fig 4A ) . A bimolecular fluorescence complementation ( BiFC ) assay confirmed the AtMYC2 and NSs interaction in plants . NSs-cEYFP and nEYFP-AtMYC2 constructs were co-expressed in transgenic N . benthamiana lines that expressed a nucleus-localized histone H2B-red fluorescent protein ( H2B-RFP ) fusion marker protein . A strong interaction ( represented by fluorescence ) was observed in the nucleus ( Fig 4B ) , while no fluorescence was observed in the negative controls ( Fig 4B ) . GST pull-down assay was used to verify the direct physical interaction between NSs and AtMYC2 in vitro . His-NSs was pulled down by GST-AtMYC2 , but not by GST alone ( Fig 4C ) . Moreover , in a co-immunoprecipitation ( Co-IP ) assay , AtMYC2-Myc was coimmunoprecipitated by YFP-NSs , but not the YFP control ( Fig 4D ) . Taken together , these results demonstrate that NSs directly interacts with MYC2 in vitro and in vivo . MYC3 and MYC4 are two closely related bHLH transcription factors that function partially redundantly with MYC2 to activate JA responses in Arabidopsis [47] . To determine whether TSWV NSs targets MCY3 and MYC4 as well , we performed a yeast two-hybrid assay and a BiFC assay . MYC2 relatives MYC3 and MYC4 were also found to interact with TSWV NSs as indicated by AD-AtMYCs ( MYC3 and MYC4 ) and BD-NSs yeast transformants turned blue when grown on SD-Leu-Trp-His medium with 0 . 04 mg/mL X-α-gal ( S3A Fig ) . In the BiFC assay , N . benthamiana coexpressing MYC3 and NSs exhibited fluorescence in the cytoplasm and nucleus , while coexpression of MYC4 and NSs led to fluorescence only in the cytoplasm ( S3B Fig ) . These results indicate that MYC family transcription factors are targeted by NSs protein . We previously showed that Arabidopsis MYC2 plays important roles in JA-regulated plant defense responses , e . g . directly regulates TPS10 transcript levels to promote plant volatile biosynthesis [38] . Thus , we hypothesized that AtMYC2 , which interacts with virulence factor NSs , is involved in the viral-induced , volatile-dependent attraction of WFT to the host plant . To validate this hypothesis , we performed a GUS staining assay using two transgenic Arabidopsis lines expressing an AtMYC2 or AtTPS10 promoter: GUS reporter gene . As shown in Fig 5A , high GUS expression was detected after 24 h of WFT infestation . This expression pattern suggests that AtMYC2 and AtTPS10 both function in defense responses against WFT in Arabidopsis . To analyze the effects of AtMYC2 and AtTPS10 on the feeding preferences of thrips , we performed two-choice assays using myc2-1 , tps10-1 , and wild-type Col-0 Arabidopsis . As shown in Fig 5B , the myc2-1 and tps10-1 mutants were more attractive to WFT than wild type . We also tested the effect of triple mutant myc234 on host preference , finding that WFT strongly preferred myc234 plants over the wild type ( Fig 5B ) . AtTPS10 encodes a monoterpene synthase that produces β-ocimene [48] . We therefore carried out a two-choice assay of β-ocimene to examine whether the attraction of tps10 is terpene-dependent . β-ocimene had a strong repellent effect on WFT ( Fig 5C ) . These results indicate that AtMYC2 is essential for terpene-dependent immunity against the thrips vector . We further examined if the TSWV NSs contributes to the preference of thrips on Arabidopsis . In two-choice assays , two transgenic Arabidopsis 35S:YFP-NSs ( NSs-1; NSs-2 ) lines were significantly more attractive to thrips compared to controls ( Fig 5D ) , supporting the conclusion that NSs protein can modify vector feeding behavior in a terpene-dependent manner . The viral transmission cycle can be roughly divided into two phases . In the first phase , the TSWV-infected plants attract non-viruliferous thrips to feed , with volatiles playing a key role in this early process ( Figs 1–5 ) . In the second phase , a ( viruliferous ) thrips population is established on TSWV-infected plants to facilitate virus transmission . To investigate whether NSs influences thrips population establishment , we performed a thrips spawning experiment with a slight modification [40] . Seven female adult thrips were allowed to feed on 35S:YFP-NSs ( NSs-1; NSs-2 ) or wild-type Arabidopsis for two weeks . We counted the number of new adults and larvae to analyze the effect of NSs on the thrips population . Plants expressing NSs were more suitable for WFT population growth than wild type ( Fig 6A ) . We reasoned that NSs targets MYCs to disable the activation of terpene synthase genes , thereby attenuating the defense of the host plant against thrips . To investigate this hypothesis , we conducted another spawning experiment using myc2-1 , tps10-1 , and myc234 mutants . More WFT were found on the mutants compared with wild type; these lines were equally suitable for WFT growth compared to the lines expressing NSs , confirming the important role for NSs in the tripartite WFT–TSWV–plant interaction ( Fig 6B and 6C ) . TSWV-infected pepper plants were more attractive to the thrips vector than healthy plants ( Fig 1A ) . Therefore , we asked whether NSs could interact with AtMYC2 orthologs in pepper . We examined the interaction between NSs and the homologous protein of AtMYC2 in pepper ( CaMYC2 ) . Our BiFC assay results showed interaction fluorescence of NSs–CaMYC2 in the nucleus , while there was no fluorescence of control ( Fig 7A ) . In Co-IP assays , CaMYC2-Myc protein was coimmunoprecipitated by YFP-NSs , but not by YFP alone ( Fig 7B ) . Taken together , our results suggest that NSs–MYC2 interaction is relatively conserved in pepper . Other orthotospoviruses also encode a NSs protein and might similarly manipulate vector behavior to accelerate their own transmission [49] . To explore whether the interaction between NSs–MYC2 is conserved among orthotospoviruses , we used Tomato zonate spot orthotospovirus ( TZSV ) , a new species of genus Orthotospovirus that threatens food security in Southwest China [50] . The evolutionary relationship of TSWV and TZSV is not very close [51] , as TSWV represents the American- and TZSV represents the Euro/Asian-type orthotospoviruses ( S4 Fig ) . We examined TZSV NSs–AtMYC2/CaMYC2 interactions by BiFC and Co-IP assays . Notably , BiFC showed interaction fluorescence of TZSV NSs–AtMYC2 and TZSV NSs–CaMYC2 aggregated in the nucleus , while the Co-IP assays again confirmed the interaction between TZSV NSs–AtMYC2 ( left panel ) and TZSV NSs–CaMYC2 ( right panel ) ( Fig 7D ) , providing evidence that TZSV NSs interacts with both AtMYC2 and CaMYC2 in vivo , consistent with NSs–MYC2 interaction in TSWV ( Figs 4 and 7A ) . These results indicated that the interaction between NSs and MYC2 may be conserved in orthotospoviruses . In summary , our results suggest that NSs targets MYCs to attenuate host defense responses to thrips , thereby manipulating terpene-dependent chemical communication between the plant and the thrips vector .
Vector-borne virus-infected plants often attract the pathogens’ vectors [1] . Here , we demonstrate a possible molecular mechanism of this virus-induced indirect manipulation through the shared host plant . Non-viruliferous thrips feeding was reported to induce a negative change in plant quality for their survival [10] . Consistent with this , we showed that the expression of various TPSs were induced strongly by herbivory ( Fig 1B ) and repellent terpenes were produced as a consequence ( S2 Fig ) . Orthotospoviruses depend on the vector thrips for transmission , and enhanced performance of WFT on virus-infected plants would be beneficial to the virus and the vector . We found that the induction of plant defense was greatly decreased in TSWV-infected plants , thus promoting the performance of WFT vector ( Figs 1 , 5 and 6 ) . Our results establish the existence of an indirect mutualistic relationship between Orthotospoviruses and the thrips vector . This indirect mutualism refers to a positive effect of virus on its insect vector . Virus suppresses plant defense against the insect vector leading to enhanced vector performance and population , which in turn promote virus transmission . Among the monoterpenes manipulated by TSWV in various plants , linalool functions as a repellent to WFT both in pepper and N . benthamiana ( Fig 2 ) . It is one of the most common defensive monoterpene compounds in the HIPVs released from plant species in response to herbivore attacks [52] . Linalool has been shown to affect the feeding behavior of insects , as well as to attract pollinators , repel herbivores , and affect insect spawning decisions [38 , 52] . It also inhibits the growth of WFT [53] , in agreement with the conclusion that linalool is an anti-WFT secondary metabolite hijacked by TSWV ( Fig 2A ) . Since volatiles are essential to herbivory responses , exogenous application of monoterpenes such as linalool may be a promising approach to avoid herbivore feeding damage and even plant pathogen transmission under field conditions , without the need for engineering in plants . Behavioral manipulation has been observed in animal-infecting bunyaviruses for many years . As early as 1980 , La Crosse virus ( LACV ) was reported to modify the feeding behavior of mosquito vectors [18] . Rift Valley fever virus ( RVFV ) was found to affect mosquito vector morbidity and mortality [19] . However , the molecular mechanism underlying this manipulation was unclear , and no specific information was available regarding viral determinants of the virus–host–vector interaction in bunyaviruses . Our study identifies NSs of TSWV as an indirect vector behavior manipulator that suppresses host plant defense responses to attract and benefit the fitness of WFT , which in turn facilitates disease dispersal from plant to plant . Notably , NSs is conserved in bunyaviruses , and TSWV NSs is an avirulence determinant that triggers a hypersensitive response in resistant plants [54] . NSs is also a well-known viral suppressor of host RNA interference in both plants and insects and is essential for TSWV transmission by WFT [16 , 23–26] . Here , we showed that the expression of NSs is sufficient to control the behavior of WFT ( Figs 3 , 5D and 6A ) by suppressing the host defense against insects through MYC proteins ( Fig 4 ) . Additionally , the non-viruliferous female thrips were reported to produce more offspring on virus-infected plants , which is in agreement with their preference for TSWV-infected plants [9 , 10 , 21] . Taken together , the infection of TSWV could counter plant defense to benefit its vector , thus promoting its spread through the NSs protein . Earlier studies showed that effectors from bacterial , fungal and oomycete pathogens converge onto common host proteins in Arabidopsis [55] . Our results suggest that viral effectors also share the same plant targets . JA signaling is essential for plant defense against pathogen and insect attack in several phytopathological systems [56 , 57] . However , plant arboviruses target JA signaling to increase the suitability of host plants for their vectors [38 , 58] . JA-dependent plant defenses affect WFT performance and preference , and TSWV infection reduces the levels of these responses . In JA-insensitive coi1-1 mutants , WFT do not show a preference for TSWV-infected plants [21] . Our results suggest that the MYC proteins involved in the JA pathway are responsible for plant terpene immunity against WFT ( Fig 5A–5C ) . MYCs are downstream genes of the JA receptor COI1 , and MYC2-orchestrated transcriptional reprogramming occurs during JA signaling [48] . Functional blocking of MYCs increases WFT preference and promotes WFT performance , including developmental duration and fecundity in Arabidopsis ( Fig 6 ) . We hypothesize that several MYC-regulated indole and aliphatic glucosinolates that function as defensive chemicals against herbivores might be repressed . Alternatively , the levels of nutrients ( such as amino acids ) are likely altered in the host , which could affect the feeding behavior and preference of thrips , as previously reported [8] . In addition , the interaction between TZSV NSs and MYC2 indicates that TZSV infection of plants may also benefit its insect vector like TSWV infection does ( Fig 7B ) . Therefore it seems like NSs of Orthotospovirus conservatively interacted with MYC2 and its homologs in plant host ( Fig 7 ) . By interrupting MYC-regulated plant defense via NSs , Orthotospovirus species appear to indirectly manipulate the preference and performance of WFT , as is the case for βC1 in Begomovirus . We previously demonstrated that βC1 of Tomato yellow leaf curl China virus ( TYLCCNV ) interacts with MYC2 to subvert plant resistance and to promote vector performance [38] . Notably , Begomovirus and Orthotospovirus species are persistently transmitted , which tend to induce attraction and promote performance of vectors on infected plants for increased transmission efficiency , indicating that viruses with same transmission mechanisms can have common manipulation tactics . Interestingly , the silencing suppressor 2b of the nonpersistently transmitted virus Cucumber mosaic virus ( CMV , Bromoviridae ) also suppresses JA signaling , and myc234 triple mutant plants were observed to attract the CMV aphid vector [58] , although CMV appears to attract vectors deceptively [15] . These similar results on evolutionarily different viruses and plant hosts suggest that manipulation of the JA pathway could be a general feature in tripartite virus–vector–plant interactions . Notably , these independently evolved virulence proteins were known as silencing suppressors that convergently targeted the host RNA silencing machinery , and our studies establish that the same occurs for the manipulation of plant–insect vector interactions . These similar effects and pathogen manipulation tactics indicate that the mechanistic and evolutionary principle for diverse pathogens seems to be convergent , even in human pathogens . For instance , CCR5 , which is the first described cellular receptor of human immunodeficiency virus ( HIV ) , is necessary and sufficient for the pathogenesis of many pathogens [59] . The HIV , Toxoplasma gondii , poxviruses ( vaccinia and myxoma ) , and Staphylococcus aureus exploit CCR5 to target and kill mammalian immune cells [60–63] . Why pathogens from different kingdoms tend to keep finding the same host targets to disrupt their defenses , and whether this is a consequence of selective pressure in evolution remain to be further determined . In summary , we have demonstrated that the emission of several monoterpenes is greatly decreased by the TSWV infection , which in turn promotes WFT preference and performance , uncovering a molecular mechanism underpinning the virus-induced manipulation through the shared host plant of the WFT vector . This work presents a mechanism by which a pathogen regulates host-derived olfactory cues for vector attraction . These results will also help to address similar tripartite interaction systems in plants , animals and humans and will allow innovative control methods through interference of vector transmission .
Pepper accession Lingfeng ( Capsicum annuum L . ) , Nicotiana benthamiana and Arabidopsis thaliana ( Col-0 ) plants were grown in insect-free growth chambers following standard procedures [38] . The Arabidopsis myc2-1 , tps10-1 , and myc234 mutants ( Col-0 background ) were described previously [38] . The 35S:YFP-NSs transgenic lines NSs-1 and NSs-2 were generated using the Agrobacterium-mediated floral-dip method [64] . A starting colony of Western flower thrips ( WFT , Frankliniella occidentalis Pergande ) ( Thysanoptera: Thripidae ) was kindly provided by Prof . Youjun Zhang ( Institute of Vegetables and Flowers , Chinese Academy of Agricultural Sciences ) . The thrips were maintained on green bean pods ( Phaseolus vulgaris L . ) in a climate chamber as described previously [65] . Tomato spotted wilt orthotospovirus ( isolate TSWV-YN ) obtained from Prof . Xiaorong Tao ( Nanjing Agriculture University ) was mechanically inoculated onto pepper and N . benthamiana as described by Mandal et al . [66] . Infected leaves were ground in 0 . 05 M phosphate buffer ( pH 7 . 0 ) and applied to the host plant using a soft finger-rubbing technique . Infected plants were tested at 10–14 dpi by RT-qPCR prior to the thrip two-choice assays . The two-choice assays on plants or leaves were performed as described previously [8 , 9] . Peppers inoculated with TSWV or buffer was used for the assay at 10–14 days post inoculation . A TSWV-infested and a control plant were confined in a pot covered with a fine mesh . For N . benthamiana , detached leaves of TSWV-infected plants and non-infected plants were separately placed in a 16 cm-Petri dish , which was covered with a moist filter paper . For Arabidopsis , plants were cultivated on solid Murashige and Skoog medium for 3–5 weeks , and whole plants were used for the two-choice assay . Fifty F . occidentalis adults were released to the center of the two tested plants or the leaves of N . benthamiana , the number of thrips that settled on each plant or leaf was counted at 12h ( pepper ) or 24 h ( N . benthamiana , Arabidopsis ) after release . For two-choice assays with individual monoterpene , 2 cm × 2 cm filter paper containing 40 μL of a 1:100 ( v/v ) solution of standard chemical substance from Sigma dissolved in n-hexane or n-hexane alone ( as a control ) was placed in a 16cm-Petri dish . Thrips were released between the two tested samples , and the thrips were counted 5 min after release . The Petri dishes were contained in a thrip culture chamber throughout the experiment to maintain consistent environmental conditions . Plants were infested with non-viruliferous thrips as described previously [56] . Twenty adult thrips ( 7–14 d after eclosion ) were grouped and starved for 3 h before the plant infestation assay . Arabidopsis plants grown on solid MS medium or soil-grown pepper and N . benthamiana plants were infested with adult thrips for the indicated time period . The thrips were gently removed and the leaf samples collected in liquid nitrogen for further analysis . For the GUS-reporter line expression assays , transgenic Arabidopsis plants were infested with thrips for 24 h , followed by GUS activity analysis . The experiment was repeated at least twice with similar results . For volatile analysis on pepper plants , plants were infested with thirty adults in a nylon mesh cage for 6 h before volatile collection . The volatiles emitted from insect‐exposed TSWV-infected and control plants were collected with a solid phase microextraction ( SPME; Supelco , Belafonte , PA , USA ) fiber consisting of 100 μm polydimethylsiloxane ( Supelco ) . Chemical analysis was performed by gas chromatography-mass spectrometry ( GC-MS ) ( Shimadzu , QP2010 ) coupled with a DB5MS column ( Agilent , Santa Clara , CA , USA , 30 m x 0 . 25 mm x 0 . 25 μm ) . The SPME fiber was thermally desorbed in the injector at 250°C for 1 min . The initial oven temperature was held at 40°C for 3 min , increased to 240°C with a gradient of 5°C/min , and maintained at 240°C for 5 min . The inlet temperature was 250°C . The collection of volatiles for each treatment was repeated 4–6 times . The collection , isolation , and identification of volatiles from N . benthamiana plants were performed as described previously [38 , 67] . Plant volatiles were collected for 12 h at a gas flow rate of 300 mL/min and analyzed by GC-MS . At least four plants per group were used . For PVX heterologous virus protein expression in pepper , the TSWV virus genes NSs , NSm , and Ncp were cloned into the PVX vector pGR208 by using gene-specific primers in S1 Table . For agroinfiltration transient expression vectors construction , the indicated DNA fragments were PCR cloning into pENTR-3C entry vector , then transformed into the agroinfiltration destination vector under the control of a CaMV 35S promoter . All constructs used for protein expression in plants were transformed into Agrobacterium tumefaciens strain EHA105 . Agrobacterium carrying the binary vectors were infiltrated into the abaxial sides of pepper and N . benthamiana leaves [49] . The Arabidopsis Mate and Plate Library was screened using yeast mating method according to the Matchmaker Gold Yeast Two-Hybrid System manufacturer’s protocol ( Clontech ) . Briefly , full-length NSs was amplified and inserted into the pGBKT7 vector by Gateway recombination , then the constructs was transformed into yeast strain Y2HGold and testing for autoactivation by using the Yeastmaker Yeast Transformation System ( Clontech ) . Then the Arabidopsis Mate and Plate Library and BD-NSs yeast clones were mated in YPDA medium . After incubation , isolated destination clones were selected from diploid-selection medium ( SD/-Leu/-Trp ) . These primary positive interactors were secondary screened on medium plates ( SD/-Leu/-Trp/-His ) and third time screened on medium plates ( SD/-Leu/-Trp/ -His/X-a-Gal ) . PCR and BLAST searches were used to obtain sequence information on corresponding AD- and BD-clones per colony . The interaction between TSWV NSs and AtMYCs were confirmed according to the manufacturer’s protocol ( Clontech ) . The pGBKT7-NSs and pGAD424-MYCs constructs were co-transformed into yeast strain Y2HGold . Yeast cotransformed with the indicated plasmids was spotted onto synthetic medium ( SD-Leu-Trp-His ) containing 10 mM 3-amino-1 , 2 , 4-triazole and 0 . 04 mg/mL X-α-gal . The empty vectors pGBKT7 ( BD ) and pGADT7 ( AD ) were used as negative controls [38] . BiFC was performed as described previously [38] . The indicated constructs were fused with the N-or C- terminal of YFP and transformed into A . tumefaciens strain EHA105 . The recombinant constructs of A . tumefaciens were infiltrated in 4–6 week old transgenic N . benthamiana ( expressing a nuclear marker-H2B-RFP ) [68] leaves via agroinfiltration . The fluorescent signals were detected at 2 dpi via confocal microscopy . His and GST tag fusion proteins were purified using His- and GST-Trap ( GE Healthcare ) according to the manufacturer’s instructions [38] . GST-AtMYC2 ( 2 μg ) and His-NSs ( 2 μg ) fusion proteins were mixed and incubated with 25 μL GST-Trap for 2 h at 4°C in a binding buffer ( 50 mM Tris-HCl , pH 7 . 5 , 200 mM NaCl , 0 . 25% Triton X-100 , and 35 mM b-mercaptoethanol ) . After six washes with binding buffer , pulled-down proteins were resuspended in 2xSDS buffer and detected by immunoblot using Anti-GST and Anti-His antibodys . A . tumefaciens carrying the 35S:MYC2-Myc or 35S:YFP-NSs constructs were infiltrated into N . benthamiana leaves . About 1g leaf tissue was collected and ground to powder in liquid nitrogen . Proteins were extracted in a cold extraction buffer ( 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 2 mM MgCl2 , 0 . 5 mM EDTA , 0 . 1% Triton , 0 . 5% NP-40 , 10% glycerol , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , one protease inhibitor cocktail/100 mL ( Sigma-Aldrich , USA ) ) . Then the protein extracts were incubated with 25 μL GFP-trap beads for 3 h at 4°C . After that , the beads were washed three times with extraction buffer and resuspended in 2xSDS buffer before used for immunoblot analysis . Total RNA was extracted from leaf and plant samples using an RNeasy Plant Mini Kit ( Qiagen ) with column DNase treatment . RNA was reverse transcribed using TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix ( TransGen Biotech , China ) . Four to six independent biological samples were collected and analyzed . RT-qPCR was performed using SYBR Green Real-Time PCR Master Mix ( Toyobo , China ) on the CFX 96 system ( Bio-Rad ) . Pepper Ca-ACT1 and N . benthamiana Nb-EF1α were used as the internal controls ( Listed in S1 Table ) . The thrip spawning assay was performed as described previously with some modifications [40] . Arabidopsis plants were grown in soil covered with Parafilm ( Bemis , USA ) to prevent any thrips from escaping and to facilitate counting . Three-week-old plants were placed in an acryl cylinder chamber ( 7 cm × 5 cm ) and covered with a fine mesh . Seven female adults ( 7–14 d after eclosion ) were allowed to infest a single plant for two weeks , and new larvae and adult thrips were counted . Eight plants of each genotype were used per experiment . The experiment was repeated at least twice with similar results . Transgenic Arabidopsis plants expressing the AtMYC2 or AtTPS10 promoter:GUS reporter gene were infested with thrips for 24 h and incubated in GUS staining buffer ( 0 . 5 mg/mL X-glucuronide , 0 . 5 mM potassium ferricyanide , 0 . 5 mM potassium ferrocyanide , 10 mM EDTA , 0 . 1% Triton X-100 , 0 . 1 M pH 7 . 0 phosphate buffer ) at 37°C overnight . The stained seedlings were cleared by washing with 70% ethanol . Untreated plants were used as a negative control . The experiment was repeated at least twice with similar results . Significant differences in gene expression and volatile organic compound levels were determined by Student’s t tests or one-way ANOVA; if the ANOVA result was significant ( P < 0 . 05 ) , Duncan’s multiple range tests were used to detect significant differences between groups . Thrip choices between different treatments were analyzed by nonparametric Wilcoxon matched pairs tests . All statistical tests were carried out with GraphPad Prism . Sequence data in this study can be found in Sol Genomics Network ( https://solgenomics . net ) , TAIR ( www . Arabidopsis . org ) or GenBank/EMBL under the following accession numbers: CaMYC2 ( CA00g50270 ) , CaMTS1 ( CA08g16370 ) , CaMTS2 ( CA08g16380 ) , CaMTS3 ( CA08g16410 ) , CaMTS4 ( CA08g16420 ) , AtMYC2 ( AT1G32640 ) , AtMYC3 ( AT5G46760 ) , AtMYC4 ( AT4G17880 ) , AtTPS10 ( AT2G24210 ) , TSWV NSs ( JF960235 . 1 ) , TSWV NSm ( JF960236 . 1 ) , and TSWV Ncp ( JF960235 . 1 ) , TZSV ( EF552433 . 1 ) . | Most bunyaviruses are transmitted by arthropod vectors , and some of them can modify the behaviors of their arthropod vectors to increase transmission to mammals , birds , and plants . NSs is a non-structural bunyavirus protein with multiple functions that acts as an avirulence determinant and silencing suppressor . In this study , we identified a new function of NSs as a conserved manipulator of vector behavior via plant . NSs suppresses jasmonate-mediated plant immunity against thrips by directly interacting with several homologs of MYC transcription factors , the core regulators of the jasmonate-signaling pathway . This hijacking by NSs enhances thrips preference and performance . Therefore , our data support the hypothesis that MYC2 is a convergent target that plant pathogens manipulate to promote their survival in plants . | [
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"microbio... | 2019 | The Orthotospovirus nonstructural protein NSs suppresses plant MYC-regulated jasmonate signaling leading to enhanced vector attraction and performance |
Zika virus ( ZIKV ) is primarily transmitted by Aedes mosquitoes in the subgenus Stegomyia but can also be transmitted sexually and vertically in humans . STAT1 is an important downstream factor that mediates type I and II interferon signaling . In the current study , we showed that mice with STAT1 knockout ( Stat1-/- ) were highly susceptible to ZIKV infection . As low as 5 plaque-forming units of ZIKV could cause viremia and death in Stat1-/- mice . ZIKV replication was initially detected in the spleen but subsequently spread to the brain with concomitant reduction of the virus in the spleen in the infected mice . Furthermore , ZIKV could be transmitted from mosquitoes to Stat1-/- mice back to mosquitoes and then to naïve Stat1-/- mice . The 50% mosquito infectious dose of viremic Stat1-/- mouse blood was close to 810 focus-forming units ( ffu ) /ml . Our further studies indicated that the activation of macrophages and conventional dendritic cells were likely critical for the resolution of ZIKV infection . The newly developed mouse and mosquito transmission models for ZIKV infection will be useful for the evaluation of antiviral drugs targeting the virus , vector , and host .
Zika virus ( ZIKV ) is an enveloped , positive sense RNA virus belonging to the Flaviviridae family and is primarily transmitted to humans via the bite of an Aedes aegypti mosquito [1 , 2] . ZIKV infections cause self-limiting fever , headache , myalgia , and conjunctivitis [1] . A recent large ZIKV outbreak in Brazil in 2015–2016 was linked to an increased incidence of microcephaly and congenital malformations in children born in the epidemic area as a result of ZIKV crossing the placenta of infected mothers [3–6] . ZIKV infection was linked to Guillain-Barré syndrome in adults [7 , 8] . After an acute infection phase , ZIKV can establish a persistent infection in the male reproductive tract that can then be sexually transmitted [9 , 10] . These varied transmission routes and different clinical presentations make ZIKV a serious public health threat . Due to the complexity of the ZIKV transmission cycle , which involves a vertebrate host and a mosquito vector , an animal model paralleling the infection process in humans would be useful to better understand the disease process resulting from ZIKV infections as well as in the evaluation of antiviral compounds and vaccine candidates . Because mice are naturally immune to a ZIKV infection , mouse strains defective in antiviral immune pathways such as AG129 , Ifnar-/- , Stat1-/- , Stat2-/- and Irf3-/-Irf5-/-Irf7-/- triple knock out mice have been used in ZIKV pathogenesis research [11–15] . For instance , ZIKV-infected Ifnar-/- mice developed weight loss , paralysis , and a systemic virus infection associated with an age-dependent mortality rate ( 20–100% ) , that is , young mice ( 3–4 weeks old ) were more sensitive to ZIKV infection than aged mice ( 11–12 weeks ) [11 , 12] . ZIKV caused a systemic infection detectable in several organs of these immunocompromised animals including the spleen , kidney , testes , and brain [11 , 12] . Immune-deficient mice also have been used to investigate the competence of Aedes mosquitoes to various ZIKV strains [2 , 16] . Interestingly , blood meals from ZIKV viremic mice are more infectious to mosquitoes than artificial blood meals of comparable doses [2] . The relative contributions of various factors from host and vector to ZIKV transmission remain to be explored . The contribution of the mosquito bite on the host's inflammatory response and how this impacts virus dissemination in vivo has been shown to play a role in infection models of Semliki Forest virus and Bunyamwera virus [15] . The role that the mosquito plays in ZIKV pathogenesis is undefined due to few infection models that involve the transmission of the virus via a mosquito [17] . STAT1 and STAT2 are key transcription factors activated by type I IFN signaling that are induced following infections with various pathogens including viruses [18] . ZIKV antagonizes type I IFN response through the suppression of STAT1 phosphorylation and STAT2 degradation [19–21] . Stat1-/- mice was recently utilized in anti-ZIKV drug development [15] . In the presented study , we found that ZIKV caused systemic infections in Stat1-/- mice that presented with high viremia and brain infections . In addition , we demonstrate that ZIKV can directly be transmitted between A . aegypti mosquitoes and Stat1-/- mice . The mosquito-dependent ZIKV transmission mouse model will be useful in studies of disease progression in the testing of novel antiviral therapies and anti-transmission strategy .
STAT1 is a key transcription factor activated following signals delivered by type I and II interferons that result in the activation of antiviral-related genes [18] . To test the sensitivity of Stat1-/- mice to ZIKV infection , adult Stat1-/- mice were intraperitoneally challenged with 4x103 to 4x106 ZIKV plaque forming units ( pfu ) /mouse that resulted in significant weight loss and 100% mortality by two weeks at the doses tested ( Fig 1A ) . In contrast , ZIKV-infected Ifnar-/- mice showed much less weight loss and only had a 20% mortality rate ( p = 0 . 0199 ) when challenged with 4x104 pfu/mouse compared to the Stat1-/- mice with the same ZIKV infection dose . It has been shown that young Ifnar-/- mice were more sensitive to ZIKV infection compared to old mice [12] . The age of Ifnar-/- mice used in Fig 1A was 9 weeks old and Stat1-/- mice were 12–16 weeks old . Age or gender differences did not affect the mortality rates observed in Stat1-/- mice . In the ZIKV-infected Stat1-/- mice ( 4×104 pfu/mouse ) , viremia peaked at 2 days post-infection ( Fig 1B , p = 0 . 0095 ) , and declined in blood over time . ZIKV-infected mice also presented with splenomegaly and were moribund . Some of the infected mice had pale livers ( Fig 1C ) . Taken together , Stat1-/- mice were more sensitive to ZIKV infection than Ifnar-/- . To examine the kinetics of ZIKV replication in Stat1-/- mice , viral RNA was isolated from various organs including liver , spleen , and brain and analyzed by quantitative real-time PCR ( RT-PCR ) . ZIKV RNA was detected in peripheral organs , spleen and liver , at early time points following infection and by day 5 began to decline ( Fig 1D ) . Because mice infected with 4x104 pfu were dead or moribund by day 7 post-infection , the low infection dose ( 1x103 pfu/mouse ) was used during the examination of tissues harvested at this time point ( Fig 1D ) . High levels of ZIKV RNA were detected in spleens 3 days post-infection suggesting that this organ could be the main replication site initially prior to systemic dissemination . In contrast , viral RNA in brain tissues was not detected until 5 and 7 days post-infection suggesting that the ZIKV replication site shifted from peripheral tissues into the brain in Stat1-/- mice . Consistent with the RNA data , ZIKV NS1 and capsid protein expression were detectable by immunoblotting in spleens harvested from mice 3 days post-infection that decreased by day 5 ( Fig 1E ) . ZIKV NS1 protein expression in brain tissue was detected on day 7 post-infection ( Fig 1F ) . We failed to detect viral protein expression in Day3 and Day5 brain tissue ( S1 Fig ) . Viral protein expression in the liver was not detected by immunoblotting ( S1 Fig ) . Similar to other flaviviruses , ZIKV NS1 protein is a secretory protein that could be used as diagnostic tool for ZIKV infection [22] . Serum NS1 expression in ZIKV-infected Stat1-/- mice gradually increased , suggesting that systemic infection in various tissues , including the brain , continued ( Fig 1G ) . Taken together , these data suggest that as the ZIKV infection progressed different organs were affected . To examine the ZIKV tissue distribution , liver , spleen , and brain sections were examined by immunohistochemistry using an anti-NS1 antibody ( Fig 2A ) . Consistent with the mRNA expression profile ( Fig 1D ) , NS1-expressing cells were rarely found in liver compared to the high expression levels observed in the spleen , mainly in the red pulp 3 days post-infection ( Fig 2A ) . NS1-expressing cells were found sporadically in brain tissues examined 7 days post-infection . To examine whether ZIKV infection induced cell death , apoptosis was examined in respective tissues using the TUNEL assay . This analysis demonstrated low levels of apoptosis in the liver compared to the substantially elevated levels observed in the spleen at both 3 and 7 days post-infection ( Fig 2B ) . The frequency of TUNEL-positive cells in the brain was mainly observed on day 7 post-infection ( Fig 2B ) . The apoptotic cells in brain might accumulate over time and contribute to the mouse death . Taken together , ZIKV replication and cell death were observed in the spleen during the early stages of infection and in the brain at later time points . TNFα and IL-6 are important proinflammatory cytokines that are upregulated during acute inflammation . Dengue virus ( DENV ) infection induces TNFα and IL-6 production in dengue patients , and serum TNFα is positively correlated with disease progression [23] . We suspected that proinflammatory cytokines might be also involved in ZIKV-associated pathology and mouse death . To investigate the role of proinflammatory cytokines in ZIKV infection , double knockout mice ( Stat1-/-× Il6-/- and Stat1-/-× Tnfa-/- ) were infected with ZIKV by direct injection . The double knockout mice were equally susceptible as Stat1-/-mice to a ZIKV infection suggesting that the upregulation of these proinflammatory cytokines might not be critical in ZIKV-induced pathology and mortality ( Fig 3A ) . In contrast , the absence of TNFα expression delayed DENV-associated death in a mouse model ( Fig 3B ) , suggesting that ZIKV-associated pathology develops via a different mechanism . In contrast to Stat1-/- mice , wild-type ( WT ) mice were resistant to a ZIKV infection . Although Ifnar-/- adult mice were sensitive to a ZIKV infection , the infected mice recovered from the infection ( Fig 1A ) . Due to the significant levels of ZIKV replication observed in the spleen ( resulting in splenomegaly ) in Stat1-/- mice . We next addressed whether anti-ZIKV-induced immune responses determined the outcome of ZIKV infection in mice . Splenocytes were isolated from WT , Stat1-/- and Ifnar-/- mice without or with ZIKV infection ( 60 h post-infection , 4x104 pfu/mouse ) and analyzed by flow cytometry with various cell lineage markers ( Fig 4 ) . Dendritic cells ( DCs ) are known to be important for the activation of adaptive immune response . Activated DCs upregulate the expression of MHCII molecule , which presents antigen peptide to prime antigen-specific T cells . In addition , activated plasmacytoid DCs produce large amounts of type I IFN immediately after virus infection , which is critical to control viral infection [24 , 25] . The amounts of SSC+ ( side scattered light ) immune cells in Stat1-/- and Ifnar-/- mice , that contain all myeloid lineages , were comparable to those in WT mice in the steady state ( S2A Fig ) . Upon ZIKV infection , SSC+ myeloid cells were increased in both Stat1-/- and Ifnar-/- mice compared to those in uninfected Stat1-/- mice ( Fig 4A and 4B , p<0 . 0001 ) . Within those myeloid cells , the percentages of CD11c+ DC populations were similar between uninfected WT , Stat1-/- and Ifnar-/- mice ( S2B Fig ) . However , the percentage of CD11c+ DCs was drastically increased in Ifnar-/- mice at 60 h post-ZIKV infection compared to those in infected Stat1-/- mice ( Fig 4C and 4D , p<0 . 0001 ) . We observed that Ly6C+CD11b+ conventional DCs ( cDCs ) as well as PDCA1+CD11b+ pDCs were significantly induced in Stat1-/- and Ifnar-/- mice in frequencies after infection compared to uninfected Stat1-/- mice ( Fig 4E–4H ) , while the DCs were similar between all uninfected mice ( S2C and S2D Fig ) . Interestingly , the induction of cDCs in infected Ifnar-/- was more prominent than in Stat1-/- mice , while the induction of PDCA1+CD11b+ DCs was more dominant in infected Stat1-/- mice . ZIKV has broad tissue tropism and macrophages could be one of the main targets during ZIKV infection [11 , 26 , 27] . We addressed F4/80+Ly6G- and MHCII+ macrophages among CD11c- myeloid cells and found no significant difference of macrophages in frequencies in uninfected WT , Stat1-/- and Ifnar-/- mice ( S2E and S2F Fig ) . After infection , the proportion of F4/80+Ly6G- macrophages was unchanged in WT and Ifnar-/- mice but slightly reduced in Stat1-/- mice ( Fig 4I and 4J ) . Further , the amount of MHCII+ activated macrophages in infected Ifnar-/- mice was more than those in infected Stat1-/- mice ( Fig 4K and 4L , p = 0 . 0046 ) . In contrast , infected Stat1-/- mice were unable to upregulate MHCII expression in macrophages . The results implied that infected Stat1-/- mice mounted an immune response , which was less competent to induce macrophage activation . Taken together , our model system showed that cDCs and splenic macrophage were not only the important target cells by ZIKV infection , but also involved in the induction of a protective immune response against ZIKV-mediated lethality . Based on our observations that Stat1-/- mice infected with ZIKV were highly viremic and pathogenic , we speculated that a complete transmission cycle might be established with Stat1-/- mice ( Fig 5A ) . Following mosquitoes take a bloodmeal from a viremic vertebrate host , arboviruses enter and replicate in mosquito midgut lumen initially . The viruses then penetrate midgut barrier and replicate in secondary organs including salivary gland . The virus can be further transmitted through mosquito saliva to another vertebrate host [28] . To establish a mosquito-dependent transmission model , ZIKV infection were first introduced to A . aegypti mosquitoes . To have ZIKV-carrying mosquitoes , ZIKV was directly injected into the mosquito thorax ( 400 pfu/mosquito , Fig 5B and 5C ) , in which ZIKV bypass the midgut barrier [29 , 30] . ZIKV replication assessed in the mosquito by plaque assay using total mosquito homogenates demonstrating that by day 7 post-infection 104 to 105 pfu/mosquito could be observed ( Fig 5B ) . Viral titers in mosquito salivary gland from day 4 or 7 post-infection were also determined . As shown in Fig 5C , ZIKV was detected in the salivary gland and the infection rates on day 4 and 7 post-thoracic injection were 62 . 5% and 100% , respectively . Similar infection rate was also observed in the midgut ( S3 Fig ) . The ZIKV-carrying mosquitoes were then allowed to take blood meals from Stat1-/- mice to assess their ZIKV transmission ability . As shown in Fig 5D , Stat1-/- mice bitten by 6–12 of Day 7 mosquitoes showed significant body weight loss and died within 10 days ( Group d in Fig 5D ) . Bites from 1–3 of day 7 mosquitoes were also sufficient to cause mouse death ( Group c in Fig 5D ) . When Stat1-/- mice were exposed to the Day 4 mosquitoes , 6–12 mosquito bites also caused a mouse death in 10 days ( Group b in Fig 5D ) . 1–3 mosquito bites caused less body weight loss and lower death rate in mice ( Group a mice in Fig 5D ) . ZIKV was recovered from blood as early as day 2 post-mosquito exposure in those ZIKV-infected mice ( Fig 5D ) , suggesting that the mosquitoes-to-mice transmission was successfully established . We next determined whether ZIKV can be also transmitted from viremic Stat1-/- mice mice to mosquitoes by allowing uninfected mosquitoes to take a blood meal from the mosquito-infected Stat1-/- mice ( Group b and d mice in Fig 5D ) . Engorged mosquitoes were collected and their virus titers determined 4 or 7 days later . A small portion of the engorged mosquitoes were collected immediately to evaluate the amount of virus taken from mouse blood . The mosquitoes ( Group e mosquitoes in Fig 5E ) which took a blood meal from mice with low ZIKV titer in blood ( Group b mice in Fig 5D ) indeed had lower virus replication in their midguts and reduced infection rate compared to the mosquitoes ( Group f mosquitoes in Fig 5E ) which took a blood meal from the mice with high virus titer in blood ( Group d mice in Fig 5D ) . The virus dissemination of ZIKV in other body parts , such as legs and wings , on Day 7 post-infection was also evaluated . As shown in Fig 5F , the dissemination rates in Group e and f were 30% and 100% , respectively . The similar dissemination rate of ZIKV in salivary gland was observed in these mosquito groups ( S4 Fig ) . The result suggested that ZIKV was transmitted from Stat1-/- mice to mosquitoes and had a successful replication in the mosquitoes . To confirm the mosquitoes that acquired their ZIKV from infected mice had virus transmission capacity to another animal , the Day 7 mosquitoes from Groups e and f mosquitoes in Fig 5E were allowed to take blood from uninfected Stat1-/- mice ( 6–9 mosquitoes/mouse ) ( Fig 5G ) . All mice bitten by mosquitoes infected with high ZIKV titer blood ( Group f mosquitoes in Fig 5E ) had a significant weight loss and 100% died within 2 weeks ( Group h mice in Fig 5G ) . In contrast , the mice bitten by the Day 7 mosquitoes from Group e ( Fig 5E ) had less weight loss and 25% death rate . The ZIKV titer in mouse blood at Day 2 post-infection was consistent with survival result ( Fig 5G ) . Taken together , a complete ZIKV mosquitoes-to-mice-to-mosquitoes-to-mice transmission cycle was established using Stat1-/- mice . We next determined the threshold of ZIKV infection in the mosquito by allowing uninfected mosquitoes to take blood meals from Stat1-/- mice exposed to various infection doses ( Fig 6A ) . ZIKV induced body weight loss and mouse death as low as 5 pfu of ZIKV challenge with slight changes in the kinetics of mouse viremia ( Fig 6B ) . Mice infected with 5–500 pfu of ZIKV infection intraperitoneally had viremia with titers ranging from 200 to 14000 ffu/ml on day 2 post-infection . The mosquitoes that took blood meals from these mice had infection rates ranging from 9% to 82% ( Fig 6C ) . Based on the data , the infectious dose at which 50% of mosquitoes become infected ( MID50 ) by feeding on mice was estimated to be 810 ffu/ml ( Fig 6D ) . In summary , ZIKV was highly infectious in both Stat1-/- mice and A . aegypti .
In the present study , Stat1-/- mice were used to establish a ZIKV infection model . Stat1-/- mice were more sensitive than Ifnar-/- mice to ZIKV infection without any discernable gender or age differences . With the different outcome of ZIKV infection in Ifnar-/- and Stat1-/- mice , cDC and macrophage activation might be the key components of protective immune responses . Furthermore , a complete ZIKV transmission cycle between mosquito and animal was established in Stat1-/- mice that will be useful in the evaluation of candidate compounds targeting both vector and host . In Stat1-/- mice , ZIKV replication took place in the spleen during the early stages of infection resulting in subsequent viremia that was detectable in the blood . Consistent with the virus replication kinetics in the spleen , viral blood titers peaked on day 2 and decreased by day 3 post-infection . Serum levels of the NS1 protein remained high at later time points in Stat1-/- mice , suggesting that virus replication was still ongoing in other organs . Differential kinetics of virus titer and NS1 level in serum were also observed in a recent study [31] . In contrast to the ZIKV NS1 expression pattern , DENV NS1 expression peaks on day 4 post-infection and disappears at later time points [32] . Measuring the serum levels of NS1 might be a useful biomarker in determining whether a persistent ZIKV infection is present in an epidemic area . Although the genome structures and transmission cycles of ZIKV and DENV are very similar , the clinical presentation following infections with these viruses is different . For example , blocking TNFα ameliorates the severity of hemorrhaging caused by DENV infections [33] . In contrast , the absence of TNFα did not show any beneficial effects in ZIKV infected Stat1-/- mice , suggesting that ZIKV causes pathology via different mechanisms . Whether inactivation of macrophages , insufficient activation of cDCs and excess production of PDCA1+CD11b+ DCs are directly involved in ZIKV pathogenesis warrants further investigation . During natural infection , arboviruses enter mosquito through a bloodmeal from a viremic vertebrate host and replicates in the midgut lumen . Vector competence is determined by whether the arbovirus can penetrate the midgut and proliferate in secondary organs including salivary gland for further transmission . When entering mosquito via intrathoracic injection , viruses bypass the midgut barrier as well as the innate immunity in mosquito lumen , and therefore higher infection rate could be reached . By taking the advantage of high infection rate in mosquitoes by thoracic injection , we therefore were able to estimate other determinants , such as the numbers of mosquito bites and extrinsic incubation time , for a successful ZIKV transmission from mosquitoes to mice . To mimic nature infection in which arbovirus infection in mosquito is mediated by oral feeding from a vertebrate host , the ZIKV-infected mice were used as blood donor for mosquito infection in the second part of our transmission model . The infection rate in mosquitoes by oral feeding was dependent on the virus titer in mouse serum and was lower than the infection rate caused by intrathoracic injection . The difference between intrathoracic injection and oral feeding on virus transmission efficiency will provide a way to evaluate the influence of various factors on midgut tissue barrier and innate immune response in mosquito to a successful arbovirus transmission . Stat1-/- mice was recently applied in ZIKV research to evaluate the efficacy of ribavirin for ZIKV treatment [34] . ZIKV was highly infectious to Stat1-/- mice and the lethal dose of ZIKV to the mice was even lower than 5 pfu , making the mouse strain a useful transmission model . We therefore could study not only the mosquitoes-to-mice transmission , but also the mice-to-mosquitoes transmission . ZIKV-infection caused high titer viremia which lasts at least three days in Stat1-/- mice , and the MID50 to A . aegypti with the viremic mouse blood was less than 1000 ffu/ml , making the transmission mouse model very useful for future applications . For example , the transmission model could be important for establishing the extrinsic incubation period ( EIP ) in mosquito . From our mouse model , the EIP was ≤ 7 days and could be shorter than the A129 mice [2 , 16] . The development of a novel mouse model that parallels the transmission cycle in humans will form the cornerstone of future research that will allow for a better study of disease progression and pathogenesis in addition to providing a novel means of testing developmental drugs and interventions . Additional transmission models using other mouse strains deficient in type I and II IFN signaling might also be developed as a means of further assessing immune responses to ZIKV in the context of different genetic backgrounds . Furthermore , the complete transmission model will be valuable to investigate aspects of vector competence for ZIKV and host factors that influence the extrinsic incubation period .
All mouse-related experiments were conducted in compliance with the guidelines of the Laboratory Animal Center of NHRI . The animal protocol ( NHRI-IACUC-105111 ) was approved by the Institutional Animal Care and Use Committee of NHRI , according to the Guide for the Care and Use of Laboratory Animals ( NRC 2011 ) . Management of animal experiment and animal care and use of NHRI have accredited by the AAALAC International . Zika virus strain PRVABC59 was obtained from the Taiwan Center for Disease Control and amplified in Vero cells ( obtained from Dr . Min-Shi Lee , NHRI , Taiwan ) . Virus titers were determined by plaque assay or focus forming assay using Vero cells as described previously [35] . Dengue virus strain D2Y98P [36] was obtained from Dr . Sylvie Alonso ( National University of Singapore , Singapore ) , amplified in C6/36 cells ( obtained from Dr . Wu-Chun Tu , National Chun Hsing University , Taiwan ) , and titrated by plaque assay using the BHK-21 cells ( obtained from Dr . Andrew Yueh , NHRI ) . Detailed information can be found in the Supplementary Methods . Antibodies to the ZIKV capsid ( GeneTex , Hsinchu , Taiwan ) and tubulin ( Sigma , St . Louis , MO ) were used for immunoblotting . ZIKV NS1 ( 172–351 ) antiserum was raised in mice and rabbits at NHRI as described in the Supplementary methods and used for immunoblotting and immunohistochemistry , and ELISA assays . Apoptosis in tissue sections was detected using the In Situ Cell Death detection kit ( Roche , Indianapolis , IN ) . Aedes aegypti ( Higgs ) eggs were hatched in deionized water under deoxygenated conditions . The hatched larvae were fed powdered yeast and goose liver ( NTN Fishing Bait ) . Newly emerged mosquitoes were fed with a 10% sucrose solution and maintained at 28°C and 80% humidity with a 12 h light and dark cycle . For direct ZIKV infection , 7–14 day-old female mosquitoes were inoculated with 400 pfu of ZKIV by thoracic injection and maintained in normal housing conditions for 7 days . Mice were bred and maintained at the Laboratory Animal Center of NHRI . Stat1-/- ( C57BL/6 background ) [18] mice were provided by Dr . Chien-Kuo Lee ( NTU , Taiwan ) . Wild-type C57BL/6 , Il6-/- , and Tnfa-/- mice were purchased from the Jackson Laboratory . Stat1-/- mice were crossed with Il6-/-and Tnfa-/- mice to establish double-knockout mice . Ifnar-/- mice ( C57BL/6 background ) were provided by Dr . Michael Karin ( UCSD , US ) . Unless otherwise specified , both male and female mice between the ages of 8–10 weeks were used in the study . Mice were anesthetized with Rompun ( 16 mg/Kg , Bayer Animal Health , Monheim , Germany ) and Ketalar ( 100 mg/Kg , Pfizer , New York , NY ) . After their ventral surfaces were shaved they were placed on top of a polyester mesh that covered a mosquito-housing cage that allowed female mosquitoes to take a blood meal . Female mosquitoes were starved for 12 h before they were allowed to take blood meals from mice . Each mouse was bitten by 6–12 mosquitoes . The ZIKV-infected mosquitoes were kept in a separated mosquito incubator and maintained at 28°C and 80% humidity with a 12 h light and dark cycle . NS1 ( 172–351 ) rabbit antiserum was used as a capture antibody ( 10 μg IgG/well ) and mouse antiserum was used as the detection antibody ( 2 μg IgG/well ) . Anti-mouse-HRP ( 1:5000 , Santa Cruz , Santa Cruz , CA ) and TMB ( eBioscience ) were used for color development . Purified NS1 ( 172–352 ) protein was used as a standard ( 1–1000 ng/ml ) to calculate NS1 serum and culture media concentrations . RNA was extracted from mouse tissues using Trizol ( Invitrogen , Carlsbad , CA ) and subsequently used as templates for cDNA synthesis using SuperScript First Strand Synthesis System ( Invitrogen ) . cDNAs were then used as templates in the quantitative-polymerase chain reaction ( PCR ) with gene specific primers and SYBR green dye to determine quantification cycle ( Cq ) by Applied Biosystems 7900HT Fast Real-Time PCR System . Relative ZIKV expression level was calculated using the ΔΔCq method with cyclophilin A ( CPH ) cDNA as an internal control . Primer sequences used in the study were: CPH Forward: atggtcaaccccaccgtgt , Reverse: ttcttgctgtctttggaactttgtc; ZIKV ( 1086–1162 ) [37] Forward: ccgctgcccaacacaag , Reverse: ccactaacgttcttttgcagacat . Spleens were mashed and digested with Liberase Blenzyme 3 ( 0 . 05U/ml , Roche , Woerden , Netherlands ) plus DNaseI ( 10 μg/ml , Sigma ) at 37°C for 20 min . Single cell suspensions of 1x106 cells were washed twice with FACS buffer ( 2% BSA/PBS , 0 . 1% NaN3 ) and maintained in the dark at 4°C throughout experiments . Flow cytometric data were acquired using a CantoII flow cytometer and FACSDiva software ( both from BD Biosciences , San Jose , CA ) . FlowJo software ( Tree Star , Inc . ) was used for data analyses . For marker expression determinations , cells were incubated for 15 min on ice with anti-mouse antibodies , including CD11b ( M1/70 ) , CD11c ( HL3 ) , CD45 ( 30-F11 ) , F4/80 ( BM8 ) , Ly6G ( IA8 ) , Ly6C ( AL-21 ) from BD Biosciences and PDCA1 ( eBio129c ) from eBioscience . Immunostained cells were washed twice in FACS buffer prior to incubation with 7AAD ( Sigma ) . Viable cells were gated from the 7AAD-negative population prior to analysis . For quantitation analysis , the percentage of specific subpopulations to the gated population was calculated in each splenocyte preparation . Unless otherwise stated , data are presented as the mean ± SEM . The difference among treatment or day groups were analyzed using one-way ANOVA with Bonferroni’s post hoc test , or Student's T test . The results of survival curve were analyzed by log-rank test . p-values ≤0 . 05 were considered statistically significant . The exact p , t , and df values can be found in S1 Table . | Zika virus ( ZIKV ) is transmitted mainly by mosquito bites and can also be transmitted between humans by sex or from pregnant women to their babies . ZIKV infection causes damage in many tissues including the brain in adults and newborns , making ZIKV infection an important health issue globally . To develop new tools for ZIKV research , we determined that a genetically modified mouse strain , Stat1-/- , was highly sensitive to ZIKV infection . We also demonstrated that ZIKV could be delivered to mice by mosquito bites and transmitted back to Stat1-/- mice . The newly developed mouse model will be useful for developing new strategies to treat ZIKV infection and for studying mechanisms to reduce mosquito-mediated transmission . | [
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"... | 2018 | Establishment of a mouse model for the complete mosquito-mediated transmission cycle of Zika virus |
Conventional diagnostic methods for human ascariasis are based on the detection of Ascaris lumbricoides eggs in stool samples . However , studies of ascariasis in pigs have shown that the prevalence and the number of eggs detected in the stool do not correlate well with exposure of the herd to the parasite . On the other hand , an ELISA test measuring antibodies to Ascaris suum haemoglobin ( AsHb ) has been shown to be useful for estimating transmission intensity on pig farms . In this study , we further characterized the AsHb antigen and screened samples from a population-based study conducted in an area that is endemic for Ascaris lumbricoides in Indonesia to assess changes in AsHb antibody rates and levels in humans following mass drug administration ( MDA ) . We developed and evaluated an ELISA to detect human IgG4 antibodies to AsHb . We tested 1066 plasma samples collected at different times from 599 subjects who lived in a village in rural Indonesia that was highly endemic for ascariasis . The community received 6 rounds of MDA for lymphatic filariasis with albendazole plus diethylcarbamazine between 2002 and 2007 . While the AsHb antibody assay was not sensitive for detecting all individuals with Ascaris eggs in their stools , the percentage of seropositive individuals decreased rapidly following MDA . Reductions in antibody rates reflected decreased mean egg output per person both at the community level and in different age groups . Two years after the last round of MDA the community egg output and antibody prevalence rate were reduced by 81 . 6% and 78 . 9% respectively compared to baseline levels . IgG4 antibody levels to AsHb appear to reflect recent exposure to Ascaris . The antibody prevalence rate may be a useful indicator for Ascaris transmission intensity in communities that can be used to assess the impact of control measures on the force of transmission .
An estimated 1 . 45 billion people worldwide are infected with three soil transmitted helminths ( STH ) Ascaris lumbricoides , Trichuris trichiura , or hookworms [1] . Among the STH , infections with A . lumbricoides are most common and its public health impact is estimated to be approximately 1 . 31 million daily adjusted life years ( DALYs ) [1] . Current STH control programs are focused on morbidity control through community based deworming of school-aged children by annual or semiannual administration of a single dose of anthelmintics such as albendazole or mebendazole [2] . In addition , the large elimination programs for lymphatic filariasis and onchocerciasis provide anthelminthics to entire at risk populations that are also effective against A . lumbricoides [3 , 4] . The intensity of A . lumbricoides infection is routinely measured by the number of eggs per gram ( EPG ) in stool by the Kato-Katz fecal thick-smear technique [5] . This method is useful for quickly assessing infection prevalence rates for evaluating the efficacy of control programs . However , the sensitivity of the Kato Katz smear is reduced in areas with low infection rates and intensities [6] . Furthermore , it is a time-consuming and cumbersome technique , and eggs in stool do not necessarily correlate well with the intensity of exposure to new infections with migrating stages of the parasite that are major contributors to morbidity caused by the parasite [7] . Therefore , it would be useful to have a practical method to measure the intensity of exposure to new infections at the population level . Research on the value of antibody assays for STH infections has been limited to date apart from interesting work on strongyloidiasis [8–10] . One reason for this is that people did not consider antibody testing to be a priority for common infections that can be diagnosed by microscopy . Also , it is commonly believed that antibody tests will not be able to distinguish between current and past infections . Only a handful of studies have evaluated antibody tests for ascariasis . Most of these studies used crude somatic antigen preparations or excretion/secretion ( E/S ) products derived from larvae or adult parasites cultivated in vitro . These antigens are difficult to procure and generally lack specificity [11–15] . Recently , Vlaminck et al . , [16] evaluated a serological test for the detection of ascariasis in fattening pigs based on the detection of IgG antibodies to A . suum haemoglobin ( AsHb ) in serum samples by ELISA . The AsHb antigen is highly produced and secreted by both the adult and larval stages [17 , 18] . Validation studies with samples from naturally and experimentally infected pigs showed that the antibody assay was superior to detection of eggs in feces for detecting exposure to the infection . A subsequent study showed that antibody reactivity to AsHb correlated with liver pathology caused by migrating A . suum larvae , and high antibody rates in pig herds were associated with low growth rates ( reduced farm productivity ) [19] . There are many parallels between ascariasis in pigs and in humans that is caused by the closely related species A . suum and A . lumbricoides , respectively . Therefore , the purpose of the present study was to investigate the potential value of anti-AsHb antibody testing for community diagnosis of human ascariasis .
Adult A . suum parasites were collected with permission from the intestines of infected pigs that were being processed as part of the normal work at a local abattoir in Ghent , Belgium . The fresh worms were snap frozen in liquid nitrogen for subsequent storage at -80°C . Tissue homogenization and mRNA extraction and cDNA construction were essentially performed as described in Rosa et al [20] . The AsHb antigen was purified according to the protocol described by Vlaminck et al [17] . The protein sequence of AsHb was obtained from the Entrez Protein Database of the National Center for Biotechnology Information ( NCBI ) , USA ( http://www . ncbi . nlm . nih . gov/protein ) with the following accession number: AAA29374 . 1 ( A . suum ) . Protein orthologs of AsHb in other nematode species were identified by a BLASTP search on WormBase ParaSite ( http://parasite . wormbase . org/ ) against the available protein sequence databases for the following species ( A . suum ( PRJEB80881 ) ; A . lumbricoides ( PRJEB4950 ) , Toxocara canis ( PRJEB533 ) , Strongyloides stercoralis ( PRJEB528 ) , Necator americanus ( PRJNA72135 ) , Ancylostoma ceylanicum ( PRJNA72583 ) , Enterobius vermicularis ( PRJEB503 ) , Brugia malayi ( PRJNA10729 ) , Wuchereria bancrofti ( PRJEB536 ) , Loa loa ( PRJNA60051 ) , and Trichuris trichiura ( PRJEB535 ) ) . The presence of signal peptides was detected using SignalP 4 . 1 software [21] . The AsHb product was amplified from parasite cDNA by reverse transcriptase- polymerase chain reaction ( RT-PCR ) using the primer pair AsHbFw ( CACCATGCGCTCATTGCTATTATTATCG ) and AsHbRv ( TCAGTGTTGCTCTTCCTTATGC ) and according to the amplification protocol described in Vlaminck et al [17] . The amplified PCR product was cloned into pET100/D-TOPO vector and transformed into One Shot TOP10 competent cells according to the manufacturers protocol ( Invitrogen , Carlsbad , CA , USA ) . Positive colonies were analyzed using PCR and the PCR products were sequenced . One positive transformant was selected and the plasmid was purified using the PureLink HQ Mini Plasmid Purification Kit ( Invitrogen ) . The plasmid DNA was used as template for the amplification of AsHb using the same primers as previously mentioned . The PCR product was sequenced to verify that the insert was in-frame for expression . The AsHb-containing vector construct was transformed into BL21 Star ( DE3 ) One Shot cells and cells were grown in Luria Broth ( Miller ) ( Sigma , St . Louis , MO , USA ) containing 50μg/ml carbenicillin ( Sigma ) . Overnight cultures of the transformed bacteria were diluted 1:100 in LB + carbenicillin and grown at 37°C with shaking to an optical density of approximately 0 . 5–0 . 8 at 600nm . Protein expression was induced by addition of isopropylthiogalactoside to the culture medium to a final concentration of 1 mM . After 4h of incubation at 37°C under vigorous agitation ( 250 rpm ) , E . coli cells were pelleted by centrifugation and suspended in 1:25 of the initial culture volume of ice-cold RIPA lysis and extraction buffer ( G Biosciences , St . Louis , MO , USA ) , incubated on shaking device at room temperature ( RT ) for 30 min and then centrifuged for 10 min at 4 . 500 g . The pellet was suspended again in RIPA lysis and extraction buffer and the previous incubation and centrifugation step was repeated twice more . The final pellet was dissolved in binding buffer ( 50mM Phosphate buffer , 0 . 5M NaCl , 10mM imidazole and 7M guanidine hydrochloride , pH 8 . 0 ) . The recombinant protein was then bound to a HIS-Select Cobalt Affinity Gel column ( Sigma ) followed by a column wash with 5x column volumes of binding buffer and eluted from the column using elution buffer ( 50mM sodium phosphate buffer , 500mM NaCl , 7M guanidine hydrochloride , 250mM imidazole , pH 8 . 0 ) . The eluate was dialyzed overnight against PBS in a Slide-A-Lyzer Dialysis Cassette , 7K MWCO ( Thermo Fisher Scientific , Pittsburg , PA , USA ) and subsequently concentrated on a Millipore centrifugal filter unit ( Millipore , Billerica , MA , USA ) . Protein dye binding and BCA protein assay ( Thermo Fisher Scientific ) were used to determine the protein concentration . Plasma samples were collected from individuals living in Mainang village on Alor Island ( Province of East Nusa Tenggara , Timor , Indonesia ) as part of a study of the impact of annual MDA with diethylcarbamazine ( DEC ) combined with albendazole ( ALB ) on Brugia timori and STH infections [22] . Only the most prevalent geohelminths , A . lumbricoides , hookworm and T . trichiura were analyzed in this study , since other species such as Hymenolepis spp . and S . stercoralis were only found in a few cases [23] . Baseline prevalence for A . lumbricoides , T . trichiura and hookworm in the whole community were 32 . 2% , 25 . 3% , and 9 . 4% respectively [23] . The STH infection rates and egg densities were assessed with single Kato Katz smears in 2002 ( prior to any mass drug administration , MDA ) and in 2009 , two years after the sixth and last annual round of MDA . For these years , community egg output was determined as the sum of EPG of all people divided by the total number of tested individuals . Prevalence rates for STH in stool in 2004 and 2007 were assessed by the formalin ether enrichment method . Additionally , at baseline , 17 . 6% of the individuals had B . timori mf in their blood [23] . Plasma samples used in the present study were collected in 2002 prior to the first round of MDA , after 2 rounds of MDA ( 2004 ) , just prior to the sixth and final round of MDA ( 2007 ) , and 2 years after the last round of MDA ( 2009 ) . Other plasma samples used in this study were from people living in the East Sepik region of Papua New Guinea . Hookworm infection rates in this study area were over 90% and no A . lumbricoides infections were detected by Kato Katz examination of one stool sample per subject in any of these study communities . Non-endemic control plasma samples were from American subjects in St . Louis , MO , USA . Protein samples were denatured and reduced in LDS 4x sample buffer ( Thermo Fisher scientific ) and separated by SDS-PAGE using Bolt 4–12% Bis-Tris Plus minigels ( Thermo Fisher Scientific ) under reducing conditions and either stained with SimplyBlue Safestain Coomassie stain ( Invitrogen ) for the visualization of the proteins or transferred onto nitrocellulose membranes for immunostaining as previously described [24] . After blotting , nitrocellulose membranes were blocked at room temperature for 1hr with 5% non-fat dry milk ( BioRad , Hercules , CA , USA ) in PBS , and then incubated with human plasma diluted 1:50 in PBS + Tween20 ( 0 . 05% ) ( PBST ) and incubated at room temperature ( RT ) for 2h . After incubation with a secondary antibody ( mouse anti human IgG4 pFc-HRP ( Southern biotech , Birmingham , AL , USA ) , blots were washed with PBST and antibody binding was detected using CN/DAB Substrate kit ( Thermo Fisher Scientific ) . AsHb was deglycosylated with PNGase F according to the manufacturer’s protocol ( New England Biolabs , Ipswich , MA , USA ) . Briefly , 10μg of AsHb was mixed with 10x denature buffer and H2O to make a 20μl total reaction volume that was denatured at 100°C for 10 min . This was followed by addition of 10X Glycobuffer 2 , 10% NP-40 and 1μl of PNGase F ( 500 , 000 U/ml ) and H2O to obtain a total reaction volume of 40μl that was incubated at 37°C for 2h . Finally , the deglycosylated product ( dAsHb ) was passed over a detergent binding spin column ( Thermo Fisher Scientific ) to remove the detergent that was added during the deglycosylation reaction . RNAseB treated with PNGase F was included as deglycosylation control . For mass spectrometric ( MS ) analysis , N-glycans released with PNGase-F from 5μg of AsHb were labeled with 2-aminobenzoic acid ( anthranilic acid , AA ) , as described [25] . MALDI-TOF-MS was performed in the negative ion reflector mode on an Ultraflextreme instrument ( Bruker Daltonics , Germany ) using DHB as matrix , as described [26] . Putative glycan structures were assigned on the basis monosaccharide compositions deduced from the observed m/z values . Phosphorylcholine ( PC ) specific monoclonal antibodies ( TEPC-15 ) ( Sigma ) were used to detect the presence of PC in AsHb and dAsHb by Western blot and ELISA . Anti-PC antibodies were detected with HRP conjugated anti-mouse IgA ( Sigma ) and nitro-blue tetrazolium/5-bromo-4-chloro-3′-indolyphosphate substrate ( Sigma ) . PC linked to bovine serum albumin was used as a positive control . Previous serological experiments performed by Santra et al . , 2001 [13] and Chatterjee et al . , 1996 [14] showed that human IgG4 responses to a fractionated adult E/S antigen of Ascaris were superior in reactivity and also showed less cross reactivity than IgG1 , IgG2 and IgG3 subclass antibodies in sera from patients infected with hookworm , Trichuris and Strongyloides . This , in combination with the results from earlier experiments performed in the lab , led to the decision to use the IgG4 subclass antibody as detecting antibody in the immunological assays described in this study . Antigen ( AsHb , rAsHb or dAsHb ) was coated at a concentration of 1μg/ml overnight at 4°C on Nunc Maxisorp flat-bottomed 96 well plates ( Sigma ) in 100μl coating buffer ( 0 . 05M carbonate/bicarbonate buffer pH9 . 6 ) . Following incubation , plates were washed 3 times with wash buffer ( PBST: 0 . 05% PBS-tween 20 , pH 7 . 2 ) . Nonspecific binding sites were blocked by dispensing 100 μl of PBS with 5% FCS in each well and incubating the plates for 2h at 4°C . For the inhibition ELISA , an extra blocking step was included where PC-groups on the AsHb were blocked by incubating the coated wells with TEPC-15 antibodies diluted 1:500 in blocking buffer for 2 h . After blocking , plates were washed as before and plasma or antibody samples were added to the wells . Plasma samples were diluted 1:50 in PBST and 100 μl of each sample was tested in duplicate . Plates were incubated for 2h at RT and afterwards washed as previously described . Secondary antibodies ( mouse anti-human IgG4 pFc’-HRP ( Southern biotech ) ) were diluted 1:2 , 000 in blocking buffer and plates incubated for 1h at RT . Finally , plates were washed and 100μl of the o-phenylenediamine dihydrochloride substrate solution ( Thermo Fisher Scientific ) was added to each well . The substrate reaction was stopped after 10 minutes by adding 50μl of stop solution ( 4M H2SO4 ) and optical densities at 490nm were recorded . The cutoff for positivity was calculated as the arithmetic mean OD + 3 times the standard deviation obtained with 10 non-endemic plasma samples from St . Louis , Missouri , USA . All statistical analyses were performed using GraphPad Prism v6 . 0 software ( La Jolla , CA , USA ) . Infection prevalence rates at baseline and subsequent time points were compared with the McNemar test . Infection intensities ( EPG ) and ELISA OD values for paired samples were compared using the Wilcoxon signed rank test . Correlations between EPG and AsHb ELISA OD values were assessed with the Spearman’s rank correlation test . The statistical significance of differences in ELISA OD values obtained with different antigens ( AsHb , dAsHb and rAsHb ) was assessed with the Wilcoxon signed rank test . The Ethics Committee of the University of Indonesia , Jakarta approved the sample collection in the Alor Island study as previously described [22] . The Institutional Review Boards at Case Western Reserve University and the Papua New Guinea Medical Research Advisory Committee approved the protocol for sample collection , and all study participants provided informed consent . The Institutional Review Board at Washington University School of Medicine waived the need for an additional review for our use of de-identified human serum samples for this in vitro study . Genbank: AAA29374 . 1 . Wormbase: GS_08371 , ALUE_0001899801 , ALUE_0001446901 , NECAME_07759 ANCCEY_14143 , TCNE_0001552801 .
In order to evaluate whether antibodies of infected humans were detecting AsHb , a select number of endemic and non-endemic control plasma samples were first used to evaluate the recognition of AsHb by Western blot and ELISA . Six of 8 endemic plasma samples with proven A . lumbricoides infection ( >25 EPG ) and 4 of 5 individuals with negative stool examinations had IgG4 antibodies that recognized AsHb ( Fig 1 ) . In contrast , none of the non-endemic control plasma samples detected AsHb on Western blot or showed strong reactivity on ELISA . A cut-off for ELISA positivity was determined , based on the OD values of the 10 non-endemic plasma samples that were tested . The cut-off was set at an OD of 0 . 38 . Specificity of the AsHb IgG4 Western blot was also assessed with plasma samples collected in an area in Papua New Guinea where hookworm infection is nearly universal but ascariasis and trichuriasis are absent . Since 7 of the 12 samples tested had IgG4 antibodies to AsHb ( S1 Fig ) , it appears that some people with hookworm infections develop IgG4 antibodies to AsHb to some degree . A BLASTP search using the AsHb protein sequence obtained from GenBank ( AAA29374 . 1 ) on Wormbase Parasite revealed two sequences in A . lumbricoides ( ALUE_0001899801 and ALUE_0001446901 ) with >99% amino acid sequence identity . Both sequences form a perfect alignment with AsHb ( S2 Fig ) . Both N . americanus and A . ceylanicum have orthologs to AsHb ( NECAME_07759 and ANCCEY_14143 respectively ) with sequences that were shorter than the AsHb ( 88 AA and 97 AA respectively ) with sequence identity in the overlapping region of 51 . 1% and 46 . 4% and E values of 1e-25 and 2 . 7e-24 respectively . The ortholog in the canine ascarid Toxocara canis ( TCNE_0001552801 ) has 69 . 6% amino acid sequence identity and also showed to contain a signal peptide . No AsHb orhtolog ( E-value < 1e-05 ) was present in Trichuris spp . ( S1 Table ) . This part of the study used ELISA to detect human IgG4 antibodies to AsHb in plasma samples collected before and after MDA . All parasitological and serological data obtained in this study is provided as supplementary information ( S2 Table ) . STH prevalence results before and after MDA are shown in Table 1 . Although the prevalence of A . lumbricoides infection decreased significantly from 38 . 2% to 15 . 7% after 2 years of MDA ( a 58 . 9% reduction; P < 0 . 01 ) , the prevalence rate rebounded to 24 . 4% in 2007 ( a 36 . 1% reduction from baseline; P < 0 . 01 ) and to 29 . 5% in 2009 ( a 22 . 8% reduction from baseline , P = 0 . 09 ) . In contrast , the average egg output in the community was 81 . 6% lower in 2009 than in 2002 ( 128 . 2 vs . 697 . 9 , P < 0 . 01 ) . Hookworm and T . trichiura infections were also reduced by MDA ( both 1 . 8% in 2007 ) , however in 2009 hookworm infection rates returned to pre-MDA levels ( 10 . 4% ) and T . trichiura rates also rebounded ( 2 . 3% ) [22] . The average community egg outputs for hookworm and whipworm did not change significantly between 2002 and 2009 ( 5 . 4 to 6 . 9 and 3 . 1 to 1 . 4 respectively ) . A total of 1 , 066 plasma samples were tested by AsHb ELISA , and these included samples collected prior to any MDA and at intervals following multiple rounds of MDA ( Table 1 ) . Employing the earlier determined OD cut-off for the ELISA , 67 . 6% of the individuals were seropositive at baseline ( Table 1 , Fig 2 and S3 Fig ) . After two years of MDA , seroprevalence was significantly reduced to 22 . 6% in 2004 ( P < 0 . 001 ) and this was unchanged in 2007 ( 23 . 0% ) . The seropositivity rate in 2009 had further decreased in comparison to 2007 ( 14 . 3% , P < 0 . 05 ) . In both 2002 and 2009 there was no significant relationship between AsHb ELISA OD values and stool egg counts for any of the STH ( S4 Fig ) . In 2002 , 66 of 90 egg positive ( 73 . 3% ) and 91 of 150 ( 60 . 7% ) egg negative individuals were seropositive by anti-AsHb ELISA . However , in 2009 only 3 of 20 egg positive ( 15% ) and 4 of 44 ( 9 . 1% ) egg negative individuals were seropositive . There was a drastic and significant reduction in both egg prevalence and seroprevalence between 2002 and 2004 in all age groups . Egg prevalence increased again in 2007 and 2009 across all age groups until it almost reached pre-treatment levels whereas seroprevalence did not change after 2004 . Similarly , the mean number of eggs excreted by the people in a specific age category was also significantly reduced from 2002 to 2009 over all age categories ( Fig 3 ) . In order to work towards better standardization of the test , we also evaluated whether infected human sera would recognize recombinant AsHb . The AsHb gene was cloned from adult worm cDNA and expressed in E . coli . The protein profile of the purified rAsHb was identical to that of AsHb after Coomassie staining ( S5 Fig ) . Antibodies in a pooled plasma sample from humans with Ascaris infection did not bind to rAsHb by Western blot ( Fig 4 ) . To test whether the presence of N-glycan groups on AsHb was important for immune recognition , the native AsHb was deglycosylated with PNGase F to remove any N-linked glycans . A shift in molecular weight was seen in the PNGase F deglycosylated AsHb ( dAsHb ) , indicating the removal of N-linked glycans . This molecular weight shift was not visible after treatment of the rAsHb with PNGase F indicating the absence of PNGase F digestible carbohydrate groups ( S5 Fig ) . Antibodies in pooled plasma from A . lumbricoides infected individuals did not bind to dAsHb by Western blot . In order to quantify this effect , IgG4 reactivity of 10 plasma samples from Indonesian individuals from 2002 to AsHb , denatured AsHb , dAsHb and rAsHb were evaluated by ELISA ( Fig 4 ) . After normalization of the data using the AsHb OD 490 as reference , a significant relative increase in antibody binding ( 59% , P < 0 . 05 ) was seen after denaturing the AsHb . The opposite was true for dAsHb and for rAsHb that were less immunoreactive than the native antigen ( -42% , P < 0 . 05 and -88% , P < 0 . 01 , respectively . In order to further characterize the glycans present on AsHb that were important for immune-recognition of the antigen , the N-glycan structures were removed from the protein backbone by PNGase F and analyzed by MALDI-TOF-MS . The glycan spectrum of the released N-glycans of AsHb is shown in Fig 4 . Major [M-H]- signals were observed at m/z 1176 . 9 , 1380 . 0 and 1583 . 2 , derived from glycans with the compositions F1N2H3 , F1N3H3 and F1N4H3 ( F , fucose; N-acetylhexosamine , H , hexose ) glycans , respectively , interpreted as α1 , 6-fucosylated trimannosyl N-glycan core structures substituted with 0–2 GlcNAc residues ( indicated in Fig 4 ) . An additional major signal was observed at m/z 1545 . 1 ( F1N3H3+165 . 1 ) indicative for the phosphorylcholine ( PC ) substituted variant of F1N3H3 which is in line with previous observations for N-glycans of A . suum [27] . Further , minor signals at m/z 1748 . 3 ( F1N4H3PC ) , 1786 . 3 ( F1N5H3 ) and 1913 . 4 ( F1N4H3PC2 ) were observed indicating that additional substitutions with HexNAc residues and PC can occur . Incubation of both AsHb and dAsHb with mouse anti-PC monoclonal antibodies ( TEPC-15 ) proved the presence of PC in native AsHb and its absence after deglycosylation with PNGase F ( S6 Fig ) . The recognition of AsHb by human IgG4 antibodies was not significantly reduced when AsHb was pre-incubated with anti-PC antibodies before addition of positive human plasma samples ( S6 Fig ) .
The results of this study suggest that measurement of antibodies to AsHb may be a useful approach for assessing exposure of human populations to A . lumbricoides infection . Egg excretion per person and antibody rates decreased in parallel following MDA while the infection prevalence rate in 2009 was not very different from the baseline rate . The implementation of MDA probably reduced the number of Ascaris eggs that were excreted in the environment . As a result , the ingestion of infective eggs and exposure to migrating stages of the parasite is likely to have decreased in all age categories in the population . This may explain why the antibody rate decreased further between 2007 and 2009 despite suspension of the MDA program after 2007 . However , since we do not have quantitative coprological data for the years 2004 and 2007 , we do not have precise information on the impact of MDA on infection intensities between 2002 and 2007 . Despite the limited sequence similarity between the AsHb orthologs identified in both hookworm species , our results confirm antigenic cross reactivity between hookworm and A . lumbricoides [28] . Thus , reduced antibody reactivity to AsHb after MDA may have been partly due to the effects of MDA on hookworm prevalence or transmission . However , the significant rebound in hookworm prevalence that occurred between 2007 and 2009 ( from 1 . 8% to 10 . 4% ) was not associated with a rise in antibody rates to AsHb . This study did not investigate antibody responses to AsHb in people infected with T . trichiura . However , the absence of an AsHb homologue in the Trichuris genome and the fact that antibodies in sera from pigs experimentally infected with T . suis had little if any reactivity with AsHb [16] suggest that humans infected with T . trichiura who have not been infected with Ascaris are not likely to have significant antibody responses to AsHb . We acknowledge that using the AsHb ELISA would be too insensitive to identify or diagnose active A . lumbricoides infection in an individual . However , it is important to notice that the goal of our study was to evaluate the use of this serological test as diagnostic marker for exposure on a community level and not for individual diagnosis of infection . Unlike for A . suum infections in pigs , where experimentally infected pigs can serve as “true gold standard” , there is no such standard available to assess the sensitivity of a serological tool for diagnosis of STH infections in humans [6] . However , since this ELISA would be used to assess transmission intensity on a community level , this is not a major obstacle . Recombinant antigens are often preferred for serodiagnosis of parasitic infections . One reason for this is that native antigens are sometimes difficult to obtain and purify . However , in the case of AsHb , the antigen is relatively easy to purify from A . suum . In addition , antibodies from infected individuals were only weakly reactive with recombinant AsHb produced in this study or with AsHb after treatment with PNGase F . This finding suggests that IgG4 human antibodies to AsHb are mostly directed against N linked glycan epitopes present on the native antigen . Shared carbohydrate epitopes pose a challenge for developing specific serology tests for helminth infections . Parasites with AsHb orthologs with low amino acid sequence identity may contain the same or similar carbohydrate epitopes that could be responsible for antigenic crossreactivity . Mass spectrometric analysis of the glycans released by PNGase F treatment of AsHb detected PC-substituted GlcNAc moieties . Blocking the PC-group with TEPC-15 antibodies did not result in a significant blocking of binding of human antibodies to AsHb by ELISA . This result suggests that other glycans motifs may be important antigenic components of AsHb and it is consistent with a previous study that reported that humans do not develop IgG4 subclass antibodies to the PC epitope [29] . The simplicity , affordability and speed of the Kato Katz test has made it the most widely used method for estimating STH infection rates and intensities in large scale control programs [30] . This has important policy implications , because current WHO guidelines for STH preventive chemotherapy are based on infection prevalence rates as assessed by a single Kato-Katz smear with little attention paid to intensity [31] . However , prevalence is not everything . Because of the high degree of aggregation of STH infections , significant reductions in average worm loads may result in small or unnoticeable changes in prevalence rates [32] . Also , infection intensities for ascariasis are often reported according to the broad WHO categories of light ( < 5 , 000 EPG ) , moderate ( 5 , 000–50 , 000 EPG ) or heavy ( >50 , 000 EPG ) . However , it is unclear whether these categories are appropriate for use in all endemic regions , because of considerable geographic variability in egg production per adult female Ascaris worm [33] . The use of diagnostic tools that are based on the detection of Ascaris eggs or DNA in the stool has significant shortcomings when it comes to the accurate estimation of true prevalence or intensity of the infection . For one thing , only a small fraction of the total number of parasite larvae that migrate through the body ever develop into adult worms in the intestine . For worms that reach the intestine , egg output per worm can vary widely because of different parasite sex ratios , the age distribution of the adult worms , and host immunity [7] . Thus the absence of A . lumbricoides eggs in the stool does not necessarily prove there has been no recent infection or exposure with larval stages . Animal studies have shown that larval stages contribute significantly to morbidity caused by Ascaris infections [34 , 35] , and this should be considered as part of the health impact of ascariasis in humans as well . Our results show that IgG4 antibody responses to AsHb do not correlate with A . lumbricoides egg output . This is consistent with results from other studies that have compared anti-Ascaris antibody responses to adult worm counts or EPG in stool [12 , 36] . Furthermore , several studies have shown that anti-Ascaris antibody responses are rather dependent on exposure and infection intensity as opposed to being protective or predictive of future levels of infection [36–39] . A significant percentage of people in Alor Island who had Ascaris eggs in their stool lacked IgG4 antibodies to AsHb , and this situation became more common later in the trial when community egg loads were reduced . It is interesting that a number people with more than 500 EPG were seronegative in 2009 . While there are several possible explanations for this , we favor the idea that antibody responses to AsHb are stimulated much more by new infections when larvae are migrating through tissues and than by the presence of low numbers of adult worms in the intestinal lumen . When the infection pressure has been reduced to low levels , exposure to new infections with migrating larvae may not be sufficient to induce or maintain IgG4 antibodies to AsHb . Furthermore , experimental infections in pigs have already shown that the number of adult worms in the gut seems almost inversely correlated with the number of eggs ingested [40 , 41] . Hence , with lower infection intensities , the chance of larvae establishing in the gut and developing into adult worms increases . It would be interesting to elucidate the effect of infection dose on anti-AsHb responses in pigs , and this might help to explain changes in antibody rates in human populations when infection pressure declines following MDA . Although AsHb serology is not sensitive for detecting active Ascaris infections in individuals , it appears to be a promising new tool for quantifying exposure to Ascaris infection at the community level . That is to say , it may provide a useful measure of egg input and incoming infection in communities even though it is not sensitive for predicting the presence of eggs in the stool in individuals . Serological surveys for antibodies to AsHb and other STH antigens could be an attractive alternative to stool examination for integrated post-MDA surveillance programs for lymphatic filariasis ( LF ) and STH , because post-MDA surveys for LF collect finger prick blood samples for use in point of care serology tests [3] , and finger prick blood could also be used for STH serology . AsHb serology could also be used as an alternative to Kato-Katz test for mapping the distribution of STH infections , because it should be useful for identifying areas with high transmission rates that have the highest need for intervention . The crossreactivity with hookworm and possibly Strongyloides stercoralis and Toxocara spp . limits the value of AsHb serology if one is interested in ascariasis alone . Work to develop a more species-specific antibody assay is ongoing . However , hookworm and Ascaris crossreactivity may not be a major flaw for the practical use of AsHb serology , since the same drugs are used to treat both of these infections . We believe that this study has provided a useful proof of principle for the value of antibody serology as an epidemiological tool for assessing STH transmission pressure in populations and for monitoring the impact of STH intervention on transmission pressure . We have shown that AsHb antibody rates correlate well with egg output per person in populations , and they are also likely to correlate well with recent egg input in individuals . However , additional research is needed on this topic . First , it will be necessary to confirm the findings from this study with samples from other areas with high rates of ascariasis . Second , it would also be interesting to evaluate changes in serology over time in different subpopulations or to use serology to try and pinpoint when children are first exposed to STH . | Ascariasis is a neglected tropical disease caused by the intestinal nematode Ascaris lumbricoides that affects hundreds of millions of people in the developing world . Current methods for diagnosis of this infection are based on detecting eggs in the stool that are excreted by adult Ascaris worms . However , these methods have limited sensitivity for recent infections , and they do not detect infections with immature parasite stages that do not always result in the establishment of adult worms in the human intestine . We have previously shown that an assay for antibodies to Ascaris hemoglobin in pig serum is useful for assessing transmission of Ascaris infections on pig farms . In this study , we developed and evaluated a similar antibody assay that is based on the detection of human IgG4 antibodies to Ascaris haemoglobin ( AsHb ) . Community antibody rates decreased rapidly following mass drug administration of the anthelmintic drug albendazole , and this decrease reflected reduced Ascaris egg excretion at the community level . This antibody test may be a useful tool for assessing the impact of control measures on the transmission of new Ascaris infections in endemic populations . | [
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"as... | 2016 | Community Rates of IgG4 Antibodies to Ascaris Haemoglobin Reflect Changes in Community Egg Loads Following Mass Drug Administration |
The microenvironment of lymphoid organs can aid healthy immune function through provision of both structural and molecular support . In mice , fibroblastic reticular cells ( FRCs ) create an essential T-cell support structure within lymph nodes , while human FRCs are largely unstudied . Here , we show that FRCs create a regulatory checkpoint in human peripheral T-cell activation through 4 mechanisms simultaneously utilised . Human tonsil and lymph node–derived FRCs constrained the proliferation of both naïve and pre-activated T cells , skewing their differentiation away from a central memory T-cell phenotype . FRCs acted unilaterally without requiring T-cell feedback , imposing suppression via indoleamine-2 , 3-dioxygenase , adenosine 2A Receptor , prostaglandin E2 , and transforming growth factor beta receptor ( TGFβR ) . Each mechanistic pathway was druggable , and a cocktail of inhibitors , targeting all 4 mechanisms , entirely reversed the suppressive effect of FRCs . T cells were not permanently anergised by FRCs , and studies using chimeric antigen receptor ( CAR ) T cells showed that immunotherapeutic T cells retained effector functions in the presence of FRCs . Since mice were not suitable as a proof-of-concept model , we instead developed a novel human tissue–based in situ assay . Human T cells stimulated using standard methods within fresh tonsil slices did not proliferate except in the presence of inhibitors described above . Collectively , we define a 4-part molecular mechanism by which FRCs regulate the T-cell response to strongly activating events in secondary lymphoid organs while permitting activated and CAR T cells to utilise effector functions . Our results define 4 feasible strategies , used alone or in combinations , to boost primary T-cell responses to infection or cancer by pharmacologically targeting FRCs .
Stromal cells create specialised lymphoid support compartments within secondary lymphoid organs . The signals they feed leukocytes have profound effects in many aspects of activation , proliferation , and differentiation [1] . Fibroblastic reticular cells ( FRCs ) construct the internal segregated structure of secondary lymphoid organs by acting as a scaffold for lymphocyte migration and secreting chemokine C-C motif ligand 19 ( CCL19 ) and chemokine C-C motif ligand 21 ( CCL21 ) to bring T cells and dendritic cells to the central T-cell zone and chemokine C-X-C motif ligand 13 ( CXCL13 ) to bring B cells to outer B cell zones . Lymphocyte survival is further supported through secretion of survival factors interleukin 7 ( IL-7 ) and B cell activating factor ( BAFF ) [2 , 3] . Several papers demonstrated that mouse lymph node–derived FRCs reduce T-cell proliferation . In mice , when T cells have been activated less than 15 h , cyclooxygenase-2 ( COX2 ) -driven prostaglandin E2 ( PGE2 ) is suppressive [4] , while comparative neutralisation experiments showed that nitric oxide plays a larger role past 15 h , when most T-cell division occurs [5–7] . T-cell function is impaired , shown through reduced interferon gamma ( IFNγ ) production [6–7] . Effects on memory T-cell differentiation have not been assessed in mice . Human FRCs are still almost entirely unstudied , though it has been shown that podoplanin ( PDPN+ ) cells analogous to mouse FRCs are found in human secondary lymphoid organs and that they secrete extracellular matrix components as well as CCL21 [8 , 9] . A recent study , citing as-yet unpublished data , said that human FRCs do not produce nitric oxide in response to IFNγ activation [9] . We therefore questioned whether prior mouse FRC research accurately modelled human FRC biology . The effects of FRCs on human T cells are unknown , and their mechanism/s of action have not been tested , though COX2 is expressed [4 , 9] . The role of human FRCs in T-cell regulation is likely to be highly relevant to human health . Mouse studies show far-reaching effects of FRCs for immunity against influenza and other pathogens [3 , 10 , 11] , and it is hypothesised that suppression of effector T-cell activation within lymph nodes reduces immune-mediated pathology against the lymph node structure [1 , 12] . Accordingly , virally infected FRCs are associated with T-cell persistence and chronic viral infection [12] . Here , we show that human FRCs block proliferation and modulate differentiation of newly activated naïve human T cells , without requiring T-cell feedback . Suppression was constitutive , and we identified 4 molecular mechanisms operating simultaneously: indoleamine-2 , 3-dioxygenase ( IDO ) , COX1 and 2 enzymes responsible for PGE2 production , adenosine 2A receptor ( A2AR ) , and transforming growth factor beta ( TGFβ ) . Coinhibition of these factors reversed FRC-mediated suppression in vitro and permitted us to observe T-cell activation on a living tonsil slice . It is important to first understand the key cells and molecules involved in regulating T-cell activation during inflammation , infection , cancer , and autoimmunity in order to treat immune-mediated pathologies and immune deficiencies . Here , we show that human FRCs , via 4 pathways—which are druggable individually or in small combinations—are a likely pharmacological target to boost the primary immune response .
To test the effect of FRCs on naïve T-cell activation , we isolated and culture-expanded FRCs from either cadaver-origin lymph nodes or live-donor tonsils using a published digestion protocol [13] or through explant culture . Cultured FRCs used in this study were identified as CD45− , CD31− , PDPN+ cells across multiple passages , and they expressed genes and proteins characteristic of published FRC phenotypes from freshly isolated mouse and human FRCs [14 , 15] ( S1 Fig ) , including expression of PDPN , CXCL12 , α smooth muscle actin ( αSMA; ACTA2 ) , lymphotoxin beta receptor ( LTBR ) , platelet-derived growth factor receptor ( PDGFR ) α and β , vimentin ( VIM ) , detectable but low levels of BAFF , mucosal vascular addressin cell adhesion molecule 1 ( MADCAM1 ) , Desmin , CD34 , and receptor activator of NF-κB ligand ( RANKL ) . As expected , chemokines CCL21 and CCL19 were switched off by cultured cells; transcription in mice has been shown to be regulated by lymphatic flow [16] . We identified a distinct subset of stromal cells present in freshly isolated tonsil but missing from our cultures; these were CD45−CD31−EpCAM- CD90+ CD73− PDPN− cells ( S1 Fig ) with unknown function . We activated carboxyfluorescein succinimidyl ester ( CFSE ) -labelled T-cells from peripheral blood mononuclear cells ( PBMCs ) in the presence of human FRCs and found that they underwent fewer divisions after 96 h ( Fig 1A and 1B ) . We then activated CFSE-labelled PBMCs and allowed T-cells to divide freely for 48 h . Cells were harvested and plated with or without FRCs and reactivated for a further 48 h . While reactivated T cells proliferated freely , the addition of FRCs after the initial activation and prior to the second activating stimulus imposed a significant brake on proliferation ( Fig 1C ) . Thus , FRCs could hamper the proliferation of both naïve and pre-activated , actively divided T cells . Next , we examined surface marker expression changes in responding T cells . After 96 h of activation with or without FRCs , T cells showed normal up-regulation of early activation marker CD69 , but significantly fewer T cells up-regulated the interleukin 2 receptor alpha ( IL-2Ra ) chain , CD25 ( Fig 1D ) . Together with data showing that FRCs could halt the proliferation of pre-activated T cells , these results suggested that the suppressive mechanism occurred well downstream of T-cell receptor ligation and did not involve steric hindrance . Suppression was responsive to dose ( Fig 1E ) , and human FRCs did not produce nitric oxide when stimulated ( Fig 1F ) . We questioned whether FRCs may be inducing apoptosis or permanently anergising responding T cells . Cell cycle analysis showed no increase in apoptosis in either CD4+ or CD8+ T cells ( Fig 1G ) . Instead , in the presence of FRCs , fewer T cells entered the DNA-synthesis phase ( S phase ) , compared to T cells that were not cocultured with FRCs ( Fig 1H ) , with no change to G0/G1 or G2/M phase ( S2 Fig ) . Separation of activated , suppressed T cells from FRCs , followed by reactivation alone in culture , showed that T cells were not permanently anergised by activation in the presence of FRCs ( Fig 1I ) . We probed the nature of the suppression mechanism by screening small molecular inhibitors , agonists , and blocking/neutralising antibodies in a similar coculture assay , in which CFSE-labelled T cells were activated in the presence or absence of FRCs and/or inhibitors and analysed after 4 days . Inhibition or blockade of TGFβ receptor , IDO , COX1/2 , and A2AR signalling all restored T-cell proliferation ( Fig 2A–2D ) to varying degrees , while interleukin 6 ( IL-6 ) and programmed cell death ligand 1 ( PD-L1 ) inhibition had no effect ( Fig 2A–2D ) , and inhibitors did not alone significantly alter T-cell differentiation phenotypes ( S3 Fig ) . All 4 mechanisms were utilised in all donors but to varying degrees , with no conserved predominance ( S4 Fig ) . This imposed challenges in how to most appropriately assess the overall effect of FRC suppression on T-cell biology in a meaningful manner , given human variance and the potential for redundancy . Donor-to-donor variance was minimal when all 4 mechanisms were targeted at once , and since all mechanisms were operational in all donors , we reasoned that this was the best means of assessing the net biological impact of FRCs on T cells . We therefore chose to suppress all mechanisms simultaneously to investigate downstream effects on T cells . To assess whether these 4 molecular targets were together sufficient to entirely block FRC suppression , we created an inhibitory cocktail of all 4 inhibitors and treated PBMCs that were exposed to stimulatory signals for 96 h . In the presence of FRCs , proliferation of both CD4 and CD8 T cells occurred at control levels ( Fig 2E and 2F ) . The inhibitor cocktail did not alone significantly increase T-cell proliferation in the absence of FRCs ( Fig 2E and 2F ) . Strikingly , the presence of FRCs influenced the fate of differentiating naive CD4+ and CD8+ T-cell populations ( CD62L+CD45RO− ) . Their presence during T-cell activation decreased differentiation of CD4+ and CD8+ T cells to a central memory phenotype ( CD62L+CD45RO+ ) while increasing the proportion of CD4+ naïve ( CD62L+CD45RO− ) T cells . Effector ( CD62L−CD45RO− ) and effector memory phenotype T cells ( CD62L− CD45RO+ ) and CD8+ naïve T cells were not affected ( Fig 3A and 3B ) . Memory phenotype cells were further profiled by expression of CD27 and by relative proliferation; both subsets yielded expected phenotypes ( S5 Fig ) . Blockade of TGFβR , IDO , COX1/2 , and A2AR signalling reversed the effects ( Fig 3A and 3B ) . Results were comparable regardless of whether anti-CD2/3/28-coated beads or PHA-L/IL-2 was used as the activating stimulus . Since CD25 is the alpha chain of the IL-2R , and since we observed selective inhibition of CD25 expression by FRCs ( Fig 1D ) , which was present as early as 24 h after stimulation and did not occur in the presence of inhibitors ( S6 Fig ) , this led us to question whether FRC-mediated effects on IL-2 signalling were evident . Coculture with FRCs did not alter signal transducer and activator of transcription 5 ( STAT5 ) phosphorylation ( Fig 4A and 4B ) , leading us to conclude that this signalling pathway was not mechanistically important; we tested other signalling molecules phosphorylated extracellular signal–regulated kinase 1/2 ( pERK1/2 ) , phosphorylated STAT1 ( pSTAT1 ) , phosphorylated STAT3 ( pSTAT3 ) , phosphorylated STAT6 ( pSTAT6 ) , and p38 mitogen-activated protein kinase ( MAPK ) and found no mechanistic insight within T cells ( S7 Fig ) . IL-2 production , as a downstream transcriptional target of IL-2 signalling , was also not altered ( Fig 4C ) . Together , these results demonstrated that an inhibition of the IL-2 signalling pathway was not driving T-cell suppression . Accordingly , the proportion of T regulatory cells ( Tregs ) was also neither increased nor decreased in stimulated cultures ( S8A and S8B Fig ) , and depletion of CD25+ cells did not prevent FRC-mediated suppression ( S8C Fig ) , suggesting that the effect of FRCs did not occur via cross-talk with Tregs . Production of IFNγ and tumour necrosis factor alpha ( TNFα ) were also unaffected by coculture with FRCs ( Fig 4C ) , suggesting that while FRCs inhibit activation and differentiation of T cells , once T cells are active , their effector functions are not impaired . This finding was confirmed using antigen-activated chimeric antigen receptor ( CAR ) T cells ( Fig 4D ) and is a finding not reproduced in mouse models , in which IFNγ production is reduced following FRC coculture [6] . Given the broad differences here observed between mice and humans , we decided to test whether T-cell cross-talk with human FRCs was required for or important to suppression . In mice , having T cells and FRCs in close proximity is important , since separation by transwell blocks the majority of the suppressive effect of FRCs [5 , 6] . Residual suppression in the presence of the transwell is likely to be due to secretion of PGE2 [4] . Unlike mouse FRCs , suppression was retained when FRCs were separated from T cells by a cell-impermeable transwell barrier ( Fig 4E ) . Moreover , FRC-conditioned media diluted 1:2 in complete media suppressed T-cell activation indistinguishably from FRCs ( Fig 4F ) , showing that FRCs secrete suppressive factors constitutively and that , unlike mouse FRCs [5 , 6] or human mesenchymal stromal cells [7] , they do not require cross-talk from activated T cells . Accordingly , steady-state cultured FRCs expressed high levels of PGE2 synthesis enzymes COX1 ( PTSG1 ) and COX2 ( PTSG2 ) ( Fig 4G ) . COX1 is usually a constitutive source of PGE2 , while COX2 is more commonly inflammation inducible yet was expressed in otherwise unstimulated FRC cultures ( Fig 4G ) [4] . COX2 , A2AR , and transforming growth factor beta receptor type 2 ( TGFβR2 ) protein staining was detectable on ERTR7+ T-zone FRCs ( TRCs ) present in frozen tonsil tissue sections ( S9 Fig ) from patients who were not suffering from infection at the time of surgery . We were unable to detect IDO staining , suggesting that this protein alone may be inducible under acute inflammatory conditions . As a method of cross-confirmation to show that FRCs preproduce suppressive factors prior to seeing T cells , we preincubated FRCs with inhibitors , prior to a wash step , and then added T cells and an activation stimulus . T cells stimulated with pre-inhibited FRCs had high CD25 expression after 24 h , similar to FRCs with inhibitors and higher than uninhibited FRCs ( S6 Fig ) , as also shown in Fig 1D . CD25 expression was used as a biomarker for FRC suppression of T cells , as T-cell division was not measurable until 48 h , and the pre-incubation step was , because of receptor turnover , not effective beyond 24 h . Next , we looked for a means to validate these in vitro results . Activation of T cells in situ on secondary lymphoid organ tissue slices has not previously been shown , to our knowledge . We decided to test whether this could be due to a suppressive effect of stromal cells , by stimulating T cells in situ within slices of freshly donated tonsil , in the presence or absence of the inhibitory cocktail . Tonsils were obtained within 2–4 h of surgery from healthy donors , immediately sectioned , and then incubated in media containing phytohaemagglutinin-L ( PHA-L ) and recombinant human interleukin 2 ( rhIL-2 ) , with or without the inhibitor cocktail added . After 96 h , T cells were imaged or isolated by mechanical disruption and analysed by flow cytometry . A significantly higher proportion of CD4+ and CD8+ T cells were observed in active cell cycle ( Ki67+ ) in the presence of the inhibitory cocktail , shown through flow cytometry ( Fig 5A and 5B ) and immunofluorescence ( Fig 5C ) . Controls showed baseline expression ( Fig 5A–5C ) . Immunofluorescent imaging showed increased Ki67 staining in the T-cell zone when inhibitors and the activating stimulus were both present ( Fig 5C ) . Ki67 staining robustly reproduced in vitro results and demonstrated that it is possible to inhibit stromal-induced T-cell suppression in situ . Taken together , these results show that human FRCs strongly influence the activation and differentiation of naïve T cells by constraining initial proliferation and skewing their differentiation away from a central memory phenotype without altering effector cytokine production or signalling . Mechanisms of action involved PGE2 , COX1/2 , TGFβ , and A2AR . Coinhibition of these factors permitted us to observe T-cell activation on a living tonsil slice for the first time , to our knowledge .
Identifying the cells and molecules that regulate T-cell activation during inflammation , infection , cancer , and autoimmunity is a fundamental first step towards creating effective therapies for immune-mediated pathologies and immune deficiencies . Human immunological studies are commonly carried out in vitro , with validation in mice . Here , we show that the presence of a human microenvironment influences T-cell activation both in vitro and in situ , with important functional and mechanistic differences from previous observations in mice . Previous studies using mouse lymph nodes have established that FRCs are important for regulation of T-cell proliferation , largely through provision of nitric oxide [5–7] with an early role for PGE2 [4 , 6] . Changes to differentiation were not observed in mice , but FRCs did compromise effector cytokine production and therefore T-cell function [6] . By contrast , our results showed key differences to mice . Human FRCs utilised 4 pathways independent of nitric oxide to control T-cell proliferation and differentiation . Inhibiting TGFβR , A2AR , IDO , and COX1/2 completely reversed the suppressive effect of FRCs and restored differentiation of T cells with a central memory phenotype to normal levels . Similarly , the inhibition of these targets in situ using tonsil slices allowed T cells to overcome the prohibitive effect that stromal cells imposed . Unlike mice , bidirectional T-cell signalling to FRCs was not required for T-cell suppression or for expression of A2AR , TGFBR , and COX2 protein by TRCs . Expression of IDO was not detected , and it is expected that IDO is induced by acute inflammation , as reported in dermal and bone marrow fibroblasts [17] . It will be of interest in the future to explore the factors and kinetics governing its induction . Once activated , polyclonal and CAR T cells both showed normal effector cytokine secretion , which again differs from mice . It is currently unclear whether these are bona fide biological differences between mice and humans or due to the preferential study of immunologically naïve mice raised in a specific pathogen-free environment . It would be interesting to see whether FRCs from mice that have undergone several rounds of a self-limiting infection would constitute a more representative model for human FRCs . The finding that T-cell function in the presence of FRCs is maintained is highly relevant to cancer immunotherapy . Secondary lymphoid organs are a primary tumour site for lymphoma and leukaemia and a prominent metastatic site for many other cancers . They are an important site for transfused CAR T-cell activity , with one study showing CAR T cells heavily infiltrating lymph nodes of patients with lymphoma at >30% of T cells [18] and another showing CAR T transcripts detectable in lymph nodes up to 3 mo post-infusion and at higher levels in lymph nodes than in blood [19] . Mouse data suggested that FRCs would limit IFNγ production by effector T cells; these results show that human FRCs only impose effects on naïve T-cell proliferation and differentiation and not effector function . Secretion of key effector cytokines IFNγ , TNFα , and IL-2 is unchanged in the presence of FRCs after T cells are activated , which is the case for CAR T therapy . Much like nitric oxide as a mechanism in mouse FRCs , human FRC molecular mechanisms are extremely complex , and the precise effects on T cells are not easily elucidated , despite their clear biological importance and decades of intense study . TGFβR signalling , the COX1 enzyme , A2AR , and IDO all have well-described suppressive effects on T-cell activation . But apart from IDO , none are uniformly anti-inflammatory; rather , each factor is capable of shaping the T-cell response in a complex manner dependent on the stimulus , costimulating factors , and the microenvironmental cytokine milieu [20 , 21] . As such , inhibitors targeting the molecules profiled in this study have been investigated for highly diverse applications . A2AR inhibitors , for example , are used to treat rheumatoid arthritis ( methotrexate [22] ) while being investigated as an immunomodulatory treatment for cancer , for which the goal is inhibition of immunosuppression [23] . The level of TGFβ signalling to naïve T cells is an important factor in enforcing their quiescence , and naïve T cells in patients with autoimmunity have reduced expression of TGFβRI and increased capacity for T-cell proliferation [24] , but TGFβ blockade enhances vaccine and immunotherapy responses [25] . With similar complexity , murine FRCs have a role in deletional and suppressive tolerance [5 , 6 , 26] while promoting healthy immune responses [3 , 10] . IDO has a well-defined role in T-cell suppression . It oxidises tryptophan to kynurenine metabolites [27] , which both deprives effector T cells of tryptophan , inducing proliferative arrest [28] , and exposes them to immunosuppressive kynurenine , which can impair T-cell growth and survival [29] . IDO is a well-described mechanism of tumour immune evasion in mice [28] and shows direct effects in human T cells [30–32] , though a phase 3 trial of combination IDO inhibitor and programmed cell death protein 1 ( PD-1 ) inhibition recently failed to improve progression-free survival compared to PD-1 inhibitor immunotherapy alone ( clinicaltrials . gov identifier: NCT02752074 ) . IDO affects the earliest stages of TCR signalling through down-regulation of Vav1 and inhibition of F-actin reorganisation [30 , 31] , as well as inhibition of the mammalian target of rapamycin ( mTOR ) pathway [29] , and , as we observed , prevents cells progressing to S phase of the cell cycle [33] . COX1 and COX2 are enzymes involved in prostaglandin synthesis . Our inhibitor is capable of blocking both , but our data show that COX1 is a major mediator of FRC suppression , since FRC-conditioned media strongly suppressed T cells , and cultured FRCs only expressed COX1 constitutively . COX1 can also be involved in the earliest inflammatory events , after which COX2 becomes the predominant inflammatory isoform [34] . In humans , PGE2 is the most abundant member of the prostanoid family , and most PGE2 is secreted by professional antigen-presenting cells ( APCs ) and stromal cells [21] . PGE2 is capable of mediating diverse effects depending on stimuli that are not well understood , but its role in suppression of T-cell activation and proliferation has been reported since 1971 [35] , with newer studies also describing skewed differentiation [36] and induction of a suppressive phenotype in non-Treg CD4+ T cells , which were capable of suppressing the proliferation of other T cells undergoing activation [37] . Reported mechanisms include down-regulation of CD25 , up-regulation of CD46 , and altered responses to costimulation [36] . Extracellular purinergic mediators , such as adenosine triphosphate ( ATP ) and adenosine , are powerful immunomodulators . They signal through A2AR as an important step in the resolution of inflammation , providing well-described suppressive influences on the function of T cells , dendritic cells , macrophages , mast cells , platelets , natural killer ( NK ) cells , B cells , fibroblasts , and neutrophils to prevent excessive tissue pathology [20 , 38 , 39] . Accordingly , methotrexate is a clinically important A2AR antagonist used to treat rheumatoid arthritis and other autoimmune diseases [22] . Adenosine production is increased in inflammation and in low-oxygen-tension microenvironments , and activation of A2AR increases intracellular cAMP , which inhibits cytokine responses . Accordingly , inhibition of A2AR awakens tumour-reactive CD8+ T cells in mouse models [40] . The oxygen tension in human lymphoid organs is likely to be low: murine lymphoid organs exhibit low oxygen tension in vivo at 0 . 5%–4 . 5% oxygen , which impacts upon the speed at which effector T cells differentiate [41] , and while data on healthy lymph nodes are lacking , low partial pressures are reported for other human organs and tissues [42] . Certain human T-cell subsets are known to express CD39 , which converts ATP to AMP [43] , while human FRCs express high levels of CD73 , which converts AMP to adenosine . A2AR signals reduce secretion of proinflammatory IL-1β and IL-6 [22] , both of which are produced at high levels by FRCs in response to inflammation [11] , and increases production of collagen I [44] . TGFβ is a highly evolutionarily conserved immunomodulatory molecule [45] . Our inhibitor blocked signalling receptor component activin receptor-like kinase 5 ( ALK5; TGFβRI ) , whose signals are transduced by phosphorylation of SMAD2 and SMAD3 proteins [46] . TGFβRI−/− mice develop a lethal inflammatory disease [47] , and neutralising TGFβ increases antitumour responses of CD8+ T cells [48] . However , TGFβ can impose both pro- and anti-inflammatory functions in responding human T cells , depending on their differentiation state and inflammatory cytokines encountered [45] . As an anti-inflammatory agent , TGFβ inhibits CD4+ and CD8+ T-cell clonal expansion and differentiation and inhibits the activation of high-affinity T cells [49–51] , relevant to our findings , and it also promotes the survival of lower-affinity T cells . These effects can occur in a paracrine fashion by acting on APCs and other cells and by reducing CD25 expression [51] . TGFβ signalling blocks the clonal expansion of T cells in vivo and blocks differentiation of T helper 1 ( Th1 ) , T helper 2 ( Th2 ) , and cytotoxic lymphocyte ( CTL ) T cells in favour of T helper 17 ( Th17; in the presence of IL-6 ) or peripheral Treg ( pTreg; in the presence of retinoic acid and IL-2 ) [50] . The described pathways could potentially also link together . PGE2 can increase the expression of IDO in dendritic cells [52] . Similarly , signalling pathways downstream of PGE2 ligands EP2–4 involve cAMP , which is derived from ATP [21] , potentially linking the suppressive actions of COX1 inhibition and A2AR inhibition . However , these potential interactions are very poorly studied and understood and require extensive further study . Suppression of T-cell proliferation within secondary lymphoid organs seems paradoxical , but these and other results [5–7] show it is conserved in mice and humans , despite utilising different mechanisms . Mouse studies clearly show that it operates in vivo , in isolated FRCs in response to inflammation [5] . Here , we show that it operates in vitro using human cells and in situ in human tissues and that FRCs also secrete key suppressive factors in the absence of current inflammation . One hypothesis is that this mechanism helps prevent bystander damage to stromal cells in a lymph node teeming with inflammatory cytokines and antigen [1 , 12] , and the mechanisms described all have well-charted effects in the resolution of inflammation [39] . Accordingly , viral infection of FRCs is associated with viral persistence in mice [12] , and a loose association has been observed in humans and rhesus macaques in studies of HIV and Ebola virus , though studies are correlative [1] . While human FRCs did not affect the secretion of effector cytokines from activated cells , they did impose effects on the proliferation of activated cells . FRCs added to pre-activated , rapidly dividing cultures halted their division , and when activated , suppressed T cells are removed from the suppressive influence of FRCs , they begin rapidly dividing within 24 h , despite being washed thoroughly to remove the inflammatory cytokine milieu , and without being given a new activating stimulus . Thus , the effects of FRCs are not limited to naïve T cells . This raises the possibility of pharmacologically targeting FRCs as a means to promote a stronger immune response . It would be valuable to study whether FRC inhibition could benefit patient groups who do not mount a robust response to vaccination , or to boost responses to cancer vaccines . The in situ activation assay revealed a clear disconnect between the study of human immunology in vitro , in which T cells are frequently activated in isolation , and in situ , in the presence of the microenvironment . Our work highlights the importance of considering the microenvironment for in vitro human immunology studies . Conditions that robustly activated T cells in culture were entirely insufficient to activate T cells in situ . Addition of the inhibitor cocktail significantly increased T cell activation in situ but not to in vitro levels , suggesting the presence of additional physical or chemical inhibitory factors that warrant further study . Similarly , in mice , rare FRCs were observed up-regulating the suppression-mediating nitric oxide synthase 2 ( NOS2 ) enzyme during an in vivo T-cell immune response [5] , which was sufficient to robustly impair T-cell activation . These processes are clearly observable yet subject to kinetics and fate decisions we have yet to fully understand—suppressive yet finely tuned to permit T-cell activation and foster immunity . The in situ activation assay has potential to test drugs in development for their effects on T-cell activation and proliferation . A technical challenge , requiring further study , is the ability to isolate slices from equivalent areas of tissue . The proportion of naïve T cells within a single donor varied hugely from slice to slice . As such , it was not yet possible to use this method to assess differences in subtler immunophenotypic changes , such as differentiation status . Another important caveat is our lack of transcriptomic data from freshly isolated human FRCs . Cultured FRCs in this study lacked a PDPN-low/negative subset that was present in freshly isolated tonsil FRCs , and the function of this subset in humans is not yet known . Together , this work suggests that FRCs utilise druggable targets ( individually or in combinations ) with the potential to boost the generation of new immune responses within secondary lymphoid organs—for example , following vaccination of relevant patient groups or for the treatment or prevention of malignancy .
All tissues were obtained from consenting donors from the National Disease Research Interchange ( NDRI ) resource centre or Human Biomaterials Resource Centre ( HBRC ) , Birmingham ( HTA licence 12358 , 15/NW/0079 ) , under project approval number REC_RG_HBRC_12–071 . All tissues were obtained and utilised in accordance with institutional guidelines and according to the principles expressed in the Declaration of Helsinki . Human tonsils were obtained from presently healthy children and adults undergoing routine tonsillectomy for a medical history of recurrent infection or obstructive sleep apnoea . Human blood was obtained from healthy adult donors . Human lymph nodes were procured from cadaveric donors , transported intact in DMEM on ice , and processed for flow cytometry or cell culture within 24 h . All tissues were obtained and utilised in accordance with institutional guidelines . Tonsils and lymph nodes were enzymatically digested using a published protocol [13] or grown through explant culture and used at passage 1–3 . Briefly , tissues were cut into small pieces and grown in a low volume of complete media with antibiotics ( alpha-MEM with 10% FBS , with penicillin , streptomycin , and a mycoplasma elimination reagent ) for 24 h to allow adhesion to the tissue culture plate . Following this , tissues were covered with media containing antibiotics and grown for 5 days to permit fibroblasts to emerge . Tissue was then discarded and cells culture-expanded in complete media without antibiotics . Using this method , a monolayer of >99% pure FRCs was achieved within 2 wk . Ten-fold expansion was taken to equal 1 passage . FRCs were defined as CD45− , CD31− , PDPN+ . Very rarely , cultures down-regulated expression of PDPN after passage 3; this did not affect their transcriptome or suppressive T-cell interactions ( not shown ) ; nonetheless , such cultures were not used experimentally to ensure uniformity . FRCs ( 2 × 104 ) were plated in a 96-well flat-bottom plate in complete media ( alpha-MEM , 10% FBS ) and allowed to adhere for 4 h . FRCs were always used in experiments prior to passage 3 . Mononuclear leukocytes were isolated from whole blood using a density gradient and then counted using a haemocytometer and Trypan Blue viability dye . Where stated , T cells were purified using the Pan T-cell isolation kit ( Miltenyi Biotec ) according to the manufacturer’s instructions and at a purity >90% , or CD25 depleted ( Miltenyi Biotec ) according to the manufacturer’s instructions and at a purity >90% . Mononuclear leukocytes or T cells ( 5 × 105 ) were added , together with a stimulant: either CD2/3/28 T-cell activation beads ( 2 beads/T cell ) ( Miltenyi Biotec ) or PHA-L ( 1 μg/ml ) + rhIL-2 ( 100 U ) , as stated in figure legends . Inhibitors were added at the following concentrations: SB431542 10 μM ( TGFβ signalling pathway inhibitor through blockade of ALK5 , 7 , 4 , Sigma ) , Indomethacin 5 μM ( Cox1/2 and PGE2 synthesis inhibitor , Sigma ) , SCH 58261 10 μM ( A2AR inhibitor , Sigma ) , 1-methyl-D-Tryptophan ( 1-MT ) 1 mM ( Indoleamine-2 , 3-dioxygenase inhibitor , Sigma ) . SCH 58261 and SB431542 10 μM were stored in DMSO . PGE2 inhibitor was reconstituted in ethanol; 1-MT was reconstituted in methanol and pH adjusted to 7 . 0 . The final volume per well was 200 μl , and all cells and inhibitors were resuspended in complete media without antibiotics . All 4 inhibitors used together at stated concentrations are referred to as the ‘inhibitor cocktail’ . FRCs ( 1–2 × 104 ) and Chinese hamster ovary ( CHO; 5 × 104–1 . 25 × 105 ) cells were plated in a 96-well flat-bottom plate in complete media ( DMEM , 10% FBS with IL-2 [25 IU/ml] ) and allowed to adhere for 4 h . CRT-3 ( 1 × 105 ) or mock-transduced T cells were added and incubated for 18 h at 37°C/5% CO2 . Culture supernatants were collected at 18 h , and the levels of IFNγ were titrated in culture supernatants using the ELISA method . Briefly , plates ( Nunc ) were coated with anti-human IFNγ Ab diluted in coating buffer ( 0 . 75 μg/ml ) and incubated at 4°C overnight . After blocking the wells using buffer containing PBS plus 0 . 05% ( v/v ) Tween 20 and 0 . 1% ( w/v ) bovine serum albumin ( BSA ) , supernatants were added to each well . Biotin-labelled mAb in incubation buffer was added to each well , and streptavidin-HRP was used as enzyme . The reaction was developed using 3 , 3′ , 5 , 5′-tetramethylbenzidine ( TMB ) substrate and stopped by adding 1 M hydrochloric acid . The plates were washed after each step using PBS with 0 . 05% ( v/v ) Tween 20 . Reading was performed using a microplate automatic reader ( Biorad ) at a wavelength of 450 nm . Tonsils <2 h from surgery were sliced into multiple 0 . 4–0 . 6 mm sections using a sterilised carbon-fibre microtome blade or embedded in low-melting-point agarose and sectioned using a vibratome , collected into ice-cold PBS . Sections were randomised between groups and cultured in complete media containing PHA-L ( 1 μg/ml ) + rhIL-2 ( 100 U ) , with or without the inhibitor cocktail described above . Multiple slices were used per treatment group . After 96 h , each slice was pushed through a cell strainer to create a single-cell suspension and stained for flow cytometry or embedded in OCT buffer and snap-frozen for sectioning and imaging . To minimise differences arising from sectioning different areas of tissue , data from multiple slices were averaged to obtain a single data point per donor . Cells were harvested at stated time points and stained for 20 min in FACS buffer ( PBS with 2% FCS and 2 mM EDTA ) using antibodies as described in S1 Table . Cells were fixed and permeabilised using a commercial kit ( BD ) and then stained for intracellular proteins using the following antibodies: cells were resuspended for flow cytometry , filtered through 100 μm mesh , and acquired using flow cytometry . Analysis utilised commercial analysis software ( TreeStar or DeNovo Software ) . tSNE analysis was performed using 1 , 000 iterations , with perplexity 30 and theta 0 . 5 , displaying a proportional number of events . Human tonsils were embedded in O . C . T . Compound ( Sakura ) and then flash frozen using dry ice . Then , 10–12 mm transverse sections were generated on a cryostat ( Bright Instrument Company ) and collected on adhesive slides ( Leica ) . Sections were air-dried for 2 h at room temperature ( RT ) and then fixed in cold acetone for 25 min . Sections were air-dried overnight for immediate immunolabelling or stored at −80°C until further use . Frozen tonsil sections were air-dried for 15 min at RT and rehydrated for 5 min with 1X PBS . Sections were then permeabilised for 15 min with 0 . 3% Triton X-100 ( ThermoFisher Scientific ) and washed 3 times with 1X PBS . Sections were blocked for 1 h in 1% BSA ( Sigma-Aldrich ) and 5% goat serum in PBS in a humidified chamber . Sections were incubated overnight at 4°C with primary antibodies diluted in 1% BSA . After incubation , sections were washed 3 times with 1X PBS and then incubated with secondary antibody for 1 h . Secondary antibodies included goat anti-rat Alexa 546 ( ThermoFisher ) , goat anti-rat Alexa 546 ( ThermoFisher ) , goat anti-rat goat Alexa 647 ( ThermoFisher ) , donkey anti-rabbit Alexa 488 ( ThermoFisher ) , goat anti-rabbit Alexa 647 ( ThermoFisher ) , donkey anti-mouse DyLight 594 ( ThermoFisher ) , and donkey anti-goat Alexa 555 ( ThermoFisher ) . Sections were then incubated with DAPI for 1 min , followed by 3 additional washes with 1X PBS . Negative controls utilised incubation with PBS with relevant serum or relevant isotype , followed by secondary antibody . Finally , sections were mounted in antifade mountant ( ThermoFisher Scientific ) for imaging . Immunofluorescence images were taken with a confocal microscope Zeiss LSM 880 , using ZEN Pro imaging system . Tonsil-derived FRCs from 3 donors ( in-house ) and bone marrow–derived MSCs from 3 donors ( Lonza and expanded in-house ) were grown in culture to P3 in the presence of hFGF ( 4 μg/ml ) and harvested in logarithmic growth phase . Total RNA was extracted using an RNA extraction kit ( Qiagen ) and purified using a cleanup kit ( Qiagen ) . Samples were quality tested using an Agilent Bioanalyzer 2100 and the Agilent RNA 6000 nano kit . RIN numbers for all samples ranged from 9 . 5 to 10 . Samples were then sent to BGI ( Hong Kong ) for library preparation and sequencing . Briefly , library preparation utilised poly-A enrichment followed by Ribozero depletion . Samples were run in a high-performance sequencing machine ( Illumina ) over 2 lanes , resulting in approximately 60 million reads per sample . Data were then trimmed for adapter sequences before analysis . Bioinformatics alignment and further analysis was done in-house using commercial software ( Partek ) . For a visual representation of gene expression , TPM was used for normalisation . The heatmap was made using Morpheus ( https://software . broadinstitute . org/morpheus ) . Data are accessible at monash . figshare . com doi: 10 . 4225/03/5a2dae0c9b455 . Data were tested for normality using D’Agostino and Pearson normality test . Normally distributed data of 2 groups were compared using an unpaired t test , or of 3 or more groups using an ANOVA with a multiple comparison test , as described in figure legends . When data were not normally distributed , 2 comparisons were made using a Mann-Whitney test . When fold-change data were compared to a normalised value of 1 , a 2-tailed Wilcoxon signed rank test was used . P < 0 . 05 was taken as significant . | The lymph node microenvironment contains an abundance of immune cells that interact with and within an intricate structural framework created by fibroblastic reticular cells . In mice , fibroblastic reticular cells are known to regulate T-cell activation , proliferation , and function , but in humans , they are poorly understood . We investigated interactions between human T cells and human fibroblastic reticular cells from tonsils and lymph nodes . When T cells were activated in the presence of human fibroblastic reticular cells , their proliferation and differentiation were reduced , without altering effector T-cell function , shown through cytokine production . We identified 4 molecular mechanisms that were responsible , concurrently used by all human fibroblast donors tested , and reversible upon addition of specific inhibitors to the cocultures . To establish the relevance of this finding outside of in vitro coculture , we showed that T-cell proliferation was increased in live human tonsil tissue slices when the fibroblastic reticular cell inhibitors were added . This work demonstrates that human fibroblastic reticular cells regulate T-cell activation and provides new information on the mechanisms used , which may be useful to design clinical strategies that improve T-cell responses . | [
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"develo... | 2018 | The human lymph node microenvironment unilaterally regulates T-cell activation and differentiation |
Bioinvasion is a major public health issue because it can lead to the introduction of pathogens in new areas and favours the emergence of zoonotic diseases . Rodents are prominent invasive species , and act as reservoirs in many zoonotic infectious diseases . The aim of this study was to determine the link between the distribution and spread of two parasite taxa ( Leishmania spp . and Trypanosoma lewisi ) and the progressive invasion of Senegal by two commensal rodent species ( the house mouse Mus musculus domesticus and the black rat Rattus rattus ) . M . m . domesticus and R . rattus have invaded the northern part and the central/southern part of the country , respectively . Native and invasive rodents were caught in villages and cities along the invasion gradients of both invaders , from coastal localities towards the interior of the land . Molecular diagnosis of the two trypanosomatid infections was performed using spleen specimens . In the north , neither M . m . domesticus nor the native species were carriers of these parasites . Conversely , in the south , 17 . 5% of R . rattus were infected by L . major and 27 . 8% by T . lewisi , while very few commensal native rodents were carriers . Prevalence pattern along invasion gradients , together with the knowledge on the geographical distribution of the parasites , suggested that the presence of the two parasites in R . rattus in Senegal is of different origins . Indeed , the invader R . rattus could have been locally infected by the native parasite L . major . Conversely , it could have introduced the exotic parasite T . lewisi in Senegal , the latter appearing to be poorly transmitted to native rodents . Altogether , these data show that R . rattus is a carrier of both parasites and could be responsible for the emergence of new foci of cutaneous leishmaniasis , or for the transmission of atypical human trypanosomiasis in Senegal .
Biological invasions are increasingly frequent due to the worldwide intensification of human-associated exchanges . They can have significant consequences on the biodiversity in many ecosystems [1] . For instance , they might lead to novel parasite-host combinations and have dramatic effects on the dynamics of diseases that affect wildlife , livestock and/or humans [2] . Disease emergence events associated with exotic pathogens imported by animal invaders have already been reported [3] . The dynamics of endemic diseases could also be affected by invasive species that act as novel hosts , or could negatively affect native host species [4] . Among invasive species , rodents indisputably represent the vertebrate group that has most often accompanied the global dispersion of humankind . Rodents act as reservoir for numerous zoonotic agents , such as helminths , bacteria or protozoa . The introduction of exotic rodents has been associated with the appearance of several new foci of infectious diseases in wildlife ( e . g . , [5 , 6 , 7] ) and human populations ( e . g . , [8] ) . However , up to now , only few studies have evaluated the effects of both native and invasive host communities on infectious disease risk in invaded ecosystems . For instance , Billeter et al . [9] showed that the risk of transmission of bartonellosis to human was higher from native rodents than from invasive black rat in Uganda . In this study , we focused on the spatial distribution of two zoonotic protozoan parasites , Leishmania spp . and Trypanosoma lewisi , in native and invasive populations of commensal rodents in Senegal . Leishmaniasis is a neglected disease that affects 0 . 9 to 1 . 6 million people worldwide and responsible for 20 , 000 to 40 , 000 deaths each year [10] . Moreover , 350 million people are considered at risk of contracting this infection [11] . Among the 30 Leishmania species described in mammals , 20 are pathogenic for humans [12 , 13] . These species are generally host-specific and restricted to particular geographical areas . The parasite is transmitted through the bite of infected female phlebotomine sand flies . In humans , they can cause different clinical forms: asymptomatic infection , visceral leishmaniasis ( VL ) , mucocutaneous leishmaniasis ( MCL ) or cutaneous leishmaniasis ( CL ) [13] . VL is mainly caused by Leishmania infantum and Leishmania donovani . Human VL has never been detected in Senegal [10 , 14 , 15] , but high human serological prevalence of Leishmania has been found in a focus near Thiès [16] , where canine leishmaniasis caused by L . infantum has been recorded since last century and recently epidemiologically described [14 , 16 , 17 , 18] . Atypical MCL due to Leishmania major ( the main causative agent of MCL is Leishmania braziliensis , whose distribution is restricted to South America [13] ) can occur in Senegal but is very rare ( Dr . Babacar Faye , personal communication , and [19] ) . Rural CL is caused by Leishmania major , and is endemic in West and North Africa , and in the Middle East . In Senegal the infection has been observed since the beginning of the 20th century [20 , 21 , 22 , 23 , 24] and is still reported [25 , Dr Babacar Faye , personal communication] . Nevertheless , public health records about the prevalence and distribution of the disease remain limited . The epidemics appear as foci that then disappear , possibly in function of seasons , and dynamics of vectors and reservoir populations [21 , 24] . Gerbils are traditionally considered to be the L . major reservoirs in the Old World [11] . However , the parasite was identified in various rodent species ( e . g . , Mastomys erythroleucus , Arvicanthis niloticus and Gerbilliscus gambianus ) near Thiès in Senegal [21 , 26 , 27] . These asymptomatic rodents ( showing no cutaneous lesion ) were found infected , by culture or molecular methods . In Senegal , L . major is transmitted by the vector Phlebotomus duboscqi [28] . P . duboscqi is found mainly in rodent burrows , termite mounds and tree holes , but also indoor; it feeds on many vertebrates , birds , reptiles and mammals ( rabbits , rodents ) and is also very anthropophilic . [18 , 28 , 29] . The genus Trypanosoma includes pathogens , such as Trypanosoma gambiense , T . cruzi and T . brucei brucei , responsible for sleeping sickness , Chagas’ disease and African animal trypanosomiasis , or nagana in livestock respectively , as well as other species considered to be non-pathogenic for humans [30] . However , atypical human trypanosomiases that are caused by species normally restricted to animals , for instance T . lewisi , have been recently described [31] . Humans are rare accidental hosts for T . lewisi , and only a few cases have been reported worldwide [30] . The infection is generally associated with mild symptoms , such as fever for a few days , but can require medical intervention in young children , and sometimes can lead to death [30] . T . lewisi has been identified for the first time in France in the 19th century , then in Poland in 1880 and 1901 [32] . It is now found in nearly all continents , including Africa where it was detected in rodents in Niger [33] and Nigeria [34 , 35] , and in one infant in Gambia [36] . Nothing more is known on its presence in West Africa , and no human infection has been reported in Senegal up to now [30] , but this atypical trypanosomiasis could be under-diagnosed , possibly due to asymptomatic carriage or to non specific symptoms . T . lewisi is described as quite host-specific , and infects rodents , mainly rats [37] . In rodents , its pathogenic potential is low . This parasite proliferates in blood and is rarely found in organs . T . lewisi is orally transmitted to rodents by ingestion of hematophagous arthropods or their faeces [38] . In Senegal , villages and towns are invaded by two major invasive rodent species ( Global Invasive Species Database [39] ) : the house mouse Mus musculus domesticus and the black rat Rattus rattus . Historical records and molecular analyses have shown that these rodents were first brought to sea ports in Senegal by Europeans during the colonial period [40 , 41 , 42] . Over the last century , both taxa have spread to inland villages and towns due to the improvement of transport infrastructures , and the native commensal rodent species have been progressively evicted from the invaded habitats [41] . The goal of this study was to evaluate Leishmania and T . lewisi infection rates in commensal native and invasive rodent communities of Senegal in localities along invasion gradients , in order ( i ) to explore the link between rodent invasion and the spread of these two trypanosomatids , and ( ii ) to assess the health risk for human populations .
Trapping campaigns within localities and private land were conducted with the authorization of the appropriate institutional and household authorities . They were carried out under the framework agreement established between the Institut de Recherche pour le Développement and the Republic of Senegal , as well as with the Senegalese Head Office of Waters and Forests . None of the rodent species investigated here has protected status ( see list of the International Union for Conservation of Nature ) . Handling procedures were performed under our lab agreement for experiments on wild animals ( no . 34-169-1 ) , and followed the official guidelines from the American Society of Mammalogists [43] . Euthanasia was performed as recommended by the Federation of European Laboratory Animal Science Associations ( FELASA ) for small rodents [44 , 45] . Sample transfers have been approved by the regional Head of Veterinary Service ( Hérault , France ) . The house mouse is now present in most of northern and central Senegal , whereas the black rat is distributed throughout the southern part of the country ( Fig 1 ) . Rodents were live-trapped in localities ( villages and towns ) along each of these two invasion gradients . We used data from historical records and longitudinal sampling surveys of rodent communities in Senegal carried out since the 1980s [41 , 46 , 47] , in order to classify sampling localities into three categories related to invasion status: ( i ) in long-established invasion localities ( LI ) , the house mouse or the black rat settled in large and permanent populations were present since more than a century , and have excluded native rodents; ( ii ) in recently invaded localities corresponding to invasion front ( IF ) , exotic rodents arrived only recently ( 10–30 years ago ) and currently coexist with native rodents; ( iii ) in non-invaded localities ( NI ) , the house mouse and the black rat have never been detected , and only native rodents are known to occur . Three to six localities were systematically sampled per invasion category along each invasion gradient ( Fig 1 ) . In addition , rodents were trapped also in Mereto , a village in the Terres Neuves region ( star in Fig 1 ) . The lack of data on rodent communities in this village ( it was not sampled for rodents before this study ) prevented its classification into a specific invasion category: the village was created before 1972 [48] , so exotic rodents may have arrived there for more than 40 years , or later . Sampling was performed between March and April 2013 in the north , and between November 2013 and February 2014 in the south ( including Mereto ) . Details on rodent trapping and identification , autopsy procedures and age determination are provided elsewhere [41 , 46 , 49] . Young animals were eliminated from the study . Each rodent was euthanized by cervical dislocation and dissected . For this study , the spleen and one ear from adult animals were collected . Tissues were stored at 4°C in 95% ethanol prior to use . DNA extraction was performed with the DNeasy Blood and Tissue Kit according to the manufacturer’s protocol ( Qiagen , Courtaboeuf , France ) . To verify the DNA quality and validate negative results , we performed a real time PCR targeting the rodent β-actin gene in all samples collected in southern Senegal with the primers bAqF ( 5’-TCCGTAAAGACCTCTATGCCAA-3’ ) and bAqR ( 5’-CAGAGTACTTGCGCTCAG-3’ ) [50] on a 7300 Real-Time PCR instrument ( Applied Biosystems , Foster City , USA ) . Each 8μl reaction mix included 1 . 6μl of 5X Evagreen ( Euromedex , Souffelweyersheim , France ) , 0 . 375μM of each primer , and 2μl of 100 times diluted DNA ( about 5-10ng ) . Cycling conditions were: 15min initial denaturation followed by 45 amplification cycles ( 95°C for 10s , 60°C for 15s , 72°C for 30s ) and a melting curve ( 95°C for 15s , 60°C for 30s , 5 acquisitions per sec . up to 95°C at 0 . 11°C/sec ) . Fluorescence detection was performed at the end of the amplification step . The expected size of the amplicon was 274b . Actin could not be amplified in 20 samples that were thus re-extracted to improve quality . Leishmania detection was performed in spleen and a few ear samples . For Leishmania diagnosis , a highly sensitive nested PCR method to amplify the minicircle kinetoplastic DNA ( kDNA ) variable region was chosen , as described by Noyes et al . [51] . For the first PCR step , the primers CSB1XR ( 5’-ATT TTT CSG WTT YGC AGA ACG-3’ ) and CSB2XF ( 5’-SRT RCA GAA AYC CCG TTC A-3’ ) were used and the following conditions: 2min initial denaturation at 94°C , followed by 45 amplification cycles ( 94°C for 30s , 54°C for 1min , 72°C for 1min ) and a final extension step of 72°C for 10min . Each 30μl reaction mix included 0 . 333μM of each primer , 42μM of each dNTP , 3μl of 10X buffer , 1U of Taq DNA polymerase ( Roche Diagnostics , Meylan , France ) , and 3μl of template DNA . For the second PCR step , 3μl of the first PCR product were used with the same programme ( but for the annealing temperature that was increased to 56°C ) and the same reaction mix except the primers that were replaced by the following: LIR ( 5’-TCG CAG AAC GCC CCT-3’ ) and 13Z ( 5’-ACT GGG GGT TGG TGT AAA ATA G-3’ ) . For this second step , the expected size of the amplicon was 500-800b depending on the Leishmania species . Leishmania complex identification was based on comparison of the size of the second PCR product on a 1 . 5% agarose gel in 0 . 5X TAE buffer with the reference strain profiles [51] . To confirm the diagnosis , amplicons from positive samples were cloned in the pGEM-T vector ( Promega , Charbonnières-les-Bains , France ) and sequenced with the LIR and 13Z primers . BLAST was used to compare the obtained sequences to those included in the NCBI and TriTrypDB databases . BLAST results were taken into account when they came from reference strains or from sequences published in peer-reviewed articles , and were well characterized in terms of species , origin and hosts , in order to avoid misidentifications . Our sequence data were also compared with sequences obtained with the same method from two L . major reference strains ( MHOM/SU/73/5ASKH and MHOM/IL/1980/Friedlin ) and from a L . major strain isolated from a Senegalese patient ( LC-DKR ) . This last strain was isolated in 2008 at the Dermatology unit of the hospital A . Le Dantec in Dakar by Pr Babacar Faye ( laboratory of Parasitology and Mycology , University Cheikh Anta Diop , Dakar ) from a patient with typical L . major lesions; L . major species was confirmed with the nested PCR described above ) . The experimental clones were named using the field sample number to which a suffix was added . Sequences were submitted to GenBank ( S1 Table , S1 Text ) . T . lewisi detection was performed in spleen . Diagnosis in mouse samples was performed using a FRET-based real time PCR method to detect 18S rRNA with the primers TRYP A1 ( 5'-AGGAATGAAGGAGGGTAGTTCG-3’ ) and TRYP A2 ( 5'-CACACTTTGGTTCTTGATTGAGG-3' ) and the probes TRYP A3 ( 5’-LC640AGAATTTCACCTCTGACGCCCCAGTPhos-3’ ) and TRYP A4 ( 5’-GCTGTAGTTCGTCTTGGTGCGGTCTFlc-3’ ) [33] , on a LightCycler LC 480 instrument ( Roche Diagnostics , Meylan , France ) . Each 12μl reaction mix included 6μl of 2X Maxima Probes master mix ( Thermo Fisher Scientific , Waltham , Massachusetts , USA ) , 0 . 5μM of each primer , 0 . 25μM of each probe , and 5μl of template DNA . After enzyme activation at 50°C for 1min and an initial denaturation step of 95°C for 10min , 50 amplification cycles were carried out at 95°C for 10s , 56°C for 10s , 72°C for 15s , followed by a melting curve ( 95°C for 15s , 56°C for 30s , 5 acquisitions per sec . up to 95°C at 0 . 11°C/sec ) . Fluorescence acquisition was performed with a Red 640 filter at the end of the annealing step . The expected size of the amplicon was 131bp . The FRET-based real time PCR was chosen for its robustness and sensitivity ( <9 . 5fg , S1 Fig ) . However , this PCR is not specific for T . lewisi , but detects also Leishmania ( S1 Fig ) . This latter parasite was not detected in samples from the first campaign realized in the north of Senegal , but was detected in some of those from the second campaign in the central/south of Senegal ( see Results ) . Therefore , we developed a more specific approach based on real time PCR amplification of T . lewisi mini-exon instead of the FRET-based real time PCR for this second campaign . The mini-exon amplification was realized with the primers ME-F ( 5'-GCTGACACCGGTTGGTTCTG -3’ ) and ME-R ( 5'-GACAGCAGCGTGAGCAATAA -3' ) . The 8μl reaction mix included 4μl 2X SYBR Green ( Roche Diagnostics , Meylan , France ) , 0 . 6μM of each primer , and 2μl of template DNA and the following cycling conditions: initial denaturation step of 95°C for 5min , followed by 50 amplification cycles ( 95°C for 15s , 59°C for 20s , 72°C for 15s ) and a melting curve ( 95°C for 5s , 59°C for 1min , 5 acquisitions per sec . up to 95°C at 0 . 11°C/sec ) on a LightCycler LC 480 instrument ( Roche Diagnostics , Meylan , France ) . Fluorescence acquisition was performed at the end of the amplification step . This PCR was specific to T . lewisi with a sensitivity of 1 . 9fg . The expected size of the amplicon was 140bp . Results were confirmed by amplicon sequencing ( S2 Text ) and comparison with sequence data from the NCBI database; sequences obtained showed homologies with the mini-exon sequence of the strain Molteno B3 ( Genbank AJ250740 . 1 ) . Statistical analyses were restricted to R . rattus as positive samples for both Leishmania and T . lewisi were mainly identified in this host species ( see Results ) . Generalized linear mixed models ( GLMMs ) assuming a binomial distribution were used to test whether prevalence levels ( infected/non-infected ) differed among invasion categories . The sampling locality was considered as a random effect . P-values were obtained by stepwise model simplification in likelihood-ratio tests and were considered significant when <0 . 05 . The models were validated by checking the normality , independence and variance homogeneity of the residuals . All model analyses were performed with the R software v 3 . 2 . 2 [52] using the package lme4 v1 . 1–14 [53] .
None of the trapped rodents presented any clinical sign of leishmaniasis . Molecular diagnosis was first performed on spleen samples . All rodents sampled in the north of Senegal ( Ma . erythroleucus and M . m . domesticus ) were negative for Leishmania ( Fig 1 ) . Conversely , in the south , 37 R . rattus , one M . m . domesticus ( from Mereto ) and two native rodent samples ( Ma . erythroleucus in Soutouta; A . niloticus in Mereto ) were positive for Leishmania ( Table 2 and Fig 1 ) . The positive samples represented 12 . 5% and 11 . 2% of all specimens in the LI and IF localities , respectively , excluding Mereto . No Leishmania infection was detected in samples from NI localities ( Fig 1 ) . In R . rattus samples ( the main host ) , Leishmania prevalence was similar between LI and IF localities ( F1 . 199 = 0 . 43 , p = 0 . 511 ) . Comparison of the amplicon sizes obtained for the R . rattus samples and the Leishmania reference strains of the Old World led to the identification of L . major in all positive R . rattus samples ( Fig 2 ) . However , the diagnosis could not be confirmed by direct sequencing of such amplicons . This can be explained by the high heterogeneity of the minicircle kDNA sequences . Each band on agarose gel could include different amplicons of equal size for a single Leishmania species . Diagnosis with more specific , but less sensitive molecular methods based on detection of Leishmania ITS1 , HSP70 or mini-exon , as well as other methods based on kDNA amplification , also failed ( S2 Fig ) . Therefore , the amplicons of all the positive nested PCR samples were cloned . The cloning was successful for seven samples ( 43 clones were successfully analysed ) , as well as for two L . major reference strains ( Friedlin and 5ASKH ) and one L . major clinical isolate ( LC-DKR ) ( 26 clones analysed for these three L . major strains ) ( S1 Table ) . Twenty of 26 sequences from reference strains displayed homologies with sequences in databases . Interestingly , six sequences of excellent quality of the reference strain clones ( for instance , the Friedlin-x and LC-DKR-p clones ) could not be extensively matched with database sequences ( <80 nucleotides ) , implying that kDNA sequences of L . major are not yet exhaustively present in databases . In addition , six sequences from the reference strains displayed homology with kDNA sequences from other strains of the same species ( for instance , the Friedlin-v clone with the LC-DKR-v clone , with 84% similarity on 572 bp ) or even of other species but with low similarity ( the Friedlin-d clone with Leishmania braziliensis , with 91% similarity on 53 bp , see S1 Table ) , in accordance with the idea that each Leishmania strain has different minicircle kDNA classes that could be either species-specific or shared with other species [54 , 55] . In the same way , 20 of 43 field sample clones displayed no similarity with the database sequences nor with the reference strain sequences . Nevertheless , two field sample clones ( clones 3394–11 and 3394-11c ) showed sequence similarity ( more than 70% identity with high probability and high score ) with L . major in databases ( e . g . LMJLV39_SCAF000371 , 73% identity in TriTrypDB on 630 bp ) and 21 field sample clones , obtained from five different samples ( 3441 , 3851 , 3167 , 3192 , 3767 ) showed strong sequence similarity to the sequence of LC-DKR-u ( Senegalese L . major control strain ) ( S1 Table ) . Interestingly , no clone from field sample showed similarity with sequences of clones of the other two reference L . major strains used in this study , 5ASKH ( isolated in Russia ) and Friedlin ( isolated in Israel ) . In conclusion , five of the seven positive field samples tested by cloning could be identified as similar to the Senegalese L . major strain . Therefore , based on the amplicon size identity and sequence comparison , it was assumed that all the other positive samples belonged to the L . major species . Altogether , these results designated R . rattus as carrier of L . major parasites . Leishmania diagnosis was also performed using DNA isolated from the ear of 64 rodents trapped in the south , chosen among nested PCR positive and negative samples using spleen DNA . Overall , specimens of different species , native or invasive , and the three locality categories ( not invaded , recently invaded , and of long-established invasion ) were represented . All tested ears were negative . It is worth mentioning that for some samples ( about 7% , generally not R . rattus samples ) , PCR products that were clearly smaller than the expected amplicon size for any Leishmania species ( about 200-300bp , S3 Fig ) were also observed . Sequence analysis of these amplicons showed similarities with GenBank rodent sequences , suggesting the unspecific amplification of the rodent host genome ( S3 Fig ) . All rodents trapped in the north of Senegal ( Ma . erythroleucus and M . m . domesticus ) were negative for T . lewisi ( FRET-based real time PCR method ) . In the south including Mereto , 59 R . rattus , six M . m . domesticus , one A . niloticus , and one Ma . erythroleucus ( spleen samples were positive for T . lewisi ( mini-exon real time PCR method ) ( Table 2 ) . Except R . rattus , all the rodents were from Mereto . No infection was detected in NI localities . In R . rattus samples ( the main host ) , T . lewisi prevalence was similar in LI and IF localities ( F1 . 199 = 1 . 11 , p = 0 . 292 ) . None of the 13 R . rattus spleen samples from Mereto was positive for T . lewisi . Some R . rattus samples were positive for both L . major and T . lewisi: 9/98 ( 9 . 2% ) in LI localities , and 3/101 ( 3% ) in IF localities ( 12/199 , 6 . 0% , for both locality categories ) ( Table 2 ) . In LI localities , L . major prevalence was higher among R . rattus individuals that were also infected by T . lewisi than among those that were not ( 26 . 5% vs 9 . 4% , GLMM F1 . 199 = 4 . 74 , p = 0 . 029 ) .
The nested PCR allowed detecting 40 positive spleen samples with amplicons of molecular weight similar to L . major species . All tested ears were negative , suggesting that ear specimens from rats with or without skin lesions should not be used for L . major detection . Direct sequencing of the positive nested PCR to confirm diagnosis was previously used by other authors [56] or by our team [18] . However , the high heterogeneity and number of minicircle kDNA sequences existing at strain level can prevent the direct sequencing . Indeed , the polymerase may amplify only one minicircle target or several at the same time , depending on the Leishmania strain and the experimental conditions . When this happens , as in our study , cloning is necessary prior to sequencing and species identification . Sequencing of field sample clones allowed finding homologies with cloned sequences of a L . major strain isolated from a Senegalese patient ( LC-DKR ) . The high-throughput sequencing of minicircle PCR products developed on vectors and Leishmania strains from the New World could overcome these issues [55] . Finally , 17 . 5% of all trapped R . rattus individuals and only two native rodents were positive for L . major in the south of Senegal ( including Mereto ) , whereas all rodents trapped in the north were negative . Gerbils are the main L . major reservoir: Rhombomys opimus ( great gerbil ) in Central Asia , Psammomys obesus ( fat sand rat ) in Middle East , and P . obesus and Meriones sp . in North Africa [57 , 58] . L . major prevalence in these species varies from 0 up to 70% , according to the diagnostic method and the season [59] . More recently , M . m . domesticus ( 42 . 9% in [60] and 7 . 1% in [56] ) and R . rattus ( 12 . 5% [56] ) also have been identified as carriers in the Middle East . So far , in West Africa , L . major was detected in native Mastomys spp . , G . gambianus and A . niloticus [21 , 26 , 27 , 61] . In Senegal , the prevalence determined by tissue culture was about 7 . 3% in Ma . erythroleucus , 5 . 8% in G . gambianus [26] , and 2 . 7% in A . niloticus [62] . However , no parasite by microscopic observation nor typical cutaneous lesion was identified in any rodent species in this country [63] . Among the previous studies conducted in Senegal in the 1980s , very few R . rattus individuals were found to be carriers , because this rodent species was not sampled at that time in the studied regions ( villages near Dakar and Thiès , in the centre of Senegal ) [21 , 22 , 27 , 64 , 65 , 66 , 67 , 68] . Thus , rats were not suspected to play a role in L . major transmission in Senegal . R . rattus has been probably introduced in Senegal from Europe , where L . major has never been detected [11] . CL is known to be endemic in Senegal since 1933 [64] . Our data showing R . rattus individuals positive for L . major in Senegal suggest that invading black rats have acquired the pathogen after their introduction . The finding that native Mastomys spp . and A . niloticus individuals , which were previously described as L . major reservoirs in Senegal , were not infected in our study could be explained by many factors , such as the ecology of native rodents compared with that of R . rattus , a different susceptibility between native and invasive rodents , Leishmania ecology ( transmission generally occurs within a very limited area , micro-foci [71] ) , L . major life cycle characteristics ( wild zoonotic cycle ) and/or the ecology of P . duboscqi ( its main vector ) that is more abundant in fields and farming areas than inside villages and towns [69] . A sampling in the surrounding of the villages invaded by R . rattus is needed to confirm the presence of a native wild rodent reservoir . Particularly , the screening of L . major in native gerbils , which are common in non-commensal habitats of southern Senegal ( e . g . , G . gambianus , G . guineae and Taterillus gracilis [46] ) , could bring information . On the other hand , no rodent was positive for L . major in the northern region invaded only by M . m . domesticus . This could be explained by the absence of L . major in this region , or by a difference in susceptibility between mice and rats . No comparative experimental study was available , but several epidemiological works showed more cases of L . major infection in R . rattus than in M . m . domesticus [56 , 70] . In Mereto , the village invaded by both R . rattus and M . m . domesticus , 16 . 7% ( n = 1/6 ) of A . niloticus , 3 . 4% ( n = 1/29 ) of M . m . domesticus and 23 . 1% ( n = 3/13 ) of R . rattus individuals were positive for L . major ( Table 2 ) , supporting again the hypothesis of a higher susceptibility of R . rattus to L . major , compared with M . m . domesticus . We found the highest prevalence of T . lewisi among R . rattus in the south of Senegal including Mereto ( 29 . 6% ) . Two native rodents ( n = 2 ) and six mice were also positive , but only in Mereto where rats and mice coexist . No infection was found in the north . T . lewisi prevalence in R . rattus is consistent with several previous works often based on microscopic observation rather than on molecular diagnostic tools . T . lewisi has been identified in the Rattini group of rodents on all continents: Europe [32 , 46 , 65] , Africa [33 , 34 , 35 , 36 , 72 , 73] , Middle East [74] , Asia [75 , 76 , 77 , 78 , 79 , 80 , 81] , North [82] , Central [83] and South [84 , 85 , 86 , 87 , 88] America , Hawaii [89] , New Zealand [90] and Australia [91] , where it caused the extinction of two native host rodent species in Christmas Island [6] . In Niger , T . lewisi prevalence is higher in R . rattus ( 71% ) than in native rodents ( 6% ) [33] . In the other cited studies , the reported prevalence ranges from 4 . 6 to 82 . 3% , and varies according to the season , sex and age of rodents [86 , 90] . Prevalence may also vary according to the tissue chosen for diagnostic . Indeed , spleen have been successfully used in previous studies because it is highly blood-supplied tissue [33 , 35] . Nevertheless , it may lead to underestimate prevalence levels of the parasite that poorly enters organs , compared to blood samples [37] . T . lewisi distribution and lack of positive samples among native rodents in our study strongly suggest that Senegal was T . lewisi-free before the arrival of R . rattus from Europe . The GLMM analysis did not find any significant difference in R . rattus prevalence between LI and IF localities , possibly due to the large variability at IF localities ( Table 2 ) . Indeed , T . lewisi was not detected in two IF localities ( SOU , BOU: Fig 1 ) , whereas its prevalence in the other two ( KED , BAN , Fig 1 , Table 2 ) was comparable to that in LI localities . These differences may be related to the variable R . rattus introduction times in IF areas . Indeed , Soutouta ( SOU ) and Boutougoufara ( BOU ) are marginal rural localities that have undoubtedly been colonized very late by R . rattus , because their connection to commercial networks dates from 2007 [92 , 93] . Conversely , Kedougou ( KED ) and Badi Nieriko ( BAN ) were colonized by R . rattus in the 1990s [41] . The prevalence heterogeneity at the IF is consistent with the hypothesis of T . lewisi introduction by R . rattus , and reflects classical enemy loss [49] in localities that have been very recently invaded . T . lewisi infection has been associated with a large mortality rate in non-Rattini species [35] , which could lead to under-estimation of parasite transmission in these hosts . If this parasite affects severely native rodents , its “spill-over” [49] from the host that introduced it would confer to rats a strong advantage at the IF . Like for L . major , the absence of T . lewisi infection among rodents in the north may suggest a lower sensitivity of mice . Mice and rats are both used as laboratory models for T . lewisi infection , but mouse experimental infection sometimes fails , while rat infection is more reproducible [76 , 94] . At first glance , the data obtained in Mereto challenge this idea: in Mereto 21% of M . m . domesticus and two native rodents ( A . niloticus and Ma . erythroleucus ) were infected , while no R . rattus ( 0/13 ) was positive for T . lewisi . Trypanosoma musculi , another trypanosomatid species very close to T . lewisi , could be responsible for the infection [95] . Indeed , T . lewisi and T . musculi , morphologically indistinguishable , were characterized on the basis of host specificity , rats being preferential hosts for T . lewisi and mice for T . musculi [95] . Moreover , these two species appear also difficult to distinguish on a molecular point of view [95 , 96] . We first tested the molecular methods used in our study for T . lewisi diagnosis on the reference strains of T . lewisi and T . musculi ( Partinico II strain kindly provided by Pr Philippe Vincendeau and Mrs Pierrette Courtois ) . The gel electrophoresis banding patterns and the sequencing did not allow distinguishing these two parasites ( S4A and S4B Fig ) . Secondly , the only published PCR protocol able to differentiate T . lewisi from T . musculi [96] was also tested . The results obtained in our assays were non reproducible , non-specific and not sensitive enough ( S4C and S4D Fig ) . Furthermore , the only two sequences available for T . musculi in Genbank database make difficult the design of a new specific and more sensitive molecular diagnostic method . Therefore , at this stage , we cannot deduce which of T . musculi or T . lewisi is the parasite responsible for infection in Mereto positive mice . The prevalent pattern of T . lewisi-positive R . rattus co-infected with L . major in LI suggests positive interactions between the two parasites . Previous studies showed that T . lewisi could weaken the immune system of its host and thus favour the acquisition of other infections , such as Toxoplasma gondii [97 , 98] or Cryptococcus neoformans [99] . Data about L . major distribution in Senegal are still limited , with the exception of the Dakar region [23] and nearby localities [21 , 22] , where it is endemic . To date , no data is available about human cases caused by L . major in the north nor in the south of this country . Classically , the transmission of L . major takes place essentially in rural environments , peridomestic and farming areas , where the burrows are , with an epidemiological cycle involving native rodents as main reservoirs [18 , 21 , 100] . We can imagine that a second transmission cycle involving R . rattus might occur predominantly inside villages and indoors . The increasing presence of R . rattus , which has a behaviour strongly linked to human activities and can move and settle in new territories , engenders a risk of emergence of new spots of human CL . It would be interesting to determine the number of cases and spatial distribution of human CL in Senegal , and to assess the available means and knowledge for its diagnosis . Only nine cases of T . lewisi infection in humans have been reported in the world , in Malaysia [101] , India [76 , 102 , 103 , 104 , 105] , Gambia [36] and Thailand [106] . Patients were often immunologically weak infants , living in poor hygiene conditions , and in close contact with contaminated rats in and around houses [36 , 76 , 101] . Symptoms were generally mild , except in one child [105] . The diagnosis was mostly based on the morphological identification of T . lewisi by microscopic examination of blood drops [76 , 101 , 102 , 103 , 104] , and was rarely confirmed with molecular tools [36 , 105 , 106] . However , human infections could be underestimated , because the identified patients lived far from health centres and T . lewisi infection is usually associated with non-specific and transient symptoms ( fever , lethargy , anorexia ) [30] . Very few epidemiological data on humans have been published and no routine and specific serological tests are available yet [107] . Nevertheless , 12 of the 187 farmers tested were serologically positive in China , without any apparent symptom [108] , reflecting the existence of asymptomatic carriers . The risk for immunologically weak people , such as patients with AIDS , remains to be evaluated . Given the high prevalence of T . lewisi in rodents in the south of Senegal , it could be interesting to assess the seroprevalence in humans in this region . This could be performed by the international network for atypical human trypanosomiases that has been set up in Africa [107] . In summary , we identified the black rat R . rattus as a potential reservoir for L . major and T . lewisi in the southern part of Senegal . These two infections appeared to obey to two different models . The invader R . rattus could have been be locally infected by the endemic parasite L . major , and is potentially more susceptible than native commensal rodents . Conversely , T . lewisi infection could have been introduced in Senegal by R . rattus , but seems to be poorly transmitted to native rodents by R . rattus , although this point remains to be investigated . The high prevalence of both parasites in R . rattus , which is anthropogenic and relentlessly gaining new territories , could increase the risk of transmission/emergence of new foci of human CL in urban areas and of sporadic cases of human trypanosomiasis . | Biological invasions ( the introduction and adaptation of living organisms to a new environment ) are increasingly frequent due to worldwide intensification of human-associated exchanges . They can lead to the introduction of pathogens in new areas and favour the emergence of diseases . Rodents are prominent invasive species , and act as reservoirs in many infectious diseases . The aim of our study was to determine the link between the distribution and spread of two parasites , Leishmania spp . and Trypanosoma lewisi , and the progressive invasion of Senegal by two commensal rodent species , the house mouse Mus musculus domesticus , and the black rat Rattus rattus . We identified R . rattus as a potential reservoir for Leishmania major and T . lewisi in the southern part of Senegal . The presence of these two pathogens in R . rattus may be of different origins . The invader R . rattus could have been locally contaminated with L . major . Conversely , T . lewisi infection could have been introduced in Senegal by R . rattus , and seems to be poorly transmitted to native rodents . Altogether , these data show that R . rattus is a carrier of both parasites , and could be responsible for the emergence of new foci of cutaneous leishmaniasis or for the transmission of atypical human trypanosomiasis in Senegal . | [
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"pr... | 2018 | Leishmania major and Trypanosoma lewisi infection in invasive and native rodents in Senegal |
Influenza A virus ( IAV ) infection can be severe or even lethal in toddlers , the elderly and patients with certain medical conditions . Infection of apparently healthy individuals nonetheless accounts for many severe disease cases and deaths , suggesting that viruses with increased pathogenicity co-circulate with pandemic or epidemic viruses . Looking for potential virulence factors , we have identified a polymerase PA D529N mutation detected in a fatal IAV case , whose introduction into two different recombinant virus backbones , led to reduced defective viral genomes ( DVGs ) production . This mutation conferred low induction of antiviral response in infected cells and increased pathogenesis in mice . To analyze the association between low DVGs production and pathogenesis in humans , we performed a genomic analysis of viruses isolated from a cohort of previously healthy individuals who suffered highly severe IAV infection requiring admission to Intensive Care Unit and patients with fatal outcome who additionally showed underlying medical conditions . These viruses were compared with those isolated from a cohort of mild IAV patients . Viruses with fewer DVGs accumulation were observed in patients with highly severe/fatal outcome than in those with mild disease , suggesting that low DVGs abundance constitutes a new virulence pathogenic marker in humans .
Acute respiratory infections are a main cause of severe illness and death worldwide . Influenza A virus ( IAV ) causes annual epidemics and occasional pandemics with potentially fatal outcome [1]; the global burden of seasonal influenza is >600 million cases , with 5 million cases of severe illness and up to 500 , 000 deaths each year . Annual influenza epidemics affect all age groups , although infants , the elderly , and individuals with underlying medical conditions are most severely affected . The existence of co-morbid conditions and the immune status may contribute to the patient outcome . Comorbid conditions for influenza include diabetes , chronic metabolic of lung , renal and cardiac diseases , immunosuppression , pregnancy and obesity [2–4] . Although comorbidities are found in many severe or even fatal cases , a considerable number of apparently healthy individuals nonetheless suffer severe infection , which suggests the coexistence of influenza strains with increased virulence among circulating viruses . We previously tested this hypothesis by characterizing two IAV strains from the AH1N1 2009 pandemic ( AH1N1pdm09 ) , one isolated from a fatal case in a person with no known previously described comorbidities ( F-IAV , fatal-case IAV ) and the other from a patient with mild symptoms ( M-IAV , mild-case IAV ) [5] . F-IAV virulence was greater than that of M-IAV in cell culture , and showed higher pathogenicity in the in vivo murine model [5] . IAV virulence and pathogenesis are dependent on complex , multigenic mechanisms involving the viral genetic characteristics , the host conditions , the virus-host interactions , and the host response to the infection . Special effort has been previously made to identify virulence determinants of AH1N1pdm09 viruses and AH1N1 seasonal viruses . As a result , some residues distributed all over the genome have been associated to increased virulence of specific viral isolates [6 , 7] . These determinants map mainly to the polymerase genes ( PB1 , PB2 , PA ) , the hemagglutinin ( HA ) , neuraminidase ( NA ) , and non-structural protein 1 ( NS1 ) ( reviewed in [8] ) . Attenuating factors have also been described in cell culture [9 , 10] . A proportion of influenza virus particles have defective genome RNAs ( DVGs ) due to internal deletions of viral segments [11–14] . The DVGs have the 3’ and 5’ ends of the parental RNA segments , and most have a single , large central deletion that generates viral RNAs of 180–1000 nucleotides [15–17] . DVGs have been found for all viral segments , but most derive from PB2 , PB1 and PA RNAs [15 , 17 , 18] . The presence of DVGs potentiates the host response in cultured cells [19 , 20] and in animal models and leads to attenuated infection [21] , possibly through recognition of double-stranded RNA by receptors that activate antiviral signaling cascades [19] ( reviewed in [22] ) . Although our understanding of influenza pathogenesis is considerable , a potential general virulence determinant in humans remains to be identified . Here we used next-generation sequencing ( NGS ) to evaluate the role of defective genomes in the pathogenicity of influenza virus circulating in the human population . We found that the low amount of DVGs accumulated in tissue culture cells correlates with increased pathogenicity in mice both , in natural isolates or recombinant viruses . To corroborate these findings we performed a genomic analysis of viruses isolated from respiratory samples of a select cohort of IAV A ( H1N1 ) pdm09-infected patients who suffered severe or fatal outcome , or from a cohort of infected patients with mild disease . The former viruses showed significantly less accumulation of DVGs than the latter . We suggest that low DVGs abundance has a major role in the severe outcome of IAV-infected patients .
We previously characterized two virus isolates , the F-IAV derived from a fatal case in a young person with no known previously described comorbidities and the M-IAV , from a young patient with mild symptoms . Comparison of the F- and M-IAV consensus sequences showed nine amino acid changes in the F isolate [5] taken the A/California/04/2009 strain as reference . Changes in the viral polymerase PA ( D529N ) and PB2 ( A221T ) subunits and the surface glycoprotein HA ( S127L ) found in <1% of viruses circulating during 2009 influenza season were considered specific and potentially responsible for the difference in virulence [5] . Since DVGs are mainly produced by the viral polymerase , PA D529N and PB2 A 221T changes in the polymerase subunits were selected as putative responsible for low DVGs production and increased pathogenicity of F-IAV . To further characterize the role of mutations in the F-IAV polymerase subunits as virulence determinants , we generated on the A/H1N1/California/04/09 virus backbone ( CAL ) , recombinant influenza viruses bearing the combination of PA D529N and PB2 A221T mutations ( PB2/PA mut; F-IAV-like polymerase ) , or viruses bearing single PA D529N ( PA mut ) or PB2 A221T ( PB2 mut ) mutations ( Table 1 ) . These viruses were grown in cell culture at a low moi ( 0 . 0001 ) to limit the production of DVGs and viruses obtained from this passage were used for the following assays . The activity of the reconstituted polymerases of these three mutant viruses and the wild-type virus was first evaluated in a mini-replicon assay , which showed not significant differences ( Fig 4A and 4B ) . Next , growth kinetics in cell culture showed that all recombinant viruses accumulates similar levels of viral proteins in a single cycle replication assay ( Fig 4C ) and replicated at a similar rate in a multiple cycle assay ( Fig 4D ) . To evaluate the pathogenesis induced by the different recombinant viruses carrying mutations present in F-IAV , we infected mice with various virus doses of CAL , PB2 mut , PA mut or PB2/PA mut viruses or with DMEM as control . Survival ( Fig 7A ) and body weight ( S9 Fig ) were monitored daily for two weeks and the lethal dose 50 ( LD50 ) for each virus was determined . CAL , PB2 mut , PA mut and PB2/PA mut viruses showed an LD50 of 1x105 , >106 , 3 x 103 and 3 . 5 x 104 , respectively ( Fig 7B ) . These data confirmed that CAL virus is pathogenic in mice [1] and indicated that the PA D529N mutation greatly increased pathogenicity , suggesting a decisive effect of this polymerase change on disease outcome . PB2 mut , which accumulates high DVG levels , was greatly attenuated compared with the CAL virus . Reconstitution of the F-like polymerase-containing virus ( PB2/PA mut ) notably reduced DVGs accumulation ( Fig 5A ) and led to higher pathogenicity compared with PB2 mut virus ( Fig 7A and 7B ) . We additionally evaluated the pathogenicity of M mut and M-PA mut viruses in the same way indicated above and the results show that the introduction of the M1+M2 mutations on the CAL wt or PA mut backgrounds led to clear virus attenuation in mice , as the LD50 increased from 1 x 105 or 3 x 103 , respectively , to >5 x 105 in both cases ( Fig 7B ) . M-PA mut virus pathogenicity was greater than that of M mut virus , as indicated by body weight loss after sublethal infection ( Fig 7C ) . To further evaluate the pathogenicity differences among viruses bearing mutations present in F-IAV , mice were infected with a sub-lethal dose ( 103 pfu ) of recombinant mutant viruses , or were mock-infected . Samples were recovered at several days post-infection ( dpi ) and viral titers determined in lung ( Fig 8A ) . PA mut-infected mice showed the highest titers at 1 , 2 , and 4 dpi and the most rapid virus replication kinetics , whereas PB2 mut-infected mice showed the lowest titers at all times tested . Next , we wanted to examine whether accumulation of DVGs play a role in the pathogenicity of influenza viruses in humans . Deep-sequencing of RNA from viruses isolated from respiratory samples of a select cohort of A ( H1N1 ) pdm09-like virus-infected patients was performed . This cohort includes patients with highly severe outcome including severe pneumonia and acute respiratory distress syndrome ( ARDS ) requiring admission to the intensive care unit ( ICU ) with mechanical ventilation and endotracheal intubation for more than 96 hours ( Fig 9A ) from 2012–2013 influenza season in Spain . For more precise characterization of the intrinsic pathogenicity of these viruses , only those isolated from patients with no known comorbidities and aged under 65 and over 4 were included ( Fig 9A ) . This cohort ( n = 4 ) is a faithful representation ( 80–100% ) of the total confirmed severe H1N1 influenza cases following these criteria in the 2012–2013 Spanish influenza season ( n = 4–5 ) ( S2 Table ) [31] . Additionally , two viruses isolated from deceased patients who accomplished these criteria , but otherwise showed underlying medical conditions were evaluated; total severe/fatal cohort n = 6 ( Fig 9A ) . These viruses were compared to those isolated from a cohort ( n = 6 ) of mild IAV patients detected through the regular influenza surveillance system .
Influenza virus pathogenicity has been studied in depth for many years , and several amino acid changes have been identified as virulence determinants [1 , 6–8 , 32] , however , a general pathogenicity determinant has not been characterized . Although DVGs have been described in natural animal infections [33 , 34] and in pandemic AH1N1pdm09- and other respiratory virus-infected individuals [35 , 36] their role in viral pathogenicity in patients has not been evaluated . The correlation between DVGs accumulation and severe disease observed in the severe/fatal- and mild-case viruses isolated from respiratory samples suggests that DVGs generation is a critical feature of severe influenza virus infection . We tested this hypothesis genetically by analyzing recombinant viruses bearing mutations identified in a fatal-outcome virus ( F-IAV , mutations PB2 A221T and PA D529N ) [5] or described elsewhere ( mutations M1 S30N + M2 V86S ) [20] . Whereas the non-pathogenic mutation PB2 A221T accumulates high levels of DVGs in cultured cells and is attenuated in mice ( PB2 mut versus CAL ) , mutation PA D529N reduces DVGs accumulation alone or in combination with PB2 A221T change ( Fig 5A ) or with M1 S30N + M2 V86 S mutations ( M-PA mut versus M mut ) ( Fig 5C ) . Moreover , recombinant viruses carrying PA D529N mutation displayed increased viral pathogenicity in the infected mice ( PB2/PA mut and PA mut ) ( Fig 7 ) . Selection criteria used for determination of putative virulent markers found in 2009 in F-IAV has been updated , and the prevalence of these changes in H1N1 viruses circulating in humans was calculated using all sequences available in the NCBI Influenza Resource database from December 2009 to March 2016 . This analysis showed that PA D529N change continues to be a rare mutation specific of F-IAV , but PB2 221 position admitted several changes including T . This data indicates that at position 221 of PB2 changes have been established in addition to the original A in the further circulating viruses after the influenza 2009 pandemic and this position might not be relevant for the increased pathogenicity of the F-IAV ( Table 1 ) . This data correlates with our findings , which indicate that PB2 A221T change is not responsible for the increased pathogenicity of the F-IAV . Here we adopted a highly restrictive approach to evaluate the potential role of DVGs accumulation as a determinant of severe influenza disease in humans , although a contribution of the immune status of the patient to the infection outcome cannot be excluded . We analyzed influenza viruses from a select cohort of patients , under 65 years and over 4 years of age , who suffered severe or fatal influenza infection . These viruses were compared with those obtained from a cohort of mild infected patients detected through the regular surveillance system . Deep sequencing identified an inverse correlation between DVGs accumulation and virus pathogenicity in these cohorts ( Fig 9 ) . Those patients with no known comorbidities infected with low DVGs producer viruses developed a severe outcome , and those who showed comorbid conditions eventually died , indicating that both factors may contribute to the fatal outcome of the infection . The later data is in agreement with our previous study where we found that , besides being infected with the virulent F-IAV , which accumulates a reduced amount of DVGs , the infected patient presented a new confirmed genetic risk factor , a truncated form of Ccr5 gene [37 , 38] . Thus , both the high virulence of the infecting virus , and the genetic risk factor , may have contributed to the fatal outcome of the patient . None of the severe/fatal cohort viruses bore PA D529N change . This result suggest that low DVGs accumulation in severe/fatal-case viruses might be mediated by various changes other than D529N in PA polymerase subunit , or in distinct polymerase subunits ( S3 Table ) , or in several viral proteins; this coincides with the complex , multigenic nature of the pathogenesis mechanisms [8] . Actually , changes in the polymerase [9] and in NS2 protein [10] modulate DVGs production in cell culture . Mutations in M1 and M2 [20] also modulate DVGs accumulation , probably by altering DVGs encapsidation in progeny virions [39 , 40] . Therefore , reduced accumulation of DVGs constitutes a virulent factor itself , regardless the mutations responsible . Regarding the possible mechanism of PA in low DVGs production , colleagues E . Fodor and G . Brownlee described some years ago that mutation A638R in the PA subunit was involved in the enormous accumulation of DGs in cell culture [9] . It was there described that this high DGs generation was due to an elongation defect by destabilization of RNA-PA subunit interaction , and that this phenomenon could be reverted by another mutation in the same PA polymerase subunit ( C453R ) . The authors proposed a putative domain involved in elongation activity in the PA-C part of this polymerase subunit . We have localized PAD529N mutation in the viral polymerase structure ( S14 Fig ) [54] , and they are spatially in the same PA-C domain nearby these previously described mutations . This data suggests that PA D529N may be involved in the same elongation process , although this activity would need to be further explored . In addition , Influenza A virus polymerase exists in different oligomerization state [55 , 56] , which allows different assembly of polymerase monomers during replication [57] . A new proposed mechanism suggests that the RNA depending RNA polymerase ( vRdRP ) dimer bound to viral RNA recruits another free polymerase dimer to form transient tetramer , which initiates replication of viral genome [58] . PA D529N mutation localizes on the interaction surface of this proposed dimerization model of the viral polymerase ( S15 Fig ) . All these data suggest that this mutation ( or other mutations on this same area ) may alter the possible polymerase dimerization or , it may modify the stability of the RNA- polymerase complex , or additionally it may change the interaction with any cellular required factor , or any combination of them . The role of DVGs in inducing the innate immune response has been demonstrated in cell culture [19] and animal models [21] . Studies in several animal models have led to proposals for the use of DG molecules or viruses modified to generate them in large numbers as protective elements against influenza virus infection [41] . It would be likely that the reduced activation of the antiviral state of cells infected with low DVGs producer viruses might induce an impaired immune response in infected animals or patients . The initially reduced antiviral state of the infected cells ( Figs 3 and 6C ) may allow the virus to grow uncontrolled for a short time and then this would induce an exacerbated immune response and inflammation which was described for severe IAV infected patients in the 2009 pandemic [42 , 43] . In viral infections , neutrophils and alveolar macrophages play a key role in clearance and control of viral growth in infected lungs , thus their substantial migration to the site of inflammation in infected tissue contributes to overall viral pathogenicity . Depletion of alveolar macrophages leads to an uncontrolled viral proliferation and fatal outcome in infected mice [30 , 44] while high influx of neutrophils in lungs and excessive inflammation has been associated with severe illness and high mortality rate in influenza infection [29] . The depletion of alveolar macrophages perfectly correlates with increased viral titers in lung tissue of PA mut and PB2/PA mut infected animals ( Fig 8A and 8C ) , emphasizing again the crucial role of these cells in viral clearance and control of viral growth . In addition , an increased influx of neutrophils , which is associated with lethal influenza virus infection [29] , has been observed in recombinant viruses carrying a mutation ( PA mut and PB2/PA mut ) ( Fig 8B ) present in a fatal-case virus , which produce reduced amount of DVGs ( Fig 5A ) . Although DVGs are produced in the polymerase segments ( PB1 , PB2 and PA ) , as previously described [13 , 18] , most viruses studied here also generate DVGs in other segments ( Figs 5B and 10 ) . The distribution of DVGs of the different cohorts is interestingly observed in some distinct viral segments , which is specially reduced ( 23-fold ) for the polymerase subunits segments in the severe/fatal—associated case viruses compared to viruses isolated from mild cases ( Fig 10 and S13 Fig ) . These findings suggest that the genomic combination of DVGs produced by each virus , and not only absolute numbers , may also contribute to pathogenicity . In summary , we establish a significant association between low DVGs accumulation and an increase in severe or fatal outcome in human influenza virus infection ( Fig 9 ) ; we provide genetic support for this association in infected cultured cells and in mice . In addition to the previous reports about the role of DVGs in natural animal infections here we present data indicating that a reduced accumulation of DVGs may be considered a new virulence marker for viral pathogenicity in humans . Evaluation of DVGs phenotype of circulating viruses might predict its potential to induce severe disease . Additional work is needed to define specific DVGs function and the mechanism by which they are produced in humans . These data could contribute substantially to the prediction of influenza disease severity and enable the development of risk-based prevention strategies and policies .
All procedures that required the use of animals complied with Spanish and European legislation concerning vivisection and the use of genetically modified organisms , and the protocols were approved by the National Center for Biotechnology Ethics Committees on Animal Experimentation and the Consejo Superior de Investigaciones Científicas ( CSIC ) Bioethics Subcommittee ( permit 11014 ) . We followed the guidelines included in the current Spanish legislation on protection for animals used in research and other scientific aims ( RD 53/2013 ) and the current European Union Directive 2014/11/EU on protection for animals used in experimentation and other scientific aims . The National Influenza Center in Madrid ( Instituto de Salud Carlos III ) and other regional laboratories from different Spanish regions , constituted the ReLEG network included in the Spanish Influenza Surveillance System ( SISS ) , which monitored the circulation of influenza viruses each influenza season as a part of the countrywide surveillance . The viruses described in this study have been detected within this surveillance activity . An informed consent is not needed for this study since the patients from whom these viruses were isolated were anonymized . Cell culture and mouse model experiments performed with recombinant viruses bearing mutations detected in a fatal case of IAV were performed in BSL2+ conditions and in a biological insulator in BSL2+ animal facilities , respectively . Cell lines used in this study were canine kidney MDCK ( ATCC ) , human lung epithelium A549 ( ATCC ) [45] and human embryonic kidney HEK293T ( ATCC ) cells [46] . Viruses used in the present study were selected according to the following criteria for the patients from whom the viruses were isolated . Patients included in the severe/fatal cohort were influenza A ( H1N1 ) pdm09 confirmed cases , aged over 4 and under 65 , admitted to intensive care unit ( ICU ) and with the information related to risk factors reflected in their clinical history . Those patients who developed highly severe disease did not display any comorbidities associated to severe influenza A virus infection , and deceased patients presented some comorbid conditions . Mild patients were influenza A ( H1N1 ) pdm09 confirmed cases , aged over 4 and under 65 , who developed mild disease and were monitored by sentinel medical centers included in the Spanish National Influenza Surveillance System . Selection of cases for this mild cohort was randomly made within the patients who meet the described above criteria and whose isolated virus were from the same Saint-Petersburg phylogenetic lineage as those from the severe/fatal cohort , accordingly to their HA gene . Respiratory samples were collected in virus transport medium ( MEM , 200 U/ml penicillin , 200 μg/ml streptomycin , 200 U/ml mycostatin and 0 . 25% bovine serum albumin fraction V ) and delivered to the Spanish National Influenza Center . All influenza A viruses were isolated at the National Influenza Centre ( CNM , ISCIII ) from respiratory samples sent for virological characterization by the Spanish Influenza Surveillance System ( SISS ) . The National Influenza Center in Madrid and other regional laboratories constitute the ReLEG network of the SISS , which monitors virus circulation each influenza season as a part of the countrywide surveillance . All viruses from either mild or severe/fatal patients were isolated from the upper respiratory tract , pharyngeal or nasopharyngeal exudates . Semi-confluent monolayers of MDCK cells were used for primary viral isolation . The monolayers were inoculated with 200 μl of homogenized samples; when the cytopathic effect was 75–100% , cultures were harvested and the supernatants used for virus stock generation by inoculation of MDCK cells . Specific mutations were engineered in expression pCAGGS plasmids derived from the CAL strain using the QuickChangeTM site-directed mutagenesis kit ( Stratagene ) as recommended by the manufacturer . These materials were developed using the Licensed technology ( Kawaoka-P99264US Recombinant Influenza viruses for vaccines and gene therapy ) . The recombinant minireplicon assay was performed essentially as described [47] . In brief , cultures of HEK293T cells ( 2 . 5 × 106 cells ) were transfected with a mixture of plasmids expressing the RNP components ( pCMVPA , 2 . 5 ng; pCMVPB1 , 12 . 5 ng; pCMVPB2 , 12 . 5 ng; and pCMVNP , 500 ng ) and a genomic plasmid expressing a viral RNA ( vRNA ) -like chloramphenicol acetyltransferase reporter gene ( pHHCAT , 500 ng ) using the calcium phosphate technique [48] . At 20 h posttransfection , total cell extracts were prepared and CAT accumulation determined by enzyme-linked immunosorbent assay ( ELISA; GE Healthcare ) , using purified CAT enzyme as a standard . Specific mutations were engineered in recombinant virus genomic pHH plasmids derived from the A/H1N1/California/04/2009 strain using the QuickChange site-directed mutagenesis kit ( Stratagene ) as recommended by the manufacturer . These materials were developed using the Licensed technology ( Ref . Kawaoka-P99264US Recombinant Influenza viruses for vaccines and gene therapy ) . The mutations were rescued into infectious virus by standard techniques [49 , 50] . Briefly , to rescue infectious virus from cDNAs , 105 293T HEK cells were cotransfected with a mixture of 12 plasmid DNAs ( 100 ng each ) including ( i ) 8 genomic plasmids each carrying a viral segment cDNA under the control of the polI promoter and ( ii ) 4 expression plasmids encoding the three polymerase subunits and the NP . Transfection was carried out at with Lipofectamine Plus ( Gibco ) under the conditions recommended by the manufacturer . At 16 h post-transfection , transfected cells were plated onto an excess of MDCK cells . When a cytopathic effect was apparent , the supernatant medium was collected and used for plaque assay on MDCK cells to estimate viral titer . The supernatant was used to produce a viral stock at low multiplicity of infection . The identity of rescued mutant viruses was ascertained by sequencing of DNAs derived from the PA and PB2 RNA segments by reverse transcription-PCR ( RT-PCR ) amplification . Supernatants of harvested cells inoculated with clinical samples or transfected with plasmids for the generation of recombinant viruses were titred by standard plaque assay . These first passages of every virus were used to inoculate fresh MDCK cells at indicated controlled low multiplicity of infection ( 0 . 0001 moi ) . All viral stocks used for further studies had a viral titer about 107 pfu/ml . For virus purification , culture supernatants of 10−4 moi-infected MDCK cells were centrifuged ( 10 min , 3110 g , 4°C ) . Supernatants were sedimented through a sucrose step gradient ( TNE buffer; 50% and 33% sucrose in 50 mM Tris-HCl , 100 mM NaCl , 5 mM EDTA , pH 7 . 5 ) ( 1 h , 274000 g , 4°C ) . The 50 to 33% interphase was collected , diluted in TNE buffer , and pelleted through a cushion of 33% sucrose in TNE ( 2 h , 112000 g , 4°C ) . For purification of viruses isolated from infected mouse lung , the previous protocol was used with modification of the sucrose gradient volume and rotors according to sample volume . For isolation , RNA in purified virions was treated with 0 . 5% SDS and 200μg/ml proteinase K in TNE ( 2 h , 37°C ) , followed by extraction with phenol-chloroform-isoamylalcohol-hydroxyquinolein and ethanol precipitation [51] . DNA was removed by DNAse treatment ( Roche ) according to manufacturer’s instructions . Quality and quantity of each RNA preparation was monitored using the Agilent 2100 Bioanalyzer ( Agilent Technologies ) ( S2 Table ) . Appropriate amounts of each sample were analyzed by high-throughput sequencing ( see below ) . For the detection of viral and cellular proteins , total cell extracts were collected and Western blot assays were performed as described [49] . Antibodies to GAPDH , β-actin ( both from Sigma ) , ISG56 , Mx1 ( both from Santa Cruz ) , NP and PB1 [57] were used . Sequencing for previously described F- and M-IAV isolated during the 2009–2010 [5] influenza season was performed with the Illumina Genome Analyzer IIx using Illumina v5 sequencing chemistry and 36 bp single reads . Base calling was performed using Illumina pipeline version 1 . 7 . 0 ( within SCS 2 . 8 ) . All other viruses were sequenced with TruSeq v3 chemistry and 50 bp single reads on an Illumina HiSeq 2000 . Total reads in each sample are indicated in S1 Table . RT-PCR for the PA or PB2 segments was used to determine the presence of DVGs and their relative amount to the full-length RNA of the same viral segment . To detect full-length segments , internal primers were used to amplify a central fragment , which is not present in DVGs . To detect DVGs , the same RNA sample and external primers of the PA segment were used in a separate reaction . Short amplification times were applied for the detection of both , internal fragment corresponding to full-length segment and DVGs , to allow detection of RNAs up to 1000nt in length . The method is illustrated in Fig 2A . The reverse transcription reaction was performed for 30 min ( 42°C ) , followed by PCR ( 35 rounds at 94°C for 30 s , 53°/58°C for 40 s , and 68°C for 40 sec using the Titan-One RT-PCR kit ( Roche ) ) . As a specificity control , the primers and RT-PCR conditions for DVGs amplification were used with a plasmid encoding the full-length PA segment , and no amplification product was obtained ( S1A Fig ) . Additionally , primers and amplification conditions for internal fragment corresponding to full-length segment were used with purified DVGs , and no amplification product was obtained ( S1A Fig ) . DVGs from cell cultured purified virions and from infected mice lung tissues were amplified by RT-PCR ( Titan-One RT-PCR kit , Roche ) as indicated above . Obtained products were amplified with Taq Polymerase ( Sigma ) for further cloning into pGEM-T vector using pGEM-T Easy kit ( Promega ) . Selected clones were sequenced by Sanger method and obtained sequences were analyzed to confirm that they corresponded to defective genomes , including the 3′and 5′ends and a large internal deletion of the full-length viral segment . Cultured human lung alveolar epithelial cells ( A549 ) were infected at 10−3 pfu/cell ( low multiplicity of infection; moi ) or 3 pfu/cell ( high moi ) . After 1 h , non-bound virus was rinsed off with acidic PBS ( pH 5 . 3 ) and at various times ( hours post-infection; hpi ) , cell supernatants were collected and used for virus titration by plaque assay . To evaluate pathogenicity of the viruses , 5 female BALB/c AnNHsd mice ( 6–7 weeks old ) were infected intranasally with different doses ( 106−102 ) of each of the recombinant influenza viruses described here , or were mock-infected . The animals were monitored daily for clinical signs and body weights for two weeks . For ethical reasons , mice were euthanized when they presented 25% body weight loss . For the kinetics experiment , 5 female BALB/c mice ( 6–7 weeks old ) were infected intranasally with a sublethal dose ( 103 pfu/50μl DMEM ) of recombinant PA mut , PB2 mut or PB2/PA mut influenza viruses , or were mock-infected ( 50μl DMEM ) . Mice were euthanized at 1 , 2 , 4 and 7 dpi by CO2 inhalation and necropsied . Lung samples were homogenized in PBS-0 . 3%-BSA-penicillin/ streptomycin ( 100 IU/ml ) using an Electronic Douncer ( IKA T10 basic , Workcenter ) . Lung samples were homogenized 1min at max speed at 4°C and debris was pelleted by centrifugation ( 2000 g , 5 min , 4°C ) . Viral titer was determined by standard plaque assay on MDCK cells . Lungs samples were kept in RMPI medium at 4°C . Tissue samples were grinded into very small pieces prior to digestion with 180 μg/ml liberase ( Roche ) and 40 μg/ml DNase I ( Roche ) in RMPI medium for 30 minutes at 37°C . Digested fragments were filtrated with 40mm Nylon Cell Strainer ( BD Falcon ) and resuspended with RMPI- 3%FBS . After centrifugation of samples ( 1640 rmp , 5 min , 4°C ) additional step for erythrocyte lysis were performed . Cell pellet was incubated for 1 . 5–2 min with 1ml of erythrocyte lysis buffer at RT . Lysis is inhibited by adding 9ml of PBS-5mM EDTA-3%FBS . Samples were then filtrated again with 40mm Nylon Cell Strainer and pelleted by centrifugation ( 1640rpm , 5 min , 4°C ) . Cell suspensions were distributed in 96 wells plate and first incubated with violet LIVE/ DEAD due ( Invitrogen ) for 30 min at 4°C , washed 2 twice with PBS and then incubated for 15 min at 4°C with Fc block CD16 rat antibody . Samples were analysed by staining cell suspension with one or more fluorochrome-labelled antibodies mix in PBS for 30 minutes at 4°C in the dark . Antibodies used were PerCP-Cy5 . 5-conjugated CD45 ( clon 30-F11 ) ( BioLegend ) , PeCy7-conjugated CD11b ( clon M1/70 ) ( BioLegend ) , APC-conjugated CD11c ( clon N418 ) ( eBIOSCIENCE ) and PE-conjugated Ly6G ( clon 1A8 ) ( BDBIOSCIENCE ) . Samples were than fixed by incubation with 4% formaldehyde for 20 min , pelleted by centrifugation ( 700 rmp , 5 min , 4°C ) and washed once with PBS . After centrifugation ( 700 rmp , 5 min , 4°C ) , cells were resuspended in 0 . 4ml PBS and kept at 4°C O/N in the dark . Flow cytometric analysis was performed on a cytometer LSR II ( BD Biosciences ) . Data were analyzed using CellQuestPro software . Animal lungs were fixed in 10% formalin , embedded in paraffin , sliced into 5 mm thick sections , and stained with hematoxylin and eosin ( H&E ) by conventional methods . UCSF Chimera 1 . 10 . 2 program was used for structural localization of specific mutations in the influenza virus polymerase . Structure of the single influenza A polymerase under accession 4WSB , or structure of the dimerized form of influenza A polymerase complex in Protein Data Bank ( PDB ) under accession 3J9B have been used as templates . Student’s t test and two-way ANOVA were used as indicated in each experiments and Figures . A non-parametric Mann-Whitney U test was applied to estimate the statistical significance of differences between RPM . GraphPad Prism v . 5 . 00 ( www . graphpad . com ) was used for analysis . | Influenza A viruses are the causative agents of annual epidemics , sporadic zoonotic outbreaks and occasionally pandemics . Worldwide , acute respiratory infections caused by influenza A viruses continue to be one of the main causes of acute illness and death . The appearance in 2009 of a new H1N1 pandemic influenza strain reinforced the search to identify viral pathogenicity determinants for evaluation of the consequences of virus epidemics and potential pandemics for human health . Here we identify a new general virulence determinant found in a cohort of severe/fatal influenza virus-infected patients , a reduced accumulation of viral defective genomes . These molecules are incomplete viral genome segments that activate the innate immune response . This data will contribute to the prediction of influenza disease severity , to improved guidance of patient treatment and will enable the development of risk-based prevention strategies and policies . | [
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"infe... | 2017 | Reduced accumulation of defective viral genomes contributes to severe outcome in influenza virus infected patients |
Eliminating Rhodesian sleeping sickness , the zoonotic form of Human African Trypanosomiasis , can be achieved only through interventions against the vectors , species of tsetse ( Glossina ) . The use of insecticide-treated cattle is the most cost-effective method of controlling tsetse but its impact might be compromised by the patchy distribution of livestock . A deterministic simulation model was used to analyse the effects of spatial heterogeneities in habitat and baits ( insecticide-treated cattle and targets ) on the distribution and abundance of tsetse . The simulated area comprised an operational block extending 32 km from an area of good habitat from which tsetse might invade . Within the operational block , habitat comprised good areas mixed with poor ones where survival probabilities and population densities were lower . In good habitat , the natural daily mortalities of adults averaged 6 . 14% for males and 3 . 07% for females; the population grew 8 . 4× in a year following a 90% reduction in densities of adults and pupae , but expired when the population density of males was reduced to <0 . 1/km2; daily movement of adults averaged 249 m for males and 367 m for females . Baits were placed throughout the operational area , or patchily to simulate uneven distributions of cattle and targets . Gaps of 2–3 km between baits were inconsequential provided the average imposed mortality per km2 across the entire operational area was maintained . Leaving gaps 5–7 km wide inside an area where baits killed 10% per day delayed effective control by 4–11 years . Corrective measures that put a few baits within the gaps were more effective than deploying extra baits on the edges . The uneven distribution of cattle within settled areas is unlikely to compromise the impact of insecticide-treated cattle on tsetse . However , where areas of >3 km wide are cattle-free then insecticide-treated targets should be deployed to compensate for the lack of cattle .
Rhodesian sleeping sickness , caused by Trypanosoma brucei rhodesiense , is transmitted by tsetse flies ( Glossina spp . ) across East and Southern Africa . The disease is the zoonotic form of Human African Trypanosomiasis ( HAT ) in which the trypanosomes are harboured by reservoir hosts , primarily in wild and domestic suids and bovids . As a consequence , treating humans only cannot eliminate the disease . Rather , in addition to treating people carrying HAT , interventions must also be directed at removing trypanosomes from reservoir hosts and eliminating the vectors [1] . In cases where the reservoirs are wild mammals , the only practicable option is to control tsetse . Tsetse also transmit species of trypanosome ( T . vivax , T . congolense , T . simiae and T . b . brucei ) that cause Animal African Trypanosomiasis in livestock . Every year , more than a million cattle are killed by tsetse-transmitted trypanosomiases across sub-Saharan Africa , despite $30–40 million being spent annually on veterinary trypanocides [2] . The development and application of more cost-effective methods of tsetse control [3]–[5] combined with a strengthened political resolve across sub-Saharan Africa to tackle trypanosomiasis [6] has revived interests in interventions against tsetse [7] , [8] . Insecticidal techniques for controlling tsetse flies ( Glossina spp . ) have been used successfully for 60 years , in many tens of thousands of square kilometres [9] . However , the control has not always proceeded as quickly and efficiently as required , for two main reasons . First , invasion from untreated areas nearby can re-infest all or much of the cleared territory [10] , as after aerial spraying in Botswana during the 1980s [11]; this problem can be solved by creating invasion barriers of odour-baited targets treated with insecticide [4] , [12] , [13] . Second , the control measures have not always been applied at the same time and intensity throughout the operational area , so that residual pockets of infestation remain , as in the early aerial spraying operations in Botswana [11] and some of the ground spraying in Zimbabwe [14] . The difficulty of even cover can be particularly serious when control is based on pyrethroid-treated cattle since the animals available for treatment are often distributed patchily , due to the animals' need for adequate grazing and water [15] , [16] . This is unfortunate since the cattle treatment is by far the most economical method of control [3] , [17] . Finding solutions to the above problems should , ideally , refer directly to abundant data from a full range of technical options tried previously in a wide variety of circumstances . However , such data are scant since full population monitoring is a luxury achievable mainly during the first few trials with a new technique [14] . Moreover , to identify confidently the limits to a technology it is necessary to use it above and below the limits . Understandably , practitioners do not deliberately attempt something that might fail . If failure does occur , by mere happenstance , actions are taken to correct the problem quickly by any means available , as when dealing with pockets of infestation left by aerial spraying in Zimbabwe [14] , Botswana [4] and Somalia ( S . Torr , unpublished data ) . Thus , there are few opportunities to assess accurately the number , distribution and dynamics of flies in the problem situations , especially since the populations there are typically sparse and hence difficult to sample . To offset the paucity of data from actual field campaigns , we have much basic information for population dynamics in the field and laboratory [18] , so allowing the modelling of tsetse control [10] , [19] , [20] . Previously we have used the simulation programme ‘Tsetse Muse’ [20] to assess the relative cost-effectiveness of insecticide-treated cattle and the sterile insect technique [20] , and the performance of aerial spraying in Botswana [4] . The present paper employs Tsetse Muse to assess how the heterogeneous distribution of baits ( insecticide-treated cattle and targets ) affects their impact on tsetse populations , and how residual pockets of infestation might be avoided and/or eliminated .
The model is detailed by [20] and can be downloaded at www . tsetse . org and the parameters adopted for its present use are indicated in Table S1 . It will be only summarised here . The numerical and spatial distributions of population components were tracked deterministically using the spreadsheet programme Microsoft Excel 2003 , it being taken that the population occurred in parallel bands of habitat that were 1 km wide , with the habitat being uniform within bands but allowed to differ between bands ( Fig . 1 ) . Outputs showed the abundance of insects along a transect that ran straight across the bands .
Prior to any intervention , the number of adult males declined to <0 . 1 at >32 km from the front in Scenario L ( Fig . 2A ) , and at >31 km in Scenario W ( Fig . 2B ) , so defining the back edges of operational areas 32 km and 31 km wide , respectively . The areas deemed initially infested with tsetse were 3 km wider , at 35 km and 34 km , respectively . Whereas the percent of females in large expanses of good habitat was 67% ( i . e . , the percent defined for the standard population ) , the percent was higher in poorer habitats , reaching 92% in the 3 km beyond the back edges , and 79% in the centre of the belt of poor habitat at 0–12 km from the front in Scenario W . This was because many of the flies in the poor habitat were not born there but moved in; females were more likely to enter because they were more mobile per day and also because they lived longer , so being able to move further in their lifetime . In Scenario L it was taken that insecticide-treated cattle were not present in areas 5–11 km wide , centred on the band at 15–16 km from the front . After 1000 days with a 10% kill in treated areas ( Fig . 5 , A ) a residual population remained in the untreated areas , and , not surprisingly , the population there was greater the wider the gap . Increasing the imposed mortality to 40% in the treated areas of Scenario L roughly halved the residual population in the gap and caused the population to decline more sharply on moving away from the gap ( Fig . 5 , B ) . If operations were continued beyond the 1000 days the residual populations in the gaps 5–7 km wide did eventually disappear . For example , with a 10% kill outside the gaps the residual population in the 5 km-wide gap expired after 1516 days , and after 4182 days with the 7 km-wide gap . When the imposed mortality outside the gaps was raised to 40% the times required decreased , being 1081 days and 2660 days , respectively . If the gaps in the distribution in insecticide-treated cattle are at least 5 km wide , then merely increasing the imposed mortality produced by the cattle – even to a level ( 40%/day ) approaching the maximum possible ( 50%/day ) - outside of the gaps is a slow or ineffective means of dealing with the flies inside ( cf A and B in Fig . 5 ) . A better solution is to use targets to fill the gaps . If the imposed mortality due to the targets is kept the same as that of the cattle , the timing of the control and the width of the invasion barriers are exactly as if the cattle treatment were used throughout . Only the cost varies , since the unit cost of targets is double that of cattle . For example , if targets substitute for cattle in a quarter of the whole treated area for the whole operational period , then the overall costs increase by half . However , deploying and servicing targets is relatively inconvenient , so in practice the density of targets might be less than cattle and/or the timing of target deployment and cattle treatment might not be perfectly synchronised . Modelling these possibilities showed , not surprisingly , that the earlier the targets were started the sooner stability was achieved after the inception of cattle treatment , and the lower were the total costs to stabilisation ( Table 2 ) . However , provided the targets were not deployed late , the total costs were less than double those of using a uniform 10% kill by cattle throughout ( Table 1 ) . In the above simulations the large gaps in cattle treatments were in the middle of the operational area . Failing to fill a gap was less serious when the gap was near the back edge , i . e . , where the population was struggling to persist , instead of within a few kilometres of the invasion front . Indeed , it was possible to achieve successful eradication without treating a large part of the rear of the operational area . For example , in Situation L with 10% imposed mortality , 10 km of the rear could be left untreated without increasing the time to stabilisation . This also reduced the cost by $58 – not much since even if cattle were operated in the rear they were not required for long . Times and costs rose if more of the rear were left , e . g . , with 15 km untreated the time increased by 485 days and costs by $537 . Although the above simulations have exposed the basic theory of bait control they are unrealistic in several ways . First , the modelling assessed tsetse abundance instantly and precisely , so that modelled control could be stopped exactly when and where it became unnecessary , but in practice the surveys of abundance take several months , during which caution demands assuming the worst and maintaining control . Second , in the field it may be suspected that the tsetse infestation is expanding , in which case it is safest to have little or no back gap . Third , while it was assumed that the control could be applied instantly everywhere , in truth the shortage of materials and supervisory capacity may require progressive implementation , in a “rolling carpet” strategy from the back edge to the invasion source . Fourth , it may be impractical to stop control on a band-by-band basis; blocks of bands 5–25 km wide are more likely to be considered . Finally , it is unwise to produce an invasion barrier of the bare minimum width; a few extra kilometres insures against temporary breakdowns in barrier upkeep . It also means that tsetse disappear from the ‘invasion zone’ – an area where the tsetse present comprise invading flies only - thereby increasing the cleared area and forming a zone where surveys would give a clearer warning of barrier breakdown . No tsetse should then be caught in the invasion zone and thus it is not required to make the difficult distinction between catching a few in one month , and one or two more than a few in the next . To simulate greater realism it was taken that in any given block of bands the control extended for three 30-day months after the first complete month in which the maximum density in the block dropped to <0 . 1 males/km2 , except that if the maximum density in the block was below the critical level on the first day of the control period in that block then the control there lasted four months . Two distinctive plans were then considered for Scenario L , involving insecticide-treated cattle ( Table 3 ) . In Plan F , aimed at fast control , the imposed mortality in treated areas was 40% , and a back gap 3 km wide was allowed , with the whole of the operational area being treated at once . Plan S was slower and more cautious , employing no back gap and an imposed mortality of 10% applied in a rolling carpet that started in a relatively small block , assuming that the control personnel wanted to prove the techniques before increasing the block size . Both plans employed a final phase which maintained an invasion barrier that was 30–50% greater than the minimum , for 12 full months . This was to allow full stock to be taken by surveys , perhaps before rolling on into the invasion source or handing the barrier upkeep to local operatives . Not surprisingly , the costs were greater than the stabilisation cost in previous modelling , since substantial safety precautions and the costs of maintaining the barrier for a year were included . Expressed per square kilometre cleared and held clear , the cost for Plan F was $90 , compared to $62 for Plan S . The greater expense of Plan F might easily be justified by the sooner benefits associated with quicker clearance; the greater cleared area could be important if the benefits per square kilometre were high . Judging from the costs of control during the clearance phases of Plan S , the cost of progressive and cautious clearance of a further 10–15 km per year , with an advancing barrier , are about double those of maintaining a static barrier for a year . The population at and near the back edge of the invasion barrier must be monitored to give early warning of any breakdown in control , and so allow correction before the population spreads far . For example , with the 13 km-wide barrier involving the 10% imposed mortality , above , let it be taken that the imposed mortality drops to 2% , perhaps because the insecticide becomes less effective due to application errors . Then the population at 1 , 2 , 4 , 8 and 12 km outside the back edge of the barrier becomes self-sustaining after 91 , 106 , 143 248 and 463 days respectively . After a year , virtually all of the territory that was the most difficult to clear would be lost , although the extensive surveys required to detect this failure might take a further six months in which more expansion would occur . By then it would be necessary to repeat almost all of the previous control . If continuous surveys are conducted within the barrier there will be a month or so of warning that the control is awry , allowing the situation to be rectified without increasing the treated area . If the population is allowed to extend for 1 km ( after 91 days ) behind the barrier , then the matter could be put right by returning the imposed mortality to 10% within the barrier and applying it in the 1 km where the self-sustaining population has spread , together with a further 3 km for safety . The situation is then restored after 46 days , so that the barrier can be returned to the normal width after five months , allowing three full months of added control while surveys confirm that corrective measures have indeed been effective . The cost of operations outside the barrier is $75 , with little increase in the threat of disease . However , if the treatments are put right only after the population has extended for 8 km ( after 248 days ) , the correction period is 94 days , making a total of seven months of control to cover also the surveys . The cost is $231 , and the disease risk has been longer and more widespread . Present indications for the speed at which tsetse invade after reducing the restrictions , and the ease with which prompt action can restore the situation , accord with field experience [24] .
In areas far from an invasion source , tsetse abundance showed a first-order decline with time , as observed in the field with a variety of tsetse species [11] , [14] , [25] , [26] . Similarly , in areas subject to constant reinvasion tsetse can penetrate various distances into an operational area according to ( i ) the density of tsetse in the invasion source and ( ii ) the distribution and abundance of baits . In Zimbabwe , tsetse were detected up to 5 km into an operational area where baits exerted a daily mortality of ∼10% in accordance with the present simulations ( see Fig . 3 ) . As a consequence of invasion , small areas ( i . e . , <10 km across ) cannot be cleared of Morsitans-group tsetse using standard densities of baits ( e . g . , 4 insecticide-treated targets or cattle/km2 ) as shown by the present simulations ( Fig . 5A ) and seen in practice [11] , [27] , [28] . The simulations showed that changes in mortality due to natural factors ( e . g . habitat ) or control efforts will alter the sex composition of tsetse populations . These results accords with field observations of high percentages of females in catches in poor habitats , e . g . , [29] , although before the demonstration that females move more than males [30] , [31] it was usually considered that high proportions of females indicated starving populations [32] . Since it was mostly females that diffused from the good to poor habitats , the model's output for the proportion of females in good habitat was slightly lower than the standard 67% if there was poorer habitat within a few kilometres . Baits also affected age structure . For instance , for tsetse 15–16 km from the invasion front , i . e . , far from the main invasion source , with the standard bait density ( 10% daily mortality rate ) applied under Scenario L , the mean age of males and females dropped to 13 and 16 days , respectively , after two months , compared to 20 and 43 days , respectively , at the start . This has important epidemiological implications particularly for the transmission of Trypanosoma brucei spp , the causative agents of sleeping sickness , which requires a development period of ∼20 days in the fly . Again , these results accord with field observations where the mean age of females caught from traps declined following the start of control operations [33] . The general agreement between the outputs of Tsetse Muse and reliable field data available for populations of medium to high density offers prima facie evidence that the modelling is not seriously awry with very sparse populations for which satisfactory field data are not available . The worst unknowns are the density-dependant changes in population dynamics [18] . However the high mortality imposed by baits are sufficient to swamp any density-dependant reductions in natural mortality , so elimination is achievable even if there were no natural deaths at low population density . The results provide several new insights that have important implications for the control of tsetse and trypanosomiasis . First , since population decline due to baits is logarithmic , the rate of decline seems to decrease on a normal scale . Such observations can be misinterpreted as being indications that an operation is becoming less effective as control proceeds . Related to this general phenomenon , because baits reduce tsetse populations rapidly on a normal scale , there is the danger of believing mistakenly that control efforts can be relaxed to complete the task . Second , while the present estimates of control costs are crude , they do highlight the general pattern of how costs change with technical options . The main finding is that without gross variation in costs there is much latitude in tailoring robust bait measures to suit the required rate of control , implementation capacities , risks and economic conditions , in a range of operational areas . Third , heterogeneities in the deployment of baits are inevitable with insecticide-treated cattle - the behaviour of cattle along with the demands of providing adequate grazing , water and security means that cattle are never evenly distributed . The present results provide a preliminary framework for understanding the likely implications of the problem and possible solutions . In particular , patchy distributions of baits will be serious if the gaps occur in good habitat , are broader than about 10 times the daily displacement of tsetse ( e . g . ∼3 km for G . morsitans , and are not recognised until many months after control begins . The problem can be solved by deploying targets in the gaps since their mode of operation is closely similar to cattle baits – both offer continuous control of resident and invading flies for as long as necessary , and can be planned to work at roughly similar rates . By contrast , treating the gaps by sequential spraying of non-residual insecticide offers no protection against invasion since during and after each application the flies can enter from unsprayed areas nearby [31] . This ensures that two months later , when all applications are complete , a residual population remains . The problem could be overcome by extending the spraying into much of the surrounding area , to kill potential invaders , but this would be very expensive [17] , especially if the gap is relatively small and so requires the high fixed costs of an aerial spraying cycle to be spread over a small area . Finally , the indication that progressive clearance by baits is not grossly more expensive than mere barrier up-keep reinforces the prospect of clearing tsetse from the whole of international fly-belts , thus eventually avoiding the invasion problem [6] . The speed of clearance could be enhanced by a combination of baits and large aerial spraying campaigns [4] with the spraying occurring mostly in places where cattle are absent and ground access is difficult . | Eliminating Rhodesian sleeping sickness , the zoonotic form of Human African Trypanosomiasis found in East and Southern Africa , can be achieved only through eliminating the vectors , species of tsetse fly ( Glossina ) . The deployment of insecticide-treated cattle is the most cost-effective means of achieving this . However , the even distribution of insecticide-treated cattle is seldom possible due to the patchy distribution of grazing , water and human settlement . We used a simulation model to explore the likely impact of such patchiness on the outcome of control operations against tsetse . The results suggest that even in areas that are highly suitable for tsetse , gaps of up to 3 km in the distribution of insecticide-treated cattle will not have a material impact on the success of an operation provided the overall mean density of cattle across all areas is adequate to achieve control ( e . g . , ∼4 insecticide-treated cattle/km2 killing 10% per day of the tsetse in the area treated ) . If the gaps are larger than 3 km , then deploying insecticide-treated targets at densities of 4/km2 in the cattle-free areas will ensure success . | [
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"trypanosomiasis"... | 2011 | Is the Even Distribution of Insecticide-Treated Cattle Essential for Tsetse Control? Modelling the Impact of Baits in Heterogeneous Environments |
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