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Vibrio parahaemolyticus is an important pathogen that causes food-borne gastroenteritis in humans . The type III secretion system encoded on chromosome 2 ( T3SS2 ) plays a critical role in the enterotoxic activity of V . parahaemolyticus . Previous studies have demonstrated that T3SS2 induces actin stress fibers in various epithelial cell lines during infection . This stress fiber formation is strongly related to pathogenicity , but the mechanisms that underlie T3SS2-dependent actin stress fiber formation and the main effector have not been elucidated . In this study , we identified VopO as a critical T3SS2 effector protein that activates the RhoA-ROCK pathway , which is an essential pathway for the induction of the T3SS2-dependent stress fiber formation . We also determined that GEF-H1 , a RhoA guanine nucleotide exchange factor ( GEF ) , directly binds VopO and is necessary for T3SS2-dependent stress fiber formation . The GEF-H1-binding activity of VopO via an alpha helix region correlated well with its stress fiber-inducing capacity . Furthermore , we showed that VopO is involved in the T3SS2-dependent disruption of the epithelial barrier . Thus , VopO hijacks the RhoA-ROCK pathway in a different manner compared with previously reported bacterial toxins and effectors that modulate the Rho GTPase signaling pathway .
Vibrio parahaemolyticus is a Gram-negative halophilic bacterium that causes acute gastroenteritis in humans after the consumption of contaminated raw or undercooked seafood . The emergence of pandemic strains poses a worldwide health threat [1] . V . parahaemolyticus possesses two type III secretion systems ( T3SSs ) : T3SS1 and T3SS2 [2] . A T3SS is a multisubunit molecular system that delivers bacterial proteins known as effectors directly to the plasma membrane or into the cytoplasm of infected host cells . The translocated effectors then modify certain functions of the host cell by disrupting normal cell signaling processes [3] . T3SS2 , which is encoded on chromosome 2 , is a major contributor to the enterotoxic effects observed in several animal models [4–7] . The T3SS2-related gene cluster is encoded in an 80-kb pathogenicity island ( Vp-PAI ) , which is conserved exclusively in pathogenic strains [8 , 9] . Recently , we demonstrated that the F-actin binding T3SS2 effector VopV is necessary for enterotoxicity [10] . During the identification of VopV , we identified several candidate effector genes that are encoded in the Vp-PAI region , but their roles in the pathogenicity of V . parahaemolyticus remain unknown . Consequently , the precise pathogenic mechanisms underlying V . parahaemolyticus infections are not fully understood . Many bacterial pathogens manipulate the actin cytoskeleton of the host cell using diverse mechanisms during infection [11] . Tissue culture analysis has shown that V . parahaemolyticus T3SS2 causes two dramatic changes in the actin cytoskeleton: the accumulation of F-actin beneath bacterial microcolonies and the induction of actin stress fibers [10 , 12] . At least three T3SS2 effectors , i . e . , VopV , VopL , and VopC , have been identified as actin cytoskeleton modification effectors . VopV exhibits an F-actin binding activity and is responsible for the F-actin accumulation phenotype [10] . VopL contains three Wiskott-Aldrich syndrome protein homology 2 ( WH2 ) motifs , and it elicits an Arp2/3-independent actin nucleation activity and the induction of actin stress fiber formation when expressed in host cells [12] . However , Liverman et al . reported that vopL deficiency only resulted in modest reductions in the amount of stress fibers formed during infection , thereby suggesting that effector ( s ) other than VopL may contribute to this activity during V . parahaemolyticus infection . Recently , we identified VopC , which deamidates Rac1 and Cdc42 , and it is homologous to a cytotoxic necrotizing factor of uropathogenic Escherichia coli . We found that this effector is involved in the T3SS2-dependent formation of actin stress fibers via the activation of Rac1 [7] . In the absence of VopC , V . parahaemolyticus induces the formation of long , branched , and curved F-actin filaments instead of actin stress fibers in Caco-2 cells . This cytoskeletal modification is completely dependent on T3SS2 . In addition , the activation of Rac1 alone is not sufficient to induce stress fiber formation in the absence of bacterial infection . These observations suggest that the formation of complete stress fibers by V . parahaemolyticus requires the coordinated action of VopC with other T3SS2 effector ( s ) . In this study , we identified a novel actin cytoskeleton-manipulating T3SS2 effector called VopO . VopO induces a high level of stress fiber formation in the host cell by activating the RhoA-ROCK pathway . We also determined that VopO binds directly to GEF-H1 , a RhoA guanine nucleotide exchange factor ( GEF ) , and that the GEF-H1-binding activity of VopO is correlated with its stress fiber formation activity . In addition , VopO-dependent stress fiber formation disrupts the epithelial barrier in vitro , as observed previously in vivo in infected intestinal tissue [5 , 13] . A number of bacterial toxins and effectors that activate or inactivate small GTPases via the direct modification or mimicry of GEFs or GTPase-activating proteins ( GAPs ) have been identified [14 , 15] , but this is the first report of an effector or a toxin that activates GEFs via direct binding . Overall , these results suggest that VopO is a novel effector with a different mode of action compared with previously reported effectors and toxins that modulate the Rho GTPase signaling pathway .
Previous studies have revealed that two effectors , VopC and VopL , are involved in T3SS2-dependent actin stress fiber formation . Recently , we demonstrated that VopC deamidates and activates Rac1 in infected cells and promotes stress fiber assembly . However , in contrast to a T3SS2-deficient mutant , the vopC deletion mutant still induces the formation of long , branched , and curved F-actin filaments in Caco-2 cells [7] . VopL has been reported to contribute to F-actin stress fiber formation [12] . Therefore , we first investigated whether the induction of T3SS2-dependent stress fibers in HeLa and Caco-2 cells is completely dependent on VopL ( S1A , B Fig . ) . In agreement with the results of a previous study [12] , in both cell types , we observed that the formation of actin stress fibers was somewhat attenuated after infection with a vopL-deficient strain ( POR-2∆vopL ) compared with the formation resulting from infection with the parent strain ( POR-2 ) . However , stress fibers were still observed in POR-2∆vopL-infected cells , whereas no fibers were observed after infection with a T3SS2-deficient strain ( POR-2∆vcrD2 ) or in an uninfected control , suggesting that an unidentified effector is essential for stress fiber formation . The small GTPase RhoA and its downstream effector Rho-associated kinase are major mediators of stress fiber formation [16] ( Fig . 1A ) . GTP binding and hydrolysis induce the conversion of RhoA between GTP-bound ( active ) and GDP-bound ( inactive ) forms [17] . The conversion of GTPases from an inactive to an active state is mediated by GEFs . Activated RhoA then propagates downstream signaling by binding to effector proteins such as ROCK . Activated ROCK leads to the formation of contractile bundles of F-actin via the phosphorylation of myosin light chain ( MLC ) [18 , 19] . Therefore , to determine whether the RhoA-ROCK pathway is required for T3SS2-dependent stress fiber formation , we employed a ROCK inhibitor ( Y27632 ) and a Rho inhibitor ( Rho inhibitor I , a permeable C3 exoenzyme from Clostridium botulinum that inhibits RhoA , RhoB , and RhoC in living cells ) . Treatment with either the ROCK inhibitor or the Rho inhibitor completely abolished the POR-2-induced formation of stress fibers ( Figs . 1B and 1C ) . We also examined the requirement for RhoA in stress fiber formation by silencing RhoA using siRNA ( Fig . 1D ) . RhoA knockdown reduced the stress fiber formation induced by POR-2 infection ( Fig . 1D ) , indicating that the RhoA-ROCK pathway is essential for T3SS2-dependent stress fiber formation . The T3SS2 effector VopC selectively deamidates and activates Rac1 and CDC42 , but not RhoA , in infected cells both in vitro and in vivo [7] . VopL binds directly to actin and enhances actin filament assembly in vitro [12] . This activity of VopL does not require any Rho GTPases . Furthermore , there are no previous reports of T3SS2 effectors activating RhoA . Overall , these results strongly indicate the existence of an unidentified T3SS2 effector that activates the RhoA-ROCK pathway , thereby inducing stress fiber formation . Next , we aimed to identify the effector responsible for T3SS2-dependent stress fiber formation . After screening candidate ORFs encoded within the Vp-PAI region , a known pathogenicity island in pathogenic strains [20] , we observed that deletion of the vopO gene ( vpa1329: Gene ID 1192024 ) caused a dramatic change in actin stress fiber formation . Stress fibers were not detected when a vopO-deficient strain ( POR-2∆vopO ) was used to infect either HeLa cells ( Fig . 2A ) or Caco-2 cells ( S1A Fig . ) . The lack of stress fiber induction when cells were infected with the POR-2∆vopO strain was rescued by in trans complementation with the vopO gene ( POR-2∆vopO/pvopO ) . Immunoblotting analysis using anti-VopO antibodies revealed that VopO is specifically secreted into the culture medium via T3SS2 ( S2A Fig . ) . Deletion of the vopO gene had no effect on the secretion of T3SS2 translocon proteins ( VopB2 and VopD2 ) [21] , the secretion of effector proteins involved in stress fiber formation ( VopL and VopC ) [7 , 12] ( S2A Fig . ) , or T3SS2-dependent cytotoxicity against Caco-2 cells [22] , which is a characteristic effect of T3SS2 in vitro ( S2B Fig . ) . The enterotoxic activity of the vopO-deficient strain ( POR-2∆vopO ) appeared to be reduced slightly compared with that of the POR-2 strain , and the enterotoxic activity of the complemented strain appeared to be slightly higher than that of the vopO-deficient strain; however , the differences between the enterotoxic effects of these strains were not significant ( S2C Fig . ) . Interestingly , VopO was involved in the T3SS2-mediated cell invasion phenotype , which was recently identified as a phenotype with VopC activity ( S2D Fig . ) [7 , 23] . These observations indicate that VopO is not required for the full secretory function of T3SS2 ( S1 Text ) , thereby suggesting that VopO is a T3SS2 effector involved in the induction of stress fiber formation and the invasive activity of V . parahaemolyticus . We then examined whether VopO is involved in the activation of the RhoA-ROCK pathway . As a positive control in the subsequent assays , we used nocodazole , a microtubule destabilizer that induces stress fiber formation via the activation of RhoA [24] . As shown in Fig . 2B , RhoA activation by the parent strain ( POR-2 ) was significantly higher than that induced by either its T3SS2-deficient derivative ( POR-2∆vcrD2 ) or an uninfected control . However , the level of RhoA activation was significantly lower in cells infected with the vopO-deficient strain ( ∆vopO ) than that in cells infected with the POR-2∆vcrD2 strain or in uninfected control cells ( Fig . 2B ) . This result suggests that balance of the Rho GTPase activation shifted from RhoA to Rac1 and Cdc42 because of activation via the other T3SS2 effector , VopC , which directly activates both Rac1 and Cdc42 [7 , 23] . Similar results were obtained by determining the amount of phosphorylated MLC ( pMLC ) ( Fig . 2C ) . The amount of pMLC increased in a functional T3SS2-dependent manner . However , the amount of pMLC was lower in POR-2∆vopO-infected cells than that in cells infected with the POR-2∆vcrD2 strain or uninfected control cells . These results suggest that VopO has an important role in the T3SS2-dependent activation of the RhoA-ROCK pathway in infected cells . Next , we used a transfection assay to determine whether VopO itself activates the RhoA-ROCK pathway and subsequently induces stress fiber formation . Both GTP-bound RhoA ( GTP-RhoA , the active form of RhoA , Fig . 2D ) and pMLC ( Fig . 2E ) increased significantly when GFP-fused VopO was transiently expressed in HeLa cells . Moreover , the activation of RhoA and the augmentation of pMLC in GFP-VopO-expressing cells coincided with the formation of thick and massive actin fibers at the site of GFP-VopO protein localization ( Fig . 2F , arrowheads ) . These findings indicate that VopO alone can activate the RhoA-ROCK pathway and induce stress fiber formation . We demonstrated the importance of the RhoA-ROCK pathway in VopO-dependent stress fiber induction . The transition of a small GTPase from an inactive to an active state is mediated by GEFs ( Fig . 1A ) . Sixty-nine types of Rho GEFs have been reported previously [25] , some of which contribute to the activation of RhoA [26 , 27] . We observed that T3SS2-dependent stress fiber formation was also blocked in dominant-negative RhoA-expressing cells ( S3 Fig . ) . Therefore , we hypothesized that VopO may interact with at least one molecule located upstream of RhoA . A T3S effector EspG of enteropathogenic Escherichia coli ( EPEC ) induces stress fiber formation and is mediated by GEF-H1 activation [28] . GEF-H1 ( Lfc in humans ) , which is a microtubule-regulated Rho GEF , plays a dominant role in RhoA activation [29] . This information let us to hypothesize that VopO might associate with GEF-H1 to induce the stress fiber . To test this hypothesis , we performed a pull-down assay using purified glutathione S-transferase ( GST ) -fused VopO ( GST-VopO ) and a HeLa cell lysate . We observed that GEF-H1 coprecipitated with GST-VopO . By contrast , two other RhoA GEFs , LARG and Ect2 [26] , did not interact strongly with VopO ( Fig . 3A ) . Interactions were not detected between VopO and RhoA or β-actin . Thus , we confirmed the direct binding of VopO with the recombinant full-length GEF-H1 protein , which was prepared using an in vitro translation system . As shown in Fig . 3B , the full-length GEF-H1 protein coprecipitated with GST-VopO . GEF-H1 contains four domains: a zinc-finger motif-containing region ( Zn ) , dbl-homology ( DH ) , pleckstrin homology ( PH ) , and α-helical coiled-coil ( CC ) domains ( Fig . 3C ) [29] . The DH and PH domains are conserved in the Rho GEF family and are required for its GEF activity [25] . By contrast , the Zn and CC domains are unique to GEF-H1 and they are involved in the regulation of GEF-H1 activity [29] . Thus , we used a pull-down assay to identify GEF-H1 domains that contribute to the binding activity to VopO ( Fig . 3D ) . An N-terminally truncated GEF-H1 ( ∆N ) protein retained relatively weak binding activity with VopO , whereas all of the truncated proteins lost their ability to bind to VopO . Because we could not identify a specific VopO-binding domain in GEF-H1 , we hypothesize that VopO might specifically recognize the higher-order structure of GEF-H1 . We then assessed the requirement for GEF-H1 in V . parahaemolyticus-induced stress fiber formation using siRNA . As shown in Fig . 3E , no changes in stress fibers were observed when the RhoA GEFs LARG and Ect2 were silenced . By contrast , cells in which only GEF-H1 was silenced exhibited diminished stress fiber formation . Overall , these findings suggest that VopO may target GEF-H1 to induce stress fiber formation . We explored this possibility in the following experiments . VopO does not share any motifs or any sequence homology with known proteins; however , the Chou-Fasman secondary structure prediction program ( http://cib . cf . ocha . ac . jp/bitool/MIX/ ) revealed that VopO possesses at least four α-helix regions: H1 , H2 , H3 , and H4 ( S4 Fig . ) . We used several truncated forms of VopO ( Fig . 4A ) to identify the α-helix region ( s ) in VopO responsible for binding to GEF-H1 . In VopO where the first C-terminal α-helix region ( ∆H1 ) was truncated , the GEF-H1-binding activity was attenuated slightly compared with that of the full-length VopO ( Fig . 4B ) . By contrast , in the VopO proteins that lacked the second C-terminal α-helix region ( H2 ) , ∆H12 or ∆H2 , the GEF-H1-binding activity was reduced dramatically . Next , we used cell transfection to evaluate the stress fiber formation activity of these truncated VopO proteins ( Fig . 4C ) . The stress fibers of ∆H1-expressing cells were somewhat weaker than those of the full-length VopO-expressing cells ( Figs . 2F and 4C ) . By contrast , ∆H12 and ∆H2 did not induce any stress fiber formation . In V . parahaemolyticus-infected cells , the stress fiber-inducing activity of the POR-2∆vopO strain was restored completely or partially by complementation with full-length or ∆H1 VopO , respectively ( Figs . 2A , 4D and 4E ) . By contrast , no stress fiber induction activity was observed in cells infected with the ∆H12- or ∆H2-complemented strains ( Figs . 4D and 4E ) . As summarized in Fig . 4A , the stress fiber-inducing activity of VopO was strongly correlated with its GEF-H1-binding activity . The interaction between GEF-H1 and microtubules is important for GEF-H1 inactivation [30] . Several T3SS effectors , such as EspG , EspG2 , and Orf3 from EPEC , release and activate GEF-H1 by disrupting the host microtubule network following stress fiber induction in infected host cells [28 , 31] . Therefore , we explored the possibility that VopO might disrupt the microtubule structure . The transient expression of DsRed-fused VopO in cells induced stress fibers ( S5A Fig . ) but did not induce the destruction of the microtubule network that is observed during transient EspG expression in host cells ( S5B Fig . ) [32] . In addition , several DsRed-VopO puncta appeared to colocalize with GFP-fused GEF-H1 but they did not significantly affect the association between GEF-H1 and microtubules ( S5C Fig . ) . Furthermore , although microtubule disruption is reported to be EspG-dependent in cells infected with EPEC [28] , VopO-dependent microtubule disruption was not observed in V . parahaemolyticus-infected cells ( S5D Fig . ) . Taken together , these results indicate that GEF-H1 is a primary target of VopO during the induction of stress fiber formation . However , the mechanism that allows VopO to activate GEF-H1 is different from that reported for microtubule-destabilizing T3S effectors . The intestinal epithelial barrier , which includes tight junctions , plays an important role in defense against the invasion of pathogens and commensal microbiota into the lamina propria [33–35] . Junctional adhesion molecule-A-deficient mice , which have a highly permeable intestinal epithelial barrier , are susceptible to enterocolitis [34] . Several studies have also reported intimate relationships between stress fiber formation and the homeostasis of the intestinal epithelial barrier [36–38] . Therefore , we investigated whether stress fiber formation induced by VopO affects the integrity of the epithelial barrier . The integrity of the epithelial barrier was evaluated by measuring the trans-epithelial resistance ( TER ) of polarized Caco-2 cells ( Fig . 5A ) . The TER value of Caco-2 cells infected with the parent strain ( POR-2 ) decreased over time . By contrast , the TER value of cells infected with a T3SS2-deficient strain ( POR-2∆vcrD2 ) was nearly identical to that of uninfected control cells . The TER value of cells infected with a vopO-deficient strain ( POR-2∆vopO ) declined significantly more slowly than that of POR-2-infected cells . We also confirmed the VopO-dependent disruption of the epithelial barrier in a FITC-dextran leakage assay [39] . A mixture of POR-2 and FITC-dextran was used to challenge the apical side of polarized Caco-2 cells and its leakage into the basolateral side was monitored . The amount of basolateral dextran increased dramatically at 12 h after the infection of cells with POR-2 compared with POR-2∆vcrD2-infected cells or uninfected control cells ( Fig . 5B ) . The basolateral dextran levels were significantly lower in POR-2∆vopO-infected cells than those in cells infected with POR-2 . Moreover , the reductions in both the TER value and the amount of basolateral dextran observed for the POR-2∆vopO strain were rescued by in trans complementation with the vopO gene ( Figs . 5A and 5B ) . The comparable cytotoxicities of POR-2∆vopO and POR-2 against Caco-2 cells ( S2B Fig . ) , suggest that disruption of the VopO-dependent epithelial barrier activity was not attributable to cytotoxicity . These results suggest that the stress fiber-inducing activity of VopO disrupts the epithelial barrier function .
T3SS2 , which is encoded in Vp-PAI on chromosome 2 of V . parahaemolyticus , is essential for enterotoxicity in several animal models , thereby indicating that it is involved in the pathogenicity of this bacterium [4 , 5] . Recently , we identified VopV as an enterotoxic T3SS2 effector in a rabbit loop assay [10] . However , the Vp-PAI region encodes many hypothetical ORFs whose biological activities and roles in virulence are not fully understood . In the present study , we determined that a functionally undetermined ORF encoded by Vp-PAI , VPA1329 ( VopO ) , is a novel T3SS2 effector of V . parahaemolyticus , which participates in disrupting the host epithelial barrier by inducing stress fiber formation . During infection , bacterial pathogens use diverse mechanisms to manipulate the actin cytoskeleton of host cells [11] . In V . parahaemolyticus , T3SS2 has been reported to induce the accumulation of F-actin beneath bacterial microcolonies and the formation of actin stress fibers within infected host cells [10 , 12] . In this and previous studies , we have demonstrated that at least three T3SS2 effectors , VopO , VopL , and VopC , are involved in T3SS2-dependent stress fiber formation [7 , 10 , 12 , 23] . Although VopL induces stress fiber formation in transfected cells , it is not essential for this process ( S1A , B Fig . ) [12] . Furthermore , the actin filament assembly-enhancing activity of VopL , dose not require any Rho GTPases . A vopC-deficient strain caused the formation of long , branched , and curved F-actin filaments with a reticular appearance; these fibers were not observed in cells infected with a T3SS2-deficient strain ( S1A Fig . ) [7] . The expression of a constitutively active form of Rac1 restored the strain’s capacity to induce normal stress fiber formation . VopO activation of the RhoA-ROCK pathway via GEF-H1 binding is essential for stress fiber formation . These observations indicate that activation of the RhoA-ROCK pathway by VopO is a requisite first step in the induction of stress fiber formation . The activity of VopL , which nucleates actin filaments , appears to enhance the efficiency of stress fiber formation . Stress fibers are contractile acto-myosin structures , which are attached to focal adhesions at both ends of the fiber . The focal complexes formed in lamellipodia are triggered by Rac1 activation [40] . Rac1-null primary mouse embryonic fibroblasts cannot form focal adhesion complexes or induce RhoA-regulated actin stress fiber formation [41] . A vopC deletion mutant lacks the ability to induce vinculin foci [7] . The focal complexes formed by VopC-activated Rac1 may be converted into focal adhesions , and this signaling cascade is necessary for the formation of robust actin stress fibers . Stress fiber formation plays a role in the maintenance of the epithelial barrier against both pathogen invasion and commensal microbiota [24 , 33 , 34 , 36 , 38] . Inappropriate induction of stress fibers disrupts tight junctions and leads to several diseases [42] . A recent in vivo study demonstrated that V . parahaemolyticus disrupts the tight junction complex of small intestinal epithelial cells prior to inducing diarrhea in an infant rabbit oral infection model [5] . The involvement of VopO in this phenomenon was difficult to determine using a rabbit ileal loop test in vivo; however , based on TER measurements and FITC-dextran leakage assays , we detected VopO-dependent disruption of the epithelial barrier ( Figs . 5A and 5B ) . These results suggest that VopO is closely involved in the disruption of tight junction complexes , which is consistent with observations in the infant rabbit oral infection model . V . parahaemolyticus is usually considered to be a noninvasive bacterial pathogen , but several recent studies have demonstrated that this bacterium can invade epithelial cells , which depends on VopC before replicating in the cytosol of the host cells [7 , 23 , 43] . In the present study , we found that a vopO deficient strain also abolished T3SS2-dependent invasive phenotype ( S2D Fig . ) . Consequently , we hypothesize that a fairly complex mechanism is required for T3SS2-dependent invasion because the invasive capacity of this bacterium is needed to activate Cdc42 , but not Rac1 and RhoA [7] , and treatment with nocodazole , which disrupts microtubules and activates GEF-H1 , inhibited the invasion of V . parahaemolyticus into HeLa and Caco-2 cells [23 , 44] . These observations indicate that , unlike VopO-dependent stress fiber formation , activation of the GEF-H1-mediated RhoA-ROCK pathway is not related to V . parahaemolyticus cell invasion . Further studies are needed to understand how VopO promotes invasion by V . parahaemolyticus . T3S effectors activate Rho family small GTPases in diverse ways . Some effectors share the common motif Trp-xxx-Glu ( WxxxE ) and functionally mimic GEFs [15 , 45] , whereas other effectors belong to the deamidating toxin family , the members of which directly modify ( via deamidation/transglutamination ) Rho family small GTPases [11] . In addition to these effectors , EspG , EspG2 , and Orf3 in EPEC disrupt the microtubule network , thereby resulting in the release and activation of GEF-H1 [28 , 31] . However , VopO does not possess a WxxxE motif ( or any type of functional motif ) , shares no sequence homology with these effectors , and does not disrupt microtubule structures in transfected or infected cells ( S5B , C , and D Fig . ) [32] . In addition , the deamidation of RhoA was not observed in cells infected with V . parahaemolyticus [7] . These results suggest that VopO hijacks the RhoA-ROCK pathway in a different manner compared with previously reported effectors and toxins . In the present study , we clearly demonstrated that VopO binds directly to GEF-H1 and that the stress fiber-inducing activity of VopO was correlated with its GEF-H1 binding activity ( Fig . 4A ) . GEF-H1 is a RhoA GEF , which is regulated by microtubule binding , phosphorylation , and protein-protein interactions [30 , 46–51] . The interaction between GEF-H1 and microtubules is particularly important for the suppression of GEF-H1 activation , where the unique N- and C-termini of GEF-H1 are responsible for its association with microtubules [30] . In a previous study , transient expression of either N- or C-terminal-truncated GEF-H1 resulted in higher GEF activity compared with the expression of full-length GEF-H1 , thereby suggesting that these domains negatively regulate GEF activity via the DH and PH domains , which are required for the GEF activity of GEF-H1 [30] . Importantly , VopO binds to GEF-H1 , which contains the C-terminal domain ( Figs . 3C and D ) , but the expression of DsRed-fused VopO did not induce obvious alterations in GFP-GEF-H1 localization according to microscopic analysis ( S5C Fig . ) . In addition , we did not observe any VopO-mediated increase in the activity of GEF-H1 with RhoA according to an in vitro Rho GEF exchange assay using mant-GTP . Therefore , the biochemical mechanism that allows VopO to target GEF-H1 and how it coordinates with other V . parahaemolyticus T3SS2 effectors during bacterial infection remain unclear . Further research is required to determine how VopO activates the RhoA-ROCK pathway via GEF-H1 . A more detailed understanding of the functional mechanism of VopO may provide new insights into how pathogenic bacteria exploit the signaling pathways of Rho family small GTPases .
V . parahaemolyticus strain RIMD2210633 ( KP-positive , serotype O3:K6 ) [2] was obtained from the Pathogenic Microbes Repository Unit , International Research Center for Infectious Diseases , Research Institute for Microbial Diseases ( Osaka University , Osaka , Japan ) . A four-primer polymerase chain reaction ( PCR ) technique was used to engineer an in-frame deletion mutation , as described previously [4] . All of the bacterial strains and plasmids used in this study are listed in the Supporting Information , S1 Table . The activation of RhoA was estimated using a G-LISA RhoA Activation Assay Biochem Kit ( Cytoskeleton Inc . , Denver , CO , USA ) or an EZ-Detect Rho Activation Kit ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) , according to the manufacturer’s instructions . One day prior to infection , the culture medium was exchanged with DMEM containing 0 . 25% fetal bovine serum . HeLa cells were infected with isogenic V . parahaemolyticus strains at a multiplicity of infection ( MOI ) of 10 for 150 min or treated with 10 μM nocodazole ( Sigma-Aldrich , St . Louis , MO , USA ) for 30 min as a positive control . After infection or nocodazole treatment , RhoA activation was evaluated by an ELISA ( the G-LISA RhoA Activation Assay Biochem Kit , Cytoskeleton Inc . , Denver , CO , USA ) . The amount of GTP-RhoA in the transfected HeLa cells was estimated via a rhotekin pull-down assay using an EZ-Detect Rho Activation Kit . The intensity of GTP-RhoA bands was measured using ImageJ software ( NIH , Bethesda , MD , USA ) . HeLa cells were infected with V . parahaemolyticus strains at a MOI of 10 for 3 h or treated with 10 μM nocodazole for 30 min . The cell lysates were probed with p ( Thr18/Ser19 ) -MLC and MLC antibodies ( Cell Signaling Technology , Inc . , Danvers , MA , USA ) . HeLa or Caco-2 cells were infected with V . parahaemolyticus strains at a MOI of 10 for 3 h or treated with 10 μM nocodazole for 1 h . To inhibit stress fiber formation , 10 μM Y27632 ( Sigma-Aldrich , St . Louis , MO , USA ) or 2 μg/mL Rho inhibitor I ( Cytoskeleton Inc . , Denver , CO , USA ) was added for 1 or 2 h prior to infection , respectively . After infection , the cells were washed with ice-cold phosphate-buffered saline ( PBS ) and fixed with 4% paraformaldehyde in PBS . The fixed cells were then stained for F-actin and DNA using Alexa-488 or rhodamine-conjugated phalloidin ( Invitrogen , Carlsbad , CA , USA ) and Hoechst 33258 ( Sigma-Aldrich , St . Louis , MO , USA ) , respectively . Images were captured using a fluorescence microscope ( Biozero BZ-8100 , Keyence , Osaka , Japan ) or a confocal laser microscope ( FLUOVEW FV10i , Olympus , Tokyo , Japan ) . The transfection of GFP fused with full-length or truncated VopO expression vectors was performed using Lipofectamine LTX reagent ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer’s instructions . At 15 h after transfection , the HeLa cells were washed with ice-cold PBS and fixed with 4% paraformaldehyde in PBS . The fixed cells were then stained for F-actin and nuclei using rhodamine phalloidin ( Invitrogen , Carlsbad , CA , USA ) and Hoechst 33258 ( Sigma-Aldrich , St . Louis , MO , USA ) , respectively . Images were captured using a fluorescence microscope ( Biozero BZ-8100 , Keyence , Osaka , Japan ) . Confluent HeLa cells were washed with ice-cold PBS and lysed in 1 mL of ice-cold RIPA buffer [50 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 0 . 1% sodium dodecyl sulfate ( SDS ) , 0 . 5% deoxycholic acid ( DOC ) , and 1% NP-40] containing an EDTA-free protease inhibitor cocktail ( Sigma-Aldrich , St . Louis , MO , USA ) . After agitation for 15 min at 4°C , the lysates were harvested with a cell scraper and centrifuged for 15 min at 10 , 000 × g . Recombinant 3xFLAG-tagged wild-type or truncated GEF-H1 proteins were prepared using TnT Quick Coupled Transcription/Translation Systems ( Promega , Fitchburg , WI , USA ) , according to the manufacturer’s instructions . The lysates from the HeLa cells or recombinant GEF-H1 proteins were incubated with GST-VopO proteins and glutathione beads ( GE Healthcare , Little Chalfont , UK ) at 4°C for 4 h . The beads were washed with RIPA buffer and eluted with SDS sample buffer , and the eluates were separated by SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) . The samples used for western blotting were separated by SDS-PAGE . After electrotransfer , the polyvinylidene fluoride ( PVDF ) membranes ( Merck Millipore , Darmstadt , Germany ) were probed with anti-GEF-H1 , p ( Thr18/Ser19 ) -MLC , MLC , anti-GST ( Cell Signaling Technology , Inc . , Danvers , MA , USA ) , anti-LARG , anti-Ect2 , anti-RhoA , anti-β-actin , anti-α-tubulin , or anti-FLAG ( Sigma-Aldrich , St . Louis , MO , USA ) antibodies , followed by horseradish-peroxidase-conjugated goat anti-rabbit or rabbit anti-mouse antibodies ( Zymed Laboratories , Inc . , South San Francisco , CA , USA ) . The blots were developed using an ECL Western Blotting Kit ( GE Healthcare , Little Chalfont , UK ) . A total of 2 x 105 Caco-2 cells were plated into Transwell chambers ( Corning Inc . , Corning , NY , USA ) with a pore size of 0 . 4 μm ( 6 . 5 mm diameter ) and cultured for 12–14 days . The medium was changed every 2 days until a steady-state TER ( 450–550 Ωcm2 ) was achieved . The cells were infected with V . parahaemolyticus strains , and the TER was measured using an epithelial voltmeter ( EVOM , WPI Inc . , Sarasota , FL , USA ) at the indicated time . The FITC-dextran leakage assay was conducted as described previously [39] . Mixtures of bacteria and 4-kDa FITC-dextran ( Sigma-Aldrich , St . Louis , MO , USA ) were used to challenge the apical side of the Transwell chambers . At 6 and 12 h after infection , the amount of FITC-dextran on the basolateral side was measured using a PowerScan HT fluorescence plate reader ( DS Pharma Biomedical Co . , Ltd , Osaka , Japan ) . Specific siRNAs for RhoA , GEF-H1 , LARG , or Ect2 were purchased from Applied Biosystems . Silencer Select Negative Control #1 siRNA ( Applied Biosystems , Foster City , CA , USA ) was used as a negative control . The siRNAs were transfected using siPORT NeoFX Transfection Agent ( Applied Biosystems , Foster City , CA , USA ) . At 24 h after transfection , the cells were subjected to western blot analysis to confirm the knockdown efficiency and used subsequently in the infection studies . All of the data are expressed as the mean and standard error based on at least three determinations per experimental condition . Student’s t tests that assumed unequal variances were used for the statistical analyses . P < 0 . 05 was considered significant .
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Many bacterial pathogens manipulate the actin cytoskeleton of mammalian cells to establish pathogenesis via invasion , to evade killing by phagocytes , to disrupt a barrier function , and to induce inflammation caused by translocation type III secretion ( T3S ) effector proteins . We demonstrated that the T3S effector protein ( VopO ) of the enteric pathogen Vibrio parahaemolyticus induced robust actin stress fiber formation in infected host cells . Furthermore , this actin rearrangement induced barrier disruption in a colon epithelial cell line . Although many types of effector proteins have been reported , VopO does not share homology with previously reported effector proteins , and no putative functional motifs could be identified . Finally , we determined that the direct binding of VopO to a RhoA guanine nucleotide exchange factor ( GEF ) is a key step in the induction of stress fiber formation . These findings indicate that VopO plays a unique role in the pathogenicity of V . parahaemolyticus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Interaction between the Type III Effector VopO and GEF-H1 Activates the RhoA-ROCK Pathway
|
Neuronal membrane potential resonance ( MPR ) is associated with subthreshold and network oscillations . A number of voltage-gated ionic currents can contribute to the generation or amplification of MPR , but how the interaction of these currents with linear currents contributes to MPR is not well understood . We explored this in the pacemaker PD neurons of the crab pyloric network . The PD neuron MPR is sensitive to blockers of H- ( IH ) and calcium-currents ( ICa ) . We used the impedance profile of the biological PD neuron , measured in voltage clamp , to constrain parameter values of a conductance-based model using a genetic algorithm and obtained many optimal parameter combinations . Unlike most cases of MPR , in these optimal models , the values of resonant- ( fres ) and phasonant- ( fϕ = 0 ) frequencies were almost identical . Taking advantage of this fact , we linked the peak phase of ionic currents to their amplitude , in order to provide a mechanistic explanation the dependence of MPR on the ICa gating variable time constants . Additionally , we found that distinct pairwise correlations between ICa parameters contributed to the maintenance of fres and resonance power ( QZ ) . Measurements of the PD neuron MPR at more hyperpolarized voltages resulted in a reduction of fres but no change in QZ . Constraining the optimal models using these data unmasked a positive correlation between the maximal conductances of IH and ICa . Thus , although IH is not necessary for MPR in this neuron type , it contributes indirectly by constraining the parameters of ICa .
Neuronal network oscillations at characteristic frequency bands emerge from the coordinated activity of the participating neurons . Membrane potential resonance ( MPR ) is defined as the ability of neurons to exhibit a peak in their voltage response to oscillatory current inputs at a preferred or resonant frequency ( fres ) [1] . MPR has been observed in many neuron types such as those in the hippocampus [2–4] and entorhinal cortex [2–6] , inferior olive [7 , 8] , thalamus [1 , 9] , striatum [10 , 11] , as well as in invertebrate oscillatory networks such as the pyloric network of the crustacean stomatogastric ganglion ( STG ) [12–14] . Neurons may also exhibit phasonance or a zero-phase response , which describes their ability to synchronize with oscillatory inputs at a preferred phasonant frequency ( fϕ = 0 ) [4 , 15–18] . Resonance , phasonance and intrinsic oscillations are related , but are different phenomena as one or more of them may be present in the absence of the others [15 , 16 , 18] . Resonant and phasonant frequencies result from a combination of low- and high-pass filter mechanisms produced by the interplay of the neuron’s passive properties and one or more ionic currents and their interaction with the oscillatory inputs [1 , 15 , 18 , 19] . The slow resonant currents ( or currents having resonant gating variables ) oppose voltage changes and act as high-pass filters . They include the hyperpolarization-activated inward current ( IH ) and the slow outward potassium current ( IM ) . On the other hand , the fast amplifying currents ( or currents having amplifying gating variables ) favor voltage changes and can make MPR more pronounced . They include the persistent sodium current ( INaP ) and the inward rectifying potassium ( IKir ) current . Most previous systematic mechanistic studies have primarily examined models with one resonant and one amplifying current , such as IH and INaP , respectively [15 , 18–20] . Currents having both activating and inactivating gating variables ( in a multiplicative way ) such as the low-threshold calcium current ( ICa ) are not included in this classification , but they are able to produce resonance by mechanisms that are less understood [16 , 21] . Although a causal relationship between the properties of MPR and network activity has not been established [but see 22] , resonant neurons have been implicated in the generation of network oscillations in a given frequency band because the resonant and network frequencies often match up or are correlated . One example is in the hippocampal theta oscillations [23] in which CA1 pyramidal cells exhibit MPR in vitro at theta frequencies of 4–10 Hz [2–4 , 24] ( but see [25] ) . Interestingly , MPR is not constant across the somatodendritic arbor in these neurons [26] . Hippocampal interneurons also show MPR in vitro , but at gamma frequencies of 30–50 Hz [3 , but see 4] , and gamma oscillations have been found to be particularly robust in network models containing resonant interneurons [27 , 28] . The crab pyloric network produces stable oscillations at a frequency of ~1 Hz , driven by a pacemaker group composed of two neuron types , the anterior burster ( AB ) and the pyloric dilator ( PD ) , that produce synchronized bursting oscillations through strong electrical-coupling [29] . The PD neuron shows MPR , with fres ~1 Hz that is positively correlated with the pyloric network frequency [12] . Previous work has demonstrated that MPR in this neuron depends on two voltage-gated currents: ICa and IH [12] . Ionic current levels in pyloric neurons are highly variable across animals , even in the same cell type [30] . It is therefore unclear how these currents may interact to produce a stable MPR in the PD neuron and whether this variability persists or is increased or decreased in the presence of oscillatory inputs . Traditionally , MPR is measured by applying ZAP current injection and recording the amplitude of the voltage response [1 , 31] . In some systems , depolarization can increase [32] or decrease [33] ) the preferred frequency . Alternatively , resonance is measured by applying ZAP voltage inputs in voltage clamp and recording the amplitude of the total current . Both approaches yield identical results for linear systems , but not necessarily for nonlinear systems . A previous study from our lab using the voltage clamp technique showed that in the PD neuron hyperpolarization decreases both fres and network frequencies [14] . Since MPR results from the outcome of the dynamics of voltage-gated ionic currents activated in different voltage ranges , changing the input voltage amplitude is expected to change fres in an input amplitude-dependent manner . This cannot be captured by linear models in which impedance is independent of the input amplitude . To our knowledge , no study has attempted to understand the ionic mechanisms that produce shifts in fres in response to changes in the voltage range . Previous studies have explored the generation of MPR by ICa and through the interaction between ICa and IH in hippocampal CA1 pyramidal neurons [16 , 17] and thalamic neurons [21] , where the resonant and network frequencies are significantly higher than in the crab pyloric network and the ICa time constants are smaller . Based on numerical simulations , these investigations have produced important results about the role of the activating and inactivating gating variables and their respective time constants play in the generation of MPR and the determination of fres . However , a mechanistic understanding of the effects of the interacting time constants and voltage-dependent inactivation that goes beyond simulations is lacking . An important finding for the CA1 pyramidal neurons is that , for physiological time constants , they exhibit resonance , but no phasonance [16] . However , for larger time constants , outside the physiological range for these neurons , they are able to exhibit phasonance . This suggests that PD neurons , which have slower time scale currents , may exhibit resonance and phasonance at comparable frequencies . If so , such a correlation between resonance and phasonance can be used to explain the influence of ionic current parameters . Our study has two interconnected goals: ( i ) to understand how the interplay of multiple resonant gating variables shapes the Z- and φ-profiles ( impedance amplitude and phase-shift as a function of input frequency ) of a biological PD neuron , and ( ii ) to understand the many ways in which these interactions can occur to produce the same Z-profile in these neurons . For a neuron behaving linearly , e . g . , with small subthreshold inputs , this task is somewhat simplified by the fact that linear components are additive . However , neurons are nonlinear and the nonlinear interaction between ionic currents has been shown to produce unexpected results [16 , 18 , 19] . To achieve these goals we measured and quantified the Z- and φ-profiles of the PD neuron . We then used a single-compartment conductance-based model of Hodgkin-Huxley type [34] that included a passive leak and the two voltage-gated currents IH and ICa to explore what combinations of model parameters can produce the experimentally observed PD neuron Z- and φ-profiles . The maximal conductances of ionic currents of neurons in the stomatogastric nervous system vary widely [35–37] . We therefore assume that the parameters that determine the Z-profile in the PD neuron vary across animals . Thus , instead of searching for a single model that fit the PD neuron Z-profile , we used a genetic algorithm to capture a collection of parameter sets that fit this Z-profile . To achieve such a fit , we defined a set of ten attributes that characterize the PD neuron Z-profile ( e . g . , resonant frequency and amplitude ) and used a multi-objective evolutionary algorithm [MOEA , 38] to obtain a family of models that fit these attributes . We then used this family of optimal models to identify the important biophysical parameters and relationships among these parameters to explain how the PD neuron Z-profile is shaped . We show how the fact that the inactivating calcium current peaks at the same phase as the passive properties , in response to sinusoidal inputs , can explain why resonant and phasonant frequencies are equal . We identify significant pairwise parameter-correlations , which selectively set certain attributes of MPR . We show that , in this neuron , IH does not produce MPR but can extend the dynamic range of ICa parameters mediating MPR . Furthermore , we identify a subset of models that capture the experimental shift in the resonant frequency with changes in lower bound of voltage oscillation . Finally , we exploit the fact that the resonant and phasonant frequencies are equal for the PD neuron to provide a mechanistic understanding of the effects of the ICa time constants on the resonant frequency by using phase information . Our results provide a mechanistic understanding for a generic class of neurons that exhibit both resonance and phasonance as the result of the interaction between multiplicative gating variables and complement the studies in [16] .
To understand how Z is generated by the dynamics of individual ionic currents at different voltages and frequencies , we examined the amplitude and kinetics of ionic currents . In voltage clamp , Z is shaped by active voltage-gated currents , interacting with the passive leak and capacitive currents , in response to the voltage inputs . To understand the contribution of different ionic currents , we measured these currents in response to a constant frequency sine wave voltage inputs ( Fig 3a inset ) at three frequencies: 0 . 1Hz , 1Hz ( fres ) and 4Hz ( Fig 3 ) . For these frequencies , we plotted the steady-state current as a function of voltage ( Fig 3b–3d left ) and normalized time ( or cycle phase = time x frequency; Fig 3b–3d right ) . At 0 . 1 Hz , the amplitudes of IH and IL +ICm sets Itotal at low ( ~ -60 mV ) and high ( ~ -30 mV ) voltages , respectively ( Fig 3b left ) . Since IH deactivation is slow , it also contributes to Itotal at high voltages ( Fig 3b right ) . At 1 Hz ( = fres ) , IH still sets the minimum of the total current , but , because of its slow kinetics , its steady-state dynamics are mostly linear ( Fig 3c left ) . However , now ICa peaks in phase ( Fig 3c right ) with the passive IL + ICm at high voltages , thus producing a smaller Itotal ( magenta bar in Fig 3c ) . The values of IH at 4 Hz are not much different from 1 Hz ( Fig 3d ) . However , ICa peaks at a much later phase ( Fig 3d right ) , which does not allow it to compensate for IL + ICm at high voltages , thus resulting in a larger Itotal ( magenta bar in Fig 3d ) . Note that at 1 Hz , the total current peaks at a cycle phase close to 0 . 5 , thus implying that that the fres and fφ = 0 are very close or equal ( Fig 3c right ) . Although Fig 3 shows the results for only one model in the optimal dataset , these results remain nearly identical for all models in the optimal dataset . The standard deviation of the currents measured , including the total current was never above 0 . 15 nA over all models . The inset in Fig 3c shows one standard deviation around the mean for the data shown in the right panel , calculated for 200 randomly selected models . An important collective property of the models we found is that the two frequencies , fres and fφ = 0 coincide ( Fig 4a and 4b ) . We analyzed the experimental data , and confirmed that the coincidence of MPR and phasonance frequencies also occurs in the biological system ( Fig 4b inset ) . This is typically not the case for neuronal models ( and for dynamical systems in general ) , not even for linear systems [18–20] , with the exception of the harmonic oscillator . However , the fact that it occurs in this system , allows us to use the current vs . cycle phase ( current-phase ) diagrams to understand the dependence of fres and fφ = 0 on the model parameters ( Fig 4c ) . The current-phase diagrams are depicted as in Fig 3b–3d , as graphs of Itotal , IL and ICa as a function of the cycle phase for each given specific input frequency ( Fig 4c ) . We do not show IH and ICm in this plot , because at frequencies near fres they do not change much with input frequency . Note that IL is independent of the input frequency ( five panels in Fig 4c ) because it precisely tracks the input voltage . In voltage clamp , fφ = 0 = 1Hz is where Itotal is at its minimum amplitude exactly at cycle phase 0 . 5 , coinciding with the peak of the input voltage ( Fig 4c , middle ) . The fact that IL precisely tracks the input voltage imposes a constraint on the shapes of ICa and Itotal . Therefore , by necessity , if the ICa trough occurs for a cycle phase below 0 . 5 , the Itotal peak must occur for a cycle phase above 0 . 5 ( Fig 4c , top two panels ) and vice versa ( Fig 4c , bottom two panels ) . This is shown by the slope of the line joining the peaks of Itotal and ICa and , at fres this line is approximately vertical ( Fig 4c middle panel ) . We use this tool to explain the dependence of the Z-profile on the time constants τmCa ( Fig 5a ) and τhCa ( Fig 5b ) . The corresponding current-phase diagrams are presented in Fig 5c and 5d , respectively . In each panel we present the current-phase diagrams for f at 1 Hz ( = fres when the parameter is at 100%; middle ) and f = fres ( sides ) when fres is different from 1Hz . To understand the dependence of Z on changes in τmCa and τhCa we have to primarily explain the dependence of the two attributes Zmax and fres on these parameters . While fres has a similar monotonic dependence on τmCa and τhCa ( as these parameters increase , fres decreases ) , Zmax has the opposite dependence on τmCa and τhCa . The opposite dependence of Zmax on τmCa and τhCa is a straightforward consequence of the opposite feedback effects ( positive for τmCa and negative for τhCa ) that these parameters exert on ICa . An increase in τmCa ( for fixed values of τhCa ) results in a smaller ICa in response to a given voltage clamp input . Because ICa is smaller and negative , this leads to an increase in Itotal and a decrease in Z at all frequencies . Similarly , an increase in τhCa ( for fixed values of τmCa ) results in a larger ICa , leading to a decrease in Itotal and an increase in Z . For a fixed value of the input frequency f ( e . g . f = 1 Hz as in Fig 5 ) , for Zmax to decrease as τmCa increases ( Fig 5a ) , the cycle phase of peak ICa is delayed thereby subtracting less from IL on the depolarizing phase . This leads to Itotal to phase advance relative to IL ( Fig 5c ) and causes fres to decrease . Similarly , for Zmax to increase as τhCa increases ( Fig 5b ) , ICa has to peak later in the cycle thereby subtracting less from IL on the depolarizing phase , which causes Itotal to peak earlier in the cycle , which in turn causes the bar also to swing from the left to the right ( Fig 5d ) . Therefore , fres decreases . Previous studies have shown that stable network output can be produced by widely variable ion channel and synaptic parameters [37 , 40] . Our biological data , similarly , showed that many of the Z- and φ-profile attributes , such as fres , Λ½ and fφ = 0 are relatively stable across different PD neurons whereas QZ shows the most variability ( Fig 1d ) . To determine whether the Z- and φ-profile attributes constrain ionic current parameters , we examined the variability of the model parameters in the optimal dataset . We found that some parameters were more constrained while others were widely variable , as measured by the coefficient of variation ( CoV; Fig 6a ) . Parameters showing large CoVs were g¯Ca , τmh , g¯H , τhCa , and V1/2Ca∞h; those showing small CoVs were g¯L and the time constant of activation of IH and ICa and half-activation voltage of ICa: τmCa , V1/2Ca∞m , g¯L ( in increasing order of CoV value ) . A small CoV value implies that the parameter is tightly constrained in order to produce the proper Z- and φ-profiles . A number of studies have indicated that the large variability in ion channel parameters is counter-balanced by paired linear covariation of these parameters [36 , 37 , 41–43] . Considering the large variability , we identified parameter pairs that co-varied ( Fig 6b ) . For this , we carried out a permutation test for the Pearson’s correlation coefficients , followed by a Student’s t-test on the regression slopes , to identify significant correlations between pairs of parameters ( see Methods ) . The strongest correlations were between the following parameters: g¯L−g¯H ( R = -0 . 93 ) , g¯L−τmCa ( R = 0 . 73 ) , g¯L−τhCa ( R = 0 . 88 ) , g¯H−τmH ( R = 0 . 68 ) , g¯H−τhCa ( R = -0 . 82 ) , g¯H−V1/2Ca∞h ( R = 0 . 76 ) , g¯Ca−V1/2Ca∞h ( R = -0 . 94 ) , and τmCa−τhCa ( R = -0 . 80 ) ( correlations selected with p < 0 . 01; Fig 6b ) . In our experiments , V1/2H∞m was fixed at -70 mV , using data from experimental measurements in crab [44] ( see Methods ) . However , we also repeated the MOEA with V1/2H∞m set to -96 mV , as reported in lobster experiments [45] , and found that all correlations observed with the former value of V1/2H∞m remain intact , but simply with a much larger maximal conductance of IH ( S1 Fig ) . In other words , shifting V1/2H∞m to the left simply results in larger g¯H in the optimal models without qualitatively changing the other findings . In particular , we found that the g¯Ca−V1/2Ca∞h correlation appeared nonlinear , but there were strong and distinct linear correlations in the two regions g¯Ca > 0 . 05 μS ( low g¯Ca ) and g¯Ca < 0 . 05 μS ( high g¯Ca; Fig 6c ) . To ensure that our partitioning of the population into different levels of g¯Ca was valid , we ran the MOEA two additional times , each time using only the mean values of g¯L , τmH , V1/2Ca∞m , and τmCa for either the low or the high g¯Ca values . These optimal models consistently separated into two non-overlapping model parameters , consistent with the low and high g¯Ca models in Fig 6c . We examined if the low and high g¯Ca models separated or showed distinct correlations in the remaining parameters . The two groups produced non-overlapping subsets of model parameters in the g¯Ca−V1/2Ca∞h graph . We calculated the Pearson’s correlation coefficient for each pair of parameters in the low and high g¯Ca groups and tested for significance as before ( see Table 1 ) . We found that only the high g¯Ca group showed a significant τmCa−τhCa and g¯H−τhCa correlations ( Table 1 ) . Additionally , both low and high g¯Ca groups showed the following correlations: V1/2Ca∞h−τhCa , g¯L−g¯H , g¯Ca−V1/2Ca∞h , and g¯H−τmH , g¯Ca−τhCa . Furthermore , when we ran the MOEA on models where g¯H was set to 0 , the only optimal models obtained fell within a narrow range of the high g¯Ca group ( S2 Fig ) , which is consistent with the distribution of high g¯Ca models in the g¯H−g¯Ca panel of Fig 6d . The lower voltage range of the PD bursting oscillation is strongly influenced by the inhibitory synaptic input from the lateral pyloric neuron ( LP ) , and previous work has shown that fres in the PD neuron is influenced by the minimum of the voltage oscillation ( Vlow ) [14] . In order to explore which subset of our optimal models faithfully reproduce the influence of the minimum voltage range , we measured the Z-profile when Vlow was changed from -60 to -70 mV ( Fig 7a ) . Decreasing Vlow significantly decreased fres ( by 0 . 24±0 . 8Hz ) , while there was no significant difference in the mean Zmax ( -0 . 15±0 . 81MΩ ) ( two-way RM-ANOVA; N = 8 , p < 0 . 001; Fig 7b , left panel ) . To explore whether the shift in fres as a function of Vlow could be captured by either low or high g¯Ca models , we measured the shift in fres and Zmax , when Vlow was changed from -60mV to -70mV . We found that fres decreased by 0 . 24±0 . 03 Hz and Zmax increased by 5 . 2±0 . 6 MΩ for high g¯Ca models , whereas fres decreased by 0 . 07±0 . 02Hz and Zmax decreased by 2 . 6±0 . 2MΩ for low g¯Ca models ( Fig 7b , right panel ) . Therefore , neither model group reproduced the experimental changes in the Z-profile , specifically , a decrease in fres and no change in Zmax . We consequently filtered the full optimal dataset ( black dots Fig 7c ) to find a subset of models that reproduced the change in fres and Zmax ( to within 5% of the representative experimental Z ( f ) shown in Fig 7a ) when Vlow was decreased to -70mV . Of the ~9000 models in the population , we found ~1000 models that produced the desired change . Interestingly , the resulting models showed a trade-off in values for g¯Ca and V1/2Ca∞h parameters that showed little overlap with the low and high g¯Ca model groups ( Fig 7c ) . To understand why this particular group ( which we will term intermediate g¯Ca ) produced small changes in Zmax when Vlow was decreased , we plotted the current-voltage relationships for ICa , IH , ICa+IH and Itotal for Vlow = -60 and -70 mV , measured at f = 1Hz ( fres at Vlow = -60mV ) and compared these models with the low and high g¯Ca models . For Vlow = -60mV , the ionic currents behaved similarly for all model groups and Itotal was maximal at -30mV ( magenta curve in Fig 7d1–7d3 ) , indicating the similarity of all models in the optimal dataset . However , when Vlow was at -70mV revealed differences in peak ICa , without affecting the peak amplitude of IH across the different g¯Ca groups ( Fig 7e1–7e3 ) . The differences in peak ICa accounted for most of the changes in Itotal across the different g¯Ca groups . The Zmax values for intermediate g¯Ca models reproduced the small shift seen in experiments because ICa was at the correct level at high voltages ( -30 mV ) when Vlow was at -70mV ( Fig 7e3 ) . The other two groups did not produce appropriate Zmax for Vlow = -70mV because either ICa was too small ( and hence Itotal too large ) , resulting in a smaller Zmax ( Fig 7e1 ) or vice versa ( Fig 7e2 ) . It was also clear that the more negative voltages allowed for an increase in IH levels and therefore larger contribution to the total current . With Vlow at -70mV , not only was there a larger peak amplitude of IH at the lower voltages , but the current at positive voltages also increased because of the very slow deactivation rate . Consequently , IH did not fully turn off when ICa peaked , so that it also contributes to shaping the upper envelope of the total current . IH kinetics were different across the groups ( Fig 7e1–7e3 ) . Taken together with the fact that when IH was removed produced only parameter values with very high g¯Ca and very low V1/2Ca∞h ( S1 Fig ) , these data suggest that IH could extend the range of ICa parameters over which MPR could be generated through compensation for variable levels of IH . The ICa in low g¯Ca models was too small when Vlow was -70 mV , because the low conductance did not allow for a significant contribution from the additional de-inactivation ( considering the higher V1/2Ca∞h in this group ) and therefore the peak current did not increase enough . Consequently , the contribution of IH at low voltages was greater than that of ICa at higher voltages ( Fig 7e2 ) . Conversely , in the high g¯Ca group , V1/2Ca∞h was more negative and so many more channels were available for de-inactivation and the contribution of ICa at higher voltages was much larger than that of IH at low voltages ( Fig 7e3 ) . These findings suggest that the balance between these two currents , that shape the lower and upper envelope of the total current response to voltage inputs , is necessary to produce the appropriate shift in fres without influencing Zmax significantly . The intermediate g¯Ca models were strongly correlated in g¯Ca−V1/2Ca∞h ( R2 = 0 . 89; p < 0 . 001 Fig 7f1 , and had a stronger correlation in the τmCa−τhCa parameters compared to all models ( R2 = 0 . 65; p < 0 . 001; Fig 7g ) . Limiting the optimal models to the intermediate g¯Ca group also revealed a correlation in the g¯Ca−g¯H parameters ( R2 = 0 . 79; p < 0 . 001; Fig 7h ) . This new correlation may be produced by the balance of the amplitudes of IH and ICa at the lower and higher voltages , respectively . To determine if any of the MPR attributes were sensitive to the correlations , we ran a 2D sensitivity analysis on a random subset of 50 models . We tested for significant difference in sensitivity across low , intermediate and high levels of g¯Ca . In particular , we tested for significant sensitivity of fres and QZ when parameters were co-varied in directions parallel ( L‖ ) or perpendicular ( L┴ ) to their respective population correlation lines . We first examined whether fres and QZ were sensitive to τmCa−τhCa for both high ( Fig 8a1 ) , low ( Fig 8a2 ) , and intermediate g¯Ca ( Fig 8a3 ) when parameters were moved along L‖ and L┴ ( blue and green line; Fig 8a1–8a3 ) . For high and intermediate g¯Ca models , fres sensitivities in the L‖ group were negative and not significantly different ( 3-way RM ANOVA; N = 50 , p > 0 . 05 ) , but both groups were significantly different from the low g¯Ca group ( 3-way RM ANOVA; N = 50 , p < 0 . 001 ) , which had a positive sensitivity ( Fig 8b ) . This result indicates that the correlation did a better job at maintaining the value of fres when the value of g¯Ca is intermediate or high . For all g¯Ca groups , we found that there was a significant interaction between the Z attribute and direction ( 2-way RM ANOVA; F ( 1 , 49 ) = 853 . 52 , p < 0 . 001 ) . When carrying out a pairwise comparison for each direction within an attribute , we found a significant difference in sensitivity between L‖ and L┴ for fres ( t ( 93 . 57 ) = 28 . 251 , p<0 . 001 ) . Similarly , for all g¯Ca groups , there was a significant difference in sensitivity between L‖ and L┴ for QZ ( t ( 93 . 57 ) = -8 . 294 , p<0 . 001 ) . Because the difference between L‖ and L┴ for QZ was negative , these results suggest that the τmCa−τhCa correlation determines fres and not QZ ( Fig 8b ) . We next examined whether fres and QZ were sensitive to the g¯Ca−V1/2Ca∞h correlation for the three model groups ( Fig 9a1–9a3 ) . For all g¯Ca groups , we found that there was a significant interaction between the Z attribute and direction ( 2-way RM ANOVA; F ( 1 , 49 ) = 1262 . 73 . 2 , p < 0 . 001 ) . When carrying out a pairwise comparison for each direction within an attribute , we found a significant difference in sensitivity between L‖ and L┴ for fres ( t ( 95 . 18 ) = 10 . 10 , p<0 . 001 ) . Similarly , for all g¯Ca groups , we found a significant difference in sensitivity between L‖ and L┴ for QZ ( t ( 95 . 18 ) = -35 . 62 , p<0 . 001 ) . Therefore , these results suggest that the g¯Ca−V1/2Ca∞h correlation determines QZ and not fres ( Fig 9b ) . Finally , we tested the sensitivity of fres and QZ to the g¯Ca−g¯H correlation in the intermediate g¯Ca group ( Fig 10a ) . We found that there was a significant interaction between the Z attribute and direction ( 2-way RM ANOVA; F ( 1 , 11 . 12 ) = 2236 . 2 , p < 0 . 001 ) . When carrying out pairwise comparisons between directions for each attribute , we found there was a significant difference in fres sensitivity between L‖ and L┴ ( t ( 93 . 93 ) = 2 . 65 , p = 0 . 0095; Fig 10 ) . Although the sensitivity of QZ was not 0 for L‖ , the difference in sensitivity values between L‖ and L┴ was also significantly different ( t ( 93 . 93 ) = 62 . 157 , p < 0 . 0001; Fig 10b ) . These results suggest that , when Vlow is at -70 mV , for this subset of models to shift fres with only small shifts in Zmax , g¯H and g¯Ca values must be balanced . It may be possible that the QZ sensitivity is not closer to zero along L‖ because V1/2Ca∞h , which is also negatively correlated with g¯Ca , should decrease too to compensate for changes in QZ .
Many neuron types exhibit membrane potential resonance ( MPR ) in response to oscillatory inputs . Several studies have shown that the resonant frequency of individual neurons is correlated with the frequency of the network in which they are embedded [2 , 6 , 12 , 14 , 22 , 46] . Moreover , networks of resonant neurons have been proposed to generate more robust network oscillations than neurons with low-pass filter properties [27 , 28] . In several cases , the underlying nonlinearities and time scales that shape the Z-profile also shape specific properties of the spiking activity patterns , thus leading to a link between the subthreshold and suprathreshold voltage responses [25 , 47] . Previous work in the crustacean stomatogastric pyloric network has shown that the resonance frequency of the pyloric pacemaker PD neurons is correlated with the pyloric network frequency and is sensitive to blockers of both IH and ICa [12–14] . However , it was not clear how these voltage-gated ionic currents and the passive properties could interact to generate MPR in the PD neurons . Previous modeling work showed that these currents participate in the generation of resonance in CA1 pyramidal neurons [16 , 17] . However , due to the differences in ICa time constants , the interaction between its activating and inactivating gating variables did not produce phasonance in CA1 pyramidal neurons , while it does in PD neurons . On a more general level , it is not well understood how the nonlinear properties of ionic currents affect their interplay . Previous studies have shown such interactions may lead to unexpected results , which are not captured by the corresponding linearizations [16–19] . This complexity is expected to increase when two currents with resonant components are involved [16 , 48] . We therefore set out to investigate the biophysical mechanism underlying such interactions by using a combined experimental and computational approach and the biological PD neuron as a case study . The two PD neurons are electrically coupled to the pacemaker anterior burster neuron in the pyloric network and their MPR directly influences the network frequency through this electrical coupling [22] . Consequently , our findings have a direct bearing on how the pyloric network frequency is controlled . Many studies of biophysical models have explored the parameter space using a brute-force technique , by sampling the parameters on a grid [40 , 49] . Although this technique provides a rather exhaustive sampling of the parameter space , using a fine grid on a large number of free parameters could lead to combinatorial explosion and result in a prohibitive number of simulations . On the other hand , a sparse sampling may miss “good” solutions . A multi-objective evolutionary algorithm ( MOEA ) can generate multiple trade-off solutions in a single run and can handle large parameter spaces very well . In contrast to a brute-force approach , the MOEA can potentially cover a much larger range with possibly hundreds of values [38] . One disadvantage of the MOEA is that , as the number of objectives increases , the search may miss a large portion of the parameter space . This occurs because randomly generated members often tend to be just as good as others , which means that the MOEA would run out of room to introduce new solutions in a given generation . To try to overcome this problem , we carefully chose the parameters of the MOEA such as population size , mutation and crossover distribution indices ( 100 , 20 and 20 , respectively ) and ensured that the sampled population covered the parameter space evenly . Additionally , we ran the MOEA multiple times , each time collecting all the good parameter sets until one has exhausted all regions of the parameter space where good models exist . In previous work , we and other authors have examined how the additive interaction of ionic currents with resonant and amplifying gating variables shape the Z and φ profiles at both the linear and nonlinear levels of description [6 , 15 , 18 , 20 , 32 , 33 , 50] . However , the role of inactivating currents in the generation of MPR is not so clear . Authors have established that ICa can generate MPR in the absence of additional ionic currents [21] , that the activation variable diminishes the propensity for MPR and the interaction with IH enhances the dynamic range of parameters producing ICa-mediated resonance [16] . Even so , to date , only a descriptive explanation of how the ionic current parameters affect certain attributes of MPR has been provided , but no study has provided a mechanistic understanding in terms of the parameters of ICa that go beyond numerical simulations . Similar to [16] , the model we used in this paper involves the interaction between resonant and amplifying components . Specifically , this model includes a calcium current with both activation ( amplifying ) and inactivation ( resonant ) gating variables , and an H-current with a single activation ( resonant ) gate . Since IH and ICa shape the lower and upper envelopes of the voltage response to current inputs , respectively [12] , given the appropriate voltage-dependence and kinetics of the currents both could play equal roles at different voltage ranges . In fact , either ICa inactivation or IH is capable of producing MPR [2 , 21] . In CA1 pyramidal neurons , the differences in Z profiles are due to the passive properties and the kinetics of IH [4] . It is possible that the kinetic parameters of IH and ICa are tuned so that they contribute nearly equally to shaping the envelopes of the voltage-clamp current . By tracking the current response to sinusoidal voltage inputs at various frequencies , we found that the fres and fφ = 0 are driven by the peak phase of ICa , and that fres and fφ = 0 are nearly equal because of the phase matching of ICa with IL . This is not always the case for neuronal models , and dynamical systems in general , not even for linear models , except for the harmonic oscillator [18–20] . In fact , as we mentioned above , this is not the case for the ICa model used in [16] , although our results on the ICa inactivation time constant are consistent with that study . In these models phase advance for low input frequencies required the presence of IH . The underlying mechanisms are still under investigation and are beyond the scope of this paper . However , the fact that it occurs was crucial to develop a method to investigate the dependence of the resonant properties , particularly the dependence of the fres on the ICa time constants , using phase information . To date , no other analytical method is available to understand the mechanisms underlying this type of phenomenon in voltage clamp . The tools we developed are applicable to other neuron types for which fres is equal to or has a functional relationship with fϕ = 0 . However , the conditions under which such a functional relationship exists still needs to be investigated . Linear correlations between biophysical parameters of the same or different currents have been reported [37] and may be important in preserving the activity of the model neuron and its subthreshold impedance profile attributes [41] . Previous studies examined combinations of parameters in populations of multi-compartment conductance-based models fit to electrophysiological data [16 , 51] and found only weak pairwise correlations suggesting that the correlations do not arise from electrophysiological constraints . In contrast , constraining the parameters of the ionic currents found to be essential for MPR in PD neuron by MPR attributes , we observed strong correlations underlying parameters when the Z and φ were constrained by the experimental data . We found that constraining the model parameters by fres produced a correlation between the values of time constants of ICa among the population of ~9000 optimal parameter sets . Furthermore , running a 2D sensitivity analysis confirmed that the time constants were constrained so that the effect of making inactivation slower was compensated for by making activation faster to maintain fres constant . The optimal model parameter sets showed a nonlinear co-variation relationship between the g¯Ca and half-inactivation voltage of ICa . However , the models could be divided into two groups , low and high g¯Ca in each of which this co-variation was close to linear . Interestingly , although ICa alone was the primary current underlying MPR , in the absence of IH ( with g¯H=0 ) the models were restricted to the high g¯Ca group . A 2D sensitivity analysis showed that co-varying parameters in each groups along their respective correlation lines preserved QZ without affecting fres , indicating that each group requires distinct changes in one parameter to compensate for effects of changes in the other . Local sensitivity analysis showed that changes in V1/2Ca∞h had opposite effects on fres between high and low g¯Ca groups . Increasing V1/2Ca∞h decreased fres in high g¯Ca models but increased it in low g¯Ca models . A previous modeling study has found that changes in V1/2Ca∞h greatly influenced the amplitude of MPR with little effect on post-inhibitory rebound in thalamic neurons [21] . It would be interesting to verify whether the mechanisms that generate MPR overlap with those that contribute to post-inhibitory rebound properties . Previous work in our lab has shown that the voltage range of oscillations significantly affects fres [13] . Here we show that decreasing , Vlow , the lower bound of the oscillation voltage of the PD neuron , from -60 to -70 mV , significantly shifted fres to smaller values without affecting Zmax . Within our optimal model parameter sets , we obtained a set of ~1000 models in the intermediate g¯Ca range that produced a similar shift in fres but no change in Zmax . Because Vlow greatly affects both ICa inactivation and IH activation , this indicated a potential interaction between these two currents . In fact , we found that because IH and ICa are activated preferentially in different voltage ranges , their amplitudes needed to be balanced to keep Zmax unchanged when Vlow was decreased . If the ratio of IH to ICa amplitudes is incorrect , then Z will amplify ( for high g¯Ca models ) or attenuate ( for low g¯Ca models ) . The intermediate g¯Ca models also showed a stronger τmCa−τhCa correlation , which may be important in matching the phase of ICa with that of IL . This group also showed a strong g¯H−g¯Ca correlation , which may provide a mechanism for controlling the changes in IH amplitude at more negative voltage with similar changes in ICa amplitude at more positive voltages . In contrast to the findings of Rathour and Narayanan [16] , in our optimal models the IH amplitude was not different across the groups with different ICa properties . However , since ICa and IH are differentially modulated [45 , 52] , their functional role may overlap when their voltage thresholds and time constants are shifted by neuromodulation . Therefore , we expect that under certain neuromodulatory contexts , IH may play more of an active role in the generation of MPR . A similar effect of two ionic currents on resonance has been observed in the hippocampal pyramidal cells that participate in the theta rhythm , in which two currents , the slow potassium M-current and IH , were found to operate at the depolarized and hyperpolarized membrane potentials respectively to generate theta-resonance [2] . In general , variability of ionic current expression in any specific neuron type should lead to great variability in network output . Yet , network output in general , and specifically the output of the crustacean pyloric network is remarkably stable across animals [30 , 53 , 54] . Our results suggest that in oscillatory networks the interaction among ionic currents in an individual neuron may be tuned in a way that the variability of the output is reduced in response to oscillatory inputs . Although our computational study may provide some insight into how such stability is achieved , it also indicates a need for additional mathematical analysis to elucidate the underlying mechanisms .
The stomatogastric nervous system of adult male crabs ( Cancer borealis ) was dissected using standard protocols as in previous studies [14] . After dissection , the entire nervous system including the commissural ganglia , the esophageal ganglion , the stomatogastric ganglion ( STG ) and the nerves connecting these ganglia , and motor nerves were pinned down in a 100mm Petri dish coated with clear silicone gel , Sylgard 186 ( Dow Corning ) . The STG was desheathed to expose the PD neurons for impalement . During the experiment , the dish was perfused with fresh crab saline maintained at 10–13°C . After impalement with sharp electrodes , the PD neuron was identified by matching intracellular voltage activity with extracellular action potentials on the motor nerves . After identifying the PD neuron with the first electrode , a second electrode was used to impale the same neuron in preparation for two-electrode voltage clamp . Voltage clamp experiments were done in the presence of 10−7 M tetrodotoxin ( TTX; Biotium ) superfusion to remove the neuromodulatory inputs from central projection neurons ( decentralization ) and to stop spiking activity [13 , 14] . Intracellular electrodes were prepared by using the Flaming-Brown micropipette puller ( P97; Sutter Instruments ) and filled with 0 . 6M K2SO4 and 0 . 02M KCl . For the microelectrode used for current injection and voltage recording , the resistance was , respectively , 10-15MΩ and 25-35MΩ . Extracellular recording from the motor nerves was carried out using a differential AC amplifier model 1700 ( A-M Systems ) and intracellular recordings were done with an Axoclamp 2B amplifier ( Molecular Devices ) . During their ongoing activity , the PD neurons produce bursting oscillations with a frequency of ~1 Hz and slow-wave activity in the range of -60 to -30 mV . Activity in the PD neuron is abolished by decentralization . The decentralized PD neuron shows MPR in response to ZAP current injection when the current drives the PD membrane voltage to oscillate between -60mV and -30mV , which is similar to the slow-wave oscillation amplitude during ongoing activity [12] . The MPR profiles are not significantly different when measured in current clamp and voltage clamp [14] . Since the MPR depends on the dynamics of voltage-gated ionic currents , it will also depend on the range and shape of the voltage oscillation . Therefore , to examine how Z ( f ) in a given voltage range constrains the properties of voltage-gated currents and how factors that affect the voltage range change MPR , we measured Z ( f ) in voltage clamp [10] . To measure the Z-profile , the PD neuron was voltage clamped with a sweeping-frequency sinusoidal impedance amplitude profile ( ZAP ) function [55] and the injected current was measured [14] . To increase the sampling duration of lower frequencies as compared to the larger ones , a logarithmic ZAP function was used: ZAP ( t ) =v0+v1sin ( 2πF ( t ) ) ; F ( t ) =flot ( fhiflo ) t/T . The amplitude of the ZAP function was adjusted to range between -60 and -30 mV ( v0 = -45 mV , v1 = 15 mV ) and the waveform ranged through frequencies of flo = 0 . 1 to fhi = 4 Hz over a total duration T = 100 s . Each ZAP waveform was preceded by three cycles of sinusoidal input at flo which smoothly transitioned into the ZAP waveform . The total waveform duration was therefore 130 s . Impedance is a complex number consisting of amplitude and phase . To measure impedance amplitude , we calculated the ratio of the voltage and current amplitudes as a function of frequency and henceforth impedance amplitude will be referred to as Z ( f ) . To measure φZ ( f ) , we measured the time difference between the peaks of the voltage clamp ZAP and the measured clamp current . One can also measure Z ( f ) by taking the ratio of the Fourier transforms of voltage and current . However , spectral leakage , caused by taking the FFT of the ZAP function and the nonlinear response , often resulted in a low signal-to-noise ratio and therefore in inaccurate estimates of impedance . Such cases would lead to less accurate polynomial fits compared to the cycle-to-cycle method described above and we therefore limited our analysis to the cycle-to-cycle method . Because the average Z-profile may not be a realistic representation of a biological neuron , we used the attributes of Z and φ measurements from a single PD neuron as our target . We characterized attributes of Z into five objective functions used for fitting by specifying five points of the profile ( Fig 11a ) . These five points were: We also constructed five objective functions to capture the attributes of φ ( f ) at five points ( Fig 11b ) : We used a single-compartment biophysical conductance-based model containing only those currents implicated in shaping Z and φ [12] . We performed simulations in voltage clamp and measured the current as: Iclamp=ICm+IL+ICa+IH where ICm is the capacitive current ( CdVdt in nA ) , Cm is set to 1 nF and IL is the voltage-independent leak current in nA . The voltage-dependent currents Icurr ( ICa or IH ) in nA are given by Icurr=g¯currmcurrphcurrq ( V−Ecurr ) where V is the ZAP voltage input ( see below ) , mcurr is the activation gating variable , hcurr is the inactivation gating variable , g¯curr is the maximal conductance in μS , Ecurr is the reversal potential in mV , and p and q are non-negative integers . For ICa , p = 3 , q = 1 and , for IH , p = 1 and q = 0 . The generic equation that governs the dynamics of the gating variables is: dxdt=1τx ( x∞ ( V ) −x ) where x = mcurr or hcurr , and x∞ ( V ) =1/[1+exp ( ( V−Vx ) /kx ) ] The sign of the slope factor ( kx ) determines whether the sigmoid is an increasing ( negative ) or decreasing ( positive ) function of V , and Vx is the midpoint of the sigmoid . A total of 8 free model parameters were defined ( Table 2 ) , which were optimized in light of the objective functions introduced above , to yield a good fit to the Z-profile attributes as described below . The slope factors kx of the sigmoid functions m∞Ca ( V ) , h∞Ca ( V ) , and m∞h ( V ) were fixed at -8 mV , 6 mV , and -7 mV , respectively . V1/2H∞m was fixed at -70 mV , using data from experimental measurements in crab [44] . The voltage-dependent time constant for IH was also taken from [44] to be where the range of τmH is given in Table 2 . Computational neuroscience optimization problems have used a number of methods , such as the “brute-force” exploration of the parameter space [51] and genetic algorithms [56] . However , the brute-force method is computationally prohibitive for an 8-dimensional model parameter space , which would require potentially very fine sampling to find optimal models . [57] . We used an MOEA ( evolutionary optimization ) to identify optimal sets of model parameters constrained by experimental Z and φ attributes . MOEAs are computationally efficient at handling high-dimensional parameter spaces and other studies have used them to search for parameters constrained by other types of electrophysiological activity [57] Evolutionary optimization finds solutions by minimizing a set of functions called objective functions , or simply objectives , subject to certain constraints . In our problem , each objective represents the Euclidean distance between the target and the model attributes of Z and φ . When optimizing multiple ( potentially conflicting ) objectives , MOEA will find a set of solutions that constitute trade-offs in objective scores . For instance , an optimal parameter set may include solutions that are optimal in fres but not in Qz or vice versa and a range of solutions in between that result from the trade-offs in both objectives . In this paper , we used the non-dominated sorting genetic algorithm II ( NSGA-II ) [38 , 58] to find optimal solutions , which utilizes concepts of non-dominance and elitism , shown to be critical in solving multi-objective optimization problems [58] . Solution x1 is said to dominate solution x2 if it is closer to the target Z ( f ) and φ ( f ) profiles in at least one attribute ( e . g . , fres ) and is no worse in any other attributes ( e . g . , QZ , Z0 , etc . ) . NSGA-II begins with a population of 100 parameter combinations created at random within pre-determined lower and upper limits ( Table 2 ) . The objective values for each parameter combination are calculated and ordered according to dominance . First , the highest rank is assigned to all of the non-dominated , trade-off solutions . From the remaining set of parameters , NSGA-II selects the second set of trade-off solutions . This process continues until there are no more parameter combinations to rank . Genetic operators such as binary tournament selection , crossover , and mutation form a child population . A combination of the parent and child parameter sets form the population used in the next generation of NSGA-II [38 , 58] . NSGA-II favors those parameter combinations—among solutions non-dominating with respect to one another—that come from less crowded parts of the parameter search space ( i . e . , with fewer similar , in the sense of fitness function values , solutions ) , thus increasing the diversity of the population . The crowding distance metric is used to promote large spread in the solution space [38] . We ran NSGA-II multiple times ( 3–5 times , until the mean values of the distributions of optimal parameters was stable ) each time for 200 generations with a population size of 100 , and pooled the solutions at the end of each run to form a combined population of ~9000 parameter combinations . The algorithm stopped when no additional distinct parameter combinations were found . The Z and φ values associated with the optimal parameter sets match the target features ( objectives ) defining Z and φ to within 5% accuracy . To test whether two parameters were significantly correlated in the population of 9000 PD models , we calculated the Pearson’s correlation coefficients for each pair of parameters and used a permutation test to determine the number of times the calculated correlation coefficient ( using a random subset of 20 models ) . The p-value was given as the fraction of R-values for the permuted vectors greater than the R-value for the original data [51] . We also used a t-test to determine whether the calculated slope of the linear fit differed significantly from zero , which gave us identical results . We repeated both procedures 2000 times , each time with a random subset of 20 models and calculated the percentage of times we obtained a p-value < 0 . 01 . We assessed how the values of fres and QZ depend on changes in parameter values by performing a sensitivity analysis as in [59] . We split the model parameters into two categories: additive , for the voltage-midpoints of activation and inactivation functions , and multiplicative , for the maximal conductances and time constants . We changed the parameters one at a time and fit the relative change in the resonance attributes as a linear function of the relative parameter change . We changed the multiplicative parameters on a logarithmic scale to characterize parameters with both low and high sensitivity . Multiplicative parameters were varied as pn+1 = exp ( ±Δpn ) p0 with Δpn = 0 . 001*1 . 15n and the sign indicating whether the parameter was increased or decreased . To ensure approximate linearity , we added points to the fit until the R2 value fell below 0 . 98 . The sensitivity was defined as the slope of this linear fit ( Fig 12 ) . For example , if a resonance attribute has a sensitivity of 1 to a parameter , then a 2-fold change in the parameter results in a 2-fold change in the attribute . We changed additive parameters by ±0 . 5 mV . We assessed the sensitivity of fres and Qz to parameter pairs ( p1 and p2 ) that were correlated . We first fit a line through the correlated values in the p1-p2 space . We then shifted this line to pass through a subset of 50 random points in p1-p2 space , resulting in a family of parallel lines , L‖ . For each point , we also produced a line perpendicular to a line L┴ . For each model , we performed a sensitivity analysis as before but used the linear fit equation L‖ or L┴ to calculate value of p2 . We fit the relative change in the Z ( f ) attribute as a linear function of the correlated change in p1 and p2 . We used the slope of the linear fit to represent the sensitivity . We used a 2-and 3-way repeated measures ANOVA and the lsmeans function in R to perform pairwise comparisons of means in testing for significant differences between each group of gCa , each direction , L‖ and L┴ , and between each Z attribute , fres and QZ . For each model , we solved a system of three differential equations for mH , mCa and hCa ( voltage was clamped ) . All simulations were performed using the modified Euler method [60] with a time step of 0 . 2 ms . The simulation code , impedance calculations , and MOEA were written in C++ . MATLAB ( The MathWorks ) and R were used to perform statistical analyses .
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Many neuron types exhibit membrane potential resonance ( MPR ) in which the neuron produces the largest response to oscillatory input at some preferred ( resonant ) frequency and , in many systems , the network frequency is correlated with neuronal MPR . MPR is captured by a peak in the impedance vs . frequency curve ( Z-profile ) , which is shaped by the dynamics of voltage-gated ionic currents . Although neuron types can express variable levels of ionic currents , they may have a stable resonant frequency . We used the PD neuron of the crab pyloric network to understand how MPR emerges from the interplay of the biophysical properties of multiple ionic currents , each capable of generating resonance . We show the contribution of an inactivating current at the resonant frequency in terms of interacting time constants . We measured the Z-profile of the PD neuron and explored possible combinations of model parameters that fit this experimentally measured profile . We found that the Z-profile constrains and defines correlations among parameters associated with ionic currents . Furthermore , the resonant frequency and amplitude are sensitive to different parameter sets and can be preserved by co-varying pairs of parameters along their correlation lines . Furthermore , although a resonant current may be present in a neuron , it may not directly contribute to MPR , but constrain the properties of other currents that generate MPR . Finally , constraining model parameters further to those that modify their MPR properties to changes in voltage range produces maximal conductance correlations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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2017
|
Mechanisms of generation of membrane potential resonance in a neuron with multiple resonant ionic currents
|
Most utility theories of choice assume that the introduction of an irrelevant option ( called the decoy ) to a choice set does not change the preference between existing options . On the contrary , a wealth of behavioral data demonstrates the dependence of preference on the decoy and on the context in which the options are presented . Nevertheless , neural mechanisms underlying context-dependent preference are poorly understood . In order to shed light on these mechanisms , we design and perform a novel experiment to measure within-subject decoy effects . We find within-subject decoy effects similar to what have been shown previously with between-subject designs . More importantly , we find that not only are the decoy effects correlated , pointing to similar underlying mechanisms , but also these effects increase with the distance of the decoy from the original options . To explain these observations , we construct a plausible neuronal model that can account for decoy effects based on the trial-by-trial adjustment of neural representations to the set of available options . This adjustment mechanism , which we call range normalization , occurs when the nervous system is required to represent different stimuli distinguishably , while being limited to using bounded neural activity . The proposed model captures our experimental observations and makes new predictions about the influence of the choice set size on the decoy effects , which are in contrast to previous models of context-dependent choice preference . Critically , unlike previous psychological models , the computational resource required by our range-normalization model does not increase exponentially as the set size increases . Our results show that context-dependent choice behavior , which is commonly perceived as an irrational response to the presence of irrelevant options , could be a natural consequence of the biophysical limits of neural representation in the brain .
At the core of many utility theories used in social and biological sciences lies a central axiom , called independence from irrelevant alternatives ( IIA ) . The IIA axiom states that the relative preference between any pair of options does not depend on what other options might be present [1]–[3] . In decision neuroscience , IIA holds in the appealing model in which separate values are computed for each different option , and values are then compared to make a choice [4] , [5] . Nevertheless , a wealth of data has clearly shown that the IIA axiom is often violated behaviorally [6] , [7] . For example , it has been shown that adding a third “decoy” option into a choice set often results in a predictable shift in the relative preference between the other two options of an initial pair . A striking example is when the decoy option is dominated by one initial option – i . e . , all of the new option's attributes are worse than the existing option attributes – but is not dominated by the other initial option . The decoy is an “irrelevant alternative” because it would never be chosen if it is dominated by another option . Introducing such a decoy results in an increased preference for the initial option that dominates the decoy [6] , [8]–[10] , a phenomenon called the attraction effect or the asymmetric dominance effect . Decoy effects can be considered an error in logical reasoning and there is some evidence that they can be exploited by consumer marketing and political strategies [11]–[13] . Interestingly , these effects are not limited to humans [14]–[16] , they increase after lesion of the medial orbitofrontal cortex in macaques [17] , and they can be mitigated by improving self-control or increasing blood glucose [18] . Considering that under realistic scenarios , choices are usually made in particular contexts [19] , exploring the neural mechanisms underlying context-dependent preference is crucial for better understanding of choice behavior in general [20] . Several explanations have been proposed to account for the preference reversal induced by the type of decoy in a choice set . Most of these models are based on verbally-described heuristics and are not mathematically formalized , which makes them difficult to test or generalize to new experimental paradigms [21] , [22] . An exception is the context-dependent “advantage” ( CDA ) model of Tversky and Simonson that coherently accounts for attraction and other context effects [7] . The CDA relies on the comparison between different attributes of the available options to account for context effects [23] . The CDA model is the precursor of more elaborate connectionist models such as the leaky competing accumulator ( LCA ) model [24] , [25] or the decision field theory ( DFT ) [26] , [27] . All these models aim to account for many types of context effects such as attraction , similarity , and compromise effects within a single framework [28] . The two popular connectionist models , the LCA and DFT , differ in a number of key features , such as the requirement of loss aversion , but like the CDA model , their core mechanism is comparison between each pair of option attributes . In most cases , psychological models such as CDA , LCA , and DFT , successfully reproduce the behavioral observations that they aim to explain . However comparing all attributes between all pairs of options in the choice set is computationally demanding , especially as the number of options and attributes grows . Other models of choice avoid these demands by assuming limited sequential attribute comparison ( e . g . , elimination-by-aspects [29] , for which there is evidence [30] ) , but those models cannot explain the attraction effect . We propose a new model to explain context effects , based on known biophysical limits of neural representation . The guiding presumption in our range-normalization ( RN ) model is that subjective values of option attributes are encoded in the firing rate of neural populations , rather than other aspects of neural firing [31] . If so , mental representations of subjective values will be bound by the same biophysical limits that govern neural representations . Namely , neural responses are bound from below by zero and from above by a few hundred spikes per second and , therefore , neurons can only represent a set of stimuli using a limited range of firing rates . Faced with a new set of stimuli to encode , however , neurons can adjust their dynamic range ( i . e . interval between threshold and saturation points ) to represent these stimuli distinguishably . We propose that this adjustment mechanism , which we call range normalization , is the principal neural mechanism underlying context-dependent effects . Normalization of the neural response is common in vision and other sensory modalities , and could be a more widespread property of neural representations [32] . To account for context effects , the range-normalization mechanism we propose here is computationally easier than comparison of all pairs of option attributes , since only the two most extreme attribute values are needed to compute the range . We implement a specific functional form of range normalization and test predictions of the outcome model using a novel within-subject design . We first describe experimental results that demonstrate within-subject decoy effects and reveal some new properties of these effects ( correlation between effects across types of decoys and decoy distance ) . Second , we describe the CDA model , how attribute comparison gives rise to context effects in this model , and its predictions in our experimental paradigm . Third , we present our RN model and its predictions for context effects . Finally , we describe new , contrasting predictions of the CDA and RN models about the influence of choice set size on context effects and the neural plausibility of these models .
Our experimental paradigm consisted of two tasks: an initial estimation task and the decoy task . We used the subject's choice from the estimation task to calculate the subject's attitude toward risk in order to tailor subject-specific target ( T ) and competitor ( C ) gambles that are equally preferred ( see below ) . This step is necessary because context effects are most strongly demonstrated when T and C are equally valuable . In the second part of the experiment ( decoy task ) , we assessed the preference between jittered versions of the T and C gambles in the presence of a third decoy gamble ( see Methods for more details ) . During the estimation task , the subject was presented with two options . These options were risky monetary gambles , described by probability p of winning a monetary reward of magnitude M , denoted . On each trial , the subject selected between pairs of gambles , always consisting of one fixed low-risk gamble , ( 0 . 7 , $20 ) , and one high-risk gamble , ( 0 . 3 , $M ) , for many different values of M ( see Methods for more details ) . The data analysis of the estimation task confirmed that all subjects appeared to understand the task and respond to changes in magnitude , preferring the high-risk gamble when its reward magnitude was large , but not when its reward magnitude was small ( Figure S1 in Text S1 ) . Logistic fitting of these choices yielded a subject-specific value of the high-risk gamble magnitude M for which the low- and high-risk gambles are equally subjectively valuable . ( Figures S2A and S2B in Text S1 ) . Across subjects , we found a wide range of values for the indifference high-risk magnitude and the sensitivity to reward magnitude ( ) , but these two quantities were not significantly correlated ( p = 0 . 33 ) ( Figure S2C in Text S1 ) . As a validity check , we computed the relative expected utility of each pair of gambles ( ) , and divided the pairs into sets with either greater than ( easy choice pairs ) , or less than ( hard choice pairs ) . If value is being inferred accurately , response times ( RTs ) should be slower for hard choice pairs that are close in subjective value . As predicted , the average RT was about 110 msec longer on trials with hard choice pairs , and that relation also held for all but one subject ( Figure S3 in Text S1 ) . On each trial of the decoy task , three monetary gambles were displayed on the screen for an 8 sec evaluation period . At the end of this period , one of the three gambles was removed from the screen and subjects had only 2 sec to choose one of the two remaining gambles in a selection period ( Figure 1A ) . Two of three initial gambles were the low-risk gamble ( target T ) and the subject-tailored high-risk gamble ( competitor C ) . The third gamble was the decoy gamble ( D ) that was randomly chosen from a set of gambles with a wide range of attribute values ( see Figure 1B and Methods for more details ) . On two thirds of the trials ( regular trials ) , the decoy gamble was removed after the evaluation period and the subject had to choose between T and C gambles . On the remaining one third of the trials ( catch trials ) , either the T or C gamble disappeared . The catch trials were included to conceal the underlying structure of the task and were subsequently discarded from the analysis ( since they do not provide choices between T and C ) . Therefore , we only analyze the regular trials to investigate how the preference between T and C gambles changed as a function of a decoy that was present at the evaluation period , but not available in the selection period . Having a long evaluation period ( 8 sec ) and a short selection period ( 2 sec ) forces subjects to evaluate and “pre-choose” options by ranking them during the evaluation period; therefore , they would be prepared to make a rapid choice in the 2-sec selection period . This ensures that presentation of the decoy during the evaluation period can influence context-dependent processes of assigning values enough to have a behavioral impact during rapid selection . This “phantom decoy” design allowed us to study the effect of dominant decoys ( decoys that are better than either T or C gambles ) as well as dominated decoys ( see below ) . We found that subjects' preference between T and C was systematically influenced by the attributes of the decoys . The first indication of the decoy influence on the subsequent choice was that the majority of our subjects did not select T and C gambles equally ( Figure S4 in Text S1 ) , though they were constructed ( from the estimation task data ) to be equally preferable . As in previous studies , we divided trials into 6 groups ( D1 to D6 ) based on the position of the decoy ( Figure 1B ) . Decoys in positions D1 and D4 are called the asymmetrically dominant decoys because they dominate either T or C ( they are less risky and also have larger reward magnitudes ) , but do not dominate both . Decoys in positions D3 and D6 are asymmetrically dominated decoys since they are either worse than the target ( D6 ) or the competitor ( D3 ) on both dimensions ( i . e . they are more risky and also have smaller reward magnitudes ) , but are only dominated by one of T and C [6] , [10] . Finally , decoys in positions D2 and D5 are similar to the target and the competitor and are better on one dimension but worse on another . They are called similar decoys [28] , [33] . We quantified decoy effects by computing the difference between the probability of selecting the target for a given decoy location , , and the overall probability of choosing the target across all trials , ( Figure 1C ) . We found that the decoys influenced subjects' preference between T and C gambles ( one-way ANOVA , p<0 . 0001 ) and the average values of over all subjects were significantly different from zero ( Wilcoxon signed rank test , p<0 . 05 ) except for decoys in position D2 . For statistical purposes , it was useful to scale decoy effects to account for the fact that some subjects had an overall target choice frequency , , which was very different from 0 . 5 ( despite the attempt to control this frequency using the estimation task ) . A scaled measured of decoy efficacy ( see Methods ) that adjusts for the target choice frequency still showed strong within-subject decoy effects ( one-way ANOVA , p<0 . 0005 ) similar to changes in preference presented earlier ( Figure 1D ) . In addition , we replicated three main findings regarding decoy effects . Firstly , we observed a robust attraction effect similar to what has been shown in previous between-subject studies [6] , [10] . That is , the asymmetrically dominated decoys D3 and D6 increased the selection of the option that dominated them: competitor C and target T , respectively ( Wilcoxon signed rank test , p<0 . 05 ) . Secondly , the asymmetrically dominant decoy D1 and D4 decreased the selection of the option which was dominated by those decoys: competitor C and target T , respectively ( Wilcoxon signed rank test , p<0 . 05 ) . We were able to study this effect due to our task design where the dominant decoy disappeared during the selection time . Thirdly , decoys in positions D2 and D5 decreased the selection of the option close to them ( C and T , respectively ) ; however , only the effect of decoys in position D5 was statistically significant ( Wilcoxon signed rank test , p<0 . 05 ) . These effects have been previously described as the similarity effects [29] , indicating that decoys take more share from the option in the choice set with which they are most similar , thereby decreasing the preference for the option similar to them . Thus , our results confirm previous between-subject findings and extend them to a within-subject design . Most preference reversals due to differences in descriptions , procedures or context are established by between-subject designs . Preference for between-subjects designs is guided by the intuition that two conditions that change a normatively irrelevant detail will be transparently equivalent if both conditions are presented in a within-subjects design; however , the normative irrelevance is cognitively inaccessible if only one condition is presented , in a between-subjects design . Establishing context-dependence in a within-subject design therefore shows its robustness . The within-subject design also adds substantial statistical power , and allows us to compute the within-subject correlation between effects for different decoys ( which a between-subject design cannot do ) . We also examined relationships between the overall decoy effects , as shown by a given subject and his/her risk aversion parameters from the estimation task . We found no relationship between the overall susceptibility of individual subjects to decoys ( defined as the average of absolute values of decoy efficacies for each subject ) and their indifference values ( r = −0 . 2 , p = 0 . 38 ) , or between the overall susceptibility and the sensitivity to the reward magnitude ( r = −0 . 21 , p = 0 . 37 ) . Next , we divided all regular trials into close and far trials , depending on the distance between the decoy and the gamble closest to it . Then we computed the decoy efficacy for each decoy location ( Figure S5 in Text S1 ) . For this analysis , decoy efficacies for close and far decoys were defined relative to the overall probability of selecting T only for the corresponding set of close or far decoys; therefore , this definition controlled for possible differences between the close and far sets of gambles . Close decoys had no significant effect ( one-way ANOVA , p = 0 . 69 ) , while far decoys had a very strong effect ( one-way ANOVA , p<10−11 ) ( Figure 2A ) . Moreover , for all decoys with significant effects over all trials ( except D4 ) , the far decoy effect was larger than the close decoy effect ( two-sample t-test , p<0 . 01 ) . We then examined the correlation between different decoy effects within-subjects . This correlation analysis provided a tool for testing whether different types of decoy effects were generated by the same mechanisms or not . We grouped decoys at different locations into three decoy types—asymmetrically dominant decoys ( D1 and D4 ) , similar decoys ( D2 and D5 ) , and asymmetrically dominated decoys ( D3 and D6 ) . We then computed the average decoy efficacy for each of these three decoy types in terms of their effects on the preference for the gamble close to or far from them . A positive ( or negative ) decoy efficacy means an increase ( or decrease , respectively ) in the preference for the gamble close to the decoy with respect to the gamble far from it . The different decoy types do influence the choice preference differently ( one-way ANOVA , p<0 . 0001 ) . Specifically , asymmetrically dominant decoys decreased preference for the gamble close to it ( Wilcoxon signed rank test , p<0 . 05 ) while asymmetrically dominated decoys increased preference for the gamble close to it ( Wilcoxon signed rank test , p<0 . 05 ) ( Figure 2B ) . There were no significant effects for similar decoys ( Wilcoxon signed rank test , p = 0 . 07 ) . Interestingly , we found a significant negative correlation between asymmetrically dominant and asymmetrically dominated decoy efficacies ( r = −0 . 57 , p = 0 . 008 ) ( Figure 2C ) . Next we tested whether the CDA model could reproduce the decoy effects observed in our experiment . First , we briefly describe the CDA model of Tversky and Simonson [7] and we present some results and predictions of this model that are relevant to our experimental paradigm . For simplicity , we assumed options have only two attributes and that the overall subjective value of an option is a weighted sum of its values on these attributes . The latter was assumed to avoid altering the original CDA model for the case where the overall value of an option is the product of its attribute values ( as for risky gambles ) . In the CDA model , the context effects arise from pairwise comparison of all options in the choice set . This pairwise comparison is performed through computing quantities termed the advantage and disadvantage . More specifically , the advantage of option T with respect to option C , , is defined aswhereSimilarly , the disadvantage of option T with respect to option C , is defined aswhere is an increasing monotonic function of ( note the change in the order of T and C in the argument of the advantage and disadvantage functions ) . Tversky and Simonson included loss aversion in their model , by assuming that the disadvantage looms greater than the advantage , that is [7] . For simplicity , we assume a linear relationship , where . The advantage and disadvantage are used to define the relative advantage of option T with respect to option C , ( 1 ) Finally , the value of an option in the choice set increases proportionally to the sum of the relative advantages between that option and each other option in the choice set . With three options T , C , and D , the overall values of options including context effects are ( 2 ) where determines the strength of the context effects , and and are the subjective values of option X before and after including the context effects . We can apply a sigmoid function to the difference in option values of T and C to obtain the choice preference between these options , before and after the decoy introduction . In order to illustrate the behavior of the CDA model over a wide range of decoy attributes , we calculated the change in the value of original options ( i . e . the options of the choice set before the decoy was introduced ) as a function of each decoy's attributes ( Figure 3A ) . This analysis showed that the maximal change in the value of a given option happens when the decoy is dominated ( both decoy attributes are smaller than the attributes of that option ) . Likewise , when the decoy is dominant ( both decoy attributes are larger than the attribute of a given option ) , the change in that option value is zero , independent of the exact location of the decoy . These option value changes happen because the relative advantage is one for dominated decoys and zero for dominant decoys . Overall , decoy introduction can only add a non-negative amount to the value of original options in the choice set . This property has undesirable consequences , which we discuss later . Next , we computed the change in the difference between the values of the original options ( and the resulting change in preference between them ) as a function of the decoy attributes ( Figure 3B ) . This analysis revealed some important aspects of the CDA model . Firstly , no change in preference occurs when both decoy attributes are smaller or larger than the attributes of both of the original options . This means that in the CDA model , such decoys are irrelevant for the choice preference . Secondly , the change in preference is larger when the decoy is dominated by the close option rather than when the decoy is dominant ( Figure 3B ) , because of loss aversion ( ) . Finally , preference reversal is stronger for decoys close to the original options than for far decoys ( Figure 3B ) . For better comparison of the results of the CDA model with our experimental data , we calculated the average models' choice behavior for decoys at locations in the attribute space that qualitatively match our experimental design ( see Methods for more details ) . The CDA model exhibits attraction and asymmetrically dominant decoy effects , but not similarity effects ( as has been previously pointed out [26] , Figure 3C ) . However , because both attraction and asymmetrically dominant decoy effects are driven by the same mechanism ( but in an opposite direction ) , the values of decoy efficacies for these decoys are anti-correlated ( data not shown ) . Moreover , as mentioned above , the decoy effects are stronger for attraction than asymmetrically dominant decoys due to the inclusion of the loss aversion concept in the CDA model ( Figure 3C ) . There is some evidence for this prediction when we group the experimental data based on the decoy type ( Figure 2B ) . However , fitting of our data using the CDA model yielded , which is closer to loss-neutrality ( Figure S6 in Text S1 ) . Finally the CDA predicts that close decoys have stronger effects than far decoys ( Figure 3D ) . This prediction of the CDA model is not supported by our experimental data ( Figure 2A ) . Here we propose a model for context effects that can account for our experimental observations and is based on plausible limits of neuronal elements in representing sensory and cognitive stimuli . Specifically , for neural representation to be useful it should be able to distinguish between any two unequal stimuli in the set of represented stimuli . However , neural firing rates are bounded between zero and a few hundred spikes per second . That is the neural representation could be variable only in the interval between a threshold and saturation points ( dynamic range ) ; outside this interval , the stimuli are represented with the same response . Nevertheless , the response of a neuron ( or a population of neurons ) to a set of stimuli can still vary , depending on the relationship between the location of the threshold and saturation points and the values of all stimuli that have to be represented in the firing activity . Considering the mentioned constraints , it is therefore plausible that the response of a neuron or a population of neurons can be adjusted to a new set of stimuli that it needs to represent ( widespread evidence of neural adaptation is reviewed in the Discussion ) . We show that this neural adjustment could explain the context-dependent preference reversal . In this model we assumed that the overall value of a given option is represented by a neural population that receives inputs from different neural populations selective to an individual option attributes ( see Method for more details ) . Assuming a linear response function , the overall value of an option , which is reflected in the firing activity of an option-selective population , is equal to a weighted sum of the neural responses to its attribute values ( 3 ) where RA is the response of population selective to option A , ri ( Ai ) is the neural response of attribute-selective population i to option A , and wAi is the weight of connections from the attribute-selective population i to the option-selective population A . For simplicity , we considered the case in which the neural response of attribute-selective populations is a linear function of stimulus value , s , when s is above a threshold ct , i and below a saturation point cs , i . In addition we normalized the response to the maximum response level so that the maximum response is represented with 1 . Note that any difference in the maximum response of neurons encoding different attributes can be absorbed into the connection weights wi's . Therefore , the neural representation attribute i can be written as ( 4 ) and so is determined by two parameters ct , i and cs , i . In order to simplify the notation , we drop the subscript i in the rest of the manuscript , but it should be understood that the neural representation could be different for each attribute . In order to express the neural response in terms of the range and configuration of represented stimuli , we define two new parameters , ft and fs , which we call the representation factors ( 5 ) where smin and snmin are the minimum and next-to-minimum values of s , and smax and snmax are the maximum and next-to-maximum values of s , respectively . The representation factors , ft and fs , determine the fraction of the value space around the minimum and maximum stimuli that are below or above the threshold or saturation points , respectively . This can be seen more clearly by expressing the threshold and saturation points , ct and cs , in terms of the representation factors ( 6 ) Note that a positive fs implies that the neuron never reaches to its maximum possible faring rate . Therefore , the representation factors determine efficiency of a neuron ( or a neural population ) in representing a set of stimuli in their firing activities ( see below ) , and so they are inherent properties of the neuron . By imposing , it is guaranteed that neural responses to different stimuli are distinct ( except when there are only two presented stimuli , for which an additional constraint needs to be imposed: ) . In order to show how neural representation depends on the representation factors defined above , we plotted the neural responses for different values of representation factors in the case in which there are only two options ( C and T ) in the stimulus set ( Figure 4A ) . For positive values of the representation factors threshold and saturation points are below and above the minimum and maximum stimuli , respectively . On the other hand , for negative values of representation factors , threshold and saturation points are above and below the minimum and maximum stimuli , respectively ( which means extreme stimuli can be represented with the same response because they lie outside the dynamic range ) . Therefore , the representation factors determine the relative position of the dynamic range of the neural response with respect to a set of represented stimuli . However , the above equations show that when a new stimulus is introduced to the stimulus set , the threshold and saturation points need to be adjusted in order for the representation factors to stay the same or adapt to the new set . Using Eq . 6 and assuming that the representation factors stay the same before and after decoy introduction ( a condition which can be relaxed as shown below ) , we computed the adjustment of neural response and changes in the response to the original options due to decoy introduction ( Figure 4B ) . The decoy may introduce a new minimum or maximum ( or a next-to-minimum or next-to-maximum ) to the stimulus set , and in all of these cases it changes the configuration of stimuli . If there were originally two options in the set , the decoy introduction always changes the neural representation and therefore changes the value of the original options . More interestingly , the values of the original stimuli before and after decoy introduction depend on the relative decoy value ( Figure 4B rightmost panel ) . This change is positive if the decoy is between the two original options or close to them , and it is negative if the decoy introduces a new minimum or maximum . Overall , the change in the differential response depends on the representation factors and decreases as the decoy becomes farther from the original options . Interestingly , we found that the ratio of the differential response after the decoy introduction to before the decoy introduction is inversely proportional to the ratio of the range of stimulus values after to before decoy introduction ( see Text S1 ) . For this reason , we call our proposed mechanism for neural adjustment the range normalization . For the above simulations we assumed that adjustment to a new set of stimuli is perfect such that the neural response in terms of representation factors stay the same . However , it is possible that due to biophysical constraints , this adjustment is not fully realized ( i . e . partial normalization ) while neurons still represent each stimulus with different responses . To incorporate partial range normalization , we set the threshold and saturation points after the introduction of the new stimulus to ( 7 ) where and are the threshold and saturation points after the decoy introduction as described by Eq . 6 , and is a quantity between 0 and 1 that determines the degree of range normalization . The extra conditions assure that all stimuli are represented with different responses . If , the neural response is not range normalized to the presentation of the new stimulus , and if , the range normalization is complete . Examples of a partial range normalization and the resulting change in the value of two original options are shown in Figure 4C ( for ) . These results showed how the degree of range normalization could control the decoy effects . A limiting factor for neural responses to distinguish between different stimuli is the ubiquitous noise in the nervous system [34] . The effects of noise on range normalization are beyond the scope of this work , however , we considered a basic consequence of noise inclusion in our range-normalization model . We assumed that in order for the neural response to be distinguishable in the presence of noise , the slope of neural response ( k ) could not be indefinitely small . Therefore , we imposed an extra constraint on the neural representation to prohibit the slope from becoming smaller than a minimum value ( ) . By adding this constraint to the RN model ( see Methods for details ) , we found that the change in the differential response to original options reaches a plateau when the decoy is very far from the original options ( Figure 4D ) . This property is psychologically plausible , however , it cannot be tested with our data since we did not use very far away decoys in our experiment . So far , we have shown how decoy introduction changes the neural response to original options based on how neurons represent a given attribute . Here we demonstrate how decoy introduction changes the preference between the original ( T and C ) options as observed in our experiment . We first show how range normalization results in the attraction effect when a decoy that asymmetrically dominates T ( but no C ) is introduced . The difference between option values before and after decoy introduction is equal to ( using Eq . 3 ) ( 8 ) ( 9 ) where is the neural response to option X after the decoy introduction . By dividing the last equation by and using Eq . 8 we obtainThe first term in the last expression is less than one because the decoy introduces a new maximum in dimension 1 , and the second term is larger than 1 as the decoy does not introduce a new minimum nor a maximum in dimension 2 ( see Figure 4 ) . Therefore , the sum of the parenthetical terms is negative so that , which shows that decoy introduction makes C preferred to T . We then simulated change in preference due to decoy introduction at different locations ( see Methods for details ) . We assumed that option attributes on a given dimension ( e . g . monetary value ) are represented by a neural population selective to that attribute ( an attribute-selective population ) . The attribute-selective populations in turn project to neural populations representing the overall value of individual options ( an option-selective population ) . The strength of these projections determines the weight of each attribute dimension on the overall value ( Eq . 3 ) . Subsequently , the outputs of the option-selective populations project to a decision-making circuit , allowing the model to choose between the available options . We found that the values of existing options are decreased or increased depending on the location of the decoy . These changes reach maximal values if the decoy is at a certain distance from the existing options . ( Figures 5A and 5B ) . The fact that decoy effects do not increase indefinitely as the decoy becomes farther from the original options is due to consideration of noise in the model . For better comparison of the behavior of the RN model with the CDA model and the experimental data , we calculated the average models' choice behavior for decoys at locations of the attribute space that qualitatively match the experimental design ( the same as in Figure 5A ) . We found that similar to the CDA model , the RN model captures attraction and asymmetrically dominant decoy effects , but it does not capture similarity effects without including asymmetry in the representation factors of the two attributes ( Figure 5C , and Figure S6 in Text S1 ) . Interestingly , the behavior of the RN with representation factors equal to zero is qualitatively similar to the CDA model with loss-neutrality ( Figure 3 ) . In order to address between-subject variability , we simulated the model over a wide range of representation factors , and we found that overall , average behavior of many simulated subjects with this model follows the same trend as the model with zero representation factor ( Figure 5D ) . However , in contrast to the CDA model , the decoy effects were stronger for far decoys than for close decoys . In addition , we found a significant anticorrelation between decoy effects for the attraction and asymmetrically dominant decoys ( Figure 5E ) . The CDA and RN models presented above , account for context effects based on very different assumptions and premises , and furthermore predict different patterns of decoy effects for far and close decoys . More importantly , different mechanisms underlying context effects in the presented models result in very different predictions regarding the influence of the choice set size on these effects , as described below . Although the CDA model captures most context effects , it is unclear how computations required by this model could be implemented biologically due to two main issues . First , in order to compute the advantage and disadvantage , every pair of options in the choice set should be compared . This causes a combinatorial problem because as the choice set becomes larger the number of required comparisons grows as , where is the number of options in the choice set . Second , the CDA model asserts that the introduction of each new option results in the addition of a non-negative value to every available option in the choice set , and therefore , as the number of options in a given choice set increases the value of every option in that set increases . This implies that the value of an option not only depends on other options in a given choice set but also on the size of that set . In order to illustrate the effect of the set size on the valuation in the CDA model , we computed the value of an option at different locations of the attribute space as a function of the number of equally preferable options in the choice set . We found that option value increases linearly with the number of options in the choice set , in every location of the attribute space ( Figure 6A ) . This is a direct consequence of the fact that in the CDA model , the relative advantage always adds a non-negative value to the overall value of a given option . Therefore , the same option has a larger value when it is part of a larger choice set ( Figure 6B ) ; in addition , the overall value of the options in the choice set exponentially increases with the choice set size ( Figure 6C ) . The former suggests that the difference between the values of two options in a given choice set should grow as the set size increases , resulting in better value discrimination in a larger choice set . The underlying mechanisms for context effects , which rely of pairwise comparison between all options in the choice set , imply that required resources for computations of context effects should increase supra-linearly with the choice set size . To demonstrate this point , we used the network structure in the LCA model [24] to calculate the required computational resource in the CDA model or any of its equivalent neural models ( see Methods for more details ) . We found that computational resources also increase exponentially with the choice set size ( Figure 6D ) . Finally , we explored the influence of the set size on the valuation in the RN model by computing changes in valuation due to decoy introduction for different number of options in the choice set ( Figure 6E ) . We found that choice set size does not have a significant effect on valuation , and the overall value of the decoy does not change with the choice set size ( Figure 6F ) . Moreover , the overall value of options in the choice set as well as the required computational resources increase only linearly with the choice set size ( Figures 6G and 6H ) . These happen in our model because the computations required for context effect do not require comparison and only depend on the configuration of option values in individual dimensions . Therefore , in contrast to the CDA model , the RN model does not predict an increase in the option values as the choice set size increases . These contrasting predictions of the model can be tested in future experiments . Table 1 summarizes the overall decoy effects predicted by the CDA and RN models , and the actual effect sizes for different decoy types . Most effects are in the predicted direction and are significant . Note that the RN model correctly predicts both the influence of distance on the decoy effects and the anti-correlation between the effects for attraction and asymmetrically dominant decoys .
The prevalent influence of context on decision-making has long been considered an “anomaly” against the normative account of human choice behavior [35] , [36] . The reason is that normative theories of choice typically assume that values are computed independently for each stimulus , rather than comparatively . The guiding metaphor for these normative theories of valuation and choice is a naïve theory of perception in which separate valued objects are perceived as encapsulated units and then integrated by a decision architecture . Of course , this view tends to disregard decades of evidence about how the visual system uses top-down encoding , neural adaptation and normalization , and gestalt principles in integrating multiple percepts . In this spirit , we propose that context effects are a natural consequence of the biophysical limits of the neural processing in the brain , as shown for other aspects of perception and choice [37]–[39] . We construct a model for context effects based on plausible biophysical mechanisms that enable neurons to efficiently adjust their responses to the set of available stimuli . Both the effects of context on neural representation and the normalization to the set of stimuli have been extensively documented in auditory [40] , [41] and visual domain [42]–[44] , where neurons are required to represent and encode external stimuli presented in very different backgrounds . Moreover , adaptation is an efficient way for the nervous system to adjust to variable statistics of the environment to improve its local information capacity or discriminability power [45]–[50] . In our model , we explored one possible class of neural adjustments ( range normalization ) during valuation and choice using two main assumptions . First , neurons utilize their entire biophysical dynamic range to represent a set of stimuli . However , it is possible that neurons never reach to their maximum biophysical firing rates and instead fire at medium rates under many conditions ( i . e . stimulus set ) . This only implies that the upper representation factor , fs , should be positive ( see Eq . 5 ) and does not qualitatively change the behavior of our model . Similarly , neurons not representing any stimulus with zero firing rate only implies positive values for the lower representation factor , ft . Second , we assume that range normalization only depends on configuration of the stimulus set and not the number of stimuli . Incorporating other parameters into response-normalization mechanisms does not contradict our proposal but it may change the resulting context effects . Here we only consider one form of range normalization to explain some of the basic effects of context on the choice preference . Future works would explore the consequences of other types of neural adjustments on the context-dependent choice behavior ( see below ) . Interestingly , response normalization is not unique to sensory neurons and processing , rather it seems to be a general property of cortical computations [51] , [52] . A recent electrophysiological study in primates has demonstrated that some neurons in the orbitofrontal cortex ( OFC ) adapt their representation of the economic values to the range of values during a given session [51] . To account for this observation , Padoa-Schioppa has proposed a “range adaptation model” in which the neurons adapt their representation ( by changing their sensitivity ) to the range of values , while their activity does not increase with the value range . In fact , in some circumstances OFC neurons appear to encode the value of the available options in a reference-dependent fashion by representing the relative value of each option in the set [53] while in other circumstance show invariance for changes of the menu [54] . Our proposed range-normalization model is more general than the range-adaptation model and differs from this model in terms of the timescale on which adaptation or normalization takes place . That is , only in a special case where the representation factors are equal is the ratio of difference in response to original options after the decoy introduction to before the decoy introduction inversely proportional to the ratio of the range of values after to before decoy introduction ( see Text S1 ) . However , in the RN model , adjustment happens on every trial with three options . In contrast , in the range-adaptation model , the range of values on a given session controls the adaptation . It is highly possible that we would also observe such adaptation on a larger timescale ( e . g . a session ) if the option set changed between sessions . Another recent study has shown that neurons in the lateral intraparietal cortex ( LIP ) show context-dependent effects by encoding the values of the saccade in the response field relative to the value of all other alternative saccade movements [52] . The authors used a divisive normalization model to account for their experimental findings . More specifically , the response to the value of the saccade in the receptive field is divided by the weighted response of the saccadic values of all options presented in the choice set , similarly to what has been proposed for sensory neurons [55] , [56] . Therefore , due to divisive normalization , the value of each given option is globally scaled by the value of all the alternative options . In contrast , in our range-normalization model , the representation of each attribute dimension depends on the set of presented values , and not their sum ( Figure 5B ) . Divisive normalization can account for relative value coding but does not predict any type of attraction effect because decoy introduction always suppresses the response to the target and the competitors without any change in the ranking of the options . However , it is possible that our proposed range normalization and the divisive normalization mechanisms play roles during different stages of decision process . Range normalization operates at the early stage of the decision process when cortical neurons have to represent individual features of each option; while divisive normalization operates at final stages ( e . g . in LIP ) when overall value associated with different actions need to be represented to control the selection processes ( e . g . saccades ) . A number of psychological models have used the attribute comparison as the basic mechanism to account for attraction and other decoy effects . The CDA model presented here was chosen as an example of such models because it accounts for the attraction and asymmetrically dominant decoy effects and provides testable predictions due to its simple , yet clear mathematical formulation . However , the CDA model or any other model that relies on attribute comparison , suffers from a few important issues . Firstly , such models predict that the values of all options increase ( or at least the best and worst option ) as the choice set increases , which implies that when presented as part of larger choice set options can be differentiated easier than when they are presented in a smaller set . This prediction is in contrast with experimental evidence showing that discriminability between items decreases with the increase of the data set [57] , and that neural representation of option values decrease as the number of alternatives increase [52] . Secondly , in such models , resources required for computation of context effects exponentially increases with the choice set . The CDA model also predicts that decoy effects are larger for closer decoys . This is somehow counterintuitive as it predicts maximal decoy effects for very similar but dominated decoys - while these decoys should have little or no effect on the preference for the close dominant option , as it might be hardly distinguishable . Recently , more sophisticated connectionist models have been proposed to capture attraction and other context effects such as the compromise and similarity effects . Two of such connectionist models are the decision-field theory ( DFT ) [26] and leaky competing accumulator ( LCA ) models [24] . While in both models attention determines which attribute to be compared at the time , these models rely on different mechanisms to account for attraction effect . The DFT model relies on bi-directional distant-dependent inhibition while the LCA model depends on the loss aversion . However , because both the DFT and LCA models require attribute comparison at some stages of processing ( similar to the CDA model ) , they both suffer from the combinatorial problem as the CDA model . In contrast , our model that relies on range normalization of neural responses , which is adjusted only once regardless of the number of options , does not suffer from this issue . There are other psychological models of context effects that do not rely on attribute comparison as the basic mechanism . Most of these models are based on heuristics and are not mathematically well formulated . These include but are not limited to the so-called weight-change , value-shift , and value-added models [21] . The weight-shift model assumes that adding a new alternative changes the relative weights of different attributes; it reduces the weight of a given attribute if the range on that attribute is extended and increases the weight if the number of different attribute values is increased . The value-shift model on the other hand , assumes that decoy changes the subjective evaluation of the attribute values , mainly based on the relative position of decoy with respect to the rest of options ( as in range-frequency theory [58] ) . Finally the value-added model assumes that decoy introduction adds values to original options , which depend on the relational properties of the decoy and each target . Our range-normalization model shares some similarities with the value-shift model in a sense that it assumes that the decoy value on a given attribute changes the value representation in that attribute independently of the other attributes . However , for a limited case where representation factors are equal , the effective weight of a given dimension is inversely proportional to the range of values on that dimension ( but there is no explicit relationship to the frequency effects in weight-shift model ) . Despite this similarity , our model relies on very different assumptions to explain the decoy effects and generates a number of novel predictions , while it is difficult to generalize the previous models because of their lack of mathematical formalization . Still another set of models , from economics and marketing [59]–[61] , assume that consumers are not sure what they prefer , but those consumers infer reasonable preferences from what options are available ( as if mere option availability is advice ) . Decoys have an influence because they shape the consumer's idea of what might be a good choice . Comparison of these models with the CDA , RN and others is an interesting area for future research . Context is a powerful modulator of how underlying preferences are constructed and choices are made , as documented by many behavioral experiments and field studies [35] , [62] . At the theoretical level , however , most of the attempts to account for context effects have neglected the computational constraints faced by the brain in order to compare choice options characterized by several different attributes . In this paper we show that considering plausible biophysical constraints of the nervous system can indeed account for a few important aspects of context effects . The range-normalization model we proposed here has a reduced computational cost relative to competing models and at the same time produces accurate empirical predictions . More importantly , it enables us to connect plausible biophysical constraints of neural representation to the biases in the human choice behavior .
All participants gave informed consent to participate according to a protocol approved by the California Institute of Technology Institutional Review Board . The experiment consisted of two parts in which subjects selected between different monetary gambles . In the first part ( estimation task ) , the subject selected between two gambles with different reward probabilities and magnitudes . We used subject's choice in this task to estimate his/her attitude toward risk and to tailor equally preferred target ( T ) and competitor ( C ) gambles . In the second part of the experiment ( decoy task ) , we assessed the preference between the target and competitor gambles in the presence of a third gamble . The subjects were told to consider every trial as equally important because at the end of the experiment , only one trial would be randomly extracted and the selected gamble on that trial would be played for real . To further encourage subjects to pay attention to every trial , we deducted $1 from the final compensation for each missed response . In total , 22 healthy Caltech male students ( 22±4 years old ) took part in the study . One subject was excluded from the data analysis since he showed an erratic pattern of gamble selection during the estimation task . This was reflected in a poor fit of his choice behavior - his sensitivity to reward magnitude , , was 7 times smaller than the mean of the group ( see Figure S2 in Text S1 for the distribution ) - which prevented a reliable estimation of his indifference point . In the estimation task , we assessed individual subjects' risk attitude using selection between two monetary gambles . The assessment procedure was an adaptation of the widely used method for estimating the indifference point which was originally developed by Holt and Laury [63] . Every subject completed four equivalent sessions , each of which consisted of 40 trials . On each trial , the subject had 4 seconds to evaluate two gambles while the instruction message “Evaluate” was on the screen . After this interval , the instruction message was changed to “Choose” and the subject had 2 seconds to indicate their choice using a keyboard . Each gamble was defined by two parameters ( p , M ) , probability p of winning a monetary reward of magnitude M , that were presented on the screen with different colors . One gamble was characterized by a small reward magnitude but a large reward probability ( low-risk or the target gamble ) . The other gamble had a large reward magnitude but a small reward probability ( high-risk or the competitor gamble ) . We fixed the magnitude and probability of the low-risk gamble ( p = 0 . 7 , M = $20±2 ) while we varied the magnitude of the high-risk gamble between $30 and $80 ( p = 0 . 3 , M = $30–$80 ) . In the second part of the experiment , we tested how presence of different decoy gambles influences the preference between the low-risk and high-risk gambles . The low-risk gamble ( T ) was set to have a magnitude M of $20±2 and a probability p of 0 . 7±0 . 05 . The high-risk gamble ( C ) was set to have a probability p = 0 . 3±0 . 05 while its magnitude was tailored individually using the indifference point from the estimation task , in order to have the subjects indifferent between T and C . Finally , decoy gambles ( D ) were designed to have a wide range of magnitude and probability values ( Figure 1 ) . Specifically , we varied probability values of the decoy between 0 . 15 and 0 . 85 , while we varied its reward magnitudes by 30% of the reward magnitude of the gamble closest to the decoy . The task sequence was as follows . Three gambles ( T , C and D ) were presented on the screen for 8 seconds ( evaluation period ) while the “Evaluate” message was on the screen . The subjects were told to evaluate the three gambles during this period . Once the evaluation time was over , the message “Evaluate” was changed to “Choose” and simultaneously , one of the three gambles was randomly removed from the screen . The subjects then had 2 seconds to choose between the two remaining gambles by pressing a keypad ( selection period ) . The decoy task was conducted in the MRI scanner ( Siemens Trio ) ; however , the fMRI data are neither analyzed nor presented here as they are beyond the scope of this paper . The main reason for not including the fMRI data here was that none of the models presented in this paper generates predictions that could be tested using BOLD-level signals . On one third of the trials ( catch trials ) , either C or T gambles disappeared . These trials were included to avoid the subject from predicting which gamble would disappear after the evaluation period , and were subsequently excluded from the analysis . On the remaining two thirds of the trials ( regular trials ) , the decoy gamble disappeared allowing us to study how the presence of this option in the choice set influence the preference between C and T . Using this design ( i . e . phantom decoy design ) , we were able to examine the effects of decoys that were preferred over C or T gambles . Finally , we used a short choice period ( 2 seconds ) to avoid subjects from reevaluating the two remaining gambles . In fact , the only way to perform this task efficiently was to rank the 3 gambles during the evaluation period and to use this ranking at the choice period . Debriefing after the study confirmed that a large majority of the subjects used this “ranking strategy” which was also reflected in the dependence of the RT on the decoy ( Figure S7 in Text S1 ) . The range-normalization model consists of three layers of neural populations: the attribute-selective , option-selective , and decision-making populations . The attribute-selective layer consists of two neural populations that represent the two attributes of the options . The attribute-selective populations project to the option-selective layer that consists of neural populations each of which represents the subjective value of an option in the choice set . The subjective values of options are determined by the weight of connections from the attribute-selective layer to the option-selective layer ( Eq . 3 ) . Finally , the outputs of option-selective populations project the corresponding populations in the decision-making layer . The decision-making network is similar to what has been previously used to simulate different reward-dependent choice behaviors [37] , [64] . Here we were only interested in the outcome of decision-making processes , therefore , we did not simulate the decision-making network on every trial . Instead , we used a sigmoid function , which has shown to describe the choice behavior of the decision-making network very well [37] , [64] , in order to compute the choice probability for a given set of inputs to the decision network . More specifically , the probability of selecting T , , is equal to ( 10 ) where RT and RC are the responses of option-selective populations for target and competitor ( Eq . 3 ) , is the strength of connections from option-selective to decision-making populations , and is a model parameters which is determined by the architecture of the decision-making network and the overall strength of its inputs [37] , [64] . In order to obtain the neural response of attribute-selective populations to a given stimulus set , we used Eq . 6 to calculate the threshold and saturation points . The threshold and saturation points uniquely define the neural response through Eq . 4 . To calculate the neural response after the decoy introduction , we first identified the minimum and maximum , and next to minimum and maximum stimuli in the stimulus set , and then we used Eq . 6 to compute the threshold and saturation points . For simulations presented in Figure 4C , we used Eq . 7 to calculate partially adjusted threshold and saturation points . For simulations presented in Figure 4D , an additional constraint for the slope of neural response was imposed as follows . For a given decoy location , we calculated the threshold and saturation points from which the slope could be determined . If the slope was below the minimum value ( 0 . 015 in simulations presented in this paper ) , in a stepwise fashion we increased and decreased the values of threshold and saturations points , respectively , until the slope value became larger than the minimum slope value . In order to simulate decoy effects in the two-dimensional attribute space , the same procedure was applied on each attribute dimension independently . For simulations presented in Figure 5D and Figure 5E , the representation factors are selected from any combinations of and for each attribute dimension . Finally , to calculate the required computational resource in our model , we assumed that an addition of each option to the choice set requires the engagement of one neural population to represent the subjective value of the new option , which requires an additional option-selective population . In contrast , in the network implantation of the CDA model , such as the LCA model , an addition of each option requires the engagement of a few neural populations that are required for comparison between each attribute of the new option and the existing options . As a result , required computational resources in CDA model increases with the number of options in the choice set , , as . All simulations were performed using custom-made codes in MATLAB . For the statistical tests presented in the paper , we have provided the conventional significant values in addition to the applied test . In order to quantify the decoy effects , we used the overall preference for the target gamble and the preference for the target gamble for a given decoy to define the decoy efficacy , Based on this definition , the decoy efficacy is bound between −1 and 1 . Note that using preference for C to define the decoy efficacy gives similar results to what presented here .
|
While faced with a decision between two options for which you have no clear preference ( say , a small cheap TV and a large expensive TV ) , you are presented with a new but inferior option ( say , a medium expensive TV ) . The mere presence of the new option , which you would not select anyway , shifts your preference toward the expensive large TV . This simple example shows how the introduction of an irrelevant option , called the “decoy , ” to the choice set can change preference between existing options , a phenomenon often called the context-dependent preference reversal . A number of models have been proposed to explain context effects . Despite their success , they are either uninformative about the underlying neural mechanisms or they require comparison of every possible pair of option attributes , a computation that is unlikely to be implemented by the nervous system due to its high computational demand and undesirable outcomes when the choice set size increases . Here we present a novel account of the context-dependent preference based on the adjustment of neural response to the set of available options . Moreover , we show results from a novel behavioral task designed to test contrasting predictions of our model and a classic model of context effects .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cognitive",
"neuroscience",
"behavioral",
"neuroscience",
"decision",
"making",
"computational",
"neuroscience",
"biology",
"computational",
"biology",
"neuroscience"
] |
2012
|
A Range-Normalization Model of Context-Dependent Choice: A New Model and Evidence
|
Seminal fluid proteins affect fertility at multiple stages in reproduction . In many species , a male's ejaculate coagulates to form a copulatory plug . Although taxonomically widespread , the molecular details of plug formation remain poorly understood , limiting our ability to manipulate the structure and understand its role in reproduction . Here I show that male mice knockouts for transglutaminase IV ( Tgm4 ) fail to form a copulatory plug , demonstrating that this gene is necessary for plug formation and lending a powerful new genetic tool to begin characterizing plug function . Tgm4 knockout males show normal sperm count , sperm motility , and reproductive morphology . However , very little of their ejaculate migrates into the female's reproductive tract , suggesting the plug prevents ejaculate leakage . Poor ejaculate migration leads to a reduction in the proportion of oocytes fertilized . However , Tgm4 knockout males fertilized between 3–11 oocytes , which should be adequate for a normal litter . Nevertheless , females mated to Tgm4 knockout males for approximately 14 days were significantly less likely to give birth to a litter compared to females mated to wild-type males . Therefore , it appears that the plug also affects post-fertilization events such as implantation and/or gestation . This study shows that a gene influencing the viscosity of seminal fluid has a major influence on male fertility .
The non-sperm component of an ejaculate can have large effects on male reproductive fitness . In internally fertilizing species , seminal proteins can modify female receptivity [1]–[3] , egg laying behavior [4]–[6] , implantation and gestation [7] , and the female's immune response to sperm and embryo [7]–[11] . Seminal proteins can also interact with the ejaculates of competitor males to influence the outcomes of fertilization [12]–[14] . In many internally fertilizing taxa , ejaculated proteins coagulate to form a hardened copulatory plug in the vaginal-cervical region of the female [15]–[22] . In spite of its wide taxonomic distribution , the molecular details that underlie its formation remain poorly understood , which limits investigations into its function . After reviewing previous biochemical insights , I present a new genetic model that offers unprecedented power to being dissecting the function of the plug . Since the first published observation of a copulatory plug in a rodent nearly 165 years ago [19] , several groups have attempted to characterize its molecular basis . Camus and Gley [23] showed that fluids extracted from the seminal vesicles coagulated in vitro upon contact with extract from the anterior lobe of the prostate ( also referred to as the coagulating gland ) [24] , [25] . Building from the Camus & Gley experiment , Williams-Ashman and colleagues showed that the rate of coagulation depended on the concentrations of seminal vesicle and/or prostate protein extracts in vitro [26] . Because these early experiments were based on crude extracts , the general term “vesiculase” was coined to describe the unknown prostate-derived protein ( s ) responsible for inducing the coagulation of seminal vesicle proteins . More detailed biochemical investigations suggested the unknown vesiculase ( s ) was a transglutaminase [27] , [28] , a protein that crosslinks glutamines and lysines via γ-glutamyl-ε-lysine dipeptide bonds and causes the bound proteins to become insoluble and coagulate . A prostate-specific transglutaminase , transglutaminase IV ( Tgm4 ) , was later characterized from humans [29]–[31] , and its protein is found in human ejaculates [32] . The ortholog in mouse is also ejaculated [33] , and functionally analogous transglutaminases have been found in mosquito ejaculates [16] . In spite of these early advances , it remains unknown whether Tgm4 is necessary for the formation of the copulatory plug . It has been suggested that some seminal vesicle proteins self-coagulate in the absence of Tgm4 [34] , that proteins other than Tgm4 induce the coagulation [35] , and that female-derived proteins may be necessary for coagulation [36] . Furthermore , there is evidence that more than one transglutaminase exists in the male reproductive tract of rodents [33] , [37] , [38] , though this could also be due to post-translational modifications [39] . Interestingly , human ejaculates do not coagulate strongly even though they have large amounts of Tgm4 [32] , calling into question its role in seminal fluid coagulation . More fully characterizing the biochemical basis of seminal fluid coagulation is critical for understanding the function of the copulatory plug . Early attempts to study copulatory plug function necessarily relied on surgical removal of male accessory glands [40]–[47] . Although copulatory plugs were abnormal and male fertility compromised in some cases , inferences were limited by the invasiveness of the procedures , the confounding effects associated with the potential alteration of hundreds of ejaculated proteins , and the failure to fully prevent a copulatory plug-like structure from forming . Other experiments showed that manual removal of the plug soon after copulation did not prevent pregnancy or parturition [48] , [49] . However , the copulatory plug may have affected fertility prior to experimental removal . To address these early experimental limitations requires a method to fully prevent the formation of a copulatory plug with minimal invasiveness . Here , I use Tgm4 knockout mice to better understand the molecular basis and functional importance of the copulatory plug , and report two main findings . First , Tgm4 knockout males failed to produce a copulatory plug after mating , demonstrating for the first time that this gene is necessary for the coagulation of seminal fluid in mice . Tgm4 knockout males therefore provide a powerful model to investigate the function of the copulatory plug . Second , in spite of normal sperm count , sperm motility , and reproductive morphology , Tgm4 knockout males sired significantly fewer litters than their wild type brothers . Analyses presented below suggest Tgm4 knockout males suffer fertility defects at two important stages: 1 ) less of their ejaculate migrates into the female's reproductive tract , and 2 ) females mated to Tgm4 knockout males produce significantly fewer litters even though a “normal” absolute number of oocytes were fertilized , suggesting additional defects in implantation and/or gestation . This study demonstrates that a gene influencing the viscosity of semen has major affects on male reproductive success .
Heterozygous “knockout first” mice were acquired from the Knockout Mouse Project ( see [50] , [51] , and Materials and Methods ) . Heterozygotes were crossed in the laboratory to generate homozygous and heterozygous knockout males , as well as homozygous wild type males that were used as controls in all experiments . All females used throughout the manuscript were homozygous wild type . All mice were essentially genetically identical except for the ∼7 kb “knockout first” cassette that spans exons 2–3 of Tgm4 . Tgm4 knockout males ( homozygous for the “knockout first” allele ) did not form a copulatory plug ( Table 1 ) , demonstrating for the first time that this gene is necessary for seminal fluid coagulation . From 13 successful 3-hour pairings to Tgm4 knockout males ( “success” being defined as the presence of sperm somewhere in the female's reproductive tract after three hours of pairing ) , complete dissection of each female's reproductive tract failed to yield a copulatory plug or plug-like structure ( Table 1 ) . In contrast , 14 of 16 successful 3-hour pairings to wild type males resulted in a copulatory plug , which normally occupies most of the vaginal canal and extends into the cervix , appearing “glued” to the epithelium . Herein , “wild type” includes males that were either heterozygous or homozygous for the wild type allele , as they were phenotypically indistinguishable from each other . I obtained similar results from 20-hour long male-female pairings: 0 of 8 females successfully paired with Tgm4 knockout males , and 11 of 15 paired to wild type males , yielded a plug . Because they cannot form a plug , Tgm4 knockout males represent a powerful genetic tool to investigate its role in reproduction . In the absence of a plug , the ejaculates of Tgm4 knockout males did not traverse the female reproductive tract properly . After mating to wild type males , female uterine horns appeared swollen , full of sperm and seminal fluid ( Figure 1A ) . In contrast , after mating to Tgm4 knockout males , female uterine horns did not swell and sperm were difficult to locate upon dissection ( Figure 1B ) . The difference in uterine horn width was statistically significant between females mated to wild type ( N = 6 ) vs . Tgm4 knockout males ( N = 15 ) ( wild type: 2 . 64 mm , SD = 0 . 30; Tgm4 knockout: 2 . 14 mm , SD = 0 . 43; t = 2 . 98 , df = 19 , P = 0 . 01 ) . The defect in ejaculate migration cannot be explained by defects in reproductive morphology of Tgm4 knockout males . Sperm count was not statistically different between wild type ( N = 19 ) vs . Tgm4 knockout ( N = 10 ) males ( mean = 133 , 900 sperm/µl , SD = 55 , 000 vs . mean = 106 , 000 sperm/µl , SD = 43 , 000 , respectively: t = 1 . 39 , df = 27 , P = 0 . 18 ) , nor was sperm motility ( mean = 0 . 96 sperm/sec , SD = 0 . 28 vs . mean = 0 . 87 sperm/sec , SD = 0 . 27 , respectively: t = 0 . 78 , df = 27 , P = 0 . 44 ) . From these same males , testis and seminal vesicle weight were analyzed in a full factorial ANCOVA to account for the potential covariation with body weight . There was not a significant difference in testis weight between Tgm4 knockout vs . wild type males ( F1 , 25 = 0 . 02 , P = 0 . 88 ) , nor was there a genotype×body weight interaction effect on testis weight ( F1 , 25 = 0 . 95 , P = 0 . 34 ) . Similarly , there was no difference in seminal vesicle weight between genotypes ( F1 , 25<0 . 01 , P = 0 . 97 ) , nor was there a genotype×body weight interaction effect on seminal vesicle weight ( F1 , 25 = 0 . 03 , P = 0 . 86 ) . Furthermore , Tgm4 knockout males successfully copulated at a rate similar to wild type; from the 3-hour pairings , 16/26 to wild type , and 13/28 pairings to Tgm4 knockout males succeeded ( Table 1 , χ2 = 0 . 7 , P = 0 . 4 ) . Therefore , both genotypes display normal copulatory behavior . As might be expected from the reduced number of sperm that make it into the female's uterus , Tgm4 knockout males fertilized significantly fewer oocytes in 20-hour assays . Among successful 20-hour pairings , Tgm4 knockout males fertilized 45 of 122 oocytes dissected from the female's oviducts ( 36 . 9% ) , compared to wild type males , which fertilized 153 of 231 oocytes ( 66 . 2% ) ( Table 1 ) . Fertilized oocytes from all successful pairings appeared healthy , with almost no signs of fragmentation . Oocytes originating from the same female are not independent observations , so I compared the proportion of fertilized oocytes on a per-female basis . Seven females successfully mated to Tgm4 knockout males yielded a mean 39 . 4% fertilized oocytes ( range 21 . 1%–72 . 7% ) , significantly lower than 12 females successfully mated to wild type ( mean = 67 . 9% , range 12 . 5%–93 . 3% ) ( t = 2 . 78 , df = 17 , P = 0 . 01 ) . The number of females analyzed ( 7 mated to Tgm4 knockout and 12 mated to wild type ) does not add up to the numbers in Table 1 ( 8 mated to Tgm4 knockout and 15 mated to wild type ) because scorable oocytes were not always recovered from oviduct dissections . It should be noted that even though Tgm4 knockout males fertilized a lower proportion of oocytes compared to wild type , they always fertilized at least 3 oocytes ( mean = 6 . 4 , range 3–11 ) , suggesting they should be able to impregnate females without difficulty . In contrast to this prediction , after being paired with females for 10–14 days , Tgm4 knockout males sired significantly fewer litters than wild type ( Table 2 ) . Of 30 pairings with Tgm4 knockout males , only 17 produced litters , significantly fewer than wild type males , which produced litters in 135 of 165 pairings ( χ2 = 7 . 93 , P = 0 . 005 ) ( Table 2 ) . Among all litters born to wild type fathers , 42/106 ( 39 . 6% ) yielded 6 or fewer pups ( the mean number of oocytes fertilized by Tgm4 knockout males , see previous paragraph ) , and 14/106 ( 13 . 2% ) resulted in 3 or fewer pups ( the minimum number of oocytes fertilized by Tgm4 knockout males , see previous paragraph ) . In other words , Tgm4 knockout males sired significantly fewer litters in spite of the fact that they appeared to fertilize enough oocytes for a healthy litter . Although Tgm4 knockout males sired significantly fewer litters ( Table 2 ) , there were no signs of maternal neglect , as judged by the likelihood a litter reached weaning age , the litter size , and the size of offspring at weaning . Specifically , 11 of 17 ( 65% ) litters sired by Tgm4 knockout males reached weaning age ( 21–28 days old ) , compared to 106 of 135 ( 79% ) litters born to a wild type male ( Table 2; χ2 = 0 . 94 , P = 0 . 3 ) . Sometimes litters do not reach weaning age because of maternal neglect . Furthermore , the number of offspring weaned per litter was not significantly different among the two male genotypes ( mean = 6 . 0 vs . 6 . 4 pups weaned per litter , SD = 3 . 5 vs . 2 . 4 , range 1–12 for both , from N = 11 vs . 106 weaned litters sired by Tgm4 knockout or wild type males , respectively: Welch's t = 0 . 59 , df = 10 . 97 , P = 0 . 57 ) , nor was weanling weight ( mean weight = 11 . 61 g vs . 12 . 14 g , SD = 3 . 0 vs . 3 . 8 from N = 66 pups weighed from 11 litters vs . 77 pups weighed from 13 litters sired by Tgm4 knockout or wild type males , respectively: Welch's t = 0 . 93 , df = 140 . 0 , P = 0 . 35 ) . The lack of statistical significance may be due to small sample sizes , but suggests that once litters are born , the pups have an equal chance of reaching healthy weaning age regardless of sire genotype .
Tgm4 knockout males failed to produce a copulatory plug , demonstrating for the first time that this gene is necessary for plug formation . In spite of normal sperm count , motility and reproductive morphology , Tgm4 knockout males suffered reduced fertility , most importantly in the significant reduction of litters born compared to wild type . Taking all the data into consideration , a model of the copulatory plug acting at two important stages of reproduction seems to explain the fertility defects of Tgm4 knockout males . First , the plug may facilitate passage of the ejaculate through the cervix and into the uterine horns and oviducts ( Fig . 1 , Table 1 ) , perhaps by sealing off the vagina and preventing backflow of the ejaculate [16] , [41] , [42] , [52]–[54] . Second , the plug may enhance the embryos' ability to implant in the female's uterus , and/or reduce the chances of abortion after implantation ( Table 2 ) . For example , the plug may contribute to the physical stimulation necessary to shift the female's physiology towards “pseudopregnancy” [53] , [55]–[57] , a state where the uterus becomes primed for implantation in mice . This second aspect of the model is supported by the reduced number of litters born to Tgm4 knockout males in spite of the fact that they fertilized between 3–11 oocytes in 20-hour assays . There does not appear to be any fertility defects that arise from differential maternal investment post-parturition . Four observations suggest that the fertility defects observed in the current study arose from the absence of the copulatory plug rather than from additional pleiotropic functions of Tgm4 . First , Tgm4 expression has so far only been detected in the prostate [58]–[60] , and never in any other tissues of a male or a female [61] , [62] , thus it should only affect ejaculate composition . Second , the only annotated domains in the Tgm4 protein are related to the formation of γ-glutamyl-ε-lysine bridges in its target proteins ( www . ensembl . org ) , suggesting that it has a limited biological role . Third , although transglutaminases may alter the sperm surface in vitro [63] , [64] , Tgm4 has never been detected on the sperm surface [65] , [66] , suggesting it does not directly affect the gamete . Fourth , Tgm4 has accumulated multiple loss-of-function mutations in some species that do not form a plug [67] , which is not predicted if Tgm4 functions outside the context of plug formation . Although the present study demonstrates the importance of the copulatory plug in non-competitive matings , it does not reject the hypothesis that the copulatory plug evolved in response to sperm competition [20] , which occurs when a female mates with more than one male during a single fertile period [68] . Copulatory plugs are larger and show stronger coagulation intensity in species with high levels of inferred sperm competition [21] , [22] , [69] , and have been lost in some species that experience low levels of sperm competition [67] , [70] , [71] . Some copulatory plug proteins evolve rapidly in species with high levels of inferred sperm competition , which is predicted if the plug inhibits female remating [67] , [70]–[73] . In mice , the copulatory plug forms immediately upon ejaculation and remains intact for approximately 24 hours [20 and unpublished data] , which is longer than the 4–12 hours that a female is able to be fertilized during her estrus cycle . Males contribute protease inhibitors in their ejaculates , which may function to preserve their copulatory plugs from female degradation [33] . Interestingly , males missing one of these protease inhibitors make a plug that degrades more quickly than wild type , which is associated with fertility defects [74] . Although the above patterns suggest the plug inhibits female remating , over 20% of wild caught pregnant females carry a litter sired by more than one male [75] , [76] , suggesting the plug is an imperfect barrier , and females or competitor males sometimes remove the plug [77]–[81] . Interestingly , copulatory plugs do not always bias fertilizations towards the first male to mate in one-female-two-male mating experiments [82]–[85] , and some evolutionary patterns do not fit the sperm competition hypothesis . For example , the socially and genetically monogamous rodent Peromyscus polionotus forms a plug [86] , [87] . By showing that the copulatory plug is correlated with normal fertility in one-male-one-female matings , the current study offers an explanation for the evolutionary maintenance of the copulatory plug in the absence of intense sperm competition . For example , the copulatory plug may prevent loss of semen [52] , promote transport of semen through the female's reproductive tract [16] , [41] , [42] , [53] , [54] , contribute to the threshold stimulation females require for proper implantation and pregnancy [55] , and/or serve as a reservoir for the slow release of sperm in the female reproductive tract [88] . In reality , the copulatory plug may have multiple functions and the genetic model presented here enables unprecedented power to begin dissecting these hypotheses . Many human seminal fluid proteins have orthologs in mouse ejaculates , including Tgm4 [33] . Even though human ejaculates do not form copulatory plugs , human seminal fluid enters a phase of coagulation and liquefaction [89] , and defects in these transitions have been associated with subfertility [90] . There are 250 known nucleotide polymorphisms in human Tgm4 mRNA , including 120 missense mutations ( www . ensembl . org version 69 ) , and Tgm4 was not detected in all ejaculates of five humans [91] . Future studies may reveal genetic and proteomic variation in Tgm4 associated with differences in human male fertility .
All mouse husbandry techniques , experimental methods , and personnel involved were approved by the University of Southern California's Institute for Animal Care and Use Committee , protocols #11394 and #11777 . The Tgm4 knockout mouse model was constructed by the multi-institutional Knockout Mouse Project [50] , [51] . A ∼7 kb “knockout first” cassette was inserted into the C57BL/6N ( 6N ) genetic background ( project #CSD30105 ) . Alternative crossing to Cre and/or FLP mice allows for further genetic modification of the knockout allele , but was unnecessary in the present study . All experimental males used in this study had 6N parents that were heterozygous for the knockout ( KO ) and wild type ( + ) allele . When possible , all three genotypes were taken from the same litter to control for simple maternal effects . Sires and dams were paired for one to two weeks , then separated so the dam gave birth in isolation . Between 21–28 days after birth , males were weaned in groups until genotyping , at which point they were separated into their own cages to avoid dominance interactions between brothers . Sexually mature males show reduced fertility when grouped together , presumably as a result of dominance interactions [92] . Females were weaned in groups of up to three individuals . All three possible male 6N genotypes - but only homozygous wild type 6N females - were used in various experiments described below . Shortly after weaning , ear snips were taken for PCR-based genotyping . DNA isolated from ear snips was genotyped with four PCR reactions . Two PCR reactions specifically amplified the wild type allele: Reaction 1 primers ( 5′-AGGTGAAAAACCAAGAAATACCATC-3′ and 5′-CTATTCCAAAACCACCAGACAGTAC-3′ ) amplified a 704 bp fragment and Reaction 2 primers ( GTGGACAGATATTCACTCTGAAGGT and GGAAACACCAATAGAAAAGTGAGTC ) amplified a 1 , 170 bp fragment . Two PCR reactions specifically amplified the knockout first allele: Reaction 3 primers ( GCTTTACATGTGTTTAGTCGAGGTT and GTTAAAGTTGTTCTGCTTCATCAGC ) amplified a 1 , 244 bp fragment and Reaction 4 primers ( GATTAAATATGATGAAAACGGCAAC and ATTATTTTTGACACCAGACCAACTG ) amplified a 1 , 349 bp fragment . DNA was amplified using 35 cycles of denaturation ( 94 C , 20 seconds ) , annealing ( 58 C , 20 seconds ) and extension ( 70 C and 40 seconds for Reaction 1 , 70 C and 80 seconds for the other three reactions ) . All PCR reactions used Fermentas 2× PCR premix . Presence/absence of bands was scored on agarose gels . Only genotypes consistent across all four reactions were included in experiments . All experimental males were individually paired with homozygous wild type 6N females . Males were between 60 and 90 days old . For the 3-hour and 20-hour assays ( see below ) , ∼28 day-old females were induced to ovulate using standard techniques [93] , [94] . Briefly , females were administered 5U Pregnant Mare's Serum Gonadotropin ( PMSG ) followed 48 hours later by 5U Human Chorionic Gonadotropin ( hCG ) . For the 10–14 day assays ( see below ) , females between 2 and 10 months old were used and ovulation was not artificially induced . Between 2–6 months of age , a subset of experimental males were sacrificed , standard measurements taken , and testes and seminal vesicles dissected and weighed . Unless otherwise stated , Student's t-tests were used to compare phenotypes among groups . In all t-tests , assumptions of normality and equal variances were confirmed using Shapiro-Wilk tests and F-tests , respectively . In a few comparisons indicated above , the two groups being compared had significantly different variances; in these cases Welch's t-test [97] was used . Importantly , no conclusions changed if Student's t-tests , Welch's t-test , or non-parametric Mann-Whitney U tests were used in any comparisons . Testis and seminal vesicle weight were each analyzed in a full factorial ANCOVA using male genotype ( knockout vs . wild type ) and body weight as factors . An ANCOVA was employed to account for the potential covariation of testis or seminal vesicle weight with body weight . To test for differences in the number of litters born to Tgm4 knockout vs . wild type males , a 2×2 contingency table was tested against a χ2 distribution . All statistical analyses were performed in R ( www . r-project . org ) or customized Python scripts ( www . python . org ) .
|
Male reproductive fitness is strongly affected by seminal fluid . In many animals , the male's ejaculate coagulates in the female's reproductive tract to form a structure known as the copulatory plug . Here , I show that male mice without a functional copy of the gene transglutaminase IV cannot form a plug and suffer severe fertility defects . In spite of normal reproductive morphology , less of the ejaculate migrates through the female's reproductive tract and Tgm4 knockout males sire significantly fewer litters than wild type . This study demonstrates that the copulatory plug and/or Tgm4 itself is necessary for normal fertility .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"animal",
"genetics",
"reproductive",
"system",
"genetic",
"mutation",
"sexual",
"reproduction",
"anatomy",
"and",
"physiology",
"gene",
"function",
"animal",
"models",
"model",
"organisms",
"sexual",
"selection",
"biology",
"mouse",
"mutagenesis",
"physiology",
"genetics",
"evolutionary",
"biology",
"evolutionary",
"processes",
"genetics",
"and",
"genomics"
] |
2013
|
Genetic Disruption of the Copulatory Plug in Mice Leads to Severely Reduced Fertility
|
Only limited information is currently available on the prevalence of vector borne and zoonotic pathogens in dogs and ticks in Nigeria . The aim of this study was to use molecular techniques to detect and characterize vector borne pathogens in dogs and ticks from Nigeria . Blood samples and ticks ( Rhipicephalus sanguineus , Rhipicephalus turanicus and Heamaphysalis leachi ) collected from 181 dogs from Nigeria were molecularly screened for human and animal vector-borne pathogens by PCR and sequencing . DNA of Hepatozoon canis ( 41 . 4% ) , Ehrlichia canis ( 12 . 7% ) , Rickettsia spp . ( 8 . 8% ) , Babesia rossi ( 6 . 6% ) , Anaplasma platys ( 6 . 6% ) , Babesia vogeli ( 0 . 6% ) and Theileria sp . ( 0 . 6% ) was detected in the blood samples . DNA of E . canis ( 23 . 7% ) , H . canis ( 21 . 1% ) , Rickettsia spp . ( 10 . 5% ) , Candidatus Neoehrlichia mikurensis ( 5 . 3% ) and A . platys ( 1 . 9% ) was detected in 258 ticks collected from 42 of the 181 dogs . Co- infections with two pathogens were present in 37% of the dogs examined and one dog was co-infected with 3 pathogens . DNA of Rickettsia conorii israelensis was detected in one dog and Rhipicephalus sanguineus tick . DNA of another human pathogen , Candidatus N . mikurensis was detected in Rhipicephalus sanguineus and Heamaphysalis leachi ticks , and is the first description of Candidatus N . mikurensis in Africa . The Theileria sp . DNA detected in a local dog in this study had 98% sequence identity to Theileria ovis from sheep . The results of this study indicate that human and animal pathogens are abundant in dogs and their ticks in Nigeria and portray the potential high risk of human exposure to infection with these agents .
Several tick-borne bacteria and parasites are important pathogens of humans and animals [1] . Being haematophagous , ticks are capable of transmitting disease agents such as viruses , bacteria and protozoa . Historically , they have been considered second only to mosquitoes in their ability to transmit disease agents [2] . Ticks attach to their hosts , facilitating transmission of infectious agents to the host and their spread to different geographical regions via traveling pets or other means of transportation [3] . Globalization and increased international trade , urbanization , climate change and increased travel and mobility of pets have resulted in rapid extension of the zoogeographical range for many tick species [1] . In areas where canine vector-borne diseases are endemic , dogs can be simultaneously or sequentially infected with more than one vector-borne agent [3] , [4] . Because blood sucking vectors contain infected host blood and pathogens , they are reliable indicators for the existence of pathogens in a specific area [5] . Therefore , it is recommended to periodically screen animals and vectors for pathogen carriage . Several molecular surveys have evaluated the existence of multiple vector borne pathogens in specific regions including , Europe [4] , Middle East [6] , Asia [7] and Africa [8] . Epidemiological surveillance of disease occurrence and prevalence is required to map local risk , to acquaint physicians and veterinarians with the prevalence of pathogens and emergence of new infectious agents and forecast possible vector-borne infection outbreaks . This can be achieved by the use of molecular diagnostic techniques , data analysis and mathematical models as well as veterinary clinical surveillance . In Nigeria , the diagnosis of vector-borne pathogens ( VBPs ) is usually based on the microscopic detection of pathogens in peripheral blood smears , sometimes serology is employed and rarely molecular methods are used . Microscopic diagnosis may lack sensitivity and is time consuming while serology usually indicates exposure rather than active infection , and might mislead due to serological cross reactions with other closely related organisms . Conversely , molecular detection is more sensitive and specific . As data on canine vector-borne infections in Nigeria is scarce [9] , [10] , this study aimed at broadening the knowledge on these canine pathogens and their ectoparasites . The objective of this study was to molecularly detect and , characterize various vector-borne pathogens in dogs and ticks in four states of Nigeria .
The study was conducted in the 4 Nigerian states; Plateau ( 9°10′N9°45′E ) , Kaduna ( 10°20′N7°45′E ) , Kwara ( 8°30′N 5°00′E ) and Rivers ( 4°45′N 6°50′E ) ( Figure 1 ) . The study protocol was read and approved by The National Veterinary Research Institute Vom , Nigeria Ethical Committee on Animal Use and Care . Animals were treated in a humane manner and in accordance with authorizations and guidelines for Ethical Conduct in the Care and Use of Nonhuman Animals in Research of the American Psychological Association ( APA ) for use by scientists working with nonhuman animals ( American Psychological Association Committee on Animal Research and Ethics in 2010 ) . One hundred and eighty one dogs from 4 states of Nigeria presented to private or government veterinary hospitals between August and December 2011 were selected . The selection criteria included dogs infested with ticks or manifesting clinical signs of tick borne diseases , such as anemia , weakness , pyrexia , anorexia and haemoglobinuria . Demographic data , signalment ( age , sex , and breed ) and clinical signs were recorded for each dog . Five ml of blood were collected from the cephalic or jugular vein into sterile EDTA tubes , and kept at 4°C until arrival at the laboratory . Ticks were collected from dogs infested at the time of presentation into test tubes containing absolute ethanol and transported to the laboratory . Thereafter , samples were preserved at −20°C and transported in a cool box to Israel for identification and DNA analysis . A total of 258 ticks were collected from 42 domestic dogs . After identification , ticks from each dog were grouped according to their species . One to three ticks from each dog were pooled for analysis . Seventy six tick pools were processed for DNA extraction . Most of the ticks selected were partially or fully engorged adult females , nymphs and larvae .
A total of 181 dogs were examined during this study , 66 ( 36 . 5% ) being males and 102 ( 56 . 4% ) were females . No information on sex was available for 13 ( 7 . 2% ) of the samples analyzed ( Table 1 ) . The age ranges of the study population were 0–6 months , 32%; 7–12 months , 30 . 9%; 13–24 months , 19 . 9%; 25–36 months , 3 . 9%; >36 months , 5% , while no information on age was available for 15 ( 8 . 3% ) dogs . Seventy eight ( 43 . 1% ) of the dogs studied belonged to a local ( Nigerian ) breed , 61 ( 33 . 7% ) belonged to pure foreign breeds , 27 ( 14 . 9% ) were of cross- breeds while no information on breed was available for 15 ( 8 . 3% ) dogs . The majority of the sampled dogs were from Plateau State ( 150; 82 . 9% ) , followed by Rivers State ( 17; 9 . 4% ) , Kaduna State ( 11; 6 . 1% ) and Kwara State ( 3; 1 . 7% ) . Overall estimate of infection with VBPs was 77 . 3% ( 140/181 ) in sampled dogs . Single infections occurred in 73 ( 40 . 3% ) dogs while co- infection with more than one pathogen occurred in 67 ( 37% ) of the dogs examined . Co- infections with H . canis were most prevalent ( 14 . 4% ) followed by E . canis and Rickettsia spp . ( 6 . 6% ) each ( Table 2 ) . Single infections occurred mostly in dogs within the age range of 9–12 months . Co-infections were mostly detected in dogs between 2–12 months of age . Hepatozoon canis was the most frequently detected pathogen in dogs ( 41 . 4% ) , followed by E . canis , Rickettsia spp . , Babesia rossi and A . platys ( 12 . 7% , 8 . 8% , 6 . 6% and 6 . 6% respectively ) . Babesia vogeli , Theileria sp . and R . conorii israelensis were detected in one dog each ( Table 1 ) . There was no significant difference ( p>0 . 05 ) in prevalence of these pathogens between the various groups of dogs studied ( Table 1 ) . Sequences of pathogens derived from dog's blood in this study were deposited in GenBank under the following accession numbers; H . canis ( JQ976620–JQ976629 ) ; B . rossi ( JQ976603–JQ976616 ) ; B . vogeli ( JQ976617 ) ; Theileria sp . ( JQ976619 ) ; E . canis ( JQ976630–JQ976641 ) and A . platys ( JQ976642–JQ976653 ) . A total of 258 ticks ( 128 adults , 124 nymphs and 6 larvae ) partially or fully engorged belonging to two genera Rhipicephalus and Haemaphysalis , removed from 42 dogs were examined for various VBPs . Ehrlichia canis , H . canis and Rickettsia spp . DNA were detected in R . sanguineus , R . turanicus and H . leachi ticks . NA of various VBPs was detected in all the different tick species examined in this study . A total of 76 tick pools were tested out of which 18 ( 23 . 7% ) , 16 ( 21 . 1% ) , 8 ( 10 . 5% ) and 4 ( 5 . 3% ) were positive for the DNA of E . canis , H . canis , Rickettsia spp . and Candidatus N . mikurensis respectively , while A . platys and R . conorii israelensis DNA were detected in one tick pool each ( Table 3 ) . Sequences from ticks were assigned the following accession numbers: H . canis ( JX027010–JX027020 ) , Candidatus N . mikurensis ( JX027021–JX027024 ) , E . canis ( JQ976654–JQ976665 ) , A . platys ( JQ976666 ) and R . conorii israelensis ( JX259321 and JX259322 ) . Pathogen DNA as single or multiple infections were detected in 26/76 ( 34 . 2% ) tick pools removed from dogs with tick infestation at the time of clinical presentation and sampling . Blood and ticks from 7 dogs ( nos . 1 , 8 , 33 , 34 , 37 , 38 and 39 ) were free from DNA of the various VBPs tested for in this study . DNA of pathogens was detected in ticks removed from 7 other dogs ( nos . 3 , 4 , 12 , 18 , 25 , 31 and 36 ) but none was detected in the blood of their dog host . Conversely , DNA of various pathogens was detected in 7 dogs ( nos . 5 , 7 , 13 , 16 , 28 , 29 and 49 ) , but no pathogen DNA was detected in ticks removed from them . Ticks collected from 4 dog ( nos . 2 , 15 , 22 and 23 ) as well as the dogs from which they were removed were both positive for E . canis DNA . Similarly , DNA of H . canis was detected in 3 dogs ( nos . 20 , 21 and 24 ) and ticks removed from each of them . However , DNA of H . canis only was detected in 3 dogs ( nos . 11 , 35 and 42 ) but DNA of both H . canis and E . canis was detected in ticks removed from them . Different pathogen's DNA was detected in 10 dogs ( nos . 6 , 9 , 10 14 , 17 , 19 , 26 , 27 , 35 and 40 ) as compared to the ticks removed from them . Nine ( 11 . 8% ) of the tick pools were co-infected by two or more pathogens . There was a significant association between H . canis DNA in dogs and ticks removed from them ( Relative risk = 2 . 69; 95% Confidence Interval = 1 . 2–5 . 8; Z = 2 . 51; p = 0 . 012 ) . However , there was no significant association between the detection of pathogen DNA in dogs blood and ticks removed from them for E . canis , ( RR = 1 . 56; 95% CI = 0 . 44–5 . 45; Z = 0 . 69; p = 0 . 49 ) , A . platys ( RR = 0 . 59; 95% CI = 0 . 025–14 . 3; Z = 0 . 32; p = 0 . 75 ) or B . rossi ( RR = 0 . 07; 95% CI = 0 . 004–1 . 13; Z = 1 . 88; p = 0 . 061 ) . Babesia spp . and Theileria spp . DNA were not detected in any of the tick pools tested . Babesia rossi and B . vogeli sequences from this study had 98–100% and 99% similarities , respectively , with the first matched BLAST result from GenBank ( Table 3 ) , while Theileria sp . had 98% sequence similarity with Theileria ovis . Ehrlichia canis sequences from blood of dogs and ticks in this study had 97–100% and 100% similarities , respectively , with the first matched BLAST result from GenBank . Two sequences had 99% similarity with Ehrlichia chaffeensis as the first GenBank match from BLAST . However , attempts to validate the identity of this species by PCR for secretory genes ( SodB/VirB 3 , VirB 4 , and VirB 9 ) did not yield confirmatory results . Four sequences from ticks had 100% sequence identity to Candidatus N . mikurensis . Anaplasma platys sequences from dogs and ticks had 97–100% and 99% sequences similarities , respectively , with the first matched BLAST result from GenBank . Similarly , H . canis sequences from dogs and ticks had 99% and 99–100% similarities , respectively , with H . canis sequences deposited in GenBank ( Table 3 ) . The rickettsial gltA gene fragment was detected in 16 of 181 ( 8 . 8% ) dog blood samples and in 8 of 76 ( 10 . 5% ) tick pools examined . Rickettsial ompA DNA was found in one ( 0 . 6% ) blood and one tick sample . Both sequences were identical and were 100% similar to ompA fragment from R . conorii israelensis . All the sequences detected in this study from dogs and the ticks removed from each of them were highly identical to each other ( 99–100% ) for all the pathogens identified ( Table 3 ) .
Ticks and other haematophagous arthropods play a major role in the epidemiology of diseases of humans and animals globally . Their distribution and abundance determines the epidemiology of vector borne infections . The results of this study provide molecular evidence for the presence of E . canis , H . canis , A . platys , B . rossi , B . vogeli , Theileria sp . , closely related to T . ovis , Candidatus N . mikurensis and R . conorii israelensis in dogs and ticks from Nigeria . DNA of at least one vector borne pathogen was detected in 77% of the dogs and 45% of the tick pools examined . This is the first report documenting the identification of Candidatus N . mikurensis , R . conorii israeliensis , A . platys and Theileria sp . in dogs and ticks from Nigeria . In fact , it is the first detection of the zoonotic pathogen , Candidatus N . mikurensis , in Africa . Candidatus N . mikurensis is an emerging pathogen first described in 2004 affecting humans and animals with varying clinical manifestation [17] . This pathogen was reported in several hosts including Ixodes spp . ticks , Rattus norvegicus , humans and dogs from Japan , Switzerland and Germany [17]–[19] . Although Ixodes ticks of medical and veterinary importance are not found in Nigeria , R . norvegicus are common and serve as small mammal hosts to multi- host ticks during their life cycle . As engorged ticks were screened in this study , it is possible that R . sanguineus ticks acquired Candidatus N . mikurensis from infected R . norvegicus or dogs . Due to the fact that this agent is a potential threat to humans , physicians should consider this pathogen in their differential diagnosis list in complicated unexplained fever of unknown etiology cases in Nigeria . Rickettsia gltA DNA was detected in 8 . 8% and 10 . 5% of dogs and ticks respectively in this study . These estimates are almost similar to the 7 . 8% previously reported [9] but lower than the 20 . 6% infection rate reported for R . africae in ticks collected from cattle and vegetation in Nigeria [10] . Another report of prevalence of 0 . 4% R . conorii and 94–100% R . africae in Rhipicephalus evertsi was made in Guinea and Liberia , West Africa [20] . In the present study , 8 . 8% of blood samples and 10 . 5% of tick pools were positive for the rickettsial gltA but only one blood sample ( 0 . 6% ) and one R . sanguineus tick pool ( 1 . 3% ) were found positive for the rickettsial ompA gene , and their sequences were 100% identical to R . conorii israelensis . This is the first report indicating the presence of the agent of Mediterranean Spotted Fever in Nigeria . Rickettsiae with ompA gene are considered to be pathogenic , while those who exclude this gene are probably non-pathogenic endosymbionts [21] . Rickettsia africae , the etiologic agent of African tick fever in humans has been detected in ticks from Nigeria [9] , [10] and other West African countries [20] but not in our study . It is possible R . sanguineus , R . turanicus and H . leachi found on dogs in this study are not competent vectors for this organism [20] . The detection of A . platys infection in Nigeria is also reported for the first time in this study . The estimate of 6 . 6% infection rate in dogs in this study is higher than the 4% reported for dogs in Italy [22] , but lower than 16% reported in Venezuela [23] . Anaplasma platys is a thrombocytotropic bacteria of dogs that causes canine infectious cyclic thrombocytopaenia characterized by clinical abnormalities such as fever , anorexia , petechial haemorrhages , and uveitis [24] . Theileria sp . with 98% sequence similarity to T . ovis from a sheep in Iran [25] was detected in one dog in this study ( Table 3 ) . Theileria spp . have been reported in dogs from South Africa [26] and Spain [27] . The Theileria sp . in this study appears to be molecularly different from the previously described species . The high estimate of H . canis ( 41 . 4% ) and E . canis ( 12 . 7% ) infections reported in dog blood in this study are higher than the 22% and 5% , respectively reported earlier in Zaria-Nigeria using microscopic examination of blood smears [28] . However , the estimate of 6 . 6% for B . rossi in this study is lower than previous reports of 10 . 2% [29] and 11 . 0% [30] , and higher than 2 . 0% for dogs in Nigeria [28] , but close to the 9 . 0% reported in Sudan [8] . Similarly , the estimate of 41 . 4% H . canis infection in this study is higher than the 20 . 3% previously reported in dogs from Nigeria , but almost similar to the 42 . 3% reported in Sudan [8] . The higher estimate of H . canis DNA in this study can be attributed to the sensitivity of the techniques used , enabling the detection of E . canis and H . canis at low bacterial and parasite loads . Babesia canis and B . gibsoni were not detected in any of the samples tested . This finding is in agreement with earlier molecular surveys in dogs from Nigeria [30] and can be attributed to the absence of their tick vectors in Nigeria . Although a case of B . rossi and B . canis co-infection in a local Nigerian dog that never left the country has been reported [31] , the source of that infection could not be elucidated . One recent study in Nigeria did not detect Ehrlichia spp . in R . sanguineus ticks [9] , while another study reported a prevalence of 5 . 7% Ehrlichia spp . in ticks collected from cattle [10] which is much lower than the 23 . 7% detected in this study . The difference in prevalence may be attributed to variation in techniques used and source of samples . Dogs are competent reservoir- hosts of several zoonotic pathogens and are infested by many blood-feeding arthropods . The role of ticks as vectors of these pathogens can be asserted considering the high sequence similarities ( 99–100% ) between the pathogens detected from the host and those detected in ticks removed from them ( Table 3 ) . Of the 18 tick pools positive for E . canis , and 16 positive for H . canis , 22% and 38% of the pools were from E . canis and H . canis positive hosts , respectively . There was a significant association between the detection of H . canis DNA in dogs and ticks removed from the same dog , but no association was found for E . canis , A . platys or B . rossi . As all ticks were removed from dogs while they were attached and most of them were partially or fully engorged , it is impossible to ascertain whether the ticks were fed with infected blood or if they served as vectors and transmitted the pathogen to their present host . Considering the fact that the tick species included in this study have multi- host life cycle , they could have acquired infection during feeding on a previous infected host and transmitted the infection during their next feeding . Dog breeding is a lucrative business in Nigeria , where dogs are used for trade and security . Dogs also serve as a food source and their meat is considered as a delicacy among some ethnic groups in Nigeria . These can expose humans directly or indirectly to zoonotic agents during handling of dogs and ticks carrying pathogens , or during processing and consumption of their meat . Further investigation is required to elucidate the role of ticks and the effect of these pathogens in causing diseases in humans in Nigeria . In conclusion , this study has confirmed that several vector borne pathogens of humans and animals are highly prevalent in Nigeria and West Africa where the incidence of tick-borne infections appears to be underestimated . Physicians and veterinarians should be aware of the existence of these pathogens in Nigeria and should include them in the differential diagnoses for clinical illnesses with compatible clinical signs . Screening targeted groups for VBPs as well as humans with fever of unknown origin or undiagnosed cases for infection with R . conorii israelensis and Candidatus N . mikurensis is recommended .
|
In Nigeria , dogs are not only kept as pets , but are also used for hunting as well as a source of animal protein among some ethnic groups . Ticks are known to infest dogs and serve as vectors for some pathogens of zoonotic and veterinary importance . There is limited information on the prevalence and distribution of vector borne pathogens in dogs and ticks in Nigeria . The aim of the study was to detect and characterize vector borne pathogens in dogs and ticks from Nigeria using molecular methods . The results of this study showed a high estimate of vector borne pathogens in Nigerian dogs ( 77 . 3% ) and ticks ( 63 . 3% ) . DNA of Candidatus N . mikurensis , an emerging pathogen of humans was detected in Rhipicephalus sanguineus and Heamaphysalis leachi ticks . Another human pathogen , Rickettsia conorii israelensis the causative agent of Mediterranean spotted fever was detected in Rhipicephalus sanguineus ticks . This is the first description of Candidatus N . mikurensis in Africa and Rickettsia conorii israelensis in Nigeria . These results indicate that the use of molecular techniques for the diagnosis of emerging infections in developing countries is of utmost importance in assisting physicians and veterinarians in making accurate diagnoses and providing the appropriate treatment for their patients .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"veterinary",
"science"
] |
2013
|
Molecular Detection and Characterization of Tick-borne Pathogens in Dogs and Ticks from Nigeria
|
Esophageal cancer occurs as either squamous cell carcinoma ( ESCC ) or adenocarcinoma . ESCCs comprise almost 90% of cases worldwide , and recur with a less than 15% five-year survival rate despite available treatments . The identification of new ESCC drivers and therapeutic targets is critical for improving outcomes . Here we report that expression of the human DEK oncogene is strongly upregulated in esophageal SCC based on data in the cancer genome atlas ( TCGA ) . DEK is a chromatin-associated protein with important roles in several nuclear processes including gene transcription , epigenetics , and DNA repair . Our previous data have utilized a murine knockout model to demonstrate that Dek expression is required for oral and esophageal SCC growth . Also , DEK overexpression in human keratinocytes , the cell of origin for SCC , was sufficient to cause hyperplasia in 3D organotypic raft cultures that mimic human skin , thus linking high DEK expression in keratinocytes to oncogenic phenotypes . However , the role of DEK over-expression in ESCC development remains unknown in human cells or genetic mouse models . To define the consequences of Dek overexpression in vivo , we generated and validated a tetracycline responsive Dek transgenic mouse model referred to as Bi-L-Dek . Dek overexpression was induced in the basal keratinocytes of stratified squamous epithelium by crossing Bi-L-Dek mice to keratin 5 tetracycline transactivator ( K5-tTA ) mice . Conditional transgene expression was validated in the resulting Bi-L-Dek_K5-tTA mice and was suppressed with doxycycline treatment in the tetracycline-off system . The mice were subjected to an established HNSCC and esophageal carcinogenesis protocol using the chemical carcinogen 4-nitroquinoline 1-oxide ( 4NQO ) . Dek overexpression stimulated gross esophageal tumor development , when compared to doxycycline treated control mice . Furthermore , high Dek expression caused a trend toward esophageal hyperplasia in 4NQO treated mice . Taken together , these data demonstrate that Dek overexpression in the cell of origin for SCC is sufficient to promote esophageal SCC development in vivo .
The human DEK oncoprotein is a predominantly chromatin-bound factor that regulates nuclear processes such as chromatin architecture , epigenetics , transcription and DNA repair [1–18] . DEK was originally identified as a fusion protein with the CAN/NUP214 nucleoporin in a patient with acute myeloid leukemia harboring the chromosomal translocation ( t6;9 ) ( p23;q34 ) [19] . Since its discovery , DEK was also shown to be increased in acute myeloid leukemia types that do not harbor the DEK-NUP214 fusion protein [20–22] and to be frequently overexpressed in solid tumors including colon , breast , gastric adenocarcinoma , ovarian carcinomas , bladder cancer , retinoblastoma , lung , pancreatic , neuroendocrine prostate cancer , hepatocellular , skin cancer , head and neck cancer squamous cell carcinoma ( HNSCC ) , and esophageal squamous cell carcinoma ( ESCC; S5 Fig ) [23–40] . Additionally , high DEK expression is associated with poor prognosis in melanoma , gastric , ovarian , breast , prostate , bladder , lung , pancreatic , skin cancer , and head and neck SCC [25 , 26 , 30 , 31 , 33–35 , 40–43] . Esophageal carcinomas are the sixth most common cause of cancer related death worldwide , and eighth in incidence worldwide [44–46] . Esophageal carcinoma occurs as either SCC or adenocarcinoma [47] . Esophageal SCC accounts for one third of esophageal cancer cases in the United States but represents more than 90% cases of esophageal cancer worldwide [47 , 48] . The most common risk factors for ESCCs , similar to HNSCC , include tobacco smoke , heavy alcohol consumption , and infection with human papillomavirus [49 , 50] . Several studies have additionally revealed that ESCC and HNSCC harbor similar genetic and molecular alterations [44 , 51–54] and are treated with similar regimen of surgery and chemoradiation [50] . However , the 5-year survival rate for patients with HNSCC is over 50% , while for patients with ESCC it remains at a dismal 5–15% [45 , 46 , 48] . Current treatment regimens frequently result in irreparable tissue damage and disfiguration that additionally highlight the need for continued identification of oncogenic drivers and targeted therapies [55] . SCC arises from keratinocytes in squamous epithelium , and the overexpression of DEK has been shown to promote cell survival , proliferation , and transformation in combination with classical oncogenes while inhibiting apoptosis , cellular differentiation and senescence [16 , 56–59] . DEK overexpression occurs through various mechanisms including gene amplification , increased transcription , and mutations in microRNAs and ubiquitin ligases responsible for DEK mRNA and protein degradation , respectively [60–70] . Several in vivo studies demonstrate the critical role of human and murine Dek in driving benign and malignant tumor growth . For example , Dek knockout ( Dek-/- ) mice are partially resistant to the formation of benign skin papillomas when treated with DMBA and TPA , a tumor initiator and promoter , respectively [16] . In a breast cancer mouse model , Dek-/- mice bred to Ron receptor tyrosine kinase transgenic mice , displayed a delayed onset of mammary tumors compared to Dek+/+ mice [71] . In another study , Dek knockout ( Dek-/- ) HPV E7 oncogene transgenic mice were protected from 4-nitroquinoline 1-oxide ( 4NQO ) -induced HNSCC and ESCC tumor growth , but not initiation , when compared to their Dek+/+ counterparts [39] . Taken together , these studies support the possible importance of Dek overexpression as a key driver of uncontrolled cellular growth and tumor development . Historically , most of the data that links DEK overexpression to oncogenic phenotypes were obtained from knockdown and knockout model systems . Only recently has DEK overexpression been investigated in vivo . In a 2017 report , Nakashima et . al . generated tetracycline inducible , whole body , Dek over-expressing mice [72] . The mice were treated with 4NQO in the drinking water for 28 weeks to induce oral lesions , then induced to overexpress Dek for 4 weeks before sacrifice . 4NQO is a chemical carcinogen that mimics the effects of tobacco smoke by forming DNA adducts and mutations similar to those seen in human HNSCC and ESCC [73 , 74] . When administered in drinking water , 4NQO stimulates susceptibility to squamous cell carcinomas in the tongue , oral cavity , and esophagus [73 , 75] . In the study , the mice over-expressing Dek for 4 weeks , post 4NQO treatment , harbored significantly increased hyperplasia in the tongue with a trend toward increased tongue tumor incidence . Interestingly , Dek overexpression significantly decreased tongue tumor diameter [72] . This suggests that a short term induction of Dek overexpression after long term carcinogen exposure has pro- and anti-tumorigenic effects . Importantly , this study demonstrated that Dek overexpression promotes cellular proliferation in tissues exposed to carcinogens . However , whether these effects are due to high Dek expression in keratinocytes as the cell of origin , and/or other cell types , remains unknown . Therefore , we targeted long term induction of the Dek transgene to the stratified squamous epithelium , and monitored resulting tumor phenotypes . To this end , a tetracycline responsive Dek and luciferase transgenic Bi-L-Dek mouse model was newly generated . Bi-L-Dek transgenic mice harbor a tetracycline response element ( TRE ) that controls the bi-directional expression of Dek and firefly luciferase . The TRE allows for temporal and tissue specific control of Dek overexpression , thus making it a versatile mouse model wherein the Dek transgene expression is controlled by tetracycline or its more stable derivative , doxycycline ( dox ) . Bi-L-Dek mice were crossed to keratin 5 tetracycline transactivator ( K5-tTA ) transgenic mice to target Dek and luciferase expression to basal keratinocytes that serve as progenitor cells for stratified squamous epithelium and are the cell of origin for squamous cell carcinoma ( SCC ) of the tongue and esophagus . The tTA protein produces a tet-off system where expression of the Dek transgene is repressed by dox . Dek overexpression and transgene repression by dox was verified in the skin , tongue and esophagus . Once validated , Bi-L-Dek_K5-tTA mice were subjected to 4NQO treatment in the presence or absence of dox . Dek caused a trend toward increased proliferation in tongue and esophageal epithelium after 4NQO treatment . Furthermore , Dek overexpression was sufficient to increase the incidence of gross esophageal , but not oral , SCC tumor formation in this system . This data suggests that Dek contributes to ESCC tumorigenesis at least partially through keratinocyte intrinsic pathways which promote cellular and tumor growth .
In order to overexpress Dek conditionally in a tissue specific manner , we utilized a construct Bi-L-Dek wherein Dek and luciferase gene expression were driven by a tetracycline response element ( TRE ) . To generate the Bi-L-Dek transgene , Dek cDNA was cloned into the Bi-L-Tet plasmid expression vector ( Clontech , Mountain View , CA , USA ) as published by others [76] , described in the Materials and Methods , and illustrated in S1A Fig . Following validation of TRE-dependent Dek and luciferase expression in vector transfected cells ( S1B–S1E Fig ) , the transgene was excised and injected into the pronucleus of fertilized mouse eggs for generation of Bi-L-Dek transgenic founders ( S1F Fig ) . Bi-L-Dek mice harbor a TRE that controls two mini cytomegalovirus ( CMV ) promoters driving bi-directional transcription of Dek and luciferase ( Fig 1A ) . Four Bi-L-Dek transgenic founder lines were assessed for transgene stability over four generations before screening for doxycycline responsive expression of Dek ( Fig 1B; the data for founder line #317 used in subsequent experiments is shown ) . To determine which lines expressed Dek and luciferase under control of the TRE , Bi-L-Dek mice were crossed to K5-tTA mice ( Fig 1C and 1D ) . The keratin 5 promoter targets tTA protein expression to the basal layer of stratified squamous epithelium including that of the esophagus , tongue , and skin ( Fig 1E ) . In this system , administration of doxycycline ( dox ) represses tTA binding to the TRE to inhibit Bi-L-Dek transgene expression ( Fig 1E ) . Founder #317 was chosen for subsequent experiments , harbors approximately three copies of the transgene ( Fig 1B ) , and is referred to as Bi-L-Dek from here on . Bi-L-Dek transgene expression in Bi-L-Dek_K5-tTA mice was validated with multiple methodologies . These included an in vivo imaging system ( IVIS ) , and the detection of Dek mRNA and protein expression by quantitative , real time polymerase chain reaction ( RT-qPCR ) , western blot analysis , in situ immunohistochemistry ( IHC ) and immunofluorescence ( IF ) ( Fig 2 ) . IVIS and ex vivo imaging confirmed luciferase expression in the skin of Bi-L-Dek_K5-tTA bi-transgenic mice and in the esophagus ( Fig 2A and 2B ) . Dek mRNA levels were induced 3 . 5 fold over endogenous levels in the skin of Bi-L-Dek_K5-tTA mice , and repression to endogenous levels was achieved by feeding with dox chow for seven days ( Fig 2C ) . Dek protein expression in the skin also increased approximately 3 fold over the levels of endogenous Dek in the Bi-L-Dek_K5-tTA mice ( Fig 2D ) . Dek overexpression in the tongue was detected by IHC along with the expected decrease in the corresponding mice on dox chow ( Fig 2E ) . Finally , we isolated keratinocytes from Bi-L-Dek_K5-tTA skin for cell culture and performed IF with antibodies against Dek and K5 , then stained with DAPI to detect DNA . As expected , Dek expression was higher in the Bi-L-Dek_K5-tTA derived keratinocytes compared to those treated with dox , or compared to keratinocytes from single transgenic control mice ( Fig 2F and 2G ) . Exogenous Dek localized to the nucleus as expected , and co-localized with endogenous Dek and DAPI ( Fig 2F ) . Altogether , Bi-L-Dek_K5-tTA mice overexpressed Dek in the squamous epithelium of the skin , tongue , and esophagus , and Dek expression was repressed by doxycycline . To assess the extent of Dek overexpression in the Bi-L-Dek_K5-tTA mice , we quantified transgene expression in the context of Dek knockout mice . Bi-L-Dek and K5-tTA transgenic mice were interbred with Dek-/- mice to generate Dek-/-_ Bi-L-Dek_K5-tTA offspring ( Fig 3A ) . IVIS confirmed luciferase expression ( Fig 3B ) , and RT-qPCR and western blot analysis confirmed Dek mRNA and protein expression , respectively , in the epidermis ( Fig 3C and S2 Fig ) . Dek mRNA levels were induced by approximately four fold in the Bi-L-Dek_K5-tTA compared to control mice ( S2 Fig ) and Dek protein levels were induced by approximately 2–3 fold in the Bi-L-Dek_K5-tTA over control mice . These levels are similar to the levels of DEK expression that can be routinely achieved in normal epithelial cells transduced with retroviral or lentiviral DEK expression vectors and are within the range of DEK levels observed in cancer cells [16 , 42 , 56 , 77–81] . To explore the broader utility of this model system , conditional Bi-L-Dek mice were bred to Dlx5/6-tTA mice to target Dek expression to neurons originating from the ventral forebrain ( S3 Fig ) . As expected , robust Dek protein overexpression was detectable in cortical interneurons and striatal projection neurons from the ventral forebrain , as previously demonstrated for other Dlx5/6-driven transgenes [82] . Additionally , we crossed the Bi-L-Dek mice with Rosa-tTA mice to produce global Dek overexpressing mice . IVIS demonstrated luciferase expression from the Bi-L-Dek transgene throughout the body , which was repressed with dox chow ( S4 Fig ) . No overt phenotypes were observed in the mice similar to results in previously published Tet-O-Dek_ Rosa26-M2rtTA mice . In all , these results further validate Bi-L-Dek-mediated transgene expression in murine epithelia , and demonstrate broad utility of this genetic mouse model for studies of Dek overexpression in other organ systems . Based on data in the cancer genome atlas ( TCGA ) , DEK is more highly expressed in ESCC compared to normal tissue and in ESCC compared to esophageal adenoma ( S5 Fig ) . However , the contribution of Dek overexpression to ESCC development is unknown . To determine if DEK contributes to ESCC development or progression , we utilized the Bi-L-Dek_K5-tTA mice with Dek overexpression targeted to basal keratinocytes that form the epithelium . Bi-L-Dek_K5-tTA mice overexpressed Dek in stratified squamous epithelium of the tongue and esophagus , and Bi-L-Dek_K5-tTA mice on dox expressed only endogenous levels of Dek . Exposure of mice to drinking water containing the soluble quinoline derivative 4NQO promotes the development of oral and/or esophageal cancer . Therefore , we exposed two groups of mice +/- Dox to 4NQO in order to determine whether Dek overexpression in basal keratinocytes is sufficient to promote SCC and if early onset of Dek overexpression increases oral and/or esophageal tumor incidence or tumor burden . The experimental design is illustrated in Fig 4A . At six weeks of age , Bi-L-Dek_K5-tTA mice in the absence of dox ( n = 7 ) or in the presence of dox ( n = 5 ) were exposed to 10ug/mL of 4NQO in their drinking water to promote SCC susceptibility . After 16 weeks , mice were given normal water , sacrificed at 45 weeks of age or when moribund , and analyzed after experimentally induced death or sacrifice . One hour prior to sacrifice , mice were injected with the thymidine analog Bromodeoxyuridine ( BrdU ) to quantify proliferation . In the absence of 4NQO treatment , Dek overexpression did not significantly increase cellular proliferation in the tongue or the esophagus when compared to control mice on dox ( Fig 4B ) . However , following 4NQO treatment there was a trend toward increased proliferation in the epithelia of Dek overexpressing tongue ( p = 0 . 07 ) and esophagus ( p = 0 . 15 ) ( Fig 4C and 4D ) . These results are in line with recently published data wherein global Dek overexpressing mice did not exhibit hyperplasia or other phenotypes under normal conditions , but displayed tongue hyperplasia after 4NQO treatment [72] . Bi-L-Dek_K5-tTA mice exposed to 4NQO or not were then analyzed for the presence of tumors in the tongue and esophagus . Detailed results are shown for each mouse in Fig 5A . After 4NQO treatment , Bi-L-Dek_K5-tTA mice continued to express higher levels of Dek protein in esophageal epithelium compared to the control group on dox ( Fig 5B ) . In addition , Dek overexpressing mice had a significantly higher incidence of gross esophageal tumors ( Fig 5C ) . Specifically , all of the Bi-L-Dek_K5-tTA mice developed at least one visible esophageal tumor ( 100% ) , in contrast to only one of five Bi-L-Dek_K5-tTA mice on dox ( 20% ) . Furthermore , one Dek overexpressing mouse harbored an excessively large tumor , while two others harbored two separate grossly apparent tumors ( Fig 5A , 5C and 5D ) . From published studies , the 4NQO protocol utilized was expected to result in a 10% incidence of gross tumors in Dek wild type mice [75 , 83–85] . This compares roughly to the 20% cancer incidence in mice exposed to dox . Overall , the number of invasive tumors and mice with multifocal tumors was not significantly different between the two groups ( Fig 5C ) ; however , survival of Dek overexpressing mice was less than 60% while all dox-treated mice survived until sacrifice at week 45 ( p = 0 . 11; Fig 5E ) . Taken together , these data provide evidence that Dek overexpression promotes esophageal squamous cell carcinoma growth . Esophagi and tongues from all mice were microscopically examined to define tumor phenotypes and quantify microscopic lesions . Histological analysis confirmed that gross tumors in Dek overexpressing mice were squamous cell carcinomas with stromal invasion confirmed histologically in 67% of the mice ( Fig 5A , 5C and 5H ) . Additional multifocal microscopic squamous cell lesions were detected in 50% of Dek overexpressing mice with all mice developing 1–3 squamous cell lesions including at least one grossly apparent tumor . In contrast , microscopic lesions predominated in dox treated mice , with one mouse harboring no lesions and the other mice harboring 1–2 squamous cell lesions including a single grossly apparent tumor ( Fig 5A , 5G and 5I ) . The single tumor apparent at necropsy in this group was a well differentiated squamous cell carcinoma with abundant keratin production which differed from the moderate to poorly differentiated squamous cell carcinomas that predominated in Dek overexpressing mice ( Fig 5I and 5H ) . Microscopic tumors in dox treated mice consisted primarily of papillary squamous cell lesions with a single focus of very superficial invasion in one lesion . This differed from the more extensive invasion and necrosis in tumors that arose in Dek overexpressing mice ( Fig 5I and 5H ) . Dek levels appeared to be high in all tumors regardless of whether these originated in the Dek overexpressing group or the dox control group , thus suggesting strong selection for the upregulation of endogenous Dek during tumorigenesis ( Fig 5F and 5G , bottom row ) . With regards to the one tumor that arose in the dox control group , endogenous upregulation or leaky transgenic expression of Dek could be responsible . No tongue tumors were identified in either group of mice . Taken together , we demonstrate for the first time that Dek overexpression promotes the growth of esophageal SCC in vivo .
A number of studies have linked DEK overexpression in various malignancies to cellular growth , motility/invasion and chemoresistance [16 , 36 , 37 , 43 , 58 , 71 , 77] . Relevant mechanisms have not been fully elucidated in each case . However , DEK loss has been shown to attenuate proliferation and survival , while inducing senescence or apoptosis , depending upon the cell type and model system studied . Required signaling pathways included those controlled by p53 and ΔNp63 to inhibit apoptosis and promote proliferation , respectively [17 , 39 , 58] , Wnt/beta-catenin to drive invasion and cellular proliferation [32] , VEGF to foster angiogenesis [7] , Rho/ROCK/MLC to support migration [86] , and NFkB to regulate cellular survival and growth [6 , 8 , 59] . One caveat regarding cancer-related interpretation of these results is that many of the experiments are based upon DEK loss of function , and thus only address the requirement for DEK in tumor cell growth and not the contribution of DEK overexpression to tumor growth . For instance , Dek knockout mice are viable and resistant to chemically induced papillomas and HPV E7 driven HNSCC . In vitro , DEK overexpression in primary keratinocytes extends life span , stimulates transforming activities of classical oncogenes , and de-regulates cellular metabolism [16 , 17 , 78] . These data are in line with , but do not prove , oncogenic activities that promote cancer development at the organismal level . Here we demonstrate that Dek overexpression targeted to the epithelium stimulates proliferation specifically in the presence of 4NQO in the tongue and also in the esophagus . Furthermore , concurrent Dek overexpression and 4NQO exposure increased the incidence of gross esophageal tumors demonstrating for the first time that Dek overexpression contributes to ESCC tumor growth in vivo . The observed increase in hyperplasia in the tongue is similar to that seen with sequential 4NQO exposure followed by ubiquitous Dek overexpression reported by Nakashima et . al . ( 72 ) . Interestingly , and in contrast to our data , Nakashima et . al . reported that Dek overexpression decreased the volume of resulting tongue tumors [72] . Key differences in the experimental designs between the two models likely account for the observed differences in tumor location and size in . Specifically , in the current study: 1 ) Dek overexpression was targeted to the basal epithelium as opposed to ubiquitous Dek overexpression including immune and stromal cells that modulate cancer cell growth , 2 ) 4NQO and Dek overexpression were concurrently administered rather than sequential exposure to 4NQO followed by Dek overexpression , 3 ) 4NQO exposure duration and dosage was 16 weeks at 10 μg/ml compared to 28 weeks at 20 μg/ml , 4 ) Dek overexpression duration was 52 weeks compared to four weeks , 5 ) exogenous Dek was unmodified and localized to the nucleusas compared to FLAG-tagged exogenous Dek protein localized predominantly to the cytoplasm , and 6 ) FVB/N mice were used compared to C57BL/6 mice . Interestingly , C57BL/6 and FVB/N harbor variations in immune phenotype , raising the intriguing possibility that immune surveillance and/or evasion account at least in part for the differing tumor phenotypes in mice with ubiquitous versus epithelial cell targeted Dek overexpression . Preliminary studies in the esophageal tumors in the current model did not reveal a significant CD3 positive T cell infiltrate by immunohistochemistry . Additional studies are needed to definitely determine the role of inflammatory cells in Dek dependent tumorigenesis , however , the lack of a prominent T-cell infiltrate suggests that differences in tumor growth in the two models cannot be simply explained by tumor infiltrating T cells acquiring an exhausted T-cell phenotype . The availability of these distinct complementary mouse models now provide a valuable system to identify cell specific functions that drive Dek induced carcinogenesis . Distinct effects of global versus tissue-specific Dek expression might reflect interesting cell-type specific functions of Dek in the tumor microenvironment including immune cells , or systemic effects on epidermal proliferation and tumor growth . The complexity of DEK functions in vivo is exemplified in studies of non-vertebrate organisms For instance , in Arabidopsis , DEK3 overexpression decreases germination efficiency under high salinity conditions , and conversely , plants deficient in DEK3 germinated significantly better compared to wild-type plants suggesting DEK3 levels are crucial for stress tolerance [87] . The overexpression of human DEK in the Drosophila eye caused a rough-eye phenotype due to caspase-9 and 3-mediated apoptosis suggesting that DEK overexpression caused ( rather than diminished ) apoptosis [88] . These non-vertebrate eukaryote model systems highlight the need for balanced DEK expression and its versatile functions in vivo . In the Bi-L-Dek_K5-tTA mouse model , Dek overexpression at the message and protein level was approximately 2–4 fold over that of endogenous Dek . This relatively modest level is in agreement with other published studies suggesting DEK expression levels are tightly regulated [1 , 16 , 58 , 77–79] . Achieving strong overexpression of DEK in vitro in our hands has been notoriously difficult , potentially due to toxicity and cell death , e . g . in the above Drosophila study [88] . Importantly , a modest level of DEK overexpression in epithelial cells has been linked to oncogenic phenotypes in vitro . These DEK dependent oncogenic activities include enhanced cancer stem cell growth , colony formation , cellular invasion , mitotic abnormalities , and metabolic de-regulation , providing evidence that subtle increases in DEK protein expression are sufficient to elicit significant cellular consequences [16 , 77–79] . In human ESCC , HNSCC , breast , bladder , colorectal , hepatocellular , and non-small cell lung carcinoma , DEK protein levels were increased in tumor versus adjacent normal tissue , and the extent of overexpression was variable . Per cell DEK protein detection in various tumor types can range from intense to weak staining by IHC , and overexpression by western blot analysis can range from 2–30 fold [23 , 25 , 26 , 31 , 34 , 39 , 42 , 77 , 80 , 81] . Overall , this patient data suggests that high levels of DEK can be tolerated by some human tumor cells , and that even modest DEK expression is associated with cancer growth and/or maintenance . In conclusion , Bi-L-Dek_K5-tTA mice subjected to 4NQO harbor trends toward increased cellular proliferation in the tongue and esophagus ( Fig 4B–4D ) and a significantly increased incidence of gross esophageal tumors ( Fig 5C ) . Tongue tumors were not detected in these same mice . Importantly , control Bi-L-Dek_K5-tTA mice on dox nonetheless developed microscopic ESCC tumors , thus suggesting that Dek overexpression does not stimulate tumor initiation , but promotes tumor growth in the esophagus . This is in alignment with previously published Dek loss of function data from HNSCC-prone K14E7 transgenic mice wherein keratinocyte proliferation and tumor growth , but not the presence of microtumors , were diminished in the absence of Dek [39] . While an abundance of data has suggested that DEK promotes tumor growth in the presence of oncogenic stimuli , the above experiments do not unequivocally rule out a role for Dek in tumor initiation . Overall larger tumors in the Dek overexpressing mice may be due to increased growth of tumors once initiated , or due to premature initiation and thus extended time for growth . In either case , Dek overexpression significantly increased the incidence of gross tumors and over 40% of Bi-L-Dek_K5-tTA mice died prior to the 45 week end point , while all mice in the dox treated control group survived . A plethora of Dek knockdown experiments have shown the importance of DEK expression for cancer cell growth and survival [10 , 16 , 39 , 58 , 77] . These data , in conjunction with evidence that transformed keratinocytes are more sensitive to DEK loss when compared to their normal or differentiated counterparts [16] , make DEK an attractive therapeutic target . Furthermore , Dek knockout mice are healthy and fertile , suggesting potential feasibility and relative safety for the targeting of DEK in cancer . However , no DEK inhibitors exist commercially nor have been published . Thus , the inducible targeting of Dek in Bi-L-Dek mice harboring ESCC tumors should now be an attractive model to interrogate the requirement of continued Dek expression for cancer maintenance and progression . Taken together , we have generated and validated a new mouse model of esophageal transformation using an inducible Bi-L-Dek transgene which is now available for broader studies of Dek in health and disease of the intact organism .
Murine Dek ( mDek ) DNA sequences were excised from the previously published R780 retroviral vector , using the restriction enzymes Sal I and Not I [16 , 71] , and cloned into the pBi-L plasmid ( Clontech , Mountainview , CA Catalog No . 631005; GenBank Accession No . : U89934 . ) cleaved with the same restriction enzymes . The resulting pBi-L-Dek construct harbors the bi-directional Pbi-1 promoter which is responsive to the tTA regulatory protein in this Tet-Off system . The Tet-responsive element ( TRE ) consists of seven copies of the 42-bp tet operator sequence ( tetO ) , and is located between two minimal CMV promoters that lack the CMV enhancer . Gene expression is silent in the absence of the tTA bound to tetO sequences and is silenced with the addition of doxycycline . The pBi-L-Dek transgene sequences were liberated using the restriction enzymes AatII and AselI . A 5247bp ( Bi-L-Dek ) DNA sequence was purified and microinjected into the pronucleus of a fertilized egg and inserted into a pseudo-pregnant mouse to produce Bi-L-Dek founders . Transgene transmission was validated , and pups from the F1 generation were mated with K5-tTA mice . Resulting F2 Bi-L-Dek_K5-tTA mice were further characterized . Four Bi-L-Dek founders were generated . One founder line never produced offspring . Another founder died before producing a pup that harbored the transgene . Of the two remaining lines , both overexpressed Dek but founder #317 was a better breeder . The murine Dek sequence that was cloned into the pBi-L-Tet vector is: 5’-ATGTCGGCGGCGGCGGCCCCCGCTGCGGAGGGAGAGGACGCCCCCGTGCCGCCC TCATCCGAGAAGGAACCCGAGATGCCGGGTCCCAGGGAAGAGAGTGAGGAGGAGGAGGAGGATGACGAAGACGATGATGAAGAGGACGAGGAGGAAGAAAAAGAAAAGAGTCTTATCGTGGAAGGCAAGAGAGAGAAGAAGAAAGTAGAGAGACTGACGATGCAAGTGTCTTCCTTACAGAGAGAGCCATTTACAGTGACACAAGGGAAGGGTCAGAAACTTTGTGAAATTGAAAGGATACATTTCTTTCTGAGTAAGAAAAAACCAGATGAACTTAGAAATCTACACAAACTGCTTTACAACAGGCCGGGCACAGTGTCCTCGTTGAAGAAGAACGTGGGTCAGTTCAGTGGCTTTCCATTCGAAAAAGGCAGTACCCAGTATAAAAAGAAGGAAGAAATGTTGAAAAAGTTTCGAAATGCCATGTTAAAGAGCATCTGTGAGGTTCTTGATTTAGAGAGGTCAGGCGTGAACAGCGAACTCGTGAAGAGGATCTTGAACTTCTTAATGCATCCAAAGCCTTCTGGCAAACCATTACCAAAGTCCAAAAAATCTTCCAGCAAAGGTAGTAAAAAGGAACGGAACAGTTCTGGAACAACAAGGAAGTCAAAGCAAACTAAATGCCCTGAAATTCTGTCAGATGAGTCTAGTAGTGATGAAGATGAGAAGAAAAATAAGGAAGAGTCTTCGGAAGATGAAGAGAAAGAAAGTGAAGAGGAGCAACCACCAAAAAAGACATCTAAAAAAGAAAAAGCAAAACAGAAAGCTACTGCTAAAAGTAAAAAATCTGTGAAGAGTGCTAATGTTAAGAAGGCAGACAGCAGTACCACCAAGAAGAATCAAAAAAGTTCCAAAAAAGAGTCTGAATCCGAAGACAGTTCTGATGATGAACCCTTAATTAAAAAATTGAAAAAGCCACCTACAGATGAAGAGCTAAAGGAAACAGTGAAGAAATTACTGGCTGATGCTAACTTGGAAGAAGTCACAATGAAGCAGATTTGCAAAGAGGTATATGAAAATTATCCTGCTTATGATTTGACTGAGAGGAAAGATTTCATTAAAACAACTGTAAAAGAGCTAATTTCTTGA-3’ K5-tTA mice were obtained internally at CCHMC and have previously been published [90] . Dek knockout mice ( Dek-/- ) have previously been published [16] . Dlx5/6-tTA mice were obtained internally at CCHMC and were generated in Dr . Kenneth Campbell’s lab by Lisa Ehrman . Dlx5/6-tTA mice have been analyzed for tTA expression , and will be fully described and characterized in a separate publication . Dlx5/6 tTA expression is similar to Cre expression in the reported Dlx5/6-Cre-IRES-EGFP ( CIE ) transgenic mouse model [82] . E2A-Cre mice were obtained from Jackson Laboratory and are strain number 003724 . The Cre transgene is under the control of the adenovirus EIIa promoter , which targets expression of Cre recombinase to the early mouse embryo . This model is useful for deletions , in the germ line , of loxP-flanked genes . E2A-Cre mice were bred to Rosa-LNL-tTA transgenic mice . These mice were also obtained from Jackson laboratories and are strain number 008600 . Rosa-LNL-tTA mice contain a loxP-flanked nonsense sequence inhibiting expression of tTA that is removed once exposed to E2A controlled Cre . Ear clips were digested with 25mM NaOH in 0 . 2mM EDTA at a pH of 12 and incubated at 95°C for 20 minutes . The reaction was neutralized with 40mM Tris-HCl . For PCR analysis , one ul of the digest with DNA was added to JumpStart Taq Ready Mix from Invitrogen ( Carlsbad , CA , product # P2893 ) using the manufacturer’s specifications . Transgenes were detected with the following primers: Bi-L-Dek: Forward: GAAATGTCCGTTCGGTTGGCAGAAGC; Reverse: CCAAAACCGTGATGGAATGGAACAACA . K5-tTA: Forward: GCTGCTTAATGAGGTCGG Reverse: CTCTGCACCTTGGTGATC . Bi-L-Dek primers that do not detect endogenous Dek ( exogenous Dek cDNA primers ) Forward: CAGTGACACAAGGGAAGGGTCAGA Reverse: AGCCACTGAACTGACCCACGT . Genomic DNA was isolated from the tails of mice from successive generations of offspring from founder 317 . A minimum of two mice were used per generation and analyzed as replicates . The DNA concentration was adjusted to 20ng/ul in each case , and 60ng of DNA was used for qPCR per sample and performed in duplicate . Primers were used to quantify the beta actin gene and a region in exon 6 of the Dek gene . This region is present in Dek+/+ mice but absent in Dek-/- mice thus allowing for a negative control . The following sequences were used: Beta actin forward: GATATCGCTGCGCTGGTCGTC Beta actin reverse: ACCATCACACCCTGGTGCCTAG Dek Exon 6 forward: AGGTCAGGCGTGAACAGCGA Dek Exon 6 reverse: TGCCAGAAGGCTTTGGATGCATTA The critical threshold ( CT ) values for Dek exon 6 primers were normalized to actin , and quantified relative to Dek wild type mice using the delta delta CT method . Values were multiplied by two to account for the two endogenous Dek alleles in WT mice and the number of Bi-L-Dek transgene insertions was determined . Error bars represent multiple mice from the same generation . Mice were injected with 15ng of luciferin per gram in body weight , and allowed to metabolize the luciferin for five minutes prior to sedation with isoflurane . Mice were imaged in the Perkin Elmer IVIS Spectrum CT , Waltham , Massaschusetts , USA . For ex vivo IVIS , mice were allowed to metabolize luciferin for eight minutes following luciferin injection , and then sacrificed with CO2 . The mice were then dissected and tissues placed in PBS containing 300ug/mL of luciferin , kept on ice , and protected from light before immediate analysis by IVIS . For validation of pBi-L-Dek expression , the plasmid was transfected into previously isolated and cultured K5-tTA expressing murine keratinocytes [91] . Cells were collected for Dek protein expression by western blot analysis . K5-tTA keratinocytes were grown in E-media supplemented with 0 . 05 mM Ca2 and 15% serum as previously published [92] . Keratinocytes were isolated from Bi-L-Dek_K5-tTA mice and single transgenic littermate controls using a previously published protocol with modifications [93] . Briefly , pups were euthanized within 48 hours of birth , rinsed in 70% ethanol , and placed in PBS . Flank skin was removed , and placed dermis side down in 1 mL of dispase ( Dispase Gibco/Invitrogen , Calsbad , CA , USA , product# 17105–041 ) and 1 mL of DMEM ( 1:1 mixture ) in a 35mm plate , and incubated overnight at 4° Celsius . The epidermis was removed and placed in 1 mL of accutase ( Sigma , St . Louis , MO , USA , product # A6964 ) for 20 minutes with agitation to release the keratinocytes . Cells were collected and centrifuged , then plated on irradiated MEFs and overlaid with CnT07 media ( CellnTec , Bern , Switzerland ) . Cells were used for experiments in passage 0 or 1 . Keratinocytes were plated onto 100 mg/ml poly-D-lysine coated coverslips , and fixed with 2% paraformaldehyde for 30 minutes . Coverslips were incubated in 0 . 1% Triton X-100 for three minutes , blocked with 5% normal goat serum , and incubated with primary antibody for one hour at 37°C . Antibody dilutions were as follows: DEK-antibody ( Cusabio , Baltimore , MD , USA ) 1:300 dilution; keratin 5 antibody ( Acris , San Diego , CA , USA ) 1:500; and sealed with a coverslip using Vectashield with DAPI ( Vector Laboratories , Burlingame , CA ) . ImageJ ( National Institutes of Health , Bethesda , Maryland , USA ) [89] was used to quantify Dek staining . Dek immunofluorescences ( IF ) images were converted to 8-bit images , followed by the identification of the location of cells with the nucleus counter ImageJ plugin . Each cell was visually validated and added to the regions of interest ( ROI ) . The mean grayscale intensity was measured in these ROIs . Quantification was from successive images to encompass the entire coverslip of keratinocytes isolated from each genotype with or without dox treatment . Tissues were lysed using mortar and pestle , resuspended in RIPA buffer ( 1% Triton , 1% deoxycholate , 0 . 1% SDS , 0 . 16M NaCl , 10 mmol/L Tris pH 7 . 4 , and 5 mmol/L EDTA ) , supplemented with a protease inhibitor cocktail ( Pharmingen , San Diego , CA , USA ) , and analyzed as described previously ( 48 ) . Primary antibodies used for DEK were as follows: DEK ( 1:1000; BD Biosciences , San Diego , CA , USA ) , pan-actin ( 1:20 , 000; a gift from James Lessard ) . Membranes were exposed to enhanced chemiluminescence reagents ( Perkin Elmer , Boston , MA , USA ) and imaged using the BioRad Chemidoc ( Hercules , CA , USA ) . All mice were maintained in a hemizygous state for the Bi-L-Dek and K5-tTA transgenes . All Bi-L-Dek mice were F3 and F4 generations from founder 317 . Bi-L-Dek mice were bred to K5-tTA mice and bi-transgenic offspring were given 4NQO water for 16 weeks at a dose of 10mg/ml starting at six weeks of age . Mice on doxycycline were continuously fed dox chow from the start of 4NQO treatment until sacrifice . After 16 weeks on 4NQO , mice were returned to normal water until sacrifice at week 45 or when determined excessively morbid by veterinary services thus warranting sacrifice . At the time of sacrifice , tumors were resected and counted , localization was noted , and tumors were measured by calipers . Tumor volume was measured by ( length x width x depth ) . All statistical analyses were performed in GraphPad Prism . The survival curve was analyzed using the log-rank ( Mantel-Cox ) test . Tumor incidence was determined significant/non-significant using the Chi Square ( and Fisher’s exact ) test . Mouse tumors and tissues were fixed in 4% paraformaldehyde , embedded in paraffin , sectioned at 5 μm thickness , and fixed onto slides . Routine H&E stained sections were analyzed for histopathology . 13 The area of microscopic tumors was determined by multiplying the widest part of the tumor by the longest part that was observed in the sections . Paraffin sections were deparaffinized in xylene and rehydrated for antigen retrieval in sodium citrate . Sections were then treated with the Mouse on Mouse peroxidase immunostaining kit ( Vector Labs , Burlingame , CA , USA ) . Sections were stained with diaminobenzidine ( DAB ) and counterstained with Nuclear Fast Red ( Poly Scientific , Bay Shore , NY , USA ) and mounted with Permount ( Fisher Scientific , Pittsburgh , PA , USA ) . Images were captured at the indicated magnifications and antibodies used are noted in each case . Antibody dilutions were used as follows: BrdU ( 1:100 , Invitrogen , Calsbad , CA , USA ) , and DEK ( 1:200 , BD Biosciences , San Jose , CA , USA; or 1:300 , Proteintech Group , Chicago , IL , USA; or 1:50 , Cusabio , Baltimore , MD , USA ) . 10x or 20x magnified images of BrdU stained tongue or esophagus were analyzed for BrdU positive cells using ImageJ ( National Institutes of Health , Bethesda , Maryland , USA ) . In ImageJ , the bottom portion of the basal cell layer of the stratified squamous epithelium was traced using the freehand tool and measured in the indicated tissue . The distance was converted into millimeters using scale bars based on magnification to determine BrdU positive cells per millimeter of epithelium . Statistical analysis was performed using GraphPad Prism with t-tests and the two-stage linear step-up procedure of Benjamini , Krieger and Yekutieli . Luciferase assays were performed using the Dual-Luciferase Reporter Assay System from Promega and following manufacturer specifications . All animal work was conducted according to Cincinnati Children's Hospital Medical Center Institutional Animal Care and Use Committee guidelines under protocol number #2017–0004 . To ameliorate animal suffering mice were euthanized with carbon dioxide when moribund as determined by veterinary services .
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The DEK oncogene is overexpressed in nearly all human cancers and portends a poor prognosis for many cancer types . High DEK expression causes cancer related phenotypes such as increased cellular proliferation , migration , and invasion in vitro . Despite the well documented link between high DEK expression and cancer , the consequences of Dek overexpression in vivo are poorly understood . To determine if Dek contributes to carcinogenesis in vivo , we generated a Dek inducible transgenic mouse model wherein Dek can be overexpressed in specific tissues and inhibited with the drug doxycycline . We targeted Dek overexpression to keratinocytes , the cell of origin for squamous cell carcinoma , and exposed the mice to the chemical carcinogen 4NQO to induce oral cavity and esophageal carcinogenesis . We found that DEK overexpression was sufficient to increase gross esophageal squamous cell carcinoma incidence and caused a trend toward increased cellular proliferation in adjacent non-tumor tissue . Importantly , we demonstrate for the first time that Dek overexpression specifically targeted to basal keratinocytes is sufficient to promote cellular and squamous cell carcinoma growth in vivo .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"keratinocytes",
"cancer",
"treatment",
"carcinomas",
"cancers",
"and",
"neoplasms",
"epithelial",
"cells",
"animal",
"models",
"oncology",
"model",
"organisms",
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"organism",
"systems",
"molecular",
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"assays",
"and",
"analysis",
"techniques",
"gastrointestinal",
"tract",
"gene",
"expression",
"and",
"vector",
"techniques",
"mouth",
"cell",
"biology",
"anatomy",
"epithelium",
"biology",
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"life",
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"squamous",
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"carcinomas",
"cellular",
"types",
"esophagus"
] |
2018
|
Dek overexpression in murine epithelia increases overt esophageal squamous cell carcinoma incidence
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MicroRNAs ( miRNAs ) are 19 to 23 nucleotide–long RNAs that post-transcriptionally regulate gene expression . Human cells express several hundred miRNAs which regulate important biological pathways such as development , proliferation , and apoptosis . Recently , 12 miRNA genes have been identified within the genome of Kaposi sarcoma–associated herpesvirus; however , their functions are still unknown . To identify host cellular genes that may be targeted by these novel viral regulators , we performed gene expression profiling in cells stably expressing KSHV-encoded miRNAs . Data analysis revealed a set of 81 genes whose expression was significantly changed in the presence of miRNAs . While the majority of changes were below 2-fold , eight genes were down-regulated between 4- and 20-fold . We confirmed miRNA-dependent regulation for three of these genes and found that protein levels of thrombospondin 1 ( THBS1 ) were decreased >10-fold . THBS1 has previously been reported to be down-regulated in Kaposi sarcoma lesions and has known activity as a strong tumor suppressor and anti-angiogenic factor , exerting its anti-angiogenic effect in part by activating the latent form of TGF-β . We show that reduced THBS1 expression in the presence of viral miRNAs translates into decreased TGF-β activity . These data suggest that KSHV-encoded miRNAs may contribute directly to pathogenesis by down-regulation of THBS1 , a major regulator of cell adhesion , migration , and angiogenesis .
Kaposi sarcoma–associated herpesvirus ( KSHV ) is the causative agent of Kaposi sarcoma ( KS ) and is associated with primary effusion lymphoma ( PEL ) and a subset of multicentric Castleman disease [1–4] . In KS tumors and PELs , the majority of cells are latently infected and express only a subset of viral genes located within the latency-associated region [5 , 6] . Recently , 12 microRNA ( miRNA ) genes have been identified within this region [7–9] . miRNAs are 19 to 23 nucleotide ( nt ) –long RNAs that post-transcriptionally regulate gene expression through selective silencing of target messenger RNAs ( mRNAs ) . Precursor miRNAs are expressed as hairpin structures from transcribed RNA that are cleaved by Drosha , exported from the nucleus through Exportin 5 , and subsequently processed by Dicer . Mature miRNAs are then incorporated into the RNA-induced silencing complex ( RISC ) , which guides their binding to 3′UTRs of target mRNAs and sequesters them to processing bodies , ultimately leading to inhibition of translation and mRNA degradation ( for review see [10] ) . Although target recognition for miRNAs is not completely understood , a seed sequence within the miRNA ( nts 2 through 8 ) is known to be critical for binding and target recognition . In this manner , a single miRNA may regulate a large number of genes [11] . Human miRNAs have so far been found to regulate fundamental biological processes such as developmental pattern formation , hematopoiesis , apoptosis , and cell cycle control ( for review see [12] ) . miRNAs have been identified within several DNA viruses , including herpesviruses ( for reviews see [13–15] ) . A total of 17 miRNAs , encoded by 12 miRNA genes , have been cloned from KSHV-infected PEL cells , and interestingly , all are located within the KSHV latency-associated region ( Figure 1A ) . This region encodes the latency-associated nuclear antigen ( LANA ) , v-Cyclin , v-Flip , and the kaposin gene family , all of which modulate host cellular gene expression and signal transduction in latently infected cells [6 , 16–21] . We hypothesize that KSHV-encoded miRNAs target host/cellular gene expression and , as a result , play a role in viral pathogenesis . As viral miRNAs have no sequence conservation to metazoan miRNAs , we are able to study them through ectopic expression without interfering with endogenous miRNAs . In this study , we have stably expressed ten KSHV miRNA genes in 293 cells and identified a total of 81 gene expression changes by microarray expression profiling . We confirmed a subset of these by quantitative real-time ( qRT ) –PCR , luciferase knockdown experiments , and Western blot analysis . Importantly , we identified thrombospondin 1 ( THBS1 ) , a potent inhibitor of angiogenesis reported to be down-regulated in KS lesions [22] , as a target of multiple KSHV miRNAs .
KSHV miRNAs are coordinately expressed with the latency-associated genes LANA , v-Cyclin , v-Flip , and kaposin , which all modulate the host cellular environment in KS tumors cells [6 , 16–21] . Hence , we hypothesized that KSHV-encoded miRNAs target host/cellular gene expression and as a result play a role in viral pathogenesis . To identify potential miRNA targets without interference from viral proteins , we introduced the miRNA cluster , which encodes ten miRNA genes within a 2 . 8-Kbp ( kilo–base pair ) region ( nts 119 , 635 to 122 , 481; Figure 1A ) , into a cytomegalovirus promoter containing expression plasmid and stably expressed the resulting construct or empty vector control in 293 cells . Cell populations rather than single-cell clones were used for subsequent experiments to avoid the risk of detecting expression differences caused by specific integration events . To confirm miRNA expression in stable 293 pmiRNA cluster cells , we performed Northern blot analysis and luciferase reporter de-repression assays . The expression level of miR-K12–1 was about 20% compared to latently infected PEL cells , which contain a high copy number of KSHV episomes ( Figure 1B ) . To detect individual miRNA expression , we performed luciferase reporter de-repression assays . First , sensor constructs each containing two complementary binding sites for a specific miRNA within the 3′UTR of pGL3 were constructed for each miRNA . Co-transfection of these constructs with individual miRNA expression vectors showed a dose-dependent inhibition , confirming sensor specificity ( Figure S1 ) . Individual KSHV miRNA expression in stable 293 pmiRNA cluster cells was examined using 2′OMe RNA antagomirs against each miRNA , which inhibit miRNA-containing RISC complexes in a sequence-specific manner [23 , 24] . As seen in Figure 1C , all nine miRNA sensor constructs show a dose-dependant de-repression between 2- and 5-fold in the presence of 2′OMe RNAs , confirming miRNA expression from the cluster . For each cell line , 293 pmiRNA cluster and 293 vector control , two independent cultures were harvested at two time points for a total of eight samples . RNA extraction , quantification , cRNA synthesis , hybridization , and washing steps were done as recommended by the manufacturer and as previously described [16 , 25] . Figure 2 shows the expression profile of genes found to be significantly changed ( p < 0 . 001 ) between both cell lines . Probe sets ( 205 ) were identified representing 177 genes . Of the 177 expression differences , the majority ( 137 ) showed decreased expression , while 39 genes showed expression increases . A cross validation ( CV ) analysis showed that of the 205 initial probe sets , a total of 81 , representing 73 genes with 3′UTRs , show 100% CV ( Table S1 ) . This analysis , based on t-values of signal intensities , gives statistically robust data but includes probe sets with relatively small fold changes . For the final data set , 65 genes were decreased in their expression between 1 . 05- and 20 . 4-fold , while eight genes were increased between 1 . 05- and 1 . 4-fold ( Table S1 ) . Eight genes showed decreases greater than 4-fold . Interestingly , five of these genes—SPP1 ( osteopontin ) , THBS1 , S100A2 ( S100 calcium binding protein A2 ) , PRG1 ( plasticity related gene 1 ) ( now named SRGN ) , and ITM2A ( integral membrane protein 2A ) —have roles in processes such as proliferation , immune modulation , angiogenesis , and apoptosis . Based on the CV support and high fold changes , we chose six genes for qRT-PCR analysis . As seen in Table 1 , the fold changes observed with qRT-PCR closely match and validate the microarray results . To determine whether the microarray gene changes are due to direct miRNA targeting , we scanned the 3′UTRs of the affected genes for potential miRNA binding sites . We initially concentrated on the seed sequences of the miRNAs ( nts 2 through 8 ) , which are known to be critical determinants of miRNA target recognition [11] . A similar analysis showed that the presence of seed matches within 3′UTRs for tissue-specific miRNAs predicted low expression levels for the corresponding genes [26] . In our analysis , we first determined the number of perfect and single mismatch seed matches for each miRNA by scanning a total of 222 , 817 nts representing 116 3′UTRs compiled from the Ensembl database ( http://www . ensembl . org ) . As controls , all 3′UTR sequences were randomly shuffled ten times , creating unrelated sequences with conserved guanine-cytosine content , and seed match scans were repeated for each shuffle . As an additional control , the 116 3′UTRs were screened for Epstein Barr virus ( EBV ) –encoded miRNA seed binding sites . EBV miRNAs lack sequence homology to both human and KSHV miRNAs . The frequency for perfect KSHV miRNA seed matches was one in every 865 nts , compared to 1 , 358 nts for shuffled sequences and 1 , 400 nts for unrelated EBV miRNAs ( Table 2 ) . The observation that perfect seed matches for KSHV miRNAs are significantly ( p < 0 . 05 ) enriched within the 3′UTRs of genes identified by microarray analysis indicates that these genes are targeted by miRNAs . 3′UTRs of SPP1 , PRG1 , ITM2A , S100A2 , RAB27A , and THBS1 were also scanned by miRanda [27] , which predicted high probability miRNA binding sites ( scores above 190 and/or free energy values below −20 kcal/mol ) within their 3′UTRs ( Figure S3 ) . In particular , THBS1 was predicted to have 34 binding sites for 12 KSHV miRNAs . Together , the bio-statistical analyses and the confirmation by qRT-PCR strongly suggest that the observed gene expression changes are due to viral miRNA expression . To test this directly , we inserted the 3′UTRs of SPP1 , PRG1 , or THBS1 into the pGL3 promoter vector downstream of the luciferase gene . Co-transfection of these constructs into 293 pmiRNA cluster cells with a mixture of ten 2′OMe RNAs resulted in significant de-repression of luciferase activity as compared with an unrelated control 2′OMe RNA ( Figure 3A ) . These results show that the 3′UTRs of SPP1 , THBS1 , and PRG1 confer miRNA-dependent repression to a heterologous reporter , further supporting that the observed expression differences are miRNA-dependent . Western blot analysis was performed to determine whether decreased mRNA levels for SPP1 , S100A2 , and THBS1 translate into inhibition of translation and , subsequently , decreased protein abundance . As seen in Figure 3B , THBS1 expression was dramatically reduced ( >10-fold ) in KSHV miRNA-expressing 293 cells when compared to vector control . SPP1 and S100A2 protein levels were not affected even though mRNA levels were decreased 20 . 4- and 5 . 1-fold , respectively . Hence , for one out of three genes examined by Western blot , we detected reduced protein levels in miRNA- expressing cells . To ensure that KSHV miRNA-mediated repression of THBS1 is not a 293 cell line specific effect , we performed transient transfection assays in BJAB cells . Either vector control or miRNA cluster was transfected into BJAB cells along with the THBS1 luciferase reporter . Luciferase readouts showed a 2-fold reduction of luciferase in response to expression of the miRNAs ( Figure 3C ) . Thus , targeting of THBS1 by KSHV-encoded miRNAs can be observed in 293 cells and in BJAB , a lymphoid cell line . We next asked whether specific KSHV miRNAs may be responsible for decreased THBS1 expression levels . We performed luciferase de-repression assays by co-transfection of the THBS1 3′UTR reporter and 2′OMe RNAs specific to individual KSHV miRNAs . Our results demonstrate that THBS1 is targeted by multiple KSHV miRNAs; in particular , miR-K12–1 , miR-K12-3-3p , miR-K12-6-3p , and miR-K12–11 lead to strong de-repression of the reporter ( Figure 4A ) . Interestingly , the observed de-repression levels closely match with the number of miRanda-predicted high affinity binding sites ( Figure S3 ) in that the four miRNAs with the highest levels of repression also have the highest number of predicted binding sites . It is important to note that we observed higher levels of de-repression than those seen in Figure 3A . However , although the total amount of 2′OMe is the same in both experiments , the concentrations of each individual 2′OMe is 10-fold greater for Figure 4 . As an additional control we tested the 3′UTR of HS6ST2 in the same assay ( Figure 4B ) . The 3′UTR of HS6ST2 is 2 , 213 bp in length ( compared to 2 , 095 for THBS1 ) , is shown to have no expression change in response to the KSHV miRNAs via microarray , and was predicted by miRanda to have few miRNA binding sites ( unpublished data ) . As shown in Figure 4B , HS6ST2 showed virtually no difference in luciferase activity for all KSHV miRNAs , with the exception of miR-K12-6-3p . These results show that the inhibition of THBS1 by the KSHV miRNAs is specific . The reason for the 6-fold de-repression with miR-K12-6-3p is not clear as there is only one high affinity binding site predicted for HS6ST2; however , the de-repression shown with THBS1 is still greater than this . Thus , our data suggest that THBS1 is targeted by multiple KSHV miRNAs rather than a specific , individual viral miRNA . THBS1 is a matricellular glycoprotein with strong anti-proliferative and anti-angiogenic activity that can activate the latent form of TGF-β by direct binding through its RFK and WxxW motifs [28 , 29] . We asked whether TGF-β activity is decreased in cells that express KSHV miRNAs and , as a result , have decreased levels of THBS1 . To this end , we performed transient transfection assays using two different TGF-β–responsive promoters ( Figure 5 ) . SBE4 contains four Smad-binding elements and is commonly used to detect active TGF-β [30] . We also tested the native matrix metalloproteinase-9 promoter ( MMP-9 ) , which is activated in response to TGF-β signaling [31] . Reporter vectors were transfected into 293 pmiRNA cluster or vector control cells and cell lysates assayed for luciferase activity . Transcription from the SBE4 reporter was reduced 11-fold in cells expressing KSHV miRNAs , while MMP-9 promoter activity was inhibited 2-fold . It is important to note that transcript levels of both TGF-β and Smad3 were not altered as determined by gene expression profiling; thus , the decreased TGF-β activity is not due to miRNA-dependent inhibition of TGF-β or Smad3 . These data demonstrate that decreased THBS1 protein levels translate into downstream TGF-β signaling .
miRNA genes have been identified in all herpesviruses studied to date , and an important question remains as to how these novel regulators contribute to viral biology . Viral miRNAs may target viral and/or host cellular gene expression . The fact that miRNAs are not conserved between virus families suggests that each virus may have evolved to target diverse sets of genes to aid its specific replication strategy ( i . e . , tissue tropism for latent , lytic , and persistent infection ) . Observations made on genetically modified KSHV viruses suggest that most KSHV miRNAs are not required for lytic replication in culture [32] . We hypothesized that KSHV miRNAs , which are coordinately expressed within the latency-associated region , modulate host cellular gene expression to promote an environment conducive to latency and persistence in the infected host . One of the most problematic areas of miRNA biology is identifying their targets and functions . Due to the rather large number of human miRNAs and the limited homology requirements for binding , in silico–based attempts for miRNA target predictions have suggested a model by which thousands of genes are targeted [11] . However , in contrast to the genetically defined roles of some Caenorhabditis elegans and Drosophila melanogaster miRNAs , only a few vertebrate miRNA targets have been determined [10 , 12 , 33] . This study aimed to experimentally determine cellular genes targeted by virally encoded miRNAs . Ectopic expression of ten KSHV miRNA genes in 293 cells resulted in significant ( p < 0 . 001 ) expression differences of 81 genes , 65 of which were down-regulated ( Table S1 ) . These results were confirmed by qRT-PCR and bioinformatics analyses , which revealed a significant enrichment for seed sequence matches within the 3′UTRs of genes changed ( Tables 1 and 2 ) . Interestingly , the number of seed matches in our analyses varied significantly between miRNAs . For example , miR-K12–1 , K12-6-5p , and K12-9-5p show significantly increased hits and therefore may target more genes ( Figure S2 ) . In a related study , Sood et al . recently demonstrated that this seed sequence analysis is effective at predicting decreased expression levels for genes containing seed binding sites to tissue-specific miRNAs [26] . While the majority of observed gene changes were moderate decreases , eight genes showed greater than 4-fold down-regulation , including SPP1 , S100A2 , ITM2A , PRG1 , and THBS1 ( Table 1 ) . We ( i ) confirmed KSHV miRNA-dependent inhibition of the 3′UTRs of SPP1 , PRG1 , and THBS1 using luciferase de-repression assays in the 293 miRNA cluster cells , ( ii ) demonstrated inhibition of THBS1 in BJAB cells , and ( iii ) found decreased protein levels for THBS1 in the presence of KSHV miRNAs ( Figure 3 ) . Additionally , we observed that THBS1 is targeted by multiple KSHV miRNAs , with the major miRNA species being miR-K12–1 , miR-K12-3-3p , miR-K12-6-3p , and miR-K12–11 ( Figure 4A ) . It is important to note that the observed effects were specific to THBS1 since the 2 , 213-bp HS6ST2 3′UTR , although of comparable length , was not similarly de-repressed . Multiple miRNAs have previously been reported to target a single 3′UTR . Stark et al . found >50% of all predicted miRNA targets contain binding sites for more than one miRNA and that as many as 12 different miRNAs can target a single 3′UTR [34] . Generally , genes that require strict regulation have longer 3′UTRs and thus more binding sites for miRNAs . Additionally , for THBS1 , miRanda [27] predicted 34 high probability binding sites for 12 different KSHV miRNAs within the THBS1 3′UTR ( see Figure S3 ) . These bioinformatic predictions are well in agreement with the levels of de-repression seen for individual miRNAs . The fact that THBS1 has a long ( 2 , 095 bp ) 3′UTR and has binding sites for multiple KSHV miRNAs suggests that inhibition of THBS1 is important for KSHV biology . To investigate the biological effects of altered THBS1 levels , we utilized TGF-β–responsive reporter constructs and demonstrated decreased TGF-β activity ( Figure 5 ) . It is important to note that transcript levels of TGF-β and Smad3 were not altered in response to miRNA expression; thus , the decreased TGF-β activity is not due to miRNA inhibition of TGF-β or Smad3 . Of those genes exhibiting the highest down-regulation in response to miRNAs , five are involved in proliferation , immune modulation , angiogenesis , and apoptosis pathways often altered in cancer . SPP1 is a secreted phosphoprotein also called osteopontin , which is known to interact with a variety of integrins as well as with CD44 , and plays a role in cell-mediated immunity . SPP1 also has demonstrated anti-proliferative activity against virally infected mucosal epithelial cells [35 , 36] . S100A2 is a calcium-binding protein that is down-regulated in many tumor types and is thought to possess tumor suppressor activity . Additionally , S100A2 interacts with the p53 family proteins p67 and p73 and increases transcriptional activity of p53 [37 , 38] . ITM2A is an integral membrane protein that may play a role in selection of T cells in the thymus and may also function in cartilage development [39] . PRG1 is a granule proteoglycan secreted by hematopoietic cells and is involved in apoptosis [40] . THBS1 is a matricellular protein that functions in cell–cell and cell–matrix adhesion and is down-regulated in a number of human cancers . THBS1 possesses both anti-proliferative and anti-angiogenic activity [28 , 41] . Angiogenesis is a hallmark of KS tumors , and the inhibition of THBS1 would aid not only this process but also proliferation of KSHV-infected endothelial-derived KS tumor cells . Interestingly , Tarabolleti et al . reported that THBS1 expression is suppressed in KS lesions [22] . However , to date , this observation has not been linked to viral protein expression in KS lesions . THBS1 is also a strong immune stimulator that can recruit monocytes to sites of vascular injury [42] and regulate T cell migration through extracellular matrix [43] . Thus , down-regulation of THBS1 may also aid in immune evasion of KSHV-infected cells . While such connections are tempting , we realize the limitations of examining the effects of viral miRNAs on a single cell line . However , 293 cells support both latent and lytic KSHV infection in vitro , and their high transfection efficiency permitted experimental confirmation of a limited number of targets by using miRNA-specific reporter assays . We have also shown that THBS1 is inhibited in a lymphoid cell line , BJAB , in a transient assay , indicating that our results are not restricted to the 293 cell line . In conclusion , this study yielded a starting list of potential KSHV miRNA targets , which warrants a further detailed analysis in all cell types infected with KSHV in vivo ( lymphoid , epithelial , and endothelial cells ) . Our approach to identify miRNA targets by ectopic expression , gene expression profiling , reporter assays , and bio-statisticial analysis has revealed cellular targets with potential roles in KS biology . These data strongly suggest that virally encoded miRNAs may directly contribute to pathogenesis and potentially tumorigenesis in the infected host .
For generation of pmiRNA cluster , a region from 119 , 635 to 122 , 481 nt within the KSHV genome [44] was amplified from BCBL-1 genomic DNA and inserted into pcDNA3 . 1/V5/HisA ( Invitrogen , http://www . invitrogen . com ) . For plasmids expressing individual miRNAs , a region of approximately 200 nts surrounding each pre-miRNA hairpin was amplified from the cluster-containing plasmid and inserted into pcDNA3 . 1/V5/HisA . Stable 293 cell lines were generated by Effectene ( Qiagen , http://www . qiagen . com ) transfections followed by G418 selection for 4 wk at 100 μg/ml . Luciferase reporter plasmids were created using the pGL3 promoter vector from Promega ( http://www . promega . com ) . Synthetic oligonucleotides containing two complete complimentary copies of a miRNA sequence separated by a 9-bp-long spacer were inserted into the 3′UTR of the luciferase gene upstream of the poly-adenylation signal . All primer sequences are annotated in Table S2 . Lipofectamine 2000 ( Invitrogen ) was used to transfect luciferase reporter constructs with or without miRNA expressing vectors or 2′OMe RNA ( Dharmacon , http://www . dharmacon . com ) complementary to a miRNA of interest . Next , 4 × 105 cells were seeded into a 6-well plate , transfected , and then incubated for 72 h . Cells were lysed with 250 μL of Cell Lysis Buffer ( Promega ) and 10 μL of lysate assayed for luciferase activity ( Promega ) . Light units are normalized to total protein , determined using the BCA protein assay kit ( Pierce , http://www . piercenet . com ) according to manufacturer's instructions . All transient transfection experiments were performed twice in triplicate . Microarray experiments were performed using 293 cells stably transfected with pmiRNA cluster or vector control . For each cell line , four independent cultures of 10-cm plates at 80% confluence were used for RNA isolation . RNA isolation was performed using the RNeasy kit as directed by the manufacturer ( Qiagen ) . RNA was labeled using the GeneChip Eukaryotic One-Cycle Target Labeling Assay as directed by Affymetrix ( http://www . affymetrix . com ) . Labeled target cRNA was used to interrogate Affymetix U133 2 . 0 plus human GeneChips that were hybridized for 16 h at 45 °C . After hybridization , the chips were washed and stained using Affymetrix fluidics protocol EukGE-WS2v5_450 . Arrays were scanned with an Affymetrix GeneChip 3000 scanner and normalization of signal intensity was performed using dChip [45] . The expression level was modeled using the perfect match only model . Probe sets whose hybridization signal intensities exhibited a significant difference ( p < 0 . 001 using a random variance model ) between pmiRNA cluster and pcDNA control cells were identified using algorithms implemented in BRB ArrayTools developed by Dr . Richard Simon and Amy Peng Lam ( http://linus . nci . nih . gov/BRB-ArrayTools . html ) . A hierarchical clustering algorithm was used to visually display the expression profiles of the probe sets found to be significant between the groups . RNA was reverse-transcribed using SuperScript III Reverse Transcriptase ( Invitrogen ) in the presence of random hexamers according to the manufacturer's protocols . qRT-PCR was performed using an Opticon II ( MJ Research , http://www . bio-rad . com ) and Dynamo HS SYBR-Green qPCR kit using cycling conditions as recommended by kit manufacturer ( Finnzymes , http://www . finnzymes . fi ) . Primers were designed across exon boundaries using Vector NTI ( Invitrogen ) and are provided in Table S2 . PCR signals were normalized to β-actin and fold changes reported as 2ΔΔCt . For Northern blot analysis , 30 μg of total RNA was loaded onto 12% 8M urea acrylamide gel and transferred onto Genescreen Plus . Probe labeling was performed using the StarFire oligonucleotide labeling system ( IDT , http://www . idtdna . com ) . Immunoblotting was performed as previously described [16] . All primary antibodies were purchased from Santa Cruz Biotechnology ( http://www . scbt . com ) . HRP-conjugated secondary antibodies were purchased from Jackson ImmunoResearch ( http://www . jacksonimmuno . com ) and blots were developed with PicoWest substrate ( Pierce ) . The 3'UTR sequences of 116 genes were obtained from Ensembl ( http://www . ensembl . org ) . 3′UTRs were then analyzed to extract all potential miRNA binding sites using an ad-hoc scanning program specifically developed to look at seed match binding . The program looks for occurrences of any number of user-provided patterns , each of which represents a target for a different miRNA , and accepts both exact matches or near-exact matches ( with at most one mismatched nucleotide ) . For each sequence , the program returns a list containing the positions of the exact matches , and a second list containing the positions of the near-exact matches and the position of the mismatched base within each matched subsequence .
Ensembl ( http://www . ensembl . org ) accession numbers for genes listed in this paper are HS6ST2 ( ENST00000370837 ) , ITM2A ( ENST00000373298 ) , PRG1 ( ENST00000242465 ) , RAB27A ( ENST00000336787 ) , S100A2 ( ENST00000368711 ) , SPP1 ( ENST00000237623 ) , and THBS1 ( ENST00000260356 ) . The Entrez Nucelotide ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=Nucleotide ) accession number for the KSHV miRNA cluster is AY973824 . The Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/projects/geo ) accession number for the microarray data is GSE7554 .
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Kaposi sarcoma–associated herpesvirus ( KSHV ) is a gamma-herpesvirus associated with Kaposi sarcoma , primary effusion lymphoma , and a subset of muticentric Castleman disease . Recently , it was found that KSHV encodes 12 microRNAs ( miRNAs ) within its latency-associated region . miRNAs are small ∼22 nucleotide-long single-stranded RNA molecules that act to inhibit gene expression by binding to target messenger RNAs ( mRNAs ) . Because miRNAs bind to these targets with limited base pairing , it has been difficult to find targets . The goal of our study was to identify cellular mRNAs targeted by KSHV-encoded miRNAs . Microarray analysis of cells expressing the KSHV miRNAs revealed a set of 81 genes that were changed . Several genes are regulators of important functions such as blood vessel growth , cell proliferation , and cell death . One target , thrombospondin 1 , is a potent inhibitor of blood vessel growth and is known to be down-regulated in Kaposi sarcoma tumors . Thrombospondin 1 , which is targeted by multiple miRNAs , also showed reduced protein levels in our study . To our knowledge , our data describe the first targets for tumorvirus-encoded miRNAs and suggest that these novel regulators may have roles in pathogenesis .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"microrna",
"targets",
"kshv",
"virology"
] |
2007
|
Identification of Cellular Genes Targeted by KSHV-Encoded MicroRNAs
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While sexual reproduction is pervasive in eukaryotic cells , the strategies employed by fungal species to achieve and complete sexual cycles is highly diverse and complex . Many fungi , including Saccharomyces cerevisiae and Schizosaccharomyces pombe , are homothallic ( able to mate with their own mitotic descendants ) because of homothallic switching ( HO ) endonuclease-mediated mating-type switching . Under laboratory conditions , the human fungal pathogen Candida albicans can undergo both heterothallic and homothallic ( opposite- and same-sex ) mating . However , both mating modes require the presence of cells with two opposite mating types ( MTLa/a and α/α ) in close proximity . Given the predominant clonal feature of this yeast in the human host , both opposite- and same-sex mating would be rare in nature . In this study , we report that glucose starvation and oxidative stress , common environmental stresses encountered by the pathogen , induce the development of mating projections and efficiently permit same-sex mating in C . albicans with an “a” mating type ( MTLa/a ) . This induction bypasses the requirement for the presence of cells with an opposite mating type and allows efficient sexual mating between cells derived from a single progenitor . Glucose starvation causes an increase in intracellular oxidative species , overwhelming the Heat Shock transcription Factor 1 ( Hsf1 ) - and Heat shock protein ( Hsp ) 90-mediated stress-response pathway . We further demonstrate that Candida TransActivating protein 4 ( Cta4 ) and Cell Wall Transcription factor 1 ( Cwt1 ) , downstream effectors of the Hsf1–Hsp90 pathway , regulate same-sex mating in C . albicans through the transcriptional control of the master regulator of a-type mating , MTLa2 , and the pheromone precursor-encoding gene Mating α factor precursor ( MFα ) . Our results suggest that mating could occur much more frequently in nature than was originally appreciated and that same-sex mating could be an important mode of sexual reproduction in C . albicans .
Sexual reproduction is a driving force for evolution and is prominent in eukaryotic organisms , with fungi adopting highly diverse strategies for sexual mating and reproduction [1 , 2] . The human fungal pathogen C . albicans is a leading cause of death due to mycotic infection , with mortality rates approaching 40% , even with current treatments [3] . C . albicans has long been thought to be asexual until the discovery of a highly complex parasexual program . In C . albicans , heterothallic ( opposite-sex ) mating between diploid a and α cells occurs to generate tetraploid a/α intermediates [4 , 5] . These tetraploid mating products undergo a parasexual process of concerted chromosome loss to generate diploid and aneuploid progeny rather than adopting a more traditional meiotic cycle [6 , 7] . As an additional layer of complexity , an epigenetic switch from the white cell type to the opaque cell type is required for efficient mating to occur [8] . Opaque a and α cells secrete a sex-specific pheromone and induce the formation of mating projections in cells with an opposite Mating type locus ( MTL ) type , thus initiating cell fusion and mating [9] . Besides differences in mating competency , white and opaque cells also differ in a number of aspects , including metabolic profiles , filamentation ability , susceptibility to antifungals , interactions with host immune cells , and virulence in different infection models [10–12] . Given the predominately clonal nature of C . albicans [13 , 14] , the frequency with which heterothallic mating occurs in nature appears remarkably low . In the model yeast S . cerevisiae , most natural isolates are homothallic and able to undergo clonal mating because of the expression of homothallic switching ( HO ) endonuclease and mating-type switching [1] . However , C . albicans does not have a homolog of HO endonuclease and is unable to undergo mating-type switching and subsequent clonal mating [15] . Although same-sex mating ( homothallism ) has been reported in C . albicans , this unisexual mating has only been observed between two a cells , when α cells are present and secrete α-pheromone ( ménage à trois mating ) , or in strains in which the BARrier 1 ( Bar1 ) protease that degrades α-pheromone has been inactivated [16] . However , there is no evidence of natural ménage à trois mating , and no natural mutants of BAR1 have been reported . Given the barriers to opposite- and same-sex mating , the biological relevance of sexual reproduction in C . albicans remains elusive . Although C . albicans mating seems to be rare in nature , environmental stressors have been reported to promote loss of heterozygosity at the MTL , drive white-to-opaque switching , and result in concerted chromosome loss , suggesting that stress may be a trigger for sexual reproduction [17] . Nutrient starvation and oxidative stresses are two of the most common environmental stressors experienced by C . albicans in nature . Here , we establish that glucose starvation and oxidative stress efficiently induce the expression of sexual pheromone precursors , leading to the formation of mating projections and enabling homothallic mating in C . albicans . A core cellular stress-responsive pathway , mediated by the molecular chaperone Hsp90 and the heat-shock transcription factor Hsf1 , is involved in this regulation through the downstream regulator Cwt1 . Cwt1 functions through the direct control of the master regulator of a-type mating MTLa2 , which regulates the expression of pheromone-encoding genes . Our study identifies a mechanism by which mating could occur much more frequently in nature than was originally appreciated , uncovers a core cellular stress-response pathway regulating this response , and sheds new light , to our knowledge , on the biology of C . albicans .
Despite the fact that cellular stressors promote loss of heterozygosity at the MTL and induce the white-to-opaque switch [17 , 18] , there still remains no evidence to directly support the hypothesis that environmental perturbations regulate sexual mating in C . albicans . In the human host , C . albicans resides on mucosal surfaces , niches that are often glucose limited [19] . Therefore , we cultured C . albicans cells in the absence of glucose but in the presence of 0 . 25% K2HPO4 , which acts as a pH-buffering reagent . We named this modified medium as YP-K ( 1% yeast extract , 2% peptone , 0 . 25% K2HPO4 , w/v ) and the control medium as YPD-K ( 1% yeast extract , 2% peptone , 2% glucose , 0 . 25% K2HPO4 , w/v ) . Opaque cells were used for all experiments in this study because they are the mating-competent form in C . albicans [8] . Upon spotting an MTLa/a C . albicans strain ( GH1013 ) [20] , we noted a wrinkled colony morphology on YP-K agar after five days of growth ( Fig 1A ) . Surprisingly , a portion of cells underwent polarized cell growth ( Fig 1A ) that was elongated and irregular in shape , closely resembling mating projections as opposed to the typical hyphal morphology [21] . When the same strain was cultured on the glucose-containing YPD-K medium , the colonies remained smooth , and cells were exclusively in the yeast form ( Fig 1A ) . This was a dose-dependent effect because incremental increases in glucose levels resulted in incremental decreases in the number of polarized cells observed ( S1 Fig ) . We also observed the development of polarized cells on YP-K medium for three independent clinical isolates of C . albicans with an MTLa/a genotype ( P37005 , L26 , and SZ306 ) ( Fig 1B ) , suggesting that this inducing effect of glucose starvation is a general feature in MTLa/a strains . The induction of mating projections was not observed in any MTLa/α and MTLα/α strains under the same culture conditions . To verify that the elongated cells were true mating projections but not pseudohyphae or true hyphae , we examined the relative expression of mating-related genes using quantitative reverse transcription PCR ( qRT-PCR ) ( Fig 1C ) . Compared to the YPD-K cultures , the relative expression levels of Mating type A1 ( MFA1 ) ( a precursor of a-pheromone ) , MFα ( a precursor of α-pheromone ) , STErile 2 ( STE2 ) ( the α-pheromone receptor ) , and Factor-Induced Gene 1 ( FIG1 ) and cell FUSion 1 ( FUS1 ) ( two pheromone-response genes ) , as well as BAR1 ( an endopeptidase that degrades α-pheromone ) , were all significantly increased in cells grown on YP-K . We also constructed four reporter strains in which MFA1 , MFα , FIG1 , and FUS1 were fused with a green fluorescent protein ( GFP ) fluorescent marker to further confirm their increased expression on YP-K medium ( S2 Fig ) . Thus , glucose depletion leads to the induction of mating-related genes and polarized cell growth , indicative of the development of mating projections in C . albicans . The induction of both a-pheromone and α-pheromone precursors in MTLa/a cells under glucose-starvation conditions suggests those cells might have lost their original sexual identity , exhibiting features of both MTLa/a and MTLα/α cells . This , in theory , could bypass the requirement of an MTLα/α cell in close proximity to induce homothallic mating between two MTLa/a partners . To further explore whether the induction of mating-related genes and polarized cell growth could lead to true homothallic mating , we performed quantitative same-sex mating assays . When two MTLa/a strains with different auxotrophic markers ( GH1350a and GH1013 ) were cultured together on YP-K medium , mating projections were observed ( Fig 2A ) , and tetraploid progeny that remained the a mating type were generated ( Fig 2B and 2C ) , confirming homothallic mating had occurred . Further , cell fusion between two MTLa/a cells and the generation of daughter cells were also observed on YP-K medium ( Fig 2A ) . In contrast , cells remained in yeast form and no mating progeny were isolated when the two MTLa/a strains were grown on YPD-K ( Fig 2 ) . These results provide the premier example of an environmentally relevant stress , glucose starvation , capable of inducing same-sex mating in C . albicans . Glucose deprivation leads to a shift in C . albicans metabolism , resulting in the activation of genes involved in the tricarboxylic acid ( TCA ) cycle and fatty acid β-oxidation [19] . These metabolic changes stimulate the production of reactive oxidative species ( ROSs ) that cause protein damage , thereby activating the unfolded protein response [22] . The intracellular levels of ROSs in cells of C . albicans grown on YP-K medium were significantly higher than those on YPD-K medium and increased with the extension of culture time ( Fig 3A ) . To assess whether oxidative stress could stimulate homothallic mating , C . albicans cells were treated with hydrogen peroxide ( H2O2 ) , a strong oxidative-stress–inducing agent . Compared to the untreated control , H2O2-treated cells underwent obvious polarized cell development on the glucose-containing medium YPD-K ( Fig 3B ) . Mating-related genes ( MFA1 , MFα , and FIG1 ) were significantly induced in H2O2-treated cells ( Fig 3C ) , and mating efficiency upon H2O2 treatment was comparable to that observed under glucose-deprivation conditions ( Fig 3D ) . To further test other environmentally relevant stress conditions , we performed same-sex mating assays using three other nutrient-poor media types: 3% agar with no additional nutrients , agar containing 3% mouse feces , and agar containing C . albicans debris . C . albicans underwent same-sex mating when cultured on these media types , with mating on mouse-feces–containing medium being most efficient ( S3 Fig ) . Notably , although mating projections morphologically resemble filaments in C . albicans , same-sex mating did not occur when cells were cultured on sorbitol medium that induces opaque cell filamentation ( S3E Fig ) . Therefore , multiple nutrient-deprivation conditions are capable of inducing homothallic mating in C . albicans . To explore the mechanism of glucose-induced same-sex mating in C . albicans , we performed global gene-expression–profile analysis . Total RNA was extracted from opaque cells grown on YPD-K or YP-K media at 25°C for 60 hours , and the samples collected were used for RNA sequencing ( RNA-Seq ) assays . We incubated cells for only 60 hours because mating projections were not induced at this time point , enabling us to minimize indirect effects on gene expression . As demonstrated in Fig 4 and S1 Data , we found 412 genes up-regulated in YPD-K medium and 408 genes up-regulated in YP-K medium ( with a change greater than 1 . 5-fold ) . As expected , a number of mating-related genes—including MFA1 , BAR1 , Candida ERK-family protein kinase ( CEK ) 1 , and CEK2—were up-regulated in YP-K medium . Heat-shock-protein–encoding genes and oxidative-stress–responsive genes were also up-regulated in YP-K medium , whereas cell-wall–related and glycosylphosphatidylinisotol ( GPI ) -anchored-protein–encoding genes were enriched in YPD-K medium . Of the differentially expressed genes , 49 have been reported as HSP90 genetic interactors ( Fig 4A and S2 Data ) [23 , 24] . Among the differentially expressed HSP90 genetic interactors , 34 genes were down-regulated and only 15 genes were up-regulated in YP-K medium . These genes were enriched in gene functions associated with stress response , cell wall , transcriptional regulation , and signaling transduction . Several observations led us to hypothesize that Hsp90 could play an important role in the regulation of glucose-starvation–induced mating-projection formation and same-sex mating . First , glucose starvation increased the intracellular level of ROSs ( Fig 3A ) that would contribute to increased protein damage , thus likely overwhelming the functional capacity of Hsp90 . Second , the treatment of oxidative reagents ( e . g . , H2O2 ) that led to the increase of intracellular ROSs induced mating-projection formation and same-sex mating in C . albicans ( Fig 3B , 3C and 3D ) . Third , a number of heat-shock-protein–encoding genes , including HSP90 , were up-regulated in the absence of glucose , further suggesting that Hsp90 functional capacity was overwhelmed ( Fig 4B and S1 Data ) . The conserved heat-shock transcription factor Hsf1 plays an important role in regulating the expression of heat-shock proteins , including the molecular chaperone Hsp90 in C . albicans and other eukaryotes [25 , 26] . Hsf1 has been implicated in regulating transcriptional changes in response to oxidative stresses and glucose starvation in S . cerevisiae [27 , 28] . Therefore , we first tested the role of Hsf1 in the development of mating projection and homothallic mating in C . albicans . Since Hsf1 is essential for cell viability , we generated a tetracycline-induced ( tetON ) -promoter–controlled conditional expression mutant , tetON-HSF1/hsf1 , in order to assess how changes in Hsf1 levels influence same-sex mating . In the absence or presence of 40 μg/mL doxycycline , the tetON-HSF1/hsf1 mutant formed wrinkled colonies and underwent robust polarized growth on YP-K medium ( Fig 5A ) . Of note , doxycycline ( 40 μg/mL ) exhibited an inhibitory effect on the induction of mating projection formation in the wild-type ( WT ) control ( Fig 5A ) . With increased exposure to glucose on YPD-K medium , mating projections were only observed in the absence of doxycycline after five days but not upon the addition of 40 μg/mL doxycycline nor in the WT control ( Fig 5A ) . Consistent with the morphological changes , mating-related genes—including MFA1 , MFα , FIG1 , and FUS1—were induced in the tetON-HSF1/hsf1 mutant on both YP-K and YPD-K media relative to the WT strain ( Fig 5B ) . Finally , we tested the effect of HSF1 depletion on homothallic mating . In the absence of doxycycline , the tetON-HSF1/hsf1 mutant underwent same-sex mating with the WT partner even on the glucose-containing YPD-K medium , albeit at a lower frequency than that observed in glucose-limiting conditions ( S1 Table and S4 Fig ) . However , in the presence of 40 μg/mL doxycycline , neither the tetON-HSF1/hsf1 mutant nor the WT control could undergo homothallic mating on YPD-K medium . Taken together , our results indicate that down-regulation of Hsf1 promotes mating-projection formation and same-sex mating in C . albicans , bypassing the requirement of glucose starvation . Next , we wanted to evaluate whether the impact of Hsf1 on homothallic mating might be mediated through its regulatory effects on HSP90 . We constructed a tetON-HSP90/hsp90 strain using an analogous tetON-promoter–controlled conditional expression strategy . As shown in Fig 6 , culturing this strain in media containing 40 μg/mL doxycycline was required to bypass a substantial growth defect of the strain . Further , opaque cells of the conditional tetON-HSP90/hsp90 strain were not stable , and therefore an ACTin 1 ( ACT1 ) promoter controlling White–Opaque Regulator 1 ( WOR1 ) , the master regulator of the opaque phenotype , was introduced in the mutant to maintain the opaque state . Similar to the tetON-HSF1/hsf1 mutant , the tetON-HSP90/hsp90 strain underwent the development of mating projections on both YP-K and YPD-K media containing 40 μg/mL doxycycline ( Fig 6A ) . However , the WT control was unable to form mating projections under the same culture conditions ( Fig 6A ) . In the presence of 100 μg/mL doxycycline , the ratio of projected cells on YPD-K medium was remarkably reduced ( Fig 6A ) . As expected , mating-related genes FIG1 and FUS1 were induced on YP-K medium , while MFA1 and MFα were induced in the tetON-HSP90/hsp90 mutant on both media relative to the WT strain in the presence of 40 μg/mL doxycycline ( Fig 6B ) . As demonstrated in S4A Fig , the relative transcript levels of HSF1 or HSP90 in the tetON-HSF1/hsf1 or tetON-HSP90/hsP90 mutant were lower than that in the WT strain even in the presence of 40 μg/mL doxycycline . Together , these data suggest that reduction of HSF1 or HSP90 levels leads to the induction of a homothallic mating program and bypasses the requirement for glucose depletion . To further characterize the regulatory mechanism of glucose starvation-induced mating , we screened a transcription factor homozygous deletion library ( in an MTLa/a background ) [29] to look for mutants capable of enhanced mating projection formation . Through this functional genomic screening approach , we identified the transcription factors Cwt1 and Cta4 that function as negative and positive regulators of the development of mating projections in C . albicans , respectively ( S5 Fig and S6 Fig ) . Homozygous deletion of CWT1 , a gene involved in the nitrosative stress response [30] , resulted in an increase in mating-projection formation on YP-K medium at three days compared to the WT control . Unlike the WT control that grew exclusively as yeast on YPD-K , cwt1/cwt1 mutants also showed polarized growth ( S5A Fig ) . As expected , mating-related genes MFA1 , MFα , FIG1 , and FUS1 were significantly increased in the cwt1/cwt1 mutant on YPD-K relative to a WT control ( S5B Fig ) . To verify the function of Cwt1 in regulating homothallic mating , we generated an additional homozygous CWT1 deletion mutant in the GH1013 background . Similar to the library mutant , the newly generated cwt1/cwt1 strain exhibited a more robust development of mating projections on both YP-K and YPD-K than the WT control ( Fig 7A and 7B ) , and also promoted same-sex mating on YPD-K medium ( S1 Table ) . Thus , Cwt1 represses homothallic mating in C . albicans . In contrast , deletion of CTA4 , a previously identified genetic interactor of Hsp90 [23 , 24] and a gene induced by nitric oxide , significantly repressed the development of mating projections in YP-K medium in C . albicans ( S6A Fig ) . Consistently , the expression of mating-related genes was not induced in the cta4/cta4 mutant in YP-K medium , unlike in the WT control ( S6B Fig ) . However , the relative expression levels of CWT1 were significantly increased in the cta4/cta4 mutant , and chromatin immunoprecipitation ( ChIP ) assays demonstrated that Cta4 bound to the promoter regions of CWT1 on YP-K medium ( S6C Fig ) . Thus , Cta4 may provide a functional connection between Hsp90 and Cwt1 and directly regulate the transcriptional expression of CWT1 . Consistently , we found that the transcription level of CWT1 was down-regulated in YP-K medium compared to the level observed in YPD-K medium ( Fig 7C ) . Moreover , in the tetON-HSF1/hsf1 background in which levels of HSF1 were reduced relative to a WT control , we also observed a significant reduction in CWT1 transcript levels ( Fig 7D ) , suggesting that the transcriptional expression of CWT1 is directly or indirectly regulated by Hsf1–Hsp90 signaling . Protein sequence analysis indicated that Cwt1 contains a Zn2Cys6 motif at the carboxyl terminus and a conserved Per-Arnt-Sim ( PAS ) domain at the amino terminus . Since the PAS domain often interacts with Hsp90 in eukaryotic organisms [31 , 32] , we next tested whether Hsp90 was able to bind to Cwt1 and regulate its activity in C . albicans . As shown in Fig 7E , co-immunoprecipitation ( IP ) assays indicated that Hsp90 and Cwt1 physically interact on YP-K , but not YPD-K , medium . Therefore , Hsf1–Hsp90 signaling may regulate the activity of Cwt1 at post-transcriptional levels as well as at the transcriptional level via Cta4 in C . albicans . MTLa2 is required for the maintenance of a-cell identity [33] . Inactivation of MTLa2 induces the expression of α-pheromone in MTLa/a cells of C . albicans [33] . Given our observation that glucose starvation induced the expression of both pheromone precursors MFA1 and MFα , we assessed whether homothallic mating induced in glucose-limiting conditions was governed by changes in MTLa2 levels . To test this , we overexpressed MTLa2 and observed a suppression in the development of mating projections under glucose-limiting conditions ( Fig 8A and 8B ) . Next , to test whether compromise of Hsf1–Hsp90–Cwt1 signaling impaired MTLa2 expression , we monitored MTLa2 levels in our tetON-HSF1/hsf1 and tetON-HSP90/hsp90 mutants . Down-regulation of HSF1 or HSP90 or deletion of CWT1 led to significantly decreased expression of MTLa2 on YPD-K medium ( Fig 8A ) , implicating this stress-response signaling in the regulation of MTLa2 expression . It has been indicated that Cwt1 has potential binding sites in the promoter regions of MTLa2 and MFα genes [34] . Using ChIP assays , we observed that Cwt1 directly binds to the promoter regions of both MTLa2 and MFα on YPD-K medium in the one-day cultures , and this binding activity was observed on both on YP-K and YPD-K media in the three-day cultures ( Fig 8C ) . These results suggest that Cwt1 directly regulates mating-related gene expression . Overall , our data suggest a model in which Hsf1–Hsp90 signaling controls the expression of Cwt1 through Hsp90 genetic interactors such as Cta4 as well as the activity of Cwt1 post-translationally through a physical interaction with Hsp90 . Cwt1 is a dimeric Zn2Cys6 zinc-finger transcription factor . The physical interaction between Hsp90 and Cwt1 could inhibit the dimerization of Cwt1 through the PAS domain in C . albicans , as observed in other eukaryotic organisms [32] . Cwt1 binds to the promoters of MFα or MTLa2 and regulates their transcriptional expression ( Fig 8D ) . It has been demonstrated that same-sex mating in C . albicans is induced by the autocrine and/or paracrine pheromone response [16] . Therefore , the activated pheromone signaling by the environmental cues could then promote the development of mating projections and same-sex mating , possibly through these two response modes ( Fig 9 ) .
The predominantly clonal nature of C . albicans greatly limits the occurrence of heterothallic mating between two competent cells with opposite mating types in its natural environment . It would be difficult for a mating-competent cell of C . albicans to find a competent cell with an opposite mating type to mate . Although the efficiency of same-sex mating is much lower than that of opposite-sex mating under laboratory conditions in C . albicans [16] , same-sex mating involving cells of a single type would lower the barrier to finding a suitable partner [35] . However , the requirement of α-pheromone production in close proximity to drive homothallic mating between two MTLa cells or the need to inactivate the Bar1 protease challenges the relevance of same-sex mating in nature [16] . This is especially true given that neither natural C . albicans mutants with loss of Bar1 function nor environmental conditions suppressing Bar1 activity have been discovered . Thus , the question remains: can C . albicans undergo frequent same-sex mating in natural environments ? In our current study , we find that glucose starvation and oxidative stress induce same-sex mating in MTLa cells of C . albicans , providing an environmentally relevant means to drive sexual reproduction in this species . C . albicans is commonly exposed to glucose starvation because glucose is limited in its natural niches such as the mouth , lower gut , and environments contaminated with human or animal excreta . However , under the same conditions , we did not observe same-sex mating in opaque Mating type α ( MATα ) cells , perhaps because of MTLa2 , a key regulator in this induction in MTLa cells . MATα/α cells may use a different environmental cue for the induction of same-sex mating . Similar to reports highlighting that the fungal pathogen Cryptococcus neoformans undergoes mating on pigeon guano [36] , we found that animal feces promoted same-sex mating in C . albicans ( S3 Fig ) . Given that the gut of human or warm-blooded animals is a major natural niche for C . albicans [12] , animal feces would be a major source for most nutritional components . Furthermore , growth on C . albicans debris medium containing no additional nutrients also induced same-sex mating ( S3 Fig ) . When yeast grows on agar-only medium , a portion of cells undergo cell death and release nutrients for the growth and survival of neighboring cells [37] . Under these glucose-limiting conditions , the frequency of same-sex mating in C . albicans ranged from 1 × 10−7 to 3 × 10−5 ( S3 Fig ) , suggesting that the average-aged colony ( containing approximately 1 × 108 cells ) can produce tens to hundreds of mating progeny . Therefore , the occurrence of same-sex mating under glucose starvation conditions could be considerably more frequent in nature than was originally thought . The notion that environmental stress may serve as a trigger to induce more frequent sexual reproduction in C . albicans has been previously proposed based on a series of observations [38] . Poor nutritional conditions increase the frequency of opposite-sex mating [39] . Oxidative stress promotes the induction of the white-to-opaque switch [40] , and recombination occurs more frequently following exposure to several types of stress , which would provide a critical step for the homozygosis of the MTL locus . Despite these lines of evidence , our work provides the seminal example of stressful conditions governing homothallic mating in C . albicans . In C . neoformans , sexual mating is stimulated by stresses such as nitrogen starvation , desiccation , and darkness [41] . Moreover , treatment of S . pombe with the oxidative agent H2O2 promotes the generation of meiotic spores [42] . Therefore , exposure to harsh environmental conditions could be a general signal for diverse fungal species to integrate environmental response pathways with increased sexual reproduction . The evolutionarily conserved regulators Hsf1 and Hsp90 play a primary and global role in orchestrating stress responses in eukaryotic organisms [43] . Glucose starvation results in the production of ROSs ( Fig 3 ) that would likely cause protein damage in C . albicans . Under these situations , the functional capacity of Hsp90 could be overwhelmed , causing it to be titrated away from its basal client proteins while it deals with more global problems of protein misfolding . Consistently , a number of heat-shock-protein–encoding genes , including HSP90 , were up-regulated in response to glucose deprivation ( Fig 4 and S1 Data ) . In our study , we implicate the Cta4 and Cwt1 transcription factors , which regulate the nitrosative or nitric oxide stress response [30] , as downstream effectors of the Hsf1–Hsp90 signaling and mating-response pathways ( Fig 8 ) . Cta4 has been previously reported as a genetic interactor of Hsp90 [23 , 24] and represses the transcriptional expression of CWT1 . Moreover , we demonstrate that Hsp90 directly binds to Cwt1 on YP-K but not YPD-K medium , perhaps through the conserved PAS motif of Cwt1 . To our knowledge , this is the first time that Hsf1 or Hsp90 have been implicated in sexual mating in fungi , and our results suggest that diverse cellular stresses capable of overwhelming the function of these regulators , including elevated temperature , may also promote sexual reproduction in C . albicans . Inactivation of the Bar1 protease or the Dipeptidyl aminopeptidase YC1 ( Yci1 ) domain protein Opaque Formation Regulator 1 ( Ofr1 ) has been shown to induce same-sex mating in C . albicans [16 , 44] . We observed that BAR1 was not down-regulated but rather highly induced upon glucose starvation ( Fig 1 ) . Further , inactivation of Ofr1 allows MTLa/α cells to undergo same-sex mating as the “a” mating type [44] . Although glucose starvation can only induce same-sex mating in MTLa/a cells but not in MTLa/α or MTLα/α cells , other unidentified environmental conditions may allow MTLa/α or MTLα/α cells to undergo homothallic mating . Therefore , glucose-starvation–induced same-sex mating appears to be independent of Bar1 and Ofr1 . In summary , we uncover a novel , to our knowledge , environmental trigger , glucose depletion , capable of acting as a signal for sexual mating in C . albicans , which not only sheds light on the biology of this pathogen but also expands the diverse repertoire of sexual reproduction modes in fungi . This strategy is different from that used by S . cerevisiae , S . pombe , and C . neoformans , despite the fact that the evolutionarily diverse yeast species achieve a same output for homothallism . Unisexual reproduction generates aneuploidy and de novo phenotypic diversity in fungi [45 , 46] , thus providing a selective advantage under stressful conditions over those organisms propagating exclusively in an asexual manner . C . albicans exists as an obligate diploid in nature , with many heteryozygous loci between homologous chromosomes . Therefore , when tetraploid intermediate cells generated from same-sex mating return to a lower ploidy state , there is substantial opportunity for the generation of genetically diverse progeny . These tetraploid strains could serve as a capacitor for generating distinct aneuploidies and randomly combined chromosome sets in order to facilitate the evolution of new traits to adapt to changing environments .
The strains used in this study are listed in supplementary S2 Table . YPD ( 20 g/L peptone , 10 g/L yeast extract , 20 g/L glucose ) and modified Lee’s glucose medium supplemented with 5 μg/ml phloxine B [47] were used for routine growth of C . albicans . Solid YPD-K ( 20 g/L peptone , 10 g/L yeast extract , 20 g/L glucose , 2 . 5 g/L K2HPO4 , 20 g/L agar ) and YP-K ( 20 g/L peptone , 10 g/L yeast extract , 2 . 5 g/L K2HPO4 , 20 g/L agar ) media were used for mating-projection induction assays . Peptone , yeast extract , and agar were purchased from BD Biosciences ( BD Bacto , Cat . Nos . , 211677 , 212750 , G8270 , and 214010; BD Biosciences , Sparks , MD , USA ) . Glucose , phloxine B , and K2HPO4 were purchased from Sigma-Aldrich ( Cat . Nos . , G8270 , P2759 , and P9666; Sigma-Aldrich , St . Louis , MO , USA ) . The pH of YPD-K and YP-K media were about 7 . 3 . Opaque cells were used for all mating and mating projection formation assays . Sorbitol medium was made according to a previous study [48] . YPD-K , YP-K , agar-only ( 3% ) , agar + C . albicans debris , or agar + 3% mouse feces media were used for quantitative mating assays . To make the C . albicans debris medium , approximately 1 × 1010 cells of SC5314 were collected from an overnight YPD culture , washed with ddH2O , frozen at −80°C , and ground with glass beads . Cell debris and 2% agar were mixed and resuspended in 100 mL ddH2O for autoclaving . To make the mouse feces medium , 2% agar and 3% mouse feces ( w/v ) were mixed and resuspended in ddH2O . Before subjecting to autoclaving , 0 . 25% K2HPO4 was added to the agar-only ( 3% ) , agar + C . albicans debris , or agar + 3% mouse feces media for pH buffering . Synthetic complete medium ( SCD ) lacking corresponding nutrients ( uridine , histidine [His] , and/or arginine [Arg] ) was used for selectable growth in quantitative mating assays . To make H2O2-containing YPD-K medium , 200 μl of 5 mM H2O2 was spread onto YPD-K medium plates . Opaque cells of GH1350a were first grown on Lee’s glucose medium at 25°C for three days . 1 × 106 cells in 10 μL of ddH2O were spotted onto different H2O2-containing YPD-K media and cultured at 25°C for three to five days . To inactivate the SacII site of tetON-promoter–containing plasmid pNIM1 [49] , the plasmid was first digested with SacII and then filled in with the Klenow fragment of DNA polymerase I . The blunt ends were ligated to generate the SacII-free plasmid pNIMsx . To construct the plasmids pNIMsx-HSP90con and pNIMsx-HSF1con for conditional knockout of HSP90 and HSF1 , respectively , one fragment of partial ORF region of HSP90 or HSF1 ( with SalI and SacII sites ) and another fragment of the corresponding 5′-UTR region ( with SacII and BglII sites ) were simultaneously subcloned into the SalI and BglII sites and replaced the GFP cassette . To construct the nourseothricin-resistant plasmid pACTS , a caSAT1 fragment was amplified from pNIM1 [49] and subcloned into the HindIII/KpnI site of pACT1 [50] . A fragment containing the MTLa2 ORF region was then subcloned into the EcoRV/HindIII site of pACTS , generating the overexpressing plasmid pACTS-MTLa2 . To create a Myc-tagged Cwt1 plasmid , the CdHIS1 cassette was amplified from plasmid pSN52 and inserted into plasmid pACT1 at the ClaI site , generating plasmid pACT1-HIS1 . A fusion PCR product containing the CWT1 ORF region and a C-terminal 13× Myc tag were prepared and subcloned into the EcoRV/KpnI site of pACT1-HIS1 , yielding plasmid pACT1-CWT1-Myc-HIS1 . A TAP-ARG4 cassette flanked by approximately 70-bp-5′– and 3′–homologous sequences of CWT1 was amplified from strain CaLC2993 with primer pair LT1266 and LT1267 and was then transformed into the WT strain SN95 ( CaLC239 ) to create TAP-tagged strain LTS1036 [51 , 52] . The fragment containing the CWT1 ORF region and a C-terminal TAP-ARG4 tag was amplified from strain LTS1036 with oligonucleotides LT1271/LT1272 and then subcloned into the EcoRV/KpnI site of pACT1 [50] , generating plasmid pACT-CWT1-TAP-ARG4 . A TAP-ARG4 cassette flanked by approximately 70-bp-5′– and 3′–homologous sequences of CTA4 was amplified from strain CaLC2993 with primer pair LT1459 and LT1460 and was then transformed into the WT strain SN95 ( CaLC239 ) to create TAP-tagged strain LTS1071 [52] . The fragment containing the CTA4 ORF region and a C-terminal TAP-ARG4 tag were amplified from strain LTS1071 with oligonucleotides LT1271/LT1462 and then subcloned into the EcoRV/KpnI site of pACT1 , generating plasmid pACT-CTA4-TAP-ARG4 . To construct the conditional knockout mutants of HSP90 in strain GH1013 , we first replaced the promoter of one allele with the tetON promoter using SacII-digested plasmid pNIMsx-HSP90con , generating mutant tetON-HSP90/HSP90 . The other allele of HSP90 was then replaced with the ARG4 cassette amplified from pRS-ARG4ΔSpeI [53] with oligonucleotides HSP90-5DR/HSP90-3DR , generating the conditional mutant tetON-HSP90/hsp90 . Since opaque cells of the tetON-HSP90/hsp90 mutant were not stable , the master regulator WOR1 was overexpressed in this mutant using plasmid pACT1-WOR1 [50] . To construct the conditional knockout mutant of HSF1 , we deleted the first allele in strain GH1013 using the URA3 cassette amplified from pGEM-URA3 [53] with oligonucleotides HSF1-5DR/HSF1-3DR , generating the mutant hsf1::URA3/HSF1 . Then , we replaced the promoter region of the second allele of HSF1 with the tetON promoter using SacII-digested plasmid pNIMsx-HSF1con , generating the mutant tetON-HSF1/hsf1 . The cartTA cassette ( reverse tet repressor ) is under the control of the white-cell–specific ADH1 promoter in the plasmid pNIM1 [49] . To increase the expression level of cartTA in opaque cells , the cassette was integrated into the opaque-specific OP4 locus in the tetON-HSF1/hsf1 and tetON-HSP90/hsp90 mutants by transformation with fusion PCR products of cartTA-ARG4 . pNIM1 and pRS-ARG4ΔSpeI were used as the primary template . To delete both alleles of CWT1 , the HIS1 and ARG4 markers flanked by CWT1 gene 5′- and 3′-fragments were amplified with fusion PCR assays and sequentially transformed into strain GH1013 as described previously [53] . The plasmids pGEM-HIS1 and pRS-ARG4ΔSpeI were used as the PCR templates . All oligonucleotides used are listed in S3 Table . To determine Cwt1-binding targets , a TAP-tagged Cwt1-ecotopic strain was constructed . The plasmid pACT-CWT1-TAP-ARG4 was linearized with AscI and transformed into strain SN95 [54] to create a TAP-tagged Cwt1-overexpressing strain ( LTS1039 ) . The function of TAP-tagged Cwt1 was verified by mating projection formation assays . To construct the MTLa2-overexpressing strain , plasmid pACTS-MTLa2 was linearized with AscI and transformed into strain GH1350a . To construct a TAP-tagged Cta4-ecotopic expression strain , the plasmid pACT-CTA4-TAP-ARG4 was linearized with AscI and transformed into strain SN95 to create TAP-tagged CTA4-ecotopic expression strain ( LTS1079 ) . To construct a GFP-tagged Hsp90 strain , a HSP90-GFP-SAT1 fusion fragment into the pACT1 plasmid [50] , generating plasmid pACT1-HSP90-GFP-SAT1 . The plasmids pACT1-HSP90-GFP-SAT1 and pACT1-CWT1-Myc-HIS1 were linearized with AscI and subsequently transformed into strain SN95 [54] , yielding strain LTS1062 with a GFP-tagged HSP90 and 13× Myc-tagged CWT1 allele . To construct the MFa1p-GFP , MFαp-GFP , FIG1p-GFP , and FUS1p-GFP reporter strains , GH1013 was transformed with PCR products of the GFP-caSAT1 cassette amplified from plasmid pNIM1 with corresponding primers . The forward and reverse primers contain a 60-bp flanking sequence homologous to the promoter and 3′-UTR regions of MFa1 , MFα , FIG1 , or FUS1 , respectively . Correct integration of the transformations was verified with PCR assays . Same-sex mating assays were performed according to our previous publications with slight modifications [55] . Briefly , opaque cells of two “a” strains ( 1 × 107 for each ) were mixed , spotted onto different media , and cultured at 25°C for three to seven days as indicated in the main text . Mating mixtures were then replated onto SCD-His , -Arg , -uridine , -His-Arg , or -Arg-uridine dropout media for prototrophic selection growth . Colonies grown out on the three types of plates were counted , and mating efficiency was calculated . C . albicans cells were incubated in liquid SCD medium with shaking at 30°C overnight , harvested , washed , and resuspended in 1× TE buffer ( 10 mM Tris , 1 mM EDTA [pH 8 . 0] ) . Cells were then fixed with 70% ethanol for two hours at room temperature and washed with 1× TE buffer before treating with 1 mg/mL RNase A and 5 mg/ml proteinase K . Propidium iodide ( PI , 25 μg/ml ) staining assays were then performed . Stained cells were washed and resuspended in 1× TE buffer for DNA content analysis . A total of approximately 30 , 000 cells of each sample were used for flow cytometry assays , and the results were analyzed using software FlowJo 7 . 6 . 1 . Quantitative real-time PCR assays were performed according to our previous publications with modifications [56] . Cells were collected from cultures grown on solid plates as described in the main text . One μg of total RNA per sample was used to synthesize cDNA with RevertAid H Minus Reverse Transcriptase ( Thermo Scientific , Waltham , MA , USA ) . Quantification of transcripts was performed in Bio-Rad CFX96 real-time PCR detection system using SYBR green . The signal from each experimental sample was normalized to expression of the ACT1 gene . For RNA-Seq assays , opaque cells of C . albicans were grown to stationary phase on Lee’s glucose medium at 25°C for five days and then spotted onto YPD-K and YP-K medium at 25°C for 60 hours of incubation . Two biological repeats were performed for each condition . Cells were harvested , and total RNA was extracted . RNA-Seq analysis was performed by the company Berry Genomics ( Beijing , China ) as described previously [57] . Briefly , approximately 10 million ( M ) reads were sequenced in each library of the samples . The library products were then sequenced using an Illumina HiSeq 2500 V4 ( Illumina , San Diego , CA , USA ) . Illumina software OLB_1 . 9 . 4 was used for base calling . The raw reads were filtered by removing the adapter and low-quality reads ( the percentage of low-quality bases with a quality value ≤3 was >50% in a read ) . Clean reads were mapped to the genome of C . albicans SC5314 using TopHat ( version 2 . 1 . 1 ) and Cufflinks ( version 2 . 2 . 1 ) software [58] . Relative gene expression levels were calculated using the fragments per kb per million reads ( FPKM ) method . To be considered significantly differentially expressed , a gene must satisfy three criteria: ( 1 ) an FPKM value higher than or equal to 20 at least in one sample , ( 2 ) a fold change value higher than or equal to 1 . 5 ( except for the Functional categories sheet of S1 Data , in which a 2-fold change cutoff was used ) , and ( 3 ) an adjusted p-value ( false discovery rate [FDR] ) lower than 0 . 05 . ChIP assays were performed as described previously [52 , 59] . Briefly , untagged ( CaLC239 , SN95 ) and pACT1-Cwt1-TAP–tagged ( LTS1039 ) C . albicans strains were spotted on YP-K medium and incubated at 25°C for one and three days . Cells were collected and fixed in 1× PBS containing 1% formaldehyde and incubated with gentle rocking for 20 min at room temperature . The crosslinking reaction was quenched by adding 2 . 5 M glycine to a final concentration of 125 mM , and cells were mixed for 5 min at room temperature . Cells were harvested , washed with 1× PBS , and homogenized in ice-cold lysis buffer using a bead beater . Sonication was performed with a Diagenode Bioruptor ( Diagenode , Denville , NJ , USA ) ( 12 min , high setting , 30 s on , 1 min off ) to obtain chromatin fragments of an average size of 250–1 , 000 bp . The chromatin was immunoprecipitated with 50 μl packed IgG Sepharose 6 Fast Flow matrix ( GE Healthcare , Chicago , IL , USA ) . The Sepharose matrix was washed , and immunoprecipitated chromatin DNA was eluted and de-crosslinked at 65°C overnight . Quantitative real-time PCR assays were performed to determine Cwt1 targets . Cells grown on YP-K and YPD-K media were harvested and suspended in lysis buffer containing 50 mM Na-HEPES ( pH 7 . 5 ) , 450 Mm NaOAc ( pH 7 . 5 ) , 1 mM EDTA , 1 mM EGTA , 5 Mm MgOAc , 5% glycerol , 0 . 25% NP-40 , 3 mM DTT , 1 mM PMSF , and EDTA-free protease inhibitor mix ( Cat . No . , 11873580001; Roche Diagnostics , Mannheim , Germany ) . Cells were lysed using a beadbeating instrument by five rounds of beating ( 50 s beating plus 1 min cooling process on ice for each round ) . The supernatant of cell lysates was collected and incubated with Sepharose beads conjugated anti-GFP monoclonal antibody at 4°C for three hours . The beads were washed for four times with the lysis buffer and then boiled for 10 min in 1× sodium dodecyl sulfate ( SDS ) sample buffer . Immunoprecipitated proteins were separated through an SDS-10% polyacrylamide gel and used for western blotting analysis using anti-Myc monoclonal antibodies ( Cat . No . , OP10; MilliporeSigma , Billerica , MA , USA ) . Cells were cultured for one to five days on YP-K or YPD-K medium at 25°C . Cells were then harvested and washed in 1 × PBS and incubated with DCFDA ( Beyotime , Shanghai , China ) for 30 min at 37°C . After washing , fluorescence intensity reflecting the ROS level was measured at 488 nm using ELISA and was normalized according to the cell numbers .
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Candida albicans is notorious as a human fungal pathogen that causes millions of incidents of thrush and systemic infection every year . Sexual reproduction plays a pivotal role in the biology and survival of pathogenic fungal pathogens . However , C . albicans is predominantly clonal , suggesting that mating and recombination between isolates would be rare in nature . Here , we report that environmental stresses induce the development of mating projections and efficient same-sex mating in C . albicans . This induction represents a novel mode of homothallism that is independent of the HO endonuclease-mediated mating-type switching observed in Saccharomyces cerevisiae and Schizosaccharomyces pombe . This represents a seminal example of how an environmentally relevant stress induces homothallic mating in fungi .
|
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2019
|
Environment-induced same-sex mating in the yeast Candida albicans through the Hsf1–Hsp90 pathway
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The ability of phagocytes to clear pathogens is an essential attribute of the innate immune response . The role of signaling lipid molecules such as phosphoinositides is well established , but the role of membrane sphingolipids in phagocytosis is largely unknown . Using a genetic approach and small molecule inhibitors , we show that phagocytosis of Candida albicans requires an intact sphingolipid biosynthetic pathway . Blockade of serine-palmitoyltransferase ( SPT ) and ceramide synthase-enzymes involved in sphingolipid biosynthesis- by myriocin and fumonisin B1 , respectively , impaired phagocytosis by phagocytes . We used CRISPR/Cas9-mediated genome editing to generate Sptlc2-deficient DC2 . 4 dendritic cells , which lack serine palmitoyl transferase activity . Sptlc2-/- DC2 . 4 cells exhibited a stark defect in phagocytosis , were unable to bind fungal particles and failed to form a normal phagocytic cup to engulf C . albicans . Supplementing the growth media with GM1 , the major ganglioside present at the cell surface , restored phagocytic activity of Sptlc2-/- DC2 . 4 cells . While overall membrane trafficking and endocytic pathways remained functional , Sptlc2-/- DC2 . 4 cells express reduced levels of the pattern recognition receptors Dectin-1 and TLR2 at the cell surface . Consistent with the in vitro data , compromised sphingolipid biosynthesis in mice sensitizes the animal to C . albicans infection . Sphingolipid biosynthesis is therefore critical for phagocytosis and in vivo clearance of C . albicans .
As a first line of defense against pathogens , the innate immune system relies on phagocytic cells that recognize and internalize foreign particulates . Phagocytosis of the fungal pathogen Candida albicans involves extensive membrane reorganization and actin remodeling at the plasma membrane for successful formation of a phagocytic cup [1–4] . Inevitably , the lateral movement of phagocytic receptors and other cofactors within the bilayer is influenced by the lipid composition of the membrane [5–8] . Nonetheless , the extent to which membrane lipids contribute to the proper operation of innate immune receptors remains largely unknown . Phosphoinositides , bioactive lipids localized mainly to the cytosolic leaflet of the plasma membrane , are essential during various stages of phagocytosis [9–14] . Formation of the phagocytic cup involves receptor clustering and cytoskeletal rearrangements at the site where the particle is initially bound . This step is highly coordinated and relies on modulation of phosphoinositide metabolism [9 , 11] . Sphingolipids are conserved in all eukaryotes , and constitute 10–15% of total membrane lipids . They are heterogeneous in length , hydroxylation status and saturation of their acyl groups [15 , 16] . Their distribution among the various biological organelles is distinct [16] . Sphingolipids are ubiquitous in the outer leaflet of the plasma membrane [17] where they are known to associate with cholesterol within the bilayer . Pathogens unavoidably interact with this class of lipids during phagocytosis . Evidence for the involvement of sphingolipids in fungal infections is mostly indirect , extrapolated from cholesterol depletion experiments [6] , performed to explore the consequences of disrupting lipid rafts , which contain both cholesterol and sphingolipids . However , like many pharmacological interventions , the extraction of cholesterol using methyl-β-cyclodextrin is a relatively blunt instrument with inevitable off-target effects [18 , 19] . Lipids are not template-encoded and are not uniquely confined to a given compartmentalized cellular organelle . This presents a challenge for the precise manipulation of their cellular levels and distribution . Consequently , it is difficult to distinguish between effects of altered lipid levels on the properties of a particular membrane or cellular compartment , and indirect effects caused by blocking steps upstream in biosynthetic or trafficking pathways . While this degree of complexity offers multiple points of attack for pharmacological and genetic intervention , manipulation of sphingolipid synthesis as a means of perturbing lipid homeostasis is comparatively underexplored . Studies of sphingolipid involvement in endocytosis of receptor-ligand complexes , or in phagocytosis of particulates such as microbes or opsonized red blood cells , has not yielded a consistent picture . Fumonisin B1 enhances phagocytosis of opsonized red blood cells , yet inhibits internalization of certain receptor-ligand combinations [20 , 21] . We are not aware of any studies that examine the role of ( glyco ) sphingolipids in phagocytosis and clearance of pathogenic fungi such as C . albicans . The biosynthetic pathway of sphingolipids and ceramides starts with the condensation of serine and palmitoyl CoA to yield 3-ketosphinganine in a reaction catalyzed by serine palmitoyl CoA transferase ( SPT ) , a protein complex comprised of two subunits , Sptlc1 and Sptlc2 [22 , 23] . Genetic ablation of either of the two subunits is embryonic lethal . Animals heterozygous for either the Sptlc1 or Sptlc2 null allele show reduced levels of sphingolipids [24] . A Chinese hamster ovary ( CHO ) cell line that lacks SPT failed to survive in the absence of exogenously added sphingoid base [25] . As to pharmacological approaches , SPT is inhibited by the small molecule myriocin [26] . Ceramide synthase accepts sphinganine and combines it with a fatty acyl CoA to yield dihydroceramide , a reaction that is blocked by the fungal metabolite fumonisin B1 . As dihydroceramide is a precursor to all ceramides , application of fumonisin B1 has been used to manipulate ceramide and other sphingolipid levels , not only in tissue culture models but also in mice [20 , 27] . Cellular sphingolipids are essential for the transport of viral glycoproteins from the Golgi apparatus to the cell surface [28 , 29] . The role of sphingolipids in endocytosis of receptor-ligand complexes , or in phagocytosis of pathogens such as C . albicans , is largely unknown . We used two approaches to manipulate sphingolipid biosynthesis . We used CRISPR/Cas9-mediated genome editing to generate dendritic cells deficient in Sptlc2 . Such Sptlc2-/- dendritic cells survive and grow . We applied the small molecule inhibitors fumonisin B1 and myriocin to interfere pharmacologically with sphingolipid synthesis . Both genetic and pharmacological blockade of sphingolipid synthesis caused a defect in phagocytosis of C . albicans by macrophages and dendritic cells . We thus find that sphingolipid biosynthesis is essential not only for efficient binding of particulates , but also for the formation of a normal phagocytic cup and subsequent internalization of the particulates . We show that sphingolipid biosynthesis is critical for cell surface expression of some pattern recognition receptors . Global membrane trafficking and endocytic pathways in Sptlc2-deficient cells are not overtly affected , at least for the parameters examined here . Mice treated with fumonisin B1 showed increased sensitivity to C . albicans infection in vivo , presumably due to a failure of phagocytic cells to properly engage the pathogen , thus leading to its uncontrolled extracellular proliferation . Therefore , our data show the importance of sphingolipids in phagocytosis and for the in vivo clearance of fungal infections .
The small molecule inhibitors myriocin or fumonisin B1 ( FB1 ) inhibit production of sphingolipids in mammalian cells . While myriocin blocks the activity of SPT-the first and rate-limiting reaction of this pathway- FB1 inhibits ceramide synthase [26 , 30–32] , causing a blockade in the production of ceramides , the backbone of all sphingolipids ( Fig 1A ) . To examine the role of sphingolipids in phagocytosis of C . albicans , we used myriocin and FB1 to manipulate sphingolipid levels in the macrophage cell line RAW264 . 7 and in the dendritic cell line DC2 . 4 . Since the turnover of sphingolipids , especially that of sphingomyelin , is slow [33] , cells were grown in the continuous presence of myriocin or FB1 for 4 days to obtain a significant reduction in sphingolipid levels . We observed no inhibition of cell growth in the presence of the inhibitors ( S2 Fig ) . We then measured the biosynthesis of sphingomyelin , the major sphingolipid in mammalian cells . Cells were metabolically labeled with the sphingomyelin precursor N-methyl-[14C]-choline , total lipids were extracted and analyzed by TLC and autoradiography . Treatment of cells with myriocin or FB1 resulted in a significantly reduced level of [14C]-sphingomyelin ( P < 0 . 0001; Fig 1B and 1C ) . However , the production of [14C]-phosphatidylcholine ( PC ) was not affected by either inhibitor ( Fig 1B and 1C ) , in support of the selectivity of these inhibitors . We next investigated the ability of RAW macrophages and DC2 . 4 cells treated with myriocin or FB1 to phagocytose C . albicans . We exposed inhibitor-treated RAW macrophages or DC2 . 4 cells to a blue fluorescent protein-expressing strain of C . albicans ( Candida-BFP ) for 60 and 90 minutes , and monitored phagocytosis by confocal microscopy . We used Alexa Flour 488-conjugated phalloidin to visualize the cells’ contours . Cells treated with myriocin or FB1 showed significantly reduced levels of C . albicans phagocytosis compared to untreated cells ( Fig 2A and 2E ) . DC2 . 4 cells treated with myriocin or FB1 showed 60–70% reduction ( p < 0 . 001 for myriocin and p < 0 . 0001 for FB1 ) , whereas RAW macrophages showed ~50% reduction ( p < 0 . 0005 for both myriocin and FB1 ) at the 60 and 90 min time points ( Fig 2B and 2F ) , with a corresponding increase in non-phagocytic cells ( p < 0 . 05; Fig 2C and 2G ) . Of the cells that were able to internalize C . albicans , fewer organisms were internalized per cell ( Fig 2D and 2H ) . Next , we examined a time-course of myriocin treatment and investigated the cellular lipidome as well as phagocytosic ability . DC2 . 4 cells were treated with myriocin for 1 , 2 , 3 or 4 days and we then performed lipid profiling by LC-MS . The level of ceramide and cerebrosides were significantly reduced after 1 day of myriocin treatment ( p<0 . 001; S1A , S1B and S1C Fig ) . The reduction in sphingomyelin was less pronounced and more gradual , showing the turnover of this lipid is slow [33] . DC2 . 4 cells treated with myriocin for 1 to 3 days showed a decrease in sphingomyelin of 35–40% ( p < 0 . 05 ) , while a ~50% reduction ( p < 0 . 05 ) was found after 4 days of treatment ( Fig 1C ) . We observed reduced levels of sphinganine ( p < 0 . 001 ) after 4 days of myriocin treatment ( S1D Fig ) . We noted a subtle increase in phosphatidylcholine in myriocin-treated DC2 . 4 cells ( S1E Fig ) . DC2 . 4 cells treated with myriocin for 1 or 2 days showed a 35–40% reduction in C . albicans phagocytosis ( S1F Fig ) . A 3-day block in sphingolipid biosynthesis caused a ~50% reduction in phagocytosis ( p < 0 . 001; S1E Fig ) whereas we saw an even more pronounced reduction ( 60–70%; p<0 . 001 ) after 4 days ( Fig 2B , see above ) . These data suggest that depletion to a certain level ( for sphingomyelin ) is essential to obtain a >60% blockade in phagocytosis . To corroborate our observations that chemical inhibition of sphingolipid biosynthesis causes a defect in phagocytosis , and to exclude off-target effects of the drugs , we generated Sptlc2-/- DC2 . 4 cell lines using CRISPR/Cas9 genome editing . Serine palmitoyl-CoA transferase ( SPT ) , which catalyzes the first and rate-limiting reaction in the sphingolipid biosynthetic pathway , is an enzyme complex composed of two subunits , SPTLC1 and SPTLC2 . We isolated two independent Sptlc2-deficient clones , for which the ablation of Sptlc2 was verified by immunoblotting of cell lysates ( Fig 3A ) using an anti-Sptlc2 serum . To measure sphingolipid synthesis , we labeled cells either with N-methyl-[14C]-choline or with N-methyl-[14C]-serine . Sptlc2 transfers serine onto palmitoyl CoA to produce the first series of long-chain bases that eventually yields ceramides . Metabolic labeling with N-methyl-[14C]-serine showed a near-complete blockade of [14C]-sphingomyelin and [14C]-glucosylceramide production in Sptlc2-/- DC2 . 4 cells as demonstrated by thin layer chromatography ( TLC ) ( Fig 3B and 3C ) . The level of [14C]-glucosylceramide , the major glycosphingolipid in mammalian cells , was below detection in Sptlc2-/- DC2 . 4 cells ( Fig 3B ) . To confirm the identity of the sphingolipids as visualized by TLC , total lipid extracts were treated with mild alkaline sodium methoxide ( Fig 3B ) to hydrolyze glycerophospholipids and so reduce their contribution to the observed signal . Cells labeled with N-methyl-[14C]-choline also showed reduced levels of sphingomyelin compared to wild type cells ( Fig 3B and 3C ) . Residual production of sphingolipids in N-methyl-[14C]-choline labeled cells likely results from a salvage pathway , in which ceramide is generated from sphingolipid turnover ( Fig 1A; [34 , 35] ) . Moreover , lipidomic profiling of Sptlc2-/- DC2 . 4 cells by LC/MS showed that Sptlc2-/- DC2 . 4 cells have significantly reduced levels of sphingosine ( p <0 . 01 ) , sphingomyelin ( p < 0 . 001 ) and ceramide ( p < 0 . 001 ) compared to control cells ( Fig 3D and 3E ) . We also examined the level of other selected major classes of lipids , noting only a subtle increase in the level of phosphatidylethanolamine ( PE; Fig 3F ) . No difference was seen for phosphatidylcholine ( PC ) or cholesterol levels in Sptlc2-/- DC2 . 4 cells compared to controls ( Fig 3F ) . We found no obvious differences between Sptlc2-/- DC2 . 4 and control cells with respect to cell division ( S2B Fig ) and morphology ( S2A Fig ) . Acquisition of nutrients from the medium , disposal of waste products , or cytokinesis must therefore proceed in a manner consistent with survival and growth . We examined the morphology of Sptlc2-/- DC2 . 4 cells by electron microscopy ( EM ) . All organellar structures remain intact , and no obvious morphological differences were detected when comparing Sptlc2-/- DC2 . 4 and controls ( S2A Fig ) . To examine the phagocytic capacity of Sptlc2-/- DC2 . 4 cells , we incubated them with Candida-BFP for 60 and 90 min , and scored phagocytosis by confocal microscopy . Sptlc2-/- DC2 . 4 cells showed significantly less phagocytosis of C . albicans ( p<0 . 0002 ) than wild type DC2 . 4 cells ( Fig 4A and 4B ) with a corresponding increase in non-phagocytic cells ( Fig 4C ) . Similarly , the numbers of C . albicans per infected cell were significantly lower than in wild type DC2 . 4 cells ( p < 0 . 0005; Fig 4D ) . Genetic ablation of Sptlc2 thus causes a defect in phagocytosis of C . albicans , underscoring the important role of an intact sphingolipid biosynthetic pathway for this process . We next examined the impact of the Sptlc2 deficiency on phagocytosis of other particulates , such as fluorophore-conjugated zymosan or IgG-coated latex beads . We observed a blockade in phagocytosis of both zymosan and IgG-coated latex beads in Sptlc2-/- DC2 . 4 cells ( p < 0 . 001; S3 Fig ) . We tracked phagocytosis of zymosan particles by Sptlc2-/- DC2 . 4 cells for more extended periods ( 4h and 6h post application ) , and still observed no internalization of zymosan ( S3C Fig ) . Combined , our data show that Sptlc2-/- DC2 . 4 cells are defective in internalization not only of C . albicans , but also of zymosan and IgG-coated beads . To investigate the role of sphingolipid biosynthesis in the production of cytokines upon fungal infection , we used zymosan to stimulate Sptlc2-/- and control DC2 . 4 cells . Zymosan is a β-glucan-containing fungal particulate preparation that evokes inflammatory signals in macrophages and dendritic cells [36 , 37] . Since pro-inflammatory cytokines are the main cytokines produced upon fungal infection [38] , we measured production of IL-6 and TNF-α . In zymosan-stimulated cells , the production of IL-6 was reduced in Sptlc2-/- DC2 . 4 cells compared to controls ( Fig 4E ) . Similarly , we found a modest reduction in TNF-α levels in Sptlc2-/- DC2 . 4 cells , and we obtained a significant difference after 24h of stimulation with zymosan ( Fig 4G ) . We also determined the ability of Sptlc2-/- DC2 . 4 cells to produce cytokines upon stimulation with the soluble ligand LPS . The production of IL-6 and TNF-α was not significantly affected in Sptlc2-deficient cells in response to LPS stimulation ( Fig 4F and 4H ) . We next investigated the role of sphingolipid biosynthesis in C . albicans infection in vivo . Because Sptlc1 and/or Sptlc2 knockout mice are embryonic lethal [24 , 39] , we used FB1 to inhibit sphingolipid biosynthesis in mice . Unlike FB1 , myriocin is known to exhibit an immunosuppressant property in vivo [26 , 40] and therefore we considered it not compatible for our in vivo study . We first established the dose and duration of FB1 treatment required to obtain reduced levels of sphingolipids without compromising the health of the treated mice . Mice received daily subcutaneous injections of 2 mg/kg FB1 . Although FB1 also inhibits sphingolipid production in C . albicans [41] , we used a far lower concentration in mice than that required for use in C . albicans . The concentration of FB1 used in mice does not affect growth of C . albicans ( S4B Fig ) . Mice treated with FB1 showed no gross differences in health or behavior compared to untreated animals ( S4A Fig ) . After 5 days , mice were sacrificed and lipidomic analysis was performed by LC/MS on lipids extracted from peritoneal macrophages and liver tissue . As expected , the level of sphinganine in FB1-treated mice increased > 30-fold and > 40-fold in peritoneal macrophages ( Fig 5A ) and liver ( Fig 5C ) , respectively . Sphingosine increased 3 to 4-fold in both peritoneal macrophages ( Fig 5A ) and liver tissue ( Fig 5C ) . Consistent with these data , FB1-treated mice showed reduced levels of ceramide , sphingomyelin , and cerebrosides ( glucosylceramide and galactosylceramide ) in both peritoneal macrophages ( Fig 5B ) and liver ( Fig 5E and 5D ) . We observed no differences in the levels of total cholesterol and the phospholipids PE and PC ( Fig 5F ) , indicating specificity of the FB1-imposed blockade . Mice treated with FB1 for 5 consecutive days received 2x104 colony forming units ( CFU ) of live or the UV-killed equivalent of C . albicans via tail vein injection . Mice continued to receive FB1 treatment daily and were closely monitored for survival and overall well-being . FB1-treated mice were highly susceptible to infection with live C . albicans , with no animals surviving beyond 9 days post infection , whereas at this dose of C . albicans , all of the untreated but infected animals survived ( Fig 5G ) . FB1-treated mice injected with UV-killed C . albicans showed no obvious signs of ill health compared to control mice . We examined the C . albicans load in kidney and brain of infected animals on day 9 . In FB1-treated mice , both organs were heavily colonized with C . albicans ( Fig 5H and 5I ) . Fungal load was also determined on day 5 . We therefore concluded that the cause of death in FB1-treated mice infected with live C . albicans was systemic candidiasis , rather than septic shock resulting from intravenous delivery of ligands for TLRs or other pattern recognition receptors . To determine the step at which sphingolipids are required during phagocytosis , we used confocal microscopy and examined the ability of Sptlc2-/- DC2 . 4 cells to bind zymosan-Alexa647 . Alexa fluor 647-conjugated zymosan was added to cells on ice ( 10 zymosan particles per cell ) , which were then transferred to 37°C for 5 and 15 min . Sptlc2-/- DC2 . 4 cells showed significantly less binding of zymosan than control DC2 . 4 cells , both at 5 and 15 min of incubation ( p = 0 . 0008; Fig 6A and 6B ) . The number of zymosan particles bound per cell was significantly higher for control cells than for Sptlc2-/- DC2 . 4 cells ( p < 0 . 0001; Fig 6C ) . Together , these results underscore the role of an intact sphingolipid biosynthetic pathway in binding particulates , which might involve any of a number of different surface structures . Why are Sptlc2-/- DC2 . 4 cells defective in phagocytosis ? Phagocytosis involves remodeling of the actin cytoskeleton to guide and shape the membrane around the pathogen to form a phagocytic cup [42 , 43] . We investigated actin-driven phagocytic cup formation in Sptlc2-/- DC2 . 4 cells . We generated Sptlc2-/- DC2 . 4 and wild type DC2 . 4 cell lines that stably express mCherry-tagged LifeAct , a biosensor that visualizes the distribution of filamentous actin in living cells [44] . After incubating the respective cell lines with Candida-BFP , we performed live cell imaging/confocal microscopy to capture phagocytic events . Wild type DC2 . 4 cells remodeled their actin cytoskeleton as expected , with clear evidence of recruitment and polymerization of actin in the course of phagocytic cup formation , and its subsequent depolymerization upon formation of a phagosome ( Fig 6D and 6E; S1 and S2 Movies ) . Accordingly , actin continues to concentrate at the base of the cup where cells contact C . albicans and remains there until closure of the phagosome . Subsequently , actin concentrations decrease at the phagocytic cup , creating a belt-shaped band of actin that stretches outwards for the successful engulfument of the C . albicans particle ( Fig 6D; Fig 9 ) . For Sptlc2-/- DC2 . 4 cells , even though random actin polymerization events occur at the cell surface , we only rarely observed the successful completion of a typical phagocytic cup ( Fig 6; S2 and S3 Movies ) . Phagocytosis is not only an actin-driven cellular activity , but is also a receptor-mediated process initiated upon recognition of particulates by pattern recognition receptors ( PRRs ) expressed at the cell surface of phagocytes [45] . The PRRs enable immune cells to discern pathogen-associated molecular patterns ( PAMPs ) found on the cell wall of most microbial pathogens [46] . Are sphingolipids essential for cell surface disposition of PRRs ? We examined PPR expression on Sptlc2-/- DC2 . 4 and control cells by flow cytometry and confocal microscopy . Surface expression of Dectin-1 , TLR2 , and FcγR was reduced in Sptlc2-/- DC2 . 4 cells compared to control cells ( Fig 7A–7C ) . Cell surface expression of TLR4 , a toll-like receptor that recognizes lipopolysaccharides , was not affected ( Fig 7A and 7B ) . Similarly , surface expression of CD45 , a receptor-linked protein tyrosine phosphatase , was not affected in Sptlc2-/- DC2 . 4 cells ( Fig 7A , 7B and 7C ) , showing at least some degree of specificity for the role of sphingolipids in surface disposition of PRRs . Despite repeated attempts to measure surface expression of Galectin-3 , DC-SIGN and mannose receptor CD206 , we were unable to detect these proteins in DC2 . 4 cell lines by flow cytometry or confocal microscopy . It is possible that the DC2 . 4 cell line and its derivatives express only very low levels , or are negative for these markers . We determined whether Sptlc2-/- DC2 . 4 cells display normal overall membrane trafficking by exploring overall secretion , and the synthesis and maturation of Class I MHC products , a type-I membrane protein expressed on all nucleated cells . Biosynthetic incorporation of [35S]methionine/cysteine in control and Sptlc2-/- DC2 . 4 cells was comparable ( S5B Fig ) . We monitored levels and composition of secreted proteins in Sptlc2-/- DC2 . 4 cells and control cells . Cells were labeled with [35S]-methionine/cysteine for 30 min and chased for different times . Secreted proteins were analyzed by SDS/PAGE and autoradiography , using cells maintained at 4°C as controls . Sptlc2-deficient and wild type cells secreted comparable amounts of protein ( S5A Fig ) . We also monitored the synthesis and maturation of Class I major histocompatibility complex ( MHC ) products by pulse-chase analysis . Sptlc2-/- DC2 . 4 cells transport class I MHC HC at rates equal to control cells , as assessed by rate and extent of acquisition of Endo H resistance ( Fig 8A ) . To verify that endocytic pathways were functional in Sptlc2-/- DC2 . 4 cells , we tested whether Sptlc2-/- cells could be infected with two viruses known to rely on distinct routes of endocytosis for host cell entry . Mature virions ( MVs ) of vaccinia virus ( VACV ) , the prototypic poxvirus , enter cells by virus-induced macropinocytosis [49] . In contrast , vesicular stomatitis virus ( VSV ) , a rhabdovirus , employs clathrin-mediated endocytosis and acid-mediated fusion from early endosomes to infect host cells [50] . Equal numbers of wild type and Sptlc2-/- DC2 . 4 cells were infected with-expressing viruses . Both virus strains encode eGFP as a non-structural protein . Successful transcription and translation require delivery of the viral genomes to the host cell cytosol . The fraction of infected cells was determined 6 h after infection by measuring the levels of eGFP using flow cytometry ( Fig 8B and 8C ) . VACV infection was robust in Sptlc2-/- cells , although the number of infected cells was moderately reduced compared to wild type cells ( Fig 8D ) . To verify that VACV infection in wild type and Sptlc2-/- DC2 . 4 cells indeed relied on macropinocytosis , infection experiments were also performed in the presence of 3-indolepropionic acid ( IPA-3 ) , an inhibitor of p21-activated kinase 1 ( PAK1 ) known to be required for macropinocytosis [48] . VACV infection was completely abrogated by IPA-3 in both wild type and knockout cells , suggesting that the infectious entry mechanism of VACV in DC2 . 4 cells indeed relies on macropinocytosis ( Fig 8E ) . The fraction of VSV-infected cells was nearly identical in wild type and knockout cells ( Fig 8B and 8D ) . Moreover , infection was sensitive to bafilomycin A1 , an inhibitor of endosomal acidification ( Fig 8D ) . Infection of DC2 . 4 cells with VSV indeed required acidified endosomes and endocytosis . We observed normal infectivity of VSV in Sptlc2-/- DC2 . 4 cells . Collectively , the use of VACV and VSV as well-defined endosomal cargo confirmed that endocytic pathways were functional in Sptlc2-/- DC2 . 4 cells , consistent also with robust growth of Sptlc2-/- DC2 . 4 cells in tissue culture , which depends in part on endocytic uptake of nutrients . Since we were unable to determine the level of gangliosides in our lipidomic analysis using LC-MS , we used a fluorescently labeled cholera toxin subunit B ( CtxB ) . CtxB binds to GM1 , a ganglioside that contains a sialic acid residue conjugated to a ceramide moiety . CtxB is commonly used as a tool to visualize lipid microdomains at the cell surface [51] . Both confocal microscopy and flow cytometry showed a significantly reduced level of CtxB binding ( ~85% ) in Sptlc2-/- DC2 . 4 cells compared to the control ( Fig 9A and 9D ) . This is consistent with our lipidomic analysis where we saw a decrease in ceramide , the backbone of all gangliosides including GM1 ( Fig 3C ) . Membrane organization in Sptlc2-/- DC2 . 4 cells must therefore be different to account for the defects in phagocytosis , while still compatible with other membrane-associated phenomena , such as the operation of the secretory pathway , endocytic entry of viruses and cell growth more generally , as described above . Next , we assessed whether addition of GM1 exogenously could restore the defect in phagocytosis in Sptlc2-/- DC2 . 4 cells . Supplementing the media with GM1 partially restores CtxB staining in Sptlc2-/- DC2 . 4 cells ( P<0 . 05; Fig 9B and 9C ) , showing successful incorporation of this ganglioside into the membrane . Exogenous addition of GM1 to Sptlc2-/- DC2 . 4 cells also partially restores phagocytosis of C . albicans ( P<0 . 05; Fig 9E ) . Our data suggest a role for GM1 as one of the gangliosides that contributes to phagocytosis .
The de novo biosynthesis of sphingolipids starts in the ER . Further conversion to higher sphingolipids takes place at the Golgi . These lipids then accumulate primarily at the outer leaflet of the plasma membrane [17] . Sphingolipids thus serve as a possible site of first contact for incoming pathogens during phagocytosis . Sphingolipids and their metabolites contribute to a variety of cellular processes , including apoptosis , cell growth and membrane transport [52–54] . The role of phosphoinositides in phagocytosis , including phosphatidylinositol 4 , 5-bisphosphate ( PI ( 4 , 5 ) P2 ) and PI ( 3 , 4 , 5 ) P3 is a matter of record [9–14] . The gram-negative bacteria Neisseria gonorrhoeae exploits acid sphingomyelinase activity during its opsonization-independent uptake by phagocytes [55] . On the other hand , inhibition of sphingolipids biosynthesis using fumonisin B1 enhances phagocytosis of opsonized red blood cells [20] . Far less is known about the significance of sphingolipids or their metabolites in phagocytosis of fungal pathogens , such as C . albicans . We present six lines of evidence to show that sphingolipids are essential for phagocytosis of C . albicans: ( i ) myriocin-mediated inhibition of SPT impairs phagocytosis of C . albicans by macrophages and dendritic cells; ( ii ) inhibition by FB1 of ceramide synthase , another crucial enzyme in the sphingolipid pathway , results in a stark reduction of C . albicans phagocytosis; ( iii ) Sptlc2-deficient dendritic cells , generated through CRISPR/Cas9 mediated genome editing , are defective in phagocytosis of C . albicans; ( iv ) exogenous addition of the ganglioside GM1 partially restores the defect in phagocytosis in Sptlc2-/- DC2 . 4 cells; ( v ) administration of FB1 in vivo sensitizes mice to C . albicans infection . This corroborates our in vitro observations and underscores the importance of sphingolipid homeostasis in clearing fungal infections; ( vi ) lipidomic analysis is entirely consistent with the specificity of the inhibitors used and with the genetic defect in Sptlc2-/- DC2 . 4 cells . Of note , sphingolipids levels-normally ~10% of total lipids- are strongly reduced , but residual sphingolipids remain , produced via salvage pathways [34 , 35] or possibly acquired from tissue culture media . Why is sphingolipid biosynthesis critical for phagocytosis , and what is the step at which this class of lipids exerts its most pronounced effect ? Phagocytosis is a complex process that involves ( i ) particle recognition through interaction of pattern recognition receptors ( PRRs ) on the surface of the phagocyte with ligands on the surface of the particle; ( ii ) assembly of actin and its associated proteins at the site of ingestion , and formation of a phagocytic cup; ( iii ) disassembly of actin at the phagosome , and ( iv ) maturation of the phagosome [3 , 42 , 56] . Our data show that Sptlc2-deficient cells are defective in the binding stage of phagocytosis . Sptlc2-/- DC2 . 4 cells do not form a typical phagocytic cup to engulf particulates . Phagocytosis is an actin-driven process , where sphingolipids may facilitate formation of actin-rich pseudopods generated through membrane curvature at the site of engulfment . The seemingly random polymerization of actin in Sptlc2-/- DC2 . 4 cells fails to further guide and shape the membrane around the particulate to complete formation of a phagocytic cup . Actin-modulating factors , including the phosphoinositides PI ( 4 , 5 ) P2 and PI ( 3 , 4 , 5 ) P3 , may not be recruited properly in Sptlc2-/- DC2 . 4 cells [57 , 58] . The lack of sphingolipids at this site may also alter expression levels and affect lateral mobility of receptors or co-factors critical for particulate recognition . Consistent with this notion , surface display of PRRs such as Dectin-1 , TLR2 and FcγR is reduced when sphingolipid biosynthesis is compromised . Sphingolipids are involved in protein transport from the trans-Golgi network ( TGN ) to the cell surface [28 , 59] . The formation of protein carrier vesicles that bud from the TGN to deliver their cargo to the cell surface requires sphingolipids [28 , 59] , and perturbation of sphingolipid biosynthesis might therefore affect cargo sorting . Sphingolipids have affinity for cholesterol and form lipid microdomains with distinct protein composition [51] . Depletion of cholesterol-far more abundant in molar concentration than sphingolipids- through application of compounds such as methyl-β-cyclodextrin affect trafficking of raft-associated proteins and some transmembrane proteins , including the influenza virus glycoprotein hemagglutinin [28 , 51] . Moreover , length and composition of transmembrane ( TM ) domains are essential for proper membrane sorting and protein insertion at the plasma membrane [60 , 61]; sphingolipids may play a role in this aspect as well . Sphingolipids can affect formation of a phagocytic cup in various ways . The lipid composition at the cell surface , where receptors and other regulatory proteins operate , is critical for successful formation of the phagocytic cup . Certain classes of proteins are recruited to the phagocytic cup , whereas others are excluded [62] . Dectin 1-the major C . albicans receptor that recognizes β-glucans on the cell wall of the fungus- clusters around the synapse-like structures where cup formation is initiated , whereas the regulatory tyrosine phosphatases CD45 and CD148 are excluded from that site [63] . Inevitably , such dynamic movement of proteins would involve extensive membrane reorganization at the plasma membrane in which sphingolipids may play a critical role . Sphingolipids are essential for membrane curvature during intra-luminal vesicle budding [64 , 65] , membrane remodeling events induced by viruses [66] , toxins [67] , or plasma membrane damage [68] . Similarly , the actin-rich pseudopods formed through membrane curvature at the site of engulfment are likely facilitated by the presence of sphingolipids ( Fig 10 ) . We find that sphingolipid biosynthesis is dispensable for VSV and vaccinia virus entry , thus demonstrating a specific role for sphingolipids in phagocytosis . While VSV uses clathrin-mediated endocytosis [50] , vaccinia virus employs macropinocytosis to enter host cells [49] . Unlike phagocytosis , macropinocytosis- an endocytic mechanism normally involved in fluid uptake- is not accompanied by formation of a recognizable coat and does not require clustering of receptors at the cell surface [69 , 70] . FB1-treated mice injected with UV-irradiated C . albicans showed no signs of disease . When inoculated with viable C . albicans , FB1-treated mice displayed a higher fungal load in both kidney and brain than control mice , consistent with an inability of the treated mice to clear the fungus through phagocytosis . This further suggests that the cause of death in these mice was systemic candidiasis as a result of a defect in phagocytosis , rather than septic shock resulting from intravenous exposure to TLR ligands . The exact mechanism that underlies this accelerated death remains to be determined . Our observation that Sptlc2-/- DC2 . 4 cells produce reduced levels of the pro-inflammatory cytokine indicates involvement of sphingolipids in the innate immune response . Sphingolipids influence the order of the lipid phase at the plasma membrane [71] . Our finding that shows Sptlc2-/- DC2 . 4 cells have a significantly reduced level of CtxB staining , the lipid raft marker , is consistent with this notion . Specializations of the eukaryotic plasma membrane include lipid domains enriched in ( glyco ) sphingolipids and cholesterol , often referred to as lipid rafts [72] . The physical characterization of these structures suggests a dynamic nature , with no agreement on either their actual size or life span in the living cell at physiological temperatures [73 , 74] . These specializations may serve to organize the disposition of signal transduction cascades through recruitment of key components [28] . In earlier work we have explored in detail the internalization of the C . albicans and analyzed the contribution of signaling platforms that include Bruton's tyrosine kinase ( Btk ) and Vav1 , in addition to the well-established role of Syk as a downstream kinase important for the function of the C-type lectin dectin-1 [75 , 76] . Enzymes such as Btk and Vav1 are amongst the candidates recruited to cytoplasmic lipid-based signaling platforms . Many of the receptors served by Syk , Btk and Vav1 take up residence in sphingolipid-rich membrane compartments to coordinate cascades of signaling events [77] . In nature , dysregulation of sphingolipid biosynthesis is linked to various diseases . Genome-wide association studies link ORM-Like protein isoform 3 ( ORMDL3 ) a member of the ORM gene family , to the onset of childhood asthma [78] . Orm proteins negatively regulate sphingolipid biosynthesis by acting as homeostatic regulators of serine palmitoyltransferase ( SPT ) , the first and rate-limiting enzyme in sphingolipid biosynthesis [79] . Mutation in the Sptlc1 gene that encodes one of the SPT subunits , causes the autoimmune disease hereditary sensory neuropathy type 1 [80] . There are also rare genetic disorders caused by mutations in proteins involved in sphingolipid metabolism [81] . Of note , none of these studies examined the properties of phagocytes . Collectively , our data show that sphingolipid biosynthesis is essential for clearance of fungal infection through phagocytosis , and hence indispensable for a proper functioning of the innate immune system . Further studies may hold the key to understanding the role of this class of lipids in the coordination of events necessary not only for the removal of such opportunistic fungi , but also for other pathogens , including the many bacteria that exploit the phagocytic pathway to subvert the host’s defense mechanisms .
Animals were maintained at the Whitehead Institute for Biomedical Research that is certified by the United States Office of Laboratory Animal Welfare ( OLAW ) under the guidance of the Public Health Service ( PHS ) Policy on Humane Care and Use of Laboratory Animals . Whitehead Institute’s Animal Welfare Assurance was approved 11/3/2009 ( IACUC , A3125-01 ) . All studies were carried out in accordance with procedures approved by the Massachusetts Institute of Technology Committee on Animal Care ( CAC# 1011-123-14 ) . Rabbit anti- Sptlc2 was purchased from Thermo Scientific Company . All flourophore-conjugated antibodies were purchased from Life Technology . Secondary anti-mouse HRP conjugated antibody was from Sigma Aldrich . Anti-HA-HRP was from Roche . Myriocin and fumonisin B1 was purchased from Sigma Aldrich and Cayman Chemical respectively . 3-indolepropionic acid ( IPA-3 ) and bafilomycin A1 were purchased from Sigma Aldrich . DC2 . 4 and RAW264 . 7 cell lines were grown in RPMI medium supplemented with 10% Inactivated Fetal calf Serum ( IFS ) at 37C and 5% CO2 . For production of retro- and lenti-viruses , low passage HEK293T cells were transfected using lipofectamine 2000 and virus-containing supernatant was harvested after 48 hours . pTK93_Lifeact-mCherry was kindly provided by Dr . lain M . Cheeseman lab ( Whitehead Institute for Biomedical Research ) . Cells stably overexpressing pTK93_Lifeact-mCherry were generated by retroviral transduction and subsequent FACS sorting was performed to enrich for mCherry-positive cells . Potential target sequences for CRISPR interference were found with the rules outlined in [82] . The following seed sequences ( CRISPR target sequences ) preceding the PAM motif that were found in the exon of Sptlc2 gene were used: Sptlc2 #1 GAACGGCTGCGTCAAGAAC; Sptlc2 #2: AGCAGCACCGCCACCGTCG Potential off-target effects of the seed sequence were confirmed using the NCBI Mus musculus Nucleotide BLAST . Generation of CRISPR/Cas9-mediated Sptlc2-knockout DC2 . 4 cell line was performed as described in [83] . Briefly , CRISPR gBlock was designed to clone into the restriction enzymatic site NheI/BamHI of CMV promoter-deleted pCDH-EF1-Hygro vector ( re-named pCDH-CMV ( - ) ) ( SBI; CD515B-1 ) as follows: cacagtcagacagtgactcaGTGTCACAgctagcTTTCCCATGATTCCTTCATATTTGCATATACGATACAAGGCTGTTAGAGAGATAATTAGAATTAATTTGACTGTAAACACAAAGATATTAGTACAAAATACGTGACGTAGAAAGTAATAATTTCTTGGGTAGTTTGCAGTTTTAAAATTATGTTTTAAAATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACCGnnnnnnnnnnnnnnnnnnnGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTTggatccTGTGCACAgtcagtcacagtcagtctac ( n: CRISPR target sequences ) The gBlock was then digested using the restriction enzymes NheI and BamHI and ligated into pCDH-CMV ( - ) vector that was linearized by digesting with the same restriction enzyme . Doxycycline inducible Cas9 expressing plasmid , pCW-Cas9 , was kindly provided by David Sabatini ( Whitehead Institute for Biomedical Research ) . Lentiviruses containing pCW-Cas9 or pCDH-EF1-Hygro-sgRNA were generated as described above . DC2 . 4 cells were infected with Cas9 lentivirus expressing Cas9 cDNA , and were cultured in media containing 7 μg/mL of puromycin ( Sigma Aldrich ) . These Cas9-inducible cells were re-infected with lentivirus carrying pCDH-CMV ( - ) -sgRNA , and were cultured in media containing 250 μg/mL of hygromycin B ( Life Technology ) . The cells stably expressing the sgRNA and Cas9 proteins were treated with 2 μg/mL of doxycycline ( Clontech ) for 3–5 days . The cells were re-plated to a 96-well plate at a density of 0 . 5 cells per well . The individual colonies were collected and the expression of Sptlc2 was examined by western-blotting using Sptlc2 antibody . The cells were either treated with 0 . 5μg/ml myriocin or 0 . 25μg/ml FB1 for 4 days and , after which cells were infected with Candida-BFP for phagocytosis experiments . Control cells were treated with only DMSO/PBS . Reduction in production of sphoingolipid biosynthesis in myriocyn/FB1 treated cells was verified by metabolic labeling and lipid analysis by TLC . Around 2 million cells were grown in 6-well-dishes and labeled with 3 μCi/mL of N-methyl-[14C]-choline or 3-L-[14C]-serine for 5 hours in Opti-MEM at 37°C . Cells were washed two times with PBS and lipid extraction was done following Bligh and Dyer method [84] . The methanol/chloroform-lipid extracts were dried by nitrogen gas . Dried lipids were re-dissolved in a few drops of chloroform/methanol ( 1:2 , vol/vol ) and loaded on a TLC plate . Where indicated , glycerolipids were removed by mild alkaline hydrolysis in 0 . 5 M sodium methoxide in MeOH for 1 h at RT . Lipids were separated by developing the TLC plate first in acetone and then in chloroform , methanol , 25% ammonia solution ( 50:25:6 , vol/vol/vol ) . Radiolabeled lipids were detected on a Phosphor-Imager ( Fujifilm BAS-2500 ) using Image Reader BAS-2500 V1 . 8 ( Fujifilm ) . Lipids were extracted from DC2 . 4 cell lines and liver tissue of mice treated or untreated with FB1 according to Folch et al . [85] but without the salt . Lipid extracts were then separated on an Ascentis Express C18 2 . 1 x 150 mm 2 . 7 μm column ( Sigma-Aldrich , St . Louis , MO ) connected to a Dionex UltiMate 3000 UPLC system and a QExactive benchtop orbitrap mass spectrometer ( Thermo Fisher Scientific , San Jose , CA ) equipped with a heated electrospray ionization ( HESI ) probe . Dried lipid samples were typically dissolved in 50 ul 65:30:5 acetonitrile:isopropanol:water ( v/v/v ) and 5 ul was injected into the LC/MS , with separate injections for positive and negative ionization modes . Mobile phase A in the chromatographic method consisted of 60:40 water/ACN in 10 mM ammonium formate and 0 . 1% formic acid , and mobile phase B consisted of 90:10 IPA/ACN , also with 10 mM ammonium formate and 0 . 1% formic acid . The chromatographic gradient was described previously [86] . The column oven and autosampler tray were held at 55°C and 4°C , respectively . The MS instrument parameters were as described previously [87] . The spray voltage was set to 4 . 2 kV , and the heated capillary and the HESI were held at 320°C and 300°C , respectively . The S-lens RF level was set to 50 , and the sheath and auxiliary gas were set to 35 and 3 units , respectively . These conditions were held constant for both positive and negative ionization mode acquisitions . External mass calibration was performed using the standard calibration mixture every 7 days . MS spectra of lipids were acquired in full-scan / data-dependent MS2 mode . For the full scan acquisition , the resolution was set to 70 , 000 , the AGC target was 1e6 , the maximum integration time was 50 msec , and the scan range was m/z = 133 . 4–2000 . For data-dependent MS2 , the top 10 ions in each full scan were isolated with a 1 . 0 Da window , fragmented at a stepped normalized collision energy of 15 , 25 , and 35 units , and analyzed at a resolution of 17 , 500 with an AGC target of 2e5 and a maximum integration time of 100 msec . The underfill ratio was set to 0 . The selection of the top 10 ions was subject to isotopic exclusion , a dynamic exclusion window of 5 . 0 sec , and an exclusion list of background ions based on a solvent blank . High-throughput profiling of lipidomic data was performed using LipidSearch software ( Thermo Fisher Scientific / Mitsui Knowledge Industries ) [88 , 89] . In addition , sphingosine and sphinganine were manually analyzed and matched to reference spectra ( http://metlin . scripps . edu ) using XCalibur QualBrowser software , and peaks were quantified by XCalibur QuanBrowser software ( Thermo Fisher Scientific ) . C . albicans strain SC5314 was cultured in YPD + Uri ( 2% bactopeptone , 1% yeast extract , 2% glucose and 80 μg/ml Uridine ) at 37°C . Generation of Candida albicans expressing blue fluorescent protein ( Candida-BFP ) is described in [75] . Zymosan A was labeled with Alexa647 carboxylic acid ( succinimidyl ester ) by incubation in 0 . 1 M Na2CO3 at room temperature . For immunofluorescence microscopy , Candida-BFP ( at an MOI of 10 ) , zymosan-Alexa647 or latex beads-rabbit IgG-FITC were added ( 10 particles per cell ) to the cells that were plated on a cover slip a day earlier . Typically , cells were fixed after 60 and 90 min post infection . The time points selected are based on our previous studies [75] . C . albicans begins to form hyphae at ~60 min post infection; phagocytes begin to show cell death ( pyroptosis ) after ~90 min . At different time points , cells were washed twice with PBS and fixed with 4% PFA for 30 min at room temperature . Cells were washed with PBS and incubated in 50mM NH4CL in PBS for 10 min and incubated for another 30 min with binding buffer ( 0 . 1% saponin , 0 . 2% BSA in PBS ) . Cells were stained with phalloidin-Alexa 488 or 568 ( Life Technology ) for 60 min and washed several times with PBS and mounted on slides for confocal microscopy . Peritoneal macrophages were harvested by peritoneal lavage with upto 10ml PBS . Cells were seeded on coverslips in DMEM ( high glucose; Gibco ) with 10% FCS plus 0 . 5μg/ml myriocin and used for phagocytosis experiments four days later . All images were captured in the W . M . Keck facility for Biological Imaging using a PerkinElmer live cell imaging spinning disk confocal system and Volocity software . Images were captured using a confocal microscope with a 63°- 1 . 40 N . A . of the Carl Zeiss Plan Apo oil objective . ImageJ was used to quantify the fluorescent intensity of the images . Animals were housed at the Whitehead Institute for Biomedical Research and maintained according to protocols approved by the Massachusetts Institute of Technology Committee on Animal Care . C57BL/6 wild type mice were purchased from Jackson Labs . C57BL/6 mice were treated daily with 2mg/kg FB1 subcutaneously for five days . Control and FB1 treated animals ( 10 mice per group ) were left uninfected ( received only PBS ) or infected with live or UV killed 2x 104 CFU of C . albicans in PBS via tail vein injection The mice continued to receive FB1 daily for the rest of the study , during which the health and overall well-being of the animals was monitored . Ten mice per group were used , and three independent experiments were performed . For the analysis of fungal load in the kidney and brain , the animals were sacrificed at the late stage of the disease ( 9 days after injection of C . albicans ) . To study the fungal loads , brain and kidney tissues of the whole organs were homogenized in PBS . Serial dilutions of the homogenates were grown on YPD plates and colonies were counted after 3 days . VACV WR E eGFP [48] encoding eGFP under the control of the J2R early ( E ) promoter in the tk locus was produced in BSC-40 cells and purified from cytoplasmic extracts through a 16% sucrose cushion in 20 mM Tris pH 9 . 0 . VSV GFP was produced in Vero cells and virus-containing cell supernatants used for infections [48] . To quantify infection by flow cytometry , DC2 . 4 wt or Sptlc2 knockout cells were seeded in 24-well plates one day before infection ( 2·105 cells/well ) . Cells were infected with the appropriate amounts of WR E eGFP or VSV GFP ( in DMEM ) . 30 minutes post infection , inoccula were removed and cells cultivated for 5:30h in full medium . Cells were trypsinized and fixed in 4% formaldehyde/PBS . Green fluorescent cells were quantified using a BD Biosciences LSRFortessa flow cytometer and the FlowJo software package . About 10 × 106 wild type and Sptlc2-/- DC2 . 4 cells were starved in methionine- and cysteine-free DMEM and starved for 30 min at 37°C . Cells were pulse-labelled for 20 min with [35S]-methionine/cysteine at 0 . 77 mCi/mL . Cells were then chased in complete media for 0 , 30 , and 90 min . At different time points during the chase cells were collected , washed once with cold PBS , and lysed in Tris buffer [150 mM NaCl , 5 mM MgCl2 , 25 mM Tris ( pH 7 . 4 ) ] containing 0 . 5% Nonidet P-40 . The lysates were precleared with immobilized protein G beads for 3 h , and MHC class I molecules were recovered using an H-2Kb heavy chain specific ( p8 ) rabbit serum [90] . Immunoprecipitated samples were subjected to Endo H treatment according to the manufacturer’s instructions . For analyzing the secretome , cells were labeled as described above and chased for the indicated time points in a media without serum . Supernatants were collected , boiled in SDS sample buffer and analysed by SDS/PAGE . Samples were visualized with autoradiography using DMSO/PPO ( 2 , 5-diphenyloxazole ) and exposure to Kodak XAR-5 film . Equal numbers ( about 0 . 5x106 ) of control and Sptlc-/- DC2 . 4 cells were incubated with antibodies for 20 min on ice . The cells were washed with cold PBS supplemented with 1% BSA and subjected to cytofluorometry immediately ( FACSCalibur; BD Biosciences ) . Control samples were prepared using the corresponding isotype antibody . FlowJo was used to analyze the data . Intensity of fluorescence was measured , and the percent maximum presented in the overlaid histograms , and mean fluorescence intensity ( MFI ) was measured to calculate the standard deviation between the experiments .
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The fungus Candida albicans is not only a commensal of the digestive system , but also a common cause of human opportunistic infections . Macrophages and dendritic cells can eliminate C . albicans by phagocytosis , a complex process that involves extensive membrane reorganization at the cell surface . The extent to which membrane lipids , including sphingolipids , contribute to the proper execution of phagocytosis remains largely unknown . Pharmacological blockade of sphingolipid biosynthesis by the small molecule inhibitors myriocin and fumonisin B1 impairs phagocytosis of C . albicans . DC2 . 4 dendritic cells genetically deficient in Sptlc2 , the enzyme that catalyzes the first and rate-limiting step in the sphingolipid biosynthetic pathway , are likewise defective in phagocytosis of C . albicans . Sptlc2-/- DC2 . 4 cells showed reduced binding of C . albicans , but overall membrane transport and protein secretion remained functional . Sptlc2-deficient cells express reduced levels of the receptors Dectin-1 and TLR2 at the cell surface , and are unable to form a normal phagocytic cup . Exogenous addition of the major ganglioside GM1 restored phagocytic ability of Sptlc2-/- DC2 . 4 cells . Mice with compromised sphingolipid production upon in vivo treatment with fumonisin B1 fail to eradicate C . albicans , consistent with the in vitro results . Sphingolipids are thus essential for clearance of fungal infection through phagocytosis , and hence indispensable for the proper functioning of the innate immune system .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Disruption of Sphingolipid Biosynthesis Blocks Phagocytosis of Candida albicans
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HIV-1 can downregulate HLA-C on infected cells , using the viral protein Vpu , and the magnitude of this downregulation varies widely between primary HIV-1 variants . The selection pressures that result in viral downregulation of HLA-C in some individuals , but preservation of surface HLA-C in others are not clear . To better understand viral immune evasion targeting HLA-C , we have characterized HLA-C downregulation by a range of primary HIV-1 viruses . 128 replication competent viral isolates from 19 individuals with effective anti-retroviral therapy , show that a substantial minority of individuals harbor latent reservoir virus which strongly downregulates HLA-C . Untreated infections display no change in HLA-C downregulation during the first 6 months of infection , but variation between viral quasispecies can be detected in chronic infection . Vpu molecules cloned from plasma of 195 treatment naïve individuals in chronic infection demonstrate that downregulation of HLA-C adapts to host HLA genotype . HLA-C alleles differ in the pressure they exert for downregulation , and individuals with higher levels of HLA-C expression favor greater viral downregulation of HLA-C . Studies of primary and mutant molecules identify 5 residues in the transmembrane region of Vpu , and 4 residues in the transmembrane domain of HLA-C , which determine interactions between Vpu and HLA . The observed adaptation of Vpu-mediated downregulation to host genotype indicates that HLA-C alleles differ in likelihood of mediating a CTL response that is subverted by viral downregulation , and that preservation of HLA-C expression is favored in the absence of these responses . Finding that latent reservoir viruses can downregulate HLA-C could have implications for HIV-1 cure therapy approaches in some individuals .
Human Leukocyte Antigen class-I ( HLA-I ) molecules present peptides from intracellular proteins at the cell surface . Classical HLA-I molecules are highly polymorphic and expressed from three loci , HLA-A , —B and—C , which differ in some respects . Polymorphism is most pronounced for HLA-B , and HLA-A/B are expressed at around 10-fold higher levels than HLA-C at the cell surface [1 , 2] . In the event of HIV-1 infection , HLA-A/B/C proteins can all present viral peptides which are recognized by CD8+ T cells , and result in the triggering of effector responses including target cell killing . Several lines of evidence provide unambiguous support for a critical role of CD8+ T cell responses in partial control of HIV-1 infection: the emergence of cytotoxic T lymphocytes ( CTL ) coincides with reduction in viral load after acute infection [3–5] , HLA-I alleles associate with outcomes of HIV-1 infection or viral sequence adaptation [6–9] , and CTL can eliminate infected cells in vitro [10] . Many pathogens evade CTLs by disrupting HLA expression , but this can incur recognition by innate immune cells . Natural Killer ( NK ) cells are regulated by inhibitory receptors for self HLA-I molecules , such as inhibitory killer immunoglobulin-like receptors ( KIR ) . Cells with decreased HLA expression fail to ligate these inhibitory receptors , resulting in NK activation and cytotoxicity [11] . Associations between KIR alleles and viral load provide evidence that NK cells can influence the outcome of HIV-1 infection in vivo , and NK cells can be observed to respond to HIV-1 infected cells in vitro [12–14] . Faced with these challenges , HIV-1 demonstrates sophisticated manipulation of HLA-I molecules , in which both the locus specificity and magnitude of downregulation appears to be important . It has long been known that HLA-A/B , but not HLA-C molecules , are downregulated by the Nef protein of HIV-1 [15–17] . Downregulation of HLA-A/B is well-established to subvert CTL mediated host immunity in vitro and in vivo [18 , 19] . The clinical importance of Nef is confirmed by rare forms of HIV-1 that lack part of Nef , which do not cause AIDS when infecting humans [20 , 21] . Although downregulated by around 5-fold on infected primary cells , HLA-A/B are not removed entirely from the surface of infected cells [2] . This may maintain some inhibition of NK cells , although other receptor-ligand interactions during infection can also clearly influence NK cell responses [22–25] . We recently found that HLA-C can be downregulated by the Vpu protein of HIV-1 , and this observation has already been replicated by 3 independent groups [26–29] . Vpu is necessary and sufficient for HLA-C downregulation , but Vpu does not affect HLA-A/B . Downregulation of HLA-C by Vpu occurs in primary cells infected with primary HIV-1 clones . Transmitted/founder viruses , viruses from chronic infection , and representatives of all subtypes of HIV-1 are capable of downregulating HLA-C [26] . This supports a biologically relevant role for downregulation of HLA-C by HIV-1 , and the finding is consistent with evidence accumulated over the past decade for a role of HLA-C expression level in HIV-1 disease . Early genome-wide association studies identified a strong effect of a variant marking HLA-C in the outcome of HIV-1 infection [8 , 9] . Unlike HLA-A/B for which individual alleles associate with HIV-1 viral load , associations at the HLA-C locus implicated large groups of alleles . It was found that host HLA-C alleles vary in expression level under normal conditions , and that HLA-C expression level as a continuous variable correlated inversely with HIV-1 viral load [30 , 31] . Higher HLA-C expression may be disadvantageous for the virus due to more potent CTL responses , as both HIV-specific CTL and viral escape mutation associate more strongly with HLA-C alleles that are expressed at higher levels [31 , 32] . A key difference between HLA-C downregulation by Vpu and the modulation of HLA-A/B by Nef , is that the former varies much more frequently between HIV-1 viruses . Indeed , downregulation of HLA-C by HIV-1 had escaped detection for so long because the widely-studied lab-adapted strain NL4-3 does not downregulate HLA-C . In the initial report of HLA-C downregulation by Vpu , 15 infectious molecular clones showed a continuous distribution in ability to downregulate HLA-C , in contrast to almost identical levels of HLA-A downregulation [26] . A comprehensive screen of 360 primary Nef variants found some variability in downregulation of HLA-A , but that the magnitude of this function was on average well-preserved [33] . This suggests that a similar level of HLA downregulation provides optimal evasion of HLA-A/B mediated immunity across individuals . Responses that target HLA-C differ much more between individuals in the selection pressure they exert on HLA expression level . Targeting of HLA-A/B and HLA-C by separate viral proteins , and their contrasting patterns of downregulation between individuals , emphasizes differences in the biological role of these HLA molecules in HIV-1 infection . The specific selection pressures which favor viral downregulation of HLA-C in some individuals , but HLA-C preservation in others , are not known . In contrast to HLA-A/B , the physiological significance of HLA-C restricted CTL is less clear . HLA-C restricted CTL responses can be detected , and viral escape mutations associating with HLA-C alleles are observed [34–38] . However , these mutations are not necessarily deleterious for the virus , and unlike for HLA-A/B individual HLA-C alleles do not strongly associate with HIV-1 viral load [7 , 39] . Viral downregulation of HLA-C is also more likely than downregulation of HLA-A/B , to increase infected cell susceptibility to NK cell cytotoxicity . This is because inhibitory KIR2DL receptors which recognize self HLA-C alleles are present in all individuals , unlike KIR3DL which bind only a subset of HLA-A/B allotypes that are absent in many individuals [40] . In vitro experiments have provided evidence consistent with HLA-C downregulation inhibiting CTL but activating NK cells . An HLA-C*03 restricted CTL clone , which recognizes an Env epitope , showed decreased inhibition of an NL4-3 virus mutated so that its Vpu was able to downregulate HLA-C [26] . KIR2DL2+ NK cells showed increased inhibition of the virus JR-CSF compared to its mutant lacking Vpu [27] . In both of these in vitro approaches , the small effect and analysis of single epitopes or effectors limits generalization to a physiological role of HLA-C downregulation in CTL or NK function . The variation in HLA-C downregulation between primary HIV-1 viruses provides an opportunity to identify the selection pressures acting on HLA-C expression in human individuals . To identify what leads to differential HLA-C downregulation by virus from different infections , it is necessary to expand the number and type of primary viruses for which this phenotype has been studied . To date 15 infectious molecular clones and viruses from six transmission pairs , have been measured for ability to downregulate HLA-C [26 , 27 , 29] . This study characterizes HLA-C downregulation by 128 viruses from the latent reservoir of 19 anti-retroviral therapy ( ART ) -suppressed individuals , 41 samples from 9 individuals with untreated infection , and cloned Vpu molecules from a further 195 chronically infected individuals spanning four viral subtypes . Adaptation of HLA-C downregulation to host genotypic variants is identified , that indicates selection pressures involved in the viral manipulation of HLA-C .
In virally suppressed individuals adhering to effective ART , a reservoir of infected cells results in viral rebound if ART is discontinued [41] . This viral reservoir is the subject of intense interest as a barrier to the cure of HIV-1 [42] . We have characterized the ability to downregulate HLA-C for 128 inducible infectious HIV-1 isolates , from peripheral blood of 19 ART-suppressed individuals . Cultures were generated for quantitative viral outgrowth assays ( QVOA ) , with limiting dilutions of donor CD4+ T lymphocytes stimulated with PHA , and induced viral isolates infected co-cultured Molt-4 T cells [43] . In these conditions viral isolates represent clonal outgrowths with negligible mutation in the two weeks of in vitro culture [44] . HLA-C downregulation by each viral isolate was quantified by comparing flow cytometry staining of HLA-C , between infected and uninfected Molt-4 cells within the same culture well ( Fig 1A ) . Isolates varied widely in their ability to downregulate HLA-C , ranging from no effect to robust downregulation ( Fig 1B ) . For viruses showing the strongest downregulation of HLA-C , this was of a magnitude comparable to that observed for the downregulation of HLA-B when primary CD4+ T cells are infected with HIV-1 in vitro [2 , 45] . 19 individuals were studied and of these , the viral isolates of 15 individuals demonstrated no or low ability to downregulate HLA-C , whereas in 4 individuals we observed downregulation of HLA-C by multiple viral isolates ( Fig 1C ) . Given the variation in HLA-C downregulation between primary viruses from different individuals and between multiple viral isolates from the reservoir of an individual ( Fig 1C ) [26] , we investigated if HLA-C downregulation changed longitudinally during early untreated infection . Using previously described paired primary infectious molecular clones from individuals , we compared HLA-C downregulation between a transmitted/founder virus and a representative virus from the same individual 6 months after infection [46 , 47] . Primary CD4+ T cells were infected in vitro and HLA-C downregulation determined by flow cytometry , comparing HLA-C staining on infected versus uninfected CD4 cells within the same culture well ( Fig 2A ) . For 3 individuals studied , there was no difference in the HLA-C downregulation by the transmitted compared to 6 month virus . This included viruses with HLA-C downregulation which was weak ( CH040 ) , moderate ( CH162 ) , and strong ( CH058 ) ( Fig 2B ) . To investigate HLA-C downregulation later in infection , we studied cloned Vpu molecules generated from the plasma of 6 untreated individuals . Cloned Vpu genes were expressed by transfection in HeLa cells using a Rev-dependent vector , and HLA-C downregulation quantified by flow cytometry comparing staining of transfected versus untransfected cells within a culture well ( Fig 2C and 2D ) . Vpu-mediated downregulation of HLA-C measured by transfection of HeLa cells , correlates robustly with HLA-C downregulation by primary HIV-1 viruses ( S1 Fig ) . For one donor sampled at 1 , 4 and 10 years post-infection , the 5 most frequent Vpu sequence variants observed at each time-point were studied , and showed variation in HLA-C downregulation between the viral quasi-species present at all timepoints ( Fig 2E ) . Phylogenetic analysis has shown that in this individual a single virus from one timepoint gives rise to all of the quasispecies present at the next , indicating that in this individual persistent diversification in viral downregulation of HLA-C occurs ( S2 Fig ) [48] . HLA-C downregulation was characterized for 3 further donors where multiple quasi-species were tested at a single timepoint ( Fig 2F–2H ) , and 2 further donors where dominant Vpu species were tested from longitudinal timepoints spanning 2 and 4 years ( Fig 2I and 2J ) . Together this analysis of 41 viruses from 9 individuals shows robust changes in HLA-C downregulation in longitudinal infection are not readily observed , but that variation between quasi-species is detected in some individuals which could result in adaptation of downregulation in chronic infection . To identify the characteristics of infections in which HIV-1 differentially modulates HLA-C , we measured the ability of Vpu from 195 different infected individuals of the BC HOMER and UARTO cohorts to downregulate HLA-C . In all cases participants were ART naïve and in the chronic stage of infection , and HLA genotypes were known for 186 of the individuals . For each individual a single Vpu sequence was cloned from plasma , expressed by transfection in Molt-4 cells , and HLA-C downregulation quantified by flow cytometry . Vpu-mediated downregulation of HLA-C measured by transfection of Molt-4 cells , correlates robustly with HLA-C downregulation by primary HIV-1 viruses ( S1 Fig ) . The observed distribution showed a natural threshold at approximately 6-fold downregulation of HLA-C , where in 117 of the infections Vpu downregulated HLA-C by <6-fold , and in 69 of the infections Vpu downregulated HLA-C >6-fold ( Fig 3A ) . Subsequent categorical analyses used this threshold to classify Vpu molecules as strong , or weak downregulators of HLA-C . When individuals were stratified by common alleles of the classical HLA-I loci , the frequency of Vpu clones that downregulate HLA-C strongly varied significantly in association with host HLA-C allotype ( p = 0 . 0009 ) , but not with HLA-A ( p = 0 . 05 ) or HLA-B ( p = 0 . 28 ) ( Fig 3B ) . At the extreme ends of this spectrum , only 1 out of 15 Vpu clones from individuals carrying HLA-C*17 was capable of downregulating HLA-C strongly , in contrast to 11 out of 14 Vpu clones from individuals carrying HLA-C*14 . This suggests immune responses which drive the virus to downregulate HLA-C , vary in their frequency between individuals with different HLA-C alleles . Inhibition of NK cells , mediated by KIR binding to HLA-C , may exert selection pressure on HLA-C expression . All HLA-C alleles resolve into two groups , C1 and C2 , based upon a dimorphism at positions 77 and 80 which determines their binding to inhibitory two-domain KIR [40] . However , we found no difference in the frequency of Vpu clones that downregulated HLA-C strongly when individuals were grouped by presence of C1 versus C2 alleles ( Fig 3C ) . This suggests that inhibition of NK cells may not be a significant factor in the selection pressures that influence Vpu-mediated downregulation of HLA . HLA-C alleles differ in their surface protein expression level on peripheral blood lymphocytes , enabling HLA-C expression level to be inferred for individuals on the basis of their HLA-C genotype [30 , 31] . For 186 Vpu molecules , the observed HLA-C downregulation showed a positive correlation with inferred HLA-C expression level in the host prior to infection ( r = 0 . 27 , p = 0 . 0005 ) ( Fig 3D ) . This indicates that the higher the HLA-C expression level of an individual that is infected with HIV-1 , the greater the downregulation of HLA-C demonstrated by the viral Vpu that adapts to this individual . To maximize statistical power our primary analyses combined individuals from the BC HOMER and UARTO cohorts . To rule out confounding by the substantial differences between these cohorts , for example in ethnicity and sex ratio , analyses were repeated stratified by cohort . Fig 3B showed the frequency of Vpu clones that downregulate HLA-C strongly , varied in association with HLA-C allotype . This association was not significant in BC HOMER and UARTO participants separately ( p = 0 . 053 and 0 . 23 respectively ) but plots of alleles with n≥5 in both cohorts show these do typically have similar effects . For example HLA-C*14 has the highest frequency of Vpu clones that downregulate HLA-C strongly in both cohorts; HLA-C*04 , 06 and 08 are intermediate; and HLA-C*03 and 07 are among the lowest in both cohorts S3A and S3B Fig ) . Fig 3D showed that the magnitude of HLA-C downregulation observed for a Vpu clone , correlated with HLA-C expression level prior to infection inferred from host HLA-C genotype . This correlation was significant in both BC HOMER ( p = 0 . 008 ) and UARTO participants ( p = 0 . 002 ) with highly similar correlation coefficients ( S3C and S3D Fig ) . The total of 195 Vpu clones studied here also included viruses from the 4 major HIV-1 group M subtypes . Both viral ability to downregulate HLA-C and variation in this phenotype between primary viruses was observed within each subtype , consistent with prior study of 14 primary infectious molecular clones [26] . In this larger dataset we detect significant difference in the frequency of HLA-C downregulation between subtypes . Vpu clones from subtype A and B viruses more frequently downregulated HLA-C strongly than those from subtypes C or D ( S4 Fig ) . Given that the proportion of Vpu clones that downregulate HLA-C differs significantly based on host HLA-C genotype ( Fig 3B ) , we hypothesized that that among individuals with an HLA-C allele that strongly selects for viral downregulation of HLA-C , there would be a direct correlation between the extent of viral adaptation to that HLA-C allele , and the observed HLA-C downregulation by Vpu . In contrast , such a relationship would not be observed in individuals carrying an HLA-C allele which does not select for viral downregulation of HLA-C . To assess this , we used a published HLA adaptation metric to quantify the extent of HIV-1 adaptation to host HLA-C alleles [49] . The strength of correlation between viral adaptation to host HLA-C and observed downregulation of HLA-C by Vpu was then determined for each HLA-C allele ( S5 Fig ) . Consistent with our hypothesis , a significant positive correlation was observed between a given Vpu’s ability to downregulate HLA-C in individuals expressing HLA-C*08 ( an allele that strongly selects for viral downregulation of HLA-C ) , but not in individuals expressing HLA-C*07 ( an allele which does not select for viral downregulation of HLA-C ) . Across HLA-C alleles , the strength of these correlations associates directly with the variation between HLA-C alleles in selection pressure for HLA-C downregulation , shown by the frequency of Vpu clones that downregulate HLA-C in individuals with that HLA-C allele ( r = 0 . 68 , p = 0 . 03 ) ( Fig 3E ) . Thus , the variation in HLA-C downregulation observed by viruses with different degrees of adaptation to an HLA-C allele , supports the finding that Vpu-mediated downregulation adapts to host HLA-C genotype . For a small number of paired Vpu molecules that differentially downregulate HLA-C , sequence variants which explain the differences in HLA-C downregulation have been identified . For example , downregulation of HLA-C by Vpu from WITO but not NL4-3 is primarily explained by the sequence differences at positions 4 and 5 of Vpu [26] . However , these positions are not sufficient to predict HLA-C downregulation in natural Vpu sequences , indicating other residues also play a role . Using the nearly 200 Vpu clones from different individuals that were characterized for ability to downregulate HLA-C , we identified Vpu sequence variants which account for variation in HLA-C downregulation in this population of primary viruses . Vpu is an approximately 80 amino acid protein with a large cytoplasmic region , a transmembrane domain , and a short N-terminal stalk . Representative sequences of primary Vpu molecules that were observed to downregulate , or not to downregulate HLA-C , are shown aligned to Vpu from NL4-3 ( Fig 4A ) . At each Vpu residue , amino acid polymorphism was tested for association with the observed downregulation of HLA-C for 191 Vpu clones . A multiple linear regression model identified 5 positions at which the residues indicated associated independently with HLA-C downregulation ( Fig 4B ) . The strongest effects are presence of glutamic acid at position 5 , or a glycine or threonine at position 16 , which when present result in Vpu molecules which downregulate HLA-C more strongly . An alanine at position 15 associated with weaker downregulation of HLA-C . The frequency of different amino acids observed at each of these 5 positions is detailed in S6 Fig . These five positions remained independently significant when viral subtype is included as a covariate ( S1 Table ) , and in analyses stratified by viral subtype or cohort each residue except for serine at position 24 remains significant in multiple subtypes and cohorts ( S2 and S3 Tables ) . Each of these 5 Vpu positions was confirmed to impact HLA-C downregulation , in the direction indicated , in the context of primary Vpu sequences compared to single amino acid mutants ( Fig 4C ) . For example , a primary Vpu clone with proline at position 3 downregulated HLA-C strongly , but a mutant constructed with serine , the next most common residue at position 3 , showed reduced ability to downregulate HLA-C . Although this population analysis identified 5 positions of importance , individual primary Vpu sequences carried no more than 3 of the residues detected to associate with HLA-C downregulation , in various combinations . When we generated a mutant Vpu with the variants that associate with greater HLA-C downregulation at all 5 of the positions implicated , this molecule demonstrated negligible downregulation of HLA-C , suggesting that some of these residues may not be compatible with one another ( Fig 4D ) . The multiple linear regression model defined in Fig 4B , when used to predict HLA-C downregulation for each Vpu clone , explains 40% of the observed variation in HLA-C downregulation for these Vpu molecules ( Fig 4E ) . A plot of the residuals between observed and predicted HLA-C downregulation confirms linear regression analysis is appropriate ( S7 Fig ) . When highlighted on the NMR structure of Vpu , the 5 positions implicated in HLA-C downregulation cluster in the transmembrane and N-terminal regions of the molecule ( Fig 4F ) [50] . These 5 positions were also observed to vary in the longitudinal Vpu clones which showed variation in HLA-C downregulation ( S2 Fig ) . Together these results suggest the transmembrane domain of Vpu is responsible for interaction with HLA-C , and that primary viruses exploit multiple alternative sequences which are able to downregulate HLA-C . Analyses of HLA chimeras also indicate the transmembrane domain of HLA-C molecules is responsible for interaction with Vpu . HEK 293T cells were transfected with Flag-tagged constructs of Vpu from a primary virus , 2_87 , which was previously shown to downregulate HLA-C , or from NL4-3 which does not downregulate HLA-C [26] . Cells were co-transfected with HA-tagged constructs of either HLA-A or HLA-C . Flag immunoprecipitation followed by western blotting with anti-Flag or anti-HA mAb , detected precipitated Vpu and associated HLA respectively . Vpu from 2_87 co-precipitated HLA-C , in contrast to lanes using Vpu from NL4-3 or the HLA-A molecule , showing an interaction between primary Vpu and HLA-C consistent with our cellular observations ( Fig 5A ) . Experiments were repeated using chimeras between HLA-A and HLA-C , where the primary Vpu co-precipitated specifically chimeras 2 and 3 with transmembrane domains from HLA-C , but not chimeras 1 and 4 lacking HLA-C transmembrane sequence ( Fig 5B and 5C ) . This indicates the transmembrane domain of HLA-C mediates interaction with Vpu . Alignment of HLA transmembrane domain sequences identified a 4 residue sequence , LAVL , which is conserved in HLA-C molecules which we have found downregulated by Vpu , but not in HLA-A/B which are unaffected by Vpu ( Fig 5D ) [26] . It was confirmed the primary Vpu could co-precipitate HLA chimera 5 , comprised entirely of HLA-A with just these 4 transmembrane residues substituted from HLA-C , but not the reciprocal chimera 6 ( Fig 5B–5D ) . Thus , studies of mutant HLA-I molecules implicate 4 residues in the transmembrane domain of HLA-C , that contribute to the interaction with Vpu .
Until recently it was believed that HIV-1 Nef subverted both CTL and NK cell immunity by downregulating the dominant CTL ligands HLA-A and -B , but preserving expression of HLA-C and HLA-E which serve as ligands for KIR2DL and NKG2A receptors that inhibit NK cells [16 , 18] . This understanding was modified by our finding that primary strains of HIV-1 can also downregulate HLA-C using the viral Vpu protein [26] . A major difference between the viral modulation of HLA-A/B compared to HLA-C , is that most HIV-1 strains downregulate HLA-A/B relatively well whereas HLA-C downregulation varies widely . The consequences resulting from HIV-1 downregulating HLA-C on infected cells in some individuals but not others are unclear . Variation between HIV-1 viruses provides an opportunity to identify selection pressures in human individuals which may be relevant . In this study we have characterized HLA-C downregulation for a wide range of primary viruses . We demonstrate that HIV-1 viruses from the latent reservoir of certain individuals downregulate HLA-C , that different quasispecies members can vary widely in their ability to downregulate HLA-C during chronic infection , and that viral downregulation of HLA-C shows a striking adaptation to the HLA genotype of the host . In a screen of 128 replication competent viral isolates from limiting dilution outgrowth assays of 19 individuals treated with effective ART , 4 individuals harbored multiple viral isolates that downregulated HLA-C . These viruses are representative of the latent viral reservoir with the ability to seed new infection when ART is ceased [41] . Consequently , it will be necessary to clear reservoir viruses that are able to downregulate HLA-C to cure HIV-1 in certain individuals . This complicates some shock and kill strategies , which have proposed to use HLA-C restricted effectors as this HLA was previously thought to be preserved on infected cells [51 , 52] . It may also provide new opportunities for individualized therapy although such approaches , if ultimately developed , would be limited in scalability at least initially . Characterization of the CTL responses in the 4 individuals in which HIV-1 downregulates HLA-C may identify CTL epitopes that could represent candidates for expansion and treatment as immune therapies , due to their in vivo selection pressure suggested by this study . The unique role of HLA-C , exerting selection pressures that result in variable modulation of its expression , may make it possible for immunotherapies to achieve enhanced efficacy if both of the opposing pressures on HLA-C expression level could be established in the same individual . In vitro infections of primary CD4+ T cells with transmitted/founder viruses , and virus from the same individual after 6 months of untreated infection , showed no change in HLA-C downregulation for 3 individuals . Analyses of 35 Vpu clones from 6 untreated individuals in chronic infection detected little longitudinal change in HLA-C downregulation , but confirmed variation between viral quasi-species present at a timepoint in several individuals , which could result in adaptation to host HLA genotype . These results indicate HLA-C downregulation by HIV-1 is not frequently affected by early innate immune pressures during acute infection [53 , 54] . A screen of Vpu molecules from nearly 200 chronically infected ART naïve individuals , found that in approximately one third of the infections viruses downregulated HLA-C strongly . Host HLA-C genotype was the strongest corollary of HLA-C downregulation identified . Viruses from individuals with HLA-C alleles such as C*07 , generally downregulated HLA-C only weakly , whereas viruses from individuals with other alleles such as C*14 were far more likely to downregulate HLA-C strongly . This differential downregulation of HLA-C is unlikely to be attributable to viral adaptation to the susceptibility of different HLA-C alleles to downregulation by Vpu . This is because primary HIV-1 viruses which downregulate HLA-C have been used to infect CD4+ cells from different individuals in vitro , and showed downregulation which was broadly similar across HLA-C types [26 , 27] . Further , the 4 HLA transmembrane residues found to be required for interaction with Vpu are conserved in all HLA-C alleles . We therefore interpret the association between downregulation and host HLA-C genotype to represent viral adaptation to differences in immune responses which are mediated by HLA-C alleles . It is well established that downregulation of HLA-A/B by Nef subverts CTL responses , so it seems likely that certain CTL responses are also the selection pressure resulting in HLA-C downregulation [18 , 19] . Consistent with this idea there is both in vitro and in vivo evidence that lower HLA-C expression levels can decrease the efficacy or frequency of HLA-C restricted CTL responses [26 , 31 , 32] . The variation in adaptation to HLA-C alleles that we observed , suggests that HLA-C alleles differ in their likelihood of restricting CTL responses which the virus responds to by downregulating HLA-C . It is not clear what differentiates HLA-C alleles that are more likely to select for HLA-C downregulation , although they tend to be expressed at higher levels . We observed a direct correlation between the level of HLA-C expression by an individual prior to infection , and the degree of downregulation observed following chronic infection . Although certain HLA-C restricted CTL responses may be evaded by viral downregulation of HLA-C , in two thirds of infections Vpu downregulated HLA-C only weakly . Thus , there appears to be some advantage to the virus in preserving HLA-C expression in most individuals . Grouping HLA-C alleles based on differential KIR binding did not result in differences in the frequency of Vpu molecules that downregulate HLA-C strongly . This might be clarified by KIR typing but suggests that inhibition of NK cells may not be a significant factor in the selection pressures that influence viral downregulation of HLA-C , because the KIR bound by these groups of HLA-C alleles differ functionally , and these HLA-C allele groups do associate with other outcomes influenced by NK cells [40 , 55–57] . Other reasons why the virus might preserve HLA-C include a possible cost to replication efficiency of using Vpu to downregulate HLA-C . Vpu has multiple functions such as antagonizing CD4 and tetherin to promote virion release from infected cells , and these may be impaired when Vpu is used to downregulate HLA-C [58 , 59] . Interactions between different conformations of HLA-C and Env have also been reported to influence virion infectivity , and could represent another mechanism impacted by HLA-C downregulation [60] . That magnitude of downregulation is part of the viral adaptation to host HLA-C alleles , is supported by the variation observed in HLA-C downregulation by viruses with different degrees of adaptation to host HLA-C alleles . Molecular characterization using primary virus derived Vpu sequences and chimeric HLA constructs identified 5 positions in the Vpu N-terminal stalk and transmembrane domain , and 4 residues in the HLA transmembrane domain , which are important in the interaction required for downregulation of HLA-C . The 5 positions of Vpu are all found on a similar face of the transmembrane/extracellular region of the molecule , consistent with residues at these sites promoting or disrupting a binding site . The 4 residues of the HLA transmembrane identified to be important are not present in any common HLA-A or—B alleles , but are conserved in all HLA-C alleles . The HLA-E transmembrane sequence differs from all classical HLA alleles in these 4 positions , so it will be interesting to determine if Vpu from different primary viruses can variably modulate HLA-E in addition to HLA-C [61] . Significantly , the Vpu variants identified are polymorphic in primary Vpu sequences , raising the possibility of predicting HLA-C downregulaton by primary virus from the Vpu sequence alone . This could identify preliminary characteristics of infections in which HIV-1 does or does not downregulate HLA-C for experimental validation , although additional effects of HIV-1 on HLA may occur in vivo . For example Vpu can suppress NF-kb activity to inhibit transcription from all HLA loci [62] . Variation between primary viruses in downregulation of HLA-C represents an opportunity to identify specific cellular immune responses that exert selection pressure on HLA expression in vivo . Comparison of cellular responses between individuals in which HIV-1 downregulates versus preserves HLA-C , has potential to identify specific CTL epitopes useful for HIV-1 therapy , and to improve understanding of the differential role of HLA-C molecules between infected individuals .
Samples from HIV-1 infected individuals were used from the following existing collections . PBL from the Reservoir Characterization Support Section of the BELIEVE consortium , approved by the George Washington University Committee on Human Research ( 021750 ) . Vpu and HLA genotypes from the BC HOMER and UARTO studies , approved by the Simon Fraser University Research ethics board ( 2016s0393 ) and the University of British Columbia-Providence Health Care Research ethics board ( H11-01642 and H08-00962 ) . All donors provided written informed consent and samples were de-identified for the present study . The mouse IgG2b monoclonal antibody DT9 was used in flow cytometry analyses to detect HLA-C [63] . This mAb has been shown to bind all alleles of HLA-C , but no common HLA-A/B alleles [30] . DT9 also recognizes HLA-E but this antigen is expressed at much lower levels than HLA-C on lymphocytes [2] . That the decreased DT9 binding of primary CD4+ cells upon HIV-1 infection reflects downregulation of HLA-C , has been confirmed by similar decreases in the staining of 3 different mAbs which recognize independent HLA-C epitopes but do not detect HLA-E [26] . Healthy individuals vary in their HLA-C expression level on PBL , and this has been shown to be a function of HLA-C genotype , with an average expression level for each allele defined ( S4 Table ) . A sum of the average expression level of each HLA-C allele has previously been shown to predict HLA-C expression level for unrelated individuals of both European and African American ethnicity [31] . QVOA were performed using a previously described protocol , with slight modifications [43] . Briefly , CD4+ T-cells from ART suppressed individuals were cultured at limiting dilutions , with 12 replicates per dilution , and stimulated with IL-2 , PHA and irradiated allogeneic feeder cells from HIV-uninfected donors in the presence of Molt-4 cells [64] . Dilutions were chosen to minimize the probability that any given dilution will have all positive or all negative wells . After 14 days of culture , wells were screened for outgrowth of a viral isolate by ELISA for p24 ( NCI Frederick ) . The following day , for the dilution with fewest CD4+ T cells that yielded wells with detectable virus , positive cultures were assayed by flow cytometry . After staining with mAb DT9 or an isotype control , detected by PE anti-mouse IgG ( Sigma Aldrich ) , cells were blocked with murine IgG and further stained with CD3-BV421 ( Sony Biotechnology ) and Far Red Viability Dye ( Thermofisher Scientific ) . Cells were then fixed and permeabilized using BD Cytofix/Cytoperm ( BD Biosciences ) followed by staining of intracellular Gag with KC57-FITC ( Beckman Coulter ) . Staining was acquired using a SP6800 Spectral Cell Analyzer ( Sony Biotechnology ) and analyzed using FlowJo ( Tree Star ) . Molt-4 cells lack CD3 expression , which enabled discrimination from primary T cells , before the median fluorescence intensity ( MFI ) of DT9 staining was compared between the HIV Gag+ ( infected ) and Gag- ( uninfected ) Molt-4 populations . Paired infectious molecular clones from the transmitter/founder and 6-month timepoints of the same infected individual have been previously been described [46 , 47] . Single genome amplification from plasma RNA at these timepoints was followed by phylogenetic analysis of viral sequences to identify the transmitted virus and a biologically active 6 month consensus virus . Plasmids containing these infectious molecular clones were expressed in HEK 293T ( ATCC , CRL-11268 ) , used to infect primary CD4+ cells in vitro , and analyzed for HLA-C downregulation by flow cytometry all as previously described [26] . Briefly , CD4+ cells were magnetically selected , stimulated with anti-CD3/28 beads and IL-2 for 3–5 days , infected with HIV-1 and cultured for a further 6 or 7 days . Cells were then incubated with mAb DT9 or isotype control , followed by PE-anti-mouse IgG ( Sigma-Aldrich ) . Free secondary antibody- binding sites were blocked with murine immunoglobulin before further staining with CD4-PB ( BioLegend ) , CD8-APC and CD3-APC . Cy7 ( both from Becton Dickinson ) , and yellow fluorescent reactive viability dye ( Invitrogen ) . Cells were then fixed and permeabilized by incubation with paraformaldehyde and saponin ( Becton Dickinson ) before staining of HIV-1 intracellular Gag with KC57-FITC ( Beckman Coulter ) . CD4 lymphocytes were discriminated as CD3+ , viability dye and CD8- , and the MFI of DT9 staining was compared between Gag+ and Gag- populations . Plasma-derived Vpu clones in a Rev-dependent CRV1 based vector have previously been described for 14 longitudinally sampled , untreated , subtype B HIV-1 infections [48] . For 2 individuals from that study , long term non-progressors 1 and 5 , multiple Vpu clones were characterized here for ability to downregulate HLA-C ( clones 1–15 and 26–28 respectively ) . For 4 untreated subtype C infected individuals ( CH596 , CH492 , CH256 , CH694 ) , for which plasma sequencing has previously been described [53 , 65 , 66] , Vpu clones were constructed in the same vector and tested for HLA-C downregulation ( clones 16–19 , 20–25 , 29–30 and 31–35 respectively ) . Sequences of these Vpu clones are given in S5 Table . Chronically infected ART-naïve individuals have previously been described from the BC HOMER cohort with subtype B infection [67 , 68] and the UARTO cohort with infections of subtypes A , C and D [69] . Overall , 25% of BC HOMER cohort participants reported they were men who have sex with men , while 39% reported a history of injection drug use , and 92% of the BC HOMER participants in this present study were male . 30% of the UARTO cohort samples included in this study were male and this cohort comprises predominantly heterosexual individuals . To characterize HLA-C downregulation a single plasma-derived Vpu sequence for 195 of these individuals , was cloned into a bicistronic pSelRRE–Vpu vector that expresses Vpu and GFP [70] . Of these 195 clones , Vpu sequences were of subtypes A ( n = 45 ) , B ( n = 75 ) , C ( n = 19 ) or D ( n = 56 ) . Sequences of these Vpu clones are given in S6 Table . A panel of 14 infectious molecular clones of HIV-1 , including viruses of subtypes B , C and D , have previously been characterized for ability to downregulate HLA-C when infecting primary CD4+ cells in vitro [26] . Vpu genes from each of these viruses were also generated in the pSelRRE–Vpu expression vector . Synthetic DNA sequences containing the desired Vpu sequence ( Synthetic Genomics Inc ) were inserted into the plasmid digested with AscI and SacII using Gibson Assembly ( New England Biolabs ) , and chemically competent cells transformed according to manufacturer instructions . Transformed cells were selected on zeocin agar plates ( Invivogen ) , single colonies grown in TB with Zeocin ( Invivogen ) , plasmid extracted , and sequence verified using primer 5’-ACCTTGTTTATTGCAGCTT-3’ for Sanger sequencing ( Integrated DNA Technologies ) . HeLa cells ( ATCC , CCL-2 ) were grown in RPMI 1640 with 10% FBS , penicillin-streptomycin , and L-glutamine . Molt-4 cells were grown in the same media supplemented with 10mM HEPES . The 35 Vpu clones obtained in CRV1 based vectors , representing multiple quasi-species and longitudinal samples from 6 individuals , were assessed for ability to downregulate HLA-C in HeLa cells as previously described [26] . Briefly , TransIT-HeLaMONSTER ( Mirus ) was used to co-transfect HeLa with the CRV1 based vector containing Rev and primary Vpu genes , and with an EGFP expressing plasmid [48] . After 24hrs trypsinized cells were incubated with mAb DT9 or isotype control , followed by APC-conjugated anti-mouse IgG ( BioLegend ) , and staining results were acquired using a FACSCalibur flow cytometer ( Becton Dickinson ) . The 195 Vpu clones derived in pSelRRE–Vpu vectors , from chronically infected UARTO and BC HOMER cohort individuals , were assessed for ability to downregulate HLA-C in Molt-4 cells . Lipofectamine ( ThermoFisher ) was used according to manufacturer instructions to co-transfect ~200 , 000 cells with the pSelRRE–Vpu plasmid containing GFP and primary Vpu genes , and with the Rev expression plasmid pSel-Rev-ΔGFP previously described [70] . After 24hrs cells were stained with DT9 and an APC-conjugated secondary as above , and staining results were acquired using a SP6800 Spectral Cell Analyzer ( Sony Biotechnology ) . 14 Vpu genes from HIV-1 infectious molecular clones were characterized for HLA-C downregulation in both HeLa cells transfected using the Mirus reagent , and Molt-4 cells transfected using Lipofectamine , as described above . All cytometry analysis was performed using FlowJo ( Tree Star ) . Vpu transfected cells were discriminated from untransfected cells by GFP fluorescence , and the MFI of DT9 staining compared between these populations to calculate fold downregulation of HLA-C . Extent of viral sequence adaptation to host HLA-C genotype was determined for 72 subtype B-infected individuals from the BC HOMER cohort for whom HLA-I genotypes were available , using a published adaptation metric [49] . Briefly , a probabilistic model is used to compare the expected HIV-1 sequence if evolving indefinitely in a host whose immune system targeted solely viral epitopes restricted by the HLA allele in question , compared to if the virus was evolving indefinitely in the absence of immune pressure . The resulting ratio of these two scenarios is scaled so that it ranges from −1 to +1 , where the extremes denote zero and complete adaptation to the HLA allele in question , respectively . This quantification of adaptation used specifically the viral Nef sequence because of the high number of HLA-associated sequence variants contained therein , and was restricted to HIV-1 subtype B as HLA-associated polymorphisms are extensively mapped for this subtype [38] . Of the 195 Vpu clones from individuals chronically infected with HIV-1 that were characterized for ability to downregulate HLA-C , 4 were excluded due to substantial sequence length polymorphism . The remaining 191 Vpu sequences were aligned and at every position of Vpu , polymorphic variants were tested for association with HLA-C downregulation as a categorical variable using Fisher’s exact test . Residues that differed with p<0 . 05 were then tested in a multiple linear regression analysis , identifying 5 positions with independently significant effects on downregulation of HLA-C . These were confirmed experimentally using single amino acid mutants of primary Vpu sequences generated by incorporating synthetic DNA sequences into the pSelRRE–Vpu vector , as described above for the construction of Vpu genes from 14 infectious molecular clones . HLA-C downregulation by mutant and primary Vpu clones was compared by transfection of Molt-4 cells , also as described above . A nine–amino acid HA epitope tag ( YPYDVPDYA ) was added to the N-terminus of HLA-A*0201 , HLA-C*0501 , or 6 chimeras of these HLA . The HA tag was added to the mature protein , by insertion just after the leader sequence , with addition of a second glycine-serine N-terminal to the tag to reproduce the cleavage site of the HLA-I molecule as previously described [71] . These constructs were synthesized by IDT with 5’ EcoRI and kozak sequences , and 3’ NotI recognition site , and inserted by Gibson Assembly into a pcDNA3 . 1 ( + ) vector digested with EcoRI and NotI ( all New England Biolabs ) . Transformed cells were selected with Ampicillin and clones sequenced with CMV3'F 5’-GGTAGGCGTGTACGGTGGGA-3’ and BGH rev 5’-TAGAAGGCACAGTCGAGG-3’ . Immunoprecipitation of Vpu and detection of co-precipitated β-TrCP has been previously described [72] . This was repeated with an adapted method substituting HLA for β-TrCP . Briefly , HEK 293T cells were co-transfected with the HA-tagged HLA plasmid and Flag-tagged Vpu . 24 hours after transfection , cells were lysed in buffer containing 50mM Tris-HCl pH7 . 5 , 100mM NaCl , 1mM EDTA , 2mM DTT , 0 . 1% NP40 and complete protease inhibitors ( Roche ) , immunoprecipitated with rabbit anti-Flag monoclonal antibody ( Sigma ) and protein G agarose beads , then analyzed by western blot using mouse anti-Flag or anti-HA antibodies . Differences in HLA-C downregulation between reservoir isolates were compared by unpaired t tests . Variation in the frequency of Vpu clones that downregulate HLA-C strongly , between individuals of different HLA genotypes , was analyzed by chi square tests . Linear regression with Spearman analysis was used to determine correlations between observed HLA-C downregulation and predicted HLA-C expression level for different individuals , and across HLA-C alleles in terms of the frequency of Vpu clones that downregulate HLA-C , and the strength of correlation between HLA-C allele specific adaptation and HLA-C downregulation . Multiple linear regression models were used to analyze Vpu positions associating with downregulation of HLA-C . Significance was based on a two-sided p value ≤0 . 05 throughout . All analyses were performed in GraphPad Prism 6 or R version 3 . 4 . 3 statistical software packages [73] .
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HLA-C is a member of the major histocompatibility complex class-I ( MHC-I ) family of molecules which are integral to many responses of innate and adaptive immunity . HIV-1 can downregulate the expression level of HLA-C on infected cells , using the viral protein Vpu , but the magnitude of HLA-C downregulation varies widely between primary HIV viruses . This provides an opportunity to identify opposing pressures on HLA-C expression in infected human individuals . We find that viral downregulation of HLA-C associates with both host and virus genotype , defining allelic differences in HLA-C and Vpu that will help to identify the specific immune responses which result in viral downregulation or preservation of HLA-C . These responses could represent candidates for immune therapy , given their demonstrated effects in vivo . We also find that HIV-1 viruses from the latent reservoir of some individuals can downregulate HLA-C , indicating that in certain individuals HIV-1 cure will require the removal of virus that is able to downregulate HLA-C . The unique role of HLA-C , exerting selection pressures that result in variable modulation of its expression , may make it possible for immunotherapies to achieve enhanced efficacy if both of the opposing pressures on HLA-C expression could be established in the same individual .
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2018
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HLA-C downregulation by HIV-1 adapts to host HLA genotype
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Defining the most penetrating correlates of protective memory T cells is key for designing improved vaccines and T cell therapies . Here , we evaluate how interleukin ( IL-2 ) production by memory CD4 T cells , a widely held indicator of their protective potential , impacts immune responses against murine influenza A virus ( IAV ) . Unexpectedly , we show that IL-2-deficient memory CD4 T cells are more effective on a per cell basis at combating IAV than wild-type memory cells that produce IL-2 . Improved outcomes orchestrated by IL-2-deficient cells include reduced weight loss and improved respiratory function that correlate with reduced levels of a broad array of inflammatory factors in the infected lung . Blocking CD70-CD27 signals to reduce CD4 T cell IL-2 production tempers the inflammation induced by wild-type memory CD4 T cells and improves the outcome of IAV infection in vaccinated mice . Finally , we show that IL-2 administration drives rapid and extremely potent lung inflammation involving NK cells , which can synergize with sublethal IAV infection to promote acute death . These results suggest that IL-2 production is not necessarily an indicator of protective CD4 T cells , and that the lung environment is particularly sensitive to IL-2-induced inflammation during viral infection .
Interleukin-2 ( IL-2 ) produced by CD4 T cells is thought to be critical for orchestrating optimal immune responses by acting as an autocrine growth and survival factor [1] as well as a paracrine cytokine to enhance the activity of other cell types , notably NK cells and CD8 T cells [2 , 3] . IL-2 production by T cells is strictly regulated by antigen recognition and costimulatory signals , resulting in its transient secretion during cognate interactions with activated APC [4] . A distinguishing feature of resting memory and memory-derived secondary CD4 T cell effectors is their ability to produce higher levels of IL-2 more rapidly than naïve and primary CD4 T effector cells [5 , 6] and CD8 T cells [7 , 8] . Consequently , memory CD4 T cells are the most physiologically relevant source of IL-2 in vivo . The capacity of Th1-polarized memory cells to co-produce high levels of IL-2 in combination with IFN-γ is widely held as a marker of superior protective capacity [9] . Indeed , memory CD4 T cells marked by dual production of IFN-γ and IL-2 provide robust immunity against influenza A virus ( IAV ) challenge in murine models [10–12] . Large numbers of memory cells capable of producing IL-2 and responding rapidly against IAV have also been characterized in human lungs [13 , 14] and transcriptional signatures , phenotypes , and functional analysis support that lung-resident memory CD4 T cells are strong producers of IL-2 [12 , 15] . Importantly , the presence of increased numbers of IAV-specific memory CD4 T cells prior to seasonal infection correlates with improved clinical outcome in human longitudinal studies [16] . Conversely , severe influenza disease has been associated with decreased levels of IL-2 in the lung [17] . These findings support the concept that IL-2 production is essential for optimizing immunity against IAV mediated by memory CD4 T cells . Here , we directly test this hypothesis . To overcome major technical and physiological barriers that prevent the straight forward investigation of the impact of memory CD4 T cell-derived IL-2 in vivo , we generated memory CD4 T cells specific for IAV in vitro from naïve TcR transgenic precursors in the presence of exogenous IL-2 [5] . This model provides IL-2-dependent signals to IL-2-deficient ( Il2-/- ) CD4 T cells during priming that are essential to form most CD4 memory populations by programing IL-7 receptor expression and rescuing effector cells from apoptosis [18 , 19] . We used WT and Il2-/- memory cells generated in this way in a well-established adoptive transfer model in which naive host mice are challenged with IAV recognized by the donor cells . This reductionist approach overcomes complications associated with long-term blockade of IL-2 or IL-2 receptors by antibody administration which has off target effects through the disruption of FoxP3+ T regulatory ( T reg ) function that can independently impact the outcome of infections , including secondary IAV challenge [20] . Additionally , while conditional knock-out models that rely on the targeted expression of cre-recombinase to delete floxed genes are widely employed , the efficiency of inducible knock systems dependent upon tamoxifen-induced cre-recombinase expression can have varied efficiency within different tissues that can confound observations [21] . Unexpectedly , we find that Il2-/- IAV-specific memory CD4 T cells provide superior protection against IAV compared to WT memory cells of the same specificity . Improved outcomes associated with Il2-/- responses include wide-ranging reductions in the constituents of pulmonary and systemic inflammation , accelerated viral clearance , improved pulmonary mechanics , and reduced weight loss . We also show that blocking the CD70-CD27 costimulatory pathway to dramatically reduce IL-2 production by memory CD4 T cells [19] tempers inflammation and morbidity in an adoptive transfer model as well as in intact vaccinated wildtype mice . Finally , we show that IL-2 administration to naive mice directly and rapidly upregulates a broad array of cytokines and chemokines in the lung and synergistically enhances inflammation induced by IAV , indicating that the lung is particularly sensitive to IL-2 driven-inflammation . NK cells are major contributors to pulmonary IL-2-induced inflammation . Moreover , NK cell depletion during IAV challenge of recipients of WT memory CD4 T cells phenocopies the improved outcomes seen in Il2-/- memory CD4 T cell recipients . Overall , our results indicate that memory CD4 T cell-derived IL-2 acts as a potent adjuvant in the lung that enhances the production of a broad early inflammatory response during acute viral infection . We find that IL-2 synergizes with pathogen-driven stimulation of innate immunity to drive robust inflammatory responses that can worsen outcomes during primary IAV infection as well as in models of memory CD4 T cell-mediated protection against lethal IAV challenge . Our findings have important implications for therapies aimed at reducing the severity of IAV infection , for the correlates of protection used to design and evaluate T cell-based vaccines , and for the consequences of respiratory infection during the clinical use of therapeutics that result in increased levels of IL-2 . This work may also help provide mechanistic insight into the immunopathological impact of memory CD4 T cells induced by vaccination in models of chronic infection [22 , 23] .
We previously found that IAV-specific lung-resident memory CD4 T cells generated from naive TcR Tg donor cells displayed robust IL-2 production when assayed at day 30 post-infection and beyond [12] . We used shielding from labeling by i . v . administered anti-CD4 Ab to discriminate lung-resident from circulating CD44hi CD4 T cells in intact IAV-primed mice and found that the shielded cells displayed strong IL-2 production via intracellular cytokine staining following stimulation with PMA and ionomycin ( Fig 1A ) . This confirms that endogenous , polyclonal lung-resident memory CD4 T cells display a strong capacity to produce IL-2 . We thus reasoned that IL-2 signals may have a greater impact on immune responses against heterosubtypic IAV infection than during primary responses against IAV where IL-2-producing CD4 T cells only reach the lung in significant numbers at 6 or 7 days post-infection [24] . Indeed , we detected significant levels of IL-2 in the lungs of mice during the first week of heterosubtypic IAV infection while IL-2 was barely detectable during the same timeframe of a primary IAV response ( Fig 1B ) . In order to access the role of IL-2 production by memory CD4 T cells during their antigenic recall , we used a previously validated model employing WT and Il2-/- TcR transgenic CD4 T cells to generate memory populations from Th1-polarized effector populations in vitro [5] . Importantly , when transferred to naïve adoptive hosts that are then challenged with IAV , these memory cell responses mirror key elements of the endogenous CD4 T cell recall response against IAV [8 , 10 , 19] . Briefly , we provided exogenous IL-2 to cultures of naïve WT or Il2-/- DO11 . 10 TcR transgenic cells [25] to program their capacity to form memory [19] . The resulting effectors cells were cultured in vitro in the absence of antigen and inflammatory signals for 3 days during which they transition to a resting state virtually indistinguishable from long-term memory CD4 T cells generated in vivo [5 , 19] . We have used such in vitro-generated memory cells in adoptive transfer studies to determine key mechanisms of CD4 T cell-mediated protection against IAV [26 , 27] . To clearly delineate protective functions of memory CD4 T cells versus those provided by memory CD8 T cells , memory B cells , and other primed populations that would not be feasible to block in intact IAV-primed mice [26] , we transferred an equal number of WT or Il2-/- DO11 . 10 memory cells to unprimed mice then infected with A/PR8-OVAII that contains the OVA323-339 epitope recognized by the DO11 . 10 TcR . We gave 5x106 memory cells , which results in ~5x105 cells able to respond factoring in a ‘10% take’ [28] . As previously discussed [26] , this number is in the range of the estimate of the total number of memory CD4 T cells generated by IAV priming in BALB/c mice , as well as in studies analyzing DR-1 “humanized” transgenic mice [29] in which the magnitude of the total HA-specific memory CD4+ T cell response detected by ELISPOT assay alone is about 1x105 cells . Given that not all cells are expected to make the cytokines assayed in the ELISPOT , and assuming that the response against HA accounts for 20–50% of the total IAV specific cells [29 , 30] , a conservative estimate of the total memory CD4 T cell pool is in the range of 2-5x105 . The recipient mice were challenged with a lethal ( 2 LD50 ) dose of IAV against which 5x106 WT memory CD4 T cells protects [26] in order to test as stringently as possible the role of IL-2 production by the memory CD4 T cells during a protective response . The earliest protective function of memory CD4 T cells upon cognate recognition of antigen during IAV challenge is to ‘jump-start’ innate inflammatory responses in the lung . This innate inflammatory response leads to marked control of viral titers within 3 days of infection ( dpi ) , is generated independently of Type I and II IFN , TNF , and pathogen associated molecular pattern ( PAMP ) recognition [31] , and is likely driven by resident memory T ( TRM ) cells . To analyze the role of IL-2 production by memory CD4 T cells in promoting this induction of innate immunity , we assessed pulmonary inflammation in naïve recipients of WT or Il2-/- memory CD4 T cells at 3 dpi with A/PR8-OVAII . As expected from previous studies [31] , WT memory CD4 T cells induced significantly higher levels of a broad spectrum of inflammatory cytokines and chemokines by 3 dpi compared to control mice not receiving memory cells ( represented as dashed lines in graphs ) ( Fig 2 ) . With the exception of TNF , IL-1α , and CCL5 that were detected at levels comparable to those in hosts with WT memory CD4 T cells , recipients of Il2-/- memory cells displayed markedly reduced levels of the factors analyzed ( Fig 2A ) , the majority of which were nevertheless significantly higher than in control mice . To evaluate how long the inflammatory factors remained reduced in the Il2-/- versus WT memory CD4 T cell recipients , we examined lung levels from 4–7 dpi . TNF , which is itself produced by WT and Il2-/- memory CD4 T cells responding to IAV [19] , remained similar . In contrast , IL-1α and IL-1β were reduced when assessed at day 4 and remained lower through 7 dpi in recipients of Il2-/- memory CD4 T cells ( Fig 2B ) . As no differences are seen in donor memory CD4 T cell numbers or in IFN-γ production at d4 and d7 ( Fig 2C–2F ) , these results suggest that IL-2 production from memory CD4 T cells promotes potent , acute , and broad production of inflammatory factors in the lung of naïve recipient mice . The kinetic timeframe of the memory CD4 T cell-driven enhanced lungs of recipient mice inflammatory response observed here mirrors previous findings and is in line with the response seen in IAV-primed mice post-heterosubtypic challenge [31] . The changes seen above could impact the ability of WT versus Il2-/- memory CD4 T cells to protect against infection . To evaluate this , we assessed a number of parameters associated with recovery from IAV challenge including morbidity , mortality , and viral clearance . All naïve recipients of memory CD4 T cells survived , while control mice not receiving memory cells succumbed by 10 dpi ( Fig 3A ) . Strikingly though , recipients of Il2-/- memory CD4 T cells began to recover weight 2–3 days earlier than WT recipients ( Fig 3B ) . The improved kinetics of weight recovery in Il2-/- memory CD4 T cell recipients correlated with modest but significantly accelerated viral clearance at 8 and 10 dpi ( Fig 3C ) . When the number of cells transferred was titrated , the enhanced protective capacity of Il2-/- memory CD4 T cells was even more evident ( Fig 3D ) , supporting the concept that reduced inflammatory responses driven by the memory CD4 T cells in the absence of IL-2 signaling correlate with improved outcomes . As in previous studies [19] , we found no differences between the frequency and proliferation of WT and Il2-/- memory CD4 T cells in the spleen , draining lymph nodes , and lungs on 4 and 7 dpi ( S1 Fig ) , indicating that the enhanced protective capacity of Il2-/- memory CD4 T cells and differences in the inflammatory response observed are not due to differences in the kinetics or peak magnitude of WT versus Il2-/- memory CD4 T cell responses . Similar patterns of improved recovery in recipients of Il2-/- memory cells were seen in nude hosts lacking T cells , JHD hosts lacking B cells , and SCID hosts deficient in both T and B cells ( S2 Fig ) . These observations argue against the possibility that altered helper functions impacting anti-viral B cells or CD8 T cell responses , or altered activity of IL-2 dependent host regulatory T cells impact the differences seen . We and others previously found significant differences in the pulmonary function of unprotected versus protected animals during the first week of IAV challenge [32–34] . We thus analyzed respiratory mechanics in naïve recipients of WT or Il2-/- memory CD4 T cell and found that recipients of Il2-/- cells demonstrated improved respiratory rates and pulmonary minute volumes from 4 to 5 dpi . On 6 dpi , both groups of memory CD4 T cells recipients began to show signs of recovery in respiratory rates and only minute volumes remained significantly different ( Fig 3E ) . Furthermore , lower levels of serum albumin , a measure of vascular leak , were detected in the bronchoalveolar lavage of Il2-/- versus WT memory CD4 T cell recipients on 5 and 6 dpi ( Fig 3F ) , indicating reduced pulmonary edema . Interestingly , histopathologic analysis of the lung did not reveal marked differences between recipients of WT or Il2-/- cells at 7 dpi ( Fig 3G and 3H ) . Together , these results indicate that early IL-2 production by memory CD4 T cells responding to IAV amplifies inflammatory responses that impair pulmonary function and promote pulmonary edema without causing measurable increases in immunopathology . Given that memory CD4 T cell derived-IL-2 appears to amplify IAV-associated inflammatory responses , modulating IL-2 production may serve as a therapeutic strategy to decrease morbidity . Treating mice with a blocking antibody against CD70 significantly reduces IL-2 and IFN-γ production from memory CD4 T cells responding in the lung at 7 dpi with IAV ( Fig 4A and 4B ) but does not impact their response kinetics or ability to control virus [19] . Similar control of IL-2 production from CD4 and CD8 T cells by CD70-dependent signals has been found in other infection models [35 , 36] . We thus asked if blocking CD70 as a means to reduce IL-2 production could improve the outcome of WT memory CD4 T cell-mediated protection . Much like the recipients of Il2-/- memory CD4 T cells in Fig 2 , recipients of WT memory CD4 T cells treated with anti-CD70 antibody showed reduced levels of IL-1α , IL-1β , IFN-γ , IL-6 , IL-17 , CCL2 ( MCP-1 ) , CXCL1 ( KC ) , and IL-12 ( Fig 4C ) , many of which are associated with exacerbated IAV infection [37–40] . In contrast to these observations , we previously found memory CD4 T cell-induced levels of IL-1 , IL-6 , CCL2 , CXCL1 , and IL-12 to be similar in WT and IFN-γ-receptor knockout mice following IAV challenge [31] , arguing against changes in IFN-γ production by memory CD4 T cells following anti-CD70 antibody treatment contributing to the patterns seen in Fig 4C . Instead , the significantly lower amount of IL-2 in lung homogenates with CD70 blockade ( Fig 4D ) , supports the position that a reduction in the amount of IL-2 available for paracrine signaling plays a major role in the tempered inflammatory responses observed . The reduced inflammatory response and corresponding small but significant reduction in weight loss and faster recovery ( Fig 4E ) seen with CD70 blockade largely phenocopies observations with Il2-/- memory CD4 T cell transfer in Figs 2 and 3 . To test whether blocking CD70 to reduce memory CD4 T cell-driven inflammation could improve outcomes in a more translational setting , naïve WT mice were primed with a cold-adapted vaccine strain of IAV ( A/Alaska; H2N2 ) and were challenged after 35 days with A/PR8 ( H1N1 ) . Groups of primed mice were treated with CD70 blocking antibody or an isotype control only during heterosubtypic A/PR8 infection . Given that memory CD8 T cell responses are largely independent of CD27:CD70 signaling [41] and since they can provide strong protection independently of the CD4 T cell recall response [42] , to specifically address the isolated impact of CD4 T cell mediated protection IAV-primed mice were also depleted of CD8 T cells prior to heterosubtypic challenge . All primed mice survived and CD70 blockade improved the time to recovery of the mice by 2–3 days ( Fig 4F ) . In contrast to CD70 blockade as well as treatment with IL-2 neutralizing antibodies , administration of exogenous IL-2 to mice challenged with IAV failed to improve recovery ( Fig 4G ) . These findings suggest that the protective efficacy of vaccine-primed memory CD4 T cells can be improved by reducing their IL-2 production . Our results imply that the improved protective efficacy of the Il2-/- memory CD4 T cells in the adoptive transfer model employed here is due largely to differences in the inflammatory response generated upon infection . IL-2 is known to activate innate and adaptive immune cells such as NK cells and CD8 T cells but also drives T reg responses that have anti-inflammatory actions [43] . Though administration of IL-2 has been reported to promote systemic inflammation and febrile illness in cancer patients [44] , detailed analysis of how IL-2 impacts the lung environment is lacking . We thus analyzed whether providing IL-2 in the absence of memory CD4 T cell transfer would directly enhance inflammatory cytokine and chemokine expression in the lung , even in the absence of IAV infection . We first administered soluble IL-2 or IL-2:anti-IL-2 antibody ( clone S4B6 ) complexes ( IL-2C ) [45–47] to unmanipulated mice by i . p . injection for 3 consecutive days and analyzed lung homogenates and serum for changes in cytokine and chemokine expression . We previously found that this regime , employing 2 μg of IL-2 , restored memory CD4 T cell generation from Il2-/- CD4 T cells to WT levels in an IAV model , indicating its ability to deliver physiologically relevant IL-2 signals in vivo [19] . Strikingly , IL-2C treatment drove strong expression of a number of analytes such as IL-6 , IFN-γ , IL-17 , and G-CSF ( S3A Fig ) detected in Fig 2 , particularly in the lung and to a lesser extent in the serum . The inflammatory factors seen in the lung following systemic IL-2 administration could originate from other sites . However , when compared to the response seen with i . p . administration , an even more robust response was seen in the lung when IL-2C was administered intranasally ( Fig 5A ) . This supports the conclusion that beyond the known ability of IL-2 to stimulate vascular leak and pulmonary edema [46 , 48] , the lung environment is extremely sensitive to rapid IL-2-dependent induction of inflammatory cytokines and chemokines , even in the absence of infection . As the 2 μg dose of IL-2C may deliver sustained IL-2 signaling not typically achieved during immune responses , we titrated the amount of IL-2 used . The pro-inflammatory impact of the IL-2C was proportional to the dose administered , with significant pro-inflammatory effects in analytes such as CCL2 arising with even 0 . 5 μg of IL-2 and broad effects seen at 1 μg . The impact of the IL-2C was abrogated by pre-treating hosts with blocking antibody against CD122 ( S3B Fig ) , confirming that IL-2 itself rather than potential contaminants in reagents was responsible for the inflammatory impact . In agreement with previous observations [45] , IL-2 and IL-2C treatment also caused the expansion of several major leukocyte populations in the spleen ( S3C Fig ) . Some of these patterns were also seen in the lung . In particular , IL-2C treatment dramatically expanded NK cells and to a lesser extent CD8 and CD4 T cells as well as inflammatory CD45+ MHC-II+ CD11b+ Ly6C+ APC [49] ( S3C and S3D Fig ) . Given the dramatic effect of IL-2C administration on the lung environment , we assessed pulmonary mechanics in uninfected animals receiving IL-2C . When compared to untreated controls , the IL-2C-driven inflammatory response correlated with decreased respiratory function ( Fig 5B ) . We next tested whether and how IL-2 impacts the outcome of primary IAV infection by treating unprimed WT mice with IL-2C for the first 3 days of a 0 . 2 LD50 A/PR8 challenge . Infected mice also treated with IL-2C displayed higher levels of a broad array of cytokines and chemokines in the lung compared to mice only infected with IAV or mice only treated with IL-2C ( Fig 6A ) , indicating strong synergy between infection-induced and IL-2-dependent inflammatory pathways . However , histopathological changes were not appreciably enhanced in mice receiving IAV and IL-2C versus mice treated with only IAV or only with IL-2C ( Fig 6B and S4 Fig ) , agreeing with the lack of histological changes in recipients of WT or Il2-/- memory CD4 T cells following IAV infection ( Fig 3 ) . Nevertheless , IL-2C treatment for 4 instead of 3 days resulted in acute death of infected mice , even when lower amounts of IL-2 were used ( Fig 6C ) . In marked contrast , when IL-2C treatment was initiated at later time-points ( 5 to 9 dpi ) that coincide with the onset of viral clearance , all mice survived ( Fig 6D ) . Thus , IL-2 signals delivered early but not at later timepoints of infection when T cell effectors reach their peak , potently enhance acute IAV-induced recruitment and/or activation of inflammatory cells in the lung and transform a mild illness to a fatal infection . Since NK cells and neutrophils were enhanced by IL-2C , and are prominently involved in driving IL-2-dependent vascular leak [50 , 51] , we determined their involvement in the IL-2-induced inflammatory response . We first characterized the dynamics of NK and neutrophil responses in the lung at 3 dpi in naïve recipients of WT or Il2-/- memory CD4 T cells , or in controls not receiving cell transfer . While total numbers of NK cells in both WT or Il2-/- memory CD4 T cell recipients were similar , significantly more activated ( CD44hi and IFN-γ+ ) NK cells were detected in WT memory CD4 T cell recipients ( Fig 7A ) . No differences in total neutrophil number or activation ( SSChi CD69hi ) were seen ( Fig 7B ) . Significantly more activated NK cells were also detected at 4 dpi in isotype antibody-treated recipients of WT memory CD4 T cells than in recipients treated with CD70 blocking antibody ( Fig 7C ) , in which reduced levels of paracrine IL-2 are detected ( Fig 4B ) . We next evaluated the ability of lung-resident memory CD4 T cells isolated from IAV-primed mice to modulate NK cell responses in the presence and absence of IL-2 neutralizing antibody to ensure that the observations made with in vitro-primed memory populations accurately recapitulate elements of the IAV-primed responses . Polyclonal IAV-primed lung-resident memory CD4 T cells were transferred intranasally to naïve recipients as previously described [12] and lung NK cell responses following IAV infection analyzed on 4 dpi . Both the number of total NK cells as well and activated NK cells were reduced when IL-2 was neutralized in lung-resident memory CD4 T cell recipients ( Fig 7D ) , mirroring findings obtained with in vitro-primed memory CD4 T cells . Finally , we tested whether memory CD4 T cell-derived IL-2 could impact NK cell responses in lungs imprinted by prior IAV infection [52] rather than in the models above assessing memory CD4 T cell responses in otherwise naive mice . We transferred WT memory CD4 T cells to naive hosts and primed with IAV . At 60 dpi , cognate peptide was administered intranasally to recall the donor CD4 T cells in the lung [31] . We analyzed lung NK cells 4 days after peptide administration in mice treated with isotype or IL-2 neutralizing antibodies and found a reduction in activated , but not total NK cells in the absence of IL-2 signaling ( S5 Fig ) . Thus , using multiple approaches , we find that IL-2 production from memory CD4 T cells following TcR stimulation significantly impacts the acute activation profile of NK cells in the lung . Given the results above and observations that NK cells can maximize neutrophil responses in vivo [53] , we next depleted NK cells in naïve mice receiving WT memory CD4 T cell prior to IAV challenge to determine whether and how NK cells impact the outcome of infection . NK cell depletion resulted in reduced weight loss and earlier recovery ( Fig 8A and 8B ) , phenocopying the improved outcomes seen in Il2-/- versus WT memory CD4 T recipients depicted in Fig 3 , as well that of animals treated with CD70 blocking antibody in Fig 4 . Even though they are known to contribute to IL-2 driven vascular leak , additional depletion of neutrophils did not appreciably alter the course of IAV infection ( Fig 8A and 8C ) . The involvement of NK cells in hampering CD4 T cell memory mediated protection prompted us to determine the extent to which IL-2-induced NK cell responses directly contribute to the production of acute inflammatory factors . To directly test this , we depleted NK cells prior to treating naïve mice with IL-2C and measured inflammatory cytokines and chemokines in the lungs . NK cell depletion decreased levels of all cytokines and chemokines enhanced by IL-2C treatment during IAV infection ( Fig 8D ) , demonstrating the IL-2-dependent ability of NK cells to markedly shape acute pulmonary inflammation . Finally , to firmly establish a detrimental role for IL-2-driven NK cell responses in the lung during IAV infection , we depleted NK cells in naïve mice prior to IL-2C administration and sublethal IAV challenge . Remarkably , NK cell depletion protected IAV-challenged WT and Rag2-/- mice from acute IL-2C-dependent death ( Fig 8E ) . In summary , our results using both reductionist models and analysis of intact mice responding to IAV strongly suggest that IL-2 produced by memory CD4 T cells can drive enhanced inflammatory responses in the lung . A major mechanism involved in driving this response is the IL-2-dependent promotion of early NK cell activity that can detrimentally impact the resolution of IAV challenge .
Defining the most incisive correlates of protective memory T cells in a disease-specific manner is critical to improve monitoring of clinical responses and to promote tailored attributes of T cells induced by vaccination . Fully understanding the impact of tissue environments on the outcome of memory T cell recall is equally important but is poorly understood . Here , we show that during memory CD4 T cell-mediated protective immune responses against IAV , their IL-2 production induces enhanced proinflammatory cytokine production and NK cell responses leading to delayed recovery and compromised lung function . Welsh and collaborators have noted the rheostat nature of NK cell function after viral infection and the pathogenic impact of too many NK cells during medium and high dose LCMV infection [54] . In the setting of IAV , the beneficial versus detrimental impact of NK cells and neutrophil responses remains controversial [55 , 56] . Our results suggest that the relative amount of IL-2 available during the early phases of the immune response can be an important contextual determinant in regulating the positive versus negative impact of these innate subsets . Our findings suggest that , at least in the case of IAV infection , the ability of memory CD4 T cells to produce high levels of IL-2 in conjunction with IFN-γ cannot alone be taken as an indicator of superior protective potential . They do not , however , necessarily contradict the general concept that multi-cytokine-producing Th1 memory cells are more protective than cells only able to secrete IFN-γ [57] . Indeed , polyfunctional memory CD4 T cells express a unique molecular signature compared to single IFN-γ producers [58] . Moreover , there are very few differences in the transcriptome of wild-type and IL-2-deficient memory CD4 T cells responding against IAV [15] . These observations support the concept that key protective molecular signatures of multi-functional CD4 memory may be independent of IL-2 expression during antigenic recall , and as we have shown , IL-2 may instead have its positive activity primarily at the effector stage to promote CD4 T cell memory generation [19] . Although we observed similar impacts using TcR transgenic and polyclonal CD4 T cells , a comprehensive understanding of how and when IL-2 produced by CD4 T cell populations impacts infection requires further study . For example , CD4 T cells with different TcR specificities have been shown to produce different amounts of IL-2 upon restimulation with cognate IAV peptides [59] , and both higher doses of antigen and higher avidity T cell responses lead to greater IL-2 production [60] . These findings indicate that responses against certain antigens may be more or less impacted by the mechanisms described here . Furthermore , optimal CD4 T cell-mediated IAV clearance requires synergy between many different specialized subsets [61–63] . Whether IL-2 from subsets other than Th1-like cells similarly impacts recall against IAV requires exploration , but we stress that the majority of CD4 T cells responding to IAV fit general Th1-like criteria . Additional studies are also required to determine if IL-2 production from memory CD4 T cells similarly impacts responses against other pathogens , as well as in other tissues . Adjuvant effects following IL-2 administration observed during systemic viral infection support that memory CD4 T cell-derived IL-2 may have similar proinflammatory effects in this setting [64] . However , that we found a stronger impact of IL-2 on inflammation in the lung than in the serum , even when IL-2 was administered systemically , suggests the lung and responses against respiratory infections may be particularly sensitive to IL-2 . Indeed earlier studies found exogenous IL-2 administration to cause cellular proliferation only in specific tissues , including the lung [65] . We speculate that IL-2 production from vaccine-induced memory CD4 T cells may in some cases be a component of their immunopathological impact , such as that observed during chronic LCMV infection [22] . We previously found that blocking CD70 during IAV priming dramatically decreased the number of memory CD4 T cells formed [19] . The impact on memory generation was due to a reduction of CD27-dependent autocrine IL-2 production induced by cognate interactions with CD70+ dendritic cells . Here , we found CD70 blockade to reduce inflammation driven by memory CD4 T cells responding to IAV . Interestingly , CD70 blockade significantly decreased IL-2 production as well as IFN-γ production by memory CD4 T cells . While higher levels of IFN-γ have been implicated in increasing susceptibility to primary IAV infection [66] , we have shown that IFN-γ signaling does not significantly impact the ability of memory CD4 T cells to drive protective inflammatory responses against IAV [26 , 31] . Thus , while we cannot formally rule out that changes in IFN-γ and other factors have no impact on the improved responses seen with blocking CD70 , our data strongly supports the hypothesis that changes in IL-2 production play a central role in this process . Collectively , our observations suggest that the CD27-CD70 pathway may be targeted as a temporal rheostat to modulate CD4 T cell immunity: during priming , targeting the CD70-CD27 pathway to enhance IL-2 production can serve to boost the efficiency of memory generation , while early during recall , blocking this pathway may help to restrain IL-2-driven inflammation originating from memory CD4 T cells . Given the potent and broad-ranging proinflammatory activity of IL-2 , we predict that mechanisms must be in place to limit its impact . The lung may be particularly sensitive to IL-2-driven inflammation because of endothelial cell expression of functional IL-2 receptors [46] . We found previously that while CD4 T cell effectors responding to IAV in the spleen and draining lymph nodes produce high levels of IL-2 , only about 10% of cells responding in the lung at the peak of the anti-viral response demonstrate robust IL-2 production [10] . This is in stark contrast to the strong IL-2 production potential observed from CD4 T cells that develops in the lungs only after the clearance of virus as well as at memory timepoints [12 , 67 , 68] . We propose that these findings reflect two distinct kinds of control on IL-2 production by CD4 T cells . First , control of T cell cytokine production appears to be an inherent property of the lung environment [69] , which may serve to buffer against IL-2-induced inflammation to maintain maximal pulmonary function . Second , further controls on IL-2 production are likely induced by infection . For example , PDL-1 , which dramatically impedes IL-2 production by activated T cells upon ligation of PD-1 [70 , 71] , is strongly upregulated in the lung early in the course of IAV infection [72 , 73] . The extent to which the capacity of lung IAV-specific CD4 T cells to produce high levels of IL-2 during the transition to memory following viral clearance [67] is resultant from downmodulation of inhibitory molecules such as PDL-1 versus CD4 T cell-intrinsic mechanisms warrants further study . Interestingly , IL-2 production by CD4 T cells is needed to promote production of the anti-inflammatory cytokine IL-10 by CD8 T cells responding to IAV [74] , which make little of their own IL-2 during heterosubtypic responses [8] . IL-10+ CD8 T cells protect mice against lethal IAV-induced inflammation in some [75] but not other [32] models . Induction of IL-10+ CD8 T cells by CD4 T cell-derived IL-2 may thus act as further layer of buffering against damaging inflammation during respiratory infections . The impact of these restraining mechanisms likely lessens following pathogen clearance . Therapeutic delivery of IL-2 , such as by systemic IL-2C treatment using the S4B6 clone and 1–2 μg of recombinant IL-2 has seen increasing use in experimental models to modulate CD8 T cell and NK cell populations in vivo to improve responses against pathogens and cancers [43] . Our results stress the importance of careful evaluation of how these treatment regimens impact inflammatory environments when interpreting experimental outcomes . Furthermore , we stress that while IL-2 production can be boosted or restored by the systemic blockade of checkpoint inhibitors [76 , 77] , how production of IL-2 by T cells responding to respiratory infection impacts patient outcome in such settings , including cancer therapy , remains to be determined . Our results argue the clinical use of IL-2 and engineered IL-2 and IL-15 [78] should be viewed with caution , given the fatal outcomes observed when IL-2C treatment synergized with mild , low-dose , IAV infection .
Experimental animal procedures were conducted in accordance with guidelines outlined by the Office of Laboratory Animal Welfare ( OLAW ) , National Institute of Health , USA . Protocols were approved by the Animal Care and Use Committee at Trudeau Institute ( Saranac Lake , NY ) protocol 00–33 , the Institutional Animal Care and Use Committee of the University of Massachusetts Medical School ( Worcester , MA ) protocol A-2198 , and the University of Central Florida ( Orlando , FL ) protocol 18–30 . Naïve CD4+ T cells were obtained from 5 to 8-week-old male or female DO11 . 10 Thy1 . 2/Thy1 . 1 and Il2-/- DO11 . 10 Thy1 . 2/Thy1 . 1 mice originally provided by A . Abbas ( UCSF ) . Recipients of cell transfers were male BALB/c . Thy1 . 2 or BALB/c . Thy1 . 1 , nude , JHD , or SCID mice that were at least 8 weeks old . In some experiments , naïve CD4 T cells were obtained from 5 to 8-week-old male OT-II Thy1 . 2/1 . 1 mice and C57BL/6 and Rag2-/- male recipients were used . Nude , Rag2-/- , JHD , and SCID mice were purchased Charles River , Taconic , or Jackson Laboratories . All other mice were obtained from Jackson Laboratories or the breeding facility at Trudeau Institute , the University of Massachusetts Medical School , or the University of Central Florida . Naïve CD4+ T cells were obtained from pooled spleen and peripheral lymph nodes as previously described [24] . Briefly , cells were purified by nylon wool and percoll density gradient separation . CD4 T cells were isolated by positive CD4 MACS selection ( Miltenyi ) . Resulting CD4+ cells routinely expressed a characteristic naive phenotype ( small size , CD62Lhi , CD44lo and CD25lo ) >97% TcR+ . TH1-polarized effectors were generated in vitro as described [5] . Briefly , naïve WT or Il2-/- CD4 T cells were cultured with an equal number of irradiated APC ( 2x105 per mL ) in the presence of exogenous IL-2 ( 20 ng per mL ) , 2 ng per mL IL-12 ( Peprotech ) , 10 μg per mL anti-IL-4 antibody ( 11B11; Bioxcell ) , and 5 μM OVAII peptide . In vitro-primed memory cells were obtained by thoroughly washing effector cultures at 4 days and re-culturing the cells in fresh media for at least 3 days in the absence of Ag and exogenous cytokines . Live cells were isolated by Lympholyte separation ( Cedarlane ) . All donor CD4 T cells were adoptively transferred in 200 μl phosphate buffered saline ( PBS ) by intravenous ( i . v . ) injection . A number of donor cells previously determined to protect against lethal IAV infection , 5 x 106 , was transferred . In some experiments , donor CD4 T cells were labeled with CFSE , as previously described [10] , prior to adoptive transfer to monitor in vivo proliferation . In some experiments , lung resident memory CD4 T cells were isolated from IAV-primed mice and 1 x 106 adoptively transferred to recipient mice through the intranasal route as previously described [12] . Influenza A/Puerto Rico/8/1934 ( PR8 ) ( H1N1 ) originating from stocks prepared at the Trudeau Institute and in use in experiments since 1997 , A/PR8-OVAII ( H1N1 ) from stock obtained from P . Doherty at St Jude’s Children’s Hospital [79] , and the cold-adapted attenuated strain A/Alaska/6/1977 CR-29 , ( H3N2 ) virus kindly provided by S . Epstein , NIH were produced in the allantoic cavity of embryonated hen eggs at the Trudeau Institute and the lethal dose ( LD50 ) , egg infective dose ( EID50 ) or tissue culture infective dose ( TCID50 ) characterized . Mice were infected intranasally under light isoflurane anesthesia ( Webster Veterinary Supply ) with the indicated doses of virus in 50 μl PBS and morbidity and mortality monitored . Donor cell injection and viral infection occurred on the same day . In some experiments , 5 μg of cognate peptide was administered intranasally to mice that had received donor memory CD4 T cells and IAV primed 60 days prior . The recovery day , or the day when animals began to regain weight following infection , was also determined . Pulmonary viral titer was determined by quantitation of viral RNA . RNA was prepared from whole lung homogenates using TRIzol ( Sigma-Aldrich ) , and 2 . 5 μg of RNA was reverse transcribed into cDNA using random hexamer primers and Superscript II Reverse Transcriptase ( Invitrogen ) . Quantitative PCR was performed to amplify the acidic polymerase ( PA ) gene of A/PR8-OVAII using an ABI Prism 7700 Sequence Detector ( Applied Biosystems ) with 50 ng of cDNA per reaction and the following primers and probe: forward primer , 5'-CGGTCCAAATTCCTGCTGA-3'; reverse primer , 5'CATTGGGTTCCTTCCATCCA-3'; probe , 5'-6-FAM-CCAAGTCATGAAGGAGAGGGAATACCGCT-3' . Data were analyzed with Sequence Detector v1 . 7a ( Applied Biosystems ) . The copy number of the PA gene per 50 ng of cDNA was calculated using a PA-containing plasmid of known concentration as a standard . The number of copies of PA gene per lung is presented . Mice were treated for the indicated days with injections of cytokine or cytokine: anti-cytokine monoclonal antibody complexes . For IL-2 complexes ( IL-2C ) , mice received 2 μg per day of recombinant IL-2 ( eBioscience ) premixed with 20 μg of anti-mouse IL-2 monoclonal antibody clone S4B6-1 ( S4B6 ) ( BD Pharmingen ) . In certain experiments , the amount of IL-2 in the complexes was varied , as indicated . Complexes were incubated at room temperature for 20 minutes ( min . ) before intraperitoneal ( i . p . ) injection in 200 μL of PBS . IL-2C in 50 μL of PBS were also administered intranasally ( i . n . ) . When IL-2 was administered as free cytokine , animals were treated with 20 μg per day in 200 μL of PBS injected i . p . For some experiments , mice were treated as indicated with 0 . 25 mg per day of anti-CD122 ( IL-2 Rβ ) antibody ( 5H4 ) to block IL-2 signaling , 0 . 25 mg per day anti-IL-2 antibodies ( S4B6 and JES6-1A12 ) to neutralize IL-2 , 0 . 5 mg per day of anti-CD70 antibody ( FR-70 ) to block CD70 signaling , 0 . 25 mg per day of anti-NK1 . 1 ( PK136 ) to deplete NK cells , 0 . 5 mg of anti Ly-6G Ab to deplete neutrophils ( 1A8 ) , or with appropriate isotype control antibody ( all Bioxcell ) . Antibody was delivered by i . p . injection in 200 μL of PBS . At different time points after virus infection , blood and lungs were obtained from euthanized animals for Luminex multiplex analysis . Lungs were harvested and homogenized in RPMI 1640 media supplemented with 2mM L-glutamine , 100 IU penicillin , 100 μg/mL streptomycin ( Invitrogen ) , 10 mM HEPES ( Research Organics ) , 50 μM 2-mercaptoethanol ( Sigma-Aldrich ) and 7 . 5% fetal bovine serum ( Hyclone ) and serum collected from blood . Alternatively , for flow cytometry , mice were euthanized by cervical dislocation followed by exsanguinated by perforation of the abdominal aorta . Lungs were perfused by injecting 10 ml of PBS in the left ventricle of the heart . Lungs and spleen were prepared into single cell suspensions by mechanical disruption of organs and passage through a nylon membrane . Flow cytometry was performed as previously described [24] using fluorochrome-labeled antibodies at manufacturer’s recommended dilutions for surface staining including anti-Thy1 . 1 ( OX-7 ) , anti-Thy1 . 2 ( 53–2 . 1 ) , anti-CD4 ( RM4 . 5 and GK1 . 5 ) , anti-CD8 ( 53–6 . 7 ) , anti-CD45 . 2 ( 104 ) , anti-γδ TcR ( GL3 ) , anti-β TCR ( H57-597 ) , anti-CD3 ( 17A2 ) , anti-CD49d ( R1-2 ) , anti-CD25 ( PC61 ) , anti-CD44 ( 1M7 . 8 . 1 ) , anti-CD69 ( H1 . 2F3 ) , anti-CD11b ( M1/70 ) , anti-Gr-1 ( RB6-8C5 ) , anti-MHC-II ( M5/114 . 15 . 2 ) , and anti-Ly6C ( HK1 . 4 ) . In some experiments , tissue resident memory CD4 T cells were identified by intravenous administration of 3 μg of fluorochrome-labeled antibody 3 minutes prior to euthanasia and tissue harvest [12] . Intracellular cytokine staining was performed as previously described [19] . Briefly , cells were treated with PMA and Ionomycin for 4 hours or stimulated overnight with cognate peptide presented by APC , with Brefeldin A added after 2 h . Cells were then surface stained , fixed for 20 min . in 4% paraformaldehyde , and permeabilized by 10 min . incubation in 0 . 1% saponin before staining for cytokine by the addition of anti-IFN-γ and anti-IL-2 fluorescently labeled antibodies . Analysis was performed using FACS Canto II and LSRII instruments ( BD Biosciences ) and FlowJo ( Tree Star ) analysis software . Levels of cytokines and chemokines in lung homogenates or serum were determined using mouse multiplex kits ( Invitrogen and Millipore ) read on a Bio-Plex Multiplex 200 Luminex reader ( Bio-Rad ) . Levels of serum albumin in the BAL fluid were determined using a Mouse Albumin ELISA Quantification Kit as per manufacturer’s instructions ( Bethyl Laboratories Inc . ) . For assessment of immunopathology following viral infection and IL-2C treatment , lungs lobes were isolated and immediately fixed in 10% neutral buffered formalin . Lung samples were subsequently processed , embedded in paraffin , sectioned , placed on L-lysine-coated slides , and stained with Hematoxylin and Eosin ( H&E ) using standard histological techniques . Sections were graded blindly from 0 to 4 , for the extent of inflammatory cell infiltration and damage of bronchi , arteries or alveoli by a certified pathologist ( S . Sell ) . Non-invasive whole-body plethysmography ( WBP ) ( Buxco ) was employed to measure respiratory rates ( breaths/min . ) , minute volumes ( mL/min . ) , and enhanced pause PenH , on conscious , unrestrained animals following IAV infection and IL-2C treatment . The minute volume is defined as the volume of air exchanged during a 1-min . interval and is calculated as follows [respiratory rate X tidal volume] . Group sizes of n = 3 to 15 were employed . Unpaired , two-tailed , Students t-tests , ∞ = 0 . 05 , were used to assess whether the means of two normally distributed groups differed significantly . One-way ANOVA analysis with Bonferroni’s multiple comparison post-test was employed to compare multiple means . Two-way ANOVA analysis with repeated measures was also employed in some experiments . The Log Rank test was used to test for significant differences in Kaplan-Meier survival curves . All error bars represent the standard deviation . Significance is indicated as * P < 0 . 05 , ** P < 0 . 005 , *** P < 0 . 001 , **** P < 0 . 0001 .
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We show that memory CD4 T cell mediated protection against influenza A virus is independent of the signature multifunctional cytokine IL-2 that is thought to define the most protective memory cells . IL-2 deficient cells are more effective than wild-type memory cells on a per cell basis at combating IAV and drive tempered early innate inflammatory responses . Our studies define a clear and surprising role for IL-2 as a cytokine adjuvant within the lung that can synergize with virus driven acute inflammatory responses to cause morbidity during sublethal respiratory viral infection .
|
[
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] |
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2019
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Memory CD4 T cell-derived IL-2 synergizes with viral infection to exacerbate lung inflammation
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Structural diversity in the peptide binding sites of the redundant classical MHC antigen presenting molecules is strongly selected in humans and mice . Although the encoded antigen presenting molecules overlap in antigen presenting function , differences in polymorphism at the MHC I A , B and C loci in humans and higher primates indicate these loci are not functionally equivalent . The structural basis of these differences is not known . We hypothesize that classical class I loci differ in their ability to direct effective immunity against intracellular pathogens . Using a picornavirus infection model and chimeric H-2 transgenes , we examined locus specific functional determinants distinguishing the ability of class I sister genes to direct effective anti viral immunity . Whereas , parental FVB and transgenic FVB mice expressing the H-2Kb gene are highly susceptible to persisting Theiler's virus infection within the CNS and subsequent demyelination , mice expressing the Db transgene clear the virus and are protected from demyelination . Remarkably , animals expressing a chimeric transgene , comprised primarily of Kb but encoding the peptide binding domain of Db , develop a robust anti viral CTL response yet fail to clear virus and develop significant demyelination . Differences in expression of the chimeric Kbα1α2Db gene ( low ) and Db ( high ) in the CNS of infected mice mirror expression levels of their endogenous H-2q counterparts in FVB mice . These findings demonstrate that locus specific elements other than those specifying peptide binding and T cell receptor interaction can determine ability to clear virus infection . This finding provides a basis for understanding locus-specific differences in MHC polymorphism , characterized best in human populations .
MHC class I antigen presenting molecules sample peptides generated intracellularly and present them on the surface of cells to CD8+ T cells bearing class I restricted T cell receptors [1]–[3] . The MHC class I multigene families in mice and humans contain three genes encoding classical antigen presenting molecules [4] , generally considered to have redundant functions . These classical MHC I molecules direct immune responses , determine host resistance to disease , and are considered key variables in vaccine design . However , certain lines of evidence indicate that the classical HLA ( A , B , and C ) and H-2 ( K , D , and L ) genes may not strictly be redundant , but instead have distinctive functions . Delineating these differences would be important for understanding the roles of class I genes in disease and shape rational development of vaccines to prevent or treat viral infections and for the immunotherapy of cancers . Immunogenetic data provide the primary support for the hypothesis that MHC class I classical genes do not function equivalently . MHC polymorphism is understood in the context of naturally selected variation in the ability of the immune system to deal with constantly changing challenges by pathogens . Allelic comparisons at the nucleotide level provide convincing evidence that natural selection for amino acid diversity in residues positioned to interact directly with bound peptide favors amino acid replacements over synonymous substitutions [5] , [6] . The nature of MHC polymorphism in mammal population is best understood from meta analysis of world wide studies of human populations . The numbers of identified alleles in human and chimpanzee populations assigned to the A , B , and C loci [5]–[8] differ substantially ( e . g . the 1641 HLA B alleles >1176 A alleles >808 C alleles as enumerated in the IMGT/HLA Database , European Bioinformatics Institute ) . The probability that any of these three loci has an equivalent number of alleles is less than 10−15 . This difference in the numbers of alleles is a strong indication that the classical class I loci are not functioning equivalently . MHC polymorphism in South American Indian populations [9]–[14] provides a snapshot of the selective forces operating over the 15 , 000 years following the settling of the new world by their Asian ancestors [15] . In these Amerindian populations in which only 8 of the defined 36 B locus allelic superfamilies from the old world have been identified , 21 subfamily members have emerged ( discounting identified old-world superfamily allelic prototypes such as B15:01 and B35:01 ) . In contrast , 8 subfamily alleles at the A locus are present , representing 4 of the 21 old world allelic families , and just 1 new variant at the C locus . Again , this pattern strongly diverges from the null hypothesis that the numbers of HLA A , B , and C alleles are evolving at the same rate in South America ( P = 0 . 0121 for comparison of A and B , and 0 . 0195 for comparison of A and C ) . A similar pattern of natural selection appears to be functioning in chimpanzee populations where the numbers and structural diversity among Patr B alleles is larger than found at the Patr A and C loci [7] , [8] . The different selection pressures operating on the A , B , and C loci , noted as well by others [5] , [16] , provides a compelling argument that each of these immune response regulatory genes do not function equivalently . Interpretations of the unequal numbers of alleles that have emerged at the class I loci in humans and chimpanzees have focused on their peptide binding properties , as it is well known that amino acid changes that affect peptide binding by MHC I molecules influence immune function . This argument focuses on functional differences distinguishing alleles of a single locus . Because there are more than 800 to 1 , 600 allelic variants at the three human loci with widely disparate abilities to bind and present peptides , explanations predicated on allele specific peptide binding properties seem unsatisfactory . Here , we propose a different explanation: locus specific differences in gene expression determine the relative importance of the class I genes for survival , driving locus specific frequencies of emerging allelic variants for the classical MHC genes . The implication is that the classical class I genes are not equally effective in directing immunity against certain pathogens , and therefore , may not be equally effective in targeting vaccine antigens against viruses and perhaps cancers . We propose that the C locus alleles will be less effective antigen presenting molecules than the A locus alleles , which in turn will be less effective than the B locus alleles . This hierarchy is noteworthy as most vaccines are designed by convenience to target the A locus antigen presenting molecules because of their more limited polymorphisms . Because the antigen presenting function of MHC I molecules are determined by their highly polymorphic peptide binding domains , variation within allelic series will overshadow differences among the proteins derived from different loci . In the laboratory mouse , the structures of genes expressed in vivo can be readily manipulated , and we have used this property to illustrate the principle underlying our hypothesis . In the mouse where large numbers of H-2 K and D classical class I alleles have been described , the genetic ability to control picornavirus-induced demyelinating disease in the spinal cord maps to the D locus class I gene cluster ( H-2 D and L ) and does not seem to be influenced by the myriad of alleles present at the K locus [14] . This suggests that ability to resist persistent infection by Theiler's murine encephalomyelitis virus ( TMEV ) might be an example of classical class I loci differing in ability to provide effective immunity against a viral pathogen . An extensive analysis of MHC mediated resistance to TMEV demonstrated that certain alleles of the D gene can direct virus-specific CD8+ T cell immunity that is responsible for clearing virus infection from the CNS [9] , [10] , [12]–[14] . Whereas only certain D region alleles provide protection against persisting virus infection , alleles of the sister locus H-2K never seem to matter . Our studies of gene conversion mediated interchange of coding sequences among the α1 and α2 coding sequences of MHC genes in the mouse indicate that natural history of these sister genes has resulted in a complete scrambling of sequence diversity [11] , [17]–[19] . This implies that structural diversity in the peptide and T cell receptor interaction domains of the D and K molecules is not likely the source of their differential ability to direct an effective and protective immune response against TMEV infection . In humans where interlocus exchange is more limited [8] , this situation is approximated by the large numbers of allelic variants at the HLA A , B and C loci encoding distinctive peptide binding sites . There are two competing hypotheses explaining the immune response phenotypes of mouse strains susceptible and resistant to persisting TMEV infection . The first is that certain MHC I alleles are capable of effectively presenting viral peptides to the CD8+ T cell compartment , and these effective alleles happen by chance to belong to the series of class I genes encoded within H-2D . The second possibility is that H-2D and H-2K genes are functionally distinct , such that while some alleles of H-2D can direct effective virus immunity , alleles of H-2K are ineffective as a group , irrespective of their ability to bind viral peptides and present them to CD8+ T cells . Here we show that structural attributes of K and D genes other than their coding sequences specifying peptide binding properties are responsible for their differential ability to direct protective immunity against a picornavirus infection . These differences influence the relative expression of H-2K and D . This finding provides structural evidence differentiating the functional properties of the classical class I loci and relates these differences to the ability to fight a virus infection . This framework provides a basis for understanding the diverging evolutionary histories of members of the “classical” MHC class I gene family in animal populations , and provides context to our understanding of the polymorphism differences evident at the human HLA A , B , and C loci .
In order to study the structural properties of the H-2D and H-2K class I genes that are responsible for directing effective viral immunity against TMEV infection , we introduced genomic clones encoding the Kb or Db ( Figure 1A ) genes into susceptible FVB mice . To control for possible gene integration positional effects , multiple independent founder lines were analyzed . One founder line of the Kb transgene and three founder lines of the Db transgene were evaluated for susceptibility to TMEV induced demyelination . TMEV infected Kb transgenic animals developed focal areas of demyelination similar to non-transgenic animals ( Figure 2A and B ) . Few of the Db transgenic mice demyelinated ( Table 1 ) , demonstrating resistance compared to the littermate controls ( Figure 2C and D ) which suggests that transfer of the genomic fragments faithfully reproduced the disease susceptibility phenotypes of interest . To evaluate whether factors other than peptide binding by MHC class I molecules determine disease susceptibility , we sought to remove the peptide binding and TCR interaction encoding domain as a variable in our analysis . Because the inability to present peptides to T cells would mask the hypothesized properties of interest , we chose to substitute the Db encoded antigen presenting domain α1α2 for its homologous counterpart in the Kb gene . This region is known to determine the peptide binding properties of MHC I antigen presenting molecules , as well as , the specificity of MHC ligand interactions with T cell receptors [3] , [20] . The resulting chimeric genomic transgene ( Kbα1α2Db ) contained the coding sequences of the α1α2 peptide binding domain and non-coding intronic sequences from the 3′ end of intron 1 , all of intron 2 , and the 5′ region of intron 3 from Db ( Figure 1A and B ) . The remaining regulatory , untranslated regions and structural features of this transgene were derived from a Kb genomic clone [21] . Equivalent expression of the Db and the Kbα1α2Db transgenes was observed in transiently transfected 293T cells ( Figure 1C ) . Eight independent FVB founder lines expressing the Kbα1α2Db transgene were established and analyzed for their susceptibility to TMEV induced demyelinating disease . Mice from 8 Kbα1α2Db founder lines developed demyelinating lesions ( Table 1 ) , similar to transgene negative matched littermate controls ( Figure 2E and F ) . We conclude from this analysis that the Kbα1α2Db chimeric transgene lacks necessary determinants to provide resistance to TMEV induced demyelinating disease . Since the level of demyelination correlates with levels of persisting virus in the CNS , we infected Db and Kbα1α2Db transgenic mice with TMEV for 21 to 24 days to evaluate whether transgenes conferred protection from chronic virus infection . We analyzed TMEV specific transcripts from chronically infected brain and spinal cord and found that the virus persisted in both tested sublines of Kbα1α2Db mice at levels similar to levels seen in the non-transgenic FVB hosts ( Figure 2G and H ) . In contrast , levels of virus were detected at low levels in the two evaluated Db transgenic founders , one of the original 3 Db founder lines , and a fourth Db founder line expressing a Db transgene with introduced LoxP sites . The critical role of Db in viral clearance is illustrated by the deletion of Db in a version of the transgene containing LoxP sites flanking the exon encoding the transmembrane region by introduction of Cre recombinase into the mice under control of the E2a promoter . Significantly higher levels of viral transcript were observed in FVB Db-LoxP Cre mice than in the FVB Db- LoxP subline ( Figure 2G ) . As shown previously [12] , the clearance of TMEV is dependent on the generation of H-2Db restricted CD8 T-cell responses to the immunodominant peptide VP2121–130 ( CD8VP2+ ) . This implies that the T-cell repertoire is available to respond to this antigen and that the antigen specific CTL are able to efficiently target virus infected cells disrupting the infection . Since Kbα1α2Db transgenic mice fail to clear virus from the CNS , we analyzed brain infiltrating lymphocytes from 6 day TMEV infected Db and Kbα1α2Db transgenic mice for the presence of CD8+ VP2121–130-specific T cells . As expected , the non-transgenic FVB ( H-2q ) infiltrates were negative for Db-restricted VP2121–130-specific CD8 T cells ( Figure 3A ) ; in contrast , the Db transgenic mice developed a robust CD8+ VP2121–130-specific response . Surprisingly , the susceptible Kbα1α2Db transgenic hosts developed a CD8+ response , equivalent to the response seen in the resistant Db transgenic hosts , demonstrating that the VP2121–130 responsive CD8 T-cells were available , activated , and recruited to the brain equivalently by both resistant and the susceptible transgenic animals . We wondered whether the presence of CD8+ T-cells specific for VP2 in the CNS suggested a defect in the ability of the T cells to target virus infected cells . We analyzed brain infiltrating leukocytes for their ability to kill VP2121–130 pulsed target cells . T cells from animals capable and incapable of eliminating persisting virus killed the peptide pulsed targets in vitro ( Figure 3B ) . This indicates that the population of T cells required for viral clearance is present within the repertoire of Kbα1α2Db transgenic mice . They are activated by virus infection , capable of killing cells presenting viral antigens , and are recruited to the infected CNS . Therefore , it appears that targeting the infected cells in vivo is deficient in Kbα1α2Db transgenic hosts . We next examined the expression of the MHC class I transgenes for evidence of differential expression . Populations of skin fibroblasts were established from animals of each parental and transgenic FVB mice . The FVB Kbα1α2Db fibroblasts had significantly reduced accumulations in RNA from their class I transgene in comparison to the FVB Db fibroblasts , before and after treatment with IFNγ ( Figure 4A ) . Direct comparison was possible because the assessed transcripts shared identical sequences that were probed using quantitative PCR . In contrast , fibroblasts from FVB , FVB-Db , and FVB-Kbα1α2Db mice were found to have equivalent amounts of RNA from the endogenous Dq locus , before and after IFNγ treatment . The response of the transgenes to IFNγ appears to be equivalent to the response of the endogenous D locus based on fold-increase induced by the cytokine . The fold increase of the Dq ( 8 . 2±0 . 9 ) and Db ( 11 . 2±2 . 0 ) genes in the FVB Db fibroblast were not statistically different , similar to that observed in the FVB Kbα1α2Db fibroblasts ( Dq , 6 . 1±0 . 5 and Db , 6 . 1±0 . 8 ) . We considered the possibility that the observed differences reflect variation in mRNA stability , but found no differences in mRNA stability between H-2Db transcripts from the two transgenes ( Figure 4A ) . We compared this to the less stable transcript derived from tumor necrosis factor alpha ( TNFα ) [22] which demonstrated a 3 to 5 fold reduction in transcript level after 6 hours of treatment with actinomycin D . We conclude that the primary functional difference observed can be attributed to the base-line expression levels of the Db and Kbα1α2Db transgenes and not to their ability to respond to IFNγ . A similar difference in expression level was observed in the brains of TMEV infected mice ( Figure 4B ) . Again , brain tissue from FVB Kbα1α2Db transgenic mice expressed equivalent levels of endogenous Dq but had reduced levels of Kbα1α2Db expression compared to FVB Db after TMEV infection . Expression of the endogenous K locus in brain cells from wild-type FVB mice infected with TMEV was also reduced relative to the D-region class I genes , most pronounced for the L locus ( Figure 4C ) . In contrast , the levels of K , D , and L expression were more comparable in the spleen of infected mice . The differential pattern of expression was seen in the spinal cord of TMEV infected transgenic and wild type animals ( Figure 4D ) . Db expression was higher in two independent transgenic lines as compared to two independent Kbα1α2Db transgenic lines . Comparison of endogenous gene expression 21 days after virus infection is complicated by viral clearance in the animals expressing the Db transgene relative to the other FVB mouse lines analyzed ( Figure 2G ) . Clearance of the virus is associated with down regulation of Dq , Lq , and Kq transcripts in the Db mice relative to the expression levels seen in FVB , Kbα1α2Db , and DbLoxP-cre mice . This expression pattern relative to virus levels is opposite to the pattern for Db and Kbα1α2Db transgenes ( top left panel , Figure 4D ) , suggesting that the functional difference in expression of the transgenes may be greater than measured in the mice 21 days after infection when virus levels are dropping in the Db transgenic mice . Also noted in these experiments was the lower level of H-2Kq transcripts relative to Dq and Lq detected in all the mice analyzed , a finding consistent with our cDNA analysis in Figure 4C . Next , we assessed the expression of the transgene and endogenously encoded class I genes at the protein level . The mean fluorescence intensity of class I molecules expressing the B22 . 249 ( Db ) defined epitope shared by Db and Kbα1α2Db was comparable in peripheral blood mononuclear cells for all the tested mouse lines , although a slight , but statistically significant lower expression was seen for the chimeric transgene encoded molecules ( Figure 5A ) . A similar expression pattern was seen using spleen cells when comparing independent founder lines ( Figure 5B ) . Expression of the Kbα1α2Db transgene was confirmed in these mice by using an antibody [23] with a reactivity pattern dependent , in part , on the α3 region of Kb ( Figure 5C ) . The central element of our hypothesis is that the expression of class I loci in tissues within the body are not equivalent and that cells , which do not adequately express class I antigen presenting molecules , can provide safe harbor for infecting virus despite the presence of infiltrating , activated , anti-viral T cells . In the specific case of CNS infection by TMEV , we hypothesize that the K locus is not universally expressed by cells infected by TMEV , while the D locus is . Therefore , we next sought to identify cells which under express Kbα1α2Db relative to Db during virus infection . One limitation with this approach is that there are many different cell types in the CNS , and our hypothesis only specifies that some cells need to under express H-2K . Therefore , this hypothesis will be difficult to assess systematically . Nevertheless , using a candidate approach , we examined class I expression by microglial cells , a brain resident cell population that can be recovered from normal and infected brain for analysis by flow cytometry [24] . Microglial cells can be discriminated from infiltrating leukocytes and other resident brain cells by their intermediate expression of the leukocyte marker CD45 . We enriched CD45 positive cells from brain homogenates using percol gradients and analyzed the CD45 intermediate cells for expression of Db and Kbα1α2Db before ( Figure 5D ) and after ( Figure 5E ) TMEV infection . H-2Db is expressed at a significantly higher level on microglial cells than is Kbα1α2Db . Following infection with TMEV , expression of Db is uniformally increased , while the increase of expression of Kbα1α2Db is more variable . Importantly , a portion of the microglial cells isolated from TMEV infected Kbα1α2Db transgenic mice do not express levels of the encoded H-2 antigen presenting molecules beyond the median level of the negative control ( shaded in Figure 5E ) . This expression pattern is consistent with our hypothesis that determinants mapping outside the peptide binding domains of the K and D class I genes differentially regulate tissue specific expression and that a population of cells in the CNS may not adequately express the relevant MHC encoded antigen presenting molecule needed to clear virus .
In this report we show , in agreement with previous reports [25] , [26] , that the ability of the H-2Db gene to incite a virus specific CTL response maps to the coding sequences of the peptide binding domain of the class I glycoprotein . However , we also found that induction of a virus specific CTL response that is recruited into the infected CNS is not sufficient to clear virus infection and protect the mice from virus induced demyelination [27] . Another , yet to be defined , attribute of the Db gene is necessary for effective immunity to persistent virus infection . This attribute of Db is not shared by the Kb gene and maps outside of the peptide binding domain coding sequences and adjacent intron sequences . We demonstrated in earlier studies using bone marrow chimeras that activated brain infiltrating CTL must be able to recognize virus on infected cells in order to clear the virus infection [27] . One possibility is that the H-2K antigen presenting molecules are not expressed sufficiently on populations of TMEV infected cells in the CNS , preventing the effective clearance of the virus by K restricted CTL . The necessary structural sequences for directing effective antiviral immunity are present within the 8 kb genomic DNA fragment introduced as a transgene in our study , as four founder lines prepared from susceptible FVB mice acquired a resistant phenotype using demyelination ( Table 1 ) or viral clearance ( Figure 2 ) as criteria . In contrast , the eight founder lines prepared using the Kbα1α2Db transgene displayed a susceptible phenotype . Therefore , a systematic analysis of recombinant transgenes should allow identification of the critical sequences determining the functional differences between these two classical class I genes . Brahic and colleagues reported that a transgene encoding a similar chimeric glycoprotein comprised of the α1α2 domain of Db in the context of the α3 , transmembrane , and cytoplasmic regions of Kb was able to clear TMEV infection from the CNS [28] . The chimeric gene used in that study contained a larger segment of Db non-coding sequences than did the chimeric transgene used in our study . One possibility is that regulatory sequences determining the ability of the D class I gene to direct effective sterilizing immunity against the virus are located within the additional Db sequences in the Brahic construct . Another possibility is that a fortuitous integration event bestowed a wider expression phenotype on the transgenic mouse used in that study . Our finding that 8 different Kbα1α2Db founder lines expressed similar demyelinating phenotypes known to be related to inability to clear chronic virus infection provides confidence in our conclusion that structural components of the transgenes , and not fortuitous integration events , determined the different phenotypes of the transgenic animals we studied . The ability of our chimeric transgene to promote the activation and recruitment of virus specific CTL to the infected CNS in a manner equivalent to the phenotype bestowed by the parental Db transgene has led us to a different conclusion from the Brahic group . We conclude that the ability of peptide binding domain to present critical viral peptide antigens and elicit cellular immunity is not sufficient to direct effective antiviral immunity to the TMEV picornavirus and that properties distinguishing D region class I genes from the K locus genes determine this difference . We had previously noted using immunohistology that the K and D region proteins of the H-2q haplotype are differentially expressed in TMEV infected brain tissues during acute and chronic infection [29] . In those studies , we found early up regulation of H-2K and H-2D protein in the brain . While H-2D expression was maintained during the chronic phase of infection , expression of H-2K diminished . Here , we have extended our analysis of the expression of H-2q haplotype genes during TMEV infection at the RNA level . We find that H-2Lq is the most prominently expressed class I gene in TMEV infected mice , with minimal expression of H-2Kq . Our comparison of the chimeric and Db transgenes showed that Db is expressed at substantially higher levels than the chimeric gene in TMEV infected CNS of transgenic mice , a pattern that mimics the endogenous Kq and Lq genes . H-2Lq and H-2Db are close evolutionary relatives as indicated by dendogram analyses based on their gene sequences [19] . Their notation as L and D locus alleles is an accident of mouse nomenclature . We have argued that all the classical D region genes can move into the D and L locus positions on the chromosome by unequal recombination [30] , and therefore , can be considered part of the same allelic series , although structural regulatory differences derived from two ancient loci now mixing by the polymorphic gene organization may be retained in mouse populations . According to our hypothesis , Db is protective because it is highly expressed in infected tissues and presents viral peptides efficiently , while Lq , also highly expressed , does not effectively present viral peptides . H-2Dq expression , while measurable , does not appear to be at the level of H-2Lq . How other D region genes in the mouse are expressed , including those of the resistant H-2d and H-2k haplotypes , remains to be determined . An important insight emanating from our studies is that the classical MHC I genes do not appear to function equivalently throughout the body . While these molecules are all capable of binding endogenously generated peptides and presenting both foreign and self antigens to CD8+ T cells , at least some of the K locus alleles ( Kq and Kb ) are expressed at lower levels than their D region counterparts ( Lq , Db , and even Dq ) by fibroblasts and some cells in the CNS . All these MHC I genes appear to be expressed more similarly in some tissues outside of the CNS , most notably spleen cells and by brain infiltrating leukocytes during infection . The implication is that regulation of MHC I gene expression in the CNS and in spleen cells differs in ways that requires more than the up regulation of a single gene regulatory factor not normally expressed in CNS tissues . Our model provides a way to identify the elements governing the regulatory mechanisms active in the CNS . Although we do not know the precise mechanism responsible for differential regulation of K and D genes in the mouse , both of our transgenes are responsive to IFNγ , but differ in basal expression in various tissues . This expression pattern is consistent with previous findings demonstrating that distinct transcriptional pathways regulate basal and activated MHC class I expression [31] and locus specific differences in the structure of MHC class I promoters [25] , [26] reflected in patterns of transcription factor binding [32] , [33] . The precise details of the mechanisms that determine differential expression of class I genes in different species may not be the same . Finally , our finding that MHC I loci have different inherent abilities to direct effective class I mediated cellular immunity to viruses provides a model for understanding the strong selection differences implicit in the structures of polymorphism of class I genes in human and chimpanzee populations . Whereas the B locus alleles are vibrantly selected for diversity in the coding blocks for their peptide binding sites , A and C locus alleles are less so . The B locus in humans and the D loci in mice occupy orthologous positions within their respective MHC [34] raising the possibility that these genes may share canonical regulatory motifs and expression patterns . Whether locus differences in polymorphism can be traced to differential regulation of the primate A , B , and C loci in the CNS or elsewhere in the body remains to be determined . Nonetheless , the implication is that the A , B , and C loci are not functioning equivalently throughout the body . This raises provocative questions about the wisdom of selecting the less polymorphic HLA-A locus antigen presenting proteins as targets for vaccine development . The requirements for initiating an immune response and for resolving infection are different . The absence of expression of A locus proteins in key tissues ( e . g . sites of infections ) could promote conditions of chronic inflammation without effective resolution of immunity at targeted tissues . We propose that differences in gene regulation may be the underlying reason why A and C diversity is lagging behind B diversity in human populations .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocols were approved by the Institutional Animal Care and Use Committee of Mayo Clinic ( #A25704 and #A12304 ) . All mice were anesthetized with isoflurane prior to intracranial virus infection . C57BL/6 mice were obtained from Jackson Laboratories ( Bar Harbor , ME , USA ) . B10 , 129 and FVB mice were obtained from the Mayo Clinic Transgenic Core Facility . Virus infection was introduced into the CNS via intracerebral inoculation with 2×105 PFU of the Daniel's strain of Theiler's murine encephalomyelitis virus ( TMEV ) . Acute infection with TMEV was analyzed at day 6 and all chronically infected animals at greater than 45 days . All animals were housed and cared for according to institutional and NIH guidelines for animal care and use . The generation of FVB Kb and FVB Db transgenic mice was described previously [12] . FVB Kbα1α2Db transgenic mice were generated by the Mayo Transgenic Core facility . To generate the chimeric construct a Sal I site was introduced into a Hind III-EcoRI fragment of H-2Kb at genomic position 136 . A Sal I/XbaI PCR fragment was generated from a cloned H-2Db vector [35] and engineered into the H-2Kb backbone vector . Function was verified in 293T cells by transfection and FACS . Briefly , 293T cells were plated in 6 well plates one day prior to transient transfection with GFP and the H-2Kb , H-2Db and H-2Kbα1α2Db constructs using Fugene 6 transfection reagent ( Roche Diagnostics Corporation , Indianapolis , IN ) . Twenty-four hours later cells were trypsinized and stained with excess H-2Db specific antibody ( B22 . 249 . R1 ) . A secondary anti-mouse IgG phycoerythrin antibody ( Accurate Chemical , Westbury , NY ) was used was used at 10 µg/mL to detect H-2Db expression . MHC I transgenic constructs were injected into FVB/Cr blastocysts to generate H-2Kb , H-2Db and H-2Kbα1α2Db transgenic mice . We ( AJJ ) generated the FVB/N-Db LoxP mouse to achieve cell-specific deletion of the H-2Db class I molecule through modification of our H-2Db transgene [12] . Using conventional molecular biology techniques , we inserted LoxP sites that flank the transmembrane exon of the Db class I gene ( exon 5 ) . This drives Db class I gene expression in the FVB/N strain ( H-2q ) . Transgenic FVB/N-Db LoxP mice elicited normal CNS infiltrating Db:VP2 121–130 epitope specific CD8 T cell responses during acute TMEV infection ( data not shown ) . To verify function of the LoxP transgene , we bred the FVB/N-Db LoxP mice to the FVB/N-Tg ( E2a-cre ) C5379Lmgd/J mouse ( Jackson: 003314 ) . This line carries a Cre transgene under the control of the adenovirus E2a promoter that targets expression of Cre recombinase in a wide variety of tissues . Thymocytes isolated from progeny FVB/N-Db LoxP mice that expressing the Cre transgene under the E2a promoter had deactivation of Db class I expression ( data not shown ) . Chronically infected mice were perfused and fixed with Trump's fixative before dissociated spinal cord sections were fixed with osmium tetroxide , dehydrated , embedded in glycol methacrylate and sectioned as described previously [36] . Spinal cord sections were analyzed for the presence of demyelination characterized by the presence of focal lesions composed of infiltrating lymphocytes , myelin debris and de-nuded axons . The 7900HT Fast Real-Time PCR System ( Applied Biosystems , Carlsbad , CA , USA ) was used to quantify viral RNA infected brain and spinal cord homogenates from mice inoculated with TMEV-wt or TMEV-L/OVA . RNA was isolated using TRIzol Reagent ( Invitrogen , Carlsbad , CA , USA ) and reverse transcribed using the Superscript cDNA synthesis kit ( Invitrogen ) . Reaction was set up using the Fast SyBR Green Master Mix Kit ( Applied Biosystems ) . cDNA was amplified using primers specific for mouse actin ( F – 5′CTGGCACCACACCTTCTACAATGAGCTG and R– 5′GCACAGCTTCTCTTTGATGTCACGCACGATTTC ) and for viral protein 2 ( VP2 ) of TMEV ( F-5′TGGTCGACTCTGTGGTTACG and R-5′ GCCGGTCTTGCAAAGATAGT ) . Cycling conditions were as follows: 50°C for 2 minutes , 95°C for 10 minutes followed by 40 cycles of 95°C at 15 seconds then 55°C for 1 minute . Amplification curves and crossing point thresholds were based on SYBR Green incorporation . Samples were normalized to actin and data are reported as fold increase over background or the appropriate control strain . Primers specific for H-2Db ( 5′GAAACACAGAAAGCCAAGGGCCAA and 5′AGTCCGACCCCAAGTCACAGCCAG ) , H-2Dq ( 5′GATCACGCAGATCGCCAAGGACAAT and 5′CGTGCAACCCCACGTCACAGCCGTACATCC ) , H-2Lq ( 5′GTCCCGCAGGCACTCACACGATCCAG and 5′CCGTCGTATGCGTACTGCTCGTACCC ) and H-2Kq ( 5′ACGACACTGAGTTGGTGCGCTTCGAC and 5′ACTCTGCTCATTGTCCTTGGCGATCT ) were used to evaluate H-2 expression in fibroblast , spinal cord and brain by semi-quantitative real-time RT-PCR . Samples were normalized to actin and fold change was calculated relative to non-transgenic or to background amplification of mice not having the amplified allele . To determine the relative contribution of different H-2 alleles to the overall expression of MHC class I in the CNS , we used a competitive PCR technology to amplify multiple MHC class I alleles and then verified their identity by sequencing . We used flanking primers that were conserved across several alleles but spanned areas of diversity that allowed us to discriminate individual alleles . We added flanking BamHI sites to each primer so that concatamers could be generated from amplified fragments ( Forward 5′CATATAATAATGGATCCTACTACAACCAGAGC , Reverse 5′GTATATTATCGGATCCGTACCCGCGGAGGAG ) . Concatamers were cloned and sequenced using standard techniques . Skin fibroblast lines were derived from ear punches obtained from FVB , FVB Db and FVB Kbα1α2Db transgenic mice according to previously described methods [37] . mRNA stability was assessed using previously described techniques [38] . Skin fibroblasts derived from FVB and transgenic FVB Db and FVB Kbα1α2Db mice were treated with 1 µg/mL of recombinant mouse interferon gamma ( R&D Systems , Minneapolis , MN ) in triplicate . Twenty-four hours later cells were either left untreated or treated with 2 . 5 mg/mL of actinomycin D ( Sigma-Aldrich , St . Louis , MO ) for 1 , 3 and 6 hours . After actinomycin D exposure RNA was extracted using TRIzol reagent . Real-time RT-PCR assessment of fold reduction in expression of H-2Db and TNFα ( Forward primer 5′GGATGAGAAGTTCCCAAATGGCCTC and Reverse primer 5′ GCTCCTCCACTTGGTGGTTTGCTA ) was performed using actin as a normalization control . Data were expressed as the fold reduction in expression from the untreated control sample . Brain and spinal cord infiltrating lymphocytes from TMEV infected mice were recovered using previously described techniques and were analyzed by flow cytometry [10] , [39] . Anti-CD45 ( 30-F11 ) and anti-CD8 ( 53-6 . 7 ) antibodies were obtained from Ebiosciences ( San Diego , CA , USA ) . FITC labeled anti H-2Db ( B22-249R1; Accurate , Westbury , NY , USA ) and PE-labeled anti H-2Kb ( AF6-88 . 5; BD Pharmingen , San Jose , CA , USA ) were used to assess MHC class I expression on PBMC , splenocytes and brain infiltrating cells . All antibodies were used in excess at concentration of 10 µg/mL . VP2121–130/H-2Db tetramers were kindly provided by the NIH Tetramer Core Facility at Emory University ( Atlanta , GA , USA ) and were used at a concentration of 12 µg/mL . HPV16 E749–57/H-2Db tetramers ( Becton-Dickinson , San Jose , CA ) were used as a non-specific control according to the manufacturers guidelines . Samples were analyzed on a BD LSR II flow cytometer ( BD Biosciences , San Jose , CA ) and analyzed using FloJo software ( Ashland , OR ) . Single color stained splenocytes were used as compensation controls . CD45-allophycocyanin staining was used to discriminate brain derived microglia that moderately autofluoresce in the FL1 channel from other brain derived cells . Comparisons between the three transgenic strains were performed on the same population of CD45 gated cells . Cytotoxic lymphocyte killing of VP2121 peptide pulsed targets was assessed by chromium release assay using EL4 cells pulsed with 1 µg/mL of peptide [10] . Normally distributed data were analyzed by ANOVA or t-test . Data failing the normality test were analyzed by ANOVA on ranks or Rank-sum test . Pairwise comparisons for ANOVA were performed using the Holm-Sidak method . Significance was determined by p less than 0 . 05 .
|
MHC I genes are best understood as regulators of antiviral immunity . In humans and mice there are 2 to 3 homologous MHC I genes encoding highly polymorphic antigen presenting molecules which present virus proteins to T lymphocytes . A world wide effort has catalogued more than 6 , 300 classical HLA MHC I alleles in human populations , making these MHC loci among the best characterized polymorphic gene families . However , there has been little progress in understanding implications of the differences in polymorphism present at the HLA A , B , and C loci . By expressing MHC I molecules capable of presenting viral antigens under regulatory determinants from different sister MHC I genes of the mouse , we address the hypothesis that locus-specific differences in the regulation of the homologous MHC I sister genes can determine whether alleles at any particular locus can effectively target protective immunity against virus infection . We find that while the ability to activate cellular immune effectors is determined by the highly polymorphic MHC I sequences encoding the peptide binding domain , the ability of these T lymphocytes to effectively clear virus from the central nervous system can also be determined by gene sequences mapping outside of this region .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"model",
"organisms",
"genetics",
"population",
"genetics",
"biology",
"immunology",
"population",
"biology",
"neuroscience",
"genetics",
"and",
"genomics"
] |
2012
|
Nonequivalence of Classical MHC Class I Loci in Ability to Direct Effective Antiviral Immunity
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DNA methylation acts in concert with restriction enzymes to protect the integrity of prokaryotic genomes . Studies in a limited number of organisms suggest that methylation also contributes to prokaryotic genome regulation , but the prevalence and properties of such non-restriction-associated methylation systems remain poorly understood . Here , we used single molecule , real-time sequencing to map DNA modifications including m6A , m4C , and m5C across the genomes of 230 diverse bacterial and archaeal species . We observed DNA methylation in nearly all ( 93% ) organisms examined , and identified a total of 834 distinct reproducibly methylated motifs . This data enabled annotation of the DNA binding specificities of 620 DNA Methyltransferases ( MTases ) , doubling known specificities for previously hard to study Type I , IIG and III MTases , and revealing their extraordinary diversity . Strikingly , 48% of organisms harbor active Type II MTases with no apparent cognate restriction enzyme . These active ‘orphan’ MTases are present in diverse bacterial and archaeal phyla and show motif specificities and methylation patterns consistent with functions in gene regulation and DNA replication . Our results reveal the pervasive presence of DNA methylation throughout the prokaryotic kingdoms , as well as the diversity of sequence specificities and potential functions of DNA methylation systems .
DNA methylation has widespread roles in the regulation of eukaryotic genomes [1–3] , but the extent to which similar processes exist in prokaryotes is unknown . Methylated DNA is found in the genomes of bacteria and archaea in the forms of 6-methyladenosine ( m6A ) , 4-methylcytosine ( m4C ) , and 5-methylcytosine ( m5C ) [4] , and is the product of DNA methyltransferase ( MTase ) enzymes [5] . MTases are often a component of restriction-modification ( RM ) systems [6] , but have also been implicated in DNA mismatch repair [7] and other epigenetic regulatory phenomena [8] . While MTase genes are present in the genomes of many prokaryotes , the overall abundance and patterns of prokaryotic DNA methylation , and the functional diversity of MTases remains largely unknown . RM systems play a central role in prokaryotic defense , and their constituent enzymes are foundational tools in modern molecular biology [6] . RM systems comprise a restriction endonuclease ( REase ) and a MTase with the same DNA binding specificity . The REase degrades DNA from viruses and other exogenous sources , while the cognate MTase methylates potential REase target sites in the host genome and thus protects them from cleavage . RM systems are classified into four main types [5 , 6 , 9 , 10] . Type I RM systems are complex , multi-subunit systems composed of separate REase and MTase subunits , and a common DNA recognition specificity ( S ) subunit [11] . The S subunit in combination with two MTase subunits methylates DNA , while the S subunit in combination with two MTase subunits and two REase subunits results in restriction . Type I RM systems recognize bi-partite motifs ( e . g . CAGNNNNNTCA ) , and cleave at large distances ( up to several kb ) from their binding site . Type II RM systems comprise separate REase and MTase enzymes , which are expected to show identical DNA binding specificity [12] . They bind short , mostly palindromic , motifs ( e . g . GATC ) , and cleave DNA within or close to the recognition site . Exceptions are the Type IIG RM systems that are single chain polypeptides containing both DNA restriction and methylation activities , bind short non-palindromic sequences ( e . g . GCCCAG ) , and cleave DNA outside of the DNA binding site [12] . In Type III systems the MTase alone contains a DNA binding specificity domain and forms a complex with the REase in order to restrict [13] . They bind short non-palindromic motifs ( e . g . CGAAT ) and cut outside of the DNA binding site . Finally , Type IV RM systems cut modified DNA and do not have a MTase component [14] . Knowledge of the binding specificities of RM systems is critical to understanding their biological functions . Traditional approaches to determine RM system specificities rely on patterns of DNA cleavage by REases , a strategy that limits discovery largely to Type II RM systems where the REase binds and cleaves DNA at the same location [5] . Owing to this limitation , while the DNA binding specificities of several thousand Type II RM systems are known , typically fewer than 100 of each of the other types of RM system are known [5] . For Type I , IIG and III systems that cut outside of the RM binding site , a more recent alternative approach is to take advantage of the identical motif specificities of methylation and restriction . In these cases , determination of the sequences methylated by the MTase can directly reveal the recognition sequence of the accompanying REase , as recently demonstrated for individual RM systems [15–21] . Beyond RM systems , MTases can also be involved in prokaryotic genome regulation [8 , 22] . These enzymes are typically observed as ‘orphan’ MTases that are found encoded in prokaryotic genomes in the absence of genes encoding a cognate restriction enzyme [23] . Examples include the Dam MTases that regulate DNA replication timing and gene expression of Gammaproteobacteria [24] and the CcrM MTases that regulate cell cycle progression of Alphaproteobacteria [19 , 25] . While genome-wide methylation analysis of individual genomes can in principle identify regulatory MTases and provide insight into the associated regulatory DNA methylation system [17 , 18 , 20 , 21 , 26 , 27] , in the absence of systematic mapping efforts it has remained unclear how common such mechanisms are in prokaryotes . It is unknown whether the MTases associated with RM systems can also play a regulatory role . MTase-encoding genes are present in the majority of bacterial and archaeal genomes , suggesting that DNA methylation may be similarly abundant . Bisulfite sequencing has enabled genome-wide surveys of 5mC methylation [28 , 29] , but a historic absence of tools for studying m6A and m4C modifications that predominate in prokaryotic DNA[30] has precluded more comprehensive studies . It has recently been demonstrated that kinetic analysis of single molecule , real-time ( SMRT ) sequencing data can directly detect many types of DNA modification [4 , 31 , 32] . While this approach is only modestly sensitive to m5C methylation , it is capable of detecting both m6A and m4C highly with a high degree of accuracy and sensitivity . The application of SMRT sequencing to a small number of prokaryotes enabled the identification of methylated motifs , and annotation of the respective MTases [15–21] . In the present study , we systematically use SMRT sequencing to uncover the patterns of DNA methylation across a large panel of more than 200 diverse bacterial and archaeal genomes to provide an overview of the epigenomic landscape of prokaryotes . In so doing we reveal the ubiquity of DNA methylation , and annotate DNA binding specificities for hundreds of MTases belonging to previously intractable types of RM systems . Furthermore , we demonstrate that a large proportion of the ‘orphan’ MTase genes encoded in prokaryotic genomes are active under normal conditions and produce patterns of DNA methylation that are consistent with gene regulatory functions . Our findings provide evidence for the pervasiveness and potentially diverse functions of DNA methylation in prokaryotic genomes .
To explore the locations and potential functions of DNA methylation across prokaryotes , we selected 230 organisms for study , including 217 bacterial and 13 archaeal species , spanning 19 different phyla and 37 different classes ( Fig 1A , S1 Table ) . These organisms were selected primarily based on their phylogenetic diversity to enable a comprehensive survey of bacterial methylation systems and maximize the chances for discovery of novel systems . For each organism , we isolated genomic DNA , and performed deep single molecule , real-time ( SMRT ) sequencing . We obtained on average 130-fold read coverage per organism , resulting in a combined dataset size of more than 79 million single-molecule reads and 105 Gb across all sequenced genomes . We aligned all SMRT sequences to the respective reference genomes , and used kinetic data analysis to identify the locations and probable types ( m6A , m4C , m5C ) of high-confidence base modifications in each sequenced genome ( see Methods ) . We then identified sequence motifs that were recurrently methylated in each genome ( Methods ) . The results of these analyses were genome-wide basepair-resolution methylation maps for each organism examined , as well as a set of modified motifs for each genome , where each motif represents the likely binding specificity of a DNA MTase . In total we identified 858 methylated motifs , with DNA modifications detected from 215 / 230 organisms ( 93% ) , and across all sequenced phyla ( Fig 1A ) . On average , we observed 3 methylated motifs per organism , with a maximum of 19 in Neisseria gonorrhoeae . Among modified motifs , the predominant base modification type detected was m6A ( 75% ) , with m4C and m5C accounting for 20% and 5% , respectively ( S1 Fig ) . The large number of m6A methylated motifs is consistent with the frequent occurrence of this modification type in the database of known MTase specificities [5] , and the ease with which this modification type is detected by SMRT sequencing . In contrast , the low frequency of m5C methylated motifs is an underestimate of the true number of such motifs across these genomes due to the lower sensitivity of SMRT sequencing to this modification type ( S2 Fig ) [16] . The fifteen organisms without detectable methylation are from across the sampled taxa , with no obvious shared characteristics . In 8/15 cases , their genomes lack predicted MTase genes ( but harbor methyl-directed restriction enzymes ) , while in other cases MTases are present but were not detectably active by SMRT sequencing ( S2 Table ) . In summary , these data reveal that DNA methylation is widespread across prokaryotes , and provide a valuable resource for exploring the specificities and functions of the MTases present in these genomes . To identify the individual MTases responsible for each methylated motif , we performed large-scale annotation of MTase binding specificities across the studied genomes . Using an integrative RM-system gene annotation pipeline ( Methods ) , we identified 1 , 459 candidate MTase genes across the 230 genomes , and classified them according to RM-system type ( panel A in S3 Fig ) . We then similarly classified the 858 detected motifs according to the type of MTase system to which they likely belong ( panel B in S3 Fig ) . Comparison of the types of methylated motifs and MTase genes within the same organism enabled us to make initial predictions of the MTase enzyme responsible for each observed methylated motif ( Fig 1B ) . For nearly all detected methylated motifs ( 849 , 99% ) , we identified at least one candidate MTase in the same genome predicted to be capable of producing the modification . In contrast , there were many ( 640 , 44% ) candidate MTase genes for which no potential modification activity was detected . Of these 227 are MTases that are predicted to produce m5C modifications that are difficult to detect by SMRT sequencing . Other cases may be MTases that are inactive due to genetic drift , mis-identified enzymes that target RNA or protein rather than DNA , or genes that are not expressed , as frequently occurs when MTases are located on prophages . In 620 cases , we were able to unambiguously match a single candidate MTase to a motif of the same type in the same genome ( Fig 1C ) , thus generating a set of high confidence annotations of MTase specificities ( S3 Table and S4 Table ) . The remaining unmatched motifs are due to several candidate MTases being present in the same genome , with insufficient evidence to make an unambiguous assignment . For almost all Type I and III MTase gene predictions , a cognate REase was identified in the same genomic region , suggesting that these constitute intact RM systems , and enabling the systematic annotation of restriction specificities ( Fig 1D , S1 Text ) . In contrast , restriction enzyme candidates could not be identified for over half ( 165/318 ) of the Type II MTases that are present ( Fig 1D ) . This is consistent with the previous observation that Type II MTases frequently occur as orphans in bacterial genomes [23] . While we cannot exclude the possibility that some novel REase genes were not identified due to sequence divergence , these 165 orphan Type II MTases represent a large group of MTases with likely non-RM functions . Comparison with known RM systems [5] indicates that our systematic analysis identified 148 RM systems with previously undescribed sequence determinants , substantially expanding the repertoire of available specificities . The discovery rate of novel enzyme specificities was particularly large for Type I , IIG , and III RM systems that have been historically difficult to study using conventional approaches ( Fig 2A ) . For example , 92% ( 161/175 ) of annotated Type I system specificities identified in our study were novel . In addition , among the Type I motifs that could not be matched to genes the majority were new specificities not seen previously . As a result , our analysis increases the number of known Type I system specificities almost four-fold ( from 76 to 293 , Fig 2B ) . Our data also reveals the extraordinary diversity of modes of DNA recognition by Type I RM systems , with variation observed in all aspects of the DNA recognition architecture ( Fig 2C ) . We also identified a substantial number of novel recognition specificities by Type IIG and Type III MTases . Among Type IIG RM systems annotated , 82% ( 56/68 ) were novel , while the same was true for 79% ( 47/59 ) of the Type III specificities ( Fig 2A ) . Unmatched motifs in these categories cannot always be unambiguously attributed as being from a Type IIG or Type III enzyme because both lead to characteristic single-strand methylation . Preliminarily we have considered short recognition sequence of 4 or 5 bases to most likely belong to the Type III family , while the longer recognition sequences of 6 or more base pairs are considered as Type IIG . Overall , the number of observed specificities across these Types of restriction system increased 2 . 7-fold ( from 144 to 385 ) as a result of our study . Previously , protection against Type I restriction enzymes was always found to be mediated by m6A modification [11] . In this study , we find examples of protection by m4C ( M . Dac11109IV in Desulfobacca acetoxidans and M1 . Mma5219I in Methanohalophilus mahii , S3 Table ) . Similar results have been obtained from other recent studies [5] , and several of these systems have now been experimentally verified ( Morgan et al . personal communication ) . Interestingly , when this happens there are two MTase genes associated with the system , one of which appears responsible for m6A methylation and the other for m4C methylation . In these cases the bipartite recognition sequence of the Type I S subunit has only G and C residues in one of the target recognition domains , which explains why m6A cannot be used to protect both halves . There are many homologs elsewhere in REBASE of systems like this , but often the specificity is unknown [5] . A similar situation has also been found for some Type III MTases where occasionally m4C is found as the protective modification both in some of the systems identified here as well as others [5] . Type IIG systems are defined by the presence of a single target recognition domain ( TRD ) for the entire RM system . They typically consist of a single polypeptide containing both the endonuclease domain and m6A MTase , as in the prototypical enzyme MmeI [33] ( S4A Fig ) . Here , we identified 76 novel Type IIG-like systems , many of which were atypical in terms of gene order , presence or absence of a DNA translocase , and differences in linkage between the endonuclease and MTase domains ( S3 Table and panels B-E in S4 Fig ) . For example , we identified several different systems in which one peptide contains an MmeI family MTase/TRD , but in which the endonuclease is encoded on a separate peptide ( AchA6III and OspHL35III , panels B and C in S4 Fig ) . Other examples such as CalB3II ( panel D in S4 Fig ) are new examples of BREX-like systems [34] . These systems use the specific methylation of the MTase protein to distinguish self from non-self in phage restriction , but appear to accomplish restriction without generating DNA cleavage . Finally , we observe novel systems that are unrelated to MmeI or BREX . For example , MexAMORF1192P is a four-protein system of two translocase proteins and separate MTase-TRD and endonuclease proteins ( panel E in S4 Fig ) . These analyses highlight the value of SMRT-sequencing in annotating novel RM systems . The examples we describe represent just a portion of the wide diversity of Type IIG-like systems that evolve from various permutations of endonuclease , MTase and translocase domains with a single DNA recognition module . The preliminary annotations of Type IIG-like MTases from this study can be propagated across many orthologs and will enable their further characterization and systematic classification . While Type II RM systems represent historically the best-studied class of RM systems , our systematic survey identified a substantial number of new Type II RM systems , some of which have unusual properties . For example , all Type II RM systems described to date are characterized by close genomic proximity of the genes encoding the REase and the MTase , respectively [5] . We observed one pair of adjacent MTases M1 . Csp12AI and M2 . Csp12AI in Clostridium sp . 12 ( A ) that were very similar to the m6A-MTase M . FokI from Flavobacterium okeanokoites . However , in Clostridium sp . 12 ( A ) the gene encoding the corresponding FokI-like restriction enzyme was not found in the immediate vicinity of M1/M2 . Csp12AI , but at a genomic location 1 . 2 megabase pairs ( Mb ) away . All three genes were tested for activity by cloning . While M2 . Csp12AI could be cloned alone , it was only possible to clone the M1 . Csp12AI gene in the presence of M2 . Csp12AI . In both cases , just as in the genome , both MTases were shown to be fully functional by PacBio sequencing of DNA ( S5 Fig ) . To exclude the possibility that the large apparent distance resulted from an incorrect genome assembly , we confirmed by PCR that the distance between the REase gene and the two MTase genes is at least 36 kb ( S6 Fig ) . These results indicate that , unlike all previously described Type II RM systems , there are Type II RM systems in which the REase and MTase genes are located at distant sites on the chromosome . Our systematic survey identified 165 candidate ‘orphan’ Type II MTases ( Fig 3A , S3 Table and S4 Table , Methods ) . These MTases are found in isolation , i . e . in the absence of corresponding restriction enzymes , but nonetheless actively methylate specific sites in the genome . This feature raises the possibility that these MTases are involved in non-RM-functions , such as gene regulation . Orphan MTases are widely distributed among prokaryotes with at least one example in 111 ( 48% ) organisms and 15/20 different phyla included in this study ( Fig 3B ) . To explore the properties and potential functions of orphan MTases in more detail , we first examined the phylogenetic conservation of orphan and RM system MTases . We determined the presence or absence of each MTase among all sequenced species related to the host organism at the genus , family or class level , and with an available reference genome sequence ( Methods ) . We considered MTases to be conserved if present in at least 50% of species within the respective taxonomic group ( Fig 3C ) . Overall , orphan MTases are far more likely to be evolutionarily conserved than RM system-associated MTases . For example , the majority of orphan MTases ( 57% ) are conserved at the genus level , while the same is true for only 9% of RM system MTases . A similar contrast between orphan and RM MTases is observed at the level of family and class ( Fig 3C ) . These results are consistent with a greater degree of conservation of orphan MTases compared with RM MTases [23] , and suggest that orphan MTases have functional roles distinct from host protection . We next performed protein sequence similarity-based clustering to identify candidate novel families of related orphan MTases . We generated initial protein clusters from all 260 Type II MTases in our study ( S7 Fig and S8 Fig ) , then extracted sub-clusters of orphan MTases from taxonomically related host organisms and with identical motif recognition sequences ( Methods ) . These analyses resulted in 19 orphan MTase families accounting for 107 / 165 orphan MTases in our study ( Fig 3D ) . The remaining 58 MTases are ‘singletons’ with no ortholog in any other genome in our dataset . The two most highly represented orphan MTase families in our study are the known regulatory orphan Dam MTases in Gammaproteobacteria , and CcrM MTases in Alphaproteobacteria , reflecting our large sampling of organisms from these taxa . Of the remaining 17 candidate families , 3 are apparent homologs of Dam MTases in Cyanobacteria and two archaeal classes , respectively . The other 12 families are novel orphan MTases of unknown function and are found in diverse prokaryotes including both bacteria and archaea . The most highly represented orphan MTase family methylates the motif 5’-RAm6ATTY-3’ ( T indicates that the A on the complementary strand is modified ) in all six Spirochaetaceae sequenced as part of this study . This motif and orphan MTase had previously been observed in Campylobacter jejuni [16] . In many cases , novel orphan MTase families are widely conserved in genomes beyond those included in our study . For example , the gene for the orphan MTase targeting 5’-TTA m6A-3’ in two Arthrobacter species in our study is present in 39 / 42 ( 93% ) of all sequenced genomes from the genus Arthrobacter . Similarly the orphan MTase targeting 5’-m4CATG-3’ in two Haloarchaeal species in our study is present in 121 / 156 ( 78% ) of all sequenced genomes from the class Haloarchaea ( Fig 3D ) . In summary , these analyses reveal the presence of several novel evolutionarily conserved families of orphan MTases of unknown function . We hypothesize that some of the newly discovered orphan MTases function similarly to the known regulatory orphan MTases Dam and CcrM , i . e . that they regulate gene expression through the presence or absence of methylation in regulatory sequences . Alternatively their function may be to regulate DNA replication , through clusters of motifs in regions of the genome associated with DNA replication control [35] . To explore these possibilities in more detail , we searched our methylome data for signatures consistent with such functions . It has previously been shown that a subset of target sites of the E . coli regulatory MTase Dam is completely unmethylated [36–38] . These unmethylated sites are the consequence of the competing activities of Dam MTase and regulatory proteins , and the presence or absence of methylation at these sites has a demonstrated impact on gene expression [39 , 40] . We therefore asked if we could recapitulate these findings for Dam MTases in our dataset , and if similar patterns are associated with novel orphan MTases . In the E . coli data from this study , the vast majority ( 17 , 544/17 , 562 , 99 . 9% ) of 5’-G m6ATC-3’ motifs are fully methylated on both strands of the genome . However , a distinct set of 18 5’-G m6ATC-3’ motifs is unmethylated on both strands of the genome ( Fig 4A ) . These unmethylated sites include six GATC positions in upstream regulatory regions of agn43 genes that are known to be regulatory targets of Dam methylation [39] . Unmethylated sites are also detected in association with the dam orphan MTase gene of Salmonella bongorii , ( Fig 4B , and S5 Table ) . In contrast , unmethylated sites are absent from the genome of Clostridium thermocellum , a bacterium harboring a 5’-G m6ATC-3’ specific MTase that is part of an RM system ( Fig 4C ) . These results suggest that the presence of small subsets of reproducibly unmethylated recognition motifs across the genome may be a distinctive signature of orphan MTases . We extended this analysis to all m6A orphan and RM-system associated MTases in our dataset with sufficient SMRT sequencing coverage for confident detection of unmethylated sites ( Methods ) . We observed widespread occurrence of unmethylated sites in association with Dam MTases across Gammaproteobacteria , as well as with the regulatory CcrM orphan MTases in Alphaproteobacteria ( consistent with recent observations of unmethylated sites in Caulobacter [21] ) . Strikingly , we also observed unmethylated sites in association with at least one MTase for the majority ( 13/16 ) of novel orphan MTase families , as well as with over half of ‘singleton’ orphan MTases ( Fig 4D , S9 Fig and S5 Table ) . In contrast , MTases of restriction systems are almost always associated with complete modification of their genomes , with only four apparently unmethylated sites observed across 41 RM MTases ( Fig 4D ) , and consistent with a role in protecting the genome from the cognate restriction enzyme . On further inspection , all four apparent unmethylated RM MTase sites have modification scores at the borderline of detection , and likely represent the background false-positive rate of detection of unmethylated sites . Overall these analyses confirm that unmethylated motifs are a common signature of novel orphan MTases , and may represent novel regulatory sites in the genome . In known cases of gene regulation by orphan MTases , functionally relevant motif sites are located in regulatory sequences upstream of genes and are unmethylated in some or all of the population [39 , 41] . We therefore asked whether the target motifs of the orphan MTases identified in this study are similarly associated with gene regulatory regions ( Fig 4E ) . In general , orphan MTase motifs ( irrespective of their methylation state ) are not significantly enriched at gene regulatory regions ( defined as 100bp upstream of CDS start to 50bp downstream of CDS start , Fig 4E , grey bars ) . However , two-thirds of orphan MTases are associated with a significant enrichment of unmethylated motifs in gene regulatory regions ( Fig 4E , black bars ) . Furthermore , unmethylated motifs are especially enriched in the promoters of genes of related function , most notably transcriptional regulators ( Fig 4E ) . For example , in Nocardia sp BMG111209 , unmethylated 5’-ATCGm6AT-3’ motifs are 5-fold enriched in gene regulatory regions , compared with fully methylated motifs ( 17/28 ( 61% ) , compared to 13% by chance ) . This enrichment increases to more than 20-fold for unmethylated sites upstream of transcriptional regulators ( 7/28 ( 25% ) unmethylated motifs compared with only 1 . 2% methylated motifs , p < 0 . 01 ) . Finally , at least in the case of dam methylases in gammaproteobacteria , unmethylated motifs overlap predicted transcription factor binding sites significantly more frequently than do methylated motifs ( S10 Fig and S5 Table ) . Overall , these results demonstrate a substantial enrichment of unmethylated motifs in regulatory regions of the genome . Since this enrichment is not merely a consequence of an elevated density of motifs in these regions , it may instead reflect the involvement of these sites in regulatory processes . The patterns of novel orphan MTases ( including ‘singleton’ MTases ) resemble those of the known MTases Dam and CcrM , further supporting the possibility that they may have shared functions in the epigenetic control of gene expression . While our analyses are generally consistent with a role for orphan DNA MTases in gene regulation , it is unclear which unmethylated sites represent targets of regulation . Indeed , previous studies of unmethylated sites have shown that while some sites are important in regulating gene expression , others may represent inconsequential blocking of DNA methylation by tightly bound transcription factors [41 , 42] . We therefore sought to prioritize our data to identify individual cases of putative regulation . We first searched for unmethylated motifs at the same genomic location across multiple related organisms . This analysis revealed 14 candidate regulatory sites across 5 different orphan MTases ( Table 1 ) . Among conserved unmethylated sites is one upstream of the glucitol/sorbitol specific PTS system ( gut locus ) . This site was previously identified in E . coli , and appeared to have no impact of gene regulation [41] , nevertheless the absence of methylation at this locus is strikingly well conserved across the Enterobacteria in our study ( S11 Fig ) . We identified eight other Dam motifs at conserved locations and unmethylated in at least two Gammaproteobacteria ( Table 1 and Fig 5A ) . We also identified conserved sites in association with three novel orphan MTases . For example , we identify conserved unmethylated sites upstream of a PadR family transcriptional regulator in both Arthrobacter species , and show that the motif in question is extensively conserved across the Arthrobacter genus ( Fig 5B ) . We next searched for the presence of unusual clusters of adjacent unmethylated motifs in related regions of the gene regulatory region , and identified seven potential regulatory regions across six orphan MTases ( Table 2 ) . Among these regions are known regulatory sites upstream of the agn43 locus in E . coli [39] , supporting the validity of this approach for finding true regulatory sites . We also identified a novel cluster of unmethylated Dam target sites upstream of a TonB-dependent receptor and putative iron uptake operon in E . coli . In addition , we identify clusters of unmethylated sites in association with three novel orphan MTases . These include a cluster of sites upstream of a GntR family transcriptional regulator and putative sugar utilization operon in Spirochaeta smaragdinae ( Fig 5C ) . More unusually we observe an extended region of reduced methylation along the entire length of an RPS synthesis gene in Nocardia sp . BMG51109 ( Fig 5D ) . Umethylated Dam motif sites are located at predicted transcription factor binding sites ( S11 Fig ) . In summary , both known and novel orphan MTases are associated with a signature of unmethylated sites in regulatory regions of the genome . Many of these sites show evidence of evolutionary conservation and unmethylated sites are overall enriched near transcription start sites , both of which are hallmarks of gene regulatory sequences and support the notion that selective absence of methylation at MTase recognition sites plays a role in gene regulation . The orphan MTases Dam and CcrM are important regulators of genome replication in Proteobacteria . Regulation occurs through the differential recognition of fully methylated or hemi-methylated DNA by cellular machinery [35] . While such methylation patterns can in principle be determined from SMRT sequencing [21] , it requires sampling of DNA from synchronized cells , which was not performed for our study . Nonetheless , the availability of large numbers of novel orphan MTase specificities makes it possible for us to search for general patterns of motif distribution ( regardless of methylation state ) consistent with a role in DNA replication control . We therefore systematically searched our methylome datasets for enriched clusters of motifs in non-coding regions of the genome . We restricted our analyses to conserved orphan MTases , and retained only those clusters of motifs that occur at orthologous locations in multiple organisms . As these analyses do not require methylome data , initial patterns of motif clusters were subject to expanded analyses of all publicly available genome sequences from related organisms ( Methods ) . In total , we identified conserved clusters of motifs in non-coding regions of the genome in association with four orphan MTases ( p < 1e-5 , Methods ) . Strikingly , all cases were located at putative origins of replication ( Fig 6 ) . First , we observed enrichment of Dam MTase motifs at the origin of replication in Enterobacteria and other Gammaproteobacteria ( Fig 6A ) . The presence of motif clusters correlates strongly with the presence of Dam orthologs in the genome , consistent with the known role of Dam in regulating DNA replication [17] . We observe similar patterns of motif enrichment for orphan MTases recognizing 5’-TTAm6A-3’ in Arthrobacter , and 5’-CTCGAG-3’ in Nocardia ( Fig 6B and 6C respectively ) . In both cases , motif clusters occur in non-coding regions between bacterial replication genes dnaA and dna polIII [43] . Furthermore , the presence of motif clusters is again strongly correlated with the presence of the respective orphan MTase . Finally , we observe an analogous system associated with a conserved orphan MTase recognizing 5’-m4CATG-3’ motifs in Haloarchaea ( Fig 6D ) . In this case , motif clusters occur upstream of orc6/cdc1 gene orthologs which encode the origin of replication complex in archaea [44] . Furthermore , motif clusters are frequently detected upstream of multiple orc6/cdc1 genes in the same genome , consistent with the presence of multiple origins of replication [44] . Again , the presence of motif enrichment correlates with the presence of the orphan MTase . In summary , these analyses confirm a pattern of motif enrichment which co-occurs with the known regulators of DNA replication , and reveals three novel systems that share this pattern including an example of an orphan MTase with a potential role in regulating DNA replication or other functions in archaea .
Despite having potentially widespread functions , the global patterns of DNA methylation in prokaryotes are largely unexplored . Here , we obtain an initial overview of the epigenomic landscape of prokaryotes by single molecule sequencing the methylomes of 230 diverse bacteria and archaea . We find that methylation is pervasive , and present in at least 95% of the organisms we sequenced . We provide base-resolution methylation state information for every organism , and collectively identify over 800 methylated motifs , corresponding to the specificities of the MTases active in these organisms . Together these data massively expand the known repertoire of prokaryotic RM system specificities , and strongly suggest the presence of additional widespread functions of DNA methylation in prokaryotes . SMRT sequencing offers a powerful approach to determine the recognition specificities of several Types of RM systems that have previously been very difficult to decipher . Type I RM systems cleave DNA at large distances from their binding site , while both Type IIG and Type III systems sometimes have difficulties in producing complete cleavage patterns . This can make them difficult to study using traditional approaches that rely on analysis of patterns of restriction digestion . However , in each of these RM system types , DNA methylation and restriction share specificity determinants such that identification of the MTase specificity automatically reveals the specificity of the cognate restriction enzyme . Furthermore , the type of methylation used by these RM systems is nearly always either m4C or m6A , both of which are readily detected by SMRT sequencing . Here , we realize this possibility by determining the specificities of 264 MTases from these RM systems , more than doubling the number of known specificities . Furthermore , the diversity of sequence specificities we reveal is astounding , with the vast majority ( 85% ) of specificities currently unique . Type II restriction enzymes have the property of recognizing a sequence and cleaving within or very close to it . This property provided a very simple experimental approach to specificity determination with the result that several thousand such specificities had been determined . Detailed experimental studies of a small number of examples suggested that their companion MTases would have the same specificity . In the present study , we show that this is generally true as abundantly exemplified by motifs recognized by Type II systems that have the clean specificity we have come to associate with Type II REases . Surprisingly , other than the Type IIG subtype , we detected very few new Type II MTase specificities , suggesting that extensive previous searches for Type II restriction enzymes for use as reagents already uncovered the majority of specificities that are present in nature . Since the phenomenon of increasing numbers of novel specificities being discovered is found among the Type I , Type IIG and Type III , but not Type II RM systems , the use of a single specificity system to guide both restriction and modification may be a key strategy employed by prokaryotes to build defense systems that ensure diversity . Our analyses also reveal abundant DNA methylation occurring independently of RM systems . It is known that ‘orphan’ MTases are common in prokaryotic genomes , but beyond a handful of well-studied regulatory MTases there is little evidence that they are active , and the general importance of prokaryotic DNA methylation beyond RM systems remains unclear . Here , we confirm the activity and sequence specificity of over 100 novel orphan Type II MTases , with at least one such active gene detected in 48% of organisms , covering 15/20 ( 75% ) phyla included in this study . Thus , there appears to be widespread prokaryotic DNA methylation beyond that involved in RM systems . In general , orphan Type II MTases are associated with patterns of incomplete methylation of their target sites , clearly discriminating them from RM system MTases . The unmethylated sites associated with orphan MTases are frequently in non-coding sequences upstream of genes , thus hinting at potential regulatory roles . Furthermore , we frequently observe that both the orphan MTases and their associated patterns of methylation are conserved across related organisms . While the functions of these systems remain to be determined , we provide evidence that several MTases may function analogously to Dam and CcrM MTases and play a role in gene regulation . For example , we identify novel conserved MTases with putative gene regulatory roles in the phyla Spirochaetae , and Actinobacteria , and a conserved MTase family with a putative role in regulating DNA replication in Haloarchaea . To our knowledge , this is the first example of regulatory DNA methylation in Archaea . Together , this suggests that genome regulation may be one of the functions of the non-RM system DNA methylation that we observe . There is reason to believe that the amount of DNA methylation , and its potential functions extend beyond those highlighted in our study . For example , Type II m5C MTases are abundant , and often appear to be orphans , but are not easily detectable by SMRT sequencing . Other MTases , such as those on prophages , appear to be inactive , but may be functional under other conditions . Furthermore , the functions of other Types of MTases may extend beyond their roles in restriction . For example , the functions of Type IIG systems are overall unclear but have recently been shown to include antiviral defense by a system that appears not to involve endonucleolytic cleavage [34] . Similarly , Type III restriction systems have been demonstrated to have important regulatory roles in phase variation [45–47] . The large numbers of novel RM systems and associated methylome data from this study will be a valuable resource for further exploration in this area . Given the extensive amount of methylation present in the majority of the genomes we have examined , it is tempting to believe that methylation is a very important modification of bacterial and archaeal DNA perhaps providing regulatory functions that we have yet to fully appreciate . Additionally , it is reasonable to assume that the evolution of DNA methylation was an early event that was important for the viability of primitive organisms . Since methylation must have preceded the evolution of restriction enzymes , it is possible that restriction enzymes evolved not initially to provide protection against bacteriophages , but rather to ensure that the methylases remained active . Their value in protecting against external threats may have been a coincidental benefit . In this scenario DNA methylation suddenly becomes a key , yet still poorly understood , component of bacterial and archaeal life–one that perhaps plays a much deeper role in prokaryotic life than we currently appreciate . In conclusion , methylome data is now easily obtained as a direct result of SMRT-sequencing , and potentially other technologies [48 , 49] . Our study demonstrates the capacity of this approach to illuminate epigenomic phenomena in prokaryotes . Since many RM systems and orphan MTases are transferred from one organism to another by horizontal transfer [50] , it seems likely that they will have significant effects on microbiomes . Our study highlights not only the importance of methylation studies , but provides initial insights into the kind of diversity that can be expected . There are undoubtedly some major discoveries to be made in this field as we delve into the details of methylation in individual organisms .
The 230 target organisms were selected based on i ) phylogenetic diversity , ii ) relevance to D . O . E mission areas in bioenergy and the environment , iii ) the presence of interesting and potentially interpretable RM systems . DNA samples were obtained from commercial sources ( American Type Culture Collection , ATCC or DSMZ ) , or from contributions to the JGI community-sequencing program ( http://jgi . doe . gov/collaborate-with-jgi/community-science-program/ ) . A complete list of bacterial strain information and DNA sources is provided in S1 Table . All sequenced organisms have publicly available reference genome sequences , and gene annotation files deposited in NCBI and IMG [51] . Accession numbers and summary statistics of reference genomes used in the analyses are provided in S1 Table . The majority of reference sequences were complete ( assembled into a single circular molecule ) , with more than 90% genomes containing less than 10 scaffolds . The inclusion of data from draft genome sequences increases our overall yield of MTase gene annotations , but limits the ability to comprehensively annotate the methylome in all cases . SMRT sequencing was performed using a library construction protocol described previously [52] . Libraries were sequenced on the Pacific Biosciences RS instrument using either C2 , C3 or C4 chemistries . The average SMRT sequence coverage per genome was 130x ( ranging from 31x to over 500x ) , with an average sub-read length of 1 . 8kb . Sequencing chemistries and sequencing yields for each DNA sample are summarized in S1 Table . DNA modification detection and motif analysis were performed using the PacBio SMRT analysis platform ( protocol version = 2 . 2 . 0 method = RS Modification and Motif Analysis . 1 , http://www . pacb . com/devnet/code . html ) . Briefly , raw reads were filtered using SFilter , to remove short reads and reads derived from sequencing adapters . Filtered reads were aligned to the reference genome using BLASR ( v1 ) [53] . Modified sites were then identified through kinetic analysis of the aligned DNA sequence data [32] . Modified sites were then grouped into motifs using MotifFinder ( v1 ) 2 . These motifs represent the recognition sequences of MTase genes active in the genome [54] . All kinetic data files have been deposited in GEO under accession numbers GSE69872 , available for review using the following link: http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=ufapcaooxtcdbal&acc=GSE69872 The full list of identified modified motifs are in S2 Table . Restriction-Modification ( RM ) genes were assigned using the SEQWARE computer resource ( Clark et al . 2012; Murray et al . 2012 ) . It comprises a large suite of program modules with specialized databases containing compilations of protein sequences of bona-fide M system components as well as non-RM system components to weed out false positives . On a daily basis , newly published sequences are collected from GenBank and downloaded for analysis by SEQWARE and incorporation into the SEQWARE databases . Many routines that run in parallel scan the new data , queue different inspection steps , and depending on the preliminary findings , pipe data into further , more detailed analysis loops . First , conserved elements of new RM systems are identified by sequence matches to known RM system genes . Most often these are MTases . Characteristic motifs [55 , 56] of the newly detected items are located , and functional domains are mapped . From these , the Type and subtype of the inspected system are inferred , as well as the identity of potentially missing components–most often restriction enzyme genes . Genes for these missing components are then picked by a contextual analysis , where attribution is guided by marginal similarities in Type/subtype characteristic component order , while skipping genes that show better matches to non-RM system genes . Homologs that harbor non-RM functions ( e . g . RNA MTases ) generate many false hits in this first round of analysis , but are then filtered out by further matching to a library of known false positives . Occasionally , fusions of RM system genes to genes of unrelated enzymes are observed . To avoid false hits produced solely by the fused parts , the non-RM system domains are masked in the search library . Newly detected systems are prepared for expert review . Items are annotated , and background supporting materials are prepared . These include hit lists , summary tables , schematics , plots and selectable alternatives for the resolution of undecided issues ( e . g . handling of frame shifts ) . Following the curator’s decisions , results are recorded , and the internal databases are rebuilt for the next round of discovery . The program suite part of SEQWARE changes frequently as new modules are incorporated to accommodate new kinds of relevant data ( shotgun , methylome ) , and as our understanding of RM systems expands . SEQWARE has been a prime supporting engine of restriction enzyme research for the last 20 years and is responsible for the bulk of the expansion of REBASE [5] . RM system gene annotations are summarized in S2 Table . MTases without detectable RM genes in the flanking genome sequence were cautiously annotated as ‘candidate orphan’ MTases . Since restriction enzymes can be hard to identify , we cannot firmly conclude that a cognate restriction enzyme gene does not exist , and therefore some of these candidate orphan MTases may in reality be part of RM systems . However , our observation that many candidate orphan MTases exhibit incomplete modification of their genomes ( Fig 4 ) is one line of evidence to suggest that the majority of these annotations are correct . In general , the sequence specificity of each putative MTase gene was predicted based on significant similarity to MTase genes of known specificity . Whenever such a gene was present and a motif of the same specificity was found , then the MTase gene was assumed to be responsible , unless more than one candidate MTase gene of the same specificity was present in which case no match was called . In the case of Type I , Type IIG and Type III genes in many cases only a single candidate gene was present for the particular kind of motif observed . Thus , for Type I genes , the recognition sequences are characteristically bipartite and usually asymmetric . For both Type IIG and Type III MTases methylation is only present on one strand . Again if only one gene was present then it could be matched unambiguously to the motif . In some cases , for Type I systems one half of the recognition sequence would match half of a known specificity in another organism . Often , this would then permit matching of the appropriate S subunit to the motif . In all cases where there were no clear and unambiguous matches , the motif was marked as unmatched ( see S3 Table and S4 Table ) . In some cases reasonable guesses could be made and these are indicated in S3 Table and S4 Table by putting the motifs and the genes likely to match them in parentheses . Nine enzymes have been characterized as restriction enzymes . Three typical Type IIG single polypeptide REase-MTase proteins have been cloned and their recognition motif determined from their endonuclease cleavage patterns: SdeAI and PliMI from the MmeI family , and RpaI , a representative of the TaqII family [33] . Similarly the endonuclease genes for the MjaI , MjaII , MjaIII , MjaIV and MjaV systems from Methanocaldococcus jannachii DSM 2661 as well as Csp12AI from Clostridium sp . 12 ( A ) , have been cloned , expressed and characterized through their endonuclease activity . The modified base and recognition motif for three MTases by cloning the MTase gene into the non-modifying host ER2796 followed by SMRT sequencing and analysis using methods described in Murray et al . 2012 ( 8 ) . In this way we also characterized the Type III MTase M . Nme18I which recognizes ACm6ACC [27] , and M1 . Csp12AI and M2 . Csp12AI from Clostridium sp . 12 ( A ) . Amino acid sequences of all annotated Type II MTases were obtained from IMG and used as queries in BLASTP ( blastall v2 . 2 . 26 ) searches of protein sequence databases of 35 , 184 bacterial and archaeal genomes in IMG . Database hits with a similarity score of 35 or more ( where similarity score = 100* ( bitscore of hit to database / bitscore of hit to self ) ) were considered potential orthologs of the MTase . Taxonomy information for all database genomes was also obtained from IMG , and used to determine the fraction of organisms across each taxonomic category with a potential MTase ortholog . MTases with orthologs in >50% of species from a taxonomic group were considered ‘conserved’ . Amino acid sequences of all annotated Type II MTases were obtained from IMG , and split into two groups according to base methylation type ( m6A or m4C and m5C ) . For each group , all versus all alignments were performed with usearch , v8 . 0 . 1616_i86linux32 [57] , using the search_global command ( with parameters–fulldp–id 0 –uc ) . Initial clusters of related MTases were identified using usearch–cluster_agg ( with parameters -id 0 . 35 -linkage min–fulldp . Using custom perl scripts , MTases were annotated with taxonomic classification of the host organism , presence or absence of cognate REase , and motif specificity . Annotated MTase clusters were then manually inspected to identify individual sub-clusters of orphan MTases with identical or closely related specificities from taxonomically related organisms . The resulting sub-clusters represent putative orphan MTase families . The ipdR ( inter-pulse duration ratio ) is the primary metric in DNA modification detection . It corresponds to the time delay in incorporation of successive bases in a sample versus an unmodified control . Unmethylated motifs were identified using inter pulse duration ratio ( ipdR ) measurements [32] , and read coverage . For each methylated motif , an ‘under-methylated’ ipdR threshold was determined by comparison of ipdR scores of bases in methylated motifs with those in unmethylated , non-motif sequences . ipdR scores for all motif sites in the genome were ranked , and an average motif ipdR calculated across the central 60% values ( to minimize the effect of unmethylated sites or other outliers ) . The average non-motif ipdR was similarly calculated from the central 60% of ranked ipdR scores from all bases of the same type in non-motif sequences in the genome . The under-methylated ipdR threshold was then defined as ( 0 . 1*average motif ipdR ) + ( 0 . 9*average non-motif ipdR ) , i . e . an approximation of the idpR score if 10% of bases were methylated . For comparison , a ‘methylated’ ipdR threshold was defined as ( 0 . 5*average motif ipdR ) + ( 0 . 5*average non-motif ipdR ) , i . e . an approximation of the idpR score if at least half of bases were methylated . Analysis of unmethylated motifs was only performed for palindromic Type II motifs that have two methylated sites ( one on each strand of the genome ) . Motif instances were considered ‘unmethylated’ if both potential methylated bases had at least twenty-fold SMRT sequence coverage , and an ipdR less than the ‘under-methylated’ threshold . Importantly , the average SMRT sequence coverage at unmethylated sites is no different from that at methylated sites ( S4 Fig ) . They are therefore high-confidence unmethylated sites , and not simply borderline cases at the thresholds for inclusion in the analysis . Gene regulatory regions were defined as 100bp upstream of the CDS start to 50bp downstream of the CDS start . Fold enrichment of all motif sequences in gene regulatory regions was determined by comparison with the average fraction of randomized control sequences in regulatory regions ( 1000 random samplings of an equal number of sites in the genome with the same length and nucleotide composition as the modified motif ) . Fold enrichment of unmethylated motifs in gene regulatory regions was determined by comparison with the fraction of methylated motifs in regulatory regions ( sites with ipdR scores greater than the ‘methylated’ threshold defined above ) . Significance of enrichment was determined using Fisher’s exact test . To determine the potential enrichment of unmethylated motifs for specific functional classes of genes , we repeated these analyses using individual subsets of regulatory regions grouped according to the COG category annotations [58] of their corresponding genes . For each orphan MTase family associated with incomplete methylation of the genome ( Fig 4 ) , we identified conserved unmethylated sites based on conservation of flanking gene sequences ( Table 1 ) . For every unmethylated site , we took the amino acid sequences of the two flanking genes , and identified best hits in each of the other genomes using BLASTP . Pairs of unmethylated sites across the two genomes were considered conserved if their flanking genes were reciprocal best hits in the respective other genome . For select conserved sites , genomic DNA sequences upstream of the putative target gene were subject to multiple alignment using MAFFT [59] and visualized in Jalview [60] . To identify potential overlap between unmethylated Dam GATC motifs and transcription factor binding sites , we obtained curated transcription factor binding site ( TFBS ) probability matrices from http://regtransbase . lbl . gov [61] , and searched for matches to these matrices using MAST [62] . We restricted our search to TFBS that were identified in gammaproteobaceria ( using the taxonomic classifications provided by regtransbase ) , and to gammaproteobacerial genomes from our study that contain at least 4 unmethylated Dam motif sites . We identified overlap between predicted TFBSs and unmethylated Dam motifs using bedtools [63] . Candidate enriched clusters of unmethylated motifs were identified as regions of the genome containing at least 3 consecutive unmethylated motifs , each separated from its nearest neighbor by less than the genome-wide average distance between motifs ( Table 2 ) . A cluster of unmethylated sites was considered significant if the probability of observing such a series of consecutive sites by chance was < = 0 . 01 , based on 10 , 000 iterations of randomly sampling n = ( number of unmethylated sites ) times from an ordered list of length l = ( total motif sites ) . We searched for unusual clusters of motifs ( regardless of methylation state ) in non-coding regions of the genome ( defined using IMG gene annotations of coding DNA sequences , and excluding RNA gene annotations ) . For each non-coding region , we calculated the local non-coding motif density ( motifs / bp ) across the 100 flanking non-coding regions . The expected number of motifs in that region was then estimated as ( non-coding region length ) * ( local motif density ) . Fold enrichment was determined as observed number of motifs / expected number of motifs . P-values were determined using the pbinom function in R ( p_val = 1-pbinom ( observed number of motifs , non-coding region length , expected number of motifs ) ) , and subject to Bonferroni correction using the number of non-coding regions in the genome . A p-value threshold of 1e-5 was used to identify non-coding regions with motif enrichment . We identified patterns of non-coding motif enrichment conserved across organisms , based on reciprocal best BLAST hits of flanking genes ( as described above ) . For methylated motifs showing conserved patterns of motif enrichment , we extended our analyses to other sequenced genomes in the same taxonomic class ( Fig 6 ) . For each genome , we calculated motif density in 500bp windows with a 50bp step-size across a 50kb region of the genome centered on the start or end of the gene closest to the motif cluster . BLAST was used to search each genome for orthologs of the responsible MTase , and determine correlation between presence of MTase and non-coding motif clusters .
|
DNA methylation is a chemical modification of DNA present in many prokaryotic genomes . The best-known role of DNA methylation is as a component of restriction-modification systems . In these systems , restriction enzymes target foreign DNA for cleavage , while DNA methylation protects the host genome from destruction . Studies in a handful of organisms show that DNA methylation may also act independently of restriction systems and function in genome regulation . However , a lack of technologies has limited the study of DNA methylation to a small number of organisms , and the broader patterns and functions of DNA methylation remain unknown . Here we use SMRT-sequencing to determine the genome wide DNA methylation patterns of more than 200 diverse bacteria and archaea . We show that DNA methylation is pervasive and present in more than 90% of studied organisms . Analysis of this data enabled annotation of the specific DNA binding sites of more than 600 restriction systems , revealing their extraordinary diversity . Strikingly , we observed widespread DNA methylation in the absence of restriction systems . Analyses of these patterns reveal that they are conserved through evolution , and likely function in genome regulation . Thus DNA methylation may play a far wider function in prokaryotic genome biology than was previously supposed .
|
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2016
|
The Epigenomic Landscape of Prokaryotes
|
Non-enveloped viruses penetrate host membranes to infect cells . A cell-based assay was used to probe the endoplasmic reticulum ( ER ) -to-cytosol membrane transport of the non-enveloped SV40 . We found that , upon ER arrival , SV40 is released into the lumen and undergoes sequential disulfide bond disruptions to reach the cytosol . However , despite these ER-dependent conformational changes , SV40 crosses the ER membrane as a large and intact particle consisting of the VP1 coat , the internal components VP2 , VP3 , and the genome . This large particle subsequently disassembles in the cytosol . Mutant virus and inhibitor studies demonstrate VP3 and likely the viral genome , as well as cellular proteasome , control ER-to-cytosol transport . Our results identify the sequence of events , as well as virus and host components , that regulate ER membrane penetration . They also suggest that the ER membrane supports passage of a large particle , potentially through either a sizeable protein-conducting channel or the lipid bilayer .
The mechanism by which non-enveloped viruses such as simian virus 40 ( SV40 ) and the murine polyomavirus ( mPy ) penetrate the host cell's membrane to cause infection is enigmatic . However , a general model describing how they breach this membrane based largely on in vitro studies is emerging [1] , [2] . In this model , the virus undergoes conformational changes by interacting with host factors , culminating in the formation of a hydrophobic viral particle or release of a lytic peptide . They then engage the limiting membrane to disrupt its integrity , enabling the virus to cross the membrane . As it is unknown whether this scenario reflects the pathway in cells , establishing a cell-based assay that monitors non-enveloped virus membrane penetration affords the opportunity to study this event's physiological mechanism . Important questions include: what reaction sequence initiates membrane penetration ? What is the nature of the viral conformational change and identity of the membrane penetrating species ? What viral and host components control the penetration process , and how is membrane transport achieved ? Here we address SV40's membrane transport process . Structurally , SV40 is composed of 72 pentamers of the VP1 coat assembled into an icosahedral viral capsid [3] , [4] . Each VP1 pentamer engages the internal proteins VP2 and VP3 through hydrophobic interactions [5] . VP1 also binds to the ∼5 kb viral DNA genome buried within the virus through electrostatic interactions . Three additional forces support the overall viral architecture . First , disulfide bonds present throughout the virus stabilize it [4] . Second , the VP1 C-terminus invades a neighboring VP1 pentamer to provide inter-pentamer support [3] . And third , calciums bound to the virus clamp together different pentamers to increase capsid stabilization [4] . To infect cells , SV40 VP1 binds to the glycolipid ganglioside GM1 on the host cell surface [6] , inducing membrane tubulation that initiates internalization [7] . The virus-receptor complex is then transported to the pH neutral caveosomes [8] or the low pH endolysosomes [9] . Regardless of the pathway , the virus subsequently sorts to the endoplasmic reticulum ( ER ) . Upon arrival of the virus-receptor complex to the ER [10] , SV40 is proposed to disassemble to cross the ER membrane and reach the cytosol [11] . From the cytosol , a subviral core particle transports into the nucleus where transcription and replication of the viral DNA ensue , leading to lytic infection or cell transformation . Reactions controlling SV40's ER-to-cytosol transport , a decisive infection event , are not fully understood . How do ER-initiated events propel the virus to the cytosol ? What is the identity of the membrane penetrating species ? What viral , ER , and cytosolic components regulate this process ? While a report suggests that the ER associated degradation ( ERAD ) machinery mediates SV40 infection [12] , how this machinery geared normally to handle endogenous proteins much smaller than SV40 ( ∼50 nm in diameter ) promotes membrane transport of the larger viral particle is unclear . Here we established a cell-based assay to elucidate SV40's ER-to-cytosol membrane penetration . Our data demonstrate that , upon ER arrival , SV40 is released into the ER lumen and undergoes sequential disulfide bond modification as it moves to the cytosol . Despite these reactions , a large and intact SV40 intermediate penetrates the ER membrane to reach the cytosol where it disassembles . We also pinpoint viral and host components that regulate the penetration process . This assay thus provides the opportunity to illuminate SV40's membrane penetration mechanism in a cellular setting .
We first tested whether brefeldin A ( BFA ) , a drug that can impede COPI-dependent retrograde transport from the cell surface to the ER , blocks arrival of SV40 to the ER and infection as reported previously [11] , [13] . A convenient method to measure SV40 ER arrival is to monitor conformational changes imparted on the virus in the ER . For instance , when SV40 arrives in the ER , ER-resident protein disulfide isomerase ( PDI ) family members disrupt its disulfide bonds [12] . When a whole cell extract ( WCE ) derived from infected cells was analyzed by non-reducing SDS-PAGE , VP1 monomer was detected [12] . Accordingly , simian CV-1 cells were incubated with SV40 ( m . o . i . 30 ) for 12 hrs at 37°C . The cells were solubilized with SDS to generate a WCE , and the samples analyzed by non-reducing SDS-PAGE followed by immunoblotting with VP1-specific antibodies . We detected formation of both VP1 monomer and a species whose size corresponds to a VP1 dimer ( Figure 1A , lane 1 ) . An additional VP1 species at the top of the gel was also detected , which is likely derived from the intact virus . The VP1 monomer and dimer levels decreased when cells were treated with BFA at infection ( 0 h . p . i . ) ( Figure 1A , compare lane 2 to 1 ) . A similar VP1 monomer level was observed when the samples were subjected to reducing SDS-PAGE ( Figure 1A , compare lanes 3 and 4 ) . BFA was added to cells 4 hrs post infection ( 4 h . p . i . ) to avoid perturbing viral entry . After 8 additional hrs , cells were harvested and analyzed as above . Under this condition , we found that the VP1 monomer and dimer levels also decreased when compared to control cells ( Figure S1A , top panel , compare lane 2 to 1 ) , indicating that BFA likely acted at an intracellular step required for ER sorting . Analyses using confocal microscopy further demonstrated that when cells were treated with BFA 4 h . p . i . , co-localization between SV40 ( green ) and ER ( red ) decreased ( Figure S1B , compare right and left panels ) . Collectively , these results indicate that ER transport is required to generate VP1 monomer and dimer . To assess BFA's effect on viral infection , control and BFA-treated cells were incubated with SV40 , and immunofluorescence microscopy was used to score expression of the virally encoded T antigen ( TAg ) in the nucleus as before [14] . We found that BFA decreased SV40 infection potently ( Figure 1B ) . This result demonstrates that ER transport is critical for SV40 infection , consistent with previous observations [11] , [13] . Thus BFA blocks SV40 trafficking to the ER and infection . To establish an ER-to-cytosol transport assay for SV40 , outlined in Figure 1C , we modified our semi-permeabilized cell-based assay developed previously to probe translocation of cholera toxin ( CT ) from the ER to the cytosol [15] . In this modified assay , SV40-infected CV-1 cells were treated with a low digitonin concentration ( 0 . 1% ) to gently permeabilize the plasma membrane while leaving intracellular membranes , including the ER membrane , intact ( Figure 1C , step 1 ) . The permeabilized cells were centrifuged at medium-speed ( 16 , 000 g ) to generate two fractions: a supernatant fraction ( S1 ) that should contain cytosolic proteins , virus that reached the cytosol from the ER , and any endosomal vesicles harboring virus that did not sediment at the medium-speed spin , and a pellet fraction ( P1 ) that should contain the plasma membrane , intracellular organelles including the ER and nucleus , and SV40 that either did not undergo ER-to-cytosol transport or did but is further imported into the nucleus . P1 contents were extracted by Triton X-100 and SDS . When S1 and P1 were subjected to reducing SDS-PAGE followed by immunoblotting , we found the cytosolic marker Hsp90 is predominantly in the S1 ( Figure 1D , compare second and fifth panels from top ) , while the ER lumenal protein PDI was present only in the P1 ( Figure 1D , compare 6th and 3rd panels from top ) . Similar to Hsp90 , the cytosolic protein actin also appeared in S1 but not P1 using this fractionation method ( Figure S1C , top and bottom panels , compare lane 1 to 2 ) . Hence , this one-step fractionation procedure efficiently separates cytosolic from ER contents , similar to our previous report [15] . When cells were incubated with wild-type ( WT ) SV40 at 4°C , a condition that blocks endocytosis , and the cells subjected to the fractionation procedure , no VP1 was detected in the S1 ( Figure 1D , lane 1 , compare first and fourth panels from top ) . In contrast , when the cells were incubated with SV40 at 37°C for 8 hrs ( 8 h . p . i . ) to allow entry , a portion of VP1 was found in the S1 ( Figure 1D , lane 2 , compare first and fourth panels from top ) . When cells treated with BFA at infection ( 0 h . p . i . ) were incubated with SV40 at 37°C for 8 hrs , the VP1 level present in the S1 decreased ( Figure 1D , top panel , compare lanes 3 to 2 ) . Similar results were observed when cells were incubated with SV40 at 37°C for 10 hrs and 12 hrs: for both time points , appearance of SV40 in the S1 was blocked significantly by BFA ( Figure 1D , top panel , compare lanes 5 to 4 and lanes 7 to 6 ) . Moreover , when BFA was added to cells 4 h . p . i . and the cells harvested after 8 additional hours , the S1 VP1 level also decreased significantly ( Figure 1E , top panel , compare lane 2 to 1 ) . Thus , by blocking ER arrival ( Figure 1A , S1A , and S1B ) , BFA also attenuates the subsequent ER-to-cytosol transport of SV40 . We showed previously that BFA also blocked ER-to-cytosol transport of CT [15] , [16] . To intoxicate cells , CT via its B subunit ( CTB ) binds to GM1 on the cell surface , becomes rapidly endocytosed into invaginating vesicles , transported to the early and recycling endosomes , then followed by retrograde sorting through the Golgi and to the ER [17] . In the ER , the catalytic CTA1 undergoes ER-to-cytosol translocation to reach the cytosol where the toxin induces cytotoxicity . We had demonstrated that BFA blocked ER-to-cytosol transport of CTA1 in both HeLa [15] and 293T [16] cells . Here , when CV-1 cells treated with BFA at intoxication were subjected to the semi-permeabilized assay ( Figure 1C ) , the S1 CTA1 level ( analyzed 90 min post-intoxication ) was significantly decreased when compared to control cells ( Figure S1D , top panel , compare lane 1 to 2 ) . This finding is consistent with our previous findings [15] , [16] and further substantiates BFA's ability to generally perturb ER-to-cytosol transport processes by disrupting ER arrival . As SV40 also relies on a nocodazole-sensitive step to reach the ER critical for infection [8] , we showed that when cells were treated with nocodazole at infection , the S1 VP1 level 12 h . p . i . was blocked completely when compared to control cells ( Figure 1F , top panel , compare lane 1 to 2 ) . Hence nocodazole effectively perturbed SV40's ER-to-cytosol transport , presumably by blocking viral transport to the ER . A more detailed time-course experiment using the semi-permeabilized system demonstrated that significant VP1 level started to appear in the S1 approximately 6 h . p . i . , although a low VP1 level appeared in the S1 at 4 h . p . i . ( Figure S1E , top panel ) . Because a previous study demonstrated that SV40 arrives to the ER approximately 6 h . p . i . [18] , the low VP1 level in the S1 at 4 h . p . i . is unlikely virus that underwent ER-to-cytosol transport . Instead , it may represent virus that either leaked from a membrane compartment due to digitonin treatment or in transport vesicles en route to the ER which did not pellet after medium-speed centrifugation . To test the former possibility , we asked whether digitonin causes leakage of CTB from membrane vesicles . CTB is used because it is much smaller than SV40 , binds to ganglioside GM1 ( akin to VP1 ) , and is also targeted to the ER similar to SV40 . Accordingly , cells were intoxicated with CT for either 5 min ( where CTB is found in vesicles/endosomes ) or 90 min ( where CTB is found in a mixture of endosomes , Golgi , and ER ) . Following digitonin treatment , cells were subjected to 16 , 000 g medium-speed centrifugation to generate S1 ( Figure S1F , see diagram and top and bottom panels , lane 1 ) . S1 was treated with or without 2% SDS and subjected to high-speed centrifugation ( 100 , 000 g ) to generate a supernatant ( sn ) and pellet fractions . Under this condition , vesicles harboring CTB should pellet , while CTB that leaked due to membrane rupture by digitonin should appear in the sn . We found that , at both time points , CTB appeared only in the pellet but not the sn ( Figure S1F , top and bottom panels , compare lane 5 to 3 ) . If SDS was added to S1 to artificially solubilize vesicles prior to high-speed centrifugation , CTB appeared in the sn but not pellet instead ( Figure S1F , top and bottom panels , compare lane 6 to 4 ) . We conclude that digitonin treatment did not cause CTB leakage from vesicles . Thus , because CTB is much smaller than SV40 , it is unlikely that digitonin disrupted any membrane vesicles to cause leakage of SV40 . To test the idea that VP1 in the S1 at 4 h . p . i . represents SV40 in transport vesicles that did not sediment after medium-speed centrifugation , we first used limited proteolysis because this method distinguishes between membrane-encased virus versus naked virus . Because of the low VP1 level in the S1 at 4 h . p . i . , a higher amount of this sample was used to visualize VP1 . We found that VP1 in the S1 at 4 h . p . i . is resistant to trypsin digestion , in contrast to virus at 12 h . p . i . ( Figure S1G , compare top and bottom panels , lanes 1 to 2 and 3 ) . These findings indicate that SV40 in the S1 at 4 h . p . i . is likely contained in membrane vesicles , while those at 12 h . p . i . are not . To further support this view , we subjected SV40 in the S1 at both 4 and 12 h . p . i . , as well as purified WT SV40 , to OptiPrep gradient flotation . The majority of VP1 at 4 h . p . i . floated to lighter density fractions when compared to purified SV40 ( Figure S1H , compare top and bottom panels ) . In contrast , VP1 at 12 h . p . i . displayed very little flotation when compared to purified SV40 ( Figure S1H , compare middle and bottom panels ) . These results demonstrate that the low SV40 level in the S1 at 4 h . p . i . is membrane-bound , presumably reflecting transport vesicles carrying SV40 that have not arrived to the ER . By contrast , virus at 12 h . p . i . is naked and not in vesicles , consistent with the property of a viral particle that has penetrated the ER membrane . We conclude that VP1 in the S1 at the 12 h . p . i . time point , as well as at the earlier 8 and 10 h . p . i . time points ( see below ) , represents the virus pool that reached the cytosol from the ER . An increase in cytosol-localized SV40 should allow more viral particles to enter the nucleus to cause infection . We found that increasing the m . o . i . increased both the S1 VP1 level at 12 h . p . i . ( Figure S1I , top panel , lanes 1–6 ) and infection ( Figure S1I , bottom graph ) . This correlation is consistent with the view that virus in S1 at 12 h . p . i . represents cytosol-localized virus poised to enter the nucleus to promote infection . To further verify that the semi-permeabilized assay reflects SV40's ER-to-cytosol transport , we reasoned that down-regulation of ER-resident factors implicated in SV40 infection should block ER-to-cytosol transport as well . As ERp57 down-regulation decreased virus infection [12] , we showed that ERp57 knock-down also decreased the S1 VP1 level at 12 h . p . i . ( Figure 1G , top panel , compare lane 1 to 2 ) . Similarly , we found that down-regulation of a novel ER-resident DNA J protein required for efficient SV40 infection also decreased the amount of S1 VP1 ( manuscript in preparation ) . Finally , as treating cells with dithiothreitol ( DTT ) was shown to attenuate infection [12] , we found that DTT treatment decreased both the S1 SV40 level ( at 12 h . p . i . ) and infection ( Figure S1J , top panel , compare lane 2 to 1 , and right graph ) . These findings further validate the semi-permeabilized system as an ER-to-cytosol transport assay . In CV-1 cells , the earliest expression of new VP1 occurred at 20 h . p . i . ( Figure 1H , middle panel , arrow ) , consistent with an earlier report in the same cell line [19] . This finding demonstrates that VP1 in the S1 derived from cells incubated with SV40 for 8 , 10 , and 12 hrs ( Figure 1D , top panel , lanes 2 , 4 , and 6 ) is input but not de novo synthesized virus . We note that TAg expressed at 14 h . p . i . ( Figure 1H , top panel , arrow head ) , suggesting that only a small proportion of virus in the P1 at the 8 , 10 , and 12 h . p . i . time points represents nuclear-localized virus . When control and BFA-treated cells were incubated with a biotinylated SV40 for 12 hrs , and the cells subjected to the ER-to-cytosol transport assay , biotinylated VP1 ( as detected by streptavidin binding ) was detected in the S1 derived from control and to a lesser extent BFA-treated cells ( Figure S1K , top panel , compare lane 1 to 2 ) . This finding further proves that the input virus reaches the cytosol . Do other viral components undergo ER-to-cytosol transport ? In addition to immunoblotting , the S1 from control and BFA-treated cells infected with SV40 for 12 hrs were subjected to PCR analyses using primers designed to amplify an SV40 genome fragment . We found presence of the viral genome in S1 derived from control but not BFA-treated cells ( Figure 1I , top panel , compare lane 1 to 2 ) . Similarly , using a VP2/VP3-specific antibody , we detected VP2 and VP3 in S1 derived from control but not BFA-treated cells ( Figure 1J , top panel , compare lane 1 to 2 ) . The higher VP3 intensity when compare to VP2 is not due to preferential antibody binding to VP3 as VP2 contains all of VP3 except VP2 has an additional N-terminal extension . Instead , this observation is likely because the input SV40 particle contains more VP3 than VP2 ( below ) , similar to a previous report [20] . These results demonstrate that VP2 , VP3 , and the viral genome are co-transported with VP1 from the ER to the cytosol . We next analyzed ER events that prime SV40 for membrane penetration by taking further advantage of the semi-permeablized system . We hypothesize that , upon ER arrival , SV40 remains bound to GM1 on the lumenal surface of the ER membrane , as the related mPy associates with its ganglioside receptor GD1a when this virus reaches the ER [10] . We postulate that SV40 is next released into the lumen by detaching from GM1 . Here it undergoes conformational changes that enable the virus to re-engage the ER membrane , ultimately penetrating this bilayer to reach the cytosol . At steady state , there should be a virus pool attached to GM1 on the ER membrane , in the ER lumen , trapped on the ER membrane in the act of penetration , and in the cytosol . Analyzing specific SV40 conformations in each pool should reveal the sequence of events and the mechanism guiding membrane penetration . P1 in our assay ought to contain SV40 attached to GM1 on the ER membrane ( as well as on the plasma membrane and other organelles ) , in the ER lumen , and trapped on the ER membrane in transit to the cytosol . In contrast , S1 should contain virus that reached the cytosol ( or in transport vesicles at the earlier time point ) . Because GM1 is enriched in membrane microdomains referred to as lipid rafts [18] , SV40 attached to GM1 should localize to lipid rafts . Contents in this microdomain are often found to be resistant to Triton X-100 extraction [21] . Thus , SV40 that reaches the ER but remains bound to GM1 is resistant to Triton X-100 extraction , while those virus released into the ER lumen or trapped on the ER membrane en route to the cytosol are extracted by this detergent . Contents resistant to Triton X-100 extraction can be extracted by SDS . Accordingly , P1 derived from cells incubated with SV40 for varying times were solubilized with Triton X-100 ( Figure 1C , step 2 ) . After centrifugation , the resulting supernatant contains the Triton X-100 extractable material ( S2 ) , while the new pellet contains Triton X-100 insoluble material that was extracted by SDS ( P2 ) . The S2 and P2 samples were subjected to immunoblot analysis . We found that while VP1 is present in P2 throughout the entire course of the experiment ( Figure 2A , bottom panel , lanes 1–7 ) , VP1 only appeared in the S2 starting at 6 h . p . i . ( Figure 2A , top panel , compare lanes 4–7 to lanes 1–3 ) . Under these conditions , PDI and most of the ER membrane protein calnexin are found in S2 but not P2 ( Figure 2A , lane 9 and 10 , compare top and bottom panels ) , as expected for an ER lumenal and membrane protein not enriched in lipid rafts . VP1's appearance in S2 derived from cells incubated with virus for 12 hrs is blocked completely when cells are pretreated with BFA ( Figure 2A , top panel , compare lane 8 to 7 ) . S2 VP1 also decreased significantly if BFA is added 4 h . p . i . ( Figure 2A , top panel , compare lane 9 to 7 ) , again demonstrating that BFA blocked an intracellular step important for SV40 sorting to the ER . As a control , we found that CTB , which is also found in lipid raft-enriched membranes , remains exclusively in the P2 and not S2 ( Figure 2B , top panel , compare lane 2 to 1 ) , indicating that Triton X-100 did not non-specifically disrupt lipid raft membrane domains to release SV40 . These results demonstrate that ER transport is required to generate Triton X-100-extractable virus , consistent with the hypothesis that SV40 detaches from GM1 upon ER arrival . Thus , while SV40 in P2 represents virus concentrated in membrane rafts due to its interaction with GM1 , SV40 in S2 represents virus that reached the ER and is released into the ER lumen , either preparing for membrane penetration or trapped on the ER membrane in transit to the cytosol . SV40's appearance in the ER starting at 6 h . p . i . in this assay is in agreement with previous studies [12] , [18] , and is consistent with the notion that SV40 arrives in the cytosol after 6 h . p . i . ( Figure S1E , top panel ) . P2 , S2 , and S1 contain SV40 at different stages of membrane penetration . To examine the nature of SV40's disulfide bonds in these fractions , samples from the three fractions generated from cells infected with SV40 for 12 hrs were subjected to non-reducing SDS-PAGE followed by immunoblotting with VP1-specific antibodies . VP1 monomer , dimer , and virus at top of the gel were detected in P2 ( Figure 2C , top panel , lane 1 ) . In S2 , a faint species corresponding to a VP1 higher oligomer , dimer , and more monomer ( when compared to its P2 level ) were observed ( Figure 2C , top panel , lane 2 ) . By contrast , only VP1 monomer was detected in S1 ( Figure 2C , top panel , lane 3 ) . When all three fractions were subjected to reducing SDS-PAGE , VP1 monomer was the only species observed ( Figure 2C , bottom panel , lanes 1-3 ) . Thus , when the virus initially arrives in the ER attached to the membrane , disulfide bond disruption is initiated , generating VP1 monomer and dimer ( Figure 2C , lane 1 ) . When the virus is released into the ER lumen or becomes subsequently trapped on the ER membrane en route to the cytosol , intact virus is converted to the VP1 higher oligomer , and the dimer is further reduced to the monomer ( Figure 2B , compare lane 2 to 1 ) . Finally , upon cytosol arrival , complete disruption of the disulfide bonds ensues , generating VP1 monomer ( Figure 2B , compare lane 3 to 2 ) . These results demonstrate a sequential rearrangement of SV40's disulfide bonds as it moves from the ER to the cytosol . We note that as monomer and dimer were not detected in any of the fractions using non-SDS biochemical methods ( below ) , they likely still consist of VP1 pentamers that remain in contact with the core viral particle via non-covalent interactions . As complete disruption of disulfide bonds that generates VP1 monomer ( in a non-reducing SDS condition ) is a hallmark of cytosol-localized SV40 , we performed a time-course experiment using a non-reducing SDS-PAGE and showed that VP1 monomer appeared in S1 at approximately 8 h . p . i . ( Figure 2D , lanes 5–7 ) . These findings further support the assertion that SV40 begins to arrive to the cytosol sometime after 6 h . p . i . , also consistent with our measurement of SV40 ER arrival at approximately 6 h . p . i . The disulfide bond arrangement of ER- and cytosol-localized SV40 is distinct ( Figure 2B , top panel , compare lane 2 to 3 ) . However , whether this difference affects the global viral conformation is unknown . We therefore evaluated the virus structures in S1 and S2 using four independent biochemical approaches . We first used conformation-specific antibodies for this purpose . Two monoclonal VP1 antibodies ( CC10 and BC11 ) were shown to neutralize SV40 infection , but did not recognize denatured virus during immunoblotting [22] . We found that these antibodies precipitated the VP1 pentamer ( not shown ) . Hence , the CC10 and BC11 antibodies recognize structural features of the intact pentamer , but not unfolded virus whose epitopes critical for antibody recognition are disordered . We reasoned that , at a sub-saturating antibody concentration where there is insufficient antibody to bind to all available VP1 , a given CC10 or BC11 antibody should precipitate more VP1 if the virus is assembled and intact than disassembled and uncoated . In contrast , at a saturating antibody concentration , a similar VP1 level would be precipitated by the antibodies regardless of the viral structural state . Thus , using antibodies at a sub-saturation condition could potentially reveal the global structural state of SV40 . Accordingly , at 12 h . p . i . , cells were subjected to the semi-permeabilized assay , and virus in S1 and S2 immunoprecipitated with a mixture of increasing amounts of the VP1 monoclonal antibodies . VP1 in S1 precipitated less efficiently than VP1 in S2 when a low ( i . e . 0 . 04 µg ) level of antibodies was used ( Figure 3A , top panel , compare lane 1 to 4 ) . However , the difference in the precipitation efficiency gradually disappeared when higher levels of antibodies ( i . e . 0 . 2 and 1 µg ) were used ( Figure 3A , top panel , compare lanes 2 and 3 to lanes 5 and 6 ) . A control antibody did not precipitate VP1 from S2 ( Figure 3A , top panel , lane 8 ) . Thus , in our experimental conditions , 0 . 04 µg represents a sub-saturating antibody concentration in which differences between the structural organization of SV40 in S1 and S2 can be revealed . Specifically , that 0 . 04 µg of the SV40 antibodies precipitated less VP1 from S1 than S2 suggests that virus in S1 underwent disassembly . VP2/VP3 in S2 co-precipitated with VP1 specifically ( Figure 3B , top panel , compare lane 2 to 4 ) , with an efficiency similar to that observed when purified WT SV40 was used as the starting material ( Figure 3B , top panel , compare lane 2 to 6 ) . In addition , the SV40 genome also co-precipitated with VP1 from S2 specifically ( Figure 3C , compare lane 2 to 4 ) . In contrast , VP2 and VP3 in S1 co-precipitated weakly with VP1 when compared to the efficiency observed using purified WT SV40 ( Figure 3D , top panel , compare lane 1 to 3 ) , even when 5-fold more S1 than S2 was used for immunoprecipitation . The SV40 genome co-precipitated with VP1 in S1 specifically ( Figure 3E , compare lane 2 to 4 ) . Our results suggest that the ER-localized SV40 is more assembled and intact than the cytosol-localized virus , and retains strong binding to the internal viral components . The cytosol-localized virus likely experienced disassembly , and displays less interaction with its internal proteins . As a second method to probe SV40's conformations in the ER and cytosol , S1 and S2 prepared from cells infected with SV40 for 12 hrs were subjected to gel filtration analyses . Our data showed that essentially all the viral particles in S2 are found in fractions similar to purified WT SV40 ( estimated to be >660 kDa in our system due to resolution of the column ) ( Figure 4A , compare second and third panels from top ) . For simplicity , these viral particles are referred to as “large” particles ( Figure 4A ) . In contrast , a virus pool in S1 was found in fractions that corresponded to “small” particles approximating 150 kDa , while another portion was located in fractions corresponding to the large particle ( Figure 4A , top panel ) . The 150 kDa species likely represents the VP1 pentamer . These results demonstrate that all the SV40 particles in the ER are large , while virus in the cytosol exists as large and small particles . We next used continuous ( 20–40% ) sucrose gradient sedimentation as a third approach to examine SV40's structure in the ER and cytosol ( Figure 4B ) . Again , whereas all the virus in S2 sedimented to bottom heavier fractions similar to purified WT SV40 corresponding to the large particle ( Figure 4B , compare second and third panels from top ) , a portion of virus in S1 was found in the top lighter fractions corresponding to the small particle and another portion in the heavier fractions corresponding to the large particle ( Figure 4B , top panel ) . The virus remained in these lighter fractions even when S1 was pretreated with Triton X-100 prior to sedimentation ( not shown ) , indicating that SV40 in these fractions is not due to flotation caused by membrane encapsulation . PCR analysis further demonstrated that the large but not small viral particles in S1 contain the viral genome ( Figure 4C , compare bottom and top panels ) . This result is consistent with our co-immunoprecipitation analysis demonstrating that the cytosol-localized SV40 binds to the genome ( Figure 3E ) . To estimate the proportion of SV40 in S1 and S2 that are small and large , these samples ( along with purified WT SV40 ) were layered over a sucrose cushion ( 20% ) and centrifuged ( Figure 4D ) . The large particle is expected to penetrate the sucrose cushion and sediment , while the small particles should remain near the top of the cushion . When the sedimented material ( labeled large ) and material near the top of the cushion ( labeled small ) were subjected to immunoblotting , approximately 50% of virus in S1 were found in the small fraction and 50% in the large fraction ( Figure 4D , compare lane 1 to 2 ) . In contrast , essentially all of the virus in S2 and a sample containing purified WT SV40 was large ( Figure 4D , compare lane 4 to 3 and 6 to 5 ) . This size distribution is consistent with the gel filtration ( Figure 4A ) and continuous sucrose sedimentation ( Figures 4B and 4C ) findings . Results using four distinct biochemical strategies ( i . e . immunoprecipitation , gel filtration , continuous sucrose gradient sedimentation , and sucrose cushion sedimentation ) demonstrate unambiguously that SV40 in the ER is a large particle , while the virus in the cytosol exists as small and large particles . The simplest explanation of these findings is that ER-localized SV40 penetrates the ER membrane as a large and intact particle , reaching the cytosol where it disassembles into small particles . The remaining core particle after cytosol-mediated disassembly , which remains relatively large and cannot be distinguished from the large ER-localized particle using either gel filtration or sucrose gradient analysis , contains the genome and is likely the predecessor to the form that enters the nucleus . Alternatively , it is possible that the ER-localized large particle disassembles into small particles in the ER , become discharged into the cytosol where they re-assemble into a large particle . To test whether the cytosol supports large particle assembly in our system , we analyzed SV40 virion formation by transfecting cells with the viral genome . Using this method , VP1 monomer should be made in the cytosol , followed by its oligomerization into pentamers in this compartment . The pentamers are expected to import into the nucleus for full assembly into the large SV40 particle . We found that when cells were transfected with the SV40 genome for 48 hrs , subjected to the semi-permeabilized assay , and the S1 and P1 analyzed by sucrose gradient sedimentation , only small particles were found in the S1 ( Figure 4E , top panel , fractions 1–4 ) . These small particles represent the cytosol-localized pentamers . By contrast , VP1 appeared in virtually all fractions in the P1 ( Figure 4E , bottom panel ) . ( The pellet was subjected to repeated freeze-thaw to extract virus from the nucleus ) . VP1 in the top fractions corresponds to nuclear-localized pentamers imported from the cytosol while those in the heavier fractions correspond to viral particles in the nucleus undergoing assembly . Thus , when cells were transfected with the SV40 genome , VP1 pentamers are generated in the cytosol and imported into the nucleus to form large particles , consistent with the established SV40 assembly process [20] . Importantly , these results demonstrate that the cytosol does not support large particle formation from small particles . We next sought to visualize the large SV40 particle in the S1 cytosol . Buffer , WT SV40 , S1 derived from mock-infected cells ( i . e . mock-infected S1 ) , and S1 derived from SV40-infected cells for 12 hrs ( i . e . SV40-infected S1 ) were immunoprecipitated with VP1-specific antibodies , and the immunoprecipitate captured by magnetic beads . Samples were subjected to SDS-PAGE and silver stained . A distinct band corresponding to VP1 was detected in samples derived only from WT SV40 and SV40-infected S1 ( Figure 5A , lanes 2 and 4 ) . In addition , a band corresponding to VP3 was also found in the WT SV40 and SV40-infected S1 immunoprecipitate ( Figure 5A , lanes 2 and 4 ) , consistent with the co-immunoprecipitation result presented in Figure 3D . When the immunoprecipitate derived from WT SV40 was subjected to negative stain EM , a mostly homogenous population of spherical particles approximately 50 nm could be seen ( Figure 5B , a and b ) . Interestingly , while spherical particles approximating 50 nm could also be observed in the SV40-infected S1 immunoprecipitate ( Figure 5C , a and b ( white arrow ) ) , others appeared to be slightly distorted , appearing as elongated spheres with what seems to be pores in the middle ( Figure 5C , b ( white arrow head ) ) . Even more distorted SV40 particles around 50 nm could also be found in the S1 immunoprecipitate . In these cases , some of their overall structures were poorly defined ( Figure 5D , a and d ) , while others appeared again to be elongated spheres with a doughnut-shaped pore in the middle ( Figure 5D , c ) or contained a clover leaf-shaped hole ( Figure 5D , b ) . Thus S1 SV40 particles are heterogeneous in structure , and likely represent the large particle pool identified in our biochemical assays . In addition to elucidating the ER membrane penetration mechanism , we characterized the viral components regulating this process . As VP2 , VP3 , and viral genome co-transport with VP1 to the cytosol ( Figure 1 ) , we asked whether these internal components control ER-to-cytosol transport . To address whether the minor coat proteins play a role , we generated SV40 mutant viruses lacking VP2 ( SV40 ( -VP2 ) ) , VP3 ( SV40 ( -VP3 ) ) , or both ( SV40 ( -VP2/-VP3 ) ) ( Figure 6A , top and bottom panel , compare lanes 2–4 to 1 ) . VP3's band intensity is higher than VP2 in WT SV40 ( Figure 6A , bottom panel , lane 1 ) , indicating more VP3 than VP2 per viral particle , as reported previously [20] . We first determined whether the mutant viruses reach the ER with equal efficiency as WT SV40 by assessing their ability to undergo both ER-dependent disulfide disruption and release from GM1-enriched lipid raft membranes . Cells were incubated with WT or mutant SV40 for 6 hrs , and the S2 prepared . When S2 was subjected to non-reducing SDS-PAGE , SV40 ( -VP3 ) displayed a similar VP1 banding pattern as WT SV40 ( Figure 6B , compare lane 3 to 1 ) . In contrast , very low signal was detected in S2 derived from cells infected with SV40 ( -VP2 ) or SV40 ( -VP2/-VP3 ) ( Figure 6B , compare lanes 4 and 2 to 1 ) . As expected , when the S2 was subjected to reducing SDS-PAGE , a similar VP1 level was seen between WT and SV40 ( -VP3 ) , and essentially no signal was detected from samples derived from SV40 ( -VP2 ) or SV40 ( -VP2/-VP3 ) ( Figure 6C , compare lanes 1 and 3 to 2 and 4 ) . The VP1 level was similar in all samples in the P2 ( Figure 6C , lanes 5–8 ) , indicating that the total cell-associated virus is the same between WT and mutant viruses . These results demonstrate that SV40 ( -VP3 ) , but not SV40 ( -VP2 ) or SV40 ( -VP2/-VP3 ) , reaches the ER with similar efficiency as WT SV40 at 6 h . p . i . ; SV40 ( -VP2 ) and SV40 ( -VP2/-VP3 ) likely entered the cells but failed to sort to the ER . We next asked whether the mutant viruses undergo ER-to-cytosol transport by assessing the S1 VP1 level at both 8 and 12 h . p . i . using the semi-permeabilized system described in Figure 1 . We found that the S1 VP1 level for all mutant viruses decreased significantly at both time points when compared to WT SV40 ( Figure 6D , top and fourth panels , compare lanes 2–4 to 1 ) . The mutant viruses also promoted infection poorly when compared to WT SV40 ( Figure 6E ) . As SV40 ( -VP3 ) reaches the ER from the cell surface with similar efficiency as WT SV40 at 6 h . p . i . , we conclude that VP3 plays a critical role in ER-to-cytosol transport . Because SV40 ( -VP2 ) and SV40 ( -VP2/-VP3 ) did not reach the ER , they are expected to not undergo subsequent ER-to-cytosol transport . Thus , our results cannot distinguish a role of VP2 in the ER-to-cytosol penetration process . Of interest , VP2 and VP3 were shown previously to be necessary for nuclear entry [23] . To address the viral genome's role in facilitating ER exit of SV40 , we enriched for SV40 that lacked the genome ( SV40 ( -genome ) ) on a CsCl gradient . As expected , infection caused by SV40 ( -genome ) was attenuated severely when compared to WT SV40 ( Figure 6F , approximately 9% of WT ) . When cells incubated with this mutant virus for 12 hrs were subjected to the semi-permeabilized assay , the S1 VP1 level decreased when compared to the VP1 level derived from cells infected with WT SV40 ( Figure 6G , top panel , compare lane 2 to 1 ) . SV40 ( -genome ) and WT SV40 underwent similar ER-dependent disulfide rearrangement ( Figure 6H , compare lane 2 to 1 ) and release from lipid raft membrane domains ( Figure 6H , compare lane 4 to 3 ) . We conclude that in addition to VP3 , the SV40 genome appears to also mediate its ER-to-cytosol transport . What might be the driving force that discharges SV40 into the cytosol from the ER membrane ? The proteasome has been shown to extract some misfolded proteins from the ER membrane into the cytosol [24] , [25] . As proteasome inhibition decreased SV40 infection [12] , we tested the proteasome's role in cytosol release of SV40 by using MG132 , a proteasome inhibitor . When DMSO or MG132 was added simultaneously with SV40 to cells for 12 hrs , VP1 in S1 decreased in cells treated with MG132 when compared to DMSO ( Figure 7A , top panel , compare lane 6 to 1; quantified in Figure 7B ) . The VP1 level in S1 was restored to a similar level as the DMSO-treated cells when MG132 was added increasingly later after incubation of cells with SV40 ( Figure 7A , top panel , compare lane 6 to lanes 2–5; quantified in Figure 7B ) . The time range when proteasome inhibition no longer affects virus arrival to the cytosol ( i . e . approximately 9–11 h . p . i . ) occurs slightly after arrival of SV40 to the cytosol ( i . e . approximately 8 h . p . i . ) . Addition of epoxomicin , a more specific proteasome inhibitor , to cells also decreased the S1 VP1 level at 12 h . p . i . ( Figure 7C , top panel , compare lane 2 to 1 ) , consistent with the MG132 effects . These findings indicate that the proteasome plays an important function in promoting virus release into the cytosol . MG132 decreased SV40 infection when this drug was added simultaneously with SV40 to cells ( Figure 7D , 0 h . p . i . , compare square to circle ) , similar to a previous finding [12] . The infection level was restored partially if MG132 was added 9 or 11 h . p . i . ( Figure 7D , circles ) , consistent with restoration of the S1 VP1 level when this drug was added at the same time points post-infection ( Figure 7A and 7B ) . The correlation between the time-dependent effects of MG132 on viral infection and release into the cytosol underscores the proteasome's role in controlling SV40's ER-to-cytosol transport . As inhibiting the proteasome prevents SV40 release into the cytosol , we hypothesized that such perturbation should concomitantly cause an increase in ER-localized virus . To assess the ER-localized SV40 level , we measured formation of viral disulfide bonded intermediates in the ER . Cells were incubated with SV40 for 12 hrs and subjected to the semi-permeabilized assay . The resulting P1 was used to generate S2 and P2 . These fractions were subjected to non-reducing SDS-PAGE followed by immunoblotting with VP1-specific antibodies . We detected formation of VP1 monomer , dimer , and a low level of the higher oligomer in the S2 ( Figure 7E , left panel , lane 2 ) . BFA added at infection blocked the generation of these products ( Figure 7E , left panel , compare lane 2 to 1 ) , consistent with results observed in a WCE sample ( Figure 1A ) . When cells were incubated simultaneously with MG132 and SV40 , the VP1 monomer , dimer , and higher oligomer levels in the S2 increased when compared to control cells ( Figure 7E , left panel , compare lane 3 to 2 ) . Similarly , proteasome inhibition also increased VP1 monomer in P2 when analyzed by a non-reducing gel ( Figure 7E , right panel , compare lane 6 to 5 ) . Thus blocking the proteasome activity caused a build-up of virus in the ER lumen and those that remained attached to GM1 on the ER membrane . These findings further demonstrate a role of the proteasome in controlling exit of SV40 to the cytosol .
Using a semi-permeabilized system , we found that SV40 is released into the ER lumen upon ER arrival , presumably by detaching from GM1 . What might be the driving force for this reaction ? ER factors may induce physical changes to VP1 that decreases its affinity for GM1 . Alternatively , when GM1 reaches the ER , it may partition into the ER bilayer , thereby reducing SV40's affinity for the membrane . In the ER , disruption of SV40's disulfide bonds by the PDI family members ERp57 and PDI imparts conformational changes on the viral particle , priming it for membrane penetration [12] . Using non-reducing SDS-PAGE , our analyses dissected this reaction into several steps . First , when SV40 attached to GM1 reaches the ER , its disulfide bonds are disrupted , generating VP1 monomer and dimer . Next , when the virus is released into the ER lumen , monomer and dimer , as well as a higher oligomer ( which could be an intermediate for the dimer and monomer ) continues to form . Because only a large viral particle was detected in the ER using non-SDS methods , disulfide bond disruption is not sufficient to generate VP1 monomer and dimer; the ER-localized SV40 likely represents VP1 pentamers that remain attached to the core viral particle via non-covalent interactions . Finally , when the virus is discharged into the cytosol , all the intermediate species undergo a thorough disruption of the disulfide bonds to produce only VP1 monomer . During these steps , SV40's interchain Cys9-Cys9 and Cys104-Cys104 disulfide bonds [4] , [12] are likely disrupted . As a species resembling VP1 pentamer ( but not monomer ) is detected in the cytosol using non-SDS methods , the monomer must be held together non-covalently . The sequential manner by which SV40's disulfide bonds are disrupted as it moves from the ER into the cytosol reveals the coordinated manner by which the host dismantles the virus . Using four independent biochemical approaches , our results unambiguously established that the conformations of the ER- and cytosol-localized viral particles are different . Specifically , we demonstrate that ER-localized SV40 is large and intact , and contains VP2 , VP3 , and the genome . No small viral particles were detected in the ER . In contrast , both large and small viral particles are present in the cytosol . These particles display weak VP1-VP2/VP3 binding . Furthermore , our EM analyses detected large 50 nm viral particles in the cytosol , although they appear to be heterogeneous in structure . The simplest interpretation of these data is that a large and intact viral particle in the ER penetrates the ER membrane to reach the cytosol where it disassembles . Another potential explanation is that the ER-localized large particle disassembles to small particles that then discharge rapidly to the cytosol where they re-assemble into a large particle . However , this complex scenario is unlikely because it would require an unprecedented efficiency in removing all the small particles from the ER to the cytosol to preclude their detection in the ER . Moreover , it is also inconsistent with the established SV40 assembly process in which the nucleus but not the cytosol supports large virion assembly [20] . In our system , we further demonstrate that the cytosol does not provide an environment conducive for large particle assembly . While a precise measurement of the large membrane penetrating species is not available , sucrose gradient analyses indicate that its size is similar to the native 50 nm SV40 virion . This proposed size raises the question of whether the virus crosses a protein-conducting channel or the ER lipid bilayer . A previous study implicated a role of Derlin-1 , a component of an ER membrane complex used during ERAD [26] , in SV40 infection [12] . Should Derlin-1 function as a channel , massive Derlin-1 oligomerization is required to accommodate viral transport . That biological membranes can support transport of large complexes is not without precedent , as a 9 nm gold particle decorated with the peroxisome-targeting signal can be transported into the peroxisome interior [27] . An alternative to the protein channel-based mechanism is a lipid-based strategy . Our in vitro findings on mPy provide insight into how this process may occur . The PDI family member ERp29 untangles the VP1 C-terminal arm of mPy in a reaction that requires reduction of the virus disulfide bonds and removal of the virus-bound calciums [28] . VP2 and possibly VP3 are then exposed , generating a hydrophobic viral particle that binds , integrates , and perforates the ER membrane [28] , [29] . These reactions initiate mPy's penetration across the ER lipid bilayer . Interestingly , a different version of the lipid-based model was hypothesized [30] . In this model , a pore in the ER membrane created when a lipid droplet leaves the membrane enables SV40/mPy to gain access to the cytosol . No experiments have validated this idea thus far . While SV40 VP2 and VP3 have been implicated in nuclear entry [23] , our findings demonstrate that at least VP3 plays a role in SV40's ER-to-cytosol transport; our results cannot distinguish any function of VP2 in this process . In vitro , SV40 VP2 and VP3 can integrate into the ER membrane [31] . Integration of these proteins into the ER membrane may create a pore through which the viral genome is injected [31] . Alternatively , VP2 and VP3 may act as lytic factors [32] , perforating the ER membrane to allow passage of a subviral particle . As the ER and nuclear membranes are continuous , a subviral particle could bypass the cytosol and reach the nucleus directly after penetrating the ER membrane . However , the findings that cytosol arrival is required for SV40 infection [33] , that interaction between VP3's nuclear localization signal and importins is necessary for nuclear entry [34] , and that ER machineries dedicated to ERAD are crucial for infection [12] , point to the ER-to-cytosol transport pathway as the dominant infectious route . As SV40's genome stabilizes its overall viral architecture [12] , its absence likely destabilizes the virus structure . This could in turn lead to incorrect conformational changes that perturb ER-to-cytosol transport . The host proteasome also plays a pivotal function in controlling SV40's ER-to-cytosol transport . Since the proteasome extracts some misfolded ER proteins to the cytosol [24] , [25] , it may also discharge SV40 into the cytosol . Establishing a cell-free reconstituted system will reveal if the proteasome plays a direct role in viral release . Our data also suggest that VP2 controls SV40 sorting to the ER from the cell surface . In addition , VP3 may also be involved in this process , should an SV40 mutant virus lacking VP3 reach the ER inefficiently after 6 h . p . i . . Further experiments are required to clarify how VP2 regulates ER sorting , and whether VP3 plays any role . Our analyses demonstrated that SV40 disassembles in the cytosol . The starting substrate for this reaction is a large particle that reaches the cytosol from the ER . Indeed , large particles approximating 50 nm were detected in the cytosol by EM . Their heterogeneous nature may reflect the various disassembly intermediates . Of particular interest is the viral intermediate containing a doughnut-shaped pore in the middle of its structure . This species might represent a viral particle in which a 5-coordinated VP1 pentamer is released to generate a pore . Release of the 5-coordinated VP1 pentamer from intact SV40 in vitro was previously hypothesized to be involved in ER-to-cytosol transport [12] . Our biochemical results also show that the large cytosol-localized virus disassembles to generate small particles approximating the size of a pentamer and lacks the genome . This disassembly reaction may be aided by the low calcium concentration in the cytosol which would promote loss of calcium ions from the cytosol-localized virus , thereby further destabilizing VP1 capsomer interaction . The remaining core particle ( relatively large particle in Figure 8 ) , which harbors the genome , is likely targeted to the nucleus to cause infection . As the cytosol-localized viral intermediates observed by EM are large , they are unlikely the species that enter the nucleus . Because previous studies showed that Hsp70 uncoats mPy in vitro [35] and binds to SV40 in cells [36] , this cytosolic chaperone may convert the large SV40 particle to the small particle in our assay . Whether cytosolic disassembly is coupled to nuclear entry is unknown , and unraveling it will provide insight into another critical step in SV40's infection pathway .
Polyclonal antibodies against Hsp90 and PDI were purchased from Santa Cruz Biotechnology , monoclonal antibodies against PDI from Abcam , large T antigen from Santa Cruz Biotechnology , MG132 and epoxomicin from EMD chemicals , BFA from Epicenter , proteinase K and monoclonal antibodies against HA from Roche , and TCEP from Thermo Scientific . All other reagents were from Sigma . The pUCSV40 encoding SV40 genome and polyclonal antibodies against VP1 were generous gifts from Dr . H . Handa ( Tokyo Institute of Technology ) , polyclonal antibodies against VP3 from Dr . H . Kasamatsu ( University of California , Los Angeles ) and monoclonal antibodies against VP1 from Dr . W . Scott ( University of Miami ) . CV-1 cells were incubated with SV40 ( m . o . i . = 3–50 ) at 4°C , washed , and incubated at 37°C . At indicated time points , cells were trypsinized ( scraped off for the mutant viruses ) , permeabilized with HN buffer ( 50 mM Hepes , pH 7 . 5 , 150 mM NaCl , and protease inhibitors ) containing 0 . 1% digitonin on ice for 10 min , and centrifuged at 16 , 100 g for 10 min . The resulting supernatant is referred as S1 . The pellet was resuspended in SDS sample buffer and is referred as P1 . Where indicated , P1 was incubated in HN buffer containing 1% Triton X-100 on ice for 10 min and centrifuged at 16 , 100 g for 10 min . This second supernatant is referred as S2 . The Triton X-100-insoluble pellet was resuspended in SDS sample buffer and is referred to as P2 . For non-reducing SDS-PAGE , NEM ( 10 mM ) was added to all buffers . SV40 monoclonal antibodies ( CC10 and BC11 ) or an HA monoclonal antibody were added to S1 and S2 and incubated on ice for 3 hrs . Protein G-Dynabeads ( Invitrogen ) were used to capture the antibody-virus complex . The beads were isolated using a magnet stand ( Dynal ) , washed with a high salt buffer ( 50 mM Hepes , pH 7 . 5 , 500 mM NaCl , 1% Triton X-100 ) , and the bound proteins eluted with an acidic buffer ( 50 mM glycine , pH 2 . 8 ) . CV-1 cells were incubated with the indicated viruses at 4°C for 2 hrs . The cells were washed and incubated at 37°C . 24 h . p . i . , cells were fixed with 1% paraformaldehyde , treated with 0 . 2% Triton X-100 , and incubated in 3% milk . The cells were stained with a mouse monoclonal SV40 large T antigen antibody , followed by Alexa Fluor-488-conjugated secondary antibody ( Invitrogen ) . In each experiment , approximately 1 , 000 cells were counted to assess the extent of large T antigen expression . S1 and S2 were loaded onto a Bio-Sil 600 gel filtration column ( Bio-Rad ) and separated with HN buffer . Forty fractions ( 0 . 5 ml each ) were collected and 0 . 1 ml of fractions 9-30 was separated by SDS-PAGE , followed by immunoblotting with VP1 monoclonal antibodies . S1 and S2 were loaded onto a 0 . 5 ml preformed 20–40% sucrose gradient and centrifuged at 49 , 500 rpm for 50 min at 4°C in an SW 55Ti rotor . After centrifugation , 10 fractions were collected from the top . S1 , S2 , and WT SV40 were layered over a 20% sucrose solution , centrifuged , and the sedimented material and material near the top of the cushion were subjected to immunoblotting . Cells were transfected ( Lipofectamine 2000 , Invitrogen ) with the SV40 genome for 48 hrs , harvested , and subjected to the ER-to-cytosol assay to generate S1 and P1 . P1 was freeze-thawed to extract virus from the nucleus . Both fractions were analyzed by sucrose gradient sedimentation . WT and SV40 mutants were purified using the OptiPrep gradient system , except SV40 ( -genome ) was purified by CsCl gradient . Briefly , SV40-infected or viral genome-transfected CV-1 cells were lysed in a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , and 0 . 5% Brij58 on ice for 30 min and centrifuged at 16 , 100 g for 10 min . The supernatant was loaded onto a discontinuous 20 and 40% OptiPrep gradient and centrifuged at 49 , 500 rpm for 2 hrs at 4°C in an SW 55Ti rotor . A viral particle fraction between 20% and 40% OptiPrep was collected with a needle . For separation of virion and empty particle , supernatant was loaded onto a 1 . 516 , 1 . 443 , 1 . 37 , 1 . 296 , 1 . 222 , and 1 . 148 g/ml discontinuous CsCl gradient ( 1 ml each ) and centrifuged at 35 , 000 rpm for 3 hrs at 4°C in an SW 41Ti rotor . Fractions corresponding to virion and empty particle were collected . Each fraction was transferred into a 5×41-mm open-top tube ( Beckman ) and centrifuged at 49 , 500 rpm for 12 hrs at 4°C in an SW 55Ti rotor . A fraction corresponding to virion or empty particle was collected . Purified SV40 was labeled with EZ-Link Sulfo-NHS-LC-Biotin ( Thermo ) according to the manufacturer's protocol . 33 nM ERp57-specific ( 5′-UGAAGGUGGCCGUGAAUUATT-3′ ) ( Invitrogen ) or control ( Ambion ) siRNAs were transfected into CV-1 cells using the Lipofectamine 2000 system according to the manufacturer's protocol . At 36 hrs post-infection , cells were infected with SV40 at m . o . i . = 5 and subjected to the ER-to-cytosol membrane penetration assay . Samples from S1 , immunoprecipitation , or sucrose gradient fractions were incubated in 10 mM Tris-HCl ( pH 8 . 5 ) containing 0 . 2 mg/ml proteinase K . After proteinase K was heat-inactivated , the samples were subjected to a PCR reaction using a set of primers ( GCAGTAGCAATCAA CCCACA [forward] and CTGACTTTGGAGGCTTCTGG [reverse] ) . CV-1 cells plated on 18 mm glass plates were washed with DMEM , chilled at 4°C , and incubated with SV40 ( m . o . i . = 1 ) at 4°C for 1 hr . Cells were washed extensively to remove unbound viruses , incubated in DMEM at 37°C for 10 hrs , fixed with 1% paraformaldehyde , incubated with a mouse monoclonal SV40 VP1 and rabbit polyclonal PDI antibody , followed by an Alexa Fluor 594 and Rhodamine conjugated secondary antibodies . Images were taken with an Olympus FV-500 confocal microscopy equipped with 100x objective . The ER images derived from the PDI signal were subjected to the FFT Bandpass Filter embedded in Image J ( NIH ) as described previously [36] . 4-fold more S1 at 4 h . p . i . was used to ensure that the VP1 level is similar between S1 at 4 and 12 h . p . i . The samples were incubated with 30 or 100 µg/ml trypsin for 1 hr on ice and the reaction was stopped by the addition of 1 mM TLCK for 10 min on ice . The samples were separated by SDS-PAGE followed by immunoblotting with SV40 VP1 monoclonal antibodies . S1 or purified SV40 was mixed with the same amount of 60% OptiPrep solution . 100 µl of the mixed sample was placed at the bottom of a Beckman centrifuge tube ( 7×20 mm ) , and 100 µl of 20% OptiPrep was loaded onto the sample . The tube was centrifuged in a Beckman TLA100 rotor for 1 hr at 100 , 000 rpm . Fractions were collected from the top ( 20 µl each ) , separated by SDS-PAGE , and immunoblotted with SV40 VP1 monoclonal antibodies . CV-1 cells were intoxicated with 30 nM CT and subjected to semi-permeabilization with 0 . 1% digitonin as described in the ER-to-cytosol membrane penetration assay . S1 and P1 fractions were analyzed by SDS-PAGE followed by immunoblotting with CTA , PDI , and Hsp90 antibodies . Cells were washed with DMEM , chilled at 4°C in 10 ml of DMEM for 20 min , and incubated with 30 nM CT at 4°C for 2 hrs . Cells were then washed with cold PBS to remove unbound CT and incubated in 10 ml of DMEM at 37°C to allow entry . At the indicated time points , cells were washed with cold PBS , scraped off the plate in 1 ml of PBS containing 10 mM NEM , and collected in a microcentrifuge tube . S1 , prepared as described above , was incubated with or without 2% SDS at 25°C for 10 min . The samples were subjected to high-speed centrifugation in a Beckman TLA100 rotor for 30 min at 100 , 000 g . The resulting supernatant and pellet fractions after high-speed spin , and the original S1 , were analyzed by SDS-PAGE followed by immunoblotting with polyclonal CTB antibodies . S1 , prepared from cells ( 7 . 5×106 cells ) infected with SV40 for 12 hrs , was incubated with 1% Triton X-100 to solubilize any membrane material , centrifuged in a Beckman TLA100 rotor for 30 min at 100 , 000 g to concentrate the virus , the resulting pellet resuspended in 100 µl of HN buffer , and subjected to immunoprecipitation as described above . The virus-antibody-bead complex was captured by a magnet stand ( Dynal ) and washed with HN buffer containing 1% Triton X-100 . The magnetic beads were resupended in 20 µl of HN buffer . For negative staining , 5 µl of each sample containing magnetic beads were absorbed onto a grow-discharged copper grid ( Electron Microscopy Sciences ) and stained with 1% uranyl acetate . The samples were observed using a Philips CM-100 at 80 kV .
|
Biological membranes represent a major barrier during viral infection . While the mechanism by which an enveloped virus breaches the limiting membrane of a host cell is well-characterized , this membrane penetration process is poorly understood for non-enveloped viruses . Indeed , most available insights on membrane transport of non-enveloped viruses are built upon in vitro studies . Here we established a cell-based assay to elucidate the molecular mechanism by which the non-enveloped SV40 penetrates the endoplasmic reticulum ( ER ) membrane to access the cytosol , a critical step in infection . Strikingly , we uncovered SV40 breaches the ER membrane as a large and intact viral particle , despite the conformational changes it experiences in the ER lumen . This result suggests that the ER membrane can accommodate translocation of a large protein complex , possibly through either a sizeable protein channel or the ER membrane bilayer . In addition to this finding , we also pinpoint viral and host components that control the ER-to-cytosol membrane transport event . Together , our data illuminate the cellular mechanism by which a non-enveloped virus penetrates the limiting membrane of a target cell during infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology",
"biology",
"microbiology"
] |
2011
|
A Large and Intact Viral Particle Penetrates the Endoplasmic Reticulum Membrane to Reach the Cytosol
|
Sex determination is a hierarchically-regulated process with high diversity in different organisms including insects . The W chromosome-derived Fem piRNA has been identified as the primary sex determination factor in the lepidopteran insect , Bombyx mori , revealing a distinctive piRNA-mediated sex determination pathway . However , the comprehensive mechanism of silkworm sex determination is still poorly understood . We show here that the silkworm PIWI protein BmSiwi , but not BmAgo3 , is essential for silkworm sex determination . CRISPR/Cas9-mediated depletion of BmSiwi results in developmental arrest in oogenesis and partial female sexual reversal , while BmAgo3 depletion only affects oogenesis . We identify three histone methyltransferases ( HMTs ) that are significantly down-regulated in BmSiwi mutant moths . Disruption one of these , BmAsh2 , causes dysregulation of piRNAs and transposable elements ( TEs ) , supporting a role for it in the piRNA signaling pathway . More importantly , we find that BmAsh2 mutagenesis results in oogenesis arrest and partial female-to-male sexual reversal as well as dysregulation of the sex determination genes , Bmdsx and BmMasc . Mutagenesis of other two HMTs , BmSETD2 and BmEggless , does not affect piRNA-mediated sex determination . Histological analysis and immunoprecipitation results support a functional interaction between the BmAsh2 and BmSiwi proteins . Our data provide the first evidence that the HMT , BmAsh2 , plays key roles in silkworm piRNA-mediated sex determination .
Insect sex determination is highly diverse in different species [1 , 2] . Destiny of the zygote in Drosophila melanogaster depends on the number of X chromosome [3–5] . Female flies carry two X chromosomes which activate the transcription of Sex-lethal ( Sxl ) and lead to female sexual development , while a single copy of X chromosome in male flies suppresses Sxl expression to determine male sexual fate [6 , 7] . Subsequently , the female-specific Sxl protein regulates splicing of transformer ( tra ) , which cooperates with the product of the non-sex-specific transformer 2 ( tra2 ) gene to regulate the alternative splicing of doublesex ( dsx ) [8 , 9] . In contrast , the insect WZ sex determination system is found in most lepidopteran insects . For example , in the lepidopteran model insect Bombyx mori , females are heterogametic ( WZ ) , while males are homogametic ( ZZ ) [10 , 11] . The B . mori W chromosome exerts a dominant control over sex determination since its presence is sufficient for feminization , and the W chromosome-derived PIWI-interacting RNA ( piRNA ) , named Feminizer ( Fem ) , has been identified as the primary factor for silkworm sex determination [12] . The Fem piRNA is arranged tandemly in the sex determination region of the W chromosome and binds to the PIWI protein BmSiwi to exert its functions [12] . In female silkworms , the Masculinization ( BmMasc ) gene is transcribed from the Z chromosome and responsible for both sex determination and dosage compensation . The Fem piRNA cleaves the BmMasc mRNA in a ping-pong cycler manner to promote the female-specific transcription of Bmdsx , resulting in the female fate of animals [10] . Inhibition of Fem leads to the production of the male-specific transcript of Bmdsx and up-regulates BmMasc in female embryos , revealing the critical roles of both Fem and BmMasc in the silkworm sex determination process , which is distinct from any other species reported [13–15] . The high diversity of sex determination mechanisms indicates that multiple factors may participate in this pathway . Epigenetic modifications are trans-regulators of gene expression that control germline cell imprinting , X chromosome gene inactivation , and gonadogenesis [16] . The histone 3 lysine 9 ( H3K9 ) demethylase , Jmjd1a , positively regulates the sex determination gene Sry in mice [17] . A lack of Jmjd1a causes the H3K9me2 mark to be retained on the Sry gene and dysregulation of Sox9 and Fox12 , resulting in male-to-female sexual reversal , as demonstrated by the appearance of a uterus in the testis [17–20] . In B . mori , siRNA-mediated knockdown of the histone methyltransferase ( HMT ) DOT1L ( H3K79 methyltransferase ) abolishes male-specific expression of Imp , an insulin-like growth factor II mRNA-binding protein thought to be a potential regulator of male-specific dsx splicing [21] . More recent researches reveal that the prevalent messenger RNA epigenetic modification , N6-methyladenosine RNA ( m6A ) , controls the alternative splicing of Sxl in Drosophila , thus functions in the sex determination process [22 , 23] . These cases indicate that epigenetic modifications , including histone methylation , are involved in sex determination . However , whether histone methylation participates in B . mori piRNA-mediated sex determination was previously unknown . The mechanism of silkworm sex determination has long been in mystery until recent identification of the W-derived Fem piRNA which functions as the initial signal for silkworm sex determination [12] . Multiply genes that potentially function in the silkworm sex determination pathway have been functional investigated since then [24 , 25] . However , how does piRNA regulate the downstream sex determination genes remain largely unknown . Here we describe that depletion of the piRNA-bound protein BmSiwi causes partial female-to-male sexual reversal , revealing its critical role in silkworm piRNA-mediated sex determination . Furthermore , we find significant down-regulation of three HMTs in BmSiwi mutant . Depletion of BmAsh2 , one of the HMTs , causes partial sexual reversal as well as dysregulation of piRNAs , TEs , Bmdsx and BmMasc . We further demonstrate that there is a functional interaction between the BmSiwi and BmAsh2 proteins . In conclusion , our data provides the first evidence that the HMT BmAsh2 plays key roles in the silkworm piRNA-mediated sex determination .
Gonad-specific expression of PIWI subfamily proteins ( PIWIs ) has been identified in the silkworm as well as other organisms [15 , 26] . In this study , we used qRT-PCR to confirm the predominant expression of two silkworm PIWIs , BmSiwi and BmAgo3 , in gonads at the larval wandering stage ( S1A and S1B Fig ) . The transcript abundance of these two PIWIs was low during the larval stages , increased more than 10-fold after pupation and peaked at the pupal and adult stages in gonads ( S1C and S1D Fig ) . Furthermore , we used immunostaining to investigate the localization of silkworm PIWIs in the gonads at the translational level . Similar to D . melanogaster , B . mori ovary possesses several ovarioles which are composed by sequentially developed egg chambers , and serve as an assembly line for oogenesis [27 , 28] . In order to distinguish the germline and somatic cells in silkworm ovary , we used a primary antibody recognizing BmVasa , which gene has been described as a conserved molecular marker for germline cells in insects , to perform the immunostaining analysis . As the results , distribution of BmVasa and BmAgo3 presented a circular pattern , surrounding the nucleus of germline cells ( Fig 1A ) . In comparison , BmSiwi localized in both the germline cells and the somatic supporting cells which were not stained by the BmVasa antibody ( Fig 1A ) . Localization of silkworm PIWIs was similar to the products of the orthologous genes in D . melanogaster , suggesting that they may participate in B . mori piRNA regulation ( Fig 1A and 1B ) [29 , 30] . In testis , both BmSiwi and BmAgo3 were detected in the spermatogonium and their distribution completely overlapped with BmVasa ( S2 Fig ) . These results indicated that BmPIWIs may function in gonadogenesis . Using the binary CRISPR/Cas9 system , we established somatic mutant lines for BmPIWIs to explore their comprehensive physiological functions ( S3A and S3B Fig ) [26 , 31] . Different types of deletions were detected around the target sites in the F1 progeny obtained when the IE1-Cas9 and U6-sgRNA transgenic lines were crossed , demonstrating efficient mutagenesis of both genes ( S3C and S3D Fig ) . In addition , the depletion efficiency was further confirmed by histological analysis using corresponding antibodies ( Fig 2A ) . Compared with wild-type ( WT ) animals , the larval ovaries from Δsiwi and Δago3 animals were oval-shaped , which was resemble to the WT testis . In details , we observed the development arrested ovarioles were shorter and vacuole filled in both mutants ( Fig 2B ) . As the result , the mature female adults produced few eggs and decreased in fecundity significantly ( Fig 2B and 2C ) . In addition , no clear individual egg chamber was observed in Δsiwi and Δago3 ovarioles since the germline cells divided excessively but differentiated defectively ( Fig 2A ) . However , the testes developed normally in both Δsiwi and Δago3 males , revealing the female-specific function of BmPIWIs ( S4A Fig ) . In conclusion , depletion of silkworm PIWIs perturbed germline cell development and arrested oogenesis specifically in females . Female Δsiwi moths developed a male-specific eighth abdominal segment and asymmetrical clasper-like structures on the genital papilla , leading to failure in mating with normal male animals ( Fig 3 and S4B Fig ) . However , neither Δsiwi males nor Δago3 females and males showed developmental defect in abdominal segmentation or the structure of the externalia ( Fig 3 and S4C Fig ) . These partial sexual reversal phenotypes indicated that BmSiwi regulates silkworm female sexual dimorphism but BmAgo3 does not . Since the alternative splicing of Bmdsx and expression amount of BmMasc were the two reporters for masculinization , hence we detected the bands of Bmdsx and expression of BmMasc in the mutants [12 , 25 , 32] . Male-specific splicing production of Bmdsx ( BmdsxM ) and an increase in BmMasc transcript abundance ( 2 . 01-fold higher than WT ) were detected in Δsiwi but not Δago3 female animals ( Fig 4A and 4B ) , indicating that BmSiwi controlled silkworm female sexual dimorphism by regulating Bmdsx and BmMasc . In addition , no significant change on Bmdsx splicing form or BmMasc expression was detected in the males of either mutant ( Fig 4A and 4B ) . RNA-seq analysis was performed using the mixed ovary samples from three individual mutants at the larval wandering stage . In Δsiwi females , we identified 1460 differentially-expressed genes ( DEGs ) in which 1325 genes were down-regulated and 135 genes were up-regulated when compared to WT . In addition , the DEGs were enriched in 268 KEGG terms and 45 GO terms ( S5A and S5B Fig ) . Only 198 DEGs ( 114 up-regulated and 84 down-regulated ) were identified in the Δago3 females , and these were enriched in 127 KEGG and 36 GO terms ( S5A and S5B Fig ) . Interestingly , the Δago3 enriched terms completely included in those of Δsiwi ( S5A and S5B Fig ) . Two GO items , “reproduction” and “reproduction process” , were identified from both mutants , confirming that BmPIWIs involve in the oogenesis ( S5C Fig ) . We also detected significant decrease of piRNA abundance in ovaries of PIWIs female mutants . Comparing to WT females , piRNA abundance decreased to 89 . 6% , 74 . 5% and 36 . 5% in Δsiwi females and 95 . 5% , 85 . 2% and 66 . 7% in Δago3 females for 28-nt , 29-nt and 30-nt piRNAs respectively ( S5D Fig ) . The relative abundance of six known piRNAs , Fem ( BmSiwi-specific binding piRNA ) , Masc ( BmAgo3-specific binding piRNA ) , Judo1 , Judo2 , Inoki and Suzuka ( the latter four of which have no previously-identified binding specificity ) , were further examined in the two mutants using qRT-PCR . Consistent with previous reports [12] , the Fem and Masc piRNAs were down-regulated in Δsiwi and Δago3 respectively ( Fig 5A ) . Three piRNAs , Judo1 , Judo2 and Inoki , were down-regulated in Δsiwi , but not Δago3 , supporting the hypothesis that they may be able to bind BmSiwi ( Fig 5A ) . However , the Suzuka was down-regulated in both mutants , likely due to a lack of binding specificity between BmSiwi and BmAgo3 ( Fig 5A ) . In addition , qRT-PCR analysis revealed that seven TEs were up-regulated in the Δsiwi female silkworms but down-regulated in the Δago3 female animals ( Fig 5B ) . The up-regulation of TEs in Δsiwi females was expected due to the decrease of its repressor , while this was the first report indicating that disruption of BmAgo3 induced TEs down-regulation . We proposed that this was caused by compensation between the primary and secondary piRNA biosynthesis pathways , although more evidences were needed [10 , 33] . In conclusion , dysregulation of piRNAs and TEs in Δsiwi and Δago3 female animals indicated a conserved function of PIWIs in insects . Epigenetic modifications were shown to affect gonadogenesis in M . musculus and D . melanogaster , raising the possibility that B . mori HMTs may participate in piRNA-mediated sex determination [16 , 34] . Based on the RNA-seq data , qRT-PCR analysis revealed that the transcripts of three HMTs , BmAsh2 , BmSETD2 and BmEggless decreased in abundance to 67% , 35% and 32% respectively in Δsiwi females comparing with WT ones , while no significant difference was found in Δago3 females ( S5E Fig ) . These three genes showed tissue-specific expression in the gonads and predominantly in the ovaries ( S6A–S6C Fig ) . We established somatic mutant lines for each HMT using the transgenic CRISPR/Cas9 system to further investigate their physiological roles ( S3E–S3G Fig ) . Δeggless animals showed no deleterious phenotype in physiology or sexual development ( Figs 2B , 2C and 3 ) . In contrast , Δash2 and Δsetd2 animals showed abnormal wing development from pupal stage , resulting in small and curly wings in adults ( S7A and S7B Fig ) . This deleterious phenotype was similar to knock-out phenotypes in D . melanogaster , in which Δash2 flies developed absent , small and homeotic wings and Δsetd2 flies showed blistered wings , indicating a conserved function of Ash2 and SETD2 in insect wing morphogenesis [35–37] . Δash2 and Δsetd2 females showed defective oogenesis phenotype similar to Δsiwi and Δago3 female moths . Histological analysis revealed that Δash2 and Δsetd2 ovaries contained shorter and vacuolated ovarioles ( Fig 2B and 2C ) . However , no defects were observed in the Δash2 and Δsetd2 male animals ( S4A and S4C Fig ) . Interestingly , only Δash2 females showed partial sexual reversal characteristics , such as the appearance of eight abdominal segments and asymmetrically differentiated genital papilla ( Fig 3 ) . Furthermore , the BmdsxM splicing form and increased BmMasc expression were detected in Δash2 females ( Fig 4A and 4B ) . These results demonstrated that BmAsh2 , but not BmSETD2 , was involved in silkworm sex determination . We further investigated the relationship between HMTs and BmPIWIs because of their similar effects on silkworm female sex determination . We found that piRNAs expressions of Fem , Judo1 , Inoki and Suzuka were down-regulated in Δash2 ovaries , consistent with the results found in Δsiwi female animals ( Fig 5A and 5C ) . However , in Δsetd2 ovaries , Fem , Judo2 and Inoki levels were comparable to those observed in WT , while Suzuka was down-regulated , and this trend was consistent with the results from Δago3 females ( Fig 5A and 5C ) . The expression of seven TEs was up-regulated in Δash2 females , while all of them , except TE1 , were down-regulated in Δsetd2 animals , supporting the hypothesis that the regulation of BmAsh2 and BmSETD2 was piRNA-dependent ( Fig 5D ) . Since BmAsh2 phenocopied BmSiwi both at the female sexual reversal phenotype and piRNA regulation , we further investigated its localization in silkworm ovary by using immunostaining . BmAsh2 distributed in both the germline and somatic cells in the ovary and accumulated in the spermatogonium of the testis , similar to the localization of BmSiwi ( Fig 1A and S2 Fig ) . Only weak signal of BmAsh2 could be detected in the Δash2 females , demonstrating that Cas9/sgRNA-mediated mutagenesis was highly efficient ( Fig 6A ) . Since Ash2 is responsible for H3K4me3 modification [38 , 39] , we next examined histone methylation using an anti-H3K4me3 antibody in Δash2 ovaries and found that the signal decreased significantly , suggesting that H3K4me3-mediated histone methylation was disrupted in Δash2 animals ( Fig 6A ) . In addition , significant decrease of BmAsh2 protein accumulation was detected in Δsiwi ovaries , being consistent to qRT-PCR results ( Fig 6B and 6C and S5E Fig ) . However , both relative mRNA and protein expressions of BmSiwi were comparable between Δash2 and WT ovaries , indicating that BmAsh2 did not function upstream of BmSiwi in silkworm sex determination pathway ( Fig 6B and 6C and S5F Fig ) . To elucidate the molecular basis of BmAsh2 involvement in sex determination , we expressed epitope-tagged BmAsh2 and BmSiwi and performed immunoprecipitation in BmN cells , which were derived from silkworm ovaries and exhibit both the primary and secondary piRNA biosynthesis processes . Successful ectopic expression for both proteins were detected in the input samples using anti-His or anti-Flag primary antibodies ( Fig 6D ) . Furthermore , the BmSiwi protein was detected in the Flag immunoprecipitation products , revealing a protein interaction between BmSiwi and BmAsh2 . In conclusion , the molecular evidence revealed that BmAsh2 plays critical roles in BmSiwi- and piRNA-mediated sex determination in B . mori ( Fig 7 ) .
PIWIs belong to the clade of gonadal Argonaute family proteins and silence TEs to maintain genomic integrity [15 , 40 , 41] . PIWI involvement in gonadal development has been demonstrated by studies showing that depletion of it caused sterility in Mus musculus , D . melanogaster and Danio rerio [13 , 15 , 42 , 43] . Absence of the piRNA-bound protein , Miwi , Mili and Miwi2 , arrested spermatogenesis at different meiosis stages in mice [13 , 44 , 45] . Drosophila Piwi depletion caused the accumulation of germline stem cell-like tumors , leading to female infertility [43 , 46] . Gonadogenesis defect was attributed to DNA damage caused by random TE insertion , which disrupted the integrity of the germline stem cell ( GSC ) genome and homeostasis between GSC self-renewal and differentiation [47 , 48] . We showed here that a deficiency in BmSiwi and BmAgo3 in the silkworm results in degenerated ovarioles with fused egg chambers and germline cell hyperplasia , revealing the conserved function of PIWIs in gonadogenesis . Since no phenotypic defect was observed in testis development , we concluded that the effect of BmPIWIs on gonadogenesis was female-specific , although high expression of BmSiwi and BmAgo3 was detected in testes . In addition to its function on oogenesis , BmSiwi , but not BmAgo3 , also was involved in female sex determination . Although BmSiwi was reported to function in Bmdsx splicing in silkworm embryos [12] , there was no previous physiological evidence reported . Here , we found that depletion of BmSiwi caused oogenesis arrestment and partial female sexual reversal , including the appearance of additional abdominal segments , asymmetrically differentiated genital papilla and a male-like clasper structure . Furthermore , dysregulation of BmMasc expression and splicing of Bmdsx further confirmed the function of BmSiwi on silkworm sex determination from molecular level . In comparison , no similar phenotype was observed in Δago3 females , supporting the conclusion that BmAgo3 does not function in silkworm sex determination . We speculated that the oogenesis arrestment observed in Δago3 females may be caused by a deficiency in a dsx-independent pathway , such as the bone morphogenetic protein ( BMP ) or epidermal growth factor receptor ( EGFR ) signaling pathway [49 , 50] . Thus , the current report provides genetic evidence for the involvement of BmSiwi in silkworm sex determination . Ash2 is part of the SET1/MLL histone methyltransferase complex and is responsible for histone 3 lysine 4 ( H3K4 ) methylation [51–55] . Drosophila spermatogenesis is controlled by multiple mechanisms , including epigenetic modifications [56] . In mouse , TE expression was repressed by CpG DNA methylation in a Mili-piRNA-dependent manner during sperm development . The repressive histone methylation at H3K9 , which was responsible for heterochromatin formation , was active on retrotransposons at the meiotic pachytene stage when DNA methylation was inactive [57] . Expression of a breast tumorigenesis key factor , piRNA-021285 , altered the methylation status of a number of related genes [58] . Drosophila TEs were silenced by PIWI-piRNA complex-dependent heterochromatin formation along with the silencing signal that spread to its adjacent genes [59] . Furthermore , PIWI-piRNA could recruit an epigenetic factor complex including the heterochromatin protein HP1a and the Su ( var ) 3-9 histone methyltransferase to the target DNA [60] . These data support the conclusion that methylation is critical for gonadogenesis . We show that the H3K4 HMT BmAsh2 was functional in piRNA-mediated sex determination in B . mori . Firstly , loss of BmAsh2 resulted in phenocopies of the BmSiwi mutant in females , which we interpreted to indicate that they function similarly in regulating silkworm sex determination . Furthermore , we detected colocalization of BmSiwi and BmAsh2 in both the germline and somatic cells in silkworm ovary . These two proteins also showed the similar localization at perinucleus in the germline cells , further confirming their important functions in piRNA regulation . More directly , we proved the direct interaction between BmSiwi and BmAsh2 proteins by immunoprecipitation assay . In conclusion , these results support the hypothesis that BmAsh2 regulates silkworm female sex determination through a piRNA-dependent pathway . Our report provides the first genetic evidence that BmAsh2 plays critical roles in BmSiwi- and piRNA-mediated silkworm sex determination .
A multivoltine , nondiapausing silkworm strain , Nistari , was used in these experiments . Larvae were reared on fresh mulberry leaves under standard conditions at 25°C [61] . The silkworm ovary-derived cell line BmN used for transfection was cultured at 25°C in TC100 insect medium [31] . Total RNA was extracted from silkworm ovaries , testes , and other tissues using TRIzol reagent ( Invitrogen ) according to the manufacturer’s instructions . The isolated RNA was purified with phenol:chloroform and subjected to first-strand cDNA synthesis using the ReverAid First Strand cDNA Synthesis Kit ( Vazyme ) . Relative mRNA amounts were measured using SYBR Green Real-time PCR Master Mix ( Toyobo ) according to a previously described method [31] . The qRT-PCR primers used here were as following: BmSiwiRTF: 5’-ATCACCCCAGAAAGACAACG-3’ , BmSiwiRTR: 5’-GCACAGTATCAGGGCAGGAT-3’ , BmAgo3RTF: 5’-GAGCAGTGCACAAAGCGATA-3’ , and BmAgo3RTR: 5’-GGCACACCTGTTTCACCTTT-3’ . As an internal control for qRT-PCR , we used a primer set that amplified a 136-bp PCR product of B . mori ribosomal protein 49 ( Bmrp49 ) [31] . Three independent biological replicates were used for qRT-PCR , and other primers are listed in S1 Table . PiRNA sequences were found by referring to Kawaoka et al . [11] , and the relative expression was measured using the stem-loop method [62] . A binary transgenic CRISPR/Cas9 system was used to construct silkworm mutants as described in Li et al . [31] . Six plasmids were constructed: the first , pBac[IE1-DsRed-IE1-Cas9] ( IE1-Cas9 ) , expresses the Cas9 protein constitutively driven by the baculovirus immediate-early gene IE1 promoter; and the other five , U6-BmSiwi sgRNA ( pBac[A3-EGFP-U6-BmSiwi sgRNA] ) , U6-BmAgo3 sgRNA , U6-BmSETD2 sgRNA , U6-BmAsh2 sgRNA and U6-BmEggless sgRNA , express small guide RNAs ( sgRNAs ) targeted to BmSiwi ( 5’- CCTGAGTTGATATATCTAGTGCC-3’ ) , BmAgo3 ( 5’-GGAGTGAGTATAGGCGGTAGAGG-3’ ) , BmSETD2 ( 5’- CCATTAGCTAGTCCAGGTCTGCC-3’ ) , BmAsh2 ( 5’-GGCAACGTGAAGGGCAGGCAAGG-3’ ) and BmEggless ( 5’- GGAGGCGGCGCAGCTCCGCGCGG-5’ ) , respectively , under the control of the silkworm U6 small nuclear RNA promoter . The plasmids were injected into preblastoderm embryos with a mixture of helper plasmids , piggyBac transposon mRNA and transgenic vectors . G0 animals were incubated at 25°C for 10–12 d until hatching , fed with fresh mulberry leaves , sib-mated or back-crossed with WT moths , and screened at late G1 embryos under a fluorescence microscope ( Nikon , AZ100 ) . Crossing the IE1-Cas9 and U6-sgRNA transgenic silkworms generates the gene-specific mutants used for the following experiments . Total RNA from the ovary of wandering stage ( when the ovary undergoes maturation ) animals was extracted from three individual animals of Δsiwi , Δago3 and WT and mixed together . For mRNA sequencing , mRNA was enriched with Sera-mag Magnetic Oligo ( dT ) Beads ( Illumina ) , fragmented to 200 nt in average , and used for cDNA synthesis . After that , the cDNA was sent to purification , end repair , nucleotide A and adapters addition ( Illumina ) . Subsequently , the modified RNA was amplified with PE 1 . 0 and PE 2 . 0 PCR primers for 15 rounds and sequenced on an Illumina HiSeq 2000 platform ( Shanghai OE BIOTECH CO . , LTD ) . Sequenced raw data was qualified , filtered , and mapped to the reference silkworm genome database ( http://silkworm . genomics . org . cn/ ) using tophat/bowtie2 . Unigene abundance was measured by fragment per kilobase of exon per million fragments mapped ( FPKM ) and used for subsequent annotation . RNA samples extracted from the ovary were also used for piRNA sequencing . Ten micrograms RNA was separated using 15% denaturing polyacrylamide gels and the small RNAs in length from 18 to 30 nt were used to construct library . Subsequently , small RNAs were sent to adaptors ligation at both the ends , cDNA synthesis and amplification were performed by using small RNA Cloning Kit ( Takara ) . After sequencing with illumine HiSeq 2500 platform , the generated reads were filtered and small RNA reads from 24 to 30 nt in length were selected for mapping to the silkworm genome ( http://silkworm . genomics . org . cn/silkdb/# ) , 121 annotated transposons and 1668 ReAS clones to identify the piRNAs as reported previously [63] . Silkworm ovaries and testes dissected from WT , Δsiwi , Δago3 , Δash2 , Δsetd2 and Δeggless animals at larval wandering stage were prefixed with Qurnah’s fixative [31] . Cross sections of 5 μm were cut with a Leica RM2235 microtome and used for staining . Sections were hydrated and stained with hematoxylin solution for 2 min , washed with running tap water for 5 min , stained with eosin solution for 2 min and dehydrated with 95% and 100% ethanol for 2 min each [64] . The stained tissues were analyzed and photographed under a microscope ( Olympus BX51 , Japan ) . Paraffin-embedded sections were rehydrated and subjected to antigen retrieval with 0 . 1% trisodium citrate containing 0 . 1% Triton X-100 for 10 min at room temperature . The samples were washed with phosphate buffered saline ( PBS ) once and blocked with 1% bovine serum albumin ( BSA ) for 1 hour at room temperature . The silkworm gonads were incubated with Rabbit anti-BmVasa ( 1:200 , Youke Biotech , indicating the germline lineage cells ) [65] , anti-BmSiwi ( 1:200 , Youke Biotech ) , anti-BmAgo3 ( 1:200 , Youke Biotech ) , anti-BmAsh2 ( 1:200 , Youke Biotech ) and anti-H3K4me3 ( 1:200 , ABclonal ) primary antibodies for 48 hours at 4°C . Samples were washed with PBS twice and treated with a FITC-conjugated Goat-anti-Rabbit secondary antibody ( diluted 1:100 with 1% BSA , YEASEN ) for 2 hours . Nuclei were stained with Hoechst ( Beyotime ) for 10 min at room temperature . After staining , samples were washed three times with PBS and analyzed with a fluorescence microscope ( Olympus , BX53 ) . Flag-tagged BmAsh2 and His-tagged BmSiwi coding sequences were cloned into the pIZT/V5-His A insect expression plasmid under the control of an optimized baculovirus immediate-early gene promoter IE2 ( OpIE2 ) . The plasmids were transfected into the silkworm ovary-derived cell line BmN using Effectene transfection reagent ( Qiagen ) according to the manufacturer’s instructions . Three days after transfection , crude proteins were extracted and used for immunoprecipitation with a mouse monoclonal anti-Flag M2 antibody ( 1:1000 , Sigma ) according to Song et al . [66] . BmSiwi was detected using a Mouse anti-His ( 1:1000 , Youke Biotech ) primary antibody . All data were analyzed using GraphPad Prism ( version 5 . 01 ) with two-way ANOVA and the Dunnett’s tests . All error bars were the means ± S . E . M . p<0 . 05 was used to determine significance in all cases .
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Sex determination is an essential and universal process for metazoan reproduction and development . Insect sex determination is highly diverse , especially for the primary signal and transductory genes . Mechanism of sex determination in the model lepidopteran insect , Bombyx mori , is largely unknown , although a piRNA , named Fem , has been identified recently as the initial factor . In the current report , we generate somatic mutants for the two silkworm piRNA-bound proteins , BmSiwi and BmAgo3 , and identify that the histone methyltransferase BmAsh2 is involved in silkworm sex determination . Loss of BmAsh2 function produces a phenocopy of BmSiwi mutation and induces partial female-to-male sexual reversal . Importantly , we find the co-localization and protein interaction between BmAsh2 and BmSiwi , further supporting critical roles of BmAsh2 in the piRNA-mediated sex determination in B . mori .
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2018
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Bombyx mori histone methyltransferase BmAsh2 is essential for silkworm piRNA-mediated sex determination
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Genome-wide association analysis in populations of European descent has recently found more than a hundred genetic variants affecting risk for common disease . An open question , however , is how relevant the variants discovered in Europeans are to other populations . To address this problem for cardiovascular phenotypes , we studied a cohort of 4 , 464 African Americans from the Jackson Heart Study ( JHS ) , in whom we genotyped both a panel of 12 recently discovered genetic variants known to predict lipid profile levels in Europeans and a panel of up to 1 , 447 ancestry informative markers allowing us to determine the African ancestry proportion of each individual at each position in the genome . Focusing on lipid profiles—HDL-cholesterol ( HDL-C ) , LDL-cholesterol ( LDL-C ) , and triglycerides ( TG ) —we identified the lipoprotein lipase ( LPL ) locus as harboring variants that account for interethnic variation in HDL-C and TG . In particular , we identified a novel common variant within LPL that is strongly associated with TG ( p = 2 . 7×10−6 ) and explains nearly 1% of the variability in this phenotype , the most of any variant in African Americans to date . Strikingly , the extensively studied “gain-of-function” S447X mutation at LPL , which has been hypothesized to be the major determinant of the LPL-TG genetic association and is in trials for human gene therapy , has a significantly diminished strength of biological effect when it is found on a background of African rather than European ancestry . These results suggest that there are other , yet undiscovered variants at the locus that are truly causal ( and are in linkage disequilibrium with S447X ) or that work synergistically with S447X to modulate TG levels . Finally , we find systematically lower effect sizes for the 12 risk variants discovered in European populations on the African local ancestry background in JHS , highlighting the need for caution in the use of genetic variants for risk assessment across different populations .
A main motivation for genome-wide association studies ( GWAS ) of disease phenotypes has been the promise of identifying markers of disease risk , which may augment the prognostic value of conventional clinical measures [1] , [2] . Although many such variants have been identified , the corresponding studies have been based almost entirely in populations of European descent . Thus , it remains to be seen if these markers will have prognostic utility in admixed individuals , such as African or Hispanic Americans , in whom each chromosome is likely to be a mosaic of blocks of DNA from different ancestral populations . African Americans , for example , carry chromosomal segments that are derived predominantly from West African and European American ancestral populations . These two populations have been subject to differing demographic history , which has led to differences in allelic frequencies and patterns of linkage disequilibrium across the genome [3] , [4] . We have previously used a form of whole-genome scanning known as admixture mapping to search for genetic variants that differ markedly in frequency between continental populations and that also contribute to disease risk [5]–[9] . In persons of recently mixed ancestry , each region of the genome is mapped according to ancestral origin using a panel of markers that are highly differentiated in frequency between West Africans and European Americans . The resulting maps are then analyzed to identify genomic regions where individuals with disease have a marked deviation in the proportion of one of the parental ancestries from the genome–wide average [10]–[13] . Such regions in principle should contain disease variants . In the current work , we have investigated to what extent the local ancestry information used in admixture mapping can inform genetic association analysis in African Americans . We chose lipid and cholesterol levels as our phenotypes of interest for several reasons: 1 ) they represent the major modifiable risk factors for coronary heart disease; 2 ) several GWAS of lipid profiles have been conducted recently in populations of European descent , yielding multiple potential risk variants [14]–[18]; and 3 ) the lipoprotein profiles of African Americans and European Americans differ , beginning in childhood [19] and persisting into adulthood [20] , with African Americans having lower levels of triglycerides and very low density lipoprotein cholesterol , and higher levels of HDL-C and apolipoprotein A-I . We have incorporated local ancestry estimates into genetic association analysis of cholesterol and TG phenotypes in the Jackson Heart Study ( JHS ) [21] . We show , herein , how using such an analytic approach in admixed populations can inform an understanding of the genetic determinants of complex disease .
Using the ANCESTRYMAP software [10] , we can make precise estimates of individual ancestry in admixed individuals , which can be correlated with continuous and dichotomous phenotypes ( see Methods ) . In the 4464 genotyped JHS participants , we found a mean African ancestry of 83±9% ( Table 1 ) . We assessed the association of overall ancestry ( hereafter called global ancestry , in contrast to local ancestry of a chromosomal segment ) with each of our three traits by linear regression ( see Methods ) . Analysis was limited to unrelated individuals , with one individual selected randomly from each family , to avoid correlations arising from relatedness . One hundred separate lists of unrelated individuals were generated by repeated random sampling . We also performed logistic regression , comparing the top and bottom quintile of the lipid distributions , to see if there were differences in ancestry for extremes of the population . After adjusting for common covariates , which accounted for 10% of the TG variance , increased African ancestry was significantly associated with decreased triglyceride levels ( Table 2 ) in both linear ( p = 6 . 5×10−5 ) and logistic regression analysis ( p = 0 . 0046 ) . For every 10% decrease in African Ancestry , there is a 3 . 9±0 . 9% increase in triglyceride level . As shown in Table 2 , HDL-C is also significantly associated with African ancestry in both linear ( p = 0 . 0009 ) and logistic ( p = 0 . 017 ) regression analysis ( 0 . 74 mg/dl HDL-C decrease per 10% decrease in African ancestry ) . For LDL-C , we included individuals who were not on cholesterol-lowering medications ( n = 3521 ) . LDL-C shows a significant association only in a comparison of upper and lower quintiles ( p = 0 . 027 ) . Decreased LDL-C levels were associated with increased African ancestry . Our observed association of increased African ancestry with increased HDL-C and decreased serum triglycerides and LDL-C is in keeping with that seen in other cohorts [23] , [24] , although we were better able to quantify these relationships given the large size of the study population and greater informativeness of the marker panel . These findings support the hypothesis that some of the population variation in lipid traits is indeed genetic , and can be attributed to variants that have different frequencies in African and European populations . Furthermore , this lipid profile of elevated HDL-C , decreased triglycerides , and decreased LDL-C would typically be considered protective towards atherosclerosis and motivates a search for contributing genetic variants . We conducted a genome-wide admixture scan by genotyping our 4464 individuals with the markers informative for ancestry as described above . We then used the ANCESTRYMAP software to estimate the probability that each chromosomal segment in the genome arose from either a European or African ancestor , and evaluated the association of this ancestry estimate with TG/cholesterol traits . The ANCESTRYMAP software is optimized for dichotomous variables , so we defined cases and controls for each of the three traits as the top and bottom quintiles . Thus in distinction to the quantitative variable analysis shown above , we have used a categorical variable for admixture mapping . Table S1 shows the results of the whole genome scan for all admixture runs . Despite the promising association of global ancestry with TG , HDL-C and LDL-C , we find no peaks meeting either our suggestive or significant thresholds of association . To quantify the lack of association of lipid profile phenotypes with ancestry , we constructed an exclusion map to rule out loci in the genome as contributing substantially to ancestry-related risk of being in the top or bottom quintile ( categorical variable ) for each trait . For each position in the genome and for each trait we estimated a credible interval for the factor by which the risk for the phenotype due to African ( or European ) ancestry differs from that due to the opposite ancestry . For all traits analyzed , and at a significance of p<0 . 05 , 96–100% of the genome could be excluded as bearing risks >1 . 5-fold due to African or European ancestry ( Table S2 ) , suggesting that no single gene makes such a strong contribution to interethnic differences in TG and cholesterol traits . We also constructed a more traditional exclusion map , excluding markers for each trait for which the LOD score was <−2 ( Figure S1 ) for a risk model of 1 . 5; this also excluded 82–89% of the genome . However , our ability to exclude genetic ancestry effects in the range of 1 . 2–1 . 5-fold risk , as seen for most common disease variants identified to date [25] , is much less ( 48–59% of genome ) . Thus we sought other , integrative methods to identify genetic determinants of interethnic variation . Whole-genome scanning by admixture mapping and GWAS has led to the identification of many new disease-associated gene variants . However , the stringent criteria for declaring genome-wide significance , resulting from the low prior probability of the multiple hypotheses tested ( >1000 in admixture mapping; >100 , 000 in GWAS ) , have limited the number of peaks followed up , and minimal overall heritability has been explained for most complex traits . Many loci that do not meet genome-wide significance might in fact be found to be associated with the trait of interest if fine-mapping strategies were pursued . Prioritization of such follow-up can be done by integrating data beyond the genome scan itself , such as meta-analysis with other GWAS [15] , [16] , integration of linkage data with GWAS [26] , or prioritization using literature-based searches for candidates [9] . Five recent manuscripts [14]–[18] have identified and/or validated 19 loci influencing TG , HDL-C and LDL-C levels in whole genome association analyses . We determined local ancestry at the most strongly associated SNP at each of these loci in our cohort to see whether local European or African ancestry is associated with variation in TG/cholesterol levels . A significant association would not only suggest that the loci are important for lipid variation in the JHS population , but that ethnic differences in lipid profile could be attributable to frequency differences in the associated risk alleles . Table 3 shows p-values for the association of local ancestry with the three lipid and cholesterol traits . Of the 27 associations tested , ancestry was associated with TG , HDL-C or LDL-C at three loci with p<0 . 05 , including one at the p<0 . 005 level . Although none of these associations would meet a p<0 . 05 significance threshold with strict Bonferroni correction ( 0 . 0018 for 27 hypotheses tested ) , we propose that for such strongly validated genes even a weakly positive result may warrant further investigation . Our local ancestry association analysis identified LPL ( p = 0 . 01 , HDL-C ) , GCKR ( p = 0 . 01 , TG ) , and ApoB ( p = 0 . 003 , LDL-C ) as plausible genes for interethnic variation in lipid profile . Of these , LPL also showed suggestive association with TG ( p = 0 . 14 ) . Furthermore , African Americans have been shown to have higher LPL activity than European Americans , and in the same study , inclusion of LPL activity in regression models rendered the association of ethnicity with triglyceride levels non-significant [27] . We therefore decided to pursue fine mapping of LPL to seek specific variants that contribute to interethnic variation in TG and HDL-C levels . Using Tagger [28] , we selected a dense panel of SNPs spanning the LPL locus and extending 10 kB upstream of the transcriptional start site and 15 kb downstream of rs10096633 ( see below ) . Tagging SNPs were selected at an r2 of 1 . 0 with MAF of ≥2% in the Yoruba West African HapMap population ( YRI ) . We then forced these SNPs into Tagger to tag this region in CEU at an r2 of 1 . 0 with MAF of ≥5% . All lipid profile GWAS to date [14]–[18] have found extremely strong association of TG with the well-known rs328 SNP or a perfect proxy . We therefore included 2 perfect proxies for this SNP in CEU , rs325 and rs17482753 , yielding a total of 95 SNPs . Both global and local ancestries can be significant confounders of the association of a SNP with a phenotype in an admixed population , such as African Americans . If a trait such as TG or HDL-C is strongly associated with overall African or European ancestry , then any SNP with frequency differentiation between Europeans and Africans may appear falsely associated with the trait , a problem known as population stratification . This confounding can be corrected by including a term for global ancestry in the regression model . Locally within the genome , however , strong admixture linkage disequilibrium ( i . e . blocks of shared ancestry ) can extend over many centimorgans . If the true risk variant differs in frequency between European Americans and Africans , any SNP within the block of admixture linkage disequilibrium ( LD ) may appear causally related to the trait if it too is sufficiently differentiated in frequency to be informative for local ancestry . This confounding occurs even after correcting for global ancestry and is in fact the basis for admixture mapping . Fortunately , it can also be addressed , by inclusion of a term for local ancestry in the regression model . A final problem is that for many genes , the pattern of LD varies depending on the ancestral origin of the chromosomal segment . In general , blocks of LD are much shorter in historically large populations such as Africans than in historically smaller populations like Europeans . Thus a SNP that is strongly associated with a trait of interest in one population may show little or no association in another population because of differing patterns of LD . This may help identify the actual risk variant since , within an admixed population , a lone causal variant would be expected to have a similar effect size on any local ancestral background . In our cohort , we calculated a linear regression residual for each trait , where the regression model included the covariates described above as well as global ancestry for each individual . This residual was tested for association by stepwise regression with local ancestry , the SNP genotype ( in an additive model ) , and a term for the SNP genotype-local ancestry interaction , computed as a product of the local-ancestry term and SNP genotype ( see Methods ) . Statistical significance for genotype , and genotype-local ancestry interaction were evaluated using ANOVA , with nested regression models . As another approach to potential confounding by differing LD patterns , we tested association of each SNP separately in a subpopulation of 1860 individuals who had >95% probability of two African ancestral chromosomes in the region ( JHS-AFR-2LPL ) , thus minimizing heterogeneity in local ancestry background , and a subpopulation of 780 individuals with >95% probability of at least one European ancestral allele ( JHS-EUR-1_2LPL , see Table 1 for demographic characteristics of these subgroups ) . We also identified a small subgroup of 65 individuals with >95% probability of two European ancestral alleles ( JHS-EUR-2LPL ) for calculation of allele frequencies and linkage disequilibrium parameters ( Table 4 , Table S3 ) . We successfully genotyped 85 of the 95 tagging SNPs . The strength of the associations for each SNP with TG and HDL-C are shown in Tables S4 and S5 with the top SNPs shown in Tables 4 and 5 . In Table 4 , three separate p-values per trait are shown for each SNP , representing association across all 3300 individuals ( p_all ) , in the JHS-AFR-2LPL ( p_afr ) , and for the significance of the genotype-by-ancestry interaction term ( p_trg_int ) . For triglycerides , six of the 85 SNPs were associated at a significance level of p<0 . 0006 ( Bonferroni correction for 85 hypotheses at p<0 . 05 significance level ) , including rs328 and all 4 SNPs having r2 = 1 with rs328 in JHS-EUR-1_2LPL: rs1011685 ( p = 2 . 0×10−5 ) , rs325 ( p = 4 . 5×10−5 ) , rs17482753 ( p = 4 . 9×10−5 ) , rs12679834 ( p = 2 . 7×10−4 ) . For these 5 SNPs pairwise r2 in JHS-AFR-2LPL ranges from 0 . 614-1 ( Table S3 ) . The strongest association with TRG was in fact for rs10096633 ( p = 2 . 7×10−6 ) , where the risk allele for elevated triglycerides is found at a frequency of 0 . 52 in YRI , and 0 . 87 in CEU ( 0 . 50 in JHS-AFR-2LPL and 0 . 82 in JHS-EUR-2LPL; Table 4 ) . rs10096633 is strongly linked to rs328 in JHS-EUR-2LPL ( r2 = 0 . 79 ) but not so in JHS-AFR-2LPL ( r2 = 0 . 059 ) . The rs328 variation leads to a premature stop codon , producing a protein two amino acids short of a full-length product . It has been presumed , based primarily on genetic association data and physiologic studies of S447X carriers [29] , that this S447X variant represents a “gain-of-function” mutation with increased LPL activity . Interestingly , the genotype-local ancestry interaction terms were significant for rs328 ( and rs325 ) ( Table 4 ) , indicating a different strength of association on the African and European local ancestry backgrounds . For the African American population as a whole , the estimated effect size was 10 . 2±2 . 4% in TG level per rs328 allele . Estimated effect sizes on the African and European local ancestry backgrounds were obtained by repeating the regression modeling using the JHS-AFRLPL and JHS-EURLPL subpopulations . In this analysis , the effect size per rs328 allele was 4 . 5±3 . 4% on the African local ancestry background ( Table 4 , p = 0 . 22 ) and 18 . 6±4 . 5% in the subgroup with one or more European LPL chromosomes . The results are similar for the highly correlated rs325 , rs17482753 , rs1011685 and rs12679834 SNPs , with all showing a significant genotype-local ancestry interaction . The significant dependence of effect size on local ancestral background suggests that the effect seen in association studies in European populations is not predominantly mediated by rs328 , but arises either from linkage of rs328 and other highly correlated SNPs to some other causal variant , or from the aggregate effect of multiple tightly linked causal variants . The effect size for rs10096633 is more comparable in the three populations: ( 6 . 2±1 . 2% , 4 . 5±1 . 6% , and 6 . 6±3 . 1% for total , JHS-AFR-2LPL , and JHS-EUR-1_2LPL , respectively; Table 4 ) . Although some SNPs show a marked difference in effect size between local ancestry backgrounds , the overall difference in genotype regression coefficients for the 85 SNPs in JHS-AFR-2LPL and JHS-EUR-1_2LPL falls short of statistical significance ( p = 0 . 07 , Wilcoxon signed-ranks test ) . To determine if a multi-SNP model could better explain the variability in TG levels , we performed stepwise linear regression combined with ANOVA ( see Methods ) for the 100 sets of unrelated JHS members to identify the top independent SNP signals . The most frequently observed model included rs10096633 , rs1031045 , rs3779788 , rs11995036 . A list of the top models is shown in Table S6a . Previous studies have established that LPL activity also influences HDL-C levels , presumably by producing remnants of triglyceride-rich lipoproteins , which can be used for HDL assembly [30] . The LPL rs328 SNP and its proxies have also been shown to be directly associated with HDL-C levels in populations of European descent [14] . We therefore looked for association of our fine-mapping LPL SNPs with HDL-C levels ( Table 5 for top SNPs , Table S5 for all SNPs ) and found association of one SNP with HDL-C at the p<0 . 0006 significance level: rs13702 ( r2 = 0 . 72 with rs10096633 in JHS-AFR-2LPL , and allele frequency of 0 . 43 in JHS-AFR-2LPL ) . The top model of independent signals for HDL-C is rs13702 , rs3289 , rs343 , rs10283151 , rs2197089 , rs6651471 , rs9644636 ( Table S6b ) . The hypothesis of a differential effect size for rs328 on different local ancestry backgrounds may be further evaluated using individuals who are heterozygotes for both genotype and local ancestry background . Presumably genotype heterozygotes might show different TG levels depending on whether the allele for higher TG resides on the African or European local ancestry background at LPL . However , when we used ANCESTRYMAP to output phased genotypes at rs328 ( data not shown ) , the number of individuals for whom we could generate confident estimates of phase and local ancestry was insufficient for comparison . In conclusion , using local ancestry estimates at the LPL locus to minimize confounding by population stratification , we have identified novel common variants within the LPL gene that are moderately differentiated in frequency between African and European Americans , and strongly associated with TG and HDL-C levels . Furthermore , analysis of differential local ancestry backgrounds suggests that the rs328 SNP explains at most a modest amount of the TG variation at the LPL locus seen in association studies in populations of European descent . To explore whether the use of local ancestry estimates informs the analysis of other SNPs known to be strongly associated with lipid and cholesterol traits , we genotyped 12 index SNPs ( from 10 genes ) that met genome-wide significance in recent lipid/cholesterol GWAS . These have not yet been evaluated for association in a large African-American population such as JHS . Table 6 shows the overall p-value and a comparison of effect sizes in the JHS-AFR-2 ( n = 1727–1945 , depending on the gene ) and JHS-EUR-1_2 ( n = 540–728 ) subgroups at the locus of interest , and the p-value for interaction of local ancestry with genotype . We replicate the original association at p<0 . 05 for 7 of the 12 SNPs ( excluding rs328 ) . The failure to replicate for the others may be a consequence of reduced power due to sample size ( the original meta-analyses exceeded 8000 individuals ) or a lower risk allele frequency arising from genetic drift . There may also be fundamental differences in effect size between European and African Americans , reflecting unidentified gene×gene or gene×environment interactions . Just as with LPL , we can compare effect sizes in JHS-AFR with JHS-EUR to look for any systematic variation with ancestral background . Given that individuals in the cohort are likely to share similar environments ( independent of local ancestry at the site of the tested variant ) , this type of internal comparison of effect sizes is superior to comparing effects across different ethnic populations , where multiple confounding factors may play a role . We see that for 10 of the 12 index SNPs ( excluding rs11591147 , which is fixed in YRI , but including rs328 ) , the magnitude of the effect is stronger in the European local ancestry subgroup than in the African local ancestry subgroup , which is significant at p = 0 . 034 by the Wilcoxon signed rank test ( using standardized residuals as the predicted variable ) . We note that some of the differences are very small; nonetheless , this estimate is probably conservative , as our JHS-EUR-1_2 subgroups include many individuals with one allele of African local ancestry at LPL . The systematic difference in effect size suggests that the majority of these index SNPs are markers for the major causal SNP ( s ) , with weaker SNP-SNP correlations seen in the ancestral West African population than in the ancestral European population , leading to smaller effect sizes . Some examples are illustrative . rs3135506 , which encodes for a S19W mutation in the endoplasmic reticulum signal peptide of ApoA5 [31] is likely responsible for the majority of the effect seen in association studies in both populations , given its similar effect sizes in JHS-AFR-2 ApoA5 ( 11 . 9±0 . 9 mg/dL ) and JHS-EUR-1_2 ApoA5 ( 14 . 8±7 . 0 mg/dL ) . Further , it is not likely that there are other major causal variants linked to rs3135506 in one population but not the other . The intronic rs780094 variant in GCKR is significantly associated with TG in JHS ( p = 0 . 00038 ) . The effect size in JHS-AFRGCKR ( 5 . 5±1 . 1% per allele ) is smaller than that seen in JHS-EURGCKR ( 8 . 4±3 . 2% per allele ) , although the p-value for interaction is not significant ( p = 0 . 56 ) . This observation can be explained by considering LD patterns in the respective HapMap populations with respect to the likely causal allele , rs1260326 , which encodes a leucine to proline change at amino acid 446 [16] . The r2 value of rs780094 with rs1260326 is 0 . 93 in CEU but only 0 . 42 in YRI . This modest difference can explain the similarly modest difference in effect sizes between JHS-EUR-2GCKR and JHS-AFR-2GCKR , although given our relatively small sample size , we cannot exclude the contribution of chance variation . Local ancestry analysis reveals a similar pattern for most of the other SNPs tested , with significant overall association and with effect size in JHS-EUR exceeding that in JHS-AFR . A more marked variation in effect size with ancestry is seen for rs662799 in ApoA5 . This SNP , found in the 5′ UTR of the ApoA5 gene , has a strong association in Willer et al [16] with an estimated effect size per allele of 16 . 9 mg/dl . An even stronger effect size is seen in JHS-EURApoA5 ( 21 . 5±5 . 5% per allele ) but the effect is negligible in JHS-AFRApoA5 ( 1 . 1±1 . 0% per allele ) , leading to a significant p-value for interaction ( p = 0 . 03 ) . rs662799 and rs328 ( LPL ) are thus the two SNPs that show a sufficiently marked difference in effect sizes between local ancestry subgroups to have a significant interaction effect .
We have demonstrated that local ancestry-based analysis in admixed populations such as African Americans adds novel insights beyond studies of genetic determinants of disease performed in populations of European descent . We used precise individual ancestry estimates to show a highly significant association of individual African/European ancestry with serum TG and HDL-C , suggesting strongly that genetic factors account for at least some of the epidemiologically observed interethnic differences . However , using local ancestry estimates in the context of whole-genome admixture mapping for TG , HDL-C , and LDL-C , we did not identify any genes with strong contributions to these trends . Thus , although it is likely that there are genetic variants whose frequency differences contribute to interethnic variability in lipid profile , these appear to have relatively modest effects . This is in keeping with our prior work on hypertension [9] , and with the generally modest effects of lipid profile variants seen in recent GWAS/meta-analysis studies [14]–[18] . Analysis of the association of local ancestry with TG/HDL-C/LDL-C at validated lipid profile loci led to prioritization of LPL for fine-mapping . Overall , we identified rs10096633 as a credible candidate for variation in TG levels , and rs13702 as a candidate for variation in HDL-C . Remarkably , analysis within the homozygote African local ancestry background showed that the extensively studied rs328 variant , which encodes a premature stop codon in LPL , is directly responsible for at most a modest amount of variation in TG levels . The S447X premature stop codon , caused by the rs328 variant in LPL , has a contentious history as a putative gain-of-function mutation , with considerable debate as to whether it demonstrates increased lipolytic activity [29] , [32] . Interestingly , it has also been associated with a reduced risk of cardiovascular disease , including myocardial infarction [33] . Largely on the basis of the observed beneficial associations of the S447X variant in population studies , therapeutic trials with viral delivery of the S447X LPL transgene have been conducted in animal models [34] , [35] , and safety trials in lipoprotein lipase-deficient patients have also begun [36] . Our findings suggest that the S447X variant may not be the major causal SNP within LPL that influences TG levels , and that it is primarily a marker for the causal variant ( s ) in European-derived populations . This finding—taking advantage of the unique short range linkage disequilibrium in chromosomes of African ancestry—emphasizes the challenges of assigning causality based on genetic association , and is cautionary for the marketing of genetic marker-based tests for disease risk assessment . When these tests are based on any variant other than the causal allele , utility may not extend to ethnic groups differing from the original study population . Prognostication in African Americans based on a SNP such as rs328 would require knowledge of LPL local ancestry for each individual while , for a SNP like rs10096633 , with more uniform effect size , this information would not be required . Although SNPs with much weaker effects on one ancestral background may just be non-functional markers , situations could also exist where they could be causal but still have differential effects . The most plausible scenario might be that multiple functional variants comprise a single haplotype more frequently in one subpopulation than in the other . This might be the case more often in European populations ( and on local European ancestral background in admixed populations ) , where LD extends over longer distances , resulting in systematically lower effect sizes on African local ancestry backgrounds for SNPs identified in GWAS studies of European-derived populations . Even in the case where there is only a single causal variant being tested , gene-gene epistatic effects from variants at other loci that are at different frequencies in the two subpopulations could influence effect size . Finally , environmental influences on effect size may also differ by ancestral background . In JHS-EUR-1LPL ( 19% of our population ) , the effect sizes for rs328 ( minor allele frequency of 12 . 5% in CEU ) and closely correlated SNPs are large , ranging from 18–22% of variation in TG levels per risk allele , and accounting for almost 4% of the residual trait variance . This effect is comparable to or exceeds the effect of rs328 ( or its proxies ) in populations of European descent , which has been estimated at 14 mg/dL [16] or 13 . 8% [17] per allele . The strongest effect size for LPL variants in JHS-AFR-2LPL is less , at 6% ( for rs10096633 and rs13702 ) . Since we tagged the LPL gene densely this raises the question of why we failed to find a variant with a large effect size on this African local ancestry background . Possible explanations include that the rs328 variant is in LD with multiple variants affecting TG levels in the European local ancestry background , that epistatic effects of other frequency-differentiated SNPs interact with that of rs328 , that the true risk variant ( s ) may lie outside our tagging boundaries , or that a strong risk allele in LD with rs328 in Europeans is at very low frequency in the West African population . We extended our local ancestry based association analysis to previously validated TG/cholesterol loci , and found replication for 7 of 12 loci at p<0 . 05 . However , we observed an overall trend of weaker effect size in JHS-AFR than JHS-EUR , with some marked differences such as rs662799 in ApoA5 , suggesting that many of these “index” SNPs are merely tagging causal alleles , with the effect size depending on the correlation between the index SNP and the causal SNP ( s ) . Again , this has clear implications for the use of SNPs identified in one ethnic population as markers of disease risk in other groups with differing demographic history . In conclusion , we have developed a local-ancestry based approach to genetic association analysis in admixed populations , and using it , we have identified several variants in the LPL gene that contribute to African/European American differences in lipid profiles . The differing patterns of linkage disequilibrium on different local ancestry backgrounds have allowed us to explore plausibility of causality for established variants . We have also laid the foundation for future studies to identify variants that would be suitable as risk markers in African American populations . Finally , our work highlights some of the challenges and opportunities that derive from extending the results of genetic association analyses across ethnic groups , and admixed groups in particular .
Linear and logistic regression analyses were used to evaluate association of global ancestry with lipid and cholesterol phenotypes . For linear regression , individuals were ranked in terms of increasing quantitative trait , and the top and bottom 0 . 5% of individuals were eliminated from further analysis as these extreme phenotypes are thought to be more likely attributable to monogenic disorders . For the LDL-C analyses , all individuals on cholesterol-lowering medication were also eliminated , as this trait is particularly sensitive to therapy . For the logistic regression analysis , “cases” and “controls” were selected as the top and bottom quintile of the distribution for each trait . A logistic regression model was then developed to predict case or control status for each trait including age , age2 , BMI , BMI2 , gender , type 2 diabetes mellitus and smoking status as possible covariates , with covariates selected for the model by stepwise forward regression . The significance of association of case/control status and global African/European ancestry was assessed using nested logistic regression models and the likelihood ratio test [37] . We randomly selected only one member of each family to be included in all genotypic analyses . To minimize bias , we generated 100 overlapping sets of 3300 unrelated individuals and conducted all statistical analyses on each set , averaging the results . For linear regression , triglyceride values were log transformed . The individual's multivariable-adjusted lipid residual was used as the phenotype in phenotype association analyses with global ancestry , local ancestry , and genotype . For genotype-phenotype association analyses , we assumed an additive model of inheritance and tested for the strength of association by ANOVA [38] with nested linear regression models , which included local ancestry , local ancestry+genotype , and local ancestry+genotype+genotype×local ancestry interaction . The genotype-local ancestry interaction term was computed as a product of the local-ancestry term and SNP genotype , and is a continuous variable ranging from of 0 to 2 . We also conducted separate linear regression in a subset of 1860 individuals with a >95% probability of two African ancestry alleles at the LPL locus and in a subset of 728 individuals with a high ( >95% ) probability of 1 or more European ancestral alleles ( local ancestry >48% European ancestry ) . For the subset of individuals with homozygous African local ancestry , we tested the association of the multivariable-adjusted lipid residual against the SNP genotype in an additive model of inheritance . Effect sizes were estimated by evaluating what effect a unit change in genotype would have on the predicted value of the trait . Multi-SNP models were identified with stepwise linear regression and the anova function in R . For each of the 100 sets of 3300 individuals , the top SNP associated with the phenotype residual of interest was identified and added to the regression model . The remaining SNPs were then each tested by comparing the model with and without the SNP by ANOVA and the most significant SNP included if the p-value for comparing was <0 . 05 . This process was continued for each of the 100 sets of individuals until no additional SNP improved the model at the p<0 . 05 threshold . For each SNP that retained significance after a Bonferroni correction , we re-estimated local ancestry at LPL by forcing it into the set of markers used to estimate local ancestry ( see below ) , so that the joint distribution of ancestry and genotype could be appropriately tested for association . Estimation of allele frequencies and r2 for SNPs was performed using the R GeneticsBase package . All statistical analyses were performed using R ( 2 . 7 . 0 ) . The samples in this study ( n = 4605 ) were all self-identified African Americans in the Jackson Heart Study [21] . Between Sept . 2000 and March 2004 , 5 , 302 African Americans were recruited from three counties , Hinds , Rankin , and Madison , which comprise the Jackson , MS metropolitan area . Unrelated JHS participants were drawn from three sources in roughly equal numbers: ( 1 ) former ARIC participants; ( 2 ) participants selected randomly from a commercial residential listing; and ( 3 ) a constrained volunteer sample for which demographic cells for recruitment were designed to mirror the overall target population . For each trait ( TG , LDL-C , HDL-C ) , the linear regression residual was calculated for each individual with either a minimally adjusted model , using age , age2 , and gender as possible covariates , or a fully adjusted model with age , age2 , BMI , BMI2 , gender , type 2 diabetes mellitus and smoking status as possible covariates . Individuals were ranked by regression residual , and the top and bottom quintiles of individuals were selected to be cases and controls respectively for the admixture scans . For each trait and each regression model ( 3 traits×2 regression models ) , we selected only one member of each family to be included in the admixture run . This was selected to be the individual with either the highest regression residual for that run or the lowest regression residual for that run . A separate run was performed for each combination , leading to a total of 12 admixture scans ( Table S1 ) . The ANCESTRYMAP software[10] was used for all analyses . The program generates local ancestry estimates by integrating information from a panel of densely spaced markers differentiated in frequency between African and European populations . The 4464 JHS individuals were genotyped on one of two panels of markers informative for West African vs . European ancestry [8]: 976 were genotyped on an older “Phase 2” Panel of 1536 markers , and 3488 were genotyped on an updated “Phase 3” Panel of 1536 markers . After quality checks , 1408 SNPs in the “Phase 2” Panel and 1447 SNPs in the “Phase 3” Panel were used for subsequent analyses . The LOD score for association , defined as the log ratio of the likelihood of the data under a disease model divided by the likelihood of the data under no disease model , was evaluated at equally spaced points across the genome . At each of these points , the disease likelihood was evaluated with a multiplicative risk model , with risk of disease integrated over the inheritance of 0 , 1 , and 2 copies of an African ancestral allele . Being based on Bayesian statistics , the ANCESTRYMAP software requires specification of a prior distribution of risk models; we used a range of ten risk models from 1 . 5-fold increased risk due to inheritance of one African ancestral allele to 1 . 5-fold increased risk due to inheritance of one European allele , with cases , controls , or both used in the analysis . For each point in the genome , we averaged the Bayes factors generated for each risk model , with the LOD score corresponding to the log-base-10 of this number . Frequency estimates for each of the SNPs in Africans and Europeans , were obtained with previously published data [6] , [39] and data from the International HapMap Project [40] . These samples provided a Bayesian prior distribution for the parental population allele frequencies as described in reference [10] . To obtain credible intervals for increased risk due to African ancestry across the genome , we repeated the procedure described in [9] . We ran ANCESTRYMAP repeatedly for 65 separate disease risk models ( 0 . 40 , 0 . 42 , 0 . 44 , 0 . 46 … , 1 . 66 , 1 . 68 and 1 . 70-fold increased risk due to one European allele ) , and searching for the maximum likelihood risk model . We evaluated LOD scores at equally spaced points across the genome , and for each point we averaged the LOD scores for the four runs of each lipid or cholesterol trait . The 95% credible intervals for increased risk due to African ( or European ) ancestry were obtained by a likelihood ratio test , with the interval including all risk models for which the log-base-10 of the likelihood of the disease model was within 0 . 883 of the maximum . We also computed an exclusion map for each trait for a 1 . 5-fold increased risk of Case status with inheritance of one copy of the African ( or European ) local ancestral allele . We evaluated the Cases-only LOD score at 3 , 622 equally spaced points across the genome , and excluded points with LOD scores <−2 . The percentage of points excluded was 84 . 2% ( increased risk due to African ancestry ) and 88 . 5% ( increased risk due to European ancestry ) for TG; 82 . 6% and 91 . 6% for HDL-C; and 81 . 8% and 88 . 9% for LDL-C . For each of the previously validated lipid and cholesterol loci , we identified the genetic position of the most strongly associated SNP using NCBI Build 35 of the public genome reference sequence ( http://genome . ucsc . edu ) and selected the closest marker ( among equally-spaced markers across the genome , see above ) to estimate the local ancestry at that locus . Local ancestry was tested for association with lipid phenotypes in a linear regression model as detailed above . http://genepath . med . harvard . edu/̃reich for our ANCESTRYMAP software . APOE – 348; HMGCR – 3156; LDLR – 3949; MVK – 4598; NCAN; PCSK9 – 255738; SORT1 – 6272; ABCA1 – 19; APOA5 – 116519; CETP – 1071; GALNT2 – 2590; LIPC – 3990; LIPG – 9388; LPL – 4023; MLXIPL – 51085; TRIB1 – 10221; ANGPTL3 – 27329; APOB 338; GCKR 2646; NCAN – 1463 .
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Single-base changes in DNA can affect biochemical measures , such as blood cholesterol or lipid levels . Such changes or “variants” can be associated with a trait either because they cause the trait or because they are linked to other causal variants . In either case , the associated variant ( s ) may be useful in predicting the trait . The chromosomes in which DNA is packaged cross over and recombine with each other in each generation , so that in historically separate populations , such as Africans and Europeans , the patterns of genetic linkage between variants differ . In the current study , we analyzed a large group of African Americans , testing genetic variants that had been associated with cholesterol and lipid levels in European-derived populations to assess their predictive value on two different genetic backgrounds within the same cohort . The ability of some variants to predict cholesterol or lipid traits was strongly dependent on genetic background , indicating that they may be tightly linked to other causal variant ( s ) in European populations and may not , themselves , be directly responsible for trait variability . We conclude that the predictive value of specific variants for risk assessment can differ critically across populations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cardiovascular",
"disorders/coronary",
"artery",
"disease",
"genetics",
"and",
"genomics"
] |
2009
|
Genetic Differences between the Determinants of Lipid Profile Phenotypes in African and European Americans: The Jackson Heart Study
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Nucleoporins are the constituents of nuclear pore complexes ( NPCs ) and are essential regulators of nucleocytoplasmic transport , gene expression and genome stability . The nucleoporin MEL-28/ELYS plays a critical role in post-mitotic NPC reassembly through recruitment of the NUP107-160 subcomplex , and is required for correct segregation of mitotic chromosomes . Here we present a systematic functional and structural analysis of MEL-28 in C . elegans early development and human ELYS in cultured cells . We have identified functional domains responsible for nuclear envelope and kinetochore localization , chromatin binding , mitotic spindle matrix association and chromosome segregation . Surprisingly , we found that perturbations to MEL-28’s conserved AT-hook domain do not affect MEL-28 localization although they disrupt MEL-28 function and delay cell cycle progression in a DNA damage checkpoint-dependent manner . Our analyses also uncover a novel meiotic role of MEL-28 . Together , these results show that MEL-28 has conserved structural domains that are essential for its fundamental roles in NPC assembly and chromosome segregation .
Metazoans have an open mitosis , in which the nuclear envelope ( NE ) disassembles during prophase to allow chromosome segregation and then reassembles around condensing chromosomes at anaphase [1] . During this process , the nuclear pore complexes ( NPCs ) are disassembled then rapidly reconstructed . ELYS , a large AT-hook domain protein , is essential for the late-mitosis rebuilding of the NPC [2] . ELYS is the first NPC component to associate with chromatin at the end of mitosis [3 , 4] and this association is required for the recruitment of the NUP107-160 subcomplex of the NPC , which in turn recruits vesicles containing the membrane-bound nucleoporins POM121 and NDC1 [4] . Thus ELYS binding to chromatin represents the first step in the post-mitotic building of the pore , and all other steps in its manufacture are dependent on this ELYS/chromatin interaction . ELYS was originally identified in a cDNA subtraction screen seeking genes expressed at high levels in the mouse embryonic sac [5] . Mouse elys knockouts die in the preimplantation stage because of cell death within the inner cell mass [6] . ELYS function is essential in all metazoa and is particularly important in rapidly dividing cells [7 , 8] . In C . elegans , the orthologous MEL-28 protein dynamically localizes to the nucleoplasm and NPC at interphase and then at the kinetochore and spindle at metaphase [9 , 10] . Consistent with its localization pattern , embryos that lack mel-28 function have severe defects with NE function , mitotic spindle assembly and chromosome segregation and are unviable . The ELYS/chromatin interaction has been studied extensively in vitro using Xenopus cell extracts . ELYS binds to chromatin during interphase but not at metaphase [11] , when it instead associates with the spindle and kinetochore [12] . Chromatin immobilization assays have shown that the most C-terminal fragment of ELYS , corresponding to amino acids ( aa . ) 2281–2408 , is sufficient for chromatin binding . This region includes the AT hook , a motif that binds to AT-rich DNA . However the aa . 2281–2408 fragment with a mutated AT hook and a C-terminal fragment that excludes the AT hook ( aa . 2359–2408 ) also bound to chromatin [4] . A nucleosome binding assay showed that a large C-terminal fragment that includes the AT hook ( aa . 2281–2408 ) was sufficient to bind to nucleosomes , whereas a piece that includes just the AT hook ( aa . 2281–2358 ) or just the region C-terminal to the AT hook ( aa . 2359–2408 ) could not bind to nucleosomes [13] . Additionally , incubation of Xenopus extracts with the C-terminal 208-aa . fragment of ELYS prevented native ELYS from binding to sperm chromatin and also prevented the recruitment of other nucleoporins to the nuclear rim , phenocopying the elys loss-of-function phenotype [11] . However , introducing a C-terminal fragment with a mutated AT hook does not disrupt nuclear pore assembly and is less effective at outcompeting the endogenous ELYS from binding to chromatin [4] . These in vitro experiments suggest that both the AT hook and other domains of the C terminus are important for the ELYS/chromatin interaction and the subsequent rebuilding of the NPC . The ELYS/chromatin association has also been studied using mouse in vitro fertilization . During fertilization in mice , sperm chromatin is rebuilt de novo using histones present in the oocyte . Experiments using in vitro fertilized mouse oocytes depleted of histones showed that ELYS does not localize to the NE of the sperm pronucleus in the absence of histones , which in turn prevents the recruitment of other nucleoporins [14] . ELYS can be artificially targeted to the NE in the absence of histones by fusing it with a domain from an inner NE protein . This chimeric ELYS protein not only localizes to the NE but also recruits the other nucleoporins . This suggests that ELYS binding to chromatin is required for its localization to the nuclear rim , which in turn allows the remainder of the nuclear pore to be built . The overall architecture of MEL-28/ELYS is similar throughout the metazoa ( see schematic representations in Figs 2C and 7B ) . All metazoan MEL-28/ELYS homologs include an N-terminal β-propeller domain , a central α-helical domain , and a C-terminal domain that includes at least one AT hook . Crystal structure determination of the N-terminal domain of mammalian ELYS showed that it forms a seven bladed β-propeller structure with an extra loop decorating each of the propeller blades [15] . In human cells , the N-terminal 1018 amino acids of ELYS ( which includes the β-propeller domain and the central α-helical domain but not the C-terminal AT hook ) is sufficient to localize the protein to NPCs [15] . Mutational disruption of the conserved loop on blade 6 of the β-propeller domain ( “loop2” ) prevents the 1–1018 aa . fragment from localizing to the nuclear rim . Despite the interest in defining the functional domains of MEL-28/ELYS , until now there have been no studies in which the phenotypic consequences of disrupting specific domains have been studied in developing animals . In this work , we have dissected the MEL-28 protein and studied its localization and function in live C . elegans embryos . We have identified regions of MEL-28 required for its roles in meiosis as well as in chromatin binding and post-mitotic nuclear pore construction . Our parallel studies in HeLa cells show that the domains required for proper localization in C . elegans are conserved in human ELYS , suggesting that conclusions from functional analyses of MEL-28 in C . elegans are broadly applicable to vertebrate ELYS .
We previously reported that C . elegans MEL-28 is broadly expressed [10] . However , a promoter study of 127 genes in C . elegans embryos suggested that MEL-28 is highly enriched in the intestinal E lineage ~200 min after fertilization [16] . We therefore revisited MEL-28 expression to analyze it in greater detail . Immunofluorescence analysis detected similar levels of MEL-28 in nuclei of all embryonic cells ( S1A Fig ) and all postembryonic tissues ( S1B Fig ) . Next , using CRISPR-Cas9 technology [17] , we generated a GFP knock-in mel-28 allele to analyze the expression of endogenous MEL-28 by live microscopy . Similar to the observations with antibodies against MEL-28 , GFP::MEL-28 localized to the NE in all cell types during embryonic and larval development and in adults ( S1C Fig ) . Thus , we conclude that MEL-28 is ubiquitously expressed throughout C . elegans development . MEL-28 strongly accumulated on condensed oocyte chromosomes ( S1C Fig; [9 , 18] ) . Moreover , we noted during our initial studies of mel-28 mutant or RNAi-treated embryos that formation and migration of the maternal pronucleus was often more severely affected than the paternal pronucleus [9 , 10] . Based on these observations we speculated that MEL-28 might have important functions in meiosis . C . elegans oocytes are arranged in a linear fashion in the proximal part of the gonad , where each oocyte is numbered relative to the spermatheca ( -1 , -2 , -3 , etc . ) [19] . The -1 oocyte completes maturation including germinal vesicle breakdown immediately before ovulation and fertilization triggers rapid progression through meiosis I and II . To examine these processes we performed live in utero recordings of animals expressing GFP::MEL-28 and mCherry::HisH2B . In the -4 oocyte , MEL-28 localized to the NE and was absent from condensed chromosomes ( Fig 1A ) . In the -3 and -2 oocytes MEL-28 gradually moved away from the NE and accumulated uniformly on meiotic chromosomes . Later , in the -1 oocyte MEL-28 redistributed to cover the surface of meiotic chromosomes ( Fig 1A; S1 Video ) , in some cases completely enclosing the chromosomes and in other cases similar to the “cup-shaped” localization of kinetochore proteins , such as KNL-1 and KNL-3 [20] . The association of MEL-28 with chromosomes persisted throughout meiosis I and II until pronuclear formation ~30 minutes after germinal vesicle breakdown ( Fig 1B; S1 Video ) . The localization pattern of MEL-28 suggested a possible role during segregation of meiotic chromosomes , similar to the situation in mitosis [9 , 10] . We therefore analyzed mel-28 ( t1684 ) embryos expressing GFP::β-tubulin and mCherry::HisH2B . mel-28 ( t1684 ) encodes a premature termination codon at aa . 766 and behaves like a strong loss-of-function of MEL-28 , presumably due to nonsense-mediated mRNA decay [10] . Maternal contribution enables homozygous mel-28 ( t1684 ) hermaphrodites to develop until adulthood but they produce only unviable embryos ( hereafter referred to as mel-28 embryos , whereas embryos produced by heterozygous siblings are referred to as control or mel-28/+ embryos ) with severe NE assembly defects [10] . Strikingly , in mel-28 embryos chromosomes failed to segregate in anaphase I ( n = 5/6 embryos ) and anaphase II ( n = 4/6 ) and , consequently , mel-28 embryos had either no ( n = 4/6 ) or a single ( n = 2/6 ) polar body , whereas control embryos had two polar bodies ( n = 6/6; Fig 1C; S2 Video ) . In addition , chromosomes in mel-28 embryos were not organized in a pronucleus but appeared scattered in the cytoplasm ( Fig 1C; 36:00 ) . To our knowledge , this is the first report describing the involvement of MEL-28/ELYS in meiosis , expanding previously described MEL-28 functions and establishing an important role in chromosome segregation during both meiosis and mitosis . To characterize which regions of MEL-28 are required for its different functions , we examined full-length and truncated versions of MEL-28 fused to GFP and tracked their localization in live C . elegans embryos . While most transgenes are expressed ( S2 Fig; S4 Fig ) , some exhibit localization patterns distinct from full-length MEL-28 ( see below ) . During interphase full-length MEL-28 was mainly localized to the NE but was also found in the nucleoplasm ( Fig 2A; S3 Video; S9 Video ) . In prophase and prometaphase , MEL-28 left the NE before complete NE breakdown and associated to the condensing chromosomes . By metaphase , MEL-28 appeared as two lines parallel to the metaphase plate , resembling the characteristic pattern of holocentric kinetochore proteins , and less abundantly to the area of the mitotic spindle ( Fig 2A–2D ) . During anaphase , MEL-28 associated to decondensing chromosomes , and re-localized to reforming NE in telophase ( Fig 2A; S3 Video ) . We next analyzed a putative coiled-coil domain placed in the central part of the protein and which might be engaged in protein—protein interactions . However , GFP::MEL-28 lacking aa . 1140–1186 localized similarly to full-length MEL-28 ( Fig 2C; S3 Video ) . During interphase MEL-28Δ1140–1186 was enriched at the NE and shuttled to kinetochores in mitosis whereas reduced signal was observed at the mitotic spindle ( Fig 2D ) . Moreover , expression of GFP::MEL-28Δ1140–1186 completely rescued the embryonic lethality of mel-28 mutant embryos ( Table 1 ) . This demonstrated that the putative coiled-coil domain as well as enrichment at the mitotic spindle is dispensable for MEL-28 function . Recently , Bilokapic and Schwartz found that the N-terminal half of ELYS containing the β-propeller and α-helical domains localized to the NE in HeLa cells [15] . However , the relevance of these domains has not been analyzed in the context of full-length MEL-28/ELYS . We first deleted the β-propeller and most of the α-helical domain ( GFP::MEL-28826-1784 ) and found that both NE localization during interphase and kinetochore localization in mitosis were abrogated ( Fig 2C ) . Instead , the truncated protein was found in the nucleoplasm and weakly associated with chromosomes during interphase and metaphase , respectively ( note that kinetochore localization appears as two parallel lines whereas a single line reflects more uniform chromosome association ) . Similar mis-localization was observed on deletion of aa . 1–507 ( GFP::MEL-28508-1784 ) or aa . 498–956 ( GFP::MEL-28Δ498–956 ) , whereas deletion of aa . 566–778 ( GFP::MEL-28Δ566–778 ) also abolished the weak association to mitotic chromosomes ( Fig 2C ) . Together , these results demonstrate that both the β-propeller and the α-helical domain are required for targeting MEL-28 to NPCs and to kinetochores . All four N-terminally truncated MEL-28 proteins accumulated in the nucleus in interphase , suggesting that the C-terminal unstructured domain of MEL-28 contains one or more nuclear localization signals ( NLS’s; see below ) . Finally , we assessed whether the truncations in the β-propeller and α-helical domains interfered with MEL-28 function . As expected from the severe mis-localization , ectopic expression of any of the four MEL-28 truncations failed to restore viability of mel-28 embryos ( Table 1 ) , suggesting that the localization of MEL-28 to NPCs and kinetochores is essential to MEL-28 function . We conclude from these experiments that the N terminus of MEL-28 is required for proper MEL-28 localization and functions . Whereas its importance for NPC localization is concordant with data on ELYS our experiments revealed a novel role in kinetochore association . Bilokapic and Schwartz identified through protein crystallization and sequence alignments two conserved loops ( loop1 and loop2 ) on the surface of the β-propeller of ELYS [15] . When they substituted 5 aa . within loop2 the structural fold of the β-propeller was maintained but NPC localization of the N-terminal half of ELYS ( aa . 1–1018 ) fused to GFP was abrogated in HeLa cells . To test the relevance of loop2 in the context of full-length protein we introduced the equivalent aa . substitutions in MEL-28 ( D409S/Y412S/R415A/V416S/P417G; MEL-28loop2mut; Fig 3A ) . In mel-28/+ embryos MEL-28loop2mut::GFP localized normally during interphase and mitosis ( Fig 3A , left panels; compare with wild type GFP::MEL-28 in Fig 2A; S4 Video; S3 Fig ) , suggesting that loop2 residues are not essential for association of full-length MEL-28 with NPCs or kinetochores . However , MEL-28loop2mut::GFP was not able to substitute for endogenous MEL-28: mel-28 embryos expressing MEL-28loop2mut::GFP were unviable ( Table 1 ) and had frequent meiosis defects as evidenced by failure in polar body extrusion and presence of multiple female pronuclei ( Fig 3A , right panels; S4 Video; Fig 3B ) . Moreover , pronuclei were abnormally small , contained less MEL-28loop2mut::GFP and did not position properly . In 83% of mel-28; MEL-28loop2mut::GFP embryos ( n = 10/12 ) female and male pronuclei did not meet before the first mitotic division . Instead , only the male pronucleus was positioned between the centrosomes , whereas female pronuclei exhibited shorter migration and remained in the anterior of the embryo . During mitosis chromosomes failed to congress to the metaphase plate ( Fig 3A; 0:00 ) and severe segregation defects were observed ( Fig 3A; 20:00–31:45 ) . We also noticed alterations in cell cycle timing , in particular for the posterior P1 blastomere at the two-cell stage . In mel-28; GFP::MEL-28 and mel-28/+; MEL-28loop2mut::GFP embryos the cell cycle of P1 lasted ~1075 sec , whereas it lasted ~1513 sec ( 41% delay ) in mel-28 embryos expressing MEL-28loop2mut::GFP ( Fig 3C ) . Other frequent defects included cleavage furrow regression ( 37%; n = 6/16 ) and abnormal positioning of cells within the eggshell ( 53%; n = 8/15 ) . To analyze if the conserved loop2 is required for MEL-28’s role in NPC assembly we performed immunofluorescence on mel-28; MEL-28loop2mut::GFP embryos and compared them with wild type , mel-28 , and mel-28; GFP::MEL-28 embryos . One-cell and four-cell stage embryos were analyzed for meiotic and mitotic defects , respectively , using mAb414 to visualize multiple Nups and specific antibodies against NPP-10C/NUP96 , which is a component of the NUP107 complex [21] . Uniform peripheral signal was observed at pronuclei of wild type and mel-28; GFP::MEL-28 one-cell stage embryos , whereas fragmented pronuclei with inconsistent Nup signal was detected in mel-28; MEL-28loop2mut::GFP and mel-28 embryos ( Fig 4A ) . Analysis of four-cell stage mel-28; MEL-28loop2mut::GFP embryos confirmed the defects in chromosome segregation observed by live imaging and revealed that although nuclei with peripheral Nup localization are formed , these are smaller than in wild type and mel-28; GFP::MEL-28 embryos ( Fig 4B ) . The NE phenotypes in mel-28; MEL-28loop2mut::GFP embryos were less severe when compared to mel-28 embryos . As previously reported , nuclear reformation and NPC assembly was strongly inhibited in mel-28 embryos although a few cells had larger nuclei with irregular NE-structure ( Fig 4B; bottom mel-28 embryo ) . From these data we conclude that MEL-28’s loop2 is essential for correct chromosome segregation both in meiosis and mitosis but not strictly required for post mitotic NPC assembly , nor for incorporation into the NE . The observation that perturbations in MEL-28’s N-terminal half do not prevent nuclear accumulation of MEL-28 prompted us to analyze the C-terminus for functional domains . We first expressed GFP::MEL-281-1744 , which lacks 40 aa . from the C-terminal end including one of the two AT-hook motifs . This short truncation did not interfere with MEL-28 localization in interphase nor during mitosis ( Fig 5; S5 Video ) . However , expression of GFP::MEL-281-1744 rescued lethality in only ~35% of mel-28 embryos ( Table 1 ) , indicating that the C-terminal AT hook of MEL-28 contributed significantly to MEL-28 activity . Next , we deleted aa . 1239–1728 , including the other AT-hook motif . This reduced slightly the NE accumulation at interphase ( Fig 5; GFP::MEL-28Δ1239–1728; S6 Video ) . Importantly , expression of GFP::MEL-28Δ1239–1728 was not able to rescue the embryonic lethality of mel-28 embryos ( Table 1 ) , which suggests that there are domains within this region required for MEL-28 function . Despite several attempts , we were unable to express a MEL-28 aa . 1–956 fragment consisting of wild type β-propeller and α-helical domains ( S4 Fig ) . In contrast , a similar fragment , but with the five aa . substitutions in loop2 described above was efficiently expressed ( MEL-281-956_l2m::GFP; S7 Video ) . MEL-281-956_l2m::GFP localized to the cytoplasm and NE , but its relative NE accumulation compared to kinetochore localization was dramatically reduced ( S3 Fig ) . As expected , expression of MEL-281-956_l2m::GFP did not rescue the embryonic lethality of mel-28 embryos ( Table 1 ) . Taken together with the results presented in Fig 2 , we conclude that although the N-terminal β-propeller and α-helical domains are the main determinants for NPC and kinetochore localization , the C-terminal portion of MEL-28 also contributes significantly . A divergent ~300 aa . MEL -28/ELYS homolog termed ELY5 was recently identified in several fungi [22 , 23] . Although our experiments presented above would suggest that the part of MEL-28 equivalent to ELY5 ( identified as aa . 696–927 by [24] ) does not contain the domains required for NPC localization we nevertheless expressed a fragment containing aa . 681–929 fused to GFP . As expected , this fragment did not localize to the NE or to kinetochores but showed instead diffuse cytoplasmic signal throughout the cell cycle ( Fig 5; GFP::MEL-28681-929; S2B Fig ) . We next expressed a series of overlapping fragments from aa . 681 to the C-terminal end . All fragments that contained aa . 846–1071 accumulated efficiently in the nucleus ( Fig 5; GFP::MEL-28681-1350 , GFP::MEL-28846-1071 , GFP::MEL-28846-1350 , and GFP::MEL-28846-1601; S4A Fig; GFP::MEL-28846-1167; S8 Video ) . A shorter fragment consisting of aa . 846–956 behaved similarly to free GFP ( S4A Fig; GFP::MEL-28846-956 ) . Nuclear accumulation was also detected for GFP::MEL-281188-1784 , but not for GFP::MEL-281161-1601 or GFP::MEL-281239-1601 ( Fig 5; S4A Fig ) . These observations are consistent with MEL-28 having at least two NLS’s mapping to the regions 846–1071 and 1601–1784 . Moreover , using the NLS prediction software “cNLS Mapper” [25] we identified several putative mono- and bipartite NLSs in these regions: two in the central region ( aa . 942–970 and 1033–1062 with scores 5 . 9 and 5 . 2 , respectively ) and three in the C-terminal region ( aa . 1606–1636 , 1682–1709 and 1741–1773 with scores 5 . 7 , 7 . 4 and 5 . 3 , respectively ) . Analysis of these C-terminal fragments also revealed that aa . 1239–1601 confer strong chromatin binding during mitosis ( Fig 5 ) . Comparing the behavior of GFP::MEL-281239-1601 and GFP::MEL-281188-1784 indicated that MEL-28’s two AT hooks are not required for chromatin association , at least during mitosis ( Fig 5 ) . Moreover , in vitro binding experiments found no difference in chromatin affinity between recombinant peptides that contained either the C-terminal 128 aa of Xenopus ELYS including the single ELYS AT hook or a variant with mutated AT hook although the former was more efficient in competition assays [4] . In agreement with the competition assay , it was independently demonstrated that the same 128-aa . peptide efficiently binds nucleosome beads but not when the AT hook is mutated [13] . However , both studies concluded that the 128-aa . peptide contains residues outside the AT hook important for chromatin and nucleosome interaction . We attempted to address this in further detail , but we were unable to detect expression of a construct encoding the C-terminal 161 aa . of MEL-28 fused to GFP ( S4B Fig; GFP::MEL-281624-1784 ) . A shorter 48-aa . fragment containing a single AT hook localized similarly to free GFP ( S4A Fig GFP::MEL-281740-1784 ) . As a complementary approach , we examined the consequences of deleting the AT hooks from full-length MEL-28 . We first compared mel-28/+ embryos expressing GFP::MEL-281-1629 ( GFP::MEL-28ΔAT ) with mel-28 embryos expressing full-length MEL-28 fused to GFP . Time-lapse confocal microscopy demonstrated that the mel-28/+; GFP::MEL-281-1629 embryos developed normally and the fluorescent protein localized similarly to GFP::MEL-28 ( Fig 6A; compare left and middle panels; S9 and S10 Videos ) . In the absence of endogenous MEL-28 , GFP::MEL-281-1629 still accumulated at the periphery of interphase nuclei and to kinetochores of mitotic chromosomes ( Fig 6A; right panels; S10 Video ) . This was in contrast to the severe phenotypes observed in MEL-28loop2mut::GFP embryos ( Fig 3A ) and suggested that MEL-28’s function in post-mitotic nuclear assembly is not strictly dependent on the AT hook domain . However , mel-28; GFP::MEL-281-1629 embryos were unviable ( Table 1 ) and displayed several defects . Most prominently , daughter nuclei were often ( n = 5/7 ) trapped at the cleavage furrow during cytokinesis of the anterior AB blastomere of two-cell stage embryos ( Fig 6A , right panels; 27:31–34:30 ) . More direct evidence for chromosome segregation failure was obtained by immunofluorescence analysis of four-cell stage embryos , which also demonstrated that NPP-10C/NUP96 and other Nups accumulated at the NE of mel-28; GFP::MEL-281-1629 embryos , albeit in an irregular pattern ( Fig 6E ) . In addition , nuclear growth was significantly reduced in GFP::MEL-281-1629 embryos ( Fig 6A , third row; Fig 6B ) , consistent with defects in NPC-mediated nucleocytoplasmic transport [26] . While nuclei from mel-28; GFP::MEL-28 and mel-28/+; GFP::MEL-281-1629 grew to the same size ( 363 . 8 ± 19 μm3 and 363 . 3 ± 63 μm3; respectively ) , the maximum volume of P1 nuclei was reduced by 32% in mel-28; GFP::MEL-281-1629 embryos ( 346 . 6 ± 44 μm3 ) . We also noticed that the nucleoplasmic pool of GFP::MEL-281-1629 was strongly diminished in mel-28 embryos compared to GFP::MEL-28 in mel-28 embryos and GFP::MEL-281-1629 in mel-28/+ embryos ( Fig 6A and 6C ) . Whereas the ratio between nucleoplasmic and cytoplasmic GFP signal was similar between mel-28; GFP::MEL-28 and mel-28/+; GFP::MEL-281-1629 embryos ( 5 . 60 ± 1 . 29 and 4 . 72 ± 0 . 99; respectively ) , the ratio was 87% lower in mel-28; GFP::MEL-281-1629 embryos ( 0 . 76 ± 0 . 18 ) . These data are compatible with a model in which GFP::MEL-281-1629 has reduced affinity for interphase chromatin and therefore accumulates at NPCs: in mel-28/+ embryos interaction of GFP::MEL-281-1629 with endogenous MEL-28 accumulates the former in the nucleoplasm , potentially interacting with chromatin . During time-lapse recordings of 2-cell stage mel-28 embryos , we realized that division of the P1 blastomere was much delayed relatively to the AB division . In wild-type embryos the P1 cell division is delayed by ~2 . 5 min compared to AB division . This P1 delay is dependent on checkpoint proteins and is thought to have evolved to protect the germ-line lineage from aneuploidy . Thus , inhibition of DNA replication or induction of DNA damage is typically associated with extended P1 delay . When we compared embryos expressing GFP::MEL-281-1629 an increase in P1 delays by 176% was observed in mel-28 versus mel-28/+ embryos ( 423 . 5 ± 61 . 9 sec versus 154 . 1 ± 59 . 2 sec; Table 2; Fig 6D ) . The presence of chromatin bridges in mel-28; GFP::MEL-281-1629 embryos ( Fig 6E ) suggested that chromosomes might be entangled , potentially as consequence of stalled replication and/or double-stranded DNA breaks . To address if the DNA damage checkpoint indeed is involved in the extended P1 delay in mel-28; GFP::MEL-281-1629 embryos , we depleted ATL-1 , the C . elegans homolog of ATR by RNAi [27] . This mitigated the P1 delay ( 285 . 7 ± 67 . 9 sec ) , which suggested that removal of the AT-hook domain from MEL-28 activates DNA damage and thereby an exaggerated delay of P1 cell division . However , depletion of ATL-1 did not fully rescue P1 cell-cycle timing , which suggests that other checkpoints are also activated in mel-28; GFP::MEL-281-1629 embryos . In conclusion , although GFP::MEL-281-1629 localizes properly to the NE and kinetochores , depletion of MEL-28’s AT-hook domain causes reduced nuclear growth , mis-segregation of chromosomes and activates the ATR DNA damage checkpoint . To explore the degree of conservation of localization domains we expressed human full-length ELYS ( ELYS1-2275 ) and 14 ELYS truncations fused to GFP in HeLa cells . As reported , ELYS1-2275 was enriched at the NE in interphase and in a pattern coincident with kinetochores in metaphase ( Fig 7A; S5 Fig; punctate localization on metaphase chromosomes was observed in single confocal sections as well as in maximum intensity projections ) . Two fragments containing the entire β-propeller and α-helical domains ( ELYS1-1101 and ELYS1-1700 ) still accumulated at the NE but had increased cytoplasmic signal , suggesting that , like for MEL-28 , sequences outside the β-propeller and α-helical domains contribute to efficient NPC targeting ( Fig 7A; S6 Fig ) . In contrast , all truncations from the N-terminal end abolished NE signal , including a deletion of ELYS aa . 1–178 ( ELYS179-2275 ) , indicating that the β-propeller is critically required for incorporation of ELYS into the NE . A short N-terminal fragment , ELYS1-329 , was also not detected at the NE , which implies that although the first 178 aa . of ELYS are needed for NPC localization , they are not sufficient . Two internal fragments , ELYS600-1101 and ELYS600-1700 , were nuclear in interphase whereas ELYS1430-1700 was mostly cytoplasmic ( Fig 7 ) . This suggests that both MEL-28 ( Fig 5; MEL-28846-1071 ) and ELYS have at least one NLS at equivalent locations within the central region of the protein . Nuclear accumulation was also observed for two non-overlapping C-terminal fragments , ELYS1851-2034 and ELYS2034-2275 . In agreement with earlier predictions [5] , this suggests the presence of NLS’s in the AT-hook-containing last 425 aa . of ELYS , similar to our mapping of a potential NLS to the AT-hook domain of MEL-28 ( Fig 5; MEL-281188-1784 ) and would represent another functional conservation between ELYS and MEL-28 . We also noted that the shortest C-terminal ELYS fragments were enriched in nucleoli , whereas longer fragments ( e . g . ELYS179-2275 , ELYS476-2275 , and ELYS600-2275 ) were excluded from these compartments ( Fig 7 ) . Interestingly , all 14 ELYS truncations localized differently from full-length ELYS during metaphase . The three N-terminal fragments ( ELYS1-329 , ELYS1-1101 , and ELYS1-1700 ) and the three internal fragments ( ELYS600-1101 , ELYS600-1700 , and ELYS1430-1700 ) were not detected on mitotic chromosomes ( Fig 7 ) . In contrast , truncations from the N-terminal end increased the abundance of ELYS on chromosomes aligned on the metaphase plate . Importantly , the pattern was more diffuse on the chromosomes compared to the punctate pattern of full-length ELYS ( S5 Fig ) . This was particularly prominent for ELYS1700-2275 and ELYS1851-2275 , but was also observed for the longer ELYS476-2275 , ELYS600-2275 , and ELYS1430-2275 fragments . These results suggest that the C-terminus of ELYS has affinity for chromatin but that the ability to interact with chromosomes is reduced in the context of full-length ELYS , which specifically localizes to kinetochores . Thus , we conclude that association with mitotic chromosomes is also conserved from C . elegans to humans . Because of the similarity between MEL-28 and ELYS in terms of structural organization despite low primary sequence homology , we propose that the functional assignments for MEL-28 domains presented in this work are likely to be relevant in more complex animals , including humans .
C . elegans MEL-28 and human ELYS have divergent amino acid sequences , with at best 23% sequence identity [10] . In spite of this , we report that the functional domains of invertebrate and vertebrate orthologs are remarkably well conserved ( Fig 8 ) . Previous work demonstrated that MEL-28/ELYS is essential for mitotic chromosome segregation in C . elegans and vertebrates [9 , 10 , 28] . Here we show that MEL-28 is also required for meiotic chromosome segregation in C . elegans oogenesis . In C . elegans , chromosome segregation during female meiosis is kinetochore-independent , and instead depends on microtubule growth in the region between separating chromosomes and lateral microtubule attachments to the separating chromosomes [20 , 29] . It may be that these lateral attachments to chromosomes are less stable in the absence of MEL-28 , leading to failure of chromosome segregation . Alternatively there could be defects to the architecture of the meiotic spindle when MEL-28 is disrupted , as has been shown for the mitotic spindle in mel-28 RNAi-treated embryos [9 , 21] . It is important to note that the cell cycle proceeds in mel-28 embryos despite the penetrant failure in meiotic chromosome segregation , which suggests that mel-28 does not affect the anaphase-promoting complex [30 , 31] . In both C . elegans and HeLa cells , full-length MEL-28/ELYS localizes to the nucleoplasm and NPCs at interphase and to the kinetochore at mitosis [9 , 10 , 12 , 28] . Here we observed that in C . elegans , localization to NPCs and the kinetochore is dependent on both the N-terminal β-propeller domain and the central α-helical domain , corresponding to the N-terminal 956 aa . residues . Mammalian ELYS NPC localization also requires the β-propeller and α-helical domains [15] and here we have shown that these domains are also necessary for the localization of ELYS to kinetochores at metaphase . Similar to previous studies of human ELYS [15] , we have found that the conserved loop decorating blade 6 ( “loop2” ) is structurally conserved amongst the vertebrate and invertebrate MEL-28/ELYS homologs . When loop2 was disrupted by five substitution mutations in mouse ELYS , this prevented a 1018-aa . N-terminal ELYS fragment ( corresponding to the β-propeller and α-helical domains ) from localizing properly to the NE [15] . We found disruption of loop2 within an equivalent N-terminal fragment of MEL-28 ( aa . 1-956 ) caused a reduction of localization at the NPC and nucleoplasm , with a corresponding increase in cytoplasmic fluorescence . Interestingly , the full-length MEL-28 fusion with the loop2 defect had the wild-type localization pattern , suggesting that domains in the C terminus contribute to nuclear rim localization . Even so , mutations of loop2 severely disrupted MEL-28 function and caused cell cycle delay , nuclear expansion defects , problems with chromosome segregation during mitosis and meiosis , and ultimately embryonic inviability . However , NPC components were recruited to the reforming nuclei relatively efficiently . This suggests that the chromosomal functions of MEL-28 are more sensitive to defects to loop2 than the nuclear pore functions of MEL-28 . In vitro analyses studying the C-terminal domain of ELYS using Xenopus extracts have suggested that there are at least two domains , including the AT hook , required for chromatin binding [4 , 11 , 13] . Our results studying the C terminus of human ELYS are consistent with this . We identified at least two domains needed for metaphase chromatin localization . The C-terminal end of ELYS corresponding to aa . 1851–2275 bound to metaphase chromatin . However the aa . 1851–2034 fragment ( which includes the AT hook ) and a smaller aa . 2034–2275 C-terminal fragment were both excluded from metaphase chromatin , suggesting that both the AT hook and the domain C-terminal to the AT hook are required for metaphase chromatin binding . The C . elegans MEL-28 data also suggest that both the AT hooks and other C-terminal domains are involved in chromatin binding . C . elegans mel-28 ( t1684 ) embryos expressing GFP::MEL-281-1629 had reduced fluorescence in the nucleoplasm at interphase , consistent with an inefficient chromatin binding . These embryos also showed defects in recruitment of NPC components that would be expected if MEL-28 could not effectively bind to chromatin [3 , 4] . We studied multiple C-terminal fragments of MEL-28 ( that also lacked the N-terminal β-propeller and the central α-helical domains ) . Such fragments that include aa . 1239–1601 localized to the metaphase chromatin , but fragments lacking this domain were excluded from metaphase chromatin . This suggests that aa . 1239–1601 , just N-terminal to the AT hooks in MEL-28 , comprise a chromatin-binding domain . Notably , MEL-28 fragments with an intact N terminus ( including the β-propeller domain and the central α-helical domain ) localized to the kinetochore regardless of the presence of aa . 1239–1601 , showing that metaphase kinetochore localization does not require this domain . With human ELYS , in contrast , fragments were completely excluded from the chromatin and kinetochores unless they contained the C terminal domain including aa . 1851–2275 . In contrast to the behavior of C-terminal MEL-28 and ELYS fragments , full-length C . elegans and human proteins were enriched at kinetochores with no apparent affinity for other parts of the metaphase chromosomes . Moreover , disruption of kinetochores blocks recruitment of MEL-28 to mitotic chromosomes [9] . However , several observations indicate that full-length MEL-28 and ELYS also interact with chromatin . Firstly , ELYS bound to chromatin in interphase Xenopus egg extracts [4 , 11 , 13] . Secondly , DamID experiments in C . elegans adults showed specific interaction of MEL-28 throughout all chromosomes [32] . As a possible explanation for the different behavior at interphase and mitosis we speculate that MEL-28 and ELYS might undergo conformational changes in mitosis that lower their affinity for chromatin . Upon deletion of N-terminal regions , the chromatin association domain ( s ) in the C-terminus of MEL-28 and ELYS become more accessible and confer binding to metaphase chromosomes . Such a “shielding” mechanism is concordant with the gradual increase in association to metaphase chromosomes as more residues are deleted from the N-terminus of ELYS . Alternatively , or in combination with conformational changes of MEL-28 and ELYS , condensed mitotic chromosomes might provide a less favorable binding site for MEL-28/ELYS . MEL-28 is efficiently targeted to the NPC and the kinetochore even without AT hooks . However , the ΔAT-hooks version of MEL-28 clearly lacks MEL-28 function; mel-28 ( t1684 ) embryos expressing MEL-281-1629 were defective in NPC assembly and nearly all died before hatching . This shows that having MEL-28 placed at the NE is not sufficient for efficient recruitment of the remaining components of the NPC but that this depends on the AT-hook domain . In addition , these embryos show chromatin bridges and activate a checkpoint associated with DNA breakage . Previous work has suggested a role for MEL-28 in chromosome congression and segregation [9 , 10] , and our observations suggest that these functions require the AT hooks . The second , or most C-terminal , of the two predicted AT hooks clustered at the C terminus is a canonical AT hook whereas the penultimate is less well conserved [10] . Interestingly , the MEL-28 fusion missing its last AT hook retained some MEL-28 function , as mel-28 ( t1684 ) animals expressing this fusion showed partial penetrance embryonic lethality , with over one third of the embryos surviving ( Table 1 ) . Since removal of both AT hooks causes 99% embryonic lethality , either the penultimate AT hook or the short domain between the AT hooks must contribute to MEL-28 function . In either case , most mel-28 ( t1684 ) embryos expressing the version lacking the last AT hook are unviable , so the last AT hook is clearly needed for full MEL-28 function . In conclusion , human ELYS and C . elegans MEL-28 have similar functional domains . Both orthologs depend on an intact β-propeller domain and central α-helical domains for NPC and kinetochore organization . The β-propeller domain contains several loops , and our work has demonstrated that loop2 , a region that contributes to ELYS localization in mammals [15] , is also critical for MEL-28 function . Both MEL-28 and ELYS also have several putative NLS’s traversing the central and C terminal regions of the protein and a C-terminal chromatin-binding domain . One major difference between MEL-28 and ELYS is that chromatin and kinetochore binding is strictly dependent on the C-terminal chromatin-binding domain in ELYS . In contrast , MEL-28 fragments lacking the C terminus are still delivered to the kinetochore as long as the N terminus is intact although in a more irregular manner . It is possible that MEL-28 kinetochore localization is more robust to perturbation because of the unique holocentric structure of the kinetochore in C . elegans .
DNA fragments to express MEL-28 full length and truncations were generated by PCR amplification ( KAPA HiFi; KAPA Biosystems , Wilmington , USA ) or restriction enzyme digestion and inserted into appropriate cloning vectors . In all cases , mel-28 introns were maintained . Plasmid details are listed in S1 Table . To construct GFP-human ELYS ( NCBI accession number: NP_056261 . 4 ) , total RNAs from HeLa , K562 and WI-38 cells were isolated by FastPure RNA kit ( TaKaRa Bio Inc . , Shiga , Japan ) , and then cDNAs were generated by using SuperScript III First-Strand synthesis system ( Invitrogen , Waltham , MA ) according to manufacturer’s protocol . The coding region of ELYS was PCR-amplified using primers listed in S2 Table and inserted into the pEGFP-C1 vector ( Clontech Laboratories , Palo Alto , CA ) at the XhoI site by In-Fusion reaction ( Clontech ) . Other ELYS fragments were amplified by PCR using the plasmid harboring full-length ELYS as a template and inserted into the pEGFP-C1 vector as describe above . DNA sequencing of all ELYS fusion plasmids was outsourced to the TaKaRa Bio Inc . Compared to the database sequence , 5 out of 5 , 6 out of 7 and 2 out of 2 clones from HeLa , K562 and WI-38 cells , respectively , contained a mutation from A to G at position 2648 , resulting in an amino acid substitution from N to S at the position 883 . Since the mutation was predominant in three different cell lines , we decided to use this ELYS sequence in this report . The wild type strain used was the C . elegans Bristol strain N2 . Transgenic strains were generated by any of three different methods: MosSCI [33] , CRISPR-Cas9 [17] or microparticle bombardment [34] . GE2633 ( mel-28 ( t1684 ) ) was obtained from the Caenorhabditis Genetic Centers . Other strains are listed in S3 Table . Strains were cultured at 15–25°C using standard C . elegans methods [35] . Rescue experiments were performed according to the promoter used to express the different MEL-28 fragments . For constitutive promoters homozygous L4 larvae were placed on individual plates to develop and lay eggs for 24 h at 20°C . Then , the adults were removed and the number of eggs was determined . Twenty four hours later embryonic lethality was calculated by counting unhatched embryos . For constructs with the hsp-16 . 41 heat shock inducible promoter , young gravid adults were incubated for 1 h at 32°C and allowed to recover and lay eggs for 24 h at 20°C . The adults were then removed and rescue of embryonic lethality was determined by the presence of viable offspring after 24 h at 20°C . We carried out RNAi as described [36] with minor adaptations . In total , 10–15 synchronized L4 hermaphrodites were placed on NGM plates ( + 1 mM IPTG + 100 μg/ml ampicillin ) seeded with E . coli producing double-stranded RNA ( alt-1 RNAi clone sjj_T06E4 . 3 from [37] ) and incubated for 20-24h at 20°C before analysis of cell cycle timing by live DIC microscopy . HeLa cells were a gift from Dr . Hiroshi Kimura ( see [38] for the cell origin ) . WI-38 cells were purchased from ATCC ( Manassas , VA , USA ) . These cells were maintained in DME medium containing 10% fetal bovine serum ( FBS ) at 37°C in a humidified 5% CO2 . K562 cells were obtained from the Riken Cell Bank ( Tsukuba , Japan ) and maintained in RPMI1640 medium containing 10% FBS . HeLa cells were grown in a glass-bottom culture dish ( MatTech , USA ) . GFP fusion plasmids ( 1 μg ) were transfected into the cells with Lipofectamine 2000 ( Invitrogen ) according to manufacturer’s protocol . After 24 hours transfection , the cells were fixed with 4% formaldehyde for 10 min , permeabilized with 0 . 1% Triton X-100 in PBS for 5 min . For immunostaining , the cells were blocked by blocking buffer ( PBS containing 10% Blocking One ( Nacalai tesque , Japan ) and 0 . 1% Triton X-100 ) , and then probed with anti-CENP-A antibody ( generous gift from Dr . Tatsuo Fukagawa ( Osaka University ) , [39] ) , followed by Alexa Fluor 568-conjugated anti-mouse IgG secondary antibody ( 1:500 , Lifetechnologies , USA ) . The cells were stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) at 100 ng/ml for 10 min at room temperature . After washing 3-times with 0 . 1% Triton X-100 in PBS , the cells were mounted on ProLong Diamond antifade mountant ( Molecular Probes , Carlsbad , CA ) . The cells were observed by confocal microscopy ( LSM510META and LSM780; Zeiss; operated by built-in software ) equipped with a C-Apo 40x NA 1 . 2 water immersion lens . C . elegans embryos and larvae were collected and processed by freeze cracking and methanol fixation as described [40] . The following primary antibodies were used: mouse monoclonal antibody ( mAb ) 414 ( Covance , Princeton , NJ , USA , 1:250 ) , mouse monoclonal antibody MH27 ( 1:50; [41] , provided by the Developmental Studies Hybridoma Bank ) , rabbit polyclonal α-HCP-3 antiserum MH3N ( 1:200; generous gift from Dr . Mark Roth [42] ) , rabbit polyclonal α-NPP10-C/NUP96 antiserum GBLC ( 1:300; [21] ) , rabbit polyclonal α-MEL-28 antiserum BUD3 ( 1:200–250; [10] ) . Secondary antibodies were Alexa Fluor 546-conjugated goat anti-mouse antibodies ( Invitrogen , 1:1000 ) , Alexa Fluor 488- and Alexa Fluor 633-conjugated goat anti-rabbit antibodies ( Invitrogen , 1:1000 ) . For DNA staining , Hoechst 33258 ( Hoechst ) was used at 5 μg/ml . Confocal images for S1A Fig were obtained with a Nikon A1R microscope through a Plan Apo VC 60x/1 . 4 objective ( Nikon , Tokyo , Japan ) using a pinhole of 1 airy unit . All other immunofluorescence images were acquired with a confocal Leica SPE microscope equipped with an ACS APO 636/ 1 . 3 objective ( Leica , Wetzlar , Germany ) using a pinhole of 1 airy unit . C . elegans samples were mounted between a coverslip and a 2% agarose pad; embryos were released by dissecting young adult hermaphrodites and mounted in 3 μL M9 buffer , whereas larvae and adults were mounted in 3 μL 10 mM levamisole HCl ( Sigma-Aldrich , St . Louis , MI , USA ) . For in utero imaging of oocytes and newly fertilized embryos , young adult hermaphrodites were anesthetized in 20 μL 5 mM ethyl 3-aminobenzoate methanesulfonate ( aka Tricaine; Sigma-Aldrich ) , 0 . 5 mM levamisole HCl , 0 . 5x M9 for 15–20 minutes prior to mounting in 3 μL of the same buffer on 2% agarose pads . Vaseline was added between the slide and the coverslip to avoid compression of the animals and melted VALAP ( 1:1:1 mixture of Vaseline , lanolin , and paraffin ) was used to seal the cover slip . Confocal epifluorescence and DIC images were recorded at 22–24°C with a Nikon A1R microscope through a Plan Apo VC 60x/1 . 4 objective ( Nikon , Tokyo , Japan ) using a pinhole of 1 . 2–1 . 4 airy unit . For preparation of Fig panels images were processed with FIJI ( fiji . sc/Fiji ) and Adobe Photoshop CS5 or CS6 ( Adobe , San Jose , CA , USA ) . Identical adjustment of brightness and contrast was applied to all comparable panels within each Fig without changing gamma . Quantification of fluorescence signal at the NE , cytoplasm and nucleoplasm was performed on raw 12 bit images . Fluorescence intensity was normalized by background subtraction; for C . elegans , images of wild type embryos acquired with identical microscope settings were used , with exception of S2B Fig Statistical analysis was performed with Origin 8 . 0 ( OriginLab , Northampton , MA , USA ) , Microsoft Excel ( Microsoft , Redmond , WA , USA ) and online Graphpad tools ( http://graphpad . com ) .
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Most animal cells have a nucleus that contains the genetic material: the chromosomes . The nucleus is enclosed by the nuclear envelope , which provides a physical barrier between the chromosomes and the surrounding cytoplasm , and enables precisely controlled transport of proteins into and out of the nucleus . Transport occurs through nuclear pore complexes , which consist of multiple copies of ~30 different proteins called nucleoporins . Although the composition of nuclear pore complexes is known , the mechanisms of their assembly and function are still unclear . We have analyzed the nucleoporin MEL-28/ELYS through a systematic dissection of functional domains both in the nematode Caenorhabditis elegans and in human cells . Interestingly , MEL-28/ELYS localizes not only to nuclear pore complexes , but is also associated with chromosomal structures known as kinetochores during cell division . Our studies have revealed that even small perturbations in MEL-28/ELYS can have dramatic consequences on nuclear pore complex assembly as well as on separation of chromosomes during cell division . Surprisingly , inhibition of MEL-28/ELYS causes cell-cycle delay , suggesting activation of a cellular surveillance system for chromosomal damages . Finally , we conclude that the structural domains of MEL-28/ELYS are conserved from nematodes to humans .
|
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"Materials",
"and",
"Methods"
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2016
|
Identification of Conserved MEL-28/ELYS Domains with Essential Roles in Nuclear Assembly and Chromosome Segregation
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Based on our initial observations showing that mice consuming a probiotic product develop more severe cryptosporidiosis , we investigated the impact of other dietary interventions on the intracellular proliferation of Cryptosporidium parvum and C . tyzzeri in the mouse . Mice were orally infected with oocysts and parasite multiplication measured by quantifying fecal oocyst output . High-throughput sequencing of 16S ribosomal RNA amplicons was used to correlate oocyst output with diet and with the composition of the intestinal microbiota . On average , mice fed a diet without fiber ( cellulose , pectin and inulin ) developed more severe infections . As expected , a diet without fibers also significantly altered the fecal microbiota . Consistent with these observations , mice fed a prebiotic product sold for human consumption excreted significantly fewer oocysts . The fecal microbiota of mice consuming no plant polysaccharides was characterized by a lower relative abundance of Bacteroidetes bacteria . Since bacterial metabolites play an important role in the physiology of intestinal enterocytes , we hypothesize based on these observations that the impact of diet on parasite proliferation is mediated primarily by the metabolic activity of the anaerobic microbiota , specifically by the effect of certain metabolites on the host . This model is consistent with the metabolic dependence of intracellular stages of the parasite on the host cell . These observations underscore the potential of dietary interventions to alleviate the impact of cryptosporidiosis , particularly in infants at risk of recurrent enteric infections .
Protozoa of the genus Cryptosporidium are important pathogens causing diarrhea in humans , ruminants and other species of animals worldwide [1] . Various Cryptosporidium species are recognized as opportunistic pathogens in patients with AIDS , where cryptosporidiosis can lead to protracted diarrhea and wasting . Although immunocompetent patients heal spontaneously within a few weeks , recent studies in developing nations have pointed to Cryptosporidium as the second leading cause of infant diarrhea [2 , 3] . The resistance of Cryptosporidium parasites to anti-protozoal drugs [4] , and the lack of alternative therapeutic options , led us to investigate the interaction between the gut microbiota and the parasite . The previously reported unexpected observation that a probiotic product can aggravate the course of cryptosporidiosis in mice [5] supports the hypothesis that parasite proliferation is impacted by diet and possibly by the effect of diet on the gut microbiota . This observation is significant because it could lead to the development of simple dietary supplements for mitigating cryptosporidiosis and perhaps other enteric infections in vulnerable infants . The benefits to intestinal health of diets rich in plant fibers are well known [6] . It has been suggested that consumption of fiber below nutritional recommendations [7 , 8] may lead to dysbiosis . A decrease in the Bacteroidetes/Firmicutes ratio has often been linked to a poor intestinal health index and to obesity [9] . Dysbiosis may also deplete the intestinal mucosal layer [10] . To what extent mucus depletion may play a role in susceptibility to cryptosporidiosis has not been investigated . Several mechanisms linking diet , microbiota and enteric infections have been proposed [11] . Bacterial metabolites , particularly those originating from the fermentation of certain plant polysaccharides , have been shown to play and important role in modulating the resistance to enteric bacterial infections [12] . Research on the interaction between the microbiota and the intestinal epithelium has shown the importance of bacterial metabolites , such as short-chain fatty acids originating from the anaerobic breakdown of plant polysaccharides [10] . The role of the intestinal microbiota in regulating the immune response and preventing inflammation has also been investigated [11 , 13] . With respect to enteric infections , much research has focused the protective role of the microbiota , a phenomenon often referred to as "colonization resistance" [14 , 15] . In contrast to what is known about the effect of diet and bacterial metabolites on the intestinal physiology , less research has focused on mechanisms linking diet and enteric infections . This limitation is particularly true for enteric protozoa [16] . With respect to cryptosporidiosis , research with germ-free severe combined immunodeficient ( SCID ) mice and SCID mice colonized with intestinal microbes conducted by Harp and co-workers showed that a normal intestinal microbiota delayed the onset of C . parvum oocyst excretion by several weeks [17 , 18] . A protective role of the gut microbiota against cryptosporidiosis was also observed in neonatal mice [19 , 20] A protein-deficient diet was also found to increase susceptibility of mice to C . parvum [21] . This phenotype was attributed to a reduced epithelial cell turnover . The effect of probiotics on the course of cryptosporidiosis was also observed by others [22 , 23] . This research uncovered a beneficial effect of Enterococcus faecalis administration to mice infected with C . parvum . None of these studies have investigated potential mechanisms mediating the observed effect on the development of C . parvum . Here we describe experiments with a mouse model of cryptosporidiosis aimed at investigating changes in the bacterial microbiome caused by dietary fiber and at relating these changes to the severity of cryptosporidiosis . The results show that relatively small changes in diet , or the administration of a prebiotic formulation , can reduce the severity of cryptosporidiosis .
C . parvum strain TU114 oocysts [24] was used in experiment 1 and 4 whereas C . tyzzeri oocysts were used in experiments 2 , 3 and 5 . C . parvum strain TU114 belongs to the anthroponotic subgroup characterized by a GP60 surface glycoprotein genotype IIc [25 , 26] . C . tyzzeri is a species commonly found in domestic mice of the species Mus musculus [27] . Oocysts for the experimental infections were purified from feces of mice on Nycodenz ( Alere Technologies , Oslo , Norway ) step gradients as previously described [28] . The age of the oocysts was 65 , 37 , 22 , 38 and 13 days for experiments 1 , 2 , 3 , 4 and 5 , respectively ( Table 1 ) . To test the effect of dietary fiber , three experiments were performed using no-fiber diet and matched control diet ( "medium-fiber diet" ) ( Supplementary Table 1 ) . In experiment 1 , 8 female CD-1 mice aged ~5 weeks were randomly divided into two groups and immunosuppressed by adding disodium dexamethasone 21-phosphate ( Sigma , cat . D1169 ) to drinking water at a concentration of 16 mg/L [29] . The immunosuppressive treatment was initiated on the day -5 post-infection ( PI ) , where day 0 is the day of infection . In experiment 2 , we used 8 female C57BL/6 mice , also divided into two groups of 4 mice . In experiment 3 , 12 female C57BL/6 mice were divided into four groups of 3 mice . In all experiments , mice were provided ad libidum with autoclaved water . In experiments 1 and 2 , each group was fed one type of diet and in experiment number 3 , two groups ingested medium-fiber diet and two groups no-fiber diet . The diet was given starting on day -5 PI , i . e . , 5 days before the animals were infected with Cryptosporidium oocysts . To test the effect of prebiotics on the microbiome and on the excretion of Cryptosporidium oocysts , we performed two experiments . In experiment 4 , 16 CD-1 mice , randomly divided into 4 groups of 4 mice , were given normal diet and were immunosuppressed by the addition of dexamethasone to drinking water at a concentration of 16 mg/L . In addition to immunosuppression with dexamethasone , vancomycin and streptomycin were added to drinking water at a concentrations of 500 mg/L and 5 g/L , respectively , starting on day -6 PI . Metronidazole at the dose of 20 mg/kg was given daily by gavage , starting at day 6 PI . Antibiotic treatment was terminated on day 2 PI . The goal of the antibiotic treatment was to deplete the native intestinal microbiome [30] , and replicate the treatment used in a previous series of experiments with probiotics [5] . From day -1 PI , the drinking water was supplemented with prebiotic ( Supplementary Table 1 ) at a concentration of 2 . 8 g/L . Lastly , in experiment 5 , 12 immunocompetent C57BL/6 mice divided into four groups were used , two were given prebiotic in the drinking water starting on day -5 PI , and the other two groups drank unsupplemented water . In this experiment all groups ingested medium-fiber diet . Experiments typically lasted 3 weeks . Upon arrival , each mouse was individually tagged and randomly assigned to a treatment groups ( Table 1 ) . Mice were orally infected on day 0 PI with approximately 2 x 104 oocysts of C . parvum strain TU114 ( experiment 1 and experiment 4 ) or C . tyzzeri ( experiment 2 , 3 and 5 ) . To obtain fecal pellets for intestinal microbiota analysis , mice were individually transferred to a 1-L plastic cup and fecal pellets collected immediately after defecation . The pellets were stored at -20°C . To collect feces for oocyst enumeration using flow cytometry , mice were individually transferred overnight to collection cages fitted with a wire bottom . Feces collected overnight were stored at 4°C . Following overnight fecal collection , mice were returned to regular cages with their original cage mates . On days when feces were collected for oocyst enumeration , mice were individually housed for 14–16 h and spent the remaining time in regular cages with their respective cage mates . Prior to processing for flow cytometry ( FCM ) , fecal pellets were suspended in water and homogenizing to a slurry . The water volume was adjusted according to the volume of feces and varied between 1 . 5 ml and 4 ml . A previously described procedure [5] was used to immuno-label oocysts . The only modification consisted in filtering the fecal slurries through a 38-μm opening Nylon mesh , ( Component Supply , Sparta , Tennessee , cat . 06725–01 ) before FCM . For each experiment , 3 samples were randomly selected for replication . Replication involved the processing and labeling of 5 separate aliquots originating from a fecal sample . The labeled samples were analyzed by FCM using a Becton Dickinson Accuri C6 cytometer . Oocyst counts for each mouse were converted to number of oocysts excreted per overnight collection event based on the sample volume analyzed by FCM and sample dilution . These values were summed over the experiment . The cumulative values obtained in this manner are designated "cumulative" oocyst counts . This values represents , for each mouse , the number of oocysts excreted over all collection periods . Feces were collected 6 times per experiment . To test the effect of each dietary treatment ( dietary fiber and prebiotics ) on oocyst output , cumulative oocyst output for each mouse was normalized against the mean cumulative oocyst output of the control mice . Specifically , the mean cumulative oocyst output of the control mice in each experiment was set equal 100% . Finally , normalized cumulative values were averaged over experiment 1–3 to test for the effect of dietary fiber , and over experiment 4 and 5 to assess the effect of prebiotics . In addition , the effect of treatment was also tested based on the individual FCM data obtained for each mouse and collection event . To ensure that oocyst counts are not impacted by diet , 3 fecal samples from negative mice fed regular diet and from the same number of mice fed diet without fiber were spiked with the same dose of oocysts and processed for oocyst enumeration . The results showed no significant effect of diet on FCM counts ( p = 0 . 14 ) . The procedures for DNA extraction , amplicon library construction and bioinformatics were previously described [5 , 31] . Briefly , fecal DNA was PCR amplified to prepare amplicons of the V1V2 variable region of the bacterial 16S rRNA gene [32 , 33] . The multiplexed amplicon library was size-selected on a Pippin HT system ( Sage BioScience , Beverly , Massachusetts ) and sequenced in an Illumina MiSeq sequencer at the Tufts University genomics core facility ( tucf . org ) using single-end 300 nucleotide strategy . To control for technical variation introduced during PCR , library preparation and sequencing , each library included two replicates of two randomly selected samples . Replication involved the separate processing of duplicated fecal samples and tagging each amplicon with a different barcode . FASTQ formatted sequences were processed using programs found in mothur [34] essentially as described [5 , 35] . Briefly , random subsamples of 5000 sequences per sample were processed . Pairwise UniFrac phylogenetic distances [36] between samples were calculated in mothur . Analysis of Similarity ( ANOSIM ) [37] was used to test the significance of clustering by treatment . Program anosim was run in mothur using a weighted UniFrac distance matrix as input . Operational Taxonomic Units ( OTUs ) were obtained using program cluster , using the OptiClust clustering method [38] . A distance cut-off of 3% was applied . Redundancy Analysis ( RDA ) was used to test the significance of association between OTU profile and oocyst concentration . The program was run in CANOCO [39] . The pseudo-F statistic was calculated by Monte Carlo with 1000 permutations of samples between treatment groups . OTU abundance values for the 150 most abundant OTUs served as dependent variables . Oocyst concentration determined by flow cytometry as described above served as independent variable . Where two experiments were pooled , i . e . , experiments 2 and 3 , any effect of the experiment was excluded by defining the experiment as covariate . GenAlEx [40] was used to draw Principal Coordinate Analysis ( PCoA ) plots using weighted UniFrac distance matrices as input . Linear Discriminant Analysis as implemented in program LEfSe [41] was used to identify statistically significant differences in OTU abundance profiles between two groups of samples defined by the dietary treatment . Sequence data from experiments 1–5 were deposited in the European Nucleotide Archive under study accession numbers PRJEB31954 , PRJEB31955 , PRJEB31958 , PRJEB31959 and PRJEB31960 , respectively . The animal experiments adhered to the National Institutes of Health’s Public Health Service Policy on Humane Care and Use of Laboratory Animals . The animal experiments were approved by the Tufts University Institutional Animal Care and Usage Committee ( IACUC ) . The IACUC approved the experiments described above and as described in document G2016-40 .
To test whether no-fiber diet affects the severity of cryptosporidiosis , immunosuppressed and competent mice were infected with C . parvum and C . tyzzeri oocysts , respectively . The intensity of the infection in mice fed medium-fiber diet or regular diet was measured by quantifying oocyst output by FCM 6 times over the duration of each experiment , where each collection event lasted approximately 16 h . In the 3 experiments designed to compare the effect of dietary fiber , mice fed a diet lacking fiber excreted 3 . 12 , 1 . 97 and 1 . 64 times more oocysts than the control mice . The effect of dietary treatment tested over the 3 experiments was statistically significant ( Mann-Whitney Rank Sum test , U = 20 , n = 13 , p = 0 . 001; Table 2 ) . Similarly , the alternative analysis based on individual fecal samples also revealed a significant effect of diet for all three experiments ( U = 56 , n = 15 , p = 0 . 02; U = 118 , n = 24 , p = 0 . 001; U = 392 , n = 36 , p = 0 . 004 ) . Fig 1 shows overnight oocyst output over time for the three experiments . We performed analogous experiments to test the effect of prebiotics , which are in essence fermentable fibers ( S1 Table ) . The impact of the dietary supplement on the severity of Cryptosporidium infection was also significant ( U = 38 , n = 14 , p = 0 . 006; Table 2 ) . This effect was apparent when prebiotics were given to mice fed no-fiber diet ( experiment 5 , U = 542 , n = 47 , p = 0 . 001 ) or regular diet ( experiment 6 , U = 305 , n = 30 , p = 0 . 033 ) . Fig 2 shows the pattern of oocyst production over time for prebiotic experiments 4 and 5 . Body weights were recorded multiple times during each experiment . In none of the experiments was a statistically significant effect of the treatment on the final weight identified . Based on the results described above and on previously published observations [5] , we investigated whether the effect of diet and dietary supplements on cryptosporidiosis could be mediated by the intestinal microbiota . To evaluate this model , the fecal bacterial microbiota was analyzed using 16S amplicon sequencing . Weighted UniFrac distances [36] between pairs of microbiota from each experiment were visualized on PCoA plots ( Figs 3 and 4 ) . In experiments 1 , 2 and 3 , ( no-fiber vs . medium-fiber diet ) , fecal sample collection was initiated on the fifth day after the onset of dietary intake , the day the mice were infected , and continued until day 23 of treatment ( day 18 PI ) . Feces were collected four times during this interval . Demonstrating an effect of diet on the intestinal microbiota , this analysis revealed a non-overlapping distribution of data points according to dietary treatment . ANOSIM R-values between diet groups for the three experiments testing the effect of diet are statistically significant ( Table 2 ) . Significant clustering according to prebiotic treatment was observed in experiments 4 and 5 based on 50 and 49 samples , respectively , collected between day 0 PI and day 15 PI . Consistent with a significant effect of the prebiotics , the ANOSIM R-value in experiment 4 was 0 . 059 , ( p = 0 . 046 ) and in experiment 5 0 . 202 ( p < 0 . 0001 ) . ( Table 2 ) . Having detected an association between dietary fiber and cumulative oocyst output , and between dietary fiber and fecal microbiota profile , we focused on the microbiome on day 0 PI . We reasoned that if the gut microbiota impacts the severity of cryptosporidiosis , the microbiota on day 0 PI would be the most relevant to examine . Since colonization of the gut epithelium by Cryptosporidium is known to impact the microbiota [35] , the analysis of the microbiota on day 0 PI eliminates the effect of cryptosporidiosis on the microbiota and enables detecting any effect of the microbiota on cryptosporidiosis . The effect of no-fiber diet on the fecal microbiota was already detectable after 5 days of treatment ( day 0 PI ) in experiments 2 and 3 ( ANOSIM R = 0 . 82 , p = 0 . 03; R = 0 . 74 , p = 0 . 001 , respectively ) . In experiment 1 , the effect was not significant ( ANOSIM R = 0 . 17 , p = 0 . 22 ) . Administration of prebiotics in experiments 4 and 5 did not significantly change the microbiota composition according to ANOSIM ( R = 0 . 10 , p = 0 . 06 , n = 16; R = 0 . 06 , p = 0 . 29 , n = 12 , respectively ) . To examine to what extent the day 0 microbiota composition correlates with total oocyst output over the course of the infection , we merged experiments 2 and 3 , which are exact replicates , to increase the power of the analysis . Experiment 1 was excluded from this analysis because the mice were immunosuppressed , because the microbiota on day 0 did not show any impact of diet and because of mortality only 31 samples were available . For the remaining 4 experiments , we analyzed the correlation between cumulative oocyst output for each mouse and the microbiota OTU profile using RDA . Of the 20 days 0 microbiota samples from pooled experiments 2 and 3 , 8 originated from experiment 2 and 12 from experiment 3 . Defining the experiment as covariate , a Monte Carlo permutation test indicated a significant correlation between cumulative oocysts output and the OTU profile ( pseudo-F = 2 . 1 , p = 0 . 0354 ) . As expected from the lack of prebiotic effect on the microbiota on day 0 , RDA of experiment 4 and 5 showed a non-significant association between day 0 microbiota profile and cumulative oocyst output ( pseudo-F = 0 . 7 , p = 0 . 4688; pseudo-F = 1 . 3 , p = 0 . 172 , respectively ) . As expected from the different treatments used in the 5 mouse experiments , the taxonomy of the fecal bacterial microbiota differed extensively between experiments . S1A Fig illustrates the magnitude of the effect of diet and antibiotics pretreatment on the microbiota . As expected , pre-treating mice with antibiotics in experiment 4 profoundly modified the microbiota when compared with microbiota from untreated mice . Removing the data points from experiment 4 from the PCoA reveals the impact of dexamethasone treatment and/or Cryptosporidium species on the microbiota ( S1B Fig ) . Since the experiments were not designed to investigate the effect of these variables , we cannot infer the relative effect of each of these 2 variables on the microbiota . This is because in experiments 1 and 4 mice were immunosuppressed before infecting them with C . parvum , whereas infection with C . tyzzeri in experiments 2 , 3 and 5 did not require immunosuppression . The position of experiment 1 data points in S1B Fig also indicates that immunosuppression and/or parasite species has a large effect on the microbiota as compared to diet . Without a direct comparison , it is difficult to infer the effect of untested variables on the microbiota . The combined samples collected on day 0 PI from experiments 2 and 3 were the primary focus of a taxonomic analysis because of the relatively large sample size ( n = 20 mice ) . Combining these two experiments is consistent with them being exact replicates ( Table 1 ) . LDA , as implemented in program LefSe , was used to identify bacterial taxa significantly associated with dietary treatment . This analysis identified 95 taxa significantly more abundant in the no-fiber microbiota and 92 in the medium-fiber microbiota ( S2 Table ) . Of the 95 taxa in the former group , only 24 ( 25% ) belonged in the phylum Bacteroidetes , which compares to 42 ( 45% ) Bacteroidetes taxa in the medium-fiber group . A Chi-square test confirms that Bacteroidetes taxa were significantly enriched in mice consuming medium-fiber diet ( χ2 = 8 . 5 , p = 0 . 003 ) . Given the wide interest in the Bacteroidetes/Firmicutes ratio as a marker of a healthy gut microbiota [42–44] , we calculated day 0 Bacteroidetes/Firmicutes from experiment 2/3 . As shown in Fig 5 , cumulative oocyst output was negatively correlated with Bacteroidetes/Firmicutes ( Pearson r = -0 . 47 , p = 0 . 04; Spearman rs = -0 . 46 , p = 0 . 04 ) . As expected from the metabolic function of the Bacteroidetes microbiota , mean Bacteroidetes/Firmicutes on day 0 PI was also significantly correlated with diet ( mean no-fiber diet = 2 . 213 , mean medium-fiber diet = 3 . 950; Mann-Whitney U = 305 , p = 0 . 001 ) . In contrast , Firmicutes relative abundance on day 0 was not significantly correlated with cumulative oocyst output ( S3 Table ) . In the other experiments this correlation was not observed on day 0 PI , but calculating the Bacteroidetes/Firmicutes ratio for the entire experiment ( all time points ) , revealed a significant effect of diet in experiment 1 ( n = 31 , U = 160 , p = 0 . 025 ) and in experiment 5 ( n = 49 , U = 181 , p = 0 . 018 ) As indicated above , this outcome could however be related to the effect of the infection of the microbiota . In experiment 4 , we did not observe a significant difference between treatment groups ( n = 50 , U = 180 , p = 0 . 9 ) . This observation is consistent with the fact that in this experiment the prebiotic supplement was given to mice fed medium-fiber diet . In addition , pretreatment of mice with antibiotics in this experiment profoundly impacted the microbiota ( S1 Fig ) .
Consistent with previously published observations [5] , the results presented here show that in the mouse a diet low in fermentable fiber impacts the intestinal microbiota and aggravates the infection with C . parvum and C . tyzzeri . Significantly , this effect was observed in two models , immunosuppressed mice infected with the human pathogen C . parvum and immunocompetent mice infected with the rodent parasite C . tyzzeri . In three experiments performed with customized diet , a statistically significant increase in the elimination of Cryptosporidium oocysts was observed in mice deprived of dietary fiber . The observation that the effect of the treatment on oocyst output did not impact the weight of the mice is likely explained by the fact that diet has a quantitative impact on the infection as opposed to a curative effect . The benefits to intestinal health of diets rich in plant fibers are well known [12 , 45] . Research on the interaction between the microbiota and the intestinal epithelium has revealed the importance of bacterial metabolites , such as SCFAs originating from the breakdown of plant polysaccharides [6] . Elucidating to what extent this interaction can impact the proliferation of an enteric pathogen could lead to the development of simple "nutraceuticals" capable of mitigating the infection . Dietary supplements would have significant advantages over drugs and vaccines , because they are cheap and do not require refrigeration , a significant advantage for distributing to vulnerable populations such as infants in developing countries . Diet could play a role for controlling cryptosporidiosis as no effective anti-cryptosporidial drugs nor vaccine is available . Moreover , such treatments are unlikely to generate resistant parasites . Although statistically significant , the effect of dietary treatments tested to date on the course of cryptosporidiosis is modest . Clearly , more effective treatments are desirable . Eradication of the infection , however , is not necessarily the most desirable outcome . An intervention which prevents diarrhea , while enabling the host to develop immunity , may be as effective at preventing the long-term consequences of recurrent infant diarrhea [46] than a complete cure . Conceivably , dietary treatments could one day be used to enhance the effect of a drug , when it becomes available , and as prophylactics . A similar study with the enteric protozoan Giardia lamblia concluded that gerbils fed a low-fiber diet were significantly more likely to become infected than animals fed a high-fiber diet [47] . This observation suggests that diet may act directly on the parasite , as Giardia multiplies extracellulary in the intestinal lumen . The observed beneficial effect on the course of giardiasis , suggests that dietary treatments may affect multiple enteric pathogens . To maximize the beneficial effect of dietary interventions on cryptosporidiosis , a better understanding of the mechanisms linking diet and parasite proliferation in the intestinal epithelium is needed . The increased severity of certain enteric infections in individuals who eat low-fiber diets can be explained by different mechanisms . A low-fiber diet may increase the abundance of bacteria that degrade the intestinal mucus layer . According to this model [10] , infection of enterocytes by enteric pathogens could be facilitated by a depleted mucus layer , thought to be one of the main innate defense mechanisms against such pathogens [48 , 49] . The effect of diet on parasite proliferation could also be linked to the production of SCFAs or other bacterial metabolites [50–54] . An example of a metabolite which may have such an effect was uncovered in research with human volunteers . Chappell and co-workers detected a significant association between luminal concentration of the bacterial metabolite indole and susceptibility to cryptosporidiosis [55] . Given the metabolic dependence of the parasite on host cell metabolites inferred from the annotation of several Cryptosporidium genomes [56 , 57] , it is also conceivable that bacterial metabolites could affect the parasite's intracellular proliferation by limiting or increasing the availability of essential molecules in the enterocyte . Metabolomics analyses will be needed to study the actual mechanism linking diet and parasite . The importance of microbial metabolites for epithelial integrity , function and immune function has been demonstrated [58] . Such mechanisms could be relevant to understanding the link between diet , microbiome and Cryptosporidium proliferation . We previously showed that administration of a probiotic product can aggravate cryptosporidiosis [5] . The prebiotics used here in experiment 4 and 5 are also found in probiotics we already tested , but combined with 14 strains of probiotic bacteria belonging to the genera Lactobacillus , Bifidobacterium and Streptococcus ( S1 Table ) . The observed mitigating effect of the prebiotics in the absence of probiotic bacteria indicates that the aggravating effect of the probiotics product may be caused by probiotic bacteria . Experiments to test the effect of probiotic bacteria , given individually or in different combinations , on the course of cryptosporidiosis may contribute to elucidating the mechanisms of interaction between the gut environment and Cryptosporidium parasites . Such experiments should combine the analysis of the microbiota and metabolites to identify mechanisms linking diet with parasite proliferation . It is interesting to note that a reduction in the severity of the infection in response to prebiotics occurred regardless of the type of diet consumed . Although an effect on cryptosporidiosis was observed in experiments 4 and 5 , the impact on the microbiome was more accentuated in experiment 5 . This is likely explained by the fact that both experiment 4 groups already ingested fibers with the diet . To study the link between diet , intestinal microbiota and the course of cryptosporidiosis , fecal transplant experiments into germ-free mice will be needed . Dietary treatments found here to be effective at reducing the severity of cryptosporidiosis in the mouse should also be tested in another model , like the pig [59] , to assess the extent to which diarrhea and increased gut motility impacts the effectiveness of the dietary treatment .
|
The infection with Cryptosporidium parasite , a condition known as cryptosporidiosis , is a common cause of infant diarrhea in developing countries . We have previously shown that mice infected with C . parvum , one of the main cause of human cryptosporidiosis , develop a more severe infection if given probiotics . To investigate the mechanism of this effect , we fed mice prebiotics and diet lacking plant fiber . We found that fermentable fiber , whether administered as a prebiotic supplement or as part of the diet , has a protective effect against cryptosporidiosis in mice . We also observed a significant association between the severity of infection and the composition of the gut microbiota . A significant inverse correlation was found between severity of cryptosporidiosis and the ratio between the abundance of bacteria belonging to the phylum Bacteroidetes and the abundance of Firmicutes bacteria . This ratio is frequently viewed as a marker of a healthy microbiota . These results raise the possibility that dietary interventions could be used to alleviate the impact of cryptosporidiosis .
|
[
"Abstract",
"Introduction",
"Material",
"and",
"methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"parasite",
"groups",
"oocysts",
"microbiome",
"microbiology",
"diet",
"cryptosporidium",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"parasitology",
"apicomplexa",
"nutrition",
"protozoans",
"cryptosporidiosis",
"microbial",
"genomics",
"digestive",
"system",
"parasitic",
"intestinal",
"diseases",
"medical",
"microbiology",
"cryptosporidium",
"parvum",
"gastrointestinal",
"tract",
"eukaryota",
"anatomy",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"organisms"
] |
2019
|
Deprivation of dietary fiber enhances susceptibility of mice to cryptosporidiosis
|
Immunity to the sand fly salivary protein SALO ( Salivary Anticomplement of Lutzomyia longipalpis ) protected hamsters against Leishmania infantum and L . braziliensis infection and , more recently , a vaccine combination of a genetically modified Leishmania with SALO conferred strong protection against L . donovani infection . Because of the importance of SALO as a potential component of a leishmaniasis vaccine , a plan to produce this recombinant protein for future scale manufacturing as well as knowledge of its structural characteristics are needed to move SALO forward for the clinical path . Recombinant SALO was expressed as a soluble secreted protein using Pichia pastoris , rSALO ( P ) , with yields of 1g/L and >99% purity as assessed by SEC-MALS and SDS-PAGE . Unlike its native counterpart , rSALO ( P ) does not inhibit the classical pathway of complement; however , antibodies to rSALO ( P ) inhibit the anti-complement activity of sand fly salivary gland homogenate . Immunization with rSALO ( P ) produces a delayed type hypersensitivity response in C57BL/6 mice , suggesting rSALO ( P ) lacked anti-complement activity but retained its immunogenicity . The structure of rSALO ( P ) was solved by S-SAD at Cu-Kalpha to 1 . 94 Å and refined to Rfactor 17% . SALO is ~80% helical , has no appreciable structural similarities to any human protein , and has limited structural similarity in the C-terminus to members of insect odorant binding proteins . SALO has three predicted human CD4+ T cell epitopes on surface exposed helices . The results indicate that SALO as expressed and purified from P . pastoris is suitable for further scale-up , manufacturing , and testing . SALO has a novel structure , is not similar to any human proteins , is immunogenic in rodents , and does not have the anti-complement activity observed in the native salivary protein which are all important attributes to move this vaccine candidate forward to the clinical path .
Sand flies are vectors of Leishmania parasites , causal agents of the neglected tropical disease ( NTD ) leishmaniasis , which is ranked among the most important NTDs in terms of global disease burden [1] and is re-emerging at alarming rates due to the ongoing conflicts in the Middle East and North Africa [2] . To date , there is no effective or licensed vaccine against human leishmaniasis , despite studies demonstrating the cost-effectiveness of developing such biotechnologies for use in resource-poor settings [3 , 4] . Sand flies deliver saliva into the skin of the host while probing for a blood meal . The saliva of blood feeding arthropods , including sand flies , has a number of potent bioactive molecules , such as anticoagulants , vasodilators , and inhibitors of platelet aggregation that assist in acquiring blood meals . In the case of sand flies , some of these bioactive components also modify the immunological environment at the host skin site of bite , favoring the establishment of Leishmania infection in the host [5 , 6] . Other biological activities of sand fly saliva have been reported and the proteins responsible for these effects have been identified [7] , including Lundep , an endonuclease that destroys neutrophil traps [8]; salivary yellow proteins that bind biogenic amines [9]; and recently SALO ( Salivary Anti-complement from Lutzomyia longipalpis ) , an inhibitor of the classical pathway of complement [10] . Although sand fly saliva was shown to exacerbate Leishmania infection , immunity to sand fly saliva protects against Leishmania infection [6 , 11] . The protection is correlated to the induction of a Th1 cellular immune response . Recently , a number of salivary proteins have emerged as vaccine candidates against cutaneous leishmaniasis , including PpSP15[12] and Linb11 [13] in rodent models , and PdSP15 in non-human primates[14] . For visceral leishmaniasis , the salivary proteins LJM17 and LJL143 were shown to induce a Th1 immune response in dogs [15] . Immunization with DNA plasmid coding for the salivary protein SALO ( formerly known as LJM19 ) was shown to protect hamsters against the fatal outcome of visceral leishmaniasis [16] and this protection was correlated with the induction of a Th1 cellular immune response [16] . Recently , a combination vaccine comprised of recombinant salivary protein SALO and a genetically modified Leishmania donovani resulted in a strong protection against visceral leishmaniasis [17] , further suggesting SALO as an important component for a visceral leishmaniasis vaccine . In this study we present the production and crystal structure of SALO genetically engineered in yeast as part of efforts to develop it as a recombinant vaccine for leishmaniasis .
All animal procedures were reviewed and approved by the National Institute of Allergy and Infectious Diseases ( NIAID ) Animal Care and Use Committee , under animal protocol LMVR4E , and handled in accordance to the Guide for the Care and Use of Laboratory Animals and with the NIH OACU ARAC . Further , the animal protocol is in full accordance with ‘The guide for the care and use of animals’ as described in the US Public Health Service policy on Humane Care and Use of Laboratory Animals 2015 ( http://grants . nih . gov/grants/olaw/references/phspolicylabanimals . pdf ) . DNA coding for SALO without signal peptide was codon optimized based on Pichia pastoris usage preference and subcloned into Pichia secretory expression vector pPICZαA ( Invitrogen ) using EcoRI/XbaI restriction sites . The correct insert sequence and reading frame of recombinant plasmid was confirmed by double-stranded sequencing using vector flanking primers α-factor and 3’AOX-1 and then transformed into Pichia pastoris X-33 by electroporation . The expression of rSALO ( P ) was induced with 0 . 5% methanol at 30°C for 72 hours and the highest expression clone was chosen for making seed stock with 20% glycerol . The large-scale expression of hexa histidine tagged rSALO ( P ) was induced with methanol in a 10L fermentor and purified by immobilized metal affinity chromatography ( see S1 Text ) . Recombinant proteins rSALO ( P ) and rSALO ( H ) were treated with glycosidases using the Enzymatic DeGlycoMx Kit from QA-Bio ( Palm Desert , CA ) , which contains a mixture of N-glycosidase F , sialidase , ß-galactosidase , glucosaminidase , and O-glycosidase following the manufacturer’s instructions . Control reactions were performed without glycosidases . Cloning , expression and purification of SALO was performed as previously described [10] . Briefly , DNA coding for SALO without the signal peptide and containing a C-terminal hexahistidine tag was synthesized by Eurofins genomics ( Huntsville , AL ) . The synthesized gene was subcloned into the VR2001-TOPO expression vector . The transfection into HEK 293-F cells and expression of rSALO ( H ) was performed at the Protein Expression Laboratory at the Frederick National Laboratory for Cancer Research ( Frederick , Maryland ) . The supernatant was recovered after 72 hours , concentrated , and buffer exchanged into PBS pH 7 . 4 using a 10K Amicon concentrator device ( Millipore ) . The protein was purified by immobilized metal ion affinity chromatography in the same buffer and eluted with imidazole . Hemolytic assays were performed as previously described [10] . Briefly , 1% normal human serum ( NHS ) in gelatin HEPES buffer with Ca+2 and Mg+2 ( 0 . 1% gelatin , 5mM HEPES , 145 mM NaCl , 0 . 15 mM CaCl2 , 0 . 5 mM MgCl2 , pH 7 . 4 ) containing SALO was added to antibody sensitized sheep erythrocytes ( 1 x 107 cells ) and incubated 30 min at 37°C . After adding 250 μl ice cold saline ( 0 . 9% NaCl ) the sample was centrifuged and the absorbance at 414nm was measured . Hemolytic assays using SGH or rSALO ( P ) in the presence of the rSALO ( P ) antiserum were performed as described above , but prior to mixing the SGH ( 0 . 5 salivary gland pairs ) or rSALO ( P ) ( final concentration 0 . 1 μM ) with the NHS in GVB2+ ( 5 mM Veronal , 145 mM NaCl , 0 . 15 mM CaCl2 , 0 . 5 mM MgCl2 , 0 . 025% NaN3 and 0 . 1% gelatin , pH 7 . 3 ) , the inhibitors were incubated with 12 . 5 μl of different dilutions of the antiserum ( 1:10; 1:100; 1:1000 or 1:10000 , in PBS ) . A control experiment using antiserum diluted 1:10 with red blood cells , was performed to determine any possible hemolytic effects of the cells hemolysis . Salivary glands were dissected from Lu . longipalpis sand flies obtained from the Vector Molecular Biology Section , LMVR , NIAID , NIH as previously described [18] . Six to eight weeks old female Balb/c mice were injected intradermally in the ear three times every 15 days with 2 μg of rSALO ( P ) mixed ( 1:1 volume ) with Magic Mouse Adjuvant ( Creative Diagnostics , Shirley , NY ) as recommended by the manufacturer . Fifteen days after the last inoculation , blood was collected to obtain the rSALO antiserum . Six to eight weeks old female Balb/c mice were injected intradermally in the right ear three times every 15 days with 2 μg of rSALO ( P ) ( without adjuvant ) . The endotoxin level of rSALO ( P ) was 0 . 00127 Endotoxin Units per injection . Delayed type hypersensitivity response or skin immune response was measured in the ear of C57Bl/6 mice as previously described [19 , 20] . The mouse ear thickness and redness were used as an indicator of a cell-mediated immune response to rSALO ( P ) [19 , 20] . Briefly , ear thickness from the from the dorsal to the ventral portion of the ear was measured using a Digital Vernier caliper ( Mitutoyo Corp . ) at 24 and 48 h following intradermal injection of rSALO ( P ) . Measurements were taken for five mice in each group and repeated at least twice . Statistical analysis was performed using the GraphPad Prism software . Multiple groups were analyzed using one-way analysis of variance followed by Tukey's multiple-comparison test . The average molecular weight of the SALO protein was determined by SEC-MALS . The system consisted of an Agilent 1260 Infinity series HPLC , coupled with a UV detector ( Agilent ) , a miniDAWN triple-angle light-scattering detector ( Wyatt Technology ) , and an Optilab rEX differential Refractive Index ( dRI ) detector ( Wyatt Technology ) . 40 μg of SALO was loaded into a TSK gel Super SW2000 column ( TOSOH Biosciences , King of Prussia , PA ) and eluted at 0 . 35 ml/min isocratically with Tris-HCL pH 8 for 30 min . Protein constants were 0 . 185 mL/g and 0 . 911 mL/ ( mg∙cm ) for dRI and UV detectors , respectively . Data collection and analysis was done with Wyatt’s ASTRA 6 . 1 . 1 software . rSALO ( P ) was buffer exchanged and concentrated to 24 mg/ml in 50 mM Tris HCl pH 8 . 0 using a 5K MW cutoff centrifugal concentrating device ( Millipore ) . The initial protein concentration was confirmed by measurement of OD280 prior to setting up crystallization experiments . Crystallization conditions were screened using commercial screens from Hampton Research at 298K . Crystals were grown by vapor diffusion in sitting drops , which were equilibrated against well containing 0 . 5 ml crystallization solution . Drops were prepared by mixing 1 . 5 μl of protein solution with an equal volume of crystallization solution . No crystals were obtained for protein produced in mammalian cells , possibly because of the presence of the N-terminus vector derived sequence . rSALO ( P ) crystallized within 16 hours from a precipitant solution containing 0 . 02M calcium chloride , 30% v/v MPD and 0 . 1 M sodium acetate pH 4 . 6 . Larger crystals with dimensions 0 . 8 mm X 0 . 5 mm X 0 . 3 mm were obtained within 48 hours by setting up larger drops using a ratio of 4 μl of protein to 1 . 5 μl of the same precipitant solution . Since crystals grew in solutions that contained adequate cryoprotectant , they were flash-cooled directly in a stream of N2 gas at 113 K prior to collecting diffraction data . X-ray diffraction data were collected at the Baylor College of Medicine core facility ( Rigaku HTC detector , Rigaku FR-E+ SuperBright microfocus rotating anode generator , with VariMax HF optics ) using the Crystal Clear ( d*trek ) package [21] . Data was integrated using MosFLM and scaled with SCALA [22] . Crystallographic data is shown in Table 1 . The structure of rSALO ( P ) was solved using single-wavelength anomalous dispersion with the anomalous signal from sulfur at Cu-Kalpha wavelength . FA values were calculated using the program SHELXC [23] . Based on an initial analysis of the data , the maximum resolution for substructure determination and initial phase calculation was set to 1 . 94 Å . The location of 89 atoms ( C , S , N , O ) were automatically determined using the program SHELXD [23] and based on the results of this automated search 82 . 08% of the model was built using the program ARP/wARP [24 , 25] . Since the difference between Rfactor and Rfree remained unreasonably high , the structure was subsequently refined in a lower symmetry space group with a dimer in the asymmetric unit . The final model was obtained by iterative manual model building cycles using the program Coot [26] followed by structure refinement with REFMAC5 [27][28] and PHENIX[29] . Structural figures were generated using PyMOL [30] . The refined coordinates and structure factors have been deposited in the RCSB protein databank under accession code 4LU2 . T-cell epitope was predicted for full SALO sequence using the program NetMHC II release 2 . 2 [31] . The program was set to default parameters that allow identification of 15-mer amino acid peptides with predicted binding affinity below 50 nM to MHC II alleles [32] . For predicted epitopes for the same MHC II allele with sequence length overlap higher than 50% , the peptide with the highest affinity score was kept . Graphs were built using in-house Perl scripts .
SALO was produced in Pichia pastoris to establish a feasible process for a product and clinical development path . This includes testing the immunogenicity of this salivary protein and resolving its crystal structure . Recombinant SALO was expressed as a soluble protein with a vector derived EF on the amino terminus using Pichia pastoris after 72 hours of methanol induction . Typical yields of rSALO ( P ) by a single immobilized metal affinity chromatography purification step were ~ 1g/L , which is 500 times higher than the 2 . 0 mg per L for rSALO ( H ) produced in HEK293 cells . Purified rSALO ( P ) appeared to be ~99% pure ( Fig 1A ) . The electrophoretic mobility of ~15kDa is likely due to the charge of the molecule and not due to post-translational modifications because the molecular weight of SALO determined by SEC-MALS ( 11 . 8 kDa ) is close to the theoretical molecular mass of 11 . 9 kDa ( Fig 1B ) . rSALO ( P ) elutes at 18 . 2 min as a single , monodisperse peak with a calculated molecular weight of 11 . 8 kDa ( Fig 1B ) which agrees with the theoretical molecular mass ( 11 . 9 kDa ) of monomeric rSALO . Recombinant SALO produced from HEK cells , rSALO ( H ) , elutes off the sizing column as two overlapping peaks ( Fig 1C ) . The main peak at 18 . 3min is ~83 . 9% of all the protein components has a molecular weight of 12 . 6 kDa , and a minor overlapping peak ( ~15 . 5% ) at 17 . 8 min with molecular weight of 13 . 7 kDa ( Fig 1C ) . The theoretical molecular weight of rSALO ( H ) is 12 . 2 kDa . Thus , rSALO ( P ) is pure and exclusively monomeric in solution ( monodisperse ) , which will simplify the downstream process for the production of a recombinant biologic for clinical development . It was previously shown that rSALO ( H ) and SGH of Lu . longipalpis containing SALO inhibited the classical pathway of complement [10] . rSALO ( P ) did not inhibit the classical pathway of complement , in contrast to rSALO ( H ) which inhibits the classical pathway of complement ( S1 Fig ) . Nevertheless , antibodies produced against rSALO ( P ) neutralized Lu . longipalpis SGH anti-complement activity in a dose dependent manner ( Fig 2A ) . Importantly , antibodies raised against rSALO ( P ) recognized both rSALO ( P ) and rSALO ( H ) , and a single band from Lu . longipalpis SGH , by Western blot ( Fig 2B ) . Furthermore , rSALO ( P ) had similar immune recall responses as rSALO ( H ) ( S2A and S2B Fig ) . C57BL/6 mice immunized with rSALO ( P ) or rSALO ( H ) induced an immune recall response as a form of a delayed-type hypersensitivity response ( DTH ) in the skin of mouse ear at 48 hours after the second and third immunization ( S2A and S2B Fig ) . This immune response was not observed in control naïve animals or at the contralateral ear where rSALO ( P ) or rSALO ( H ) was not injected ( S2A and S2B Fig ) . The crystal structure of SALO was refined with a dimer in the asymmetric unit in the space group P 42 with statistics shown in Table 1 . We chose the dimer because refining the structure of SALO as a monomer in a higher symmetry space group ( P 42212 ) , resulted in >12% difference between RFactor and RFree and increased disorder in loop regions . The SALO dimer ( Fig 3A ) appears to be crystallographic and PISA analysis shows no appreciable buried surface area at the dimer interface . SALO is ~80% alpha helix and ~20% loop . Each SALO monomer has an overall topology comprised exclusively of helices , stabilized by disulfide bonds and connected by short loops ( Fig 3B ) . SALO also has large segregated exposed charged regions ( Fig 3C ) . There are three predicted human T cell epitopes in SALO , EDCENIFHDNAYLL ( peptide 1 ) , VAKIIRECIAQVSTQ ( peptide 2 ) and KFSEIYDCYMKKKIC ( peptide 3 ) , ( Table 2 ) . All three epitopes are located on surface exposed helices ( Fig 4 ) . The structure of SALO is unique and could not have been predicted from any known protein structures . SALO is comprised entirely of helices and belongs to the all-alpha protein class with EF hand like fold . Pfam analysis using PDBSum ( http://www . ebi . ac . uk/pdbsum/ ) reveals that the C-terminal of SALO ( residues 50–105 ) contains the Pfam domain family PF01395 , otherwise known as the odorant-binding domain of insect proteins . Members of this family have limited sequence identity and their proposed shared function is to bind insect pheromones or odorants [33 , 34] . Additional studies are required to clarify if SALO can indeed bind odorants . The structure of the salivary protein PdSP15 from the sand fly Phlebotomus duboscqi has been reported , and like SALO , its structure is all helices connected by loops [35] . While SALO only shares 19 . 6% sequence similarity to PdSP15 , their C-terminal odorant binding domains superpose quite well ( Fig 5A–5C ) . Additionally , secondary structure alignment reveals a series of conserved residues including disulfide bonds that connect the central helices ( Fig 5C & 5D ) . Interestingly , two of the three predicted T-cell epitopes ( peptide 2 and peptide 3 ) are located in the structurally conserved C-terminus odorant-binding domain ( Fig 5D ) . It remains unknown what roles these conserved residues play in odorant binding or the functions of SALO and similar proteins .
Immunization with sand fly salivary protein SALO protects against leishmaniasis ( visceral and cutaneous ) , either as a DNA vaccine or as a recombinant protein [16 , 37] . Furthermore , this vaccine candidate was recently shown to inhibit the classical pathway of complement [10] . To move this vaccine candidate towards the clinical path , we solved the structure of SALO and expressed it in P . pastoris . Further , we developed a process for pilot production of SALO . Results from the current work demonstrate the feasibility of expressing SALO in P . pastoris for future scale manufacturing . We previously showed that rSALO ( H ) has anti-complement activity . In this work , we reproduce this finding ( S1 Fig ) and demonstrate that rSALO ( P ) lacks anti-complement activity , and is monomeric . Interestingly , both native SALO from salivary glands and rSALO ( H ) form multi-species as previously shown by Western blot [10] . It is possible that some or one of these multiple species are required for anti-complement activity , and that the single species observed in rSALO ( P ) may not be the active form of the protein . Regardless , rSALO ( P ) has the desirable features of a vaccine candidate: it is monomeric , monodisperse and does not have anti-complement activity while retaining its immunogenicity . After immunization , both rSALO ( H ) and rSALO ( P ) induced a robust delayed hypersensitivity response in mice . Of note , though rSALO ( P ) lacks anti-complement activity , antibodies against it inhibit the anti-complement activity from Lu . longipalpis sand fly salivary gland homogenate , suggesting that the overall structure of SALO is conserved regardless of the expression source . Our structural analyses also reveal that SALO does not share any appreciable tertiary or quaternary structural similarity to any known mammalian protein families . Furthermore , SALO is not found in any other insect vectors or other organisms [38] , displaying appreciable sequence homology only to proteins found in New World sand flies of the genus Lutzomyia and Nyssomyia [39] . Interestingly , our current studies reveal that SALO and PdSP15 , another salivary vaccine candidate that was previously shown to protect non-human primates against vector-transmitted L . major infection [14] , have conserved structural features in their odorant binding protein domain , which contains two of the three predicted CD4+ T cell epitopes , strongly suggesting that the odorant binding protein domain may be relevant for their immunogenic properties . Both SALO and PdSP15 produce a robust cellular immune response that is protective against leishmaiasis [14 , 16] . In light of our current findings , further studies are necessary to determine the importance of the odorant binding protein domain in the antigenicity of these salivary proteins . In summary , this work demonstrates that rSALO ( P ) is suitable for further scale-up , manufacturing , and testing as a vaccine candidate against leishmaniasis . The structure of SALO is novel and unique to sand flies with no resemblance to any protein sequence or structure from humans . rSALO ( P ) retains its immunogenicity and importantly it lacks anti-complement activity , overcoming a potential obstacle for its development as a vaccine . The attributes of recombinant rSALO ( P ) and its feasibility for future large-scale production make this molecule an attractive target as a component of a Leishmania vaccine for humans .
|
Immunity to sand fly salivary proteins has been shown to confer protection against leishmaniasis in rodent models . Recombinant salivary protein SALO ( Salivary Anticomplement of Lutzomyia longipalpis ) was shown to protect hamsters against the fatal outcome of visceral leishmaniasis caused by Leishmania infantum and to protect against cutaneous leishmaniasis caused by Leishmania braziliensis . Because of the potential use of this sand fly salivary protein as a component of a vaccine against human visceral leishmaniasis further characterization of SALO needs to be performed as well as a development plan for future scale manufacturing . In this work we present the successful expression and purification of recombinant SALO using Pichia pastoris . SALO from insect saliva inhibits the classical pathway of complement , an activity that may interfere with its role as a vaccine candidate . Here we show that recombinant SALO produced from Pichia pastoris , rSALO ( P ) , does not have the anti-complement activity , and antibodies against rSALO ( P ) inhibit the anti-complement activity of sand fly saliva , suggesting that the overall structure of SALO is conserved regardless of expression source . The high-resolution structure of SALO was determined by single atom anomalous dispersion and refined to 1 . 94 Å . SALO is a small compact helical protein with no appreciable structural similarity to anti-complement or any reported mammalian protein structures . Therefore , SALO is a feasible candidate to be incorporated as a component of an effective anti-Leishmania vaccine .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
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2017
|
Structure of SALO, a leishmaniasis vaccine candidate from the sand fly Lutzomyia longipalpis
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The emergence and spread of antibiotic resistance among Acinetobacter spp . have been investigated extensively . Most studies focused on the multiple antibiotic resistance genes located on plasmids or genomic resistance islands . On the other hand , the mechanisms controlling intrinsic resistance are still not well understood . In this study , we identified the novel subclass of aminoglycoside nucleotidyltransferase ANT ( 3" ) -II in Acinetobacter spp . , which comprised numerous variants distributed among three main clades . All members of this subclass can inactivate streptomycin and spectinomycin . The three ant ( 3" ) -II genes , encoding for the three ANT ( 3" ) -II clades , are widely distributed in the genus Acinetobacter and always located in the same conserved genomic region . According to their prevalence , these genes are intrinsic in Acinetobacter baumannii , Acinetobacter pittii , and Acinetobacter gyllenbergii . We also demonstrated that the ant ( 3" ) -II genes are located in a homologous recombination hotspot and were recurrently transferred among Acinetobacter species . In conclusion , our findings demonstrated a novel mechanism of natural resistance in Acinetobacter spp . , identified a novel subclass of aminoglycoside nucleotidyltransferase and provided new insight into the evolutionary history of intrinsic resistance genes .
The genus Acinetobacter includes a broad group of biochemically and physiologically versatile bacteria that are ubiquitous in many environments [1] . Acinetobacter baumannii is the most clinically significant Acinetobacter species , and is among the most concerning causes of hospital-acquired bacterial infections [2] . Several other species of the genus may also cause human infections , such as A . nosocomialis and A . pittii , and less frequently A . ursingii , A . haemolyticus , A . lwoffii , A . parvus , and A . junii [3] . In the last few decades , the rate of hospital-acquired infections caused by Acinetobacter spp . have increased markedly worldwide [4 , 5] . Of more concern is the rapidly increasing rate of multidrug resistance among Acinetobacter isolates , which has reduced the number of effective therapeutic options [1] . A study in US hospitals demonstrated that the rate of multidrug-resistant ( MDR ) Acinetobacter isolates increased from 7 to 30% between 1993 and 2004 [6] . In Chinese hospitals , more than half of A . baumannii isolates in 2014 were resistant to at least 10 frequently used antibiotics , and the rate of resistance to imipenem and meropenem has increased markedly ( from 31–39% in 2005 to 62 . 4–66 . 7% in 2014 ) [7] . Additionally , the frequency of detection of multi-resistant non-baumannii isolates such as A . pittii , A . junii , and A . johnsonii has increased considerably , which demonstrated the exceptional adaptation of Acinetobacter spp . to antibiotic pressure . The emergence and spread of antibiotic resistance among Acinetobacter spp . have been investigated extensively . Many antibiotic resistance genes ( ARGs ) are located on plasmids or genomic resistance islands in Acinetobacter isolates [8 , 9] . In addition to ARGs located on mobile elements , several chromosomally encoded efflux systems and β-lactamases have been reported to contribute to the resistance of A . baumannii . The corresponding genes confer resistance when their expression changes due to mobilization or mutation of their associated regulatory elements . For instance , the adeABC operon , which encodes an efflux pump , is not expressed in environmental A . baumannii isolates; and the MDR phenotype is caused by mutations in adeRS located upstream of adeABC , leading to over-expression of the pump [10] . By providing a strong promoter , insertion of ISAba1 upstream of blaOXA-51 in A . baumannii also increased the expression of the β-lactamase gene , which conferred resistance to carbapenems [11 , 12] . Interestingly , ISAba1 was also responsible for blaOXA-51 mobilization , as indicated by detection of a plasmid-borne ISAba1-blaOXA-51-like gene in baumannii and non-baumannii species of Acinetobacter [13 , 14] . Another example is the emergence of the mobile blaNDM-1 carbapenemase gene , which was initially captured by the genome of an Acinetobacter species where it was integrated in the transposon Tn125 , enabling its efficient spread in various pathogens [15] . Finally , recent works have shown that the aphA6 and aac ( 6' ) -Ih genes , which encode aminoglycoside-modifying enzymes , are intrinsic in A . guillouiae and A . gyllenbergii , respectively , but were acquired by A . baumannii by means of IS mobile elements [16 , 17] . When captured by mobile elements , chromosomally-encoded conserved genes can thus act as a source of acquired resistance . Despite the important role of intrinsic genes in resistance , a substantial gap remains in our understanding of the intrinsic mechanisms responsible for the antibiotic resistance of Acinetobacter spp . Good candidates for intrinsic resistance are transferases , which are present in bacterial chromosomes at a high frequency , and which have a diverse range of antibiotic substrates . For example , nucleotidyltransferases , phosphotransferases , and acetyltransferases can modify aminoglycosides , macrolides , chloramphenicols , and lincosamides [18] . In the present study , we scanned the publicly available bacterial genomes of Acinetobacter spp . to detect putative antibiotic transferases . Putative aminoglycoside nucleotidyltransferase ( ant ) genes were found in conserved chromosomal regions in various Acinetobacter spp . Functional analyses indicated that the putative encoded transferases from different species can increase streptomycin and spectinomycin resistance of Escherichia coli . According to the distant phylogenetic relationship between these proteins and the reported ANT ( 3" ) -I nucleotidyltransferases , we propose that the Acinetobacter ANT ( 3" ) should refer to a new subclass of aminoglycoside nucleotidyltransferases , designated ANT ( 3" ) -II . The genes encoding for ANT ( 3" ) -II are intrinsic in at least Acinetobacter baumannii , Acinetobacter pittii and Acinetobacter gyllenbergii . Moreover , we also demonstrated that these intrinsic genes recurrently transferred among Acinetobacter species by homologous recombination .
Comparison of the amino acid sequences of known aminoglycoside , macrolide , chloramphenicol , and lincosamide transferases with complete and draft genomes of Acinetobacter spp . from GenBank resulted in identification of 90 protein sequences with more than 30% amino acid identity with known resistance genes and less than 80% amino acid identity with each other . Among them , five were encoded by genes located in conserved chromosomal regions and had not hitherto been reported as antibiotic resistance determinants . These putative proteins shared the highest amino acid sequence identity ( 39–44% ) with aminoglycoside nucleotidyltransferases/adenylytransferases ( ANT ) of the class ANT ( 3" ) -I ( sometimes designated Aad ) , which catalyze adenylylation at the 3'' [ANT ( 3'' ) ] position [19] ( S1 Table ) . They are present in A . baumannii , A . pittii , A . junii , A . parvus , A . gyllenbergii , and A . ursingii , as well as 24 Acinetobacter isolates that have not been identified to the species level . We generated neighbor-joining , maximum-parsimony , and maximum-likelihood phylogenetic trees to evaluate the relationships between the putative ANTs and all known reported ANT ( 3" ) -I enzymes ( Fig 1 , S1 Fig ) . We used ANT ( 9 ) sequences from Staphylococcus aureus and Enterococcus faecalis as outgroups , which catalyze adenylylation at the 9[ANT ( 9 ) ] position [19] . The three phylogenetic methods yielded trees with similar topologies , in which all protein sequences of the genus Acinetobacter clustered apart from known ANT ( 3" ) -I enzymes , as a highly supported monophyletic clade ( with 100% bootstrap confidence ) , designated ANT ( 3" ) -II . These proteins share 64–72% amino acid identity with each other and 35–44% amino acid identity with known ANT ( 3" ) -Ia enzymes . ANT ( 3" ) -II formed three highly supported monophyletic clades , which were designated Cluster IIa , IIb , and IIc ( Fig 1 ) . Cluster IIa included putative ANTs of A . baumannii , A . pittii , and A . junii . All IIa members share at least 96% amino acid identities with each other . Cluster IIb consisted of only four sequences , all of which were from unknown Acinetobacter species , and shared more than 96% amino acid identities with each other . Finally , Cluster IIc included all remaining ANT ( 3" ) -II homolog sequences detected in Acinetobacter genomes , and shared 68–100% amino acid identities with each other . These three groups shared at most 72% amino acid identity with each other and their phylogenetic relationships was not consistent depending on the reconstruction method used ( S1 Fig ) , supporting the clustering in three independent groups . The putative ant ( 3" ) -II genes from A . baumannii , A . junii , and A . pittii ( IIa ) ; Acinetobacter sp . NIPH 758 ( IIb ) ; and A . parvus , A . gyllenbergii , and A . ursingii ( IIc ) were functionally expressed in E . coli ( Fig 1 ) . All of the above candidate genes increased the resistance of E . coli to streptomycin and spectinomycin by 32–128-fold , compared to the control ( E . coli harboring the null vector pUC18 only ) ( Table 1 ) . However , the expressed ANTs did not result in increased resistance to other aminoglycosides , suggesting a substrate specificity for the transferase , consistent with other enzymes of the ANT ( 3" ) class [19] . To assess the contribution of ANT ( 3" ) -IIa to intrinsic resistance in A . baumannii , the ant ( 3" ) -IIa gene was inactivated in A . baumannii ATCC 19606 and the susceptibility of the mutant strain to aminoglycosides was evaluated ( Table 2 ) . Inactivation of ant ( 3" ) -IIa decreased streptomycin and spectinomycin resistance by 16-fold and 2-fold , respectively , which can be fully restored by a complementation plasmid expressing ant ( 3" ) -IIa ( Table 2 ) , demonstrating that ANT ( 3" ) -IIa contributes to intrinsic resistance in A . baumannii . To characterize the products of enzymatic inactivation , ANT ( 3" ) -IIa from A . baumannii ATCC 19606 was over-expressed in E . coli BL21 ( DE3 ) and purified . The molecular weight of ANT ( 3" ) -IIa was 32 kDa ( S2 Fig ) . Enzyme activity was assayed using liquid chromatography-mass spectrometry ( LC-MS ) . With streptomycin [m/z 582 . 4 ( M+H ) +] as a substrate , a reaction product with an ( M+H ) + ion at m/z 911 . 3 was detected in the reaction system ( S3 Fig ) , indicative of addition of a single adenyl group to the antibiotic , which was confirmed by the product ion at m/z 911 . 3 . This result suggests that the ANT ( 3" ) -IIa enzyme of A . baumannii can adenylate streptomycin , and validates its functional classification as a streptomycin nucleotidyltransferase . The ant ( 3" ) -IIa gene in A . baumannii isolates was located at the same chromosomal locus , within a more than 20-kbp-long region highly conserved among most strains ( S4 Fig ) , and with no evidence of mobile element in the vicinity of the gene . Moreover , the gene was present in 1027 of 1406 A . baumannii genomes . The conserved location and high prevalence of the gene among A . baumannii isolates ( 73% ) suggest it to be intrinsic in this species . Similarly , ant ( 3" ) -IIa in A . pittii , and ant ( 3" ) -IIc in A . gyllenbergii are located in conserved chromosomal regions and are also present in most sequenced isolates , again indicating that they are intrinsic in the corresponding species . Although the ant ( 3" ) -II genes are also located in the same chromosomal region in all other Acinetobacter genome sequences we investigated , determination of whether these genes are intrinsic is hampered by the impossibility to cluster the sequenced isolates into well defined species . The unexpected inclusion of A . junii ANT ( 3" ) -IIa sequences among A . baumannii sequences in the phylogenetic tree ( Fig 1 ) prompted further investigation of the phylogenetic relationships among ANT-carrying Acinetobacter strains . The position of strains carrying ant ( 3" ) -II genes was mapped on the Acinetobacter phylogenetic tree based on the whole core-genome proteins [3] . First , the observed pattern revealed that almost all strains carrying an ant ( 3" ) -II gene ( with the exception of A . ursignii ANC3649 ) cluster in a single clade of the phylogeny ( Fig 2 ) . Second , numerous strains lacking an ant ( 3" ) -II gene are also scattered in this clade . Third , even when looking at branches including ant ( 3" ) -II-carrying strains only , the ANT ( 3" ) -II distribution of Fig 1 is globally inconsistent with the strains' phylogeny . Nearly half of the nodes linking ant ( 3" ) -II-carrying strains do not match the phylogenetic relationships observed for the ANT ( 3" ) -II sequences ( red nodes in Fig 2 ) . Such discrepancies between accessory and core gene phylogenies are generally indicative of horizontal gene transfer ( HGT ) , usually mediated by mobile genetic elements . However , no mobile element or mobile element remnant could have been detected in the vicinity of the ant ( 3" ) -II genes in any of the genomes investigated . To explore whether HGT caused the observed inconsistencies even so , we first focused on the unexpected position of A . junii ANT ( 3" ) -IIa among A . baumannii ANT ( 3" ) -IIa proteins , considering that both species are distantly related in the core-genome tree ( Fig 2 ) . We aligned the 10-kbp downstream and upstream regions of ant ( 3" ) -IIa from the A . baumannii ATCC19606 genome , the A . junii CIP 64 . 5 genome , and two other representative A . junii genomes devoid of the gene ( Fig 3 ) . Consistent with the phylogenetic analysis , the 4 . 3-kbp region including ant ( 3" ) -IIa in A . junii CIP 64 . 5 was 98% identical to those of A . baumannii , while the flanking regions were only 70–77% identical . More interestingly , an inner portion of the 4 . 3-kbp region , which included ant ( 3" ) -IIa and the following two genes , was replaced by an unrelated 1 . 8-kbp DNA sequence in A . junii CIP 107470 and by a small , unrelated 300-bp sequence in A . junii NIPH 182 ( Fig 3 ) . Such pattern is typical of gene acquisition through homologous recombination ( HR ) . To validate the occurrence of a HR event between A . baumannii and A . junii at the ant ( 3" ) -IIa locus , we performed an HR analysis on the 14-kbp region surrounding ant ( 3" ) -IIa by recombination detection program ( RDP ) analysis . Seven independent HR detection algorithms were selected , all of which provided highly significant support for a 4 . 3-kbp recombination event in A . junii CIP 64 . 5 with A . baumannii ATCC 19606 as the donor ( S2 Table ) . All methods located the maximal recombined region between positions 103 , 874 and 108 , 115 bp in the A . junii CIP 64 . 5 sequence , which included ant ( 3" ) -IIa . These results confirm that ant ( 3" ) -IIa in A . junii CIP 64 . 5 originated from A . baumannii and was integrated in the genome through homologous recombination . Another clear-cut inconsistency between the ANT ( 3" ) -II and Acinetobacter phylogenies is the clustering of the A . gyllenbergii NIPH 230 ANT ( 3" ) -IIc with A . parvus-related ANT ( 3" ) -IIc . Analysis of the genomic context of ant ( 3" ) -IIc in A . gyllenbergii NIPH 230 and A . parvus CIP 102637 showed that the sequence of the gene and its surrounding region ( 1 . 5 kbp ) is 100% identical between the two strains , while the remaining flanking regions are only 74–84% identical ( S5 Fig ) . Other discrepancies include the presence of ant ( 3" ) -IIc in A . ursingii ANC 3649 , the presence of the strains carrying ant ( 3" ) -IIb in an inner branch of the ant ( 3" ) -IIc clade , and the position of A . parvus CIP 108168 and CIP 102529 away from the other A . parvus strains in the ANT ( 3" ) -II phylogeny ( Figs 1 and 2 ) . All these discrepancies can be attributed to HGT mediated by HR , although the lack of representative genomes and sequence incompleteness prevented us from fully resolving the HR events leading to the observed patterns ( S6–S8 Figs ) . The high number of observed HR events involving ant ( 3" ) -II genes across Acinetobacter species prompted us to investigate the rate of recombination ( precisely , the intensity of DNA transfer ) at this locus compared to the global genome recombination rate . We first ran the ordered Painting algorithm [20] on all the A . baumannii genomes present in Fig 1 ( see Methods ) . In A . baumanni , ant ( 3" ) -IIa ranked as the 126th most recombined gene out of the 2282 genes present in all investigated genomes . Although such rank is indicative of a high recombination rate , it cannot be considered as a recombination hotspot according to the criterion proposed in [20] . When the analysis was performed on all Acinetobacter genomes carrying an ant ( 3" ) -II gene , the locus ranked 17th most recombined gene out of the 1694 genes present in all genomes , and fell in the range of the predicted recombination hotspots ( Fig 4A ) . Interestingly , the two genes directly adjacent to ant ( 3" ) -IIa in A . baumannii AYE ( encoding a PadR-family transcriptional regulator and a small conserved hypothetical protein ) ranked 12th and 21th most recombined genes , respectively ( Fig 4B ) . On the contrary , guaA ( encoding a GMP synthase ) ranked 118th despite being also adjacent to ant ( 3" ) -II genes ( Fig 4B ) . These results indicate that the ant ( 3" ) -II genes are part of a small recombination hotspot of ca . 2 kbp involved in the frequent horizontal transfer of the resistance gene across Acinetobacter species .
Intrinsic resistance genes are defined as conserved chromosomal genes , conferring phenotypic resistance in a given species [21] . Traditional intrinsic resistance genes encode either multidrug resistance efflux pumps that pump out intracellular antibiotics or outer membrane proteins that reduce the permeability of the outer membrane , decreasing antibiotic uptake [22] . Recently , a large number of other types of intrinsic resistance genes have been identified in E . coli , Pseudomonas aeruginosa , and Staphylococcus aureus by screening libraries of mutants obtained through precise gene deletions or transposon-tagged insertions that belong to a variety of cellular functional categories [23–26] . In the present study , bioinformatics screening of complete genome sequences and extensive molecular biology and biochemistry experiments were used to identify a new subclass of aminoglycoside nucleotidyltransferases , ANT ( 3" ) -II , in Acinetobacter spp . ANT ( 3" ) -II enzymes are phylogenetically distant from all known ANT ( 3" ) -I and the genes encoding for ANT ( 3" ) -II are always located at the same chromosomal locus in Acinetobacter spp . , while the genes of ANT ( 3" ) -I are part of mobile genetic elements widely distributed in gram-negative bacteria . Furthermore , our findings revealed that ANT ( 3" ) -II is intrinsic in A . baumannii , A . pittii and A . gyllenbergii , bringing to light a novel natural mechanism of resistance in these clinically important pathogens . Understanding the origins of antibiotic resistance would enable tracing their evolution , and predicting the emergence of clinical resistance . It has been proposed that certain antibiotic resistance genes in human bacterial pathogens originated from antibiotic-producing microorganisms , which use these genes to prevent suicide during drug production [27 , 28] . Recent studies have shown that intrinsic genes , particularly in the genus Acinetobacter , can become mobile . The intrinsic genes aph ( 3’ ) -VI and aac ( 6’ ) -Ih , which encode aminoglycoside-modifying enzymes that originated from A . guillouiae and A . gyllenbergii , respectively , were captured by an IS element and disseminated in other Acinetobacter species [16 , 17] . Similarly , A . radioresistens might be the origin of the blaOXA-23 carbapenem resistance determinant in A . baumannii [29] . The findings of our study demonstrated that , contrary to the above examples , the ant ( 3" ) -II genes were not mobilized through mobile elements but are located in a recombination hotspot , leading to frequent allele replacement or ant ( 3" ) -II gene acquisition in various Acinetobacter species . The impact of HR on the diversification of surface molecules and on the transfer of two resistance genes ( an activated cephalosporin resistance gene and the blaNDM-1 gene ) have been reported previously in A . baumannii [30–32] . However , the role of this molecular process in antibiotic resistance dissemination between Acinetobacter species is shown here for the first time . Moreover , the results suggest that this dissemination can occur even between very distantly related species of the genus , as exemplified by the ant ( 3" ) -IIc acquisition in A . ursingii . This phenomenon parallels the extensive horizontal exchanges of an intrinsic macrolide resistance gene among environmental and pathogenic species of the Bacillus cereus group we reported previously [33] . In the B . cereus group , acquisition of the intrinsic resistance gene was also mediated by HR . The fact that this process occurs in both Gram-positive and Gram-negative bacteria , and in both environmental and clinical isolates , suggests that transfer of resistance via HR may be more common than previously appreciated . However , the high divergence of ant ( 3" ) -II sequences and their distribution mostly among non-clinical Acinetobacter spp . indicate that the first acquisition of this gene in a subgroup of Acinetobacter and its high rate of horizontal transfer likely predates the development of antibiotic for therapeutic use . It has become clear that clinical antibiotic resistance genes originated from intrinsic genes of environmental bacterial species , for which their primary function in the natural environment have been questioned [18 , 28] . These general considerations would also apply for the ant ( 3" ) -II genes . On the one hand , ant ( 3" ) -II may function as a streptomycin/spectinomycin resistance gene in nature , since these two antibiotics are naturally produced by soil-dwelling bacteria [34 , 35] . The high rate of horizontal transfer would thus be an ecological response to the patchy distribution of antibiotic producers in the soil . Similarly , the sequence diversification of ANT ( 3" ) -II may have been an evolutionary response to the production of new antibiotics variants by the producer , following the arms race hypothesis [36] . Exchange of ant ( 3" ) -II variants or even creation of mosaic variants would thus help the Acinetobacter spp . to adapt to the antibiotic compounds produced locally . On the other hand , the ant ( 3" ) -II genes primary function in nature may be unrelated to antibiotics resistance . More and more examples are now reported in the literature , suggesting that it could be a general trend [37–39] . Under this hypothesis , the underlying reasons of the diversification and high rate of horizontal transfer of ant ( 3" ) -II genes remains the same , except that the adaptive pressure is not known . A last possibility could be that the selection pressure actually acts on the flanking region rather than on the ant ( 3" ) -II locus itself . Indeed , the recombination hotspot region expands over 1 . 5 kbp downstream of ant ( 3" ) -II genes and include two other genes . The closest one particularly ( ABAYE3738 in A . baumannii AYE , encoding a PadR-family transcriptional regulator ) , show even higher recombination intensity than ant ( 3" ) -II . The high rate of ant ( 3" ) -II genes horizontal transfers could thus be just a by-product of its physical position before this gene . HR facilitates only permanent integration of a gene from an extra chromosomal DNA fragment into the chromosome , but the mechanism that could mediates cellular uptake of DNA fragments in Acinetobacer spp . remains unclear . Natural transformation has been described in some strains of A . baylyi [40] , A . baumannii [41 , 3] , and A . calcoaceticus [42] , suggesting that DNA uptake could occur via transformation . Genomic analysis of the genus Acinetobacter indicated the presence of most of the 13 key T4P ( type IV pili ) and competence-associated components in all genomes [3] . These studies suggest that members of the genus Acinetobacter have the potential to develop natural competence . Alternatively , general transduction has been suggested between A . baumannii strains , facilitated by one of the prophages present in the chromosome [32] . The extent to which these two processes provide the raw DNA material for HR and contribute to the genome plasticity observed in this genus is of great interest and requires further investigation . Intrinsic resistance genes could provide an attractive therapeutic target to encourage the development of novel drugs that rejuvenate the activity of existing antibiotics [43] . We have shown that the deletion of ant ( 3" ) -II significantly decreases the resistance level of A . baumannii to streptomycin . Although no inhibitor of aminoglycoside-modifying enzymes is currently available , there is extensive effort to find how to overcome the action of these enzymes in pathogens [44] . Therefore , the demonstration that A . baumannii become more susceptible to streptomycin when ant ( 3" ) -IIa is silenced blazes a trail for future treatments against this extremely multi-resistant pathogen . This work identified a novel subclass of intrinsic aminoglycoside nucleotidyltransferases in environmental and clinically important Acinetobacter species . The corresponding genes ant ( 3” ) -II not only conferred phenotypic resistance in a given species but were also frequently horizontally transferred between different Acinetobacter species by homologous recombination . Overall , these results provide insight of Acinetobacer’s natural resistance and enhance our understanding of the evolutionary history of intrinsic resistance determinants in bacteria .
Escherichia coli DH5α was used as a host for the expression of putative transferase resistance genes in vitro . The genomic DNA of A . baumannii ATCC 19606 was extracted to use as template for polymerase chain reaction ( PCR ) . All strains were grown on Luria-Bertani ( LB ) broth at 37°C . The MIC for each strain was determined using broth microdilution assays , as recommended by the Clinical and Laboratory Standards Institute ( CLSI , http://www . clsi . org/; CLSI 2010 ) . Eleven open reading frames , representative of the putative ant ( 3" ) -II subclass were chosen from various Acinetobacter to determine their antibiotic resistance phenotypes . One DNA sequence , corresponding to the putative ant ( 3" ) -IIa from A . baumannii ATCC 19606 , was amplified using the primers antF/R listed in S3 Table . Other genes from A . baumannii ATCC 17978 , A . baumannii AYE , A . junii CIP 64 . 5 , A . pittii CIP 70 . 29 , A . pittii PHEA-2 , A . parvus CIP 108168 , A . ursingii ANC 3649 , A . gyllenbergii NIPH 230 , A . sp . NIPH 1859 , and A . sp . NIPH 758 were synthesized by Sangon Biotech ( Shanghai , China ) . The PCR product and synthesized DNA were cloned into multiple cloning sites of the pUC18 vector , resulting in recombinant plasmids carrying the target genes . E . coli DH5α was transformed with these recombinant plasmids , and clones were selected by blue-white screening on LB agar plates supplemented with 100 μg/ml ampicillin . The transformants containing the inserted fragments were confirmed by enzyme digestion and sequencing . Resistance of transformants to aminoglycosides ( streptomycin , spectinomycin , kanamycin , gentamycin , amikacin , and tobramycin ) were determined using the microdilution method , with induction by 0 . 5 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) , as recommended by the CLSI ( http://www . clsi . org/; CLSI 2010 ) . The plasmid pEX18Gm was used as a suicide vector to knock out ant ( 3'' ) -IIa in A . baumannii . The upstream and downstream regions ( ~500 bp ) of the gene were amplified using genomic DNA of A . baumannii ATCC 19606 as a template , and combined by overlap PCR . The desirable fusion PCR product was ligated to pEX18Gm , resulting in a recombinant plasmid . The plasmid was transformed into E . coli S17-1λpir , to create a donor strain . A . baumannii ATCC 19606 was used as a recipient strain . Using a conjugation experiment , a merodiploid ( containing the plasmid integrated with one HR event ) was obtained by plating cells on LB plates containing 100 μg/ml ampicillin and 20 μg/ml gentamycin . The merodiploid was inoculated sequentially into LB liquid medium free of antibiotic for propagation at 37°C . After several rounds of propagation , serial dilutions were spread on LB agar plates with 20% sucrose . After overnight incubation at 37°C , colonies were selected on gentamycin-containing LB agar plates and LB agar without antibiotics . The gentamycin-sensitive clones , which have lost pEX18Gm through a second HR event , were verified as correct ant ( 3'' ) -IIa-deficient mutants by PCR and sequencing . The primers used for construction of gene-deficient mutants are listed in S3 Table . A 1337-bp DNA fragment containing the A . baumannii origin of replication was synthesized by Sangon Biotech ( Shanghai , China ) and then cloned into plasmid pKF18k-2 ( Takara ) using PvuII restriction site . Inserts were selected in kanamycin 50 μg/ml and verified with PCR and enzyme digestion . The resulting plasmid was designated pKFAb , which can replicate in E . coli and A . baumannii . To construct the complementation plasmid the ant ( 3" ) -II gene was amplified from the A . baumannii ATCC 19606 chromosome using antF/R primers ( S3 Table ) and cloned into plasmid pKFAb with EcoRI and HindIII restriction sites by transformation of E . coli Top10 . The resulting recombinant plasmid pKFAb::ant was verified by enzyme digestion and sequencing . At last the construct pKFAb::ant was introduced into competent A . baumannii ( Δant ) by electroporation , and transformants were selected on LB plates supplemented with streptomycin 150 μg/ml . ant ( 3" ) -IIa from A . baumannii ATCC 19606 ( EEX02086 ) was inserted into pET28a ( + ) expression plasmid . The resulting recombinant plasmid was transformed into E . coli BL21 ( DE3 ) . A fresh overnight culture was used to inoculate 500 ml LB media in a 1/100 inoculum size . Culture was incubated at 37°C with shaking at 200 rpm up to OD600 of 0 . 6–0 . 8 , followed by continued cultivation for an additional 16 h by addition of 0 . 5 mM IPTG at 25°C . Subsequently , cells were harvested by centrifugation , washed with 50 mM Tris-HCl buffer ( pH 7 . 5 ) . Then cells were resuspended in 30 ml of 50 mM Tris-HCl buffer ( pH 7 . 5 ) and disrupted by using a ultrasonic cell disruptor ( SCIENTZ , Ningbo ) . The cell lysate was clarified by centrifugation at 12 , 000×g for 20 min , and the supernatant ( soluble protein fraction ) was used for protein purification . Recombinant protein was purified with the His-Bind protein purification kit ( Novagen , Madison , WI , USA ) according to the manufacturer's instructions . To remove any residual proteins , imidazole and salts from the collected fractions , these fractions were pooled and re-separated with Superdex 200 gel chromatography with Tris-HCl buffer using a fast protein liquid chromatography system ( Äkta FPLC , Amersham Biosciences , USA ) . The purified protein was concentrated by ultrafiltration using a Millipore Ultra-15 ( 10 kDa ) centrifugal filter ( Millipore , USA ) and stored at −70°C . The ANT ( 3" ) -IIa enzymatic reaction consisted of 50 mM HEPES ( pH7 . 5 ) , 1 mM dithiothreitol , 15 mM MgCl2 , 1 mg streptomycin , 3 mM ATP , 0 . 5 mg of purified ANT ( 3" ) -IIa protein , and 2 units of inorganic pyrophosphatase in a total volume of 1 ml [45] . The reaction was performed at 37°C for 24 h and stopped by the addition of cold acetonitrile . The supernanant fraction was then applied onto a Millipore Ultra15 ( 10 kDa ) centrifugal filter tube . After centrifugation at 12 , 000×g for 20 min , the protein was removed . The streptomycin and reaction product was eluted with deionized water . Prior to analyses , samples were filtered through a 0 . 22 μm filter and then subjected to LC-MS analysis on an Agilent 1260/6460 Triple quadrupole LC/MS system . The analytic column was ZORBAX SB-Aq ( Agilent , 2 . 1×100 mm , 3 . 5 μm ) . A mixture of 2% methanol and 0 . 1% formic acid ( v/v ) in water under an isocratic elution program was used as the mobile phase . The flow rate was set at 0 . 1 ml min-1 , and the column temperature was set at 30°C . The fragmentor voltage was 260 V . A capillary voltage of 4 . 0 kV and atomizing gas pressure of 35 psi were used . The flow rate of drying gas was 12 ml min-1 , and the temperature of the solvent removal was 350°C . N2 was used as the collision gas and a collision voltage of 45 V was used for product ion scan . The LC-MS data were acquired and analyzed using a MassHunter Workstation Software ( Version B . 04 . 00 , Agilent , Santa Clara , CA , USA ) . To discover new antibiotic transferases , two databases were constructed . One database consisted of a collection of amino acid sequences of known antibiotic transferases , including aminoglycoside , macrolide , chloramphenicol , and lincosamides transferases obtained from the Antibiotic Resistance Genes Database ( ARDB; http://ardb . cbcb . umd . edu/index . html ) , and completed with more recently described transferase resistance genes . The other was the complete collection of assembled and draft Acinetobacter genome sequences downloaded from the NCBI FTP Site ( ftp://ftp . ncbi . nlm . nih . gov/ ) in summer 2014 . The antibiotic transferase sequences were searched against the genomes using TBLASTN . And the proteins sharing more than 30% amino acid identity and 50% coverage with known antibiotic transferases were selected for expression analyses . Putative ANT ( 3" ) -II amino-acid sequences from Acinetobacter species were extracted from the respective genomes and the full set of ANT ( 3'' ) -I ( AadA ) protein sequences described in [19] were downloaded from Genbank . The protein dataset was completed with the recently described AadA25 protein [46] , four additional AadA sequences located in Acinetobacter species , and two ANT ( 9 ) -I sequences to serve as outgroups . The accession numbers for all these proteins are listed in S4 Table . The sequence dataset was aligned using the CLUSTALW software with standard parameters [47] . Phylogenetic trees were constructed using NJ , MP and ML methods implemented in MEGA 6 software [48] . All phylogenetic trees were constructed using the partial deletion parameter set to 80% and 1000 bootstrap replicates . Other parameters were set to default for NJ and MP analyses , while the WAG+F model , with gamma-distributed rates among sites , was used for ML analysis . The construction of the Acinetobacter core-proteins phylogenetic tree was described previously [3] . Briefly , orthologous core proteins shared by the analyzed Acinetobacter genomes were retrieved from bi-directional best hits . The 950 resulting families of proteins were concatenated and aligned to construct an approximated maximum-likelihood using FastTree with the WAG matrix of protein evolution , a gamma correction for variable evolutionary rates , and 100 bootstraps . HR detection at the ant ( 3" ) -IIa locus was performed using the RDP4 package , which includes several HR detection methods [49] , on a 14 kbp conserved region surrounding the gene in A . baumannii and A . junii genomes . The region was extracted from the four sequences presented in Fig 1 and aligned using CLUSTALW , with a gap penalty of 20 . The alignment was then carefully corrected visually , as alignment errors might critically alter HR detections , as explained in the RDP4 manual . The alignment was examined using the RDP [50] , GENECONV [51] , BootScan [52 , 53] , Maximum Chi Square ( MAXCHI ) [54] , Chimaera [55] , SisterScan [56] , and PhylPro [57] methods . We retained only events unambiguously detected by the seven methods , using the following RDP4 parameters: a corrected P-value < 0 . 000001 , with phylogenetic evidence , and with overlapping signals disentangled . HR investigations were made by running automated RDP analysis of the alignment of four sequences , and significant recombination events were detected . First , complete and draft genome sequences of the 31 A . baumannii strains carrying ant ( 3" ) -IIa ( Fig 1 ) were downloaded from the Genbank database . The genome sequence of A . baumannii AYE was selected as reference , and each of the remaining genomes were aligned on this reference using progressiveMauve v2 . 4 . 0 [58] . Resulting pairwise alignments were then combined , and all positions in AYE identical in all strains as well as positions with missing data ( gaps in one or more sequence ) were discarded . The SNPs and their position in the remaining core genomic regions were provided as input to orderedPainting [20] . Next , the same analysis was performed on the whole Acinetobacter genus , again with A . baumannii AYE as reference . Genome sequence of all Acinetobacter from Fig 1 carrying ant ( 3" ) -II were downloaded from Genbank , except that only three A . baumannii ( AYE , ATCC19606 , and NIPH601 ) and three A . pittii ( PHEA-2 , ABBL015 , and NBRC110504 ) were retained . The output of orderedPainting is a recombination intensity at each polymorphic site measured as a statistic Di . We therefore averaged the intensity value for each gene annotated in AYE . The top one percentile of gene-based recombination intensity was selected as indicative of recombination hotspot , as proposed in Yahara et al . 's report [20] .
|
The level of interest in intrinsic resistance genes has increased recently , and one of reasons is that their mobilization could lead to emergence of resistant pathogens . Insertion sequences ( ISs ) or plasmids can capture intrinsic resistance genes and disseminate them in bacterial populations . In this study , we identified a novel subclass of aminoglycoside nucleotidyltransferases which are intrinsic in A . baumannii and other Acinetobacter species . The genes encoding the aminoglycoside nucleotidyltransferase were frequently horizontally transferred between different Acinetobacter species by homologous recombination . This work reports a novel mechanism of natural resistance in Acinetobacter and an overlooked pathway for the dissemination of resistance among species in this genus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"antimicrobials",
"taxonomy",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"drugs",
"microbiology",
"antibiotic",
"resistance",
"streptomycin",
"phylogenetics",
"data",
"management",
"antibiotics",
"phylogenetic",
"analysis",
"pharmacology",
"dna",
"molecular",
"biology",
"techniques",
"bacteria",
"bacterial",
"pathogens",
"homologous",
"recombination",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"antimicrobial",
"resistance",
"medical",
"microbiology",
"microbial",
"pathogens",
"evolutionary",
"systematics",
"molecular",
"biology",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"biochemistry",
"acinetobacter",
"acinetobacter",
"baumannii",
"nucleic",
"acids",
"genetics",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"dna",
"recombination",
"evolutionary",
"biology",
"organisms"
] |
2017
|
A new subclass of intrinsic aminoglycoside nucleotidyltransferases, ANT(3")-II, is horizontally transferred among Acinetobacter spp. by homologous recombination
|
Intestinal schistosomiasis and soil-transmitted helminth ( STH ) infections constitute major public health problems in many parts of sub-Saharan Africa . In this study we examined the functional significance of such polyparasite infections in anemia and undernutrition in Rwandan individuals . Three polyparasite infection profiles were defined , in addition to a reference profile that consisted of either no infections or low-intensity infection with only one of the focal parasite species . Logistic regression models were applied to data of 1 , 605 individuals from 6 schools in 2 districts of the Northern Province before chemotherapeutic treatment in order to correctly identify individuals who were at higher odds of being anaemic and/or undernourished . Stunted relative to nonstunted , and males compared to females , were found to be at higher odds of being anaemic independently of polyparasite infection profile . The odds of being wasted were 2-fold greater for children with concurrent infection of at least 2 parasites at M+ intensity compared to those children with the reference profile . Males compared to females and anaemic compared to nonanaemic children were significantly more likely to be stunted . None of the three polyparasite infection profiles were found to have significant effects on stunting . The present data suggest that the levels of polyparasitism , and infection intensities in the Rwandan individuals examined here may be lower as compared to other recent similar epidemiological studies in different regions across sub-Saharan Africa . Neither the odds of anaemia nor the odds of stunting were found to be significantly different in the three-polyparasite infection profiles . However , the odds of wasting were higher in those children with at least two parasites at M+ intensity compared to those children with the reference profile . Nevertheless , despite the low morbidity levels indicated in the population under study here , we recommend sustainable efforts for the deworming of affected populations to be continued in order to support the economic development of the country .
Individuals living primarily in rural areas of low-income countries commonly harbor multiple parasitic infections , including infection with multiple helminth species [1] , [2] , [3] , [4] , [5] , [6] , [7] . In particular , intestinal schistosomiasis and polyparasitic soil-transmitted helminths ( STHs ) infections constitute major public health problems in sub-Saharan Africa [5] , [7] , [8] . Despite the considerable attention in epidemiological literature to the profile of the aforementioned infections , there are very few human studies that have examined the morbidity implications of polyparasitism [9] , [10] , [11] . Investigating the implications of polyparasitism morbidity is particularly relevant for healthcare providers in many developing countries where they must decide screening and treatment strategies in resource-limited settings [10] . The United Nations' fifth report on world nutrition emphasized that malnutrition is the largest contributor to ill-health in the world and that diet-related risk factors for chronic disease are responsible for a large share of the burden of disease in low mortality developing countries [12] . Furthermore , this same report underscored that the effect of such malnutrition is exacerbated by the 4 to 5 billion individuals in the developing world who simultaneously suffer from iron deficiency and its related form of anemia , whilst it also highlighted the high prevalence of anemia throughout the developing world . The link between hookworm infection and anaemia is well known , and the mechanism of effect through intestinal blood loss has been described [13] , [14] , [15] , [16] , [17] , [18] . Recent large scale studies have suggested links between heavy intensities of Schistosoma mansoni infections ( the intestinal type of schistosomiasis mainly found in sub-Saharan Africa ) with anaemia and lowered haemoglobin counts [19] , [20] , [21] . The mechanisms underlying S . mansoni associated anaemia are likely multifactorial ( e . g . , iron deficiency due to extra-corporeal loss , splenic sequestration , autoimmune haemolysis and anaemia of inflammation ) and have also been documented [22] , [23] . Moderate or high intensities of Trichuris trichiura are also associated with higher risks of anaemia in the presence of other STHs [24] , while the impact of Ascaris lumbricoides on anaemia is less clear [10] . Different types of helminth infection may affect nutritional status in different ways ( e . g . , nutrient absorption , and degree of mucosal damage ) [25] . Previous studies indicated various mechanisms through which hookworm , S . mansoni , T . trichiura and A . lumbricoides infections might alter nutritional status [11] , [22] , [26] , [27] . Several studies have found positive associations between malnutrition and the aforementioned intestinal parasites , but they have always limited their focus to single helminth species rather than looking at combinations of helminth species present [28] , [29] , [30] , [31] . In addition , Ezeamama and colleagues [9] have emphasized the lack , and at the same time the need for , epidemiological studies that examine the effect of polyparasite infections at various intensities in a range of morbidities . In the present study , we have used uniquely detailed data from the Rwandan national Neglected Tropical Disease ( NTD ) control programme in order to refine and understand the functional significance of polyparasite infections in anaemia and undernutrition in mainly school aged children from two districts in Northern Rwanda . The objectives of this study were to examine the distribution and the intensities of such polyparasite infections as well as to elucidate whether if individuals concurrently infected with multiple helminth species have measurably increased odds of being anaemic and/or undernourished .
Ethical approval for Monitoring & Evaluation ( M & E ) surveys was obtained from the Rwandan National Ethical Committee and Columbia University's International Review Board . The aim of the survey was explained to the participants , their parents , guardians and teachers before data collection . Moreover , only children who had completed their assent form and presented a consent form signed by their parents were entered in these surveys . Rwanda is a landlocked country in the Great Lakes region of east-central Africa , bordered by Uganda , Burundi , the Democratic Republic of the Congo and Tanzania . It is one of the smallest countries of Africa ( 26 , 338 km2 ) , but is home to approximately 10 . 1 million people thus supporting the densest population in continental Africa , with most of the population engaged in subsistence agriculture . A verdant country of fertile and hilly terrain with altitudes varying from 950 m to 4519 m , the small republic bears the title “Land of a Thousand Hills” . The Rwanda MoH through the Centre for Treatment and Research on AIDS , Malaria , Tuberculosis and Other Epidemics ( TRAC Plus ) - a centre for infectious disease control - was charged with planning and implementing data collection with the assistance from the National Reference Laboratory and the Access project . For the M & E survey , schools in both districts were randomly selected from three sample frames to allow the programme to be evaluated in 2 low- , 2 medium- and 2 high-schistosomiasis prevalence schools . More precisely these sample frames were defined as follows: It should be noted that the aforementioned lakes are located in different districts and they were selected on the basis that distance to the lakeshore has been proven useful to screen schools in the greater region [32] . The required sample sizes for children were calculated based on schistosomiasis prevalence/intensity data from schools in various African countries with similar age ranges assuming expected reductions in S . mansoni intensities over two annual treatments through EpiSchisto software ( http://www . schoolsandhealth . org/epidynamics . htm ) ; more technical details have been described elsewhere [33] and hence they are not repeated here . In addition , 120 adults were randomly selected in two villages from the two aforementioned districts , which were located less than 1 km away from each of the lakes . This adult sub-group was included with the aim of monitoring the future impact of Mass Drug Administration ( MDA ) on S . mansoni-related hepatic fibrosis , where highest morbidity/symptomology tends to be displayed in this older age group . However , for the purposes of the current analyses , we decided to include data from individuals of up to 20 years old , thereby inclusive of the end of the growing period for late maturers . These data were collected during February to April in 2008 , based on results of mapping surveys in 2007 ( data not presented here ) with the aim to determine pre-treatment levels of the infection status and some clinical indicators . The parasite burden was determined by duplicate examination from different microscopists of one stool specimen , at the same time , from each study participant for the presence of S . mansoni , T . trichiura , A . lumbricoides and hookworm ( Ancylostoma duodenale ) by the Kato-Katz method . This was due to logistical and financial reasons and can be justified within the scale of a large-scale control programme , although we are fully aware that replicate stool samples over several days are ideally required to accurately estimate intensity of schistosomiasis and STH . The mean number of eggs per gram ( EPG ) of stool for each parasite was used to define infections of low and moderate/high ( M+ ) intensity in accordance with WHO-established intensity cutoff values for S . mansoni , T . trichiura , hookworm and A . lumbricoides infections . Parasite infection profiles were based on infection status of the study participants; these parasite infection profiles were created using a similar technique developed in a study conducted in rice-farming villages in Leyte , The Philippines [9] . Given possible concurrent infection by up to four parasites at one of three potential intensity levels ( none , low , or M+ ) for each species , there were 34 = 81 possible unique categories of polyparasite infections . A total of 47 of the 81 categories were found in the current Rwandese study population . The sub profiles were finally condensed into the following 4 infection profiles corresponding to putatively different risk levels for anaemia and undernutrition: Heights were measured with height poles which had a fixed head board and can thus be considered comparable to that of the NHANES stadiometer ( http://www . cdc . gov/nchs/products/elec_prods/subject/video . htm ) . More precisely , the stature meter was placed to the floor and for each individual the tape was pulled up until zero reached the red line . The upper part of the pole was then firmly and accurately attached to the wall and fixed with screws . Finally the meter was pulled down onto the head of individual to get the measurement . Weights were measured with electronic balances . Children were asked to remove their shoes and all heavy clothes if they wore any and this was done in the morning by the survey team . All persons performing these measurements were fully trained and experienced in the use of these protocols , and the same staffs were used throughout to ensure standardization . Finger prick blood samples were also obtained from each individual , sufficient for accurate Hb measurement using a Hemocue photometer [34] . Indices of the anthropometric status of the studied children were based on the 2000 growth reference curves designed by the Centre for Disease Control ( CDC ) as this population more closely resembles those in countries like Rwanda since it includes both human milk and formula-fed infants; these were computed using the Nutstat program within Epi Info V 3 . 4 . The fact that the 2000 CDC growth charts consist of sex specific charts for infants , birth to age 36 months ( length-for-age , weight-for-length , weight-for-age , and head circumference-for-age ) and older children , 2 to 20 years ( stature-for-age , weight-for-age and Body Mass Index ( BMI ) -for-age ) led us also to the decision of excluding data of individuals more than 20 years old . Low Body Mass Index is considered an indicator of acute under-nutrition ( thinness or wasting ) and is generally associated with failure to gain weight or a loss of weight [35] . The Z-score cut-off point recommended by WHO , CDC , and others to classify low anthropometric levels is 2 Standard Deviation ( SD ) units below the reference median for this specific index . A cut-off of -2 BMI Z-scores was calculated to classify underweight individuals . The z-scores of height-for-age that were less than 2 SD below the reference median served to define stunted individuals . In order to examine the adjusted odds ratios ( ORs ) of anaemia , wasting and stunting , we tested a range of different approaches of statistical modeling to correctly identify individuals who have had higher morbidity as assessed from the outcomes aforementioned . Because the modeling of the between school variation through random effects logistic regressions did not prove appropriate for the statistical analysis of our data , we also employed the Generalized Estimating Equations ( GEE ) approach whenever this was analytically possible . If the GEE algorithm did not converge , we used conventional logistic regression models . The GEE method does not explicitly model between-cluster variation; instead it focuses on and it estimates its counterpart , the within-cluster similarity of the residuals; it then uses this estimated correlation to reestimate the regression parameters and to calculate standard errors which are reasonably accurate and hence lead to the generation of confidence intervals with the correct coverage rates [36] . Data management and statistical analyses were performed using SAS V9 ( SAS Institute Inc . , Cary , NC , USA ) . For all the odds ratios studied , we fitted the random effects logistic regression models by using PROC NLMIXED while we employed the GEE method by using PROC GENMOD . Particularly for the odds of anaemia , we have included as explanatory variable the parasite infection profiles I-III ( as defined in the previous section ) ; we also consider the nutritional status as defined by stunting as an effect modifier . We therefore display estimates with and without considering the effect of stunting; we also included the interaction term of stunting with the parasite infection profiles if the change in deviances between relevant nested models was significant at the 5% significance level . Similarly , when we modeled the odds of wasting and stunting respectively , we have included as explanatory variable the parasite infection profiles I-III while we consider anaemia status as an effect modifier . Potential confounders of the relationships between anaemia stunting , wasting and helminth infection were decided to be included in light of known confounders of these associations based on published literature [11] , [20] , [21] , [37] , [38] , [39] and these were the categories of age , sex , and the district where study participants were living in . Mean Hb concentration of different groups of individuals recruited in the current study were also initially examined through a random effects at the school level multivariate linear regression model by using PROC MIXED . Likelihood ratio tests indicated that these random effects were not significant and thus it was finally decided to omit them . However , because of the non-independence found in our data we finally decided to employ the GEE method by using PROC GENMOD . We tested as explanatory variables the categories of age , sex , district and parasite infection profiles I-III . We also tested the two-way interaction terms of parasite infection profiles I-III with district and stunting and retained them in the model if the change in deviances between the relevant nested models was significant at the 5% significance level . Covariates in all aforementioned multivariable models with p<0 . 05 were considered significantly associated with outcomes .
A total of 1605 children and adolescents were recruited , for a participation rate of 88% , and provided complete parasitologic and anthropometric data . They were aged 5 to 20 years old , with a median age of 10 years and 47 . 7% of the recruited individuals were male . The observed prevalences of wasting , stunting and anaemia were respectively estimated as following: 8 . 1% ( 95% CI: 6 . 8 to 9 . 4 ) , 38 . 5% ( 36 . 1 to 40 . 9 ) and 4 . 9% ( 95% CI: 3 . 9 to 6 . 1 ) . The mean observed Hb concentration was estimated to be 13 . 8 g/dL ( 95% CI: 13 . 7 to 13 . 8 ) . Table 1 contains the characteristics of the study population by subprofile classification infection category . The most prevalent co-infections were those of low intensity of A . lumbricoides and T . trichiura ( 21 . 2% ) . The adjusted ORs of anaemia from the GEE multivariate logistic regression models are presented in Table 2 . Deviance tests indicated ‘Model 3’ as the most appropriate one; this model shows that individuals who are stunted are almost 1 . 5 times more likely than non-stunted to be anaemic ( OR: 1 . 6 , P = 0 . 041 ) . Children of 11–13 years old were significantly less likely than children of 5–7 years old to be anaemic ( OR = 0 . 572 , P = 0 . 026 ) . In addition , males are almost twice more likely to be anaemic compared to females ( OR: 1 . 9 , P = 0 . 024 ) . Neither the interaction terms of stunting or district with the parasite infection profiles nor any other examined variable here were found to be significant factors for the odds of being anaemic . GEE did not converge for the modeling of the odds of being wasted and this is most likely to be explicable by the fact that there was not sufficient information in order to estimate the binomial probability structure by taking into account the intra-subject correlation . Consequently , we used Maximum Likelihood ( ML ) and the results of such multivariate logistic regression models for the odds of being wasted are presented in Table 3 . Deviance tests as well as Akaike's information criterion ( AIC ) indicated ‘Model 2’as the best one among the tested models . This model shows that only children of 11–13 years old were significantly more likely than the younger children ( age group: 5–7 years old ) to be wasted ( OR = 1 . 8 , P = 0 . 033 ) . Furthermore , study participants from Burera district were significantly more likely to be wasted when compared with study participants from Musanze district ( OR = 3 . 3 , P<0 . 001 ) . It is noteworthy that children with concurrent infection of at least 2 parasite species at M+ intensity - that is , those with polyparasite infection profiles III - were almost twice marginally significantly more likely to be wasted than children with the reference polyparasite infection profile ( OR = 2 . 2 , P = 0 . 054 ) . Neither the interaction terms of anaemia status or district with the parasite infection profiles nor any other examined variable here , were found to be significant factors for the odds of being wasted . Table 4 contains the results from the GEE multivariate logistic regression models for the odds of being stunted . Deviance tests indicated ‘Model 3’ as the most appropriate one; this model shows those children of 11–17 years old to have significant positive ORs if compared with the youngest age group examined here ( i . e . 5–7 years old ) , ( more specifically , 11–13 years old: OR = 2 . 4 , P = 0 . 001; 14–17 years old: OR = 1 . 4 , P = 0 . 044 ) . However , adolescents of 18–20 years old were significantly less likely than the youngest age group to be stunted ( OR = 0 . 4 , P = 0 . 003 ) . Male individuals were significantly more likely than females to be stunted ( OR = 1 . 9 , P<0 . 001 ) . Study participants from Burera district were significantly less likely to be stunted than the study participants from Musanze district ( OR = 0 . 4 , P<0 . 001 ) . Anaemic study participants were significantly more likely than non anaemic to be stunted ( OR = 1 . 7 , P = 0 . 020 ) . Neither the interaction terms of anaemia status or district with the parasite infection profiles nor any other examined variable here were found to be significant factors for the odds of being stunted . Finally , Table 5 contains the results from the GEE linear regression model for the mean Hb concentration and the mean differences in different groups of the study population here . Deviance tests indicated ‘Model 3’ as the most appropriate one; this model shows that on average Hb concentration in the study population was 13 . 109 g/dL ( 95% CI: 12 . 904–13 . 314 ) . All different categories of age yielded significant associations with increased Hb levels compared to the youngest age group examined here ( i . e . , 5–7 years old ) . Study participants who were stunted when compared to non-stunted had significantly lower Hb counts by 0 . 270 g/dL , respectively ( P<0 . 001 ) .
Concurrent multiple parasite infections were found to be the norm in our study population , as has been reported in studies published elsewhere [1] , [2] , [5] , [6] , [9] , [10] . However , in the current study population , none of the concurrent polyparasite infections were found to be significantly associated with higher odds of anaemia , wasting , stunting nor mean lowered Hb concentration . Nevertheless , results did indicate that those study participants with concurrent infection with at least 2 parasites at M+ intensity were marginally significantly more likely to be wasted ( P = 0 . 054 ) relative to those with no infection or infection with 1 parasite species at low intensity , thereby validating the impact of higher intensity infections on health [40] . Potential reasons for the general lack of association of the concurrent polyparasite infections with anaemia in the current Rwandan population might be that anaemia itself appears to be relatively uncommon in this area . One reason for the latter may relate also to the fact that malaria incidence in the two districts studied here is lower that the rest of the country , as well as to the decrease of malaria prevalence in Rwanda in general as an unpublished WHO Draft of Mid Term Evaluation Report of the Rwandan Malaria Strategic Plan 2005–2010 , reveals . In addition , as Table 1 indicates , the majority of the study participants ( i . e . 21 . 2% ) had low intensities of A . lumbricoides and T . trichiura while very few of them had M+ intensities of hookworm and S . mansoni infections . Such a distribution is likely to have limited the power of this study – potentially making it difficult to achieve statistical significance where one existed for co-infections of M+ intensities . M+ intensities of the latter two helminth infections have been recently shown to be significant factors for anaemia in other similar epidemiological studies [20] , [21] , and such combined findings highlight how different factors contribute to anaemia in different parasite transmission and eco-epidemiological settings . Indeed we would recommend further similar studies in the eastern part of Rwanda where there is a higher prevalence of hookworm as shown by the STH mapping survey conducted last year by the NTD control programme ( unpublished data ) and malaria together with other country and epidemiological settings , to further elucidate the potential association of polyparasitism to human morbidity . The present study also indicated children of 11–13 years old to be significantly less likely than children of 5–7 years old to be anaemic . This finding might be explained by the fact that the youngest children have recently experienced the high iron demand of early childhood . We also found males compared to females to be significantly more likely to be anaemic . A previous study has discussed that among younger children , boys are more anaemic than girls but the reasons for this remain still unknown [41] . We have also attempted to examine if the differences between the sexes in the odds of being anaemic varied by age , but when the statistical interactions of age and sex were included in the relevant model , the algorithm did not converge . Stunted children compared to non stunted were also demonstrated to be more likely to be anaemic . This result is supported by the observation that iron deficiency which leads to anaemia also contributes to poor growth while it has been demonstrated that supplementation of iron to anemic children has a positive effect on linear growth [42] . The effect of the polyparasite infection profile on anaemia was not found to vary according to stunting . We also assessed the association between the polyparasite infection profiles and acute under-nutrition . Children with concurrent infection with at least two parasites at M+ intensity relative to those with no infection or infection were found to be marginally significantly more likely to be wasted ( P = 0 . 054 ) . This finding might be explained by decreased appetite experienced in those individuals who harbored two or more parasites at M+ intensity . However , significant differences in the odds of wasting observed within the two districts studied here still remain unclear as we do not think that the latter would differ in dietary patterns or socio-economic status . Regarding chronic undernutrition and concurrent polyparasite infections , the present cross-sectional study did not find any significant association with the exception of age and anaemia status being revealed as a significant factor for stunting . Older individuals , with the exception of the age group of 18–20 years old , were found to be more likely to be stunted than the younger age group studied here ( i . e . 5–7 years old ) . This could imply prior malnutrition in these individuals as has previously been reported in Zanzibar and Burkina Faso [43] , [44] . Furthermore , the findings of decreased odds of stunting in the older age of 18–20 years old suggest compensatory growth in height for this age group and this is consistent with results from longitudinal Senegalese data [45] . Anaemic compared to non-anaemic individuals were also found to be significantly more likely to be stunted and the causal pathways for such results have been discussed in the previous paragraph . However , the effect of the polyparasite infection profile on stunting was not found to vary according to anaemia status . Finally , it should be noted that stunting - an indicator of chronic undernutrition - was the most prevalent form of undernutrition observed in this study . This has been also found by the Rwanda demographic health survey 2005 where the Northern province had the highest prevalence of severe chronic malnutrition [46] . Nevertheless , for the same reasons as mentioned above , any explanation for the significant differences in the odds of stunting observed within the two districts studied here still remain unclear . Our investigation has some limitations . As mentioned above , due in part to the overdispersed nature of helminth infections eggs in stool and daily variation in excretion , the ideal protocol is to use replicate faecal samples over several ( ideally a minimum of three ) consecutive days [47] . Unfortunately due to the logistical and financial constraints inherent within the scale of such a large-scale control programme , such ideals cannot realistically be met and hence duplicate Kato Katz thick smears were taken from a single day's stool per individual instead . We are aware that such an assessment method is likely to have introduced some misclassification in the measurement of the intensities of helminth infections and consequently in the allocation of study participants to the polyparasite infection profiles . In addition , although we do recognize that anthropometric measurements should be taken according to the standardized protocols used by NHANES to develop the growth charts , some modification to these gold standard measures are necessarily within the field conditions of Mass Drug Administration . Nevertheless , we are confident that every possible precaution was employed by the Rwanda field team in order to obtain accurate and high quality reproducible data . Furthermore , we believe that is it high unlikely that the examined associations of this study were biased by unmeasured confounding factors such as socio-economic status of the study participants despite the fact that helminth infections are known to be intimately linked with poverty [48] , [49] , [50] , [51] . The reason for this is that surveyed participants most likely would belong to the poorest populations of the country with no significant variations in their socio-economic status and thus with no effect in the examined outcomes here . Therefore overall , despite the aforementioned potential limitations , this study represents one of the few quantitative , comprehensively analyzed studies on the epidemiology of helminth infections , anaemia and undernutrition in Rwanda covering a broad age range with an extension particularly in the adolescents' years . In conclusion the results of this study suggest that low-intensity polyparasite infections are more prevalent in Northern Rwanda , relative to high intensity polyparasitism , at least in terms of the major species of parasites under focus in the current study , and such co-infections appear not to have , alone , a great impact on anaemia and undernutrition . Consequently based on the current findings we would support the argument that sufficient chemotherapy programmes to prevent high infection intensities build up in these people even without achieving parasite eradications , should be promoted . Finally , as currently there is a move towards drugs for integrated NTDs , we would urge for similar analytical studies in order to fully evaluate risks and benefits of such initiatives in helminth endemic regions .
|
The helminth infections—schistosomiasis , hookworm , ascariasis and trichuriasis—are the main neglected tropical diseases ( NTDs ) to thrive in sub-Saharan Africa . Here we assess the distribution and the intensities of such polyparasite infections in two districts of the Northern Province in Rwanda and determine whether these are associated with anaemia , lowered haemoglobin levels and recent and/or chronic undernutrition . Rwanda is a small landlocked country in Central Africa where no research or control efforts on NTDs has been conducted since before the genocide in 1994 . The current study aimed to elucidate , for the first time post-genocide , the burden of NTDs on the health of the Rwandan people and potential associated morbidity . Despite the fact that we observed low morbidity levels and intensities of polyparasite helminth infections , we recommend sustainable efforts for the deworming of the Rwandan people to be continued in order to offer a worm-free physical and cognitive development to the children of Rwanda and hence support the economic development of the country .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"public",
"health",
"and",
"epidemiology/epidemiology",
"infectious",
"diseases/helminth",
"infections",
"public",
"health",
"and",
"epidemiology/global",
"health",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"mathematics/statistics"
] |
2009
|
Polyparasite Helminth Infections and Their Association to Anaemia and Undernutrition in Northern Rwanda
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The last 20 years has seen a significant series of outbreaks of Buruli/Bairnsdale Ulcer ( BU ) , caused by Mycobacterium ulcerans , in temperate south-eastern Australia ( state of Victoria ) . Here , the prevailing view of M . ulcerans as an aquatic pathogen has been questioned by recent research identifying native wildlife as potential terrestrial reservoirs of infection; specifically , tree-dwelling common ringtail and brushtail possums . In that previous work , sampling of environmental possum faeces detected a high prevalence of M . ulcerans DNA in established endemic areas for human BU on the Bellarine Peninsula , compared with non-endemic areas . Here , we report research from an emergent BU focus recently identified on the Mornington Peninsula , confirming associations between human BU and the presence of the aetiological agent in possum faeces , detected by real-time PCR targeting M . ulcerans IS2404 , IS2606 and KR . Mycobacterium ulcerans DNA was detected in 20/216 ( 9 . 3% ) ground collected ringtail possum faecal samples and 4/6 ( 66 . 6% ) brushtail possum faecal samples . The distribution of the PCR positive possum faecal samples and human BU cases was highly focal: there was a significant non-random cluster of 16 M . ulcerans positive possum faecal sample points detected by spatial scan statistics ( P<0 . 0001 ) within a circle of radius 0 . 42 km , within which were located the addresses of 6/12 human cases reported from the area to date; moreover , the highest sample PCR signal strength ( equivalent to ≥106 organisms per gram of faeces ) was found in a sample point located within this cluster radius . Corresponding faecal samples collected from closely adjacent BU-free areas were predominantly negative . Possums may be useful sentinels to predict endemic spread of human BU in Victoria , for public health planning . Further research is needed to establish whether spatial associations represent evidence of direct or indirect transmission between possums and humans , and the mechanism by which this may occur .
Mycobacterium ulcerans is an environmental , potentially zoonotic bacterial pathogen , which in humans causes the progressive ulcerative skin condition Buruli Ulcer ( BU ) , a neglected tropical disease which is endemic in at least 30 countries worldwide [1] . The majority of the disease burden is in West and sub Saharan Africa , however there is a significant and ongoing outbreak in temperate south-eastern Australia in the state of Victoria ( where the disease is also referred to as Bairnsdale ulcer ) [2] , [3] . In all settings , the geographic distribution of human BU case clusters is highly focal . The exact method of disease transmission is unknown: BU foci in Africa have been associated with natural bodies of fresh water such as rivers and lakes , prompting the hypothesis that human infection is acquired through skin abrasions by physical contact with contaminated water [4] , [5] or from the bites of infected aquatic insects such as water bugs ( Naucoridae ) [6] . It has also been observed that new endemic areas emerge in areas adjacent to recent soil disturbance and flooding [7] . In south-east Australia , infection is consistently associated with coastal areas [8] , [9] and the mechanism of transmission remains elusive , although several studies have indicated that mosquitoes may have a role [2] , [10] . The bacterium infects a wide range of terrestrial mammals in Australia , including both domestic animals [11]–[13] and native wildlife [14] , [15] . More recently , an extensive survey conducted in an area endemic for human BU on the Bellarine Peninsula ( Point Lonsdale; see map , Figure 1 ) revealed that a large proportion of faecal samples from common ringtail ( Pseudocheirus peregrinus ) and common brushtail ( Trichosurus vulpecula ) possums contained high concentrations of M . ulcerans DNA [16] . That study showed a strong association between BU endemicity of an area and the proportion and DNA concentration of M . ulcerans positive possum faecal samples . More recently , BU has emerged in a previously non-endemic area of the Mornington Peninsula , in the towns of Sorrento and Blairgowrie ( Figure 1 ) distant from previous historical foci further to the east ( Phillip Island , and the Frankston-Langwarrin area of outer Melbourne ) . From 2006 to the present , 12 new cases of human BU have been confirmed in patients who were either residents ( n = 6 ) or visitors ( n = 6 ) to this region , with no known contact with any of the established endemic areas for BU such as the Bellarine Peninsula , and no recent history of travel to endemic areas either interstate or overseas . In light of previous research showing the potential for possums in BU endemic areas to excrete M . ulcerans DNA , the aim of this study was to determine whether the presence and/or relative abundance of M . ulcerans DNA in possum faecal samples could be associated with a newly established focus of human BU cases in a previously non-endemic area . We undertook a systematic survey of ground collected possum faeces in the area of the emergent Mornington Peninsula BU focus with the objective to analyze the distribution of M . ulcerans positive possum faecal samples and to look for spatial associations with human BU case addresses .
The towns of Sorrento ( population 1448 ) and Blairgowrie ( population 2161 ) , are located near the western tip of the 750 km2 Mornington Peninsula , approximately 90 km south of Melbourne ( Figure 1 ) . The terrain is predominantly low-lying coastal scrubland ( <50 m above sea level ) , with an average annual rainfall of approximately 730 mm . There are no substantial water courses or large bodies of fresh water in either of the two towns; open drainage ditches with accumulations of standing water are uncommon . The sample sites in each town consist of similar networks of single-track asphalt or gravel roads with grass verges , connecting rows of large dwellings set in spacious fenced grounds with fairly abundant scrub and tree cover including coastal tea trees ( Leptospermum laevigatum ) as well as numerous introduced cultivar species in gardens . A significant proportion of properties in this region are not occupied by permanent residents , but used as holiday homes or temporary tourist accommodation . A case of Buruli Ulcer ( BU ) was defined as a human patient with at least one suggestive clinical lesion from which M . ulcerans DNA was detected by real-time IS2404 PCR . The identity of the M . ulcerans strain was confirmed via Variable Number of Tandem Repeat ( VNTR ) typing [17] of the cultured isolate or using DNA extracted from the clinical specimen at the Victorian Infectious Diseases Reference Laboratory ( VIDRL ) . A patient was suspected of having acquired BU from the Sorrento/Blairgowrie area if he/she was a resident of ( n = 6 ) , or a visitor to ( n = 6 ) , that area and had not reported recent contact ( <12 months ) with any other BU endemic area . Addresses were available for all 6 residents , whereas the addresses of visitors' holiday homes were available in 3 cases . The remaining 3 non-resident patients visited holiday homes of unknown location within the Sorrento/Blairgowrie area . Samples of possum faeces were collected from ground level ( roadside verges ) at points arranged in a predetermined grid pattern 200 m apart . Where there was no tree cover at the indicated grid point ( and hence , no possum faeces ) , samples were collected from the nearest available location where faecal pellets could be found . Faeces originating from common ringtail and common brushtail possums ( hereafter referred to as ringtail and brushtail possums ) were collected and distinguished based on their characteristic size and shape by experienced field workers and with the aid of a track and scat manual [18] . Where possible intact scats , which had not started to break down due to weather and invertebrates , and estimated to be less than a week old , were selected . The sample spacing interval was chosen in an attempt to minimize resampling of faeces from the same animals between adjacent points , since possums are highly territorial and radio-tracking data show that they generally restrict their movements within a radius of approximately 100 m or less ( A . Legione , unpublished data ) . Faecal samples from each sampling location were stored separately in sterile ziplock plastic bags and transported cool to the laboratory for storage at +4°C prior to DNA extraction , typically within a week of collection . DNA was extracted from possum faecal material using the FastDNA SPIN Kit for Soil ( MP Biomedicals , Solon , OH ) . Faeces ( approximately 100 mg ) was added to the kit-supplied Sodium Phosphate and MT Buffer in Lysing Matrix E tubes , and was homogenized for 40 s at setting 6 on a FastPrep Instrument ( MP Biomedicals , Solon , OH ) . Tubes were then centrifuged at maximum speed in a bench microfuge for 10 minutes to pellet debris , before the supernatant was removed and mixed with the supplied protein precipitation solution . After centrifugation at maximum speed for 5 min , 200 µl supernatant was transferred for extraction using an automated robotic system ( Corbett X-tractor gene , Qiagen ) , following the manufacturer's recommendations . Extracted DNA ( 100 µl ) was stored at −20°C . Two microliters of DNA template were used in subsequent real time PCR reactions targeting three independent regions in the M . ulcerans genome ( IS2404 , IS2606 and KR ) , as described previously [19] . Based on the difference in cycle threshold ( Ct ) values between IS2606 and IS2404 ( ΔCt [IS2606-IS2404] ) these assays are able to distinguish between M . ulcerans strains , which typically cause disease in mammals , and other members of the M . ulcerans/M . marinum complex ( with fewer copies of IS2606 ) which may be present in the environment , but are not associated with the human outbreak . An estimate of M . ulcerans bacterial load per gram of possum faeces was obtained based on the previously established correlations between IS2404 PCR Ct values and bacterial loads in spiked possum faeces [16] . These calculations enabled comparison of the relative amounts of M . ulcerans DNA between samples in the present survey , expressed in 10-fold orders of magnitude up to ≥106 organisms per gram of faeces , and should be considered semi-quantitative rather than absolute . Culture of M . ulcerans was not attempted since our previous research has shown this to be an insensitive diagnostic method when applied to possum faeces , due to overgrowth of contaminants [16] . VNTR typing was performed using 1 µl DNA template in 25 µl reaction volume , using conditions described previously [17] . PCR products were visualized on 2% agarose gel with ethidium bromide staining , and product size was estimated with reference to a 100 bp DNA ladder ( Promega , Wisconsin , USA ) . Products of the expected size were purified using the High Pure PCR Purification Kit ( Roche Diagnostics , Australia ) and sequenced using the BigDye Terminator v3 . 1 Cycle Sequencing Kit ( Applied Biosystems , Foster City , CA ) according to the manufacturer's instructions . VNTR sequences were compared with those from a well characterized Victorian M . ulcerans isolate ( Strain Ref: MU_JKD8049 ) , which was obtained from a BU patient linked to Point Lonsdale in 2004 [20] . Scan statistics were used to detect and evaluate clusters of positive possum faecal samples in a purely spatial setting , using a Bernoulli model ( binary outcome ) [21] . This analysis was carried out in SaTScan V8 . 0 ( http://www . satscan . org/ ) .
Mycobacterium ulcerans DNA was detected by real-time PCR in 20/216 ( 9 . 3% ) ground collected ringtail possum faecal samples and 4/6 ( 66 . 6% ) brushtail possum faecal samples . There was a significant non-random clustering of positive possum faecal samples identified by spatial scan statistics ( P<0 . 0001; 16/30 samples were positive within a circle of radius 0 . 42 km; see Figure 2 ) . There was a visually apparent spatial correlation between the occurrence of positive possum faeces and the addresses of 6 human BU cases . Four patients who resided locally and two patients who had holiday homes in the area were located within the cluster radius described above: due to requirements of patient confidentiality we are unable to show the specific locations of patients' addresses in Figure 2 . Due to the small number of human cases to date , it is not yet possible to confirm statistically significant clustering of human BU cases in this area . Additionally , one human case ( resident ) was located adjacent to an outlier positive faecal sampling point ( again we are unable to depict this case location due to confidentiality requirements ) in an area of predominantly negative possum faecal samples . Finally , two human BU case addresses were located in areas where possum faeces was not sampled ( one holiday house address , and one resident ) . The calculated values for ΔCt ( IS2606-IS2404 ) from M . ulcerans PCR positive faecal samples were ≤3 . 32 ( 95% CI = 1 . 56–2 . 61 ) , confirming that all the sequences detected were attributable to M . ulcerans and not another member of the M . ulcerans/M . marinum complex which typically give higher ΔCt values ( 95% CI = 6 . 94–8 . 07 ) [19] . IS2404 real-time PCR Ct values ranged from 24–39 , corresponding with M . ulcerans burdens in faeces ( estimated as described previously ) ranging from ≥106 to 100 organisms per gram of possum faeces . The median estimated bacterial load in M . ulcerans positive ringtail faeces was 103–104 organisms per gram , which was similar to the median load in positive brushtail faeces . The two faecal samples with the highest M . ulcerans DNA concentrations ( ≥106 organisms per gram ) were from ringtail possums sampled within the cluster of positive possum faecal samples as described above . The DNA concentration in these two samples was sufficiently high to allow sequencing of VNTR locus 14 ( both samples ) and 9 ( 1 sample only ) . The nucleotide sequences obtained were identical to those from the strain of M . ulcerans causing human BU disease in Victoria ( MU_JKD8049 ) .
Human BU incidence in south-eastern Australia is on the increase , particularly in the last two decades [2] , [8] . The progressive extension of the westernmost extremity of the endemic area from the original Bairnsdale region more than 260 km to the east , and the frequent emergence of new endemic foci are current public health concerns . In the most recently studied focus on the Bellarine Peninsula , BU was first reported in 1998 , and is now endemic in three small towns near the Eastern tip of the peninsula: in Point Lonsdale ( see Figure 1 for location map ) , the infection rate calculated in 2011 ( 26 cases ) was equivalent to 770/100 , 000 population ( C . Lavender , unpublished data ) . Tracking of the geographic shift of endemic areas ( such as the recent emergence of BU on the western extremity of the Mornington Peninsula ) using traditional epidemiological survey methods is complicated by the long incubation period of the disease in humans ( median 4 . 5 months , IQR = 109–160 days ) [22] , requiring time-consuming analysis of patients' historical movement patterns over an extended period to identify their exposure location . This is particularly challenging in patients who acquire infection from very brief visits to an endemic area as in one documented case , during a stay lasting only a few hours [3] . Further complications in tracking the location of patients' exposure arise because many BU-affected areas are popular holiday resorts that experience high numbers of visiting non-residents particularly in summer . Conversely , survey sampling of roadside-collected possum faeces is straightforward , detection of M . ulcerans DNA by real-time PCR can be done within hours using an automated robotic platform , and such a sampling procedure does not raise issues of informed consent , since it does not require examination or interview of human patients . There is increasing interest in the use of wildlife sentinels to monitor the emergence and spread of a number of zoonotic diseases such as West Nile disease , rabies , and anaplasmosis [23] , [24] . As a first step towards validating possum faecal surveys as a public health tool to monitor BU emergence , we show here that detection of M . ulcerans DNA in possum faeces was associated with a recent outbreak of BU in a previously non-endemic area of the Mornington Peninsula . A significant non-random cluster of Mycobacterium ulcerans PCR positive possum faeces was closely adjacent to the addresses of 6 of the total 9 Sorrento/Blairgowrie human BU patients for whom we have obtained residential and holiday home addresses , and the highest M . ulcerans bacterial loads in possum faeces coincided with this cluster . It should be noted that in the present study , sampling was carried out on roadside verges underneath overhanging branches of trees growing along the fence line of residences: we cannot rule out the possibility that conditions within fences and boundaries differ from those outside , however this seems unlikely since possum movement is not restricted by such artificial barriers at ground level as being arboreal , they are highly adapted for climbing . Although it was not possible to accurately determine the age of the individual faecal pellets , all were collected from areas exposed to rain and invertebrates which increase the rate of degradation of such samples [25] and on this basis were estimated to be up to a week old . Since pre-outbreak sampling ( before 2006 ) was not done , it is not yet possible to confirm the temporal relationship between possum and human infections . It is noteworthy that we have identified a small number of positive possum scat in a survey of nearby area of the Mornington peninsula ( approximately 2 km distant from the present study site ) which as yet has no human BU cases ( data not shown ) – any developments will be reported in future research . Interestingly , the geographic location of the current outbreak area of Sorrento is adjacent to a new housing development , built on the site of a golf course in the mid-1990s . It is highly likely that significant soil disturbance would have taken place at that time . VNTR typing showed that the M . ulcerans in possum faeces on the Mornington Peninsula was indistinguishable from the strain causing human disease in south-east Australia , as was previously demonstrated in possum faeces collected on the Bellarine Peninsula [16] . It is not yet known whether this finding reflects transmission of M . ulcerans between possums and humans , or simply a common environmental source of infection . Consistent with previous research [19] , we found that sequencing of VNTR loci could be achieved only from IS2404 positive faecal samples with high M . ulcerans bacterial loads ( ≥106 organisms per gram in the present study ) . Also in agreement with previous work sampling possum faeces in Victoria [26] , no other members of the M . ulcerans/M . marinum complex were detected in faecal samples collected in Sorrento and Blairgowrie . Relative to the ubiquitous nature of ringtail possum faeces in the environment , brushtail possum faecal specimens were found rarely ( in 6 sampling grid locations only ) . However the proportion of M . ulcerans IS2404 positive brushtail faecal specimens was higher than that of ringtail samples ( 66 . 6%; 4/6 vs 9 . 3%; 20/216 ) , and it is interesting that positive samples from both species of possum coincided spatially in Sorrento , adjacent to a focus of human BU . Population survey work would be required to determine if ringtail and brushtail possums do indeed coexist in the outbreak area and not elsewhere , which may support the hypothesis of M . ulcerans as a cyclozoonosis . Conversely , in previous work in Point Lonsdale on the Bellarine Peninsula ( for map , see Figure 1 ) , the proportion of positive brushtail samples was lower than that of ringtail possum faecal samples ( 29%; 8/28 vs 43%; 70/164 ) [16] . The overall number of sample points with brushtail faecal specimens in Point Lonsdale ( n = 28 ) was greater than in the current study in Sorrento/Blairgowrie ( n = 6 ) , despite similar sizes of the two sampling areas ( approximately 5 km2 ) . It is not known if this finding reflects a higher population density of brushtail possums in a well-established BU endemic area , than in the location of a more recent outbreak: confirmation of this would require a survey of the live possum population to estimate overall numbers using established techniques such as spotlighting or trapping [27] . The limited distribution ( i . e . highly focal nature ) of both human cases and positive possum samples at Sorrento/Blairgowrie contrast the distribution pattern at Point Lonsdale , where human cases and infected possums were more widespread across the whole township [16] . This distribution pattern probably reflects the recent nature of the outbreak in the former , and the considerably longer term presence of the disease agent in populations of both possums and humans in the latter . It will be insightful to reassess the Sorrento/Blairgowrie site again in several years . As a future research priority , we need information on the degree to which relative population density of ringtail and brushtail possums influences endemicity and/or emergence of BU in humans . Specifically , longitudinal follow-up is needed of human BU disease incidence , possum population dynamics and the prevalence of possum faecal M . ulcerans DNA , in the above described emergent endemic area on the Mornington Peninsula . Live trapping of possums has not yet been done in this area to confirm the presence of possums with M . ulcerans positive skin lesions , as described in the previous work on the Bellarine Peninsula [16] , as distinct from those showing only positive faecal samples . This distinction is important since faecal shedding could occur by simple ingestion of the pathogen e . g . on vegetation , and subsequent excretion , whereas the development of active clinical disease in possums shows the potential for establishment of a long lasting infectious reservoir host . Mycobacterium ulcerans in superficial skin lesions would also be available for uptake by biting insects which could potentially act as vectors of BU , as discussed in previous research [2] , [3] , Overall , a better understanding of spatial and temporal associations between human and possum M . ulcerans infection is likely to be the key to elucidation of the transmission mechanism of BU in south-east Australia .
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Mycobacterium ulcerans causes the disfiguring human skin disease Buruli ulcer ( BU ) . The mechanism of transmission and reservoir for human infection remain unknown . In previous research , we reported the detection of M . ulcerans DNA in the faeces of possums ( small tree-dwelling marsupials ) in an area of South-East Australia ( the Bellarine peninsula ) where the largest recorded outbreak of human BU has been in progress for the last decade . The current study was carried out in a new outbreak area ( the Mornington peninsula ) , and describes the detection of M . ulcerans DNA in possum faeces collected from the ground , in locations which correspond closely with the addresses of human BU cases . The association of new human BU cases with areas where M . ulcerans positive possum faeces are found contributes further evidence to the possible role of possums as an environmental reservoir of infection . Possums may be useful sentinel animals to monitor the spread of BU in Australia .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"bacteriology",
"emerging",
"infectious",
"diseases",
"medical",
"microbiology",
"biology",
"microbiology",
"bacterial",
"pathogens"
] |
2014
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Potential Wildlife Sentinels for Monitoring the Endemic Spread of Human Buruli Ulcer in South-East Australia
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Poxviruses have evolved unique proteins and mechanisms to counteract the nuclear factor κB ( NF-κB ) signaling pathway , which is an essential regulatory pathway of host innate immune responses . Here , we describe a NF-κB inhibitory virion protein of orf virus ( ORFV ) , ORFV073 , which functions very early in infected cells . Infection with ORFV073 gene deletion virus ( OV-IA82Δ073 ) led to increased accumulation of NF-κB essential modulator ( NEMO ) , marked phosphorylation of IκB kinase ( IKK ) subunits IKKα and IKKβ , IκBα and NF-κB subunit p65 ( NF-κB-p65 ) , and to early nuclear translocation of NF-κB-p65 in virus-infected cells ( ≤ 30 min post infection ) . Expression of ORFV073 alone was sufficient to inhibit TNFα induced activation of the NF-κB signaling in uninfected cells . Consistent with observed inhibition of IKK complex activation , ORFV073 interacted with the regulatory subunit of the IKK complex NEMO . Infection of sheep with OV-IA82Δ073 led to virus attenuation , indicating that ORFV073 is a virulence determinant in the natural host . Notably , ORFV073 represents the first poxviral virion-associated NF-κB inhibitor described , highlighting the significance of viral inhibition of NF-κB signaling very early in infection .
Orf virus ( ORFV ) , the prototype member of the genus Parapoxvirus ( PPV ) of the Poxviridae , is the etiologic agent of contagious pustular dermatitis or orf , a ubiquitous disease of sheep and goats [1] . Orf is characterized by inflammatory , often proliferative lesions affecting the skin and the oral mucosa [2] . Lesions evolve through the stages of erythema , pustules and scabs , and are usually restricted to areas surrounding the virus entry sites [1 , 2] . If not complicated by secondary infections , orf lesions usually resolve in 6 to 8 weeks [3] . ORFV is highly epitheliotropic , and only keratinocytes or their counterparts in the oral mucosa have been shown to support viral replication in vivo [4 , 5] . Keratinocytes provide the first physical barrier to invading pathogens , and function as immune sentinels initiating inflammation and promoting skin healing after injury [6] . Keratinocytes express different cytokine receptors , such as tumor necrosis factor ( TNF ) receptor 1 ( TNFR1 ) and interleukin-1 receptor ( IL-1R ) , and multiple pattern recognition receptors ( PRRs ) such as toll-like receptors ( TLRs ) for recognition of pathogen-associated molecular patterns ( PAMPs ) of bacterial or viral origin [7] . Additional PRRs , such as the cyclic GMP-AMP Synthase ( cGAS ) , retinoic acid -inducible gene 1 ( RIG-I ) -like receptors and NOD-like receptors ( NLRs ) recognize viral nucleic acid in the cytoplasm [8] . Engagement of these receptors initiates downstream pro-inflammatory signaling cascades [6 , 7] , including the nuclear factor-kappa B ( NF-κB ) signaling pathway , which mediates innate immune responses and contributes to skin homeostasis [9 , 10] . NF-κB comprises multiple transcription factors ( NF-κB-p65 [RelA] , RelB , c-Rel , NF-κB-p50/p105 and NF-κB-p52/p100 ) that bind as homo- or heterodimers to specific DNA regulatory sequences to control expression of a wide range of cellular genes involved in innate immunity , inflammation , cell proliferation and differentiation , and apoptosis [11–13] . In unstimulated cells , NF-κB dimers are sequestered in the cytoplasm through binding to the inhibitor kappa-B alpha ( IκBα ) [13] . Most TLRs and IL-1 receptors transmit signals to the IκB kinase ( IKK ) complex via adaptor proteins interleukin receptor-associated kinase 1 ( IRAK1 ) and TNF Receptor Associated Factor 6 ( TRAF6 ) . However , TNFR1 , TLR3 and TLR4 rely on Receptor-interacting protein kinase 1 ( RIPK1 ) for activation of the IKK complex [14] . The IKK complex consists of the regulatory subunit IKKγ/NF-κB essential modulator ( NEMO ) and two kinases , IKKα and IKKβ [15] . In the canonical NF-κB pathway , various stimuli lead to phosphorylation of IκBα via IKKβ resulting in IκBα ubiquitination and subsequent proteasomal degradation [11 , 13] . Released p65/p50 dimers translocate to the nucleus where they bind κB-responsive DNA elements , recruit transcription co-regulators , and activate or repress gene expression [16] . Binding of NF-κB subunits to κB responsive elements and effective recruitment of transcriptional partners , however , are tightly regulated by posttranslational modifications of the NF-κB transcription complex and/or histones surrounding NF-κB target genes [16] . Given the central role played by NF-κB in regulating and integrating cellular processes such as inflammation and apoptosis it is not surprising that viruses have evolved strategies to counteract the NF-κB signaling pathway [17] . Poxviruses , in particular , are known to encode many NF-κB inhibitors , with selected viruses encoding multiple inhibitory functions [18 , 19] . Notably , poxviral NF-κB inhibitors target mainly cytoplasmic events leading to NF-κB activation [18 , 19] . For example , vaccinia virus ( VACV ) encodes at least ten cytoplasmic NF-κB inhibitors that target events leading to activation of the IKK complex ( A52R , A46R , B14 , C4 , N1L , K7 and M2L ) , degradation of IκBα ( A49 , K1L ) , or activation of the protein kinase RNA ( PKR ) -double-stranded RNA ( dsRNA ) signaling pathway ( E3L ) [20–28] . Similarly , ectromelia virus , the causative agent of mousepox , encodes four F-box and ankyrin domain-containing proteins ( EVM002 , EVM005 , EVM154 and EVM165 ) and a BTB/Kelch protein ( EVM150 ) that prevent IκBα degradation and NF-κB-p65 nuclear translocation , respectively , by modulating ubiquitin ligase activity [29–31] . Recently , a molluscum contagiosum virus ( MCV ) -encoded protein MC132 was shown to inhibit NF-κB activation by interacting with- and targeting NF-κB-p65 for proteasomal degradation [32] . Several poxviral proteins specifically target the IKK complex , a bottleneck for most NF-κB activating signals , including those involved in nucleic acid sensing and response to infection [19] . Two VACV proteins prevent phosphorylation and subsequent activation of IKK complex . VACV B14 directly interacts and inhibits the activity of IKKβ , while VACV N1L interacts with multiple subunits of the IKK complex [21 , 23] . MCV FLICE-like proteins ( vFLIPs ) MC159 and MC160 also target the IKK complex , with MC159 interacting with NEMO and preventing activation of IKKβ and MC160 inducing cytoplasmic degradation of IKKα [33 , 34] . In general , multiple NF-κB inhibitors encoded by a given poxvirus function at different levels of the NF-κB signaling pathway; however , viruses encoding inhibitors acting at the same level have been described [19] . While targeting multiple branches of the NF-κB pathway , poxviral inhibitors are not completely redundant in vivo as viruses harboring single gene deletions affecting NF-κB inhibitors have been shown to influence aspects of disease [35] . Notably , apart from VACV E3L ( ORFV020 ) , homologues of the known poxviral inhibitors of NF-κB are absent in parapoxviruses , suggesting that these viruses have evolved novel proteins to counteract the NF-κB signaling pathway . Recently , we have described three NF-κB inhibitors encoded by ORFV , ORFV024 , ORFV002 , and ORFV121 [36–38] . ORFV024 was shown to inhibit phosphorylation of IKK kinases , thus preventing activation of IKK complex . ORFV121 , a virulence determinant , was shown to bind to- and inhibit phosphorylation and nuclear translocation of NF-κB-p65 . And , ORFV002 was shown to inhibit nuclear phosphorylation of NF-κB-p65 by interfering with NF-κB-p65 and mitogen- and stress activated kinase-1 ( MSK1 ) interaction [37 , 39] . Here , we show that ORFV073 , a virion protein unique to parapoxviruses , is an inhibitor of NF-κB signaling that prevents activation of the IKK complex and subsequent nuclear translocation of NF-κB-p65 at early times post-infection . Notably , ORFV073 represents the first poxviral virion-associated NF-κB inhibitor described , highlighting the significance of viral inhibition of NF-κB signaling very early in infection .
Primary ovine fetal turbinate cells ( OFTu ) were kindly provided by Howard D . Lehmkuhl ( USDA ) and were maintained at 37°C with 5% CO2 in minimal essential medium ( MEM ) supplemented with 10% fetal bovine serum ( FBS ) , 2 mM L-glutamine , 50 μg/ml gentamicin , 100 IU/ml penicillin , and 100 μg/ml streptomycin . HeLa cells ( American Type Culture Collection ) stably expressing green fluorescent protein ( GFP ) ( GFP/HeLa ) or ORFV073-GFP ( 073GFP/HeLa ) fusion protein were maintained in Dulbecco's modified essential medium ( DMEM ) supplemented as above with the addition of neomycin ( G418; 500 μg/ml; Gibco ) . ORFV strain OV-IA82 [40] was used to construct an OV-IA82 ORFV073 gene deletion virus ( OV-IA82Δ073 ) and for experiments involving wild-type virus infection . OV-IA82Δ073 was used as parental virus to construct a flag tagged ORFV073 revertant virus ( OV-IA82RV073Flag ) . To construct ORFV073-His expression plasmid , the ORFV073 coding sequence was PCR-amplified from the OV-IA82 genome with primers 073His-Fw ( HindIII ) -5’ TAATAAATAAGCTTAAAATGGCGGGACGCGCGCGTTTTTC-3’and 073His-Rv ( EcoRI ) -5’-GACTTCGCGAATTCGGGGCAGTAGTTACAAAAACGTTT-3’ and cloned into the vector pcDNA/V5-His ( Thermo Fisher Scientific , Waltham , MA ) . Similarly , to construct ORFV073-GFP ( ORFV073-GFP ) expression plasmid , the ORFV073 coding sequence was PCR-amplified from the OV-IA82 genome with primers 073GFP-Fw ( XhoI ) -5’-AGAATCTCGAGATGGCGGGACGCGCGCGTTTTTC-3’ and 073GFP-Rv ( BamHI ) -5’-AGCACTGGATCCGGGGCAGTAGTTACAAAAAC-3’ and cloned into the vector pEGFP-N1 ( Clontech , Mountain View , CA ) . DNA sequencing of plasmids confirmed fidelity of constructs . pcDNA3 . 1+IBKG/C- ( K ) -DYK and pcDNA3 . 1+TRAF6/C- ( K ) -DYK expression plasmids for NF-κB essential modifier ( NEMO ) and TNF receptor associated factor 6 ( TRAF6 ) , respectively were purchased from Genscript ( Piscataway , NJ ) . Plasmid pCMV-RIPK1 for receptor-interacting protein kinase 1 ( RIPK1 ) was kindly provided by Dr . Lin-Feng Chen ( Department of Biochemistry , University of Illinois at Urbana-Champaign ) . To generate OV-IA82Δ073 , a recombination cassette containing ORFV073 left ( LF; 526 bp ) and right ( RF; 526 bp ) flanking regions , and GFP gene driven by vaccinia virus VV7 . 5 promoter was synthesized and cloned in vector pUC57 ( pUC57-073LF-GFP-073RF ) ( Genscript , Piscataway , NJ ) . Similarly , to generate OV-IA82RV073Flag , ORFV073 left ( LF; 586 bp ) and right ( RF; 586 bp ) flanking regions with ORFV073 coding sequence in frame with 3xflag sequence , and red fluorescent protein ( RFP ) sequences driven by VV7 . 5 promoter was synthesized and cloned into vector pUC57 ( pUC57-073LF-0733xflag-RFP-073RF ) ( Genscript , Piscataway , NJ ) . DNA sequencing of constructs confirmed sequence integrity and identity . To obtain OV-IA82Δ073 , OFTu cells were infected with OV-IA82 and transfected with recombination vector pUC57-073LF-GFP-073RF . Similarly , to obtain OV-IA82RV073Flag , cells were infected with OV-IA82Δ073 and transfected with recombination vector pUC57-073LF-0733xflag-RFP-073RF . Fluorescent plaques indicative of recombinant virus replication were selected and subjected to virus purification by limiting dilution and plaque assays as previously described [37] . Integrity and fidelity of sequences in recombinant viruses were confirmed by PCR and DNA sequencing . To obtain semi-purified ORFV for infection experiments , OFTu cells cultured in five T175 were infected with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag ( multiplicity of infection , MOI = 0 . 1 ) and harvested at 90–95% cytopathic effect ( CPE ) . Cultures were freeze and thawed three times , spun down ( 1500 rpm , 5 min ) to remove cellular debris , and then ultracentrifuged ( 25000 rpm , 1 h ) to pellet virus . Virions were resuspended in MEM , and viral titers were determined by the Spearman and Karber’s method and expressed as tissue culture infectious dose 50 ( TCID50 ) /ml . For virion protein studies , OV-IA82Δ073 and OV-IA82RV073Flag were purified by double sucrose gradients with modifications [41] . OFTu cells ( 10 T175 flasks ) were infected with OV-IA82Δ073 or OV-IA82RV073Flag ( MOI = 0 . 1 ) , harvested at advanced CPE , and centrifuged to obtain supernatant and cell pellet fractions . Supernatants were ultracentrifuged to pellet extracellular enveloped virus ( EEV ) as described above and cell pellets were freeze and thawed three times to release intracellular mature virus ( IMV ) and centrifuged to remove cellular debris . Both EEV and IMV preparations were centrifuged through a sucrose cushion followed by double sucrose gradient purification . EEV and IMV-containing bands were collected and resuspended in 250 μl 10 mM Tris Hcl . Whole cell lysates ( 10 μg ) from mock and OV-IA82RV073Flag infected cells ( MOI = 10 ) ( 24 h p . i ) and purified OV-IA82Δ073 and OV-IA82RV073Flag EEV and IMV virion proteins ( 10 μg ) were resolved by SDS-PAGE , blotted to nitrocellulose membrane and probed with primary antibody against flag ( Catalog no . A00187-200; Genscript ) or ORFV086 structural protein [42] . Blots were developed with HRP-conjugated goat anti mouse secondary antibody ( sc-2031; Santa Cruz ) and chemiluminescent reagent ( Super Signal West Femto , Thermo Fischer ) . A retroviral expression system ( pLNCX2; Clontech ) was used to construct HeLa cells constitutively expressing GFP ( GFP/HeLa ) or ORFV073-GFP ( ORFV073GFP/HeLa ) fusion protein . GFP or ORFV073-GFP DNA sequences were cloned into plasmid pLNCX2 and transfected into the packaging cell line GP2-293 using Lipofectamine 2000 . After 48 h , supernatants containing GFP or ORFV073-GFP-encoding recombinant retrovirus particles were harvested and used to infect HeLa cells . Selection , amplification and maintenance of the individual clones were performed in the presence of G418 ( 500 μg/ml; Gibco ) . Expression of control GFP or ORFV073-GFP was monitored by fluorescence microscopy and Western blot using antibody against GFP ( sc-9996; Santa Cruz Biotechnology ) . Analysis of ORFV073 sequence and prediction of subcellular localization was performed with PSORT II ( https://psort . hgc . jp/form2 . html ) , NoLS ( http://www . compbio . dundee . ac . uk/www-nod/ ) and NucPred ( http://www . sbc . su . se/~maccallr/nucpred/ ) . Alignment of PPV ORFV073 amino acid sequences was performed using CLUSTAL Omega ( EMBL-EBI ) . Virus strains and GenBank accession numbers used for the alignment are as follows: BPSV strain BV-AR02 , NC 005337 . 1; PPV red deer ( PPV-RD ) strain HL953 , NC 025963 . 1; PCPV strain F00 . 120R , GQ329669 . 1; ORFV strains D1701 , HM133903 . 1; NA1/11 , KF234407 . 1; NZ2 , DQ184476 . 1; IA82 , AY386263 . 1; B029 , KF837136 . 1; OV-SA00 , NC 005336 . 1; GO , KP010354 . 1; NP , KP010355 . 1; SJ1 , KP010356 . 1; YX , KP010353 . 1 . To assess ORFV073 protein expression , OFTu cells were mock infected or infected with OV-IA82RV073Flag ( MOI = 10 ) for 2 , 4 , 6 , 8 , 10 , 12 or 24 h post infection ( h p . i . ) . Whole cell protein extracts ( 50 μg ) were resolved by SDS-PAGE , blotted to nitrocellulose membranes and probed with primary antibody against flag and glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) ( sc-25778; Santa Cruz ) . Blots were developed with appropriate HRP-conjugated secondary antibodies ( sc-2031 and sc-2004; Santa Cruz ) and chemiluminescent reagents . The transcription kinetics of ORFV073 during ORFV infection in OFTu cells was examined by RT-PCR following procedures previously described [36] . Transcription of ORFV073 , ORFV085 ( late gene control ) and ORFV127 ( early gene control ) was assessed by PCR using the primers 073GFP-Fw ( XhoI ) and 073GFP-Rv ( BamHI ) ( described above ) , 085LintFw-5’-ACGCCTAGCAGCAGGTACA-3’ and 085LintRv-5’-GCTACGTGACGGTGATCAAG-3’ , and 127EintFw-5’-CTCCTCGACGACTTCAAAGG-3’ and 127EintRv-5’-TATGTCGAACTCGCTCATGG-3’ respectively . To determine the subcellular localization of ORFV073 and structural protein ORFV086 , OFTu cells cultured in chamber slides ( ibidi , Martinsried , Germany ) were mock infected or infected with OV-IA82RV073Flag and OV-IA82 , respectively ( MOI = 10 ) . At 30 min and 1 , 2 , 6 , 8 , 12 , 16 and 24 h p . i . , cells were fixed with 4% formaldehyde for 20 min , permeabilized with 0 . 2% Triton-X for 10 min , blocked with 1% bovine serum albumin for 1 h and then incubated with primary mouse monoclonal antibody against flag ( no . A00187-200; Genscript ) or ORFV086 [42] overnight at 4°C . Cells were then incubated with Alexa Fluor 488-labeled secondary goat anti mouse antibody ( no . A-11001; Thermo Fisher Scientific ) for 1 h , counterstained with DAPI for 10 min , and examined by confocal microscopy ( A1 , Nikon ) . To examine the possibility of localization of ORFV073 in endosomes , co-localization of ORFV073 with endosomal marker ( Caveolin-1 ) was performed . OFTu cells mock infected or infected with OV-IA82RV073Flag ( MOI = 10 ) were fixed at 16 and 24 h p . i , permeabilized and blocked as describe above . Cells were sequentially incubated with primary mouse monoclonal antibody against flag ( no . A00187-200; Genscript ) and rabbit polyclonal antibody against Caveolin-1 ( no . sc-894 , Santa Cruz ) , and Alexa Fluor 488-labeled secondary goat anti mouse antibody ( no . A-11001; Thermo Fisher Scientific ) and Alexa Fluor 647-labeled secondary goat anti rabbit antibody ( no . A-21244; Thermo Fisher Scientific ) . Cells were then counterstained with DAPI and examined by confocal microscopy ( A1 , Nikon ) To examine co-localization of ORFV073 with ORFV086 , OFTu cells mock infected or infected with OV-IA82RV073Flag ( MOI = 10 ) were fixed at 16 and 24 h p . i , permeabilized and blocked as describe above . Cells were sequentially incubated with primary rabbit monoclonal antibody against flag ( no . 14793 , Cell Signaling ) and mouse monoclonal antibody against ORFV086 , and Alexa Fluor 488-labeled secondary goat anti rabbit antibody ( no . A-11008; Thermo Fisher Scientific ) and Alexa Fluor 647-labeled secondary goat anti mouse antibody ( no . A-21236; Thermo Fisher Scientific ) . Cells were then counterstained with DAPI and examined by confocal microscopy ( A1 , Nikon ) . The replication characteristics of OV-IA82Δ073 was assessed in OFTu cells . Cells cultured in 6-well plates were infected with OV-IA82 or OV-IA82Δ073 using MOI 0 . 1 ( multi-step growth curve ) or 10 ( single-step growth curve ) and harvested at 6 , 12 , 24 , 36 , 48 , 72 and/or 96 h p . i . Virus titers at each time point were determined as described above . To compare the cytopathic effect ( CPE ) induced by OV-IA82 and OV-IA82Δ073 , OFTu cells were infected with OV-IA82 or OV-IA82Δ073 ( MOI = 10 ) and evaluated under an inverted light microscope at 48 h p . i . ( Leica DMI 4000B; 20X ) . To assess the effect of ORFV073 on NF-κB regulated gene transcription , OFTu cells were mock infected or infected with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag ( MOI = 10 ) and harvested at 1 and 2 h p . i . in the presence of Trizol reagent ( Thermo Fisher , Waltham , MA ) , and RNA samples were processed and reverse transcribed as previously described [36] . The expression of interleukin-8 ( IL-8 ) , prostaglandin endoperoxide synthase 2 ( PTGS2 ) , C-C chemokine ligand 20 ( CCL20 ) and NF-κB inhibitor alpha ( NF-κBIA ) genes was assessed using Custom Plus TaqMan Gene Expression Assays ( Applied Biosystems ) based on ovine gene sequences in GenBank . Real-time PCR and data analysis were performed as previously described [36] . Statistical analysis of the data was performed by using Student’s t test . To investigate the effect of ORFV073 on nuclear translocation of NF-κB-p65 following ORFV infection , OFTu cells were mock infected or infected ( MOI = 10 ) with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag . Cells were fixed at 30 min and 1 , 2 , 4 , 6 , 8 , 12 and 24 h p . i . as described above , sequentially incubated with antibody against NF-κB-p65 ( no . 8242; Cell Signaling ) and with Alexa Fluor 488-labeled goat anti rabbit antibody , counterstained with DAPI , and examined by confocal microscopy . Cells ( n = approximately 300 per sample ) from randomly selected fields were scored for nuclear NF-κB-p65 and results depicted as the mean percentage of cells expressing nuclear NF-κB-p65 for each time point . Statistical analysis of data was performed by using Student’s t test . To examine the effect of ORFV073 expression on TNFα-induced nuclear translocation of NF-κB-p65 , HeLa cells stably expressing GFP ( GFP/HeLa ) or ORFV073-GFP fusion protein ( 073GFP/HeLa ) were treated with 20 ng/ml of TNFα ( Cell Signaling , Danvers , MA ) . Cells were fixed at 30 min and 1 h post-treatment , sequentially incubated with primary antibody against NF-κB-p65 , and Alexa Fluor 594-labeled goat anti rabbit secondary antibody ( no . A-11037 , Thermo Fisher Scientific ) , counterstained with DAPI , and examined by confocal microscopy . Cells ( n = approximately 200 per sample ) from randomly selected fields were scored for nuclear NF-κB-p65 and results depicted as the mean percentage of GFP/073GFP expressing cells containing nuclear NF-κB-p65 for each time point . Statistical analysis of data was performed by using Student’s t test . To evaluate the effect of protein synthesis inhibitor cycloheximide ( CHX ) on nuclear translocation of NF-κB-p65 during ORFV infection , OFTu cells mock treated or treated with CHX ( 50 μg/ml ) ( Sigma-Aldrich , St . Louis , MO ) for 30 min were mock infected or infected with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag ( MOI = 10 ) in absence or presence of CHX ( 50 μg/ml ) for 1 h . Nuclear translocation assays and data analysis were performed as described above . As a control for CHX activity , OFTu cells mock treated or treated with CHX ( 50 μg/ml ) for 30 min were mock infected or infected with OV-IA82RV073Flag and harvested at 30 min and 1 h p . i . Whole cell protein extracts ( 50 μg ) were resolved by SDS-PAGE , and transferred to nitrocellulose membranes and probed with antibody against p53 ( sc-6243; Santa Cruz ) and actin ( sc-8432; Santa Cruz ) as described above . HeLa cells stably expressing GFP ( GFP/HeLa ) or ORFV073-GFP ( 073GFP/HeLa ) were treated with TNFα ( 20 ng/ml ) and harvested at 5 , 10 and 15 min post treatment . OFTu cells mock infected or infected with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag ( MOI = 10 ) were harvested at 30 min and 1 h p . i . Whole cell protein extracts ( 50 μg ) were resolved by SDS-PAGE , blotted to nitrocellulose membranes and probed with antibody against phospho-IKKα/β ( Ser176/180 ) ( no . 2697; Cell Signaling ) , phospho-IκBα ( Ser32/36 ) ( no . 9246; Cell Signaling ) , phospho-NF-κB-p65 ( Ser536 ) ( no . 3033; Cell Signaling ) , IKKα/β ( sc-7607; Santa Cruz ) , IκBα ( sc-371; Santa Cruz ) , NF-κB-p65 ( sc-7151; Santa Cruz ) , GAPDH or GFP ( sc-9996; Santa Cruz ) . Blots were processed as described above . Densitometric analysis of the blots was performed with ImageJ software version 1 . 6 . 0 ( National Institutes of Health , Bethesda , MD ) . Statistical analysis of densitometry data was performed by using the Student’s t test . To investigate the kinetics of NF-κB activation following ORFV infection , OFTu cells were infected with OV-IA82 or OV-IA82Δ073 ( MOI = 10 ) and harvested at 30 min , 1 h , 2 h , 4 h , 6 h , 8 h and 12 h p . i . Whole cell protein extracts ( 50 μg ) were resolved by SDS-PAGE , blotted to nitrocellulose membranes , probed with phospho-NF-κB-p65 , NF-κB-p65 and GAPDH antibodies , and developed as described above . To assess the effect of ORFV073 on NEMO levels , OFTu cells , mock infected or infected with OV-IA82 or OV-IA82Δ073 ( MOI = 10 ) were harvested at 30 min , 45 min , 1 h , 1 h 30 min and 2 h p . i . , and cytoplasmic protein extracts were prepared using NE-PER Nuclear and Cytoplasmic Extraction Reagents following manufacturer’s protocol ( Thermo Fisher , Waltham , MA ) . Extracts ( 50 μg ) were resolved by SDS-PAGE , blotted to nitrocellulose membranes , probed with NEMO ( sc-8330 , Santa Cruz ) and GAPDH antibodies , and developed as described above . Densitometric and statistical analysis of the blots was performed as described above . To investigate the potential interaction of ORFV073 with cellular proteins NEMO , RIPK1 and TRAF6 , OFTu cells co-transfected with 1 μg of pcDNA/V5-His ( control plasmid ) or pcDNA/V5-073His ( ORFV073-His ) together with either pcDNA3 . 1-NEMO , pCMV-RIPK1 or pcDNA3 . 1-TRAF6 were harvested 24 h post transfection and nuclear extracts were prepared using NE-PER Nuclear and Cytoplasmic Extraction Reagents ( Thermo Fisher , Waltham , MA ) . Co-immunoprecipiation was performed using Nuclear Complex Co-IP Kit ( Active Motif , Carslbad , CA ) following manufacturer’s protocols . Nuclear protein extracts were co-immunoprecipitated with antibodies against His ( no . A00186; Genscript ) , NEMO ( sc-8330 , Santa Cruz ) , RIPK1 ( no . 3493 , Cell Signaling ) or TRAF6 ( sc-7221 , Santa Cruz ) overnight at 4°C , and then incubated with 50 μl of pre-washed protein G agarose beads ( no . 16–266; Millipore ) at 4°C for 2 h . Beads were washed four times with high stringency buffer and eluted proteins ( 2x Laemelli buffer ) resolved by SDS-PAGE , blotted to nitrocellulose membranes , probed with antibodies against His , NEMO , RIPK1 or TRAF6 and developed as described above . Light chain specific secondary antibody against Rabbit IgG ( no . ab99697; Abcam ) was used for NEMO blots . To evaluate the effect of ORFV073 on ORFV virulence in the natural host , five-month-old lambs were randomly allocated to three experimental groups , OV-IA82Δ073 ( n = 4 ) , OV-IA82RV073Flag ( n = 4 ) and mock ( n = 3 ) . Following anesthesia , the mucocutaneous junction of the lip near the right labial commissure and the inner sides of hind limbs were scarified along 2 cm and 5 cm-long lines , respectively , and virus inoculum ( 0 . 5 ml ) containing 107 . 5 TCID50/ml was applied topically to each inoculation site using cotton swabs . The scarified areas of the lips were monitored for 21 days for the presence of characteristic orf lesions . Criteria assessed were extent of erythema , papules , pustules , and attached scab . Each criterion was scored according to the width of the lesion along the line of scarification: 1 , lesion < 0 . 5 cm across; 2 , lesion 0 . 5 cm-1 cm across; 3 , lesion > 1 cm across , and the total daily score for each lamb was the sum of scores of the four lesion types . Skin biopsy specimens were collected at days 2 , 5 , 8 , 12 and 21 p . i . , fixed in 10% buffered formalin , embedded in paraffin , sectioned , and stained with hematoxylin and eosin using standard methods . All animal procedures were approved by University of Nebraska-Lincoln Institutional Animal Care and Use Committee ( IACUC; protocol 1318 ) and were performed in accordance with the Guide for the Care and Use of Agricultural Animals in Agricultural Research and Teaching .
ORFV073 encodes for an arginine-rich 188-amino acid , basic protein with predicted molecular weight of 21 . 9 kDa . ORFV073 is highly conserved among ORFV isolates exhibiting 95%-99% amino acid identity ( aa id ) , and less similar to orthologs in pseudocowpox virus ( PCPV , 89% aa id ) , parapoxvirus of the Red Deer ( PPV-RD , 70% aa id ) , and bovine papular stomatitis virus ( BPSV , 63% aa id ) . Notably , PCPV contains two PPV073 paralogs arranged back to back in the genome ( PCPV073 and PCPV073 . 5; 45% aa id ) , which are likely the result of gene duplication followed by divergent evolution [43] . A divergent ORFV073 homolog ( SQPV0840 , 36% aa id ) is found in squirrelpox virus , a member of a novel chordopoxvirus genus closely related to PPV . Interestingly , mouse betaherpesvirus 1 ( i . e . murid cytomegalovirus , a virus that circulates in wild mice ) encodes a protein of unknown function ( m170 ) similar in size to PPV073 and with a region of approximately 50 residues sharing 56% aa id to PPV073 ( OV-IA82 amino acid positions 71–122 ) ( Fig 1 ) . While PPV073 orthologs contain a predicted nuclear localization signal ( NLS ) at their carboxyl-termini ( OV-IA82 amino acid positions 149–182; underlined in Fig 1 ) , no NLS was predicted for SQPV0840 and m170 . The expression kinetics of ORFV073 was assessed by Western blot . ORFV073 was increasingly detected between 10 and 24 hours post-infection ( h p . i . ) ( Fig 2A ) . Consistent with this observation , ORFV073 transcription was detected only at late times during ORFV infection ( 6 to 24 h p . i . ) ( S1 Fig ) . ORFV073 transcripts were markedly decreased at 12 and 24 h p . i . in the presence of AraC , an inhibitor of DNA replication and of late poxviral gene transcription ( S1 Fig ) . Together , these results indicate that ORFV073 belongs to the late class of poxviral genes . To determine the subcellular localization of ORFV073 , OFTu cells were mock infected or infected with OV-IA82RV073Flag and examined by immunofluorescence at 30 min and 1 , 2 , 6 , 8 , 12 , 16 and 24 h p . i as described in Material and Methods . Prior to the 16 h p . i sampling point , no convincing ORFV073 specific staining was observed in infected cells . ORFV073 was found predominantly in perinuclear regions and the nucleus of infected cells , and within small circular to ovoid structures ( 340 . 2±44 . 8nm ) in proximity to the perinuclear region and the cell membrane at 16 and 24 h p . i . Similarly sized structures ( 387 . 4±51 . 6nm ) were observed following staining for virion structural protein ORFV086 ( Fig 2B ) . ORFV073 and ORFV086 co-localized in perinuclear regions and the smaller sized structures ( S2A Fig ) . To rule out the possibility that the smaller ORFV073 stained structures were endosomes , co-localization studies of ORFV073 and endosomal marker ( Caveolin-1 ) were performed . No co-staining was observed ( S2B Fig ) . Results suggest that ORFV073 , a late viral protein , may be a virion component . The replication kinetics of ORFV073 gene deletion virus ( OV-IA82Δ073 ) was compared with that of wild-type virus ( OV-IA82 ) in primary ovine cells ( OFTu ) . No differences in replication kinetics and viral yields were observed in multi-step or one-step growth curves between the two viruses ( Fig 3A and 3B ) . Also , no differences in cytopathic effect , and plaque size and morphology were observed between the viruses ( Fig 3C ) . These data indicate that ORFV073 is nonessential for ORFV replication in OFTu cells . On preliminary microarray analysis increased transcription of multiple NF-κB regulated genes MMP13 ( 8 . 5-fold ) , MMP1 ( 7 . 3-fold ) , CASP4 ( 3 . 4-fold ) and IL-6 ( 2 . 5-fold ) was observed in cells infected with OV-IA82Δ073 compared to cells infected with wild-type virus , suggesting that ORFV073 inhibits NF-κB function . To validate this observation , real-time PCR analysis of gene expression was conducted . To rule out any confounding effect from cytokines that potentially might be present in the virus inocula , viruses used in these studies were semi-purified as described in Materials and Methods . Increased transcription of NF-κB-regulated genes IL8 ( 222 . 1 and 418 . 6-fold ) , PTGS2 ( 22 and 31 . 2-fold ) , CCL20 ( 168 . 1 and 429 . 2-fold ) and NFKBIA ( 8 and 12 . 2-fold ) was observed in cells infected with OV-IA82Δ073 compared to wild type OV-IA82 at 1 and 2 h p . i . , respectively ( Fig 4A ) . To assess whether ORFV073 affects NF-κB-p65 nuclear translocation , OFTu cells were infected with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag , or mock infected , and NF-κB-p65 localization was examined by immunofluorescence . Infection with OV-IA82Δ073 but not OV-IA82 or OV-IA82RV073Flag led to rapid nuclear translocation of NF-κB-p65 as early as 30 minutes p . i . ( Fig 4B and 4C ) . The effect was transient as levels of nuclear NF-κB-p65 returned to those in wild-type virus-infected cells between 2 and 4 h p . i . ( Fig 4C , P<0 . 05 ) . Consistent with the nuclear translocation kinetics , levels of phosphorylated NF-κB-p65 ( Ser536 ) , which accumulates in the cytoplasm prior to nuclear translocation , are increased at early times p . i . with OV-IA82Δ073 ( Fig 4D ) . Together , data show that ORFV073 is a NF-κB inhibitor acting transiently very early in infection . To investigate the role of ORFV073 in NF-κB inhibition , OFTu cells were infected with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag for 30 min or 1 h , and phosphorylation of IKKα/β , IκBα and NF-κB-p65 was assessed by Western blot . Infection by OV-IA82Δ073 led to marked and early phosphorylation of IKKα/β ( Ser176/180 ) , IκBα ( Ser32/36 ) and NF-κB-p65 ( Ser536 ) ( Fig 5A ) . Densitometric analysis showed that relative fold increases of phosphorylated forms in OV-IA82Δ073-infected cells were 101 . 7 and 123 . 7 for IKKα/β ( Fig 5B ) , 54 . 2 and 33 . 7 for IκBα ( Fig 5C ) , and 5 . 5 and 2 . 5 for NF-κB-p65 ( Fig 5D ) , at 30 min and 1 h p . i . , respectively . To assess the effect of ORFV073 on NEMO , OFTu cells were mock infected or infected with OV-IA82 or OV-IA82Δ073 for 30 min , 45 min , 1 h , 1 h 30 min and 2 h , and expression of NEMO was assessed by Western blot . Virus infection resulted in a significant increase in NEMO levels in wild-type virus-infected cells at 30 min ( 2 . 0 fold ) and OV-IA82Δ073 infected cells at 30 min ( 3 . 04 fold ) , and 1 h p . i . ( 3 . 39 fold ) compared to mock infected cells ( Fig 6A and 6B ) . However , NEMO levels in OV-IA82Δ073 infected cells were significantly higher at 30 min ( 1 . 53 fold ) , 45 min ( 1 . 41 fold ) and 1 h ( 1 . 31 fold ) than those observed in wild-type virus-infected cells ( Fig 6A and 6C ) . Together , results indicate that ORFV073 prevents NF-κB activation early in infection by inhibiting activation of the IKK complex . This is likely the result of a ORFV073-dependent event that leads to reduced accumulation of NEMO in wild-type virus-infected cells compared to levels found in OV-IA82Δ073 infected cells . The early inhibitory effect of ORFV073 on NF-κB signaling is at variance with it being expressed at late times p . i . This observation , together with ORFV073 staining small circular to ovoid structures in infected cells ( Fig 2B and S2A Fig ) raised the possibility that ORFV073 may be a virion component available during and/or immediately after virus entry . To examine this possibility , extracellular enveloped virus ( EEV ) and intracellular mature virus ( IMV ) were purified from OFTu cells infected with OV-IA82RV073Flag . Western blot analysis showed a major band with a size corresponding to ORFV073-3xflag predicted molecular weight ( approximately 25 kDa ) in the IMV fraction and a noticeably weaker band in the EEV fraction which might represent possible contamination with IMV . Higher molecular weight forms of ORFV073 of approximately 30 kDa ( observed in two of six independent experiments ) and a doublet of 40 kDa ( observed in all six independent experiments ) were detected in the EEV fraction . These ORFV073 specific bands were not observed in western blots of OV-IA82Δ073 virions or uninfected cell lysates ( Fig 7A , top panel ) . Higher molecular weight forms of ORFV073 in EEV suggest possible covalent modification of virion-incorporated ORFV073 during particle maturation and morphogenesis . As a control , the virion core protein ORFV086 was detected as a predominant 21 kDa band together with previously described higher molecular weight forms in both EEV and IMV fractions [42] ( Fig 7A , bottom panel ) . To assess whether early inhibition of NF-κB-p65 nuclear translocation by ORFV073 involves de novo viral protein synthesis in the infected cells , OFTu cells were pre-treated with the protein synthesis inhibitor cycloheximide ( CHX ) for 30 min followed by infection with OV-IA82 , OV-IA82Δ073 or OV-IA82RV073Flag for 1 h in presence of the drug . Under these conditions expression of p53 , a cellular protein with short half-life , was inhibited ( S3 Fig ) . NF-κB-p65 nuclear translocation was inhibited in both OV-IA82 and OV-IA82RV073Flag -infected cells regardless of CHX treatment ( Fig 7B and S4 Fig ) . Together , these results indicate that ORFV073 is a virion component responsible for early inhibition of NF-κB signaling . The effect of ORFV073 in TNFα induced nuclear translocation of NF-κB-p65 was assessed by immunofluorescence in HeLa cells stably expressing GFP ( GFP/HeLa ) or ORFV073GFP fusion protein ( 073GFP/HeLa ) . Upon TNFα induction , ORFV073GFP-expressing cells exhibited significantly reduced nuclear translocation of NF-κB-p65 ( 35 . 2 and 28 . 7% ) compared to control cells expressing GFP alone ( 86 . 6 and 79 . 3% ) at 30 min and 1 h after TNFα induction , respectively ( Fig 8A and 8B , P<0 . 05 ) . Thus , in the absence of any other viral protein , ORFV073 is able to inhibit TNFα-induced NF-κB signaling . The effect of ORFV073 in TNFα induced activation of NF-κB-p65 was further investigated by examining phosphorylation of IKKα/β ( Ser176/180 ) , IκBα ( Ser32/36 ) and NF-κB-p65 ( Ser536 ) in HeLa cells stably expressing GFP or ORFV073GFP fusion . ORFV073 expression markedly reduced the TNFα induced phosphorylation of IKKα/β ( 65 . 7 , 49 . 6 and 65 . 9% ) , IκBα ( 83 , 83 . 4 and 87 . 4% ) and NF-κB-p65 ( 35 . 7 , 39 . 8 and 46% ) in cells expressing 073GFP compared to control GFP expressing cells at 5 , 10 and 15 min after TNFα induction , respectively ( Fig 9A , 9B , 9C and 9D , P<0 . 05 ) . While constant levels of IKKα/β , NF-κB-p65 and GAPDH were observed in mock and TNFα-treated cells , reduced levels of total IκBα were noted in ORFV073GFP cells following TNFα treatment , likely due to proteasomal degradation of IκBα following its phosphorylation . Together , results indicate that ORFV073 inhibits both virus infection-and TNFα-induced NF-κB-p65 activation by preventing activation of the IKK complex . Results above demonstrated that ORFV073 functions at or upstream of IKK complex in NF-κB signaling pathway . To examine the potential mechanism ( s ) underlying ORFV073 function , reciprocal co-immunoprecipitation of ORFV073 with various mediators of the TNFα-induced NF-κB signaling pathway was performed . OFTu cells were co-transfected with control plasmid or pORFV073-His together with pNEMO , pRIPK1 , or pTRAF6 . Cells were harvested 24 h post-transfection and nuclear extracts obtained as described in Material and Methods . Reciprocal interaction was observed between ORFV073 and NEMO following either anti-His or anti-NEMO antibody pull downs ( Fig 10 ) . Reciprocal co-immunoprecipitation of ORFV073 with RIPK1 and TRAF6 were not observed . These results show that ORFV073 interacts with NEMO , the regulatory subunit of the IKK complex . Interaction of ORFV073 with NEMO in uninfected cells and elevated levels of NEMO in cells infected with OV-IA82Δ073 early during infection suggest that ORFV073 interferes with assembly and/or activation of the IKK complex thus affecting subsequent activation of NF-κB signaling . The effect of ORFV073 in virus virulence was investigated in sheep , a natural ORFV host . Animals were inoculated with OV-IA82Δ073 ( n = 4 ) , OV-IA82RV073Flag ( n = 4 ) or PBS ( control group , n = 3 ) in the right labial commissure and the inner side of the thighs , and disease course was monitored for 21 days . All virus-inoculated animals developed clinical orf ( Fig 11A ) . However , clinical disease was less severe in sheep infected with OV-IA82Δ073 ( Fig 11B ) . No significant differences in disease onset and time to lesion resolution between animal groups were observed . By day 5 p . i . , lesions in all four OV-IA82RV073Flag -infected sheep exhibited scabby tissue deposition and pustules at the lesion margins . In OV-IA82Δ073 -infected sheep , however , pustule development was not observed and deposition of scabby tissue was seen in only one animal at this time point ( sheep #62; Fig 11A , 5 dpi ) . Lesions in two OV-IA82RV073Flag -infected sheep continued to evolve by further scabby tissue deposition during the following week ( sheep 21 and 124 ) , while scabs in the other two animals were shed leaving pustules exposed . In contrast , changes in sheep inoculated with mutant virus progressed modestly and a clinical pustular stage was never observed ( Fig 11A , Day 9 p . i . ) . Lesions started to regress by day 12 p . i . , with one animal per group exhibiting scabby lesions at day 16 p . i . ( sheep 62 and 124 ) , and clinical resolution was complete in all virus-infected sheep by day 21 p . i . Punch biopsies were collected from inoculation sites in the thighs at various times post-infection and processed for histology . By 2 dpi , skin samples from all animals showed epidermal hyperplasia , active re-epithelialization , and various degrees of inflammatory cell infiltration . All OV-IA82RV073Flag -infected sheep showed foci of ballooned degenerated keratinocytes , a morphological indication of advanced virus replication . In contrast , none of the OV-IA82Δ073 -infected sheep exhibited ballooned degeneration by this time . ( Fig 12 , left panels ) . By day 5 p . i . , with the exception of sheep 62 , samples from all infected animals exhibited ballooning degeneration of keratinocytes . Congruent with the gross pathology , OV-IA82RV073Flag -infected sheep samples showed large , often broken and hemorrhagic pustules . In contrast , lesions in OV-IA82Δ073-infected sheep contained small , intact micropustules contained by a mildly hyperkeratotic stratum corneum ( Fig 12 , right panels ) . These pustules never developed further beyond this stage . Data indicate that infection of sheep with OV-IA82Δ073 , a virus lacking ORFV073 , resulted in delayed infection of keratinocytes and absence of a clinical pustular stage .
NF-κB is a key regulator of early host responses against pathogens , playing a critical role in inflammation and integrating many cellular processes including cell proliferation , differentiation , and survival [9 , 10] . The parapoxvirus ORFV has evolved multiple strategies to counteract activation of the NF-κB signaling pathway , with encoded NF-κB inhibitors targeting both cytoplasmic and nuclear events leading to NF-κB activation [36–38] . Here , we describe a ORFV virion protein , ORFV073 , that inhibits activation of the IKK complex and subsequent NF-κB signaling at very early times post-infection . Parapoxviral genes involved in host range , immune modulation/evasion and virulence largely map to the terminal genomic regions [40 , 44] . Somewhat surprisingly , ORFV073 , is located approximately in the center of the central conserved region of the genome , between two highly conserved poxviral genes ( ORFV072 , which encodes for a transcription termination factor , and ORFV074 , which encodes for the small subunit of the mRNA capping enzyme ) . ORFV073 is highly conserved among the ORFV isolates and , while less similar to orthologs in other parapoxviruses , it still exhibits a higher degree of conservation than that observed for other known parapoxviral host range and immune evasion genes [40] . ORFV073 genomic location and its high degree of conservation may suggest the overall significance of this nonessential gene for viral perpetuation and transmission under selective pressures operating in nature . Interestingly , the finding of a 50 aa region in ORFV073 with homology to a herpesvirus protein of unknown function ( mouse cytomegalovirus m170 ) suggests that yet unmapped ORFV073 functions may extend across virus families . Notably , ORFV073 is a virion protein that inhibits NF-κB signaling at very early times in infected cells ( ≤ 30 min . p . i . ) ( Figs 4C , 5A , 7A and 7B ) . Our experiments with virus lacking ORFV073 suggest that early infection events such as virus entry and uncoating are efficiently sensed by PRRs , leading to NF-κB activation . Recently , tumor necrosis factor receptor ( TNFR ) -associated factor 2 ( TRAF2 ) was reported to be involved in VACV fast entry via plasma membrane fusion [45] . TRAF2 functions downstream of TNFR1 and TNFR2 mediating activation of both canonical and non-canonical NF-κB signaling pathways [46] . If TRAF2 is activated in some manner during virus entry , subsequent activation of intracellular signaling pathways , including the NF-κB pathway , would be the expected outcomes . In the context of wild-type ORFV infection , virion-associated ORFV073 is immediately available to interfere with any potential TRAF2-induced NF-κB activation by inhibiting IKK activation possibly by interaction with NEMO . In contrast , a virus lacking ORFV073 in the virion , such as OV-IA82Δ073 described here , would be unable to block NF-κB early activation and nuclear translocation of NF-κB-p65 . While other scenarios are also possible , results here illustrate the importance of preventing NF-κB activation early in infection . In the context of the virus-infected cell , relatively few poxviral NF-κB inhibitors with clearly defined early functions have been described . VACV K1L protein was shown to prevent degradation of IκBα between 2 and 3 h p . i . in infected cells [25] . Similarly , VACV B14 was shown to reduce phosphorylation of IκBα at 2 and 4 h p . i [47] and VACV M2L was shown to inhibit phosphorylation of extracellular signal-regulated kinase 1 and 2 ( ERK1/2 ) at 2 h p . i . and subsequent activation of NF-κB signaling [24] . Likewise , ORFV ORFV121 and ORFV002 were shown to inhibit NF-κB-p65 phosphorylation and acetylation , respectively , at relatively early times p . i [37 , 38] . ORFV073 inhibits NF-κB-p65 activation by preventing activation of IKK complex ( Figs 5A and 9A ) . ORFV073 interaction with NEMO , the regulatory subunit of the IKK complex , likely underlies this inhibition ( Fig 10 ) . The early inhibition of IKK complex in wild-type ORFV-infected cells , is coincident with the reduced levels of NEMO in wild-type virus-infected cells compared to levels observed in OV-IA82Δ073-infected cells during the first hour p . i . , suggesting that altered NEMO protein stability and/or trafficking might occur in the presence of ORFV073 ( Fig 6A and 6C ) . Other poxviral NF-κB inhibitors are reported to specifically target the IKK complex , the bottleneck for most NF-κB-activating signals [19] . ORFV ORFV024 was shown to inhibit activation of IKK complex by preventing phosphorylation of IKK kinase [36] . VACV B14 and N1L were shown to interact with IKKβ and multiple components of IKK complex , respectively inhibiting subsequent activation of IKK complex [23 , 47] . Similarly , MCV MC159 and MC160 , were shown to interact with NEMO preventing IKKβ activation and induce degradation of IKKα , respectively [33 , 34] . ORFV073 is a late viral protein found predominantly in the nucleus of infected cells at 16 to 24 h p . i . ( Fig 2A and 2B ) . Late expression of ORFV073 in the viral replicative cycle is consistent with it being a virion component and functioning early in the next round of infection; however , the predominant nuclear localization of the protein at late times p . i . suggests it may have additional functions , related or unrelated to the NF-κB signaling pathway . Other poxviral NF-κB inhibitors with nuclear functions have been described . For example , parapoxviral ORFV002 is a nuclear inhibitor of the NF-κB signaling pathway that affects NF-κB-p65-mediated transcription [37] . The myxoma virus virulence factor M150R colocalized with NF-κB-p65 in the nucleus of TNFα-stimulated cells suggesting a potential role in regulation of the NF-κB signaling pathway; however , its effect on NF-κB-mediated gene transcription has not been demonstrated [48] . A nuclear function leading to decreased NF-κB-mediated gene expression was reported for VACV , but the actual viral protein ( s ) and mechanism ( s ) responsible for the inhibition are still unknown [49] . Recently , VACV K1 protein was shown to localize in both cytoplasm and nucleus of the cell , and prevent NF-κB-p65 acetylation [50] . Interestingly , ORFV073 interacts with NEMO in the nucleus of ORFV073 transfected cells . In addition to the canonical and non-canonical NF-κB pathway , NEMO is also involved in the atypical NF-κB pathway [51] . In response to genotoxic stress , which conceivably could occur during later stages of virus infection , NEMO translocates to the nucleus where it undergoes ataxia telangiectasia mutated checkpoint kinase ( ATM ) -mediated ubiquitination . NEMO and ATM are then trafficked to the cytoplasm where they activate IKKβ which results in activation of the canonical NF-κB pathway [11 , 52] . Although no significant differences in NF-κB-p65 nuclear translocation were observed between wild type- and OV-IA82Δ073-infected cells at late times p . i . ( Fig 4C and 4D ) , possible effects of ORFV073-NEMO interactions on NF-κB signaling in the nucleus cannot be excluded . Other , as yet uncharacterized , late nuclear functions for ORFV073 unrelated to the NF-κB signaling pathway are also possible . The actual role of poxviral NF-κB inhibitors for aspects of infection biology in vivo remains poorly understood [18 , 21 , 36 , 37 , 53] . Here , deletion of ORFV073 from the ORFV genome resulted in attenuation of ORFV in sheep , indicating that ORFV073 contributes to ORFV virulence in the natural host . The delayed infection of keratinocytes and absence of a clinical pustular stage in sheep infected with OV-IA82Δ073 likely reflect improved ability of the host to control the infection in the absence of ORFV073 . Studies with other ORFV-encoded NF-κB inhibitors have shown that single genes either had no effect on disease pathogenesis , resulting in a wild-type disease phenotype in sheep ( ORFV002 , ORFV024 ) [36 , 37] or , as for ORFV121 , a viral protein which binds to- and prevents nuclear translocation of NF-κB-p65 , led to a markedly attenuated disease phenotype [38] . Remarkably , single gene deletions of most poxviral NF-κB inhibitors resulted in only modest effects on viral pathogenesis and virulence [22 , 36 , 37] . The multiple NF-κB inhibitors encoded by a poxvirus together with the possibility of overlapping or complementing functions may explain this observation . Alternatively , specific poxviral NF-κB inhibitors may exert only subtle and perhaps transient host range effects on specific infected cells or the infected tissue microenvironment . Regardless , the impact of these subtle changes on virus fitness in nature may be difficult to fully ascertain under experimental conditions . To our knowledge , ORFV073 is the first poxviral NF-κB inhibitor found in virions . As early infection events are likely conserved among poxviruses [54] , it is reasonable to speculate that other poxviruses encode yet to be identified virion proteins which inhibit NF-κB activation very early in infection and that early inhibition of NF-κB signaling is of greater biologic significance than currently appreciated .
|
Successful infection of the host by poxviruses relies on control of innate immune responses by virus-encoded immunomodulators . In particular , poxviruses evolved to counteract the NF-κB pathway by encoding multiple inhibitors targeting various levels of NF-κB signaling . We identified a NF-κB inhibitor encoded by ORFV , ORFV073 , that is unique to Parapoxvirus ( PPV ) . In contrast to previously described poxviral NF-κB inhibitors , ORFV073 is a virion protein available immediately following virus entry . Consistent with this possibility , ORFV073 efficiently inhibited NF-κB signaling very early during infection . Results also showed that this inhibition is important for ORFV pathogenesis in the natural host . Regulation of NF-κB signaling by virion proteins early in infection may be more prevalent among poxviruses and of greater biological significance than currently appreciated .
|
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"Discussion"
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2017
|
A parapoxviral virion protein inhibits NF-κB signaling early in infection
|
The anthelmintic praziquantel ( ±PZQ ) serves as a highly effective antischistosomal therapy . ±PZQ causes a rapid paralysis of adult schistosome worms and deleterious effects on the worm tegument . In addition to these activities against the parasite , ±PZQ also modulates host vascular tone in blood vessels where the adult worms reside . In resting mesenteric arteries ±PZQ causes a constriction of basal tone , an effect mediated by ( R ) -PZQ activation of endogenous serotoninergic G protein coupled receptors ( GPCRs ) . Here , we demonstrate a novel vasodilatory action of ±PZQ in mesenteric vessels that are precontracted by high potassium-evoked depolarization , an effect previously reported to be associated with agonists of the transient receptor potential melastatin 8 channel ( TRPM8 ) . Pharmacological profiling a panel of 17 human TRPs demonstrated ±PZQ activity against a subset of human TRP channels . Several host TRP channels ( hTRPA1 , hTRPC3 , hTRPC7 ) were activated by both ( R ) -PZQ and ( S ) -PZQ over a micromolar range whereas hTRPM8 showed stereoselective activation by ( S ) -PZQ . The relaxant effect of ±PZQ in mesenteric arteries was caused by ( S ) -PZQ , and mimicked by TRPM8 agonists . However , persistence of both ( S ) -PZQ and TRPM8 agonist evoked vessel relaxation in TRPM8 knockout tissue suggested that canonical TRPM8 does not mediate this ( S ) -PZQ effect . We conclude that ( S ) -PZQ is vasoactive over the micromolar range in mesenteric arteries although the molecular mediators of this effect remain to be identified . These data expand our knowledge of the polypharmacology and host vascular efficacy of this clinically important anthelmintic .
Schistosomiasis is a socioeconomically devastating helminth infection afflicting over 200 million people worldwide [1] . The resulting disease burden of chronic schistosomiasis is estimated to encumber third world economies with an annual loss of 70 million disability-adjusted life years [2 , 3] . In infected individuals , the prolific egg laying capacity of paired adult worms ( >1000 eggs/day deposited in tissues , [4] ) triggers localized inflammatory responses around eggs trapped within host tissues . Chronic infections progress toward fibrosis and obstructive disease in gastrointestinal tissues and liver ( S . mansoni , S . japonicum ) , genitourinary disease ( S . haematobium ) , anemia , undernutrition and a heightened risk for other comorbidities . Effective drug therapy for schistosomiasis is therefore a healthcare priority [1–3] . The drug praziquantel ( ±PZQ ) has served as the stalwart antischistosomal therapy since the 1980s and the need for ±PZQ is significant [5] . Thankfully , the drug has remained effective over three decades of clinical use , although there are certainly features of ±PZQ that are less than optimal . These include anxiety over the emergence of drug resistance in face of selective pressures imposed by mass distribution efforts , a refractoriness of juvenile worms to PZQ , our lack of understanding over the molecular target ( s ) of PZQ and an inability to improve on PZQ by chemical derivatization of the drug [6 , 7] . Certainly , a better understanding of how ±PZQ works would catalyze future drug development efforts toward the next generation of antischistosomal compounds . Addition of ±PZQ to adult schistosomes causes an acute Ca2+ influx , rapid paralysis of the musculature and a more chronic tegumental damage that aids immunological elimination of worms from the host . Efficacy in vitro and in vivo is associated with the action of ( R ) -PZQ as the more active enantiomer ( eutomer ) in the clinical formulation [8 , 9] , underpinning effort to develop an enantiopure clinical formulation [10] . ±PZQ also displays activity against target ( s ) in the host [11 , 12] , including vasoconstriction of the mesenteric blood vessels inhabited by the adults worms , an effect caused by ( R ) -PZQ stimulation of endogenous serotoninergic GPCRs [13] . The distomer ( S ) -PZQ also exhibits host bioactivity: it is associated with an unpleasant bitter taste effect [14] and effects a transient translocation ( ‘hepatic shift’ ) of S . mansoni worms from the splanchnic beds to the liver on administration [9] despite the appreciated lack of efficacy of ( S ) -PZQ against worms in vitro . Recent work has revealed activity of ±PZQ against the human transient receptor potential melastatin 8 channel ( TRPM8 , [15] ) , although the efficacy of the individual enantiomers at regulating TRPM8 are undefined . TRP channels belong to a superfamily of ion channels that respond to a broad diversity of stimuli and chemotypes underpinning many elements of our sensory physiology [16 , 17] . Schistosome TRPs are themselves promising targets for their druggability [18 , 19] . Collectively , both recent reports underscore considerable progress in defining activities and target ( s ) of ±PZQ action in the human host [13 , 15] . Here , we report a novel vasodilatory action of ( S ) -PZQ in contracted mesenteric vessels . Based on previously published data implicating TRPM8 channels in this vasodilatory effect in rat mesenteric arteries [20] , further prioritized by the work of Babes et al . [15] showing activation of TRPM8 by ±PZQ , activity of ±PZQ on endogenous TRPs that regulate myogenic tone was suspected . This study was designed to investigate the interaction of ( R ) -PZQ and ( S ) -PZQ with human TRPs , and test the possibility that such interactions regulate mesenteric vessel tone .
±PZQ was purchased from Sigma and individual enantiomers– ( R ) -PZQ and ( S ) -PZQ–were resolved following protocols published by Woelfle et al . [10] . Icilin and WS-12 were from R&D Systems and all other ligands were sourced from Sigma-Aldrich . HEK-293 cell lines were sourced from ATCC ( CRL-1573 ) and found to be negative for mycoplasma contamination . Cell culture reagents were from Invitrogen . Human TRPM8 cDNA was a VersaClone from R&D Systems ( RDC0188 ) . Plasmids encoding human TRPA1 and human TRPV1 cDNA were purchased from DNASU plasmid repository ( HsCD00080227 and HsCD00081472 , respectively ) . The TRP channel coding sequences were subcloned into pCS2+ to introduce a COOH-term myc tag using the InFusion HD method ( Clontech ) , HindIII/XhoI restriction enzymes ( NEB ) and the following primers: TRPM8 F–TGGGGACGTCGGAGC-aagctt-gccaccatgtcctttagagcag; TRPM8 R–AAATCGATGGGATGC-ctcgag-tttgattttattagcaatctctttcagaagacc; TRPA1 F-GGACGTCGGAGC-aagctt- atgaagcgcagcctgagg; TRPA1 R-TCGATGGGATGC-ctcgag-aggctcaagatggtgtgtttttgc; TRPV1 F–GGACGTCGGAGC-aagctt-atgaagaaatggagcagcacag; TRPV1 R-TCGATGGGATGC-ctcgag-cttctccccggaagcgg ( where upper case specifies vector-specific sequences , italics indicate restriction sites , and lower case indicates TRP channel specific sequences ) . Primers are listed in a 5’ to 3’ orientation . Swiss Webster mice ( female , 10–13 weeks ) were sourced from Charles River Laboratories . Measurements of mouse mesenteric vessel tone were made using wire myography using a four channel myograph system ( DMT , Aarhus , Denmark ) . Vessel strips isolated from second order mesenteries were equilibrated for ≥30 min in gassed ( 95% O2 , 5% CO2 ) , physiological saline solution ( PSS , 130mM NaCl , 4 . 7mM KCl , 1 . 18mM KH2PO4 , 1 . 17mM MgSO4 , 14 . 9mM NaHCO3 , 5 . 5mM dextrose , 0 . 026mM EDTA , 1 . 6mM CaCl2 , pH 7 . 4 at 37°C ) . To identify the optimal pre-stretch value for experiments , a normalization factor ( IC1/IC100 ) was calculated for individual test strips [21 , 22] , defined as the ratio of the internal circumference at which the maximum response to vasoconstriction ( KCl , plus 40μM norepinephrine ) was observed ( IC1 ) , divided by the internal circumference at which a transmural wall pressure of 100mm of Hg is attained on a length-tension plot overlayed with a La Place transformation isobar ( IC100 ) . After vessel equilibration , reactivity was measured under isometric conditions in response to KCl ( KPSS , 74 . 7mM NaCl , 60mM KCl , 1 . 18mM KH2PO4 , 1 . 17mM MgSO4 , 14 . 9mM NaHCO3 , 5 . 5mM dextrose , 0 . 026mM EDTA , 1 . 6mM CaCl2 , pH 7 . 4 at 37°C ) or indicated ligands as detailed for individual experiments . Homozygous TRPM8 knockout ( KO ) mice , harboring a premature truncation within the cytoplasmic NH2-terminal domain of TRPM8 [23] , were sourced from the Jackson Laboratory ( Trpm8tm1Jul/Trpm8tm1Jul , female , 16–18 weeks ) . For these experiments , CR7BLBL/6J mice were used as age and strain matched controls . ±PZQ , ( R ) -PZQ and ( S ) -PZQ were screened against a panel of 17 human TRP channels ( SB Drug Discovery , Glasgow ) . For all hTRPs , except for TRPM5 , individual channel constructs were stably expressed in HEK cell lines . TRMP5 was expressed in a stable CHO cell line . In preparation for the assays , cells were trypsinized , counted and seeded ( 50 , 000 cells/well ) in black , clear-bottomed 96 well plates and incubated overnight . The following day , cells were loaded with a fluorescent indicator ( FLIPR Calcium 5 Assay kit for TRPA1 , TRPV1 , TRPV2 , TRPV3 , TRPV4 , TRPV5 , TRPC1 , TRPM2 , TRPM3 and TRPM8 , or a membrane potential dye ( FLIPR Membrane Potential Red Assay Kit for TRPC3 , TRPC4 , TRPC5 , TRPC6 , TRPC7 , TRPM4 and TRPM5 ) prepared according to the manufacturer’s instructions in HEPES buffered Hank’s balanced salt solution ( HBSS ) . Dye solution ( 10μl ) was added to appropriate wells and incubated at 37°C for 1 hour . All assays were performed at room temperature . Compounds were tested at 0 . 3 , 1 , 3 , 10 , 30 , 100μM and 300μM in triplicate in both agonist and antagonist mode to determine EC50 and IC50 values , which were compared with reference compounds . Compounds were screened at a final DMSO concentration of 0 . 5% . Plates were screened using a Flexstation ( Molecular Devices , FX01138 ) , monitoring fluorescence values every ~1 . 52 seconds . For ‘agonist mode’ testing , 10μl of the appropriate test compound , or standard agonist , was added after 20 seconds and fluorescence monitored for 2 minutes at λex = 485nm , λem = 525nm for Ca2+ imaging and λex = 530nm , λem = 565nm for membrane potential measurements . For ‘antagonist mode’ testing , test compounds and standard inhibitors were added to appropriate wells and incubated for 10 minutes at room temperature prior to addition of standard agonist compound . HEK293 cells ( ATCC CRL-1573 . 3 ) were cultured in DMEM supplemented with 10% fetal bovine serum ( FBS ) , penicillin ( 100 units/ml ) , streptomycin ( 100 μg/ml ) , and L-glutamine ( 290 μg/ml ) . Cells were transiently transfected ( Lipofectamine LTX , Thermo Fisher ) at a density of 2x106 cells per T-25 cell-culture flask with TRP channel cDNA . For Ca2+ imaging assays , HEK293 cells were seeded onto 8-chambered coverglass slides ( Thermo Fisher , 115411PK ) , at a density of 1x104 cells one day prior to imaging . Cells were washed twice with HBSS , and incubated with fluo-4-AM ( 4μM ) , pluronic F127 ( 0 . 4% ) and probenecid ( 2 . 5mM ) for 25 minutes at room temperature . Cells were then washed twice with HBSS , and left at room temperature ( 30 minutes ) for de-esterification . Dishes were mounted on an Olympus IX81 microscope and fluorescence changes ( λex = 488nM , λem = 513±15nm bandpass filter ) monitored using a Yogokawa spinning disk confocal ( CSU-X-M1N ) and an Andor iXon Ultra 888 EMCCD camera . Tissue harvesting followed ethical regulations approved by the University of Minnesota IACUC committee ( Protocol #1606–33903 ) . Animal husbandry procedures followed requirements outlines in the Public Health Service Policy on Humane Care and Use of Laboratory Animals and the Animal Welfare Act .
The contractile tone of vessel strips isolated from mouse mesenteric arteries was evaluated using wire myography . A typical experiment trace is shown in Fig 1 , where mounted vessel strips exhibited a sustained contraction to high K+ media ( KPSS ) that rapidly reversed upon solution exchange ( Fig 1A ) . At resting tone , addition of ±PZQ caused a marked contraction , consistent with recent data showing vasoconstriction mediated by ( R ) -PZQ activation of host 5-HT2B receptors ( Fig 1A , [13] ) . However , an additional action of ±PZQ was observed in vessels precontracted by KPSS exposure . Addition of ±PZQ to vessels contracted with KPSS caused a marked relaxation ( Fig 1A ) . This vasodilatory effect of ±PZQ was dose-dependent , and sufficient to relax the contracted vessel by 61±9% at high concentrations of ±PZQ ( 100μM , Fig 1B ) . Relaxation evoked by ±PZQ was phasic , with successive additions of ±PZQ ( 10μM ) resulting in a dose-dependent relaxation of vessel tone toward precontracted levels ( Fig 1C ) . The ability of the separated enantiomers , ( R ) -PZQ and ( S ) -PZQ to cause this partial vasodilation of KPSS-precontracted vessels was examined ( Fig 2 ) . The decrease in tension evoked by ±PZQ was mimicked by addition of ( S ) -PZQ ( Fig 2A ) . In contrast , bath application of ( R ) -PZQ was associated with an initial , small contraction possibly reflecting residual serotonergic tone ( Fig 2A ) . To quantify these effects , measurements of changes in tension 1 minute after addition of ( S ) -PZQ or ( R ) -PZQ to capture these initial changes in myogenic tone . These data confirmed that the vasodilatory action of ±PZQ on precontracted mesenteric artery strips was predominantly mediated by ( S ) -PZQ ( Fig 2B ) . Phasic vasorelaxation of mouse mesenteric arteries has previously been associated with the action of agonists of the transient receptor potential melastatin 8 channel ( TRPM8 , [24 , 25] ) under a similar contractile paradigm . This observation has especial relevance given recent data showing that ±PZQ activates TRPM8 in both heterologous expression experiments , as well as in assays for endogenous TRP activity in dorsal root ganglion neurons [15] . These observations merited profiling of ±PZQ action against a broad panel of human TRP channels ( hTRPs ) , including TRPM8 . Therefore , a primary screen was performed against stable cell lines expressing individual hTRPs , using either a Ca2+-sensitive fluorescent dye , or a membrane-potential reporter as a readout for channel activity . Responses to ±PZQ , ( R ) -PZQ and ( S ) -PZQ were measured in triplicate in both ‘agonist-mode’ ( addition of ±PZQ , ( R ) -PZQ or ( S ) -PZQ ) and ‘antagonist-mode’ ( inhibition of response to a channel activator by either ±PZQ , ( R ) -PZQ and ( S ) -PZQ ) . If functional effects were resolved , EC50 ( ‘agonist-mode’ ) or IC50 ( ‘antagonist-mode’ ) values were determined and represented as a heat-map for ease of comparison ( Fig 3A ) . Several conclusions can be drawn from this primary screening dataset . First , ±PZQ displayed activity against only a subset of screened hTRPs—hTRPA1 , hTRPC3 , hTRPC7 and hTRPM8 . Second , these effects occurred over the micromolar range . Third , these effects were predominantly attributable to ( S ) -PZQ activity as the more active enantiomer , or—in the case of TRPM8 - ( S ) -PZQ as the sole active enantiomer . Finally , the ability of PZQ enantiomers to both stimulate and inhibit hTRP activity implied action as partial agonists . Individual dose response curves for hTRPA1 , hTRPC3 , hTRPC7 and hTRPM8 activation by each ligand are shown ( Fig 3B–3E ) . Given the efficacy of ( S ) -PZQ at causing vasorelaxation ( Fig 2 ) , the stereoselectivity of ( S ) -PZQ at hTRPM8 ( Fig 3 ) and the proposed role for TRPM8 in mesenteric vascular beds [24 , 25] , secondary assays were performed using single cell confocal Ca2+ imaging to validate ( S ) -PZQ action at human TRPM8 . Untransfected , and human TRPM8 transfected , HEK293 cells were challenged with ±PZQ , ( R ) -PZQ and ( S ) -PZQ and menthol ( a TRPM8 agonist ) . In untransfected HEK293 cells , neither ±PZQ or menthol elevated cytoplasmic Ca2+ levels , while addition of acetylcholine ( ACh ) as a positive control caused Ca2+ transients through activation of endogenous muscarinic GPCRs ( Fig 4A ) . However , in TRPM8 expressing cells , addition of menthol rapidly elevated cytoplasmic Ca2+ ( Fig 4B ) , and this response was caused by Ca2+ entry as menthol-evoked Ca2+ signals were not observed in Ca2+-free media ( Supplementary Fig 1 ) . In TRPM8 , expressing cells , addition of ±PZQ or ( S ) -PZQ evoked cytoplasmic Ca2+ signals , while ( R ) -PZQ was without effect ( Fig 4B ) . Representative fluorescence traces for each of these experiments is shown in Fig 4C . Finally , Ca2+ transients evoked by either ( S ) -PZQ or menthol were blocked by the TRPM8 antagonist AMTB . The cumulative data for all the confocal Ca2+ imaging experiments is shown in Fig 4D . Analysis of the dose dependency of ( S ) -PZQ action on hTRPM8 revealed micromolar sensitivity ( EC50 = 19 . 2±5 . 3 μM , Fig 4E ) . Collectively , these data validated the primary screen results evidencing stereoselective activation of TRPM8 by ( S ) -PZQ . As a negative control for these experiments , we analyzed responses from human TRPV1-expressing cells: no activity of ±PZQ against TRPV1 was observed in the primary screen ( Fig 3A ) . In untransfected HEK293 cells , neither the addition of the TRPV1 agonist capsaicin ( 1μM ) nor addition of ±PZQ evoked a Ca2+ response ( Fig 5A ) . However , in TRPV1 expressing cells , addition of capsaicin evoked Ca2+ signals which could be blocked by the TRPV1 antagonist , capsazepine ( 10μM , Fig 5B ) . No responses to ±PZQ ( 100μM ) were observed under similar conditions ( Fig 5C ) . The cumulative dataset from these assays is shown in Fig 5D . These data were consistent with the primary screen showing no activation of human TRPV1 by ±PZQ . Next , we performed secondary validation assays on TRPA1 , shown to be activated by both PZQ enantiomers in the primary screen ( Fig 3 ) . In untransfected HEK293 cells , addition of ±PZQ ( 100μM ) , or the TRPA1 agonist allyl isothiocyanate ( AITC , 100μM ) was without effect , suggesting a lack of endogenously expressed TRPA1 channels ( Fig 6A ) . However , in cells heterologously expressing hTRPA1 , addition of AITC resulted in an elevation of cytoplasmic Ca2+ , an effect which could be blocked by preincubation with the TRPA1 antagonist , AM-0902 ( 1μM ) , thus demonstrating functional expression of the hTRPA1 channel in transfected cells ( Fig 6B ) . Addition of ±PZQ ( 100μM ) to hTRPA1 expressing cells also elicited Ca2+ responses , which were also blocked by preincubation with AM-0902 ( 1μM ) ( Fig 6C ) . In contrast to hTRPM8 , addition of either ( R ) -PZQ or ( S ) -PZQ resulted in activation of hTRPA1 ( Fig 6D ) . Cumulative data for these TRPA1 assays in HEK293 cells are shown in Fig 6E . These data confirm the results of the primary screen showing activation of TRPA1 by ( S ) -PZQ and ( R ) -PZQ . Having established hTRPM8 as one target of ( S ) -PZQ ( Figs 3&4 ) , we returned to evaluate ( S ) -PZQ action within mesenteric blood vessels at endogenous levels of channel expression . First , various TRPM8 agonists were examined . These included menthol , icilin ( a more potent small molecule structurally unrelated to menthol ) and WS-12 ( another potent menthol derivative ) . Each of these agents completely relaxed KPSS-contracted vessel strips at high concentrations ( menthol 300μM , icilin 50 μM and WS-12 50μM , Fig 7A–7C ) . While suggestive of action at TRPM8 , these compounds are known to display broader action within the TRP family , as well as affinity for other Ca2+ channels [26 , 27] . Therefore , we repeated these experiments in mesenteric vessels isolated from a TRPM8 knockout mouse ( TRPM8 KO ) . In the TRPM8 KO background , the vasorelaxant effect of the TRPM8 ligands persisted ( Fig 7A–7C ) . The ability of ( S ) -PZQ to relax KPSS-evoked contractions was also examined in both models ( Fig 7D ) , and the relaxant effect was preserved in TRPM8 KO tissue . The extent of relaxation ( ~30% of peak KPSS-evoked tone ) was similar in WT and TRPM8 KO tissue ( Fig 7E ) . These results indicate that TRPM8 does not mediate the vasorelaxation evoked by ( S ) -PZQ , and that the relaxation observed with TRPM8 agonists was caused by broader action against other targets . We therefore conclude that while ( S ) -PZQ is vasoactive over the micromolar range in mesenteric arteries , this effect is not mediated by TRPM8 .
Here we demonstrate functional interactions between the resolved enantiomers of ±PZQ and a subset of human TRP channels over the micromolar range ( Fig 3 ) . These interactions may have significance for understanding the mechanism of action of ±PZQ in both host and parasite .
In terms of host biology , this concentration range is compatible with ( R ) -PZQ and ( S ) -PZQ concentrations attained within the splanchnic vasculature during ±PZQ treatment [13 , 28 , 29] . While the majority of human TRP channels were unaffected by ( R ) -PZQ and ( S ) -PZQ ( Fig 2 ) , the subset of TRP channels engaged by PZQ enantiomers ( hTRPA1 , hTRPC3 , hTRPC7 , hTRPM8 ) are all expressed in host blood vessels inhabited by adult worm pairs , where their activation causes vasorelaxation . Activation of TRPC3 in mesenteric endothelium mediates agonist-evoked vasodilation [30–32] , via various signaling mechanisms ( nitric oxide ( NO ) -dependent signaling , hyperpolarization ) . TRPC7 , which complexes with TRPC3 [33] , mediates store-operated Ca2+ entry in portal vein myocytes [34] . TRPA1 activation also causes vasodilation: in mesenteric beds , this is mediated via TRPA1 activation releasing calcitonin gene related peptide ( CGRP ) from perivascular nerves . [35 , 36] . Finally , TRPM8 is highly expressed in mesenteric artery and pharmacological activation of TRPM8 channels relaxes contracted vessels [20 , 24 , 25] , effects attenuated in TRPM8 knockout mice [24] . These data suggest vasodilation of contracted blood vessels as a possible physiological outcome of host TRP channel engagement by ±PZQ . We note TRPM5 , a transducer of bitter taste signaling was not activated by ( S ) -PZQ ( Fig 3 ) . While taste is a side effect associated with ( S ) -PZQ [14] , another target in the bitter tasting pathway must explain this association . The potential role for PZQ engagement of TRPs in vasodilatory responses was further bolstered by recent data showing that ±PZQ acts a partial agonist of TRPM8 over the micromolar range [15] . Expanding upon this discovery , we demonstrate here that ±PZQ activation of TRPM8 is mediated exclusively by the ( S ) -PZQ enantiomer ( Figs 3&4 ) , and given that ( S ) -PZQ is responsible for the vasodilatory effect observed in the myography experiments ( Fig 2 ) , these correlations prompted consideration of TRPM8 as the prime candidate for ( S ) -PZQ regulation in vivo . However , analysis of vessel responses in TRPM8 KO tissue were inconsistent with this hypothesis , as vasorelaxation by either ( S ) -PZQ or TRPM8 agonists was unaffected by the loss of TRPM8 ( Fig 6 ) . Instead , vasodilation by TRPM8 ligands in response to K+-evoked depolarization likely reflects ‘off-target’ actions of these drugs . While this does not detract from evidence of host TRP regulation by the PZQ enantiomers ( Figs 3&4 ) , results from TRPM8 KO tissue do leave the molecular basis of the ( S ) -PZQ evoked vasorelaxation unresolved . One possibility is broader effects of ±PZQ on other PZQ-sensitive TRP channels ( Fig 3 ) expressed in different cell types within the splanchnic circulation to coalesce vasodilatory cues on contracted vessels . Another possibility is that ( S ) -PZQ could be acting directly as a voltage-operated calcium channel blocker , consistent with data demonstrating non-specific blockade of voltage-operated Ca2+ channels ( Cav ) by TRPM8 ligands in isolated arteries [26] . If indeed ( S ) -PZQ were to act as a Cav blocker , then the original Cav activation hypothesis of PZQ action [37–39] merits further attention . Could ±PZQ be acting in an analogous way to the Cav ligand ±BayK8644 [40] , where one enantiomer acts as a Cav agonist ( ( R ) -PZQ ) —as implied previously [37–39] , and one enantiomer ( ( S ) -PZQ ) as a Cav blocker—as implied here ? Investigation of these possibilities is beyond the scope of the current study . While conventionally viewed as a ‘selective’ antiparasitic therapy , our observations reinforce recent data demonstrating that the clinical racemate ±PZQ is vasoactive in the host [13] . Two discrete actions on host mesenteric vasculature are relevant , mediated by discrete enantiomers–first , constriction of basal tone ( ( R ) -PZQ activation of 5-HT2B receptors , [13] ) and second , dilation of contracted vessels by ( S ) -PZQ . Both actions would occur on administration of ±PZQ , and could combine to optimize blood flow and perfusion pressure throughout the mesenteric vasculature to help flush ( R ) -PZQ paralyzed worms to the liver . Such changes in vascular tone may underpin the ‘hepatic shift’ seen in vivo on administration of either ( R ) -PZQ or ( S ) -PZQ [9] even though ( S ) -PZQ is lacks activity against adult schistosomes in vitro . The host targets of ( R ) -PZQ ( 5-HT2B ) and ( S ) -PZQ ( TRPM8 ) provide a rare example of enantiomers within a clinical formulation that target structurally distinct effectors ( a GPCR versus a non-selective cation channel ) . The commonality between these targets may be realized by considering PZQ as a ‘tryptaminergic pharmacophore’ , a view supported from studies of PZQ action in flatworms [41] . In addition to modulating serotoninergic binding pockets of GPCRs , tryptaminergic ligands also modulate the activity of TRPM8 [42] , a channel notorious for activation by broad chemotypes . Tryptaminergic ligands of TRPM8 include 5-benzyloxytryptamine [43 , 44] , certain N-substituted tryptamines [45] and indole alkaloids [46] . Finally , in terms of interaction of PZQ enantiomers with human TRP channels , some comment on commonalities and discrepancies with prior results is warranted . Most importantly , our data confirm the key discovery of Babes et al . [15] that ±PZQ acts as a partial agonist at human TRPM8 over the micromolar range ( ±PZQ EC50 ~25μM by microfluorimetry [15] , ±PZQ EC50 = 19±5μM by confocal imaging , Fig 4E ) . This activity is attributable to the ( S ) -enantiomer ( Fig 4E ) . Other human TRP channels were also regulated by ±PZQ ( Fig 3 ) and here our results contrast with prior data [15] . Babes et al . demonstrated a lack of activity of ±PZQ against TRPA1 ( ≤100μM ) , and show low potency activation of TRPV1 by ±PZQ ( 100μM ) . In contrast , our data show the opposite: ±PZQ activates TRPA1 ( Figs 3&6 ) , with no apparent activation of TRPV1 under our experimental conditions ( Figs 3&5 ) . The reason for these discrepancies is currently unclear but merits further investigation given the existence of homologs to TRPA1 in parasitic schistosomes , but not to TRPV1 and TRPM8 [18] .
Discovery of TRPs as human targets of ±PZQ is also informative for efforts to define the parasitic target ( s ) of ±PZQ , as precedent has now been established for ±PZQ action as both a GPCR ligand and TRP channel modulator . Despite the molecular divergence between human and flatworm proteins and ligand binding pockets [19 , 47] , it is not unreasonable to anticipate ( R ) -PZQ or ( S ) -PZQ affinities for flatworm target ( s ) within both the GPCR or TRP channel families . Both 5-HT2BR ( Gq coupled ) and the individual TRP channel targets ( hTRPA1 , hTRPC3 , hTRPC7 , hTRPM8 ) elevate cytoplasmic Ca2+ , and the ability of ±PZQ to dysregulate Ca2+ homeostasis in both parasitic schistosomes and free-living flatworms is well appreciated [6 , 48–50] . Moreover , the activity of serotonergic GPCRs and TRP channels can be coupled through amplifying interactions–GPCR mediated Ca2+ store depletion activates TRP mediated Ca2+ entry , which can itself stimulate serotoninergic pathways [51–53] . Perhaps the unique host-parasite polypharmacology of ±PZQ to engage reinforcing parasite targets deleterious to worm viability together with host pathways that mediate beneficial responses combating infection underpins the unique clinical efficacy of ±PZQ that has proved difficult to replicate over 35 years of clinical usage .
|
Praziquantel is a key drug for combating diseases caused by parasitic flatworms . It is the therapeutic mainstay for treatment of schistosomiasis , a disease that afflicts over 200 million people worldwide . In this study , we investigate potential molecular targets of praziquantel , and demonstrate interactions with several members of the transient receptor potential ( TRP ) ion channel family over the micromolar range . These interactions with endogenous host TRP channels may contribute to regulation of vascular contractility in the blood vessels where the mature parasites reside .
|
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2018
|
Activation of host transient receptor potential (TRP) channels by praziquantel stereoisomers
|
Cercospora zeae-maydis causes gray leaf spot of maize , which has become one of the most widespread and destructive diseases of maize in the world . C . zeae-maydis infects leaves through stomata , which is predicated on the ability of the pathogen to perceive stomata and reorient growth accordingly . In this study , the discovery that light was required for C . zeae-maydis to perceive stomata and infect leaves led to the identification of CRP1 , a gene encoding a putative blue-light photoreceptor homologous to White Collar-1 ( WC-1 ) of Neurospora crassa . Disrupting CRP1 via homologous recombination revealed roles in multiple aspects of pathogenesis , including tropism of hyphae to stomata , the formation of appressoria , conidiation , and the biosynthesis of cercosporin . CRP1 was also required for photoreactivation after lethal doses of UV exposure . Intriguingly , putative orthologs of CRP1 are central regulators of circadian clocks in other filamentous fungi , raising the possibility that C . zeae-maydis uses light as a key environmental input to coordinate pathogenesis with maize photoperiodic responses . This study identified a novel molecular mechanism underlying stomatal tropism in a foliar fungal pathogen , provides specific insight into how light regulates pathogenesis in C . zeae-maydis , and establishes a genetic framework for the molecular dissection of infection via stomata and the integration of host and pathogen responses to photoperiod .
Each year , foliar diseases caused by plant pathogenic fungi cause incalculable losses to global food production . Despite the significance of fungal foliar diseases , the current understanding of how fungi infect plants has important gaps . Arguably , the fungal foliar pathogen in which infection is most thoroughly investigated at the molecular level is Magnaporthe oryzae , which generates enormous turgor pressure inside specialized infection structures known as appressoria to directly penetrate the epidermis of rice leaves [1] . While informative , this model is only fractionally representative of how foliar fungal pathogens infect plants . Many fungi exclusively infect leaves through natural openings such as stomata [2] , although the molecular basis of this phenomenon is not fully understood . As early as 1905 , experiments conducted with artificial leaf surfaces in which drilled holes were substituted for stomata revealed that penetration of stomata by rust fungi is thigmotropic , i . e . , regulated by changes in leaf topography associated with stomata , a finding that was substantiated during ensuing decades of research [3]–[5] . However , mechanisms explaining how non-thigmotropic fungi such as Cercospora spp . find their way into stomata were debated for more than a half-century . In 1916 , Pool and McKay postulated that hyphae of the sugar beet pathogen C . beticola sense nearby stomata , possibly through chemoattraction [6] . Their hypothesis resulted from observations that hyphae of C . beticola nearly always took the shortest possible path to gain entry to the closest stomata , and defied the conventional wisdom of the era , namely that hyphae of C . beticola and most other fungi encountered stomata randomly [7] . In the late 1970s and early 1980s , histological studies performed with C . beticola and C . zeae-maydis confirmed that reorientation of hyphal growth to stomata was non-thigmotropic [8] , [9] . These results supported the original idea of stomatal tropism advanced by Pool and McKay , and today , debate over the existence of non-thigmotropic stomatal tropism in fungi has been largely put to rest [10] . Somewhat surprisingly , despite the importance of stomata as portals of entry , relatively little is known mechanistically about how pathogens find and penetrate stomata or how plants defend themselves against this method of attack . There is ample evidence that foliar pathogens other than fungi display stomatal tropism , including certain bacteria and oomycetes [11] , [12] . For example , Pseudomonas syringae moves towards stomata during infection of Arabidopsis , which the bacterium utilizes to infiltrate mesophyll tissues of leaves . However , upon perceiving the pathogen , the plant closes its stomata as a component of the defense response [12] . To usurp this mechanism of plant defense , the bacterium produces coronatine , a secondary metabolite that induces calcium channel opening in guard cells , thus leading to an increase in stomatal aperture-which , in turn , allows the pathogen access to mesophyll tissues [12] . What is known of the interaction between P . syringae and Arabidopsis gives rise to fascinating questions regarding foliar fungal pathogens that infect leaves through stomata . For example , are the mechanisms through which fungi and bacteria sense stomata similar ? Do fungal foliar pathogens produce secondary metabolites structurally or functionally analogous to coronatine that alter stomatal aperture ? Are plants able to defend themselves against stomatal invasion by fungi as was recently demonstrated in bacteria ? Information about molecular mechanisms underlying infection of leaves by fungi through stomata may come from a more thorough understanding of the association between light and fungal pathogenesis . Although plants regulate stomatal aperture in response to environmental cues such as temperature and humidity , the basal regulation of stomatal aperture is circadian , governed by an endogenous molecular clock that is entrained by daily cycles of day and night [13] . Plants have evolved complex molecular mechanisms to perceive changes in the quality , quantity , and periodicity of light , and these signal transduction pathways also regulate stomatal aperture [14] , [15] . Fungi have also developed sensitive molecular mechanisms to detect light . Most known fungal responses to light are mediated by blue ( ∼410–520 nm ) light and are regulated by members of the white collar-1 ( wc-1 ) family of photoreceptor-encoding genes [16] , [17] . Originally identified in Neurospora crassa , wc-1 encodes a dual-function blue light photoreceptor/transcription factor that forms a heterodimer with the protein encoded by white collar-2 to form the White Collar Complex ( WCC ) . The WCC governs virtually all of the light-dependent responses in N . crassa , including the innate circadian clock [16] , [17] . Genes similar to wc-1 have subsequently been identified in numerous fungi , suggesting that the molecular mechanisms underlying responses to light may be at least partially conserved across the fungal kingdom , although few light-responsive genes with clear roles in foliar pathogenesis have been identified . In this study , we investigated molecular mechanisms underlying pathogenesis in the maize foliar pathogen Cercospora zeae-maydis , with particular emphasis on understanding the molecular basis of infection through stomata . An initial discovery that light was required for stomatal tropism and infection in C . zeae-maydis led to the discovery of a putative blue-light photoreceptor ( encoded by CRP1 ) homologous with WC-1 of N . crassa . Functional characterization of CRP1 through targeted mutagenesis revealed that blue light regulates multiple aspects of pathogenesis , and that some , but not all , blue-light responses in C . zeae-maydis are regulated by CRP1 . Our findings identify a novel molecular mechanism through which fungi utilize light as a signal to regulate stomatal tropism and pathogenesis , which has led to the formulation of new hypotheses regarding the coordination of fungal pathogenesis and plant defense responses during the initiation and development of foliar diseases .
To gain insight into environmental factors influencing stomatal tropism and infection through stomata , a series of experiments was conducted in which the influence of temperature , relative humidity , and photoperiod on pathogenesis were explored . Unexpectedly , plants exposed to constant darkness failed to develop visible signs of infection . To further dissect this observation , we utilized a GUS-labeled reporter strain of C . zeae-maydis to assess the effect of constant darkness on distinct stages of pathogenesis , including germination of conidia , formation of appressoria , and the initiation of necrotic lesions . In constant darkness , germination of conidia was not significantly different than in light/dark cycles ( data not shown ) . However , in constant darkness , C . zeae-maydis failed to form appressoria ( Figure 1A ) , whereas the fungus displayed stomatal tropism and formed appressoria normally when exposed to a 12 hr light/dark cycle ( Figure 1B ) . From these observations , we concluded that light is required for infection , although further experimentation was required to conclusively determine whether light was specifically required for the induction of appressorium formation , or whether light was required for the fungus to sense stomata , in which case the defect in appressorium formation could have been an indirect result from incubation in darkness . To gain additional clues into the molecular regulation of light responses in C . zeae-maydis , we evaluated the effect of specific wavelengths of light on morphogenesis and secondary metabolism . On favorable culture media , the formation of asexual spores ( conidia ) is repressed by constant light in C . zeae-maydis , whereas constant darkness induces conidiation [18] . Conversely , the biosynthesis of cercosporin , a photosensitizing perylenequinone that accumulates as a red pigment in culture , is known to be induced by light in Cercospora spp . but is produced in very low levels in constant darkness [19] , [20] . To identify specific wavelength ( s ) of light responsible for these phenomena , we constructed customized fungal growth chambers with acrylic filters that specifically transmitted blue , green , orange , or red light . However , because the transmission spectrum of the green-light filters overlapped with the blue-light filters , and the transmission spectrum of the orange-light filters overlapped with the red-light filters , we focused on defining blue- and red-light responses to avoid potential ambiguities associated with spectral overlap . Cultures of C . zeae-maydis grown exclusively in either white or blue light synthesized high levels of cercosporin , whereas cercosporin biosynthesis in cultures grown exclusively in red light was indistinguishable from cultures grown in constant darkness ( Figure 2A ) . Additionally , cultures grown exclusively in constant blue light consistently displayed an earlier onset of cercosporin biosynthesis and higher levels of biosynthesis compared to cultures grown in constant white light ( data not shown ) . In contrast to cercosporin biosynthesis , cultures grown in constant white or blue light failed to produce conidia on conducive media , whereas cultures grown in constant red light or darkness produced abundant amounts of conidia ( Figure 2B ) . To determine if responses to blue light were conserved among Cercospora species , we evaluated cercosporin biosynthesis and conidiation in C . beticola , C . kikuchii , and C . sorghi . Conidiation and cercosporin biosynthesis in these three species was similar to C . zeae-maydis in response to light ( Figure S1 , Table S1 ) , indicating that the regulation of cercosporin biosynthesis and conidiation by blue light is at least partially conserved throughout the genus . Together , these findings demonstrated that blue light plays a key role in regulating at least two critical stages of pathogenesis among Cercospora spp . and thus implicated blue light-responsive signaling pathways in foliar pathogenesis . Observations that cercosporin biosynthesis and conidiation were regulated specifically by blue light focused gene discovery efforts on putative blue-light photoreceptors in C . zeae-maydis , with the ultimate goal of gaining a molecular foothold into understanding stomatal tropism and appressorium formation in response to light . In many filamentous fungi , the well-characterized white collar regulatory complex ( WCC ) originally characterized in N . crassa is essential for blue-light-mediated processes [21]–[23] . The WCC consists of the interacting transcription factors White Collar-1 ( WC-1 ) and White Collar-2 ( WC-2 ) , of which WC-1 is the limiting component of the complex [24] , [25] . To clone the wc-1 ortholog from C . zeae-maydis , we performed PCR with degenerate primers designed from highly conserved regions of wc-1 and putative orthologs identified in sequenced Dothidiomycete genomes . After cloning and sequencing a small segment of a putative ortholog in C . zeae-maydis , the remainder of the gene ( designated CRP1 for Cercospora regulator of pathogenesis ) was obtained by genome-walker PCR and sequencing cosmid clones . Conceptual translations of CRP1 indicated that the gene contains an open reading frame ( ORF ) of 3 , 535 bp . Additionally , comparison of the CRP1 ORF with mRNA from wc-1 and putative orthologs from other filamentous fungi indicates that the CRP1 ORF contains four putative introns; intron 1 ( 50 bp ) , intron 2 ( 62 bp ) , intron 3 ( 45 bp ) , and intron 4 ( 72 bp ) starting 1 , 245 bp , 1 , 732 bp , 2 , 831 bp , and 3 , 163 bp from the start site of the ORF , respectively . Conceptual translation of CRP1 also revealed a predicted protein of 1 , 101 amino acid residues with a predicted molecular weight of 120 . 01 kDa and isoelectric point of pH 8 . 20 ( Figure 3 ) . Crp1 is predicted to contain a nuclear localization signal residing at amino acid residues 954–963 [26] ( Figure 3A ) . Global comparison of the amino acid residues of Crp1 and WC-1 indicate the two proteins share 49 . 9% similarity and 37 . 0% identity . Additionally , the two proteins are predicted to share a similar domain architecture [27] , [28] ( Figure 3A ) . Further evidence supporting the function of Crp1 as a blue-light photoreceptor was obtained through comparisons of the conceptually translated protein with WC-1 and Vivid ( VVD ) of N . crassa . Crp1 is predicted to contain a Light , Oxygen , Voltage ( LOV ) domain , a Per-ARNT-Sim ( PAS ) -Fold domain , a PAS domain , and a Zinc-finger DNA binding domain . The flavin-binding LOV domain , a member of the PAS domain superfamily , has been implicated in sensing and responding to environmental stimuli [29] , [30] . The LOV domain is well conserved in numerous organisms , including bacteria , fungi , and plants; exceptional conservation is found within the fungal kingdom [29] , [31] ( Figure 3B ) . VVD of N . crassa is a blue-light photoreceptor of 186 amino acids that contains a single LOV domain [32] , [33] . The LOV domain of VVD has been characterized extensively [30] , [32] . Ribbon diagrams depicting the tertiary structure of the LOV domain contained in VVD of N . crassa and the predicted tertiary structure of the LOV domain contained in Crp1 indicated a percent identity of 45 . 27% and an e value of 8 . 30e-36 [30] , [34] ( Figure 3C ) . Moreover , Crp1 contains two additional highly conserved domains involved in light signaling and protein interaction – a PAS-Fold domain and a PAS domain [24] , [35] , [36] . In addition to the three light sensing domains , Crp1 contains a conserved Zinc-finger DNA binding domain common among GATA transcription factors that regulate changes in gene expression in response to light [21] , [37] . The most striking difference in functional regions between Crp1 and WC-1 is the apparent lack of the WC-1 terminal poly-glutamine ( Poly-Q ) regions in Crp1 ( Figure 3A ) . In WC-1 , the Poly-Q terminal regions are thought to function in transcriptional activation , as in the GATA family transcription factors [38] . The lack of the terminal Poly-Q regions in Crp1 could indicate a novel mechanism of activation in response to light . Intriguingly , Crp1 does contain a number of low-complexity regions ( LCR ) near its terminal regions ( Figure 3A ) . The LCRs are implicated in numerous biologically important functions , including protein-protein interactions , transcription , transcriptional regulation , and translation [39] . BLAST analysis ( tblastx ) querying the translated sequence of CRP1 against the GenBank nucleotide collection indicated that CRP1 shares high sequence similarity with characterized and predicted genes in a wide range of fungi within the clades Ascomycota , Basidiomycota , and Zygomycota . The highest sequence similarity was obtained from species within the subdivision Pezizomycotina , and Crp1 groups tightly within a clade formed by Dothideomycetes [40] ( Figure 4 ) . Subsequent protein-protein BLAST ( blastp ) analyses of individual fungal genomes revealed that most fungi from within the Ascomycota and Basidiomycota have one putative ortholog of CRP1 , although members of the Zygomycota examined in this study possessed three genes encoding proteins highly similar to Crp1 . In addition , members of the Ascomycota subphyla Saccharomycotina and Taphrinomycotina [41] , which include both the budding and fission yeasts , do not encode a protein orthologous to Crp1 . Notably , most of the divergent regions of the putative orthologs were found near the N- and C-termini; the central region containing both the LOV domain and the PAS-Fold domain were highly conserved in all orthologous proteins examined ( Figure 3A ) . A phylogenetic analysis of Crp1-like proteins within the Zygomycota and the fungal subkingdom Dikarya produced a well-supported tree corresponding closely to the branch order expected from fungal phylogeny [42] , [43] , indicating the analyzed sequences likely arose from a common ancestor ( Figure 4 ) . To further dissect the role of light in the regulation of stomatal tropism and pathogenesis , we disrupted CRP1 in C . zeae-maydis by homologous recombination ( Figure S2A ) . We obtained two independent gene-disruption mutants of CRP1 ( Δcrp1-24 and Δcrp1-40; Figure S2B ) that were morphologically indistinguishable during growth on a wide variety of culture media ( data not shown ) . To determine the role of CRP1 in pathogenesis , maize plants were inoculated individually with the wild-type strain , the Δcrp1-24 mutant , or the Δcrp1-40 mutant . In the wild-type strain , hyphae frequently reoriented growth in the direction of stomata ( Figure 5A ) , and nearly 70% of hyphae that encountered stomata formed appressoria ( Figure 5B ) . However , hyphae of the Δcrp1 mutants failed to exhibit stomatal tropism , and frequently grew around or across stomata ( Figure 5A ) . The Δcrp1 mutants were capable of producing appressoria over stomata , but at a nearly10-fold reduction in frequency compared to the wild-type strain ( Figure 5B ) . Together , these results indicated that CRP1 regulates both stomatal tropism and appressorium formation in C . zeae-maydis . Observations that the Δcrp1 mutants failed to display stomatal tropism and produced low levels of appressoria prompted the question whether CRP1 plays a broader role in colonization of host tissues and virulence . When C . zeae-maydis infects maize , penetration of leaves through stomata is followed by a latent phase of infection , in which the pathogen colonizes host tissue asymptomatically before switching to a necrotrophic phase of growth . The necrotrophic phase of pathogenesis results in distinctive rectangular lesions defined laterally by the minor veins of leaves . Based on the 10-fold reduction in appressorium formation in the mutant ( Figure 5B ) , we expected to see a 10-fold or greater reduction in the number of lesions if CRP1 regulated stomatal penetration but not colonization . However , if CRP1 were to regulate additional components of pathogenesis , we expected either a complete failure of lesion formation or the development of non-characteristic lesions ( e . g . , smaller ) in leaves infected by the mutant strains . On maize leaves inoculated individually with the wild type , Δcrp1-24 mutant , or Δcrp1-40 mutant , chlorotic flecks appeared within three days after inoculation ( Figure 6 ) . However , within seven days , leaves inoculated with the wild-type strain began to develop expanding , necrotic lesions , whereas leaves inoculated with the Δcrp1-24 or Δcrp1-40 mutants remained unchanged ( Figure 6 ) . After 14 days , characteristic rectangular lesions were consistently observed on leaves inoculated with the wild-type strain , whereas leaves inoculated with the Δcrp1-24 or Δcrp1-40 mutants failed to develop lesions in repeated inoculation attempts ( Figure 6 ) . Together , these observations indicate that CRP1 is required for the necrotrophic phase of pathogenesis , possibly by regulating the viability of appressoria and/or the transition from latent infection to the induction of necrosis . To determine whether CRP1 regulated the blue light-dependent repression of conidiation , we compared the abilities of the wild-type strain and the Δcrp1-24 and Δcrp1-40 mutants to produce conidia in constant light . Unlike the wild type , which grew vegetatively on V8-agar in constant while or blue light , the Δcrp1-24 and Δcrp1-40 mutants produced conidia at levels comparable to the wild-type strain grown in constant red light or darkness ( Figure 7A ) , thus confirming that the repression of conidiation by blue light is mediated through CRP1 . Surprisingly , the Δcrp1-24 and Δcrp1-40 mutants also produced large numbers of mature , viable conidia during growth in constant light on culture media that are typically unfavorable for conidiation ( Figure 7B ) , thus reflecting a global de-repression of conidiation resulting from disruption of CRP1 . In sum , these observations indicate that CRP1 mediates the light-dependent repression of conidiation in C . zeae-maydis but may also regulate or respond to additional environmental inputs that influence asexual reproduction . To determine if CRP1 regulates the induction of cercosporin biosynthesis by light , the accumulation of cercosporin in growth media was evaluated in the wild type and Δcrp1 mutants during growth in various light conditions . When the wild type was grown on conducive media in constant white light , cercosporin accumulated as a visible pigment in the growth medium by 72 h after inoculation ( Figure 8A ) . In comparison , the accumulation of cercosporin in culture media was delayed in the Δcrp1-24 and Δcrp1-40 mutants by approximately two days , but reached wild-type levels after seven days of growth ( Figure 8A ) . Unexpectedly , in red light and darkness , the Δcrp1-24 and Δcrp1-40 mutants produced substantial amounts of cercosporin during growth on 0 . 2× PDA medium ( Figure 8B ) , which indicated that CRP1 is required to repress cercosporin biosynthesis in the absence of blue or white light . These findings raise two distinct possibilities: either disruption of CRP1 leads to a constitutive , light-independent de-repression of cercosporin biosynthesis , or C . zeae-maydis possess another blue-light photoreceptor . In previous work , we identified and characterized PHL1 of C . zeae-maydis , which belongs to the cryptochrome/6-4 photolyase gene superfamily [18] . Functional characterization of PHL1 indicated that the gene encodes a photolyase involved in light-dependent DNA repair , although PHL1 and putative orthologs in other fungi appear to regulate their own expression through unknown mechanisms [18] . To explore the possibility that PHL1 regulates aspects of blue light-dependent gene expression beyond UV-damage repair , we created a double mutant disrupted in both CRP1 and PHL1 ( designated Δcrp1Δphl1 ) . When grown on 0 . 2× PDA medium in constant light , the accumulation of cercosporin in culture media was delayed in the Δcrp1Δphl1 mutant , as observed in the Δcrp1-24 and Δcrp1-40 mutants ( Figure 8A ) . However , contrary to expectations that cercosporin production by the Δcrp1Δphl1 mutant would be less than the single knockout mutants , the Δcrp1Δphl1 mutant produced wild-type levels of cercosporin after seven days of growth , thus indicating that PHL1 has a minimal role in the light-dependent induction of cercosporin biosynthesis . Photoreactivation , a light-dependent mechanism through which UV-induced DNA damage is repaired , can be assessed by comparing survival after UV exposure between cultures allowed to recover in light versus darkness . In previous work , we determined that PHL1 is required for photoreactivation , and may possess an autoregulatory mechanism for gene expression [18] . To further dissect mechanisms through which C . zeae-maydis perceives light , we investigated whether CRP1 was required for photoreactivation . The wild type as well as the Δcrp1-24 , Δcrp1-40 , Δphl1 , and Δcrp1Δphl1 mutants were exposed to UV light and allowed to recover either in constant white light or darkness as previously described [18] . After three days of recovery in constant light , the wild-type strain grew robustly , but failed to grow during recovery in constant darkness; this result is consistent with previous results [18] , confirming that photoreactivation occurs in the C . zeae-maydis wild-type strain ( Figures 9 , S3 ) . However , the Δcrp1-24 and Δcrp1-40 mutants did not survive UV irradiation regardless of exposure to light or darkness during recovery ( Figures 9 , S3 ) . To clarify the mechanism through which CRP1 regulates UV damage repair , we measured the expression of PHL1 and CPD1 in the wild type , Δcrp1-24 mutant , and Δcrp1Δphl1 double mutant during photoreactivation . PHL1 is predicted to encode a 6-4 photolyase , whereas CPD1 is predicted to encode a cyclobutane pyrimidine dimer ( CPD ) photolyase in C . zeae-maydis [18] . After 1 h of photoreactivation , expression of PHL1 and CPD1 was highly induced ( 8 fold and 12 fold , respectively ) in the wild-type strain , whereas no induction of PHL1 was observed in the Δcrp1-24 mutant ( Figure 10 ) . Additionally , expression levels of CPD1 were similar in the Δcrp1-24 mutant and the Δcrp1Δphl1 double mutant during photoreactivation ( Figure 10 ) , indicating that either PHL1 does not directly regulate CPD1 expression , or CRP1 is required for any regulatory interaction between CPD1 and PHL1 . Although CPD1 expression was slightly induced during photoreactivation in both the Δcrp1-24 and Δcrp1Δphl1 mutants ( Figure 9 ) , the magnitude of the induction was comparably low in both mutants compared to the wild type . Together , these results indicated that CRP1 regulates the induction of PHL1 and CPD1 during photoreactivation .
Infecting leaves through stomata is a critical component of pathogenesis for many fungal foliar pathogens , including Cercospora spp . , but the process is poorly understood at the molecular level . This study presents the first molecular confirmation that stomatal tropism and infection are not random or arbitrary processes in C . zeae-maydis , thus validating predictions for Cercospora ssp . first outlined nearly a century ago . However , the precise mechanism through which CRP1 regulates early infection events requires further elucidation . One intriguing finding in this study was that disruption of CRP1 substantially reduced , but did not completely eliminate , the formation of appressoria over stomata; however , despite numerous , repeated foliar inoculations , we never observed the induction of foliar necrosis by the Δcrp1 mutants . Given that infection of maize by C . zeae-maydis is characterized by a latent period of infection after the formation of appressoria and preceding the visible manifestation of necrotic lesions , there are at least two possible mechanisms that could explain the failure of the Δcrp1 mutants to induce foliar necrosis . One possible explanation is that appressoria formed by the Δcrp1 mutants have subtle morphological and/or physiological defects , and thus fail to facilitate the entry of the pathogen into the sub-stomatal cavity due to the formation of defective penetration pegs or an inability to suppress host detection . An inability to suppress host detection seems somewhat unlikely , however , considering that we never observed a distinct physiological response in the host ( e . g . , a hypersensitive response ) induced by the Δcrp1 mutants compared to the wild type; a visible manifestation of defense would be the anticipated result of a failure to suppress host detection . A second explanation is that the Δcrp1 mutants may successfully penetrate leaves and enter the latent phase of infection , but fail to transition to a necrotrophic growth habit . In this scenario , the light-dependent induction of critical phytotoxins such as cercosporin and possibly other genes and/or metabolites required for the necrotrophic phase of pathogenesis would require the function of Crp1 . This hypothesis is difficult to test , however , because of the interplay between CRP1 and early infection events , and inoculation techniques that bypass the formation of appressoria for foliar entry ( such as wounding ) are not yet available in this pathosystem . Although the induction of cercosporin biosynthesis by light is well established among Cercospora species [19] , [44] , [45] , the mechanisms through which light is perceived and transduced to activate cercosporin biosynthetic genes have been elusive . Over thirty years ago , Lynch and Geoghehan ( 1979 ) postulated that cercosporin may function as a photopigment among the Cercospora , a hypothesis based on an observed overlap between wavelengths of light that strongly induced cercosporin biosynthesis in C . beticola [46] and the wavelength of maximum absorption ( λmax ) of cercosporin [45] . In the ensuing decades , many categories of chromoproteins have been discovered and characterized at the molecular level , including broadly conserved plant and fungal photoreceptors [47] . However , to date , no family of fungal proteins has been identified that could plausibly utilize cercosporin as a chromophore . Additionally , studies subsequent to the work of Lynch and Geoghehan ( 1979 ) revealed that cercosporin reacts with plasma membranes to function as a phytotoxin [19] , and that Cercospora species possess transport proteins required to avoid self-toxicity from intracellular accumulation of cercosporin [48] . These observations strongly suggest that the primary biological function of cercosporin is a phytotoxin rather than a photoreceptor-associated chromophore . Furthermore , this conclusion is supported by our findings that at least one of the blue light-dependent morphological phenotypes of C . zeae-maydis ( e . g . , the repression of conidiation ) is regulated by CRP1 during growth on media that fully repressed cercosporin biosynthesis . In this study , we found blue light to be the specific range of light that induces cercosporin biosynthesis , but the precise mechanism through which this occurs is not fully clear . Disruption of CRP1 delayed the onset of cercosporin biosynthesis in blue and white light but did not fully eliminate cercosporin biosynthesis , as would be expected in a simplistic regulatory model in which CRP1 was the sole blue-light photoreceptor responsible for transducing light as a stimulus . One possible explanation is that C . zeae-maydis possesses an as-yet undescribed blue-light photoreceptor that is at least partially functionally redundant with Crp1 . The persistence of some blue-light responses in N . crassa despite the deletion of wc-1 and wc-2 suggests the existence of an additional blue-light photoreceptor [33] . The sequenced genomes of numerous filamentous fungi contain orthologs of plant and mammalian blue-light photoreceptors , including cryptochromes and phototropins , although none has been demonstrated to function as a blue-light photoreceptor to date . However , the postulated existence of a novel blue-light photoreceptor does not adequately explain the de-repression of cercosporin biosynthesis in red light and constant darkness in the Δcrp1 mutants . A second possible explanation for how light regulates cercosporin biosynthesis is that CRP1 regulates the expression and/or degradation of an inhibitor of cercosporin biosynthesis . Cercosporin biosynthesis is known to be inhibited in constant light when plates are inoculated at high spore density [49] , which indicates the existence of an inhibitory feedback mechanism that can at least conditionally supersede the activation of cercosporin biosynthesis by light . In this model , the basal state for cercosporin biosynthesis in C . zeae-maydis could be ‘off’ due to the function of an inhibitor , which would be degraded through the function of CRP1 in response to blue light . CRP1 could also regulate the biosynthesis of the inhibitor in a light-dependent or –independent manner , which would explain the derepression of cercosporin biosynthesis in normally non-inducive light conditions . A more thorough molecular characterization of Crp1 and a better understanding of its interaction with signaling pathways in C . zeae-maydis is required to support either of these working models . Zonate , concentric rings radiating from the site of infection are common among fungal foliar diseases and could plausibly result from periodicity underlying pathogenesis . The colonization of host tissue by hemibiotrophic fungi such as C . zeae-maydis is hypothesized to result from a defined succession of events , beginning with the secretion of resistance-suppressing proteins and/or secondary metabolites , followed by the growth of new hyphae behind the advancing front of secreted hydrolytic enzymes that induce necrosis , and culminating in the production of conidia after successful colonization . In the example of C . zeae-maydis , once the fungus penetrates the intercellular space , light would induce the production of cercosporin , a potent disruptor of host-cell membranes , and the fungus would presumably colonize new tissue and produce conidia at night . In this model , the fungus would alternative between invasive growth ( daytime ) and reproductive growth ( nighttime ) during colonization of leaves . Under field conditions , lesions caused by C . zeae-maydis are often scalariform in appearance; our observations indicate the laddering pattern of lesions results from bands of conidiophores interspersed by zones of vegetative growth ( data not shown ) . Additionally , a blue light-entrained circadian rhythm underlying hyphal melanization in the soybean foliar pathogen C . kikuchii was recently described [50] . These observations gave rise to the hypothesis that circadian rhythms regulate components of pathogenesis or fungal development among Cercospora spp . , including C . zeae-maydis , and perhaps other groups of fungal foliar pathogens . Some 100 years after the earliest documented observations that fungi can penetrate host leaves through stomata and that light plays a role in fungal pathogenesis , this study uncovered a crucial molecular component of both phenomena . By demonstrating the requirement of a putative blue-light photoreceptor in the perception of stomata , the formation of appressoria , and the subsequent colonization of host tissue , our findings raise the possibility that fungal pathogens utilize light to synchronize key elements of pathogenesis , and also challenge assumptions that the penetration of stomata by fungi is a chance occurrence . The identification of CRP1 provides a novel avenue through which the molecular components of light perception and pathogenesis can be explored . Additionally , our findings underscore the utility of Cercospora species as alternative models in which to study the molecular dynamics of light signaling and molecular clocks in fungal biology and plant-fungal interactions .
Wild-type C . zeae-maydis ( strain SCOH1-5 ) was isolated from diseased maize plants collected in Scott County , Ohio in 1995 . From the C . zeae-maydis SCOH1-5 strain , Δcrp1-24 , Δcrp1-40 , and Δcrp1Δphl1 were generated for this study and are described in greater detail below . All strains were maintained on V8-agar medium in constant darkness to promote conidiation; conidia were harvested with sterile water and quantified with a hemocytometer . To assay conidiation and cercosporin production in response to light , freshly harvested conidia from dark-grown cultures were plated on V8-agar or 0 . 2× potato dextrose agar ( PDA; B&D ) medium , respectively . A data set of Crp1-like proteins was assembled from public data repositories ( GenBank , DOE Joint Genome Institute , and Broad Instiute ) by blastP [51] . Sequences were initially aligned with Muscle [52] . Subsequently , ambiguously aligned regions were removed using Gblocks [53] . The amino acid data set was analyzed using maximum likelihood methods under the Whelan and Goldman ( WAG ) +gamma+estimation of proportion of invariant sites model in RAxML v 7 . 0 . 0 [54] , [55] on the available CIPRES web-portal [56] . Internal branch support was evaluated in RAxML based on 1 , 000 bootstrap replicates . To control the wavelengths of light available to cultures , light filters were constructed from blue ( 400–530 nm ) , green ( 450–600 nm ) , orange ( 540–700 nm ) , or red ( 600–700 nm ) acrylic glass ( American Acrylics , Skokie , IL ) . Spectral properties of all acrylic glass filters were determined with a Beckman DU-530 scanning spectrophotometer . For all experiments , light was provided by conventional 40 W fluorescent bulbs ( GE ) . To control the intensity of light , cultures were positioned at varying distances from light sources or covered in layers of cheesecloth so that all cultures received a constant supply of 5 µE of light . All light measurements were taken with a LiCor integrating spectrophotometer . To extract cercosporin , cultures were grown on 0 . 2× PDA for four days . The plates were flooded with 5 ml of 5 N KOH , and incubated for 30 min . From each plate , 1 ml of extract was diluted 1∶4 and measured with a Beckman DU-530 spectrophotometer at a wavelength of 480 nm as previously described [18] . Experiments contained three biological replications and experiments were repeated four times with similar results . For genomic DNA extractions , fungal tissue was obtained from four-day-old cultures grown in liquid YEPD medium . Fungal genomic DNA was extracted by a CTAB protocol as described previously [57] . DNA sequences were determined by the Purdue University Genomics Center ( West Lafayette , IN ) . PCR primers were obtained from Integrated DNA Technologies ( Coralville , IA ) . For GUS staining , tissues were incubated in a solution containing 0 . 1 M NaPO4 , 10 mM EDTA , 0 . 1% Triton X-100 , 1 mM K3Fe ( CN ) 6 , and 2 mM X-Gluc ( 5-Bromo-4-chloro-3-indoxyl-beta-D-glucuronide cyclohexylammonium salt; Gold Biotechnology , St . Louis , MO ) for 30 min at 37°C . To identify CRP1 , degenerate PCR primers CRP1degF ( 5′-ATHAAYTAYMGNAARGGAGG-3′ ) and CRP1degR ( 5′-CCRCARCTRTTRCANAATC-3′ ) were designed by aligning amino acid and nucleotide sequences of predicted genes from Magnaporthe oryzae , Fusarium graminearum , Aspergillus clavatus , and Ustilago maydis . Genomic DNA of C . zeae-maydis was amplified with primers CRP1degF and CRP1degR to generate a 0 . 9-kb product that was cloned into pGEM-T EZ ( Promega ) and sequenced . The remainder of CRP1 was obtained by genome-walker PCR ( Clontech ) and sequencing clones containing CRP1 identified in a cosmid library containing 8× coverage of the C . zeae-maydis genome ( kindly provided by Dr . Won-Bo Shim , Texas A&M University ) . In total , we sequenced 5 , 925 bp of the CRP1 locus , including the entire open reading frame of CRP1 , 1 , 040-bp upstream from the putative start codon , and 1 , 350-bp downstream from the putative stop codon . For functional analysis of CRP1 , we targeted the gene for disruption via single homologous recombination in wild-type strain SCOH1-5 . A 644 bp region of CRP1 was amplified with primers Crp1KOf ( 5′-CCGGATCCATCCATGAAGGCG-3′ ) and Crp1KOr ( 5′-GAGGATCCTGCCAAACTGCG-3′ ) and cloned into vector pKS-HYG [58] to create pCRP1-KO . Then , a single-homologous disruption cassette was amplified from pCRP1-KO with primers Crp1KOf and HygR ( 5′-CGATCAGAAACTTCTCGACAG-3′ ) . The PCR product was precipitated and used to transform protoplasts of C . zeae-maydis as previously described [59] . Hygromycin-resistant colonies were screened by PCR with primers A1 ( 5′-ATCTCGAGGTGTACGCATGGTGCTA-3′ ) and H3 ( 5′-CGGCAATTTCGATGATGCAGCTTG-3′ ) to identify two independent strains disrupted in CRP1 ( Δcrp1-24 and Δcrp1-40 ) . Additionally , to create a Δcrp1Δphl1 strain , CRP1 was disrupted in the Δphl1-1 background . The methodology was essentially the same as described above for disruption in the wild-type strain . However , because PHL1 was disrupted with a cassette conveying resistance to hygromycin , plasmid pKS-GEN conveying resistance to geneticin was used in place of pKS-HYG [60] . Infected leaves were collected in 24 h intervals after infection . Leaves were examined at the Purdue Life Science Microscopy Facility with a JEOL JSM-840 Scanning Electron Microscope . Maize inbred B73 , which is highly susceptible to infection by C . zeae-maydis , was inoculated in a greenhouse when plants were approximately five weeks old . Leaves were inoculated with 1 ml of conidia suspension ( 105 conidia/ml ) by an atomizer attached to an air compressor . Inoculated plants were incubated under opaque plastic bags for five days to promote symptom development . In each experiment , at least three plants were inoculated with each strain . The pathogenicity assays were performed three times with similar results . From each strain , 2 . 5 µl of conidia suspension containing 1000 , 100 , or 10 conidia were spotted onto V8-agar medium . The plates were exposed to UV light ( 3 mW/cm2 ) for 90 min and incubated for three days in constant light or darkness . As a control , without UV treatment , plates spotted by the conidia suspension ( 2 . 5 ul ) were incubated for three days in constant light or darkness . Each experiment evaluated by two plates was repeated two times with similar results . Total RNA was extracted with Trizol reagent ( Invitrogen ) and purified with an RNeasy miniprep purification kit ( Qiagen ) . For analyses of gene expression , cDNA was generated with random primers by Stratascript RT-PCR system ( Stratagene ) . Expression of PHL1 ( forward primer 5′-AGTTCTGGGATTGCTGGACCGAAA-3′ , reverse primer 5′-TCTCGCCACCTTTATGAGGCGAA-3′ ) and CPD1 ( forward primer 5′-CTCGAATAGAGCATCGTCGTATTCCC-3′ , reverse primer 5′-TGGCATGGCGGGAGTTTTACAAG-3′ ) was measured by quantitative PCR . The PCR reactions were performed in an MXP-3000 real-time PCR system ( Stratagene ) , and the reaction conditions were followed by previously described [18] . To normalize expression data , TUB2 was amplified with primers Tub-rtf ( 5′-GGCTGGTGAGTGGTGCGAAA-3′ ) and Tub-rtr ( 5′-GCTCAACAGCGATCTGCGCA-3′ ) . Expression of PHL1 and CPD1 was normalized to TUB2 expression and calculated as fold differences in expression relative to expression in the wild type before exposure to UV light . The calculation was based on 2−ΔΔCt method . Sequence of genes and proteins mentioned in this study can be found in the GenBank ( http://www . ncbi . nlm . nih . gov/ ) with following accession numbers: Cercospora zeae-maydis CRP1 ( HQ646376 ) , C . zeae-maydis PHL1 ( EU443730 ) , C . zeae-maydis CPD1 ( EU814871 ) , C . zeae-maydis TUB2 ( EU402967 ) , Neurospora crassa wc-1 ( CAA63964 . 2 ) , N . crassa vvd ( AAK08514 . 1 ) , Cryptococcus neoformans BWC1 ( AY882437 ) , Phycomyces blakesleeanus MADA ( DQ229146 . 1 ) , Aspegillus clavatus lreA ( XM001270598 ) , Bipolaris oryzae BLR1 ( AB273633 ) .
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Fungal diseases of crop plants are a significant threat to global food security . Improving host resistance is the most cost-effective and environmentally sound strategy for sustainable disease management . However , many devastating diseases of important crops have proven impossible to manage through genetic resistance alone , thus underscoring the need for new strategies to improve resistance . For example , although many fungal pathogens utilize natural openings in leaves to infect host plants , little is known about the underlying molecular dialogue between the host and the pathogen , or whether resistance during this stage of pathogenesis can be improved through breeding . In this study , we explored the ability of a common fungal foliar pathogen of maize to sense and penetrate pores in the leaf epidermis involved in gas exchange ( stomata ) . We determined that light is required for the fungus to sense stomata and identified a blue-light photoreceptor in the fungus that mediates stomatal tropism and infection . Intriguingly , orthologous photoreceptors in other fungi entrain endogenous circadian rhythms , and plants are known to regulate stomatal aperture in response to daily cycles of light and darkness . This study raises the distinct possibility that plants and pathogens coordinate their responses to photoperiod , thus providing a novel insight into fungal pathogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbial",
"metabolism",
"sustainable",
"agriculture",
"plant",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"developmental",
"biology",
"plant",
"science",
"microbial",
"growth",
"and",
"development",
"fungal",
"reproduction",
"morphogenesis",
"mycology",
"microbial",
"pathogens",
"biology",
"pathogenesis",
"agriculture",
"fungal",
"biochemistry",
"ecology"
] |
2011
|
Regulation of Stomatal Tropism and Infection by Light in Cercospora zeae-maydis: Evidence for Coordinated Host/Pathogen Responses to Photoperiod?
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Antiretroviral therapy ( ART ) effectively controls HIV infection , suppressing HIV viral loads . However , some residual virus remains , below the level of detection , in HIV-infected patients on ART . The source of this viremia is an area of debate: does it derive primarily from activation of infected cells in the latent reservoir , or from ongoing viral replication ? Observations seem to be contradictory: there is evidence of short term evolution , implying that there must be ongoing viral replication , and viral strains should thus evolve . However , phylogenetic analyses , and rare emergent drug resistance , suggest no long-term viral evolution , implying that virus derived from activated latent cells must dominate . We use simple deterministic and stochastic models to gain insight into residual viremia dynamics in HIV-infected patients . Our modeling relies on two underlying assumptions for patients on suppressive ART: that latent cell activation drives viral dynamics and that the reproductive ratio of treated infection is less than 1 . Nonetheless , the contribution of viral replication to residual viremia in patients on ART may be non-negligible . However , even if the portion of viremia attributable to viral replication is significant , our model predicts ( 1 ) that latent reservoir re-seeding remains negligible , and ( 2 ) some short-term viral evolution is permitted , but long-term evolution can still be limited: stochastic analysis of our model shows that de novo emergence of drug resistance is rare . Thus , our simple models reconcile the seemingly contradictory observations on residual viremia and , with relatively few parameters , recapitulates HIV viral dynamics observed in patients on suppressive therapy .
Antiretroviral therapy ( ART ) effectively controls HIV infection , suppressing HIV viral loads to below detectable levels in most patients . However , infection remains: cessation of treatment is usually followed by HIV rebound to high levels [1] . Ultra-sensitive assays , with detection thresholds as low as 0 . 3 virions per mL of plasma , reveal the presence of viremia in patients on treatment [2] . What is unclear is the source of this persistent , low-level viremia; does it derive from ongoing rounds of viral replication , or activation of infected cells in the latent reservoir , or some combination of the two [3] . Our aim is to employ simple mathematical models to gain insight into the source of residual viremia in HIV-infected patients . HIV cell infection is usually followed by virus production and cell death . However , a small fraction of infected cells instead enter a state of latent infection [4 , 5] , in which HIV has integrated into the host cell DNA but there is little , if any , virus production . The virus’ cytopathic effects seem negligible , and these cells seem unaffected by therapy or host immune responses . The reservoir of these cells is established early during primary infection [6–8] . While in a latent state infected cells may undergo homeostatic proliferation [9] , which promotes reservoir stability . The latent reservoir represents only a very small fraction of the total CD4+ T cell population but it is very long-lived; patients on treatment show a decaying reservoir with a half-life estimated to be between 6 and 44 months on average , so the time to complete eradication may be up to 70 years [10] . Eradication of the latent reservoir is considered to be one of the major hurdles to curing HIV infection [11] . Importantly , for our purposes , upon latent cell activation , viral production and ensuing cell death resume [12] . Mechanisms for the generation and maintenance of latency and subsequent activation remain unclear [4 , 13 , 14] . The evidence supporting latent cell activation as the only source of residual plasma viremia is as follows: ( 1 ) Intensification of ART , by adding an additional drug , has no appreciable impact on CD4 counts [15] or viral load [16] . ( 2 ) During suppressive ART , plasma virus shows little or no development of drug resistance mutations [17 , 18] . ( 3 ) Clonal sequences of plasma virus indicate a close relationship with virus archived in the latent reservoir [19 , 20] . ( 4 ) HIV envelope proteins in gut-associated lymphatic tissue show no evidence of evolution in patients on ART initiated during primary infection [21] . ( 5 ) Genotypic studies of pre- and post-treatment virus show a too-close relationship for the source of rebound virus to be ongoing viral replication [22] . However , there is also evidence supporting the notion of ongoing replication . For example , a genotypic study of episomal HIV cDNA collected prior to viral rebound showed evidence of recent evolution [23] , suggesting that fresh rounds of cell infection with HIV contribute to residual viremia . Also , while the level of residual plasma viremia has been shown to correlate with the size of the CD4+ T cell viral reservoir in patients on ART , it does not correlate with markers of immune activation , suggesting that reactivation of the latent viral reservoir may not be the sole source of residual plasma viremia [24] . Residual viral replication may also occur in productively infected CD4+ T cells in various lymphoid tissues , without being reflected in plasma viremia [24 , 25] . The mathematical modeling work below reconciles these contradictory observations . We make two underlying assumptions: that latent cell activation does occur in patients , and that R , the reproductive ratio , i . e . , the average number of new cell infections induced by a single infected cell , during suppressive ART is less than 1 . We show that , even though R < 1 , the contribution of viral replication to residual viremia can be non-negligible if therapy is not sufficiently potent . Further , we shall show that , although the contribution of viral replication to residual viremia can be significant in such cases , low genetic variability can still be maintained , consistent with de novo emergence of drug resistance being very rare . Thus , recent evolution is possible , matching the observation in [23] , but long term evolution is unlikely , matching the observations in [15 , 17 , 19 , 20] .
The reproductive ratio R = ( 1 − ε ) pβT/δ ( c + βT ) is a key parameter in our model in determining the amount of residual replication . The fraction f determines the level of predicted latent reservoir re-seeding in patients on treatment , which can be significant if R is large . These parameters are therefore central in characterizing ongoing viral dynamics in patients on treatment . We now discuss realistic ranges for those parameters . Our primary results below rely upon the reproductive ratio R = ( 1 − ε ) pβT/ ( c + βT ) δ only , since f is small . However , for the purposes of illustrative simulation , we input the parameters individually rather than as the group parameter R . Where possible , model parameter estimates are taken from the literature [2 , 10 , 35–40] , as listed in Table 1 . For most antiretroviral therapy , the associated in vivo drug efficacy ε is poorly characterized . Recently raltegravir , an integrase inhibitor , has been estimated to have efficacy 0 . 94 in a combination therapy including emtricitabine and tenofovir disoproxil fumarate , and 0 . 997 during monotherapy [41] . Integrase inhibitors are not yet included in most recommended antiretroviral therapy combinations [27] , but combination therapy with raltegravir seems to be no more effective than other types of drugs in treatment-naïve patients [42 , 43] . We therefore choose for our baseline net drug efficacy ε = 0 . 99 , slightly better than the efficacy of raltegravir in the combination therapy used by Andrade et al . ( 2015 ) [41] . We will use this drug efficacy to fix the latently infected cell activation rate , next , assuming the viral load on long-term therapy V0 = 3 . 1 copies/mL [2] . With a fixed at this value we will then explore the sensitivity of our results to drug efficacies in the range ε = 0 . 9–0 . 999 . Beyond the net measured latent reservoir half-life , t 1 / 2 L = ln ( 2 ) / η 2 , model parameters relating to latent reservoir dynamics , η1 and a , remain poorly understood . However , the largest negative eigenvalue in our model ( 4 ) should correspond to the observed long term decay of latently infected cells , η2 . We choose the latent reservoir decay rate in the absence of replenishment by de novo infection , η1 , as a function of this net latent reservoir decay η2 . As shown in Sec . B . 3 in S1 Text , η 1 = η 2 - a δ f R η 2 - δ ( 1 - ( 1 - f ) R ) . ( 5 ) We choose the latent cell activation rate a so that in model ( 4 ) , at some arbitrary time after being on therapy for a long period , designated t = 0 , the latent reservoir size L0 and viral load V0 are in quasi-equilibrium , i . e . , a = δ ( 1 - ( 1 - f ) R ) - η 2 c V 0 p L 0 , ( 6 ) see Sec . B . 3 in S1 Text for details . Note that this approach imposes an additional constraint on our parameters; a > 0 requires that δ [1 − ( 1 − f ) R] > η2 . We interpret this constraint as the net decay rate of productively infected cells in the presence of new infections but in the absence of new latent cell activations , δ [1 − ( 1 − f ) R] ( c . f . Eq ( 4 ) ) must be more rapid than the net decay rate of the latent reservoir , η2 . Assuming a drug efficacy of ε = 0 . 99 , an on-therapy quasi-steady state viral load V0 = 3 . 1 copies/mL , and corresponding latent reservoir size L0 = 1 per 106 cells , we obtain a baseline activation rate of a = 1 . 74 × 10−3 day−1 , which corresponds to an average time of activation for a single latently infected cell of 575 days . This is about 3 . 5 times the estimated lifespan of a human memory CD4+ T-cell [44] , so only a minority of latently infected cells are expected to become activated before they die . Nonetheless , we estimate that there are aL ≈ 174 latent cell activations per day , assuming 1011 CD4+ T-cells body-wide . Pinkevych et al . ( 2015 ) estimated that on average , after therapy is interrupted , active viral replication is initiated once every 6 days . This does not imply that there is an average of one new latent cell activation every six days , as there also needs to be ensuing rounds of viral replication following the activation of a latently infected cell that cause viral rebound , rather than a chain of infection that ultimately dies out . Therefore , the actual value of aL remains unclear . We use aL ≈ 174 latent cell activations per day but our qualitative results are not sensitive to this choice , see Sec . E in S1 Text . When investigating viral dynamics under drug efficacies ε ≠ 0 . 99 we recalculate the associated reproductive ratio , R = ( 1 − ε ) R* and then re-compute the associated on-therapy quasi-steady state viral load , V0 = apL0/c ( δ ( 1 − ( 1 − f ) R ) − η2 ) from Eq ( 6 ) .
We have presented a simple HIV viral dynamics model , extended from the standard model [29] , that recapitulates the following features of HIV infection in patients on suppressive therapy: Our primary assumption is that latent cell activation drives viral dynamics on therapy . This assumption is supported by the observation that clonal sequences of plasma virus indicate a close relationship with virus archived in the latent reservoir [19 , 20] , and is an increasingly well-accepted hypothesis [26 , 34 , 51 , 54] . An important aspect of our analysis is that our results rely primarily on the with ( i . e . change in to within ) in-host basic reproductive ratio of HIV in patients on effective therapy , R . In particular , since the fraction of new infections that result in latency , f , is very small [34] , the fraction of residual viremia attributable to viral replication in patients on suppressive therapy is approximately R , Eq ( 12 ) . Further , the probability distribution on the number of rounds of replication achievable after the activation of a latently infected cell , before the lineage dies out , is a function of R only . We made a reasonable choice of R but no clear estimate exists for patients on suppressive therapy . Our model predicts that estimation of the reproductive ratio of a patient on therapy , rather than individual parameters that make up the ratio ( e . g . viral production rate p , drug efficacy ε ) would allow us to effectively characterize ongoing replication in patients on therapy , analyzing for example the probability of emergent drug resistance across different individuals . The implication of our modeling on the low probability of emergent drug resistance re-enforces results from Ribeiro et al . ( 2000 ) [55] . There the authors argued that , since the proportion of infected cells produced over time in patients on ART is very small relative to the number of infected cells in patients pre-therapy , for drug resistant variants to emerge , they most likely already exist in the infected cell population at initiation of therapy . To this argument we add the fact that the proportion of infected cells in patients on therapy that have resulted from any viral replication is approximately R , the viral reproductive ratio in patients when on therapy , further reducing the probability of drug resistance emerging from ongoing viral replication . The assumption of high drug efficacy implies that patients are adherent to therapy , which may not always be the case [56] . Patients who are not adherent , or patients who have developed some resistance to therapy , may have low drug efficacy . In that case we would expect a high reproductive ratio R near 1 , and therefore a high proportion ( approximately R ) of residual viremia to be associated with ongoing viral replication . We used ε = 0 . 6 as an illustrative example of this case , with R = 0 . 92 and therefore 92% of residual viremia due to ongoing viral replication ( see Fig 2c ) . Although a latent cell activation would be followed , in this case , by more rounds of viral replication than for higher drug efficacy , ultimately the lineage would still die out ( see Fig 3b and 3c ) . More rounds of viral replication implies more chances for a drug resistant variant to emerge , but the probability is still small; there are too few rounds of replication to be assured of the right mutation ( see Fig 4 ) . It is important to note however , that ε = 0 . 6 > εc , the critical drug efficacy below which therapy is not suppressive . Our modeling predictions are contingent on R < 1 . They are not valid , for example , for cases where adherence to therapy in a patient is such that average drug efficacy dips below this critical value εc , which gives R > 1 . Our model suffers from a number of other limitations . Importantly , we model dynamics of latent cell activation very simply; we assume no clonal expansion , which may occur since latently infected cells are mainly memory cells [9 , 51 , 57–61] , and we assume that an activated latently infected cell is the same as a productively infected cell , which may not be the case . We also assume new latently infected cells decay at the same average rate as pre-existing latent populations . In these pre-existing latent populations , activation by common cognate antigens likely already occurred , yielding a slow activation rate; new latently infected cells may still be specific to common antigens and hence have a more rapid activation rate . It is also a one-compartment model , that is , we do not model dynamics in different tissues individually , in particular lymphatic tissue where drug concentrations may be lower than in blood [25] , and where residual replication may occur in productively infected CD4+ T cells without being reflected in plasma viremia [24 , 25] . Viral and cell transport between tissues may play an important role in promoting HIV infection in patients on therapy [3 , 24] . In spite of these limitations , we have shown that our models , with relatively few parameters , recapitulate HIV viral dynamics observed in patients on suppressive therapy . We used a variant of the model to predict that viral replication cannot replenish the reservoir in a patient on therapy . Current strategies for HIV functional cure target the latent reservoir , with reservoir eradication as the goal . Our prediction implies that these reservoir eradication strategies will not be obstructed by latent reservoir replenishment in HIV+ patients on effective therapy .
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In HIV+ individuals , antiretroviral therapy ( ART ) effectively controls HIV viral loads to below levels detectable by routine tests . However , more sensitive tests can detect some residual viremia . The source of this virus is a matter of debate: does it derive from ongoing viral replication , or from viral production following activation of latently infected cells ? Experimental observations support both sides of the argument: in patients on therapy , HIV shows no long-term evolution , and emergence of drug-resistant mutants is rare , implying no ongoing viral replication , but there remains short-term evolution , implying the opposite . To reconcile these observations , we propose a mathematical model of latently and productively infected cells and virus . Using our models we predict that , in patients on suppressive ART , the contribution of viral replication to residual virus , while small , yields short term-evolution . But even if the contribution is large , for example if adherence to therapy is poor , long-term evolution can still be limited , and de novo emergence of drug resistance is rare . Thus , our simple models reconcile the seemingly contradictory observations on residual viremia .
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[
"Abstract",
"Introduction",
"Models",
"Discussion"
] |
[] |
2016
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Residual Viremia in Treated HIV+ Individuals
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As an inhibitor of cyclin-dependent kinases , p16INK4A is an important tumour suppressor and inducer of cellular senescence that is often inactivated during the development of cancer by promoter DNA methylation . Using newly established lymphoblastoid cell lines ( LCLs ) expressing a conditional EBNA3C from recombinant EBV , we demonstrate that EBNA3C inactivation initiates chromatin remodelling that resets the epigenetic status of p16INK4A to permit transcriptional activation: the polycomb-associated repressive H3K27me3 histone modification is substantially reduced , while the activation-related mark H3K4me3 is modestly increased . Activation of EBNA3C reverses the distribution of these epigenetic marks , represses p16INK4A transcription and allows proliferation . LCLs lacking EBNA3A express relatively high levels of p16INK4A and have a similar pattern of histone modifications on p16INK4A as produced by the inactivation of EBNA3C . Since binding to the co-repressor of transcription CtBP has been linked to the oncogenic activity of EBNA3A and EBNA3C , we established LCLs with recombinant viruses encoding EBNA3A- and/or EBNA3C-mutants that no longer bind CtBP . These novel LCLs have revealed that the chromatin remodelling and epigenetic repression of p16INK4A requires the interaction of both EBNA3A and EBNA3C with CtBP . The repression of p16INK4A by latent EBV will not only overcome senescence in infected B cells , but may also pave the way for p16INK4A DNA methylation during B cell lymphomagenesis .
In vitro , EBV can very efficiently induce the activation and continuous proliferation of resting human B lymphocytes . The resulting lymphoblastoid cell lines ( LCLs ) carry the viral genome as extra-chromosomal episomes and express only nine ‘latent’ EBV proteins . There are six nuclear antigens ( EBNAs 1 , 2 , 3A , 3B , 3C & LP ) , three membrane associated proteins ( LMP1 , LMP2A & 2B ) and in addition several untranslated RNA species . Together these factors activate the quiescent B cells and sustain their proliferation while maintaining the viral episome in its extra-chromosomal state ( reviewed in [1] ) . Current data on the persistence of EBV in humans are consistent with the viral genome residing long-term in the resting memory B cell compartment . This occurs in at least of 90% of the world's population . However , to establish persistence EBV generally infects resting ( naïve ) B cells in Waldeyer's ring of the oropharynx and drives these to proliferate as activated B-blasts . This transient expansion of an infected B-blast population is generally accompanied by migration into germinal centres and differentiation to become centroblasts and centrocytes and finally resting memory B cells . The precise series of events that the EBV-positive B cells undergo to reach the memory compartment is unknown , but it appears to involve the regulated , sequential silencing of EBV genes encoding latency-associated proteins [2] , [3] . Although infection is generally asymptomatic , EBV can be the causative agent in the benign self-limiting lymphoproliferation , infectious mononucleosis ( IM ) . Uncontrolled proliferation of latently infected B cells in the immunocompromised may result in a chronic form of IM , a chronic polyclonal B-lymphoproliferative disorder called post-transplant lymphoproliferative disease ( PTLD ) or rarely the development of malignant lymphoma . Individuals co-infected with malaria ( mainly children ) or HIV ( mainly adults ) may be at increased risk of developing EBV-associated B cell lymphomas , including Burkitt's lymphoma ( BL ) and diffuse large B cell lymphoma [4] . EBNA3A , EBNA3B and EBNA3C are considered to comprise a family which probably arose in primate gamma-herpesvirus evolution by a series of gene duplication events since they have the same gene structure ( ie a short 5′ coding exon and a long 3′ coding exon ) , are arranged in tandem in the EBV genome and share limited but significant amino acid sequence homology . EBNA3 transcripts are alternatively spliced from very long mRNAs generally initiated at the Cp latency promoter and LCLs have only a few copies of these transcripts per cell , suggesting their expression is very tightly regulated and the turnover of the EBNA3s is slow [1] , [5] . Although they are related and the three proteins have limited homology , there is nothing to suggest that they have extensively redundant functions . Genetic studies using recombinant viruses originally indicated that EBNA3A and EBNA3C are essential for the efficient in vitro transformation or immortalisation of B cells , whereas EBNA3B is dispensable [6] , [7] . However , under the appropriate conditions , with feeder cells present , it has been possible to establish EBNA3A-negative LCLs ( [8]; our unpublished data ) . Each EBNA3 protein binds to the cellular DNA-binding factor RBP-JK ( also known as CBF1 ) . This is the same protein that binds to , and targets to DNA , the EBV transactivator EBNA2 and the NOTCH-IC effector of the NOTCH signalling pathway . EBNA3A , EBNA3B and EBNA3C can repress Cp reporter plasmids and plasmids containing multiple RBP-JK/CBF1 binding sites derived from Cp ( [9] , [10] , [11] , [12]; P . Young and MJA , unpublished data ) . Since Cp is generally the promoter for all EBNA mRNA initiation in LCL cells , the EBNA3 proteins probably contribute to a negative auto-regulatory loop . In addition all three EBNA3s exhibit robust repressor activity when targeted directly to DNA by fusion with the DNA-binding domain of Gal4 and they all interact with one or more cellular factor ( s ) involved in transcriptional repression or silencing; these include histone deacetylases ( HDACs ) and CtBP ( [11] , [12] , [13] , [14] , [15] , [16] , [17]; P . Young and MJA , unpublished data ) . CtBP ( C-terminal binding protein ) was initially discovered as a cellular factor interacting with the C-terminus of adenovirus E1A oncoprotein and subsequently identified as one of a highly conserved family of co-repressors of transcription ( reviewed in [18] ) . Most of the factors that bind to CtBP and negatively regulate transcription contain at least one conserved Pro-Leu-Asp-Leu-Ser ( “PLDLS” ) CtBP-interaction domain ( or close variant ) that is necessary and probably sufficient for the interaction . CtBP-containing complexes can coordinate biochemical and enzymatic events that convert transcriptionally active chromatin directly to a repressive or silent state ( [19] , [20] ) . Moreover there is also good evidence that CtBP is involved in the regulation of cell proliferation since it has been shown that CtBP forms a link between human polycomb group ( PcG ) proteins and pRb [21] and that CtBP and PcG complexes both regulate elements in the CDKN2A locus [22] , [23] . EBNA3A and EBNA3C each bind to CtBP in vitro and in vivo but this binding correlates only partially with their ability to repress transcription when targeted to DNA in transient reporter assays . However , the interaction correlates extremely well with their ability to behave as cooperating nuclear oncogenes when introduced into primary rodent fibroblasts with oncogenic Ha-RAS [16] , [17] , [24] . Since in this type of assay , the oncogene Ha-RAS alone triggers exit from the cell cycle and premature senescence via the induction of the p16INK4A and/or p19ARF proteins encoded by the CDKN2A locus [23] , [25] , this suggests that EBNA3A and EBNA3C can each rescue primary fibroblasts from growth arrest and senescence . Further evidence that EBNA3C deregulates the cell cycle came with the demonstration that when over-expressed it overcomes a mitotic metaphase checkpoint and induces polyploidy and multi-nucleation , eventually leading to cell death [26] . However , the molecular details of its action in mitosis have remained elusive . A reported interaction with CHK2 suggests that it could have a role in the transition from G2 to mitosis , but this has not yet been proven [27] and although it has been reported that EBNA3C may repress the transcription of the mitotic regulator BUBR1 in one B cell line , this has not been extended to LCLs [28] . Several recent reports indicate that EBNA3C can also directly associate with multiple other factors involved in the regulation of cell cycle progression and/or the G1/S checkpoint . These include Cyclin A; the ubiquitin ligase SCFSKP2; the tumour suppressor pRb; the oncoprotein MYC; MDM2 in a complex with p53 and p53 alone [29] , [30] , [31] , [32] , [33] , [34] . It remains to be determined if these interactions occur in infected B cells and whether they are functionally significant . The most direct and compelling evidence that EBNA3C modulates the cell cycle during EBV-mediated transformation of B cells into LCLs comes from Maruo and colleagues . Using a recombinant Akata EBV made conditional for EBNA3C function by fusing EBNA3C with a modified oestrogen receptor , they revealed that EBNA3C represses expression of the cyclin-dependent kinase inhibitor p16INK4A in LCLs . Removing the inducer of EBNA3C activity ( 4-hydroxytamoxifen ) from the culture medium results in an accumulation of both p16INK4A mRNA and protein and in reduced cell proliferation [35] . EBNA3A also cooperates with Ha-RAS in the transformation and immortalization of REFs and there is again a remarkably good correlation between EBNA3A binding to CtBP and its ability to cooperate with oncogenic ras [17] . Furthermore , a 4-hydroxytamoxifen-dependent LCL conditional for EBNA3A function showed that in the absence of EBNA3A , cell proliferation gradually declines . Although it was not indicated whether this involved regulation of the CDKN2A locus [36] , a more recent report of a microarray screen has indicated that repression of p16INK4A transcription in LCLs is associated with EBNA3A expression [8] . We recently demonstrated that EBV represses transcription of the gene encoding the pro-apoptotic BCL-2-related family member BIM [37] , [38] . The repression of BIM transcription initially involves a polycomb repressive complex , PRC2 , where the histone methyltransferase EZH2 , which together with SUZ12 and EED , is responsible for establishing the epigenetic modification H3K27me3 ( tri-methylation of lysine 27 on histone H3 ) ( reviewed in [39] , [40] ) . H3K27 methylation of BIM may then be followed by DNA methylation of sites within the CpG-island flanking the BIM transcriptional initiation site ( [38] and our unpublished data ) . Since EBNA3A and EBNA3C are necessary for the chromatin remodelling and epigenetic repression of BIM , here we have examined the p16INK4A locus and the roles EBNA3A and EBNA3C play in regulating its epigenetic status .
An EBNA3C-HT fusion protein ( 3CHT ) in the B95-8 background was constructed with the same linking sequence and 4-hydroxytamoxifen-sensitive murine estrogen receptor that has previously been described in the Akata background [35] . The connection between the 3C and HT is a single proline residue between the last amino acid of EBNA3C and amino acid 281 of the murine estrogen receptor alpha ( modified by G525R to make it 4-hydroxytamoxifen-specific . This fusion was recombined into the B95-8 bacterial artificial chromosome ( BAC ) [41] using previously described methods [37] , [42] to produce two independent BACs containing 3CHT ( A and C ) . A set of CtBP-binding-mutant viruses were generated in which the EBNA3A and/or EBNA3C binding site ( s ) for CtBP were replaced with previously characterised mutations that lack the ability to bind CtBP [16] , [17] . This was achieved by a sequential set of recombinations , initially mutating the pair of CtBP binding sites in EBNA3A ( to generate the 3ACtBP mutant ) . The EBNA3C binding site for CtBP in this was then mutated to create a virus genome lacking all CtBP binding sites among the EBNA3s ( E3CtBP ) . Then the EBNA3A mutant sequence was replaced with wild-type sequence , leaving only the EBNA3C sequence as mutant ( 3CCtBP ) and finally the EBNA3C sequence was reverted to wild-type sequence , generating the CtBP revertant ( revCtBP ) . Established LCLs were cultured in RPMI-1640 medium ( Invitrogen ) supplemented with 10% fetal calf serum , penicillin and streptomycin . LCL 3CHT were cultured with addition of 400nM of 4-hydroxytamoxifen ( HT , Sigma ) . After the infection of primary B cells , LCLs were grown to a volume and density suitable for freezing multiple aliquots ( typically about 60ml at a density of 3×105 cells/ml or greater ) . This took 4–8 weeks for WT-EBV , revertant and 3CHT LCLs and 6–12 weeks for the EBNA3A and CtBP mutant LCLs . Cells recovered from liquid nitrogen were cultured for about 10 days ( with HT if necessary ) before the start of any experiment . At the end of an experiment the cells were discarded . Twenty-four hours before any experimental treatment , cells were seeded at a density of 2 . 5×105 cells/ml . Virus was produced by transfection of recombinant BACs into HEK293 cells [ ( ATCC , CRL-1573 ) , a kind gift of Claire Shannon-Lowe , University of Birmingham] and selection of clonal Hygromycin B-resistant cell lines , which were screened for integrity of EBV genome by episome rescue and pulsed-field gel analysis of BAC restriction digests ( not shown ) . Infectious virus was produced by the transfection of EBV-BAC-containing 293 cells with BZLF1 and BALF4 expression constructs [43] , and after 4 days , supernatant was filtered through 0 . 45 µm filters . Virus titre was assessed by infection of Raji cells and counting green cells on an inverted fluorescent microscope after enhancement of GFP expression by overnight treatment with 5 nM TPA and 1 . 25 mM sodium butyrate . Virus titres were typically in the range of 50 to 250 raji green units ( rgu ) per microlitre of tissue culture supernatant . B-cells for generation of LCL-3CHT-A and -C and EBNA3A-knockout LCLs ( and for limiting dilution experiments – see below ) were isolated from buffy coat residues ( UK blood transfusion services ) by centrifugation over ficoll . CtBP mutant LCLs and 3CHT-LCL B , D and E ( described herein ) were generated by infection of PBLs isolated from donated EBV-seronegative blood ( a kind gift of Ingo Johannessen , University of Edinburgh ) . Three donors were used ( D1 , D2 and D3 ) . 3CHT-LCL B , D and E were made by infection of in blood from donors 1 , 3 and 2 respectively with EBV-3CHT-A virus . Essentially , between 50 µl and 1 ml of virus was added to 106 PBLs ( typically 2–8% of which are B-cells by FACS for CD20; not shown ) in a well of a 24 well plate , and cultured initially in RPMI supplemented with 15% FCS , supplemented with Cyclosporine A ( 500 ng/ml ) for the first 2–3 weeks . Once LCLs had grown out into large culture volumes , the FCS level in the medium was reduced to 10% . Virus stocks were diluted to 2×104 Raji green units ( rgu ) per ml in RPMI supplemented with 10% FCS , and ten-fold serial dilutions used to generate virus concentrations down to 0 . 2 rgu/ml . The virus was added to an equal volume of PBLs at 2×106 cells per ml , and 1 ml was seeded per well in a 24 well plates . Two virus preparations from independent 293 cell producer lines were used for each virus mutant , and 6 wells for each virus concentration . 24 wells with no virus were used to control for spontaneous immortalisation of B-cells . After a week , the culture volume was increased to 2 ml and half the culture volume was replaced weekly thereafter . For the first 2 weeks , the medium was supplemented with cyclosporine A ( 500 ng/ml ) . Cell growth was monitored and wells were scored positive or negative after 40 days , based on the presence of clumps of cells characteristic of LCLs . To assess growth rate of CtBP-mutant LCLs , 5×104 cells per ml of cells were seeded in 10 ml in a flask . 0 . 5 ml was removed every day and live cells ( by trypan blue staining to exclude dead cells ) were counted on a haemocytometer . To analyse cell cycle distribution , 2×106 LCL cells were fixed in 80% ethanol , incubated in PI solution [PBS containing 18 µg/ml propidium iodide ( PI ) and 8 µg/ml RNase A ( Sigma Aldrich ) ] at 4°C for 1 h before flow cytometric analysis . To quantify cells in S phase , cells were pulsed with 10 µM 5-bromo-2′-deoxyuridine ( BrdU ) ( Sigma Aldrich ) for 1 h at 37°C , harvested immediately after the pulse , fixed in 80% ethanol and co-stained with FITC-conjugated anti-BrdU mAb ( Becton Dickinson ) for 1 h at room temperature and PI solution for 1 h at 37°C . Western blotting was performed essentially as described previously [16] . Briefly , proteins extracted using RIPA buffer , or in some cases whole cell lysates were resolved by sodium dodecyl sulphate–polyacrylamide gel electrophoresis ( SDS–PAGE ) and transferred to Protran nitrocellulose membranes ( Schleicher and Schuell Bioscience , Dassel , Germany ) . Membranes were blocked with 5% milk powder in PBS/0 . 05% Tween 20 , probed with appropriate primary and HRP-conjugated secondary antibodies . ECL kit ( Amersham Biosciences , Chalfont St Giles , UK ) was used for visualization . Following primary antibodies were used: mouse monoclonal anti-γ-tubulin ( Sigma , T6557 ) , sheep polyclonal anti-EBNA3A ( Exalpha , USA ) , mouse monoclonal anti-EBNA3C ( A10 , kind gift from Prof . Martin Rowe , University of Birmingham ) , mouse monoclonal phospho-independent anti-Rb ( BD Pharmingen , 554136 ) , rabbit polyclonal anti-phospho-Rb ( Ser 807–811 ) ( Cell Signaling , 9308 ) , mouse monoclonal anti-E2F1 ( Millipore , mixed clones cKH20 and KH95 , 05-379 ) , mouse monoclonal anti-p16INK4A ( clone JC8 , kind gift from Dr Gordon Peters , Cancer Research UK ) , rabbit polyclonal anti-p130 ( Santa-Cruz , c-20 , sc-317 ) , rabbit polyclonal anti-p107 ( Santa Cruz , c-18 , sc-138 ) , mouse monoclonal anti-EBNA2 ( clone PE2 , Dako ) , mouse monoclonal anti-LMP1 ( clone CS1-4 , Dako ) , mouse monoclonal anti-EBNA-LP ( clone JF186 , gift of Paul Farrell ) , rat monoclonal anti-LMP2A ( clone 14B7 , AbCam 59026 ) , rabbit polyclonal anti-CtBP [16] and rat monoclonal anti-EBNA3B ( clone 6C9 , provided by Elizabeth Kremmer , Munich; Popp et al , submitted for publication ) . For Q RT-PCR , RNA was extracted from approximately 5×106 cells for each cell line using the RNeasy mini kit from Qiagen and following the manufacturer's instructions . One microgram of each RNA sample was reverse-transcribed using SuperScript III First-Strand Synthesis Supermix for qRT-PCR ( Invitrogen ) . Between 0 . 5–1% of cDNA product ( equivalent to 5–10 ng RNA ) was used per qPCR reaction . qPCR was performed on an ABI 7900HT real-time PCR machine using Platinum Sybr Green qPCR SuperMix UDG kit ( Invitrogen ) . The cycling conditions were 95°C for 20 sec , followed by 40 cycles of 1 sec at 95°C , 20 sec at 60°C on a fast block . Dissociation curve analysis was performed during each run to confirm absence of non-specific products . Sequences of the assays used are listed in Table S1 . Standard curves , used to standardise amplification efficiency , were produced by six 5-fold serial dilutions of a mixture containing all cDNA samples used . Results were analyzed with qbasePLUS software ( Biogazelle , Ghent , Netherlands ) . Precise normalization was achieved using internal average control calculated from the controls ( housekeeping genes – bold in Table S1 ) with highest stability rating ( usually ALAS1 and GNB2L1 ) . The calculated errors in the graphs are the standard errors from three replicate qPCR reactions for each mRNA . Chromatin immunoprecipitations for methylated histone H3 were performed essentially as described previously [38] . Precipitated DNA was cleaned using QIAquick Gel Extraction Kit ( Qiagen ) and was assayed by qPCR . Input DNA Ct was adjusted from the 5% used in the qPCR to 100% equivalent by subtracting 4 . 32 ( Log2 of 20 ) cycles . ‘Percent input’ precipitated was then calculated by 100×2∧ ( Ct adjusted input – Ct IP ) . Non-specific background was estimated by precipitation with IgG ( data not shown since all values were below 0 . 03% of input ) . The error bars represent standard deviations from triplicate pPCR reactions for both input and IP . Sequences of the primers used in these assays are listed in Table S2 .
In order to examine in detail the regulation of p16INK4A and cell proliferation by EBV , initially a recombinant EBV encoding a conditional EBNA3C was constructed . We employed the B95 . 8 EBV-BAC system used previously to generate EBNA3-knockout ( KO ) viruses and the design of fusion proteins described by Maruo and colleagues for fusing the carboxyl-terminus of EBNA3C to a modified estrogen receptor [35] , [37] . EBNA3C fused to this estrogen receptor is dependent on 4-hydroxytamoxifen ( HT ) in the culture medium for its function and stability . Two independently generated viruses encoding the EBNA3C-HT-fusion ( 3CHT ) were used to establish multiple lymphoblastoid cell lines ( called LCL 3CHT ) . LCL 3CHT – with one notable exception described below – required HT in the culture medium for their proliferation . When HT was removed , after 3–7 days there was a dramatic reduction in the amount of EBNA3C detected by western blotting ( for a representative example see Figure 1 ) . This reduced expression is probably because the inactivated fusion binds to heat shock proteins and is targeted for proteasome-mediated degradation and is consistent with the behaviour of the equivalent fusion in the Akata strain EBV [35] . The LCLs were validated further by western blot analyses probing for each of the EBV latent proteins . As was previously reported in Akata , no consistent differences in steady state levels were seen . In the absence of HT a very slight increase in LMP1 was sometimes observed – this was also reported previously ( Figure S1; [35] and data not shown ) . In order to confirm that inactivation of EBNA3C compromises cellular DNA synthesis , BrdU incorporation was assessed . Two LCL 3CHT ( -A & -C ) cells were cultured with HT , then for 14 or 33 days after HT had been removed from the culture medium . Cells were then pulsed for 1 hour with BrdU , harvested , fixed and stained with anti-BrdU-FITC and propidium iodide . The cells were analysed by flow cytometry ( see for example Figure 2A ) . In both LCL 3CHT , BrdU incorporation after 14 days without HT was reduced to about 40% of that in similar control cells cultured with HT and after 33 days the reduction in the proportion of cells entering S phase was even more profound ( Figure 2B ) . To establish whether the growth arrest could be reversed upon reactivation of EBNA3C-HT , HT was re-added after 14 or 33 days growth in its absence . Surprisingly , although the levels of EBNA3C-HT were re-established quite rapidly ( within about 24 hours: see Figure 1 ) , there was a significant delay before BrdU incorporation matched that of the controls . These cells clearly do not behave like a synchronised G1/S-arrested population released into S phase . When HT was re-added after 14 days without HT , it took 12 days to return to the level of proliferation seen in control cells ( Figure 2C ) . When the HT was re-added after 33 days without HT , the period required to achieve full proliferation was extended to 16 days ( Figure 2D ) . Since the restoration of cell proliferation after reactivation of EBNA3C is dependent on the time elapsed without functional EBNA3C , it is possible that a subpopulation of cells is being driven into in a state of irreversible arrest similar to senescence . These would be refractory to further pro-proliferative signals from EBNA3C and they would gradually accumulate . Alternatively there may be an intrinsic delay associated with the molecular processes necessary to re-induce cell cycle progression in individual arrested cells . It should be noted that , although viable , these non-proliferating cells did not stain positive for the operational mark of senescence β-galactosidase ( data not shown ) . Maruo and colleagues described how in the absence of functional EBNA3C , the cyclin-dependent kinase inhibitor p16INK4A accumulates [35] . We needed to establish that similar changes in p16INK4A levels occur in the B95 . 8-derived LCL 3CHT , and determine whether the response to EBNA3C reactivation correlated with ( and could account for ) the reduction in p16INK4A expression and the changes in proliferation of LCL 3CHT described above . The DNA sequence of the unique first exon of p16INK4A is GC rich and is predicted to have extensive secondary structure under the conditions used for qPCR ( based on DNA sequence analysis using Visual OMP ) . A quantitative RT-PCR assay specific for p16INK4A transcripts , which detects the amplicon located within the unique first exon of p16INK4A ( Table S1 ) , is quantitative only in the presence of large amounts of template . Therefore , a second assay that quantifies an amplicon located within the second and third exon shared by p16INK4A and p14ARF was designed ( CDKN2A assay; Table S1 ) , which is quantitative over a 5-log range of template concentration . These assays were used to show that the regulation of p16INK4A in B95 . 8-BAC LCL 3CHT is reversible ( Figure 3 ) . After 14 days in culture in the absence of HT , there was a 2-2 . 5-fold increase in CDKN2A transcripts relative to the control population . After re-adding HT into the medium , CDKN2A transcripts gradually decreased over the next 12 days ( Figure 3A ) . This result was confirmed with the p16INK4A-specific assay ( Figure 3B ) . Extending the period of culture in the absence of HT to 33 days resulted in a 3 to 5-fold increase in the level of CDKN2A transcripts . After re-adding HT to the medium , CDKN2A transcripts gradually decreased to the levels found in an actively proliferating culture , but it seemed to require at least 16 days ( Figure 3C ) . This was also consistent with the results using the p16INK4A-specific primer set ( Figure 3D ) . Echoing the transcript data , p16INK4A protein expression was gradually reduced after reactivation of EBNA3C in LCL 3CHT ( Figure 3E ) . We conclude that regulation of p16INK4A expression in LCL 3CHT undergoing changes in proliferation seems to be exclusively or at least predominantly at the level of transcription as has been previously shown in various types of cell , including LCLs [23] , [35] . Taken together , the data confirm that p16INK4A accumulates in LCL 3CHT cultured without HT and this inversely correlates with the proportion of cells entering S phase . The data show for the first time that when EBNA3C is reactivated by the re-addition of HT to the culture medium , the reverse occurs and the correlation between EBNA3C activity , p16INK4A transcription and proliferation holds true . In order to determine the consequences of p16INK4A accumulation on the rest of the Rb-axis in the absence of functional EBNA3C , LCL 3CHT were cultured with HT ( controls ) and without HT for 14 and 33 days . Subsequently , HT was re-added on day 14 or on day 33 . In arrested cells , Rb became hypophosphorylated ( as revealed by both pan-specific and phospho-specific anti-Rb antibodies ) ; as the arrest intensified after extended time without HT , reduced phosphorylation was accompanied by a slight reduction in the expression of Rb ( Figures 4A and B ) . Simultaneously the Rb-related p130 protein accumulated and the amount of p107 was reduced ( Figure 4B , annotated 014 and 033 ) . This was particularly apparent after 33 days . Consistent with the data showing recovery after re-addition of HT and the reconstitution of functional EBNA3C , Rb was gradually re-phosphorylated and up-regulated ( Figure 4B ) ; concomitantly , expression of p130 decreased as cells entered the proliferation cycle , and p107 expression increased . The repression of p16INK4A by PcG silencing complexes adding H3K27me3 marks to chromatin has been well characterized in primary fibroblasts . In pre-senescent proliferating fibroblasts the H3K27me3 mark forms a broad peak centred on the first exon of p16INK4A; induction of senescence is associated with displacement of PcG silencers and a profound reduction of H3K27me3 on the chromatin associated with p16INK4A exon 1 [40] , [44] , [45] , [46] . We hypothesised that the up-regulation of p16INK4A transcription in the absence of functional EBNA3C might result from loss of PcG-mediated repression . Therefore ChIP analyses were performed to assess the level of H3K27me3 in the p16INK4A locus ( see schematic in Figure 5A ) in LCL 3CHT cultured with and without HT . Upon EBNA3C inactivation by the removal of HT , H3K27me3 gradually decreased at the p16INK4A exon 1 , as transcription increased ( Figure 5B and C ) . This process could be reversed by reconstitution of functional EBNA3C after re-addition of HT ( see below ) . The time taken for H3K27me3 depletion correlated with the length of time in culture without functional EBNA3C and was consistent with the rate of p16INK4A induction . Equivalent results were obtained when similar ChIP experiments were performed on an independent LCL 3CHT and using two different sets of p16INK4A exon 1-specific primers ( Figure S2 and data not shown ) . Recently it became clear that the transcriptional status of p16INK4A is not determined by H3K27 tri-methylation alone , but rather by the interplay between H3K27me3 and H3K4me3 modifications [44] . Therefore ChIP analyses were performed to assess the quantities of both modifications at the p16INK4A locus in LCL 3CHT cultured with and without HT ( Figures 5D and E ) . EBNA3C inactivation ( labelled -30 days ) affected epigenetic marks at the p16INK4A locus in a manner consistent with transcriptional activation; the repressive H3K27me3 mark was reduced while the activation-related H3K4me3 increased . After EBNA3C reactivation ( labelled +20 days ) , the epigenetic modifications at p16INK4A locus were apparently reversed . Further ChIP assays were used to confirm the specificity of p16INK4A regulation . The quantities of H3K27me3 and H3K4me3 at p16INK4A exon 1 ( primer set C – indicated by boxes ) were compared to the quantities at various other sites in the CDKN2A locus , including a region ( site A ) located 4 . 5kb downstream of p14ARF transcription start site ( Figures 5A , D and E ) . As described previously [45] in human primary fibroblasts H3K27me3 marks are broadly distributed across the CDKN2A locus , peaking in exon 1 , but extending into the region corresponding to A . Similarly , this region in LCL 3CHT was associated with some degree of H3K27me3 – particularly in cycling cells . However – whether or not EBNA3C is active – H3K4me3 is detected on exon 1 ( site C ) , but at site A it is always completely absent . In parallel , ChIP experiments were performed with an IgG antibody of the same isotype as the anti-H3K27me3 or -H3K4me3 antibodies to assess the level of background and no significant binding was observed at any site ( data not shown ) . The presence of two inversely regulated modifications excludes the possibility that the reduction in histone methylation is due to nucleosome re-positioning away from p16INK4A resulting in a reduction of the total histone H3 at the locus . Relatively high quantities of H3K4me3 were detected at the p16INK4A exon 1 ( site C ) compared to site A , even when EBNA3C was active ( in LCL 3CHT with HT and with HT re-added ) and therefore repressing p16INK4A . This suggests that p16INK4A exon 1 in LCLs might contain a ‘bivalent’ or ‘poised’ chromatin domain [47] . To avoid biases due to the genetic background of a single donor , we decided to confirm our findings using newly established LCL 3CHT lines from several different donors . It was soon noted that one of these cell lines did not arrest after the removal of HT from the medium . Western blotting with a pan-specific anti-Rb antibody failed to detect Rb protein in this cell line ( LCL 3CHT-E ) cultured with or without HT ( Figure 6A ) . Consistent with this , qPCR showed that LCL 3CHT-E expressed low levels of Rb mRNA in comparison to other LCL 3CHT lines ( Figure 6B ) . It is well established that functional Rb can regulate p16INK4A levels through a negative feedback loop and that Rb-negative tumours ( such as carcinoma of cervix ) and tumour-derived cell lines can express high levels of p16INK4A [23] , [48] . However , even in this LCL in which Rb cannot be detected , p16INK4A was still repressed in the presence of active EBNA3C , and this was relieved after EBNA3C inactivation by the removal of HT ( Figure 6A ) . When ChIP analyses for H3K27Me3 ( and H3K4Me3 ) marks across the CDKN2A locus were performed on the LCL expressing no detectable Rb ( LCL 3CHT-E ) similar patterns to those seen for LCLs expressing Rb protein were seen ( compare Figures 6C and D with Figures 5D and E ) . That is , high levels of H3K27Me3 occupied exon 1 ( site C ) in the presence HT , while there were low levels of this repressive mark in its absence . The reverse was true for H3K4me3 . This reinforces our view that the epigenetic regulation of p16INK4A by EBNA3C is independent of Rb expression . Recently EBNA3A was shown to regulate p16INK4A in a microarray study using EBNA3A-knockout ( KO ) LCLs [8] . The efficiency with which LCLs can be established using EBNA3A-KO is lower than with wild type virus , but by infecting peripheral blood leukocytes ( PBLs ) including macrophages that transiently act as feeder cells , we generated two independent EBNA3A-KO LCLs . Since EBNA3A and EBNA3C co-operate to epigenetically regulate the cellular gene BIM in BL31 cells , we wanted to ask whether EBNA3A might also cooperate with EBNA3C in modifying chromatin at the p16INK4A locus . The two EBNA3A-KO LCL were validated by probing western blots of protein extracts for the major latent EBV proteins; as previously described for these knockouts in a BL background there were no consistent differences in EBV gene expression ( [37]; data not shown ) . Although only two independent EBNA3A-KO lines were investigated , further characterization showed that – consistent with the report from Hertle and colleagues [8] – p16INK4A protein expression is elevated in both EBNA3A-KO LCLs relative to WT ( B95 . 8 ) -BAC infected LCL ( Figure 7A ) . A comparison of steady state levels of H3K27me3 and H3K4me3 at the p16INK4A locus revealed that in the absence of EBNA3A , the ratio of H3K27me3 to H3K4me3 associated with exon 1 was reversed relative to LCLs established with WT EBV BACs ( Figures 7B and C ) . The low level of H3K27me3 and high level of H3K4me3 are consistent with a more transcriptionally active locus and the higher levels of p16INK4A protein detected in the EBNA3A-knockout lines . This suggests that EBNA3A , together with EBNA3C , is involved in the chromatin remodelling of p16INK4A . In the EBNA3A-KO LCLs used for microarray analysis , the level of Rb transcripts was reported to be lower than in LCLs carrying WT EBV [8] . The two EBNA3A-KO LCLs described here expressed similar levels of Rb to the WT EBV infected cells . However , there was substantially more of the hypophosphorylated form when EBNA3A was not expressed ( data not shown ) . We showed previously that EBNA3A and EBNA3C mutants that are unable to bind CtBP are severely impaired in their ability to transform primary rat embryo fibroblasts in co-operation with activated Ha-RAS [16] , [17] . Since this assay , in part , measures the ability of proteins to overcome p16INK4A-mediated premature senescence and because CtBP is involved in the repression of p16INK4A in primary human fibroblasts and keratinocytes [22] , it seemed appropriate to ask whether the interaction of CtBP with EBNAs 3A and C is necessary for the modulation of p16INK4A expression in LCLs . Mutations in EBNA3A and EBNA3C that completely ablate their capacity to bind CtBP have been described ( [16] , [17]; schematic in Figure 8A ) . These mutations were serially engineered into the B95 . 8 EBV-BAC creating CtBP-binding mutants of EBNA3A and EBNA3C , both individually and together , along with revertant virus ( Figure 8B ) . These viruses were used to establish LCLs from PBL and fully validated by mutation-specific PCR , CtBP IP and western blotting for EBV latent proteins ( Figure S3 ) . Although latent EBV gene expression generally appeared unaffected by these point mutations it was soon noticeable that population growth was impaired . Limiting dilution analysis of infected PBL was performed in an attempt to quantify this impairment . In terms of the number of wells that contained clumps of cells characteristic of LCL outgrowth after 40 days , there was no substantial difference between the immortalization efficiency of the wild-type and revertant viruses , as compared to the CtBP mutants ( data not shown ) . However , it was clear that the rate of outgrowth of the CtBP mutant LCLs was considerably slower than the wild-type LCLs . This effect is strikingly illustrated by the image of the plate in which the LCLs were grown ( Figure 8C ) . Both the colour of the culture medium and the visible cell clumps in the wells show the much more rapid growth of the revertant LCLs as compared with the mutants . Also notable was the tendency of the mutant EBVs to sometimes grow as a single large clump of cells in the presence of a large number of smaller clumps ( Figure 8C , eg wells A4 and C5 ) . This may indicate natural selection driving phenotypic changes in the cells to allow the more robust clones to emerge as LCLs . Even once LCLs are established , CtBP-mutant LCLs continue to show a growth defect , exhibiting reduced rate of population growth relative to wild-type and revertant LCLs ( Figure 8D ) . They also tended to have a much lower maximum cell density , with CtBP-mutant LCLs struggling to grow much beyond 0 . 7×106 cells/ml . When p16INK4A transcripts and protein were quantified by qRT-PCR and western blotting respectively ( Figures 9A , B and C ) it was apparent that all the mutants express more p16INK4A mRNA and protein than either the WT-BAC or revertant-LCL cells . We assume that this increase in p16INK4A contributes to the impaired outgrowth of the mutant-carrying LCLs . CtBP has been implicated in PcG-mediated repression [19] , [49] , [50] , [51] and may be directly involved in the chromatin remodelling of p16INK4A [22] . ChIP analysis within p16INK4A exon 1 showed that all the CtBP-mutant LCLs exhibited a significant reduction of the H3K27Me3 mark relative to a WT-EBV LCL . There was generally a corresponding increase in the level of the activation mark H3K4me3 relative to WT ( Figure 10A ) . A detailed comparison of a WT-BAC LCL and a revertant LCL ( revCtBP ) with a double CtBP-mutant LCL ( E3CtBP ) across the p16INK4A locus was also performed . The double CtBPM mutant profile closely resembled those of LCL 3CHT cells grown without HT and therefore lacking a functional EBNA3C . In contrast the WT and revertant profiles – as would be expected – resembled LCL 3CHT cultured in the presence of HT ( compare Figures 10B and C with Figures 5D and E ) . We conclude that the interaction of both EBNA3A and EBNA3C with CtBP is important for EBV-mediated chromatin remodelling and repression of p16INK4A during B cell transformation . This may explain why these protein:protein interactions are particularly important for the efficient outgrowth and establishment of LCLs . Although the E3CtBP LCL examined here appears to have an active p16INK4A locus and relatively high levels of p16INK4A protein , the cells are clearly capable of cell division – leading us to suspect that some other element of the Rb-axis might be compromised . An examination of these cells , and other CtBP-mutant cells , showed that proliferation was probably not completely inhibited because in both E3CtBP and 3ACtBP LCLs expression of Rb is often reduced ( see for example Figure S4 ) . Since there will be considerable selection pressure during the establishment of an LCL , this is probably the result of unidentified compensatory lesions occurring in the Rb-axis early after infection and the outgrowth of the more robustly proliferating clones .
Although at least one virus – Paramecium bursaria Chlorella virus 1 – encodes its own histone methyltransferase that catalyses the methylation of histone H3K27 and represses a multitude of host genes [55] , this is unlikely to be the case of EBNA3C or EBNA3A . Even though the crystal structure of neither is available and their secondary structure is difficult to predict [5] , sequence homology studies fail to identify a potential methyltransferase domain in either EBNA3A or EBNA3C . The consensus of opinion is that the regulation of p16INK4A is primarily under the control of members of the polycomb group of proteins ( PcG ) . As already indicated , these multi-component repressor complexes generate histone modifications – including H3K27me3 – that are characteristic of silent chromatin . These marks are heritable and may affect the whole CDKN2A locus [23] . In Drosophila , repression by PcG complexes spreads from polycomb response elements ( PREs ) located within a genomic regulatory locus , but the equivalent of PREs have not been identified in mammalian cells . The mechanism of targeting PcGs specifically to the p16INK4A locus is unknown and , more generally , how the DNA-binding specificity of PcG complexes is achieved remains a key question in mammalian biology . The most likely candidates for PcG recruiting factors are sequence-specific transcription factors or long non-coding RNAs ( nc-RNAs ) [40] , so a major challenge for the immediate future is to establish whether EBNAs 3A and 3C together deregulate or interact with sequence specific transcription factors or nc-RNAs that normally regulate p16INK4A and/or BIM . No consistent changes in the expression of PcG proteins or the H3K27me3-specific demethylase JMJD3 have been identified in LCLs expressing inactivated or mutated EBNAs 3A or 3C ( [8]; our unpublished observations ) . Furthermore , although it has been shown that LMP1 is a negative regulator of p16INK4A in fibroblasts and epithelial cells [56] , [57] , LMP1 expression was not reduced in any of the mutant LCLs studied here or reported elsewhere ( Figures S1 and S3; [8] , [35] . We therefore do not think LMP1 signalling plays a significant role in the repression of p16INK4A in B cells . Epigenetic changes are by definition heritable , but not always irreversible . Certain loci exhibit a high degree of plasticity and are poised for rapid activation . Examples include families of developmental genes such as those in the HOX locus and tumour suppressor genes that become active when aberrant pro-proliferative signals are detected [23] , [40] . Our data suggest that the repression of p16INK4A locus by EBNA3C ( presumably cooperating with EBNA3A which is constitutively expressed ) in the LCL 3CHT system is reversible . H3K27me3 is known to cause the local formation of heterochromatin that is labile or readily reversible , but this type of histone methylation can also facilitate methylation of DNA of the same region , particularly in the development of cancer . DNA methylation represents a more stable modification and can ‘fix’ the repression of the locus . It has been shown that promoter regions bound by PcG which remain largely unmethylated on CpG dinucleotides in normal tissues , serve during tumorigenesis as a map to direct DNA methylation . CpG islands methylated de novo in cancer have often been previously marked by the presence of PcG proteins and H3K27me3 [58] , [59] , [60] , [61] , [62] , [63] . We have shown previously that this is the case for BIM in EBV-positive lymphomas [38] . It is therefore also likely that during EBV-mediated lymphomagenesis p16INK4A is repressed as a result of EBNA3A/C-induced histone modifications that then act as a focus for DNA methyltransferases to initiate the more stable cancer-associated CpG methylation . This is certainly consistent with the limited data on EBV-positive BL , that suggest the p16INK4A locus is nearly always silenced by DNA methylation of exon 1 in tumour-derived cell lines and in many BL biopsies [64] , [65] . EBNA3C is absolutely essential for the transformation of primary B cells by EBV , therefore the successful establishment of LCL 3CHT reassures us that EBNA3C modified by a C-terminal fusion retains most of the functions required for transformation . However , there appear to be at least two limitations of the EBNA3C-HT fusion system . Firstly , even in the presence of HT the repression of p16INK4A is not complete . The amount of p16INK4A is higher than in WT-EBV LCL and is almost equivalent to the accumulation seen in EBNA3A-KO and CtBP-mutant LCLs ( Figures 7A and 9C ) . The elevated levels of p16INK4A in LCL 3CHT , even in the presence of HT might indicate that EBNA3C activation is compromised in a subpopulation of cells and these cells exit from the cell cycle . Alternatively , and perhaps more likely , fusion of EBNA3C with the large estrogen receptor domain might partially inhibit EBNA3C function in all cells in the population , even in the presence of HT . A second limitation to the utility of these cells for studying EBNA3C function is the considerable delay between removing or re-adding HT from/to the culture medium and the change in levels of p16INK4A . The first signs of p16INK4A increase are seen about 10 days after removing HT from the medium and the first signs of p16INK4A repression are apparent no sooner than two days after re-addition of HT . This may be caused by the EBNA3C-HT fusion not being quite equivalent to WT EBNA3C ( as discussed above ) or could indicate that p16INK4A is not a direct target of EBNA3C . A similar phenomenon occurs during the induction of p16INK4A by activated RAS . It takes approximately 5 days to fully activate p16INK4A by RAS . Since such an extended timeframe is not compatible with the signalling dynamics of the RAF-MEK-ERK cascade , it has been suggested that mutant RAS leads to the accumulation of intracellular stresses and subsequent activation of p38 [23] . It is possible that EBV latent gene expression minus EBNA3C or EBNA3A sets up some sort of dis-equilibrium that results in intracellular stress , but preliminary data suggest that this is not mediated by p38 activation ( data not shown ) . Alternatively , it has been suggested that the slow kinetics of p16INK4A activation might reflect the need to displace either repressive histone or repressive PcG-mediated DNA methylation [23] , [60] , [66] and this could be the case in B cells latently infected with EBV . The role of CtBP in EBNA3A/3C-mediated repression of p16INK4A is intriguing but not yet understood . CtBP was discovered because of its binding to adenoviral oncoprotein E1A . Binding of E1A to CtBP antagonizes the function of CtBP , and E1A mutants unable to bind CtBP show enhanced efficiency of transformation [67] . In contrast , EBNA3A and EBNA3C mutants unable to bind CtBP were less effective in transforming and immortalizing primary rat embryo fibroblasts in cooperation with Ha-RAS [16] , [17] . Furthermore we showed that Marek's disease virus , a herpesvirus that induces T cell lymphoma in chickens , requires its nuclear oncoprotein MEQ to bind CtBP for tumorigenesis [68] . Consistent with the role of CtBP in rodent cell transformation by EBNA3A and EBNA3C , LCLs produced by immortalization with CtBP-binding mutant viruses grow out much more slowly than WT-EBV LCLs and fail to effectively repress p16INK4A . Taken together the data suggest that binding of EBNA3A and EBNA3C to CtBP augments transformation efficiency and LCL outgrowth by aiding the establishment or maintenance of the H3K27me3 mark on p16INK4A exon 1 . CtBP-containing complexes have been previously linked to chromatin remodelling including demethylation of H3K4 , and CtBP has been shown to recruit PcG silencers to certain genes in mammalian cells [19] , [50] . Although both EBNA3A and EBNA3C can be immunoprecipitated from LCLs with CtBP [16] , [17] and under standard conditions EBNA3A and EBNA3C can be reciprocally co-precipitated ( our unpublished data ) we have been unable to ChIP EBNA3A/3C-CtBP complexes on either the p16INK4A or BIM promoters ( data not shown ) . We do not know whether this is because of technical limitations of the reagents that are available or whether these promoters are not actually direct targets of the complexes . It may be necessary to develop new regents to address this issue . Although we do not yet understand this requirement for CtBP-binding , the data are consistent with recent reports that the C-terminus of EBNA3C – and specifically the PLDLS CtBP-binding site – is necessary to completely rescue proliferation in Akata-derived E3C-HT LCLs cultured without HT [69] , [70] . Serendipitously an LCL 3CHT cell line with no detectable Rb protein was produced . In this cell line , p16INK4A was repressed in the presence of functional EBNA3C and de-repressed after EBNA3C inactivation . We therefore assume that regulation of p16INK4A by EBNA3C in LCL 3CHT is an Rb-independent phenomenon . Repression of p16INK4A in the Rb-negative LCL 3CHT line is unlikely to be functionally relevant , because the main target of p16INK4A is absent . Since EBNA3C repressed p16INK4A even when no additional proliferation advantage was to be gained , this implies that repression of p16INK4A is a specific consequence of EBNA3C working in collaboration with EBNA3A and CtBP . Decreased expression of Rb protein was also seen in E3CtBP and 3ACtBP LCLs , ( Figure S4 ) and decreased Rb mRNA has been reported in EBNA3A-KO LCLs [8] . Since the absence of Rb will confer a common proliferative advantage , it is probable that elevated p16INK4A expression in these various cell lines creates a strong selection pressure for the loss , or reduced expression of Rb during transformation . In summary , we have described a novel mechanism used by EBV to overcome stress-induced growth arrest by preventing the induction of p16INK4A and , by a similar mechanism , enhance cell survival by preventing the induction of pro-apoptotic BIM [37] , [38] . To our knowledge this ability to epigenetically inactivate a crucial cell cycle inhibitor and a potent inducer of cell death makes EBV unique among the known DNA ‘tumour viruses’ [71] . Understanding the precise molecular details of EBV-mediated chromatin remodelling of p16INK4A and the role that CtBP plays in this process should cast new light on the nature of viral oncogenesis and the mysteries of polycomb-mediated gene repression .
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We previously showed that two Epstein-Barr virus latency-associated proteins—EBNA3A and EBNA3C—contribute to enhanced B cell survival by inhibiting the expression of the death-inducing protein BIM . This repression involves remodelling of the BIM gene promoter by polycomb proteins and DNA methylation within an unusually large CpG-island that flanks the transcription initiation site . Here we show that the same two proteins , EBNA3A and EBNA3C , functionally cooperate in the polycomb-mediated chromatin remodelling of another tumour suppressor gene , p16INK4A , that encodes a cyclin-dependent kinase inhibitor capable of blocking cell proliferation . Both EBV proteins can bind the highly conserved co-repressor of transcription CtBP , and these interactions appear to be required for the efficient repression of p16INK4A . Thus by utilising the polycomb system to induce the heritable repression of two major tumour suppressor genes—one that induces cell death ( BIM ) and one that induces growth arrest ( p16INK4A ) —EBV profoundly alters latently infected B cells and their progeny , making them significantly more prone to malignant transformation .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"molecular",
"biology/histone",
"modification",
"virology/persistence",
"and",
"latency",
"molecular",
"biology/transcription",
"initiation",
"and",
"activation",
"cell",
"biology/cellular",
"death",
"and",
"stress",
"responses",
"cell",
"biology/cell",
"growth",
"and",
"division",
"virology",
"infectious",
"diseases/viral",
"infections",
"virology/viruses",
"and",
"cancer",
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"oncology/myelomas",
"and",
"lymphoproliferative",
"diseases",
"oncology/hematological",
"malignancies"
] |
2010
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Epigenetic Repression of p16INK4A by Latent Epstein-Barr Virus Requires the Interaction of EBNA3A and EBNA3C with CtBP
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There is an increasing need to evaluate the impact of chemotherapeutic and vector-based interventions as onchocerciasis affected countries work towards eliminating the disease . The Esperanza Window Trap ( EWT ) provides a possible alternative to human landing collections ( HLCs ) for the collection of anthropophilic blackflies , yet it is not known whether current designs will prove effective for onchocerciasis vectors throughout sub-Saharan Africa . EWTs were deployed for 41 days in northern Uganda and south eastern Tanzania where different Simulium damnosum sibling species are responsible for disease transmission . The relative efficacy of EWTs and HLCs was compared , and responses of host-seeking blackflies to odour baits , colours , and yeast-produced CO2 were investigated . Blue EWTs baited with CO2 and worn socks collected 42 . 3% ( 2 , 393 ) of the total S . damnosum s . l . catch in northern Uganda . Numbers were comparable with those collected by HLCs ( 32 . 1% , 1 , 817 ) , and higher than those collected on traps baited with CO2 and BG-Lure ( 25 . 6% , 1 , 446 ) , a synthetic human attractant . Traps performed less well for the collection of S . damnosum s . l . in Tanzania where HLCs ( 72 . 5% , 2 , 432 ) consistently outperformed both blue ( 16 . 8% , 563 ) and black ( 10 . 7% , 360 ) traps baited with CO2 and worn socks . HLCs ( 72 . 3% , 361 ) also outperformed sock-baited ( 6 . 4% , 32 ) and BG-Lure-baited ( 21 . 2% , 106 ) traps for the collection of anthropophilic Simulium bovis in northern Uganda . Contrasting blackfly distributions were observed on traps in Uganda and Tanzania , indicating differences in behaviour in each area . The success of EWT collections of S . damnosum s . l . in northern Uganda was not replicated in Tanzania , or for the collection of anthropophilic S . bovis . Further research to improve the understanding of behavioural responses of vector sibling species to traps and their attractants should be encouraged .
Following the implementation of the Mectizan ( ivermectin ) Donation Program in 1987 , methods of onchocerciasis control switched from vector-based interventions to mass drug administration through community directed treatment with ivermectin ( CDTI ) [14] . Whereas it has been established that ivermectin treatment can eliminate the disease in certain endemic foci , the conditions under which CDTI alone is effective have not been fully explored [15–17] . It is therefore essential that methods for monitoring entomological and parasitological indices of onchocerciasis transmission are available in intervention and post-intervention settings as countries work towards elimination [18 , 19] . For EWTs to be effective in evaluating the impact of chemotherapeutic and vector-based programmes , they should collect appropriate numbers of the same vector populations as those biting humans . They should also collect vectors with the same age structure ( parity rates ) as those biting humans , or collect them in a condition that enables age structures to be calibrated . The current WHO guidelines for entomological evaluation of O . volvulus transmission in CDTI settings require that HLCs are used for the collection of anthropophilic blackflies [20 , 21] . The method is robust , sensitive , and well accepted by communities , and is therefore preferable to more invasive methods of O . volvulus surveillance such as Ov-16 serology testing in children [21] . However , human participants collecting biting flies are potentially exposed to a range of vector-borne pathogens , although with appropriate training , the risk is generally considered no higher than for others living in disease endemic areas . Despite this , obtaining the necessary ethical approval can often delay the implementation of research and surveillance programmes [22] . Attempts to develop new , or to utilise or modify existing traps for the collection of host-seeking , anthropophilic blackflies , have been met with mixed or limited success [2] . Light traps [3 , 4] , sticky traps and silhouettes [23–26] , BG-Sentinel traps [7] , modified Challier-Lavessiere tsetse traps [5 , 6] , and other novel traps [27] have been successfully used to collect blackflies in various physiological states , yet repeating collections using these methods has sometimes proved difficult [8 , 9] . Early investigations into the response of blackflies to long-range visual and olfactory stimuli , including colour , shape , and CO2 , were mainly confined to Nearctic species including Simulium venustum and Simulium vittatum [28–32] . Several studies indicate that host-seeking blackflies generally prefer to land on darker colours and matt surfaces [30 , 31 , 33] , and it is also thought low UV reflectance and strong contrast of traps against their background is important in attraction [28 , 32 , 34] . Comparatively little research has been dedicated to similar investigations for Simulium damnosum sensu lato ( s . l . ) , the principal vector of O . volvulus in Africa . The limited data that exists is consistent with colour-choice experiments for other blackflies , in that host-seeking S . damnosum s . l . appear to be attracted to dark colours [5 , 24 , 25 , 35] . However , results of behavioural studies should be interpreted cautiously , and Walsh ( 1980 ) stresses that they should not be generalised for species other than those being investigated [25 , 28] . This is likely to be especially relevant when studying S . damnosum s . l . , a complex of sibling species composed of at least 55 morphologically indistinguishable cytospecies and cytoforms of unknown taxonomic status , each with unique ecological and behavioural traits [36 , 37] . Simulium damnosum s . l . , like other haematophagous Diptera , are attracted to CO2 and host odours [38 , 39] . CO2 is a powerful mediator of host-seeking behaviour which can greatly enhance blackfly collections [23 , 24] , yet the biological mechanisms of blackfly attraction to olfactory and visual stimuli are poorly understood [38] . Following experiments in a Cameroonian rainforest , Thompson ( 1976 ) demonstrated that the presence of ‘exhaled breath’ , industrial CO2 , and worn clothing , improved trap collections [24 , 40] . He concluded that chemicals present in human sweat are likely to be important in attracting S . damnosum s . l . [40] , and that visual and olfactory cues are of greatest importance in attracting savannah and forest sibling species respectively [24] . More recently , EWTs and BG-Sentinel traps baited with worn shirts , trousers ( pants ) and synthetic chemicals ( BG-Lure and octenol ) have been shown to be more effective in attracting blackflies than unbaited traps [7] . Young et al . ( 2015 ) have since used gas chromatography and electroantennography to identify chemicals present in human sweat which are potentially attractive to S . damnosum s . l . in Burkina Faso and Simulium ochraceum s . l . in Mexico [13] . They then demonstrated that EWTs baited with candidate compounds collected 2–3 times the number of these species in the field compared to traps baited with CO2 alone , although the authors acknowledge that catch numbers were low and that further research is needed [13] . In 2013 , Rodriguez-Pérez et al . published results of the development and trial of the EWT in Mexico , which involved investigating the attractiveness of coloured fabrics , CO2 sources , and host odours to S . ochraceum [7] . EWTs constructed using blue fabric outperformed those made with red , yellow and black fabrics when baited with either industrial CO2 released at 150-200mL/min , or CO2 produced by mixing sugar , yeast ( Saccharomyces cerevisiae ) and water ( quantities not specified ) . There was no statistically significant difference in the number of blackflies collected on traps regardless of the CO2 source . With the addition of host odours in the form of a worn shirt or BG-Lure , CO2-baited blue EWTs approached the attractiveness of HLCs in one of two trials . In the second trial , the baited EWT was only half as effective as the HLC [7] . Toé et al . ( 2014 ) further developed the EWT in Burkina Faso for the collection of Simulium damnosum sensu stricto ( s . str . ) and Simulium sirbanum , but used black traps baited with BG-Lure and yeast-produced CO2 as the basic design [11] . EWTs of differing heights were first compared . ‘Short’ traps , standing within 15cm of the ground were more effective than ‘tall’ traps , although the difference was only statistically significant at one of two sites investigated . The addition of a vertical blue stripe to the black background further enhanced collections , but again , this was only statistically significant at one of the two sites . Short , striped EWTs baited with CO2 and BG-Lure caught similar numbers of S . damnosum s . l . as those baited with CO2 and worn trousers . In a final experiment , EWTs baited with CO2 and worn trousers collected numbers comparable with HLCs , whereas those baited with worn trousers alone collected numbers similar to unbaited traps . The authors also reported the collection of Simulium adersi and Simulium schoutedeni from the traps , and questioned the importance of fermentation products other than carbon dioxide in the attraction of vector flies [11] . The various sibling species of the S . damnosum complex are behaviourally and ecologically unique in traits such as breeding habitats , dispersal capabilities , degree of anthropophily , and their capacity to transmit disease [37] . It is not yet known whether different sibling species will respond differently to EWTs , and whether current trap designs will prove to be effective for S . damnosum s . l . collections throughout onchocerciasis affected areas of sub-Saharan Africa . This study therefore aimed to compare the relative efficacy of EWTs with HLCs for the collection of anthropophilic blackflies in onchocerciasis transmission zones of Uganda and Tanzania , where different sibling species of the S . damnosum complex are responsible for disease transmission . Responses of host-seeking blackflies to odour baits , colour schemes , and yeast-produced CO2 were also investigated .
Experimental work took place for a total of 41 days at five locations in Uganda ( 26 days ) , and one in Tanzania ( 15 days ) , between 28 June 2015 and 19 September 2016 ( Table 1 ) . Collections were made in the districts of Lamwo , Moyo and Nwoya in the Madi/Mid-North onchocerciasis transmission zones of northern Uganda . Savannah grassland predominates and S . damnosum s . str . is thought to be the principal vector of O . volvulus [41 , 42] . Small numbers of S . sirbanum also breed along the Pager River northeast of Kitgum [43] . In addition , a member of the Simulium bovis species-group also forms a significant proportion of the anthropophilic blackfly population in the Mid-North [44] . Both S . damnosum s . l . and S . bovis occupy similar breeding habitats [45 , 46] . In Lamwo district , these are mainly along the larger rivers including the Achwa ( Aswa ) and Pager [47 , 48] . In Moyo , there is thought to be little local breeding of S . damnosum s . l . , and it is likely that biting blackflies migrate from a series of rapids along the Nile in neighbouring South Sudan [43 , 49] . The Murchison Nile forms the southern boundary of Nwoya district and is a major source of blackfly breeding [49] . There are historical reports of S . damnosum s . l . breeding along the Ayago River , a tributary of the Nile , and the Kibaa and Murchison River tributaries have also been cited as possible sources of infestation [49 , 50] . Rainfall lasts from April to November , with peaks occurring early and late in the rainy season . The climate is hot and dry from December to March [51] . Collections in Tanzania were made at Chikuti on the north side of the Mahenge Mountains in the Mahenge onchocerciasis transmission zone of Ulanga district . The area is characterised by Precambrian limestone , and the presence of riverine , dry lowland and submontane forests [52] . The mountains are drained by numerous stony streams and rivers that are favourable to blackfly breeding [53] . Again , the principal vector of onchocerciasis is S . damnosum s . l . [35] . The cytoforms present in Mahenge are ‘Nkusi’ , Simulium plumbeum ( = ‘Hammerkopi’ and ‘Ketaketa’ ) , ‘Sebwe’ and ‘Turiani’ [35 , 54 , 55] . ‘Nkusi’ is thought to be the predominant anthropophilic species , and S . plumbeum may have a limited role in human biting . Both ‘Sebwe’ and ‘Turiani’ are zoophilic [35 , 54] . Simulium nyasalandicum ( originally reported as S . woodi ) also contributes to biting in small numbers , mainly in the south of the transmission zone [35 , 56] . Rainfall lasts from November to May , and peaks between March and May . The dry season lasts from June to October [35 , 52] . Traps were constructed using locally-sourced materials . Frames were composed of a light-gauge steel and trap faces measured approximately 1m2 ( Fig 1 ) . Traps stood on 0 . 25m sharpened legs which were easily pushed into the ground . The basic design included a blue tarpaulin screen that was hung tightly inside the frame . Blue was chosen as the base-colour as blue traps yielded the greatest number of blackflies during collections by Rodriguez-Pérez et al . ( 2013 ) in Mexico [7] . A black central stripe ⅓ the width of the blue screen was painted onto the trap using a matt black emulsion ( Sadolin Paints ( U ) Limited , Uganda ) during initial experiments in Uganda in 2015 . The paint was allowed to dry for two days before traps were deployed . During subsequent collections in Tanzania and Uganda ( 2016 ) , the black paint was replaced with black tarpaulin which was sewn together with the blue tarpaulin to form the screen . A CO2 outlet and host odour attractants were attached to the top corners of the EWT frame ( Fig 1 ) . Traps were covered with a black plastic sheet when not in use . Tangle-Trap insect trap coating paste ( Contech , Victoria , BC , Canada ) was used to coat EWTs in Uganda . It was not possible to acquire the same product for trapping work in Tanzania due to manufacturing problems . EWTs in Tanzania were therefore coated with Temmen-Insektenleim ( Temmen GmbH , Hattersheim , Germany ) . Both products were thinned using ≈150mL locally purchased white spirit ( Sadolin Paints ( U ) Limited , Uganda ) , before being applied to traps at least 24h prior to their deployment . A sugar-yeast based source of carbon dioxide was produced in the field following methods outlined by Smallegange et al . ( 2010 ) [57] . However , quantities of ingredients were adjusted to provide sufficient CO2 output ( >80mL/min for at least 11 hours ) following incubation at 30°C during preliminary laboratory experiments ( S1 Fig ) . Dry baker’s yeast ( 50g ) , sugar ( 500g ) and water ( 2 . 5L ) were mixed in 10L ( Uganda ) or 12L ( Tanzania ) containers immediately prior to blackfly collections commencing . PVC tubing extended from a hole in the container to an outlet at a top corner of the EWT . Containers were briefly shaken before being placed next to traps . Fresh sugar-yeast mixtures were prepared each day by community members assisting with blackfly collections . Traps were either baited with host odours emanating from a pair of worn socks , or BG-Lure ( Biogents AG , Regensburg , Germany ) , a synthetic mosquito attractant containing chemicals found on human skin ( ammonia , lactic acid , and caproic acid ) [58] . Worn socks were provided by villagers in exchange for a new pair of socks , and were tied to the top corner of the EWT opposite the CO2 outlet and replaced every three days . Worn socks have been shown to be effective for up to 8 days for the collection of mosquitoes [59] . HLCs were made by trained community-based participants following standard methods [20] . A team of two people worked alternate hours between 07:00 and 18:00 , collecting blackflies landing on their exposed legs . Flies were collected in individual tubes and hourly catches were recorded . Blackflies were removed from EWTs using forceps after applying a drop of white spirit to specimens in order to partially dissolve the adhesive . A 10x magnification hand lens was used to verify identification of insects where necessary . All blackflies were preserved in >95% ethanol and were identified in the laboratory using morphological keys in Freeman & De Meillon ( 1953 ) [60] . The member of the S . bovis species-group present in northern Uganda was identified based on the morphology of male pupae collected at Apyeta Bridge in 2015 . To confirm identification , specimens were compared with reference material at the Natural History Museum , London , UK . The identity of adult S . bovis group flies collected on traps and by HLC was inferred based on the pupal identifications . Biting flies other than blackflies were removed from traps and preserved during collections made in 2016 only . In all experiments , blackfly count was the response variable and was modelled as a function of trap type , the main covariate of interest . Location , collection site and rainfall were included as additional covariates . A generalized linear framework with a negative binomial distribution was used to take into account the overdispersion observed in the count data . The Akaike Information Criterion was used to select the most appropriate model for each data set , and models were verified by means of diagnostic plots . When more than one anthropophilic blackfly species was active at a study location , data for each species were analysed separately . Data were excluded from analysis for a particular species if blackfly collections were low ( <5/day using all methods ) , or if the species was absent . The negative binomial model was also used to analyse the distribution of blackflies on traps , and to investigate interactions between blackfly attachment on columns and rows . Heat maps of blackfly attachment to traps were produced using log transformed data to improve graphical representation of blackfly distribution . Analyses were performed within the R version 3 . 3 . 2 statistical computing environment [61] . Blackfly collections involving human participants were subject to review and approval by the institutional review board at the Institute of Tropical Medicine , Antwerp , Belgium ( 960/14 , 1089/16 ) ; the Higher Degrees , Research and Ethics Committee , Makerere University School of Public Health , Kampala , Uganda ( 2014/244 ) ; and the Medical Research Coordinating Committee at the National Institute for Medical Research , Dar es Salaam , Tanzania ( NIMR/HQ/R . 8a/Vol . IX/2212 ) . Formal approval to conduct studies in Uganda was granted by the Uganda National Council for Science and Technology ( HS 1701 ) . All participants were adults over the age of 18 years who provided written informed consent .
Pairs of traps baited with CO2 and worn socks ( EWT Socks ) were as effective as the HLC for the collection of S . damnosum s . l . in northern Uganda , while pairs of traps baited with CO2 and BG-Lure ( EWT BG-Lure ) were the least effective overall ( Fig 2A ) . However , there was a significant interaction effect of trap type and location on blackfly collections ( p = 0 . 002 ) . The EWT Socks outperformed the HLC and EWT BG-Lure at Ayago Bridge and Gwere Luzira , whereas the reverse was true at Pamulu . After 15 trap days , the EWT BG-Lure collected 25 . 6% ( 1 , 446 ) , the EWT Socks 42 . 3% ( 2 , 393 ) , and the HLC 32 . 1% ( 1 , 817 ) of the total S . damnosum s . l . catch ( Table 3 ) . There was a significant effect of trap type on the number of S . bovis collected in Lamwo district ( p = 0 . 008 ) . Unlike for the collection of S . damnosum s . l . , there was no interaction effect of trap type and location on collections ( p = 0 . 58 ) ( Fig 2B ) . The HLC clearly outperformed EWTs of both types at Apyeta Bridge and Beyogoya ( p<0 . 001 ) , and there was weak evidence to suggest the EWT Socks was the least effective trap overall ( p = 0 . 074 ) . After 6 trap days , the EWT BG-Lure collected 21 . 2% ( 106 ) , the EWT Socks 6 . 4% ( 32 ) , and the HLC 72 . 3% ( 361 ) of the total S . bovis catch ( Table 3 ) . More than 99% of blackflies recovered from EWTs in Uganda were morphologically indistinguishable from those collected by HLC . This was not the case in Tanzania where S . damnosum s . l . comprised 100% of the catch by HLC , but only 86 . 3% ( 360/417 ) and 85 . 6% ( 563/658 ) of the catch on the EWT Black and EWT Blue traps respectively . There was a significant effect of trap type on S . damnosum s . l . collections at Chikuti ( p<0 . 001 ) where the HLC clearly and consistently outperformed EWTs of each colour scheme ( Fig 2C ) . There was no overall difference in efficacy between the EWTs , and despite the EWT Blue outperforming the EWT Black at two of the three collection sites , there was insufficient evidence to suggest S . damnosum s . l . preferred one colour scheme over another ( p = 0 . 28 ) . After 15 trap days , the EWT Black collected 10 . 7% ( 360 ) , the EWT Blue 16 . 8% ( 563 ) , and the HLC 72 . 5% ( 2 , 432 ) of the total S . damnosum s . l . catch ( Table 3 ) . Rainfall restricted trapping to five days at Ayago Bridge in Uganda during September 2016 , although this was sufficient to demonstrate that freshly prepared sugar-yeast mixtures ( producing CO2 ) enhanced S . damnosum s . l . collections ( p<0 . 001 ) ( Fig 2D ) . After 5 trap days , the EWT CO2+ collected 68 . 9% ( 2 , 394 ) and the EWT CO2- 31 . 1% ( 1 , 082 ) of the total S . damnosum s . l . catch ( Table 3 ) . Trap site was a significant explanatory variable ( p<0 . 001 ) and blackfly activity was noticeably higher at one of the two collection sites . Both sites were situated in areas of cleared bush surrounded by tall vegetation , although the most productive site had greater exposure to direct sunlight . When exposed to direct sunlight , S . damnosum s . l . would primarily land on the shaded side of traps . The vertical distribution of blackflies ( all species ) was similar for both the EWT CO2+ and EWT CO2- in Uganda where 62 . 8% and 66 . 9% of specimens were removed from the bottom rows of respective traps ( Table 4 ) . Blackfly numbers decreased with increasing height on the traps ( p<0 . 001 ) regardless of whether CO2 was present or absent . In contrast , blackflies ( all species ) in Tanzania showed greater attraction to the top row of EWTs ( p<0 . 001 ) ( Table 4 ) . Again , the percentage of blackflies differed little between the traps , with 60 . 4% and 58 . 0% being removed from the top rows of the EWT Blue and EWT Black respectively . Blackfly numbers decreased with decreasing height on EWTs of both colour schemes ( p = 0 . 021 ) . The horizontal distribution of blackflies on the EWT Blue indicated a preference towards the outer columns where the CO2 outlet ( left ) and worn socks ( right ) were located ( p = 0 . 002 ) . There was also a slight preference towards the left column on the EWT Black , although blackflies were otherwise more evenly distributed across columns than on the EWT Blue . Log transformed counts of blackfly distribution are illustrated in Fig 3 . Only five biting flies other than blackflies were removed from traps in Tanzania and all were Tabanidae of the genera Haematopota and Tabanus . Biting flies were more diverse and abundant at Ayago Bridge in Uganda and included both male and female Glossina f . fuscipes and Glossina pallidipes . Glossinidae were identified to species using morphological and molecular methods in the laboratory of Prof Stephen Torr ( Liverpool School of Tropical Medicine , UK ) . Stomoxys calcitrans and several unidentified Haematopota and Tabanus species were also collected ( Table 5 ) . The biting flies recovered from traps were of sexes exhibiting anthropophilic behaviour for each species .
Whereas pairs of blue EWTs baited with CO2 and BG-Lure appeared to be less effective than in previous studies in Mexico and Burkina Faso [7 , 11] , those baited with CO2 and worn socks regularly collected numbers comparable with HLCs in northern Uganda . A notable exception was at Pamulu , where the EWT Socks caught the fewest flies . Blackfly activity varied greatly from site to site at each location , and it rained on the day the EWT Socks was positioned at the site with highest activity at Pamulu . The negative impact of rain on trap performance was compounded by the limited number of catching days ( 3 ) at this location . There was no rain at Gwere Luzira , so traps were unaffected . In addition , the higher number of trapping days ( 9 ) at Ayago Bridge meant the impact of rain on overall trap performance was less apparent than at Pamulu . In contrast to the success of the Ugandan collections , EWTs baited with CO2 and worn socks performed relatively poorly compared to HLCs for the collection of S . damnosum s . l . in Tanzania . It is not clear why , although given that different S . damnosum sibling species were present in the study areas of each country , it seems plausible that they might respond differently to traps . The host-oriented behaviour of Glossinidae has been extensively studied and there is evidence of both interspecific and intraspecific variation in response to host kairomones [62 , 63] . Similar differences in behavioural response may exist for the many sibling species of the S . damnosum complex , and the recent study of blackfly attraction to human semiochemicals by Young et al . ( 2015 ) should provide a good starting point for further research [13] . In the meantime , the most appropriate odour bait is probably worn clothing , that is easy to obtain and reflects odour profiles of local populations . EWTs performed poorly for the collection of S . bovis in northern Uganda . This is a species that generally feeds on cattle , although frequent human biting has been reported in the past from Nigeria and northern Cameroon [45 , 64] . It has been proposed that anthropophily may develop in the absence of its usual bovine host [45] . Pairs of EWTs baited with worn socks collected just 6 . 4% ( 32/499 ) of the total S . bovis catch ( Table 3 ) . EWTs baited with BG-Lure performed slightly better , collecting 21 . 2% ( 106/499 ) of the total catch . However , the difference in trap efficacy can probably be explained by the presence of a herd of cattle , rather than attraction to the lures . Of the 106 S . bovis collected over six days on traps baited with BG-Lure , 65 . 1% ( 69 ) were collected on a single day at Apyeta Bridge . On that day , cattle passed within a few metres of the BG-Lure-baited traps . The observed number of blackflies was noticeably higher on these traps immediately after the cattle had passed . Whereas flies “carried” by the cattle might have dispersed and enhanced collections on all trap types , the impact was much more evident on those closest to the herd . A similar event occurred at Gwere Luzira where the presence of cattle also coincided with a high ( 240 ) S . damnosum s . l . catch on sock-baited EWTs . Again , there were noticeable differences in the number of blackflies on these traps before and after the event . Such confounding factors will need to be taken into consideration if attempting to calibrate trap collections with human biting rates . Care will also need to be taken to place traps away from shared animal hosts of human biting blackflies . Uniformity of experiments would have been improved by standardising the washed status of HLC participants and also the amount of time socks were worn for in advance of trapping . Baiting traps with socks from both HLC participants might also have reduced bias caused by variation in human attractiveness to blackflies [59] . HLCs consistently outperformed EWTs of each colour scheme in Tanzania . Possible reasons for differences in trap-efficacy observed between countries are discussed in the following sections . As a result of the poor relative performance of traps in Tanzania , there was insufficient evidence to demonstrate that S . damnosum s . l . preferred one colour scheme over the other . Further investigations of colour preference among S . damnosum sibling species are warranted . Freshly prepared sugar-yeast mixtures clearly enhanced the number of blackflies collected on EWTs . Despite concerns raised that fermentation products other than CO2 are likely to attract vector flies other than those seeking a blood meal , the impact appears to have been negligible [11 , 57] . Since no male blackflies were collected on traps , despite non-vector species breeding in the adjacent river , it is likely that CO2 is the most important compound in attraction . However , it should be noted that various Hymenoptera and Diptera were frequently attracted to the jerry can containing the sugar-yeast mixture . Comparing the parity rates and gonotrophic status of HLC and EWT-collected flies would help further clarify whether sugar-yeast mixtures are only attracting host-seeking vectors . The contrasting distribution of blackflies of all species on EWTs in Uganda and Tanzania appears to indicate differences in S . damnosum s . l . behavioural response , although differences in species composition present obvious limitations to the study . Perhaps the simplest explanation would be to refer to the previously mentioned work of Thompson ( 1976 ) in Cameroon [24] . If savannah sibling species are more reliant on visual host-seeking cues [24] , are naturally inclined to fly close to the ground [38 , 65 , 66] , and tend to land low on their host [65 , 66] , this could sufficiently explain the distribution of blackflies on traps in Uganda . The percentage of blackflies removed from the bottom ( 62 . 8%/66 . 9% ) and middle ( 24 . 5%/25 . 8% ) rows of the EWT CO2+ and EWT CO2- ( Table 4 ) , compares well with a study of savannah S . damnosum s . l . in northern Cameroon [66] . Here , Renz and Wenk ( 1983 ) demonstrated that most flies fed on the ankles ( 53%/51% ) and calves ( 28%/27% ) of standing and sitting volunteers respectively [66] . The percentage of blackflies removed from the top ( 60 . 4%/58 . 0% ) and middle ( 21 . 8%/24 . 1% ) rows of the EWT Blue and EWT Black at Chikuti in Tanzania shows a considerably contrasting distribution . It could be that the behaviour of sibling species present in the Mahenge Mountains more closely resembles the forest sibling species described by Thompson ( 1976 ) [24] . It is possible that they are more reliant upon olfactory cues when host-seeking , explaining why greater numbers were removed from the top rows of traps where odour baits were positioned [24] . Host preferences of sibling species present in Mahenge may offer another explanation . It is known that the vertical distribution of haematophagous Diptera can be influenced by their hosts [67 , 68]; that no blackfly species is exclusively anthropophilic [37] , and that degrees of anthropophily vary among human biting members of the S . damnosum complex [69] . Little is known about the respective blood hosts of S . damnosum s . l . in Mahenge , although ‘Nkusi’ is probably responsible for the majority of human biting [35] . It is also known to feed on cattle in addition to humans in western Uganda [70] . The remaining cytoforms , S . plumbeum , ‘Sebwe’ and ‘Turiani’ are either mainly or entirely zoophilic [35 , 54] , and zoophilic blackflies can also be specific in their preferred feeding sites on a host [71] . For example , East African S . vorax and S . nyasalandicum prefer to bite the ears and underside of cattle , respectively [71] . Many ornithophilic blackfly species also prefer to bite the area around the head and neck of their hosts [72 , 73] . Studies of Glossinidae have shown that odour-oriented responses attract flies towards their hosts , but final responses are to visual cues [63 , 74] . Again , similar mechanisms of host-location might also exist for blackflies [63] . It is not known whether EWTs were sampling the same sibling species as HLCs during studies in Uganda and Tanzania . PCR-based identification of S . damnosum s . l . collected using each method might have highlighted any differences in sibling species composition [75] . The use of unbaited EWTs , or EWTs with odour baits positioned at different heights , might have clarified the importance of visual and olfactory cues in each study area . Preserving blackflies according to the area of the trap on which they landed , rather than according trap type , would have enabled the distribution of S . damnosum s . l . and other species to be represented more accurately . Also , blood meal analyses of flies collected on EWTs or breeding in nearby rivers might have yielded information about host preference . The lack of male S . damnosum s . l . and S . bovis on traps might suggest that EWTs specifically target host-seeking females , but this should be considered in relation to the distance of collection sites from breeding sites . Little is known about dispersal distances of male blackflies , although it is generally thought they disperse shorter distances than females [71 , 76] . With the exception of adult collection sites at Apyeta Bridge which were adjacent to the Achwa River , those at Pamulu ( 13km ) , Gwere Luzira ( 16km ) , Beyogoya ( 7 . 5km ) and Ayago Bridge ( 11km ) , were a considerable distance from places of known S . damnosum s . l . breeding ( Table 1 ) . At Chikuti , they were also 5km from known breeding sites in the Mbalu River . It was unsurprising that biting flies other than blackflies were recovered from traps since blue and black target traps are commonly used for the collection of diurnally active haematophagous Diptera , including the genera collected during this study [63] . Given that only blood-feeding sexes of each species were recovered implies that EWTs are attractive to host-seeking flies [77] . Ideally , the same adhesive would have been used to coat EWTs in both Uganda and Tanzania , but this was not possible due to manufacturing problems . Both Tangle-Trap and Temmen-Insektenleim are clear , odourless adhesives commonly used to trap insects [78 , 79] . They do not oxidise to form a surface film and remained sticky throughout the trapping experiments . Adhesives with these physical properties are known to be effective for collecting tsetse and other Diptera [80 , 81] . Whereas the use of different products might have had an effect on the relative blackfly catch in each country , it is unlikely that this could sufficiently explain the differences in trap efficacy observed . Differences in locally-sourced products such as sugar , yeast and container-size almost certainly affected rates of CO2 production in each country . Temperatures to which sugar-yeast mixtures were exposed are also likely to have had an impact . Concerns about the impact of prolonged exposure to high temperatures on CO2 production were addressed by conducting semi-field experiments at Gulu University ( Gulu , northern Uganda ) in September 2016 ( S2 Fig ) . Experiments were conducted for four days in mean daily ( 07:00–18:00 ) temperatures of up to 36 . 8°C ( min . 20 . 2°C , max . 46 . 0°C ) . Results showed that mean daily CO2 production did not drop below 173 . 79mL/min when using sugar-yeast mixtures as previously described . It is therefore also unlikely that differences in trap efficacy observed between countries were caused by effects of high temperatures on CO2 production . Further field-based research into the effects of consumables and environmental variables on CO2 production and trap efficacy is needed . The choice of trap materials and their interactions with the environment affected trap performance and ease of use . The matt black emulsion initially used to paint stripes on the blue tarpaulin screen frequently peeled when removing overnight covers , although this problem was easily overcome by replacing the paint with black tarpaulin during trap construction . The adhesives used were costly if imported and affected specimen quality . It was necessary to apply a drop of white spirit to partially dissolve the glue before removing a specimen as previously recommended by Toé et al . ( 2014 ) [11] . This improved specimen quality , although specimen removal was consequently laborious if catch numbers exceeded 500 blackflies a day , and only a single person was working to remove them . Rodriguez-Pérez et al . ( 2013 ) previously stated that a single person can easily maintain five traps , and this is true providing that catch numbers are relatively low [7] . The prolonged presence of an individual at a trap also served to attract even greater numbers of blackflies . Specimen desiccation was a problem in Tanzania where blackflies were removed from traps twice daily , but was less so in Uganda where specimens were removed three times daily . It was also necessary to frequently clean traps and reapply adhesives following rainfall , which often left soil and detritus covering the base of EWTs . This was particularly important in Uganda where blackflies were mostly found on the lower third of traps . Trap placement was particularly important to the success of collections with significant site-to-site variation in blackfly activity frequently encountered . Although no attempts were made to standardise trap placement , sites with partial shade and some direct sunlight appeared to collect most flies . Traps performed poorly in sites that were too exposed , while those placed in heavily shaded areas often caught the fewest flies . Esperanza Window Trap collections of S . damnosum s . l . in Uganda were very encouraging , with pairs of traps baited with yeast-produced CO2 and worn socks proving to be as efficacious as HLCs . However , successes of the Ugandan collections were not replicated in Tanzania where HLCs clearly and consistently outperformed EWTs of both colour schemes . Behavioural responses of S . damnosum s . l . to EWTs appeared to differ between study countries and this was highlighted by differences in the distribution of blackflies on traps . Responses of S . damnosum s . l . to visual and olfactory stimuli should be investigated further in East Africa given the diversity of sibling species present . Further research should also investigate whether EWTs sample the same sibling species as HLCs in areas such as Mahenge where anthropophilic and zoophilic S . damnosum s . l . occur sympatrically [35] . Since several non-anthropophilic Simulium species were collected on traps , it seems reasonable to assume that non-anthropophilic S . damnosum s . l . could also be present . The relatively poor performance of EWTs for the collection of anthropophilic S . bovis should raise awareness of potential limitations of EWTs for the collection of anthropophilic blackflies in areas where species other than S . damnosum s . l . transmit O . volvulus . Current EWT designs have shown promise for the collection of S . damnosum s . l . in Burkina Faso and northern Uganda [11] . Further research and development should be encouraged to improve understanding of behavioural responses of blackflies to traps and their attractants in order to develop them as a tool for onchocerciasis surveillance in sub-Saharan Africa .
|
Using human bait to collect blood-feeding insects is an ethically sensitive issue . Whereas researchers investigating insect-borne diseases such as sleeping sickness , leishmaniasis and malaria have a range of traps at their disposal , those investigating blackflies and river blindness ( onchocerciasis ) still rely on this method . Alternatives to human bait are needed to monitor disease transmission as onchocerciasis control programmes approach their elimination phase . The recently developed Esperanza Window Trap provides one such possibility . We built these traps based on previously published methods while conducting blackfly research in Uganda and Tanzania in order to evaluate their efficacy and ease of use . Our results show that in Uganda the traps worked well for the collection of Simulium damnosum , the blackfly primarily responsible for onchocerciasis transmission in sub-Saharan Africa , but were less effective at collecting the same species in Tanzania . Blackfly behaviour and response to traps will probably vary from one country to another . Esperanza Window Traps show promise for blackfly collections , but further research and development are needed to determine how broadly they can be used .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"onchocerca",
"volvulus",
"chemical",
"compounds",
"ruminants",
"helminths",
"geographical",
"locations",
"tropical",
"diseases",
"uganda",
"vertebrates",
"parasitic",
"diseases",
"animals",
"mammals",
"diptera",
"onchocerca",
"tanzania",
"neglected",
"tropical",
"diseases",
"onchocerciasis",
"africa",
"behavior",
"chemistry",
"insects",
"arthropoda",
"people",
"and",
"places",
"helminth",
"infections",
"carbon",
"dioxide",
"biology",
"and",
"life",
"sciences",
"nematoda",
"physical",
"sciences",
"cattle",
"amniotes",
"bovines",
"organisms"
] |
2017
|
Esperanza Window Traps for the collection of anthropophilic blackflies (Diptera: Simuliidae) in Uganda and Tanzania
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Pathogens hijack host endocytic pathways to force their own entry into eukaryotic target cells . Many bacteria either exploit receptor-mediated zippering or inject virulence proteins directly to trigger membrane reorganisation and cytoskeletal rearrangements . By contrast , extracellular C . trachomatis elementary bodies ( EBs ) apparently employ facets of both the zipper and trigger mechanisms and are only ~400 nm in diameter . Our cryo-electron tomography of C . trachomatis entry revealed an unexpectedly diverse array of host structures in association with invading EBs , suggesting internalisation may progress by multiple , potentially redundant routes or several sequential events within a single pathway . Here we performed quantitative analysis of actin organisation at chlamydial entry foci , highlighting filopodial capture and phagocytic cups as dominant and conserved morphological structures early during internalisation . We applied inhibitor-based screening and employed reporters to systematically assay and visualise the spatio-temporal contribution of diverse endocytic signalling mediators to C . trachomatis entry . In addition to the recognised roles of the Rac1 GTPase and its associated nucleation-promoting factor ( NPF ) WAVE , our data revealed an additional unrecognised pathway sharing key hallmarks of macropinocytosis: i ) amiloride sensitivity , ii ) fluid-phase uptake , iii ) recruitment and activity of the NPF N-WASP , and iv ) the localised generation of phosphoinositide-3-phosphate ( PI3P ) species . Given their central role in macropinocytosis and affinity for PI3P , we assessed the role of SNX-PX-BAR family proteins . Strikingly , SNX9 was specifically and transiently enriched at C . trachomatis entry foci . SNX9-/- cells exhibited a 20% defect in EB entry , which was enhanced to 60% when the cells were infected without sedimentation-induced EB adhesion , consistent with a defect in initial EB-host interaction . Correspondingly , filopodial capture of C . trachomatis EBs was specifically attenuated in SNX9-/- cells , implicating SNX9 as a central host mediator of filopodial capture early during chlamydial entry . Our findings identify an unanticipated complexity of signalling underpinning cell entry by this major human pathogen , and suggest intriguing parallels with viral entry mechanisms .
An essential early event in the lifecycle of many human and animal pathogens is entry into non-phagocytic host epithelial cells . Viruses , bacteria and parasites all engage with host cell surfaces prior to inducing the reorganisation of the plasma membrane and underlying cytoskeleton to promote their internalisation . Invasive bacteria like Salmonella and Listeria species are typically > 1 μm in diameter and promote their internalisation either by injecting virulence effector proteins that subvert host signalling to reversibly induce cytoskeletal reorganisation , or through surface ligand mimicry hijack receptor-mediated endocytosis , respectively [1] . By contrast , the infectious extracellular form of Chlamydia trachomatis , termed the elementary body ( EB ) , is much smaller in diameter ( ~400 nm ) than its archetypal Gram-negative cousins . Nevertheless , EBs must also induce actin-dependent internalisation into non-phagocytic cells , a pivotal step in the lifecycle of this obligate intracellular bacterium [2] . How C . trachomatis promotes cell entry is incompletely understood , but it is often considered as an example of the trigger mechanism epitomised by the enteroinvasive bacterium Salmonella typhimurium . Salmonella employs a type III secretion system ( T3SS ) to inject multiple , semi-redundant effectors into host cells that coordinate the reorganisation of the host actin cytoskeleton . Two effectors reversibly stimulate the cellular Rho-family GTPases Cdc42 and Rac1 , two effectors bind to actin directly to modulate filament dynamics , and a further effector acts as a phosphoinositol phosphatase mimic , modulating membrane plasticity and co-stimulating the Rho GTPases [3–7] . C . trachomatis EBs also exploit a T3SS and deliver effectors into the host cell that reversibly stimulate Rac1 . Although the mechanism remains incomplete , a major factor is the T3SS effector translocated actin recruiting phosphoprotein ( TARP ) that nucleates polymerisation directly by binding to actin , and indirectly upon tyrosine phosphorylation by acting as a scaffold for Rac1 guanine nucleotide exchange factors [8–11] . A second effector post-translationally modifies the GTPase itself , possibly to subsequently downregulate signaling [12] . A number of host receptors have also been linked to cell entry by different chlamydial species . For instance , C . pneumoniae Pmp21 binds epidermal growth factor receptor ( EGFR ) to induce EB entry by receptor-mediated endocytosis [13] , an event more reminiscent of the zipper mechanism exemplified by Listeria [14] . The role of receptors in C . trachomatis entry is however less clear , as none are essential [15] . Although Rac1 stimulation is sufficient for the formation of lamellipodia , this signalling activity cannot exclusively account for the complex actin ruffles , pedestal-like structures and filopodia present at EB entry sites [2 , 16] . This view was further reinforced by our cryo-electron tomography of early interactions between C . trachomatis EBs and cultured cells [17] , when EBs were captured in association with phagocytic cups , trapped by actin-rich filopodia and present within membrane ruffles reminiscent of macropinosomes . These combined data support a view that multiple or redundant entry pathways are likely to operate in parallel . As expected , chlamydial entry thus shares many similarities with other bacterial entry pathways . However , there are also striking parallels with viral entry mechanisms . The small size of EBs , their association with filopodia [2] , entry-associated phosphorylation and signaling [16 , 18 , 19] , and also the requirement for protein disulphide isomerase-associated reduction [20] and promiscuous receptor interactions [21–23] are all factors common to viral entry mechanisms [24–28] . In this study we have quantified the cytoskeletal rearrangements and membrane reorganisation at C . trachomatis entry foci , and systematically investigated the underlying signalling pathways , initially by employing inhibitor screens in a manner analogous to studies of viral entry [29 , 30] .
Although our cryo-electron tomography revealed an unexpected diversity of cellular structures at C . trachomatis entry sites [17] , this technique did not enable the visualisation of sufficient events to statistically distinguish whether they represent sequential assemblies or distinct pathways . Consequently , we exploited confocal microscopy to categorise a significantly larger number of bacterial entry foci , initially by observing F-actin recruitment during C . trachomatis infection of human retinal pigment epithelial ( RPE1 ) cells . Telomerase-immortalised RPE1 cells are widely applied to study endocytic pathways [31 , 32] , and are permissive to C . trachomatis infection [33] . Distinct F-actin structures could be defined using light microscopy that correlated with those observed by cryo-electron tomography [17] . Differential fluorescence staining was used to discriminate extracellular and intracellular bacteria ( S1 Fig , see Materials and Methods ) . From 10 minutes post-infection , C . trachomatis LGV2 EBs were captured in association with filopodia , F-actin cup , tail or ring-like assemblies ( Fig 1A and additional examples in S2 Fig ) . To examine the progression of these events over time , cells were additionally fixed 30 and 120 minutes post-infection . The most striking phenotype was the difference in the number of EBs in association with filopodia , which decreased from 33 ± 3% at 10 minutes to 13 ± 6% after 120 minutes ( Fig 1B ) . This revealed that filopodial association is a significant early event during EB entry into cultured RPE1 cells . To determine whether filopodial association is a conserved early event , F-actin structures were next equivalently quantified following the infection of HeLa cells with C . trachomatis LGV2 ( S3A Fig ) . The frequency of filopodial association at 10 minutes post infection was even higher in these cells , at 51 ± 4% EBs , decreasing significantly by 120 minutes post infection to 23 ± 3% ( S3B Fig ) . Comparable EB-associated F-actin structures formed when RPE1 cells were infected with C . trachomatis serovar D ( S4A and S4B Fig ) . Under these experimental infection conditions , RPE1 and HeLa cells have similar overall infection kinetics , and comparable proportions of EBs in association with F-actin structures at 10 , 30 and 120 minutes post-infection ( S5 Fig ) . These quantitative data illustrate a spatio-temporal conservation in the F-actin superstructures formed during C . trachomatis entry , which occur independently of bacterial serovar and host cell type . Furthermore , they highlight filopodial association as a conserved and quantitatively significant early event during initial EB-host cell interaction . These initial data thus support a model that Chlamydia might utilise similar mechanisms to viruses and exosomes for entry into host cells , whereby filopodial capture and surfing precedes internalisation [24] . Filopodia are induced by the activation of the GTPase Cdc42 and the associated actin nucleation-promoting factor ( NPF ) N-WASP [34 , 35] , although additional efficiency factors are also required including IRTKS- and Ena/VASP-family proteins [36 , 37] . However , previous studies have shown Rac1-WAVE-Arp2/3 signalling is clearly required for C . trachomatis invasion , whereas Cdc42 is not [38 , 39] . To evaluate whether additional host factors are required for filopodial association and EB uptake , cells were infected in the presence of small molecule inhibitors targetting a broad panel of cellular factors known to influence actin dynamics and endocytic processes . Initially , to facilitate high-throughput screening , cultured RPE1 cells were infected with C . trachomatis LGV2 for two hours in the presence of three different concentrations of each inhibitor , where the mid concentration was the established IC50 or effective concentration . Inhibitors were then removed by washing , and the infection allowed to proceed until 24 hours post-infection ( hpi ) , when the infected cells were fixed and the number of inclusions present in comparison to control , mock-treated cells enumerated . Based on the initial results , a second smaller-scale inhibitor screen was conducted to directly assess the effects of a subset of the inhibitors , which influenced inclusion formation in the first screen , on bacterial entry by applying differential fluorescence staining to discriminate extracellular and intracellular bacteria directly . This allowed effects on entry and nascent inclusion formation to be distinguished . For the initial screening at 24 hpi , effects were arbitrarily considered as significant when the mid-concentration of an inhibitor reduced the number of inclusion-containing cells to ≤ 75% of the control ( this threshold is represented by a dotted line on plots in S6 Fig ) . Inclusion morphology was also examined following each treatment by parallel immunofluorescence ( of which selected examples are shown in S7 Fig ) . Consistent with the major roles for the Sos/Abi1/Eps8 and Vav2 guanine nucleotide exchange factors ( GEFs ) in the activation of the small GTPase Rac1 during C . trachomatis entry [11] , the EHop inhibitor that specifically prevents Vav2-mediated Rac1 activation [40] induced a clear and significant dose-dependent decrease in the number of inclusion-containing cells , whereas NSC that alternatively targets Rac1-specific GEFs TrioN and Tiam1 had a lesser effect [41] ( compare EHop and NSC in S6 Fig ) . Unexpectedly , inhibitors targeting the GTPase Cdc42 resulted in significant reduction in inclusion number ( S6 Fig , ML141 and Casin ) . Conversely , the inhibition of RhoA and Arf GTPases did not reduce inclusion formation to ≤ 75% of the control , our significance criteria ( S6 Fig , RhoA and Arf6 ) , in agreement with previous studies [38] , although Arf6 activity has been implicated in the internalisation of the related C . caviae [42] . Finally , all three dynamin inhibitors tested decreased inclusion formation significantly and dose-dependently , with dynasore and MiTMAB preventing inclusion formation at the highest concentration ( S6 Fig , Dynasore , MiTMAB and OcTMAB ) , in agreement with the established role of dynamin in mediating lipid transport early during inclusion biogenesis [43] . Despite the well-recognised requirement for actin reorganisation , equivalent assays performed using small molecule inhibitors that target actin dynamics exhibited contrasting effects on inclusion formation in RPE1 cells . Cytochalasin D treatment induced a dose-dependent decrease in inclusion formation , with an 88 ± 4% reduction at mid concentration ( S6 Fig , actin polymerisation cytoD ) , whereas cells treated with latrunculin B only exhibited a 16 ± 7% decrease at the highest concentration tested ( S6 Fig , actin polymerisation latB ) , despite the fact that both cytochalasin and latrunculin are classical inhibitors of actin polymerisation , albeit via distinct modes of action [44 , 45] . Jasplakinolide , which stabilises F-actin , prevented inclusion formation at the mid concentration ( S6 Fig , actin stabilisation ) [46] . Consistent with a role for Rho GTPase signalling , CK636 and CK548 that target the Arp2/3 actin nucleation complex and inhibit actin polymerisation either by preventing the Arp2 and Arp3 subunits of the complex entering their active conformation or by binding to the hydrophobic core of Arp3 [47] , induced a dose-dependent reduction in inclusion formation , reflected by a 37 ± 8% and 48 ± 9% decreases at the mid concentration , respectively ( S6 Fig , CK636 and CK548 ) . In addition to Arp2/3-directed nucleation of branched F-actin networks , unbranched filament nucleation by formins apparently also contributes , as the formin inhibitor SMIF induced a 76 ± 6% reduction in inclusion formation at the mid concentration , although this was excluded from further analysis as it did not reduce inclusion formation ≤ 75% of the control ( S6 Fig , SMIF ) . These data demonstrate that our assays using C . trachomatis LGV2 and cultured RPE1 cells specifically recapitulate previous findings implicating the specific stimulation of Rac1 via a subfamily of cellular GEFs [11] . Given the early role of filopodia ( Fig 1 ) , we investigated the potential contribution of the NPF N-WASP , which stimulates filopodia formation by activating the Arp2/3 complex via Cdc42-dependent and -independent pathways [48 , 49] . We exploited wiskostatin that specifically inhibits N-WASP activity by stabilising the auto-inhibited conformation [50] . Intriguingly , N-WASP inhibition resulted in 49 ± 12% and 92 ± 5% decreases in inclusion formation at the mid-IC50 and high-concentrations , respectively ( S6 Fig , wiskostatin ) . These data implicate N-WASP as an apparently dominant mediator of early inclusion formation . Correspondingly , the macropinocytosis inhibitor EIPA induced a dose-dependent decrease in inclusion formation ( S6 Fig , macropinocytosis ) , with ~50% of the remaining inclusions containing RBs which were morphologically abnormal ( S7 Fig , EIPA ) . By contrast , treatment with filipin and cholesterol oxidase , which target lipid raft and caveolae-mediated endocytosis [51 , 52] , did not significantly affect the number of inclusions formed in comparison to the mock-treated controls ( S6 Fig , filipin & cholesterol oxidase ) , or the morphology of the inclusions or RBs ( S7 Fig , cholesterol oxidase ) . Tip , which targets myosin VI and consequently clathrin-mediated endocytosis ( CME ) [53] , reduced inclusion formation by 15 ± 6% at the mid concentration , although the inhibitor itself was significantly cytotoxic when applied at higher concentrations ( S6 Fig , Tip and S8 Fig , Tip ) . These initial inhibition experiments using inclusion formation 24 hpi as a phenotypic read-out in RPE1 cells , both confirmed recognised mediators of bacterial entry and early inclusion biogenesis ( Rac1 , dynamin , Arp2/3-dependent actin polymerisation ) and implicated previously unrecognised factors ( Cdc42 , N-WASP , macropinocytosis ) . However , these data alone are insufficient to distinguish a role for these factors in C . trachomatis entry rather than in the subsequent development and trafficking of early bacteria-containing vacuoles . Based on the initial results ( S6 Fig ) , a more restricted inhibitor screen was performed using EHop ( Rac1 ) , Casin ( Cdc42 ) , MiTMAB ( dynamin ) , wiskostatin ( N-WASP ) , CK636 ( Arp2/3 ) , cytochalasin D and latrunculin B ( actin polymerisation ) , EIPA ( macropinocytosis ) and Rhosin ( RhoA ) , to assess the effect of these inhibitors directly on bacterial entry using differential ‘inside-outside’ immunofluorescence staining ( S1 Fig , see Materials and Methods ) . In agreement with their effects on inclusion formation ( S6 Fig and S7 Fig ) , inhibition of Rac1 , N-WASP , macropinocytosis and the Arp2/3 complex each reduced bacterial entry in a dose-dependent manner ( Fig 2 and S9 Fig ) . Indeed , the N-WASP inhibitor wiskostatin could block C . trachomatis entry , without inducing substantial cell cytotoxicity ( Fig 2 , N-WASP and S8 Fig , Wiskostatin ) . Consistent with the dominant role of Rac1 signalling [2 , 16] , entry was not as profoundly suppressed by the Cdc42 inhibitor ( compare Rac1 and Cdc42 in Fig 2 ) , in agreement with previous studies [38 , 39] . Conversely , RhoA and dynamin GTPase inhibitors had a limited effect on C . trachomatis LGV2 entry ( Fig 2 RhoA and dynamin ) , in agreement with a role for dynamin in mediating lipid transport post-entry , early during inclusion biogenesis [43] . Unexpectedly , neither cytochalasin D nor latrunculin B decreased bacterial entry as anticipated ( Fig 2 , cytochalasin D and S9 Fig , latrunculin B ) , although cytochalasin D treatment significantly inhibited the entry of Salmonella typhimurium into RPE1 cells , reducing internalisation to 18 ± 9% of mock-treated controls under equivalent conditions at IC50 ( S10A Fig ) , when F-actin organisation is clearly disrupted ( S10B Fig ) . Intriguingly , treatment with cytochalasin D or latrunculin B reproducibly stalled a population of EBs in association with patches of F-actin at the cell periphery ( immunofluorescence panels in Fig 2 , cytochalasin D and S9 Fig , latrunculin B ) . To extend the inhibitor screening , it was important to establish whether the implicated signal transducers were specifically recruited to C . trachomatis entry foci , and to gain insights into the spatio-temporal dynamics of this process . Initially , RPE1 cells transiently expressing Cdc42-GFP , Rac1-GFP , RhoA-GFP , Arf1-GFP or Arf6-GFP were infected with C . trachomatis LGV2 and observed over a two hour timecourse . In agreement with the inhibitor-based screen , Rac1-GFP and Cdc42-GFP were transiently recruited from 10 minutes post infection ( Fig 3 , Rac1-GFP and Cdc42-GFP , and fluorescence intensity plots through the central plane of the EB ) , whereas Arf1-GFP and RhoA-GFP were not enriched at entry foci ( Fig 3 , Arf1-GFP and RhoA-GFP ) . At this timepoint , only 7–10% of bacteria are intracellular ( S5 Fig ) , thus the recruitment of Rac1 and Cdc42 ( 8 . 6 ±0 . 9% and 6 . 7 ±1 . 3% respectively ) reflects the transient association of these with nearly every invasion-competent EB at 10 minutes post-infection . Arf6-GFP was observed with lower frequency in membrane ruffles adjacent to some EBs ( Fig 3 , Arf6-GFP ) , consistent with its role in membrane ruffling and Rac1 trafficking [54 , 55] . No enrichment of endogenous clathrin , caveolin-1 or flotillin-1 was evident at entry foci under equivalent conditions ( S11 Fig , clathrin , caveolin-1 and flotillin-1 ) , consistent with the lack of inhibition of inclusion formation by Tip , cholesterol oxidase and filipin ( S6 Fig ) . These data support the view that Cdc42 and Rac1 GTPases are dominant early host mediators of C . trachomatis entry into RPE1 cells . Since the NPFs N-WASP and WAVE bridge the activated Rho-family GTPases Cdc42 and Rac1 to the Arp2/3 complex , respectively , we next examined the recruitment of N-WASP and the WAVE complex following C . trachomatis LGV2 infection of RPE1 cells . The WAVE subunit PIR121-GFP was recruited to entry foci ( Fig 4 , PIR121 ) , consistent with the reported requirement for Rac1 and the WAVE complex during C . trachomatis infection [38 , 39] . N-WASP-GFP was recruited more frequently to C . trachomatis entry foci than PIR121-GFP from as early as 10 minutes post infection ( Fig 4 , N-WASP ) , in agreement with our findings that N-WASP inhibition can also block bacterial entry ( Fig 2 , N-WASP ) . There was no apparent specific preference of the GTPase-GFP or NFP-GFP for specific EB-associated F-actin structures . Given that N-WASP is transiently recruited to C . trachomatis entry foci and N-WASP inhibition has dose-dependent effects on bacterial entry , we validated the role of N-WASP in entry using knockout mouse embryonic fibroblasts ( N-WASP-/- ) . In these cells , chlamydial adhesion was reduced by >70% compared to isogenic wild-type control cells ( S12A Fig ) , while C . trachomatis entry was significantly reduced by 54 ± 17% ( S12B and S12C Fig ) . These data confirm the importance of N-WASP during entry processes , yet imply the role of N-WASP is more complex than simply the requirement in entry alone . The fact that upstream adhesion of C . trachomatis is also affected may be indicative of a role for N-WASP and consequently the actin cytoskeleton in stablising chlamydial adhesion , which may subsequently impact bacterial entry efficiency . Alternatively , the absence of N-WASP in the knockout cells may disturb F-actin organization and influence normal C . trachomatis adhesion processes indirectly . Nevertheless , these data clearly support the view that N-WASP plays a significant yet previously uncharacterised role in the early interactions between C . trachomatis and the host cell . The requirement for Rac1 , Cdc42 , N-WASP , the Arp2/3 complex and macropinocytosis-associated sodium-proton exchangers inhibited by EIPA , and the dynamic recruitment of Rac1 , Cdc42 , WAVE and N-WASP to entry foci , together with the association of EBs with phagocytic cups , suggested that C . trachomatis entry shared many similarities to growth factor and virus-induced macropinocytosis [56] . An additional hallmark of macropinocytosis is the associated activity of phosphoinositol-3-kinase ( PI3K ) , and the sequential generation of the phosphoinositide signalling intermediates phosphatidylinositol-3 , 4 , 5-trisphosphate [PI ( 3 , 4 , 5 ) P3] and phosphatidylinositol-3-phosphate ( PI3P ) at the plasma membrane . Consequently , we next investigated PI3K activity and used reporters to determine the localisation of 3-phosphoinositide species during chlamydial internalisation . RPE1 cells were infected with C . trachomatis LGV2 in the presence of the pan-PI3K inhibitors wortmannin and LY294002 [57] , and the effect on bacterial entry assessed using the fluorescence ‘inside-out’ assay . Neither wortmannin nor LY294002 significantly inhibited C . trachomatis entry ( Fig 5A ) , in agreement with previous data in HeLa cells showing wortmannin-insensitive chlamydial internalization [2] . However , Akt-PH-GFP , which reports PI ( 3 , 4 , 5 ) P3 when expressed in cultured cells , was recruited to EBs 10 and 30 minutes post infection ( Fig 5B , Akt-PH-GFP ) . Strikingly , the PI3P reporter PX-p40-GFP not only accumulated at entry sites , but also persisted around motile early vacuoles encapsulating EBs ( Fig 5B , PX-p40-GFP and S1 Movie ) . These data suggest that C . trachomatis enters via a macropinocytosis-like route , with features subtly distinct from the canonical pathway . This would not be without precedent , as a wortmannin- and LY294002-insensitive pathway generates PI ( 3 , 4 , 5 ) P3 during Salmonella entry [58] . Despite this apparent difference in the requirement for PI3K , 40 ± 26% of C . trachomatis EBs co-localised with the fluid-phase marker 10 , 000 MW dextran after 30 minutes , whereas no equivalent association was observed with the CME marker transferrin ( Fig 5C , and Fig 5D , compare dextran and transferrin ) , consistent with the lack of clathrin recruitment ( S11 Fig , clathrin ) . Fluid-phase and transferrin uptake were not significantly enhanced during infection ( Fig 5C , compare NI and I panels ) , although dextran uptake was inhibited by EIPA treatment . Using this approach , it was not possible to determine whether dextran-labelled EB-containing macropinosomes were derived from a defined class of F-actin-rich surface structure . However , when cells were infected with C . trachomatis in the presence of 70 , 000 MW dextran , by comparison there did not appear to be equivalent coincidence between 70 , 000 MW dextran and C . trachomatis EBs ( S13 Fig ) , suggesting a limit in the capacity of the uptake vesicle . This is in accordance with the tight encapsulation of C . trachomatis EBs observed by cryo-electron tomography at this time point [17] . Taken together , these data reveal that C . trachomatis LGV2 entry can proceed via atypical macropinocytosis-like events that share hallmarks of the archetypal cellular pathway , yet also exhibit key differences . Macropinosomes are derived from membrane ruffles and protrusions folding back and fusing with the plasma membrane to form large vesicles [59] . Although the signalling underlying cellular macropinocytosis is incompletely understood Rac1 , WAVE , Cdc42 and N-WASP , along with other downstream effectors such as PAK1 , are implicated in this process [56] . Phosphoinositide signalling is also central , with PI ( 3 , 4 , 5 ) P3 present in phagocytic cups being rapidly dephosphorylated to PI3P on the nascent macropinosome [60 , 61] . The sorting nexin ( SNX ) protein family is implicated in membrane trafficking , cargo sorting and endocytosis , and is characterised by a phosphoinositide-binding phox ( PX ) domain . The PX domain confers phosphoinositide binding specificity , with some PX domains binding PI ( 3 ) P [62] . The SNX-PX-BAR subfamily contain a BAR domain , and of these SNX1 , SNX5 , SNX9 , SNX18 and SNX33 are implicated in macropinosome formation [63–65] . To further characterise the macropinocytic-like entry pathway of C . trachomatis , we explored whether SNX-PX-BAR proteins were involved early during bacterial entry . Cultured RPE1 cells expressing individual SNX-PX-BAR family proteins epitope-tagged at their C-terminus ( SNX1-Myc , SNX2-Myc , SNX4-Myc , SNX5-Myc , SNX6-Myc , SNX7-Myc , SNX8-Myc , SNX9-Myc , SNX18-Myc , SNX30-Myc , SNX32-Myc and SNX33-Myc ) were infected with C . trachomatis LGV2 . Infection was allowed to proceed for 10 minutes prior to fixation and analysis of SNX-PX-BAR-Myc recruitment to entry foci . Of the twelve SNX-PX-BAR-Myc proteins analysed , SNX9-Myc was observed most frequently at entry sites ( Fig 6A , SNX9 and Fig 6B , SNX9-Myc ) . However , since all twelve ectopically-expressed SNX-PX-BAR-Myc derivatives were also present at entry sites at low frequency using our applied scoring criteria ( Fig 6A ) , we next verified the localisation of endogenous SNX9 with an anti-SNX9 polyclonal antibody by indirect immunofluorescence . This antibody recognised endogenous SNX9 in accordance with literature ( S14A Fig anti-SNX9; [66 , 67] ) , and within F-actin pedestals generated following infection of RPE1 cells with enteropathogenic Escherichia coli ( EPEC ) ( S14B Fig; [68] ) . Endogenous SNX9 was frequently present at sites of EB-host cell interaction in RPE1 cells infected with C . trachomatis ( Fig 6B , SNX9 ) , in agreement with the recruitment of SNX9-Myc ( Fig 6B , SNX9-Myc ) . In addition to these studies using fixed cells , RPE1 cells expressing GFP-SNX9 were also observed by live imaging . In trans expression of GFP-SNX9 expression generated two distinct phenotypes , dependent on the level of expression . High expression induced extensive membrane tubulation , whereas lower expression generated a punctate distribution reminiscent of the endogenous protein ( compare left and right GFP-SNX9 panels in S14A Fig GFP-SNX9; [66 , 69] ) . Consequently , cells exhibiting membrane tubulation were excluded from further analysis . Functionality of GFP-SNX9 in low-expressing cells was additionally confirmed by verifying localisation within F-actin pedestals generated following infection with EPEC ( S14C Fig ) . Cultured RPE1 cells expressing low levels of GFP-SNX9 were infected with C . trachomatis LGV2 . GFP-SNX9 was clearly recruited to cell-associated EBs ( Fig 6C; S2 Movie ) . Live imaging showed that EBs adhere in close proximity to peripheral membrane ruffles enriched in GFP-SNX9 , following which intense GFP-SNX9 puncta form that directly overlap with the EB , dissipating ~400 seconds later ( S2 Movie ) . These combined approaches reveal the specific and transient recruitment of the SNX-PX-BAR family protein SNX9 to C . trachomatis entry sites . Given that SNX9 is transiently recruited to C . trachomatis entry foci , we next investigated the possible roles for SNX9 during infection . The effect of SNX9 knockdown on C . trachomatis entry was therefore assessed . Cultured RPE1 cells were treated with pooled SNX9 siRNA for 72 hours or equivalently with control non-targeting scrambled siRNA , prior to infection with C . trachomatis LGV2 . Infection was allowed to proceed for two hours prior to fixation and quantification of intracellular bacteria by inside-outside differential fluorescence staining ( Fig 7A ) . Knockdown of SNX9 resulted in a 20 ±8% reduction in the number of internalised bacteria ( p<0 . 05 ) compared to the equivalent non-targeting control siRNA treated population , revealing SNX9 contributes to bacterial entry . To establish whether the effect of SNX9 knockdown is limited to invasion , cells were treated with siRNA for 48 hours , prior to infection with C . trachomatis LGV2 . Infection was allowed to proceed for 24 hours prior to fixation and quantification of the number of inclusion containing cells ( Fig 7B , Fig 7C ) . No significant difference was observed in the overall number of infected cells ( Fig 7C ) . However , RPE1 cells treated with SNX9 siRNA had a significant number of smaller-sized inclusions ( Fig 7D ) . While the spread of inclusion diameters in control non-targeting siRNA ( 10 . 4 ± 3 . 4 μm ) and SNX9 siRNA ( 8 . 1 ± 3 . 8 μm ) treated cells were similar , the minimum and maximum inclusion diameters in the SNX9 siRNA treated cells were smaller than those in control non-targeting siRNA treated cells . This suggests that SNX9 influences inclusion growth , or the phenotype could be a secondary effect arising from the reduction in C . trachomatis entry ( Fig 7A ) . However , as these knockdown experiments inevitably represent a mixed population , we exploited human adherent HAP1 cells that recapitulate the chlamydial infection cycle [70] to generate a SNX9 knockout cell line ( SNX9-/- ) . While bacterial adhesion remained unchanged in comparison to isogenic controls when SNX9-/- cells were infected C . trachomatis LGV2 ( Fig 8A ) , EB entry was significantly reduced by 25 ± 9% ( Fig 8B ) . As the membrane-scission protein dynamin interacts with SNX9 [71 , 72] , we examined the structure of early C . trachomatis-containing vacuoles at 3 hours post infection of HAP1 wild type and SNX9-/- by cryo-EM tomography [17] . The vacuoles formed in SNX9-/- cells were morphologically indistinguishable from those formed in the wild type background ( Fig 8C ) , suggesting SNX9 does not participate in membrane scission or early vacuole formation subsequent to the entry defect . Furthermore , there was no apparent change in appearance or formation of the inclusion ( Fig 8D ) , the generation of infectious progeny ( Fig 8E ) , the number of infected cells ( Fig 8F ) or the inclusion diameter in the SNX9-/- cells ( Fig 8G ) . These phenotypic data using knockout cells thus demonstrate that the functional role of SNX9 is limited to the early phase of EB entry . SNX9 has an established role in the reorganisation of the actin cytoskeleton , as it interacts directly with multiple proteins that regulate filament dynamics , including the Arp2/3 complex , N-WASP , Cdc42 and RhoA [66 , 73 , 74] , and is recruited to F-actin-rich structures during fluid-phase endocytosis [73] . To examine whether SNX9 might contribute to cytoskeletal reorganisation during C . trachomatis entry , F-actin morphology during bacterial entry into wild type and SNX9-/- HAP1 cells was compared using the assays developed previously to assess RPE1 and HeLa cells ( Fig 1 and S3 Fig ) . Adherent wild type and SNX9-/- cells appear phenotypically similar prior to infection , and both populations exhibited protrusions , filopodia and lamellipodia; however , SNX9-/- cells exhibited a small yet significant reduction in the numbers of filopodia per cell in non-infected cells ( Fig 9A , compare WT and SNX9-/- in ‘NI’ cells ) . During infection , there was a modest increase in overall filopodia numbers per cell in the WT background , while this was not observed in the SNX9-/- cells , and the reduction in filopodia numbers per cell compared to the WT was greatly reduced ( Fig 9A , compare WT and SNX9-/- cells during infection ‘I’ ) . This is likely to be an oversimplification however , due to the limitations of confocal microscopy to analyse filopodia on the cell surface , and additional information about the cell surface structures during infection would likely be visible using higher resolution microscopy techniques . When both populations were infected with C . trachomatis LGV2 and the resulting actin structures compared 30 minutes post infection , the number of bacteria in contact with filopodia decreased by > 50% in the SNX9-/- background , whereas the number of EBs in association with cup , tail , or ring-like structures were not significantly different ( Fig 9B , sedimentation ) . As filopodial capture is an early event , we investigated whether this effect occurred only when EBs were artificially sedimented onto the HAP1 cells , by comparing the phenotypes when cells were infected by co-incubation with a population of cells from which bacteria are actively egressing , previously developed for electron tomography [17] . This natural infection captures all the progressive processes that occur during cell entry , rather than experimentally imposing synchronous contact between EBs and the host cell plasma membrane by sedimentation . When wild type and SNX9-/- HAP1 cells were infected in this more natural way , fewer EBs were again observed in contact with filopodia in the SNX9-/- relative to the WT cells ( Fig 9B , egress ) . Moreover , entry was strikingly reduced by 59 ± 8% in the SNX9-/- cells ( Fig 9C ) , compared to the 25 ± 9% reduction following sedimentation ( Fig 8B ) . Taken together , these data demonstrate that SNX9 is required for early filopodia formation during C . trachomatis entry , an effect that can be partially compensated by artificial sedimentation of the bacteria into contact with the host cells .
In this study , we aimed to further understand the pathways exploited by C . trachomatis to enter eukaryotic host cells . We demonstrated that during internalisation , distinct F-actin structures interact with cell-associated EBs , including an initial contact between EBs and filopodia , and bacterial association with more complex F-actin cup , tail and ruffle structures . In addition to the recognised roles of the Rac1 GTPase and its associated NPF WAVE , we have revealed an additional unrecognised pathway sharing key hallmarks of macropinocytosis: i ) amiloride sensitivity , ii ) fluid-phase uptake , iii ) recruitment and activity of the NPF N-WASP , iv ) the localised generation of phosphoinositide-3-phosphate ( PI3P ) species and v ) involvement of the SNX-PX-BAR protein SNX9 in early filopodial capture . Macropinocytosis-like pathways underlie cell entry by diverse viruses , including HIV , Vaccinia , Adenovirus , Ebola , Influenza A and the Herpes Simplex Virus [29 , 75–79] . Indeed , Chlamydiae were originally described as a virus due to the absolute requirement of the host cell for survival , yet in many ways Chlamydiae remain atypical bacteria , particularly as EBs do not exclusively utilise either zipper or trigger mechanisms of entry [1] , and for bacteria they are relatively small . In this study we extend viral parallels to C . trachomatis entry . We have shown that filopodia associations with EBs occur at a high frequency during invasion in a process reminiscent of the filopodia-mediated capture of viruses , including Vesicular Stomatitis Virus , Murine Leukaemia Virus and HIV [24 , 80] . In this process , viral particles associate with and either ‘surf’ along or induce filopodial retraction towards the cell body where they are internalised at endocytic ‘hot spots’ [24] . In the context of our data which show a decrease in EB association with filopodia over time , filopodial capture is likely the initial point of contact for EBs and may explain the distinct orientation of the T3SS towards the cell during infection [17] , likely facilitating interaction of EBs with the surface of microvilli-dense cells of mucosal membranes they preferentially infect . Indeed , many viruses follow this filopodia-mediated capture with membrane ruffling and uptake via macropinocytosis , furthering similarities between EB and viral uptake . The involvement of a macropinocytosis-like pathway in C . trachomatis entry is supported by several observations: requirement for Rho GTPases , sensitivity to EIPA , coincidence of EBs and a fluid phase marker during entry , dynamin independence and the association of EBs with PI ( 3 , 4 , 5 ) P3 and PI3P during early entry [56] . We have also shown a requirement for N-WASP activity during chlamydial entry , suggestive of N-WASP involvement in F-actin rearrangements during internalisation , including filopodia formation [35] . Furthermore , while Cdc42 does not have a major role in C . trachomatis entry ( [38]; this study ) , there is a limited , although significant reduction in entry upon Cdc42 inhibition . Co-activation of Rac1 and Cdc42 is central to membrane ruffling [81 , 82] and macropinocytosis [59 , 83] , so the coordinated inter-dependent action of Rac1 and Cdc42 would not be unexpected in membrane ruffling and macropinocytosis-like uptake of C . trachomatis , especially as a similar pathway underlies C . caviae entry [84] . Indeed , our data suggest that there was no specific preference of Rac1- , Cdc42 , N-WASP or PIR121 ( WAVE ) GFP reporter fusions for particular EB-associated F-actin superstructures . Further careful investigation of endogenous regulators is now required , as this either reflects a limitation induced by the expression of the reporters or implies functional redundancy . Further similarities remain to be assessed , such as whether PAK1/2 , myosin II , and CtBP1 are involved in entry [85–88] , yet Chlamydia-specific adaptations would not be unexpected . Typical macropinocytic ruffles are much larger and differences in signalling requirements during macropinocytic uptake of smaller-sized cargoes have been described [87 , 89 , 90] . Indeed , despite the clear association of EBs with 3-phosphoinositide species , paradoxically C . trachomatis entry is largely PI3K-independent . However , this is not without precedent , as for example the association of Salmonella with PI ( 3 , 4 , 5 ) P3-rich membrane ruffles is PI3K-independent , and mediated by as yet unknown mechanisms involving the inositol phosphatase mimic SopB [58] . As expected , cytochalasin D and latrunculin B significantly inhibited inclusion formation and Salmonella entry in our hands . However , phalloidin staining revealed F-actin patches accumulated beneath cell-associated EBs , which were reported as internalised by the ‘inside-outside’ assay despite the presence of the inhibitors . Both the F-actin accumulation and this apparent internalisation were unexpected . The latter may merely reflect a difference in antibody accessibility under these conditions , but the accumulation of F-actin may indicate that effector-mediated actin polymerisation might locally limit the effect of actin depolymerising agents . This is not without precedent , as a similar phenomenon was observed after cells infected by Salmonella typhimurium were treated with cytochalasin D , where stabilised F-actin patches were similarly evident beneath adherent bacteria [91] , and even during bead uptake by phagocytes treated with cytochalasin B [92] . Cytochalasin D may therefore not prevent F-actin accumulation , despite inhibiting actin-dependent processes . As with PI3K- and myosin X-dependent processes , the efficiency of drug-mediated inhibition of actin polymerisation may be dependent on particle size [89 , 90 , 93] , and consequently the short dense F-actin present beneath ~400 nm diameter EBs represent relatively poor targets . Despite the ‘dogma’ of cytochalasin D-mediated inhibition of pathogen entry , conflicting effects have been reported for Chlamydia . Ward and Murray [94] reported only a 50% reduction in chlamydial entry into cytochalasin D-treated cells , whereas Carabeo and colleagues later reported a 41-fold reduction in entry [2] . However , entry of related C . psitacci was only decreased by 10% [95] . As TARP-mediated actin polymerisation can occur both directly and via Rac1-dependent pathways [8 , 11] , these redundant mechanisms may be differentially susceptible to inhibitors in different strains and target cells . It is clear that the mechanisms by which a small bacterium like C . trachomatis triggers host actin polymerisation now demands further investigation . We reveal a functional relationship between EB internalisation and PX-BAR-domain containing protein SNX9 , which mediates F-actin rearrangements during the early entry process . Indeed , global increases in filopodia formation during infection ( Fig 9A ) , similar to those observed previously [2] , as well as specific association between C . trachomatis EBs and filopodia ( Fig 9B ) , are decreased in the SNX9-/- cells . How SNX9 is facilitating this process remains an open question , yet one attractive hypothesis is that SNX9 acts as a scaffold for the recruitment and activation of N-WASP to bring about filopodia formation , membrane ruffling and macropinocytic uptake of the EB [66 , 96 , 97] . SNX9 interaction with factors important for filopodia formation have been previously identified , and include N-WASP and Arp2/3 [66 , 73 , 96] . A more recent study also described a direct interaction between Cdc42 and SNX9 and intriguingly linked SNX9 expression to increased filopodia formation [74] , suggesting our observation that initial filopodial capture of EBs is impaired in SNX9-/- cells is a direct consequence of impaired F-actin rearrangements ( Fig 10 ) . Similar to the effect of SNX9 depletion on Salmonella invasion [58] , in our study SNX9 knockdown or knockout did not abrogate entry completely . As some SNX-PX-BAR proteins are reported to have redundant roles , for instance SNX18 can compensate for SNX9 deficiency in CME , which may in part arise from the ability of SNX-PX-BAR family proteins to form heterodimers [98 , 99] , there may be compensatory effects during entry into these SNX9 depleted cells that might also allow chlamydial entry to proceed . SNX9 is recognised not only for coordination of membrane remodelling , but also as a scaffold to integrate F-actin reorganisation , endocytic traffic and Rho GTPase activity to fulfill roles in both cellular homeostasis and disease [74 , 100] . Consequently , SNX9 is an attractive candidate for hijack by opportunistic bacterial pathogens . SNX9 has been implicated in both the entry and infection of Salmonella and pedestal formation by EPEC and related EHEC [68 , 69 , 101 , 102] . These pathogens utilise T3SS effectors to subvert SNX9 activity , for example during Salmonella invasion localised SopB-mediated increases in PI ( 3 , 4 ) P2 recruit SNX9 to membranes to facilitate ruffling and N-WASP signalling . In this process , comparable decreases in Salmonella entry are observed when SNX9 is depleted [101] . It is tempting to speculate that a chlamydial effector interacts with SNX9 during cell entry , triggering oligomerisation of SNX9 to amplify SH3-domain mediated interactions akin to the signal amplification induced by EPEC/EHEC [68 , 69 , 102] . Whether or not SNX9 is recruited to membranes through an association with a chlamydial T3SS effector remains to be established , but an additional possibility is that manipulation of phosphoinositides during entry temporally and spatially control SNX9 recruitment , similar to the indirect recruitment of SNX9 mediated by Salmonella SopB [101] . To date no chlamydial entry effectors that directly interfere with phosphoinositide signalling have been have been identified , unlike in most other bacterial pathogens studied , for example phosphoinositide phosphatase mimics such as Salmonella SopB or Shigella IpgD [7 , 103] . However , the T3SS effector TARP binds to the p85 subunit of PI3K , whereas the T3SS effector TepP can interact with both p85 and p110 PI3K subunits and contributes to PI3K activation on early inclusions [11 , 104] . These effects are intriguing given the apparent PI3K-independent PI3P and PI ( 3 , 4 , 5 ) P3 interaction with C . trachomatis we observe during early entry and now warrant further investigation . Chlamydial entry and early T3SS effectors are not well defined , a fact reinforced by the observation that only C . trachomatis TARP harbors the N-terminal repeat regions required for Rac1 activation , suggesting additional factors are required by other species [10 , 16] . In this study we identified roles for N-WASP and macropinocytosis during the internalisation of C . trachomatis using a panel of small molecule inhibitors , in addition to the established roles of Rac1 and Arp2/3 [38 , 39] . This is in contrast to other studies which have implicated clathrin mediated endocytosis in EB uptake [105–107] . However , consistent with the data presented here , including a lack of clathrin recruitment to chlamydial entry sites ( S11 Fig ) , our detailed dissection of entry structures by cryo-EM never revealed an electron-dense clathrin coat present at chlamydial entry foci [17] . Our data reveal new insights into the diversity of signalling underlying the entry of C . trachomatis into host cells . We revealed a key initial interaction between EBs and host cell filopodia mediated by the SNX-PX-BAR protein SNX9 . This shares similarities with virus-like entry routes , and precedes a macropinocytosis-like pathway . Further studies of the underlying molecular mechanisms will reveal insights into the hijack of host cell function by this important obligate intracellular pathogen .
All cell culture reagents , unless otherwise specified , as well as Alexa Fluor dyes and Texas Red-conjugated phalloidin were purchased from Invitrogen . The following primary antibodies were used: mouse anti-chlamydial MOMP-LPS ( Argene , 11–114 ) , rabbit anti-Chlamydia ( Abcam , ab31131 ) , mouse anti-clathrin heavy chain X22 ( Thermofischer , MA1-065 ) , rabbit anti-caveolin-1 ( BD Biosciences , 610059 ) mouse anti-flotillin-1 ( BD Biosciences , 610821 ) , rabbit anti-myc-tag 71D10 ( Cell signalling , mAb #2278 ) and mouse anti-SNX9 ( Abcam , ab118996 ) . Tetramethylrhodamine dextran ( TRITC-dextran ) 10 , 000 MW was purchased from Life Technologies and Transferrin Alexa Fluor 647 conjugate was purchased from Thermofischer . Inhibitors used in these experiments were as follows: Rhosin ( Rhosin , Calbiochem 555460 ) , EHop ( EHop-016 , Sigma SML0526 ) , NSC ( NSC 23766 , Tocris 2161 ) , Secin ( SecinH3 , Tocris 2849 ) , ML141 ( ML 141 , Tocris 4266 ) , Casin ( Casin , Tocris 3872 ) , EHT ( EHT 1864 , Tocris 3872 ) , Dynasore ( Dynasore , Abcam ab120192 ) , MiTMAB ( MiTMAB , Calbiochem 324411 ) , OctMAB ( OcTMAB , Tocris 4225 ) , Rho Inhibitor ( Rho inhibitor , Cytoskeleton Inc ) , SMIF ( SMIFH2 , Tocris 4401 ) , CK636 ( CK636 , Sigma C7374 ) , CK548 ( CK548 , Sigma C7499 ) , Lat B ( Lantrunculin B , Sigma L5288 ) , Wiskostatin ( Wiskostatin , Sigma W2270 ) , Jasplakinolide ( Jasplakinolide , Invitrogen J7473 ) , Cyto . D ( Cytochalasin D , Sigma C8273 ) , Cholesterol oxidase ( cholesterol oxidase , Sigma C5421 ) , Filipin ( Filipin , Sigma F4767 ) , Tip ( 2 , 4 , 6-Triiodophenol , Alfa Aesar A17145 ) , EIPA ( 5- ( N-Ethyl-N-isopropyl ) amiloride , Sigma A3085 ) . Homo sapiens retinal pigment epithelial cells hTERT-RPE-1 ( RPE1 ) cells ( ATCC ) were cultured in Dulbecco’s Modified Eagle’s Medium/Nutrient Mixture F-12 Ham ( DMEM/F12 ) supplemented with GlutaMax , 10% fetal calf serum ( FCS ) and penicillin-streptomycin . Cells were transfected with plasmids using Turbofect according to the manufacturer's instructions ( Fermentas ) . Individual plasmids used were SNX1 , SNX2 , SNX4 , SNX5 , SNX6 , SNX7 , SNX8 , SNX9 , SNX18 , SNX30 , SNX32 , SNX33 ( pCi c-Myc ) , SNX9 ( pCi N-EGFP ) , RhoA ( pEGFP-C3 ) , Rac1 , Cdc42 ( pcDNA3 . 1-EGFP ) , Arf1 , Arf6 , Akt-PH , PLC-PH , SidC , PX-P40 ( pEGFP ) , PIR121 ( pDEST ) , N-WASP ( pKC425 ) . Cells were transfected with 20 nM each of SNX9 siRNA , Hs_SNX9_7 SI02777656 and Hs_SNX9_8 SI02777663 ( Qiagen ) using Hiperfect ( Qiagen ) according to the manufacturer’s instructions . Homo sapiens cervix adenocarcinoma ( HeLa ) , Cercopithecus aethiops kidney fibroblast ( Cos7 ) cells ( ATCC ) and Mouse embryonic fibroblast cells N-WASP-/- ( kind gift from Dr Michael Way ) were cultured in Dulbecco's modified Eagle's medium ( DMEM , high glucose with GlutaMAX ) containing 10% FCS and penicillin-streptomycin . Homo sapiens adherent fibroblast-like cells derived from male chronic myelogenous leukemia ( CML ) cell line KBM-7 ( HAP1 ) and HAP1 SH3PX1-/- ( SNX9-/- ) cells were purchased from Horizon Genomics . HAP1 cells were cultured in Iscove's Modified Dulbecco's Medium ( IMDM ) supplemented with 10% FCS and penicillin/streptomycin . C . trachomatis LGV2 or C . trachomatis D serovars were propagated in HeLa cells as previously described and stored in SPG buffer at −80°C [108] . Infections were carried out by diluting the stored LGV2 serovar in infection medium ( DMEM/F12 or DMEM , 10% FCS , 25μg ml−1 gentamicin ) resulting in a multiplicity of infection ( MOI ) of ~5–30 EBs per cell . In sedimentation infection cells seeded 24 h previously were overlaid with infection medium and centrifuged at 900 x g for 10 min . At an appropriate time-point , cells were fixed with paraformaldehyde ( PFA ) for immunostaining . Bacterial inclusion-forming units ( IFU ) were determined as described [109] . For the egress method , cells were infected according to [17] with minor modifications . Briefly , RPE1 or HAP1 cells were seeded into 100 mm dishes . 24 h later cells were infected with C . trachomatis LGV2 ( MOI 5–30 ) by sedimentation . The following day , non-infected cells ( RPE1 or HAP1 ) were seeded onto 12 mm coverslips in a separate 24-well plate . At 48 hpi when infected cells begin to release new EB progeny , the coverslips were introduced to the 100 mm dish and incubated for an appropriate time-point at 37°C to allow infection directly with the released EBs . Enteropathogenic E . coli ( EPEC ) was inoculated into Luria-Bertani broth and incubated overnight at 37°C with shaking . EPEC overnight cultures were diluted 1:25 into DMEM supplemented with 10% FCS and 100 mM HEPES pH 7 . 4 and grown to mid-log phase , conditions which maximise T3SS activity [110] . Cell monolayers were infected with an appropriate dilution of mid-log phase cultures in DMEM supplemented with 10% FCS and 25 mM HEPES pH 7 . 4 without antibiotics at an MOI of 50 . Infected cells were incubated for 4 h at 37°C prior to fixation . Salmonella enterica overnight cultures were diluted 1:25 in 2 ml of Luria-Bertani broth and incubated for 4 h at 37°C with shaking . Cell monolayers were pre-treated with media without antibiotics containing 1% ( v/v ) serum and 2μm cytochalasin D . After 5 minutes , this media was replaced with medium containing appropriately diluted bacterial stock and cytochalasin D at an MOI of 50 . Cells were centrifuged at 160 × g , 10 minutes , room temperature to synchronize the infection . After incubation at 37°C 5% CO2 for 1 h , cells were washed three times and remaining extracellular bacteria killed by incubation of the cells in infection media supplemented with 25 μg/ml gentamicin 37°C 5% CO2 , for 1 h . Cells were washed again and lysed in 0 . 05% ( v/v ) Triton-X-100 . Serial dilutions of cell lysates were plated on LB agar and the percentage of intracellular bacteria compared with the original inoculum was determined for both cytochalasin D treated and mock treated cells . For immunolabelling , cells were cultured on 12 mm coverslips in 24-well dishes . When appropriate , cells were fixed by exchanging media for 4% paraformaldehyde ( PFA ) in phosphate buffered saline ( PBS ) at room temperature . Cells were incubated in PFA for 20 min before neutralisation using an equal volume of 50 mM NH4Cl in PBS for at least 20 min prior to antibody labelling . Following fixation , cells were permeabilized in 0 . 05% Triton X-100 ( 10 min ) , rinsed in PBS and then washed in PBS containing 0 . 1% ( w/v ) BSA . Primary antibodies were diluted in PBS / 1% ( w/v ) BSA , added to coverslips and incubated for 2 h . Secondary antibodies ( Alexa Fluor 488 , 546 and 633 ) or Texas Red phalloidin ( for visualisation of F-actin ) were diluted in PBS / 1% ( w/v ) BSA , added to coverslips and incubated for 1 h . Coverslips were mounted with Mowiol ( Sigma ) and observed using a confocal microscope ( TCS Sp5 AOBS; Leica ) . For live-cell imaging of GFP-SNX9 expressing cells , cells were infected as described above with CellTracker CMTR ( Thermofischer ) labelled C . trachomatis LGV2 EBs , according to the method devised by Boleti and colleagues [111] . At an appropriate time-point , infected cells were fixed in 1% PFA for 20 min and quenched with 50 mM NH4Cl in PBS . Following fixation , cells were rinsed in PBS and then in PBS containing 0 . 1% ( w/v ) BSA . Cells were stained as above , incubating with anti-Chlamydia primary antibody , followed by secondary antibody ( Alexa Fluor 488 ) . Cells were then permeablised in 0 . 05% Triton X-100 for 10 min . Cells were then stained again with the same anti-Chlamydia primary antibody followed by secondary antibody ( Alexa Fluor 633 , pseudocoloured blue ) and Texas-Red phalloidin . Extracellular bacteria are therefore labelled twice with Alexa Fluor 488 ( green ) and 633 ( blue ) appearing cyan and allowing them to be distinguished from intracellular bacteria labelled only with Alexa Fluor 633 which appear blue . The percentage of intracellular bacteria was then calculated [total bacteria–extracellular bacteria]/total bacteria x 100 ) and expressed relative to control . RPE1 cells were plated into 24-well plates containing glass coverslips . 24 h later the cell media was removed and replaced with media containing 1% ( v/v ) serum and inhibitors at the indicated concentrations . After 5 min , this media was replaced with media containing C . trachomatis EBs and the inhibitor . Cells were infected as described above and incubated at 37°C for 2 h . For entry experiments at 2 h post-infection , quantification of invasion efficiency was carried out as described previously in inhibitor and mock-treated control cells . For quantification of the effects on inclusion formation , inhibitor was washed out at 2 h post-infection , and the infection allowed to proceed until 24 h post-infection when cells were fixed and stained . Cells were seeded at an appropriate density in 24-well plates containing 12 mm coverslips . 24 h later cells were infected as described above in infection media containing 1% serum and either TRITC-dextran at a concentration of 1 mg ml-1 for 30 min or transferrin Alexa Fluor 647 conjugate at a concentration of 200 μg ml-1 for 30 min . Following ligand uptake , the plates were incubated on ice and the medium was removed . Cells were then washed twice with ice-cold PBS and for 2 min in ice-cold stripping buffer containing 150 mM NaCl , 100 mM glycine , 5 mM KCl and 1 mM CaCl2 at pH 4 . 5 . Cells were washed twice with ice-cold PBS prior to fixation in PFA . The total fluorescence of transferrin Alexa Fluor 647 conjugate within cell boundaries was corrected by background fluorescence in ImageJ . Mean transferrin fluorescence in arbitrary units was quantified in infected and non-infected cells . Dextran uptake was measured according to a modified version of the protocol reported by Wang and colleagues [112] . Briefly , the 3D objects counter tool in ImageJ was used to quantify dextran uptake as 3D objects or puncta ( macropinosomes ) within z-stacks , using a minimum size filter of 0 . 2 μm2–20 μm2 for specificity of macropinosome identification and to minimise background fluorescence .
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Chlamydia trachomatis remains the leading bacterial agent of sexually transmitted disease worldwide and causes a form of blindness called trachoma in Developing nations , which is recognised by the World Health Organisation as a neglected tropical disease . Despite this burden , we know comparatively little about how it causes disease at a molecular level . Chlamydia must live inside human cells to survive , and here we study the mechanism of how it enters cells , which is critical to the lifecycle . We study how the bacterium exploits signalling pathways inside the cell to its own advantage to deform the cell membrane by reorganising the underlying cell skeleton , and identify new factors involved in this process . Our findings suggest intriguing similarities with how some viruses enter cells . A better understanding of these processes may help to develop future vaccines and new treatments .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
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"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"intracellular",
"pathogens",
"gene",
"regulation",
"chlamydia",
"trachomatis",
"pathogens",
"immunology",
"microbiology",
"sexually",
"transmitted",
"diseases",
"chlamydia",
"antibodies",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
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"specimen",
"preparation",
"and",
"treatment",
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"infectious",
"diseases",
"chlamydia",
"infection",
"contractile",
"proteins",
"actins",
"small",
"interfering",
"rnas",
"immune",
"system",
"proteins",
"proteins",
"medical",
"microbiology",
"gene",
"expression",
"microbial",
"pathogens",
"biochemistry",
"cytoskeletal",
"proteins",
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"acids",
"physiology",
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"rna",
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] |
2018
|
Chlamydia exploits filopodial capture and a macropinocytosis-like pathway for host cell entry
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Histone demethylases have emerged as important players in developmental processes . Jumonji domain containing-3 ( Jmjd3 ) has been identified as a key histone demethylase that plays a critical role in the regulation of gene expression; however , the in vivo function of Jmjd3 in embryonic development remains largely unknown . To this end , we generated Jmjd3 global and conditional knockout mice . Global deletion of Jmjd3 induces perinatal lethality associated with defective lung development . Tissue and stage-specific deletion revealed that Jmjd3 is dispensable in the later stage of embryonic lung development . Jmjd3 ablation downregulates the expression of genes critical for lung development and function , including AQP-5 and SP-B . Jmjd3-mediated alterations in gene expression are associated with locus-specific changes in the methylation status of H3K27 and H3K4 . Furthermore , Jmjd3 is recruited to the SP-B promoter through interactions with the transcription factor Nkx2 . 1 and the epigenetic protein Brg1 . Taken together , these findings demonstrate that Jmjd3 plays a stage-dependent and locus-specific role in the mouse lung development . Our study provides molecular insights into the mechanisms by which Jmjd3 regulates target gene expression in the embryonic stages of lung development .
Gene expression is epigenetically regulated through DNA methylation as well as covalent chromatin modifications such as acetylation , phosphorylation , ubiquitination , sumoylation , and methylation of histones . Histone methylation state is dynamically regulated by histone methyltransferases and demethylases [1]–[5] . The trimethylation of histone 3 ( H3K4 ) at lysine 4 is usually associated with the activation of gene expression , whereas trimethylation of histone 3 at lysine 27 ( H3K27 ) is associated with the repression of gene expression [1]–[5] . The polycomb repressive complex , which contains the H3K27 methyltransferase Ezh2 [5] , [6] , dimethylates and trimethylates H3K27 ( H3K27me2/3 ) . Recently , the H3K27 demethylase Jumonji domain containing-3 ( Jmjd3; KDM6B ) was found to catalyze the demethylation of H3K27me2/3 in vitro [7]–[11] . Despite the identification of Jmjd3 as a key H3K27 demethylase , little is known regarding its in vivo function . Jmjd3 expression is induced by vitamin D and proinflammatory stimuli in macrophages and is required for the expression of INK4A-ARF , Nodal , and Irf4 in fibroblasts , mouse embryonic stem cells ( ESCs ) , and macrophages , respectively [12]–[16] . Recently , we showed that Jmjd3 plays a vital role in induced pluripotent stem cell reprogramming by regulating INK4a/Arf expression and PHF20 degradation [17] . Several studies using Jmjd3 knockout ( KO ) mice have demonstrated the importance of Jmjd3 in differentiation and development in vivo . Jmjd3 has been shown to play a crucial role in the regulation of macrophage development and differentiation [15] and mesoderm differentiation and cardiovascular lineage commitment in mouse ESCs [18] . Furthermore , the embryonic and postnatal lethality of Jmjd3 deletion in mice indicate the critical requirement for Jmjd3 during development [15] , [18] , [19] . However , the role and mechanism of action of Jmjd3 in differentiation and developmental processes remain largely unknown . Lung development is a complex process that requires the participation of many transcription factors and developmentally regulated genes at several different stages . This complex process begins with the formation of airways from embryonic lung buds that originate from the foregut endoderm and branch into the millions of alveoli required for the first breath after birth [20]–[22] . Lung alveoli are lined by type I and II pneumocytes that are required for gas exchange and surfactant production to reduce surface tension , respectively . Failure of the lung to expand after the first breath is one of the most common causes of neonatal morbidity and mortality from diseases such as infant respiratory distress syndrome [22] , [23] . Among the surfactant proteins expressed in type II cells , including surfactant protein A ( SP-A ) , SP-B , SP-C , and SP-D [24] , [25] , SP-B is required for postnatal lung function and survival [26]–[28] . Complete deficiency of SP-B in mice and humans results in lethal neonatal RDS , which is characterized by a virtual absence of lung compliance and increased amounts of incompletely processed proprotein SP-C [26] , [27] , [29]–[31] . Loss or partial reduction of SP-B expression has been observed in patients without SP-B gene mutation [27] , but whether such losses or reductions in SP-B expression are associated with epigenetic alterations remains largely unknown . To investigate the function and mechanisms of Jmjd3 in vivo , we generated Jmjd3 global and tissue-specific knockout mice . We found that Jmjd3 ablation induces perinatal lethality associated with respiratory failure caused by defective lung development . Tissue-specific deletion of Jmjd3 and tamoxifen ( TM ) -induced temporal deletion revealed Jmjd3 is dispensable in the later embryonic development ( after E9 . 5 ) stages . Gene expression profiling , tissue staining , and chromatin immunoprecipitation-sequencing ( ChIP-Seq ) analyses showed that Jmjd3 deficiency markedly reduces a set of genes critical for lung development , in particular SP-B . Jmjd3-mediated changes in target gene expression are associated with alterations in H3K27 and H3K4 methylation levels in the proximal promoter region . Jmjd3 regulates SP-B expression in a locus-specific manner through interactions with the transcription factor Nkx2 . 1 and the epigenetic protein Brg1 . Together , our findings show that Jmjd3 plays a stage-dependent and locus-specific role during embryonic lung development . This function of Jmjd3 is associated with the epigenetic regulation of lung surfactant protein gene expression .
To study the in vivo functions of Jmjd3 during development , Jmjd3 KO mice were generated by homologous recombination technique [17] . Heterozygous Jmjd3+/− mice were fertile and viable . However , homozygous Jmjd3-deficient mice were not recovered at weaning time ( P21 ) from the heterozygous Jmjd3+/− mating pairs ( Table S1 ) , suggesting that Jmjd3 deficiency induces postnatal lethality . To confirm this , the embryos were genotyped and found to be consistent with a Mendelian ratio distribution at the late gestation stages ( E11 . 5 , E14 . 5 , E17 . 5 , E19 . 5 ) up to postnatal day 0 ( P0 ) . However , homozygous Jmjd3-deficient mice died shortly after birth ( P0 ) with kyphosis and severe lordosis ( Figure 1A ) . Jmjd3-deficient newborns and their wild-type ( WT ) littermates had pink skin color at birth; however , Jmjd3-deficient newborns became cyanotic within minutes due to respiratory failure ( Figure 1A ) . Consistent with this observation , we found that the lungs of Jmjd3-deficient mice were not inflated with air and much smaller in size compared with the lungs of WT mice ( Figure 1B ) . Hematoxylin & eosin ( H&E ) staining revealed that Jmjd3-deficient lungs were arrested in the late canalicular stage with limited sac spaces , undilated acinar bibules , and buds apparent in the peripheral regions . In contrast , WT lungs contained well-developed pre-alveoli and thinned out mesenchyme ( Figures 1C and S1A ) . The alveolar sacs and associated capillary beds were less developed in Jmjd3-deficient lungs compared with WT lungs . At E17 . 5 , Jmjd3-deficienct lungs were smaller with less developed lobes compared with WT lungs ( Figure S1B ) . H&E staining revealed the severe development defects ( Figure S1C ) . Together , these observations indicate that Jmjd3 is essential for lung development . In addition to the lung , Jmjd3 deficiency caused other embryonic defects . The yolk sac vascular plexus was less developed in Jmjd3-deficient embryos than in WT embryos at E14 . 5 ( Figure 1D ) . Jmjd3−/− embryos developed subcutaneous edema in the upper back at E14 . 5 , reflecting defective muscle and skeletal development ( Figure 1E ) . Consistent with these observations , H&E staining of sagittal sections of Jmjd3-deficient E11 . 5 embryos revealed multiple developmental defects , including delayed ganglia , somite , and spinal cord development ( Figure 1F ) . We also observed that the umbilical hernia at E18 . 5 was not absorbed in Jmjd3-deficient mice , which is hazardous for newborn mice survival ( Figure 1G ) . Because Jmjd3 has been associated with immune cell-mediated inflammation [32] , we examined the spleens of Jmjd3-deficient and WT embryos . The spleens of Jmjd3-deficient embryos were smaller and had multiple hyperemic areas compared with WT embryos ( Figure 1H ) . H&E staining also revealed the accumulation of blood cells in Jmjd3-deficient spleens ( Figure 1I ) . Jmjd3-deficient mice were also markedly smaller than their WT littermates at birth ( Figure 1A , 1J ) . These results indicate that Jmjd3 plays an important role in embryonic development . Because Jmjd3-deficient newborn mice died shortly after birth of respiratory failure , we sought to determine whether tissue-specific ablation of Jmjd3 recapitulates the global deletion phenotype . To do this , we first crossed Jmjd3f/f mice with CCSP-Cre mice in which Cre is expressed under the control of the CCSP promoter in Clara cells at early E15 [33] . Lung structure was similar between Jmjd3f/f:CCSP-Cre and WT mice ( Figure 2A , 2B ) , suggesting that Jmjd3 may not be important in Clara cells during lung development after E15 . Next , we crossed Jmjd3f/f mice with SPC-Cre mice in which the SPC promoter drives the expression of Cre recombinase gene in distal progenitor cells at E11 . 5 [34] . We found that Jmjd3f/f:SPC-Cre mice were viable from birth to adulthood without any dramatic defects in the lung architecture ( Figure 2A , 2C ) . To rule out the possibility that the H3K27me2/3 demethylase UTX compensates for the loss of Jmjd3 function in Jmjd3 deficient mice , we generated both Jmjd3 and UTX specific deletion mice by crossing Jmjd3floxed and Utxfloxed with SPC-Cre mice . Jmjd3f/f:Utxf/f:SPC-Cre mice exhibit a similar phenotype to that of Jmjd3f/f:SPC-Cre pups , indicating that UTX does not compensate for the loss of Jmjd3 ( Figure S2 ) . Because SPC and CCSP-driven expression of Cre are detectable after lung development initiation [35] , [36] , we reasoned that Jmjd3 is not critical in epithelial cells once lung development is initiated . To test this possibility , Jmjd3f/f mice were crossed with Wnt1-Cre mice in which Cre expression is driven by the Wnt1 gene promoter [37] , [38] . The Wnt1 protein is predominantly expressed in the inner cell mass of the blastocyst during the early stages of embryonic development ( E7 . 5 ) [39] . Wnt1-driven Cre is mainly expressed in neural crest cells [38] and intrinsic innervation of the lung [37] . Intrinsic nerve ganglia , which are derived from neural crest cells , are required for normal lung development and function [36] . Although the Wnt1-Cre model is often used to study neural crest formation , it is also used to study gene function during organogenesis in mice [39] . We found that all Jmjd3f/f:Wnt1-Cre newborn pups died within 8 h of respiratory failure ( Figure 2D ) . H&E staining showed that lung structure was severely affected with fewer sac spaces and undilated acinar bibules at different embryonic stages ( Figure 2E ) , suggests the potential involvement of Jmjd3 in the early embryonic stage of lung development . Because Jmjd3 is required for the early embryonic lung development , we further studied the importance of Jmjd3 at different embryonic stages . To do this , we crossed Jmjd3f/f mice with CAG-Cre/ESR mice in which Cre expression is globally induced with TM . Jmjd3f/f:CAG-Cre/ESR offspring from TM-treated maternal mice at E4 . 5 stage died shortly after birth and exhibited a phenotype similar to that of Jmjd3-deficienct embryos , whereas Jmjd3f/f and WT offspring of the same litter survived ( Figure 3A , 3B ) . In contrast , the survival rate was increased in Jmjd3f/f:CAG-Cre/ESR offspring from TM-treated maternal mice at E9 . 5 or later stages ( Figure 3A , 3C ) . Polymerase chain reaction ( PCR ) analysis showed Jmjd3 deletion in the lethal and surviving Jmjd3f/f:CAG-Cre/ESR pups ( Figure 3B , 3C ) . H&E staining showed that the lung structure of Jmjd3f/f;CAG-Cre/ESR offspring from TM-treated maternal mice at E4 . 5 was comparable to that of Jmjd3−/− newborns , whereas lung structure was similar between Jmjd3f/f:CAG-Cre/ESR surviving offspring from TM-treated maternal mice at E9 . 5 or later and WT mice ( Figure 3D ) . The lungs of Jmjd3f/f:CAG-Cre/ESR adult mice treated with TM did not show any structural abnormalities ( Figure 3D ) . Taken together , these findings indicate that Jmjd3 is dispensable in the later E9 . 5 embryonic stage of lung development . Having established a role for Jmjd3 in lung development , we next investigated the underlying molecular mechanisms by identifying the target genes of Jmjd3 in lung tissue . Affymetrix microarray analysis was performed on RNA samples isolated from WT and Jmjd3−/− E17 . 5 lungs . Of the 45 , 000 probes analyzed , 244 genes were downregulated and 190 genes were upregulated in Jmjd3-deficient lung tissues compared with WT lung tissues ( Table S2 ) . Representative genes with altered expression ( >2 fold ) are shown in Figure 4A . Importantly , Jmjd3 deficiency downregulated the expression of genes known to play important roles in lung development and function , including aquaporin-5 ( AQP-5 ) , SP-A , SP-B , SP-D , and Clara cell 10 kDa secretory protein ( CC10 ) . Consistent with this observation , qPCR and immunoblot analyses showed that AQP-5 , SP-B , SP-D , and CC10 mRNA and protein expression were markedly reduced in Jmjd3-deficient lung tissues compared with WT lung tissues ( Figure 4B , 4C ) . These results were further supported by histological analysis of AQP-5 , SP-B , and CC10 protein expression in Jmjd3-deficient lung tissues ( Figure 4D ) . SP-A RNA , but not protein , expression was significantly reduced in Jmjd3-deficient lung tissues compared with WT controls , whereas SP-C expression was not appreciably different ( Figure 4B , 4C ) . Although UTX and Ezh2 are involved in the control of H3K27 methylation , their expression levels were not affected by Jmjd3 deficiency in lung tissues ( Figure 4B , 4C ) . As a recent report implicated respiratory rhythm generator ( RRG ) and pre-Bötzinger complex dysfunction in the impaired respiratory function of Jmjd3 deficient mice [19] , we analyzed the expression of RRG-related and pre-Bötzinger complex genes in Jmjd3-deficient brainstem and lung tissues . qPCR analysis showed that the expression of several pre-Bötzinger complex-specific genes including Esrrg , Kirrel3 , and March4 was unchanged at E17 . 5 , but increased rather than decreased at P0 in the Jmjd3 deficient brainstem compared with the WT brainstem ( Figure S3A ) . Furthermore , the expression of these pre-Bötzinger complex-specific genes was not different between Jmjd3-deficient and WT E17 . 5 lung tissues ( Figure S3B ) . Respiratory muscles , including diaphragm and intercostal muscles , play an important role in breathing-like movement . Expression analysis of genes in diaphragm development including Fog2 , COUP-TFII and Wt1 by qPCR did not reveal a significant difference between WT and Jmjd3-deficient mice ( Figure S3C ) . To determine the stage-specific requirement of Jmjd3 in the regulation of lung development genes , we used the TM inducible and Wnt1-Cre deletion models . SP-B , CC10 , and AQP-5 immunostaining were negative in the lung tissues of Jmjd3f/f:CAG-Cre/ESR offspring from TM-treated maternal mice at E4 . 5 but were readily detectable in the lung tissues of Jmjd3f/f offspring from similarly treated maternal mice ( Figure 4E ) . This finding indicates that Jmjd3 deletion at E4 . 5 effectively reduces SP-B , CC10 , and AQP-5 expression in lung tissues , leading to defects in lung development and function . SP-B and AQP-5 immunostaining were also dramatically decreased in lung tissues of Jmjd3f/f:Wnt1-Cre mice at P0 ( Figure 4E ) . Together , these results suggest that Jmjd3 ablation specifically reduces the expression of AQP-5 , SP-B , SP-D , and CC10 genes in lung tissues and underscores the importance of Jmjd3 in the temporal and spatial regulation of genes that are critically required for lung development during embryonic development . Jmjd3 functions as a H3K27 demethylase in vitro [7]–[11]; therefore , we determined whether Jmjd3 ablation affects H3K27 methylation in vivo in the lung . Global H3K27 di- and trimethylation ( H3K27me2 and H3K27me3 , respectively ) were markedly increased in Jmjd3-deficient lungs compared with WT lungs ( Figure S4A ) . H3K4me3 was also slightly higher in Jmjd3-deficient lungs than in WT lungs , whereas H3K9 methylation was similar between Jmjd3-deficient and WT lungs ( Figure S4A ) . We also examined the methylation status in Jmjd3-deficient mouse embryonic fibroblasts ( MEFs ) by immunofluorescence staining and found increased H3K27me2 and H3K27me3 in Jmjd3−/− MEFs ( Figure S4B ) . Furthermore , immunoblot analysis also revealed that both H3K27me2 and H3K27me3 were markedly increased in Jmjd3−/− MEFs compared with WT MEFs , whereas appreciable differences in the methylation of H3K4 and H3K9 were not observed ( Figure S4C ) . To address whether the increase in H3K27 di- and trimethylation was directly related to Jmjd3 deletion , we examined the methylation status of Jmjd3-deficient MEFs transiently transfected with a FLAG-tagged Jmjd3-expressing retrovirus . H3K27 dimethylation and trimethylation were decreased in FLAG-tagged Jmjd3-overexpressing Jmjd3−/− MEFs ( Figure S4D ) , suggesting that Jmjd3 deletion alters global H3K27 methylation levels in MEFs and lung tissues . To further define changes in the H3K4 and H3K27 methylation status of Jmjd3 target genes in lung tissue , we performed ChIP-Seq analysis on WT and Jmjd3-deficienct lung tissues . Among the 35 , 000 RefSeq genes , 553 genes had increased H3K27me3 around 5 kb of the transcription start site ( TSS ) and 292 genes had decreased H3K27me3 in Jmjd3-deficient lungs compared with WT controls ( Figure 5A; Table S3 ) . The number of genes with increased H3K4me3 was greater than the number of genes with decreased H3K4me3 in Jmjd3-deficient lungs ( 106 vs . 50; Tables S3 , S4 ) , which was consistent with the immunoblot analysis of H3K4 methylation ( Figure 5A ) . ChIP-Seq analysis of individual genes revealed that H3K27me3 amounts in the upstream promoter regions of the TSS of SP-B were markedly increased in Jmjd3-deficient lung tissues compared with WT controls , whereas H3K4me3 in the gene body regions of SP-B was slightly reduced in Jmjd3-deficient lung tissues ( Figure 5B ) . The amounts of H3K4me3 around the TSS and gene body regions of AQP-5 , SP-D , and CC10 were also markedly decreased in Jmjd3-deficient lung tissues compared with WT controls ( Figure S5A ) . In contrast , we did not observe any appreciable differences in H3K4 and H3K27 methylation in the promoter and body regions of SP-C , ABCA3 , Nkx2 . 1 , Hoxb1 , Hoxa5 , Ezh2 , and UTX , whereas H3K4me3 , but not H3K27me3 , was slightly decreased in the SP-A gene ( Figure S5A ) . ChIP-Seq analysis also revealed that H3K27me3 and H3K4me3 methylation levels in the promoter regions or gene bodies of the RRG-related genes Phox2b , Tshz3 , Task2 , Maoa , Phox2a , Reln , and March4 were not different between Jmjd3-deficient and WT E17 . 5 lung tissues ( Figure S5B ) . To further validate the ChIP-Seq results , we performed ChIP-PCR analysis on WT and Jmjd3-deficient lung tissues at E17 . 5 . A strong band was detected around 2 kb upstream of the TSS of SP-B ( Figure 5C ) , indicating that Jmjd3 is recruited to the regulatory region of the SP-B gene . In addition , Jmjd3 was also bound to the upstream regions of the TSS of AQP-5 , SP-A , SP-D , and CC10 , but not to those of SP-C ( Figure S5C ) . Next , we assessed the effect of Jmjd3 on the histone methylation status of the SP-B promoter region using ChIP-qPCR . H3K27me3 was markedly increased in the A , B , C , and E regions of the SP-B gene promoter in Jmjd3-deficient lungs compared with WT controls ( Figure 5D ) . Increased levels of H3K27me3 were also observed in the promoter regions of CC10 and SP-D , but not in those of AQP-5 , in Jmjd3-deficient lungs ( Figure S5D ) . In contrast , the methylation levels of H3K4me3 in the promoter regions ( i . e . , B and C regions near the TSS ) of AQP-5 , CC10 , and SP-D were markedly lower in Jmjd3-deficient lungs than in WT controls ( Figure S5D ) . These results indicate that Jmjd3 ablation in lung tissues affects H3K27me3 and H3K4me3 levels in the promoter and gene body regions of target genes . To determine whether Jmjd3-mediated changes in histone methylation status were chromosome or locus-specific , we randomly analyzed the H3K27 and H3K4 methylation level of genes located in the region ( ∼280 kb ) containing SP-B on chromosome 6 and the region ( ∼160 kb ) containing AQP-5 on chromosome 15 . Our ChIP-Seq data showed that H3K27 and H3K4 methylation of both SP-B and AQP-5 were altered , whereas H3K27 and H3K4 methylation in neighbouring loci were not significantly changed . This data suggests that Jmjd3 targets specific loci for histone demethylation and does not affect histone methylation in adjacent chromosome regions ( Figure 5E ) . Jmjd3 is a general H3K27 demethylase without specific DNA binding properties and the mechanisms by which it regulates specific gene expression remain unknown . Therefore , we reasoned that Jmjd3 might be recruited to specific gene promoters through interactions with key transcription factors and/or epigenetic proteins . To test this possibility , we first determined whether Jmjd3 regulates target gene expression by interacting with Nkx2 . 1 . Nkx2 . 1 ( also known as thyroid transcription factor 1 ) is a key transcriptional activator of SP-B gene expression [40] . Coimmunoprecipitation and immunoblot analyses revealed that Jmjd3 interacted with Nkx2 . 1 in 293T cells expressing HA-tagged Jmjd3 and FLAG-tagged Nkx2 . 1 ( Figure 6A ) . To determine whether Jmjd3 affects the transcriptional activity of Nkx2 . 1 , we performed a luciferase assay on 293T cells cotransfected with Nkx2 . 1 , Jmjd3 , and mouse SP-B promoter-linked episomal luciferase vector ( containing Nkx2 . 1 binding sites ) [40] , [41] . Jmjd3 alone did not affect luciferase activity; however , it significantly increased the ability of Nkx2 . 1 to enhance SP-B-mediated luciferase activity ( Figure 6B ) . In contrast , an interaction between exogenous UTX and Nkx2 . 1 was not observed in 293T cells ( Figure S6 ) . Thus , the Nkx2 . 1 interaction appears to be specific to histone demethylase Jmjd3 . These findings suggest that Jmjd3 regulates SP-B expression by interacting with Nkx2 . 1 in the SP-B promoter region . Brg1 , an ATPase subunit of the Swi/Snf chromatin remodeling complex , has been shown to cooperate with Nkx2-1 to regulate SP-B expression [40] . Jmjd3 interacted with Brg1 in 293T cells expressing HA-tagged Jmjd3 and FLAG-tagged Brg1 ( Figure 6C ) , consistent with the results of a previous study [42] . Furthermore , Jmjd3 significantly enhanced Nkx2 . 1 and Brg1-mediated SP-B promoter activity ( Figure 6D ) . To determine the region of Jmjd3 that interacts with Nkx2 . 1 and Brg1 , we generated four HA-tagged Jmjd3 truncated constructs: Jmjd3N ( 1–450 aa ) , Jmjd3M ( 450–1200 aa ) , Jmjd3C ( 1201–1682 aa ) , and Jmjd3NM ( 1–1200 aa ) ( Figure 6E ) . The N-terminus of Jmjd3 ( Jmjd3N ) strongly interacted with Nkx2 . 1 , whereas the C-terminus of Jmjd3 ( Jmjd3C ) specifically interacted with Brg1 ( Figure 6F , 6G ) . Importantly , the endogenous interaction of Jmjd3 with Nkx2 . 1 and Brg1 was readily detected in WT , but not in Jmjd3-deficient , lung tissues at E17 . 5 ( Figure 6H ) , suggesting that Jmjd3 may be required for Nkx2 . 1/Brg1 complex formation on the SP-B promoter region . However , the interaction between Nkx2 . 1 and Brg1 was still detected in Jmjd3-deficient lung tissues , although to a lesser extent compared with WT tissues ( Figure 6H ) . Therefore , Jmjd3 may also influence the interaction between Nkx2 . 1 and Brg1 . The role of Jmjd3 in the Nkx2 . 1/Brg1 interaction was confirmed by ChIP-qPCR , which showed a marked reduction in Nkx2 . 1 and Brg1 binding to SP-B promoter regions in Jmjd3-deficient lung tissues ( Figure 6I ) . Taken together , our results suggest that Jmjd3 activates SP-B expression by specifically interacting with Nkx2 . 1 and Brg1 to form Nkx2 . 1-Brg1-Jmjd3 complexes . We next asked whether the demethylase activity of Jmjd3 is required for regulating SP-B expression . To address this , a luciferase assay was performed on 293T cells cotransfected with SP-B promoter-linked episomal luciferase vector , Nkx2 . 1 , and Brg1 in the presence of WT or mutant Jmjd3 ( a loss of function mutation in H1390A in the catalytic domain of the demethylase ) . WT Jmjd3 significantly enhanced Nkx2 . 1 and Brg1-mediated SP-B promoter activity , whereas mutant Jmjd3 failed to do so ( Figure 7A ) . To further test the ability of mutant Jmjd3 to rescue SP-B expression , WT and mutant Jmjd3 constructs were ectopically expressed in human H441 cells stably transfected with Jmjd3-specific shRNA . WT , but not mutant , Jmjd3 rescued endogenous SP-B mRNA and protein expression ( Figure 7B , 7C ) . We next determined whether the catalytic domain ( located at the C-terminus ) of Jmjd3 was sufficient to regulate SP-B expression . Full-length Jmjd3 markedly enhanced the luciferase activity of the SP-B promoter compared with truncated Jmjd3 containing only the catalytic domain ( Figure S7 ) . Taken together , these results suggest that full-length Jmjd3 is required for regulating SP-B expression in a demethylase activity-dependent manner .
In this study , we show that Jmjd3 is important for organogenesis during embryonic development , as evidenced by the multiple organs defects induced by global Jmjd3 deficiency . The importance of Jmjd3 in embryonic lung development appears to be associated with its epigenetic regulation of target genes required for lung development and function . However , our results obtained from lung epithelial cell-specific deletion of Jmjd3 crossed with SPC-Cre and CCSP-Cre mice suggest Jmjd3 is dispensable for lung development at E9 . 5 or later embryonic stages . This notion is further supported by results obtained with Jmjd3 deletion in TM-inducible Cre mouse models . To investigate whether Jmjd3 plays a role in the lung mesenchyme or the communication between the mesenchyme and epithelium cells , further experiments using other Cre mice such as Dermo-Cre for Jmjd3 deletion are needed and currently under way . Our study identifies key target genes including SP-B , CC10 and AQP-5 , involved in lung development and function . Changes in jmjd3 target gene expression are associated with locus-specific methylation alterations of H3K27 and H3K4 , providing new insights into the role and molecular mechanism of action of Jmjd3 in embryonic lung development . Our results show that the global deletion of Jmjd3 results in perinatal lethality in mice shortly after birth . Similarly , several studies have also shown that Jmjd3 deficiency is associated with a lethal phenotype in mice; however , the severity of the lethal phenotype varies according to the deletion strategy used to generate Jmjd3 KO mice [15] , [18] , [19] . In the present study , deletion of the demethylase catalytic domain of Jmjd3 induces perinatal lethality 30 min after birth . Satoh et al . [15] reported similar findings with conventional deletion of the same Jmjd3 catalytic domain . In both our own and the Satoh et al . [15] studies , the perinatal lethality of the Jmjd3 global deletion phenotype is associated with respiratory failure caused by defects in lung development . By contrast , Jmjd3 deletion mice generated using a gene trap strategy to insert a neo-cassette between exons 1 and 2 exhibit postnatal lethality , some pups survive up to one day [19] . The surviving pups may be attributed to the leaky expression of Jmjd3 associated with the gene-trap strategy . Despite similar postnatal lethality caused by respiratory failure , lung development and structure are normal in the Jmjd3 TRAP insertion mice [19] , suggesting that two inactivation strategies ( targeted deletion of functional domain and gene-trap ) show different phenotypes in lung development and function . Furthermore , Ohtani et al . [18] showed that mice with complete abrogation of Jmjd3 expression through a deletion ( exons 4 to 5 ) -induced frameshift exhibit a more severe phenotype characterized by embryonic lethality before E6 . 5 . Taken together , these studies show that mice generated by different Jmjd3 deletion strategies develop different phenotypes . It is likely that Jmjd3 deletion causes multiple defects in different organs or tissues by affecting different Jmjd3 target genes through different mechanisms . In our study , Jmjd3-deficient mice die shortly after birth of respiratory failure associated with defective lung development . Consistent with our findings , Satoh et al . [15] showed that conventional deletion of the Jmjd3 catalytic domain results in premature lung development in mice; however , target genes and mechanistic study of Jmjd3 in lung development was not provided in their study . In a recent study , Burgold et al . [19] also found that the perinatal lethality of Jmjd3 deficiency is caused by respiratory failure . However , lung development was normal in Jmjd3-deficient mice , and postnatal lethality was associated with Jmjd3-mediated disruption of the embryonic respiratory neuronal network and RRG . To determine whether Jmjd3 deficiency affects RRG-related or pre-Bötzinger complex gene expression in lung tissues at E17 . 5 , we fail to provide evidence that RRG-related or pre-Bötzinger complex gene expression is altered in our Jmjd3-deficient mice . Consistent with these findings , Jmjd3 deficiency does not affect H3K27me3 or H3K4me3 methylation levels in either the promoter regions or gene bodies of RRG-related genes . Furthermore , we show no appreciable difference in the expression of Fog2 , COUP-TFII and Wt1 genes in respiratory muscles at P0 between WT and Jmjd3-deficient mice . Different phenotypes observed in different Jmjd3 inaction mice might be due to differences in leaky expression , targeted deletion of catalytic domain or whole coding region . It has been demonstrated that Jmjd3 affects different target gene expression or proteins through demethylase activity-dependent and independent mechanisms [15] , [17] , [43] . Taken together , these studies suggest that more than one mechanism is responsible for the observed different phenotypes . Our findings presented here indicate that Jmjd3 deficiency downregulates a set of genes including SP-B critical to lung maturation and function at E17 . 5 . SP-B , a surfactant expressed in type II cells , is required for postnatal lung function and survival [26]–[28] . Complete SP-B deficiency in mice and humans results in lethal , neonatal RDS [23] , which is characterized by a virtual absence of lung compliance and increased amounts of incompletely processed proprotein SP-C [26] , [27] , [29]–[31] . Similar to the Jmjd3 deletion phenotype , SP-B-deficient mice die of respiratory failure immediately after birth [30] . Together , these findings indicate that the impaired respiratory function in the Jmjd3 deletion phenotype is associated with Jmjd3-mediated regulation of lung-specific gene expression . To provide definitive evidence that the phenotype of Jmjd3-deficient mice is caused by downregulation of SP-B gene , experiments using Jmjd3-deficient mice crossed with SP-B-inducible mice in a SP-B deficient background are under way to determine whether inducible SP-B expression could rescue the phenotype of Jmjd3-deficient mice . Since Jmjd3 has been shown to regulate gene expression in a demethylase activity-independent [43] and -dependent manner [15] , we determine whether the demethylase activity of Jmjd3 is required for the regulation of target gene expression . Jmjd3 deletion alters the H3K27 and H3K4 methylation levels in the promoter and gene body regions of a select set of target genes associated with lung function and development . A strong correlation between H3K4 and H3K27 methylation level and AQP-5 , SP-B , SP-D , and CC10 expression is also observed , whereas a weak correlation is observed in many other genes . Our findings are consistent with a previous study showing that changes in H3K27me3 level and gene expression are associated in a limited number of genes in Jmjd3-deficient macrophages [15] . SP-B deficiency caused by genetic mutations has been implicated as the principal cause of infant RDS; however , loss or partial reduction of SP-B expression has also been observed in patients without SP-B gene mutations [27] . These observations suggest the potential involvement of epigenetic mechanisms in surfactant expression . Using ChIP-qPCR , we found that Jmjd3 deficiency markedly increases H3K27me3 at the SP-B promoter in lung tissues . Thus , Jmjd3 deletion may lead to the loss of SP-B epigenetic regulation . The chromatin modifier Brg1 has been shown to cooperate with Nkx2-1 to regulate SP-B and SP-A expression [40] . Consistent with these reports , we found that Jmjd3 specifically upregulates SP-B expression by interacting with Nkx2-1 and Brg1 . Our ChIP-Seq analysis also indicates that Jmjd3 demethylation is locus-dependent . There are several possible reasons for this . First , the H3K27 demethylase UTX may functionally overlap with Jmjd3 and control methylation in the same or different set of genes . A recent study showed that UTX deletion results in embryonic lethality associated with cardiac defects [44] . In our study , we found that UTX does not compensate for the loss of Jmjd3 as evidenced by the similar phenotype between Jmjd3f/f:Utxf/f:SPC-Cre and Jmjd3f/f:SPC-Cre mice . Furthermore , Jmjd3 , but not UTX , specifically interacts with Nkx2 . 1 to regulate SP-B expression , indicating that these two demethylases regulate distinct subsets of genes . Second , recent studies have indicated that the methylation status of enhancer regions is critically important in the control of gene expression [45] , [46] . Thus , Jmjd3 may also control gene expression by affecting H3K27me3 level in gene enhancer regions rather than promoter regions . Third , similar to UTX , Jmjd3 may also form a complex with other histone modification proteins , such as H3K4 methyltransferases [32] , [47] . Thus , Jmjd3 deletion may potentially affect both H3K27 and H3K4 methylation in the promoter and gene-body regions of target genes , which is supported by our results . Finally , Jmjd3 is recruited to the SP-B promoter through N-terminal and C-terminal interactions with Nkx2 . 1 and Brg1 , respectively . Therefore , Jmjd3 activates SP-B expression by acting as a bridge to promote the formation of Nkx2 . 1-Brg1 stable complexes . In summary , through the generation of conventional and conditional Jmjd3 KO mice , our study provides in vivo evidence that Jmjd3 is required for the early lung development , but dispensable for lung development at E9 . 5 or later embryonic stages . Furthermore , we have identified important Jmjd3 target genes that are critical for lung development and potential mechanisms by which Jmjd3 interacts with Brg1 and NKx2 . 1 to regulate histone methylation status in the promoter regions of these lung development genes .
Jmjd3 floxed mice ( Jmjd3f/f ) and Jmjd3−/− mice were generated by homologous recombination technique [17] . Mice were bred and maintained at the pathogen-free animal facilities of Baylor Medical School and the Houston Methodist Research Institute . Animal procedures were approved by the Animal Care and Use Committee at Baylor College of Medicine and Houston Methodist Research Institute . To generate inducible Jmjd3 deletion in mice , TM ( Sigma T5648 ) was dissolved in corn oil ( Sigma ) at a concentration of 10 mg/ml and intraperitoneally ( IP ) injected into 8-week-old mice at 9 mg TM/40 g of body weight [48] , [49] . Pregnant mice received 3–4 mg TM/40 g of body weight by IP injection for 2 consecutive days . Pregnant mice were injected with TM at different time points to recover E4 . 5 , E9 . 5 , and E14 . 5 embryos . The newborn pups were genotyped with tail and lung tissue , and lung structure and development were assessed . The Wnt1-Cre strain was obtained from the Jackson Laboratory ( 022137 ) . SPC-Cre , and CCSP-Cre and Utxfloxed mice were kindly provided by Dr . Brigid Hogan ( Duke University Medical Center ) , Dr . Frank DeMayo ( Baylor College of Medicine ) and Dr . Kai Ge ( National Institutes of Health ) , respectively . Total RNAs were isolated from E17 . 5 lung tissues and primary MEFs using Trizol reagent ( Invitrogen ) . A total of 1 µg of RNA was converted to cDNA using Superscript III Reverse Transcriptase ( Qiagen ) with random hexamer primers . qPCR was carried out using SYBR Green mix on the ABI 7000 PCR machine . Jmjd3 expression in heterozygous and homozygous WT MEFs and WT and Jmjd3−/− E17 . 5 lung tissues was determined using the following Jmjd3 primer set: ( forward ) 5′-AAGTGGGGACAAGGAGACCT and ( reverse ) 5′-AAGTGGGGACAAGGAGACCT . WT and Jmjd3−/− E17 . 5 lung tissues were also evaluated for the expression of SP-A ( 5′-CTCCAGACCTGTGCCCATATG and 5′-ACCTCCAGTCATGGCACAGTAA ) , SP-B ( 5′-ACGTCCTCTGGAAGCCTTCA and 5′-TGTCTTCTTGGAGCCACAACAG ) , SP-C ( 5′-ACCCTGTGTGGAGAGCTACCA and 5′-TTTGCGGAGGGTCTTTCCT ) , SP-D ( 5′-CTCTGAGGCAGCAGATGGA and 5′-ATCAGGGAACAATGCAGCTT ) , CC10 ( 5′-TCCTAACAAGTCCTCTGTGTAAGA and 5′-AGGAGACACAGGGCAGTGACA ) , AQP-5 ( 5′-ATGAACCCAGCCCGATCTTT and 5′-ACGATCGGTCCTACCCAGAAG ) , ABCA3 ( 5′-TTGCCCTCATTGGAGAGCCTG and 5′-TCCGGCCATCCTCAGTGGTGGG ) , Nkx2 . 1 ( 5′-TCCAGCCTATCCCATCTGAACT and 5′-CAAGCGCATCTCACGTCTCA ) , UTX ( 5′-ATCCCAGCTCAGCAGAAGTT and 5′-GGAGGAAAGAAAGCATCACG ) , Ezh2 ( 5′-GCCAGACTGGGAAGAAATCTG and 5′-TGTGCTGGAAAATCCAAGTCA ) , Hoxa5 ( 5′-TCTCGTTGCCCTAATTCATCTTT and 5′-CATTCAGGACAAAGAGATGAACAGAA ) , Hoxb1 ( 5′-TTGCCCTGGAAACTGTAAAG and 5′-AATTTGCCAACAACCCATC ) , Esrrg ( 5′- TGCACCGGGCTCTGTCAAGGAA and 5′-AATCCATGTGCGCCCGACAACC ) , Reln ( 5′-TCCAGGCTCAGCACCAAGCCAA and 5′- TGGATCTTGCCTTCTGACGCCCTT ) , Kirrel3 ( 5′-AGAAAGTCACAGCTCCGCTCGGT and 5′-ACGGGAGGGTTGCAGAAAGGCT ) , March4 ( 5′-GCCCCTCCCCTTGGTTCCATCAAA and 5′-CGAGGAGGAGAAAGCGAAGCCACT ) , Fog2 ( 5′-CGCCTTTGTGGTGGACTTTGACT and 5′-GCTTCTCGTTGCCTCCCACTACA ) , COUP-TFII ( 5′-AGTACTGCCGCCTCAAAAAG and 5′-CGTTGGTCAGGGCAAACT ) , Wt1 ( 5′-AATGCGCCCTACCTGCCCA and 5′- CCGTCGAAAGTGACCGTGCTGTAT ) and β-Actin ( 5′-GTGGGCCGCTCTAGGCACCA and 5′-TGGCCTTAGGGTTCAGGG ) . Microarray analysis was performed on RNA samples prepared from E17 . 5 Jmjd3+/+ and Jmjd3−/− lungs using the Affymetrix Genechip microarray system . RNA quality assessment and microarray analysis were performed by the Microarray Core Facility of Baylor College of Medicine . RNA quality was checked using the NanoDrop ND-1000 spectrophotometer and Agilent 2100 Bioanalyzer . Total RNA samples of 250 ng were labeled using the 3′ IVT Express Kit with the new standard Affymetrix linear amplification protocol and then reverse transcribed to produce double-stranded cDNA . The cDNA product was used as a template for the in vitro transcription of biotin-labeled cRNA . The labeled cRNA was quantified using the NanoDrop ND-1000 spectrophotometer . A total of 15 . 0 µg of the labeled cRNA was fragmented and the concentration rechecked . A hybridization cocktail containing Affymetrix spike-in controls and fragmented labeled cRNA was loaded onto a GeneChip array . The array was hybridized for 16 h at 45°C with rotation at 60 rpm and then washed and stained with a strepavidin , R-phycoerythrin conjugate stain . Signal amplification was done using biotinylated anti-streptavidin . The stained array was scanned on the Affymetrix GeneChip Scanner 3000 . The images were then analyzed and quality control metrics were recorded using Affymetrix GCOS software version 1 . 1 . 2 . Pairwise comparisons were made between WT and Jmjd3-deficient lungs . Raw P values were adjusted by the Benjamini-Hochberg method for 5% false discovery rate to yield adjusted p values . The criteria for significance of differentially regulated genes were established as ≥1 . 3-fold change with an adjusted P value<0 . 05 . Pathways were analyzed ( Ingenuity Systems ) to determine the ratios of known genes within each pathway that were significantly changed between WT and KO lungs . Lung tissues were collected , ground into fine powders in liquid nitrogen , and transferred to lysis buffer ( 50 mM Tris-HCl [pH 7 . 4] , 0 . 1% SDS ) supplemented with protease inhibitor cocktail ( cOmplete Mini Protease Inhibitor , 14132300 ) and 2 µM phenylmethylsulphonyl fluoride ( PMSF ) . Cells were washed in phosphate-buffered saline ( PBS ) , harvested , and lysed in RIPA buffer supplemented with protease inhibitor cocktail and 2 µM PMSF . After sonication and centrifugation , protein supernatants were collected , and protein concentrations were determined using a Bradford Protein Assay Kit ( Sigma ) . Protein extracts were boiled in SDS sample buffer for 5 min , loaded directly onto a 4–12% SDS gel , transferred onto nitrocellulose membranes ( Bio-Rad ) , blocked with 5% milk , and incubated with primary and corresponding secondary antibodies . The following antibodies were used: anti-H3K4me1 ( 07-436 ) , anti-H3K4me2 ( 07-030 ) , anti-H3K4me3 ( 07-473 ) , anti-H3K9me1 ( 07-450 ) , anti-H3K9me2 ( 07-441 ) , anti-H3K9me3 ( 07-442 ) , anti-H3K27me1 ( 07-448 ) , anti-H3K27me2 ( 07-452 ) , and anti-H3K27me3 ( 07-449 ) from Millipore; anti-SP-A ( sc-7700 ) , anti-AQP-5 ( sc-9891 ) ; anti-SP-B ( sc-13978 ) , anti-SP-C ( sc-13979 ) , anti-SP-D ( sc-13980 ) , and anti-CC10 ( sc-25555 ) from Santa Cruz Biotechnology; anti-H3 ( 9715 ) and Ezh2 ( 4905 ) from Cell Signaling; anti-CD11b ( 17-0113-81 ) , anti-F4/80 ( 15-4801-82 ) , and anti-Gr1 ( 17-5931-81 ) from eBioscience; mouse anti-Flag , anti-HRP-flag , and anti-β-actin from Sigma; rabbit anti-UTX ( ab36938 ) from ( Abcam ) ; and anti-Jmjd3 ( ab1022a ) from Abgent . Tissues and embryos were fixed in 10% neutral formalin . The fixation time was dependent on embryonic stage: E<12 . 5 were fixed for 2 h , E12 . 5-E17 for 4 h , and E>17 . 5 for 24–48 h as previously described [50] . After fixation , tissues and embryos were embedded in paraffin and cut into 5-µm thick sections . H&E staining and immunostaining were performed according to standard protocols [51] . MEFs were isolated from WT and Jmjd3 KO embryos at E11 . 5 [51] . Immunocytofluorescence was performed according to a standard protocol using primary antibodies as mentioned above and corresponding goat anti-FITC-conjugated rabbit secondary antibodies ( 115-096-003; Jackson ImmunoResearch Laboratories ) or goat anti-Texas Red-conjugated mouse secondary antibodies ( 81–6114; ZyMax Grade ) . Chromatin was prepared from lung tissues of E17 . 5 embryos . ChIP-PCR and ChIP-qPCR were performed using Jmjd3 antibody ( Abgent ) and selected histone marks , respectively . Immunoprecipitated chromatin DNA and input were used for ChIP-PCR and ChIP-qPCR with specific primers . Briefly , whole lungs of E17 . 5 embryos were quickly chopped into tiny pieces with a scalpel and fixed in 1% formaldehyde in 1X PBS at room temperature for 15 min . Glycine was added to a final concentration of 0 . 125 M . Samples were rotated at room temperature for another 5 min , centrifuged at low speed for 5 min , and the supernatants decanted . The pellets were washed once with ice-cold 1X PBS and disaggregated mechanically through a 0 . 5-µm cell strainer in 1 ml of ice-cold 1X PBS . Immunoprecipitation was performed on single cell suspensions using the ChIP Assay Kit according to the manufacturer's instructions ( Millipore , 17–295 ) . Antibodies used included anti-Jmjd3; anti-mono- , di- , and trimethylated H3K4 antibodies; anti-mono- , di- , and trimethylated H3K9 antibodies; and anti-mono- , di- , and trimethylated H3K27 antibodies . DNA binding of Jmjd3 and methylation of lung marker genes including AQP-5 , SP-A , SP-B , SP-C , SP-D , and CC10 were evaluated by ChIP-PCR and ChIP-qPCR using the following specific primers: AQP-5-AF , 5′-AACCTGCGGAGGGGGAAGGT; AQP-5-AR , 5′-CGTCCCCACCCCCACTCCAT; AQP-5-BF , 5′-CACCACCCCAGGGTCCCCAA; AQP-5-BR , 5′-CCTGCTCTGCGCTCGGCAAT; AQP-5-CF , 5′-CAGGAGAGCCCAGCACGCAC; AQP-5-CR , 5′-CAGTGTAGACTGGCCCGGCG , SP-A-AF , 5′-GCACACGTACGGAAGCCGGA; SP-A-AR , 5′-CCTGCGGTGCTCAGTGGCAA; SP-A-BF , 5′-CCTTTCTGCTTCTTTCCTATGGCCG; SP-A-BR , 5′-AGCAAAACATCAGAACAACCAAAACTA; SP-A-CF , 5′-ATGGCTGCTTCCTGTCCGGC; SP-A-CR , 5′-CCCGCACACAGAGCCTGCAA , SP-B-AF , 5′-AGGGCCCGGACACATAGAGG; SP-B-AR , 5′-CTGAGGCCCAGGGCAGAGGT; SP-B-BF , 5′-GGGTGTGAGGTGACACGCCG; SP-B-BR , 5′-CTGTGGTGGGGGTGACCACG; SP-B-CF , 5′-TGTCACCAGTGGCACAGTGGAA; SP-B-CR , 5′-AGGTGAGCACTGCCATACCAGG; SP-B-DF: 5′-GTAGAGGATTGAGAAGCCT; SP-B-DR , 5′-CAGCCTGACTTTGTTCAC; SP-B-EF , 5′-CGTGACTCTCTGAAGAAGGC; SP-B-ER: 5′-AAACGACACCCTGGAAGTG; SP-B-FF , 5′-CCCTTGTTTGACGGTGAA; SP-B-FR: 5′-TGGCTGCCTACTGCTTAGT; SP-C-AF , 5′-ACAGGCAATCCCAGATCGCTGA; SP-C-AR: 5′-GTCCCAGCCATCTCTGCCCCT; SP-C-BF , 5′-AGAGGGGCAGAGATGGCTGGG; SP-C-BR , 5′-GCTTGGGACAGCACCTGGGG; SP-C-CF , 5′-AGGCCCAGTCCTTCACCCCTG; SP-C-CR , 5′-GCCTACTGGAGGATGGACAGTCA; SP-D-AF , 5′-TCAGAGGACTAATGACAGCCTT; SP-D-AR , 5′-CAGCAGGGACAGACATACCA; SP-D-BF , 5′-GGAAGATGGAAGAACAAGGG; SP-D-BR , 5′-CTACAAAGGCAGCAACCTG; SP-D-CF , 5′-TGTGTGTGTGTGTGTGTGTGA; SP-D-CR , 5′-CACCTCTGTTTGTCAGGCTC; CC10-AF , 5′-TCCCACCAGCACCATAGTA; CC10-AR , 5′-CACCTTCTCCATTTCCACA; CC10-BF , 5′-CTAACAATGCCCAAGAATCG ; CC10-BR , 5′-GGAGACCCTTCAGGAATCA; CC10-CF , 5′-CTCCACTGCCTGAATACTCC; CC10-CR , 5′-ACTTGGTCATCTTCTCCGTG . Lungs of E17 . 5 Jmjd3+/+ and Jmjd3−/− embryos were collected and prepared for ChIP-PCR as previously described [17] . A total of 200 ng of DNA was used for the ChIP-Seq library construction . Illumina sequencing was performed as previously described [52]–[54] . Sequencing reads from H3K4me3 and H3K27me3 ChIP-Seq libraries were aligned to the mouse mm8 genome using ELAND software . To reduce PCR amplification bias , only one uniquely mapped read per genomic position was retained . The histone modification changes upon Jmjd3 knockout were assessed using the fold changes of background-subtracted read densities around 5 kb and 2 kb of the TSSs . The statistical significance of the fold change was assessed using the MA-plot-based method . The fold change and the false discovery rate cutoffs were set to 1 . 5 and 10% , respectively . Data are represented as mean ± standard deviation ( SD ) where indicated . Statistical analysis was performed using the Student's t test with GraphPad Prism 4 . 0 software . A P value<0 . 05 was considered significant .
|
A chromosome in the eukaryotic nucleus is an organized package of DNA coiled around histone proteins . DNA contains genes and other nucleotide sequences as well as histone proteins including H1 , H2A , H2B , H3 , and H4 . Gene expression is dynamically regulated by DNA and histone modifications , such as methylation and demethylation . The protein Jumonji domain containing-3 ( Jmjd3 ) is a critical demethylase that regulates gene expression . Here , we found that Jmjd3 plays an important role in the regulation of mouse lung development . Global Jmjd3 deletion results in perinatal lethality that is associated with respiratory failure caused by defective lung development . Tissue and stage-specific deletion show that Jmjd3 is dispensable for mouse lung development in the later stages ( after E9 . 5 ) . Jmjd3 deficiency downregulates the expression of genes critical for lung development through interactions with specific transcription factors and epigenetic protein complexes . Our findings provide new insights into the role and molecular mechanism of action of Jmjd3 in embryonic lung development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"histology",
"anatomy",
"genetics",
"biology",
"and",
"life",
"sciences"
] |
2014
|
Stage-Dependent and Locus-Specific Role of Histone Demethylase Jumonji D3 (JMJD3) in the Embryonic Stages of Lung Development
|
Chagas' disease is an important neglected public health problem in many Latin American countries , but population-based epidemiological data are scarce . Here we present a nationwide analysis on Chagas-associated mortality , and risk factors for death from this disease . We analyzed all death certificates of individuals who died between 1999 and 2007 in Brazil , based on the nationwide Mortality Information System ( a total of 243 data sets with about 9 million entries ) . Chagas' disease was mentioned in 53 , 930 ( 0 . 6% ) of death certificates , with 44 , 537 ( 82 . 6% ) as an underlying cause and 9 , 387 ( 17 . 4% ) as an associated cause of death . Acute Chagas' disease was responsible for 2 . 8% of deaths . The mean standardized mortality rate was 3 . 36/100 . 000 inhabitants/year . Nationwide standardized mortality rates reduced gradually , from 3 . 78 ( 1999 ) to 2 . 78 ( 2007 ) deaths/year per 100 , 000 inhabitants ( −26 . 4% ) . Standardized mortality rates were highest in the Central-West region , ranging from 15 . 23 in 1999 to 9 . 46 in 2007 ( −37 . 9% ) , with a significant negative linear trend ( p = 0 . 001; R2 = 82% ) . Proportional mortality considering multiple causes of death was 0 . 60% . The Central-West showed highest proportional mortality among regions ( 2 . 17% ) , with a significant linear negative trend , from 2 . 28% to 1 . 90% ( −19 . 5%; p = 0 . 001; R2 = 84% ) . There was a significant increase in the Northeast of 38 . 5% ( p = 0 . 006; R2 = 82% ) . Bivariable analysis on risk factors for death from Chagas' disease showed highest relative risks ( RR ) in older age groups ( RR: 10 . 03; 95% CI: 9 . 40–10 . 70; p<0 . 001 ) and those residing in the Central-West region ( RR: 15 . 01; 95% CI: 3 . 90–16 . 22; p<0 . 001 ) . In logistic regression analysis , age ≥30 years ( adjusted OR: 10 . 81; 95% CI: 10 . 03–10 . 65; p<0 . 001 ) and residence in one of the three high risk states Minas Gerais , Goiás or the Federal District ( adjusted OR: 5 . 12; 95% CI: 5 . 03–5 . 22 , p<0 . 001 ) maintained important independent risk factors for death by Chagas' disease . This is the first nationwide population-based study on Chagas mortality in Brazil , considering multiple causes of death . Despite the decline of mortality associated with Chagas' disease in Brazil , the disease remains a serious public health problem with marked regional differences .
American trypanosomiasis ( Chagas' disease ) is an anthropozoonic vector-borne parasitic infection caused by the protozoan parasite Trypanosoma cruzi [1]–[4] . Other important infection routes include blood transfusion , vertical transmission , organ transplantation , and oral transmission [1] , [2] . There are acute ( often asymptomatic ) and chronic phases of the disease . In the case of untreated acute disease , the infection may persist for many years and even decades . Chronic disease manifests as indeterminate , cardiac , gastrointestinal and neural forms [2] . As a chronic condition , the infection may be associated with other chronic diseases further increasing mortality [5] . Chagas' disease is the sixth most important tropical infection in the world in terms of global burden of disease [6] , with high social and economic impact throughout the endemic area [7]–[9] . Chronic Chagas' disease is a public health problem faced by many Latin American countries , from Mexico in the north to Argentina in the south . There are about 15 to 18 million people infected in Latin America , and approximately 100 million are at potential risk for infection [3] , [8] . An estimated 14 , 000 people die from the disease each year worldwide [3] . Single autochthonous cases were notified in the southern United States , but thousands are estimated [4] , [10] . Chronic cardiac disease is the leading cause of disability-adjusted life years ( DALYs ) lost in young economically active adults in endemic countries , with considerable social , public health and economic consequences [2] . The migration of infected individuals to non-endemic countries has become an emerging public health problem in these regions , mainly in the United States and European countries [3] . The principal means of transmission in these countries are from blood transfusion , organ transplantation and mother-to-child transmission [11] . In Brazil , the number of new Chagas' disease cases has been reduced dramatically in recent years , owing mainly to reduction of vector transmission ( mostly by the kissing bug Triatoma infestans ) and control of infection by blood transfusion [8]–[11] . However , there is still a plethora of people living with chronic forms of the disease . About 2 to 3 million people are estimated to be infected in Brazil [7] , [12] , 600 , 000 of them with chronic heart or digestive complications , causing death in about 5 , 000 individuals each year [13] . Despite the importance for health policy and planning , mortality statistics from endemic countries have not yet been used systematically to assess the impact of control measures against Chagas' disease [14] . To fill this gap , we present the first Brazilian nationwide population-based study on Chagas mortality , using multiple causes of death , as available on death certificates . We analyzed mortality trends over time , geographical regions mostly affected , and factors associated with the occurrence of death caused by Chagas' disease .
Brazil has a population of approximately 190 million inhabitants . The country is divided into 26 States and one Federal District . The Federation is further grouped into five major regions ( North , Northeast , Southeast , South and Central-West ) with different geographic , economic and cultural characteristics . We analyzed all death certificates of individuals who died between 1999 and 2007 in Brazil . As data source we used the Brazilian death certificates , standardized by the Mortality Information System ( Sistema de Informação de Mortalidade - SIM ) of the Ministry of Health , a national electronic database . SIM data are public domain and were obtained from the website of the Department of the Unified Health System , DATASUS ( http://tabnet . datasus . gov . br/tabdata/sim/dados/cid10_indice . htm ) . Death certificates contain demographic ( age , gender , education , race , marital status , municipality of residence and municipality of occurrence of death ) and clinical information ( underlying and associated causes of death ) . It is the physicians' responsibility to complete the death certificate forms . Until 1995 , reference codes were based on the International Classification of Diseases ( ICD ) in its 9th revision , and after 1996 in its 10th revision [15] . We identified Chagas-related deaths using category B57 ( “Chagas' disease” ) including all subcategories ( B57 . 0 to B57 . 5 ) which represent both acute and chronic clinical forms , according to the Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems ( ICD-10 ) [15] . Population estimates were obtained from the Brazilian Institute of Geography and Statistics ( Instituto Brasileiro de Geografia e Estatística - IBGE ) based on a national population census in 2000 and yearly official estimates ( 1999–2007 ) . The census is a fundamental source of information from the entire Brazilian population , and data collection and analysis are subject to supervision and quality control . This study was based on publicly available secondary anonymous data , with no possibility of identification of individuals . Thus , approvement of the study by a Ethical Review Board was not necessary . A total 243 data sets with about 9 million entries were downloaded and processed ( one data set for each of the 27 federal states and each of the 9 years , from 1999–2007 ) . In a first step , we checked data sets for completeness in relation to the total number of deaths . Field codes from different data sets were standardized and variables not considered in the analysis eliminated . We then identified all death certificates in which Chagas' disease was recorded in any line of the certificate as cause of death ( both underlying and associated causes ) . We created new variables for causes of death , as in many cases more than one cause was noted in a line . Then , Chagas-related mortality rates for multiple causes of death and underlying causes of death from 1999–2007 were calculated . These mortality rates were calculated by dividing the number of deaths in each calendar year by the population , and presented per 100 , 000 . We calculated the standardization of mortality rates by age using the direct method , considering the Brazilian population in 2003 as standard [16] . The proportional mortality rate was calculated by dividing the number of deaths from Chagas' disease by the total number of deaths multiplied by 100 . We present standardized specific mortality and proportional mortality rates , stratified by region of residence , year of occurrence of death , and age group . Mortality analysis in this study focuses on multiple causes of death ( including both underlying and any other causes ) , and on the usual approach of underlying causes of death ( the disease or condition , which led directly to the death ) [16] . Analysis of time trends of mortality rates was performed using polynomial regression models . The polynomial model aimed to find the curve that would best fit the data in order to describe the relationship between the dependent variable ( mortality associated with Chagas' disease ) and the independent variable ( year of death ) . We first made scatter diagrams of indicators of mortality and the years of death , to visualize the mathematical function that would best represent the relationship between variables . From this observation , we estimated the regression models . We tested the following regression models where the values of Y and X are the dependent and independent variables respectively and β0 , β1 , β2 and β3 are the coefficients of regression [15]: a ) linear ( 1st order ) : Y = β0+β1X; b ) 2nd order: Y = β0+β1X+β2X2; c ) third order: Y = β0+β1X+β2X2+β3X3; d ) exponential: Y = eβ0+β1X . The choice of the best-fitting model was based on an analysis of the diagram , the coefficient of determination ( R2 ) , the statistical significance and residual analysis ( true homoscedasticity assumption ) . When the trend was not statistically significant , the model was chosen according to the coefficient of determination and when two models were similar , the statistical point of view , we chose the simplest model ( lowest order ) . Trends were considered statistically significant when the models showed p<0 . 05 . We further performed bivariable and multivariable analysis to identify association between demographic variables available ( gender , age , race , state and region of residence , residence in state capital , year of death ) and death due to Chagas' disease . We calculated relative risks ( RR ) with their respective 95% confidence intervals and applied the chi-squared test to estimate significance of the differences between relative frequencies . In logistic regression analysis the variables included were gender , age ≥30 years , and residence in a high risk state Minas Gerais , Goiás or Federal District . The cut-off point of 30 years was based on the natural history of the disease and disease control measures implemented in the 1970s and 1980s [1] , [9]–[13] . Region of residence and place of occurrence were excluded due to collinearity with the variable on high risk states included . The information on race/color lacked information in more than 10% of cases and was thus not included; in addition , information on skin color is not standardized in Brazil , and interpretation of this variable is limited . Calculation of the indicators and the preparation of tables and figures were performed in Microsoft Excel spreadsheets . We used SPSS for Windows version 15 . 0 ( Statistical Package for the Social Sciences; SPSS Corporation , Chicago , USA ) for calculation of polynomial regression models . Bivariable and multivariable statistical analysis was performed using the programs STATA version 11 ( Stata Corporation , College Station , USA ) and Epi Info for Windows version 3 . 5 . 1 ( Centers for Disease Control and Prevention , Atlanta , USA ) .
Trends of standardized specific mortality rates , stratified by Brazilian regions , are shown in Figure 1 . Nationwide standardized mortality rates related to Chagas' disease reduced gradually from 1999 to 2007 by 26 . 4% , from 3 . 78 to 2 . 78 deaths/100 , 000 inhabitants ( Figure 1 ) . Chagas as underlying cause of death decreased by 33 . 2% , from 3 . 28 to 2 . 19 deaths/100 , 000 inhabitants . On the other hand , there was a relative increase by 20 . 4% of the rate of associated causes of death , from 0 . 49 to 0 . 59 per 100 , 000 inhabitants between 1999 and 2007 . There were distinct regional patterns . The standardized mortality rates were highest in the Central-West region , with rates ranging from 15 . 23 in 1999 to 9 . 46 in 2007 , a decrease of 37 . 9% . Polynomial regression shows that during the study period this decrease was linear with a significantly high R2 value of 82% ( Table 1 ) . Southeast and South regions showed a decrease of 28 . 8% and 41 . 8% , respectively ( Figure 1 ) . In contrast , mortality rates increased significantly during the study period in the Northeast region . The North presented the lowest coefficients , with a stabilization ( Figure 1 , Table 1 ) . Proportional mortality rates over time , stratified by Brazilian regions , are shown in Figure 2 . Similar to specific rates , the inclusion of multiple causes of death as compared to underlying causes increased mortality rate by 20% ( 0 . 50% vs . 0 . 60% ) . There was a progressive decrease of proportional mortality as the underlying cause in Brazil , from 0 . 53% to 0 . 45% ( 15% decline ) , but an increase of mortality as associated cause , from 0 . 08% to 0 . 14% ( 75% increase ) , resulting in an overall stabilization of proportional mortality considering multiple causes of death i . e . all mentions ( Figure 2 ) . The Central-West showed highest proportional mortality among regions ( 2 . 17% ) , with a significant linear decreasing trend of multiple causes , from 2 . 28% to 1 . 90% ( 19 . 5% , R2 = 84% ) between 1999 and 2007 ( Figure 2 , Table 1 ) . In contrast , there was a significant increase in the Northeast of 38 . 5% ( R2 = 82% ) . The three other regions showed stabilization of proportional mortality ( Table 1 , Figure 2 ) . Bivariable analysis of variables associated with Chagas' disease as a multiple cause of death is presented in Table 2 . All variables available from death certificates were significantly associated with Chagas mortality . Highest relative risks were found in older age groups and those residing in the Central-West . The result of logistic regression is depicted in Table 3 . Similarly , age ≥30 years and residence in one of the three high risk states maintained important independent risk factors for death by Chagas' disease .
We performed the first nationwide population-based study on Chagas mortality in Brazil considering multiple causes of death , and provided a comprehensive overview of mortality associated with Chagas' disease and trend over time . The data show that Chagas' disease is still a public health problem in the country . In general , mortality indicators tended to decline , but there were different patterns between regions . Advanced age and region of residence were identified as important risk factors for death by Chagas' disease . The presence of Chagas' disease as one of the leading causes of death among elderly in Brazil indicates that the consequences of infection acquired in the past are still present for a significant portion of this population [17] . A previous Brazilian study using deaths from Chagas' disease as the underlying cause between 1981 and 1998 found a 44 . 4% decrease in mortality rates due to Chagas' disease in 1981–1983 as compared to 1996–1998 , and described similar regional patterns [18] . The high risk of death in Minas Gerais and Goiás states is consistent with areas of high endemicity and vector transmission in previous decades , as reflected by the percentage of infection and infestation observed in entomological and serological surveys performed in 1975–1983 [19] and 1975–1980 [20] , respectively . The high mortality rates in the Federal District can be explained by intense migration of rural populations from endemic areas [18] , [21] . In fact , about 1/5 of migrants in the Federal District come from rural endemic areas [21] . This scenario highlights future challenges and the need for continued disease surveillance of main transmission routes in these endemic regions [19] , [22] , but also for new control strategies including oral transmission in the Amazon region and secondary vectors such as Triatoma brasiliensis in the Northeast region [18] , [19] , [23] . In addition to these measures , adequate access to health services and social assistance to the many chronic chagasic patients should be guaranteed [22] . Regional differences of the number of deaths due to Chagas' disease reflect how effective the measures were adopted for the control of vector transmission and transfusion in the past , but also may be explained by other factors: transmission will need the presence of a suitable vector; population immigration of infected people will influence transmission dynamics; and there is unequal recognition of the disease , quality of care and diagnostic capacity in the different regions [24] . This is exemplified by the elimination of transmission of Chagas' disease via the main vector T . infestans which is not frequent in the North and Northeast and thus did not have a considerable impact on transmission dynamics in these two regions [18] , [22] . This indicates that control and primary and secondary prevention of Chagas' disease in the North and Northeast has been neglected , resulting in the emergence of new cases and constant or even increasing mortality rates [25] . The fact that Chagas' disease is mostly a slowly progressive and chronic disease and that most deaths are related to an infection acquired many years earlier suggests that the number of new cases in the Northeast may have remained constant or even increased over years . Possible difficulties in controlling secondary vector species such as T . brasiliensis and Triatoma pseudomaculata , along with oral and congenital transmission may be contributing factors for the maintenance of T . cruzi in the Northeast region [25] . Only a small fraction of deaths were caused by acute Chagas' disease , with highest figures in the North region . Interestingly , deaths due to acute disease were increasing during the study period . In fact , in the Amazon region the majority of new cases is caused by oral transmission often leading to acute disease , mainly through the consumption of unpasteurized natural products ( such as the palm products açai and bacaba juice ) , and an increased number of outbreaks of acute disease has been reported in recent years [26] . Our data further suggest that with the elimination of T . infestans as the main vector in the country , there is a need to develop sustainable control methods to reduce the chance of occurrence of cases directly dependent on secondary transmission by other vectors [9] and new strategies to control emerging oral transmission in the Amazon region [26] . Similar to previous studies , our data show that males and advanced age groups were at higher risk for death related to Chagas' disease [14] , [21] , [24] . However , our results did not provide sufficient evidence for the association between males and Chagas mortality . Whereas in bivariable analysis male gender appeared to be protective , in multivariable analysis being male was a risk factor for mortality . This may be caused by the fact that male sex per se is not a risk factor , but gender-specific behavior patterns causing a higher prevalence in males , besides social , cultural and behavioral differences between populations studied [27] . Rassi Jr . et al . [28] describe a controversy about the prognostic value of male mortality from Chagas' disease , shown to be associated with a worse prognosis in some studies [29] , [30] , but that this finding was not replicated in others [31]–[34] . However , most studies involved restricted subgroups , in contrast to our data . The highest Chagas death rates in the older age groups corroborates previous studies [14] , [21] , [24] and confirms that there is a marked tendency of aging of chagasic patients . This transition can be explained mainly due to a cohort effect , a result of exposure to infection with T . cruzi in the past [35] , considering that transmission routes of Chagas' disease ( vector-borne and blood transfusion ) have been widely controlled in Brazil [9] , [36] . The decrease in early mortality has also been attributed to reduced reinfection rates , leading to reduced severity of clinical signs of infection [12] , [14] with consequently longer survival of patients [37] . Elderly patients with Chagas' disease are a particularly vulnerable population , considering the harmful effects of a combination of Chagas' disease and other chronic degenerative diseases [17] . The frequent association of chronic disease causes significant demand for health services and medications that predispose to numerous risks , whereas the association between Chagas' disease and other chronic diseases may increase mortality and worsen the quality of life of those who are in such an unfavorable condition [17] . The risk of death due to Chagas' disease was higher among blacks and mixed race compared to the white population , indicating social disparities in the determination of death related to Chagas' disease . This fact corroborates the findings of Gonçalves et al . [34] that during a follow-up of a cohort of chronic chagasic patients from an endemic area found that different races or black color were poor prognostic factors for mortality due to Chagas' disease . However , in more than 10% of death certificates this information was missing , limiting the validity and reliability of this result . As there were only 54 deaths reported in indigenous people in our study , no further conclusions can be made regarding this subgroup . Our data also point to an important topic regarding notification of Chagas' disease . Historically , only acute cases are subject to compulsory notification , reflecting the focus of control actions against Chagas' disease as an acute condition and on reduction of transmission . The Brazilian Ministry of Health has been discussing compulsory notification of chronic forms , considering the burden of chronic disease in the country and the fact that reactivation of Chagas' disease in the presence of HIV infection is considered an AIDS-defining condition in Brazil [38] . Our study will provide further evidence for the need of introduction of chronic forms as a notifiable disease . Our study contains some limitations . The number of Chagas-related deaths may have been underreported , despite the important progress over the period under study both in the coverage of the Mortality Information System ( SIM ) , and the quality of information on causes of deaths . Quality of data may also vary between regions in the country [14] . We included multiple causes of deaths , i . e . the mention of Chagas' disease in any field rather than only the underlying cause , to reduce this error . The sociodemographic conditions , such as race/color , education and usual occupation , considered as possible factors predictive of mortality associated with Chagas' disease , showed a considerable proportion of unknown data . Despite these limitations , we consider the results of this study of high validity and highly representative , since all death certificates during the period 1999 to 2007 were included in Brazil , a country of continental dimensions . Our data show that mortality rates increased by 21% when multiple causes of death were considered , as compared to underlying causes of death . In fact , other authors have highlighted previously the importance of not only including underlying causes in mortality statistics , to better reflect the true epidemiology of Chagas' disease [14] , [39] . We conclude that the wealth of information provided by the analysis of mortality from multiple causes contributed to the identification of the epidemiological situation of mortality associated with Chagas' disease and to predict future trends . Thus , our study provides comprehensive and reliable information for planning and evaluation of control measures of Chagas' disease in Brazil . In addition to control measures against transmission of disease , one of the main challenges is the obvious need to improve clinical care and surgery , ensuring adequate attention to the large number of cases Chagas' disease that accumulated over the last decades .
|
American trypanosomiasis ( Chagas' disease ) is a parasitic disease which remains a public health problem in Latin America , but studies investigating the dynamics in populations under risk are scarce . We conducted a nation-wide study based on about 9 million Brazilian death certificates from 1999–2007 . Epidemiological characteristics of Chagas-related deaths , temporal trends and associated factors were investigated . Chagas' disease was mentioned in about 54 , 000 ( 0 . 6% ) death certificates , resulting in a mean standardized mortality rate of 3 . 36/100 , 000 inhabitants/year . Nationwide mortality rates reduced gradually , from 3 . 78 ( 1999 ) to 2 . 78 ( 2007 ) deaths/year/100 , 000 inhabitants ( −26 . 4% ) . The vast majority of deaths ( 97 . 2% ) were caused by chronic form of the disease . There were marked regional differences: mortality rates were highest in the Central-West region . In contrast to the rest of the country , there was a significant increase in the Northeast ( 38 . 5% ) . Risk factors independently associated with Chagas mortality were age >30 years ( adjusted OR = 10 . 60; 9 . 90–11 . 33 , p<0 . 001 ) and residing in the states of Minas Gerais , Goiás or Federal District ( adjusted OR = 4 . 89 , 4 . 81–4 . 98 , p<0 . 001 ) . We performed the first nationwide population-based study on Chagas mortality , considering multiple causes of death in Brazil . A comprehensive overview of mortality associated with Chagas' disease is provided . Chagas' disease is still a major public health problem in the country .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"epidemiology",
"global",
"health",
"public",
"health"
] |
2012
|
Epidemiology of Mortality Related to Chagas' Disease in Brazil, 1999–2007
|
Recently , a growing number of biological research and scientific experiments have demonstrated that microRNA ( miRNA ) affects the development of human complex diseases . Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases . However , since uncovering associations via experimental methods is expensive and time-consuming , novel and effective computational methods for association prediction are in demand . In this study , we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction ( MDHGI ) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method , the miRNA functional similarity , the disease semantic similarity , and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network . Compared with previous computational models based on heterogeneous networks , our model took full advantage of matrix decomposition before the construction of heterogeneous network , thereby improving the prediction accuracy . MDHGI obtained AUCs of 0 . 8945 and 0 . 8240 in the global and the local leave-one-out cross validation , respectively . Moreover , the AUC of 0 . 8794+/-0 . 0021 in 5-fold cross validation confirmed its stability of predictive performance . In addition , to further evaluate the model's accuracy , we applied MDHGI to four important human cancers in three different kinds of case studies . In the first type , 98% ( Esophageal Neoplasms ) and 98% ( Lymphoma ) of top 50 predicted miRNAs have been confirmed by at least one of the two databases ( dbDEMC and miR2Disease ) or at least one experimental literature in PubMed . In the second type of case study , what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms . As a result , 100% ( Lung Neoplasms ) of top 50 related miRNAs have been indexed by at least one of the three databases ( dbDEMC , miR2Disease and HMDD V2 . 0 ) or at least one experimental literature in PubMed . Furthermore , we also tested our prediction method on the HMDD V1 . 0 database to prove the applicability of MDHGI to different datasets . The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases ( HMDD V2 . 0 , dbDEMC , and miR2Disease ) or at least one experimental literature .
MicroRNA ( miRNA ) are one class of important short noncoding RNA ( ~22nt ) molecules that mostly inhibit gene expression at the post-transcriptional level [1–4] . In 1993 , lin-4 was the first miRNA detected as a result of research on the timing of C . elegans larval development [5] . Unlike conventional protein coding genes , lin-4 coded for a 22 nucleotide regulatory RNA rather than a protein [5 , 6] . Since then , thousands of miRNAs have been discovered in many living organisms , and currently 2588 miRNAs in the human genome have been annotated [7] . Because each miRNA is probably able to control the expression of hundreds of target genes , the whole miRNA pathway is a critical mechanism for gene expression control [2 , 8–13] . Recently , more and more studies have shown that miRNAs play critical roles in diverse important biological processes . Therefore , it is no surprise that miRNA could be associated with cancers [14 , 15] and other kinds of diseases [16] . As indicated by increasing evidences , miRNAs are emerging as novel potential biomarkers or diagnostic/therapeutic tools for diseases [17–22] . For example , miR-708 affects the progress of bladder carcinoma through direct inhibition of Caspase-2 [23] . MiR-29c down-regulation results in derepression of its target DNA methyltransferase 3a , which promotes the development of ischemic brain damage [24] . Another example is that let-7b expression has a positive correlation with patient age ( R = 0 . 472; p<0 . 001 ) [25] . Higher Nuclear opalescence or cataract scores for Nuclear color ( N ) , Cortical ( C ) and Posterior ( P ) was discovered positively associated with higher expression of let-7b in patients with age-related cataracts ( p<0 . 001 ) [25] . Identifying potential miRNA-disease associations enhances the understanding towards molecular mechanisms and pathogenesis of diseases . As a biomarker , miRNA can be used for disease diagnosis; and as drug targets , miRNAs can be applied to disease treatment . Since carrying out experiments is an expensive and time-consuming process , only a small number of miRNA-disease associations have been confirmed by traditional experimental approaches . Proposing computational models to predict disease-related miRNAs is a worthful supplement to experiments . Researchers should spare no effort to excogitate a more accurate prediction method so that reasonable candidates can be provided for future biological experiments [26] . In recent years , several computational methods have been developed to predict potential miRNA-disease associations and some of them performed well [27–33] . Based on the assumption that functionally related miRNAs are more likely to be associated with diseases which have similar phenotypes , Jiang el al . [34] proposed a network-based approach by combining miRNA similarity network , disease similarity network with miRNA-disease association network . After that , based on the hypergeometric distribution , a scoring system was constructed to acquire the scores of potential miRNA-disease associations . Focusing on the functional link between miRNA targets and disease genes , Shi et al . [35] devised a computational model by performing random walk on a protein-protein interaction ( PPI ) network and focusing on the functional links between miRNAs targets and diseases genes in PPI network . Mørk et al . [36] developed miRNA-Protein-Disease ( miRPD ) association prediction model by linking miRNAs to diseases via the underlying proteins involved . However , these methods did not exhibit a commendable predictive performance because their performance depended largely on miRNA-target interactions which have a high ratio of false-positive and false-negative samples . Some other computational models without relying on miRNA-target interactions have been proposed in the past few years . Xuan et al . [37] developed the method called Human Disease-related MiRNA Prediction ( HDMP ) to predict miRNAs associated with diseases . The association score between a miRNA and a disease was computed by summing up the sub-scores for the miRNA’s k neighbors , and the sub-score for a neighbor was calculated via multiplying the weight of the neighbor with the functional similarity between the neighbor and the miRNA . However , HDMP could not be applied to new diseases without any known associated miRNAs because predictions were made mainly from the information of miRNAs’ neighbors . Based on the global similarity measures , Chen et al . [38] developed a method named Random Walk with Restart for MiRNA-Disease Association prediction ( RWRMDA ) by implementing random walk on the miRNA functional similarity network to prioritize candidate miRNAs for disease of interest . Nonetheless , this method was unable to predict miRNAs associated with the diseases without any known related miRNAs . Another computational model named MIRNAs associated with Diseases Prediction ( MIDP ) was developed by Xuan et al . [39] based on random walk on a miRNA network derived from miRNA-associated diseases and semantic similarity of their associated diseases . The model assigned higher transition weights to labeled nodes than unlabeled nodes , which efficiently exploited the prior information of nodes and the various ranges of topologies . Besides , since they extended the walking on a miRNA-disease bilayer network , MIDP could also be used to prioritize candidate miRNAs for diseases without any known associated miRNAs . Later , a method named Matrix Completion for MiRNA-Disease Association prediction ( MCMDA ) [40] was proposed to predict potential associations by utilizing the matrix completion algorithm to update the adjacency matrix . However , the algorithm also suffered from a limitation of not being applicable to new diseases and new miRNAs . Recently , Chen et al . [41] proposed another model called Within and Between Score for MiRNA-Disease Association prediction ( WBSMDA ) . After integrating similarity for miRNAs and diseases , within-score and between-score were calculated and combined to obtain the final score for potential miRNA-disease association inference . Later , Chen et al . [42] presented a model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction ( HGIMDA ) by combining the integrated miRNA similarity network , the integrated disease similarities network and the known miRNA-disease associations network into a heterogeneous graph . After that , they constructed an iterative equation by summarizing all paths with the length equal to three from which they can infer potential association between a disease and a miRNA . In addition , several other computational models were based on machine learning algorithms . For example , based on the features which were extracted from MiRNA Target-Dysregulated Network ( MTDN ) model by assessing topological properties of miRNAs and changes in miRNA expression , Xu et al . [43] implemented a Support Vector Machine ( SVM ) classifier to distinguish positive miRNA-disease associations from negative ones . However , even till today it is still difficult to obtain negative samples , and this fact seriously decreased the prediction accuracy of MTDN . Chen et al . [44] further presented Regularized Least Squares for MiRNA-Disease Association prediction ( RLSMDA ) method based on semi-supervised learning in the miRNA space and the disease space . What is worth mentioning is that RLSMDA could identify related miRNAs for diseases without any known associated miRNAs . Chen et al . [45] developed another computational model called restricted Boltzmann machine for multiple types of miRNA-disease association prediction ( RBMMMDA ) , the core of which was restricted Boltzmann machine ( RBM ) . The model built a two-layer undirected graphical model containing layers of visible and hidden units . Compared to previous models , RBMMMDA could obtain not only new miRNA-disease associations but also the corresponding association types . The method named Ranking-based KNN for miRNA-Disease Association prediction ( RKNNMDA ) [46] was first implemented to search for k-nearest neighbors both for miRNAs and diseases by using the K-Nearest Neighbors ( KNN ) algorithm . Then these k-nearest neighbors were reranked according to the SVM ranking model . Finally , weighted voting was carried out on the ranking results to obtain the final ranking of all potential miRNA-disease associations . The drawback of RKNNMDA was that bias might be caused to miRNAs with more known associated diseases . Identifying miRNAs associated with diseases is beneficial for the development of diagnostic/treatment tools for diseases . Using traditional experimental methods for association detection is demanding and so computational models for miRNA-disease association prediction are needed to complement to experiments . Because previously developed computational methods have some aforementioned limitations , it is essential to develop a new method that exploits more useful information and make more reliable predictions . However , there are also some difficulties of predicting potential disease-related miRNAs , such as the rare known miRNA-disease associations , the unavailable negative miRNA-disease associations , the relatively limited biological datasets about miRNAs , and the universality to new diseases without any known associated miRNAs as well as new miRNAs . What’s more , considering that some of the existing computational models are only based on one of the matrix decomposition algorithm and network algorithm , it is of great significance to fully take advantage of these two methods to develop a new calculation model for miRNA-disease association prediction . In this study , we developed an effective computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction ( MDHGI ) . We first rebuilt a new adjacency matrix by using Sparse Learning Method ( SLM ) to decompose the original adjacency matrix obtained from known miRNA-disease associations . Then we combined the miRNA functional similarities network , the disease semantic similarities network , the Gaussian interaction profile kernel similarities network , and the new adjacency matrix into a heterogeneous graph . Finally , we implemented normalization on integrated similarity for miRNAs and diseases and iteration algorithm on the graph to obtain a predicted association score scores for all miRNA-disease pairs . To evaluate the effectiveness of MDHGI , global and local Leave-One-Out Cross Validation ( LOOCV ) as well as 5-fold cross validation were carried out . The AUCs of global and local LOOCV were respectively 0 . 8945 and 0 . 8240 , and the model obtained an average AUC of 0 . 8794+/-0 . 0021 in 5-fold cross validation . In the case studies of four important human cancers , 49 , 49 , 50 , and 50 out top 50 predicted miRNAs for Esophageal Neoplasms , Lymphoma , Lung Neoplasms , and Breast Neoplasms were respectively confirmed by different databases or experimental literatures in PubMed . These results proved that MDHGI was effective in predicting potential miRNA-disease associations and it had significant advantages over previous methods . Our main contribution in this article is to perfect the HGIMDA model and further improve its accuracy by taking full advantage of the two methods ( matrix decomposition and network algorithm ) . Besides , the idea presented in this article may have new inspiration for other researchers and the model we proposed is also a supplement to methodological research .
Actually , since the data in the databases is derived from the collected experimental literatures , it is a common practice for researchers to utilize known miRNA-disease associations data in HMDD as the training set . As previous studies have done [27 , 39 , 47–49] , in this paper , the known miRNA-disease associations dataset was extracted from the HMDD V2 . 0 database . The dataset contained 5430 validated associations between 495 miRNAs and 383 diseases . To facilitate subsequent calculations , we constructed an adjacency matrix A ∈ Rm×n to store the known miRNA-disease associations and other miRNA-disease pairs . In the adjacency matrix A , m and n are respectively defined as the number of miRNAs and diseases . Besides , element A ( ri , dj ) is set to be 1 if miRNA ri is associated with disease dj , otherwise 0 [42] . In previous studies [50–54] , many researchers made use of the DAG to describe a disease in their calculation models . According to the National Library of Medicine ( http://www . nlm . nih . gov/ ) , we can obtain the relationship of various diseases based on the disease Directed Acyclic Graph ( DAG ) constructed from the MeSH descriptor of Category C . For example , for the DAG of lung neoplasms ( See Fig 1 ) , ‘respiratory tract diseases’ points to ‘lung diseases’ . All nodes in the DAG are connected by a direct edge from a more general term , we call it parent , to a more specific term , and we call it child [55] . Here , a disease D was described by DAG = ( D , T ( D ) , E ( D ) ) , in which we defined all ancestor nodes of D and D itself as T ( D ) and the edge set including the direct edges from parent nodes to child nodes as E ( D ) . In DAG ( D ) , the contribution of disease d to the semantic value of disease D was defined as: {D1D ( d ) =1ifd=DD1D ( d ) =max{Δ*D1D ( d′ ) |d′∈childrenofd}ifd≠D ( 2 ) where Δ is the semantic contribution decay factor . Here , based on the previous literature [37] , we denoted the value of Δ to 0 . 5 . Moreover , the semantic value of disease D was defined as: DV1 ( D ) =∑d∈T ( D ) D1D ( d ) ( 3 ) Since the larger part of DAG was shared by two diseases , the higher semantic similarity value they would get , the semantic similarity score between disease di and dj were defined as follows: SS1 ( di , dj ) =∑t∈T ( di ) ∩T ( dj ) ( D1di ( t ) +D1dj ( t ) ) DV1 ( di ) +DV1 ( dj ) ( 4 ) It is obvious that a disease which appears in less DAGs contributes to the semantic similarity of disease at a high level . Considering the inexact approach in disease semantic similarity model 1 that the contribution of diseases in the same layer of DAG ( D ) to the semantic value of D were treated as the same . We defined the contribution of disease d in DAG ( D ) to the semantic value of disease D as follows: D2D ( d ) =−log[thenumberofDAGsincludingt/thenumberofdiseases] ( 5 ) We then defined the semantic similarity between disease di and dj in the similar way as the disease semantic similarity model 1 . Wang et al . [56] developed the MISIM method to calculate the miRNA functional similarity between a miRNA pair ( ri and rj ) . The whole process of MISIM can be divided into four steps . In the first step , we need to identify the diseases set D ( ri ) ( diseases associated with ri ) and D ( rj ) ( diseases associated with rj ) for miRNA ri and rj , respectively . Next , the semantic value of all diseases in these two sets are computed according to the corresponding DAG . Third , the semantic similarity for each disease pairs between D ( ri ) and D ( rj ) can be calculated based on their semantic value . Finally , the functional similarity of ri and rj is calculated based on the semantic similarity obtained in step three . From the website http://www . cuilab . cn/files/images/cuilab/misim . zip , we downloaded the miRNA functional similarity data . Then the miRNA functional similarity matrix MS was constructed , in which the element MS ( ri , rj ) indicated the similarity value between the miRNA ri and the miRNA rj . Based on the notion that functionally similar miRNAs are usually associated with similar diseases , the Gaussian interaction profile kernel similarity can be constructed as another algorithm for similarity measurement between two miRNAs/diseases [57 , 58] . It is obvious that the ith row and jth column of adjacent matrix A respectively represents the information whether the miRNA or the disease are associated with each of the diseases or the miRNAs . For convenience , we denoted vector IV ( ri ) and IV ( dj ) to represent the ith row vector and jth column vector , respectively . Therefore , the Gaussian interaction profile kernel similarity of diseases and miRNAs could be computed as follows: GD ( di , dj ) =exp ( −βd‖IV ( di ) −IV ( dj ) ‖2 ) ( 8 ) GR ( ri , rj ) =exp ( −βr‖IV ( ri ) −IV ( rj ) ‖2 ) ( 9 ) where the adjustment coefficients βd and βr could be defined as follows: βd=β'd/ ( 1n∑i=1n‖IV ( di ) ‖2 ) ( 10 ) βr=β'r/ ( 1m∑i=1m‖IV ( ri ) ‖2 ) ( 11 ) where β′d and β′r are the original bandwidths and both of them were defined as 1 based on the previous study [59] . The integrated disease similarity could be obtained through combining the disease semantic similarity and the disease Gaussian interaction profile kernel similarity . What makes a difference to the integrated miRNA similarity is that if disease di and dj have their own DAG ( i . e . these two diseases have semantic similarity ) , then the final integrated similarity is the average between SS and GD . Otherwise the integrated disease similarity equals to the value of Gaussian interaction profile kernel similarity . Furthermore , the integrated miRNA similarity could be obtained by combining the miRNA functional similarity with miRNA Gaussian interaction profile kernel similarity: SR ( ri , rj ) ={ ( MS ( ri , rj ) +GR ( ri , rj ) ) 2riandrjhasfunctionalsimilarityGR ( ri , rj ) otherwise ( 14 ) In this study , the proposed method , MDHGI , fully extends the advantages of matrix factorization and network algorithm to make prediction for miRNA-disease associations . The flow chart of the algorithm is shown in Fig 2 . Actually , the data we used to train our model are normally far from perfect . Considering that , a portion of the miRNA-disease associations in the real data would be redundant , and also some other miRNA-disease associations would be missing from the real data . Hence , the adjacency matrix for miRNA-disease associations can be decomposed into two parts . The first part is a linear combination of the original adjacency matrix and a low-rank matrix and the second part is a sparse matrix with most entries being zeros and can be considered as the noise or the outliers . The method is used to look for the lowest-rank matrix which is further utilized to reconstruct a new adjacency matrix that will be used in the next calculation . Firstly , we decomposed A as follows: A=AX+E ( 15 ) Obviously , there were infinite many solutions for Eq ( 15 ) . However , since we wished X to be of low rank , where rank of a matrix was the maximum number of linearly independent column ( or row ) vectors in the matrix , and E to be sparse , we could enforce the nuclear norm or trace norm on X and sparse norm on E . Mathematically , Eq ( 15 ) could be thus relaxed as minX , E‖X‖*+α‖E‖2 , 1s . t . A=AX+E ( 16 ) where ‖X‖*=∑iσi ( i . e . , σiisthesigularvaluesofX ) ( 17 ) ‖E‖2 , 1=∑j=1n∑i=1n ( Eij ) 2 ( 18 ) α is a positive free parameter which was used to balance the weights of low-rank matrix and sparse matrix . Here , according to the existing method [60] , the value of α was defined as 0 . 1 . Minimizing the trace norm of a matrix contributed to the lower-rank matrix , meanwhile the sparse norm was capable of identifying noises and outliers . If the matrix A in AX in the right side of Eq ( 16 ) is set as identity matrix , then the model is degenerated to the robust PCA . Therefore , Eq ( 16 ) could also be regarded as a generalization of the robust PCA [61 , 62] . Eq ( 16 ) could be rewritten into an equivalent problem as minX , E , J‖J‖*+α‖E‖2 , 1s . t . A=AX+E , X=J ( 19 ) The Eq ( 19 ) above , which is a constraint and convex optimization problem , can be solved by off-the-shelf interior point solvers after being reformulated as a semidefinite program [63] . However , the interior point solvers are not suitable for large matrices since they rely much on second-order information of the objective function . Thus , we should take advantage of both the first-order information and the special properties of this class of convex optimization problems to overcome the scalability issue . The iterative thresholding ( IT ) algorithm , accelerated proximal gradient ( APG ) algorithm , exact augmented Lagrange multipliers ( EALM ) algorithm and inexact augmented Lagrange multipliers ( IALM ) algorithm are several methods to solve the problem of Eq ( 19 ) . However , for IT algorithm , as shown in the original literature [55] , the iteration process converges extremely slowly ( about 104 iterations to converge ) . As for APG algorithm , although the APG's computing speed has improved when compared to IT algorithm , it is still not as fast as IALM . Especially , the solution to Eq ( 19 ) obtained from the IALM is much more accurate than that by APG . Moreover , even though the convergence rate of EALM is as fast as IALM , the latter requires less number of partial SVDs . In general , the IALM algorithm is a relatively more efficient algorithm to solve the problem of Eq ( 19 ) . Thus , in this paper , we utilized IALM [64] method by first converting Eq ( 19 ) to an unconstraint problem and then minimizing this problem based on augmented Lagrange function such that L=‖J‖*+α‖E‖2 , 1+tr ( Y1T ( A‑AX‑E ) ) +tr ( Y2T ( X‑J ) ) +μ2 ( ‖A‑AX‑E‖F2+‖X‑J‖F2 ) ( 20 ) where μ ≥ 0 is a penalty parameter . The problem above could be solved by minimizing with respect to J , X , and E , respectively . Besides , after fixing the other variables and then updating the Lagrange multipliers Y1 , Y2 , Eq ( 20 ) would be settled . The detailed steps of how to solve Eq ( 20 ) is shown in Fig 3 . We defined the solution of Eq ( 20 ) as X* and E* . If Aij represents the association between miRNA ri and disease dj , then X* ∈ Rn×n could be considered as a similarity matrix that described the similarity between diseases . While if Aij represents the associations between disease di and miRNA rj ( as the transposition of the adjacency matrix in Eq ( 15 ) ) , then X* ∈ Rm×m describes the similarity between miRNAs . After obtaining X* , the solution of Eq ( 20 ) , we could compute the new associations between each pair of miRNAs and diseases by projecting the adjacency matrix onto the lower-dimensional space as A*=AX* ( 21 ) From the matrix A* , we reacquired the miRNA-disease associations information which were further combined with the integrated similarity for miRNAs and diseases into a heterogeneous graph . By analyzing the heterogeneous graph , for disease d and miRNA m , we could further define their potential association probability as follows if they had no known associations . According to the equation above , the potential association probability between miRNA m and disease d could be calculated by summarizing all paths with the length equal to three ( See Fig 4 ) . Moreover , considering the iteration of above process , we obtained iterative equation through representing the equation as matrix multiplications . Pi+1=aSR×Pi×SD+ ( 1‑a ) A* ( 23 ) Here , the decay factor a was denoted to 0 . 4 based on the previous study [65] . For the iteration , we can treat this process like that every node with prior information disseminates the information obtained in the previous iteration to its neighbors . Due to the relation between the end-points and the probability of looking into an edge among the same end-points in a random network with the same node degrees , the weight of an edge was normalized according to the degrees of its end-points [66] . Based on the previous literature [65] , miRNA-disease association probability matrix P would converge when SR and SD were properly normalized utilizing Eqs ( 24 ) and ( 25 ) , respectively . Moreover , we have given the specific proof process as Theorem 1 in the S1 Text SR ( mi , mj ) =SR ( mi , mj ) ∑l=1nmSR ( mi , ml ) ∑l=1nmSR ( mj , ml ) ( 24 ) SD ( di , dj ) =SD ( di , dj ) ∑l=1ndSD ( di , dl ) ∑l=1ndSD ( dj , dl ) ( 25 ) Here , we set the cutoff as 10−6 . The iteration above would become stable when the change between Pi and Pi+1 measured by L1 norm was less than the given cutoff . In addition , for convenience , we have made a web server at http://chengroup . cumt . edu . cn/tool/mdhgi/ . After opening the website and selecting the disease name of interest in the box , researchers will get the prediction results of potential disease-related miRNAs . For more details , please see the website's ‘Guide’ .
Here , we used two types of cross validation to evaluate the performance of MDHGI , namely , LOOCV and 5-fold cross validation . LOOCV could be further divided into global and local LOOCV , in which each known association was in turn considered to be the test sample and the others were treated as the training samples . In global LOOCV , each of the known miRNA-disease associations was in turn considered as the test sample and all unknown miRNA-disease pairs were treated as candidate samples , while in local LOOCV , candidate samples only contained those miRNAs without any known associations with the investigated disease in the test sample . In 5-fold cross validation , we randomly divided all known miRNA-disease associations into five subsets with equal size . Then each subset was in turn considered as the test sample and the rest four subsets were treated as training samples . In the same way as LOOCV , all unknown miRNA-disease pairs were regarded as candidate samples . Subsequently , we obtained a predicted association score matrix by MDHGI , and ranked the score of each test sample against the scores of the candidate samples . This partition-prediction-ranking procedure was repeated 100 times to obtain a sound estimate of the mean and variance of MDHGI’s prediction accuracy . In each cross validation scheme , the model would be considered to successfully predict an association if the ranking of a test sample was above a given threshold . Moreover , we drew a receiver operating characteristics ( ROC ) curve through plotting the true positive rate ( TPR , sensitivity ) versus the false positive rate ( FPR , 1-specificity ) at different thresholds . Sensitivity referred to as the percentage of the test samples whose ranks surpassed the given threshold , while specificity denoted the percentage of negative miRNA-disease associations whose ranks were below the threshold . Then , we calculated the area under the ROC curve ( AUC ) to evaluate the predictive performance of MDHGI . AUC = 1 would indicate that all test samples were perfectly predicted , while AUC = 0 . 5 would mean the model only had random prediction performance . As shown in Fig 5 , MDHGI obtained an AUC of 0 . 8945 in global LOOCV and an AUC of 0 . 8240 in local LOOCV . These results proved that MDHGI exhibited a sound performance in predicting potential miRNA–disease associations . However , the AUCs for MaxFlow [67] , RKNNMDA [46] , HGIMDA [42] , RLSMDA [44] , HDMP [68] , WBSMDA [41] , and MCMDA [40] in global LOOCV were 0 . 8624 , 0 . 7159 , 0 . 8781 , 0 . 8426 , 0 . 8366 , 0 . 8030 , and 0 . 8749 , respectively . In local LOOCV , these models’ AUCs were 0 . 7774 , 0 . 8221 , 0 . 8077 , 0 . 6953 , 0 . 7702 , 0 . 8031 , and 0 . 7718 , respectively . In addition , the AUCs in local LOOCV for RWRMDA [38] , MIDP [39] and MiRAI [69] were 0 . 7891 , 0 . 8196 and 0 . 6299 , respectively . Both RWRMDA and MIDP were not applicable to global LOOCV , because , based on random walk , they could not uncover missing associations for all the diseases simultaneously . Moreover , MiRAI was also not included in global LOOCV . In MiRAI , for a disease/miRNA associated with more miRNAs/diseases , the association scores between the disease/miRNA and its candidate miRNAs/diseases tended to be higher . Therefore , the association scores obtained for different diseases were not comparable . MiRAI had a low AUC because our training dataset was sparse . Since the dataset only contained 5430 validated associations between 495 miRNAs and 383 diseases , the majority miRNAs/diseases were associated with only a few diseases/miRNAs . While in the original literature [69] , the dataset contained only 83 diseases with at least 20 known associated miRNAs . As for 5-fold cross validation , in comparison with MaxFlow , RKNNMDA , RLSMDA , HDMP , WBSMDA and MCMDA whose average AUCs were 0 . 8579+/-0 . 001 , 0 . 6723+/-0 . 0027 , 0 . 8569+/-0 . 0020 , 0 . 8342+/-0 . 0010 , 0 . 8185+/-0 . 0009 and 0 . 8767+/-0 . 0011 , respectively , the average AUC for MDHGI was 0 . 8794+/-0 . 0021 . This further confirmed the superior prediction accuracy and the performance stability of our model . In addition , we have supplemented seven experiments by assigning different weight parameters to miRNA-miRNA edges and disease-disease edges , while the weight for miRNA-disease edges remains unchanged ( See Table 1 ) . The reason why we carried out these experiments is that we can make quantitative analysis for the reliability of the data ( the miRNA functional similarity and the disease semantic similarity ) . As shown in Table 1 , with the diminution of the weight for miRNA-miRNA edge and disease-disease edge , the values of the AUC for Global LOOCV , Local LOOCV and 5-fold cross validation decreased . What is worth mentioning is that all of the three types of AUCs descended in a very slow way , which proved our model’s stability in a certain degree . From the results , we can conclude that the data of miRNA functional similarity and disease semantic similarity we used are reliable . In order to further demonstrate the prediction accuracy of MDHGI , we carried out case studies on two important human complex diseases by prioritizing candidate miRNAs for the diseases using our model with the training dataset from HMDD V2 . 0 [70] . Just like the validation databases in some existing methods [27 , 39 , 47–49] , we verified the top 50 predictions with two other prominent miRNA-disease association databases , namely , dbDEMC [71] and miR2Disease [72] . The first type of case study was implemented on Esophageal Neoplasms and Lymphoma . In our model , we utilized the known miRNA-disease associations in HMDD V2 . 0 as the training set . After ranking all candidate miRNAs for each investigated disease based on their predicted scores , the top 50 predicted miRNAs were picked out and verified in other two prominent miRNA-disease association databases ( i . e . , dbDEMC and miR2Disease ) . Besides , the results showed that 232 of the 5430 known miRNA-disease associations in HMDD V2 . 0 also existed in miR2Disease and 546 associations also existed in dbDEMC after comparing the HMDD V2 . 0 with miR2Disease/dbDEMC . Nonetheless , since only candidate miRNAs ( miRNAs unassociated with the investigated disease based on HMDD V2 . 0 ) for an investigated disease were ranked and verified , there was no overlap between the training samples and the prediction lists . Hence , none of the top 50 predicted miRNAs existed in HMDD V2 . 0 and the verification of miRNAs in the prediction lists was completely independent of HMDD V2 . 0 . Esophageal cancer is a commonly-diagnosed cancer arising from the esophagus—the food pipe that runs between the throat and the stomach . Based on the estimates of the esophageal cancer burden in the United States in 2017 , the new cases and deaths from esophageal cancer will be 16940 and 15690 , respectively [73] . Recent research showed that the first miRNA we predicted ( hsa-mir-200b ) suppresses invasiveness and modulates the cytoskeletal and adhesive machinery in esophageal squamous cell carcinoma cells via targeting Kindlin-2 [74] . Moreover , the data provided by Chen et al . [75] offered the convincing evidence that combined expression of hsa-mir-133a and hsa-mir-133b ( 2nd in the prediction list ) might predict chemosensitivity of patients with esophageal squamous cell carcinoma ( ESCC ) undergoing paclitaxel-based chemotherapy which implied its importance in applying ‘personalized cancer medicine’ in the clinical treatment of ESCC . Another example is that aberrant expression level of hsa-mir-16 ( 3rd in the prediction list ) could suppress cell apoptosis while promote growth by regulating the reversion-inducing cysteine-rich protein with Kazal motifs ( RECK ) and the ex-determining region Y-related high-mobility-group box transcription factor 6 ( SOX6 ) which play important roles in the pathogenesis of ESCC [76] . MDHGI was implemented to identify potentially related miRNAs for Esophageal Neoplasms and ranked the miRNAs in terms of their association scores . As a result , 10 out of the top 10 , 18 out of the top 20 , and 43 out of the top 50 predictions were manually confirmed in database dbDEMC and miR2disease ( See Table 2 ) . Besides , to further confirmed our prediction results , we also manually verified the top 50 predicted miRNAs in PubMed . The result showed that 49 , 49 and 46 out of the top 50 predictions were respectively confirmed by at least one , two , and three experimental literatures in PubMed ( See S1 Table ) . Lymphoma is a group of blood cell tumors that develop from lymphocytes ( a type of white blood cell ) . Hodgkin lymphoma ( HL ) and non-Hodgkin lymphoma ( NHL ) are the two main types of lymphoma [77] . Recent experimental studies showed that hsa-mir-223 ( 1st in the prediction list ) regulates cell growth and targets proto-oncogenes in mycosis fungoides/cutaneous T-cell lymphoma [78] . Besides , it also has been verified that plasma hsa-mir-155 , hsa-mir-203 , and hsa-mir-205 ( 2nd in the prediction list ) are biomarkers for monitoring of primary cutaneous T-cell lymphomas ( TCTL ) [79] . Moreover , the study of Yang et al . suggested that hsa-mir-10b ( 3rd in the prediction list ) contributes to osteoblast differentiation through targeting B cell lymphoma 6 ( Bcl6 ) which provides a novel insight into understanding the molecular mechanism underlying osteoblast differentiation and suggests a potential target for inhibiting bone loss [80] . Taking lymphoma as the investigated disease and implementing MDHGI for potential miRNA-lymphoma association prediction , nine out of the top 10 , 15 out of the top 20 and 44 out of the top 50 potential lymphoma-associated miRNAs were manually verified in database dbDEMC and miR2disease ( See Table 3 ) . Furthermore , in the same way as the validation of esophageal cancer , 49 , 48 and 46 out of the top 50 predictions were respectively confirmed by at least one , two and three experimental literatures in PubMed ( See S2 Table ) . To facilitate further validation and research , we have provided the complete prediction list of potential miRNAs associated with all the 383 human diseases in HMDD V2 . 0 , together with the association scores predicted by MDHGI ( See S3 Table ) . In addition , to illustrate the applicability of MDHGI to new diseases , namely , diseases that have no known associated miRNAs , we carried out another case study on Lung Neoplasms . Known associations for this disease were removed from the training dataset , so that predictions would only be made from the information of other diseases’ related miRNAs and the similarity measures . After implementing MDHGI , we obtained the ranking of Lung Neoplasms’ candidate miRNAs in terms of their association scores ( See Table 4 ) . The data provided by Babu et al . suggested that increased expression of hsa-mir-20a ( 1st in the prediction list ) in lung cancer may decrease iron export which will lead to intracellular iron retention and cell proliferation [81] . Besides , recent research showed that hsa-mir-17 ( 2nd in the prediction list ) and hsa-mir-92 families play important roles in cisplatin resistance and can be used as potential biomarkers for better predicting the clinical response to platinum-based chemotherapy in non-small cell lung cancer ( NSCLC ) [82] . Shen et al . provided the evidence that down-regulation of hsa-mir-18a ( 3rd in the prediction list ) sensitizes NSCLC to radiation treatment and it may help to develop a new approach to sensitizing radioresistant lung cancer cells by targeting hsa-mir-18a [83] . Respectively , 10 , 20 and 50 out of the top 10 , 20 and 50 predictions were manually confirmed in HMDD V2 . 0 , dbDEMC and miR2Disease . Similarly , we also manually verified the top 50 predicted miRNAs in PubMed . The result showed that 50 out of the top 50 predictions were confirmed by at least three experimental literatures in PubMed ( See S4 Table ) . Finally , we trained our model with the dataset from the HMDD V1 . 0 to demonstrate that MDHGI would perform equally well on different datasets . Breast Neoplasms was used as the investigated disease . As a result , there were respectively 10 , 20 , and 48 out of the top 10 , 20 and 50 predictions manually confirmed in the three databases mentioned above ( See Table 5 ) . Besides , 50 out of the top 50 predictions were confirmed by at least three experimental literatures in PubMed ( See S5 Table ) . Taking first-ranked hsa-let-7e as an example , research confirmed that umonji/Arid1 B ( JARID1B ) promoted breast tumor cell cycle progression through epigenetic repression of hsa-let-7e [84] . Recent experimental studies showed that breast cancer patients with low hsa-let-7b ( 2nd in the prediction list ) expression had poor prognoses which indicated that hsa-let-7b might act as cancer suppressor gene in breast cancer development and progression by inhibiting the expression of BSG [85] . Moreover , the results of Sun et al . suggested that hsa-mir-223 ( 3rd in the prediction list ) increases the sensitivity of triple-negative breast cancer stem cells ( TNBCSCs ) to TRAIL ( tumor necrosis factor-related apoptosis-inducing ligand ) -induced apoptosis by targeting HCLS1 ( hematopoietic cell-specific Lyn substrate 1 ) -associated protein X-1 ( HAX-1 ) [86] . According to the results presented , MDHGI consistently achieved an excellent predictive performance in each of the four case studies . With the continuous experimental research on miRNA-disease associations , we expect that more and more miRNAs in the prediction lists generated by our model would be verified in the future .
This paper introduced the computational method called MDHGI in which we combined the sparse learning method with the heterogeneous graph inference method to calculate potential miRNA-disease association scores . In the process of low-rank matrix decomposition , the sparse norm could effectively handle training datasets with a high level of noises and a low quality , which were commonly faced by biological researchers . However , some elements with the value of 1 in the adjacency matrix might turn into 0 after using the sparse learning method , which means the corresponding known miRNA-disease associations information might be removed . To overcome the disadvantage , the heterogeneous graph inference method was used by integrating the Gaussian interaction profile kernel similarity , the disease semantic similarity , the miRNA functional similarity , and miRNA-disease associations which were reacquired from the recalculated adjacency matrix into a heterogeneous graph . The excellent performance of MDHGI was demonstrated by experimental results from both cross validation and case studies on Esophageal Neoplasms , Lymphoma , Lung Neoplasms and Breast Neoplasms . It could be concluded that MDHGI should serve as an effective tool for predicting potential miRNA-disease associations , and would be helpful in human disease prevention , treatment , diagnosis , and prognosis . The reliable performance of MDHGI came from the following factors . Firstly , by decomposing the original data into a clean ( a linear combination of low-rank matrix and the adjacency matrix ) and noise ( sparse matrix ) parts , we could obtain a clean data about the associations between miRNAs and diseases . Secondly , more and more disease-miRNA association data had been discovered and confirmed . Due to the data-dependent property of sparse learning method , the increasing number of known associations improved the prediction accuracy . Thirdly , MDHGI could be used to make predictions for new diseases which have no known related miRNAs and miRNAs without any known associated diseases . Lastly , MDHGI could effectively uncover missing miRNA-disease associations for all diseases simultaneously . Therefore , MDHGI is a superior model over previous ones . Limitations also exist in this method . Firstly , though current studies benefited from the increased known data , it is never a finished work to expand data . Secondly , it is obvious that assigning different penalization parameters for the three different types of edges ( miRNA-miRNA edges , disease-disease edges and miRNA-disease edges ) would be more accurate for the prediction performance . However , there are some difficulties that make us unable to do this work . Firstly , for the moment , we don't know how to properly give different weights to vertexes and edges in the network . Secondly , since all the known miRNA-disease associations we utilized in our model were based on databases ( i . e . , different experimental literatures ) , it is very difficult for us to quantify the reliability of different edges . Hence , taking full account of your suggestions , we will conduct our research in this area in the next step . In addition , the parameter set in the algorithm is difficult to optimize , and deserves further research . Finally , MDHGI might cause bias to miRNAs which have more associated disease records and vice versa . Therefore , we would develop optimization strategies to improve the accuracy of this prediction method in the future .
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Identifying potential miRNA-disease associations enhances the understanding towards molecular mechanisms and pathogenesis of diseases , which is beneficial for the development of diagnostic/treatment tools for diseases . Compared with traditional experiment methods , computational models can help experimenters reduce the cost of money and time . In order to computationally predict potential miRNA-disease associations , we developed MDHGI by combining the sparse learning method with the heterogeneous graph inference method . We performed MDHGI on different database and the experiment results indicated that MDHGI had significant advantages over previous methods both in leave-one-out cross validation and 5-fold cross validation . Besides , we also carried out three different kinds of case studies on four important human complex diseases to further demonstrate the prediction accuracy of MDHGI . In consequence , 98% , 98% , 100% and 100% out of the top 50 candidate miRNAs for the four diseases were confirmed by different databases or experimental literatures in PubMed , respectively . Thus , it could be concluded that MDHGI could make reliable predictions and should serve as an effective tool for predicting potential miRNA-disease associations .
|
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2018
|
MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction
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An outstanding problem in neuroscience is to understand how information is integrated across the many modules of the brain . While classic information-theoretic measures have transformed our understanding of feedforward information processing in the brain’s sensory periphery , comparable measures for information flow in the massively recurrent networks of the rest of the brain have been lacking . To address this , recent work in information theory has produced a sound measure of network-wide “integrated information” , which can be estimated from time-series data . But , a computational hurdle has stymied attempts to measure large-scale information integration in real brains . Specifically , the measurement of integrated information involves a combinatorial search for the informational “weakest link” of a network , a process whose computation time explodes super-exponentially with network size . Here , we show that spectral clustering , applied on the correlation matrix of time-series data , provides an approximate but robust solution to the search for the informational weakest link of large networks . This reduces the computation time for integrated information in large systems from longer than the lifespan of the universe to just minutes . We evaluate this solution in brain-like systems of coupled oscillators as well as in high-density electrocortigraphy data from two macaque monkeys , and show that the informational “weakest link” of the monkey cortex splits posterior sensory areas from anterior association areas . Finally , we use our solution to provide evidence in support of the long-standing hypothesis that information integration is maximized by networks with a high global efficiency , and that modular network structures promote the segregation of information .
Information theory , which largely measures communication between transmitter-receiver pairs ( for e . g . a telephone sender and receiver ) [1] , has been key to understanding information transmission in the feedforward paths of the brain’s sensory periphery [2–8] . But , traditional information-theoretic measures are of limited utility as soon as signals enter the recurrent networks that form the rest of the brain . That is because these measures are designed to quantify feedforward information flow . Until very recently , no theoretically sound measures were available to quantify and analyze information that is integrated by entire recurrent networks . Recent work in information theory has risen to meet the challenge of quantifying the integration of information across the recurrent networks that bridge spatially distributed brain areas . Over the last decade , several measures of network-wide information integration have been proposed [9–16] , which all generally define information integration as how much more information flows in a whole network than in the sum of its parts . The intuition can be phrased like this: if you cut a network into disconnected parts , forcing those parts to evolve over time independently of one another , how much less information is carried over time in the network ? If we can estimate this difference accurately , we’d have a value—in bits—of how much information is integrated in a network . Most of these measures of information integration have faced serious theoretical issues , such as exceeding the total information in a network , falling below 0 bits , or being impossible to estimate from time-series data [11] . To remedy this problem , mathematicians have recently derived a new , theoretically sound measure of information integration called “geometric integrated information” , which is immune to the criticisms leveled against most previous measures [17 , 18] ( that said , we note that a mathematically similar measure called “stochastic interaction” was derived almost two decades ago [9] , and that its time-reverse equivalent was recently lauded as a theoretically sound option for measuring information integration [11] , but that this measure has been shown to exceed a system’s total mutual information in time [14]—a criticism to which geometric integrated information is immune . We also note that there might be other sensible upper-bounds for a measure of integrated information , such as channel capacity or “effective information” , as in [19] ) . This means that , in principle , neuroscientists could use geometric integrated information to push past the feedforward circuits of the brain’s sensory periphery , and begin to make sense of the information being integrated across the recurrently connected modules of the rest of the brain . But there’s a hitch . Calculating any of the proposed measures of information integration , including geometric integrated information , is computationally intractable for networks with more than about 20 nodes ( e . g . 20 neurons or voxels ) . That is because all such measures of information integration require identifying what is called the “minimum information bipartition” ( MIB ) of a network , which is the bipartition that splits the network into two maximally independent sub-communities [9–18] . This makes measuring integrated information in large networks impossible , because finding the MIB requires a brute-force search through all possible bipartitions of a network—a combinatorial search whose computation time explodes super-exponentially with network size . The reason we need to find the MIB is that a network’s capacity for information integration is characterized by where information integration is lowest , which is very much like defining the strength of a chain by the strength of its weakest link: if one link is weak , then the whole chain is weak . For example , if a network has unconnected sub-networks , then the integrated information of that network is 0 bits . In general , to accurately determine a network’s value of integrated information , one has to find the MIB of that network . Note that , in principle , the partition that yields the global minimum of integrated information might split a network into more than two sub-communities . But , because the number of possible n-partitions explodes with the Bell number ( e . g . a network of 8 nodes can be partitioned 4 , 140 ways , a network of 10 nodes can be partitioned 115 , 975 ways , and a network of 12 nodes can be partitioned 4 , 213 , 597 ways ) , we follow most of the Integrated Information Theory literature [9–18] and restrict partitions to bipartitions , which still capture a network’s overall capacity for information integration , and are at least computationally tractable for small networks . But , even with the restriction to bipartitions , the application of Integrated Information Theory is computationally challenging . As mentioned above , a brute-force search to find the bipartition that minimizes integrated information becomes computationally intractable quickly ( e . g . a 20-node network can be bipartitioned 524 , 287 ways and a 30-node network can be bipartitioned 536 , 870 , 911 ways ) . Given the computational intractability of finding the MIB of large networks , our question is this: for a given set of time-series data recorded from nodes in a connected network , is there a way to approximate the minimum information bipartition without a brute-force search ? There have been several proposed solutions to this problem . In our own earlier work [20] , we proposed using graph clustering to quickly find the MIB—a proposal also voiced by others [11]—though neither we nor others have yet successfully demonstrated that graph clustering does in fact find good partitions across which to calculate integrated information . Other proposed solutions have used optimization algorithms to find the MIB [21] , but these are either prohibitively slow or split brain networks into one-vs-all partitions , which do not reflect how complex biological systems are likely organized [19 , 22] . Here , we build upon and empirically validate our earlier proposal that the MIB can be identified through graph clustering . We show that a network partitioning method called “spectral clustering” [23 , 24] , when applied to correlation matrices of neural time-series data ( Fig 1 ) , reliably identifies or approximates the MIB of even large systems . We demonstrate this in several steps . First , we show that spectral clustering can find the exact MIB in small , brain-like networks ( 14-16 nodes ) of coupled oscillators . Then , we move onto large networks of coupled oscillators ( 50-300 nodes ) , where we forced the MIB onto the networks by structurally severing them in half , and show that spectral clustering can find good approximations of the MIB in these large oscillator networks as well . Third , we show that spectral clustering can find the exact MIB in small samples of monkey ECoG data . Fourth , we apply spectral clustering to data from all available recording sites in two monkey brains—which are so large that it would likely take centuries to determine their ground-truth MIB—and show that spectral clustering quickly finds a partition across which integrated information is smaller than or nearly equivalent to the value of integrated information across partitions identified by an optimization-based solution to this search problem ( which can take weeks to run ) . We note that we also tried using two other community detection algorithms , namely the Weighted Stochastic Block Model algorithm [25] and the Louvain Algorithm for modularity maximization [26] , but that our early experimentation with these algorithms did not yield results nearly as strong as did spectral clustering in identifying the MIB . That said , we leave open the possibility that other community detection algorithms might approximate networks’ MIBs as well as spectral clustering does . We use our spectral clustering-based method to report two novel empirical findings: 1 ) The MIB of ECoG recordings in the macaque cortex splits posterior sensory areas from anterior association areas , and 2 ) Supporting predictions from neural connectomics research , we show that networks with a high global efficiency ( i . e . a short average path length ) produce high integrated information and that strongly modular networks produce low integrated information . Because we believe that this measure will be empirically valuable for understanding how different brain states or task conditions rely on different modes of information integration between neurons or brain regions , we have made our code publicly available as a toolbox at https://figshare . com/articles/Information_Integration_in_Large_Brain_Networks/7176557 .
As mentioned in the Introduction , a number of measures of integrated information based on time-series data have been proposed . Only very recently [17 , 18] , a measure was derived that is at the same time computable from time-series data and properly bounded between zero bits and the total mutual information in time and space in a system . This measure , called “geometric integrated information” , or ΦG , is defined as the minimized Kullback-Leibler divergence between the “full model” p of a system X , which fully characterizes all the spatiotemporal influences within the system , and a “disconnected model” q . In the disconnected model , the network of interest is partitioned into statistically disconnected sub-communities , which evolve over time independently of one another: q ( X t i | X t - τ ) = q ( X t i | X t - τ i ) ∀ i ( 1 ) where the index i labels the statistically disconnected sub-communities ( so , for a bipartition , i iterates from 1 to 2 ) , and Xt and Xt−τ describe present and past states of the system , respectively ( t and t−τ are discrete time indices ) . X t i and X t - τ i refer to non-empty subsets ( corresponding to sub-communities ) of the variables constituting Xt and Xt−τ; Xt and Xt−τ are n-dimensional real-valued random vectors , i . e . X t : = ( X t 1 , X t 2 , . . . , X t n ) , where X t j for j = ( 1 , … , n ) are real-valued random variables . In other words , for a given multivariate time-series , with n variables ( e . g . neurons , electrodes , or voxels ) and m time-points , Xt−τ is a matrix of observations of all n variables from time 1 to time m−τ , and Xt is a matrix of observations of all n variables from time τ to time m . Geometric integrated information is then defined as: Φ G = min q D K L [ p ( X t , X t - τ ) | | q ( X t , X t - τ ) ] ( 2 ) where DKL[p , q] stands for the Kullback-Leibler divergence between two distributions p and q . Geometric integrated information has a simple and quick-to-compute formulation for multivariate Gaussian signals [17] , and all data analyzed in this paper are approximately multivariate normal ( S2 Fig ) . ( We note that for Gaussian variables no recourse to information geometry is necessary to minimize the KL divergence in Eq 2 , and so arguably there is no direct sense in which this measure is “geometric” for Gaussian variables . That said , because the framework of information geometry is necessary for calculation of this measure in the non-Gaussian case , we follow [17] and still call this measure “geometric integrated information” in the Gaussian case ) . Like many information-theoretic measures , geometric integrated information can be computed in Gaussian data using the framework of linear regression . As is commonly done in time-series analysis across a range of fields , we can model the evolution in time of a Gaussian system using a simple linear regression model: X t = A X t - τ + E ( 3 ) where Xt corresponds to the present of the system and Xt−τ corresponds to the past of the system , A corresponds to the regression matrix estimated from the data , and E corresponds to the error or residuals in the linear regression . Both A and E can be computed from the covariance matrices of the data . The regression matrix A is given by the normal equation: A = Σ X t X t - τ ( Σ X t - τ X t - τ ) - 1 ( 4 ) where Σ X t X t - τ is the variance between the present and the past of the system X . The covariance of the error matrix E can also be computed from the covariance of the data , and is precisely equivalent to the conditional variance of the present , given the past of the system: Σ E E = Σ X t | X t - τ ( 5 ) where Σ X t | X t - τ = Σ X t - τ X t - τ - A ( Σ X t X t - τ ) T ( 6 ) The covariance of E is all we’ll need for the complete model of the system’s evolution . Oizumi et al [17] prove that the disconnected model of the system can also be expressed in terms of linear regression: X t = A ′ X t - τ + E ′ ( 7 ) where A′ is a regression matrix like A , but all elements describing interactions across the MIB have been set to zero ( i . e . A′ is a diagonal block matrix ) . If we have correctly identified the MIB of the network , and therefore set all the right elements of A′ to zero , then the covariance of E′ , which is the only thing we now need to calculate integrated information , is: Σ E ′ E ′ = Σ E E + ( A - A ′ ) Σ X t - τ X t - τ ( A - A ′ ) T ( 8 ) There is no ( known ) closed-form solution for A′ and ΣE′ E′ , but these matrices can be estimated using iterative methods . In this paper , we estimate these matrices using the augmented Lagrangian method provided by [27] . Finally , we insert Eqs 6 and 8 into the standard formula for the Kullback-Leibler divergence between two Gaussians with identical means . After a simple algebraic transformation the estimate of integrated information , in bits , can be written: Φ G = 1 2 log | Σ E ′ E ′ | | Σ E E | ( 9 ) where |ΣE′ E′| refers to the determinant of the error matrix in our disconnected model , and |ΣEE| refers to the determinant of the error matrix in the connected model . If the sub-communities of a network evolve in time mostly independently of one another , then these determinants will be close and ΦG will be small . If , on the other hand , there are strong inter-dependencies between the sub-communities of a network , then these two determinants will diverge and ΦG will be large . To find the minimum information bipartition , we need to perform a brute-force search through all possible bipartitions of a network , and find the bipartition that minimizes integrated information . Unfortunately , this will usually lead to strongly asymmetrical partitions , in which one or two nodes are split from the rest of the system—and such partitions are usually of little functional relevance [11 , 14–16] . While how to best handle such asymmetric partitions remains an open problem in the Integrated Information Theory literature [11 , 14] , there have been a number of proposed solutions for finding more balanced and functionally meaningful partitions . Here , we use the solution originally suggested in [19] and also used in [15 , 16] , which is to find the bipartition that minimizes integrated information , normalized by the factor K: K = min k [ H ( M k ) ] ( 10 ) where H ( Mk ) refers to the entropy of a sub-community Mk . For a multivariate Gaussian system M , the entropy H ( M ) = 1 2 ln ( | 2 π e Σ ( M ) | ) , where the bars denote the matrix determinant and Σ ( M ) is the covariance matrix of the variable M . Normalized integrated information thus equals Φ G K . Minimizing the normalized version of integrated information biases the search toward partitions that are more balanced in the number of nodes , and away from partitions in which a single node is isolated from the rest of the network . Thus , strictly speaking , the MIB of a network is the bipartition , out of all possible bipartitions , that minimizes Φ G K , and the integrated information of that network is ΦG , not normalized by K , across that partition . ( Note that normalization was not discussed in the paper in which geometric integrated information was originally derived [17] , but that it has already been shown that without normalization , the bipartition that minimizes geometric integrated information is often the one-vs-all partition [21] ) . Recall that earlier , we mentioned that a previously proposed optimization-based solution for quickly finding the MIB often splits networks into one-vs-all partitions , which are difficult to interpret in terms of biological function . This solution , proposed by [21] , makes use of the Queyranne algorithm for minimizing sub-modular functions , and was shown to accurately identify bipartitions that minimize non-normalized integrated information . Problematically , these bipartitions are often one-vs-all splits—which is precisely what normalization was designed to avoid . Thus , finding the MIB using the Queyranne algorithm can be considered a valid option if a researcher wants to find a partition that minimizes non-normalized integrated information , as opposed to normalized integrated information . Our goal , however , is to find a quick and accurate method for identifying bipartitions that minimize normalized integrated informaton , because we share others’ conviction [19] that this yields more biologically meaningful results . Finally , note that ΦG is calculated over a time-lag τ ( Eqs 1–8 ) . If , for example , τ is set to 50 ms , then ΦG will tell you , in bits , how much information is carried over 50 ms using the network connections that cross the MIB of your system . While the choice of a partition across which to calculate integrated information ( i . e . , the MIB ) is well-defined , the choice of a time-lag τ is not . For the purposes of this study , we chose a time-lag that , on average , maximized integrated information for the system at hand ( S3 Fig ) . This choice was based on previous papers [13 , 28] , which , based on phenomenological arguments , maintain that the time-scale of neural information integration that is most relevant to cognitive and perceptual processes is the scale that maximizes integrated information—a claim about which we are agnostic , but which our method could help elucidate in future research . That said , we note that in general , it is common to estimate time-delayed information measures such as transfer entropy for various time-lags , and then to choose the time-lag that maximizes the information measure of interest . This procedure has been shown to accurately capture the time scales of delayed system interactions [29] . As a critical innovation , which enables the estimation of ΦG for large networks , we propose to reduce the search space for the MIB using graph clustering on the correlation matrix of neural time-series data ( Fig 1 ) . We searched the literature for a graph clustering algorithm that is biased toward balanced partitions , like the search for the MIB . We therefore chose to use spectral clustering [23] to partition our networks , because it is known to quickly find bipartitions that approximately but robustly minimize the “normalized cut function” in graph theory , which is the sum of weights that cross a partition normalized by the sum of weights between the entire network and the communities on either side of that partition ( see Methods for more details ) . While the normalized cut function is mathematically distinct from the function being minimized in search for the MIB ( i . e . , Φ G K ) , in both cases normalization is being used to find roughly equal-sized communities , and so we hypothesized that both should yield similar partitions . To use a network partitioning algorithm , we need a way to estimate network structure from time-series data . To address this challenge , we drew on insights from neural connectomics research . Network neuroscientists often treat the correlation matrix of neural time-series data as a “functional network” describing neural interactions , and apply graph clustering algorithms like spectral clustering to neural correlation matrices to partition the brain into distinct functional sub-networks [22 , 30–35] . Following this insight , our method takes the correlation matrix of time-series data , transforms it using a power adjacency function ( following [36] ) and thresholds the transformed matrix across a range of cutoffs ( following [37–41] ) , applies spectral clustering at each threshold , calculates ΦG ( normalized ) across each resulting candidate network partition , and picks as the estimate of the MIB the partition that yields the lowest value of ΦG ( normalized ) . See the Methods for more details on how we used spectral clustering to approximate the MIB . As a first step in assessing how well spectral clustering on the correlation matrix of time-series data recorded from a network can find the MIB of that network , we begin with a simulation of coupled oscillators . Among the variety of existing oscillator models , we chose to test our method in brain-like networks of coupled stochastic Rössler oscillators [42] because , when weakly coupled , their activity approximates a multivariate normal distribution [43] ( S2A–S2C and S2F–S2K Fig ) , similar to the ECoG data we analyze later in this paper ( S2D and S2E Fig ) . Besides oscillators’ frequency and the amplitude of noise injected into the oscillators , all parameters in the model were taken from previous literature ( see Methods ) . We simulated 25 , 000 time-points of oscillatory signals from 50 14-node networks and 50 16-node networks . These networks were generated using a novel algorithm based on Hebbian plasticity , which produces connectivity patterns that recapitulate basic features of brain connectomes , including a modular structure and rich between-module connectivity [22] , and a log-normal degree distribution [44] ( see Methods ) . To assess the performance of spectral clustering in identifying the MIB from time-series data , we need a best guess at the “ground truth” MIB of a system . When the underlying transition probabilities of a system are known , the ground-truth MIB can simply be determined by a brute-force search through all possible bipartitions of a system and identifying the bipartition that minimizes normalized integrated information . Identifying the ground-truth from time-series data , however , requires infinite observations . Thus , when we refer to the “ground-truth” MIB throughout this paper , we simply mean the bipartition , identified through a brute-force search through all possible bipartitions , that minimizes an estimate of normalized integrated information from finite observational data . We found that in 95/100 of our small simulated networks , there was a difference of 0 bits between ΦG ( normalized ) across the spectral clustering-based bipartition and the lowest value of ΦG ( normalized ) identified through a brute-force search through all possible bipartitions ( Fig 2a ) . In other words , in almost all networks tested , our spectral clustering-based approach gives the exact same result as does a brute-force search for the MIB . We further found that the Rand Index [45] ( a common measure of partition similarity ) between the ground-truth MIB and the spectral bipartition was 1 ( indicating a perfect match ) for those same 95 networks ( Fig 2b ) . Finding partitions that are highly similar to the MIB in these networks is important , since the more dissimilar a partition is from the MIB , the larger ΦG ( normalized ) will tend to be across that partition; in other words , the further off you are from the MIB , the less accurate your estimate of integrated information will tend to be ( S4A and S4B Fig ) . To test the statistical stability of these results , we computed running averages of both the Rand Indices and the differences between estimated ΦG values ( e . g . the running mean Rand Index of the first two 14-node networks , then the first three 14-node networks , then the first four 14-node networks , etc . ) . We then took the approximate derivatives of the running averages for both network sizes , and used two-sample t-tests to accept the null hypothesis ( α = 0 . 05 ) that the approximate derivatives were indistinguishable from 0 for both tests , for both network sizes . This means that the results reported in Fig 2 are statistically stable at a sample size of 50 networks ( i . e . adding more samples would not likely change the means significantly , as the differences in the running average are already approximately zero at just 50 networks ) . To further check whether this result generalizes across different network dynamics , we used the same networks to generate multivariate autoregressive simulations and performed the exact same analysis , and found that spectral clustering also accurately identifies the MIB for autoregressive data ( S7 Fig ) . We used the same running average and approximate derivative test to confirm that our results for the autoregressive dynamics in S7 Fig are also statistically stable at a sample size of 50 networks . Finally , we also compared our approach to another proposed method for quickly identifying the MIB from time-series data . This method uses the Queyranne algorithm for fast minimization of sub-modular functions [21] . Though in past work the Queyranne algorithm has been successfully used to minimize non-normalized integrated information , we used the Queyranne algorithm to try to find a bipartition that minimizes normalized integrated information . The difference between ΦG ( normalized ) across the Queyranne bipartition and ΦG ( normalized ) across the MIB was 0 bits ( indicating a perfect match ) in only 1/50 14-node networks ( mean difference = 0 . 0031 bits ) and in 2/50 16-node networks ( mean difference = 0 . 0026 bits ) . The Rand Index between the Queyranne partition and the MIB was 1 for the same networks for which the difference in ΦG ( normalized ) was 0; the mean Rand Index was 0 . 576 across all 14-node networks and 0 . 582 across all 16-node networks . The Queyranne algorithm also performed poorly in minimizing normalized integrated information in autoregressive simulations generated from these same small brain-like networks ( S7 Fig ) . Thus , spectral clustering does a better job of estimating the MIB in small brain-like networks than does the Queyranne algorithm . Moreover , even when trying to minimize normalized integrated information , which is biased toward balanced partitions , the Queyranne algorithm often found partitions that isolate one node from the rest of the network . This occurred in 23/50 of the 14-node networks ( while none of the MIBs identified through a brute-force search yielded one-vs-all partitions ) and in 26/50 of the 16-node networks ( while only one of the MIBs identified through a brute-force search was a one-vs-all partition ) . Such partitions are usually of little functional relevance—hence why normalization is introduced in searching for the MIB [19] . Moreover , the partitions found by the Queyranne algorithm were also generally dissimilar from the partitions found by our spectral clustering approach: the mean Rand index between the spectral partitions and the Queyranne algorithm partitions was 0 . 57 for the 14-node networks and 0 . 59 for the 16-node networks . Having passed this basic test in small networks , we next asked whether spectral clustering can accurately identify the MIB in large systems . To test this , we used our algorithm for generating brain-like connectivity ( see Methods ) to create networks which ranged from 50 to 300 nodes in size . Networks of these sizes cannot be exhaustively searched for their MIB , so we forced the MIB onto these networks by cutting them in half . If a network is cut into two parts , then , with infinite data , the MIB will converge onto where the network has been cut and ΦG across this cut will be 0 bits . For these networks , we generated 100 , 000 time-points of data using the stochastic Rössler oscillator model , since in larger systems more data are necessary for more accurate estimation of multivariate information measures . We were unable to test the accuracy of the Queyranne algorithm for these networks , because the computation time for using the algorithm to minimize normalized integrated information increased exponentially , making its application to networks with more than 50 nodes prohibitively expensive; that said , we note that the algorithm is far faster in minimizing non-normalized integrated information , as shown in [21] . Spectral clustering again performed remarkably well . The mean absolute difference between ΦG across the spectral partition and ΦG across the ground-truth cut was less than 0 . 001 bits ( normalized ) for all network sizes ( Fig 3A ) , indicating a close match . Note that , objectively , ΦG should be zero in these cut networks , and we would expect estimates of ΦG to converge to zero bits with infinite data; as a sanity check , we utilized a well-established method for extrapolating estimates of information measures to what they would be if infinite data were available , and found that this brought estimates significantly closer to zero bits for these cut networks , as expected ( S1 Fig ) . The Rand Index between the spectral partition and the ground-truth cut was greater than . 8 for 37/40 of the 50-node networks , 39/40 of the 100-node networks , 29/40 of the 150-node networks , 21/40 of the 200-node networks , 18/40 of the 250-node networks , and 10/40 of the 300-node networks . Given that the estimates of ΦG across the spectral partition and the ground-truth cuts were very close even in the 200- to 300-node networks ( for which the spectral partitions were similar to the ground-truth cut less often ) and also both extrapolated to around the ground-truth of zero bits , these results suggest that there are sometimes multiple minima for normalized integrated information ( i . e . in these cut networks , there are sometimes several bipartitions across which there is little to no information integration ) . To test the statistical stability of these results , we computed running averages of both the Rand Indices and the differences between estimated ΦG values ( e . g . the running mean Rand Index of the first two 50-node networks , then the first three 50-node networks , then the first four 50-node networks , etc . ) . We then took the approximate derivatives of the running averages for each network size , and used two-sample t-tests to confirm that the approximate derivatives were statistically indistinguishable from 0 for both tests , for each network size . This means that the results reported in Fig 3 are statistically stable at a sample size of 40 networks ( i . e . adding more samples would not likely change the means significantly ) . Finally , we again checked whether this result generalizes across different network dynamics , by generating autoregresive simulated data from these large , cut networks . We found that spectral clustering performed even better ( nearly perfectly ) for the autoregressive simulations ( S8 Fig ) , again supporting the robustness and generalizability of our method . We again used a running mean of the results , together with approximate derivatives , to confirm that the results for the autoregressive data in S8 Fig were also statistically stable at a sample size of 40 networks . We next applied the same spectral clustering method to one minute of ECoG data from two macaque monkeys , Chibi and George [46] . After pre-processing ( see Methods ) , data for 125 electrodes distributed across the left cortex of each monkey were available . These data were multivariate normal ( S2D and S2E Fig ) . To enable comparison between graph clustering-based partitions and the ground-truth MIB , we divided these data into overlapping sets of fourteen electrodes each , resulting in 112 sets of electrodes for each monkey . The difference between ΦG across the MIB and ΦG across the partitions identified by spectral clustering was 0 ( indicating a perfect match ) for 46/112 of the datasets from Chibi’s brain ( mean difference = 0 . 0001 bits ) and in 67/112 of the datasets from George’s brain ( mean difference = 0 . 0002 bits ) ( Fig 4A ) . The Rand Index comparing the spectral partition and MIB was 1 for those same datasets ( Chibi mean Rand Index = 0 . 79 , George mean Rand Index = 0 . 87 ) ( Fig 4B ) . As was the case for our simulated networks , the more dissimilar partitions in the monkeys’ brains were from the MIB , the larger ΦG ( normalized ) tended to be across those partitions ( S4 Fig ) . The Queyranne algorithm again performed worse than spectral clustering , yielding perfect matches to the ground-truth in only 18/112 of the datasets from Chibi’s brain ( mean Rand Index = 0 . 6 ) and 22/112 from George’s brain ( mean Rand Index = 0 . 64 ) . Moreover , as was the case for our simulated data , the Queyranne algorithm separated one node from the rest of the system in the majority ( 145/224 ) of all ECoG datasets ( as opposed to the ground-truth MIBs , which separated one node from the rest of the system in only 39/224 datasets ) . Finally , the partitions found by the two algorithms were generally dissimilar: the mean Rand index between the spectral partitions and the Queyranne algorithm partitions was 0 . 65 for the electrode clusters in Chibi’s brain and 0 . 67 for George’s brain . As a test of how well spectral clustering could approximate the MIB for all electrodes , we asked whether it could minimize ΦG ( normalized ) in the whole cortex of each monkey . We therefore calculated ΦG across the spectral clustering-based bipartition of the entire left cortex for both monkeys . We found that this estimate of the MIB split posterior sensory areas from anterior association areas in both brains ( Fig 4C and 4E ) . To test the statistical robustness of this result , we compared both our estimated ΦG ( normalized ) values and our estimated MIBs for both monkey cortices to results from 100 Amplitude Adjusted Fourier Transform surrogate datasets [47]; we found that our estimated ΦG ( normalized ) values were significantly higher than the surrogate distributions for both monkeys , and that the similarities between the MIBs estimated for the monkey cortices and the MIBs estimated for the surrogate datasets were at chance levels , suggesting that the results for the full monkey brains are not artifactual ( S9 Fig ) . We then compared ΦG across the spectral clustering-based partitions to ΦG values calculated across partitions identified by a Replica Exchange Markov Chain Monte Carlo ( REMCMC ) algorithm . The REMCMC method for estimating the MIB is described in detail in [21]; the algorithm used in this paper is the same as that used in [21] , except that it searched for a bipartition that minimized normalized ( rather than non-normalized ) integrated information . We also terminated the algorithm after 10 days , since it failed to reach convergence for either monkey dataset by that point . Since the algorithm tries to minimize normalized integrated information across six parallel sequences , it produces six guesses for the MIB . We also tried using the Queyranne algorithm for the monkey brains , but the algorithm failed to terminate even after two weeks of running , and so we did not include the Queyranne algorithm in this analysis . For George’s brain , normalized integrated information across the spectral clustering-based partition was lower than it was across all six bipartitions identified by the REMCMC method ( Fig 4E ) . In Chibi’s brain , the REMCMC algorithm found two partitions across which normalized integrated information was very slightly lower ( 0 . 0002 bits ) than it was across the spectral clustering-based partition; interestingly , the two REMCMC partitions ( which yielded the same value of normalized integrated information ) were not only dissimilar to each other ( Rand Index = 0 . 5 ) , but were also both dissimilar to the spectral clustering-based partition ( Rand Indices = 0 . 5 , 0 . 55 ) , suggesting that there were several local minima of normalized integrated information for Chibi’s brain . In all , these results show that our spectral clustering-based method reliably minimizes ΦG ( normalized ) of the entire macaque cortex , suggesting that it successfully finds or approximates the MIB in large neural data . The ability to quickly measure information integration in large networks allowed us to assess what network architectures best support information integration , and what that might imply about how brains could be organized to integrate information . We here test for the first time , in silico , several graph-theoretic measures that have been hypothesized to track neural information integration . Note that in the neural connectomics literature , these graph-theoretic measures are often applied to either structural networks , such as the physical connectivity between brain regions that might be revealed through diffusion tractography , or to functional networks , such as correlation matrices calculated from functional magnetic resonance imaging recordings [48] . Because analyses of structural networks are more straightforward than analyses of functional networks ( primarily because there is considerable debate surrounding what constitutes a functional network ) , we here focus on the relationships between structural networks and integrated information . We hope to more systematically investigate the relationship between integrated information and functional networks in future work . The most commonly invoked graph-theoretic measure of a network’s capacity to integrate information is global efficiency [37 , 48–51] . Global efficiency is related to the inverse of the average shortest path between nodes in a network . Formally , the global efficiency E of a network G is defined as follows: E ( G ) = 1 n ( n - 1 ) ∑ i ≠ j ∈ G 1 d ( i , j ) ( 11 ) where n is the number of nodes in the network and d ( i , j ) is the shortest path between given network nodes i and j . In high efficiency networks , any node can be reached by any other node with only a few steps . For about a decade , network neuroscientists have assumed that the global efficiency of a brain network quantifies its ability to concurrently exchange information between its spatially distributed parts; for this reason , it has been assumed that global efficiency sets an upper limit on neural information integration [37 , 48 , 50 , 51] . Conversely , it has been assumed that the modularity of brain networks ( and of complex networks more generally ) limits the integration of information , primarily by segregating network dynamics [22 , 33 , 51] . The modularity of a network is defined by Newman’s Q: Q = 1 2 m ∑ i j [ A i j - k i k j 2 m ] δ ( c i , c j ) ( 12 ) where Aij is the adjacency between nodes i and j , ki and kj are the sums of the adjacencies involving i and j , respectively , ci and cj are the modules to which nodes i and j have been assigned , respectively , m = 1 2 ∑ i j A i j , and δ ( ci , cj ) equals 1 if ci = cj and 0 otherwise . Networks that can be easily subdivided into distinct sub-communities or modules will have a high Q , whereas networks with little community structure ( such as random networks ) will have a low Q . We used the Brain Connectivity Toolbox’s [51]modularity_und . m function , which implements Newman’s spectral community detection algorithm [52] , to compute network modularity . To directly study the relationship between network efficiency , modularity , and integrated information , we followed the network generation procedure introduced by Watts and Strogatz in their canonical paper on small-world networks [53] . In their paper , Watts and Strogatz begin with completely regular lattice networks , in which nodes are only connected to their neighbors; they then systematically increase a parameter p , which is the probability that a given node will re-wire a local connection and connect to any random node in the network . A p of 0 yields a completely regular lattice network , a p of 1 yields a completely random network , and intermediate values of p yield “small-world” networks , which are highly clustered like regular lattice networks but also have short characteristic path lengths like random networks ( Fig 5A ) . The parameter p also systematically controls the global efficiency of the network: higher values of p produce networks with higher global efficiency [49] ( Fig 5B ) . We also show that p systematically decreases network modularity ( Fig 5C ) . Since up until this point we have only shown that our spectral clustering-based approach can find the MIB of brain-like networks of coupled oscillators , autoregressive signals generated from brain-like networks , and in real brain data , we first checked whether spectral clustering can also find the MIB in small lattice networks , small-world networks , and random networks of coupled oscillators . Consistent with our earlier results , we found that spectral clustering found the exact MIB ( determined through a brute-force search ) in almost all 14- and 16-node Rössler oscillator networks of these types that we tested ( S6 Fig ) . As such , we felt confident that it would also give us accurate estimates of integrated information in large networks of these types . We therefore iterated through 19 values of p: the first 10 values were logarithmically spaced between 0 . 001 and 0 . 1 ( following [53] ) , and the following nine values were linearly spaced between 0 . 1 and 1 . For each value of p , we created 50 100-node networks , which all had the same number of edges and a mean degree of 6 , and ran the Rössler oscillator model on those networks to produce 25 , 000 time-points of oscillatory signals . To ensure that any differences in integrated information in the resulting network dynamics were attributable to network connectivity rather than coupling strength , we set the oscillators’ coupling parameter to 0 . 25 for all networks in this analysis ( rather than determine the coupling strength through a master stability function , as we do elsewhere—see Methods ) . We found that , as predicted by work in neural connectomics [37 , 48 , 50 , 51] , networks’ global efficiency was tightly coupled to their capacity for information integration . Increasing the rewiring probability p systematically increased both a network’s global efficiency ( Fig 5B ) and how many bits of information are integrated across that network ( Fig 5D ) , and decreased the networks’ structural modularity . Interestingly , both global efficiency and integrated information reach a plateau around p = 0 . 4 , though it is unclear from our present results why this is the case . Finally , when looking across all networks , there was a strong and significant correlation ( r = 0 . 91 , p < 10−324 ) between the networks’ global efficiency and how much information they integrate ( Fig 5D ) and a strong and significant anti-correlation ( r = -0 . 90 , p < 10−324 ) between the networks’ structural modularity and how much information they integrate ( Fig 5E ) . This supports the widely held hypothesis that global efficiency determines how many bits of information a network can integrate and that modularity limits information integration , at least in the case of coupled oscillator networks . It would be interesting to see whether this relationship between network efficiency and integrated information extends to systems with non-Gaussian dynamics—a possibility we hope to explore in future work . The results reported thus far show that our spectral clustering-based approach can accurately approximate the MIB of a system from time-series data . As a final analysis , we show that it is also much faster to run than either a brute-force search or the Queyranne algorithm for large systems , since its run time scales much less steeply ( Fig 6 ) . We simulated 25 , 000 time points of data using our Rössler oscillator model and artificial brain-like networks ( see Methods ) ranging from 10 to 120 nodes in size . We estimated integrated information using a brute-force search for the MIB in the 10- to 18-node networks , used the Queyranne algorithm for networks of 10- to 50-nodes in size , and used our spectral clustering approach for all network sizes . We empirically measured how long it took to run each of these algorithms on Matlab , using a 64-bit linux CentOS . In Fig 6 we plot the average run time across five samples of each network size . We found that , as expected , the run time for the brute-force search for the MIB scales super-exponentially; we further found that the run time for our approach scales much less steeply than does the run time for the Queyranne algorithm , which means that our method is not only more accurate than the Queyranne algorithm in finding bipartitions that minimize normalized integrated information , but is also much faster for large systems . That said , we again emphasize that the Queyranne algorithm is a valid and fast option for minimizing non-normalized integrated information [21] .
We have presented in this paper a method for measuring integrated information in large systems , using time-series observations from those systems . Specifically , we presented a robust approximate solution to the search for the minimum information bipartition of large networks , a problem that has impeded efforts to measure integrated information in large brain networks . Our proposed method for quickly partitioning brain networks to find the MIB is drawn from well-established methods in neuroimaging ( for a recent review of the use of graph clustering on neural correlation matrices to identify functional sub-networks of the brain , see [22] , and for the specific use of spectral clustering in such analyses , see [35 , 38] ) . Although the Queyranne algorithm has previously been shown to successfully find bipartitions that minimize non-normalized integrated information [21] , the algorithm usually finds one-vs-all network partitions , even when trying to find a partition that minimizes normalized integrated information ( as we report here ) . That said , we agree with Kitazono and colleagues [21] that it would be fruitful to consider methods that combine our spectral clustering-based approach with their Queyranne algorithm-based approach . It is worth pointing out that although spectral clustering found the MIB or partitions close to the MIB in the majority of both real and simulated signals for which the ground-truth MIB could be computed , it did not always yield perfect results . While it is still unclear what conditions ensure that spectral clustering will find the exact MIB , we note that in the analyses performed here , the performance of spectral clustering was correlated with the strength of interactions between units separated by the spectral partition ( S4 Fig ) . Importantly , our solution passed a number of basic but challenging tests involving artificial and real brain recordings . As a first application of our result , we investigated the relationship between integrated information and network structure . We found that , consistent with earlier predictions [37 , 48 , 50 , 51 , 54] , networks with a high global efficiency produce high integrated information and that networks with high structural modularity produce low integrated information ( Fig 5 ) . This observation may help in pinpointing brain structures with high levels of information integration . For example , it has been assumed that the cerebellum does not integrate much information because of its highly modular architecture , while the rich , recurrent cross-module connectivity of the thalamocortical system has been assumed to allow for high levels of information integration [55–57] . Our simulation-based results support this hypothesis , though the truth of the matter will clearly need to be determined on the anvil of experiment . We also found that our method for identifying the MIB of large systems split posterior sensory areas from anterior association areas in both monkey cortices we tested ( Fig 4 ) . In strict mathematical terms , this means that activity in posterior and anterior regions evolved largely independently over time . We note that both monkeys were awake and resting while the data we analyzed were collected; it would be interesting to see whether the demarcation of independent information-processing sub-networks might vary as a function of cognitive task or brain state . Because our solution to the problem of searching for the MIB in large networks has made it possible to measure integrated information in real brains , we envision the described solution becoming a broadly applicable tool for neuroscience . In particular , our solution can help to elucidate the function of recurrent brain networks , just as the information-theoretic measure of channel capacity revealed coding schemes in feedforward brain circuits [2–8] . Our method can also be used to directly test the Integrated Information Theory of Consciousness [13] , for example by measuring changes in information integration during states of unconsciousness , like anesthesia . With respect to the applicability of our method to the Integrated Information Theory of Consciousness , it is worth pointing out one fascinating result here , which was that in the macaque brains , integrated information peaked at a time-lag of around 100 ms ( S3 Fig ) , which roughly corresponds to the observed timescale of conscious human perception [58 , 59] . This matching of time scales is one prediction of the Integrated Information Theory of Consciousness [13 , 28] , though this correspondence should be investigated more systematically in future empirical work . Given the potential usefulness of measuring integrated information in complex systems more generally , our method may also be of use to researchers in other fields as well . To facilitate such research , we have made our Matlab toolbox publicly available .
We here describe our algorithm for generating artificial brain-like networks or “connectomes” . First , following insights from the evolutionary neuroscience literature [60] , the number of modules in our networks was equal to the log of their number of nodes , rounded up . The sizes of the modules in these networks were random , though the sizes of the modules did not vary significantly because each node had an equal probability of being assigned to any given module . Undirected edges were cast between nodes according to two different probabilities: for a pair of nodes i and j where i ≠ j , an edge was cast between j and i according to a probability pint if both nodes were in the same module and with probability pext if they were in different modules . For a given network with M modules and for a given module with n nodes , if n ≥ 4 , then p i n t = 4 . 5 n and p e x t = 3 . 3 n M; otherwise p i n t = 4 n and p e x t = 3 . 75 n M . To mimic a basic Hebbian process , the nodes that made the most connections were then rewarded with even more connections and the nodes that made the fewest connections were punished by having their connections pruned . The process works like so: after edges have been cast according to the two probabilities pint and pext , find q , such that around 38% of nodes have made fewer than q connections ( this parameter of 38% was chosen somewhat arbitrarily , but it reliably led to a log-normal degree distribution as desired ) . Create a vector x with elements [q − 1 , q , q + 1 , … , f + 5] , where f is the largest number of connections that any node in the network made in the previous step of casting out connections . Create a second vector y of the same length as vector x . The first f 4 elements of y are set to 1 , and the last l − f + 1 elements of y are set to Z , where Z = N + log N 7 , N is the number of nodes in the network , and l is the length of vectors x and y . The middle w elements of y , where w = f - f 4 + 1 , are replaced with the vector [ 1 , 1 + Z w , 1 + 2 Z w , . . . , Z ] . A sigmoid function S is fit to x and y . For every node in the network , random connections are pruned or added , such that every node now has S ( c ) connections , where c is the number of edges the node had before pruning or adding connections . All networks were checked to ensure that in a given network , any node could be reached by any other node . The resulting networks recapitulated basic features of brain networks , including a modular structure with rich cross-module connectivity [22] , as well as a log-normal degree distributions with long right tails [44] . To simulate oscillatory brain signals from our artificial networks , we used a stochastic Rössler oscillator model . We chose to simulate data using Rössler oscillators because , as has been previously shown [43] , they follow a multivariate normal distribution when weakly coupled ( S2 Fig ) . The system of Rössler oscillators is modeled by the following differential equations: x ˙ i = - w y i - z i - σ ∑ h = 1 N g i h x h ( 13 ) y ˙ i = w x i + a y i + d η i ( 14 ) z ˙ i = b + ( x i - c ) z i ( 15 ) where , following previous literature [43 , 61] , a = 0 . 2 , b = 0 . 2 , and c = 9 . The oscillation frequencies w were normally distributed around a mean of 10 with a standard deviation of . 1 . d was set to 750 , and ηi is Gaussian noise . gih are the coefficients of the network’s Laplacian matrix , and σ is the coupling strength between oscillators . For all simulations other than the ones reported in Fig 5 ( where the coupling was 0 . 25 for all networks ) , σ was determined using a master stability function . Master stability functions give the lower and upper bounds for the coupling strengths that ensure network synchronizability . For networks of coupled Rössler oscillators , the lower-bound for the coupling strength is 0 . 186 divided by the second top eigenvalue of the network’s Laplacian matrix , and the upper-bound is 4 . 614 divided by the last eigenvalue of the network’s Laplacian matrix [62 , 63] . For each network , σ was set to the half-way point between these lower- and upper-bounds . The equations were integrated with a Euler algorithm , with dt = 0 . 001 . For our time-series , we took the y component of these equations , which yielded rich synchronization dynamics and followed a multivariate normal distribution ( S2 Fig ) . As shown in [23] , spectral clustering provides an approximate but robust solution to the “normalized cut” or Ncut problem in graph theory . The problem is motivated by a body of work on how to partition a graph G = ( V , E ) , with V vertices and E edges , into disjoint subsets A , B , A∪B = V , A∩B = ∅ . The Ncut problem entails finding a network cut which minimizes the following measure: N c u t ( A , B ) = c u t ( A , B ) a s s o c ( A , V ) + c u t ( A , B ) a s s o c ( B , V ) ( 16 ) where cut ( A , B ) is the sum of edges ( binary or weighted ) crossing a particular cut , assoc ( A , V ) is the sum of edges between community A and the entire network , and assoc ( B , V ) is similarly the sum of edges between community B and the entire network . Dividing cut ( A , B ) by the normalization factors assoc ( A , V ) and assoc ( B , V ) helps ensure that the clusters separated by the bipartition are relatively balanced in size , and as such serves the same function as the normalization function K ( Eq 10 ) in the search for the MIB . Shi and Malik [23] developed a fast spectral clustering algorithm that can quickly find a partition that ( approximately but robustly ) minimizes the Ncut function . The algorithm applies k-means clustering to the eigenvectors corresponding to the top k eigenvalues of a network’s Laplacian matrix , where k is the number of communities being split ( so , for a bipartition , k = 2 ) . Though many other clustering methods are available , we chose spectral clustering because it is particularly well-suited for normalized clustering problems , and as such is appropriate for the search for the MIB . The principle contribution of this paper is the empirical finding that the MIB of a network can be approximated by applying spectral clustering to correlation matrices of time-series data . To get a range of candidate partitions from a single correlation matrix , we first applied a power adjacency function [36] to the correlation matrix C , such that every correlation value rij in C is mapped onto a continuous edge weight wij: w i j = ( r i j + 1 2 ) β ( 17 ) The value chosen for β determines the shape of the power adjacency function . We iterated through 10 values of β , logarithmically spaced between 1 and 10 . For every resulting power adjacency transformation of C , we then iterated through a range of cutoff values ( from the 0th to the . 99th percentile of weights in steps of 0 . 005 ) , and for every iteration , all edge weights less than that cutoff value were set to 0 ( following [37–41] ) . Spectral clustering was then applied to the Laplacian matrix computed from each adjacency matrix , as well as to the Laplacian matrix computed from the un-thresholded correlation matrix . In total , this resulted in 2189 candidate partitions for each dataset . ΦG ( normalized ) was calculated for each of these candidate partitions , and we chose among these the partition that minimized ΦG ( normalized ) as our spectral clustering-based alternative to the MIB ( identified through a brute-force search ) . Note that , to our knowledge , there is no analytic guarantee that the MIB will be among these 2189 candidate partitions , and so the work presented here can be seen as a numerical experiment strongly motivating the proposal that there is a relationship between the MIB and the spectral partition of the correlation matrix of time-series data . In future work , we hope to analytically study this relationship in greater depth . ECoG data from the left cortex of two monkeys , Chibi and George , is publicly available on neurotycho . org [46] . Data from 128 electrodes were available for over an hour of recording from both monkeys . We selected the first 50 , 000 ms of data from both monkeys . The data were then down-sampled to 500 Hz , demeaned , de-trended , and band-stop filtered for 50 Hz and harmonics , which is the line noise in Japan ( where the data were collected ) . Data were then re-referenced to the common average across electrodes . We then visually inspected the data for artifacts . Segments of data with artifacts that spread across more than one electrode were removed from all electrodes , and individual electrodes with consistent artifacts that did not spread to their neighbors were removed entirely ( electrodes 14 , 28 , and 80 were removed for George , and electrodes 17 , 53 , and 107 were removed for Chibi ) . The pre-processed ECoG data were approximately multivariate normal ( S2 Fig ) , allowing for the fast measurement of integrated information .
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Information theory has been key to our understanding of the feedforward pathways of the brain’s sensory periphery . But , traditional information-theoretic measures only quantify communication between pairs of transmitters and receivers , and have been of limited utility in decoding signals in the recurrent networks that dominate the rest of the brain . To address this shortcoming , a theoretically sound measure of information integration has recently been derived , which can quantify communication across an entire brain network . This measure could be pivotal in understanding recurrent brain networks . But , a computational hurdle has made it impossible to quantify this measure in real brains . We present an approximate but robust solution to this hurdle , and use our solution to test long-held assumptions about how brain networks might integrate information .
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2019
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Information integration in large brain networks
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Plasmacytoid dendritic cell ( pDC ) -mediated protection against cytopathic virus infection involves various molecular , cellular , tissue-scale , and organism-scale events . In order to better understand such multiscale interactions , we have implemented a systems immunology approach focusing on the analysis of the structure , dynamics and operating principles of virus-host interactions which constrain the initial spread of the pathogen . Using high-resolution experimental data sets coming from the well-described mouse hepatitis virus ( MHV ) model , we first calibrated basic modules including MHV infection of its primary target cells , i . e . pDCs and macrophages ( Mφs ) . These basic building blocks were used to generate and validate an integrative mathematical model for in vivo infection dynamics . Parameter estimation for the system indicated that on a per capita basis , one infected pDC secretes sufficient type I IFN to protect 103 to 104 Mφs from cytopathic viral infection . This extremely high protective capacity of pDCs secures the spleen's capability to function as a ‘sink’ for the virus produced in peripheral organs such as the liver . Furthermore , our results suggest that the pDC population in spleen ensures a robust protection against virus variants which substantially down-modulate IFN secretion . However , the ability of pDCs to protect against severe disease caused by virus variants exhibiting an enhanced liver tropism and higher replication rates appears to be rather limited . Taken together , this systems immunology analysis suggests that antiviral therapy against cytopathic viruses should primarily limit viral replication within peripheral target organs .
Protection against life-threatening infections is a major function of the immune system . The systems biology view of the induction of the protective immune responses suggests that the kinetics of innate immune responses critically impinge on the development of pathogen-specific adaptive immune responses [1] . The major services provided by cells of the innate system located in secondary lymphoid organs ( SLO ) are ( i ) an early sensing of pathogen-associated molecular patterns ( ii ) the reduction of pathogen spread throughout the host by capturing pathogens , and ( iii ) the sustained stimulation of the adaptive responses over sufficient periods of time [2] . To mediate these challenging functions of pathogen capturing and containment , and long-lasting antigen presentation , efficient cell protection mechanisms are needed , especially in the case of cytopathic virus infections . Plasmacytoid dendritic cells ( pDCs ) are a CD11clow DC subset that is characterized by a particular set of phenotypic markers and special functional properties [3] , [4] . One of the major functional characteristics of pDCs is the expression of pathogen recognition receptors , such as Toll-like receptor ( TLR ) -7 and -9 , which endow these cells with the ability to rapidly produce large amounts of type I interferons ( IFNs ) following encounter with RNA or DNA viruses [5] . Hence , by providing a first wave of antiviral IFN , pDCs immediately limit viral spread and set the stage for antigen-specific immune responses . The mouse hepatitis virus ( MHV ) infection represents a well-understood paradigmatic system for the analysis of type I IFN responses . MHV is a member of the Coronaviridae family that harbor a number of viruses causing severe diseases in animals and humans , such as acute hepatitis , encephalitis , infectious bronchitis , lethal infectious peritonitis , and the severe acute respiratory syndrome ( SARS ) [6] , [7] . In systemic MHV infection , spleen and liver represent major target organs [8] , and primarily hematopoietic cell-derived type I IFN controls viral replication and virus-induced liver disease [9] . We could recently show that pDCs are the major cell population generating IFN-α during the initial phase of mouse coronavirus infection [8] . Importantly , mainly macrophages ( Mφ ) and , to a lesser extent conventional DCs , respond most efficiently to the pDC-derived type I IFN and thereby secure containment of MHV within SLOs [10] . Thus , the type I IFN-mediated crosstalk between pDCs and Mφs represents an essential cellular pathway for the protection against MHV-induced liver disease . In system biology terms , MHV infection triggers a complex array of processes at different biological scales such as protein expression , cellular migration , or pathological organ damage . To focus on the front edge of the virus-host interaction , the present analysis specifically addresses the early dynamics ( i . e . the first 48 h ) of the type I IFN response to MHV since this is decisive for the outcome of the infection . The reductionist's view of the most essential processes underlying the early systemic dynamics of MHV infection , liver pathology and the first wave of type I IFN production is summarized in Figure 1A . Our studies on the role of pDCs in establishing the type I IFN-mediated protection of Mφs against cytopathic MHV infection suggested that the spleen may function as a ‘sink’ contributing to the elimination of the virus from the system . Under the condition of pDC-deficiency or lack of type I IFN responsiveness of Mφs , a severe disease is observed [8] , [10] indicating that the operation of the spleen might switch from a ‘virus sink’ to a ‘virus source’ mode . This bi-modal function , i . e . the ability of the spleen to either eliminate or disseminate the virus , is outlined in Figure 1B . The switch between the two modes most likely depends on the number of pDCs in spleen , their activation status , the dose of infection , and the kinetics of virus spread . Thus , for an improved understanding of pDC function in cytopathic virus infection , it is of fundamental importance to determine the robustness and fragility of the early type I IFN response within SLOs in relation to the ability of the virus to counteract the IFN system and to replicate and cause severe disease in peripheral tissues . Because of the inherent complexity of the virus-host system , experimental approaches to examine the dynamical aspects of such multiscale interactions are limited . Therefore , we have used mathematical modeling in conjunction with high-resolution experimental data to predict kinetics and severity of infection in relation to variations in virus and host parameters . Our results suggest that the spleen represents a robust sink system for cytopathic virus infection able to cope with substantial variations of the IFN secretion and virus production in the spleen . However , the system is very fragile to minor increases in the virus growth rate in peripheral tissues .
To estimate the kinetic parameters of MHV-pDC interaction , we used in vitro data on MHV infection of bone marrow-derived pDCs as described previously [8] . The data set characterizes the response of pDCs infection with MHV at a multiplicity of infection ( MOI ) of 1 ( Figure 2A ) . To delineate a quantitative effect of IFN-α on virus production , additional data from similar experiments conducted with pDCs from mice deficient for the type I IFN receptor ( ifnar−/− ) were used ( Figure 2B ) . In addition to the MHV/IFN-α data , we considered data on survival kinetics of MHV-infected pDCs from wt and ifnar−/− mice generated independently in a separate series of experiments . The MHV-pDC interaction parameters appearing in the basic model of the type I IFN response ( described in Materials and Methods ) were estimated by fitting simultaneously the data sets on wt and ifnar−/− cells . The maximum likelihood approach for the log-transformed data was used to quantify the model parameters with the resulting best-fit description of the data by the model shown in Figure 2A and 2B . The resulting calibrated model for the in vitro pDC response to MHV was further validated by comparing its predictions with in vitro infection at an MOI of 0 . 1 and 0 . 01 ( Figure S1 ) and also , by determining the fraction of infected cells deduced from experimental data sets using an enhanced green fluorescence protein ( EGFP ) expressing recombinant MHV [10] ( Figure 2A and B ) . The parameter values summarized in Table 1 provide additional insight into the ‘numbers game’ between the virus and pDCs: ( i ) the average MHV secretion rate of infected pDCs is rather low with ∼1 . 7 pfu cell−1 h−1 , ( ii ) the IFN-α level required for 2-fold inhibition of MHV production is about 46 pg/ml , ( iii ) the average secretion rate of IFN-α per infected pDC is ∼4 . 4*10−4 pg h−1 or , equivalently , ∼15586 molecules h−1 . The latter estimate takes into account that the molecular weight of IFN-α is about 17000 atomic mass units ( a . u . ) and 1 a . u . = 1 . 67×10−24 g . To identify the parameters of MHV infection and type I IFN-mediated protection of Mφs , we considered experimental data sets that were generated using a broad spectrum of IFN treatment conditions [10] , [13] . These data sets included ( i ) the early kinetics of MHV replication in Mφs at MOI = 1 ( Figure 2C ) and 0 . 0001 ( Figure S2 ) , ( ii ) Mφ infection ( MOI = 1 ) after treatment with 500 IUnits ( 1 IU≅8 . 333 pg ) of recombinant IFN-α ( Figure 2D ) and ( iii ) Mφ infection ( MOI = 1 ) after pre-treatment with pDCs derived supernatant containing 500 , 200 , 50 and 10 pg/ml of IFN-α ( Figure S3 ) . The core data set using MOI = 1 was supplemented by Mφ survival data generated as described previously [10] . As shown in Figure 2C and D , the experimental data for MHV infection kinetics ( MOI = 1 ) in wt Mφs and after IFN treatment , are in close agreement with the model prediction . The essential parameters ( Table 1 ) suggest that ( i ) MHV production by a single Mφ is with 37 pfu h−1 much larger than that of pDCs , ( ii ) the concentration of IFN-α required for 2-fold inhibition of MHV production is about 0 . 1 pg/ml , and ( iii ) the per cell secretion rate of IFN-α is about 100-times smaller in Mφs ( 3×10−6 pg h−1 or equivalently , 106 molecules h−1 ) compared to pDCs . The calibrated modules for the in vitro infection of pDCs and Mφs thus provide valuable basic building blocks that allowed to proceed with the modeling of early kinetics ( 0–48 hours ) of MHV growth and the IFN response in vivo . To further validate the calibrated modules , we considered an experimental in vitro system mimicking ‘in vitro spleen infection’ . To this end , we first determined the cellular composition of spleen in terms of pDC and Mφ population sizes during early MHV infection . At the beginning of infection with 5×103 pfu , the geometric means for pDCs and Mφs were 6 . 6×105 cells and 5 . 2×106 cells , respectively ( relative variation ∼10% ) , and increased about two-fold by 36 hours following infection . Here , we considered intermediate values , i . e . the ones observed 18 hours post infection , so that 7×105 pDCs and 6×106 Mφs were used to model the infection dynamics in spleen . To evaluate the qualitative consistency of the in vitro parameter estimates with the actually observed phenotype of MHV infection in vivo , we modeled the infections of the mixture of the above numbers of pDCs and Mφs with increasing virus doses ( 5×101 , 5×103 , 5×105 pfu ) . As shown in Figure S4 , the model consistently predicts that the virus growth is robustly controlled and that the extent of the activation of the type I IFN response depends on the virus kinetics . To set up the mathematical model for systemic MHV infection in mice , we proceeded in stages . First , we estimated the virus transfer rates between blood , spleen and liver together with the rate of alanine aminotransferase ( ALT ) increase using the compartmental model described in the Materials and Methods section , equations ( 5 ) – ( 8 ) . A reference data set characterizing the MHV growth in spleen , blood and liver and serum ALT kinetics after intravenous ( i . v . ) infection with 5×103 pfu and 5×105 pfu as shown in Figure 3A–D was used . Next , we integrated the description of MHV infection in spleen ( given by equations ( 9 ) ) with virus compartmental dynamics in the liver and blood to formulate the systemic model of MHV infection specified by the set of delay-differential equations ( 6 ) – ( 9 ) . It is most likely that both the splenic microarchitecture and the trafficking of the virus between organ compartments have an effect on the kinetics of MHV infection of splenic target cells as compared to the in vitro system . The compartmental model parameters listed in Table 2 were estimated via the maximum likelihood approach constrained by a detailed description of MHV interaction with the populations of pDCs and Mφs in spleen . To accommodate for the observed differences between the in vitro and in vivo systems , we set out to refine some of the spleen model parameters to accurately describe the systemic virus data ( Figure 3 ) . The number of reliably identifiable parameters in the mathematical model is limited by the amount and quality of the corresponding sets of experimental data which are available . To move from in vitro to in vivo MHV infection the following considerations were taken into account . First , the morphology of spleen is drastically different from the in vitro cell suspensions , which directly implies that the rate of target cell infection might differ . The second factor is the spatial location of Mφs versus pDCs in spleen . Finally , the number of Mφs in spleen is about 10-fold larger than the pDC population , so that the in vitro estimated infection rate of Mφs would lead to an overwhelming virus growth in spleen before enough pDCs get activated to produce a protective amount of type I IFN . Therefore , the kinetics of Mφ infection in spleen has been considered in the first instance . The above reasoning in conjunction with the parsimony principle and numerous trials to fit the in vivo data with different selections of adjusted parameters led us to conclude that a minimal set of three parameters ensured a consistent fitting of the vivo infection data: the infection rate ( reduced by 60-fold ) , the 50% inhibition threshold ( increased by 10-fold ) and the IFN secretion rate ( reduced by 3 fold ) for Mφs ( Table 1 ) . Taken together , the stepwise developed mathematical model , comprised of calibrated , refined and validated elementary modules , tightly fits the observed in vivo kinetics of MHV infection , and thus , provides a quantitative computational tool to assess the sensitivity of MHV infection dynamics to variations in the basic parameters of virus-host interactions . As a first step in the analytical modeling process , we examined the effect of pDC numbers and activation status on the protection of Mφs in spleen and the prevention of severe liver disease . As readout , i . e . the prediction of pDC performance criteria , we considered the maximum fraction of infected Mφs in spleen and the peak level of serum ALT during the first 48 hours post i . v . infection with various doses of MHV . As shown in Figure 4A , the decrease of the pDC population in spleen by 10-fold results in increased virus titers but still keeps virus growth under control . However , further depletion of pDCs leads to an overwhelming virus growth . Mφs in spleen represent about a 10-fold larger population of cells able to secrete MHV at a rate that is 10-times higher than pDCs . Therefore , protection of Mφs against the infection represents an important task that pDCs have to ensure . Indeed , antibody-mediated depletion of pDCs considerably increased infection of splenic Mφs ( Figure S5 ) . Figure 4B ( left panel ) shows the quantitative model predictions of how the fraction of infected Mφs in spleen depends on the number of pDCs and the dose of infection . Ten-fold reduction of pDCs in spleen still ensures that more than 90% of Mφs remain uninfected for low to intermediate infection doses . However , a further decrease of the pDC population breaks their ability to keep the number of infected Mφs below 10% . Because there is an inherent delay in activation of pDCs before the type I IFN secretion starts , we modeled the situation when a certain fraction of splenic pDCs is pre-activated at the start of the infection . Figure 4C ( left panel ) shows the predicted dependence of the infected Mφs on the number of pre-activated pDCs for MHV infection with 50 pfu . The results allowed us to quantify the upper limit for the protective capacity ( ) of pDCs , if we define it as the ability to protect 90% of Mφs against infection . As few as 2000 activated pDCs suffice to protect 6×106 Mφs , which leads to the estimate of Mφ per pDC . To clarify how the pDCs in spleen contribute to control against severe disease , we evaluated the peak ALT level for i . v . infections with different MHV doses and different pDC numbers ( Figure 4B , right panel ) . If we define the ALT threshold for protection against severe disease to be 103 IU/L , then the host is protected against infection with physiological doses when the number of pDCs in spleen is unchanged ( 7×105 ) or 10-fold reduced . The protection is lost if spleen contains only 7×103 pDCs and the dose of infection is larger than 100 pfu . This suggests that the protection unit of pDCs ( ) required to prevent severe disease after low dose infection is around 7×103 pDCs . Pre-activation of pDCs leads to a more efficient control of the infection-associated disease as shown in Figure 4C ( right panel ) . The reduction in the total number of non-activated splenic pDCs strongly affects the severity of disease . However , rather modest pre-activation of as few as 200 pDCs leads to a reduction of peak ALT below the threshold of severe disease ( ) . SLOs function to protect again severe disease by eliminating the virus from the system . To examine how the sink function of spleen depends on the availability of pDCs , we calculated the ratio of the number of viruses produced locally in spleen versus virus eliminated via trapping by target cells . The results summarized in Figure 4D show that the capacity of the spleen to eliminate the virus depends on the number of pDCs . Furthermore , the extent of the sink function depends on the dose of infection , i . e . high dose infection leads to a full activation of the capacity of spleen to work as a sink for the virus . Overall , the spleen preserves its sink function as long as the pDC population is above 104 cells . Viruses have evolved various mechanisms to reduce the efficacy of innate immune mechanisms [14] . Therefore , we examined a situation of virus-mediated inhibition of type I IFN synthesis by pDCs . Figure 5A shows the overall kinetics of the virus in spleen and liver together with serum ALT level after low- , intermediate- and high dose i . v . infections of hosts with either normal IFN secretion rate pg cell−1 h−1 , or 10- to 100-fold reduction . Reduced IFN production by pDCs has a stronger impact on virus kinetics in spleen ( Figure 5A , upper row ) but leads to minor perturbations of virus growth in the liver ( Figure 5A , middle row ) , or ALT levels in serum ( Figure 5A , bottom row ) . To quantitatively estimate the robustness of the pDC-mediated protection for spleen and liver we examined at a higher resolution the effect of reduced IFN synthesis on the peak viral load with infection doses ranging from 5 to 5×104 pfu . Figure 5B shows that the spleen is well-protected , i . e . the peak virus titer stays below 105 pfu/ml . However , once the IFN secretion rate is reduced by 100-fold , the virus infection is out of control as manifested by maximum titers of ∼107 pfu/ml for all doses . In contrast to the spleen , the peak virus titer in the liver increases with higher virus doses and reduced type I IFN secretion rates ( Figure 5C ) . Likewise , the severity of disease , characterized by the peak ALT levels , depends on the dose of infection and the IFN secretion rate by pDCs in a way similar to the virus titer in liver ( Figure 5D ) . If we define a severe disease by ALT levels above 103 IU/L , then 7×105 pDCs in spleen keep the host protected during the first two days of infection with virus doses up to 500 pfu even when the IFN secretion rate drops down to 1% of its normal value . Viruses can acquire mutations that result in a faster replication in target cells and a high-virulence phenotype [15] , [16] . A recently published study on cytopathic influenza A virus infection [15] provides quantitative details of the scale of virulence-enhancing mutations and the resulting increase in virus growth rate . It follows from the analysis of these virus growth data that the difference in the intrinsic growth rate is about 30% . With this estimate as a reference value , we used the mathematical model of MHV infection to evaluate the limits of protection against severe disease for increasing virus replication rates . Since various MHV strains display significant differences in their ability to replicate in different organ systems [17] , two complementary scenarios were considered: the increase in virus growth rate in the peripheral organs ( liver ) versus SLOs ( spleen ) . Figure 6A shows that pDCs in spleen provide very limited protection against severe disease for faster replicating strains of the virus . Indeed , only a 15% increase in the growth rate of MHV in the liver leads to infection with ALT levels rising to 103 IU/L within two days . The decrease of pDC numbers in spleen makes the situation more fragile to even smaller increases in the virus growth rate . The contour lines shown in Figure 6A are the curves along which the value of ALT in serum at 48 h post infection remains the constant . The quantitative analysis of the contour lines slope suggests that 1% increase in the replication rate of the virus in the liver requires about 50% increase in the initial pDCs number in the spleen for the ALT level to have the same particular value . On the contrary , pDCs provide a robust protection against severe disease when the virulence-enhancing mutation leads to faster replication only in target cells located in spleen ( Figure 6B ) , i . e . splenic pDCs protect against severe disease for up to 30-fold increase in the viral replication rate in splenic Mφs . Taken together , these analyses indicate that the spleen represents a robust sink system able to cope with substantially enhanced virus production as long as this gain of viral fitness remains restricted to this SLO .
An important hallmark of the innate immune response during cytopathic virus infection is the ability of Mφs to contain viral particles in SLOs . For example , Mφs in the marginal sinuses of lymph nodes , capture viral particles from the incoming lymph stream [18] , and marginal zone Mφs in spleen bind viruses decorated by complement and natural antibodies and reduce thereby dissemination of viruses to peripheral organs [19] . Coronaviruses can suppress early type I IFN responses in particular cell types including Mφs [14] , [20] , thus leaving these cells vulnerable to the cytopathic effects of the viral infection . Such blocking of innate type I IFN induction has not been observed for MHV and SARS coronavirus infection of mouse and human pDCs , respectively [8] . Likewise , human pDCs seem not to be sensitive to the inhibitory effects of the potent IFN antagonist NS1 of influenza virus [21] . Hence , pDCs are not only specialized for immediate viral recognition and type I IFN production through TLR-7 and -9 expression and the constitutive expression of IRF-7 [5] , they probably also exhibit unique counter-measures against viral IFN inhibitors . The interplay between pDCs and Mφs is thus a critical cellular axis for the preservation of SLO integrity during early cytopathic virus infection . The systems immunology approach presented in this study provides a better understanding of the robustness and fragility of the pDC-mediated protection of Mφs and eventually , the host against cytopathic virus infection . We implemented a genuine systems methodology using a building block approach in which the elementary ‘modules of response’ were calibrated using in vitro data . In addition , in vivo observations were used to further estimate essential parameters which combine the modules in a ‘closed’ system . A key to the development of this predictive mechanism-based modular mathematical model were model-driven experimental studies which provided comprehensive data sets for an accurate and reliable quantification of model parameters and validation of the model predictions . Using this approach , we identified the limits of the spleen's capability to function as a sink for the virus produced in a peripheral target organ . The robust sink function of the spleen is guaranteed by the high protective capacity of single pDCs which protect 103 to 104 Mφs from cytopathic viral infection . Furthermore , we determined the minimal protective unit of pre-activated pDCs in spleen to be around 200 cells which can rescue the host from severe disease . The presented results suggest that the splenic sink function remains operational as long as viral mutations do not permit accelerated growth in peripheral tissues . Our model suggests that maintaining the sink function of SLOs is one of the major functions of pDCs . This notion is supported by findings on the life cycle of pDCs , namely that after development in the bone marrow , pDCs cells enter the blood circulation , and subsequently home to SLOs [22]–[25] . The rapid accumulation of pDCs in lymph nodes following exposition to inflammatory stimuli [26] , [27] further corroborates that SLOs are – at least in the very early phase of an infection – the most important compartment of pDC activity . Potential effects of pDCs migrating to non-lymphoid organs such as the liver in later stages of an infection have not been considered in our model . It will be interesting to incorporate data on pDC populations accumulating in peripheral organs during the later phases of MHV infection in future modeling approaches . Likewise , it will be important to address the potential functions of pDCs in the modulation of innate and adaptive immune responses [4] in the terms of systems immunology . A combination of the present mathematical framework with , for example , novel experimental models of pDC-deficiency [28] , [29] will open new avenues to describe the dynamical aspects of such multiscale interactions . Furthermore , the presented combination of experimental studies and mathematical modeling may be used to further explore the contribution of virus-encoded factors modulating tumor necrosis factor-enhanced liver inflammation [30] or the role of important host factors such as the prothrombinase Fgl2/fibroleukin which critically regulate virus-induced liver disease [31] . The last decade of research in immunology is characterized by a tremendous advance in the high-throughput experimental technologies yielding detailed information on the system state at various levels of resolution . This inspired a turn in basic and applied immunology from reductionist dissection to systems integration with mathematical modeling being an essential tool [32]–[35] . However , the translation of the powerful modeling methodologies developed in applied mathematics , such as the mathematical systems theory and computational techniques into research tools appropriate for a multiscale analysis of immunological phenomena remains a challenge [1] , [36] , [37] . Indeed , recent reviews on the application of mathematical analyses in immunology indicate that progress has mainly been made in those studies which model the immune processes at a single resolution level , rather than bridge multiple scales of description [1] , [38]–[40] . The within-host population dynamics of antigen-specific immune responses and pathogens and the single cell regulation of lymphocyte activation represent the two most advanced fields . Compared to antigen-specific responses , the development of mathematical models for the description and analysis of innate immune processes has remained a poorly investigated area [41]–[43] . Our study addresses the above two challenges of the integrative modeling of antiviral immune responses: ( 1 ) it bridges the cell-cytokine-virus population dynamics level with the physiological function of the spleen in the host-pathogen interaction and ( 2 ) provides a high-resolution quantitative description of the early type I IFN response to cytopathic virus infection . Taken together , the data-driven mathematical modeling of pDC biology provides novel insight in systems' level phenomena such as the pDC protective capacity , the pDC unit of protection , and the sink versus source function of the spleen . Furthermore , this systems immunology approach has generated an in-depth to understanding of the sensitivity of virus-host interaction indicating that antiviral compounds directed against cytopathic viruses should mainly target viral spread within non-lymphoid target organs because pDC-derived type I IFNs within SLOs secure efficient protection of vulnerable target cells .
Experiments were performed in accordance with federal and cantonal guidelines ( Tierschutzgesetz ) under the permission numbers SG07/62 and SG07/63 granted by the Veterinary Office of the Canton of St . Gallen . C57BL/6 ( B6 ) mice were obtained from Charles River Laboratories ( Sulzfeld , Germany ) . Type I IFN receptor deficient mice ( ifnar−/− ) [44] on the B6 background were kindly provided by Martin Bachmann , Cytos AG , Schlieren , Switzerland . MHV A59 was generated from a molecularly cloned cDNA [45] based on the Albany strain of MHV A59 and propagated on L929 cells . EGFP-recombinant MHV was previously described [13] . Mice were sacrificed at the indicated time points and organs were stored at −70°C until further analysis . Blood was incubated at RT to coagulate , centrifuged , and serum was used for ALT measurements using a Hitachi 747 autoanalyzer . Virus titers in organs were determined from frozen organs after weighing and homogenization by standard plaque assay using L929 cells . pDCs were obtained from spleens of B6 mice following digestion with collagenase type II as described previously [8] and infection kinetics following incubation with EGFP-recombinant MHV was determined by flow cytometry [10] . Mφs were isolated from the peritoneal cavity of B6 mice and cell survival was determined with the Cell Proliferation MTS Assay ( Celltiter 96 Aqueous one solution cell proliferation assay ) from Promega . The persistence of virus and type I IFN in medium displays an exponential kinetics . The corresponding decay rate constants for MHV ( dV ) and IFNα ( dI ) were estimated by a linear regression procedure for the log-transformed values of the virus titer and IFN concentration using GraphPad Prism v . 4 software ( http://www . graphpad . com ) . The parameter estimates and their 95% Confidence Intervals ( CIs ) are presented in Table S1 . The data on target cell persistence ( C ( t ) ) display kinetics which differs for some cell types or under certain conditions from the exponential ( denoted by E ) behavior . It is rather consistent with the Gompertz kinetics ( denoted by G ) as described by equation In contrast to the exponential decay , Gompertz kinetics allows the death rate to increase over time and is particularly appropriate for describing cohort-type cell population dynamics . The parameter kC represents the tempo of the per capita death increase , and for kC small compared to the duration of experiment , the Gompertz equation reduces to the exponential one . Fitting the cell persistence data using either E-model or G-model showed that The best-fit estimates for the death rate parameters for pDCs and Mφs are given in the Table S1 . To check whether the increased complexity of the G- versus E- model of decay is justified by the pDCs data in hand , we evaluated for the models the Akaike criterion of the information loss ( AIC ) , defined as [46] , and the model description length [47] evaluated fromwhere nd is the total number of scalar observations , L is the number of optimized parameters , and are the best-fit least-squares function and the maximized likelihood function , I ( p ) is the Fisher information matrix , and is the domain of the parameter space on which the model is defined . Both criteria of the model parsimony turned out to be smaller for the Gompertz model: the was 30 versus 38 and the value was 19 . 5 versus 22 . 1 . To describe the antiviral IFN-α response in vitro , we reduced the complexity of the IFN system to four principal constituents: the virus titer/ml , V ( t ) ; the amount of type I IFN per ml , I ( t ) ; the density of uninfected target cells ( pDC or Mφ ) , C ( t ) ; the density of infected target cells , CV ( t ) . The rate of change of the virus population is determined by virus secretion from infected cells , which starts after some latent period ( time-delay ) , and the elimination through the infection of target cells with the rate constant and a natural decay at rate . The virus dynamics is modeled by the delay-differential equation: ( 1 ) The protective effect of the type I IFN is assumed to reduce the mean per cell virus production rate by 50% at the IFN concentration specified by the inhibition constant θ . The rate of change of the amount of IFN-α in the system results from the IFN production by virus infected target cells , which occurs with some secretion delay , and the decay of the free interferon molecules: ( 2 ) The parameters of the equation , i . e . the average per cell secretion rate , the delay and the decay rate determine the IFN-α concentration dynamics . The loss of IFN-α due to interaction with the target cell receptors is neglected . The rate of change of the number of infected target cells is modeled by the following equation ( 3 ) The first term in the right-hand side describes the emergence of the infected cells due to the virus infection of uninfected target cells whereas the second one accounts for the death of infected cells . The per capita death rate is either constant , or depends on time according to Gompertz law . Finally , the rate equation for the density of uninfected target cells reads ( 4 ) It considers the transition of uninfected cells to infected cells due to virus infection and the death of cells at per capita rate , which can be also time-dependent . The infection of mice with MHV leads to virus spread and growth in different organs . Virus population dynamics in any anatomical compartment results from a superposition of intra-compartmental production-elimination and inter-compartmental transfer of the virus . The compartmental model in order to describe the pathological consequences of MHV infection requires consideration of the virus population dynamics in spleen , liver and blood . The simplest equations for the rate of change of virus populations in the compartments are as follows: ( 5 ) ( 6 ) ( 7 ) The virus populations in spleen and liver are assumed to follow a logistic growth with the outflow-inflow rates depending in a linear way on the virus concentration in the corresponding compartments . The parameters denote the intrinsic growth rate , carrying capacity and transfer rate to and from blood for spleen . Similar parameters characterize the processes in the liver . The blood compartment functions to transfer the virus to spleen , liver and other organs with the rates , respectively . The intravenous injection of virus dose is represented by the following initial conditions: . The following estimates of the volumes ( ml ) for spleen , liver and blood were used: , , [48] . The infection of target cells with MHV induces a cytopathic effect leading to an earlier cell death . The primary cell targets of MHV in the liver compartment are hepatocytes . The severity of the virus-induced liver disease is characterized by the liver enzyme ALT concentration in serum . The rate of change of ALT in blood is modeled the following equation: ( 8 ) where the increase of serum ALT concentration is proportional to the virus population in the liver , with the parameter characterizing the release rate of ALT into blood . The second term takes into account that there is some homeostatic turnover of ALT in serum with the decay rate . The compartmental model of virus growth and ALT kinetics provides a tool to quantify the transfer coefficients of the virus for the spleen-blood-liver system ( 5 ) – ( 7 ) as well as the disease severity due to the virus presence in the liver . An integrative mathematical model to study the protective function of pDCs in MHV infection is assembled from the building block models described above . The model considers spleen , liver and blood compartments , in which MHV replication is described at different resolution levels . For spleen , a detailed description of the virus-target cells interaction is considered which includes MHV , IFN-α , pDCs , Mφs dynamics as modeled by the subset of equations ( 9 ) : ( 9 ) Eighteen h post MHV infection , spleen contains about 7×105 pDCs and 6×106 Mφs and there is a continuous turnover of these cell populations . Therefore , at any given moment the infected target cells represent a heterogeneous population with respect to the infection time and live expectancy rather than a cohort of cells with synchronous kinetics . This is taken into account by assuming exponential death kinetics for cell populations in spleen , with the rate constants corresponding to the in vitro derived estimates . The influx-elimination processes are described by the homeostasis terms for uninfected pDCs and Mφs ( see the last two terms in the last two equations ) . The equation for virus in spleen considers the virus transfer between spleen and blood . The subset of equations for virus dynamics in blood , liver and the serum ALT level remains the same as equations ( 6 ) – ( 8 ) . The following initial data for the delay differential equation system ( 6 ) – ( 9 ) specifying the intravenous infection were usedand for . For the numerical solution of the initial value problem for the system of delay-differential equations ( 6 ) – ( 9 ) we used either the MATLAB code dde23 ( http://www . mathworks . com ) or our original solver for stiff systems of delay equations [49] . The process through which the available information is used in order to estimate as accurately as possible the systems' dynamics is known as data assimilation . To calibrate the mathematical model using the sampled data , we optimally combined heterogeneous observations ( coming from distinct series of experiments ) with model predictions by optimizing a “cost function” , which expresses the distance between observations and the corresponding model values . The available data vary essentially in terms of the sample sizes , which is common for studies of virus and cell population dynamics . The most numerous data sets are the virus kinetics data . The corresponding samples followed a log-normal distribution . Assuming further that the errors in observations at successive times are independent and the variance of observation errors is constant for all observation times , we applied the maximum likelihood ( ML ) approach to parameter estimation as we described in detail in [48] . We searched for a vector of best-fit parameters , by maximizing the likelihood function specifying the probability of obtaining the observed data . Under the above assumptions , this optimization is equivalent to minimizing the value of the total misfit between the available observations and the model as defined by . The first term specifies the squared deviation between the log-transformed model and experimental time-series data , and the second one refers ( when applicable ) to effects of specific treatment ( recombinant IFN-α ) or infection scenario in vitro ( e . g . coculture experiments with pDCs and Mφs ) on the virus titer at a given time . The parameter estimation was carried out using either MATLAB 7 . 0 routines ( http://www . mathworks . com ) or Absoft Pro Fortran Developer ( http://www . absoft . com ) and IMSL function minimization code ZXMIN based on a quasi-Newton method . The number of reliably identifiable parameters in the mathematical models is limited by the amount and quality of the corresponding sets of experimental data which are available and also by the model structure . As the ‘large-scale’ systemic model of MHV infection was built using a modular ( rather than monolithic ) approach broadly accepted in systems biology [50] , we examined the identifiability properties of the basic modules . Having the best-fit parameter estimates and their confidence intervals , we examined a posteriori algebraic identifiability [51] of the modules following the multiple time points method [52] . The method is based upon elimination of unobserved state variable from the original system of equations to obtain the identification equation by a combination of high-order derivatives of the observed variables and the availability of the measurements at a number of time points . The algebraic identifiability analysis of the basic IFN response module ( equations 1–4 ) showed that if virus titer , IFN concentration and the fraction of live cells are the observable state characteristics , then the time-lag of virus production , the 50% inhibition threshold and the virus secretion rate represent a group of functionally related parameters , i . e . their estimates depend on each other with the statistical uncertainty further to be characterized by the confidence intervals . This dependence is consistent with them entering together the virus production term in equation ( 1 ) of the basic module . The other implication is that the structure permits the other model parameters to be identified . The identifiability test of the compartmental model ( equations 5–8 ) suggests that it is identifiable , provided that the observable variables are the virus in spleen , blood and liver and the serum ALT blood , which is the current case . Furthermore , a global sensitivity analysis was performed which allowed to rank the influence of random variations in the model parameters on the variation in the serum ALT level . The methodology and the results are presented in Table S2 and the accompanying text .
|
Human infections with highly virulent viruses , such as 1918 influenza or SARS-coronavirus , represent major threats to public health . The initial innate immune responses to such viruses have to restrict virus spread before the adaptive immune responses fully develop . Therefore , it is of fundamental practical importance to understand the robustness and fragility of the early protection against such virus infections mediated by the type I interferon ( IFN ) response . Because of the inherent complexity of the virus-host system , we have used mathematical modeling to predict the sensitivity of the kinetics and severity of infection to variations in virus and host parameters . Our results suggest that the spleen represents a robust sink system for systemic virus infection and that this system is able to cope with substantial variations in IFN secretion and virus production . However , the system is very fragile to only minor increases in the virus growth rate in peripheral tissues . Collectively , the mathematical approach described in this study allows us to identify the most robust virus and host parameters during early cytopathic virus infection and can serve as a paradigm for systems immunology analyses of multiscale virus-host interaction of many life-threatening cytopathic virus infections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/animal",
"models",
"of",
"infection",
"immunology/innate",
"immunity",
"computer",
"science/numerical",
"analysis",
"and",
"theoretical",
"computing",
"computational",
"biology/systems",
"biology",
"virology/host",
"antiviral",
"responses"
] |
2010
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A Systems Immunology Approach to Plasmacytoid Dendritic Cell Function in Cytopathic Virus Infections
|
It has long been suspected that the rate of mutation varies across the human genome at a large scale based on the divergence between humans and other species . However , it is now possible to directly investigate this question using the large number of de novo mutations ( DNMs ) that have been discovered in humans through the sequencing of trios . We investigate a number of questions pertaining to the distribution of mutations using more than 130 , 000 DNMs from three large datasets . We demonstrate that the amount and pattern of variation differs between datasets at the 1MB and 100KB scales probably as a consequence of differences in sequencing technology and processing . In particular , datasets show different patterns of correlation to genomic variables such as replication time . Never-the-less there are many commonalities between datasets , which likely represent true patterns . We show that there is variation in the mutation rate at the 100KB , 1MB and 10MB scale that cannot be explained by variation at smaller scales , however the level of this variation is modest at large scales–at the 1MB scale we infer that ~90% of regions have a mutation rate within 50% of the mean . Different types of mutation show similar levels of variation and appear to vary in concert which suggests the pattern of mutation is relatively constant across the genome . We demonstrate that variation in the mutation rate does not generate large-scale variation in GC-content , and hence that mutation bias does not maintain the isochore structure of the human genome . We find that genomic features explain less than 40% of the explainable variance in the rate of DNM . As expected the rate of divergence between species is correlated to the rate of DNM . However , the correlations are weaker than expected if all the variation in divergence was due to variation in the mutation rate . We provide evidence that this is due the effect of biased gene conversion on the probability that a mutation will become fixed . In contrast to divergence , we find that most of the variation in diversity can be explained by variation in the mutation rate . Finally , we show that the correlation between divergence and DNM density declines as increasingly divergent species are considered .
Until recently , the distribution of germ-line mutations across the genome was studied using patterns of nucleotide substitution between species in putatively neutral sequences ( see [1] for review of this literature ) , since under neutrality the rate of substitution should be equal to the mutation rate . However , the sequencing of hundreds of individuals and their parents has led to the discovery of thousands of germ-line de novo mutations ( DNMs ) in humans [2–6]; it is therefore possible to analyse the pattern of DNMs directly rather than inferring their patterns from substitutions . Initial analyses have shown that the rate of germ-line DNM increases with paternal age [4] , a result that was never-the-less inferred by Haldane some 70 years ago [7] , maternal age [6] , varies across the genome [5] and is correlated to a number of factors , including the time of replication [3] , the rate of recombination [3] , GC content [5] and DNA hypersensitivity [5] . Previous analyses have demonstrated that there is large scale ( e . g . 1MB ) variation in the rate of DNM in both the germ-line [3 , 5] and the somatic tissue [8–12] . Here we focus exclusively on germ-line mutations . We use a collection of over 130 , 000 germ-line DNMs to address a range of questions pertaining to the large-scale distribution of DNMs . First , we quantify how much variation there is at different scales and investigate whether the variation in the mutation rate at a large-scale can be explained in terms of variation at smaller scales . We also investigate to what extent the variation is correlated between different types of mutation , and to what extent it is correlated to a range of genomic variables . We use the data to investigate a long-standing question–what forces are responsible for the large-scale variation in GC content across the human genome , the so called “isochore” structure [13] . It has been suggested that the variation could be due to mutation bias [14–18] , natural selection [13 , 19 , 20] , biased gene conversion [21–24] , or a combination of all three forces [25] . There is now convincing evidence that biased gene conversion plays a role in the generating at least some of the variation in GC-content [26–28] . However , this does not preclude a role for mutation bias or selection . With a dataset of DNMs we are able to directly test whether mutation bias causes variation in GC-content . The rate of divergence between species is known to vary across the genome at a large scale [1] . As expected this appears to be in part due to variation in the rate of mutation [3] . However , the rate of mutation at the MB scale is not as strongly correlated to the rate of nucleotide substitution between species as it could be if all the variation in divergence between 1MB windows was due to variation in the mutation rate [3] . Instead , the rate of divergence appears to correlate independently to the rate of recombination . This might be due to one , or a combination , of several factors . First , recombination might affect the probability that a mutation becomes fixed by the process of biased gene conversion ( BGC ) ( reviewed by [26] ) . Second , recombination can affect the probability that a mutation will be fixed by natural selection; in regions of high recombination deleterious mutations are less likely to be fixed , whereas advantageous mutations are more likely . Third , low levels of recombination can increase the effects of genetic hitch-hiking and background selection , both of which can reduce the diversity in the human-chimp ancestor , and the time to coalescence and the divergence between species . There is evidence of this effect in the divergence of humans and chimpanzees , because the divergence between these two species is lower nearer exons and other functional elements [29 , 30] . And fourth , the correlation of divergence to both recombination and DNM density might simply be due to limitations in multiple regression; spurious associations can arise if multiple regression is performed on two correlated variables that are subject to sampling error . For example , it might be that divergence only depends on the mutation rate , but that the mutation rate is partially dependent on the rate of recombination . In a multiple regression , divergence might come out as being correlated to both DNM density and the recombination rate , because we do not know the mutation rate without error , since we only have limited number of DNMs . Here , we introduce a test that can resolve between these explanations . As with divergence , we might expect variation in the level of diversity across a genome to correlate to the mutation rate . The role of the mutation rate variation in determining the level of genetic diversity across the genome has long been a subject of debate . It was noted many years ago that diversity varies across the human genome at a large scale and that this variation is correlated to the rate of recombination [31–33] . Because the rate of substitution between species is also correlated to the rate of recombination , Hellmann et al . [31 , 32] inferred that the correlation between diversity and recombination was at least in part due to a mutagenic effect of recombination , an inference that has been confirmed by recent studies of recombination [3 , 34 , 35] . However , no investigation has been made as to whether variation in the rate of mutation explains all the variation in diversity , or whether biased gene conversion , direct and linked selection have a major influence on diversity at a large scale .
To investigate large scale patterns of de novo mutation in humans we compiled data from three studies which between them had discovered more than 130 , 000 autosomal DNMs: 105 , 385 from Jonsson et al . [36] , 26 , 939 mutations from Wong et al . [6] , and 11016 mutations from Francioli et al . [3] The datasets are henceforth referred to by the name of the first author . We divided the mutations up into 9 categories reflecting the fact that CpG dinucleotides have higher mutation rates than non-CpG sites , and the fact that we cannot differentiate which strand the mutation had occurred on: CpG C>T ( a C to T or G to A mutation at a CpG site ) , CpG C>A , CpG C>G and for non-CpG sites C>T , T>C , C>A , T>G , C<>G and T<>A mutations . The proportion of mutations in each category in each of the datasets is shown in Fig 1 . We find that the pattern of mutation differs significantly between the studies ( Chi-square test of independence on the number of mutations in each of the 9 categories , p < 0 . 0001 ) . This appears to be largely due to the relative frequency of C>T transitions in both the CpG and non-CpG context; a discrepancy which has been noted before[37 , 38] . In the data from Wong et al . [6] the frequency of C>T transitions at CpG sites is ~13% whereas it is ~16–17% in the other two datasets . For non-CpG sites the frequency of C>T transitions is ~24% in all studies except that of Wong et al . in which it is 26% . It is not clear whether these patterns reflect differences in the mutation rate between different cohorts of individuals , possibly because of age [3 , 4 , 6] or geographical origin [39] or whether the differences are due to methodological problems associated with detecting DNMs . To investigate whether there is large scale variation in the mutation rate we divided the genome into non-overlapping windows of 10KB , 100KB , 1MB and 10MB and fit a gamma distribution to the number of mutations per region , taking into account the sampling error associated with the low number of mutations per region . We focussed our analysis at the 1MB scale since this has been extensively studied before . However , we show that the variation at 1MB forms part of a continuum of variation . We also repeated almost all our analyses at the 100KB scale with qualitatively similar results ( these results are reported in supplementary tables ) . We find that the amount of variation differs significantly between the three studies ( likelihood ratio tests: p < 0 . 001 ) , although , the differences are quantitatively small at the 1MB ( Fig 2 ) and 100KB ( S1 Fig ) scales . The variation between datasets might be due to differences in age or ethnicity between the individuals in each study , or methodological problems–for example , there might be differences between studies in the ability to identify DNMs . We can test whether callability is an issue in the Wong dataset because Wong et al . [6] estimated the number of trios at which a DNM was callable at each site . If we reanalyse the Wong data using the sum of the callable trios per MB , rather than the number of sites in the human genome assembly , we obtain very similar estimates of the distribution: the coefficient of variation ( CV ) for the distribution is 0 . 27 when we use the number of sites and 0 . 24 when we use the sum of callable trios . As expected the number of DNMs per site is significantly correlated between the datasets ( 1MB Francioli v Wong r = 0 . 15 , p<0 . 001; Francioli v Jonsson r = 0 . 19 p<0 . 001; Wong v Jonsson r = 0 . 29 , p<0 . 001 ) . The correlation is weak , but this is likely to be in part due to sampling error . If we simulate data assuming a common distribution , estimating the shape parameter as the mean CV of the distributions fit to the individual datasets , the mean simulated correlations are: Francioli v Wong r = 0 . 20; Francioli v Jonsson r = 0 . 29; Wong v Jonsson r = 0 . 41 . This suggests that a substantial proportion of the variation is common to the three datasets , however in each case less than 5% of the simulated correlations are less than the observed correlation suggesting that some portion of the variation in the three datasets is uncorrelated . The CV of the gamma distribution fitted to the density of DNMs is 0 . 18 , 0 . 27 and 0 . 15 for the Francioli , Wong and Jonsson datasets respectively ( Fig 2 ) . The level of variation is significant ( i . e . the lower 95% confidence interval of the CV is greater than zero ) , however the level of variation is modest ( Fig 2 ) . A gamma distribution with a coefficient of variation of 0 . 18 is one in which 90% of regions have a mutation rate within 30% of the mean ( i . e . if the mean is one , between 0 . 7 and 1 . 3 ) . The gamma distribution fits the distribution of rates qualitatively quite well ( S2 Fig; S3 Fig for 100KB ) , even though a goodness-of-fit test rejects the model at both the 100KB and 1MB scales in all three datasets ( p<0 . 001 in all cases ) . At the 1MB the observed distribution is more peaked than the fitted gamma distributed; there are too many regions with very low , very high and intermediate numbers of DNMs . If we include estimates of the distribution for 10KB , 100KB and 10MB we find , as expected , that the variance in the mutation rate declines as the scale gets larger ( Figs 3 and 4 ) . This is more marked for the Francioli dataset than for the Wong and Jonsson datasets ( Figs 3 and 4 ) . If we plot the CV of the fitted gamma distribution against the window size we find that the log of the CV of the gamma distribution is approximately linearly related to the log of the window size for the Francioli and Wong datasets ( Fig 4 ) ; the relationship appears curvi-linear for the Jonsson dataset . The fact that the CV declines gradually across scales suggests that the variation at the 1MB scale is part of a continuum of variation at different scales . The linearity of the relationship in two of the datasets suggests that a simple phenomenon may underlie the variation at different scales . If all the variation at the larger scales is explainable by variation at a smaller scale , then the CV at scale x should be equal to the CV at some finer scale , y , divided by the square-root of x/y; on a log-log scale this should yield a slope of -0 . 5 . The slope for each dataset is shallower than this ( Francioli b = -0 . 25; Wong b = -0 . 10; Jonsson b = -0 . 16 ) . This therefore suggests that there is variation at a larger scale that cannot be explained by variation at a smaller scale . To test whether this is the case , we ran a series of one-way ANOVAs; testing variation at the 100KB scale using 10KB windows , 1MB using 100KB windows and 10MB using 1MB windows . The results were significant for all datasets ( p<0 . 001 in all cases ) . If we estimate the distribution for individual mutational types we find that in many cases the lower CI on the CV is zero; this might be because we do not have enough data to reliably estimate the distribution for each individual mutational type . We therefore combined mutations into a variety of non-mutually exclusive categories . In each case we estimated the distribution for the relevant category of sites–e . g . in considering the distribution of CpG rates we consider the number of CpG DNMs at CpG sites , not at all sites . We find that the estimated distributions are similar for different mutational types except that there is rather more variation at CpG sites in the Francioli dataset ( Fig 3; 100KB results S1 Table ) . Although the distributions are fairly similar for different mutational types , likelihood ratio tests demonstrate that there are significant differences between mutational categories ( S2 Table for 1MB and 100KB results ) ; this is particularly apparent for the Jonsson dataset , probably as a consequence of the size of this dataset . Never-the-less the differences between different mutational categories are relatively small . Given that there is variation in the mutation rate at the 1MB scale and that this variation is quite similar in magnitude for different mutational types , it would seem likely that the rate of mutation for the different mutational types are correlated . We find that this is indeed the case . We observe significant correlations between all categories of mutations in the three datasets ( Table 1; S3 Table for 100KB ) . The correlations are weak but this is to be expected given the large level of sampling error . To compare the correlation to what we might expect if the two categories of mutation shared a common distribution and were perfectly correlated , we simulated data under a common distribution , estimating the CV of the common distribution as the mean of the distributions fitted to the two mutational categories . We find that generally the observed correlations are similar , and not significantly different , to the expected correlations . In some cases , we observe that the simulated correlation is actually consistently weaker than the observed correlation; this may reflect the inadequacy of the gamma distribution in describing the distribution of rates . The fact that the rates of Strong to Weak base pairs ( S>W ) and W>S mutation covary ( Table 1 ) suggests that mutational biases are unlikely to generate much variation in GC-content across the genome . To investigate this further , we used two approaches to test whether there was variation in the pattern of mutation that could generate variation in GC content . First , we used the DNM data for each window to predict the equilibrium GC content to which the sequence would evolve , fitting a model by maximum likelihood ( ML ) in which this equilibrium GC-content could vary across the genome . The ML estimate for the mean equilibrium GC-content is similar in all datasets at ~0 . 32 . The ML estimate and its 95% CIs for the standard deviation for the equilibrium GC-content are 0 . 02 ( 0 , 0 . 060 ) , 0 . 001 ( 0 , 0 . 036 ) and 0 . 011 ( 0 , 0 . 024 ) for the Francioli , Wong and Jonsson respectively; in each case confidence intervals encompass 0 , suggesting that a model with no variation in equilibrium GC-content fits the data well . Furthermore , the upper confidence interval is small , suggesting that at most variation in the pattern of mutation generates little variation in GC-content . However , the ML method does not rule out the possibility that there is some variation in the pattern of mutation . Furthermore , the method does not take into account the difference in the mutation rate between CpG and non-CpG sites . We therefore used a second approach in which we grouped windows together based on their current GC-content . We then estimated the mutation rates for the 9 categories of mutation using the DNM data and used these estimated mutation rates in a simulation of sequence evolution , in which we evolved the sequence to its equilibrium GC content . We find no correlation between the equilibrium GC content to which the sequence evolves and the current GC content ( Fig 5; S4 Fig for 100KB ) . It has been suggested that the mutation rate at a site is predictable based on genomic features , such as replication time , by Michaelson et al . [5] , or the 7-mer sequence in which a site is found , by Aggarwala et al . [40] . To investigate whether these models can explain the variation at large scales we used the models to predict the average mutation rate for each 100KB or 1MB region and correlated these predictions against the observed number of DNMs per site . We find that the density of DNMs is significantly correlated to the rates predicted under the 7-mer model of Aggarwala et al . [40] . This correlation is significantly positive for the Wong and Jonsson datasets , as we might expect , but significantly negative for the Francioli dataset ( Table 2; S4 Table for 100KB results ) . To compare these correlations to what we might expect if the Aggarwala model explained all the variation at large scales , we simulated the appropriate number of DNMs across the genome according to this model . The observed correlation is significantly smaller than the expected correlation for all datasets , however , the observed and expected correlations are quite similar for the Wong dataset suggesting that much of the variation in DNM density in this dataset is explainable by the model of Aggarwala et al . [40] . However , the model explains almost none of the variation in the Jonsson dataset . In contrast , the density of DNMs is significantly positively correlated to the predictions of the Michaelson model in the Francioli and Jonsson datasets , but not for the Wong dataset . However , in all cases the correlation is substantially and significantly smaller than it could be if the model explained all the variation ( Table 2; S4 Table for 100KB results ) suggesting that this model fails to capture much of the variation at the 1MB and 100KB scales . To try and understand why there is large scale variation in the mutation rate , we compiled a number of genomic variables which have previously been shown to correlate to the rate of germline or somatic DNM , or divergence between species: male and female recombination rate , GC content , replication time , nucleosome occupancy , transcription level , DNA hypersensitivity and several histone methylation and acetylation marks [3 , 5 , 9 , 41 , 42] . Surprisingly , the three datasets yield different patterns of correlation . The overall density of DNMs is significantly positively correlated to male and female recombination rates across all datasets , but otherwise there is no consistency ( Table 3; 100KB results S5 Table ) ; for example , DNM density is negatively correlated to replication time ( later replicating regions have higher mutation rates ) in the Francioli and Jonsson datasets , but positively correlated in the Wong dataset , and despite containing 10-times as much data , the correlation is weaker in the Jonsson than the Francioli dataset . Overall , the correlations are more similar in their direction in the Francioli and Jonsson datasets . Many of the genomic variables are correlated to each other . If we use principle components to reduce the dimensionality , the first principle component ( PC ) explains 58% of the variation in the genomic variables , the second 13% , the third and fourth 6 . 9 and 5 . 7% of the variation . We find that the density of DNMs is significantly negatively correlated to the first PC in the Francioli data ( r = -0 . 14 , p<0 . 001 ) , significantly positively in the Wong data ( r = 0 . 14 , p<0 . 001 ) and uncorrelated in the Jonsson data ( r = -0 . 013 , p = 0 . 54 ) . All are significantly positively correlated to the second PC ( Francioli , r = 0 . 14 , p<0 . 001; Wong , r = 0 . 27 , p<0 . 001; Jonsson , r = 0 . 15 , p < 0 . 001 ) , uncorrelated to the third component and Wong and Jonsson are significantly correlated to the fourth component but in opposite directions ( Wong , r = -0 . 059 , p = 0 . 005; Jonsson , r = 0 . 1 , p<0 . 001 ) . It is possible that the differences between Wong and the other datasets are due to biases in the ability to call DNMs . However , analysing the Wong data using the number of callable trios at each site does not qualitatively alter the pattern of correlation in the Wong dataset ( Table 3 ) or the correlations to the principle components of the genomic features ( PC1 , r = 0 . 11 p<0 . 001; PC2 , r = 0 . 25 , p<0 . 001; PC3 , r = -0 . 019 , p = 0 . 37; PC4 , r = -0 . 048 , p = 0 . 019 ) . To investigate whether these patterns are consistent across mutational types , we calculated the correlation between the density of each mutational type ( e . g . CpG C>T mutations at CpG sites ) and the first two PCs of the genomic features . For the Francioli and Jonsson datasets the patterns are perfectly consistent; all mutational types , if they show a significant correlation , are significantly negatively correlated to the first PC , and significantly positively correlated to the second ( S6 Table ) . For the Wong data , the patterns are more heterogeneous; all mutational types are positively correlated to the second PC , but some mutational types are significantly positively correlated to the first PC and others significantly negatively correlated . In order to try and disentangle which factors might be most important in determining the rate of mutation we used stepwise regression . We find , as expected , that the models selected for the three datasets are different ( Table 4 ) ; only male recombination rate is common to and correlated in the same direction in all three models . The differences are not due to variation in the ability to call DNMs in the Wong dataset since repeating the analyses using the sum of callable trios rather than sites , does not alter the patterns ( Table 4 ) . At the 100KB scale , replication time joins male recombination factor as a common factor in all three datasets ( S7 Table ) . The differences between the three datasets could be due to paternal age since Francioli et al . [3] showed that the correlation between DNM density and replication time was only evident amongst individuals born to young fathers ( <28 years ) , and paternal age differs between the three studies: the average paternal age was 27 . 7 years in the Francioli dataset ( Laurent Francioli pers comm ) , 33 . 4 years in the Wong data [6] and 32 . 0 in the Jonsson data ( calculated from their supplementary data ) . To investigate whether this could explain the differences between the datasets we divided the DNMs into those discovered in individuals with young ( <28 years ) and old fathers ( ≥28 years ) , and regressed the normalised DNM density ( dividing by the mean DNM density for each dataset in each age cohort ) against replication time and PC1 . We find no evidence that the relationship between DNM density and replication time ( or PC1 ) is stronger in individuals born to young fathers in the Wong and Jonsson datasets ( Table 5 ) . The amount of variation explained by the multiple regression models is small– 0 . 044 , 0 . 10 and 0 . 042 for Francioli , Wong and Jonsson respectively—but this might be expected given the small number of DNMs per MB and hence the large sampling error . To investigate how much of the explainable variance the model explains we sampled rates from the gamma distribution fitted to the distribution of DNMs across the genome and generated DNMs using these rates and then correlated these simulated rates to the true rates ( i . e . those sampled from the gamma distribution ) . The average coefficient of determination for the simulated data is 0 . 11 , 0 . 39 and 0 . 42 for the Francioli , Wong and Jonsson datasets respectively suggesting that the regression model explains ~37% , ~26% and ~10% of the explainable variance for the three datasets . In all cases , none of the simulated datasets have a coefficient of determination that is as low as the observed . The rate of divergence between species is expected to depend , at least in part , on the rate of mutation . To investigate whether variation in the rate of substitution is correlated to variation in the rate of mutation we calculated the divergence between humans and chimpanzees , initially by simply counting the numbers of differences between the two species . There are at least three different sets of human-chimpanzee alignments: pairwise alignments between human and chimpanzee ( PW ) [43] found on the University of California Santa Cruz ( UCSC ) Genome Browser , the human-chimp alignment from the multiple alignment of 46 mammals ( MZ ) [44] from the same location , and the human-chimp alignment from the Ensembl Enredo , Pecan and Ortheus primate multiple alignment ( EPO ) [45] . We find that the correlation depends upon the human-chimpanzee alignments used and the amount of each 1MB window covered by aligned bases ( Fig 6 ) . The correlation is significantly negative if we include all windows for the UCSC PW and MZ alignments at the 1MB scale , but becomes more positive as we restrict the analysis to windows with more aligned bases . In contrast , the correlations are always positive when using the EPO alignments , and the strength of this correlation does not change once we get above 200 , 000 aligned bases per 1MB . Further analysis suggests there are some problems with the PW and MZ alignments because divergence per MB window is negatively correlated to mean alignment length ( r = -0 . 31 , p < 0 . 0001 ) for the PW alignments and positively correlated ( r = 0 . 57 , p < 0 . 0001 ) for the MZ alignments ( S5 Fig ) . The EPO alignment method shows no such bias and we consider these alignments to be the best of those available . Therefore , we use the EPO alignments for the rest of this analysis . To gain a more precise estimation of the number of substitutions we used the method of Duret and Arndt [21] , which is a non-stationary model of nucleotide substitution that allows the rate of transition at CpG dinucleotides to differ to than that at other sites . As expected the divergence along the human lineage ( since humans split from chimpanzees ) is significantly correlated to the rate of DNMs ( Francioli , r = 0 . 20 p<0 . 001; Wong , r = 0 . 16 , p<0 . 001; Jonsson , r = 0 . 31 , p<0 . 001 ) . However , the correlation between the rate of DNMs and divergence is not expected to be perfect even if variation in the mutation rate is the only factor affecting the rate of substitution between species; this is because we have relatively few DNMs and hence our estimate of the density of DNMs is subject to a large amount of sampling error . To investigate how strong the correlation could be , we follow the procedure suggested by Francioli et al . [3]; we assume that variation in the mutation rate is the only factor affecting the variation in the substitution rate across the genome between species and that we know the substitution rate without error ( this is an approximation , but the sampling error associated with the substitution rate is small relative to the sampling error associated with DNM density because we have so many substitutions ) . We generated the observed number DNMs according to the rates of substitution , and then considered the correlation between these simulated DNM densities and the observed substitution rates . We repeated this procedure 1000 times to generate a distribution of expected correlations . Performing this simulation , we find that we would expect the correlation between divergence and DNM density to be 0 . 30 , 0 . 44 and 0 . 68 for the Francioli , Wong and Jonsson datasets respectively , considerably greater than the observed values of 0 . 20 , 0 . 16 and 0 . 31 respectively . In none of the simulations was the simulated correlation as low as the observed correlation . There are several potential explanations for why the correlation is weaker than it could be; the pattern of mutation might have changed [39 , 46–48] , or there might be other factors that affect divergence . Francioli et al . [3] showed that including recombination in a regression model between divergence and DNM density significantly improved the fit of the model; a result we confirm here; the coefficient of determination when the sex-average recombination rate is included in a regression of divergence versus DNM density increases from 0 . 039 to 0 . 14 , 0 . 026 to 0 . 12 and 0 . 095 to 0 . 18 for the Francioli , Wong and Jonsson datasets respectively; similar patterns are observed for male and female recombination rates separately . As detailed in the introduction there are at least four explanations for why recombination might be correlated to the rate of divergence independent of its effect on the rate of DNM: ( i ) biased gene conversion , ( ii ) recombination affecting the efficiency of selection , ( iii ) recombination affecting the depth of the genealogy in the human-chimpanzee ancestor and ( iv ) problems with regressing against correlated variables that are subject to sampling error . We can potentially differentiate between these four explanations by comparing the slope of the regression between the rate of substitution and the recombination rate ( RR ) , and the rate of DNM and the RR . If recombination affects the substitution rate , independent of its effects on DNM mutations , because of GC-biased gene conversion ( gBGC ) , then we expect the slope between divergence and RR to be greater than the slope between DNM density and RR for Weak>Strong ( W>S ) , smaller for S>W , and unaffected for S<>S and W<>W changes . The reason is as follows; gBGC increases the probability that a W>S mutation will get fixed but decreases the probability that a S>W mutation will get fixed . This means that regions of the genome with high rates of recombination will tend to have higher substitution rates of W>S mutations than regions with low rates of recombination hence increasing the slope of the relationship between divergence and recombination rate . The opposite is true for S>W mutations , and S<>S and W<>W mutations should be unaffected by gBGC . If selection is the reason that divergence is correlated to recombination independently of its effects on the mutation rate , then we expect all the slopes associated with substitutions to be less than those associated with DNMs . The reason is as follows; if a proportion of mutations are slightly deleterious then those will have a greater chance of being fixed in regions of low recombination than high recombination . If the effect of recombination on the substitution rate is due to variation in the coalescence time in the human-chimp ancestor , then we expect all the slopes associated with substitution to be greater than those associated with DNMs; this is because the average time to coalescence is expected to be shorter in regions of low recombination than in regions of high recombination . Finally , if the effect is due to problems with multiple regression then we might expect all the slopes to become shallower . Since the DNM density and divergences are on different scales we divided each by their mean to normalise them and hence make the slopes comparable . The results of our test are consistent with the gBGC hypothesis; the slope of divergence versus RR is greater than the slope for DNM density versus RR for W>S mutations and less for S>W mutations ( Fig 7 ) ; we present the analyses using sex-averaged RR , but the results are similar for either male or female recombination rates , and for 100KB windows ( S6 and S7 Figs and S8 and S9 Tables ) . These differences are significant in the expected direction for all comparisons except W>S from the Wong data ( Table 6 ) ( significance was assessed by bootstrapping the data by MB regions 100 times and then recalculating the slopes ) . There are no significant differences between the slope for W<>W and S<>S mutations and the slope for substitutions , consistent with gBGC , except for the Jonsson dataset in which the DNM slope is significantly less than the slope for substitutions . This latter result suggests that there might also be an effect of linked selection , but this result should be treated with caution given that the other two datasets show the opposite pattern . Just as we expect there to be correlation between divergence and DNM rate , so we might expect there to be correlation between DNA sequence diversity within the human species and the rate of DNM . To investigate this , we compiled the number of SNPs in 1MB and 100KB windows from the 1000 genome project [49 , 50] . There is a positive correlation between SNP density and DNM density in all datasets ( Francioli r = 0 . 18 p<0 . 001; Wong r = 0 . 31 , p<0 . 001; Jonsson r = 0 . 43 , p<0 . 001 ) . Using a similar strategy to that used in the analysis of divergence we calculated the correlation we would expect if all the variation in diversity was due to variation in the mutation rate by assuming that the level of diversity is known without error , and hence is a perfect measure of the mutation rate ( we have on average 31 , 000 SNPs per MB , so there is little sampling error associated with the SNPs ) . We then simulated the observed number of DNMs according to these inferred mutation rates . The expected correlations are 0 . 24 , 0 . 35 and 0 . 58 in the Francioli , Wong and Jonsson datasets , which are slightly higher than the observed correlation , significantly so for Francioli and Jonsson ( p<0 . 01 in both cases ) . The observed correlations are 74% , 89% and 74% of the expected correlations for Francioli , Wong and Jonsson respectively . A similar pattern is observed for individual mutational types at both the 1MB and 100KB scale , with some being greater and others smaller than expected ( S10 Table ) . These results suggest that much of the variation in diversity at the 1MB scale is due to variation in the mutation rate . Although much of the variation in diversity appears to be due to variation in the mutation rate we tested for the effect of gBGC . We find the slopes are consistent with gBGC for the Francioli dataset , but the other datasets show inconsistent patterns; in the Wong data , the slope of DNM versus RR is significantly greater than the slope of SNP density versus RR across all mutational categories and the opposite pattern is found in Jonsson ( p<0 . 01 in all cases ) ( Fig 7 ) . The divergence between species , usually humans and macaques , is often used to control for mutation rate variation in various analyses . But how does the correlation between divergence and the DNM rate in humans change as the species being compared get further apart ? Terekhanova et al . [48] showed that the rate of S<>S and W<>W substitutions ( chosen to eliminate the influence of gBGC ) along the human lineage at the 1MB scale is correlated to that along other primate lineages , but that the correlation declines as the evolutionary distance increases . This suggests that the mutation rate evolves at the 1MB relatively rapidly . However , they did not consider DNMs in detail . To investigate further , we compiled data from a variety of primate species–human/chimpanzee/orangutan ( HCO ) considering the divergence along the human and chimp lineages , human/orangutan/macaque ( HOM ) considering the divergence along the human and orangutan lineages , and human/macaque/marmoset ( HMM ) considering the divergence along the human and macaque lineages . This yields two series of divergences of increasing evolutionary divergence: the human lineage from HCO , HOM and HMM , and chimp from HCO , orangutan from HOM and macaque from HMM . We estimated the divergence using the non-stationary method of Duret and Arndt [21] that treats CpG sites separately . We do not restrict ourselves only to DNMs in the aligned regions but used all DNMs in each window . In this way , the average number of DNMs per window is independent of the evolutionary divergence . As expected , we find that the correlation between the density of DNM and the rate of substitution declines as the evolutionary divergence increases , except the correlation between the density of DNMs in the Francioli dataset and the divergence along the human lineage since the divergence from orangutan which is slightly lower than the correlation with divergence since humans split from macaques ( Fig 8 ) . It is also notable that the decrease in the correlation is quite modest in many cases .
We have considered the large-scale ( 1MB or 100KB ) distribution of DNMs along the human genome using an analysis of 3 datasets obtained by the sequencing of trios ( an individual and their parents ) . Unfortunately , there are significant differences between these datasets; most conspicuously they show different patterns of correlation to genomic variables . For example , the density of DNMs at the 1MB scale is significantly negatively correlated to the density of H3K4me1 epigenetic marks in the dataset of Francioli et al . [3] , significantly positively correlated in Wong et al . [6] and uncorrelated in Jonsson et al . [36] despite this being by far the largest dataset . However , these correlations to genomic variables are weak , and explain only a small fraction of the explainable variance , and there are many commonalities between datasets , which likely represent true patterns . There appears to be rather little variation in the mutation rate at a large scale in all datasets . However , there is variation at a large scale that cannot be explained by variation at smaller scales , and large-scale variation forms part of a continuum of variation across different scales . Furthermore , the level of variation for different mutational types is similar and different mutational types covary together . There is no evidence that variation in the pattern of mutation generates variation in GC content that would underlie the maintenance of isochores . In all datasets , the correlations to genomic variables are weak and explain little of the explainable variance . We confirm that the correlation between the mutation rate , as measured by DNM density , and divergence , is not as strong as it could be across datasets , and demonstrate that this is in part due to BGC . In contrast , we find that variation in diversity at large scales is largely a consequence of variation in the mutation rate . Finally , we demonstrate that the correlation between the rate of DNM and the rate of substitution , declines as increasingly divergent species are considered . It is possible that the differences between datasets are due to parental age , since Francioli et al . [3] found that the correlation between DNM density and replication time was only evident in individuals born to young fathers , and paternal age differs between our datasets . However , like Besenbacher et al . [51] , we find no evidence that paternal age affects the relationship between the mutation rate and replication time or genomic variables , as summarised by the first principle component of the genomic variables , in either the Wong or Jonsson datasets . It is also possible that the differences between the datasets are due to ethnicity , since it has been shown that the rate and pattern of mutation , at the single nucleotide scale , varies over short timescales , such that it can vary between human populations [39 , 46 , 47]; for example , the rate of TCC to TTC is elevated in Europeans [39 , 46] . It has also been demonstrated that the mutation pattern evolves at larger scales . Terekhanova et al . [48] considered the correlation between the rate of S<>S and W<>W substitution along the human and other primate lineages at the 1MB scale . They showed that the strength of the correlation declines as more distant species are considered suggesting that the mutation rate evolves at this scale . However , the rate of decline was fairly slow , and human populations would not be predicted to show very different patterns from this analysis . Furthermore , it seems that the populations considered by the three studies were dominated by individuals from the same population , Europeans: Dutch in the study of Francioli et al . [3] , Icelanders in Jonsson et al . [36] and mostly North American Europeans in Wong et al . [6] ( see [52] for ethic details ) . Without any other obvious explanation , it therefore seems likely that the differences between datasets are due to sequencing technology , or the pipelines used to call the DNMs . The Francioli [3] and Jonsson [36] datasets were largely sequenced using Illumina Hiseq at 13x and 35x coverage respectively . The Wong [6] dataset was sequenced using the DNA nanoball technology at 60x coverage . The datasets were subject to a variety of different methods to call DNMs . One potential problem is a GC-bias that has been documented for Illumina sequencing [53] , in which high and low GC-content reads are under-represented [54] . To investigate whether this might be the cause of the differences between datasets we regressed the number of DNMs per MB against GC content , and the square of the GC content , to allow for non-linearity . We find that both linear and quadratic terms are significant for the Francioli ( p<0 . 05 for both terms ) and Wong ( p<0 . 001 for both terms ) datasets , but neither coefficient is significant in the Jonsson dataset . In the Francioli dataset high GC-content regions have fewer DNMs , whereas in Wong it is the low GC-content regions that have a deficit ( Fig 9 ) . If we take the residuals from the regression and correlate these against genomic variables we find consistent patterns across datasets ( Table 7 ) : the GC-content corrected DNM density is significantly positively correlated to male and female recombination rates , and significantly negatively correlated to replication time and H3K4me3 across all datasets . There are some other significant correlations to histone marks in each of the datasets , with the sign of the correlation being consistent across datasets . If we calculate the principle components for the genomic variables , excluding GC-content , we find that the first four components explain 55 , 14 , 7 . 4 and 6 . 2% of the variance respectively . We find consistent patterns of correlation across datasets in terms of the sign of the correlation ( Table 7 ) —the GC-corrected density of DNMs is negatively correlated to the first PC , but only significant for Jonsson , significantly positively correlated to the second PC in all datasets , uncorrelated to the third and only significantly correlated to the fourth in Jonsson ( Table 7 ) . Despite the fact that the GC-corrected densities of DNMs show similar correlations to genomic variables , we do not find similar models selected by forward selection in a multiple regression ( Table 7 ) . Only one feature is common to all datasets–replication time . The differences between the datasets may reflect the strong correlations between genomic variables , which makes it difficult for any procedure to select the correct model . The fact that correcting for GC content yields similar patterns of correlation to genomic variables , suggests that there is a GC-bias in detecting DNMs . However , regressing against GC-content does not necessarily yield the correct pattern , because there may be a genuine relationship between the mutation rate and GC-content . For example , if we regress the number of W<>W and S<>S substitutions , chosen to remove the influence of BGC , between human and chimpanzee against GC-content we find a U-shaped relationship , unlike that seen for any of the DNM datasets ( Fig 9 ) ; this might reflect the true pattern . Are there any clues as to which dataset reflects the true pattern of correlation ? If we consider the correlation between S<>S and W<>W substitutions , and genomic variables ( 1MB Table 3; 100KB S5 Table ) we find the correlations most closely parallel those in the Francioli dataset; when there is a significant correlation both the substitution date and the Francioli DNMs are significant in the same direction . In contrast , the sign of the correlation is usually the same in the substitution and Jonsson datasets , but the correlations are often non-significant in the Jonsson data . Wong shows very different patterns with some significant correlations in opposite directions . If the differences between datasets are due to sequencing and processing technology then this has important implications for understanding the reasons the mutation rate varies across the genome because no two datasets show identical patterns of correlation between DNM density and genomic variables . We would suggest that unless a pattern can be shown to be consistent across datasets generated by different sequencing and processing technologies then it must be treated with some caution . There are two additional points to make about correlations to genomic variables . First , it is evident that many genomic variables are highly correlated to each other so disentangling them will be difficult . Applying multiple regression may not be informative because few of the genomic variables are known without error , so the variables which come out as correlated may not be the causative factors , but those known with the least error . Second , genomic variables explain rather little of the variance in the rate of DNM . This may be because the genomic variables are measured with considerable error , or it may be that we are not assaying the factors which are important; but what these might be , is far from clear . The evolution of the large-scale variation in GC-content across the human genome has been the subject of much debate [25]; mutation bias [14–18] , selection [13 , 19 , 20] and biased gene conversion [21–24] have all been proposed as explanations . There is good evidence that biased gene conversion has some effect on the base composition of the human genome [24 , 26–28] . However , this does not preclude a role for mutation bias . We have tested the mutation bias hypothesis using the DNM data and two different tests . We find no evidence that the pattern of mutation varies across the genome in a way that would generate variation in GC-content . Instead we provide additional evidence that biased gene conversion influences the chance that mutations become fixed in the genome . We find that previous models of mutation rate variation do not explain the variation in DNM density seen in our datasets at large scales . This is perhaps not surprising . The model of Michaelson et al . [5] was derived by regressing a small number ( ~600 ) of DNMs against a suite of genomic variables at multiple scales . So , whilst the model took into account genomic variables at large scales it was principally aimed at estimating the rate of mutation at a single site . The model of Aggarwala et al . [40] estimated the mutation rate at individual sites based on the 7-mer context . It therefore contained no explicit information about large-scale variation . As expected the rate of divergence between species is correlated to the rate of DNM , however , the strength and even the sign of the correlation depends on the alignments being used . The correlations between divergence and DNM density are actually negative if no filtering is applied to the UCSC alignments , and there is a negative correlation between divergence and alignment length for the pairwise alignments from the UCSC genome browser , and a positive correlation for the multi-species alignment . It is clear that there are problems with these alignments and that they should be used with caution . As Francioli et al . [3] showed , the correlation between divergence and DNM density is worse than it would be if variation in the mutation rate was the only factor affecting divergence . This is perhaps not surprising because the substitution rate depends both on the rate of mutation and the probability of fixation , both of which may vary across the genome . Francioli et al . [3] further demonstrated that although the rate of DNM is correlated to the rate of recombination , divergence is correlated to the rate of recombination independently of this effect . There are at least four explanations for the effect of recombination on divergence: ( i ) biased gene conversion , ( ii ) direct selection , ( iii ) linked selection and ( iv ) problems with multiple regression . We have provided evidence for an effect of biased gene conversion , but no clear evidence of three other factors–i . e . the slope of the regression between DNM density and RR is not significantly different to the slope of the regression between divergence and RR for S<>S and W<>W mutations , except in the Jonsson data . However , whilst the slope of DNMs versus RR is lower than the slope of divergence versus RR in Jonsson , we see the opposite pattern in the other two datasets . The fact that there is no obvious effect of indirect selection is surprising given the results of McVicker et al . [29] . They showed that the divergence between humans and chimpanzees was significantly lower near exons and other regions of the genome subject to evolutionary constraint . A similar reduction was not observed in the divergence of human and macaque and human and dog , suggesting that the pattern was not due to selection outside exons , or regions identified as being subject to selection ( though see Phung et al . [30] who detected a correlation between divergence and proximity to functional DNA in the divergence between humans and rodents ) . They therefore inferred that the reduction was due to the effect of linked selection reducing diversity in the human-chimpanzee ancestor . There are several possible reasons why we see no evidence of this effect in our analysis . First , our test may not be powerful enough . Second , the effects may be counteracted by direct selection which is expected to affect the slope of the regression between divergence and RR in the opposite direction to indirect selection . Third , the scale , magnitude and variation in the effects of indirect selection may be not large enough to affect the relationship between divergence and the rate of mutation; if there is little variation in the magnitude of the indirect effects of selection across the genome at the 1MB ( or 100KB ) level then indirect selection will have no effect on the correlation between the rate of mutation and divergence . McVicker et al . [29] and Phung et al . [30] presented evidence of indirect selection affecting the divergence between humans and chimpanzees , but over short scales of <100KB . It is possible that at fine scales indirect selection may be more important . In contrast to the pattern with divergence , we find that much of the variation in diversity , at least at the 1MB and 100KB scales , can be explained by variation in the mutation rate . This suggests that much of the correlation between diversity and RR [31–33 , 55–57] is due to variation in the mutation rate not to linked selection . However , the correlation between DNM density and diversity is not as strong as it could be and this could be due to linked selection . Considering that much of the variation in diversity is due to variation in the mutation rate , it is perhaps not surprising that the analysis of DNM density versus RR and SNP density versus RR slopes are inconclusive . The results from Francioli are consistent with BGC affecting the relationship between SNP and DNM density , but the data from Wong and Jonsson are not . Divergence between species has often been used to control for mutation rate variation in humans ( for example [29 , 55 , 58 , 59] ) . This is clearly not satisfactory given that the correlation between divergence and rate of DNM is only about half as strong as it could and this correlation gets worse as more divergent species are considered ( see also Terekhanova et al . [48] ) . Unfortunately , correcting for mutation rate variation is likely to be difficult because attempting to predict mutation rates from genomic features is unreliable , given that regression models explain less than half the explainable variation . Furthermore , the largest amounts of variation are at the smallest scales ( Fig 4 ) where we have the lowest density of DNMs . We find , as others have before [3 , 5] , that the rate of germ-line DNM is correlated to a number of genomic features . However , we find that these features explain less than 50% of the explainable variance leaving the majority of the variance unexplained . Our inability to predict the mutation rate might be because the genomic features have not been assayed in the relevant tissue , the germ-line , or that there are important features that have yet to be assayed . Interestingly , Terekhanova et al . [48] showed that this unexplained component of the substitution rate evolves more rapidly than the explained component . They demonstrated that the substitution rates at the 1MB level in a range of primate species were almost as well correlated to genomic features in humans , as the substitution rate along the human lineage . This implies that the variance in the substitution rate not explained by genomic features , evolves rapidly , given that the correlation between the substitution rate in humans and other lineages declines as they get more distant . There is clearly much we do not currently understand about the why there is large scale variation in the mutation rate and how it evolves through time . Understanding these patterns is challenging given that different datasets show different patterns . Never-the-less there are some patterns which are common to all datasets .
Details of DNM mutations were downloaded from the supplementary tables of the respective papers or from the relevant web-sites: 105 , 385 mutations from Jonsson et al . [36] , 26 , 939 mutations from Wong et al . [6] and 11016 mutations from Francioli et al . [3] . The data from Jonsson et al . was mapped to hg38 so the liftover tool was used to map these to hg19 . Only autosomal DNMs were used . Three sets of alignments were used in this analysis , all based on human genome build hg19/GRCh37: ( i ) the University of California Santa Cruz ( UCSC ) pairwise ( PW ) alignments [43] for human-chimpanzee ( hg19-panTro4 downloaded from http://hgdownload . cse . ucsc . edu/goldenpath/hg19/vsPanTro4/ ) ( ii ) the UCSC MultiZ ( MZ ) 46-way alignments [44] downloaded from http://hgdownload . cse . ucsc . edu/goldenpath/hg19/multiz46way/ and ( iii ) Ensembl Enredo , Pecan , Ortheus ( EPO ) 6 primate multiple alignment , release 74 , [45] downloaded from ftp://ftp . ensembl . org/pub/release-74/emf/ensembl-compara/epo_6_primate/ . We found that the EPO alignments were the most reliable–see main text–and they were used for the majority of the analyses . All SNPs from the 1000 genomes project phase 3 [50] were downloaded from http://hgdownload . cse . ucsc . edu/gbdb/hg19/1000Genomes/phase3/ . After removing all multi-allelic SNPs and , structural variants and indels we were left with 77 , 818 , 368 autosomal SNPs . After filtering out windows which had less than 50% of nucleotides aligning between human-chimpanzee-orangutan and no recombination rate scores we were left with 71 , 917 , 321 SNPs . We considered how well the variation at the 100KB and 1MB scale was predicted by two models of mutation rates: the rates estimated by Aggarwala et al . [40] based on the 7-mer context surrounding a site , and the rates estimated for each site by Michaelson et al . based on a variety of genomic features . The rates for Aggarwala et al . [40] were taken from their S7 Table , and the context of each site was used to predict the average mutation rate for each 100KB or 1MB window using their model . The mutability indices from the Michaelson et al . study [5] were provided by the authors . The analysis of the model of Michaelson et al . [5] is more complex since they give the probability of detecting a DNM in their data at each site in the genome , referred to as the mutability index ( MI ) , but these do not translate directly into mutation rates . Using their DNM data we tabulated the number of sites in the genome with a given MI along with the number of DNMs from their study that had been observed at those sites . Because DNMs are not observed at some MIs we grouped MIs into groups of ten starting from the first MI with at least one DNM . We then regressed the log of the number of DNMs over the number of sites against the mean MI ( see S8 Fig ) . The regression line was estimated to be log ( mutation rate ) = -6 . 73 + 0 . 0103 x MI . Using this equation , we predicted the mutation rate at each site in the genome . Michaelson et al . [5] give MIs mapped to hg18; we lifted these over the hg19 using the liftover tool . Male , female and sex-averaged standardised recombination rate data [60] were downloaded from http://www . decode . com/additional/male . rmap , which provides recombination rates in 10KB steps . For each 100KB and 1MB windows the recombination rate was calculated as the mean of these scores with a score assigned to the window in which the position of its first base resided . GC content was calculated directly from the human genome ( hg19/GCRh37 ) for 100kb and 1Mb windows . All other feature data was taken from the ENCODE project [61] and downloaded from the UCSC genome browser . Where possible we used data from the embryonic stem cell line H1-hESC . The mean value was taken for each genome feature across the window . For replication time data , we downloaded the ENCODE Repli-seq wavelet smoothed signal data [62 , 63] , provided in 1KB steps , for the GM12878 , HeLa , HUVEC , K562 , MCF-7 and HepG2 cell lines . Replication times were assigned to windows based upon their start coordinates . We computed the mean replication time for all autosomes for 100KB and 1MB windows across all 6 cell lines . We measured transcription rate using RNA-seq data . Nucleosome occupancy was taken from the GM12878 cell line , histone modifications and RNA-seq data from the stem cell line H1-hESC . We only included windows in our analysis in which >50% of the window had data from all features . SPSS version 22 and Mathematica version 10 were used for all statistical analyses . To estimate the mutation rate distribution we use the method of [8] . In brief , we assume that the mutation rate in each window is αu¯ where u¯ is the average mutation rate per site and α is the rate above or below this mean . α is assumed to be gamma distributed . The number of mutations per window is assumed to be Poisson distributed with a mean αu¯l where l is the length of the window . This means that the number of mutations per window is a negative binomial . In considering a particular category of mutations , such as CpG transitions , we considered the number of CpG transition DNMs at CpG sites . We fit the distribution using maximum likelihood using the NMaximize function in Mathematica . Initial analyses suggested that the maximum likelihood value of the mutation rate parameter was very close to the mean estimate of the mutation rate; as a consequence , to speed up the maximization we fixed the mutation rate to its estimated mean and found the ML estimate of the shape parameter of the gamma distribution . We investigated the correlation between different types of mutation across windows by fitting a single distribution to both types of mutation , estimating the shape parameter of the shared distribution as the mean of the CV of the ML estimates of distributions fitted to the two categories independently . We then used this distribution to simulate data; we drew a random variate for each window from the distribution assigning this as the rate for that window . We then generated two Poisson variates with the appropriate means such that the total number of DNMs for each type of mutation was expected to be equal the total number of DNMs of those types . To test whether the mutation pattern varied across the genome in a manner that would generate variation in the mutation rate we fit the following model . Let us assume that the mutation rate from strong ( S ) to weak ( W ) base pairs , where strong are G:C and weak are A:T , be μ ( 1 − fe ) , where μ is the mutation rate and fe is the equilibrium GC-content to which the sequence would evolve if there was no selection or biased gene conversion . Let the mutation rate in the opposite direction be μfe and the current GC-content be f . Then we expect the proportion of mutations that are S->W to be x ( fe , f ) =fμ ( 1−fe ) fμ ( 1−fe ) + ( 1−f ) μfe=f ( 1−fe ) f ( 1−fe ) + ( 1−f ) fe ( 1 ) Let us assume that fe is normally distributed . Then the likelihood of observing i S>W mutations out of a total of n S>W and w>S mutations is L=∫01N ( fe;fe¯ , σ ) B ( n , i , x ( fe , f ) ) dfe/∫01N ( fe;fe¯ , σ ) dfe ( 2 ) The total log-likelihood is therefore the sum of the log of Eq 2 for each MB or 100KB window across all the windows in the genome . The maximum likelihood values were obtained by manually searching for the ML values in Mathematica . In a number of analyses , we simulate DNMs under assumed model; for example , using the 7-mer model of Aggarwala et al . [40] . In these simulations , we calculate the expected number of DNMs given the window’s mutation rate , the number of relevant sites and the total number of DNMs , and then generated a random Poisson variate from this expectation . In each simulation , we generated 1000 simulated datasets .
|
Using a dataset of more than 130 , 000 de novo mutations we show that there is large-scale variation in the mutation rate at the 100KB and 1MB scales . We show that different types of mutation vary in concert and in a manner that is not expected to generate variation in base composition; hence mutation bias is not responsible for the large-scale variation in base composition that is observed across human chromosomes . As expected , large-scale variation in the rate of divergence between species and the variation within species across the genome , are correlated to the rate of mutation , but the correlation between divergence and the mutation rate is not as strong as it could be . We show that biased gene conversion is responsible for weakening the correlation . In contrast , we find that most of the variation across the genome in diversity can be explained by variation in the mutation rate . Finally , we show that the correlation between the rate of mutation in humans and the divergence between humans and other species , weakens as the species become more divergent .
|
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2018
|
Large scale variation in the rate of germ-line de novo mutation, base composition, divergence and diversity in humans
|
Pneumococcal conjugate vaccines ( PCVs ) have substantially reduced morbidity and mortality of pneumococcal disease . The impact of the 7-valent PCV on all-serotype invasive pneumococcal disease ( IPD ) among children was reported to vary between high-income countries . We investigate the ability to predict this heterogeneity from pre-vaccination data . We propose a parsimonious model that predicts the impact of PCVs from the odds of vaccine serotype ( VT ) among carriers and IPD cases in the pre-PCV period , assuming that VT are eliminated in a mature PCV programme , that full serotype replacement occurs in carriage and that invasiveness of the NVT group is unchanged . We test model performance against the reported impact of PCV7 on childhood IPD in high-income countries from a recent meta-analysis . The odds of pre-PCV7 VT IPD , PCV schedule , PCV coverage and whether a catch up campaign was used for introduction was gathered from the same analysis . We conducted a literature review and meta-analysis to obtain the odds of pre-PCV7 VT carriage in the respective settings . The model predicted the reported impact on childhood IPD of mature PCV programmes; the ratio of predicted and observed incidence risk ratios was close to 1 in all settings . In the high income settings studied differences in schedule , coverage , and catch up campaigns were not associated with the observed heterogeneity in impact of PCV7 on childhood all-serotype IPD . The pre-PCV7 proportion of VT IPD alone also had limited predictive value . The pre-PCV7 proportion of VT carriage and IPD are the main determinants for the impact of PCV7 on childhood IPD and can be combined in a simple model to provide predictions of the vaccine preventable burden of IPD .
The Word Health Organisation estimates that Streptococcus pneumoniae is associated with about 5% of all-cause child mortality globally; over 90% of these pneumococcal deaths occur in low income countries [1] . Pneumococcal conjugate vaccines ( PCVs ) are part of the routine infant immunization schedule in most high income countries , resulting in a substantially reduced burden of serious pneumococcal disease [2–4] . PCVs are also being introduced into the routine vaccination programmes of low and middle income countries , partly with the financial support of Gavi , the Vaccine Alliance [5–7] . PCVs provide protection against nasopharyngeal carriage and disease for serotypes included in the vaccine ( VT ) ; these serotypes have been associated with the majority of invasive pneumococcal disease ( IPD ) globally [8] . Protection against VT nasopharyngeal carriage opens an ecological niche which is filled by the non-vaccine pneumococcal serotypes ( NVT ) ; a process termed serotype replacement [9–11] . This increase in NVT colonization prevalence results in an increased rate of NVT disease; however , because these serotypes are inherently less likely to cause disease among young children than VT strains , there is a substantial net benefit [12] . Understanding the interplay between VT protection and NVT replacement is essential for the assessment of the total impact of PCVs [13 , 14] . Despite being consistently beneficial , substantial heterogeneity in the relative impact of PCV7 on all-serotype IPD rates has been observed across settings , with impact estimates in children younger than 5 years ranging from 24% to 83% in mature programmes [12] . This heterogeneity is thought to result from interactions of vaccine coverage , vaccination schedule , serotype distribution , demographic structure and social mixing patterns , catch up campaigns at introduction , time since PCV introduction , and disease surveillance sensitivity . The contribution of each of those factors to the observed heterogeneity in PCV impact on all-serotype IPD is unclear . PCVs are amongst the most expensive vaccines that are routinely used for infant vaccination . Although the Advance Market Commitment ( AMC ) and the support of Gavi , the Vaccine Alliance substantially reduced the PCV price for low income countries [15] and pooled procurement might help reducing the costs for middle income countries [16] PCVs pose a considerable investment that requires robust evidence about its likely impact . A better understanding of the main factors that determine the impact of PCVs is essential to reduce the uncertainty around the impact and cost-effectiveness estimates of PCVs in PCV-naive countries , as well as for the assessment of the likely impact of future PCV compositions and to inform programme maintenance justifications . While better impact predictions may help with a faster introduction of PCVs globally , the justification of existing pneumococcal immunisation programmes will become particularly important for countries that have introduced PCVs with financial support from Gavi , the Vaccine Alliance and will graduate from that support . These countries have to evaluate the merits of vaccination at or below the agreed tail price of PCV under the AMC agreements , however , measuring disease impact is only possible in a limited number of countries . Disease impact models are therefore important for many countries . Available methods to predict the likely impact of PCV on disease include models accounting for carriage and disease serotype distribution and replacement as the main drivers for PCV impact [14 , 17 , 18] and more complex transmission models [19–21] . To date little validation of the models capability to accurately predict post vaccination changes in pneumococcal disease is available . We evaluate the ability of the pre-vaccination pneumococcal serotype distribution in both nasopharyngeal carriage and disease , vaccine coverage , schedule and catch up campaigns to predict the impact of PCV7 on invasive pneumococcal disease in children less than five years old and the importance of each of those factors for the accuracy of the prediction .
To predict the impact of PCV on pneumococcal disease we employ a model that is similar to previous approaches and uses changes in pneumococcal carriage to predict the impact of PCV on IPD [14 , 17 , 18 , 22] . For simplicity we assume a perfectly monitored homogenous population . Note that the methods can be derived similarly if imperfect sensitivity of carriage and/or disease surveillance and a heterogeneous population is assumed , as long as surveillance sensitivity and population heterogeneity does not change after vaccination . In this population and in the absence of PCV vaccination the rate of pneumococcal disease ( D ) per person-time can be expressed as a function of the rate of carriers ( C ) per person-time and the average ratio at which a carriage episode results in disease ( Φ = D/C ) , stratified by vaccine and non-vaccine serotypes respectively: D = CvtΦvt+ CnvtΦnvt . The parameter Φ is also called the case to carrier or invasiveness ratio for VT or NVT , which corresponds to the mean of the serotype specific invasiveness ratios weighted by serotype-specific carriage prevalence . We further assume that ( i ) vaccine serotype carriage will eventually be eliminated through routine use of PCV ( Cvt* = 0 ) , the superscript star indicating post vaccination , ( ii ) that a proportion , λ∈[0 , 1] of pre-PCV VT carriage is replaced by NVT carriage ( Cnvt* = λCvt+Cnvt ) , and ( iii ) that the invasiveness ratio of the non-vaccine serotypes group remains unchanged after vaccination ( Φnvt* = Φnvt ) . Then the rate of pneumococcal disease in a mature PCV-vaccination programme ( D* ) , when the programme has been in place long enough for direct and indirect effects to become fully established , can be expressed as a function of pre-vaccination pneumococcal carriage rates and the invasiveness of the NVT group of serotypes: D* = λCvt+CnvtΦnvt . Then the expected Incidence Rate Ratio ( IRR ) , simplifies to: IRR = D*D = λc+1d+1 , where c and d are the odds of VT carriage and disease , respectively prior to vaccination . That is c = Cvt / Cnvt and d = Dvt / Dnvt . Note that , although technically c and d are the odds based on disease and carriage rates , it is equivalent to calculate the odds based on counts even if carriage and disease data arise from samples of different sizes or proportions of VT and NVT among pneumococcal carriage and disease rates . For convenience we will mainly refer to proportions hereafter . Therefore , if c and d can be obtained from representative samples of the population , the expected percentage change in IPD after vaccination , one of the key measures of vaccine impact , can be estimated from pre-vaccination data alone ( given an informed assumption on the level of replacement ( λ ) is available ) . To test the performance of our predictor and the potential importance of other factors in accounting for the observed heterogeneity of PCV impact we compared the change in IPD rate after routine use of PCV7 with model predictions that use pre-vaccination data on the proportion of VT in IPD and pneumococcal carriage in a sample of the population from the same study site or country . We assumed that carriage and disease in these sub-population were representative of that in the respective study site or country . We further assumed that the mean duration of carriage for VT and NVT is similar which allowed the use of carriage prevalence for the calculation of the odds of VT carriage . We studied two predictions: ( i ) our main prediction that assumes complete serotype replacement in nasopharyngeal carriage ( λ = 1 ) as is observed in most settings where PCV7 was introduced for routine vaccination and ( ii ) an alternative prediction that assumes no serotype replacement in carriage ( λ = 0 ) . This prediction requires no carriage data because it reduces to IRR = DnvtD . This prediction illustrates the impact of assuming that all vaccine preventable IPD is eliminated and does not take into account serotype replacement To calculate the predicted IRRs and its corresponding distributions we assumed that both the proportion of VT among carriers and among IPD were samples from binomial distributions and drew 10 , 000 bootstrap samples . Where the proportion of VT carriers was derived through the Bayesian meta-analysis we drew the bootstrap samples from the respective posterior distribution instead ( S2 Fig ) . Similarly , we assumed that the observed IRR were samples from log-normal distributions with confidence bounds matching those reported by Feikin et al [12] . We calculated the marginal distribution of observed IRRs for a specific schedule , coverage range or the use of a catch-up campaign upon implementation by bootstrap sampling from the respective observed IRR distributions ( S3 and S4 Figs ) . The different impact of , e . g . a 3+1 schedule versus a 2+1 schedule , on the observed IRR was estimated through the ratio of the marginal distribution of IRRs of the 3+1 schedule settings and the marginal distribution of IRRs of the 2+1 schedule settings . A ratio centered around 1 indicates that the average impact of PCV7 in settings with either a 2+1 or a 3+1 schedule was similar . We estimated the setting specific performance of our predicted IRRs by calculating the ratio of the predicted IRR to the observed IRR . A ratio of 1 is indicative of a perfect prediction . Non-parametric bootstrapping methods were used to infer confidence intervals . All analysis was performed in R version 3 . 1 [25] .
The summary analysis of PCV impact on IPD three years after introduction of PCV7 [12] included data from 13 sites that met the inclusion criteria: indigenous and non-indigenous Australia , Calgary , Denmark , England and Wales , Crete , the Netherlands , Scotland , Switzerland , the US general population ( Active Bacterial Core Surveillance ) , Alaska , Navajo Nation and Northern California ( Kaiser Permanente ) . We identified childhood NP carriage information stratified by VT and NVT from the pre-PCV era from 9 of the 13 sites ( Table 1 ) . No carriage information was available in any healthy subpopulation of Calgary , Crete , Scotland and Switzerland . In England and Wales , Netherlands and Alaska more than one carriage study was identified . There was little or moderate heterogeneity between the study estimates of VT carriage proportion within the different sub populations of the same setting . Respective studies were pooled through Bayesian random effects meta-analysis to provide a single estimate of the proportion of VT among carriers for each setting ( Table 1 and S2 Fig ) . Fig . 1 illustrates how the serotype distributions in pneumococcal carriage and disease shape the predicted incidence risk ratios in the prediction model: the higher the proportion of VT in disease , the higher the predicted impact ( lower IRR ) and the lower the proportion of VT in carriage the higher the predicted impact . In particular this shows how serotype replacement in nasopharyngeal carriage and differences in serotype distribution in carriage prior to vaccine introduction can lead to vastly different vaccine impact predictions in two settings with similar contribution of VT to the pneumococcal disease burden . There was little difference between the average observed impact of PCV7 between settings with either different schedules or different coverage levels ( S3 and S4 Figs ) . The impact in settings using a 2+1 schedule was slightly higher than in 3+1 schedule settings and the impact in 3+0 and 3+1 schedule settings was similar; ratio of IRRs 0 . 87 ( 0 . 20 to 2 . 91 ) and 1 . 00 ( 0 . 24 to 3 . 61 ) . In settings with an average vaccine coverage of under 70% and those with a coverage between 70% to 90% the impact of PCV7 was similar to the impact in settings where vaccine coverage had been over 90%; ratio of IRRs 1 . 00 ( 0 . 39 to 2 . 42 ) and 0 . 95 ( 0 . 26 to 4 . 18 ) . Settings which used a catch-up campaign for introduction of PCV7 reported on average a 25% ( -65% to 325% ) higher impact than those which had not . Assuming no serotype replacement in carriage ( λ = 0 ) for the prediction led to consistent overestimation of vaccine impact ( Fig . 2 ) . With the assumption of complete serotype replacement in carriage ( λ = 1 ) , however , we were able to closely predict the impact of routine PCV7 use on paediatric IPD 3 years after introduction ( Fig . 2 and Fig . 3 ) . The corresponding ratios of predicted and observed IRRs are provided in Table 1 .
Routine pneumococcal conjugate vaccination has led to sustained reductions of all-serotype invasive pneumococcal disease in children , albeit of varying absolute and relative magnitudes across various countries and surveillance sites . Here we aimed to understand the factors related to the observed heterogeneity of PCV7 impact on IPD in children under five years of age 3 years after the start of routine vaccination so that estimates of that impact could reliably be produced for countries without disease impact data . Our analysis shows that the proportion of VT-IPD in the pre-PCV period , which is sometimes used as a measure of the potential vaccine preventable burden of S . pneumoniae , is consistently overestimating the observed impact of PCVs on overall IPD because it ignores the effect of serotype replacement . However , when supplemented by the odds of VT carriage pre-PCV in the same population our proposed model , assuming full serotype replacement in carriage , is highly predictive for the observed relative impact ( IRR ) of PCVs on overall IPD . We find that in the studied sites neither differences in schedule nor vaccine coverage substantially contributed to the heterogeneity of IPD impact observed from surveillance three years after the introduction of PCV7 . While there is little doubt that sufficient vaccine coverage is essential for the success of a PCV programme we find that even in those settings where the average vaccine coverage during the first 3 years after implementation was below 70% the observed impact of vaccination was similar to settings with higher coverage . This may be due to the strong herd protection induced by PCV7 in high income countries , even at low coverage levels , which has helped to control VT transmission and as a consequence VT IPD . We find some evidence of a higher impact of vaccination in settings that introduced PCV with a catch-up campaign , albeit with substantial heterogeneity of PCV impact between those settings . The method for prediction of the impact of PCV7 builds on the idea that , because of serotype replacement , the case to carrier ratio of serotypes or serotype groups is an important determinant for the success of pneumococcal conjugate vaccines , as has been proposed earlier in similar model approaches [9 , 14 , 17 , 18 , 22 , 26] . A model using the inherent link of carriage and IPD has been proposed for monitoring IPD through post PCV nasopharyngeal carriage in the absence of IPD surveillance and has been shown to provide valid predictions for most of the 5 studied sites [18] . Similar models have used pre-vaccination carriage prevalence and IPD incidence to predict the impact of pneumococcal vaccines on IPD . One was validated against a single estimate of IPD impact from US ABC data [22] . We present here a minimalistic tool that utilises information on the serotype distribution of carriage and IPD before vaccination to predict disease impact . We show that the predicted IPD impact closely matches the observed impact in those nine sites and that the model is able to replicate the substantial heterogeneity of impact among sites . We find that a pre-PCV serotype specific case series of IPD ( e . g . through sentinel surveillance or surveillance data where the denominator data are unclear ) , rather than IPD incidence as used in previous approaches , and a cross-sectional carriage study , provide sufficient data to predict the percentage reduction in IPD after maturity of the PCV programme has been reached . If supplemented by pre-PCV pneumococcal disease incidence data , the absolute impact of PCV can be estimated . This study for the first time uses the observed impact of PCV7 from multiple sites to rigorously test the predictive ability of the model while showing that differences in schedule , coverage and the use of catch-up campaigns are unlikely to have had a major contribution to the observed heterogeneity in PCV7 impact on IPD . However we cannot fully rule out that other factors , including changes in testing practices and antibiotic usage , have contributed to the heterogeneity in IPD impact among settings . The methodology employed here bases predictions on the combined information of serotype distribution in IPD and carriage prior to vaccination . It is essential that these data come from the same or well matched populations for the prediction to be valid . While data on IPD usually result from routine surveillance of a defined population which ideally is representative of the epidemiology of IPD in the country , nasopharyngeal carriage surveys may be conducted among a group that may not be representative of the population studied for IPD . If the serotype distribution in the carriage study population is not representative of that in the disease surveillance population the resulting estimate can be misleading . Similarly , if serotypes that are hardly observed in carriage ( serotype 1 ) or epidemic serotypes ( serotypes 1 or 5 ) contribute substantially to the local IPD burden the performance of the model may be impaired . Here we test the predictive ability of the model using IPD surveillance data from large geographic regions paired with data on nasopharyngeal carriage from relatively small population samples of the same geographic regions . We find that in instances where carriage estimates are available from different population subgroups than that of the IPD surveillance population ( see Table 1 ) there was only limited heterogeneity between them suggesting that those samples were representative of the carriage epidemiology in the country . However , the pneumococcal serotypes are affected by secular changes [27] which could also result in a temporal mismatch of carriage and IPD data . The impact of PCV on IPD incidence in Greece and Canada was only reported in Crete and Calgary ( IRR 0 . 61 ( 0 . 06 to 6 . 50 ) and 0 . 56 ( 0 . 22 to 1 . 42 ) ) for which no carriage data was available . However , carriage data were available from other regions of Greece and Canada [28–31] . If used to predict the impact of PCV7 we estimated a likely IRR of 0 . 29 ( 0 . 00 to 1 . 49 ) and 0 . 46 ( 0 . 20 to 0 . 78 ) for Greece and Canada respectively assuming full serotype replacement . Our predictions are formed on the grounds of three major assumptions: ( i ) that vaccine serotypes will eventually be eliminated in the post vaccination era , ( ii ) that VT carriage is replaced by NVT carriage to a proportion λ = 1 ( complete replacement ) and ( iii ) that the average pathogenicity of non-vaccine serotypes remains unchanged after vaccination . In most settings almost complete elimination of both vaccine serotype carriage and disease has been observed [9–12] and the competition of NVTs has a potential supporting role in this which could help elimination even under low coverage or high infection pressure [19 , 32 , 33] . However , evidence that vaccine serotypes are eliminated in a mature programme is still sparse in low- and middle-income settings and it is yet unclear if in settings with high transmission PCVs will still induce sufficient herd protection to interrupt VT transmission . Hence by assuming elimination of VT carriage and disease in our model we predict the potentially vaccine preventable burden when accounting for serotype replacement . We explore the prediction for scenarios without serotype replacement and with complete serotype replacement and use the latter as the most likely scenario which we validated against the observed impact of PCV7 on childhood IPD . While full serotype replacement has been frequently reported from carriage surveys in mature pneumococcal conjugate vaccination programmes [34 , 35] not all studies fully support this finding [10] . The presented model provides the flexibility to explore deviating assumptions on the proportion to which NVT replace VT in carriage . The average pathogenicity of the NVT group can change following vaccination if NVT serotypes of high pathogenicity disproportionally replace compared with NVT serotypes of lower pathogenicity as was observed following PCV7 when there was a substantial rise in both carriage and disease due to serotype 19A . However , the carriage prevalence rank order of serotypes were found to be associated with the size of their capsule and therefore is thought to be generally stable [36] and to increase proportionally after vaccination [14] . Furthermore the invasive potential of serotypes has found to be a globally stable property which is also constant with time [37–39] . We excluded Norway from the analysis where unlike any other setting a 40% reduction in post vaccination NVT IPD was reported [12] . Pre-PCV7 carriage information was reported from two sites in Norway leading to a pooled estimate for the proportion of VT among carriers of 0 . 52 ( 0 . 22 to 0 . 80 ) [40 , 41] . From the serotype distribution in both carriage and IPD we predict an IRR for all-serotype IPD of 0 . 52 ( 0 . 31 to 1 . 21 ) in a mature programme . By contrast , Feikin et al . estimate 0 . 17 ( 0 . 13 to 0 . 23 ) from surveillance data ( S5 Fig ) . However , the estimate for the observed impact is based on the extrapolation of a pre vaccination increase in IPD which runs the potential risk of overestimation of the vaccine impact [42] . Without accounting for pre vaccination trends the IRR from routine IPD surveillance is estimated at 0 . 46 ( 0 . 32 to 0 . 65 ) , as was estimated elsewhere [12 , 43] , and is in line with our prediction . While only in Norway a significant reduction of NVT IPD in children was estimated it may reflect the inherent limitations of an ecological design to estimate the impact of vaccination after the start of routine vaccination; its susceptibility to other factors that impact on IPD incidence . We further studied the sensitivity of the models predictive value to the delay between the start of vaccination and the impact estimate from surveillance . As our base case we presented all analysis in comparison to the observed IRR three years after the introduction of PCV7 . Three years were chosen as a trade-off between allowing sufficient time for herd effects and serotype replacement to stabilise and including as many data sets in the analysis as possible . We find that the predictive ability of our model is not affected by the choice of longer post vaccination periods ( S6 Fig ) . As estimates of PCV7 impact from low- and middle-income countries are only recently becoming available , we could only validate our method against the use of PCV7 in high-income countries and its effects in children . The validity of our results should hold for other pneumococcal disease endpoints , including non-bacteraemic pneumonia , other age groups , in particular the elderly , and conjugate vaccine formulations of higher valency; data to validate this expectation are only recently becoming available . The importance of carriage data to estimate the dynamic effects of vaccination has been increasingly recognised [44] . However , still only limited information on nasopharyngeal carriage in older age groups , particularly in elderly is collected [24] and in many settings no carriage information is available . The potential importance of pneumococcal carriage for supporting the licensing of pneumococcal conjugate vaccines has recently been outlined [44] . We add to this by making the case for data on pneumococcal carriage to supplement that on IPD for predicting the likely impact of PCV vaccination . We present evidence that the heterogeneity in the observed impact of PCV7 on IPD is largely due to a combination of carriage and IPD of VTs and that other factors including vaccine schedule , coverage or the use of catch up campaigns have a minor role . This method could prove useful to assess the potential impact of future conjugate vaccine formulations , aid with the impact assessments of PCVs into countries where population based surveillance of IPD is not possible , and provide an impact prediction tool to countries who have not yet introduced PCV . With a growing body of evidence on the impact of different PCV formulation and from low income countries further validation will be essential to determine the full potential of this simple model .
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Pneumococcal vaccines ( PCVs ) that protect children against 7 , 10 and 13 of the most pathogenic pneumococcal serotypes have substantially reduced childhood morbidity and mortality . A recent analysis that evaluated the impact of the 7 valent PCV in multiple high income settings in North America , Europe and Oceania found that the magnitude of all-serotype invasive pneumococcal disease reduction varied greatly between settings ( 24%-83% ) . We explored potential sources for that variation , including differences in disease epidemiology before vaccination , vaccine coverage , vaccine schedules and the use of catch-up campaigns for introduction . We find that differences in reported disease impact among mature PCV programmes are likely to be unrelated to the differences in the vaccine programme but can be predicted from a simple model based on pre-vaccination epidemiology , in particular the proportion of vaccine serotypes detected among patients with invasive pneumococcal disease and the proportion of vaccine serotypes that are found in the nasopharynx of healthy individuals . This model presents a useful tool to estimate the potential impact of PCVs ( as a relative rate reduction ) , highlights the essential role of pre-vaccination carriage in healthy individuals for disease impact of PCVs and can estimate the prevented burden of disease where disease surveillance is unavailable .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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The Serotype Distribution among Healthy Carriers before Vaccination Is Essential for Predicting the Impact of Pneumococcal Conjugate Vaccine on Invasive Disease
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A wide range of biological processes are regulated by sumoylation , a post-translational modification involving the conjugation of SUMO ( Small Ubiquitin-Like Modifier ) to protein . In Arabidopsis thaliana , AtSIZ1 encodes a SUMO E3 ligase for SUMO modification . siz1 mutants displayed defective secondary cell walls ( SCWs ) in inflorescence fiber cells . Such defects were caused by repression of SND1/NST1-mediated transcriptional networks . Yeast two-hybrid assay indicated that SIZ1 interacts with the LBD30 C-terminal domain , which was further confirmed using bimolecular fluorescence complementation and immunoprecipitation . Mass spectrometry and co-immunoprecipitation indicated that SIZ1 mediates SUMO conjugation to LBD30 at the K226 residue . Genes controlling SCW formation were activated by the overexpression of LBD30 , but not in the LBD30 ( K226R ) mutant . LBD30 enhancement of SCW formation resulted from upregulation of SND1/NST1-mediated transcriptional networks . This study presents a mechanism by which sumoylation of LBD30 , mediated by SIZ1 , regulates SCW formation in A . thaliana .
Plant cells are surrounded by walls that provide structural support and regulate growth . All plant cells form primary cell walls , which are synthesized during cell expansion and differentiation , while specialized cell types can also deposit a secondary wall on the inside of the primary wall once cell elongation has finished . Examples of the SCW are found in vascular tissues , such as in fiber cells and tracheary elements , as well as in other mechanically important tissues , for example , collenchyma cells . The major constituents of the SCW are cellulose , non-cellulosic polysaccharides and lignin . These polymers are cross-linked , providing cell walls with both mechanical strength and hydrophobic properties . Such characteristics are needed for upright growth , long-distance transport of solutes[1] , selectivity of nutrient and water transport in root endodermis[2] , defense against pathogens[3] , and phenomena such as pod shattering[4] , anther dehiscence[5] and flower abscission [6] . In the cells undergoing SCW biosynthesis , SCW cellulose synthase complexes in the plasma membrane produce β- ( 1–4 ) glucan chains that assemble into microfibrils in the orientation guided by cortical microtubules [7] . The microfibrils are extruded into the cell wall matrix and interact with Golgi-synthesized hemicellulose , generally xylan and mannan , to form a stable network [8] . Lignin monomers are transported to the space within the polysaccharide network where they are oxidized and polymerized to make matured SCW [9] . Genes responsible for the SCW biosynthesis process are regulated by a group of transcriptional activators and repressors , which constitute a hierarchical regulatory network controlling SCW formation in various locations [10] . For example , SND1 and NST1 control SCW deposition in fiber cells [11–13] while VND6 and VND7 are responsible for vessel cells SCW formation in A . thaliana [14 , 15] . Increasingly , post translational regulation of SCW formation is also being studied . For example , N-glycosylation regulates the enzyme activity of PtrMAN6 in suppression of SCW formation in Populus [16] . The phosphorylation of cellulose synthase AtCesA7 affected SCW cellulose biosynthesis in A . thaliana [17] . Sumoylation , conjugation of SUMO to substrate proteins , is a reversible and dynamic protein modification that regulates a range of biological processes [18] . SUMO conjugation forms a covalent bond between the C-terminal glycine carboxyl group of SUMO and the ε-amino group of a lysine residue , mostly occurring at the consensus motif ΨKXD/E ( Ψ , hydrophobic amino acid; K , lysine for conjugation; X , any amino acid; D/E , acidic amino acids ) of target proteins [19] . Completion of sumoylation requires an enzymatic cascade of SUMO E1 activating enzyme , SUMO E2 conjugating enzyme and SUMO E3 ligase[18] . This process can be reversed through desumoylating proteases [20] . Generally sumoylation results in either stabilization of the target protein by protecting it against ubiquitylation [21 , 22] or destabilization by promoting the sumoylated protein for proteoasomal degradation[23] . Sumoylation can also alter protein cellular localization and modulate protein function or enzymatic activity[24] . In plants sumoylation plays a variety of roles in stress responses , growth , flowering , photomorphogenesis , nutrient homeostasis , and other biological processes[25 , 26] . AtSIZ1 is an SP-RING ( SIZ/PIAS-type ) E3 ligase identified from Arabidopsis thaliana . It contains five structural domains including SAP ( Scaffold attachment factor A/B//acinus/PIAS ) domain , PINIT domain , SP-RING ( SIZ/PIAS-RING ) domain , SXS domain and PHD ( Plant Homeodomain ) [27] . These domains determine AtSIZ1 subcellular localization , enzyme activity , and action in responding to biotic and abiotic stresses [27] . AtSIZ1 plays various roles in growth [28] , flowering[29 , 30] , light response[31 , 32] , immunity[33 , 34] and metabolism of nutrient elements , such as phosphate[35] , nitrogen [36] and copper[37] . AtSIZ1 is also implicated in sugar signaling [38] . Recent studies have shown that AtSIZ1 meidated sumoylation is involved in plant response to various stresses[26] , including cold[39] , heat stress[40] , drought stress[41] as well as in signaling processes such as abscisic acid[42 , 43] , salicylic acid[44] , auxin[45] and gibberellin signaling pathways[46] . In this study , we observed SCW defects in the A . thaliana siz1 mutants . Genetic and biochemical analyses indicate that the SCW defects were caused by failure of the LBD30 sumoylation which was mediated by SIZ1 . The study reveals a mechanism that sumoylation functions as a regulatory expedient in SCW formation in A . thaliana .
We screened an A . thaliana T-DNA insertion pool ( Col-0 background ) for the phenotypic abnormality of SCW formation in the inflorescence stem through microscopy observation . Two T-DNA insertion alleles , siz1-2 and siz1-3 , which impair AtSIZ1 SUMO E3 ligase function [35] ( Fig 1A ) , displayed morphological The siz1 mutant plants were smaller with shorter inflorescence stems compared to WT ( Fig 1A ) . To determine whether SIZ1 directly affects SCW formation , we employed an RNAi strategy to inhibit AtSIZ1 expression specifically in the cells forming secondary walls . The promoter of the fiber cell-specific SND1 was used to drive SIZ1-RNAi in PSND1AtSIZ1-i transgenic plants ( S1A Fig ) . In transgenic lines , expression of AtSIZ1 was suppressed by about 50% ( S1E Fig ) . The wall thickness of the fiber cells in inflorescence stem was reduced compared to WT ( S1B , S1C and S1F Fig ) . These suggest that SIZ1 plays a role in SCW formation in inflorescence fiber cells . To investigate how SCW formation is changed in the siz1 mutants , we analyzed the chemical composition of their cell walls and examined expression of the SCW-related genes . In inflorescence stem crystalline cellulose and lignin were reduced by more than 20% in siz1 plants compared to WT ( Fig 2A and 2B ) . The xylose from non-cellulosic polysaccharides was also significantly decreased in siz1 plants ( Fig 2C ) . Expression of the genes responsible for SCW formation was significantly suppressed in siz1 plants . These genes included transcription factor genes ( SND1 , NST1 , MYB46 and MYB103 ) [1] ( Fig 2D ) , SCW cellulose synthase genes ( CesA4 , CesA7 and CesA8 ) [1] ( Fig 2E ) , lignin biosynthesis genes ( PAL1 , CCoAOMT and 4CL1 ) [1] ( Fig 2F ) and xylan biosynthesis genes ( IRX8 , IRX9 and IRX14 ) [1] ( Fig 2G ) . These results indicated that AtSIZ1 is involved in regulating the transcriptional network that controls SCW formation . AtSIZ1 promoter was active in cortex cells and interfascicular fibers of the inflorescence stem undergoing SCW formation ( S2 Fig ) . SIZ1 is a nuclear-localized protein [27] and functions in facilitating SUMO conjugation to target proteins [47] . Using AtSIZ1 as the bait against a cDNA library made from A . thaliana inflorescence stem undergoing SCW formation , we conducted yeast two-hybrid ( Y2H ) screening to identify its target proteins for sumoylation . Among 191 identified candidates , four were found to be different parts from the ASYMMETRIC LEAVES2/LATERAL ORGAN BOUNDARIES DOMAIN ( AS2/LBD ) protein , LBD30 , encoded by At4g00220 locus [48 , 49] . We examined LBD30 expression in public databases and found that it is highly expressed in the inflorescence stem ( S3 Fig ) . LBD/AS2 family proteins have a characteristic LOB domain at N terminus that possess DNA-binding ability [49 , 50] . We re-examined the interaction between AtSIZ1 and LBD30 in an Y2H system and found that AtSIZ1 interacted with LBD30 through its C-terminus ( LBD30-C , amino acids 121–228 ) ( Fig 3A ) . This interaction was verified through SIZ1 interacted with LBD30 , but expression of LBD30 was not altered in siz1 mutants ( S4 Fig ) . LBD30 is predicted to contain a sumoylation motif ( ΨKXE ) with K226 as a potential SUMO conjugation residue ( S1 Table ) and showed a high possibility to be sumoylated among a list of SCW formation-related proteins [1] ( S1 Table ) . Then we examined whether LBD30 could be SUMO conjugated at the ΨKXE motif . Using tandem mass spectrometry analysis , a mutant AtSUMO1 ( T91R ) protein , which allows production of a signature peptide containing a diglycine remnant at the sumoylation site[51] , was identified at K226 in LBD30 ( Fig 4A ) . To verify this sumoylation , recombinant LBD30 was generated and the sumoylated LBD30 was detected in sumoylation assay ( Fig 4B ) . When LBD30 was mutated to generate a K226R variant ( LBD30K226R ) , the substitution of K226 to R resulted in failure of SUMO1 conjugation to LBD30 ( Fig 4C ) without affecting its nuclear localization ( S5 Fig ) . Furthermore , we examined if the mutant LBD30 can be sumoylated by AtSIZ1 in planta . By combinational expression of LBD30 or LBD30K226R , AtSIZ1 and AtSUMO1 in tobacco leaves , immunoblotting indicated only LBD30 is SUMO-conjugated ( S6 Fig ) . Next , AtSUMO1 and LBD30 or LBD30K226R were co-expressed in siz1-2 and WT A . thaliana . AtSUMO1 conjugation to LBD30 was only detected in the transgenic plants with WT background expressing LBD30 and AtSUMO1 ( Fig 4D ) . These demonstrated that SIZ1 mediates LBD30 sumoylation at the K226 residue . We investigated the effect of LBD30 sumoylation in the transgenics overexpressing LBD30 and LBD30 ( K226R ) . Overexpression of LBD30 caused drastic phenotypic changes , severe dwarfism , short petioles and downward curled leaves . Ectopic lignin deposition was detected in cotyledons in 24 out of 28 T1 transgenic plants ( Fig 5A–5C ) . In contrast , overexpression of LBD30K226R showed little phenotypic changes ( Fig 5A and 5D ) . Similarly , expression of LBD30 in siz1-2 caused no phenotypic change in 28 transgenic plants out of 36 T1 plants and minor changes in remaining 8 plants compared to the siz1-2 plants ( Fig 5A , 5E and 5F ) . LBD30 sumoylation played a role in development and secondary cell wall biosynthesis . Then , we investigated whether the SCW defects in siz1 plants is caused by failure of LBD30 SUMO modification . We examined the transcripts of SND1 and NST1 in the transgenics overexpressing LBD30 ( S7A Fig ) . The transgenic plants were unable to develop normal inflorescence stem ( Fig 5A ) but expression of SND1 and NST1 was drastically up-regulated in the 2 weeks-old seedlings ( Fig 5G ) . This upregulation of SND1 and NST1 expression was insignificant in the transgenics carrying LBD30K226R or in the transgenics overexpressing LBD30 in siz1 mutant background ( Fig 5G ) . When LBD30 was overexpressed in nst1/snd1 double mutant[11–13] , ectopic lignin deposition in cotyledons was not detected in the transgenics ( 31/31 T1 plants ) and the nst1/snd1 double mutant ( S7B and S7C Fig ) . On the other hand , we evaluated the effect of LBD30 sumoylation on SND1 and NST1 expression using a dual luciferase assay in A . thaliana protoplasts . The effecter was constructed by using a 35S promoter to drive expression of LBD30 and LBD30 ( K226R ) . A firefly luciferase driven by SND1 or NST1 promoter was used as a reporter ( Fig 5H and 5I ) . LBD30 showed a significantly higher activity in activation of SND1 or NST1 promoter than LBD30 ( K226R ) ( Fig 5H and 5I ) , suggesting that LBD30 SUMO conjugation affected SND1 and NST1 expression . Thus , the SUMO modification of LBD30 played a role in regulating SCW formation through the SND1/NST1-directed transcriptional network .
In higher plants all cells form primary cell wall . In some type cells , additional SCWs are formed inside the primary wall , providing plants with mechanical support for erect growth and channels for long-distance transportation of water , nutrients , and photosynthetic products . Formation of the SCWs in various type cells need to be precisely regulated in a spatio-temporal manner during growth and development [52] . To ensure a precise deposition of SCWs in some type cells , multiple levels of regulation have to be developed in plants . Disturbance of the regulatory networks causes abnormal growth and development [1] . At the transcriptional level , complex regulatory networks are involved in SCW formation [8 , 53] . SCW formation in different cell types is initiated through cell type-specific transcription regulators [11–15] . Many signaling molecules regulating SCW formation have yet-to-be characterized [54] . At the protein level , post-translation modifications , such as protein phosphorylation and N-glycosylation [16 , 17] , are also being studied for their roles in regulating SCW formation . While a large number of proteins are modified with SUMO-conjugation and such modification affects a variety of biological processes [18] , this study presents a detailed picture of how sumolyation can lead to the upregulation of SCW formation . Specifically , we found LBD30 sumoylation is required for activation of the SND1/NST1-mediated transcriptional networks in SCW formation . AtSIZ1-mediated sumoylation is involved in a variety of growth and development processes such as flowering , response to light , immunity and nutrient element metabolisms in A . thaliana [31–34 , 36 , 55] . In this study , we observed that siz1 mutants displayed defective SCWs in interfascicular fiber cells . Analysis indicated that SIZ1 interacted with LBD30 and catalyzed its sumoylation at K226 position in the sumoylation motif . LBD30 is a transcription factor belonging to the Lateral Organ Boundaries Domain ( LBD ) family[56 , 57] . LBD30 and its homolog LBD18 in A . thaliana were preferentially expressed in vascular tissues and LBD18 played a role in regulating tracheary element differentiation[57] . Defective SIZ1 or mutated LBD30 at K226 position led to loss of LBD30 function during the formation of SCW in interfascicular fiber cells . The evidence indicated that LBD30 , when it was sumoylated by SIZ1 , played a role in activating the SND1/NST1-mediated transcriptional networks ( Fig 6 ) which regulate SCW formation in the fiber cells of inflorescence stem . Generally , stress conditions cause activation of SCW formation [58 , 59] . Several transcription factors sumoylated by AtSIZ1 are related to stress responses , including ICE1 in freezing stress[39] , HsfA2 in heat stress[60] , PHR1 in phosphate ( Pi ) deficiency[35] , MYB30 and ABI5 in the abscisic acid-dependent drought stress[42 , 43] . It is worthy of further study whether LBD30 sumoylation acts as a linking device between stress responses and SCW formation . Generally LBD family proteins regulate plant development through interaction with other transcription factors [50] . A number of transcription factors have been identified to bind to SND1 and NST1 promoters to activate their expression[1 , 61] . In this study , we found that transcription factor LBD30 was sumoylated by SIZ1 and such protein modification affected activation of the SND1/NST1-mediated transcriptional networks for SCW formation in fiber cells . Though it remains to be investigated how LBD30 sumoylation performs its function in activation of the transcriptional networks , one possibility is that LBD30 sumoylation may affect the transcription factor interactions that are necessary for activation of SND1/NST1 expression . This possibility might justify the observation that LBD30 sumoylation showed different strength of effect on SND1 and NST1 expression between transgenics and protoplast system . Interaction of LBD30 with other factors in planta affected the SND1 and NST1 promoter activity . The finding that LBD30 sumoylation acts as another layer of regulation to aid in the precise control of SCW formation provides additional insight into a key process that is essential for upright growth and the long-distance transport of water and solutes in plants and has implications in cell wall modification via regulation of LBD30 sumoylation in crop improvement .
The A . thaliana Col-0 ecotype ( WT ) and the T-DNA insertion mutant lines , siz1-2 ( SALK_065397 ) [35] , siz1-3 ( SALK_034008 ) [35] and snd1/nst1 double mutant ( CS67921 ) [11–13] , were grown in a phytotron at 22°C with a photoperiod of 16 h of light and 8 h of darkness . Transformation of A . thaliana was performed using the Agrobacterium tumefaciens-mediated floral dip method [62] . Transgenic plants were selected on MS medium containing 50 μg/ml hygromycin . Positive T2 transgenic plants were used for further analysis , with the exception of LBD30 overexpressing plants in the Col-0 background , where T1 plants were used because the T1 transgenic displayed severe growth defects and hardly produced seeds . cDNAs for AtSIZ1 ( At5g60410 ) , LBD30 ( At4g00220 ) , AtSUMO1 ( At4g26840 ) and the promoter regions of AtSIZ1 ( 3535bp ) , SND1 ( 2858bp ) and NST1 ( 2913bp ) were PCR-amplified from a cDNA pool of A . thaliana as well as from genomic DNA with specific primers listed in S2 Table . For the Y2H assay , the coding region of LBD30 and AtSIZ1 were inserted respectively into the pGBKT7 and pGADT7 plasmids ( Clontech ) and introduced into AH109 yeast cells ( Clontech ) following the manual . For BiFC analysis , LBD30-YC and YN-AtSIZ1 were constructed as previously described [63] and mobilized into A . tumefaciens strain GV3101 and transformed into Nicotiana benthamiana tobacco leaf cells [63] . For purification of recombinant proteins , the LBD30 , a mutated LBD30 ( K226R ) and AtSIZ1 coding regions were cloned into the pET-28b ( Novagen ) and pGEX-4T-1 ( GE Healthcare ) plasmids to produce the His6-LBD30 , His6-LBD30 ( K226R ) and GST-AtSIZ1 fusion proteins , respectively . The site directed mutagenesis of LBD30 ( K226R ) was generated according to Hieff MutTM Site-Directed Mutagenesis Kit ( Yeasen Biotech ) . For protein expression in plants , the full coding regions of AtSIZ1 , LBD30 , LBD30 ( K226R ) and AtSUMO1 were subcloned into the binary pCambia 1300 vector to produce chimeric MYC-AtSIZ1 , LBD30-HA , LBD30 ( K226R ) -HA and FLAG-AtSUMO1 fusions under the control of the constitutive CaMV 35S promoter . These constructs were coexpressed in Nicotiana benthamiana tobacco leaves for transient expression , and transformed into A . thaliana to produce stably transformed plants . Constructs encoding LBD30 or LBD30 ( K226R ) and green fluorescent protein ( GFP ) fusion proteins under the control of the CaMV 35S promoter were generated and introduced into A . thaliana protoplasts [64] to investigate subcellular localization . For transcriptional activation analysis , the coding regions of LBD30 and LBD30 ( K226R ) and the promoter regions of the SND1 and NST1 genes were cloned into the effector ( 35S-transcription factor ) and reporter ( firefly luciferase ) vectors ( pGreenII vector , Promega ) and then coexpressed in A . thaliana protoplasts[64] . For analysis of AtSIZ1 expression , an AtSIZ1 promoter fragment was cloned and fused to a β-glucuronidase ( GUS ) reporter gene in the pCambia1301 vector for A . thaliana transformation . To investigate the function of AtSIZ1 in inflorescence stems , two different genomic DNA fragments specific to AtSIZ1 were amplified separately to form hairpin structures under the control of the SND1 gene promoter . These constructs were designed to cause RNAi suppression ( SND1promoter-AtSIZ1RNAi1 and SND1promoter-AtSIZ1RNAi2 ) specifically in A . thaliana inflorescence stems . The basal internodes of inflorescence stems of 8-week-old plants with the same flowering date were collected as described before . Briefly , the internodes were fixed in FAA overnight and embedded in paraffin ( Sigma-Aldrich 18635 ) after dehydration through a graded ethanol series . Ten-micrometer-thick sections were cut and stained with toluidine blue for light microscopy . Free-hand cross sections of A . thaliana inflorescence stems were stained with 0 . 5% phloroglucinol ( Sigma-Aldrich P3502 ) ( w/v ) in 12% HCl for 3 min , and immediately observed under a bright-field microscope ( OLYMPUS BX53 ) . For transmission electron microscopy , ultrathin sections were cut and observed as described [65] . To visualize lignin auto-fluorescence under UV light and the sub-cellular localization of GFP-fusion proteins , A . thaliana cotyledons were grown on MS plates and A . thaliana leaf protoplasts were observed using a fluorescent microscope ( OLYMPUS BX53 ) . For the BiFC analysis , tobacco leaf cells were stained with DAPI [66]and visualized using a confocal microscope ( LSM 510 META; Zeiss ) . Fluorescence stems from at least three independent 8-week-old A . thaliana WT or mutant plants were collected and ground in liquid nitrogen to a fine powder to prepare alcohol insoluble residue ( AIR ) as previously described [67] . After the de-starched procedure [67] , the crystalline cellulose content and monosaccharide composition were analyzed according to a previously published protocol [68] . The lignin content was determined following the methods in [69] . Total RNA isolated from the lower center part of the inflorescence stem of 4-week-old A . thaliana plants and whole seedlings of 2-week-old WT , mutants and transgenic plants were extracted using the E . Z . N . A . Total RNA Kit ( Omega ) according to the manufacturer’s instructions . cDNA was synthesized by treatment with reverse transcriptase and oligo ( dT ) primer ( TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix , Transgene Biotech ) and quantitative PCR assays were conducted with a MyiQ real-time PCR detection system ( Bio-Rad ) using SYBR Green ( TransStart Top Green qPCR MIX ) following the user manual . The A . thaliana ACT2 gene ( AT3G18780 ) was used as an internal control to normalize the data . The mathematical analysis for qPCR quantification was delta-delta Ct method [70] . The quantitative PCR ( qPCR ) experiment was performed in biological triplicates . Free-hand cross-sections of the lower internodes of the inflorescence stems from 4 week old AtSIZ promoter-GUS transgenic A . thaliana were examined for GUS activity as previously described [71] . To identify AtSIZ1 interacting proteins , a Y2H library was generated using cDNA derived from 4-week-old A . thaliana inflorescence stems and used to screen for target proteins , using the Make Your Own Mate & Plate Library System ( Clontech ) , according to the manufacturer`s directions . For the BiFC analysis , the constructs were transformed into Agrobacterium strain GV3101 , and the resulting strains were used to transform N . benthamiana leaf cells , either individually or in combination . The leaves were examined after 48 h of incubation . To investigate the physical interaction between AtSIZ1 and LBD30 in vitro , recombinant His6-LBD30 and GST-AtSIZ1 proteins were expressed in Escherichia coli and purified with Ni-NTA Agarose ( Qiagen ) and Pierce GST Agarose ( Thermo Scientific ) , according to the manufacturer’s instructions . GST and GST-tagged AtSIZ1 proteins from the cell lysates were first immobilized on the GST Agarose ( Thermo Scientific ) . After washing away unbound proteins with 1×PBS , the immobilized GST and GST-AtSIZ1 proteins were incubated with the cell lysate of Escherichia coli expressing His6-LBD30 . After several washing steps with 1×PBS , the complexes were eluted with 2×SDS loading buffer and boiled at 100°C for 5 min . The eluted proteins were separated by SDS-PAGE , transferred to a PVDF membrane and the protein was immunoblotted with an anti-His antibody ( 1:5000 dilution , Abmart ) . In vivo AtSIZ1 and LBD30 interactions were analyzed by co-immunoprecipitation ( co-IP ) . Myc-tagged AtSIZ1 and HA-tagged LBD30 were expressed transiently in tobacco leaf cells . Proteins were extracted by grinding the leaves in liquid nitrogen and thawed in extraction buffer [50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1mM EDTA , 0 . 2% Triton X-100 ( Sigma Aldrich ) , 10% glycerol , 1mM PMSF , 2% PVPP ( polyvinylpolypyrrolidone ) and 1x concentration of protease inhibitor cocktail ( Roche ) ] for 30 min . The homogenate was then filtered through a 0 . 22 μm filter membrane ( Millipore ) and 1mL of the filtrate was incubated with 50 μL agarose conjugated anti-Myc mouse monoclonal antibody ( Abmart ) or 50 μL anti-HA rat monoclonal antibody Affinity Matrix ( Roche ) for 3 h at 4°C . The beads were washed three times with wash buffer ( 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 0 . 2% Triton X-100 ) , and the bound proteins were eluted with 2×SDS-PAGE loading buffer and boiled at 100°C for 5 min . The eluted proteins were immunoblotted as above and incubated with anti-HA mouse monoclonal antibody ( 1:3000 dilution , Abmart ) or anti-Myc mouse monoclonal antibody ( 1:3000 dilution , Abmart ) . The in vitro sumoylation was performed using the SUMOlinkTM SUMO-1 Kit ( Active Motif ) . Briefly , recombinant His6-LBD30 and His6-LBD30 ( K226R ) proteins were expressed in E . coli and purified . A total of 3 μg of target protein was added to 20 μl reaction buffer and incubated at 30°C for 3 h . The reaction was stopped by adding 10 μl of 2×SDS-PAGE loading buffer . Sumoylated of His6-LBD30 was detected by immunoblot analysis using an anti-His mouse monoclonal antibody ( 1:5000 dilution , Abmart ) and a SUMO-1 rabbit antibody ( 1:2000 dilution , Active Motif ) . The reaction mixture was also separated by SDS-PAGE . After staining with Coomassie Blue R-250 , the sumoylated His6-LBD30 protein band was cut into 1 mm wide pieces for digestion and liquid chromatography-tandem mass spectrometry ( LC-MS ⁄MS ) analysis of the LBD30 sumoylation site . Protein digestion for LC-MS/MS analysis was performed by the Beijing Protein Institute . Briefly , the protein bands were destained with 50% v/v acetonitrile ( ACN ) [72] and 25 mM ammonium bicarbonate and dried in 100% ACN and the gel slices were incubated with a 10 ng μl-1 trypsin solution in 25 mM ammonium bicarbonate at 37°C for 12h . The extracts were then dried in a stream of N2 and resuspended in 5% ACN in 0 . 1% v/v formic acid FA . LC-MS ⁄MS analysis was performed using an Ultimate3000 liquid chromatography system ( Dionex ) connected to a Q Exactive mass spectrometer ( Thermo Scientific ) as decribed previously [72] with modifications . The extracts were separated by a C18 reverse-phase column with a 1 hour gradient of mobile phase ( phase A , 5% ACN in 0 . 1% FA; phase B , 95% CAN in 0 . 1% FA ) at a flow rate of 300 nL / min . The separated sample was then injected into the mass spectrometer and a method of full scans were acquired with AGC target value of 1E6 , resolution of 70 , 000 FWHM at 200 m/z , and maximum ion injection time ( IT ) of 100 ms . The mass spectura were extracted by BioWork version 3 . 3 . 1 sp1 ( Thermo Fisher ) . All MS/MS samples were analyzed using Mascot software ( Marix Science ) . For the in vivo sumoylation assay , the Myc tagged AtSIZ1 , the FLAG-tagged AtSUMO1 and the HA-tagged LBD30 or LBD30 ( K226R ) were expressed in tobacco leaves . The FLAG-AtSUMO1 transgenic A . thaliana plants ( Col-0 background ) were crossed with LBD30-HA or LBD30 ( K226R ) -HA transgenic A . thaliana plants ( siz1-2 background ) . F2 progeny of transgenic plants with WT and siz1-2 background overexpressing FLAG-AtSUMO1 and LBD30-HA or LBD30 ( K226R ) -HA were obtained . Total proteins were extracted and immunoprecipitated with an anti-FLAG mouse monoclonal M2 affinity gel ( Sigma-Aldrich ) . The sumoylated LBD30 was detected by immunoblotting with an anti-HA Rat monoclonal high-affinity antibody ( 1:2000 dilution , Roche ) after IP . Protoplasts used in the transient effector-reporter analysis were isolated from 2-week-old A . thaliana seedlings as previously described [64] . The coding sequences of LBD30 and LBD30 ( K226R ) were cloned into the effector plasmid . The promoters of SND1 and NST1 were cloned into the firefly luciferase reporter vector ( pGreenII , Promega ) . The Renilla luciferase gene driven by the CaMV 35S promoter served as a control to normalize for transformation efficiency . Luciferase activities were measured with a dual-luciferase reporter assay system ( Promega ) .
|
Secondary cell wall ( SCW ) is essential for upright plant growth and long-distance transport of water and solutes . Regulation of SCW formation at the transcriptional level has been much studied . Here we show that AtSIZ1 , a small ubiquitin-related modifier ( SUMO ) E3 ligase , mediates the sumoylation of transcription factor LBD30 , a process that regulates SCW formation . Elucidating a role of sumoylation in the post-translational regulation of SCW formation , complementing the body of research on regulation at the transcriptional level , provides additional insight into a key process that is essential for upright growth and the long-distance transport of water and solutes in plants .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"biotechnology",
"plant",
"anatomy",
"engineering",
"and",
"technology",
"gene",
"regulation",
"brassica",
"plant",
"science",
"model",
"organisms",
"sumoylation",
"genetically",
"modified",
"plants",
"experimental",
"organism",
"systems",
"inflorescences",
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"genetic",
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"arabidopsis",
"thaliana",
"bioengineering",
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] |
2019
|
SUMO modification of LBD30 by SIZ1 regulates secondary cell wall formation in Arabidopsis thaliana
|
Most available drugs against visceral leishmaniasis are toxic , and growing limitations in available chemotherapeutic strategies due to emerging resistant strains and lack of an effective vaccine against visceral leishmaniasis deepens the crisis . Antineoplastic drugs like miltefosine have in the past been effective against the parasitic infections . An antineoplastic drug , cisplatin ( cis-diamminedichloroplatinum II; CDDP ) , is recognized as a DNA-damaging drug which also induces alteration of cell-cycle in both promastigotes and amastigotes leading to cell death . First in vivo reports from our laboratory revealed the leishmanicidal potential of cisplatin . However , high doses of cisplatin produce impairment of kidney , which can be reduced by the administration of antioxidants . The present study was designed to evaluate the antileishmanial effect of cisplatin at higher doses ( 5 mg and 2 . 5 mg/kg body weight ) and its combination with different antioxidants ( vitamin C , vitamin E and silibinin ) so as to eliminate the parasite completely and reduce the toxicity . In addition , various immunological , hematological and biochemical changes induced by it in uninfected and Leishmania donovani infected BALB/c mice were investigated . A significant reduction in parasite load , higher IgG2a and lower IgG1 levels , enhanced DTH responses , and greater concentration of Th1 cytokines ( IFN-γ , IL-2 ) with a concomitant down regulation of IL-10 and IL-4 pointed towards the generation of the protective Th1 type of immune response . A combination of cisplatin with antioxidants resulted in successful reduction of nephrotoxicity by normalizing the enzymatic levels of various liver and kidney function tests . Reduction in parasite load , increase in Th1 type of immune responses , and normalization of various biochemical parameters occurred in animals treated with cisplatin in combination with various antioxidants as compared to those treated with the drug only . The above results are promising as antioxidants reduced the potential toxicity of high doses of cisplatin , making the combination a potential anti-leishmanial therapy , especially in resistant cases .
Pentavalent antimonial compounds like sodium stibogluconate and N-methylglucamine antimoniate have been the mainstay of antileishmanial therapy [1] . They remain the conventional treatment of children and adults all over the world except in Bihar ( India ) where Sb is no longer useful owing to high failure rates due to resistance [2] , [3] and also have the disadvantage of toxicity , parenteral administration and need for long duration of therapy [4] . Secondary treatment regimens with amphotericin B and pentamidine are effective but these are also parenteral , have to be administered for prolonged periods and therefore , are expensive and potentially toxic [2] . Liposomal formulations of amphotericin B target the cells that host the parasite and have decreased nephrotoxicity but are prohibitively costly . Paromomycin have advantages of high level of efficacy and low rates of adverse reaction , but the drawback is its high cost [5] . Oral drugs sitamaquine ( WR 6020 ) and miltefosine are the two promising oral antileishmanial compounds . Miltefosine ( hexadecylphosphocholine ) is a membrane activating alkyl phospholipid , having cure rates of approximately 90–95% . It has an obvious advantage in being an active oral agent and hospitalization is thus not required [3] but is teratogenic in animals [3] so cannot be used in pregnant women . Considering the fact that therapeutic interventions against visceral leishmaniasis ( VL ) are limited and facing serious concerns of toxicity , high cost and emerging resistance , there is a greater interest in new drug developments which are cost effective , efficient and easily available to people suffering from leishmaniasis . An antineoplastic drug , cisplatin ( cis-diamminedichloroplatinum II; CDDP ) a platinum-containing compound , is recognized as a DNA-damaging drug [6] and is known to augment the cytotoxic T-lymphocyte mediated antitumor immunity [7] , [8] . It has been found to have antileishmanial activity in vitro at a concentration of 0 . 25–64 µM and has been shown to lead towards an apoptosis like cell death of both promastigotes and amastigotes [9] . First in vivo report from our laboratory also showed a significant reduction in parasite load and enhanced DTH responses which suggested the generation of the cell-mediated immune responses . Though the protective efficacy of the drug [10] was demonstrated , it could not completely eliminate the parasite at low dosages of 0 . 5 and 1 mg/kg b . wt . In spite of its good antineoplastic activity against various cancer treatments , its clinical use was rapidly limited due to unexpected and very severe renal toxicity [11] . The kidney selectively accumulates cisplatin and its analogues to a higher degree than other organs , probably through mediated transport [12] . Cisplatin treatment also induces extensive death of cells in the proximal and distal tubules and loop of Henle [13] . High doses of cisplatin produce impairment of kidney and are recognized as the most important dose limiting factor [14] . Mild nephrotoxicty has also been reported with the cisplatin at a dose of 1 mg/kg body wt . [10] . Nephrotoxicity of cisplatin has been reported to be reduced by the administration of antioxidants [15] , [16] . At higher doses , cisplatin may be used in combination with antioxidants which might suppress the drug-induced toxic effects , and may help in complete elimination of the parasite from the host reticulo-endothelial system . All these factors led to designing of the present study where we attempted to test the leishmanicidal activity of cisplatin at high dosages of 2 . 5 and 5 mg/kg b . wt . Since , cisplatin causes nephrotoxicity at higher dosages , we have studied the nephroprotective potential of different antioxidants i . e . vitamin C , vitamin E and silibinin .
Leishmania donovani promastigotes of strain MHOM/IN/80/Dd8 , originally obtained from the London School of Hygiene and Tropical Medicine , London , were used for the present study and maintained in vitro at 22±1°C in modified NNN medium by serial subcultures after every 48–72 h . 5–6 weeks old inbred BALB/c mice , weighing 20–25 g were obtained from IMTECH and Central Animal House of Panjab University , India . They were fed with water and mouse feed ad libitum . Experiments were carried out according to the guidelines of the Committee for the purpose of Control and Supervision of Experiments on Animals ( CPCSEA , Registration No . 45/1999/CPCSEA ) . The ethical clearance for conducting various experiments mentioned in the study on BALB/c mice was taken from Institutional Animal Ethics Committee ( IAEC ) of the Panjab University , Chandigarh in its meeting held on 25 . 08 . 2008 ( Approval No . 1334-50/CAH/3 . 09 . 2008 ) . cis-diamminedichloroplatinum ( II ) dichloride ( CP ) was purchased from Sigma Aldrich Co . , USA and was dissolved in distilled water to get the required concentration of 5 mg/kg body weight ( b . wt . ) and 2 . 5 mg/kg body weight ( b . wt . ) . Sodium stibogluconate ( SSG ) was dissolved in distilled water in water bath at 72°C to get the required concentration of 40 mg/kg b . wt . Vitamin C ( Ascorbic acid ) , vitamin E [ ( ± ) -α-Tocopherol] and Silibinin were also purchased from Sigma Aldrich Co . , USA . Vitamin C was dissolved in distilled water , Vitamin E was dissolved in corn oil and silibinin was dissolved in saline to get the required concentration of 200 mg/kg b . wt of vitamin C , 100 mg/100 g b . wt . of vitamin E and 200 mg/kg b . wt . of silibinin .
Mice treated with cisplatin showed significant reduction in the parasite load as compared to infected untreated controls . Cisplatin at the dosage of 5 mg/kg b . wt . showed significantly lesser parasite burden as compared to those treated with 2 . 5 mg/kg b . wt . of cisplatin . The reduction in parasite burden in cisplatin treated groups was observed to be 97% ( 5 mg/kg b . wt . ) and 91% ( 2 . 5 mg/kg b . wt . ) on 30 p . t . d . The percent decrease in parasite load was found to be in the range of 84–97% in animals treated with cisplatin along with different antioxidants . Similar trend was observed in mice treated with 2 . 5 mg/kg b . wt . of cisplatin . Results were also comparable with the group of infected animals treated with SSG ( positive control ) , where reduction in parasite burden was found to be in the range of 84–97% on 1 , 15 and 30 p . t . d . ( Fig . 1 ) . A profound delayed type hypersensitivity response was induced by cisplatin treated L . donovani infected animals , suggesting the generation of cell-mediated immune responses . The percentage increase in footpad thickness in the infected animals treated with 5 mg/kg b . wt . of cisplatin was found to be significantly higher than those treated with 2 . 5 mg/kg b . wt . of the drug . In infected animals where antioxidants were given along with cisplatin at the dosage of 5 mg/kg b . wt . , the DTH response was significantly increased from 1 to 30 p . t . d . The increase in DTH response varies from 28–46% in animals treated with cisplatin along with antioxidants . Furthermore , treatment with cisplatin ( 2 . 5 mg/kg bwt . ) in combination with different antioxidants , significantly increased the DTH response on 30 p . t . d . as compared to infected controls . When cisplatin treated animals were compared with SSG treated animals then the increase was found to be comparable ( Fig . 2 ) . The IgG levels were found to be highest in the infected untreated controls as compared to the infected cisplatin treated animals . With increase in post treatment days , the IgG antibody response in infected mice treated with cisplatin ( 5 mg/kg b . wt . and 2 . 5 mg/kg b . wt . ) was observed to be significantly lower as compared to control animals and the maximum antibody response was produced in infected untreated animals . IgG levels in the infected animals treated with 5 mg/kg b . wt . of cisplatin were found to be significantly lower than those treated with 2 . 5 mg/kg b . wt . of the drug . When cisplatin treated animals were compared with SSG treated animals then the decrease in antibody titre was found to be comparable ( Fig . 3A ) . IgG1 and IgG2a antibody responses were also evaluated by ELISA using specific anti-mouse isotype antibodies . Similarly , in addition to IgG levels , decreased IgG1 levels were found in infected cisplatin treated animals as compared to infected untreated controls . Treatment of infected animals with cisplatin ( 5 mg/kg b . wt . and 2 . 5 mg/kg b . wt . ) significantly increased the IgG2a antibody titre on 30 p . t . d . as compared to infected animals . Treatment of animals with cisplatin led to a sudden increase in the IgG2a levels on 1 p . t . d . and further decreased on 30 p . t . d . but was still higher than infected controls . The increase was more pronounced in infected animals treated with cisplatin at a dosage of 5 mg/kg b . wt . as compared to infected animals treated with cisplatin at dosage of 2 . 5 mg/kg b . wt . When antioxidants were supplemented along with cisplatin at the dosage of 5 mg/kg b . wt . , the IgG2a antibody titre was found to be higher than the infected controls . This increase in IgG2a antibody titre varies from 0 . 188±0 . 003–0 . 146±0 . 003 in cisplatin+vitC+vitE+silibinin , 0 . 187±0 . 005–0 . 148±0 . 002 in cisplatin+vitC+vitE and 0 . 187±0 . 0 . 005–0 . 147±0 . 0 . 009 in cisplatin+silibinin treated animals on 30 p . t . d . Similar trend was observed in mice treated with 2 . 5 mg/kg bwt . of cisplatin . When cisplatin treated animals were compared with SSG treated animals then the increase in antibody titres was found to be comparable ( Fig . 3B and 3C ) . The cytokine responses ( IFN-γ , IL-2 , IL-4 and IL-10 ) in supernatants of spleen cells , cultured in the presence of crude antigen ( 50 µg/ml ) , were analyzed for different groups of animals . Th1-specific cytokines , that is , IFN-γ and IL-2 levels were significantly greater in infected mice treated with cisplatin ( 5 mg/kg b . wt . and 2 . 5 mg/kg b . wt . ) as compared to infected untreated animals and uninfected treated mice . The levels of IFN-γ and IL-2 decreased from 1 p . t . d . to 30 p . t . d . , however they were still higher than infected controls . The levels of these cytokines were comparable when cisplatin treated animals were compared with cisplatin and antioxidants treated animals . Also , when the cisplatin treated animals were compared with SSG treated animals then the increase in IFN-γ and IL-2 levels were found to be comparable ( Fig . 4A and 4B ) . The levels of Th2-regulated cytokines , IL-4 and IL-10 , were minimum in the infected animals treated with cisplatin . Spleen cells from infected mice , however , produced much more IL-4 than the cisplatin treated groups . This effect of cisplatin in down-regulating IL-4 and IL-10 production was seen in almost all the animals treated along with different antioxidants on different post treatment days . The levels of IL-10 and IL-4 produced by splenocytes of infected and cisplatin treated mice were comparable to that induced by SSG treatment . ( Fig . 5A and 5B ) . A decrease in hemoglobin levels were observed in infected and uninfected cisplatin ( 5 mg/kg b . wt . and 2 . 5 mg/kg b . wt . ) treated animals as compared to normal control animals . When antioxidants were given along with the drug in infected animals , hemoglobin levels were found to be in normal range of 8–10 g/dl . Leucopenia was observed in infected and uninfected cisplatin ( 5 mg/kg b . wt . and 2 . 5 mg/kg b . wt . ) treated animals while leucocytosis was observed in infected untreated animals . TLC was found to be in normal range of 7000–12000/mm3 when antioxidants were supplemented along with cisplatin . When compared with SSG , the results were found to be comparable ( Fig . 6A and 6B ) . Quantitative estimation of SGOT and SGPT activity revealed maximum activity in infected mice treated with cisplatin followed by uninfected cisplatin treated mice and then infected untreated mice . Enzyme activity in mice treated with 5 mg/kg b . wt . of cisplatin was found to be maximum in comparison to those treated with 2 . 5 mg/kg b . wt . of the drug and thus showed a sharp decline from 1 to 30 p . t . d . when antioxidants were supplemented along with the cisplatin at the dosage of 5 mg/kg b . wt . The percent decrease in SGOT level was found to be 96–80% in cisplatin+vitC+vitE+silibinin , 97–82% in cisplatin+vitC+vitE and 97–93% in cisplatin+silibinin treated animals on 30 p . t . d . ( Fig . 7A ) . The percent decrease in SGPT level was found to be 94–91% in cisplatin+vitC+vitE+silibinin , 93–90% in cisplatin+vitC+vitE and 91 . 86–91 . 98% in cisplatin+silibinin treated animals on 30 p . t . d . as compared to infected cisplatin treated mice ( Fig . 7B ) . Similarly , SGOT and SGPT levels were found to be significantly reduced when antioxidants were given with cisplatin at the dosage of 2 . 5 mg/kg bwt . When compared with SSG , the results were found to be comparable . The alkaline phosphatase and acid phosphatase activity was found to be in normal range of 4–11 KA units and 0 to 0 . 6 U/L respectively in all groups of mice . The activity of lactate dehydrogenase was found to be maximum in infected mice treated with cisplatin followed by infected untreated mice and uninfected untreated mice . When antioxidants were supplemented along with cisplatin , the significantly reduced levels of LDH were found on 1 p . t . d . as compared to cisplatin treated mice and normal range of 114–240 IU/L was observed on all post treatment days . When compared with SSG , the results were found to be comparable ( Fig . 7C ) . Treatment of infected and uninfected mice with cisplatin ( 5 mg/kg bwt and 2 . 5 mg/kg bwt ) led to a sudden increase in the concentration of blood urea , BUN , uric acid and creatinine . The increase was more pronounced in mice treated at the dosage of 5 mg/kg b . wt . as compared to cisplatin at the dosage of 2 . 5 mg/kg b . wt . To reduce the nephrotoxicity induced by cisplatin , antioxidants ( vitamin C , vitamin E and silibinin ) were supplemented along with cisplatin . The levels of serum urea , BUN , uric acid and creatinine were found to be within the normal range of 10–45 mg/dl , 5–21 mg/dl , 3–6 . 7 mg/dl and 0 . 85–1 . 35 mg/dl respectively in animals treated with cisplatin along with antioxidants . The results were quite comparable to the SSG treated mice where normal levels were found on different post treatment days ( Fig . 8A and 8B ) . A significant decrease was found in electrolyte levels when infected animals were treated with cisplatin and leads to hyponatremia , hypomagnesemia , hypocalcemia , hypokalemia , hypochloremia and hypophosphatemia . The decrease in electrolytes was more pronounced in mice treated with 5 mg/kg b . wt . of cisplatin in comparison to those treated with 2 . 5 mg/kg b . wt . of cisplatin . When the antioxidants were supplemented to reduce the nephrotoxicity , the normal electrolyte levels were attained and the serum sodium , potassium , phosphorus , chloride , calcium and magnesium concentration was found to be within the range of 135 to 155 mmols/l , 3 . 6 to 5 . 5 mmols/l , 2 . 5–5 mg/dl , 98–109 mmols/l , 8 . 7 to 10 . 5 mg/dl and 1 . 3 to 2 . 5 mg/dl respectively ( Fig . 9A , 9B , 9C and Fig . 10A , 10B ) . The death rate was maximum in animals treated with cisplatin ( 5 mg/kg bwt and 2 . 5 mg/kg bwt ) . This increased from 30–85% and 25–75% in animals treated with cisplatin at the dosage of 5 mg/kg bwt . and 2 . 5 mg/kg bwt . on different post treatment days . When antioxidants were given along with the drug in infected animals , death rate was found to be 0% . The results were found to be comparable to SSG treated animals where death rate was found to be 0% on 30 p . t . d .
The results of the present study demonstrated the antileishmanial efficacy of high doses of cisplatin . In addition treatment of L . donovani infected animals with cisplatin along with antioxidants ( vitamin C , vitamin E and silibinin ) ameliorated the nephrotoxicity caused by administration of high dosage of cisplatin . The drug-induced protective immune responses were associated with a reduction in parasite burden as assessed by LDU in liver . It has been observed that mice treated with higher dosage of cisplatin ( 5 mg/kg b . wt . ) showed better results by reducing the parasite load by 97% as compared to low dosage ( 2 . 5 mg/kg b . wt . ) which proves its efficacy against the amastigote stage of L . donovani . These findings correlate with the earlier studies from our laboratory where the drug was found to be more effective at a concentration of 1 mg/kg b . wt . as compared to 0 . 5 mg/kg b . wt . [10] . SSG at a dosage of 40 mg/kg/day for 5 days intraperitoneally resulted in reduction of parasite burden by 97% from liver . Similar observations have been made by Trotter et al [22] who found that 47 mg/kg/day of pentostam for 5 days cured 90% of infection in NMRI mice . Our studies are also consistent with several previous studies which confirmed that SSG therapy cured more than 95% of patients in Bihar [23] . In Pakistan , Nepal and Sudan , cure rates in the range of 98%–100% have been reported [24]–[26] The effectiveness of the newly developed antineoplastic drug , cisplatin ( cis-diamminedichloroplatinum II; CDDP ) was comparable with that of SSG as treatment with cisplatin resulted in reduction of parasite load to a similar extent . Our studies were in accordance with the findings of Geib et al [27] where irinotecan/cisplatin treatment related deaths were observed in 5 cases ( 19% ) , mainly due to infectious complications due to excessive toxicity . Cisplatin should be used in combination with antioxidants to overcome the side effects induced by cisplatin and to increase the survival rate of animals . Our study has shown that after antioxidant supplementation along with cisplatin , no death was reported . Several cellular studies , animal and human studies [28]–[31] have demonstrated that vitamins A , E , C , and K , as well as beta-carotene and selenium—as single agents or in combination—all protect against the toxicity of adriamycin and actually enhance its cancer-killing effects and increase the survival rate . Delayed type hypersensitivity ( DTH ) is an immunologic response that has been frequently used as a correlate for protection against or sensitization to Leishmania antigen in humans and experimental models of Leishmania infection [32] . The results demonstrate a positive correlation between enhanced DTH responses and reduced parasite load demonstrating the generation of Th1 type of protective immune responses [33] generated by cisplatin . This response was found to be more pronounced in mice treated with 5 mg/kg b . wt . as compared to those treated with 2 . 5 mg/kg b . wt . of cisplatin since maximum reduction in parasite burden occurs after treatment with high dosage of cisplatin . We have earlier reported increased DTH responses in cisplatin treated animals , when two different doses ( 1 mg/kg b . wt . and 0 . 5 mg/kg b . wt . ) of cisplatin were tested . Higher dose revealed enhanced DTH response as compared to a lower dose of cisplatin [10] . The positive DTH response , which is an indicator of development of cell-mediated immune responses , also develops after treatment with the drug indicating thereby that this drug not only brought about reduction in parasite load but also helped in regaining the cell-mediated immune responses which is very important for complete recovery . None of the two doses of the drug caused depression of DTH response , thereby indicating that drug treatment helped in reversal of immunosuppression caused by the parasite . Since , IgG2a and IgG1 kinetics indirectly reflect the Th1/Th2 responses , the relative production of these isotypes are used as a marker for the induction of Th1-like and Th2-like immune responses . The analysis of IgG isotypes disclosed a dichotomous response to visceral infection . Treated animals produced low levels of IgG and IgG1 in comparison to the infected controls but IgG2a levels were reported to be slightly higher . A successful cure results when levels of IgG2a increase with low levels of IgG1 in treated groups driving the immune response towards Th1 type . The results demonstrate a positive correlation of low IgG1 with high cell mediated immune response , in terms of DTH and vice versa . There is a correlation between the clinical outcome of the infection and the cytokine response profile . Control of visceral leishmaniasis in mice is believed to require IFN-γ , produced by spleen cells , which drives the immune response towards a Th1 phenotype by IL-2 [34] . To study the type of immune response generated in the treated animals , the cytokine levels were estimated in the splenic lymphocyte cultures of all groups of mice . Our results suggest that treatment with cisplatin preferentially induces a type 1 immune response which resulted in significant protection in mice against L . donovani infection . The infected and cisplatin treated animals showed the maximum concentration of Th1-specific cytokines , IFN-γ and IL-2 and least concentration of Th2-specific cytokines , IL-4 and IL-10 pointing towards the potential of the drug to generate protective immune response against L . donovani . High levels of IFN-γ were also reported in the patients exposed to whole cell extracts of the antigen [35] . Cisplatin ( DDP ) is one of the conventional anticancer agents endowed with immunomodulating features [36] . When the dose of cisplatin was increased to 5 mg/kg b . wt . , all mice treated with this dosage of drug showed increased production of Th1 specific cytokines suggesting the generation of Th1 type of immune response and the decrease in parasite load showed protective nature of therapy . The increase was also found to be prominent at low dosage ( 2 . 5 mg/kg b . wt . ) but was lesser than at high dosage . Cisplatin is known to boost the cytotoxic T-lymphocyte mediated antitumor immunity [7] , [8] . Park et al [37] also showed that cisplatin treatment increased the levels of IFN-γ and IL-2 . IL-10 has been suggested to play a role in counterbalancing the exacerbated polarized response that may develop following cure [38] . The studies of Lehman et al [39] confirmed that in visceral leishmaniasis a Th1 dominated immune response is protective against L . donovani parasites and furthermore , the capacity to produce IFN-γ rather than the presence of IL-4 determines the efficacy of the immune response in susceptible miceHigh levels of IL-4 and IL-10 in control animals supported a view that marked up-regulation of these two cytokines is accompanied by susceptibility , disease progression and depressed Th1 type of cell mediated immunity with decreased production of IFN-γ and IL-12 [40] . Our results confirmed these reports as progressive L . donovani infection promote the production of IL-4 and IL-10 and at the same time , suppressed the production of Th1 cytokines such as IFN-γ and IL-2 . Similarly in humans , during active VL , the immune response was predominantly of Th2 type , with the absence of IFN-γ in Leishmania antigen activated PBMC culture supernatants [41] . So , treatment of mice with cisplatin significantly brought down the IL-4 and IL-10 levels and enhanced the IFN-γ and IL-2 levels after therapy which points towards the shifting of Th2 immune response to Th1 type of immune response , depicting its protective role . While toxicities induced by cisplatin include ototoxicity , gastrotoxicity , myelosuppression , and allergic reactions [42] , [43] the main dose-limiting side effect of cisplatin is nephrotoxicity [44] , [45] . Nephrotoxicity increases with the dose and frequency of administration and cumulative dose of cisplatin [44] . As cisplatin causes nephrotoxicity , it has been suggested that the toxic effects of cisplatin may be related to free radical induced damage which can be reduced by the supplementation of antioxidants [46] leading to the possibility that cisplatin may be used in combination with antioxidants which might suppress the drug-induced toxic effects . To assess the drug induced side effects , various haematological and biochemical studies were carried out . Regarding the hematological parameters , anemia is a frequent manifestation of visceral leishmaniasis that appear with the disease after an incubation period ranging from one month to several years [47] which is in accordance with our study where anemia was found in mice infected with L . donovani . Cisplatin treatment at both dosages brought about a significant reduction in the white blood cells and anemia . This is in accordance with an earlier study by Khynriam and Prasad [21] where cisplatin treatment of tumor bearing mice caused a decrease in total leucocytes and severe anemia when given repeatedly Our findings were also in accordance with the studies of Nair et al [48] where body weight , hemoglobin levels and leucocyte counts were decreased after cisplatin injection in mice . In a previous study , a reduction in ototoxicity , renal toxicity , and hematologic toxicity in animals treated with cisplatin plus supplementation with high doses of antioxidants [46] has been reported which is further correlated with our findings where antioxidant supplementation showed no discrepancy in the hematological parameters . Liver enzymes are the earliest to show a rise even before the appearance of clinical signs ( hepatic damage , pulmonary tuberculosis ) and their monitoring at various intervals is thus an index of the extent of damage to the liver [49] . It has already been observed that all the medications used to treat visceral leishmaniasis may be associated with significant increase in levels of liver enzymes during treatment which may be due to the killing of the parasites in liver , rather than to direct medication induced hepatotoxic effects [50] and thus hepatocyte damage is considered as a non-desirable side effect [51] . Infected animals treated with cisplatin at the dose of 5 mg/kg b . wt . and 2 . 5 mg/kg b . wt . showed increase in SGOT , SGPT and LDH levels depicting hepatocellular damage . Hepatotoxicty is a rare side effect of cisplatin . However , it is known that cisplatin is significantly taken up in human liver [52] . Some reports suggest that cisplatin-induced hepatotoxicity may be dose-related [52] . High doses of cisplatin have been found to produce hepatotoxicity , with apoptosis as the major lesion , and metallothionein protects against cisplatin-induced liver injury [53] . Our study has shown pronounced increase in liver enzymes in mice treated with high dosage as compared to those treated with low dosage . The increase in hepatic enzymes is more pronounced in infected and treated animals which may be because the parasite causes structural and functional derangement of liver . Zicca et al [52] showed that the high dose of cisplatin ( 7 . 5 mg/kg ) administered to rats caused an evident liver damage characterized by significant increase of glutamic oxaloacetic transaminase and γ–glutamyl transpeptidase plasma activities [52] . Furthermore , Lactate dehydrogenase ( LDH ) enzyme is considered to be a specific marker for tissue damage [54] . LDH is a key enzyme in energy metabolism located in the cell cytoplasm and alkaline phosphatase is a phosphohydrolase enzyme attached to the cell wall by glycosyl phosphatidyl inositol anchors . Activities of these enzymes in urine are physiologically very low . Therefore , any increase in their activities suggests proximal tubular cell damage [55] . Cisplatin treatment at both the dosages brought about increase in LDH level which is related to the studies of Sudhakar et al . [56] where cisplatin treatment significantly increased the enzyme activities of SGOT , SGPT and LDH levels . It has been established that lipid peroxidation might participate in the hepatotoxicity in cisplatin-treated animals despite activation of antioxidant enzymes [57] . It has been suggested that antioxidant enzymes represent the protective response against cisplatin toxicity in the livers of tumor-bearing animals [53] . Many antioxidants have been studied to protect tissue from cisplatin's side effects . In the present study , administration of antioxidants reduced the side effects causing the hepatocellular damage by cisplatin administration . This could be substantiated with the observation of Molander et al . [58] , who have also reported that the serum levels of the transaminases return to normal as the liver parenchyma heals and the liver cells regenerates . Silibinin is the most biologically active component with regard to antioxidant and hepatoprotective properties [59] . Pretreating rats and mice with silymarin before exposure to chemical hepatotoxins , such as carbon tetrachloride , thallium , acetaminophen and halothane , significantly reduced lipid peroxidation and hepatotoxicity [60] which is consistent with our study where silibinin administration showed protective response against hepatic damage . Recent work by Seo and Lee [61] provides evidence that low vitamin C intake as ascorbic acid acts primarily as an antioxidant and prevents hepatotoxicity . Both the findings are correlated to our study where supplementation of α-tocopherol and ascorbic acid showed the antioxidant effect and reduced the hepatotoxicity which might be caused by the killing of the parasite or by the administration of cisplatin . SSG treatment brought about a transient increase in SGOT and SGPT which returned to their normal levels within 15 to 30 p . t . d . , thereby indicating that drug treatment may cause reversal of damage caused by the parasite and shows recovery of the damaged liver . It may be possible that liver finds it difficult to deal with the load of the drug and parasite but after increase in post treatment days , it may be capable of recovery . Our results clearly support the findings of Crofton and Andrews [62] that liver enzymes improved as patients progressed under chemotherapy . Cisplatin has been shown to cause nephrotoxicity in patients [63] as well as in a variety of animal species [64] . On studying the renal parameters , increase in levels of serum urea , BUN , uric acid , creatinine and decrease in electrolytes like Mg , Na etc . was observed in the cisplatin ( both doses ) treated animals . The increase in renal parameters after cisplatin treatment points towards the nephrotoxic effect of the drug . The findings correlate with earlier studies of some workers where a marked increase in blood urea nitrogen and creatinine in serum on treatment with cisplatin at different doses has been reported [65] . The increase of serum creatinine and urea levels was 7 and 5 . 7-fold , respectively after treatment with cisplatin at a dosage of 16 mg/kg b . wt . [66] . Kaur et al [10] also reported an increase in the renal parameters of mice on cisplatin administration . In our study , loss of electrolytes has been observed , resulting in hyponatremia , hypomagnesemia , hypocalcemia , hypokalemia and hypophosphatemia when infected mice were treated with cisplatin . This decrease was more transient with high dose of cisplatin ( 5 mg/kg b . wt . ) . Cisplatin ( CDDP ) is a well-known chemotherapeutic agent that is associated with hyponatremia . Cisplatin regimens can lead to a more or less pronounced hyponatremia in 4 to 10% of cases due to salt wasting with hypomagnesemia and normokalemia [67] . This is in accordance with our study where cisplatin administration caused hyponatremia in Leismania donovani infected mice . However , in relevance to our study moderate hyponatremia ( 131 mmol/l ) without any other biological or clinical disturbances was noticed on day 6 of cisplatin administration in the studies by el Weshi et al . [67] . Hypomagnesemia is a well-known side-effect in patients undergoing chemotherapy with cisplatin containing regimens and very little is actually known about the clinical importance of hypomagnesemia as induced by cisplatin . When cisplatin induces renal injury , declining values of serum magnesium seem to be one of the earliest signs and can be found in the presence of otherwise normal tubular function [68] . Ariceta et al . [69] found , that the minimal cumulative dose required to induce hypomagnesemia was 300 mg/m2 of cisplatin . Buckley et al . [70] followed 50 patients receiving cisplatin in doses of 50 mg/m2 at four weeks intervals . They found that the incidence of hypomagnesemia increased during treatment from 41% after one course of chemotherapy to 100% in patients receiving six courses of chemotherapy [71] Our results are in accordance to a previous study where administration of cisplatin to dogs resulted in an increase in potassium clearance [72] which leads to hypokalemia . This might result from the proximal tubular injury by cisplatin leading to an increased delivery of sodium , potassium and water to the distal nephron , which creates a sodium-load dependent potassium secretion . Hypocalcemia occurs frequently among patients receiving cisplatin and the actual frequency is probably dependent on the administered dose . Correction of magnesium blood levels usually should improve the hypocalcemia [73] . Although , mild hypocalcemia is reported in high dose cisplatin treatment , severe hypocalcemia has not been reported in low dose cisplatin treatment [74] as in our previous study where administration of cisplatin at low dosage caused no change in calcium levels [10] . Cisplatin induced suppression of renal antioxidant enzyme activity in previous studies [75] , [76] suggests that the diminution of these renal antioxidant systems caused by the drug can be prevented by the supplementation with antioxidants . The balance between oxidant and antioxidant system seemed to be disturbed in our study due to cisplatin administration , and to obviate impairment of this balance supplementation of antioxidants prior to administration of cisplatin is required . Cisplatin induced suppression of renal antioxidant enzyme activities has also supported by Ajith et al [75] and Cetin et al [76] . The present study suggested that administration of antioxidants ( vitamin C , vitamin E and Silibinin ) before cisplatin injection causes the reversal of renal damage . A higher dose of cisplatin was selected in the study to explore the protective effect of the antioxidants when cisplatin caused maximum damage to the kidneys . We found that vitamin C , vitamin E and flavonoid ( silibinin ) significantly sheltered the cisplatin-induced nephrotoxicity by impairing the antioxidant system . In the study by Weijl et al [77] , cancer patients received cisplatin-based chemotherapy , in which half the patients were given a dietary supplement that consisted of vitamin C , vitamin E and selenium . These patients showed recovery with respect to the severity of the nephrotoxicity induced by cisplatin . It has also been shown that both vitamins E and C decreased lipid peroxidation and augmented the activity of antioxidant enzymes in the kidneys of diabetic rats [78] . In another study , lipid peroxide levels were reduced and levels of antioxidant enzymes and thiol compounds were increased following administration of α-tocopherol and ascorbic acid in lead-induced oxidative stress [79] It has been reported that administration of cisplatin at the dosage of 6 mg/kg b . wt . , intraperitoneally , at an interval of 120 hours , resulted in a significant increase in the concentration of blood urea nitrogen and creatinine . However , when vitamin E and cysteine were administered along with the cisplatin , the results were partially reversed [65] , which supports our findings where normal range of blood urea nitrogen , urea and creatinine levels after the administration of cisplatin ( 5 mg/kg b . wt . and 2 . 5 mg/kg b . wt . ) along with different antioxidants were reported . Our results were also in accordance with the studies of Ajith et al [80] and Appenroth et al [17] where significant reduction in various liver and kidney function tests was reported when both vitamin C and vitamin E were administered together . Silibinin possesses anti-oxidant and membrane-stabilizing properties that have already been elucidated in hepatocytes challenged with a variety of radical-generating drugs [81] . Silibinin partly or totally ameliorated cisplatin induced alterations in parameters associated with proximal tubular function . Administration of cisplatin caused a decline in kidney function within a day following treatment but administration of silibinin caused the reversal of kidney function tests within normal range . Our results were in accordance with the studies of Gaedeke et al [15] where similar results were evaluated . Hypomagnesemia observed by Mavichak et al . [82] was also reported from our study after cisplatin treatment but not after treatment with cisplatin in combination with silibinin signifying its strong antioxidant potential . However , silymarin caused a marked decrease in potassium excretion , suggesting that this constituent is a potassium-sparing diuretic [83] . The above findings correlates with our study where silibinin in combination with vitamins and even alone ameliorated the cisplatin induced alterations in the kidney function tests . Concomitant treatment of antioxidants rendered protection from oxidants attack . All the enzymatic levels which were reported to be higher in the infected controls and the infected plus cisplatin treated animals , ended up being normal in the animals treated with cisplatin and vitamin C , vitamin E and silibinin combination . Hence , we establish that higher dosage of cisplatin is effective in diminution of parasite burden in visceral leishmaniasis . However , it is recommended that higher dose should be used in combination with antioxidants which help in suppression of drug-induced toxic effects . The results presented in the study are promising in context of reduction in parasite burden , enhancement of immune responses and reduction in the toxic effects . The encouraging results could lead to studies with combination of immunomodulators/herbal extracts and in other animal models for further development of cisplatin as an antileishmanial therapy . To examine the immunomodulatory effect of cisplatin , further studies can be carried in immunosuppressed models .
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Leishmaniasis , a neglected tropical disease ( NTD ) caused by Leishmania , has been put on the World Health Organization agenda for eradication as a part of their Special Programme for Tropical Diseases Research . Visceral leishmaniasis ( VL ) is a life-threatening disease when no treatment is given . Most of the drugs still used to treat VL are often expensive , difficult to administer , have serious side effects , and several are becoming ineffective because of increasing parasite resistance . Cisplatin is a first-generation platinum-containing drug , used in the treatment of various solid tumors . We have for the first time characterized the in vivo effect of cisplatin in murine experimental visceral leishmaniasis , but at higher doses it is nephrotoxic . Considering the above findings , the present study was designed to evaluate the protective efficacy of the drug in combination with various antioxidants to reduce or prevent cisplatin-induced nephrotoxicity . Drug treatment induces a higher secretion of Th1 cytokines , diminution in parasite burden , and the supplementation of antioxidants which are antagonists of the toxicity helps in reducing the nephrotoxicity .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine"
] |
2012
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Evaluation of Nephroprotective and Immunomodulatory Activities of Antioxidants in Combination with Cisplatin against Murine Visceral Leishmaniasis
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Transcription factors are key components of regulatory networks that control development , as well as the response to environmental stimuli . We have established an experimental pipeline in Caenorhabditis elegans that permits global identification of the binding sites for transcription factors using chromatin immunoprecipitation and deep sequencing . We describe and validate this strategy , and apply it to the transcription factor PHA-4 , which plays critical roles in organ development and other cellular processes . We identified thousands of binding sites for PHA-4 during formation of the embryonic pharynx , and also found a role for this factor during the starvation response . Many binding sites were found to shift dramatically between embryos and starved larvae , from developmentally regulated genes to genes involved in metabolism . These results indicate distinct roles for this regulator in two different biological processes and demonstrate the versatility of transcription factors in mediating diverse biological roles .
A major scientific endeavor is aimed toward understanding how the regulatory information embedded in the genome is deployed to direct the complex process of development [1] . With the completion of the genomic sequence of many model organisms , much effort is now focused on identifying the precise regions of the genome that regulate specific developmental events . Of particular interest are regions that serve as binding sites for developmentally important transcription factors . Through these sites , a transcription factor controls the spatial and temporal expression of genes that function in diverse developmental processes . Identification of the DNA binding sites of a factor links it to its direct target genes , and permits a fuller understanding of the mechanisms by which different transcription factors control the development of an organism . Ultimately , understanding transcriptional regulation of development requires identification of the regulatory network as a whole . The binding sites of many transcription factors under similar conditions must be determined , as well as how binding sites for a given transcription factor change over time as development progresses . To this end , we have developed a high-throughput experimental system to categorize the binding sites of many transcription factors using chromatin immunoprecipitation ( ChIP ) in the developmental model organism , the nematode C . elegans . C . elegans provides many advantages to deciphering developmental regulatory networks [2] . The invariant cell lineage of C . elegans provides an excellent framework to interpret how regulatory networks control development . Additionally , the spatial and temporal expression of both transcription factors and their targets can be followed using live GFP imaging techniques . The small size and simple growth conditions of C . elegans facilitate large-scale biochemical analyses such as ChIP . Finally , because the genome is relatively compact , individual genes are small and close together , which simplifies multiple steps of the process , from cloning procedures to downstream bioinformatics analysis . We have established an experimental system to systematically tag C . elegans transcription factor genes with a fluorescent epitope tag , create transgenic animals expressing a tagged factor , and perform chromatin immunoprecipitation followed by deep sequencing ( ChIP-Seq ) to identify binding sites for that factor [3] , [4] . We first applied this approach to the large subunit of RNA polymerase II , AMA-1 , and demonstrate that tagged AMA-1 can recapitulate binding by endogenous AMA-1 . We then focused on the sequence-specific transcription factor PHA-4/FOXA because of its well-studied role as a master regulator of pharynx development during embryogenesis [5]–[7] , as well as a novel role in improved survival under starvation conditions that we describe here . We identified binding sites for PHA-4/FOXA under two developmental conditions: during embryogenesis and during the first stage of larval development ( L1 ) under starvation conditions . We found that the binding sites and associated gene targets of PHA-4 in embryogenesis are generally associated with organ development , whereas the targets in L1 are primarily associated with metabolism , reflecting the expected biology of each condition . Interestingly , we find that several targets preferentially bound in starved L1s are involved in autophagy . These data establish that we have laid the foundation for systematic identification of genome-wide transcription factor binding sites during C . elegans development and demonstrate new roles for key regulators in diverse biological processes .
Identification of binding sites in vivo is critical to understand how transcription factors operate in regulatory networks to control development . We therefore have established a pipeline to facilitate this endeavor in C . elegans ( Figure 1 ) . To briefly summarize , we first generated constructs in which each transcription factor is tagged in frame with a dual GFP:3xFLAG tag at the carboxyl terminus . This tag provides both direct visual evidence of spatial and temporal expression in vivo , as well as two different epitopes that can be utilized for biochemical experiments . We used recombineering to insert the tag directly into a fosmid that contains the entire locus of interest as well as extensive flanking regions ( Sarov et al . , in prep ) . This approach increases the likelihood that the transcription factor will have the essential regulatory information to allow it to be expressed correctly in vivo . These constructs were introduced into worms via microparticle bombardment , which produces animals bearing low-copy number , integrated transgenes [8] . We then isolated lines carrying an integrated transgene , and examined them for expression of the GFP-tagged factor by fluorescence microscopy . We determined the developmental stage at which maximal expression occurs , as well as whether the transcription factor is localized to the nucleus as expected . Additionally , we examined the size of the tagged transgenic protein by immunoblot analysis using both anti-FLAG and anti-GFP antibodies . Finally , we tested whether the protein can be immunoprecipitated with an antibody to GFP ( anti-GFP; Materials and Methods ) , followed by immunoblot analysis . If all of these quality control measures were passed , we then grew the transgenic animals to the desired developmental time , harvested and crosslinked the sample , and performed chromatin immunoprecipitation ( ChIP ) using anti-GFP to collect chromatin preferentially bound by the GFP-tagged factor [9] . This chromatin was then subjected to Illumina-based sequencing , as was non-immunoprecipitated ( input ) chromatin from the same sample , which served as a control . We examined whether a transgenic tagged factor could recapitulate the binding sites of the endogenous , untagged factor . To directly compare the binding properties of a tagged factor expressed from a transgene with that of the endogenous protein , we first performed ChIP for both the tagged and native versions of AMA-1 , the large subunit of RNA polymerase II ( RNA Pol II ) . AMA-1 is well-suited for this comparison because commercially available antibodies against RNA Pol II recognize C . elegans AMA-1 , and perform well in ChIP assays [10] , [11] . Additionally , AMA-1 is abundantly expressed in the nucleus of all cells of the animal [12] , [13] . We therefore established a transgenic strain that expresses AMA-1:GFP:3xFLAG ( referred to as AMA-1:GFP thereafter ) in all nuclei , recapitulating the wild type expression pattern ( Figure S1A ) . We grew duplicate populations of AMA-1:GFP animals to the L4 stage , which was chosen to provide a stringent test case , as it provides a particularly biologically complex stage that can be difficult to replicate . We then performed ChIP using two different antibodies: anti-GFP , which recognizes the tagged AMA-1 , and anti-Pol II ( 8WG16 , pan-Pol II ) , which recognizes both tagged and native proteins in both phosphorylated and non-phosphorylated forms ( Figure S1B ) . The DNA from each immunoprecipitation was purified and the ends subjected to sequencing using the Illumina platform , as was input DNA isolated from crosslinked and sonicated cells ( non-immunoprecipitated ) . The binding profiles of both samples were determined and then compared ( Figure 2A; Figure S1C ) . The overall correlation between anti-GFP and anti-Pol II ChIP samples was extremely high ( 0 . 934; Figure 2B ) , indicating that the tagged AMA-1:GFP had a binding profile highly similar to that of native RNA Pol II . Indeed , the correlation between IPs performed on the same biological samples was higher than that for IPs performed with the same antibody on different biological replicates ( Figure S1C ) . Importantly , the tagged AMA-1 did not exhibit significant ectopic binding sites not found with endogenous RNA Pol II , indicating that the transgenic system does not induce a major increase of non-specific binding . Moreover , the binding sites identified in the two samples have the similar characteristic of broad peaks distributed over the length of the gene , as expected for genes undergoing active transcription ( Figure S2 ) . We conclude that the addition of the GFP:3xFLAG tag does not disrupt the ability of AMA-1 to interact with its endogenous target genes , and that our anti-GFP antibody works very well in ChIP experiments in C . elegans . We next determined the binding sites for a key transcription factor , PHA-4/FOXA . PHA-4 is a master organ identity gene that is required for the specification and formation of the pharynx . The expression of several hundred genes in the developing embryonic pharynx is dependent upon PHA-4 , many of which are likely direct targets [14] , [15] . Moreover , PHA-4 is required continuously after birth [14] and plays a role in diet-induced longevity in adults , in the absence of another FOX family transcription factor , DAF-16 [16] , [17] . In addition to these previously described functions , we discovered an additional function for PHA-4 in promoting the survival of first stage larvae ( L1 ) undergoing starvation ( Figure 3 ) . L1 animals were transiently subjected to pha-4 ( RNAi ) or a negative control Cherry ( RNAi ) and incubated in the absence of food ( Materials and Methods ) . After eight days of starvation , larvae were transferred to food and tested for their ability to mature beyond the L1 stage . pha-4 ( RNAi ) animals exhibited a significantly reduced survival rate at 30% , compared to 75% from the negative control ( Figure 3A ) . However , no difference in survival was observed for up to four days of starvation , indicating that pha-4 ( RNAi ) larvae were healthy , and had not suffered developmental defects ( data not shown ) . Conversely , transgenic expression of pha-4 from its native promoter was sufficient to prolong starvation survival relative to a control , from a mean survival of 8 . 3±0 . 2 days in wild type to 9 . 4±0 . 2 days in a strain expressing tagged PHA-4 ( Figure 3B ) . Thus , PHA-4 participates in diverse biological processes at different stages of development , with roles in embryonic pharynx development , L1 starvation survival , and adult longevity . To identify PHA-4 binding sites in the genome , PHA-4:GFP:3xFLAG ( referred to as PHA-4:GFP thereafter ) transgenic animals were created via our pipeline . Animals bearing an integrated transgene had nuclear-localized expression in the pharynx and intestine in embryos , and in pharynx , intestine and rectum in larvae , confirming published expression patterns [5]–[7] , [18] ( Figure 4A ) . Moreover , immunoblot analysis using anti-GFP demonstrated a tagged protein somewhat larger than 90kD , the approximate size expected of the largest PHA-4 isoform containing the GFP:3xFLAG tag ( Figure 4B ) . Finally , we crossed animals bearing the PHA-4:GFP transgene to pha-4 ( q90 ) mutants , and rescued the embryonic lethality of these mutants . To compare the binding patterns of PHA-4 under different conditions , we collected and crosslinked PHA-4:GFP transgenic animals during embryogenesis when the pharynx is forming , and during the L1 larval stage under starvation conditions . Biologically independent duplicate samples were collected . The samples were immunoprecipitated with anti-GFP to identify PHA-4 binding sites , and also with anti-RNA Pol II antibodies to identify the location of RNA Pol II , which will help to define the transcriptional state of genes associated with PHA-4 . The immunoprecipitated chromatin , along with control input DNA from the same animals , was then sequenced to the depth of >106 reads per sample . Figure 4C shows the binding patterns of PHA-4 and RNA Pol II at both developmental times for a representative region of the genome , as well as a closer view of the binding patterns at the gene smk-1 . smk-1 encodes a potential co-factor for PHA-4 [16]; our data suggest that it might also be a regulatory target of PHA-4 ( Figure 4D ) . We also collected RNA from wild type embryos and L1 samples and performed cDNA deep sequencing [19] to identify expressed genes through an independent method . Using a peak-scoring algorithm [20] , we identified discrete PHA-4 binding sites for each sample . A total of 4350 and 4808 binding sites were defined in embryos and starved L1 larvae , respectively ( p<10−5; Table 1 and Dataset S1 , S2 ) . We found a high correlation between replicate experiments for each stage ( 0 . 85 for embryos , 0 . 88 for L1; data not shown ) . We also developed a target-calling algorithm ( Figure S3 ) to assign these binding sites to candidate gene targets . Genes within 2 kb of a binding site were designated as candidate regulatory targets of PHA-4 , which resulted in the assignment of over 90% of the sites to one or more genes ( Figure 5A ) . A binding site could be assigned to more than one gene if it fell within a gene-dense interval ( Materials and Methods ) . In total , 4816 protein-coding genes are candidate PHA-4 targets in embryos , and 4621 genes are candidate PHA-4 targets in L1 larvae . Only 280 binding sites lie >5 kb from annotated genes . Presumably these either act a distance or regulate genes that have not yet been annotated , such as non-protein coding genes . Overall , these data indicate that PHA-4 has a broad role in directly regulating the expression of many genes in the C . elegans genome , in agreement with previously published studies [14] . We used several methods to validate these binding sites . We first used ChIP-qPCR to directly test whether we could detect enriched binding of PHA-4 at 94 individual candidate sites taken from both embryonic and L1 data sets ( Table S1 ) . We found that 76% of the embryonic sites and 74% of L1 sites were reproducibly enriched two-fold or higher by ChIP-qPCR of a biologically independent replicate . Thus , many PHA-4 binding sites identified by ChIP-Seq are verified through an independent detection method . Additionally , we compared our results to an earlier expression analysis that had identified genes expressed during pharynx development in embryos [14] ( Figure S4 , Materials and Methods ) . We compared our list of genes to the list of known of pharynx development genes and found that over 38% were bound by PHA-4 in our embryonic ChIP-Seq experiment , which is significantly higher compared to a randomized set ( 90/238; p<1 . 7×10−13 ) . Moreover , seven of these pharynx-expressed genes had been previously demonstrated to be bound directly by PHA-4 using a gel shift assay [14] , and six of the seven were bound by PHA-4 in our experiments at sites containing the previously identified PHA-4 consensus sequence . Finally , we examined the sequence underlying PHA-4 binding peaks to identify de novo consensus binding sequences enriched under the peaks relative to the genome . Five of the six consensus sequences identified in either of the two stages were variations of the known PHA-4 binding consensus sequence TRTTKRY [14] , primarily TGTBTSY ( B = [TGC] , S = [GC] , Y = [TC] , p<10−4 ) ( Figure S5 ) . Intriguingly , the PHA-4 binding site sequence in embryos differs from those identified in starved L1s . Moreover , a second , unrelated site was identified in embryos that was not found in starved L1s , GAGAGAG/C ( 3 . 3-fold; p<10−26 ) . This GAGA element , was previously noted as associated with timing of pharynx development in embryos [15] . The GAGA sequence was not enriched among PHA-4 binding sites in starved L1 larvae , or in a control dataset consisting of HTZ-1 binding peaks [11] , indicating that it is specific to the PHA-4 embryo dataset . These observations suggest that PHA-4 might have different co-factors at the two developmental stages that direct it to distinct targets and distinct binding sites in response to developmental and environmental cues . We conclude that many of the global PHA-4 binding sites we have identified likely reflect functionally relevant binding events in the C . elegans genome . To determine the degree to which binding sites change under different conditions , we compared the PHA-4 binding profiles at the two stages ( Figure 5B ) . The two datasets exhibit extensive overlap ( 2367 targets ) , but also have many sites present in one stage but not the other . Of the PHA-4 embryogenesis targets , 1975 ( 45% ) are not found on the list of PHA-4 L1 targets , while 1676 ( 41% ) of PHA-4 L1 targets are not found among the embryo set . This observation indicates that the binding profile of PHA-4 shifts substantially under distinct developmental conditions . To globally categorize the types of genes that are differentially regulated , we determined the Gene Ontology ( GO ) functional categories that are enriched among each set of stage-specific PHA-4 targets ( Figure 5C and 5D ) . We found that the embryo set is enriched for developmental processes , whereas the L1 set of targets is enriched for metabolic processes and defense responses ( Table S2 and Table S3 ) . Although these functional categories are quite broad , this shift in the basic functions of the targets is consistent with the shift in the function of PHA-4 from organ development to an altered metabolic response to promote survival of starvation conditions . To investigate these differences in greater detail , we individually annotated a subset of candidate gene targets . We first selected target genes based on the presence of strong binding sites ( p<10−10 ) 0–2kb upstream of the gene , in the candidate regulatory region . The subset of those targets that had already been assigned a three-letter name , and presumably had some functional information available , were then divided into common , embryo-only , and starved L1-only sets consisting of 202 , 312 , and 294 genes , respectively ( Dataset S3 ) . We explored gene function by reviewing available data summaries in public databases , such as Wormbase , and noting multiple trends and distinctions between the datasets ( Table S4 ) . Many genes throughout all three datasets have been described as expressed in pharynx or intestine , or are known to have a role in muscle development or function . Additionally , genes encoding ribosomal proteins are targeted in all three datasets , with the most found in the common , or shared , dataset . Intriguingly , multiple components of the RNAi pathway are also candidate PHA-4 targets , as are splicing regulatory factors . Several striking differences were obvious between the two developmental conditions we examined . For instance , the target set in embryos includes many components of G-protein signaling , but the L1 set was devoid of this signaling pathway . Conversely , the L1 set had multiple examples of modulators of the TGFβ-signaling pathway , which is involved in controlling both body size and dauer formation [21] , whereas the embryo set did not . Additionally , the embryo set contains many genes that encode chromatin regulators , including multiple members of the SynMuv B pathway , NuRD components , and histone modifying proteins . Intriguingly , multiple members of the dosage compensation machinery are apparently targeted by PHA-4 binding in embryos , such as dpy-22 , dpy-27 , dpy-30 , and sdc-2 . In contrast to the embryo set , the L1 set of targets with likely roles in transcription primarily consist of sequence-specific transcription factors rather than chromatin-modifying proteins . Most notably , over five times as many nuclear hormone receptors were bound by PHA-4 in starved L1s compared to embryos ( 28 vs . 5 , respectively ) . Additionally , the metabolism-related factors in starved L1s consist largely of multiple regulators of sterol and fatty-acid metabolism , as well as cytochrome P450 and glutathione-S-transferase components . The starved L1 set also include several components involved in acetylcholine metabolism and signaling , which is involved in neuromuscular synapse transmission . Starved L1s also have increased PHA-4 binding at various types of membrane-bound proteins , including several multidrug resistance proteins , P-glycoproteins , tetraspanins , and serpentine receptors . Many fewer of these types of proteins were noted in the shared or embryo sets . This shift in functions between stages is exemplified by PHA-4 target genes involved in autophagy . Autophagy in multicellular organisms can be induced by environmental stresses including food limitation . Moreover , autophagy genes are essential for dauer development and life-span extension by diet restriction in C . elegans [22]–[24] . Recent genetic assays indicate that the autophagic response to dietary restriction is a transcriptionally regulated response that requires PHA-4 activity [24] . Four genes known to be involved in autophagy ( bec-1 , lgg-1 , gpd-2 , and unc-51 ) and found that all four are strongly bound by PHA-4 in starved L1 larvae , but PHA-4 exhibits minimal binding in embryos ( Figure S6 ) . Thus , our data suggest that PHA-4 is directly involved in inducing the expression of autophagy genes in response to starvation . We then correlated gene expression levels with PHA-4 binding using the RNA-sequencing data we gathered in embryos and L1 larvae . Overall , we found that 87% of genes bound by PHA-4 at either stage are expressed , indicating that PHA-4 rarely functions as a repressor at either stage . In support of this observation , we found that the expression levels of 74% of the embryo-specific PHA-4 target transcripts decreased in L1 larvae , when PHA-4 is no longer bound . The converse is also true: 69% of the L1-specific PHA-4 targets are expressed at lower levels in embryos , when PHA-4 is no longer bound ( Figure 6 ) . This finding indicates that PHA-4 might be directly involved in promoting the expression of most of its gene targets . Finally , we tested whether RNA Pol II “stalling” at transcription start sites ( TSS ) is affected by binding of PHA-4 in a stage-specific fashion . Stalling is the accumulation of RNA Pol II at the TSS , and has been experimentally defined as the presence of a peak of RNA Pol II binding at the TSS that is four-fold higher than binding within the gene body [25] . Stalling occurs preferentially at developmentally or environmentally regulated genes , presumably to hold RNA Pol II poised to respond rapidly upon the appearance of the appropriate cue . Stalling has been observed at ∼10% of genes in Drosophila and C . elegans previously [25] , [26] , but in our samples , we found that less than 2% of genes exhibited stalling in embryos and L1 larvae , likely due to experimental and culture differences ( Dataset S4 and S5 ) . However , PHA-4 binding clearly occurs at stalled genes at both stages more frequently than expected ( Figure 7 ) . Of the 277 genes with RNA Pol II stalling in L1 larvae , 49% are bound by PHA-4 , which is twice the fraction of genes bound by PHA-4 genome-wide ( 23% ) . This effect was even more pronounced in embryos . Among the 251 genes with RNA Pol II stalling in embryos , 85% are bound by PHA-4 , despite PHA-4 binding to only 20% of genes in the genome . This observation is consistent with the idea that PHA-4 regulates genes in response to developmental and environmental cues that influence the spatial and temporal regulation onset of gene expression .
We have established a pipeline to identify transcription factor binding sites in vivo in C . elegans . This pipeline is designed to take advantage of the stability of fosmid-based transgenes , as well as their reliability in reproducing native expression patterns . The transgenic lines emerging from this pipeline tend to have between one and three copies of the transgene , and exhibit minimal , if any , over-expression ( Sarov et al . , in prep ) . Our initial trials with the RNA polymerase II subunit AMA-1 indicate that the transgenic , tagged version of a transcriptional regulator can indeed successfully recapitulate the DNA binding properties of the native factor . This pipeline can now be used on additional factors , and because the same antibody is used for every immunoprecipitation , will provide fairly uniform investigation of the binding sites of multiple factors , and aid in the dissection of regulatory networks in development . As a first step toward this major goal , we identified candidate gene targets of PHA-4 in vivo at two distinct developmental stages . We chose PHA-4 as the initial factor for binding site identification for three primary reasons . First , it is a well-characterized factor with fundamentally important , yet distinct , functions at different times in development . Second , a handful of direct transcriptional targets of PHA-4 have been independently identified and validated , providing some key positive controls . Finally , PHA-4 , unlike AMA-1 , is expressed tissue-specifically , primarily in digestion-related tissues such as the pharynx and intestine . Thus , it provides a test case for whether ChIP can be performed on transcription factors with restricted expression . A little over half of the PHA-4 targets we identified are in common between these two stages , suggesting that PHA-4 does have a general function in regulation of gene expression . However , over 40% are preferentially bound in one stage relative to the other , indicating that the ability of PHA-4 to mediate different processes likely occurs through a shift in the sets of targets it regulates . These data indicate that transcription factors can have diverse and key roles in distinct biological processes and underscore the importance of identifying binding sites under multiple conditions . Several interesting differences in PHA-4 binding were noted between the two stages . For instance , among the many examples listed , several genes encoding members of the dosage compensation complex were preferentially bound by PHA-4 in embryos relative to L1s . During embryogenesis , PHA-4 helps specify the pharynx at the same time that the dosage compensation complex ( DCC ) is beginning to implement a two-fold reduction of transcription levels from the entire X chromosome . Little is known about how the dosage compensation complex interacts with tissue-specific programs , and our data suggests that PHA-4 helps to control the levels of the DCC in order to provide more or less dosage compensation in that tissue as needed . Possibly , master regulators in other tissues also regulate DCC levels in order to bring the level of dosage compensation in alignment with the needs of a specific tissue . We have also demonstrated a novel role for PHA-4 in promoting the survival of larvae during starvation . Reduced PHA-4 levels resulted in decreased survival , while conversely expression of PHA-4:GFP in a wild type background increased survival . In particular , the increased survival indicates that the role of PHA-4 in this process is a regulatable function . This function is in keeping with its noted role in regulating environmental responses , as well as controlling longevity and dauer formation [16] , [18] . Identification of the PHA-4 binding sites under the starvation condition illuminates some aspect of this function . A quite striking increase in genes involved in fatty acid metabolism and sterol biosynthesis were seen in L1s relative to embryos . Accordingly , many nuclear hormone receptor genes , which encode proteins that bind steroid hormones , were preferentially bound by PHA-4 . The nuclear hormone receptor gene family in C . elegans is much expanded relative to other organisms , and many of the ligands for these proteins are unknown . It is possible that a subset of these proteins respond to endogenous steroid hormones generated in response to starvation , and that PHA-4 mediates their induction . Overall , the experimental ChIP-Seq pipeline we developed has produced global binding data , expanding the view of how PHA-4 works as both a master regulator of organ development and a mediator of starvation survival . PHA-4 primarily functions as an activator in both situations , based on our analysis of gene expression concomitant with binding analysis . It is likely that the different binding patterns of PHA-4 are mediated by potential cofactors such as SMK-1 [16] , as well as interactions with other transcription factors such as the GAGA-binding protein suggested by the motif analysis here , and other studies [15] . The binding sites of these factors can be identified using the tagging system and experimental pipeline that we have established , and integrated with the PHA-4 binding data to understand the functional relationship of these factors . Ultimately , the global DNA binding datasets we gather will greatly facilitate formulation of developmental gene regulatory networks in C . elegans .
A 30–40 Kb fosmid containing the entire pha-4 or ama-1 locus , along with flanking regions , was selected from an available fosmid library ( http://eleans . bcgsc . bc . ca/ ) . Using recombineering [27] , a tag containing GFP and three tandem copies of the FLAG epitope was engineered in frame at the carboxyl terminus of each gene . Additionally , the marker gene unc-119 was placed into the backbone of the fosmid ( Sarov et al . , in prep ) . The fosmid clones containing the tagged genes were then prepped , and introduced into unc-119 ( ed3 ) mutant worms using microparticle bombardment [8] . Strains were tested for 100% rescue of the Unc-119 phenotype , indicating integration of the transgene . Integrated lines were then examined by fluorescence microscopy for expression of the tagged protein . For starvation assays with PHA-4:GFP young embryos were released by bleach treatment and placed into modified S basal medium at 20°C ( day zero ) . Three samples of 30 ul each ( representing over 500 worms ) were removed daily and plated with food to determine how many animals could mature beyond the L1 stage after two days incubation . For starvation assays using RNAi , embryos were incubated in dsRNA according to Ahringer [28] with the following changes . DNA template for dsRNA synthesis was prepared by PCR . Primer sets for GFP were 5′-TAATACGACTCACTATAGGAATTTTCTGTCAGTGGAGAGGGTG-3′ and 5′-TAATACGACTCACTATAGGTCCATGCCATGTGTAATCCCAG-3′ and amplified from bSEM538 ( pPD126 . 25 ) . Primer sets for PHA-4 were 5′-TAATACGACTCACTATAGG-3′ and 5′-TAATACGACTCACTATAGGGATCCAACATCCATCACGACC-3′ and amplified from bSEM865 . In vitro transcription was performed using PCR products as template with the Ampliscribe T7 Transcription Kit ( Epicentre Biotechnologies ) . RNA was then treated with DNase and extracted using phenol/chloroform and ethanol precipitation . RNA was resuspended in a final concentration of 2 ug/ul . Gravid hermaphrodites were bleached and embryos harvested and diluted to 100 embs/ul . In one PCR tube 2 ul RNAi soaking buffer ( 1 . 25×M9 , 15 mM spermidine , 0 . 25% gelatin ) , 8 ul dsRNA at 2 ug/ul and 1 ul of embryo suspension was added . Three tubes per sample per day were prepared . Worms were incubated at 20°C for appropriate number of days . Day one was 24 hours after bleaching . To determine viability , three samples were taken and put onto plates with OP50 and are counted for worms bigger than L1 stage 2 days later . Variability in the number of worms per plate occurs because of pipetting variability , so numbers can go above 100% for one plate vs . the starter plate . The difference in buffers between the two types of starvation assays altered the survival times of worms in the two assays; animals incubated in RNAi buffer survived longer than in S basal . Liquid culture of worm strains was performed as described [9] with some modifications . Synchronized cultures of worms were grown on 10–20 150×15 mm plates until animals were gravid . The worms were then washed from plates using M9 buffer and bleached to obtain embryos . Embryos were transferred to 25–50 ml liquid media ( S medium and nystatin ) , and incubated overnight at 20°C at 230 rpm rotation without food to obtain a synchronized first stage larval L1 culture . The worms were then transferred to 500 ml S medium with the anti-fungal nystatin and concentrated HB101 , which serves as a food source . The worms were then grown at 20°C with shaking to the desired developmental stage before harvesting . Additional food was added as necessary . For starved L1s , PHA-4:GFP worms were collected after 6h without exposure to bacteria . To harvest , worms were centrifuged in 50 ml conical tubes at 3000 g for 2 minutes at room temperature . The worm pellet was then washed repeatedly with M9 buffer and centrifuged as before until bacteria were removed . If the sample was destined for IP followed by immunoblot , the pellet was directly subjected to this procedure ( described below ) . If the sample was destined for ChIP-Seq , the sample was then resuspended in 47 ml M9 and 2 . 8 ml 37% formaldehyde solution , and crosslinked for 30 minutes at room temperature with rotation at 50–100 rpm . The worms were then washed with 50 ml 100 mM Tris pH 7 . 5 to quench formaldehyde solution , washed two times with 50 ml M9 , and once with 10 ml FA buffer ( 50 mM HEPES/KOH pH 7 . 5 , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate; 150 mM NaCl ) supplemented with protease inhibitors ( Roche Cat#11697498001 , cOmplete Protease Inhibitor Cocktail Tablets ) . Worms were then collected in a 15 ml conical tube by centrifugation at 3 , 000g for 30s . The supernatant was discarded and the embryo pellet was stored at −80°C . Chromatin immunoprecipitation was performed as described [9] , with the following modifications . Approximately 0 . 5 ml of packed embryos/larvae was resuspended in 3 ml FA buffer plus protease inhibitors ( 2 tablets protease inhibitors , 250 ul 100 mM PMSF , 50 ul 1M DTT in 50 ml FA buffer ) . Using a Branson sonifier microtip , the sample was sonicated on ice/salt water 15 times at the following settings: 50% amplitude , 10 sec on , 59 . 9 sec off , avoiding overheating . Samples were transferred to microfuge tubes and spun at 13 , 000g for 15 minutes at 4°C . The protein concentration of the supernatant was then determined by Bradford assay . Extract corresponding to ∼2 . 2 mg of protein was added to a microfuge tube and the volume brought to 400 ul with FA buffer+protease inhibitors . Then 20 ul of 20% sarkosyl solution was added , and the tube spun at 13 , 000g for 5 minutes at 4°C . The supernatant was then transferred to a new tube , and 10% of the material removed and stored at −20°C for future use as input DNA . To the remainder , 15 ug of affinity-purified GFP ( polyclonal goat IgG; produced in Hyman lab ) or control IgG antibodies was added to the extract to detect the tagged transcription factor . Alternatively , 10 µL of mouse ascites containing the 8WG16 mouse monoclonal antibody was added ( Covance , Cat . #MMS-126R ) to detect RNA polymerase II . The immunocomplexes were rotated at 4°C overnight ( 16–20 h ) . Then 25 ul of protein A ( anti-Pol II samples ) or protein G ( anti-GFP samples ) conjugated to sepharose beads ( Amersham Biosciences ) were added to each ChIP sample and washed four times with 1 ml FA buffer , and spun at 2500g for 2 min to collect the beads . After the washes , the beads were suspended in one bed volume of FA buffer , and 40 ul of the bead slurry was added to each ChIP sample and rotated at 4°C for 2 h . The beads were then washed twice for 5′ each at room temperature in 1 ml of FA buffer and once in FA with 1M NaCl . Each wash was gently rotated , and beads collected between each wash by spinning for 1–2 minutes at 2500g . FA with 500 mM NaCl was then added to the beads and the beads were transferred to a new tube and rotated for 10 min . The beads were then washed in TEL buffer ( 0 . 25 M LiCl , 1% NP-40 , 1% sodium deoxycholate , 1 mM EDTA , 10 mM Tris-HCl , pH 8 . 0 ) for 10 min and twice in TE for 5 min . To elute the immunocomplexes , 150 ul Elution Buffer ( 1% SDS in TE with 250 mM NaCl ) was added and the tube incubated at 65°C for 15 min , with brief vortexing every 5 min . The beads were spun down at 2500g for 2 min and the supernatant transferred to a new tube . The elution was repeated and supernatants combined . At this point , input samples were thawed and treated with the ChIP sample as follows . To each sample , 2 ul 10 mg/ml RnaseA was added and incubated at room temperature for 1–2 hours . Then 250 ul Elution Buffer with 1 ul of 20 mg/ml proteinase K was added to each sample and incubated for 1–2 hours at 55°C , then transferred to 65°C for 12–20 h to reverse crosslinks . The DNA was then purified with the Qiaquick PCR purification kit ( Qiagen ) , and eluted with 50 ul H2O . A 5 ul aliquot of the input DNA was then run on a 2% agarose gel to check the extent of shearing , with an expected range between 200–800 bp . The immunoprecipitated DNA was either interrogated by qPCR or subjected to high-throughput sequencing library preparation ( below ) . All ChIP experiments were completed with two or more biological replicates . Immunoblotting was performed on worm lysates as well as immunoprecipited TF/DNA complexes . Immunoblot analysis of immunoprecipitated AMA-1:GFP and PHA-4:GFP was performed on non-crossed-linked worm lysates that had been subjected to the ChIP protocol until the multiple wash steps . Then 50 ul lysis buffer was added to the immunocomplex bound beads , and the beads were boiled for 5min before loading onto the gel . Ready Gel Precast Gels ( 4–15% polyacrylamide ) from Bio-Rad Laboratories were used according to manufacturer's instructions . For AMA-1:GFP detection , anti-GFP goat polyclonal antibody was used , and for PHA-4:GFP detection anti-GFP from Roche ( cat# 11814460001 ) was used , along with the species-appropriate secondary antibodies . To monitor enrichment of known or newly identified target genes , qPCR amplification of ChIP DNA was performed . Primers used are described in Table S1 . Each PCR reaction of 10 ul was run through the following program in a Roche LightCycler 480 machine using the SYBR Green I Master kit ( Roche 04 707 516 001 ) according to manufacturer's instructions . PCR program: Step 1: 95°C for 5 min; Step 2: 95°C for 30 sec; Step 3: 55°C for 30 sec , Step 4: 72°C for 1 min . Repeat steps 2–4 44×; Step 5: 72°C for 5 min; Step 6: 4°C . The protocol for library preparation was adapted from the protocol “Preparing Samples for Sequencing Genomic DNA” by Illumina , and optimized with the following alterations . ChIP DNA was end-repaired using the ‘End-It DNA End Repair Kit’ from Epicentre , Cat#ER0720 , then an ‘A’ base was added to the 3′ ends of the ChIP DNA using Klenow ( 3′ to 5′; NEB Cat# M0212s ) . The ChIP DNA was then ligated with the adapter mix from the Illumina kit using LigaFast from Promega ( Cat#M8221 ) . The DNA was then purified using the QIAquick PCR Purification Kit and protocol between each step . The DNA was isolated from a 2% Invitrogen E-gel ( Invitrogen Cat# G5018-02 ) by cutting a gel slice between 150∼350 bp , which excludes adapter-adapters migrating at ∼120 bp . The DNA was then purified from the gel slice using the Qiagen Gel Extraction Kit , and subjected to PCR amplication with Phusion DNA polymerase ( NEB Cat# F-531 ) and Illumina primers using the following PCR protocol: 30 sec at 98°C , [10 sec at 98°C , 30 sec at 65°C , 30 sec at 72°C] for 16 cycles , followed by 5 min at 72°C . The DNA was then purified on a QIAquick MinElute column and the 150∼350 bp band gel-isolated . Out of a 20 ul elution , 2 ul were used to measure the DNA concentration ( ng/ul ) and A260/A280 using a Nanodrop spectrophotometer . DNA with >5 ng/ul concentration is now ready for sequencing . Worms were grown to the desired stage and pelleted as described above . Total RNA was extracted by TRIzol ( Invitrogen ) according to the manufacturer's protocol ( TRIzol: pellet = 2∶1 ) . PolyA RNA was purified using the Applied Biosystem ( Ambion ) MicroPoly ( A ) Purist kit . PolyA RNA was fragmented using Fragmentation Reagent ( Ambion ) . First strand cDNA was synthesized from polyA RNA using a mixture of oligo dT and random primer ( Invitrogen ) . Double stranded cDNA synthesis was performed using the SuperScript double stranded cDNA synthesis kit ( Invitrogen ) . RNA-Seq libraries were prepared for sequencing using the Illumina protocol as described [29] . RNA-Seq scoring was performed as previously described [30] . The RNA seq dataset has been submitted to GEO ( accession number GSE16552 ) . To assess the expression level of a given transcript , the DCPM ( average depth of coverage per million reads ) is calculated from RNA-Seq using a published method [30] . The change of expression level is determined by the DCPM of each transcript at different stages . The transcript with higher DCPM at a certain stage will be labeled as up-regulated gene at this stage . All mapping and analysis are based on genome WS170 of C . elegans . The annotation of the genome includes 27 , 322 transcripts ( 20 , 084 genes ) , which were confirmed from a previous study [30] , where most of the transcription start sites ( TSS ) were defined . If no TSS was found , it was set as 150 base pairs upstream of the ATG site . Raw data from the Illumina Genome Analyzer I and II were analyzed with Illumina's Firecrest , Bustard and GERALD modules for image analysis , basecalling and run metrics respectively , and a PhiX174 control lane was used for matrix and phasing estimations , as per the manufacturer's instructions . Then , the sequence reads were mapped to the C . elegans genome using Illumina's ELAND program in standalone mode . For each sample , the numbers of total and mapped reads were determined ( Table S5 ) . ChIP-Seq with two separate biological replicates with either the anti-GFP antibody ( Germany ) or anti-Pol II antibody ( Clone 8WG16 , Covance Research Products Inc ) were pooled together for signal calling . Significant “ChIP hits” were created using a 200 bp sliding window and scoring was performed with the PeakSeq program [20] . The hits were further filtered by using various p-values of PeakSeq ( Figure S7 ) . The Integrated Genome Browser ( IGB , Affymetrix ) was used to view images of signal tracks and to overlay them onto the C . elegans genome . Each GFP and POL II sample was compared over input DNA signal . To build signal tracks for comparing samples with different number of sequencing reads , the y-axis was normalized for each sample according to the total number of mapped reads . In order to show the concordance of two antibodies ( anti-GFP and anti-Pol II ) for the AMA-1 binding experiments , we compared the hits from PeakSeq with p value cut-off 0 . 001 . Every PeakSeq hit was divided into 600 bp bins . Then the tag count of each bin was normalized against its background input . The normalized tag counts of two antibodies were correlated significantly ( average correlation coefficient , R , is 0 . 934; Figure 2B ) . To determine which genes showed elevated Pol II or GFP signal over TSSs , we bypassed the first pass of Peak-Seq that determines potential binding regions by simulation . Instead , we directed Peak-Seq to examine 24 , 678 regions corresponding to TSS sites with a 300 bp pad on each side of the TSS . Peak-Seq was then used to determine whether these 600 bp regions were enriched relative to input DNA . All ChIP-Seq datasets have been submitted to GEO ( accession numbers GSE15535 , GSE15628 , GSE14545 ) , and all tracks are available for viewing at the modENCODE website ( www . modencode . org ) . The high genic density of the C . elegans transcriptome makes it often the case that several genes are within a few kilobases of a binding site , and it is necessary to select the most likely targets amongst them . We therefore wrote an algorithm that first searches for all transcripts within 5kb of the midpoint of a binding site . The distance between the binding site and each transcript is computed as one of three possibilities: binding site is upstream a certain number of bases from the TSS , downstream a certain number of bases from the TES , or within the gene . Transcript isoforms are then grouped into genes assigning the distance to it as that of the closest isoform . The genes are then ranked by the likelihood of being the target according to the following criteria: most likely target is that which has binding site within it , next most likely is that which is downstream of the binding site ( if multiple targets are downstream they are ranked by their distance ) , and the least likely are those that are upstream of the binding site ( if multiple targets are upstream they are ranked by their distance ) . The targets are then grouped into the following four bins: ( 1 ) target genes which have an internal binding site or that are less than 2kb downstream of the binding site , ( 2 ) targets that are within 2kb–5kb downstream , ( 3 ) targets that are less than 2kb upstream of binding site , and ( 4 ) targets that are within 2kb–5kb upstream . Finally , the binding site is said to target all genes in the first non-empty bin . Examples of how assignment of a binding site to candidate target genes occurs are shown in Figure S3 . To determine whether Pol II is stalled in a gene , we created a differential signal map by subtracting the tag count of the factor from that of the input at each position . This map was used to calculate the average tag count for promoter regions and over the bodies of transcripts . For this analysis , the promoter region is defined as ±300 bp from the TSS . The transcript body is the region 600 bp downstream of the TSS to the end of the transcript . If the ratio of promoter∶body average transcript count is greater than 4 , Pol II is considered stalled . For lower ratios , Pol II signal is deemed to be either uniform or absent depending upon whether Peak-Seq detected Pol II enrichment in the transcript . This is the same method used by Zeitlinger et al . [25] . A stringent q-value cut off defined by PeakSeq , 1×10−5 , was used to define the genes targeted by PHA-4 in embryos and starved L1 stages ( Figure S7 ) . The binding site has to be within or upstream 2000 bp of the targeted gene . GoStat ( http://gostat . wehi . edu . au/cgi-bin/goStat . pl ) was used for finding the over-represented and under-represented GO terms [Table S2 ( embryos ) and Table S3 ( L1 ) ] . GO categories were taken from the “biological process” level . 1975 and 1676 unique targeted genes at embryonic and L1 stages are analyzed respectively , of which 1328 and 905 are annotated in the GO database for embryonic or L1 stage . The top ten enriched GO terms at each stage are listed in Figure 3C and 3D . The same target list for GO analysis was used for Gene Set Enrichment Analysis ( GSEA ) [31] in embryos . An expression dataset of 8 , 769 genes were analyzed in a previous microarray study on pharynx development in embryos , comparing two mutants , par-1 ( excess pharynx ) and skn-1 ( no pharynx ) [14] . Of these , 2348 are defined as PHA-4 target genes from our embryonic target list . GSEA [31] ( http://www . broad . mit . edu/gsea/ ) showed significant enrichment of these targets among the up-regulated genes , which means they are more highly expressed in par-1 embryos with excess pharynx , than in skn-1 embryos that lack pharynx . The motif analysis was performed by MEME ( http://meme . sdsc . edu/ ) . MEME [32] was used to discover the motifs and generate the position weight matrices ( PWMs ) for PHA-4 in embryos and starved L1s . For the embryonic stage , the input data to MEME was the central 200 bp corresponding to the center of the peak of the bound region . All the input sequences were sorted by their p-values reported by PeakSeq and the top 200 sequences were chosen for motif discovery . For the L1 stage , sorting by p-values failed to find a significant match of the known PHA-4 consensus motif , because of a higher signal from the input sample . Instead , sequences were sorted by their signal ratios over input and the top 200 sequences with a more stringent window of 100 bp were chosen . To calculate the enrichment of the observed consensus motif , MAST [32] was used to search for sequences that contain the motif represented by the PWMs generated by the aforementioned . The input data to MAST was the central 1000 bp corresponding to the peak . All the sequences were sorted by their p-values and the top 200 sequences were chosen . For the background , 1000 bp was taken from 1000 bp upstream of the central for each sequence with p-value<0 . 05 . The p-value cutoff for each motif match was <0 . 0001 . The enrichment was calculated by comparing the number of sequences matched the motif in the bound regions to that in the background regions . The p-values of enrichment in both embryos and L1s are close to 0 .
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The C . elegans transcription factor PHA-4 is a member of the highly conserved FOXA family of transcription factors . These factors act as master regulators of organ development by controlling how genes are turned off and on as tissues are formed . Additionally they regulate genes in response to nutrient levels and control both longevity and survival of the organism . However , the extent to which these factors control similar or distinct gene targets for each of these functions is unknown . For this reason , we have used the technique of chromatin immunoprecipitation followed by deep sequencing ( ChIP–Seq ) , to define the target binding sites of PHA-4 on a genome-wide scale , when it is either functioning as an organ identity regulator or in response to environmental stress . Our data clearly demonstrate distinct sets of biologically relevant target genes for the transcription factor PHA-4 under these two different conditions . Not only have we defined PHA-4 targets , but we established an experimental ChIP–Seq pipeline to facilitate the identification of binding sites for many transcription factors in the future .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/genomics",
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/genome",
"projects",
"genetics",
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"genomics/gene",
"function",
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"biology/organogenesis",
"genetics",
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] |
2010
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Genome-Wide Identification of Binding Sites Defines Distinct Functions for Caenorhabditis elegans PHA-4/FOXA in Development and Environmental Response
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While the hypothalamo-pituitary-adrenal axis ( HPA ) activates a general stress response by increasing glucocorticoid ( Gc ) synthesis , biological stress resulting from infections triggers the inflammatory response through production of cytokines . The pituitary gland integrates some of these signals by responding to the pro-inflammatory cytokines IL6 and LIF and to a negative Gc feedback loop . The present work used whole-genome approaches to define the LIF/STAT3 regulatory network and to delineate cross-talk between this pathway and Gc action . Genome-wide ChIP-chip identified 3 , 449 STAT3 binding sites , whereas 2 , 396 genes regulated by LIF and/or Gc were found by expression profiling . Surprisingly , LIF on its own changed expression of only 85 genes but the joint action of LIF and Gc potentiated the expression of more than a thousand genes . Accordingly , activation of both LIF and Gc pathways also potentiated STAT3 and GR recruitment to many STAT3 targets . Our analyses revealed an unexpected gene cluster that requires both stimuli for delayed activation; 83% of the genes in this cluster are involved in different cell defense mechanisms . Thus , stressors that trigger both general stress and inflammatory responses lead to activation of a stereotypic innate cellular defense response .
The pituitary gland is at the center of the hypothalamo-pituitary-adrenal ( HPA ) axis that mediates the response to stress [1] , [2] . Under normal conditions , the stress response is an integrated collection of tissue responses that place the organism in a state of alertness in order to fight or flight in the face of aggression . The output of the HPA axis during the stress response is exerted by circulating glucocorticoids ( Gc ) . Indeed , Gc are synthesized by the adrenals in response to pituitary adrenocorticotropic hormone ( ACTH ) which itself is responsive to hypothalamic corticotropin-releasing hormone ( CRH ) that integrates neural inputs into this neuro-endocrine pathway . Gc exert their metabolic effects and a stress response through action on a wide range of tissues including liver , muscle and adipose tissues . The metabolic effects of Gc are profound and failure to maintain Gc levels within the normal range as in Addison disease ( hypocortisolism ) results in weight loss , muscle weakness , fatigue and low blood pressure . Cushing syndrome is caused by excess Gc and in Cushing disease , this excess is due to pituitary corticotroph adenomas . Cushing syndrome is associated with accumulation of body fat , cardiovascular and metabolic effects that can ultimately lead to hypertension , diabetes and osteoporosis [3] . It is therefore critical that activation of HPA axis and Gc synthesis be restored to normal levels following the stress response . Negative feedback is exerted by Gc themselves both at the level of hypothalamus where they repress transcription of the CRH gene and release of CRH , and at the pituitary level where they repress transcription of the pro-opiomelanocortin ( Pomc ) gene and the release of POMC-derived ACTH [1] . The inflammatory response is a response to biological stresses and various aggressions including those caused by infections [4] . Many effects of the response to inflammation are mediated through cytokines that act on multiple tissues and importantly on the HPA axis . Indeed , inflammation-induced cytokines , such as IL6 , stimulate hypothalamic production of CRH and act directly on pituitary corticotroph cells to stimulate Pomc gene transcription and ACTH release . LIF , a member of the IL6 family , also contributes to stimulation of POMC expression , both during development and in adult function [5] . At the level of pituitary corticotroph cells , the action of LIF and IL6 are additive with those of hypothalamic CRH [6] . The HPA axis is thus at the center of the so-called immuno-neuroendocrine interface [7] . The action of LIF/IL6 in pituitary corticotroph cells was shown to be mediated in part through activation of STAT3 [8] . STAT3 action on the Pomc promoter was mapped to a composite regulatory element that also contains the NurRE , a binding site for dimers of orphan nuclear receptors of the Nur subfamily [9] , [10] . The Nur subfamily of orphan nuclear receptors includes NGFI-B ( Nur77 ) , NURR1 and NOR1 [11] and it was shown that homodimers or heterodimers between members of this subfamily can activate the NurRE in response to CRH as long as at least one moiety of the dimers is NGFI-B [12]–[14] . Thus , a composite regulatory element integrates LIF/IL6 and CRH signaling . Gc repress Pomc gene transcription and in particular , antagonize Pomc activation by CRH and LIF [15] . Feedback repression of the Pomc gene by the Gc receptor ( GR ) is mainly exerted at the level of the NurRE/Stat3 composite regulatory element [16] , [17] . GR repression at the NurRE involves a mechanism of trans-repression that depends on protein∶protein interactions between GR and NGFI-B , rather than direct GR contact with DNA [16] . Further , the weak direct interaction between GR and NGFI-B requires the presence of the Swi/Snf ATPase Brg1 for stable formation of a trans-repression complex [18] . Brg1 is also required to recruit HDAC2 to this repressor complex and this repression involves chromatin remodeling . Thus , the NurRE/Stat3 regulatory element of the Pomc gene is a critical target for most stimulatory and inhibitory inputs into this system . In the present work , we have used whole-genome approaches to identify STAT3 target sites as revealed by ChIP-chip analysis using whole-genome tiling arrays [19]–[21] and to correlate these with the transcriptome of LIF and Gc responses . These analyses defined gene clusters that contribute to the repressor effects of Gc on corticotroph cell function , in particular the inhibitory Gc effect on cell proliferation . Most interestingly , the work revealed a class of genes that have delayed responses to LIF+Gc: a large number of these genes contribute to the cell defense response . Using a highly LIF- and Gc-dependent gene of this group , lipocalin 2 ( Lcn2 ) , we show synergistic recruitment of STAT3 and GR at a genomic regulatory module that integrates LIF and Gc responses . Further , LIF and Gc synergism is exerted on Lcn2 expression and other cell defense genes in various tissues in vivo and the gene profile of this action is very similar to that of LPS , a strong inducer of the inflammatory response . Collectively , this work highlights a general cell defense response that is dependent on the combined action of LIF or other cytokines released during inflammatory and immune responses and Gc produced by the HPA axis . This delayed stimulatory Gc action likely overlaps with hepatic acute-phase and innate immune responses [22] , [23] , and it contrasts with the anti-inflammatory properties of these steroids used therapeutically .
In order to assess the cellular response to LIF/STAT3 , the time course of STAT3 activation in response to LIF in AtT-20 cells , a model of mouse pituitary corticotroph cells , was determined by Western blot analysis of phospho-STAT3 ( Figure 1A ) . This analysis indicated a peak of phospho-STAT3 at about 20 minutes following LIF treatment . In principle , activated phospho-STAT3 should lead to promoter occupancy of STAT3 target genes and thus the time course of promoter recruitment was assessed by chromatin immunoprecipitation ( ChIP ) in AtT-20 cells for a panel of STAT3 target genes ( Figure 1B ) . For most of these genes , maximal promoter occupancy was achieved between 10 and 20 minutes after LIF stimulation . Genomic targets of LIF activated STAT3 were therefore identified by ChIP-chip analysis of AtT-20 cells treated with LIF for 20 minutes . Three independent STAT3 ChIP and control IgG samples were hybridized on the Affymetrix Mouse Tiling 2 . 0R Array Set , covering the entire non-repetitive mouse genome with a 35 bp resolution . The raw data were processed using the MAT software package [24] . A threshold P value of 10−5 was used to select peaks of specific STAT3 immunoreactivity throughout the genome , yielding a calculated false discovery rate ( FDR ) of 3 . 3% [25] . This analysis revealed a total of 3 449 putative STAT3 target sites in the mouse genome , after removal of 74 sites by redundant sequence filtering ( complete list in Table S1 ) . The chromosomal distribution of these sites is shown in Figure 1C . The mean length of genomic regions exhibiting a positive ChIP signal is 804 bp . In order to test the reliability of those results , 42 genomic sites with P values ranging from 10−5 to 10−148 were randomly picked and STAT3 recruitment at each of these sites was tested on separate ChIP using QPCR: all 42 tested sites were confirmed to be positive ( Figure 1B–C and data not shown ) . The position of STAT3 binding sites on the mouse genome was analyzed relative to transcription start sites ( TSS ) of UCSC known genes . They were mapped either as upstream relative to known TSS , downstream from known TSS within the gene body or relative to the 3′ end of UCSC known genes ( Figure 2A ) . This analysis clearly showed a preferential localization of STAT3 binding sites within 5 kb of TSS , with 19 . 4% of the total site number within this interval and 9 . 4% within 1 kb of TSS . Tiling array data for specific loci previously known to have STAT3 binding sites are also shown in Figure 2 . For example , the promoter region of the Pomc gene is known to have a STAT3 binding site at −387/−379 bp [8]–[10] , and the tiling array data show a peak of STAT3 recruitment over this promoter region ( Figure 2B ) . Similarly , the promoter of the Stat3 gene itself is known to have a STAT3 binding site , and thus is subject to auto-regulation . The tiling array shows a peak of STAT3 recruitment ( Figure 2C ) that overlaps the reported STAT3 binding site at −338/−331 bp [26] . The Socs3 gene is involved in negative feedback regulation of STAT3 signaling and the Socs3 promoter has a STAT3 binding site at −64/−72 bp [27] that overlaps the observed peak of STAT3 recruitment ( Figure 2D ) . In addition to these sites , the tiling array data revealed numerous other STAT3 binding sites in the Stat3/Stat5 and Socs3 loci; the biological relevance of these putative regulatory regions will need to be evaluated . Interestingly , STAT3 binding sites were found in close proximity to all Stat genes , except Stat6 . Finally , STAT3 binding sites were found in the vicinity and promoter region of some microRNA genes , for example around the miR-21 gene ( Figure 2E ) that was implicated in the STAT3-dependent growth promotion activity of IL6 [28] . The DNA binding sequence for STAT3 has been defined experimentally through the work of numerous investigators . For example , the binding motif used by the Genomatix software to identify putative STAT3 binding sites is shown in Figure 2F and compared with a consensus that we derived from 24 published genomic STAT3 binding sites . We have used two non-biased algorithms designed to identify recurring motifs within the STAT3-bound DNA fragments ( Figure 1C ) ; the AlignAce algorithm and the Consensus algorithm identified a consensus binding motif that is very similar to the previously documented binding sites for STAT3 ( Figure 2F ) . No other motif was found to be enriched within the ensemble of STAT3 genomic targets . We also searched the 3 449 STAT3 target sequences for known transcription factor binding motifs with MatInspector ( Genomatix ) software and again , we found no other enriched motif compared to 10 randomly picked genomic sequences of the same total length . In AtT-20 cells , the stimulatory effect of LIF on Pomc gene transcription is antagonized by Gc and GR . In order to assess whether this antagonism is reflected at the level of STAT3 genomic recruitment , we performed STAT3 ChIP in cells treated either with LIF , the synthetic Gc dexamethasone ( Dex ) or both for 20 minutes and determined STAT3 recruitment by QPCR for a panel of STAT3 target genes ( Figure 3A ) . While some genes such as Pomc showed moderately enhanced STAT3 recruitment in response to LIF+Dex compared to LIF , other genes such as metallothionein 2 ( Mt2 ) revealed marked synergism in STAT3 recruitment in cells treated with LIF+Dex ( Figure 3A ) . This suggests that recruitment of one factor potentiates recruitment of the other factor to target regulatory sequences . About a third of tested genes showed greater STAT3 recruitment for LIF+Dex compared to LIF treated cells while another third showed decreased recruitment and the remaining third showed no effect . In order to assess whether potentiation of STAT3 recruitment is reciprocal , similar ChIP analyses were performed for GR recruitment to the same loci and these analyses again showed potentiation of GR recruitment following LIF+Dex treatment for the same subset of genes , such as Pomc and Mt2 ( Figure 3B ) . It is noteworthy that so many randomly chosen STAT3 target loci are also Gc/GR targets . Sequential ChIP were performed for STAT3 and GR on three loci using AtT-20 cells treated with LIF+Dex . These analyses confirmed that for the Pomc , Mt2 and Lcn2 loci , both GR and STAT3 are present together on the same chromatin fragments ( Figure 3C ) . These data clearly suggest that a subset of LIF target genes may be subject to the combined action of LIF and Gc . In order to correlate STAT3 genomic binding sites with regulation by LIF or Gc of adjacent candidate target genes , we performed expression profiling experiments . Duplicate RNA samples from AtT-20 cells treated with/without LIF and/or Dex for 3 h and 18 h were hybridized on Affymetrix MOE expression arrays . The data were pre-processed using GC-RMA normalization within the FlexArray software [29] , [30] . A total of 2 396 regulated probesets were identified ( complete data provided in Table S2 ) following a Local-pooled-error test , using a 2-fold change threshold and a P value smaller than 0 . 05 [31] . The number of genes up or down regulated by these treatments is presented in Figure 4A . Whereas a large number of genes were up and down regulated by Dex , few genes are affected by LIF ( mainly up regulated ) . This low number of modulated genes was unexpected because we identified 3 449 STAT3 binding sites in presence of LIF . Most significantly , a large number of new genes are regulated in response to both LIF+Dex , at both 3 h and 18 h post-treatment ( Figure 4C , D ) . It is noteworthy that early and late response genes are quite different with a limited number of genes showing sustained changes of expression at both 3 h and 18 h ( Figure 4B ) . These data clearly suggest that a class ( es ) of gene ( s ) is dependent on both LIF and Gc for regulation . In order to correlate LIF regulated genes identified in these profiling experiments with genomic sites of STAT3 binding identified by ChIP-chip , we searched for STAT3 binding sites within 5 or 50 kb of the TSS of hormone responsive genes ( Figure 4E ) . This analysis showed that 62/42% of LIF regulated genes have STAT3 binding site within 5 kb of their TSS , and 76/64% within 50 kb of the TSS , at 3 h/18 h respectively . This proportion is smaller for Dex and LIF+Dex-regulated genes , reaching about 30% of genes within 50 kb of TSS . This is higher than the random expectation value of 18% , calculated for all genes on the Affymetrix MOE 2 . 0 microarray . Clustering analysis using Smooth correlation in the Genespring GX 7 . 3 software was performed on the expression profiling data of hormone-treated AtT-20 cells . A heat map ( Figure 5A ) of this clustering identified groups of genes that are similarly regulated ( Figure 5B ) . Clustering analysis was performed using the Smooth correlation K means approach . These clusters of co-regulated genes contain from 77 to 549 probesets ( Table S3 ) . The DAVID software was used to search for over-represented Gene Ontology ( GO ) classes of gene functions [32] . Clusters #1 , 3 , 4 and 8 did not contain significant numbers of genes associated with similar biological processes ( GO gene lists in Table S4 ) . Cluster #9 regroups genes that are repressed by Dex at both time points: this cluster contains significant enrichment for genes encoding transcription and nuclear functions ( P≤10−5 ) and cell processes ( P≤10−6 ) . Interestingly , cluster #7 is highly enriched in genes involved in control of cell cycle and mitosis ( P≤10−14 ) and these genes ( Figure 5C ) are primarily repressed by Dex at 18 h ( Figure 5A and 5B ) . It is reassuring to find this cell cycle and mitosis cluster associated with Gc repression since the growth of AtT-20 cells is known to be inhibited by these steroids [33] . The most striking cluster to be identified in this work is represented by the 179 probesets of cluster #2 ( Figure 5B ) . These genes have the particularity of being specifically upregulated at 18 h by the combined action of LIF+Dex , but not by Dex or LIF alone . Gene Ontology analysis of this cluster reveals a highly significant ( P≤10−8 ) number of genes that are associated with cell defense response ( Figure 5D ) . To a lower extent , we found other genes implicated in cell defense response in cluster #5 ( Table S4 ) , which contains the genes activated by LIF at 3 h or 18 h independently of the presence of Gc . The delayed ( 18 h ) response of cluster #2 genes is suggestive of a secondary response . In order to ascertain whether this is the case , we assessed responsiveness to LIF+Dex of a representative panel of cluster #2 genes in the presence/absence of the protein synthesis inhibitor cycloheximide ( Figure 5E ) . This experiment clearly showed that the bulk of this LIF+Dex response is secondary and dependent on de novo synthesis of an intermediate regulator ( s ) . Of the genes that are subject to synergistic activation by LIF+Dex , the Lcn2 gene showed the most striking potentiation . In order to validate the great synergism observed between LIF+Dex effects on Lcn2 mRNA levels in the microarray analyses , we performed RT-QPCR quantification of Lcn2 mRNA in AtT-20 cells treated for 18 h with either or both agents . These quantifications indicate that the Lcn2 gene is responsive to LIF alone ( 23-fold ) , highly induced by Dex ( 10 278-fold ) , but phenomenally subject to synergism between these two signals ( 156 026-fold ) as shown on a log scale in Figure 6A . This striking upregulation is also revealed by Lcn2 Western blot analysis of AtT-20 cell culture medium ( Figure 6B ) . No STAT3 binding was found at the Lcn2 promoter ( data not shown ) , but the STAT3 whole-genome ChIP-chip experiment revealed significant enrichment at about 22 kb upstream of the Lcn2 gene within an intergenic region ( Figure 6C ) and no other gene is regulated by either LIF and/or Dex in the Lcn2 vicinity . In order to assess the possibility that this STAT3 binding region might represent a regulatory sequence for Lcn2 expression , we performed analytical ChIP for STAT3 and GR in this genomic region using cells treated or not with hormones . These data indicated significant potentiation of STAT3 and GR recruitment over this putative regulatory region ( Figure 3A , 3B ) . Sequential ChIP analyses also demonstrate STAT3 and GR co-occupancy on this genomic region ( Figure 3C ) . This −22 kb region may therefore act as a hormone sensitive enhancer for regulation of Lcn2 expression . In order to test this hypothesis , a luciferase plasmid reporter was constructed with/without the putative 1133 bp enhancer domain and assessed for transcriptional activity upon transfection in AtT-20 cells . This assay revealed marked transcriptional activity of the putative enhancer ( Figure 6D ) and further , the enhancer-containing reporter plasmid was found to be responsive to LIF , Dex and LIF+Dex treatment ( Figure 6E ) . Thus , these data clearly suggest that an enhancer is present at −22 kb upstream of the Lcn2 gene and that this enhancer is in part responsible for the marked synergistic activation of Lcn2 transcription by LIF+Dex . Notwithstanding the likely involvement of a cycloheximide-dependent regulator ( s ) for long term Lcn2 induction ( Figure 5E ) , the data implicate direct actions of STAT3 and GR at the Lcn2 enhancer . Lcn2 is a secreted protein that is present in blood and its plasma concentration is greatly enhanced following bacterial challenges [34] , [35] . In order to test whether LIF+Dex also stimulate Lcn2 expression in vivo , mice were injected with either LIF , Dex or LIF+Dex and analyzed for serum Lcn2 . The effect of LIF+Dex was compared to the documented stimulation of Lcn2 expression by lipopolysaccharides O127:B8 ( LPS ) . While Dex on its own did not stimulate serum Lcn2 at 3 h of treatment , injection of LIF led to a small increase in serum Lcn2 but the combined LIF+Dex treatment was even more effective , approaching the response obtained with LPS injection ( Figure 7A ) . At 20 h of treatment , a small response to Dex was observed but again the greatest increase was observed in LIF+Dex treated mice . Circulating Lcn2 is likely produced by a variety of sources including liver [34] . It is therefore possible that the synergistic stimulation of Lcn2 gene expression observed in AtT-20 cells may be a reflection of a general cellular response to these agents . In order to test this , RT-QPCR was used to measure Lcn2 mRNA levels in pituitary and liver of mice injected with LIF , Dex and LIF+Dex ( 3 and 20 h ) , together with a reference group of mice injected with PBS or LPS ( Figure 7B to 7E ) . These data indicate that the synergistic action of LIF+Dex is not unique to the pituitary . Liver production of Lcn2 could thus account for a significant proportion of blood Lcn2 observed in animals treated with LIF+Dex . To assess whether the cell defense mechanism activated in AtT-20 by LIF+Dex ( cluster #2 ) is active and generalized in vivo , we randomly selected genes within this cluster . mRNA levels were measured by RT-QPCR in five tissues ( pituitary , liver , testis , lung and heart ) from mice treated for 20 h with PBS , LIF+Dex or LPS . As above , this experiment was performed in mice that have normal Gc levels using a pharmacological dose of Dex together with LIF . In all five tissues , the two treatments produced comparable patterns of gene activation ( Figure 7F ) . It thus appears that the cell defense mechanisms activated by LIF+Dex are very similar to those activated by LPS , in agreement with the stimulatory effect of LPS on cytokines , ACTH and Gc [36] . Many genes synergistically activated by LIF+Dex are part of the hepatic acute-phase and innate immune response [22] , [23] . In view of this widespread in vivo response , we verified whether similar responses would be observed in cell lines other than AtT-20 . Furthermore , we tested the responses to the LIF-related cytokine IL6 that is also induced during the inflammatory response . We used the 10T½ cells that co-express the LIF and IL6 receptors , like AtT-20 cells , but also the NIH 3T3 cells that only express the IL6 receptor , as shown by RT-QPCR ( Figure 7G ) . These analyses showed LIF+Dex as well as IL6+Dex synergism in all three cell lines ( Figure 7H ) .
The mapping of STAT3 binding sites on the mouse genome in LIF-stimulated cells identified 3 449 high confidence sites ( Figure 1 ) . This number stands in stark contrast with the relatively limited number of LIF-regulated mRNAs identified in profiling experiments ( Figure 4 ) . Although it is possible that a large number of target genes are regulated less than the 2-fold threshold of expression profiling data , it is more likely that this small number of LIF-regulated genes reflects the dependence of STAT3 on other transcription factors for activity . This action includes a moderate stimulatory effect on Pomc gene expression: within the context of Pomc regulation , LIF action is mostly meaningful in association with the stimulatory action of CRH signaling and the downstream Nur orphan nuclear receptors [10] . Nonetheless , it appears that activation of STAT3 by phosphorylation ( Figure 1A ) leads to promoter occupancy of a large number of target genes ( Figure 1B , 1C ) , independently of other signaling pathways . These STAT3 targets include cell-specific genes such as Pomc ( Figure 2B ) and genes involved in STAT3 signaling itself ( Figure 2C , 2D ) . The STAT3 target genes defined through ChIP-chip analysis also include a large number of genes that are co-regulated by Gc . Independently of this co-regulation , non-biased analysis of STAT3 genomic binding regions only revealed one conserved sequence motif , the STAT3 binding site itself ( Figure 2F ) . This conserved motif is entirely consistent with the previously defined STAT3 binding site [27] , [39] . It is noteworthy that this analysis did not reveal enrichment of any other motif: it might have been expected that some transcription factor binding motifs might have been enriched in association with STAT3 targets since STAT3 has already been shown to act in association with a variety of factors including GR [40] . Failure to detect particular enrichment of one binding motif with STAT3 binding sites may reflect the fact that STAT3 binding sites is associated with a large array of conserved binding motifs for many structural classes of DNA binding proteins and/or that these other factors act by protein∶protein interactions with STAT3 . The localization of binding peaks within STAT3 binding regions corresponded quite closely to the position of known STAT3 binding sites ( Figure 2B to 2E ) . For example in the Pomc promoter ( Figure 2B ) , a binding peak was observed at −465 bp whereas the published STAT3 binding site is located at −387/−379 bp [8]–[10] . A surprising finding of this study has been the large number of genes that exhibit potentiation of LIF effects ( activation or repression ) by Gc ( cluster #1 , 2 , 7 and 8 ) . The Venn diagrams ( Figure 4C and 4D ) clearly illustrate the large number of genes that are subject to Gc potentiation of LIF activity . Interestingly , a similar proportion ( about 2/3 ) of randomly chosen STAT3-binding loci showed enhancement or antagonism of STAT3 recruitment in presence of LIF+Dex compared to LIF alone ( Figure 3A ) . Also , many of these loci showed enhanced GR recruitment in LIF+Dex compared to Dex-treated cells ( Figure 3B ) . The potentiation of GR recruitment to STAT3 loci may involve direct protein interactions between these effectors as such interactions have been documented [41] . Direct STAT3:GR interactions may cause transcriptional synergism [41] but they may also reflect transcriptional antagonism as observed for trans-repression of LIF and/or CRH-induced Pomc transcription by GR . Indeed , Gc repress Pomc transcription without direct DNA binding by GR: the present work showed enhanced GR and STAT3 recruitment to the Pomc promoter in Dex+LIF-treated cells compared to Dex or LIF alone ( Figure 3A , B ) and we have similarly showed enhanced NGFI-B and GR recruitment to this promoter in CRH+Dex-treated cells compared to either treatment [18] . The potentiation of genomic recruitment of one factor by another is thus a clear indication of transcriptional interactions , but it does not predict whether an interaction may be synergistic or antagonistic on transcription . In addition to its repressor effect on Pomc transcription [42] , Gc inhibit the growth of AtT-20 cells [33] . Cluster #7 genes are repressed by Dex at 18 h but not 3 h irrespective of the presence of LIF and it is enriched in genes involved in cell cycle control and mitosis ( Figure 5A–C ) . This gene cluster therefore contains the ensemble of gene functions that may work coordinately to repress cell proliferation . It will be interesting to assess whether a similar group of genes is also involved in the growth inhibitory effects of Gc on immune or other cells . A unique cluster of genes was identified in the present work and is represented by cluster #2 ( Figure 5D ) . This 179 probesets ( 150 genes ) cluster is highly enriched in genes involved in cell defense response . Upon removal of 40 genes of unknown function , the remaining 110 genes with known or suspected function were queried for involvement in various processes . Of these , a total of 91 genes were previously associated with various cell defense mechanisms , such as innate responses to viruses or to bacteria , or acute phase response . This group thus represents 83% of genes with documented function in cluster #2 . The group includes genes of the innate response to viral infection that are interferon induced ( ISGs ) [43]: examples of this group include the six 2′-5′-oligoadenylate synthetase ( Oas ) genes , the Mx1 and Mx2 genes , Irf7 and Pkr ( Figure S1 ) . Interestingly , the interferon genes themselves and Toll-like receptors were not induced by LIF+Dex . Similarly , the bacterial infection and acute phase response genes [44] , [45] Tpl2 , Saa3 , Haptoglobin and Serpina3 were all found in cluster #2 but the α2-macroglobulin gene was not . It should be mentioned however that other ISGs and cell defense genes were induced in these experiments under different regulatory modalities and therefore they are found in clusters other than #2 . The genes of cluster #2 thus represent an innate defense mechanism that is triggered by joint activation of the inflammatory response and HPA axis . This innate cell defense response may be evolutionary conserved as it has been suggested for the functions of Mx and Oas genes [46] , [47] . The most striking example of a LIF+Dex-dependent gene is Lcn2 that is induced more than 150 000-fold in AtT-20 cells ( Figure 6A ) . Whereas the Lcn2 promoter does not exhibit any STAT3 or GR recruitment ( Figure 6C and data not shown ) , their activities are likely conferred , at least in part , upon the Lcn2 gene by a putative enhancer element identified 22 kb upstream of the Lcn2 gene ( Figure 6C–E ) . Interestingly , the putative Lcn2 enhancer exhibits potentiation of GR binding upon LIF/STAT3 action and the reverse ( Figure 3 ) . However , it is clear that direct action of STAT3 and GR on the Lcn2 locus is not the only mechanism of activation since at 18 h post-stimulation , most of the response to LIF+Dex is dependent on de novo protein synthesis ( Figure 5E ) . In fact , most of cluster #2 genes exhibit an analogous secondary response . Lcn2 regulation thus exemplifies a cell defense response that appears to be shared by many cells and tissues [48] , [49] . We have ascertained this in vivo by injection of LIF , Dex , or both in normal mice and compared these responses with LPS challenge in pituitary and liver . Lcn2 expression was induced by LIF in both tissues and Dex treatment exerted synergistic activation at 3 h post-treatment ( Figure 7A–E ) . Less synergism of Dex action with LIF was observed in vivo compared to tissue culture cells ( Figure 6A ) , but the in vivo experiments were conducted in mice with normal adrenal function and Gc levels . In order to test the responsiveness of cluster #2 genes in various tissues in vivo , a similar experiment was conducted in mice injected with LIF+Dex compared to LPS-injected animals . As shown graphically in Figure 7F , the response patterns to these agents are similar in five tissues . It is noteworthy that tissues not usually associated with the acute phase response , share this response pattern . These conclusions are also supported by experiments using different cell lines ( Figure 7G and 7H ) . Thus , LIF/IL6 and Gc appear to elicit an innate cell defense response . With regards to Gc , this positive action has been interpreted as pro-inflammatory [22] but it may be more appropriately interpreted as a local cell defense response that is distinct and complementary to the systemic anti-inflammatory actions of Gc . It is interesting to suggest that the innate cell defense response identified in the present work may constitute an ancestral defense mechanism .
AtT-20 cells were maintained in DMEM supplemented with 10% fetal bovine serum and antibiotics . The cells were transfected with 500 ng of luciferase reporter construct using Lipofectamine reagent ( Invitrogen ) . The following day , cells were stimulated for 4 h with either PBS as vehicle , LIF 10 ng/ml ( Chemicon ) , dexamethasone ( Dex ) 10−7 M ( Sigma ) , or a combination of LIF+Dex . Whole cell extracts ( WCE ) were prepared and analyzed on SDS-PAGE as described [18] . Western blots were revealed using STAT3 ( sc-482 ) , phospho-STAT3 ( sc-7993 ) , α-Tubulin ( sc-32293 ) and Lcn2 ( sc-50351 ) antibodies from Santa Cruz Biotechnology . AtT-20 cells were grown to 60–70% confluence and stimulated with 10 ng/ml LIF and/or 10−7 M Dex for 20 min . ChIP were performed as described previously [50] , with little modifications . Briefly , chromatin was crosslinked with 1% formaldehyde added directly to the culture medium ( 5 min at room temperature ) . Crosslinking was stopped with glycine 125 mM in PBS for 5 min , followed by chromatin preparation . Sonicated chromatin was immunoprecipitated with either rabbit IgG ( Sigma G2018 ) , GR ( sc-1004 ) or a combination of phospho-STAT3 ( sc-7993 ) and STAT3 ( sc-482 ) antibodies and collected using protein-A/G beads ( Santa Cruz Biotechnologies ) . After washes and decrosslinking , DNA was purified using QIAquick columns following manufacturer's directives . For sequential ChIP , chromatin immunoprecipitates were gently eluted with elution buffer ( 10 mM Tris-HCl pH8 , 1% SDS ) for 20 min at 65°C . Supernatants were diluted to 0 . 5% SDS , 0 . 5% Triton , 0 . 05% NaDOC , 10 mM Tris-HCl pH8 and 140 mM NaCl , and complemented with 0 . 5 mg/ml BSA , 0 . 05 mg/ml yeast tRNA and 0 . 025 mg/ml phage λ DNA . The second immunoprecipitation was performed as described above for single ChIP . Enrichment was assed by QPCR with Qiagen QuantiTect SYBR green PCR kit . The list of oligonucleotides used is available upon request . Three independent STAT3 and control IgG ChIP samples were amplified , fragmented , biotin labeled and hybridized on Affymetrix Mouse Tiling 2 . 0R Array Set as recommended by the company . Raw data were processed with the MAT software [24] to calculate peak intensity and determine statistically significant enrichment of specific genomic regions . A P value cut-off of 10−5 was applied and redundant sequences were subtracted following BLAT search . Thus , the STAT3 whole-genome ChIP-chip yielded 3 449 sites with a predicted false discovery rate ( FDR ) of 3 . 3% . De novo motif analyses were done using two different sequence alignment algorithms . First , 800 bp masked sequences were retrieved from UCSC genome browser for each of the STAT3 binding sites: those included 400 bp upstream and downstream of MAT defined enrichment peaks . These sequences were processed using AlignAce [51] and Consensus [52] . The graphical representation of the position weight matrices obtained from these analyses were generated with WebLogo [53] . The same sequence set was challenged against all known transcription factor binding motifs using the MatInspector software ( Genomatix ) . The resulting occurrence of each motif was compared to the mean number of predicted binding sites in 10 randomly picked genomic sequence sets . Total RNA was extracted from AtT-20 cells previously treated for 3 or 18 h with vehicle , 10 ng/ml LIF and/or 10−7 M Dex , using RNeasy columns ( Qiagen ) . Two biological replicates of each condition were hybridized on Affymetrix MOE 430 2 . 0 arrays , except for Dex 18 h that was hybridized on the previous version of MOE A and B arrays . Hybridization and scanning were done at the McGill University and Genome Québec Innovation Centre . Data were normalized using GC-RMA [29] , [30] on the FlexArray application . The variance between replicates is smaller than 0 . 001 . We used the Local-pooled-error test ( LPE ) to assess differential gene expression between control and hormone treated cells [31] . Gene expression with fold changes greater than 2 ( P≤0 . 05 ) were considered significant . Genes from cluster #2 were picked randomly for RT-QPCR validation . AtT-20 cells were treated with LIF+Dex ( 10 ng/ml and 10−7 M respectively ) in presence or absence of cycloheximide at 10 µg/ml ( Sigma ) . We also treated AtT-20 , 10T½ and NIH 3T3 cells with LIF ( 10 ng/ml ) , IL6 ( 10 ng/ml ) , Dex 10−7 M alone or in combination for 18 h . Total RNA was extracted as described above and gene expression was quantified with the Qiagen OneStep RT-QPCR kit . The genes with expression changes in at least one condition ( LIF , Dex , LIF+Dex , at 3 h or 18 h ) were uploaded into GeneSpring GX 7 . 3 software ( Agilent ) for analysis . Smooth correlation was used to do unbiased clustering . Following this , K-mean clustering using Smooth correlation was used to separate genes with the same expression reactivity . We determined that 9 clusters is the most segregating setting for our dataset . The gene lists extracted from those 9 clusters were uploaded into the DAVID website [32] to search for enriched biological processes . The Affymetrix MOE 430 2 . 0 gene list was used as reference . Thresholds were set at a minimum of 5 genes per Gene Ontology class and a P value ≤ 0 . 001 . Groups of six CD1 male mice aged between 10 and 14 weeks were injected intraperitoneally with either PBS , 100 µg/kg LIF , 400 µg/kg Dex , LIF+Dex or 100 µg/kg LPS ( O127:B8 , Sigma ) and sacrificed after 3 h . Similar groups were sacrificed at 20 h following 5 injections , except for LPS ( only one LPS injection and 4 PBS injections ) . Mice were anaesthetized with 0 . 025 ml/g of avertin 2 . 5% . 1 ml of blood was collected by cardiac puncture . Serum proteins ( 100 µg ) were loaded onto SDS-PAGE and Lcn2 protein was revealed by Western blot . Lcn2 is a small 26 kDa protein and the upper part of gels was stained with Coomassie blue as loading control . Pituitary , liver , testis , lung and heart were dissected out following sacrifice . Total RNA was extracted from these tissues using RNeasy column as described by Qiagen . cDNA was produced using SuperScript III ( Invitrogen ) and gene expression was measured by QPCR with Qiagen QuantiTect SYBR green . Lcn2 and other mRNA levels were normalized in respect to β-actin mRNA . The oligos sequences are available upon request . Animal experimentation was approved by the IRCM Animal Care and Use Committee , in conformity with regulations of the Canadian Council on Animal Care .
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Global biological responses involve pleiotropic , general components exhibited by many cells/tissues together with cell-specific responses . Typically , such responses are dependent on multiple signaling pathways that integrate different inputs to trigger concerted tissue/cell responses . In studying LIF action in the context of immune-endocrine regulatory interactions , we found that LIF regulates expression of a surprisingly small number of genes . In contrast , the mapping of LIF-activated STAT3 transcription factor recruitment by genome-wide ChIP-chip led to the identification of a much larger set of putative regulatory sites . In view of the cross-talk between cytokine and glucocorticoid ( Gc ) signaling in response to stress and inflammation , we investigated the contribution of Gc to LIF action . Interestingly , the discrepancy between the number of LIF-regulated genes and LIF-dependent STAT3 genomic targets was partly explained by widespread Gc potentiation of LIF action . We further show requirement on both signaling pathways to elicit a pleiotropic and stereotypic innate cellular defense response , together with cell-specific responses such as antagonism between cytokines and Gc on expression of pituitary POMC . Thus , this stereotypic innate cell defense response is defined by the convergence of pathways activated by the stress and inflammatory systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/genomics",
"cell",
"biology/gene",
"expression",
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/gene",
"expression",
"computational",
"biology/molecular",
"genetics",
"immunology/immune",
"response",
"immunology/innate",
"immunity",
"physiology/endocrinology",
"computational",
"biology/genomics",
"computational",
"biology/signaling",
"networks",
"molecular",
"biology/translational",
"regulation",
"computational",
"biology/systems",
"biology"
] |
2008
|
Regulatory Network Analyses Reveal Genome-Wide Potentiation of LIF Signaling by Glucocorticoids and Define an Innate Cell Defense Response
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X-chromosome inactivation ( XCI ) in female lymphocytes is uniquely regulated , as the inactive X ( Xi ) chromosome lacks localized Xist RNA and heterochromatin modifications . Epigenetic profiling reveals that Xist RNA is lost from the Xi at the pro-B cell stage and that additional heterochromatic modifications are gradually lost during B cell development . Activation of mature B cells restores Xist RNA and heterochromatin to the Xi in a dynamic two-step process that differs in timing and pattern , depending on the method of B cell stimulation . Finally , we find that DNA binding domain of YY1 is necessary for XCI in activated B cells , as ex-vivo YY1 deletion results in loss of Xi heterochromatin marks and up-regulation of X-linked genes . Ectopic expression of the YY1 zinc finger domain is sufficient to restore Xist RNA localization during B cell activation . Together , our results indicate that Xist RNA localization is critical for maintaining XCI in female lymphocytes , and that chromatin changes on the Xi during B cell development and the dynamic nature of YY1-dependent XCI maintenance in mature B cells predisposes X-linked immunity genes to reactivation .
Maintaining the proper dosage of gene expression is essential for the survival of all mammals . To balance the unequal number of sex chromosomes between females ( XX ) and males ( XY ) , female mammals transcriptionally silence a single X-chromosome through the process of X-chromosome inactivation ( XCI ) , thereby equalizing the expression of X-linked genes between the sexes [1 , 2] . XCI is initiated during early embryonic development by expression of the long noncoding RNA Xist , which is transcribed from a single X-chromosome that will become inactivated [3–7] . Xist RNA transcripts function in cis as molecular scaffolds to recruit protein complexes for deposition of heterochromatic modifications , including H3K27me3 , H2a-Ubiquitinylation ( H2a-Ub ) , the histone variant macro-H2a , and H4K20me , across the chromosome , resulting in transcriptional repression [8–10] . The memory of the transcriptionally silent Xi is maintained with each cell division throughout the entire lifetime of somatic cells , so that dosage compensation of X-linked genes is faithfully preserved . Continuous expression of Xist RNA also plays a role in maintaining transcriptional repression of the Xi during the maintenance phase of XCI . Human pluripotent stem cells that have irreversibly silenced XIST lack enrichment of heterochromatic marks on the Xi , and exhibit overexpression of X-linked genes [11–13] . Deletion of Xist in hematopoietic stem cells also compromises XCI maintenance in the blood lineages , and increased expression of X-linked genes in female mutant mice results in hyper-proliferation and development of myeloid cancers [14] . X-linked genes are frequently overexpressed in female lymphocytes from lupus patients and mouse models of autoimmunity [15–18] , which suggests that XCI may be perturbed in these cells [19 , 20] . The transcription factor Ying Yang 1 ( YY1 ) contributes to the initiation and maintenance phases of XCI . YY1 is a multifunctional protein containing four zinc fingers that can either activate or repress transcription [21–23] . The Xist promoter region contains YY1 binding sites and YY1 enhances Xist expression during XCI initiation and maintenance in mammalian cells [24] . The YY1 protein can also bind RNA [25 , 26] , and the Repeat C region of Xist contains three YY1 binding sites required for tethering Xist RNA to the chromatin of the Xi in post-XCI somatic cells [27] . YY1 mediates the long-distance DNA interactions required for V ( D ) J recombination of the immunoglobulin loci and for class switch recombination , and B cell specific YY1 deletion arrests B cell development at the pro-B cell stage [28–32] . We recently found that YY1 deletion or disruption in activated female T and B cells disrupts Xist RNA localization to the Xi with minimal impact on Xist expression [33] , suggesting that YY1 may regulate XCI maintenance through mediating Xist RNA localization in lymphocytes . The X-chromosome contains the highest density of immunity-related genes [34] , and these genes are subject to XCI to ensure appropriate expression levels in somatic cells [35–37] . Surprisingly , despite their expression of Xist RNA , mammalian naive T and B cells do not localize Xist RNA and heterochromatic marks to the Xi [33] . However , these modifications are present on the Xi in activated cells . Here , we determine when epigenetic modifications are first lost from the Xi in the B cell lineage , and evaluate the molecular mechanism by which YY1 mediates the return of Xist RNA and heterochromatic modifications to the Xi for XCI maintenance during B cell activation . Together , these data elucidate a novel , lymphocyte specific mechanism for XCI maintenance and provide a foundational framework for understanding the origins of increased X-linked gene expression in female lymphocytes from autoimmunity patients .
We previously observed that mature splenic and recirculating B cells lacked canonical Xist RNA clouds and heterochromatic modifications [33] . Thus , we determined when Xist RNA first disappears from the Xi during B cell development ( Fig 1A ) . To answer this question , we isolated hematopoietic stem cells ( HSCs; Lin- , IL-7Ra- , c-kit+ , Sca-1+ ) , common lymphoid progenitors ( CLPs; Lin- , IL-7Ra+ , c-kit+/lo , Sca-1lo ) and common myeloid progenitors ( CMPs; Lin- , IL-7Ra- , c-kit+ , Sca-1- , FcγRlo , CD34+ ) from bone marrow of female mice using fluorescence activated cell sorting ( S1A Fig ) [38 , 39] . Sorted cells were immediately fixed for Xist RNA fluorescence in situ hybridization ( FISH ) using labeled short oligo probes . We classified Xist RNA localization patterns into four groups: Type I cells have robust Xist RNA localization on the Xi; Type II cells exhibit diffuse Xist RNA signals within a nuclear region encompassing the X-chromosome; Type III cells have Xist RNA pinpoints across the nucleus; Type IV cells lack Xist RNA signals ( S2 Fig ) [33] . We found that HSCs , CLPs , and CMPs exhibited Type I and II Xist RNA patterns , similar to fibroblasts , which are typically 80–90% Type I ( Fig 1B and 1C and S3 Fig ) . Next , we examined CLP-derived B cell progenitors in the bone marrow , ( Fig 1A ) , including pro-B cells ( B220lo , AA4 . 1+ , CD19+ , CD43+ ) , pre-B cells ( B220lo , AA4 . 1+ , CD19+ , CD43- , CD23- , IgM- ) , and immature B cells ( B220lo , AA4 . 1+ , CD19+ , CD43- , CD23+/- , IgM+ ) from female mice [40 , 41] ( S1B Fig ) , and then performed Xist RNA FISH . Remarkably , we found that pro-B cells completely lacked any Xist RNA signals ( Fig 1B and 1C and S3 Fig ) . Pre-B cells had Type II and III diffuse patterns in about 30% of the cells , yet the majority of the cells lacked a detectible Xist RNA signal ( Type IV ) ( Fig 1C ) . Immature B cells also mostly lacked Xist RNA signals , and had similar levels of Type III patterns ( 35% ) as pre-B cells ( Figs 1C and S3 ) . We also examined recirculating mature B cells in bone marrow ( B220hi , AA4 . 1- , CD19+ , CD23+ ) ( S1B Fig ) and observed predominately faint and diffuse Type IV and Type III Xist RNA patterns ( Fig 1C , and S3 Fig ) . Finally , we isolated germinal center ( GC ) B cells and plasma cells from spleens of non-immunized female mice ( S1C Fig ) , and found that the Xist RNA localization patterns were predominantly Type III yet contained some Type I and II Xist RNA patterns ( Fig 1B and 1C ) . In summary , Xist RNA disappears from the Xi at the pro-B cell stage and is absent through mature B cells , and is partially restored in a subset of cells ( see below ) because some GC and plasma cells are likely activated . We hypothesized that the absence of robust Xist RNA ‘clouds’ on the Xi in developing B cell subsets could result from reductions in Xist expression . To address this , we examined the steady-state levels of Xist RNA in HSCs , CLPs , pro-B , pre-B , immature B , and recirculating mature B cells from bone marrow , and in naïve and in vitro stimulated mature splenic B cells ( S4A Fig ) . We detected Xist expression in all developing B cell subsets , including lymphocyte progenitors with robust Xist RNA clouds , although the relative Xist RNA levels decreased slightly from the pro-B to the pre-B stage ( p < 0 . 001; Fig 1D and S4B and S4C Fig ) . We also observed that Xist RNA transcripts were greater in recirculating B cells relative to immature B cells and earlier subsets ( p < 0 . 001; Fig 1D and S4C Fig ) . We next asked if the reduced Xist expression and lack of Xist RNA signals was accompanied by a reduction in heterochromatin marks on the Xi during B cell development . We performed sequential Xist RNA FISH followed by immunofluorescence to detect H3K27me3 and the ubiquitinylated histone H2A ( H2A-Ub ) in B cell subsets from bone marrow . We found that the majority of pro-B cells contained foci for both H3K27me3 and H2A-Ub ( Fig 1E and 1F ) , despite the lack of Xist RNA transcripts localized on the Xi in these cells . Pre-B cells also exhibited foci for H3K27me3 and H2A-Ub which co-localized with an X-chromosome ( S5A Fig ) , albeit at decreased levels compared to pro-B cells . Immature B cells had fewer nuclei with H3K27me3 ( ~25% ) and H2A-Ub ( ~15% ) foci ( Fig 1E and 1F ) . Recirculating B cells from bone marrow had the fewest nuclei with foci for these heterochromatin marks . We conclude that the Xi is dramatically restructured during female B cell development , and that loss of Xist RNA localization , together with reduced Xist expression , from the Xi initiates the gradual disappearance of heterochromatin modifications . Naïve female lymphocytes are the first physiological example of somatic cells where Xist is transcribed , yet Xist RNA does not localize to the Xi [33] . However , we have also shown that Xist RNA localization is restored in activated lymphocytes . To characterize the dynamic behavior of Xist RNA in activated lymphocytes , we performed a time course experiment to follow the visual appearance of Xist RNA transcripts on the Xi after in vitro stimulation of mature CD23+ follicular splenic B cells using CpG treatment . We performed RNA FISH in activated cells collected every 4 hours ( Fig 2A ) . Unstimulated cells ( 0 hr ) lacked detectible Xist RNA signals , consistent with a Type IV pattern . We observed Type III Xist RNA patterns , with pinpoints dispersed across the nucleus , starting at 4 hrs , and continuing to 12 hrs post stimulation ( Fig 2A and 2B and S6A Fig ) . At 16 hrs post stimulation , Type I and II Xist RNA patterns appeared ( Fig 2A and 2B and S6A Fig ) . Cells stimulated for 24–30 hrs have the highest percentages of Type I and II Xist RNA localization patterns ( Fig 2A and 2B and S6B Fig ) . After 36 hrs , the percentage of Type I and II Xist RNA patterns were reduced 10–20% ( S6B Fig ) . Xist RNA levels remained constant during the first 24 hrs of B cell stimulation ( S7A Fig ) . In our experience , we have not observed differences with the kinetics of Xist RNA localization in activated B cells across different mouse strains ( C57Bl/6 , BALB/c , 129/SvJae , Mus Castaneus ) or in female mice from different ages ( 2–6 months ) . We conclude that Xist RNA localization to the Xi occurs in two distinct phases during B cell activation . During the first phase ( Phase 1 ) , Xist RNA transcripts cluster together across the nucleus ( 4–12 hrs post-stimulation ) , becoming visible as dispersed pinpoints with RNA FISH . In the second phase ( Phase 2 ) , occurring at 16–30 hrs post-stimulation , Xist RNA pinpoints localize to the Xi and form the bright and dense canonical Xist RNA ‘clouds’ in a localized nuclear territory at the periphery . In order to generate an immune response , B cells can be stimulated in different ways , utilizing distinct signaling pathways . Each of these methods exhibits differences with cell division rates , the timing of cell cycle progression , and propensity for differentiation into plasma cells , which influences cellular responsiveness of B cell subsets [42–44] . We investigated whether the activation method would affect the timing or distribution of Xist RNA localization patterns in mature splenic B cells . We stimulated splenic CD23+ B cells three different ways: ( 1 ) CpG DNA , a TLR9 agonist that induces cell proliferation [45 , 46]; ( 2 ) lipopolysaccharide ( LPS ) , which stimulates B cells through TLR4 signaling; or ( 3 ) anti-mu antibody , which signals through the B cell receptor ( BCR ) [47 , 48] . We isolated splenic B cells from 5 different female mice ( 4 mice for anti-mu experiments ) , stimulated cells for 24 hrs , then quantified the localization patterns of Xist RNA using RNA FISH . We found that LPS and CpG stimulation had very similar distributions of Types I , II , III patterns ( Fig 2C and 2D ) , yet CpG stimulation seemed to yield slightly more ( 3–5% ) Type IV cells compared to LPS treatment ( Fig 2D ) . Surprisingly , B cell stimulation using anti-mu resulted in predominantly Type III Xist RNA patterns ( 75–95% ) , with less than 10% of nuclei containing Type I or II clustered Xist RNA transcripts ( Fig 2C and 2D ) . To determine if anti-mu induced alteration in Xist RNA localization was delayed relative to CpG and LPS , we assessed Xist RNA localization for 48–72 hrs after stimulation . We observed that Type III patterns predominate across all timepoints , with very few Type I cells and 10–20% Type II cells ( Fig 2E ) . By 72 hrs post-stimulation , the percentages of Type I/II Xist RNA patterns were reduced for all three methods of B cell activation ( S6C Fig ) . We conclude that B cell activation using CpG or LPS generated the highest amount of Type I/II Xist RNA patterns , and that anti-mu stimulation results in predominantly poorly localized Type III Xist RNA nuclear distribution . The stability of Xist RNA transcripts might influence its localization to the Xi in activated B cells , where long-lived Xist RNA would be predicted to remain associated with the Xi . First , we investigated the half-life ( t1/2 ) of Xist RNA transcripts in mature naïve B cells . We isolated splenic CD23+ B cells from female mice , treated cells with actinomycin D to inhibit RNA transcription for 0–11 hr , isolated RNA every 2–3 hr within this time period , and then used quantitative RT-PCR ( qRT-PCR ) to quantify Xist RNA ( the housekeeping gene RPL13A was used for normalization ) . We found that Xist RNA transcripts rapidly decreased with actinomycin D treatment compared to DMSO treatment alone ( Fig 3A ) , with a t1/2 of 1 . 9 hrs in mature naïve splenic B cells . This is significantly shorter than the t1/2 for Xist in female embryonic stem cells ( ESCs ) ( ~ 6 hrs ) and female mouse embryonic fibroblasts ( MEFs ) ( ~ 3–4 hrs ) [49] . To calculate the t1/2 for Xist RNA in CpG-stimulated splenic B cells , we treated activated B cells at 12 hrs post-stimulation with actinomycin for 8 hrs , then collected RNA at 2 hr intervals for qRT-PCR ( Fig 3B ) . We found that Xist RNA has a t1/2 of 2 . 6 hrs in activated B cells , similar to naïve B cells . Because Xist RNA has a short half-life in mature B cells , we asked whether any Xist RNA that had become localized to the Xi at either 12 or 24 hrs post-stimulation would have been preferentially stabilized at the Xi . To address this question , we performed Xist RNA FISH using activated B cells treated with actinomycin or DMSO ( control ) for 2–12 hr intervals ( Fig 3B and 3C ) . Significant levels of Xist RNA mislocalization from the Xi occurred after 2 hrs of actinomycin treatment , and no Type I/II patterns were present after 6 hrs ( Fig 3D ) , indicating Xist RNA localization does not preferentially stabilize transcripts . Thus , Xist RNA is short-lived in B cells , and its localization to the Xi requires continuous transcription . As Xist RNA transcripts dynamically relocalize to the Xi during the first 24 hrs following B cell stimulation , we determined if such co-localization coincided with the appearance of H3K27me3 enrichment on the Xi . We activated splenic B cells with CpG , then performed sequential Xist RNA FISH followed by H3K27me3 IF for cells at 5 hrs , 12 hrs , and 24 hrs post-stimulation . We quantified the number of cells containing an Xist RNA signal ( not distinguishing between Types I , II , or III ) , the number of cells containing an H3K27me3 focus , and cells with co-localization of both signals . Cells at 5 hours post-stimulation had strong H3K27me3 signals spread throughout the nucleus ( Fig 4A ) , consistent with limited transcriptional activity of naïve B cells . At 5 hrs post stimulation , more than half of the cells lacked the colocalized Xist RNA/H3K27me3 signal ( Fig 4A and 4C and S8 Fig; purple bars ) , typically observed in activated B cells and female fibroblasts . However , we found some examples of cells with a Type II Xist RNA pattern that contained a H3K27me3 focus , and also a few cells with a H3K27me3 focus without Xist RNA signal ( Fig 4B ) , suggesting that H3K27me3 is unlikely to function as an imprint to direct the return of Xist RNA back to the Xi during splenic B cell activation . Sequential IF followed by DNA FISH for detection of the X-chromosomes confirmed that H3K27me3 foci overlapped with a signal for an X-chromosome ( S5B Fig ) . The number of activated B cells with co-localization of Xist RNA signals and H3K27me3 foci increased at 12 hr and 24 hrs post-stimulation ( Fig 4C and S8 Fig; blue bars ) . Nuclei with Type I Xist RNA patterns had the most robust H3K27me3 foci , most notable at 24 hrs post-stimulation ( Fig 4A ) . The timing of H3K27me3 focal enrichment during B cell stimulation suggests that: ( 1 ) Xist RNA transcripts are localized on the Xi before or at the same time as H3K27me3 deposition; and ( 2 ) H3K27me3 marks are enriched on Xi during the second phase of Xist RNA localization . We previously showed that YY1 is necessary for the formation of Type I Xist RNA clouds in activated lymphocytes [33] , thus we speculated whether YY1 protein or RNA levels would correlate with the disappearance of Xist RNA localization on Xi during B cell development . To address this question , we isolated uncommitted progenitor cells ( HSCs , CLPs ) , developing B cell subsets ( pro-B , pre-B , immature B ) and recirculating B cells from bone marrow , and mature B cell subsets from spleen using female mice . We measured YY1 protein levels by flow cytometry and YY1 RNA levels using qPCR ( Fig 3E and S9A Fig ) . Uncommitted progenitor cells ( HSCs , CLPs ) have predominantly Type I Xist RNA localization patterns , yet CLPs have less YY1 protein compared to HSCs ( Fig 3E ) . YY1 RNA levels were relatively consistent across all B cell subsets ( S9A Fig ) . We found that pro-B cells , which lack detectible Xist RNA signals ( predominantly Type IV ) , have the greatest amount of YY1 protein among bone marrow derived cells . Among splenic B cell subsets , YY1 protein levels were higher overall compared to bone marrow derived cells , and germinal center B cells had the greatest amount of YY1 across all samples . Thus , there was no correlation between YY1 RNA or protein levels and Xist RNA localization patterns in developing B cells , indicating that decreased YY1 protein levels do not account for the disappearance of Xist RNA from the Xi during B cell development . Because Xist RNA and heterochromatin marks are localized to the Xi in activated lymphocytes , we asked whether the nuclear distribution of YY1 would change during B cell activation . We examined the nuclear distribution of YY1 protein using IF in naïve and activated splenic B cells , and observed that activated splenic B cells had more YY1 signal compared to naïve B cells ( Fig 3F ) , as described recently [50] . Consistent with this , we found that YY1 RNA levels increase after 4 hrs of stimulation , and remain constant through 24 hrs post-stimulation ( S7B Fig ) . However , we observed some overlap between YY1 and Xist RNA signals , yet we did not observe dramatic focal enrichment of YY1 protein that co-localized with Type I/II Xist RNA patterns in activated splenic cells ( Fig 3F ) . Activation of splenic B cells through the BCR ( using IgM/anti-mu ) or Tlr9 ( using CpG ) did not affect YY1 protein levels , determined by measuring fluorescence intensity from immunofluorescence staining ( S9B Fig ) . We conclude that there are no changes with YY1 nuclear localization in activated B cells . We previously showed that disrupting expression of the transcription factor YY1 in T and B cells ablates Xist RNA localization to the Xi in activated cells [33] . We therefore next asked whether Xist RNA localization , acting through YY1-mediated recruitment , was necessary for the return of heterochromatin modifications to the Xi in activated B cells . We isolated CD23+ splenic B cells from wildtype and floxed YY1 female mice , treated the cells with recombinant TAT-CRE recombinase protein ( to delete Yy1 ) [31] , and stimulated the cells for 3 days using CpG . YY1 deletion after Tat-Cre treatment was confirmed at the RNA , DNA , and protein level ( S10A Fig ) . Next , we performed sequential Xist RNA FISH followed by IF to examine heterochromatin enrichment on the Xi ( Fig 5A ) . Ex vivo YY1 deletion greatly reduced the number of H3K27me3 and H2A-Ub foci in activated B cells from 80–85% to 20% ( Fig 5B ) , and Type I and II Xist RNA patterns were also reduced ( Fig 5A ) , as observed previously [33] . These results indicate that YY1-mediated Xist RNA localization to the Xi in activated B cells is strongly associated with the enrichment of H3K27me3 and H2A-Ub on this chromosome . Next , we investigated whether the loss of heterochromatin marks on the Xi , resulting from YY1 deletion and the mislocalization of Xist RNA , would affect X-linked gene expression in female activated splenic B cells . We used ex vivo YY1 deletion in splenic XX or XY B cells from activated mice , with or without TAT-CRE , for RNA-seq analyses . Looking specifically at genes from the X-chromosome ( manuscript in preparation for whole genome analyses of YY1 deletion ) , we determined the differentially expressed genes ( DEGs ) in Tat-Cre treated XX and XY samples ( using FDR cutoff of 95% ) , removing the 11 genes present in both lists ( Fig 5C ) . Next we compared the 68 female-specific DEGs ( 48 down-regulated; 20 up-regulated ) between female wildtype and Tat-Cre treated B cells ( Fig 5C; S1 Table , first tab ) . This list includes the immunity-related genes Cxcr3 and Itm2a ( Fig 5D ) , both of which are overexpressed in human female B cells relative to male B cells [33] . We also identified all of the DEGs comparing female wildtype to YY1-deleted cells , and found 211 X-linked upregulated genes , consistent with the loss of Xist RNA , H3K27me3 , and H2A-Ub modifications from the Xi ( S2 Table ) . Gene Ontology ( GO ) analysis of the 211 upregulated X-linked genes , comparing XX wildtype to YY1-deleted cells ( S2 Table ) , revealed that affected pathways included chromatin and chromosome organization , mitochondrion , and also ubiquitin-like conjugation ( Fig 5E ) . We also observed that 266 X-linked genes were down-regulated after YY1 deletion in activated splenic B cells ( S1 Table ) , including Xist ( log2FC = -1 . 17 ) and neighboring genes Jpx ( log2FC = -1 . 02 ) and Ftx ( log2FC = -0 . 70 ) , which activate Xist expression [51–53] . Such findings are expected , as YY1 is a transcriptional activator for Xist in post-XCI cells [24] , thus YY1 deletion would be expected to reduce Xist RNA levels and expression of target genes positively regulated by Xist expression . Using qPCR , we confirmed that Xist RNA levels decreased by 50% 48 hrs after Cre-mediated deletion ( S10A Fig ) . In conclusion , YY1 is required to maintain XCI and transcriptional silencing of X-linked genes through regulating Xist RNA localization to the Xi . Because YY1 deletion impairs Xist RNA localization to the Xi in stimulated B cells , we next asked which protein domain ( s ) of YY1 were required for the localization ( Fig 6A ) . To address this question , we deleted YY1 ex vivo in activated splenic B cells , then performed rescue experiments by adding back YY1 mutant proteins each missing distinct domains known to be critical for YY1 function . To select for transfected cells , human-versions of YY1 ( hYY1 ) mutant proteins were co-expressed from a viral vector that also contained GFP with a separate internal ribosomal entry sequence ( IRES ) . Expression levels ( RNA and protein ) for each hYY1 mutant after rescue were comparable ( S10B Fig ) . We used FACS to sort infected cells ( GFP+ ) from uninfected cells ( GFP- ) 48 hrs after infection for each hYY1 mutant , and performed Xist RNA FISH quantifying the percentage of nuclei with Types I or II Xist RNA patterns . Infection using an empty vector control resulted in predominantly Type III and IV Xist RNA patterns , with very few Type I/II cells ( ~3–5%; for 3 independent experiments ) ( Fig 6B and 6C ) , similar to our previous observations for YY1 deletion in splenic B cells [33] . Full-length hYY1 protein rescued Xist RNA localization to the Xi , with nearly identical levels of Type I/II Xist RNA patterns as wildtype B cells ( Fig 6B and 6C ) . The N-terminal domain ( NTD ) of hYY1 ( 1–200 amino acids ) contains the transcriptional activation domain and interacts with histone acetyltransferases and histone deacetylases [54 , 55] , as well as RNA binding activity that is independent of the zinc finger domain [26] . However , we found that expression of the 1–200 amino acid protein did not rescue the Xist RNA localization defect ( Fig 6B and 6C ) . Next , we asked whether the C-terminal domain of hYY1 ( CTD; 201–414 amino acids ) that contains the REPO domain important for polycomb repressive complex 1 ( PRC1 ) and polycomb repressive complex 2 ( PRC2 ) interactions [56 , 57] as well as four zinc fingers ( ZNF ) responsible for DNA binding activity [58] would rescue Xist RNA localization defects . HYY1 sequence 201–414 rescued Xist RNA localization to the Xi ( Fig 6B and 6C ) , indicating that the critical domain for recruitment was located in this region . Surprisingly , we found that the DNA binding domain of YY1 alone , and not the REPO domain , was completely sufficient for Xist RNA localization in activated B cells , as expression of only the hYY1 four ZNF region was sufficient to rescue Xist RNA localization in activated B cells to wildtype levels ( Fig 6B and 6C ) . As a control , we examined Xist RNA localization patterns for each sort of GFP-negative cells , and observed similar percentages of Type I/II Xist RNA patterns as empty-vector cells ( S11 Fig ) . Importantly , expression of the hYY1 zinc finger construct in YY1 rescue experiments did not significantly alter Xist expression ( S10C Fig ) , which supports a novel role for YY1 in Xist RNA localization separate from transcriptional activation . These results indicate that the YY1 DNA binding domain is critical for localizing Xist RNA to the Xi in activated splenic B cells .
Female mammals use XCI for dosage compensation of X-linked gene expression . However , in contrast to other somatic cells , lymphocytes use a unique and dynamic two-step mechanism to maintain XCI ( Fig 7 ) . In our study , we addressed the timing of Xist RNA loss from the Xi during B cell development . We found that Xist RNA is localized to the Xi in HSCs and CLPs , which display localization patterns similar to fibroblasts and other somatic cells . Intriguingly , Xist RNA is absent from the Xi beginning at the pro-B cell stage , and is missing from this chromosome throughout B cell development . Using sequential RNA FISH and IF , we demonstrated that the heterochromatin modifications H3K27me3 and H2A-Ub , usually enriched on the Xi , progressively disappear from the Xi during B cell development . Our observations of the presence of Xist RNA and H3K27me3 on the Xi for HSCs and CLPs , and the absence of these marks on pre-B , and immature B cells are in agreement with a previous study [59] . Our study indicates that the chromatin of the Xi progressively changes during B cell development , initiated with the loss of Xist RNA , and heterochromatin marks gradually disappear from the Xi as pro-B cells differentiate to immature B cells . Because Xist RNA is expressed in these cells , we propose that Xist RNA localization to the Xi , rather than Xist expression , is necessary to maintain enrichment of heterochromatin modifications on this chromosome , and that its absence on the Xi may initiate the chromatin reorganization of the Xi during differentiation of CLPs to pro-B cells . Our work demonstrates that mature activated B cells , most notably in vitro stimulated follicular B cells , but to a lesser degree GC B cells and plasma cells , have robust Xist RNA clouds localized to the Xi . Some of the GC and plasma cells isolated from non-immunized mice were likely activated , which could be why we observe Type I and II patterns . Using TLR agonists to stimulate splenic B cells in vitro , we followed the nuclear locations of Xist RNA transcripts with RNA FISH over time . We found that Xist RNA relocalizes to the Xi in two steps , which we term Phase 1 and 2 ( Fig 7 ) . During Phase 1 , occurring 4–12 hrs post-stimulation , Xist RNA transcripts are clustered and dispersed across the nucleus in the Type III pattern . We propose that Xist binding proteins , preferentially expressed in activated rather than naïve B cells , sequester Xist RNA transcripts , which are continuously expressed and likely diffuse away from the site of transcription ( Fig 7 ) . At present , the identity of Xist RNA binding proteins that could regulate Phase 1 of Xist localization during B cell stimulation is unknown . There are over 200 distinct RNA binding proteins that function in XCI initiation and maintenance in embryonic stem cells and MEFs [60–62] , many of which function in chromatin remodeling and nuclear organization . We speculate that the Xist RNA pinpoints visible at 4–8 hrs post-stimulation are not individual Xist RNA molecules , which would have low signal intensity , but are instead clusters of Xist RNA nucleated by one or more RNA binding proteins , thereby generating a brighter fluorescence signal . Alternatively , Xist RNA tertiary structural changes may affect fluorescence detection during B cell stimulation . In this study , we observed that Xist RNA signals become localized to nuclear regions encompassing the Xi during Phase 2 of localization , occurring 16–24hrs after stimulation , where the majority of cells exhibit Type I and II patterns . We found that YY1 is required for Phase 2 of Xist RNA localization , because YY1 deletion ex vivo results in accumulation of Type III cells and reduction of Type I Xist RNA localization patterns [33] . YY1 is a direct activator of Xist , and YY1 deletion ex-vivo reduced steady-state levels of Xist RNA about 2-fold ( S1 Table; S10A Fig ) , which could contribute to the observed loss of heterochromatin mark enrichment at the Xi in stimulated B cells . It is possible that YY1 may also function for Phase 1 of Xist localization , because YY1 protein is still present during this time ( 4–12 hrs post-stimulation ) in our ex vivo Tat-mediated deletion system . We propose that YY1 is one of several proteins that function in Xist RNA localization in stimulated B cells , as we recently described a similar function for hnRNP U in human T cells [33] . In Phase 2 of Xist RNA localization , Xist RNA can be recruited either directly or indirectly by YY1 to the Xi . YY1 can bind both DNA [21 , 63] and also RNA , as Xist RNA can be immunoprecipitated using a YY1 antibody after UV crosslinking of female MEFs [27] . The RNA binding domain was recently mapped to the N-terminal domain excluding the zinc finger domain at the CTD [25] . However , another recent publication reported that the C-terminal zinc finger domain of YY1 can bind RNA [26] . In our study , we used a variety of different hYY1 mutant proteins to rescue Xist RNA localization in YY1 deleted splenocytes , and found that the zinc finger of YY1 is the only domain required for returning Xist RNA to the Xi ( Fig 6 ) . Importantly , ectopic expression of the hYY1 zinc finger domain ( nor any of the hYY1 mutant proteins ) did not affect Xist expression , indicating that YY1 transcriptional activation occurs independently of Xist RNA localization in activated B cells . Our results indicate that the DNA binding domain of YY1 may tether Xist RNA transcripts to the genomic DNA of the Xi . At present , it is unclear whether YY1 directly binds Xist RNA in activated splenocytes , or if YY1-interacting partners directly bind Xist RNA . YY1 interacts with various chromatin modifiers including Polycomb group proteins [56] and HDAC2 [64] in splenic B cells , and it is possible that YY1 tethers the Xist RNA-bound protein ( s ) to the Xi during Phase 2 of Xist localization in activated splenic B cells ( Fig 7 ) . The REPO domain of YY1 recruits Polycomb group proteins to DNA sequences [56] , thus we were surprised that deletion of this domain still resulted in rescue of Xist RNA localization in YY1-deleted splenocytes . It is possible that that the EZH2 subunit of PRC2 , which binds Xist RNA in fibroblasts and differentiating embryonic stem cells [10 , 65] , is directly recruited by Xist RNA to the Xi independently of YY1 . Our data demonstrate that H3K27me3 modifications co-localize with Type I and II Xist RNA signals as early as 5 hrs post-stimulation in activated splenic B cells . Because co-localization frequency increases with time post-stimulation , one interpretation is that Xist RNA becomes localized at the Xi at the same time as PRC2 is depositing H3K27me3 modifications across the Xi . It is well established that Xist RNA can bind to EZH2 subunit of PRC2 in MEFs and differentiating mESCs [10 , 65] , supporting a role for Xist RNA-mediated recruitment of PRC2 for localization at the Xi during B cell stimulation . However , at 5 hrs post-stimulation , we see evidence of some nuclei containing H3K27me3 foci that lacked Xist RNA ( Fig 4B; bottom row ) . This observation suggests that there might be epigenetic modifications , perhaps DNA methylation , across the Xi that mark this chromosome for the return of H3K27me3 and H2A-Ub marks during B cell stimulation . DNA methylation patterns are an important marker that preserves the epigenetic state after cell division , where DNA replication produces a new daughter strand requiring modified nucleosomes to be recruited to maintain gene silencing [66–68] . It is possible that the DNA methylation modifications at CpG regions of promoters across the Xi do not change during B cell stimulation , and may serve as an imprint to direct the return of Xist RNA . At present , it is unknown why the Xi loses heterochromatic modifications and Xist RNA during female B cell development , and how this might be beneficial for mature B cells . The X-chromosome is enriched for immunity-related genes , and there are some X-linked genes , such as Btk [69 , 70] , that are required for B cell development to continue beyond the pro-B cell stage . To our knowledge , X-linked genes are not required for V ( D ) J recombination , which occurs at the pro-B cell stage . We speculate that the euchromatic-like features of the Xi in developing B cells and the dynamic localization of Xist RNA and heterochromatin marks in activated mature cells might enable rapid reactivation of specific immune-related X-linked genes in response to pathogenic infections . Sex differences are observed in immune responses [71] , and our study demonstrates that B cell development is different in females compared to male cells . Our findings provide a mechanism for the genetic basis for sex-biased autoimmune susceptibility involving B cells that overexpress immunity-related X-linked genes TLR7 and TLR8 [15–18 , 72 , 73] . In conclusion , our study provides a foundational framework that explains how X-linked genes can become over-expressed in female B cells . The chromatin of the Xi changes during the early stages of B cell commitment , beginning with the loss of Xist RNA localization at the Xi . Because YY1-mediated Xist RNA localization to the Xi during lymphocyte activation is a critical factor for regulating X-linked gene expression , we propose that monitoring changes in Xist RNA localization in female lymphocytes is an important parameter for diagnosing perturbations to XCI in female-biased autoimmune disorders involving pathogenic B cells .
Animal experiments were approved by the University of Pennsylvania Institutional Animal Care and Use Committee ( IACUC ) . Euthanasia via carbon dioxide was used for animal sacrifice prior to spleen isolation . Splenic B cells were isolated and from wildtype female mice ( 2–6 months ) of various genetic backgrounds ( C57BL/6 , Balb/cJ , and 129S1 ) , or from yy1flox/flox mice on a C57BL/6 background ( 8–12 weeks ) . We did not observe differences with the kinetics of Xist RNA localization in activated B cells from different strains or ages . CD23+ mature follicular B cells were isolated using the MACS purification system with anti-CD23-biotin ( eBioscience ) and streptavidin microbeads ( Miltenyi Biotec ) , and cultured as previously described ( Wang et al . , 2016 ) . B cells were activated with 1 μM CpG ( InvivoGen ) , 5 μg/mL LPS ( Sigma ) , or 20 μg/mL anti-IgM . Sequential RNA fluorescence in situ hybridization ( FISH ) and immunofluorescence ( IF ) was performed using established protocols [13 , 74] . For Xist RNA FISH , two Cy3-labeled 20-nucleotide oligo probes were designed to recognize regions within exon 1 ( synthesized by IDT ) [33] . DNA FISH was performed with X-chromosome probes ( MetaSystems Probes ) . For IF , cells we blocked with 0 . 2% PBS-Tween 0 . 5% BSA . Histone H3K27me2me3 ( Active Motif; Cat . 39155 ) and Ubiquityl-histone H2a Lys119 ( Cell Signaling; Cat . #8240 ) were diluted 1:100 . Images were obtained using a Nikon Eclipse microscope and were categorized by types of Xist RNA localization patterns as described previously ( Wang et al . , 2016 ) . Statistical significance was calculated using chi-squared tests and ANOVA . Naïve and CpG activated splenic CD23+ B cells were treated with 5 μg/mL actinomycin D or DMSO . For activated cells , actinomycin D ( Invitrogen ) was added 12 or 24 hours after stimulation , and cells were harvested at various time points for both RNA FISH and qRT-PCR . RNA was isolated using TRIzol reagent ( Invitrogen ) , and cDNA was synthesized with qScript cDNA SuperMix ( Quanta ) . To determine the half-life , levels of Xist RNA were determined by qPCR using Power SYBR Green ( Applied Biosystems ) and the following Xist qPCR primer pair: 5’-CAGAGTAGCGAGGACTTGAAGAG-3’ , and 5’-GCTGGTTCGTCTATCTTGTGGG-3’ . Half-life experiments were performed twice , and the results from one representative experiment is shown . Samples were normalized to the level of Xist at time 0 for pre-actinomycin D treatment . Half-life was calculated using a linear regression equation , defining the amount of time ( in hours ) for half of the amount of Xist RNA ( at time 0 ) to remain . Statistical significance was calculated using two-tailed t-tests and ANOVA . Bone marrow or spleen cells were stained with antibodies for fluorescence activated cell sorting ( FACS ) analyses as previously described [50] . Briefly , cells were stained with fluorochrome-conjugated or biotinylated antibodies to mouse . Staining was done in PBS/1% BSA containing mouse IgG Fc fragments ( Jackson Immunoresearch , Cat # 115-006-020 ) . Dead cells and doublets were excluded and sorting was performed on a FACS Aria II machine using the following markers: Hematopoietic stem cells ( HSC ) : Lin- ( CD4[ , CD8 , B220 , GR1 , F4/80 ) , IL-7R- , Sca-1+ , c-kit+; Common lymphoid progenitors ( CLP ) : Lin- , IL-7R+ , Sca-1lo , c-kitlo; Common myeloid progenitors ( CMP ) : Lin- , IL-7R- , Sca-1- , c-kit+ , FcγRlo , CD34+; Progenitor B ( Pro-B ) : B220lo , AA4 . 1+ , CD19+ , CD43+; Precursor B ( Pre-B ) : B220lo AA4 . 1+ , CD19+ , CD43- , CD23- , IgM-; Immature B ( Imm-B ) : B220lo , AA4 . 1+ , CD19+ , CD43- , CD23+/- , IgM+; Recirculating mature B ( Recirc-B ) : B220hi , AA4 . 1- , CD19+ , CD23+; Follicular B ( FO B ) : CD19+ , B220+ , AA4 . 1lo/- , CD43- , CD23+ , CD21/35lo/-; B1-B: CD19+ , B220+ , CD43+; Transitional B: CD19+ , B220+ , AA4 . 1+ , CD43-; Marginal zone B ( MZ ) : CD19+ , B220+ , AA4 . 1lo/- , CD43- , CD23lo/- , CD21/35+; Plasma B: IgD- , DUMP ( CD4 , CD8 , F4/80 , GR1 ) - , CD19+/- , CD138hi; Germinal Center B ( GCB ) : IgD- , DUMP ( CD4 , CD8 , F4/80 , GR1 ) - , CD19+ , CD138- , GL7hi , FAShi . Antibodies ( Clone , Cat # ) were purchased from BioLegend: CD19 ( 6D5 , 115543 ) , CD23 ( B3B4 , 101608 ) , CD4 ( RM4-5 , 130312 ) , CD8 ( 53–6 . 7 , 100721 ) , CD21/35 ( 7E0 , 123418 ) , CD43 ( S11 , 143204 ) , IL-7R ( A7R34 , 135023 ) , Sca-1 ( D7 , 108124 ) , ckit ( 2B8 , 105812 ) , CD34 ( MEC14 . 7 , 119308 ) , B220 ( RA3-6B2 , 103229 ) , AA4 . 1 ( AA4 . 1 , 136510 ) , IgD ( 11-26c . 2a , 405725 ) , F4/80 ( BM8 , 123112 ) , FcγR ( 93 , 101318 ) , CD138 ( 281–2 , 142516 ) , eBiosciences: GR1 ( RB6-8C5 , 15-5931-82 ) , and from BD Biosciences: IgM ( R6-60 . 2 , 562565 ) , Fas/CD95 ( Jo2 , 557653 ) . Data were analyzed using FlowJo software . We performed four independent sorting experiments using 2–5 C57BL/6mice for each sort ( see S4 Fig ) , and the results and Ct values for all the experiments are shown in Figs 1 and S4 . To determine Xist RNA expression levels in each B cell subset , we calculated the ratio of Ct differences relative to mature splenic naïve cells ( Ratio = 2^ddCt ) , and used the housekeeping gene RPL13A [primer pair 5’-AGCCTACCAGAAAGTTTGCTTAC-3’ and 5’-GCTTCTTCTTCCGATAGTGCATC-3’] for normalization . We also used primers for another housekeeping gene ( 18S ) for normalization , and obtained similar results as RPL13A . Statistical significance was determined using one-way ANOVA post-hoc with Tukey correction . Splenic CD23+ B cells were purified from yy1flox/flox animals ( described above ) , then conditional YY1 knockout in splenic B cells was achieved using TAT-CRE enzyme purified from bacteria . Cells were washed three times with opti-MEM ( Invitrogen ) and then incubated with TAT-CRE for 45 min at 37°C . To inactivate TAT-CRE , fetal bovine serum was added to a final concentration of 10% . The cells were washed once with splenic B cell medium and then cultured at 37°C in a 5% CO2 atmosphere . The cells were activated ex vivo with 5 μg/ml LPS to stimulate proliferation [31] . Knockdown of endogenous YY1 was confirmed by qRT-PCR ( primer pair: 5’-CGACGGTTGTAATAAGAAGTTTG-3’ and 5’-ATGTCCCTTAAGTGTGTAG-3’ ) and Western blots ( sc414; Santa Cruz ) . After 24 hrs of YY1 deletion , cells were transduced with retrovirus supernatant containing either empty vector ( pMX ) or different human YY1-pMX constructs by 90 min infection . At 48 hours post infection , GFP positive and negative cells were isolated by FACS , and Xist RNA localization patterns were analyzed by RNA FISH . HYY1 levels were determined using human YY1-specific exonic primers within the CTD [primer pair: 5’-CACATGTGCGAATCCATACC-3’ and 5’-TGGTTGTTTTTGGCCTTAGC-3’] and also by Western blots , using an antibody for YY1 ( sc414 Santa Cruz Biotechnology ) . Human YY1 ( hYY1 ) , hYY1 1–200 , hYY1ΔREPO , hYY1 zinc finger 288–414 , and hYY1 201–414 sequences were cloned into the pMX-GFP vector BamHI-SnaB1 sites [32] . All hYY1 constructs were cloned in frame with GFP . High titer retrovirus supernatant was prepared using Fugene-6 ( Promega ) transfection into HEK293 cells along with retroviral envelope plasmid pHIT123 . Viral supernatants were collected and concentrated with Retro-X-Concentrator ( Clontech ) . To determine levels of YY1 , immunofluorescence was performed in naïve and CpG activated follicular B cells at an antibody dilution of 1:200 ( Santa Cruz ) . Bone marrow and splenic cells were isolated from female mice ( one mouse for each experiment ) , washed , and then stained for surface antigens in PBS with 2% bovine serum albumin ( BSA ) for 30 min at 4°C . Following washing , cells were treated with Cytofix/Cytoperm buffer ( eBiosciences ) and then stained for YY1 ( clone EPR4652 , Abcam ) for 30 min at 4°C . Data were collected on a BD LSR II flow cytometer and analyzed with FlowJo software ( Tree Star ) . The average fluorescence units were quantified for each cell subset and statistical significance was determined using two-tailed t-tests; representative results from one experiment is shown . Mu-IgM/anti-CD40 stimulated follicular B cells were isolated from 3 month old female or male yy1flox/flox animals ( three separate animals each ) treated with or without TAT-CRE . About 48 hours post-stimulation and YY1 deletion , total RNA was isolated using TRIzol ( Invitrogen ) and libraries were prepared with an Illumina TruSeq Stranded mRNA LT kit . Samples were run on Illumina NextSeq 500 and bioinformatic analyses were conducted as previously described [75] . Briefly , R ( v3 . 3 . 1 ) , RStudio ( v1 . 0 . 44 ) , and the Bioconductor suite of packages ( http://www . bioconductor . org ) were used to perform X-linked gene expression analyses . RNA-Seq reads were aligned with Tophat to the GRCm38/mm10 mouse reference genome and HTSeq-count and edgeR were used to normalize data . Statistical significance was determined with log2 fold change ( lfc ) and false discovery rates ( FDR ) ( FDR <0 . 05 , lfc > 0 . 5 or <-0 . 5 ) . Heatmaps were constructed in RStudio ( gplots , RColorBrewer ) , and expression levels for differentially expressed X-linked genes are shown . Gene Ontology ( GO ) term enrichment was performed using DAVID v6 . 8 . A selection of Functional Annotation Clustering terms with an enrichment score >1 ( p < 0 . 05 ) are shown . The RNAseq data are available in the Gene Expression Omnibus ( GEO ) database under the accession number GSE104097 .
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Females are predisposed to develop various autoimmune disorders , and the genetic basis for this susceptibility is the X-chromosome . X-linked genes are dosage compensated between sexes by X-chromosome Inactivation ( XCI ) during embryogenesis and maintained into adulthood . Here we show that the chromatin of the inactive X loses epigenetic modifications during B cell lineage development . We found that female mature B cells , which are the pathogenic cells in autoimmunity , have a dynamic two-step mechanism of maintaining XCI during stimulation . The transcription factor YY1 , which regulates DNA looping during V ( D ) J recombination in B cells , is necessary for relocalizing Xist RNA back to the inactive X in activated B cells . YY1 deletion ex vivo in mature B cells impairs heterochromatin mark enrichment on the inactive X , and results in increased X-linked gene expression . We demonstrate that the DNA binding domain of YY1 is sufficient for localizing Xist RNA to the inactive X during B cell stimulation . Our study indicates that Xist RNA localization is critical for maintaining XCI in female lymphocytes . We propose that chromatin changes on the Xi during B cell development and the dynamic nature of YY1-dependent XCI maintenance in mature B cells predisposes X-linked immunity genes to reactivation .
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2017
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Loss of Xist RNA from the inactive X during B cell development is restored in a dynamic YY1-dependent two-step process in activated B cells
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Meconium ileus ( MI ) , a life-threatening intestinal obstruction due to meconium with abnormal protein content , occurs in approximately 15 percent of neonates with cystic fibrosis ( CF ) . Analysis of twins with CF demonstrates that MI is a highly heritable trait , indicating that genetic modifiers are largely responsible for this complication . Here , we performed regional family-based association analysis of a locus that had previously been linked to MI and found that SNP haplotypes 5′ to and within the MSRA gene were associated with MI ( P = 1 . 99×10−5 to 1 . 08×10−6; Bonferroni P = 0 . 057 to 3 . 1×10−3 ) . The haplotype with the lowest P value showed association with MI in an independent sample of 1 , 335 unrelated CF patients ( OR = 0 . 72 , 95% CI [0 . 53–0 . 98] , P = 0 . 04 ) . Intestinal obstruction at the time of weaning was decreased in CF mice with Msra null alleles compared to those with wild-type Msra resulting in significant improvement in survival ( P = 1 . 2×10−4 ) . Similar levels of goblet cell hyperplasia were observed in the ilea of the Cftr−/− and Cftr−/−Msra−/− mice . Modulation of MSRA , an antioxidant shown to preserve the activity of enzymes , may influence proteolysis in the developing intestine of the CF fetus , thereby altering the incidence of obstruction in the newborn period . Identification of MSRA as a modifier of MI provides new insight into the biologic mechanism of neonatal intestinal obstruction caused by loss of CFTR function .
Cystic fibrosis ( CF; MIM 219700 , http://www . omim . org ) is an autosomal recessive condition caused by mutations in the cystic fibrosis transmembrane conductance regulator ( CFTR; MIM 602421 ) gene [1] . The earliest manifestation of CF is meconium ileus ( MI ) , a prenatal obstruction of the small intestine at the ileocecal junction . Meconium , the intestinal contents of the developing gut that form the first bowel movement , has an abnormally high protein content in CF neonates thought to be due to defective proteolysis [2]–[4] . Impaction of the tenacious meconium results in intestinal obstruction in approximately 15% of CF newborns . This complication presents as abdominal distention , failure to pass meconium , and vomiting and was near universally fatal in CF newborns until effective treatment ( enema or surgery ) was developed . The long term effects of MI have been a matter of debate as some investigators have reported worse outcomes while others observed no significant differences from CF subjects without MI [5]–[7] . Genetic modifiers have been implicated in the development of MI for over 40 years as recurrence risk for this complication in siblings with CF ( ∼0 . 25 ) has consistently been shown to be higher than that in unrelated individuals with CF ( ∼0 . 15 ) [8]–[12] . Concordance analysis in monozygous and dizygous twins demonstrated that the heritability of MI approaches 1 . 0 , confirming that modifier genes play a substantial role in MI [12] . CFTR also contributes to risk as MI almost exclusively manifests in CF subjects with exocrine pancreatic insufficiency ( PI ) , which is highly correlated with CFTR genotype [13] , [14] . Furthermore , the incidence of MI appears to vary among CFTR mutations that confer PI . For example , the amino acid substitution p . Gly551Asp ( “G551D” ) has been associated with reduced risk of MI compared to the most common CFTR mutation that causes a deleterious in-frame deletion of one amino acid ( p . Phe508del , “delta F508” ) [15] , [16] . Initial attempts to identify MI modifier genes in humans utilized localization results from mouse studies . Mice with disruption of Cftr present with an MI-like phenotype; however , it differs from human MI in several respects . First , intestinal obstruction in CF mice causes mortality shortly after birth and at the time of weaning with the introduction of solid food [17]–[19] . Life-threatening obstruction in humans occurs in the perinatal period , while episodes of variable severity termed distal intestinal obstruction syndrome ( DIOS ) can also occur throughout life ( 5 to 12 episodes per 1 , 000 patient-years ) , especially in adults with PI and as a post-operative complication of surgical intervention , particularly organ transplantation [20] , [21] . Second , obstructive lesions in CF mice have been observed in the jejunum , ileum and colon , compared to predominantly ileo-colic localization in humans [22] . Third , pancreatic exocrine disease is much less prominent in mouse models of CF [17] , [23] , [24] . On the other hand , there are instructive genetic similarities between mice and humans with CF . Cftr alleles influence the rate and severity of murine intestinal obstruction [17] , [25]–[28] and strain-specific differences in the penetrance of intestinal obstruction indicate that different modifier genes underlie obstruction at birth and at weaning [19] . Candidate gene approaches in CF mice have revealed that decreased expression of the sodium hydrogen exchanger 3 ( Nhe3 ) or mucin 1 ( Muc1 ) or over-expression of the chloride calcium channel activated 3 protein ( Clca3/Gob5 ) can reduce intestinal obstruction at weaning [29]–[31] . Newer animal models of CF have provided additional clues in the search for modifiers of MI . CFTR knock-out ferrets and pigs have been shown to develop intestinal obstruction that is anatomically and temporally equivalent to that observed in humans; however , the phenotype is highly penetrant in these animals ( 75% and 100% , respectively ) [32] , [33] . Animal models of CF and heritability studies in humans suggest that intestinal obstruction due to loss of CFTR function is a consistent feature , and that the incomplete penetrance of this trait in humans with CF is likely due to the presence of genetic modifiers . We present here the results of a regional association analysis of a linked locus on chromosome 8 [12] , and report the identification and functional confirmation of a modifier gene for MI in humans and mice with CF .
To investigate a region on chromosome 8 that had previously shown linkage to MI [12] , transmission analysis of SNPs was performed using families from the Cystic Fibrosis Twin and Sibling Study ( TSS ) . As MI is associated with an increased recurrence risk among siblings , we enriched for genes that modify this phenotype by analyzing 133 families in which at least one subject was affected with MI ( 26 MI concordant and 91 discordant pairs , 2 concordant and 8 discordant sets of 3 , and 6 singletons; Table 1 ) . Since MI rarely occurs in the absence of PI , individuals were excluded if their CFTR genotypes were associated with exocrine pancreatic sufficiency ( PS ) ( n = 7 ) or if they clinically demonstrated PS ( n = 5 ) . All SNPs on the Illumina 610-Quad genotyping panel that passed rigorous quality control ( 2 , 896 SNPs ) [34] were included from an approximately 9 Mb region within the linkage locus where the SNP LOD score exceeded 1 . 0 ( boundaries: rs2945913-rs2285274; green shaded area in linkage plot inset , Figure 1 ) . Parental genotypes were utilized to test the transmission of SNP alleles using pedigree-based association testing ( PBAT ) , an extension of the transmission disequilibrium test that is robust against population stratification [35] , [36] . A cluster of SNPs proximal to and within MSRA ( MIM 601250 ) showed evidence of association with MI when an additive mode of inheritance was assumed ( Figure 1 ) . One SNP within this cluster 5′ to MSRA , rs614197 , exceeded the threshold for region-wide significance ( P = 8 . 35×10−6 , Bonferroni corrected P = 0 . 024 ) . However , this SNP was not associated with MI in a sample of unrelated CF patients from the Canadian Consortium for Genetic Studies ( CGS; Table 1 ) . Lack of significant association between MI and rs614197 in the CGS sample led us to question whether the initial observation in the TSS families was spurious , or if detection of association could be confounded by interacting loci [37] or by heterogeneity of effect of alleles at the locus [38] , [39] . To test the latter concept , we used haplotype analysis to search for additional genetic variation associated with MI in the region surrounding MSRA . Haplotypes derived from a sliding window of three consecutive SNPs across a 2 Mb region centered at rs614197 ( boundaries: rs17700611-rs4240673 ) were tested for transmission distortion under an additive genetic model ( Figure S1 ) . We used combinations of three SNPs as a compromise between the number of haplotypes generated and the penalty incurred by multiple testing . Two haplotypes exceeded the threshold for significant regional association after Bonferroni correction for 2 , 890 different haplotypes observed in the 133 families studied ( Table 2 ) . The combination of rs10903323 T , rs4840475 G and rs17151637 A ( T-G-A ) spanning a 3 . 5 kb region in intron 3 of MSRA ( Figure 2 ) was significantly over-transmitted to individuals without MI , or in other words had a protective effect on MI ( 54 informative families , raw P = 1 . 08×10−6; Bonferroni P = 3 . 13×10−3 ) . In the entire TSS cohort , the T-G-A haplotype frequency was 14 . 9% ( 13 . 0% in subjects with MI , 25 . 0% in those without MI ) . A second , overlapping haplotype ( Figure 2 ) containing two of the same SNP alleles as the T-G-A haplotype ( rs4840475 G , rs17151637 A , plus rs6601427 C; Table 2 ) similarly demonstrated association with the absence of MI ( 48 informative families , raw P = 1 . 17×10−5; Bonferroni P = 0 . 034 ) . Another haplotype located just 5′ to MSRA showed nearly significant over-transmission to individuals with MI , indicating that it conferred risk for MI ( 59 informative families , raw P = 1 . 99×10−5; Bonferroni P = 0 . 057 ) . Interestingly , this haplotype included rs614197 , the SNP that was associated with MI in the initial single marker analysis ( rs586123 G , rs614197 G and rs2055729 C; Table 2 ) . The SNPs comprising these three haplotypes displayed weak linkage disequilibrium ( LD ) with the exception of the second and third SNP in the 5′ haplotype ( r2 = 0 . 55; Figure 2 ) . Thus , the haplotypes are likely detecting additional genetic variation that cannot be assessed by the tagging SNPs on this array platform . To evaluate whether the MSRA alleles identified here by association could explain the former linkage signal on chromosome 8 , the frequency of the T-G-A haplotype that showed the strongest association signal was calculated in siblings with CF that were concordant for MI . Sixteen sib-pairs contributed positively to the LOD score while the remaining 14 sib-pairs had a negative LOD score . The frequency of the T-G-A MSRA haplotype was lower in siblings that contributed to linkage ( 5 of 64 chromosomes , 7 . 8% ) compared to siblings that did not ( 12 of 44 chromosomes , 21 . 4%; P = 0 . 033 ) . Given that the T-G-A haplotype is associated with a decreased rate of MI , it appears that the observed linkage with risk for MI in our previous study was due to the increased sharing of other MSRA haplotypes that confer a higher risk of MI . A replication study was performed using CF patients recruited by the CGS . The CGS is representative of the general CF population in Canada and comprises approximately 70% of all Canadian CF patients [40] . The overall rate of MI in this group was 15 . 9% ( n = 220 with MI , 1 , 163 without MI ) , which is consistent with the incidence of MI reported in numerous Caucasian CF populations [8] , [10] , [16] . The frequency of the T-G-A haplotype in the 1 , 335 CGS subjects in whom haplotypes were ascertained was similar ( 15 . 4% ) to that observed in the TSS ( 14 . 9%; Table 2 ) . The T-G-A haplotype showed association with MI in the CGS sample under an additive model ( OR = 0 . 72 , 95% CI [0 . 53–0 . 98] , P = 0 . 04 ) , in agreement with the initial finding in the TSS . The incidence of MI in subjects with 0 copies of the T-G-A haplotype was 17 . 3% ( 165/954 subjects ) , 13 . 1% ( 46/351 ) in those with 1 copy and 10% ( 3/30 ) in those with 2 copies . The concept that variation in CFTR influences the risk of MI is evident from the observation that a PI state ( primarily determined by CFTR genotype ) is required for the development of MI [14] . However , there is a finer correlation between CFTR genotype and MI as two CFTR mutations that are highly correlated with PI , p . Gly551Asp and p . Gly542X , have been shown to decrease or increase MI risk , respectively , from that conferred by the common mutation p . Phe508del [14]–[16] . As p . Gly551Asp is present at a relatively high frequency among European CF alleles ( ∼2% [41] ) , we evaluated the association between this mutation and MI in the TSS and CGS cohorts . The incidence of MI in p . Gly551Asp-bearing subjects was 7 . 8% in the TSS ( n = 51 ) and 5 . 9% in the CGS ( n = 51 ) , compared to 20 . 5% ( n = 507 ) and 17 . 9% ( n = 851 ) in p . Phe508del homozygotes ( P = 0 . 026 , 0 . 033 ) , respectively . Combining these two samples of CF subjects demonstrated that the odds of MI in subjects with p . Gly551Asp was about a third of that in p . Phe508del homozygotes ( OR = 0 . 32 , 95% CI [0 . 13–0 . 76]; P = 0 . 010 ) , comparable to the report by Hamosh , et al [15] . The finding that variation in the disease-causing gene alters the incidence of MI even among PI subjects suggested that the relationship between MI and the MSRA haplotype could be confounded by variation in CFTR . To control for genetic heterogeneity in CFTR , we tested for association between the T-G-A haplotype and MI by evaluating 1 , 017 subjects with the same genotype ( p . Phe508del homozygotes ) drawn from the TSS MI families ( n = 166 ) and CGS studies ( n = 851 ) . A meta-analysis conducted using logistic regression coefficients and standard errors [42] from individual TSS and CGS analyses revealed that the T-G-A haplotype retained an additive protective effect ( P = 0 . 001 ) , indicating that MSRA modifies MI independently of variation in CFTR . Intestinal obstruction at the time of weaning is the primary cause of death in mouse models of CF [22] , [43] . As the effect of the MI-associated haplotypes upon MSRA expression was unknown , we elected to introduce null alleles of Msra into mouse models of CF with high and low rates of mortality due to intestinal obstruction to detect whether loss of Msra expression reduced or exacerbated the rate of obstruction . Intestinal obstruction in a null CF mouse model ( C57BL/6J Cftr−/− ) leads to high mortality ( >80% [17] ) by 40 days of age while lower rates of mortality [44] occur in a CF mouse model ( C57BL/6JR117H/R117H ) with a targeted knock-in of a missense mutation ( p . Arg117His ) associated with residual CFTR function [45] , [46] and very low rates of MI in humans with CF [47] . Mice heterozygous for the Cftr null ( Cftr+/− ) or p . Arg117His allele ( Cftr+/R117H ) were crossed to mice with one or two Msra null alleles to produce CF mice ( Cftr−/− or CftrR117H/R117H ) with wild-type ( +/+ ) , heterozygous ( +/− ) , and null ( −/− ) Msra genotypes . As expected , Cftr−/− mice displayed a sharp drop in survival around the time of weaning when solid food is introduced to the diet ( Figure 3 ) . The median survival of the Cftr−/− mice was 22 days , consistent with the high rates of mortality due to intestinal obstruction reported in other CF ‘null’ mice [17] , [18] , [24] , [48] , all of which would have been homozygous for wild-type Msra ( i . e . Msra+/+ ) . However , survival was significantly improved in Msra+/− and Msra−/− CF mice compared to their Msra+/+ littermates ( P = 0 . 022 and P = 1 . 2×10−4 , respectively , by log-rank test; Figure 3A ) . At the end of the follow-up period , 61% of Msra−/− and 42% of Msra+/− mice were still living compared to 17% of Msra+/+ mice . The increasing trend in survival across genotypes ( Ptrend = 1 . 3×10−4 ) mirrors the additive effect of the MSRA haplotype observed in humans . As anticipated , CftrR117H/R117H mice displayed reduced mortality , notably through the weaning period , compared to Cftr-null mice ( 64 . 3% of Msra+/+ mice alive at 40 days , n = 14; Figure 3B ) . However , there was no difference in the rate of survival between Msra+/+ mice and mice with one Msra null allele ( Msra+/−: 76 . 5% alive at 40 days , n = 51; P = 0 . 33 , log-rank test ) or two null alleles ( Msra−/−: 70 . 6% alive at 40 days , n = 51; P = 0 . 51 ) . Mortality due to intestinal obstruction was confirmed in all animals for which the carcass was identified intact , and these were primarily animals that succumbed after weaning . Thus , loss of Msra expression increased survival in Cftr−/− mice by reducing the rate of fatal intestinal obstruction . Excessive mucus accumulation in the crypts and lumen along with goblet cell hyperplasia are characteristic findings in the small and large intestine of Cftr−/− mice [17] , [22] , [43] . As goblet cells are the primary source for mucus in the intestine , we sought to determine if the goblet cell content of villi observed in the ileum of 15 day old mice is affected by Msra expression ( Figure 4A ) . In wild-type ( WT; i . e . non-CF ) mice , goblet cell counts per villus ranged from 7 . 6% to 19 . 7% with a median of 11 . 1% ( Figure 4B ) . The fraction of goblet cells in Msra−/− mice was similar to WT , ranging from 5 . 8% to 19 . 9% with a median of 13 . 0% , while the fraction of goblet cells in Cftr−/− mice had a wider range , 12 . 4% to 90 . 1% , and higher median ( 23 . 7% ) than WT or Msra−/− mice ( Figure 4B ) . The fraction of goblet cells in WT and Msra−/− mice and the increased proportion in Cftr−/− mice is comparable to the numbers reported in other studies [29] , [31] . Like the Cftr−/− mice , goblet cell fraction in the ileum of Cftr−/−Msra−/− mice varied widely , ranging from 16 . 3% to 90 . 1% with a median of 25 . 9% ( Figure 4B ) . Cftr−/− and Cftr−/−Msra−/− mice displayed considerable heterogeneity in the fraction of goblet cells per section with both groups having a subset of villi where ∼90% of cells were goblet cells ( two sections in Cftr−/− and four in Cftr−/−Msra−/− ) . As goblet cell fractions were not normally distributed for the Cftr−/− and Cftr−/−Msra−/− mice , we evaluated these differences using the non-parametric Mann-Whitney test . WT and Msra−/− mice had similar distributions ( P = 0 . 97 ) whereas both differed significantly from Cftr−/− ( WT: P = 7 . 6×10−5; Msra−/−: P = 2 . 6×10−4 ) . Similarly , Cftr−/−Msra−/− mice differed from WT ( P = 2 . 4×10−5 ) and Msra−/− mice ( P = 3 . 8×10−5 ) . However , the distributions in Cftr−/− and Cftr−/−Msra−/− did not differ when all observations were included ( P = 0 . 67 ) or when the sections with goblet cell fractions exceeding 89% were excluded ( median 22 . 2% vs . 22 . 3% , respectively; P = 0 . 26 ) . Thus , loss of Msra expression does not appear to affect goblet cell hyperplasia in the ileum of CF mice despite reducing intestinal obstruction and increasing survival .
Neonatal intestinal obstruction ( also known as meconium ileus or MI ) has incomplete penetrance ( 15% ) and high heritability ( ∼1 . 0 ) suggesting a prominent role for modifier genes in this complication of CF [12] . Both candidate gene and genome-wide studies indicate that multiple genetic modifiers of low effect contribute to this trait in humans and in mice [12] , [19] , [49] , [50] . The polygenic etiology of MI combined with its low incidence in CF present a substantial challenge to identifying the responsible genetic modifiers . However , by employing both linkage and transmission methods in a family-based study followed by replication in an unrelated sample of CF patients , we were able to implicate the MSRA gene on chromosome 8 . To test whether manipulation of Msra expression modified intestinal obstruction in the CF mouse , we elected to use a null allele of Msra to avoid temporal or spatial issues that might have complicated a transgenic over-expression strategy . As we did not know if loss of expression would increase or decrease the rate of obstruction , we employed two CF mouse models with different rates of mortality due to intestinal obstruction at the time of weaning . Our hypothesis was that the CF null model ( with high rates of obstruction ) would reveal whether loss of Msra function decreased obstruction while the p . Arg117His model ( with lower rates of obstruction ) would reveal whether loss of Msra function increased obstruction . Indeed , reduction of Msra expression in the null CF mouse model resulted in a significant decrease in intestinal obstruction . The lack of effect in the p . Arg117His CF mice suggested that the modifying effect of Msra did not exceed the reduction in obstruction conferred by residual function of CFTR bearing p . Arg117His . Together , the two CF mouse models indicated that loss of Msra afforded protection from intestinal obstruction during the time of weaning . The Msra null allele was generated from 129-derived embryonic stem cells , and mortality from obstruction is nearly 100% in mice on the 129 background [17] . Thus , it is unlikely that variation in the 129-derived region surrounding Msra is responsible for the reduced intestinal obstruction . Furthermore , the Msra region in mice displays minimal synteny with the region surrounding MSRA on chromosome 8 in humans . For example , tankyrase ( TNKS ) , a gene adjacent to the 5′ end of MSRA in humans is not adjacent to Msra on chromosome 14 in the mouse but is located on mouse chromosome 8 . The mouse studies provide compelling evidence supporting the contention that MSRA modulates MI in humans . Interestingly , non-CF Msra+/− mice have previously been shown to have reduced lifespan thought to be the consequence of enhanced vulnerability to oxidative stress compared to wild-type animals [51] . In contrast , we observed longer survival in CF mice lacking Msra . Our opposing finding suggests that in the disease context of CF , having less Msra affords a protective benefit . The initial starting point in our modifier gene search was a 9 Mb region on chromosome 8 within a region linked to risk for MI in 30 concordant sibling pairs [12] . Association analysis of 133 families using transmission disequilibrium testing identified a single SNP ( rs614197 ) that achieved region-wide significance . Lack of significant association between MI and this SNP in the CGS sample motivated us to search for evidence of untyped alleles using haplotype analysis . The association of both ‘protective’ and ‘risk’ haplotypes with MI led us to surmise that alleles of different effect exist in or near MSRA . We then tested whether the ‘protective’ MSRA haplotype had a similar influence on MI in an independent CF sample . While the TSS sample was potentially subject to ascertainment bias due to its recruitment criteria ( i . e . having a sibling with CF ) , the CGS sample was ideal for replication as recruitment was based only on a diagnosis of CF , and the sample comprises 70% of the patients with CF in Canada . The significant correlation between the ‘protective’ haplotype and reduced incidence of MI in the CGS sample and conformity with an additive model provided reassurance that variants in MSRA modified MI risk . Finally , as several studies , including the present study , have indicated that the CFTR genotype can affect the rate of MI [14]–[16] , we evaluated whether CFTR allelic variation contributed to the association between the MSRA haplotype and MI . The T-G-A haplotype associated with MI in an additive fashion in individuals with identical CFTR genotypes ( p . Phe508del homozygotes ) , thereby demonstrating that MSRA modifies MI independently of variation in CFTR , the disease-causing gene . Heterogeneity of effect appears to explain the observed linkage of risk for MI to the region encompassing MSRA . As noted above , the T-G-A haplotype demonstrating robust association with MI conferred protection from MI rather than risk for MI . However , this is not the most common haplotype ( ∼15% ) ; thus most siblings carry other haplotypes derived from the alleles of the three SNPs that , by definition , confer higher risk for MI than the protective T-G-A haplotype . As predicted by this assumption , the T-G-A haplotype was significantly underrepresented in siblings concordant for MI who contributed to the linkage signal on chromosome 8 . By the same token , the T-G-A haplotype was over represented in siblings who did not contribute to linkage . Hence , the observed modest linkage in siblings concordant for MI was the result of sharing of neutral MSRA haplotypes or haplotypes conferring risk for MI , and lack of sharing of alleles associated with protection from MI . Sequencing of the coding regions of MSRA in three CF subjects with MI and no T-G-A haplotypes and in three subjects without MI and two T-G-A haplotypes did not identify any plausible causative variants ( data not shown ) . Thus , we conclude that the haplotypes are tagging as yet unidentified genetic variation within or near MSRA . Central roles for luminal hydration and mucus production in intestinal obstruction in CF mice at the time of weaning have been supported by the manipulation of expression of two genes . In one study , reduced expression of the sodium hydrogen exchanger ( Nhe3 ) led to decreased intestinal sodium absorption , thereby increasing the hydration of luminal contents , alleviating obstruction , and improving survival [29] . Similarly , knock-out of the mucin gene Muc1 in CF mice improved survival due to reduced intestinal mucus content and less obstruction at the time of weaning [30] . As noted by many others [22] , [43] as well as in this study , goblet cell hyperplasia is a consistent histologic feature in the small and large intestines of CF mice . However , the role of goblet cells in intestinal obstruction in CF mice is not clear [43] . Reduced expression of Nhe3 relieved obstruction and eliminated goblet cell hyperplasia [29] while increased expression of the goblet cell marker Clca3 ( Gob5 ) relieved obstruction and increased goblet cell hyperplasia in CF mice [31] . Rozmahel and colleagues suggested that Clca3 may reduce mucin release from goblet cells , given the observation of increased goblet cell size , thereby reducing luminal mucus content and intestinal obstruction in the CF mice [31] . Evidence of association between MI and the p . Ser357Asn variant in CLCA1 , the human ortholog of the murine Clca3 , in 682 European CF subjects suggests that this goblet cell marker may also contribute to intestinal obstruction in humans [52] . Thus , alteration of mucus content in the CF intestine , either by reduction in goblet cell numbers or down-regulation of mucus release , appears to affect the rate of intestinal obstruction . Our analysis indicated that loss of Msra expression did not affect goblet cell hyperplasia in the Cftr−/− ileum despite mitigating intestinal obstruction; thus the link between Msra and intestinal obstruction in the context of CF is not immediately clear . In humans , loss of CFTR function leads to a combination of impaired pancreatic secretion of proteolytic enzymes and deficient luminal hydration of meconium [53] , [54] . Consequently , meconium from CF neonates is abnormally proteinaceous compared to that of normal neonates , and it has been proposed that the altered viscoelastic properties of meconium predispose fetuses and neonates with CF to intestinal obstruction [55] . In young CF mice , pancreatic exocrine function is relatively preserved [17] . However , inadequate hydration and excessive mucus secretion leads to distension of the crypts of Lieberkühn in the ileum and colon and formation of concretions that appear to play a role in obstruction [17] , [22] , [23] , [56] . MSRA encodes an antioxidant enzyme that modifies the activity of certain proteins by reducing methionine residues [57] . It is expressed in the intestine and other tissues , particularly the liver , kidney , and brain [58]–[60] . A possible link between MSRA and MI may be that MSRA can modulate the activity of proteolytic enzymes [61] . Maximizing the activity of any residual enzymes produced by the fetal pancreas would likely contribute to the breakdown of intestinal proteins in utero , thereby reducing the risk of obstruction . Evidence that MSRA modifies intestinal obstruction provides new opportunities to investigate the above concepts and the mechanisms underlying intestinal obstruction in mice and in humans lacking CFTR .
This study was approved by the institutional/ethical review boards of all participating institutions . Written , informed consent or assent was obtained from all subjects before enrollment in the study . Experiments on mice were approved by the Case Western Reserve University Institutional Animal Care and Use Committee . Study subjects were derived from the North American CF Modifier Gene Consortium , which is comprised of three independent collections of CF subjects . Subjects in the discovery sample were part of the Cystic Fibrosis Twin and Sibling Study ( TSS ) at Johns Hopkins University ( n = 1 , 125 subjects ) . Enrollment was based on conclusive diagnosis of CF [62] . Methods for isolation of patient DNA [63] and identification of CFTR mutations [64] , [65] have been previously described . The diagnosis of MI was based on the presence of the following features in the newborn period: lack of passage of stool within 24 hours after birth , evidence of intestinal obstruction on abdominal radiograph ( ground-glass appearance of intestine , air-fluid levels , and/or intra-abdominal calcifications ) , evidence of colonic abnormality ( microcolon on radiograph ) , and treatment for obstruction ( enema or surgery ) . Individuals with clinically defined PS , a CFTR mutation associated with PS , or unknown pancreatic status were excluded ( n = 143 ) , as was one subject with unknown MI status . The primary analysis was conducted in 133 “MI families” in which at least one sibling was affected with MI ( 270 subjects , 169 parents ) . Case/control analyses were restricted to persons of self-reported European descent to minimize the potential for spurious associations due to race-related differences in allele frequencies; whereas the primary family-based transmission analysis , which was robust against population stratification , included an additional 23 individuals of non-European or mixed descent . Findings from the primary analysis were tested in an independent sample which has been described elsewhere [34] . The replication population consisted of 1 , 573 CF subjects from the CGS [40] . All subjects were defined as having PI . Exclusion of non-whites yielded 1 , 383 subjects ( including 56 sib-ships ) for analysis . Rates of MI in subjects carrying the CFTR p . Gly551Asp mutation ( c . 1652G>A ) or who were homozygous for p . Phe508del were evaluated in the entire TSS sample and the CGS sample . Subjects with p . Gly551Asp carried this mutation in trans with another PI-associated mutation: p . Cys343X ( c . 1029delC ) , c . 1585-1G>A , p . Lys1177SerfsX15 ( c . 3528delC ) , c . 489+1G>T , p . Glu585X ( c . 1753G>T ) , p . Phe508del , p . Gly542X ( c . 1624G>T ) , p . Gly551Asp , p . Asn1303Lys ( c . 3909C>G ) , p . Arg553X ( c . 1657C>T ) , p . Val520Phe ( c . 1558G>T ) , or p . Trp1282X ( c . 3846G>A ) . Mutation legacy names can be found in the Cystic Fibrosis Mutation Database ( http://www . genet . sickkids . on . ca ) . Linkage disequilibrium between SNPs was assessed using Haploview ( http://www . broad . mit . edu/mpg/haploview ) [66] . DNA extracted from either whole blood or transformed lymphocyte cell lines was hybridized to the Illumina Infinium 610-Quad SNP array platform for whole genome genotyping at the McGill University and Génome Québec Innovation Centre . Genotyping was performed and stringent quality control measures were employed simultaneously in both cohorts , and the quality of SNP calls was deemed to be very high ( 0 . 004% discordance between replicate samples ) . SNPs were excluded from analysis in all cohorts if the call rate was <90% , if the minor allele frequency was <1% , or if the Mendelian error rate was >1% . For family-based studies , any marker displaying non-Mendelian inheritance was dropped from analysis for any family with the error . A detailed description of additional quality control measures can be found in Wright , et al [34] . For the primary study , family-based association testing of SNPs and haplotypes was performed using the PBAT module [67] implemented within the Golden Helix HelixTree software package ( Golden Helix , Inc . Bozeman , MT , USA; http://www . goldenhelix . com ) . A sliding-window approach was employed to test the transmission of haplotypes composed of three adjacent SNPs ( frequency >1% ) . A 2 Mb region was selected for haplotype testing as the number of possible unique haplotypes would be nearly equal to the number of SNPs tested in the initial analysis . Therefore , to be considered significant , a haplotype would have to reach the same Bonferroni-corrected threshold ( or higher ) that was set for SNPs in the initial analysis . An additive genetic model was applied under a null hypothesis of linkage and no association , and standard phenotypic residuals were used as offsets to increase the power of the test statistic . Bonferroni correction was applied by multiplying nominal P values by the total number of SNPs or haplotypes tested . The SNP association plot was generated using LocusZoom 1 . 0 ( http://csg . sph . umich . edu/locuszoom ) . For case/control analyses , haplotypes were derived in the primary and replication populations using an expectation-maximization ( EM ) algorithm implemented in Golden Helix . Statistical analysis was performed in Stata10 ( StataCorp , College Station , TX , USA ) . Comparison of MI status to the number of copies of haplotypes was performed using Fisher's exact test ( using only subjects with haplotypes that could be determined with 100% posterior probability ) . Odds ratios comparing the odds of MI in subjects with 0 , 1 or 2 copies of the chr8 haplotype , thus assuming an additive model , were estimated using logistic regression . For subjects with more than one haplotype assignment , EM probability estimates were used to weight haplotypes . For TSS subjects , parental and sibling genotypes were utilized when possible to resolve phase . For studies including siblings ( TSS and CGS ) , empiric standard errors account for the possibility of sib-pair correlation [68] . Mice heterozygous for a Cftr null allele , B6 . 129P2-Cftrtm1Unc [17] or for the Cftr missense allele p . Arg117His , B6 . 129S6-Cftrtm2Uth [44] , and either homozygous or heterozygous for a null allele of Msra [51] were generated as breeders to produce CF mice carrying the three genotypes of Msra ( +/+ , +/− , and −/− ) used in this study . Mice were housed at constant temperature ( 22°C ) on a 12 hour light/dark cycle . Cages were checked daily for births and for monitoring health and survival of animals to 40 days . Mice were weaned at 21 days of age onto an enriched diet ( 9F Sterilizable Rodent Diet 7960 , Harlan Teklad , Madison , WI ) and provided water ad libitum . Mice were of mixed background , but predominantly C57BL/6J ( >87%; crossed a minimum of two generations to C57BL/6J and some animals up to ten generations ) . Genotyping was carried out on 7 to 10-day old animals and if death occurred before this point , genotypes were determined from carcasses . Cftr genotyping was performed as previously described [44] . Msra genotyping was carried out using two primer sets to generate a 579-bp product for the wild-type allele ( forward 5′-GTGTGAGAATAAACAGATGTTCTATGC-3′ and reverse 5′-GGGTTGAGTACACTCCTTTCA-3′ ) or a 320-bp product for the mutant null allele ( forward 5′-AAAGCGCCTCCCTACCCG-3′ and reverse 5′-ACTGTGCCCAGTTTAGTCCGTG-3′ ) . Samples were amplified by an initial denaturation for 5 min at 95°C followed by 35 cycles of 95°C for 30 sec , 59°C for 30 sec , and 72°C for 30 sec . PCR products were fractionated on a 1% agarose gel . Kaplan-Meier survival curves were plotted and differences in survival were analyzed by the non-parametric test of trends and log-rank test of equality using Stata10 . For histology , freshly harvested tissues were fixed in 10% formalin . Tissue was embedded in paraffin , sectioned at 5 µm thickness on a microtome , and mounted on glass slides for microscopy . Deparaffinized slides were stained with Alcian blue and periodic acid Schiff ( PAS ) stains , then counterstained with acidified Harris hematoxylin . Comparison of goblet cell proportions between groups was performed using the non-parametric Mann-Whitney test .
|
Cystic fibrosis ( CF ) is a monogenic disease with considerable phenotypic variability . About 15% of newborns with CF suffer from an intestinal obstruction called meconium ileus ( MI ) , and studies in CF twins have shown that modifier genes play a substantial role in the development of this complication . We used a family-based study design to enrich for genetic modifiers of MI and found that variations in the MSRA gene , represented by combinations of SNPs , or haplotypes , were protective against this manifestation of CF . We investigated association between one of the MSRA haplotypes and MI in an independent sample of CF patients and showed that it had a similar protective effect . Furthermore , CF mice lacking Msra expression had lower mortality due to intestinal obstruction at the time of transitioning to solid food and lived longer than CF mice with normal Msra , thus supporting the protective effect of the haplotype we observed in human CF subjects . The identification of modifiers of MI such as MSRA offers new insight into the mechanism of this life-threatening complication of CF .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biology",
"human",
"genetics",
"genetics",
"and",
"genomics"
] |
2012
|
Variation in MSRA Modifies Risk of Neonatal Intestinal Obstruction in Cystic Fibrosis
|
The functional effects of most amino acid replacements accumulated during molecular evolution are unknown , because most are not observed naturally and the possible combinations are too numerous . We created 168 single mutations in wild-type Escherichia coli isopropymalate dehydrogenase ( IMDH ) that match the differences found in wild-type Pseudomonas aeruginosa IMDH . 104 mutant enzymes performed similarly to E . coli wild-type IMDH , one was functionally enhanced , and 63 were functionally compromised . The transition from E . coli IMDH , or an ancestral form , to the functional wild-type P . aeruginosa IMDH requires extensive epistasis to ameliorate the combined effects of the deleterious mutations . This result stands in marked contrast with a basic assumption of molecular phylogenetics , that sites in sequences evolve independently of each other . Residues that affect function are scattered haphazardly throughout the IMDH structure . We screened for compensatory mutations at three sites , all of which lie near the active site and all of which are among the least active mutants . No compensatory mutations were found at two sites indicating that a single site may engage in compound epistatic interactions . One complete and three partial compensatory mutations of the third site are remote and lie in a different domain . This demonstrates that epistatic interactions can occur between distant ( >20Å ) sites . Phylogenetic analysis shows that incompatible mutations were fixed in different lineages .
In a half century of molecular phylogenetics there never has been a systematic investigation of the functional and fitness effects of amino acid replacements in evolution . Experimental studies focus on those few mutations that change protein function [1] . Of the remaining thousands of replacements nothing is said – they may or may not be of functional consequence . Sequence analyses use statistical approaches to explore modes of evolution [2] , [3] . Rarely does fitting alternative evolutionary models to observed data disallow alternative explanations . For example , to some [4]–[6] an elevated ratio of amino acid replacements to silent substitutions between species ( dn/ds ) suggests evidence for the action of positive selection . To others [7] it suggests relaxed selection against slightly deleterious amino acid replacements during population bottlenecks . Both interpretations are viable . Non-additive interactions among mutations ( epistasis ) are critical to protein structure and function [1] , [8] and consequently to speciation [9] , the evolution of sex [10] , recombination [11] , dominance [12] , robustness [13] and human disease [14] . Non-additive interactions force sites to functionally co-vary during evolution [15] , [16] . Computational methods that ignore phylogenetic structure [17]–[29] fail to distinguish between co-variation arising from functional causes and co-variation arising though shared common ancestry . The latter , an ineluctable product of shared history , is reflected in the bifurcating hierarchy of a phylogenetic tree ( a tree must collapse to a star burst if no sites co-vary ) . Computational methods that account for phylogenetic structure [30]–[38] have identified sites likely to functionally co-evolve [32] , [36] . The relative scarcity of such sites accords with the observation that most amino acid replacements occur at the surfaces of proteins where solvent exposed side chains are less likely to interact [39] , [40] . On the other hand it may simply reflect a lack of statistical power in many , though not all [32] , of computational methods used . An alternative approach identifies pathogenic missense mutations in one species that have no obvious detrimental effect in a related species [41] , [42] . This approach does not detect deleterious mutations of minor phenotypic effect . Computationally derived predictions need empirical verification [1] . Gloor et al . [43] used site directed mutagenesis to confirm the epistasis predicted between co-evolving residues in yeast phosphoglycerate kinase . Experiments with yeast iso-2-cytochrome c [44] also identified epistatic interactions between sites . However , two other studies , one with game bird lysozymes [45] , [46] and one with vertebrate p53 domains [47] , failed to find any evidence of epistasis . Several other site directed mutagenesis studies identified epistatic interactions among positively selected replacements in TEM-1 β-lacatamase [48] , vertebrate steroid receptors [49] and visual pigments [50] and in coral red fluorescent proteins [51] . In no case , however , have experiments been designed to explore the prevalence of epistasis in molecular evolution in general . Here , we explore the prevalence of epistasis in molecular evolution from the distribution of functional effects caused by individual mutations introduced to one sequence from a homologue in another species . We studied the leuB encoded β-isopropylmalate dehydrogenase ( IMDH ) because: 1 ) the enzyme has a conserved well defined role in leucine biosynthesis [52] , [53]; 2 ) high resolution x-ray crystallography of divergent IMDHs ( <35% identical ) reveals a conserved protein fold [54]–[57]; sequence alignments show that divergent IMDHs rarely differ by more than a few insertions and deletions; and 4 ) the relationship between enzyme performance ( kcat/Km . NAD ) and fitness has been determined using Escherichia coli as a model system [58] . The IMDHs from two mesophyles , E . coli and P . aeruginosa , differ at 168 of 365 sites including six small indels ( Figure S1 ) located in flexible loops external to the core structure . Conserved in fold and function , E . coli and P . aeruginosa IMDHs provide excellent material with which to investigate protein evolution arising through sequence divergence in the absence of major changes in structure and function .
We constructed 168 site directed mutants of E . coli leuB ( each with a single mutation from P . aeruginosa leuB ) and then expressed and purified each enzyme and determined its kinetic parameters . The resulting distribution of enzyme performances ( kcat/Km . NAD ) is strongly skewed to the left and only a single outlier with increased performance lies on the right ( Figure 1A ) . The error distribution , obtained by repeatedly assaying wild-type E . coli IMDH , is Gaussian , , ( Figure 1B ) . This same distribution is expected of amino acid replacements that do not affect function . The 52 mutants with relative performances above E . coli wild-type form a half-Gaussian distribution , , , similar to the error distribution . This suggests mutations have no detectable effect on enzyme performance , 63 reduce it , and one increases it . Pair-wise t-tests ( for unequal replication and unequal variances [59] ) combine with a 5% false discovery rate [60] to identify 61 mutants of changed performance: 56 have decreased performance and 5 have increased performance , with only the single outlier having performance increased by more than 15% . The assumption that mutations act independently , and hence additively , leads to a predicted performance for P . aeruginosa IMDH that is clearly wrong . The sum of the individual mutational effects and the E . coli kcat/Km . NAD is negative: . This is a physical impossibility . The assumption that mutations act multiplicatively is also wrong . In simple transition state theory , where ΔG′ is the difference in free energy between the ground state and the transition state , R is the gas constant and T is °Kelvin [61] . The difference in free energy between the transition states of the mutant and wild-type enzymes is . With five mutants completely inactive the sum of the ΔΔG′ and is minus infinity and the predicted performance is . In fact P . aeruginosa IMDH has a , , slightly lower than that of E . coli IMDH which has a , . The inescapable conclusion is that amino acid replacements at many sites interact . IMDH evolution is characterized by rampant epistasis that remains cryptic until revealed by experiment . That only 64 of the 168 sites affect function certainly underestimates the number that interact epistatically . To understand this , consider two cases in which two sites are involved in a simple pair-wise interaction ( Figure 2 ) . In each case , only one of the two sites reduces function when an amino acid in one species is mutated to that found in another . Mutating the second site restores the ancestral functional state . Mutations at both sites may reduce function if three or more amino acid replacements arose during the course of evolution ( Figure S2A ) – although this is not guaranteed . In a simple network of pair-wise interactions only two of three sites might be identified ( Figure S2B ) . We expect some of the remaining 104 sites to engage in epistatic interactions . Zuckerkandl [15] first proposed that amino acid replacements at one site in a protein might influence the acceptability of amino acid replacements at other sites . Fitch and Markowitz [16] suggested that as species diverge from a common ancestor their sets of variable sites also diverge to explore different regions of sequence space . Mutating a currently invariant site in one species by introducing an amino acid from a homologous protein in another species risks producing a loss-of-function mutant . The many functionally compromised mutants in this study amply confirm the insight of these early pioneers . Mutations affecting function ( <80% wild-type performance ) are scattered throughout the IMDH structure ( Figure S3 ) . Solvent accessibility , distance to the catalytic center ( Asp 251 ) , secondary structure and rate of amino acid replacement per site do not correlate significantly with performance for the mutants analyzed here . Only one mutation , F73L , likely affects catalysis by contacting a substrate directly ( Figure S4 ) . We screened for compensatory mutations of F73L , A94D and A284C , all of which lie near the active site and all of which are among the least active mutants . We combined each deleterious mutation with each of the 167 remaining mutations , expressed and purified each double mutant , and assayed their activities . Four mutations compensate the F73L mutation ( Table 1 ) . F120A , identified in the original screen because it produces a 50% increase in wild-type performance , now produces a 6-fold increase in performance so that the F73L , F120A double mutant , while not as active as the E . coli wild-type enzyme , is as active as the P . aeruginosa enzyme . Three other compensatory mutants , F132L C136I and I179V , do not individually affect wild-type performance . Performance is completely restored to E . coli wild-type levels in the F73L , I179V double mutant . Performance is partly restored in the F73L , F132L and F73L , C136I double mutants . No mutations were found to restore function to A94D and A284C . Our results are compatible with several types of interactions . Using E . coli IMDH performance as a standard suggests a simple pair-wise interaction between L73 and I179 because only L73F and I179V are fully compensatory . Using the lower P . aeruginosa IMDH performance as a standard suggests a high-order interaction with function compromised only when residues L73 , F120 , C136 , I179 are combined . Replacing any one amino acid , L73F , F120A , C136I , or I179V destroys the 4-way interaction to restore full performance . That no mutation restores function to A94D and A284C demonstrates several compensatory mutations are essential; at least two sites ( A and B ) must each interact with each original mutation ( X ) to form a simple chain ( A-X-B ) . Whereas previous experimental studies [41]–[45] assumed interacting residues would be in close physical contact , all compensatory mutations for F73L in the large domain lie more than 20 Å distant in the small domain , close to a hinge in the b-sheet on which the two domains swivel ( Figure 3 ) . This suggests a common mode of action , possibly related to repositioning the F73L-shifted nicotinamide ring for catalysis . Our experimental strategy , of moving replacements from one homologue into another and screening for compensatory mutations , is useful in that it provides a general means to identify interacting sites regardless of the mechanisms involved . The predicted fitness effects of most mutations are tiny . Previous work [56] established that wild-type E . coli IMDH lies on a fitness plateau ( Figure 4A ) . In this limit of adaptation [62] , increases in performance do not improve fitness and even large reductions in IMDH performance produce small fitness effects ( Figure 4B ) . Indeed , 65% of the selection coefficients are predicted to be less than 10−5/generation . Selection during starvation growth with glucose as the sole limiting resource is far greater than in nature where leucine is both widely available and abundant [63] . Many mutations , including the one with increased performance , are likely selectively neutral , or very nearly so . The cryptic epistasis we revealed is consistent with two modes of neutral evolution: the covarion process [64] and the nearly neutral process [65] . In the covarion process , neutral and/or beneficial mutations are fixed in different lineages that , when brought together in the same protein , are deleterious ( Figures 2 , Figure S2 ) . In the nearly neutral process successive slightly deleterious alleles are fixed by random genetic drift ( particularly during population bottlenecks ) until a compensatory mutation arises that , on restoring full activity , is fixed by positive selection ( particularly after a population expands ) . The concave fitness function for E . coli IMDH ( Figure S4 ) , typical of dominance curves [12] , provides the fitness plateau on which fitness could gradually drift downwards as slightly deleterious mutations sequentially fix before a beneficial compensatory mutation restores full activity . The two processes can be distinguished by determining the order in which mutations arise during the course of evolution . Phylogenetic analysis suggests that the most recent common ancestor ( MRCA ) of E . coli and P . aeruginosa had amino acids FAFVV at sites 73 , 120 , 132 , 136 and 179 ( Figure 5 ) . On the lineage leading to E . coli mutations V136C and V179I arose first ( the order is indeterminate ) before mutation A120F . Each mutation is compatible with F73 . On the lineage leading to P . aeruginosa mutations F73L and F132V arose ( the order is indeterminate ) before mutation V132L and finally mutation V136I . The presence of V179 is expected to compensate the potentially deleterious interaction between L73 and F132 in the event that the F73L mutation arose first . Hence , the pattern of replacements supports the nearly neutral process because a potentially deleterious mutation never arose before a compensatory mutation in the same lineage . Our demonstration of rampant cryptic epistasis in IMDH is entirely in accord with a recent insightful analysis of protein evolution that invoked extensive epistasis to account for the retarded divergence seen in ancient proteins [66] . There the case was made for a rugged fitness landscape characterized by multidimensional sign epistasis that forces sites to be conserved for billions of years until the right combination of amino acids at other sites to allows them to evolve . Our failure to identify compensatory mutations for A94D and A284C is indicative of multidimensional sign epistasis . That a single replacement is sufficient to compensate the F73L mutation demonstrates that epistasis need not always be multidimensional , however . In an earlier study [67] , a mutant library in which 52 natural amino acid replacements from 15 subtilisin orthologues had been recombined was screened for function . Sequence comparisons of the unscreened and the screened libraries suggested that almost all possible pair-wise combinations of amino acids can coexist and that functional co-dependencies are rare . These conclusions seemingly stand in contradiction to ours . The subtilisin experiment suggests that 7 of pairs compromise function for . In other words about a half percent of pair-wise interactions are deleterious . For E . coli IMDH , the probability , f , that introducing an amino acid from an orthologue has no effect on function is , where D is the number of residues that differ between the two sequences . For E . coli IMDH we have , and hence . The two-fold difference between the two estimates of p is small considering the differences between the enzymes and the experimental methods employed . The take-home lesson is that epistatic interactions may be rare individually , but their cumulative impact on evolution rapidly increases with divergence , D . The phenomenon is akin to the snowball effect describing the accumulation of Dobzhanski-Muller incompatibilities during speciation [68] . The simplest model of sequence evolution is a Poisson process in which each site accumulates mutations at a constant rate λ . The expected number of mutations accumulated at time t is simply λt and the variance in the number of substitutions at time t is also λt . This gives the Poisson molecular clock a characteristic variance to mean ratio of . However , sequence analyses show that the molecular clock is the over-dispersed with [69]–[75] . Various hypotheses have been proposed to explain this over-dispersion including episodic bursts of selection , increased rates of fixation of deleterious alleles during population bottlenecks , fluctuating neutral spaces and variable mutation rates [73] , [76]–[84] . Cryptic epistasis , in causing constraints at sites to vary and hence substitution rates at sites to vary , undoubtedly contributes to over-dispersion in the molecular clock . Simulations show that ignoring changes in substitution rates ( heterotachy ) can induce systematic errors in phylogenetic reconstruction , including topological inaccuracies , long-branch biases and other effects [85]–[91] . Simulations also show that ignoring co-dependencies among sites causes the amount of evolution to be underestimated , particularly on branches deep in a tree [92] . The resulting impression of rapid ancient radiations with an indeterminate branching order makes identifying the origins of some taxonomic groups difficult [93] , [94] . Taking explicit account of co-dependencies within data has been shown to aid phylogenetic inference [92] , [95] . While recent advances accommodate temporal variability in substitution rates within sites [87] , even going so far as to model pair-wise interactions between sites in close proximity using predefined statistical potentials calculated from structural data [88] , general phylogenetic practice does not [89] . The extensive cryptic epistasis we have revealed suggests that the usual practice of ignoring co-dependencies among sites needs reconsidering . Ancestral sequence resurrection is a popular experimental approach to explore ancient phenotypes and adaptations [1] . Accurately inferred ancestral sequences are essential , otherwise there can be little confidence in the experimental results . Caution is warranted when interpreting functional patterns that mimic in silico reconstruction biases [96] . Current methods ignore functional co-dependencies among sites; the consequences for the accuracy of inferred ancestral sequences is largely unexplored . On the one hand , coupling between sites represents a loss of degrees of freedom ( knowing the residue at one site allows inferences to be made about the residues at coupled sites ) that leads to overconfidence in reconstructed trees [97] . This is particularly problematic if attempting to reconstruct ancestral sequences during a supposed rapid ancient radiation . On the other hand , the same loss of degrees of freedom means that fewer inferences are made , which should improve accuracy . Simulations suggest that the conditions producing phylogenetic uncertainty also make the ancestral state identical across plausible trees [98] . This helps make ancestral sequence reconstructions robust to phylogenetic uncertainty . Our dissection of epistatic interactions with site 73 shows that amino acid replacements accumulated during evolution can interact without affecting protein function . Nevertheless , cryptic epistasis may impact functional evolution . Reconstructing ancestral proteins on either side of an ancient functional change neglects epistatic interactions that earlier prevented the change and that later prevented the new function reverting [99] or changing in response to a new selective pressure . Such canalizing epistasis both retards functional evolution and thwarts attempts to engineer enzymes rationally [100] . Protein breeding experiments commonly use mutant libraries , generated either by recombining related sequences [101] or by allowing sequences to accumulate ‘neutral drift’ mutations [102] , to circumvent the canalizing effects of cryptic epistasis . We speculate that the rampant cryptic epistasis , inferred by computational methods [66] and detected in experiments on IMDH , might be sufficiently extensive to resist functional changes on evolutionary time scales . Only when rare neutral mutations relieve its canalizing effects can new functions evolve . This model potentially explains why protein evolution is characterized by long periods of functional stasis punctuated by rapid functional shifts .
E . coli K12 strains MG1655 , JW5807 ( Keio Collection ) [103] , MM294D and BL21-gold-DleuB::kanr have been previously described [53] , [58] . A derivative of E . coli strain BL21-gold ( Stratagene ) was constructed by P1 transduction [104] of the ΔleuB-leuC::kanr construct from strain JW5807 . LB medium was supplemented with 15 g/l Bacto agar for plates [104] . TALON Superflow metal affinity resin and TALON xTractor Buffer were purchased from Takara Bio USA ( Madison , WI ) . Unless specified otherwise , chemicals were purchased from Sigma-Aldrich ( St . Louis ) and restriction enzymes were purchased from New England Biolabs ( Ipswich , MA ) and Fermentas ( Canada ) . dl-threo-3-isopropylmalic acid was purchased from Wako Pure Chemical Industries ( Japan ) . All mutants sequenced at the BioMedical Genomics Center , University of Minnesota . The leu operon , from mid leuA through leuC , was acquired by genomic PCR from MG1655 . The genomic PCR product and pMML22KBA-KYVY [58] were digested with restriction enzymes RsrII and SphI . The vector and insert were ligated by quick ligation ( Fermentas ) , to create pLeuB7 . The construct was transformed by RbCl transformation [105] into MM294D and selected on LB/Amp ( 100 µg/ml ) , overnight at 37°C . The 5′-primer is designed with 15–20 bases , then the bases to be mutated , followed by a minimum of 12 bases at the 3′ end . The 3′-primer is complementary to the first 15–20 bases 5′-primer . Thus , the primers are staggered and only the 5′-primer encodes the mutations to be introduced . Cytosine residues in plasmid pLeuB7 were methylated by the CpG Methyltransferase M . SssI ( New England Biolabs ) according to the manufacture's instructions [106] . Methylated DNA and a nonmethylated control were diluted 1∶25 , and 2 µl transformed into E . coli strain MM294D by using the RbCl/CaCl2 method [105] . After transformation cells were plated on LB/ampicillin ( 100 µg/ml ) and incubated overnight at 37°C . Restriction sites ( Figure S5 ) and the 168 single mutants were introduced into wild-type E . coli leuB using the protocol in Table 2 [107] . Five microliters of each finished reaction was run on a 1% agarose gel to verify the PCR worked , and 2 µl was transformed [105] into MM294D , plated on LB/ampicillin ( 100 µg/ml ) and incubated overnight at 37°C . The presence of mutations was confirmed by sequencing . Mutant enzymes with kinetic characteristics different from wild-type had their entire leuB gene re-sequenced to confirm that no other mutations had inadvertently been introduced . Double mutants incorporating F73L , A94D and A284C with other mutations were constructed by restriction digestion and ligation using strain MM294D as a host . F73L , A94D , A284C were restriction digested and inserts with L24V , S156E , and Y360A ligated to form parent vectors F73L , L24V , F73L , S156E , A94D , L24V , A94D , S156E , A284C , S156E , and A284C , Y360A . L24V removes the AflII site , S156E removes the BamHI site , and Y360A removes the SnaBI site . Parent vectors were then restriction digested and inserts , obtained from restriction digests of other single mutants , were ligated in . After transformation [105] cells were plated on LB/ampicillin ( 100 µg/ml ) and incubated overnight at 37°C . Colonies were grown in LB/ampicillin ( 100 µg/ml ) and the plasmids purified . Double mutants were identified by the presence of a restored AflII , BamHI or SnaBI restriction site . Those remaining mutations close to F73L , A94D and A284C that could not be introduced by restriction digestion and ligation were introduced by PCR mutagenesis and the entire gene sequenced . In all 694 mutants were constructed: 17 restriction sites were introduced into pLeuB7 , 170 single mutants were made ( the exact position of one single amino acid deletion could not be reliably identified and so three mutates deleting residues 150 , 151 and 152 , were constructed ) , and 3×169 double mutants were made . Mutant IMDHs were over-expressed from plasmids in a derivative of E . coli strain BL21-gold ( Stratagene ) formed by P1 transduction [104] of the ΔleuB-leuC::kanr construct from strain JW5807 . Transformed cells were grown overnight at 37°C in 5 mL of LB containing ampicillin ( 100 µg/ml ) and 0 . 2 mM IPTG . Following centrifugation , cells were resuspended in 1 mL of BD TALON xTractor Buffer ( Becton-Dickenson ) . After 10 min rocking at room temperature , the sample was then centrifuged for 20 min at 11 , 200×g and the supernatant transferred to a TALON 2 mL disposable gravity column containing 2 mL of equilibrated BD TALON Superflow metal affinity resin . The protein was then eluted following the manufacture's protocol with the exception that potassium salts were substituted for sodium salts . All enzymes were purified to homogeneity as judged using Coomassie stained SDS-PAGE gels . Double mutants were screened for compensatory mutations at 37°C in 25 mM MOPS , 100 mM KCl , 1 mM DTT , pH 7 . 3 in the presence of fixed concentrations of 0 . 2 mM dl-threo-3-isopropylmalic acid and 5 mM MgCl2 and 0 . 1 mM NAD . The concentration of NAD lies far below the Kms of the single mutants ( 459±51 mM for F73L , 687±35 mM for A94D , 777±34 mM for A284C ) . With each mutant unsaturated the rate of the reaction is proportional to kcat/Km making improvements in performance readily detectable . Kinetics were performed at 37°C in 25 mM MOPS , 100 mM KCl , 1 mM DTT , pH 7 . 3 in the presence of fixed concentrations of 0 . 2 mM dl-threo-3-isopropylmalic acid and 5 mM MgCl2 , and with concentrations of NAD varied from 1/4 to 10× the apparent Km . Reactions were initiated by adding 10 µL of mutant IMDH ( diluted in 50 mM potassium phosphate , 300 mM KCl , 150 mM imidazole , 10 mM β-mercaptoethanol , pH 7 . 0 ) to 1 ml of the reaction mix in a 1 cm semi-UV ( methylacrylate ) cuvette ( Fisher Scientific ) . Reaction rates were determined spectrophotometrically by measuring the production of NADH at 340 nm using a molar extinction coefficient of 6220 M−1 cm−1 , in a thermostated Cary 300 Bio with a 6×6 Peltier block ( Varian ) . Inhibition constants were determined in the presence of varying fixed concentrations of reduced coenzyme . Kinetic parameters Vmax , Km and Vmax/Km were determined using nonlinear regression as implemented in JMP ( SAS Institute ) . Maximum turnover rates , , were calculated with enzyme concentrations , [E] , determined spectrophotometrically by Bradford assay [108] ( Bio-Rad ) using bovine IgG as the standard . Each single mutant was independently expressed , purified and kinetically characterized twice . A total of 537 amino acid sequences ( downloaded from GenBank via the NCBI web site http://www . ncbi . nlm . nih . gov/ ) were aligned using ClustalW software [109] . X-ray structures ( IMDHs 1HEX , 1CNZ , 1CM7 , IV53 , 1W0D , 1WPW , 1VLC , and 1A05 ) were downloaded from the PDB web site ( http://www . pdb . org/pdb/home/home . do ) and superimposed using Swiss-Pdb Viewer software [110] . Superpositioned structures were used as a guide to adjust the alignments of highly divergent sequences . A bootstrapped neighbor joining tree was constructed with PHYLIP [111] using a JTT [112] substitution matrix with deep branches swapped and assessed by maximum likelihood . A consensus tree was generated with Mr . Bayes [113] based on a gamma distributed , mixed model of amino acid evolution . The MCMC was run for 75000 generations sampling every 50 generations with a burn-in of 500 . Both trees produced similar results when ancestral sites were reconstructed by fastml [114] . With Bayesian posterior probabilities <0 . 9 accounting for <15% of sites ( mostly in flexible loops ) , the amino acid identities at most sites in the deduced sequence of the most recent common ancestor are reliably inferred .
|
Many bioinformatics and functional genomics predictions are derived from evolutionary patterns of amino acid replacement in protein sequence alignments . Most computational methods assume that replacements in one sequence will be tolerated in all related sequences . Here , we evaluate—by direct experiment—the functional impact of amino acid replacements accumulated during the course of evolution . Our initial results show that cryptic interactions among amino acid replacements are common and that most are deleterious . This result has implications not only for the evolution of function , recombination , sex , dominance , robustness , disease , and even speciation , but also for practical applications—in conservation biology ( e . g . to decide which organisms to preserve ) and in vaccine design ( e . g . using consensus or reconstructed ancestral sequences ) . Analyzing one interaction in detail , we find that compensatory mutations need not lie in close proximity to the original mutation as generally supposed . This result suggests that unsuspected structure–function relationships can be revealed by analyzing patterns of site-to-site interactions among amino acid replacements in evolution .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry/molecular",
"evolution",
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics"
] |
2010
|
Pervasive Cryptic Epistasis in Molecular Evolution
|
Cestodes are a diverse group of parasites , some of them being agents of neglected diseases . In cestodes , little is known about the functional properties of G protein coupled receptors ( GPCRs ) which have proved to be highly druggable targets in other organisms . Notably , serotoninergic G-protein coupled receptors ( 5-HT GPCRs ) play major roles in key functions like movement , development and reproduction in parasites . Three 5-HT GPCRs from Echinococcus granulosus and Mesocestoides corti were cloned , sequenced , bioinformatically analyzed and functionally characterized . Multiple sequence alignment with other GPCRs showed the presence of seven transmembrane segments and conserved motifs but interesting differences were also observed . Phylogenetic analysis grouped these new sequences within the 5-HT7 clade of GPCRs . Molecular modeling showed a striking resemblance in the spatial localization of key residues with their mammalian counterparts . Expression analysis using available RNAseq data showed that both E . granulosus sequences are expressed in larval and adult stages . Localization studies performed in E . granulosus larvae with a fluorescent probe produced a punctiform pattern concentrated in suckers . E . granulosus and M . corti larvae showed an increase in motility in response to serotonin . Heterologous expression revealed elevated levels of cAMP production in response to 5-HT and two of the GPCRs showed extremely high sensitivity to 5-HT ( picomolar range ) . While each of these GPCRs was activated by 5-HT , they exhibit distinct pharmacological properties ( 5-HT sensitivity , differential responsiveness to ligands ) . These data provide the first functional report of GPCRs in parasitic cestodes . The serotoninergic GPCRs characterized here may represent novel druggable targets for antiparasitic intervention .
The parasitic flatworms Echinococcus granulosus sensu lato ( s . l . ) and Mesocestoides corti are tapeworms belonging to the class Cestoda , with E . granulosus s . l . belonging to Taeniidae and M . corti to Mesocestoididae family . The Echinococcus species are important parasites of wildlife , domestic animals and people worldwide . The larval stage of almost all parasites of the E . granulosus s . l . complex ( which includes the species Echinococcus granulosus sensu stricto and Echinococcus canadensis ) cause human cystic echinococcosis or hydatidosis , one of the 17 neglected diseases prioritized by WHO [1] . The larval stage ( tetrathyridia ) of M . corti has a remarkable capacity of asexual reproduction in the peritoneal cavity of mice and some other mammalian hosts [2] . This parasite is a well established model for laboratory studies and the tetrathyridium is used to examine drug effects on neuromuscular activity [3] . According to Mansour [4] the survival of parasitic helminths in their natural habitat is dependent on their ability to maintain themselves in situ in the face of peristaltic , blood or lymph movements . Most cestode parasites have specialized sucker-like organs to move within and attach to the host . They also exhibit well-coordinated rhythmical movements which could help to locate and maintain themselves in the host [4] or to serve the reproductive function in the parasite [5] . Any interference with coordination of the parasite movement could result in conveyance to an environment hostile for their survival or expulsion from the host [6] . These important functions can be accomplished only by the activity of different kind of muscles [7] innervated by a well-developed nervous system . Serotonin or 5-hydroxytryptamine ( 5-HT ) is an ancient molecule and neurotransmitter with diverse roles in organisms [8] . In invertebrates , the action of 5-HT on neuromuscular junction depends on the species and the type of preparation under consideration [9] . For example , in insects it has been shown that application of 5-HT on neuromuscular junctions appears to slightly depress synaptic strength [10] . In crustaceans , it has been demonstrated that 5-HT enhances synaptic transmission at neuromuscular junctions [11] . In the leech , 5-HT exposure has a relaxing effect on skeletal muscle but enhances muscle force and work production during locomotion and feeding [12] . Finally , in the sea cucumber , 5-HT inhibited evoked contractions induced by acetylcholine [13] . Mansour et al . [14] were among the first investigators who reported the existence of 5-HT in parasitic helminths . Work in free living planarians also highlights the diversity of serotonin receptors in flatworms [15] . Functionally , 5-HT is myoexcitatory in several species of cestodes and trematodes [3 , 4 , 16 , 17] . However , the mechanism by which 5-HT exerts these effects remains unclear: recent work proposed the action on 5-HT receptors located on nerves [18] , older reports suggest a direct effect in muscles [7] , with a combination of both these effects being likely . Finally , other studies relate the motility of these worms with the activation of the glycolytic enzyme phosphofructokinase [19] . The diversity of effects is presumably enabled through the existence of multiple 5-HT receptors [20] . Evidence has accumulated that 5-HT receptors can signal through cyclic AMP ( cAMP ) [7 , 21] and PKA [22] although other second messenger pathways may also be involved [20] . The seminal idea of Mansour [17] about the potential use of 5-HT receptors as pharmacological targets in parasites has received support from recent data in planarians [23] and Schistosoma mansoni [18 , 24 , 25] . Pharmacological profiling of heterologously expressed flatworm 5-HT receptors revealed different pharmacological profiles between parasitic and human serotonin receptors and inhibitory effects of various 5-HT antagonists on motility [25] . These data strongly suggest that 5-HT receptors from parasites could be used as targets for pharmacological intervention [25] . With the exception of 5-HT3 , which is a serotonin gated ion channel , serotonin receptors belong to the rhodopsin family or the class A of G-protein coupled receptors ( GPCRs ) . GPCRs respond to a broad range of physicochemical entities ranging from photons , protons , calcium ions , and small molecules encompassing odorants , neurotransmitters , peptides and glycoproteins , making GPCRs versatile chemical sensors [26] . Indeed the senses of sight , smell and taste are mediated by GPCR signalling . Given the central role played by GPCRs in nearly all physiological processes , they represent attractive targets for drug discovery across a broad spectrum of diseases [26] . Serotoninergic pathways have been implicated in the aetiology of numerous disease states , including depression , schizophrenia , anxiety , social phobia , migraine , obsessive–compulsive and panic disorder [27] . Some selected examples of therapies are: clozapine , an effective agent for chronic schizophrenia [28]; the 5-HT1A receptor partial agonist buspirone , used as an anxiolytic [28]; the antipsychotic risperidone , which targets serotoninergic and dopaminergic receptors [28] and sumatriptan , a selective 5-HT1 receptor agonist used for migraine [28 , 29] . Recent data demonstrate the anthelmintic praziquantel is itself a human serotoninergic ligand [30] . The availability of genome data from Echinococcus spp [31 , 32 , 33] and M . corti ( http://parasite . wormbase . org/ Mesocestoides_corti_prjeb510 / Info / Index / , Helminth Genomes Consortium ) has now permitted us to search for 5HT-GPCR coding genes in these cestodes . Currently , the treatment used for echinococcosis and other cestode infections in humans relies on benzimidazoles , mainly albendazole , which is used alone or in combination with praziquantel [34 , 35] . However , arising of resistance to albendazol and also to praziquantel was reported for many helminths [35 , 36] . These drugs have been reported to be ineffective in 40% of cases [34] , are only parasitostatics ( specially for E . multilocularis ) , requiring of life-long treatments and are not tolerated by many patients [37] . The scarcity of anthelmintic drugs available and the emergence of resistant parasites , makes the discovery of new anthelmintic drugs an imperative need . One possible way to achieve this goal is to characterize G-protein coupled receptors in cestodes as potential pharmacological targets . In this work , we analyzed the effect of 5-HT on motility of the tetrathyridia of M . corti . The evidence for involvement of 5-HT receptors in the cestode neuromuscular activity was reinforced by the utilization of a fluorescent probe derived from a potent agonist for the human serotoninergic receptor 5-HT1A [38] . Isolation , sequencing , bioinformatic characterization and functional testing of 5-HT GPCRs were performed in two species of cestodes , with prioritized sequences modeled to identify characteristics that could be critical for receptor function . Overall , our work assesses the importance of 5-HT in the neuromuscular activity of cestodes , and identifies new 5-HT GPCRs as potential targets for drug therapy .
Gene models coding for metabotropic 5-HT receptors were searched using the terms “5-hydroxytryptamine receptor” , “serotonin receptor” , “biogenic amine 5-HT receptor” , “G protein coupled 5 hydroxytryptamine receptor” in Echinococcus multilocularis [31] , Echinococcus granulosus [32] , Echinococcus canadensis [33] and Mesocestoides corti ( http://parasite . wormbase . org/Mesocestoides_corti_prjeb510/Info/Index/ , Helminth Genomes Consortium ) databases . The retrieved gene models were also used as a query in the Blast tool from the NCBI for nucleotide ( BLASTN 2 . 7 . 0+ ) and proteins ( BLASTP 2 . 7 . 0+ ) . The presence of conserved domains in the gene models found was determined using the Conserved Domain Database tool from the NCBI ( https://www . ncbi . nlm . nih . gov/cdd/ ) . The prediction of hypothetical open reading frames from nucleotide sequences was performed using the translate tool from the expasy proteomic tools ( http://web . expasy . org/translate/ ) . The localization and length of transmembrane domains was predicted using TMpred ( http://embnet . vital-it . ch/software/TMPRED_form . html ) . The multiple sequence alignment was performed using the program MULTALIN [39] from PRABI ( https://npsa-prabi . ibcp . fr/cgi-bin/npsa_automat . pl ? page=/NPSAHLP/npsahlp_alignmultalin . html ) . The multiple sequence alignments were then visually inspected and manually edited when necessary . The molecular weight and isoelectric point of the predicted proteins was calculated with the program Compute pI/Mw tool ( http://web . expasy . org/compute_pi/ ) . Cloned 5-HT7Egran1 , 5-HT7Egran2 and 5-HT7Mco1 and predicted cestode sequences gene models ECANG7_00799 ( 5-HT1Egran1 ) , ECANG7_02049 ( 5-HT1Egran2 ) and MCOS_0000684301 ( 5-HT7Mco2 ) were aligned with cloned serotonin receptor sequences from the following invertebrates; Dugesia japonica ( 5-HT1Dj1 to 5-HT1Dj3 , 5-HT4Dj1 to 5-HT4Dj5 , 5-HT7Dj1 to 5-HT7Dj7 , PMCID: PMC4569474 ) , Schistosoma mansoni ( 5-HT7Sm , GenBank accession number KX150867 ) , Caenorhabditis elegans ( UniProt IDs G5EGH0 for 5-HT1Ce , O17470 for 5-HT2Ce and Q22895 for 5-HT7Ce ) and Drosophila melanogaster GenBank accession numbers CAA77570 . 1 ( 5-HT1ADro ) , CAA77571 . 1 ( 5-HT1BDro ) , CAA57429 . 1 ( 5-HT2ADro ) , NP_001262373 . 1 ( 5-HT2BDro ) and NP_524599 . 1 ( 5-HT7Dro ) . Alignment of amino acid sequences was performed with Clustal Omega v1 . 2 [40] , gaps removed with Gap Strip/Squeeze v2 . 1 . 0 ( 75% gap tolerance ) [41] , and an unrooted maximum likelihood phylogenetic tree was generated with PhyML v3 . 1 ( 500 bootstrap replicates ) [42] . For molecular modelling studies , proteins studied in this work were searched using BLAST [43] against UniProtKB/Swiss-Prot databases . Protein domains were screened against PFAM , and Prosite databases using PFAM_scan [44] or HMMscan 3 . 0 . Protein structure models were obtained using PHYRE2 [45] and SWISS-MODEL [46–49] . PDB database was used for homology searching and 3SN6 and 4IAR with ergotamine ligand ( http://www . rcsb . org/pdb ) [50] was used for structural comparison analyses . For all the new sequences , the template used to model the transmembrane segment of the protein was the deposited structure 3SN6 , and the internal segment ( intracellular loop 3 ) modelled using Phyre using the template 2RH1 . Ramachandran Plots were produced using Rampage ( http://mordred . bioc . cam . ac . uk/~rapper/rampage . php ) . Relevant and conserved protein residues for the ligand binding were identified and its distances from the ligand were measured . Analysis of the 5-HT7Egran1 modeled protein revealed that 91 . 7% of the residues are within the favoured regions , 4 . 3% in allowed regions and 4% in outlier regions of the Ramachandran plots . For the modeled 5-HT7Egran2 protein , 88 . 6% of the residues are within the favoured regions , 8 . 3% in allowed regions and only 3 , 1% in outlier regions were as for 5-HT7Mco1 , 85 . 3% of the residues are within the favoured regions , 9 . 5% in allowed regions and 5 , 2% in outlier regions . All this data shows the good quality of the generated homology models ( S1A , S1B and S1C Fig ) . Compounds UCM120 and UCM2550 were synthesized as described previously [38 , 51] and dissolved in DMSO before dilution to the needed concentration . Ergotamine and tryptamine were also dissolved in DMSO whereas Lysergic acid diethylamide ( LSD ) , tyramine , octopamine , acetylcholine , histamine and dopamine were dissolved in distilled H2O . 5-HT was dissolved in distilled H2O at stock concentration of 5 mM . Stock solutions were either made up on the day of the experiment or taken from aliquots stored at -80°C for no longer than 1 week prior to use . After 0 . 22 μm filtration with Millex GV filter units ( Millipore , Ireland ) , stock solutions were diluted to the corresponding final concentration ( e . g . 0 . 1; 1; 10; 100; 500; 1000 and 2000 μM for 5-HT ) in RPMI medium with high glucose ( Gibco , USA ) . 5-HT ( H9523 ) , LSD ( L7007 ) , ergotamine ( E1200000 ) , tryptamine ( 193747 ) , tyramine ( T90344 ) , octopamine ( O0250 ) , acetylcholine ( A6625 ) , histamine ( H7125 ) and dopamine ( H8502 ) were obtained from Sigma Chemical Company . Echinococcus granulosus protoscoleces were obtained under sterile conditions by needle aspiration of hepatic hydatid cysts of porcine origin , provided by abattoirs from Buenos Aires and Santa Fe provinces , Argentina . The livers used for parasite extraction were from animals that were not specifically used for this study and all the material obtained was processed as part of the normal work of the abattoir . Samples from animals at the abattoir were collected under consent from local authorities . Protoscolex viability was assessed using the eosin exclusion test after three washes with PBS , with 50 μg/ml of gentamicin to remove cyst wall debris [52] . Only samples showing more than 95% viability were used . A fraction of the protoscoleces was used for probe imaging , other fraction for motility experiments and the remaining protoscoleces were used for species/genotype determination by sequencing a fragment of the mitochondrial cytochrome c oxidase subunit 1 ( CO1 ) , as previously described [53] . The resulting species and genotype of all protoscoleces used in this work were from Echinococcus canadensis G7 . Three biological replicates were used with each replicate corresponding to protoscoleces obtained from a single cyst . Mesocestoides corti larvae ( tetrathyridia ) were maintained by alternate , serial passages in Wistar female rats and BALB/c female mice as previously described [54] . The tetrathyridia larvae used in drug testing were obtained after 3 months of intraperitoneal inoculation . Only tetrathyridia from up to the third serial passage in mice were used for the experiments . Three biological replicates were used with each replicate corresponding to tetrathyridia obtained from a single mouse host . Larvae freshly collected from mice were washed three times in PBS and used immediately for drug tests . Wistar female rats and BALB/c female mice were housed at the animal facilities of Instituto de Investigaciones en Microbiología y Parasitología Médica ( IMPaM ) , Facultad de Medicina , Universidad de Buenos Aires ( UBA ) -Consejo Nacional de Investigaciones Científicas y Tecnológicas ( CONICET ) , Buenos Aires , Argentina , in a temperature-controlled light cycle room with food and water ad libitum . Experiments involving the use of experimental animals were carried out according to protocols approved by the Comité Institucional para el Cuidado y Uso de Animales de Laboratorio ( CICUAL ) , Facultad de Medicina , Universidad de Buenos Aires , Argentina ( protocol “in vivo passages of cestode parasites from Mesocestoides corti” number CD N° 1127/2015 ) . Cyst puncture was performed following the approved protocol by the same institution ( protocol “Hydatid cysts puncture from natural infections” number CD N° 3723/2014 ) . Total RNA from E . granulosus was extracted from protoscoleces that were crushed under liquid nitrogen and processed using Trizol reagent ( Invitrogen ) . The RNA obtained was treated with RNase-Free DNase ( Fermentas ) , ethanol precipitated and reverse transcribed using Superscript III reverse transcriptase ( RT ) ( Invitrogen ) and gene specific reverse primer complementary to 5-HT GPCR gene models ( S1 Table ) . One cDNA for each selected gene model was synthesized . The same procedure was followed to obtain RNA from M . corti tetrathyridia and cDNA synthesis . The gene models ECANG7_06088 and ECANG7_06092 from E . granulosus and MCOS_0000684401 from M . corti , retrieved as explained in the section “Bioinformatic analysis” were used for primer design ( S1 Table ) and a PCR product for each serotoninergic GPCR was obtained . The cycling parameters for ECANG7_06088 were 95°C for 5´ ( initial denaturalization ) , then 98°C for 20´´ , 56°C for 30´´ and 72°C for 2´ ( repeated 5 times ) , then 98°C for 20´´ , 66°C for 30´´ and 72°C for 2´ ( repeated 35 times ) and finally , 72°C for 10´ ( final extension ) . For ECANG7_06092 were 95°C for 5´ ( initial denaturalization ) , then 98°C for 20´´ , 55°C for 30´´ and 72°C for 2´ ( repeated 5 times ) , then 98°C for 20´´ , 64°C for 30´´ and 72°C for 2´ ( repeated 35 times ) and finally , 72°C for 10´ ( final extension ) . Finally , for MCOS_0000684401 were 95°C for 5´ ( initial denaturalization ) , then 98°C for 20´´ , 55°C for 30´´ and 72°C for 2´ ( repeated 5 times ) , then 98°C for 20´´ , 72°C for 30´´ and 72°C for 2´ ( repeated 35 times ) and finally , 72°C for 10´ ( final extension ) . PCR reactions were performed using the KAPA HIFI polymerase ( Biosystems ) using the cDNA previously obtained with each reverse primer as a template . Amplification products were visualized by agarose gel electrophoresis and Gel Red staining and the bands of interest were extracted from the gel using the QIAquick Gel Extraction Kit ( Qiagen ) , used for a non-templated adenine adding or A-tailing procedure employing a nonproofreading DNA polymerase ( Pegasus , Embiotec ) and finally cloned into the TOPO TA Vector ( Invitrogen ) . The recombinant plasmids were used for Escherichia coli ( DH5α ) transformation and the transformed bacteria were grown in LB with ampicillin and kanamycin . The selected colonies were then used for plasmid purification using the GeneJet Plasmid miniprep kit ( Fermentas ) and sequencing using an Applied Biosystems Big Dye terminator kit ( Applied Biosystems ) on an ABI 377 automated DNA sequencer . The cloned cDNA products obtained with E . granulosus primers were designed from the ECANG7_06088 and ECANG7_06092 gene models and named 5-HT7Egran1 and 5-HT7Egran2 respectively ( S1 Text ) following the naming scheme proposed by Tierney [55] . The cDNA cloned from the gene model MCOS_0000684401 from M . corti was named 5-HT7Mco1 ( S1 Text ) . For the motility index measurement , the method of Camicia et al . [16] was followed with minor modifications . In the case of E . granulosus , protoscoleces were mantained in RPMI medium with high glucose ( Gibco , USA ) with 50 μg/ml of gentamicin for no more than 48 hours in order to avoid the potential differentiation of the protoscolex towards premicrocyst [16] . The day before the experiment the protoscoleces were transferred to U-shape 96-well microplates ( Greiner Bio-One , Germany ) with approximately 125 parasites per 100 μl per well . This number of parasites per well was empirically determined as the best number of worms to perform the experiments . Movement was measured as described for C . elegans [56] using a worm tracker device ( WMicrotracker Designplus SRL , Argentina ) which determines the motility index by the light-microbeam ( 100 μm wide; k = 880 nm; intensity <1 mW ) scattering produced by the movement of parasites . Quantification of the signal was performed by applying a mathematical algorithm previously described for sperm viability determination ( Patent number: US4176953 , year of submission: 1978 , year of publication: 1979 ) . To study the effect of 5-HT on the motility of M . corti tetrathyridia , only one worm per well was used as this was the empirically determined optimal number . Addition of more tetrathyridia to each well plate did not result in a better signal . M . corti tetrathyridia were used fresh , immediately after the mice was opened . One tetrathyridium per well in 100 μl of RPMI with high glucose ( Gibco ) was pipetted to U-shape 96-well microplates and incubated overnight at 37°C in a 5% CO2 atmosphere . The following day , basal activity recordings were performed for 2 h at 37°C in the worm tracker . After the initial 2 h , 25 μl of the same medium containing 5-HT at the indicated concentrations were added and the motility of parasites was recorded in the resultant 125 μl of medium for another 2 hours . Twenty-five microliters of fresh medium alone were added to the controls before registering motility in the resultant 125 μl . In order to avoid any potential difference between individuals not related to the effect of the 5-HT , the activity found in each well was divided by the activity found in the same well in the basal record . Each experiment was performed using 16 technical replicates per treatment condition . In order to count with biological replicates , four independent experiments , with tetrathyridia coming from different mice were performed . We evaluated the effect of UCM2550 , a 5-HT1A agonist in mammals and the parent ligand of UCM120 , [38 , 57] on protoscolex movement . Protoscoleces were incubated in 96-well microplates as described in the section “Motility index measurement” , and basal activity was registered for 2 hours at 37°C . After this first acquisition they were subjected , in groups of 16 replicates , to the inidicated concentrations of the previously mentioned drug , and recorded for an additional 2 hours in the resultant 125 μl . Control wells were incubated in 125 μl of medium with or without 2% of DMSO ( the vehicle used to dissolve the drugs ) and no differences were observed between those groups . Three independent experiments , with protoscoleces from different hydatid cysts ( biological replicates ) , were performed . The effect of the drug on protoscolex viability was tested for toxicity at the maximal concentrations used . In some experiments , alongside to the worm tracker motility analyses , the parasite motor activity was also measured with a digital video camera ( Kodak easy share Z915 digital camera ) coupled to an inverted microscope ( Nikon , model TMS-F ) . Fresh parasites were processed in the same way as in previous sections in U-shape 96-well microplates , then incubated overnight at 37°C in a 5% CO2 atmosphere and finally video recorded with a digital camera after drug treatment . Images were obtained for a period of 15 seconds and the data were analyzed with ImageJ software ( version 1 . 48 , NIH , USA ) . Protoscoleces showed complex motor movements characterized by sucker and rostellum movements and repeated body bends . To quantify this type of movements the method of Patocka et al . [18] was followed with some modifications . The videos were transformed to stacks of TIFF images by using the Filmora video editor ( Wondershare Filmora , version 8 . 2 . 3 , downloaded from https://filmora . wondershare . es/ ) and then imported into ImageJ . First , the stacks were first converted to 8 bit type images . Next , the background was subtracted and then converted to a binary image by threshold adjust . Then , the maximum intensity of Z projection ( which shows all the elements in the movie ) was calculated together with the minimum intensity projection ( which shows only the constant elements in the movie ) . Then subtracting the minimal from maximal projections with the “image calculator” it was possible to remove everything constant in the movie , including immobile animals . Finally , the resultant pixels were quantified using the “Analyze particles” command and the integrated density in the summary result table showed the pixels that changed during the course of each movie . This procedure was repeated for each video and a graphic representation of pixel change was done for each treatment ( S2 Fig ) . Eight technical replicates and three independent experiments , with protoscoleces coming from different hydatid cysts ( biological replicates ) , were performed . Protoscoleces were fixed for 4 hours at room temperature ( RT ) in 4% ( w/v ) paraformaldehyde ( PFA ) in PBS ( pH 7 . 4 ) and washed for 24 hours at RT in PBS containing 0 . 35% ( v/v ) triton X-100 ( TX-100 ) , 0 . 1% ( w/v ) sodium azide ( NaN3 ) , 0 . 1% ( w/v ) BSA ( Sigma ) , 6 . 25 x 10−3 ( w/v ) digitonin ( Dig ) and 0 . 5% DMSO ( PBS/TX-100/NaN3/BSA/Dig/DMSO ) . Approximately , 10 μl of pellet of protoscoleces were aliquoted in eppendorf tubes and incubated for 120 hours ( five days ) at 4° C in the presence of 100 μM of the probe UCM120 ( which is a 5-HT1A agonist in mammals labeled with a dansyl group as fluorescent tag ) [38] in constant agitation in a final volume of 300 μl . Specimens were then washed three times with PBS and fixed for 3 hours at room temperature ( RT ) in 4% ( w/v ) paraformaldehyde ( PFA ) in PBS/TX-100/NaN3/BSA/Dig/DMSO . The protoscoleces were washed again , mounted in glycerol/PBS ( 8:1 v/v ) and then viewed under standard fluorescence microscope ( Nikon Eclipse E600 ) or in a confocal scanning laser microscope ( CSLM , Fluoview 1000 Olympus ) . The individual pictures obtained from the optical sections were used for the reconstruction of the whole specimen using the program ImageJ . In order to determine if the signal obtained was specific , several controls were run in parallel: protoscoleces without probe or protoscoleces in the presence of 1000 μM of serotonin as negative control . This experiment was performed several times with similar results . The transcriptional expression levels ( in RPKM , or reads per kilobase per million reads ) for each serotoninergic receptor in Echinococcus granulosus sensu stricto ( G1 genotype ) were from Zheng et al . ( [32] , Supplementary Table 28 ) . HEK-293 cells ( ATCC CRL-1573 . 3 ) were cultured in growth media [DMEM ( Gibco ) , 10% heat inactivated fetal bovine serum ( Gibco ) , penicillin ( 100units/mL ) , streptomycin ( 100μg/mL ) and L-glutamine ( 290μg/mL ) ] and used for assays between passages 5 and 25 . For cestode GPCR heterologous expression assays , cells were transfected ( Lipofectamine 2000 , Invitrogen ) at 80% confluency approximately 16 hours after seeding within T-25 culture flasks with a 1:1 ratio of human codon optimized cestode GPCR cDNA ( subcloned into a pcDNA3 . 1 ( - ) mammalian expression vector ) and cDNA encoding the pGloSensor 22-F plasmid ( Promega ) . The following day , cells were trypsinized , centrifuged ( 300g/5min ) , resuspended in DMEM supplemented with 1% dialyzed FBS ( Gibco ) and plated in 96 well , solid white plates ( Corning , cat # 3917 ) . After overnight culture to allow adherence , media was exchanged for assay buffer ( HBSS supplemented with 0 . 1% BSA , 20mM HEPES ( pH 7 . 4 ) , and GloSensor reagent ( Promega ) . cAMP-luminescence assays were performed following addition of a phosphodiesterase inhibitor ( 3-isobutyl-1-methylxanthine , IBMX; 200μM ) using a GloMax-Multi Detection System plate reader ( Promega ) as described previously [24] . Statistical analyses were carried out using the GraphPad Prism Software package , version 6 for Windows ( GraphPad Software , CA , USA ) . To analyze the effects of serotonin in motility assays performed with the WMicrotracker device , the relative motility indices of all replicate wells for each concentration of the different independent experiments were made into one cohort for one-way ANOVA . To determine significant differences a Bonferroni and Dunnett post-tests were performed comparing all concentrations with the control . In the experiment evaluating the effects of UCM2550 on protoscolex motility , the mean motility index obtained with the WMicrotracker was compared before and after the addition of the drug with a Student’s t-test . For image based assays , pixel changes in control and treatments were calculated and analyzed with one way ANOVA and Dunnett post-test were performed comparing all the concentrations with the control . Differences from control with P < 0 . 05 were considered statistically significant inductions or inhibitions of motor activity .
Six gene models in total were retrieved by blast searches from which four gene models were from Echinococcus spp . databases and only two from the M . corti database . From all them , only ECANG7_06088 , ECANG7_06092 ( E . granulosus ) and MCOS_0000684401 ( M . corti ) , were used for primer design since the rest of the gene models were either incomplete ( lacking one or more transmembrane segments ) , or missing important residues for function , or they could not be amplified in PCR reactions . By using sequence specific primers , three cDNAs were cloned by standard RT-PCR being two of the sequences obtained from E . granulosus ( 5-HT7Egran1 and 5-HT7Egran2 ) and one from M . corti ( 5-HT7Mco1 ) . The 5-HT7Egran1 cDNA has 2100 bp and encodes a protein of 659 amino acids with a predicted molecular weight of 73 . 8 kDa and a pI close to 10 ( pI = 9 . 83 ) . The second cDNA cloned , 5-HT7Egran2 , has 1731 bp and encodes for a protein of 576 amino acids with a predicted molecular weight of 65 kDa and a pI close to 9 ( pI = 9 . 12 ) . Finally , the M . corti cDNA 5-HT7Mco1 , has 2091 bp and encodes for a protein of 696 amino acids with a predicted molecular weight of 78 . 6 kDa and a pI of 9 . 45 . Domain searches showed that all the cloned sequences have best hits with the 5-HT7 domain of serotonin receptors . S3 Fig shows a multiple sequence alignment between the Homo sapiens , C . elegans , S . mansoni , E . granulosus and M . corti sequences encoding for serotoninergic G-protein coupled receptors . The bioinformatic analysis of the cloned sequences shows several characteristics of the molecular signature of a GPCR . For example , all three sequences have seven conserved hydrophobic transmembrane segments consistent with GPCR architecture ( S3 Fig ) . Topological modeling ( TMpred ) predicts an extracellular N-terminus and an intracellular C-terminus . Intracellular loop 3 and , for 5HT7Egran1 and 5HT7Mco1 the carboxy-terminal regions , were longer than the corresponding regions in the human counterpart ( S3 Fig ) . The multiple sequence alignment also highlights conserved amino acid residues that are invariant within the rhodopsin family of GPCRs [58] . The first of them is the DRY motif present in the third transmembrane domain probably involved in the stabilization of the ground state [59 , 60] . The second relevant motif found was the NPxxY motif located toward the cytoplasmic end of the seventh transmembrane domain and potentially implicated as crucial in the recruitment of β-arrestin , which is a mechanism of receptor inactivation [59–61] . The third is the PIF motif , which is a critical trigger motif for receptor activation [59 , 60] . And the last one , in the transmembrane segment 6 , there is a cluster of aromatic residues referred as “toggle switch” in which it was reported that the movement of some of these residues could be important for receptor activation [60] . Taking the residue position from the human 5HT1B receptor as a reference sequence [62] and the Ballesteros and Weinstein nomenclature [63] , the residues conserved in all the cestode sequences were: C1223 . 25 which makes putative disulphide bond contact with the C199El2 in the extracellular loop 2; D1293 . 32 , which forms a salt bridge with the positively charged amino group of ergotamine in the human receptor [62]; C1333 . 36 , which recognizes the amine portion of the ligand; T1343 . 37 , which forms hydrogen bond with the indole group; A2135 . 46 , which interacts with the indole ring in the ligand; W3276 . 48 , F3306 . 51 and F3316 . 52 , which form the orthosteric binding pocket . The residue Y3597 . 43 is hypothesized to form hydrogen bond with N6 of ergotamine in the human receptor . The residue conserved in all but not in 5-HT7Egran2 , is T2165 . 43 , which interacts with the indole ring in the ligand and is replaced by C in this position in the 5-HT7Egran2 sequence . The conserved S2155 . 42 of TM5 is replaced by alanine in all the cestode sequences and , interestingly , the same substitution was observed in the S7 . 1 receptor from the planaria D . japonica [15] and also in the Sm5HTR from S . mansoni [18] . TM5 residues ( S4 Fig ) are important players in ligand recognition [62 , 64]: with the exception of the alanine at position 5 . 42 , the other crucial residues ( 5 . 43 and 5 . 46 ) are conserved in mammalian serotonin receptors , particularly in subtypes 1 ( 5-HT1 ) and 7 ( 5-HT7 ) . Besides ligand binding , other residues potentially involved in G protein contacts could be identified ( S3 Fig ) . For example , the residue L3166 . 37 in the cytoplasmic end of TM6 upon activation contacts a universally conserved leucine of the G protein in the β2 adrenergic receptor-Gs structure [65] . Moreover , in the V155 position of the human 5-HT1 receptor ( F139 in the human beta 2 adrenergic receptor ) , located in the IL2 helix , it was reported a F ( phenylalanine ) or L ( leucine ) for Gs coupling which is coincident with the residues observed in serotonin receptors from cestodes at this position [66] ( S3 Fig ) . The phylogenetic analysis ( Fig 1 ) shows that all the cloned sequences grouped with the 5HT7 subtype ( S7-like clade ) of invertebrate serotoninergic G-protein coupled receptors , which are Gs coupled [67] . Transcriptomic data obtained from Zheng and coworkers [32] , reveals that two of the receptors cloned here ( 5-HT7Egran1 and 5-HT7Egran2 ) are highly expressed in the activated protoscolex stage ( S5 Fig ) with the levels of expression of 5-HT7Egran2 being particularly high . The levels of expression of both receptors in activated protoscolex stage are at least fourfold higher than those observed in other stages of the parasite . No expression of these two receptors was detected in the oncosphere . Molecular models of 5-HT7Egran1 , 5-HT7Egran2 and 5-HT7Mco1 were developed to explore potential structural similarities and differences between the parasitic and human receptors . Fig 2 shows the models of each cloned serotoninergic GPCR compared with the crystal structure of human 5-HT1BR ( 4IAR ) [62] . A striking structural similarity in the transmembrane domains can be observed ( Fig 2 , models A , C and E ) . According to the models obtained , four important interactions with ergotamine could occur at the transmembrane segments three ( III ) and five ( V ) , ( Fig 2 , models B , D and F ) . The indicated residues comprise part of the orthosteric binding pocket of the cestode GPCRs . Previously , we have shown that 5-HT induces E . granulosus protoscolex motility [16] . In spite of some evidence about 5-HT action on M . corti motility [3] , the kinetics of this important neurotransmitter in motility over longer periods of time ( >30 minutes ) was unknown . For this reason , we analyzed further the effect of 5-HT on the larvae of M . corti . The addition of serotonin to tetrathyridia resulted in the stimulation of the motility above basal levels ( S6 Fig , S1 Video ) . The channel activity traces show a stimulation in frequency of movement at high doses of 5-HT compared to the basal condition ( S6A Fig ) . The dose-response curve showed a significant rise of motility starting from 100 μM of 5-HT ( *p< 0 . 05 ) until the highest concentration tested of 2000 μM over a period of two hours ( ****p< 0 . 001; S6B Fig ) . Dose-response curves were also collected over different intervals of time ( 30 , 60 , 90 and 120 minutes ) . These graphs showed that the shape of the curve changes with time ( S6C Fig ) . At the highest concentrations tested ( 500 to 2000 μM ) , the relative motility tends to drop with time after the first 30 minutes of incubation , losing their significance respect to control , probably reflecting an inactivation process . At the lowest dose ( 100 μM ) the motility tends to rise during the first 90 minutes of incubation and then also it tends to fall after 120 minutes of incubation ( S6C Fig ) . Similar concentrations of 5-HT ( 1000 μM; ***p< 0 . 001 ) are necessary to see significant changes in motility over a period of 30 minutes by video imaging ( S6D Fig ) . Based on the bioinformatic prediction that these sequences encode serotoninergic GPCRs , responses to 5-HT were compared between cells expressing individual cestode GPCRs and untransfected cells . For each of the clones , addition of 5-HT in transfected cells evoked a rapid and dose dependent stimulation of cAMP accumulation ( Fig 3A , 3B & 3C ) . One notable feature of two of the GPCRs– 5-HT7Egran2 and 5-HT7Mco1 –was resolution of a background rate of cAMP accumulation immediately on IBMX addition prior to addition of 5-HT ( the period between the arrows in Fig 3B & 3C ) . This was not observed with the 5-HT7Egran1 clone ( Fig 3A ) . Because of this basal coupling , the fold-change in luminescence signal on addition of 5-HT was lower with– 5-HT7Egran2 ( ~3 . 3-fold ) and 5-HT7Mco1 ( ~2 . 5-fold ) than observed in cells expressing 5-HT7Egran1 ( ~20-fold ) . Full dose response relationships were performed for each GPCR , and compared with endogenous responses to the same concentrations of 5-HT in untransfected cells ( Fig 3D , 3E & 3F ) . The EC50 for 5-HT evoked cAMP generation was 171±57nM in cells expressing 5-HT7Egran1 . For the two GPCRS exhibiting basal coupling , the EC50 for 5-HT evoked cAMP production was in the picomolar range measured as 543±107pM for 5-HT7Egran2 and 255±63pM for 5-HT7Mco1 . Responses to 5-HT were considerably smaller in untransfected cells ( Fig 3D , 3E & 3F ) . To investigate the specificity of these GPCRs for 5-HT , responses to higher concentrations ( 10μM ) of other neurotransmitters ( tryptamine , tyramine , octopamine , histamine , dopamine and acetylcholine ) were examined ( Fig 4 ) . In all cases , 5-HT elicited maximal cAMP accumulation . At higher doses ( 10 μM ) , tryptamine also proved an activator of 5-HT7Egran2 ( EC50 ~0 . 8μM ) and 5-HT7Mco1 ( EC50 ~1μM ) but not 5-HT7Egran1 ( Fig 4 ) . The first 2 receptors were at least 1000-fold less sensitive to tryptamine than to 5-HT . Responses to other ligands were considerably smaller , likely representing responses from endogenous GPCRs in HEK-293 cells ( histamine , dopamine ) or comparable with levels of luminescence seen with vehicle control . From this dataset we conclude that each of the cestode sequences encodes a bona fide serotoninergic GPCR . Our modelling studies supported an interaction of the ergot alkaloid ergotamine with the binding pocket of the cestode GPCRs ( Fig 2B , 2D & 2F ) . Therefore we assayed responsiveness of each GPCR to ergotamine , as well as the synthetic hallucinogen lysergic acid diethylamide ( LSD ) which has proved a useful probe ligand for studying 5-HT receptor properties [59] . Ergotamine activated each of the cestode 5HT7Rs but with variable efficacy ( Fig 5A , 5B & 5C ) , behaving as a low efficacy partial agonist at 5-HT7Egran1 and a higher efficacy partial agonist at 5-HT7Egran2 and 5-HT7Mco1 . Each GPCR responded to ergotamine over a similar concentration range ( EC50s of 290±49nM , 234±37nM and 354±62nM for 5-HT7Egran1 , 5-HT7Egran2 and 5-HT7Mco1 respectively , Fig 5A , 5B & 5C ) . Responses to LSD were then examined . Two of the receptors ( 5-HT7Egran1 and 5-HT7Mco1 ) were potently activated by addition of LSD but no response was observed in cells expressing 5-HT7Egran2 when probed in ‘agonist’ mode ( Fig 5D , 5E & 5F ) . Rather , LSD attenuated the basal coupling of 5-HT7Egran2 and blocked cAMP accumulation in response to a subsequent addition of 5-HT ( ‘antagonist mode’ ) , demonstrating LSD acted as an antagonist of 5-HT7Egran2 . Full dose response relationships to LSD were performed ( Fig 5G , 5H & 5I ) which demonstrated EC50s for activation of 35±6nM ( 5-HT7Egran1 ) and 4 . 4±1 . 0nM ( 5-HT7Mco1 ) , and an IC50 for inhibition of 5-HT responses of 2 . 9±0 . 2nM ( 5-HT7Egran2 ) . Collectively , these functional expression data demonstrate that while each of these GPCRs is activated by 5-HT , they exhibit distinct pharmacological properties ( sensitivity to 5-HT , differential responsiveness to tryptamine , ergotamine and LSD ) . In order to assess the localization of serotoninergic G-protein coupled receptors in larval stages of cestodes , we used a fluorescent probe with high affinity for mammalian serotoninergic GPCRs . Since the first attempts with M . corti tetrathyridia were unsuccessful and owing to the very limited availability of the probe , we concentrated our efforts in protoscoleces of E . granulosus , taking into account the higher size of M . corti tetratyridia compared to E . granulosus protoscoleces . The fluorescent probe—UCM120 , a high affinity agonist of the human 5-HT1A receptor ( Ki = 2 nM , Fig 6A ) [38]—displayed a distinctive staining pattern in protoscoleces of E . granulosus . A discontinuous and punctiform pattern of staining could be observed particularly concentrated in suckers but some of this pattern could also be observed in the body ( Fig 6B and 6C ) . Several controls were performed in order to determine the specificity of pattern observed . When protoscoleces where incubated in the presence of the probe and an excess of serotonin ( 1 mM ) was added , the staining pattern previously mentioned disappeared ( Fig 6D ) . The hooks remained strongly stained as seen in controls ( Fig 6D ) , suggesting this represented non-specific staining or autofluorescence . When protoscoleces where incubated in the presence of the non fluorescent analog UCM2550 , motility was inhibited ( Fig 6E , S2 Video ) . The dose-response curve obtained with the WMicrotracker device showed a significant fall of motility ( relative to basal ) starting from 100 μM ( *p< 0 . 05 ) until the highest concentration tested of 1000 μM during a period of two hours ( *p< 0 . 05; Fig 6E ) . However , using a video based method , lower concentrations produced significant inhibition of motility , starting from 0 . 1 μM ( **** P ≤ 0 . 0001; Fig 6F ) .
This work provides for the first time , functional evidence for the existence of serotoninergic GPCRs in cestodes . Bioinformatic predictions suggesting the presence of several 5-HT-GPCR coding genes were validated by functional studies . From diverse genomic and transcriptomic databases , several gene models with putative seven transmembrane segments and amino acid identity with serotoninergic GPCRs were identified in E . granulosus and M . corti . The information obtained was used for the cloning of three putative 5-HT GPCRs of cestodes: 5-HT7Egran1 , 5-HT7Egran2 and 5-HT7Mco1 . The multiple sequence alignment of these clones with serotoninergic and other aminergic GPCRs suggests that the sequences obtained correspond to serotoninergic GPCRs . This result is principally supported by the multiple sequence alignment analysis of residues potentially involved in ligand binding in transmembrane domain 5 . Many aminergic ligands form critical interactions with residues in TM5 , in particular the residues 5 . 42 , 5 . 43 and 5 . 46 [60 , 62] . These residues can be used to discriminate between serotoninergic and other aminergic GPCRs and much of the specificity of GPCRs could be attributable to TM5 properties [68] . The crucial residues 5 . 42 and 5 . 46 are generally conserved in mammalian serotonin receptors , particularly in subtypes 1 ( 5-HT1 ) and 7 ( 5-HT7 ) . It was reported that those residues are usually less polar in serotoninergic ( no more than one polar residue ) than the corresponding residues of adrenergic , dopaminergic and histaminergic receptors [62] . The substitution of the polar serine by the non polar alanine at position 5 . 42 observed here in cestode 5HT-GPCRs was reported before in some invertebrate receptors , for example in D . japonica [15] and S . mansoni [18] . Indeed , the residues “AA” in the position 5 . 42 and 5 . 46 respectively in planarian receptors were proposed as diagnostic for assignment to the clade 7 of the invertebrate receptors [15] . The threonine residue in position 5 . 43 is also conserved in cestode and trematode sequences as it was in mammalian receptors . In addition , several motifs commonly found in aminergic receptors , that could be critical for receptor function , were also found in the cestode sequences . Phylogenetic analysis showed a close relationship between the cloned cestode receptors and the clade 7 according to the invertebrate classification . These subtype of receptors are coupled to Gs predicting an increase of cAMP levels upon stimulation . Consistent with the bioinformatic prediction , all the cloned cestode receptors produced high levels of cAMP upon incubation with 5-HT . The cestode 5-HT GPCRs were found to be expressed in motile life cycle stages such as protoscolex and adult worms , as well as in the hydatid cyst , suggesting that those receptors could have other activities besides motor control . The molecular modeling of the cloned receptors and the comparison with the mammalian counterpart showed a striking resemblance at the structural level , in coincidence with the results obtained by multiple sequence alignment . The position of the orthosteric binding pocket and the docking with ergotamine are in line with these structural similarities . In agreement with bioinformatic predictions , the increase in larval motility upon incubation with serotonin suggests the presence of serotoninergic receptors . In concordance with previous work [3] , the addition of serotonin to M . corti tetrathyridia caused a significant stimulation of motility in a dose response fashion . Previously , we have shown that E . granulosus protoscoleces responded to serotonin [16] using the same method . The stimulation of M . corti tetrathyridia motility exerted by serotonin was observed with higher concentrations ( starting from 500 μM during 30 minutes of incubation , using a light scattering method ) than those previously reported ( 100 nM , during 30 minutes of incubation ) [3] and this could be attributed to differences in the biological samples used and/or the different sensitivity of the equipment employed in both cases . Other factors such as preincubation time and medium could also contribute to the differences observed . We were unable , by using imaging techniques , to find a significant stimulation of motility below 1000 μM of serotonin after 30 minutes of incubation with the drug . The increase on motility obtained in the presence of this neurotransmitter was observed until 60 minutes , but later the activity tended to fall . This could be due to an inactivation process after overstimulation of serotoninergic receptors [61] . In order to confirm the bioinformatic and the phylogenetic predictions , we heterologously expressed the cloned sequences and analyzed the intracellular response to receptor ligands . Remarkably , the three cloned sequences yielded elevated levels of cAMP accumulation upon incubation with serotonin . This response was not observed with other neurotransmitters . Interestingly , for two of the cloned receptors the EC50 was in the picomolar range . To the best of our knowledge , this is the lowest EC50 value reported for 5-HT GPCRs and would identify them as the most sensitive 5-HT receptors reported in any invertebrate . Differences between the three cloned receptors were also observed in their responses to three tested ligands: tryptamine was an activator of 5-HT7Egran2 and 5-HT7Mco1 but not 5-HT7Egran1; ergotamine activated the three GPCRs with variable efficacy , behaving as a low efficacy partial agonist at 5-HT7Egran1 and a higher efficacy partial agonist at 5-HT7Egran2 and 5-HT7Mco1; the hallucinogen LSD was a potent agonist [69] of 5-HT7Egran1 and 5-HT7Mco1 but an antagonist at 5-HT7Egran2 . This differential response to LSD could be related to the divergence of 5-HT7Egran2 from 5-HT7Egran1 and 5-HT7Mco1 as seen in the multiple sequence alignment and phylogeny ( Fig 1 ) . The EC50 for 5-HT in 5-HT receptors from invertebrates [18 , 70 , 71] , as well as from the vertebrate host [72] , show values in the nM to μM interval . Unexpectedly , the receptors cloned showed distinct pharmacological properties characterized by an extremely high sensitivity ( picomolar EC50 value ) to 5-HT in 5-HT7Egran2 and 5-HT7Mco1 . Another remarkable difference with other receptors was that the allucinogen LSD , which has an agonist activity in several 5-HT7 receptors from invertebrates [73–76] and vertebrates [69] , behaved as an antagonist in the 5-HT7Egran2 receptor . The unique properties of these highly expressed tapeworm receptors may be exploitable for drug design . Another noteworthy discovery was a high level of basal coupling observed for two of the GPCRs , which could relate to their high affinity for serotonin ( picomolar EC50 values ) . Basal coupling may have resulted from the presence of low levels of contaminating 5-HT in media in the face of the high receptor affinity for 5-HT . Alternatively , basal activity may represent constitutive activity of these receptors . There are several examples of constitutive activity in GPCRs of diverse ligand specificity [77] , including serotoninergic GPCRs from vertebrates [78] and invertebrates [79] . The physiological significance of this basal coupling activity–if manifest outside the heterologous assays system used here—is not clear but constitutive activity of these GPCRs may provide tonic support for basal neuromuscular activity [77] . There is clear discordance between the concentration of serotonin needed to activate the receptors heterologously expressed in vitro ( picomolar-nanomolar range ) and the concentration to induce motility in vivo ( micromolar range ) in larval stages of cestodes . There are several characteristics of each assay system which could explain this difference . For example , in the in vitro system a high density of receptors are directly exposed to the media , while in vivo endogenous levels of receptors will be localized within internal compartments of the parasite . Rapid uptake of serotonin by endogenous cells could also contribute to the low potency of 5-HT on intact worms . Differences in receptor environment may also play a role , including interacting proteins such as G proteins with different affinities for these receptors in both systems . Finally , in the whole parasite several receptors subtypes are probably activated at the same time: some receptors will stimulate the cAMP generation by Gs coupling ( e . g . 5-HT7Egran1 and 5-HT7Egran2 in Echinococcus granulosus ) while others may decrease cAMP through Gi coupling [for example , receptors encoded by gene models ECANG7_00799 ( named as 5-HT1Ecan1 in Fig 1 ) and ECANG7_02049 ( named as 5-HT1Ecan2 in Fig 1 ) are yet to be characterized] . The fluorescent images obtained showed a particular pattern of punctiform and discontinuous staining concentrated in the suckers of the protocolex , structures involved in parasite attachment to the host intestine . The addition of excess 5-HT caused the disappearance of this signal and moreover , the addition of the analogous UCM2550 to protoscoleces in motility experiments , resulted in inhibition of motility supporting the results obtained by imaging methods . In this case , serotonin could be exerting its effects through serotoninergic receptors located at the surface of the protoscolex ( particularly at the suckers ) and in this way the parasite could sense the serotonin from the host . Neuroactive substances such as serotonin could play a leading role not only at neuromuscular control but also in development and reproduction in flatworms [5 , 80] and other invertebrates [81] . The precise role played by serotonin at the neuromuscular junction in flatworms is still unknown . In other invertebrates , for example at the crustacean neuromuscular junction , it was shown that the application of 5-HT increases the total synaptic pool size of releasable synaptic vesicles in response to an action potential in the presynaptic terminal [82] . Moreover , it was shown in the same system that 5-HT accelerates the kinetics with which the transmitter is released and increase the total amount of transmitter released [83] . Perhaps , it could be hypothesized that a similar mechanism of action could be operating in a similar way at neuromuscular junctions in cestodes after 5-HT addition but more experiments are needed to test this hypothesis . In E . granulosus it was found that serotonin could have a major role in development [16] . However , the potential role played in development by the GPCRs described here is at present unknown . The innervation of reproductive structures by the serotoninergic nerve elements could be also of major importance for parasite propagation [5] . In E . granulosus , the genital atrium and associated reproductive ducts are richly innervated with serotoninergic nerve cells bodies and nerve fibres [84] . For all these reasons , targeting the serotoninergic structures of the parasite will convey potential benefits beyond the essential function of parasite attachment: development and reproduction could also be impaired . Considering the particular sensitivity to 5-HT and their different pharmacology , we view the serotoninergic GPCRs described here are promising drug targets for Echinococcus spp . A more detailed characterization of the response of the receptors cloned here to other known serotoninergic ligands will advance our structural understanding of these binding sites to allow identification of selective cestode 5HT receptor antagonists . Cloning and successful heterologous expression of these GPCRs now permits the possibility of high throughput screening campaign for the first time . The development of more specific agents against the parasitic over the mammalian host could deliver novel drugs for treatment of hydatid disease . In conclusion , we have identified for the first time several genes encoding for serotoninergic G-protein coupled receptors from cestodes based on: i ) seven transmembrane domains and conserved motifs commonly found on this kind of receptors , ii ) striking structural similarity found by homology modeling , iii ) high levels of cAMP accumulation in functional studies after heterologous expression of cDNAs in HEK-293 cells showing overall agreement with in vitro responses of parasites to serotonin and 5-HT GPCR ligands . The cloned receptors showed distinct pharmacological properties with differential sensitivity to 5-HT and differential responsiveness to tested ligands . As these genes are expressed in clinically relevant parasite stages , these GPCRs merit evaluation as novel targets for chemotherapy to combat neglected diseases caused by cestodes .
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Cestode parasites are flatworms with the ability to parasitize almost every vertebrate species . Several of these parasites are etiological agents of neglected diseases prioritized by WHO , such as hydatid disease , or hydatidosis , a zoonosis caused by species of the genus Echinococcus that affects millions of people worldwide . Due to the scarcity of anthelmintic drugs available and the emergence of resistant parasites , the discovery of new anthelmintic drugs is mandatory . Neuromuscular function has been the target of commonly used drugs against parasitic diseases to impact movement , parasite development and reproduction . Here we describe three new proteins , some of them highly expressed in cestodes which could be relevant for motility . Using different approaches , the three proteins were identified as G protein coupled receptors for serotonin , an important neurotransmitter and a known modulator of cestode motility . These new receptors exhibit unique characteristics including a particular sensitivity to serotonin as well as a distinctive pharmacology , which will assist their targeting for chemotherapeutic intervention .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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] |
2018
|
Unique pharmacological properties of serotoninergic G-protein coupled receptors from cestodes
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A key question in decision-making is how people integrate amounts and probabilities to form preferences between risky alternatives . Here we rely on the general principle of integration-to-boundary to develop several biologically plausible process models of risky-choice , which account for both choices and response-times . These models allowed us to contrast two influential competing theories: i ) within-alternative evaluations , based on multiplicative interaction between amounts and probabilities , ii ) within-attribute comparisons across alternatives . To constrain the preference formation process , we monitored eye-fixations during decisions between pairs of simple lotteries , designed to systematically span the decision-space . The behavioral results indicate that the participants' eye-scanning patterns were associated with risk-preferences and expected-value maximization . Crucially , model comparisons showed that within-alternative process models decisively outperformed within-attribute ones , in accounting for choices and response-times . These findings elucidate the psychological processes underlying preference formation when making risky-choices , and suggest that compensatory , within-alternative integration is an adaptive mechanism employed in human decision-making .
Decision-making under risk is ubiquitous in daily activities , such as deciding whether to take an umbrella when the weather forecast predicts 50% chance for rain , or whether to purchase a lottery ticket with a winning probability of 1% . Such decisions are difficult because the outcomes of the alternatives are only known with some probability , and thus they are subject to risk tradeoffs . For example , when deciding between a lottery that offers $100 with a probability of 50% and an offer of $40 with certainty , one needs to balance between the appeal of the attractive amount ( $100 ) and the risk of getting nothing ( rather than gaining $40 for certain ) . Choices between such lotteries were the subject of intensive research in economics and experimental psychology that investigated how humans make risky decisions , starting from the normative Expected-Utility ( EU; [1] ) , followed by random utility models [2] and culminating with Cumulative Prospect Theory ( CPT; [3–6] , see also Transfer of Attention eXchange [TAX] , for a related type of model [7] ) . Yet despite the impressive success of CPT in accounting for risky choice data ( e . g . , the dependence of risk-aversion on the magnitude of the outcomes' probabilities [8] ) , the theory has been criticized for making assumptions that are inconsistent with capacity limitations of human online information processing , and for not explicating the process by which the preferences are formed [9 , 10] . Several process theories were developed to account for risky choice . First , heuristic models , such as Priority Heuristic ( PH ) , suggest that preferences are not formed via a compensatory process of averaging over all outcomes ( like in EU and CPT ) , but rather via a sequential process of comparing the alternatives over one specific attribute ( probability or amount ) at a time , in a specified order , and stopping at the first instance in which a termination criterion is satisfied [9] . Second , a number of models have relied on the sequential-sampling framework [11–14] , which successfully accounted for choices in perceptual tasks , in order to develop a process model of risky choice . For example , in Decision Field Theory ( DFT; [15] ) , as attention fluctuates between the alternatives , the preference dynamically evolves by integrating amounts , which are sampled with a frequency that is associated with their ( subjective ) probabilities [16] . In the Decision by Sampling model ( DbS; [17–19] ) , like in PH , the sampling involves comparisons between the values of the alternatives on a specific attribute ( i . e . , amounts or probabilities , but not both ) . However , unlike PH , DbS does not assume a fixed order of attribute sampling , nor that the decision is settled at a single comparison , but rather a stochastic sampling , which continues until the accumulated difference of favorable comparisons reaches a decision boundary . Critically , as opposed to EU or CPT , in DbS the processing takes place within-attributes ( i . e . , comparison between amounts or between probabilities ) . Finally , in the Parallel Constraint Satisfaction model ( PCS; [20] ) , a compensatory within-alternative process similar to EU ( i . e . , multiplication of amounts and probabilities ) is carried out in a parallel and automatic manner; this process is mediated by a connectionist network of bottom-up and top-down connections . Although several qualitative predictions of the PCS model have been confirmed [20] , this model has not been tested quantitatively in risky choice . More recently , a number of studies have relied on eye-fixations during choice between alternatives , to gain insight into the preference formation process . For example , Krajbich , Rangel and colleagues have shown that an extension of the Drift Diffusion Model ( DDM; [12 , 13] ) , the attentional DDM ( aDDM ) , accounts well for observed preferences between consumer products , food items and 50–50 monetary gambles [21–24] . To do so , the aDDM assumes that the value of the sampled alternative is modulated by eye-fixations , so that the values of the non-fixated alternatives are attenuated compared with the fixated ones . In the domain of risky choice , a number of studies have contrasted within-alternative and within-attribute models , and reported partial support for both [20 , 24–28] . In particular , Glöckner and Herbold [20] analyzed risky choice while monitoring eye-movements , and provided evidence against the PH model and in favor of the PCS and DFT models ( see also [29] for similar results ) . Finally , in a recent investigation of eye-movements during risky choice , Stewart , Hermens , & Matthews [30] concluded that , while eye-movements contribute to choice preference , this contribution is mostly independent of the values sampled . In other words , the more one looks at an alternative the more likely s/he is to choose it , independently of the magnitude of amount or probability . The aim of the current study is to develop and contrast process models of risky choice , which are constrained by the eye movements of participants making decisions . In particular , we adopt an integration-to-boundary framework , which allows to predict both choices and their decision-time , and we extend the aDDM [21 , 22 , 31] approach to the domain of risky choice ( see also [24] for a recent extension to 50–50 monetary gambles ) . In this regard , a central question is whether the preferences are formed by integrating global alternative-values , based on multiplicative interactions between amounts and probabilities ( within-alternative processing ) , or by sampling and integrating attribute-comparisons ( within–attribute processing ) . Furthermore , using process models that include attentional modulation of fixated information , we wish to account for individual differences in risk preference . While previous work has highlighted the impact of task-complexity ( e . g . , number of alternatives and attributes ) in determining the decision strategy adopted by the participants ( e . g . , [32] ) , here we focus on the simplest type of risky choice ( between pairs of alternatives , each consisting of a probability p to win amount x , see Fig 1A ) . Thus , our aim here is not to determine which of these two types of processes prevail in any choice scenario ( both can take place , subject to task-conditions and individual differences ) . Rather , we wish to test if , at least for this simple case , the more normative ( within-alternative and multiplicative ) strategies are within the capacity of participants resources . Towards this end , we carry out a systematic investigation of risky choice with simple two-outcome lotteries , while eye-fixations are monitored . To anticipate our results , we provide a clear demonstration that within-alternative and multiplicative evaluations are being used , subject to individual differences that correlate with choice normativity .
We began by examining the basic psychometric properties of our choice-data . Analysis of the "catch-trials" showed that the participants chose the better option ( higher in both amount and probability ) in 97% of these trials ( SD = 6% ) . Next , we conducted a mixed-effect logistic regression on the choice data , with the Expected-Value ( EV ) differences ( x1∙p1 –x2∙p2 ) as a predictor , and with random intercepts and slopes at the participant level . The results indicated that the participants were sensitive to EV differences , and preferred lotteries with higher EVs over lotteries with lower ones ( β = 0 . 40 , p < . 001; Fig 2A ) . Additionally , using a Pearson correlation analysis , we showed that the reaction time ( RT ) of a decision decreased as the absolute EV difference between the lotteries increased ( r = -0 . 8 , p < . 001; Fig 2B ) . This finding is consistent with previous process models such as the PCS [20] , the aDDM [21] , and the DFT [16] , indicating that the participants take longer to decide when the evidence ( as measured by the EV-difference ) is smaller . Finally , we evaluated the risk-preferences of the participants . To this end , we focused on choice problems with similar EVs ( |ΔEV| ≤ 1 , Nchoice problems = 26 ) , and examined the proportion of trials in which high-payoff/low-probability lotteries ( riskier options ) were preferred over low-payoff/high-probability lotteries ( safer options ) . Following the CPT regularity of differential risk-attitudes for low vs . medium/high probabilities ( see S1 Text ) , we examined the risk-preferences separately for these two probability domains: i ) low-probability cases , in which one of the lotteries has p < . 25 ( e . g . , $24 with p = . 1 vs . $6 with p = . 5 ) , and ii ) high-probability cases , in which both lotteries have p ≥ . 25 ( e . g . , $30 with p = . 5 vs . $15 with p = 1 ) ; the . 25 cutoff was selected to match CPT ( see S1 Text ) . A paired samples t-test indicated that , consistent with CPT , the participants showed higher levels of risk-aversion for medium/high probabilities as compared to low ones ( t ( 30 ) = 3 . 84 , p < . 001 ) . Follow-up one-sample t-tests ( against . 5 ) indicated that the participants showed risk-aversion for medium/high probabilities ( t ( 30 ) = 4 . 49 , p < . 001 ) ; no risk-aversion , however , was obtained for low probabilities ( t ( 30 ) = -0 . 11 , p = . 9 ) . On average , the participants made 9 . 05 fixations ( SD = 3 . 56 ) per trial , with a mean duration of 407ms ( SD = 244 ms ) per fixation . Also , on average across participants , there was no significant difference between the proportion of fixations towards amounts and probabilities ( t ( 30 ) = 0 . 78 , p = . 44 ) . There was , however , a remarkable difference between participants in this proportion , which was correlated with participants’ risk preferences: the more a participant fixated on amounts , the more likely he or she was to choose the riskier alternatives ( r = . 48 , p = . 006; S1A Fig ) . To understand this relationship we examined individual differences in fixating the higher of two amounts/probabilities , as this can explain risk-biases ( looking more at higher amounts or at lower probabilities leads to risk-seeking according to the aDDM [21 , 22 , 24] ) . Importantly , we find that the more a participant tends to fixate on amounts the more s/he fixates on the larger of them ( r = . 47; p = . 007; S1B Fig ) , and similarly for probabilities ( r = . 46; p = . 007; S1C Fig ) . Finally , the frequency of fixations on the higher of the two amounts was positively correlated with risk-seeking ( r = . 58; p < . 001; Fig 2E ) , and the frequency of fixations on the higher of the two probabilities was negatively correlated with risk-seeking ( r = . 45; p = . 01;S1D Fig ) see also [24 , 33] . We also examined the eye-trajectories in relation to their transitions between the four attributes ( x1 , p1 , x2 , p2 ) . The transitions between decision attributes ( amounts and probabilities ) were classified into three categories [20 , 25 , 30]: i ) Within-alternative transitions–transitions between attributes that belong to the same alternative . ii ) Within-attribute transitions–transitions between different alternatives , within the same attribute . iii ) “Diagonal” transitions–transitions between the amount of alternative A and the probability of alternative B and vice versa . Fig 2C and 2D show one example each for within-alternative and within-attribute trials , respectively . An Analysis of Variances ( ANOVA ) revealed significant differences of the transition probabilities between the three transitions types ( F ( 2 , 60 ) = 431 . 1 , p < . 001 ) . Post-hoc comparisons showed that the participants made more within-alternative than within-attribute transitions ( p < . 001 ) , as well as more within-attribute than diagonal ones ( p < . 001 ) . The proportion of within-alternative transitions ( out of all transitions ) was subject to individual differences and was correlated with EV-choice performance ( ΔEV ) , such that the higher the fraction of within-alternative transitions the higher was the proportion of the alternative with the higher EV to be chosen ( r = . 57 , p < . 001; Fig 2F ) . Recent research has demonstrated that attentional mechanisms play a key role in the development of preferences [24 , 34–38] . In particular , it was shown that the more an alternative is fixated on , the more likely it is to be chosen [21 , 30 , 39] . We first estimated the benefit of looking time ( or of the number of fixations ) on choice , using a measure that was developed in another study ( [40] ) . Specifically , using logistic regression we predicted the choices from the EU-difference between the two alternatives ( with parameters fitted for each subject on all his/her choices ) . Then , for each trial , we computed the deviation between the actual choices ( coded as 0 or 1 ) , and the probabilities predicted by the EU-difference . This residual was averaged separately for trials in which the choice had a positive or a negative gaze-advantage ( we did this twice , for total gaze duration and for number of fixations ) . We then computed the difference between these measures to obtain the average difference in choice probability for the items with a positive versus negative final gaze advantage , when corrected for the influence of their ( EU ) values . As shown in Fig 3 ( see also [40] for similar results on non-risky choices ) , there is a marked gaze-advantage in predicting the choice . At the group level , this advantage has a mean value of 0 . 29 ( SD = 0 . 14 ) for the number of fixation ( Fig 3 , upper panel ) , and a mean value of 0 . 24 ( SD = 0 . 11 ) for the total gaze duration ( Fig 3 , middle panel ) . Finally , we found a small , but significant prediction enhancement , for the number of fixation predictor ( t ( 30 ) = 2 . 87 , p = 0 . 008; Fig 3 , lower panel ) . Second , we have confirmed the impact of gaze on choice , using a multiplicative computation of the alternatives' values based on EU ( or CPT ) values and a monotonic function of gaze-time ( or number of fixations; see S2 Text for details ) . As illustrated in Fig 4A , we examined an EU × time regression model ( similar conclusions were obtained for CPT based models , see S2 Text ) , in which the EU value of each alternative increases with its dwell time on the two alternatives ( α is the risk-parameter of EU , τ is a saturation parameter , and β is a slope parameter ) . Additionally , we examined a similar regression model , in which dwell-times were replaced with the number of fixations each alternative is sampled ( Fig 4B ) . Comparison of these models with the traditional EU ( which does not take eye-movements into account ) showed that using eye-movements significantly improved prediction accuracy and AIC comparted with the traditional EU ( Fig 4C ) . Note also , that the prediction accuracy and AIC which were obtained using the number of fixations , equal ( for EU ) or surpass ( for CPT ) , the prediction accuracy and AIC obtained using the measure of dwell-time . In addition , in both gaze based regressions ( number of fixations and dwell time ) , the fitted values of saturation-parameter τ were lower than 1 , indicating that , for example , looking twice as long at an alternative increases its value by a factor of less than 2 . One way to understand this non-linear saturation is in relation to a leak of the accumulated values [14 , 41 , 42] . In such leaky integration models , the accumulated evidence saturates at an asymptotic value , and remains constant even if more integration time is allowed . Accordingly , at each fixation one samples and accumulates a value , however , as the trial proceeds the accumulated value leaks , resulting in a type of recency . Indeed , the percentage of match between the fixated alternative and the final choice as a function of fixation number ( backwards from the end ) showed a clear recency pattern ( Fig 5; note that an aDDM model without leak can also generate a recency pattern [43] , therefore a quantitative model comparison is needed to determine if leak is required to account for the actual pattern ) . The central aim of this study is to develop and contrast two classes of process models that differ in the way attentional ( or eye ) transitions affect the integration of amounts and probabilities . Both types of models assume that: a ) fixated objects receive enhanced attention , b ) attention modulates the weight of value integration [21] , and c ) recently sampled values are weighted more than earlier ones [14 , 41 , 42] . The models differ , however , on how the values are integrated into preferences . Note that we do not aim to test specific models but rather distinguish between broad classes of models based on certain principles , in particular , between within-attribute vs . within-alternative models [20 , 25 , 32 , 44] . While the former is used in models such as PH and DBS , the latter is used in models such as EU , CPT and PCS . We also examined a more hybrid model , which still relies on multiplicative within-alternative computations , but also allows some extent of competition between the attributes . The most complex of the models ( in terms of number of parameters ) is the within-alternative process model , which has four free parameters . The first two , α and γ correspond to the CPT parameters for risk aversion and probability weighting [3] , respectively , θ corresponds to the aDDM attentional modulation parameter , and λ is the activation-leak . As we show in S5 Fig , we carried out a recovery exercise , showing that our fitting procedure is able to provide a good recovery for all those parameters over a wide range of values that correspond to those found in the actual data . This non-trivial result is helped by the fact that our 94 choice problems systematically span the choice space . As shown in Table 1 , the within-alternative process models with attention modulation and leak gave the best fit and showed the highest cross-validation prediction accuracy . They outperformed both the within-attribute process models , as well as the traditional , non-integration to boundary models ( compensatory and non-compensatory heuristics ) . These results speak against the hypothesis that the participants accumulate only the differences of the attended attributes . We also found that the within-attribute models with perfect ( rather than leaky ) integration ( Normalized and Binary differences ) , resulted in much worse AIC , prediction accuracy , and cross-validation ( therefore in Table 1 we report only the within-attribute models which include leak as a free parameter ) . We note that the within-alternative choice models required a significant degree of information leak ( λgroup = 0 . 58 ) . As shown in S3 Table , we explicitly tested four versions of within-alternative models that include an attentional modulation but no activation-leak , all of which resulted in much poorer prediction-accuracy and AIC fit values . By contrast , the leaky within-alternative process models outperformed ( on prediction accuracy , AIC and cross-validation ) the regression models that include either EU or CPT together with the number of fixations ( see S2 Text and Table 2 ) . This suggests that considering dynamic processes , such as attentional shifts and leak of activation improves prediction accuracy and fit measures beyond what is achievable by using only the number of fixations . Note that the within-alternative two-layer leaky accumulators model outperforms the single-layer accumulator model . This result suggests that the perception of the attributes is dynamic and is subject to modulation by attentional processes . Finally , the hybrid model resulted in fits ( AIC and prediction accuracy ) that did not exceed those of the within-alternative model ( see S3 Table ) , and with a moderate mutual inhibition value ( . 13 ) which does not trigger a full all-or-none dynamics . Due to its complexity , we leave a full investigation of this model to future research . Finally , we carried out a comparison of the predictive accuracy of our best performance model–the two-layer leaky accumulators—with that of the traditional EU and CPT models across all decisions as a function of EV-differences . The comparison demonstrates that the difference in prediction accuracy is especially large for difficult choices ( low EV-differences , 1–3 Quantiles; Fig 7 ) , suggesting that attentional modulations are particularly significant in difficult decisions [48] . We contrasted the within-alternative and the within-attribute models , in accounting simultaneously for choices and decision-times . To this end , we adopt an integration-to-boundary framework , which assumes that preferences are accumulated until they cross a decision criterion [49 , 50]; this introduced a few more parameters ( for the boundary ) into the model ( see S1 Methods Model Fitting ) . The models are now set to estimate the probability of a subject’s choice conditioned on its decision time and fixations . This probability is accumulated for all choice trials of the participant to a total likelihood , which is used to optimize the boundary parameters . Two families of decision boundaries were tested , for each of the models: i ) the standard fixed ( time-invariant ) boundary , which introduces a single new boundary parameter , and ii ) a collapsing ( time-variant ) boundary model , which introduces three new parameters ( see S1 Methods Optimization procedure: choices and decision-times , for further details regarding the implementations of these two types of models ) . The collapsing boundary model has been the focus of recent investigations in decision neuroscience [41 , 42] , and appears to be favored in experimental tasks that span over longer time intervals ( more than 2–3 sec [51–53] , but see [54] for an alternative explanation based on across-trial variability parameters ) . The results show that , with both decision boundary families , the two-layer leaky accumulator model outperformed all the other models . Among the two types of boundary families , the best fits were obtained for the collapsing boundary models ( AIC and cross-validation ) , despite the cost of the two extra parameters . For this reason , we only report below the results for this type of boundary ( see S4 Table for comparison of all within-attribute and within-alternative models using fixed and collapsing boundaries ) . We find that the within-alternative/two-layer leaky accumulator model ( AIC = 14 , 492 ) decisively outperformed the within-attribute/normalized differences model ( AIC = 15 , 815; ΔAIC = 823 ) , in accounting for decision-times ( conditioned on the actual fixation patterns ) . Finally , we used these models to predict the distribution of decision times ( measured in number of fixations ) , for novel but statistically matched patterns of fixations . To this end , for each trial we simulated a fixation sequence that is based on a statistical model of the participant’s fixations towards the four attributes as a function of their values [21 , 30] . The results indicate that for the two-layer leaky accumulator model , the predicted and actual decision-time distributions show a good match , however for the normalized differences model , the tail of the predicted decision-time distribution deviates from that of the actual decision-time distribution ( Fig 8 ) . Our best within-alternative integration model accounts also for the empirical correlation we reported between the proportion of fixations a participant makes to the higher of the two amounts and his or her risk-preference bias ( Fig 2E; see also [24] ) . To show this , we simulated choices for each participant , based on his or her fitted model-parameters and the participant's actual fixation sequence . The correlation between the model's risk-preference prediction and the proportion of fixations to the higher amount ( r = . 58 , p < . 001 ) , was exactly equal to the empirical correlation obtained in the data ( Fig 2E ) . Next , we sought to demonstrate that this relation is associated with the fixation pattern and not merely with differences in model parameters . To this end , we simulated choices for each participant , by using his or her actual fixation sequences , however , this time we used model parameters that correspond to the group mean ( rather than the individually fitted parameters ) . This resulted in a significant correlation ( r = . 52; p = . 002; Fig 9A ) between the risk-preference and the proportion of fixating on the higher amount . This correlation between risk-biases and fixation-pattern relies upon the model’s attentional component , which gives higher weights to the attributes on which the participant fixates . For example , assume that a participant is asked to choose between A: ( $20 , 0 . 5 ) and B: ( $10 , 1 ) . If s/he fixates more the amount of alternative A than the amount of alternative B , higher weights would be given to the former , and thus the riskier alternative ( A ) would be preferred by the model over the safer one ( B ) . Finally , we address an important question: which preference-formation mechanism ( within-alternative or within-attribute ) results in better normative performance , and thus can be regarded as more adaptive ? To answer this , we simulated the two types of models based on the participants' best fitted parameters and actual fixation sequences , and we examined two measures of normative choice predicted by each model: i ) the fraction of EV-choices ( for simplification we discuss normativity in terms of EV , but the same would hold in terms of EU ) , and ii ) the fraction of transitivity violations–a direct measure of choice irrationality ( [55 , 56]; see S4 Text ) . As seen in Fig 9B , the normative performance is higher for the within-alternative model than for the within-attribute model , for both measures: EV-choices: t ( 30 ) = 6 . 27 , p < . 001 and transitivity violations: t ( 30 ) = 5 . 15 , p < . 001 . This is expected because our within-alternative model , like CPT , assumes a multiplication between subjectively transformed amounts and probabilities , which also maintains choice-consistency . Although the normative model requires a multiplication of objective values whereas our model requires a multiplication of subjective values , this discrepancy is relatively minor compared with non-multiplicative strategies ( i . e . , within-attribute integration or heuristics ) . Moreover , we have found that the more within-alternative transitions a person makes , the higher is his or her fraction of EV choices ( Fig 2F; see also [28] ) . This correlation can be naturally understood , since the participants rely on a within-alternative multiplicative mechanism , and this operation is likely to be more precise following an actual transition between amounts and probabilities ( i . e . , a fixation on one attribute of an alternative followed immediately by a fixation to the other attribute of the same alternative ) , than following a non-direct transition ( where one of the to-be-multiplied attributes is based on memory or defaults ) . Consistent with this , we found a correlation across participants between the prediction accuracy of the within-alternative model and the proportion of within-alternative transitions ( r = 0 . 39 , p = . 03 ) .
The main aim of our study was to elucidate the mechanisms by which different attributes ( amounts and probabilities ) are integrated to generate an overall subjective value of choice alternative . To this end , we focused on choices between simple lotteries and developed process models of risky choice , which are constrained by eye-fixations and we assumed a fixation-based attentional modulation . In addition , we introduced activation-leak and examined two types of decision-boundaries , in order to account for decision times . Within these models we specifically contrasted within-alternative multiplicative models and within-attribute type models , and carried out a systematic parametric investigation of choices between simple lotteries ( x1 with p1 vs . x2 with p2 ) , while tracking participants' eye-fixations . First , we replicated previous findings indicating that participants prefer lotteries with higher EVs . In particular , the choice probability of the alternative with the higher EV increases ( and choice-RT decreases ) with the EV-difference between the lotteries . Nevertheless , participants also exhibited risk biases that are probability-dependent , being risk-averse at high/medium probabilities , but not at low probabilities . Second , we found that , on average , the eye-scan patterns were dominated by within-alternative as compared to within-attribute or diagonal transitions ( Fig 2C–2D , respectively ) , and that individual differences on this eye-scan pattern correlate with EV-choice ( see also [28] , for a similar result ) . Third , we used eye-fixations to constrain a number of process models that accumulate preference across fixations , using an aDDM approach with two attributes [21 , 57] . Here we contrasted two types of integration-to-boundary process models: i ) within-attribute models , and ii ) within-alternative models . As shown in Table 1 , the latter resulted in the best predictive accuracy and measures of fit . Importantly , the two-layer model also accounted well for decision times ( Fig 8 ) and for individual differences in risk biases ( Fig 9 ) . Finally , the worst performance in our task was obtained for the non-compensatory heuristic models . For example , the best of the heuristics ( the PH ) resulted in a worse fit than even the simple EV model ( see cross-validation measure in Table 1 ) . The conclusions favoring the within-alternative multiplicative models may need to be qualified for the task conditions we used here . First , we used simple lotteries with single non-zero outcomes ( x with p , 0 with 1-p ) . It is possible that the amount of within-attribute ( non-compensatory ) processing would increase when more complex choices are used [25 , 32] . While we cannot rule out this possibility , recent research in the domain of probabilistic inferences [58–60] and risky choice [20 , 61] , indicates that when decision processes are monitored via eye-tracking ( which does not slow down the decision process ) rather than via mouse pressing techniques ( e . g . , [10] ) , participants are able to use compensatory strategies for relatively high complexity levels ( see also [62] for a multi-attribute choice task ) . Second , our results need to be qualified to the use of analog ( graphic rather than symbolic ) representation of the data . We used this format of representation to reduce the possibility that our participants ( who are students that may be familiar with EV-principles , and are required to do 104 choice problems ) , adopt an explicit EV calculation strategy . We believe that such a strategy is less likely with analog information , and thus our results favoring an implicit multiplicative mechanism are even more remarkable . Third , the alternatives in our experiment were aligned only vertically ( one lottery was placed over the other , with the amounts and probabilities placed left/right , see Fig 1A ) . It is possible to argue that this layout favors within-alternative processing ( left to right or right to left transitions ) . Note , however , that a horizontal layout ( left/right alternatives ) triggers a strong bias favoring within-attribute processing , in particular , comparing the horizontally aligned amount bars . Nevertheless , we report in S4 Fig data from a pilot Experiment ( N = 13 ) using a horizontal alternative layout , which shows that even under such within-attribute favorable conditions , we still find dominance for within-alternative transitions . Here we only wish to support the following conclusion: humans possess the capacity to spontaneously deploy an ‘economic’ ( multiplicative across-dimension ) type computation ( which is analog rather than symbolic; [63] ) , supporting the idea that humans are closer to normative principles than previously thought ( see also [60 , 64] ) . Future research will be needed to further quantify to what extent the use of this mechanism ( or strategy ) is contingent upon task complexity and stimuli type . There are several important properties of our winning process model that we want to highlight . First , it assumes two layers of leaky accumulators , one for the estimation of subjective amounts and of subjective probabilities , and the one for the evaluation of the integrated subjective values ( the combination of subjective amounts and probabilities ) . Second , it assumes that the units in the second layer are updated via a multiplication of the activation of the corresponding , first layer units ( Fig 6A ) . This is similar to how the CPT model generates subjective utilities . In fact , we find a high correlation between the utility function’s curvature parameter ( α ) of the classical CPT and the corresponding parameter of our process model ( r = . 91 , p < . 001 ) , with higher α -values for the classical CPT ( see S2 Fig ) . This suggests that the classical CPT-parameters reflect a combination of several processes , such as attention allocation and subjective-value transformation [65] . Note also , that the model assumes an activation-leak , a feature that allows it to account for recency effects in the data ( see Fig 5 ) , and prevents a double-integration that would occur in the two-layer model in its absence . Third , in addition to predicting choices , the model also predicts decision times , describing the preference formation dynamics under the integration to boundary framework with inputs that correspond to a multiplicative transformation of subjective amounts and probabilities . In particular , we found support for a collapsing boundary , consistent with a recent normative analysis of value based decisions [66] , and with choice studies that span longer intervals [51–53] . Other process models of risky choice , such as DFT [15 , 16] also assume an implicit multiplicative interaction between amounts and probabilities . In DFT , however , this is not due to the multiplication of amounts and probabilities but rather to the sampling frequency of the amounts , which changes with the corresponding probabilities . This implies that observers look ( or attend ) more to a given amount if the corresponding probability is higher . In our data , while we find that the relative number of fixations to an amount increases with its probability , this increase was quite minor ( about 1% ) , and therefore cannot explain the multiplicative interaction [20] . However , it is possible that eye-fixations under-estimate the differential of covert attention modulation . Future research is also needed to better understand the neural mechanisms underlying these computations [67–69] . While the computation of subjective amounts and probabilities can be understood to involve simple psychophysical transformations over amounts ( unbounded scale; [70] ) and probabilities ( bounded scales; [71] ) , the nature of the multiplicative interaction between neural activations requires future investigations . Note that a multiplicative interaction is also assumed in the PCS risk model [20] . To do so , PCS had to assume different neural substrates for amounts ( neural activations ) and for probabilities ( synaptic weights ) . The latter assumption , however , may be difficult to justify for one-shot decisions , which allow little opportunity for learning synaptic weights . We thus suggest that the multiplicative interactions involve neural activations . While less standard than linear interactions [72] , a number of neural mechanisms have been proposed to mediate multiplication of neural activity in neural systems [73 , 74] . Future research is also needed to extend the scope of this investigation from simple lotteries to more complex ones ( with multiple outcomes ) and from binary to multiple choices .
The experiment was approved by the ethics committee at Tel-Aviv University ( 1321253 ) , consent was given by a written form . Here we briefly describe the key features of the models applied ( for a full description see S3 Text ) . In all of the models ( except for the Heuristics models ) , the probability of choosing each alternative is calculated using an exponential version of Luce’s choice rule [76 , 77]: P ( x1 , p1;x2 , p2 ) =11+e−β ( U1−U2 ) where U1 and U2 are the utilities of the alternatives , and β is a free parameter indicating the sensitivity of the model to their difference .
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Decision-making under risk requires a selection between alternatives , such as lotteries , which offer a reward with a specified probability . Human decision between such alternatives is at the center of the normative decision theory , which assumes that decisions are rationally made by forming a value for each alternative and selecting the alternative with the highest value . To this day , there is still a considerable debate on how such values are computed . While the normative theory assumes that values of the alternatives reflect the statistically expected rewards , more recent theories have argued that alternative-values are not computed , and choices are only based on sequentially comparing the alternatives on amounts or on probabilities . Here , we carried out an experimental investigation of risky decision-making , in which participants chose between pairs of simple lottery alternatives that systematically span a range of probabilities and amounts , while we tracked their eye positions during the decision-making process . We found that the participants are sensitive to the expected-utility of the alternatives , as predicted by the normative decision theories , but they also exhibit risk-biases that correlate with the eye-scanning patterns . We then carry out computational modeling , comparing preference-formation models on their ability to account for both choices and their reaction-times . The results provide strong support for normative models , which assume that the values of the alternative are computed via a multiplicative function of the amounts and probabilities . These results suggest that humans are closer to normative principles than previously assumed , and motivate further investigation into the neural mechanism that mediates these multiplicative computations .
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2019
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The formation of preference in risky choice
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In humans , the absence or irreversible loss of hair cells , the sensory mechanoreceptors in the cochlea , accounts for a large majority of acquired and congenital hearing disorders . In the auditory and vestibular neuroepithelia of the inner ear , hair cells are accompanied by another cell type called supporting cells . This second cell population has been described as having stem cell-like properties , allowing efficient hair cell replacement during embryonic and larval/fetal development of all vertebrates . However , mammals lose their regenerative capacity in most inner ear neuroepithelia in postnatal life . Remarkably , reptiles , birds , amphibians , and fish are different in that they can regenerate hair cells throughout their lifespan . The lateral line in amphibians and in fish is an additional sensory organ , which is used to detect water movements and is comprised of neuroepithelial patches , called neuromasts . These are similar in ultra-structure to the inner ear's neuroepithelia and they share the expression of various molecular markers . We examined the regeneration process in hair cells of the lateral line of zebrafish larvae carrying a retroviral integration in a previously uncharacterized gene , phoenix ( pho ) . Phoenix mutant larvae develop normally and display a morphologically intact lateral line . However , after ablation of hair cells with copper or neomycin , their regeneration in pho mutants is severely impaired . We show that proliferation in the supporting cells is strongly decreased after damage to hair cells and correlates with the reduction of newly formed hair cells in the regenerating phoenix mutant neuromasts . The retroviral integration linked to the phenotype is in a novel gene with no known homologs showing high expression in neuromast supporting cells . Whereas its role during early development of the lateral line remains to be addressed , in later larval stages phoenix defines a new class of proteins implicated in hair cell regeneration .
During development of the vertebrate inner ear , a subset of neuroepithelial cells specialize to give rise to hair cells and supporting cells [1]–[3] . These two cell populations assume distinct and complementary functions . The hair cell is a highly differentiated mechanoreceptor cell , transducing sound waves in the cochlea or acceleration and head movements in the vestibular organ , into electrical signals [4] , [5] . The supporting cells provide cohesive support [6] , [7] and have secretory functions [8] , [9] . More importantly , they have been clearly implicated in the addition of new hair cells , both during normal growth and during restoration of the sensory epithelium after damage in various animal models [10]–[14] . Thus , among supporting cells , there exists a tissue-specific population of progenitor cells . However , in mammals , this “stem cell like” property is lost shortly after birth in most neuroepithelia of the inner ear [15] . With the exception of some limited regeneration in a sub-region of the vestibular organ [16] , [17] , post natal hair cell loss is permanent and irreversible . Consequently , a large majority of deafness cases in humans are linked to absent or damaged hair cells . To restore the regenerative capacity of supporting cells is an obvious therapeutic aim , but our understanding of the regenerative process is incomplete . Because birds , amphibians , reptiles , and fish have retained the ability to regenerate hair cells [14] , [18]–[25] they provide opportunities to find genes involved in the regeneration process and its maintenance . Fish and amphibians have an additional organ related to the inner ear called the lateral line , which is used to detect water currents [26]–[28] . It is a superficial organ running along each side of the body which consists of stereotypically distributed patches of neuroepithelium , called neuromasts . These are discrete organs made of hair cells that project into the aqueous environment and supporting cells that surround them . In the zebrafish embryo , the lateral line neuromasts first appear in the head approximately 2 days post fertilization ( dpf ) and in parallel begin to develop along the entire length of the trunk and tail [29] . Hair cells are fully functional by 4dpf [30] and as the larva grows into an adult , additional neuromasts are continually added to the embryonic pattern [31] , [32] . In adult fish , newly formed neuromasts are thought to originate from preexisting ones , with supporting cells budding off and migrating to their new locations [33] . Additionally , this sensory organ is known to continuously replace hair cells in larvae and adults [34] . The neuromasts are also able to replace hair cells after all existing ones have been have been destroyed [35] , [36] . Several waterborne agents have been used to eliminate hair cells from neuromasts , including aminoglycosides , platinum-based drugs , and metal ions . Copper is a potent ototoxic agent , killing hair cells in the lateral line , as early as 3dpf [37] . After 5dpf , the hair cells also become sensitive to antibiotics of the aminoglycoside family , in particular to neomycin , presumably coinciding with their functional maturity [38] , [39] . At all time points , regeneration of hair cells has been documented , mainly resulting from the division of supporting cells and subsequent differentiation into new hair cells [14] , [40]–[44] . We utilized an in vivo assay using both copper and neomycin to follow regeneration of hair cells in neuromasts of 6dpf to 8dpf larvae . Using this assay , we characterized two allelic mutant lines generated by retroviral insertion [45] , which we have named phoenix ( pho ) . Homozygous mutants did not display obvious morphological or behavioral phenotypes and developed a functional lateral line as ascertained by FM1-43 incorporation . However , when phoenix larvae were treated with either copper or neomycin , they showed a strongly reduced regeneration of the hair cells . In parallel , we demonstrated that another form of regeneration , the growth of the tail after amputation , was not affected in the phoenix mutants , arguing that the regeneration defect was specific to hair cells . We show that , the number of supporting cells was not significantly different before or after copper and neomycin treatments in neuromasts of wild-type versus phoenix mutant larvae . Furthermore , we monitored cell death over the time course of our assay in treated wild-type and mutant neuromasts and did not find significant differences in supporting cell death . Strikingly , we found that the proliferation rate in this progenitor cell population was strongly reduced in mutants . Thus , impaired proliferation is tightly correlated to the observed deficit of regenerated hair cells in the phoenix mutant . We characterized the gene carrying the retroviral integration linked to the phenotype , and find that it is a novel gene , with no previously described homologs . Expression at 3-4dpf is upregulated in the supporting cells of the neuromasts . Thus , Phoenix is the first documented member of a novel gene family , which has an important role in the regeneration of hair cells in the lateral line .
We have utilized an assay to monitor hair cell regeneration in the lateral line of 5dpf zebrafish larvae , using transient exposure to copper sulphate ( 10mM ) or neomycin ( 200mM ) dissolved in the water . Our intention was to test for defective hair cell regeneration from a collection of mutant lines previously identified in a retroviral integration screen [45] . The major advantage of using retroviral constructs , over chemicals like ENU , as a mutagenic agent is the rapid identification of the mutated gene . This allows the spatio-temporal expression of the mutated genes to be compared to the observed phenotypes , facilitating selection of the mutants of interest . We reasoned that mutant larvae which develop a normal and functional lateral line and that exhibit expression of the mutated gene in neuromasts would be good candidates to test for defects in the regeneration of the hair cells . We found such a mutant line , which we called phoenix . Homozygotes displayed no behavioral defects ( response to sound or mechanical stimulation was normal , data not shown ) or visible morphological abnormalities until 5dpf , when the swim bladder failed to inflate . Later , around 7 to 8 dpf , mutants display necrosis in the liver , and death ensues at approximately 14 dpf . Because the gene was expressed in the lateral line neuromasts ( see below ) , we performed a detailed assessment of this organ in 2 to 12 dpf larvae ( Figure 1 ) . Semi-thin sections through neuromasts ( Figure 1A ) of phoenix mutants ( lower panels , 5dpf left panel and 9dpf right panel ) were indistinguishable from wild-type larvae ( upper panels ) . Camera lucida drawings ( Figure 1B ) of each section show the cilia ( green ) of hair cells and their nuclei ( red ) and the nuclei of the supporting cells ( blue ) . Likewise , ultra-thin sections observed by electron microscopy ( EM ) ( 10dpf larvae shown in Figure 1C ) did not present obvious differences between wild-type ( top panel ) and mutant neuromasts ( lower panel ) . Camera lucida drawings ( Figure 1D ) show , like in Figure 1C , the cilia ( green ) of hair cells and their nuclei ( red ) and the nuclei of the supporting cells ( blue ) . The cytoplasm of hair cells is outlined in dark red and an apoptotic body is shown in yellow in the lower panel . Note that apoptotic bodies , as the one present in the mutant , were found at the same rate in untreated wild-type and mutant sections , being probably products of the regular turnover of the supporting cells , which has been described previously [34] . We further observed wild-type and mutant neuromasts in three different transgenic backgrounds ( Figure S1 ) . As shown in live images , these lines express GFP in all the cells of the neuromast in the cldnB::GFP line [46] ( Figure S1A ) , in the hair cells in the pou4f3::GFP line [47] ( Figure S1B ) or in a subset of supporting cells in the ET20::GFP line [48] ( Figure S1C ) . Camera lucida drawings were added to each panel , for better illustration of the live images . To test the functionality of the hair cells in wild-type and phoenix mutant larvae , we used the well-described technique of monitoring the absorption of the vital dye FM1-43 [49] . We imaged live wild-type and mutant larvae in the cldnB::GFP line background ( Figure S1A , GFP in green in first and third columns , FM1-43 in red in second and third columns . We added camera lucida drawings of the merged images in the fourth column ) . Again , we did not find a significant difference between the absorption of the dye in wild-type ( top ) and mutant ( bottom ) hair cells , nor did we see any significant observable structural differences . Therefore , although the gene is expressed in the lateral line , its initial development appears unaffected in mutant larvae . The first allele ( hi43 ) of the phoenix mutant line was generated by retroviral mutagenesis as previously reported [45] . The genomic location of the retroviral integration was isolated and used to genotype the offspring to maintain the mutant line through numerous generations [45] . We identified a BAC spanning the genomic locus from a zebrafish BAC library and sequenced it in its entirety . Using GENSCAN [50] , we predicted the genomic structure of phoenix ( Figure 2A ) . The gene spanned ∼16 . 5 kb with seven exons , the final predicted exon being unusually long as it was comprised of 7758bp with the entire exon consisting of an open reading frame . The size of the exons and introns is indicated in Figure 2A . The retroviral integration in hi43 ( black triangle ) landed in the first exon and was adjacent to the first splice donor site . To confirm that the retroviral insertion disrupted the surrounding gene , we designed primers to perform RT-PCR on total RNA extracts of 7 dpf wild-type and mutant larvae and on the zebrafish tissue culture cell line Pac2 , which also expressed the gene [51] . The expected 835 bp RT-PCR product was seen ( Figure 2B , left arrow ) , in wild-type larvae ( lane 2 ) , and in the Pac2 cell line ( lane 1 and 3 ) , but was absent in the mutant larvae ( lane 4 ) . However a shorter 366 bp product was generated in the mutant larvae exclusively ( Figure 2B , right arrow ) . We cloned and sequenced all of the observed PCR fragments . In pho mutants , aberrant splicing occurred , fusing exon 1 to exon 7 and causing a deletion and a frame-shift truncation of the presumptive protein product ( Figure 2C ) . Additionally , the aberrant splicing event resulted in an arginine replacing the last serine in a predicted abATPase signature , therefore potentially abrogating the putative enzymatic activity ( Figure 2C ) . To further prove that the deficient regeneration of the hair cells in the lateral line was caused by a mutation in the phoenix gene , we acquired a second commercially available allele ( Znomics , Inc . line: ZM_00003486 ) . This mutant line carried a retroviral insertion in the first exon ( 75bp downstream of the ATG ) of the phoenix gene ( green triangle Figure 2A ) . The insertion of the provirus in this position most probably led to a null mutation . All of the phenotypes observed in the original hi43 allele were present in the second recovered mutation and all subsequent experiments were performed in both allelic mutant lines . We did not attempt to phenocopy the regeneration phenotype using morpholino injection , as our observations started at 6dpf . This time-point is beyond the time-window of efficacy of morpholinos , which get diluted over time after the numerous cell divisions occurring in the growing embryo/larva . Taken together , our data strongly indicate that we have correctly identified the genetic defect responsible for the observed phenotype . Using RT-PCR , we identified several alternatively spliced variants of the pho gene ( Figure 2C ) . We compared the different isoforms against various RefSeq databases using BLAST [52] . The best predicted homologs in other species for the pho gene were of low quality and were all predicted proteins , therefore providing little information . However , using genomic homology comparisons from the UCSC Genome Browser ( http://genome . ucsc . edu/ ) we were able to identify the syntenic region for four other fish species: Takifugu rubripes ( fugu ) , Tetraodon nigroviridis ( tetraodon ) , Oryzias latipes ( medaka ) , and Gasterosteus aculeatus ( stickleback ) . The genes flanking phoenix in zebrafish are aspa and c11orf54 homologs . In all fish species examined , this synteny was maintained , but a synteny break occurred at this location in other vertebrate genomes . Located between aspa and c11orf54 in all four fish species was a large predicted ORF . The corresponding predicted genes ( GENSCAN00000016645 in fugu , chr14 . 906 . 1 in medaka , chrVII . 1390 . 1 in stickleback and chr7 . 288 . 1 in tetraodon ) had very weak but noticeable identity with the zebrafish phoenix gene . The overall identity of the fish homologues was still not significant ( the best match was fugu to tetraodon at 35% identity in a limited region , with no other pair-wise comparison showing higher than 24% identity ) . The main feature of all proteins was a low complexity sequence with a large number of proline and lysine residues . Using ClustalW2 ( http://www . ebi . ac . uk/Tools/clustalw2/index . html ) , we showed that the only significant identity in these predicted transcripts was in the amino-terminal end of the proteins ( Fig . 2D red box and 2C yellow rectangle ) . One small region was highly conserved in all transcripts ( SDS-X ( 2-3 ) -SLF-[ILV]-TQ , red box in Figure 2D ) , which may represent a functional motif for this class of proteins . The high rate of divergence across the different fish species suggested that , the function of the protein can tolerate significant alterations in the primary sequence . Because these fish homologs were not identified through the usual BLAST comparisons , if homologs in other vertebrates exist , they will be difficult to find using traditional approaches . It is likely they will have to be identified through functional similarities instead of primary sequence homology . To gather more information on the phoenix gene , we analyzed the putative phoenix product , looking for protein motifs and domains , using protein structure databases such as PFAM and CDD . We identified one putative functional domain using Motif Search ( http://motif . genome . jp ) . It contained an abATPase signature , PSVHSPPSDS ( P-[SAP]-[LIV]-[DNH]-X ( 3 ) -SXS ) , encoded in the first exon with the last serine overlapping the splicing site ( Figure 2C ) . It is important to note , that evidence for the ATPase signature was not found in the other fish species , but those transcripts were computationally predicted and it is possible that there were missing exons or were incorrectly sequenced . In the largest phoenix splice form , there was a strongly predicted single membrane-spanning domain ( Bioweb , Pasteur Paris , Figure 2C ) . Therefore , the majority of the protein appeared to be a very long , poorly structured , tail with a single potential anchorage point to a membrane . All the shorter gene products were predicted to be cytoplasmic . No secretion signal peptide was found ( CBS Website , University of Denmark ) . We performed in situ hybridization ( ISH ) on embryos from 8 hours post fertilization ( hpf ) through 5 dpf . The expression of the phoenix gene was first detected in the anterior and posterior lateral line system ( Figure 2E ) beginning at 2 dpf , and was uniformly expressed in all neuromasts and still maintained in this organ at 5dpf . Phoenix mRNA was found in a “ring-like” structure ( Figure 2F , left panel ) , clearly staining the supporting cells , while it was absent from the center of the neuromasts , where hair cells were located ( Figure 2F , right ) . Additionally , we found expression in discrete areas of the inner ear , which could correspond to the neuroepithelial patches ( Figure S2B ) . The pou4f3::GFP line allowed us to observe in live larvae the hair cells in the inner ear which seemed unaffected by the mutation in wild-type ( left ) and mutant ( right ) cristae ( Figure S2A ) Taken together , these observations clearly show that , we have identified a new gene with an upregulated expression in the supporting cells of the lateral line in zebrafish . Based on the various predictions , we speculate that Phoenix is most likely a structural protein , potentially carrying an enzymatic ATPase activity at its N-terminus . Previous reports have demonstrated that copper [37] and neomycin , an antibiotic of the aminoglycoside family , effectively kill hair cells in the lateral line [38] , [39] , [41] . We treated 5dpf larvae with copper sulphate ( 10mM ) for 2 hours or with neomycin ( 200mM ) for 1 hour . After rinsing the larvae , we stained with Yo-Pro1 , a vital dye that specifically accumulates in hair cells and binds irreversibly to DNA , allowing easy visualization of the hair cells in live larvae [38] . Untreated control wild-type ( Figure 3A , left top panels ) and phoenix mutant ( Figure 3A , left lower panels ) larvae at 5dpf showed a bright uniform staining of the hair cells , which adopt the shape of a rosette . Most hair cells were ablated immediately after the copper treatment ( +0h ) in all observed wild-type ( top panel ) and phoenix mutant ( lower panel ) larvae . At most , one remaining hair cell could be seen , but its irregular shape indicating that it was probably a dying cell ( Figure 3A , second column ) . We concluded that we could efficiently destroy the lateral line hair cells with this treatment . Likewise , 4h after exposure to neomycin , we found that most hair cells were absent ( data not shown ) . We next monitored , over the following three days , the reappearance of hair cells in wild-type and mutant larvae after both treatments ( copper shown in Figure 3A , neomycin not shown ) . At one day post treatment ( +24h , third column ) , we found an average of 6 stained hair cells in the wild-type ( top panel ) , but at most one in the mutant ( bottom panel ) neuromasts . At two days post treatment ( +48h , fourth column ) , we found on average 8 stained hair cells in the wild-type ( top panel ) , but at most two in the mutant ( bottom panel ) neuromasts . Three days post treatment ( +72h , fifth column ) , we found averages of 12 stained hair cells in the wild-type ( top panel ) vs . at most three in the mutant ( bottom panel ) neuromasts . Therefore we conclude that the regeneration process is severely impaired in phoenix mutant larvae . We further quantified the treatments , counting Yo-Pro1 positive hair cells in 10 head and all trunk and tail neuromasts in wild-type and mutant larvae , after both copper and neomycin treatments and at all three time points during the recovery period . To factor in subtle developmental delays that we might have missed in the mutant , we counted untreated larvae at comparable stages . Numbers are presented as a percentage , 100% being the total number of hair cells present in untreated wild-type and mutant larvae at comparable stages . After copper treatment ( Figure 3B ) at all three time points , we found close to a five-fold difference in the number of Yo-Pro1 positive cells in wild-type when compared to phoenix neuromasts . After neomycin treatment ( Figure 3C ) we found nearly a two-fold difference at all stages . We conclude that in the absence of the phoenix product , neuromasts are not able to efficiently regenerate the destroyed hair cells . To determine if phoenix's role in regeneration was specific to the lateral line , or represented a general inability to regenerate damaged tissues , we tested larval tail regeneration . We transected tails of 3dpf untreated wild-type ( top panel ) and mutant ( lower panel ) larvae and followed them to 9dpf ( Figure 3D left panels ) . As described previously , the tails of wild-type larvae first closed the injured tips of the notochord and neural tube , followed by fin fold outgrowth [53] . Likewise , in mutant larvae , we observed a complete regeneration of the tail ( Figure 3D , left lower panel ) . Furthermore , to exclude the fact that the treatments with the ototoxic agents could be interfering with the tail regeneration , we sectioned tails , immediately after copper ( middle panels ) or neomycin treatment at 5dpf ( right panels ) . We monitored tail regeneration at 8dpf for copper and 9dpf for neomycin . While tail regrowth did not reach completion in wild-type or in mutant larvae , we saw no differences in the growth rate between wild-type and mutant larvae with either treatment . To further exclude more subtle differences , we quantified the tail growth after copper exposure ( Figure 3E top graph ) in 8dpf wild-type ( green bar ) and mutant ( yellow bar ) larvae and did not find a significant difference . Similarly , after neomycin treatment ( Figure 3E , bottom panel ) , the growth of the tail in 9dpf wild-type ( blue bar ) and phoenix ( pink bar ) larvae was identical . Thus , we conclude that the phoenix mutation affects hair cell but not tail regeneration , indicating a defect specific to the lateral line . Taken together , these observations strongly suggest that phoenix has an important role in the regeneration process of hair cells in the lateral line . The differences in regeneration capacity of hair cells in phoenix versus wild-type larvae could be due to a difference in susceptibility of the progenitor cells to the ototoxic agents used in our assays . There is substantial evidence pointing to supporting cells as the source of new hair cells in neuromast regeneration . The new hair cells are likely to arise from a population of dividing progenitor cells [42]–[44] , however it is not clear if all or only a subset of the supporting cells represent cells capable of forming new hair cells [42]–[44] . In order to visualize all the supporting cells ( including all the cells capable of initiating regeneration ) , we took advantage of a transgenic zebrafish line generated by an enhancer trap event , which expresses GFP in all supporting cells of the neuromast ( MB/SB unpublished ) . We named this transgenic line SCM1 ( Supporting Cell Marker 1 ) . It was crossed into the phoenix mutant background in order to obtain mutant transgenic individuals . We evaluated the effect of either copper or neomycin treatment on the number of supporting cells in wild-type and mutant treated larvae after copper or neomycin exposure . We confirmed that there were no significant differences in the appearance of the supporting cells in the mutant transgenic larvae by performing the regeneration assay , followed by immuno-staining against GFP to detect supporting cells and anti-Myosin VI antibody to detect hair cells . In figure 4A , we present a schematic drawing , of the stainings in a transverse view ( top ) and a dorsal view ( bottom ) of a neuromast , as seen in both untreated wild-type and mutant neuromasts . We found centrally located hair cells ( red ) , surrounded by the supporting cells ( green ) that form cytoplasmic furrows around hair cells , as described previously [54] , [55] . In the control untreated wild-type ( Figure 4B , fist column ) and phoenix mutant ( second column ) neuromasts , hair cells were present and centrally located as visualized by anti-myosin VI antibody staining ( red , middle and lower panels ) . The supporting cells , as expected , surrounded them as visualized with an anti-GFP antibody staining ( green , top and lower panels ) . After copper ( shown in Figure 4B ) and neomycin ( not shown ) treatments , we could see a clear difference between wild-type and mutant neuromasts in the number of hair cells ( red , middle and lower panels ) at all stages of recovery , +24h ( columns 3 and 4 ) , +48h ( columns 5 and 6 ) and +72h ( not shown ) . Importantly , we did not see an obvious difference at any of the observed stages post-treatment , in the number or appearance of supporting cells ( green , top and lower panels ) in phoenix mutants compared to wild-type larvae . This finding suggests that , while hair cell regeneration is greatly decreased in phoenix mutant neuromasts , the number of supporting cells appear unaffected . To further support this finding , we used confocal imaging to count supporting cells in the immuno-stained larvae ( Figure 4C and 4D ) in wild-type ( green bars ) and phoenix mutants ( yellow bars ) . We counted the supporting cells in 4 different neuromasts in the head ( Figure 4C ) and in two different trunk neuromasts ( Figure 4D ) in each larva . Cell numbers were determined for untreated or copper treated larvae at +0h , +4h , +24h and +48h after the treatment . We did not find significant differences in any of the observed stages between wild-type and mutant larvae . Another possible explanation for the observed phenotype is that mutant progenitor cells are dividing but that the newly formed precursor cells do not survive because they are hyper-sensitive or unstable after the chemical exposure . To detect dying cells in the supporting cell layer in the mutant neuromasts , we analyzed semi-thin sections ( n = 2/2 wild-type and mutant larvae , respectively in Figure 4E ) . We reconstructed each neuromast using between 12 and 20 successive sections . We show representative examples of a wild-type ( left panels ) and a mutant ( right panels ) neuromast , untreated ( Figure 4E , top panels ) and 4 hours after neomycin treatment ( lower panels ) . Before and after treatment , wild-type and mutant neuromasts were indistinguishable . As outlined in the camera lucida drawings under each section , the wild-type neuromast had three hair cells ( nuclei outlined in red ) and ten supporting cells visible ( nuclei outlined in blue ) . In the mutant neuromast , three hair cells and ten supporting cells were visible . Four hours after neomycin treatment hair cells were completely destroyed in the wild-type and in the mutant neuromast ( Figure 4E , lower panels ) . The supporting cells appeared unaffected in both wild-type and phoenix mutant neuromasts . Ten nuclei in the wild-type and ten in the mutant neuromast were visible ( as outlined in the camera lucida in blue ) . Thus , at all observed stages , we did not notice a significantly higher cell death rate among the supporting cell population in phoenix mutant larvae . Next , we examined cell death in live larvae using acridine orange [37] . We stained larvae at +24h , +48h and +72h post treatment . Dying cells displayed strong staining as shown in two wild-type ( Figure 4G left panels ) and two mutant ( Figure 4G right panels ) neuromasts at +24h post copper treatment . We counted the acridine orange positive cells in ten head and all trunk and tail neuromasts ( Figure 4F ) after copper treatment in the wild-type ( green line ) and mutant ( yellow line ) , or after neomycin treatment ( wild-type: blue , mutant: pink ) . At all stages observed we did not find a significant difference . Additionally , we looked at cell death in the neuromasts , using TUNEL stainings and did not find any obvious difference in the rate of dying cells at all stages observed ( data not shown ) . We therefore conclude that the copper and neomycin treatments do not significantly affect the number of supporting cells in either wild-type or mutant larvae . Moreover , we rule out the possibility that progenitor cells may be dying in phoenix neuromasts as we did not observe an increase in cell death rates in mutant larvae compared to wild types . Taken together these results indicate that the regenerative capacity rather than the survival of supporting cells is affected by the phoenix mutation . Since the number of supporting cells in mutant neuromasts is not unlike that of wild type neuromasts , we turned our attention to differences in cell division to explain the regeneration phenotype observed in phoenix larvae . To test the hypothesis that the phoenix mutation is affecting proliferation of the progenitor cell population , we exposed SCM1 transgenic wild-type and mutant larvae , treated with copper or neomycin , to BrdU for 6h prior to fixing them , at +24h , +48h and +72h after chemical treatment . We subsequently double-stained the larvae with an anti-BrdU antibody ( red in Figure 5A , middle and right panels ) and with an anti-GFP antibody ( green in left and right panels ) . At +24h after copper ( Figure 5A ) and neomycin ( not shown ) treatments , the BrdU-labeled transgenic wild-type ( Figure 5A , top panels ) and the transgenic phoenix mutant ( lower panels ) larvae showed a striking difference in the number of BrdU positive cells . On average , we found eight BrdU positive cells in the transgenic wild-type neuromasts , in mutant neuromasts we typically found no positive cells while at most we found two . Thus the number of BrdU positive cells in the progenitor cells population in the mutant neuromasts is drastically reduced , most likely explaining the decrease in the number of newly regenerated hair cells in phoenix mutant embryos . To obtain quantitative data to support the above conclusion and to follow the dynamics of BrdU incorporation over time , we counted BrdU positive cells after copper treatment ( Figure 5B ) in at least ten wild-type ( green line ) and ten phoenix mutant ( yellow line ) neuromasts per larva . BrdU was added to the medium 6h before fixation allowing us to determine rates of DNA synthesis in equivalent time-windows during the 3 days regeneration period . Since recovery to a normal number of hair cells is reached at around 72 hours after treatment , this time point represents the rate of BrdU incorporation in undamaged neuromasts , or the BrdU baseline . At +24h of recovery , we had an eight-fold difference in the number of BrdU positive cells between wild-type and mutant neuromasts . This difference was still two-fold at +48h of recovery and both returned to the base line frequency at +72h . We performed similar tests after neomycin treatment ( Figure 5C , wild-type: blue line and mutant: pink line ) . At +24h , we found close to a two-fold difference of BrdU labeled cells , and at +48h a 33% decrease between wild-type and mutant neuromasts . Taken together , these results show that the lack of the Phoenix protein has a significant negative impact on proliferation in the regenerating neuromast , which is not due to a reduction in the number of supporting cells . Thus , phoenix has an important role in ensuring proper proliferation of the supporting cells during hair cell regeneration in neuromasts .
We have isolated a mutation in a previously uncharacterized gene in zebrafish that we have named phoenix because of an observed deficiency in the regeneration of the hair cells of the lateral line . Identical phenotypes were observed in two different alleles , caused by independent retroviral integrations in the phoenix gene in different genetic backgrounds . This unambiguously links the phenotype to this particular gene , which encodes a rapidly evolving protein with no obvious homologs in the current genomic databases . The predicted Phoenix protein is a long peptide of low complexity with a predicted ATPase domain reminiscent of many cytoskeletal proteins . This would argue in favor of a structural protein , possibly interacting with the cytoskeleton and subcellular membranes . How it is linked to the ability of the supporting cells to regenerate will require further investigation . It also remains to be discovered whether mammalian cells have a similar molecule; we were unable to detect a related sequence in the mammalian genomes . Interestingly , inner ear hair cells in mammals are unable to regenerate from supporting cells postnatally and , therefore , exploring the molecular differences between supporting cells between mammals and other vertebrates becomes of interest when trying to understand differences in regenerative capacity . We show that the expression of the phoenix gene is strongly upregulated in the supporting cells of the lateral line and that the lateral line morphology is essentially normal in the phoenix mutant larvae at all stages observed , from 2dpf to 12dpf . However it is important to note that , because we did not assess a possible maternal contribution for this particular gene , we cannot definitively exclude a potential role for phoenix during early development in general or of the lateral line in particular . Nevertheless , our regeneration assay performed in larvae between 6 and 8dpf , clearly shows that the number of regenerated hair cells is reduced by five-fold after copper treatment and by two-fold after neomycin treatment in phoenix mutant neuromasts . Therefore , the zygotic expression of the phoenix gene is clearly crucial for proper regeneration of the hair cells in the lateral line . Observations at later stages never showed full recovery to a wild-type number of hair cells in the mutant neuromasts , demonstrating that the deficit in the regenerative process is irreversible . We have also found expression of phoenix in the neuroepithelia of the inner ear , but unfortunately regeneration of the hair cells in the ear cannot be assessed with our assay , as water-borne ototoxic compounds do not diffuse into the otic capsule . We further show that the regeneration deficiency is specific to the lateral line , as we did not see a similar effect during tail regeneration . In combination with the restricted expression of the gene in supporting cells of the lateral line , this strongly suggests that , zygotic phoenix is involved specifically in the regeneration of hair cells in the lateral line , independent of the ototoxic treatment ( copper or neomycin ) used . It is intriguing that while the two treatments trigger similar responses , they are significantly different in the amplitude of the elicited effect . The reason there is a nearly 2 . 5 times greater inability of the supporting cells to respond properly after copper compared to neomycin treatments , is not clear . However , it is to be noted that the wild-type regeneration is also significantly slower ( about two times ) after the copper treatments when compared to neomycin treatments , as we found and as it has been described previously [37] , [43] . One possibility , as has been suggested previously [44] , is that there is more than one mechanism of regeneration occurring in the supporting cells . The supporting cells probably do not represent a uniform population . For instance , one type of supporting cells , the “early responders” may be unaffected in the case of hair cells killed with neomycin , but might be more severely affected by the copper treatment leaving them unable , either temporarily or permanently , to respond and enter division . In less mature embryos ( 3dpf ) , the concentration of copper used ( 10 µM ) has been shown to eliminate post-mitotic precursors , thus requiring cell division and longer times for regeneration when compared to lower doses of the metal [37] . Additionally , it was recently shown in a screen to identify genes that modify hair cell resistance in the lateral line , that different ototoxic agents can elicit different pathways for protection and survival of hair cells in treated neuromasts [56] . It is therefore also possible that the regenerative response in supporting cells depends partially on the type of ototoxic agent applied . Namely , the trigger or the response to the trigger of regeneration in supporting cells could rely on different pathways , partially overlapping or complementing each other depending on the ototoxic chemical . Further investigation will allow us to distinguish between these possibilities . It appears that phoenix is upstream of the differential response between neomycin and copper , as the differences in the rates of response in mutant larvae remain similar to the differences seen in wild-type larvae . We showed that the death rate of supporting cells was unaltered in mutant neuromasts as observed by three different approaches ( histology , acridine orange and TUNEL staining ) . Furthermore , making use of the MSC1 transgenic line that expresses GFP exclusively in the supporting cells , we demonstrated no loss in the number of those cells in mutant neuromasts . It is to be noted that the counting of cells in the neuromast is technically challenging . Although we did not see obvious differences , we cannot exclude a possible subtle reduction in the number of supporting cells . If a key subset of cells critical to the initiation of regeneration is missing , even a subtle difference in number might have a drastic effect on regeneration , which we cannot exclude at our level of analysis . However , it was previously shown that the regeneration process is happening in many supporting cells simultaneously , encompassing most of the neuromast [44] making this possibility unlikely . We clearly show that a significant number of supporting cells are present and that the phoenix mRNA is expressed in all supporting cells , therefore the most likely explanation is that the regenerative potential of all supporting cells is severely impaired . To assess proliferation of the progenitor cells , we follow the incorporation of BrdU during the S-phase of the cell cycle in neuromasts . The incorporation of BrdU is detected in cells that have exited the cell cycle and have differentiated . Therefore , it provides no information on the post-mitotic state of the BrdU positive cells . However , as the number of regenerated hair cells correlates closely to the number of BrdU labeled cells and as we do not see additional cell death in either wild-type or mutant neuromasts , this would suggest that the progenitor cells that actually enter S-phase proceed through the entire cell cycle and into differentiation . In the phoenix mutant larvae , this regenerative process is happening in a significantly reduced number of supporting cells . Further work is required to address the timing of the blockage occurring in the supporting cells in mutant neuromasts , but it is clearly occurring before DNA synthesis initiates . This invaluable information will help to elucidate the mechanism of action of the novel phoenix gene in the regeneration process of hair cells in the lateral line .
Fish care and husbandry were performed according to [57] in compliance with NIH guideline for animal care . The hi43 allele of phoenix was recovered in a retroviral screen performed at MIT [58] . The genomic locus of the retroviral integration and the putative cDNA were determined as described in [45] . Genotyping was done with primers GGAGATCGACAGCGCCCTGAAG and AAACTGCTGAGGGCTGCTGGACCGCATC . The zm allele ( ZM_00003486 ) was purchased ( Znomics , Inc ) and carriers were genotyped using the primers CGAGACCCCGCCGCCTGATGTT and GACGCAGGCGCATAAAATCAGTC . The MSC1 transgenic line was a gift from B . Weinstein and carriers were identified by detecting specific expression of GFP in the lateral line . The cldnB::GFP line [46] was obtained from M . Allende , the pou4f3::GFP line [47] from H . Baier and the ET20::GFP line [48] from Vladimir Korzh . All active agents were added to system water at 28°C . During the staining/treatment , larvae were kept in cell strainers ( BD Falcon ) in 6 well plates ( Costar , Corning , Inc ) ) at a maximum of 35 larvae/well . This allowed rapid transfer of the larvae during the post-treatment rinsing . All larvae were rinsed 6×3 minutes with system water and then transferred into large Petri dishes with abundant fresh system water . After the various treatments , only healthy looking larvae displaying no additional morphological or behavioral defects were kept for subsequent analysis over the following 72 hours . The number of hair cells and the AO positive cells were counted in 10 head and all the trunk and tail neuromasts , in each larva . We added YoPro-1 ( Molecular probes ) for 15 minutes at 2mM , as described in [38] , FM1-43 ( Molecular probes ) for 1minute at 2mM , as described [49] and Acridine orange ( AO ) hemi ( zinc chloride , Sigma ) for 5 minutes at 2mg/ml , as described [37] . Copper ( Copper ( II ) sulfate , Sigma ) was added for 2h at 10mM as described in [37] and Neomycin ( sulfate , Calbiochem ) for 1h at 200mM as described in [38] , [39] . Monitoring , counting and imaging of the lateral line in live larvae , after using the different life dyes , was done on an inverted Zeiss AXIOVERT200M equipped with an Apotome Grid Confocal . Larvae were anesthetized with MS222 ( 0 . 005% ) and mounted on a cover slip in 2% Methylcellulose ( Sigma ) . Five day old larvae were allowed to recover for an hour after copper or neomycin treatment . Subsequently , larvae were anesthetized with 0 . 005% MS-222 ( Sigma ) and placed on a clean glass slide and a portion of their tail was amputated with a scalpel . We cut not just the fin , but also a portion of the actual tail including the neural tube , the notochord , and the somites , to evaluate the regeneration of other tissues in addition to the fin itself . Three day old untreated larvae were similarly processed . To follow and image the tail growth over 3 to 6 days post amputation , we mounted anesthetized larvae on cover slips in 2% methylcellulose . Measurements were made on pictures taken from live larvae on the inverted Zeiss AXIOVERT200M , using the Axiovision software from Zeiss . The region of amputation was usually easily identifiable , as it presented healing scars , allowing a reasonably accurate measurement of the newly grown tail . BrdU ( Sigma ) was diluted to 10mM in system water and larvae were exposed for 6h before fixation at the chosen time points ( 24 , 48 and 72 hours post treatment ) o/n with 4% formaldehyde ( Electron Microscopy Sciences ) in 1× PBS ( Quality Biological , Inc . ) . Double staining was done as described [40] , [44] , with a fluorochrome labeled mouse monoclonal antibody against BrdU ( Molecular probes ) and a fluorochrome labeled rabbit polyclonal antibody against GFP ( Abcam ) . Larvae were mounted on slides in Aquapolymount ( Polyscience , Inc ) and at least 10 neuromasts/larva were counted . The imaging was done on an upright confocal microscope ( Zeiss AXIOVERT ) Untreated and copper treated larvae were fixed o/n with 4% formaldehyde ( Electron Microscopy Sciences ) in 1× PBS ( Quality Biological , Inc . ) , at various stages of interest and subsequently stored in 100% methanol . After progressive rehydration ( 25% , 50% 75% and 100% PTW ( PBS1× , 0 . 001% Tween and 0 . 001% DMSO ) ) , larvae were treated with acetone for 7mn at −20°C . Subsequently , we rehydrated and rinsed them 3×5mn in PTW . Next we digested them with 1mg/ml collagenase ( Sigma ) in PTB ( PTW + 10% goat serum +10% BSA ) for 35mn . After 5×5mn rinses with PBT , we pre-incubated the larvae 4 hours in PBT . Larvae were incubated o/n with the polyclonal rabbit primary antibody ( 1/200 ) against Myosin VI ( Proteus Biosciences , Inc ) and a fluorescently labeled monoclonal mouse primary antibody ( 1/200 ) against GFP ( Abcam ) . The next day we rinsed the larvae 6×10 mn in PTW and preincubated them again for 4 hours in PBT . The fluorescently labeled secondary anti-rabbit antibody ( 1/500 ) was added o/n . The next day 6×10 mn rinses were performed . Larvae were mounted on slides in Aquapolymount ( Polyscience , Inc ) for imaging on an upright confocal microscope ( Zeiss AXIOVERT ) . Larvae were fixed overnight at in 2 . 5% glutaraldehyde ( Sigma ) and 4% paraformaldehyde prepared from paraformaldehyde ( Sigma ) in 0 . 1M sodium cacodylate buffer ( Sigma ) . Larvae were then rinsed and post-fixed 1h at room temperature in reduced osmium ( 1∶1 mixture of 2% aqueous potassium ferrocyanide ) as described previously [59] . After post-fixation the cells were dehydrated in ethanol and processed for Epon ( Sigma ) embedding . Semi-thin sections ( 300 nm ) were cut and collected on a glass slide , and subsequently stained using toluidine blue ( Sigma ) . The analysis and imaging were done on an inverted Zeiss Axiovert200M . Ultra thin sections ( 80 nm ) were cut on a Reichter-E ultramicrotome , collected on copper grids and stained with lead citrate ( Sigma ) for 2 min . Sections were then examined with a CM 10 Philips electron microscope at 80kV . Performed as described previously [60] . We designed an antisense probe , using the following primers TCAACTGATGTATTTCCTGGGC and GTTTTGCTCCACTATCTGACCTTT . The probe was hybridized at 62°C . Larvae were mounted on slides in Aquapolymount ( Polyscience , Inc ) for imaging on an upright confocal microscope ( Zeiss AXIOVERT ) . We screened the zebrafish BAC library ( Chori 211 , BacPac consortium ) with a probe generated from total RNA ( 5 dpf larva ) , with the primers AGATCTTGAGATTGCCGAATGT and CATCTCTCTCACCTTCTTCAGTGAC . The BAC was sequenced , by shotgun assembly and brought to finished quality ( ≤1 error per 50kb ) at the National Intramural Sequencing Center ( NISC ) . Sequences were aligned to the zebrafish genome ( UCSC Genome Browser http://genome . ucsc . edu/ ) and syntenic regions identified using genomic chain comparisons in Takifugu rubripes ( fugu ) , Tetraodon nigroviridis ( tetraodon ) , Oryzias latipes ( medaka ) , and Gasterosteus aculeatus ( stickleback ) . Genescan or N-SCAN [50] predicted transcripts between the two flanking genes were identified and compared using ClustalW [61] ( http://www . ebi . ac . uk/Tools/clustalw2/index . html ) . The BLOSUM protein matrix [62] was used with a window of 7 and gap penalty of 2 . We prepared total RNA extracts from embryos and Pac2 cells ( which is an embryonic zebrafish cell line described in [51] in Trizol ( Invitrogen ) . For the RT-PCR , we retro-transcribed specific cDNA using superscript II Reverse transcriptase ( Invitrogen ) , following the manufacturer's protocol . For the cloning of all transcripts shown in Figure 2B and 2C , we used the same set of 2 nested forward primers CCATATCAAAACAGAGCTGTGCTAC and GTCAAGGCAGAGTAAGCAAGTGACACTG , both located in the 5′UTR , respectively starting 142bp and 121bp upstream of the start codon . For the cloning of transcripts 2 , 3 and 6 , they were respectively coupled with the reverse primers GTTCTGTTTTCTTTTCCTTGTCAACGCC and CCTTCTCTCCCTGATAAAGTCTGGCAACC both found in the 5′end of exon 7 . For the cloning of the transcript 1 , we coupled them respectively with the reverse primers CATTCATTCATTCATTCATTTTCCT and TTTCCTTTTGCTTAGTCCCTTATTT both found in the 3′end of exon 2 . For the cloning of the transcript 4 we coupled them respectively with the reverse primers TTTTACATCTACAGAATCGTGAAAAA and AATTGTGATTCTCATTTTAGCCAGA , both found in the 3′ end of exon 6 . For the cloning of the transcript 5 we coupled them respectively with the reverse primers TTTTTCTTTCATAGCGAACACAAAG and TTTAAACTGTCAGCTCTTGATGC , both found in 3′end of exon 5 . To verify the quality of the Total RNA from different stages , which we used for the RT-PCR , we amplified a 159bp fragment of b-actin using the following primers , GACCCAGACATCAGGGAGTGATGG and AGGTGTGATGCCAGATCTTCTCCAGT . All sub-cloning for subsequent sequencing were done using the TOPO TA cloning Kit ( Invitrogen ) , following the manufacturer protocol . Sequencing was performed by ACGT , Inc . The analysis of the sequences was done using the Sequencher ( Gene Codes Corp . ) program , with the genomic sequence of the phoenix gene ( as previously isolated in the BAC ) as a reference . At all time points and in all graphs , the p values for wild-type versus mutant larvae were calculated using a Student's T test ( two tailed ) with two samples of equal variance and considered significant when p was <0 . 05 . Error bars in the different graphs represent the standard deviation , or standard error , depending on the number of samples ( n ) analyzed . Stacks of images collected on the inverted microscope were treated with the Axiovision software from Zeiss . Stacks of images collected on the confocal microscope were treated with the LSM software from Zeiss . All images or stack of images were exported as tiff files , which were subsequently processed with Photoshop .
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By screening for regeneration deficient zebrafish mutations , we identified a zebrafish mutant line deficient in a highly specific regeneration process , the renewal of hair cells in the lateral line . Although this organ is specific to fish and amphibians , it contains essentially the same mechanosensory cells ( the hair cells ) that function in the ear for sound and balance detection in all vertebrates . Mammals are unusual vertebrates in that they have lost the ability to regenerate functional hair cells after damage by sound or chemical exposure . All other vertebrates retain their ability to regenerate their hair cells after damage , but this process is not well understood at the molecular level . The retroviral insertion linked to the phoenix mutation is in a new gene family class that is specifically required for the supporting cells to enter into mitosis after hair cell damage . What is particularly unusual about this mutation is that it appears not to affect the normal development and differentiation pathways , but only seems to affect the cells' post-differentiation regeneration .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"function",
"genetics",
"and",
"genomics/gene",
"discovery",
"developmental",
"biology/stem",
"cells"
] |
2009
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Phoenix Is Required for Mechanosensory Hair Cell Regeneration in the Zebrafish Lateral Line
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Cotton bacterial blight ( CBB ) , an important disease of ( Gossypium hirsutum ) in the early 20th century , had been controlled by resistant germplasm for over half a century . Recently , CBB re-emerged as an agronomic problem in the United States . Here , we report analysis of cotton variety planting statistics that indicate a steady increase in the percentage of susceptible cotton varieties grown each year since 2009 . Phylogenetic analysis revealed that strains from the current outbreak cluster with race 18 Xanthomonas citri pv . malvacearum ( Xcm ) strains . Illumina based draft genomes were generated for thirteen Xcm isolates and analyzed along with 4 previously published Xcm genomes . These genomes encode 24 conserved and nine variable type three effectors . Strains in the race 18 clade contain 3 to 5 more effectors than other Xcm strains . SMRT sequencing of two geographically and temporally diverse strains of Xcm yielded circular chromosomes and accompanying plasmids . These genomes encode eight and thirteen distinct transcription activator-like effector genes . RNA-sequencing revealed 52 genes induced within two cotton cultivars by both tested Xcm strains . This gene list includes a homeologous pair of genes , with homology to the known susceptibility gene , MLO . In contrast , the two strains of Xcm induce different clade III SWEET sugar transporters . Subsequent genome wide analysis revealed patterns in the overall expression of homeologous gene pairs in cotton after inoculation by Xcm . These data reveal important insights into the Xcm-G . hirsutum disease complex and strategies for future development of resistant cultivars .
Upland cotton ( Gossypium hirsutum L . ) is the world’s leading natural fiber crop . Cotton is commercially grown in over 84 countries , and in the United States , is responsible for $74 billion annually [1 , 2] . Numerous foliar diseases affect cotton throughout the world’s cotton growing regions . Historically , one of the most significant foliar diseases has been bacterial blight , caused by Xanthomonas citri pv . malvacearum . Cotton bacterial blight significantly limited cotton yield in the late 20th century . In the 1940’s and 1950’s , breeders identified and introgressed multiple resistance loci into elite germplasm [3–5] . This strategy proved durable for over half a century . In 2011 , cotton bacterial blight ( CBB ) returned and caused significant losses to farmers in the southern United States , including in Arkansas and Mississippi . Nonetheless , CBB has received little research focus during the last several decades because , prior to 2011 , losses from this disease were not substantial . Modern molecular and genomic technologies can now be employed expeditiously to deduce the underlying cause of the disease re-emergence and pinpoint optimized routes towards the development of durable resistance . CBB is caused by X . citri pv . malvacearum ( Xcm ) ; however , the pathogen has previously been placed within other species groupings [6–9] . The Xcm pathovar can be further divided into at least 19 races according to virulence phenotypes on a panel of historical cotton cultivars: Acala-44 , Stoneville 2B-S9 , Stoneville 20 , Mebane B-1 , 1-10B , 20–3 , and 101-102 . B [10 , 11] . Historically , the most common race observed in the U . S . has been race 18 , which was first isolated in 1973 [12] . This race is highly virulent , causing disease on all cultivars in the panel except for 101-102 . B . However , this diagnostic panel of cotton varieties used to race type strains is no longer available from the USDA/ARS , Germplasm Resources Information Network ( GRIN ) . CBB can occur at any stage in the plant’s life cycle and on any aerial organ . Typical symptoms include seedling blight as either pre- or post-emergent damping-off , black arm on petioles and stems , water-soaked spots on leaves and bracts , and most importantly boll rot [10] . The most commonly observed symptoms are the angular-shaped lesions on leaves that can coalesce and result in a systemic infection . Disease at each of these stages can cause yield losses either by injury to the plant or direct damage to the boll . No effective chemical treatments for the disease have been released to date . Methods to reduce yield loss as a result of CBB include acid de-linting cotton seed prior to planting , field cultivation practices to reduce sources of overwintering inoculum and planting cultivars with known sources of resistance [3 , 4 , 8 , 13 , 14] . Xanthomonads assemble the type three secretion system ( T3SS ) , a needle-like structure , to inject diverse type three effectors ( T3Es ) into the plant cell to suppress immunity and promote disease [15–19] . For example , transcription activator-like ( TAL ) effectors influence the expression levels of host genes by binding directly to promoters in a sequence-specific way [20] . Up-regulated host genes that contribute to pathogen virulence are termed susceptibility genes and may be modified through genome editing for the development of resistant crop varieties [21] . Plants have specialized immune receptors , collectively known as nucleotide-binding leucine rich repeat receptors that recognize , either directly or indirectly , the pathogen effector molecules [22 , 23] . Historically , this host-pathogen interaction has been termed the ‘gene-for-gene’ model of immunity , wherein a single gene from the host and a single gene from the pathogen are responsible for recognition [24] . Recognition triggers a strong immune response that often includes a localized hypersensitive response ( HR ) in which programmed cell death occurs around the infection site [25] . Nineteen CBB resistance loci have been reported in Gossypium hirsutum breeding programs; however , none have been molecularly identified [8 , 13] . Here we combine comparative genomics of the pathogen Xcm with transcriptomics of the host to identify molecular determinants of Cotton Bacterial Blight . This will inform the development of durable resistance strategies .
In 2011 , farmers , extension specialists , and certified crop advisers in Missouri , Mississippi , and Arkansas observed cotton plants exhibiting symptoms of CBB . Widespread infected plant material was observed throughout much of the production area , but appeared to be centered around Clarksdale , Mississippi . In Fig 1 , we collate reports from this outbreak and overlay these data with US cotton planting statistics to reveal that this disease has spread through much of the cotton belt in the southern U . S . ( Figs 1 and S1 , S1 Table ) . Since 2016 , CBB has been reported from at least eight out of the sixteen states that grow cotton ( Fig 1 ) . In 2014 , we collected diseased cotton leaves from two sites across Mississippi and confirmed pathogen causality following Koch’s postulates [26] . In addition , PCR amplification of the 16S rRNA gene confirmed that the causal agent was a member of the Xanthomonas genus . Multi locus sequence type ( MLST ) analysis and maximum-likelihood analysis were performed using concatenated sections of the gltA , lepA , lacF , gyrB , fusA and gap-1 loci for increased phylogenetic resolution ( Fig 2A ) . The newly sequenced strains were named MS14002 and MS14003 and were compared to four previously published Xcm genomes and thirty-six additional Xanthomonas genomes representing thirteen species ( Tables 1 and S2 ) . MS14002 and MS14003 grouped with the previously published Xcm strains as a single unresolved clade , further confirming that the current disease outbreak is CBB and is caused by Xcm . The species designation reported here is consistent with previous reports [6 , 7] . Race groups have been described for Xcm strains by analyzing compatible ( susceptible ) and incompatible ( resistant ) interactions on a panel of seven cotton cultivars . Different geographies often harbor different pathogen races [7] . Consequently , one possible explanation for the recent outbreak of CBB would be the introduction of a new race of Xcm capable of overcoming existing genetic resistance . Only 2 varieties of the original cotton panel plus three related cultivars , were available and these cultivars were not sufficient to determine whether a new race had established within the U . S . Thirteen Xcm strains were sequenced using Illumina technology to determine the phylogenetic relationship between recent isolates of Xcm and historical isolates . Isolates designated as race 1 , race 2 , race 3 , race 12 and race 18 have been maintained at Mississippi State University with these designations . Additional isolates were obtained from the Collection Française de Bactéries associées aux Plantes ( CFBP ) culture collection . Together , these isolates include nine strains from the US , three from Africa , and one from South America and span collection dates ranging from 1958 through 2014 ( Fig 1 , Table 1 ) . Illumina reads were mapped to the Xanthomonas citri subsp . citri strain Aw12879 ( Genbank assembly accession: GCA_000349225 . 1 ) using Bowtie2 and single nucleotide polymorphisms ( SNPs ) were identified using Samtools [27 , 28] . Only regions of the genome with at least 10x coverage for all genomes were considered . This approach identified 17 , 853 sites that were polymorphic in at least one genome . Nucleotides were concatenated and used to build a neighbor-joining tree ( Fig 2B ) . This analysis revealed that recent U . S . Xcm isolates grouped with the race 18 clade . Notably , the race 18 clade is phylogenetically distant from the other Xcm isolates . Xanthomonads deploy many classes of virulence factors to promote disease . Type three effectors ( T3E ) are of particular interest for their role in determining race designations . T3E profiles from sixteen Xcm isolates were compared to determine whether a change in the virulence protein arsenal of the newly isolated strains could explain the re-emergence of CBB . Genomes from 13 Xcm isolates were de novo assembled with SPAdes and annotated with Prokka based on annotations from the X . euvesicatoria ( aka . X . campestris pv . vesicatoria ) 85–10 genome ( NCBI accession: NC_007508 . 1 ) . T3Es pose a particular challenge for reference based annotation as no bacterial genome contains all effectors . Consequently , an additional protein file containing known T3Es from our previous work was included within the Prokka annotation pipeline [15 , 29] . This analysis revealed 24 conserved and 9 variable Xcm T3Es ( Fig 3A ) . Race 18 clade isolates contain more effectors than other isolates that were sequenced . The recent Xcm isolates ( MS14002 and MS14003 ) were not distinguishable from the historical race 18 isolate , with the exception of XcmNI86 isolated from Nicaragua in 1986 , which contains mutations in XopE2 and XopP . Analysis of the genomic sequence of T3Es revealed presence/absence differences , frameshifts and premature stop codons . However , this analysis does not preclude potential allelic or expression differences among the virulence proteins that could be contributing factors to the re-emergence of CBB . Therefore , newly isolated strains may harbor subtle genomic changes that have allowed them to overcome existing resistance phenotypes . Many commercial cultivars of cotton are reported to be resistant to CBB [30–32] . Based on these previous reports , we selected commercial cultivars resistant and susceptible ( 6 of each ) to CBB . In addition , we included 5 available varieties that are related to the historical panel as well as 2 parents from a nested association mapping ( NAM ) population currently under development [33] . All varieties inoculated with the newly isolated Xcm strains exhibited inoculation phenotypes consistent with previous reports ( Fig 3B and 3C ) . In these assays , bright field and near infrared ( NIR ) imaging were used to distinguish water-soaked disease symptoms from rapid cell death ( HR ) that is indicative of an immune response . These data confirm that existing resistance genes present within cotton germplasm are able to recognize the newly isolated Xcm strains and trigger a hypersensitive response . Together , the phylogenetic analysis , effector profile conservation and cotton inoculation phenotypes , confirm that the recent outbreak of Xcm in the US represents a re-emergence of a race 18 clade Xcm and is not the result of a dramatic shift in the pathogen . The USDA Agricultural Marketing Service ( AMS ) releases reports on the percentage of upland cotton cultivars planted in the U . S . each year ( www . ams . usda . gov/mnreports/cnavar . pdf ) . Most of these varieties are screened for resistance or susceptibility to multiple strains of Xcm by extension scientists and published in news bulletins [30 , 31 , 34–38] . These distinct datasets were cross referenced to reveal that only 25% of the total cotton acreage was planted with resistant cultivars in 2016 ( Fig 3D , S3 Table ) . This is part of a larger downward trend in which the acreage of resistant cultivars has fallen each year since at least 2009 when the percentage of acreage planted with resistant varieties was at 75% . Differences in virulence were observed among Xcm strains at the molecular and phenotypic level . In order to gain insight into these differences , we selected two strains from our collection that differed in T3E content , virulence level , geography of origin and isolation date . AR81009 was isolated in Argentina in 1981 and is one of the most virulent strains investigated in this study; MS14003 was isolated in Mississippi in 2014 and is a representative strain of the race 18 clade ( S2 Fig ) . The latter strain causes comparatively slower and diminished leaf symptoms; however , both strains are able to multiply and cause disease on susceptible varieties of cotton ( S3 Fig ) . Full genome sequences were generated with Single Molecule Real-Time ( SMRT ) sequencing . Genomes were assembled using the PacBio Falcon assembler which yielded circular 5Mb genomes and associated plasmids . Genic synteny between the two strains was observed with the exception of two 1 . 05 Mb inversions ( Fig 4 ) . Regions of high and low GC content , indicative of horizontal gene transfer , were identified in both genomes . In particular , a 120kb insertion with low GC content was observed in AR81009 . This region contains one T3E as well as two annotated type four secretion system-related genes , two conjugal transfer proteins , and two multi drug resistant genes ( Fig 4 insert ) . MS14003 contains three plasmids ( 52 . 4 , 47 . 4 , and 15 . 3kb ) while AR81009 contains two plasmids ( 92 . 6 and 22 . 9kb ) . Analysis of homologous regions among the plasmids was performed using progressiveMauve [39] . This identified four homologous regions greater than 1kb that were shared among multiple plasmids ( Fig 4 ) . Both strains express TAL effector proteins as demonstrated through western blot analysis using a TAL effector specific polyclonal antibody ( Fig 5 ) [40] . However , the complexity of TAL effector repertoires within these strains prevented complete resolution of each individual TAL effector using Illumina sequencing . In contrast , the long reads obtained from SMRT sequencing are able to span whole TAL effectors , allowing for full assemblies of the TAL effectors in each strain . The AR81009 genome encodes twelve TAL effectors that range in size from twelve to twenty three repeat lengths , six of which reside on plasmids . The MS14003 genome encodes eight TAL effectors that range in size from fourteen to twenty eight repeat lengths , seven of which reside on plasmids ( Fig 5 ) . Three partial TAL effector-like coding sequences were also identified within these genomes and are presumed to be non-functional . A 1-repeat gene with reduced 5’ and 3’ regions was identified in both strains directly upstream of a complete TAL effector . In addition , a large 4kb TAL effector was identified in AR81009 with a 1 . 5 kb insertion and 10 complete repeat sequences . The tool AnnoTALE was used to annotate and group TAL effectors based on the identities of the repeat variable diresidues ( RVDs ) in each gene [41] . Little homology was identified among TAL RVD sequences within and between strains; only two TAL effectors were determined to be within the same TAL class between strains ( TAL19b of AR81009 and TAL19 of MS14003 ) and two within strain MS14003 ( TAL14b and TAL16 ) . An RNA-sequencing experiment was designed to determine whether AR81009 and MS14003 incite different host responses during infection ( Fig 6A and 6B ) . Isolates were inoculated into the phylogenetically diverse G . hirsutum cultivars Acala Maxxa and DES 56 [33] . Infected and mock-treated tissue were collected at 24 and 48 hours post inoculation . First , we considered global transcriptome patterns of gene expression . Fifty-two genes were determined to be induced in all Xcm-G . hirsutum interactions at 48 hours ( Fig 6C , S4 Table ) . Of note among this list is a homeologous pair of genes with homology to the known susceptibility target MLO [42–45] . Gene induction by a single strain was also observed; AR81009 and MS14003 uniquely induced 127 and 16 G . hirsutum genes , respectively ( Fig 6C ) . In contrast , the average magnitude of gene induction between the two strains was not significantly different ( S4 Fig ) . Both Xcm strains caused more genes to be differentially expressed in DES 56 than in Acala Maxxa . Among the 52 genes significantly induced by both strains , sixteen conserved targets are homeologous pairs , whereas seventeen and fifteen genes are encoded by the A and D sub-genomes , respectively ( Tables 2 and S4 ) . It has been previously reported that homeologous genes encoded on the G . hirsutum A and D sub-genomes are differentially regulated during abiotic stress [46] . A set of approximately 10 , 000 homeologous gene pairs were selected and differential gene expression was assessed ( Fig 7 ) . For each pairwise comparison of Xcm strain and G . hirsutum cultivar , a similar number of genes were differentially expressed in each of the A and D subgenomes . However , some homeologous pairs were up- or down-regulated differentially in response to disease , indicating a level of sub-genome specific responses to disease . For example , SWEET sugar transporter gene Gh_D12G1898 in the D genome is induced over fourfold during infection with Xcm strain AR81009 , while the homeolog Gh_A12G1747 in the A genome is induced to a much smaller extent . SWEET sugar transporter genes have been reported to be targets of and upregulated by Xanthomonas TAL effectors in Manihot esculenta , Oryza sativa , and Citrus sinensis [21 , 40 , 47 , 48] . In rice and cassava , the SWEET genes are confirmed susceptibility genes that contribute to disease symptoms . The previously reported susceptibility genes and the SWEETs identified here , are clade III sugar transporters ( S5 Fig ) . The NBI Gossypium hirsutum genome encodes 54 putative SWEET sugar transporter genes . Of these 54 genes , three were upregulated greater than fourfold in response to inoculation by one of the two Xcm strains ( Fig 8 ) . Predicted TAL effector binding sites were identified using the program TALEnt [49] . MS14003 significantly induces the homeologs Gh_A04G0861 and Gh_D04G1360 and contains the TAL effectors M14b , M28a , and M28b , which are predicted to bind within the 300bp promoter sequences of at least one of these genes . Of note is TAL M28a , which is predicted to bind both homeologs ( S6A Fig ) . In contrast , AR81009 induces Gh_D12G1898 to a greater extent than its homeolog Gh_A12G1747 . TAL effectors A14c and A16b from AR81009 are predicted to bind to the Gh_D12G1898 and Gh_A12G1747 promoters; however , TAL A14a is predicted to bind only the Gh_D12G1898 promoter ( S6B Fig ) . We note that while Gh_A12G1747 did not pass the fourfold cut off for gene induction , this gene is slightly induced compared to mock inoculation .
Cotton Bacterial Blight was considered controlled in the U . S . until an outbreak was observed during the 2011 growing season in Missouri , Mississippi and Arkansas [50] . Until 2011 , seed sterilization , breeding for resistant varieties , and farming techniques such as crop rotation and sterilizing equipment prevented the disease from becoming an economic concern [51] . The number of counties reporting incidence of CBB has increased from 17 counties in 2011 to 77 counties in 2015 [38 , 52 , 53] . This paper investigates the root of the re-emergence and identifies several routes towards control of the disease . When CBB was first recognized as re-emerging , several possible explanations were proposed including: ( 1 ) A highly virulent race of the pathogen that had been introduced to the U . S . ; ( 2 ) Historical strains of Xcm that had evolved to overcome existing resistance ( e . g . an effector gene change or host shift ) ; and ( 3 ) Environmental conditions over the last several years that had been particularly conducive to the disease . Here , we present evidence that the re-emergence of CBB is not due to a large genetic change or race shift in the pathogen . Rather , the re-emergence of the disease is likely due to agricultural factors such as large areas of susceptible cultivars being planted . The presented data do not rule out potential environmental conditions that may also have contributed to the re-emergence . In this context , environmental conditions include disease conducive temperature and humidity as well as potentially contaminated seed or other agronomic practices that may have perpetuated spread of the disease outbreaks . Importantly , the presented data confirm that the presence of resistance loci could be deployed to prevent further spread of this disease . However , since many of the most popular farmer preferred varieties lack these resistance traits , additional breeding or biotechnology strategies will be needed to maximize utility . Notably , the current Xcm isolates characterized in this study all originate from Mississippi cotton fields in 2014 . During the 2015 and 2016 growing seasons , resistant cotton cultivars were observed in Texas with symptoms indicative of bacterial infection distinct from CBB . Additional work is underway to identify and characterize the causal agent ( s ) of these disease symptoms . Recent work on CBB in the US has focused on the most prevalent US Xcm race: race 18 . However , races are not necessarily phylogenetically distinct clades . Race 18 isolates have been reported overseas , indicating that there may be independent origins of the race or cross-continent movement of this pathogen . Phenotypic race delineations were created before modern genetic and phylogenetic techniques were developed . However , modern genetics presents the opportunity to begin classifying strains based upon phylogenetic and effector profiles rather than phenotypes on a limited range of host varieties . Here , we identify all known and putative race 18 isolates as phylogenetically grouped into a single clade and distinct from other Xcm isolates . Future efforts can further explore phylogenetic relatedness among diverse isolates . While resistant cotton cultivars were identified for all strains in this study , variability in symptom severity was observed for different strains when inoculated into susceptible cultivars . Two strains in particular , MS14003 and AR81009 , have different effector profiles as well as different disease phenotypes . Comparative genomic analysis of the two pathogens revealed many differences that may contribute to the relative disease severity phenotypes . Similarly , transcriptomic analysis of two cultivars of G . hirsutum inoculated with these strains confirm that the genomic differences between the two strains result in a divergence in their molecular targets in the host . Over the past decade , susceptibility genes have become targets for developing disease tolerant plants [54 , 55] . These genes are typically highly induced during infection [56] . Therefore , RNA-Seq of infected plants has become a preferred way to identify candidate susceptibility genes . Once identified , genome editing can be used to block induction of these genes [57] . We report a homeologous pair of genes that are homologs of the MLO gene as targeted by both Xcm strains in both cotton cultivars . These genes are excellent candidates for future biotechnology efforts . Because the potential importance of these genes in cotton biology is unknown , their role in cotton physiology must first be explored . Knock-out mutations of MLO genes in other systems has led to durable resistance against powdery mildew as well as oomycetes and bacteria such as Xanthomonas [42 , 45] . The dual purpose of host susceptibility genes has been observed previously . For example , the rice Xa13 ( aka . Os8N3 and OsSWEET11 ) gene is required for pollen development but also targeted by a rice pathogen during infection [58] . Xa13 is a member of the clade III SWEET sugar transporters implicated in many pathosystems . In this case , the induction of Xa13 for pathogen susceptibility is mediated by a TAL effector . Of the 54 SWEET genes in the G . hirsutum genome , at least three are significantly upregulated during Xcm infection . In contrast to MLO , no single SWEET gene was induced by both pathogen strains in both hosts . Analysis of SWEET gene expression after inoculation revealed a context for polyploidy in the G . hirsutum-Xcm pathosystem . This relatively unexplored area of plant-microbe interactions arose from our observation of a potential difference in induction magnitude between the homeologous Gh_A12G1747 and Gh_D12G1898 SWEET genes . Further analysis revealed many examples of preferentially induced or down-regulated homeologs in response to Xcm infection . Characterization of sub-genome specialization may lead to new insights regarding durability of resistance and susceptibility loci in polyploid crops . Future research may investigate the diploid ancestors of tetraploid cotton to further explore the evolution of host and pathogen in the context of ploidy events [59] . Multiple putative TAL effector binding sites were identified within each up-regulated SWEET promoter . These observations suggest that TAL M28a from MS14003 may induce the homeologs Gh_A04G0861 and Gh_D04G1360 . Further , TAL effector A14a from AR81009 is likely responsible for the upregulation of Gh_D12G1898 . Whether additional TAL effectors are involved in these responses is not clear . Genome organization in the host , such as histone modifications or other epigenetic regulations may also be affecting these interactions . Future research will investigate these mechanisms further . Collectively , the data presented here suggest that the wide-spread planting of CBB-susceptible cultivars has contributed to the re-emergence of CBB in the southern U . S . It is possible that a reservoir of race 18 Xcm was maintained in cotton fields below the level of detection due to resistant cultivars planted in the 1990s and early 2000s . Alternatively , the pathogen may have persisted on an alternate host or was re-introduced by contaminated seed [9 , 10] . Regardless of the cause of the re-emergence , the genomic comparisons among pathogen races and host cultivars has identified several possible routes towards resistance . These include the use of existing effective resistance loci as well as the potential disruption of the induction of susceptibility genes through genome editing . The latter is an attractive strategy in part because of recent progress in genome editing [60 , 61] . In summary , within a relatively short time frame , through the deployment of modern molecular and genomic techniques , we were able to identify factors that likely contribute to the re-emergence of cotton bacterial blight and generate data that can now be rapidly translated to effective disease control strategies .
New Xcm strains were isolated from infected cotton leaves by grinding tissue in 10mM MgCl2 and culturing bacteria on NYGA media . The most abundant colony type was selected , single colony purified and then 16S sequencing was used to confirm the bacterial genus as previously described [62] . In addition , single colony purified strains were re-inoculated into cotton leaves and the appearance of water soaked symptoms indicative of CBB infection was confirmed . Both newly isolated strains as well as strains received from collaborators were used to generate a rifampicin resistance version of each strain . Wildtype strains were grown on NYGA , then transferred to NYGA containing 100μg/ml rifampicin . After approximately 4–5 days , single colonies emerged . These were single colony purified and stored at -80C . The rifampicin resistant version of each Xcm strain was used in all subsequent experiments reported in this manuscript unless otherwise noted . Cotton varieties from the original cotton panel for determining Xcm race designations were obtained from the USDA/ARS , Germplasm Resources Information Network ( GRIN ) . Varieties included in the G . hirsutum NAM population were provided by Vasu Kuraparthy [33] . Other commercial varieties were obtained from Terry Wheeler and Tom Allen . Disease assays were conducted in a growth chamber set at 30°C and 80% humidity . Xcm strains were grown on NYGA plates containing 100μg/ml rifampicin at 30°C for two days before inoculations were performed . Inoculations were conducted by infiltrating a fully expanded leaf with a bacterial solution in 10mM MgCl2 ( OD600 specified within each assay ) . The field tests were conducted as follows: Cotton cultivars are planted in two row plots ( 10–11 m in length , 1 m row spacing ) , in a randomized complete block design with four replications . Approximately 60 to 80 days after planting , Xcm was applied to the test area similar to that described in Wheeler et al . ( 2007 ) [37] . Briefly , Xcm is grown in trypticase soy broth ( 30 g/L ) for 1 ½ days and then 19 L of the concentrated bacterial solution ( 108 cfu/ml ) are diluted into 189 L of water ( resulting in 106 cfu/ml ) . The surfactant Silwet L-77 ( polyalkyleneoxide modified heptamethyltrisiloxane , Loveland Industries , Greely , CO ) is added at 0 . 2% v/v . The suspension of bacteria are sprayed over the top of the cotton at a pressure of 83 kpa and rate of 470 L/ha . The nozzles used were TeeJet 8008 . Symptoms were typically visible 14 days after application and plots were rated for incidence of symptoms 17–21 days after application [34–37] . Area of cotton planted per county in the United States in 2015 was obtained from the USDA National Agricultural Statistics Service: www . nass . usda . gov/Statistics_by_Subject/result . php ? 7061F36A-A4C6-3C65-BD7F-129B702CFBA2§or=CROPS&group=FIELD%20CROPS&comm=COTTONUSDA . Estimated percentage of upland cotton planted for each variety was obtained from the Agricultural Marketing Service ( AMS ) : www . ams . usda . gov/mnreports/canvar . pdf . Illumina based genomic datasets were generated as previously described [29] . Paired-end Illumina reads were trimmed using Trimmomatic v0 . 32 ( ILLUMINACLIP:TruSeq3-PE . fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 ) [63] . Genome assemblies were generated using the SPAdes de novo genome assembler [64] . Strain information is reported in Supplemental Table 1 . Similar to our previously published methods [29] , the program Prokka was used in conjunction with a T3E database to identify type three effector repertoires for each of the 12 Xcm isolates as well as four Xcm genomes previously deposited on NCBI ( S2 Table ) [65] . Multi-locus sequence analysis was conducted by concatenating sequences of the gltA , lepA , lacF , gyrB , fusA and gap-1 loci obtained from the Plant-Associated Microbes Database ( PAMDB ) for each strain as previously described [66] . A maximum-likelihood tree using these concatenated sequences was generated using CLC Genomics 7 . 5 . A variant based dendrogram was created by comparing 12 Illumina sequenced Xcm genomes to the complete Xanthomonas citri subsp . citri strain Aw12879 reference genome ( Genbank assembly accession: GCA_000349225 . 1 ) on NCBI . Read pairs were aligned to the reference genome using Bowtie2 v2 . 2 . 9 with default alignment parameters [27] . From these alignments , single nucleotide polymorphisms ( SNPs ) were identified using samtools mpileup v1 . 3 and the bcftools call v1 . 3 . 1 multi-allelic caller [28] . Using Python v2 . 7 , the output from samtools mpileup was used to identify loci in the X . citri subsp . citri reference genome with a minimum coverage of 10 reads in each Xcm genome used Python version 2 . 7 available at http://www . python . org . Vcftools v0 . 1 . 14 and bedtools v2 . 25 . 0 were used in combination to remove sites marked as insertions or deletions , low quality , or heterozygous in any of the genomes [67 , 68] . Remaining loci were concatenated to create a FASTA alignment of confident loci . Reference loci were used where SNP's were not detected in a genome . The resulting FASTA alignment contained 17853 loci per strain . This alignment was loaded into the online Simple Phylogeny Tool from the ClustalW2 package to create a neighbor joining tree of the assessed strains [69 , 70] . Trees were visualized using FigTree v1 . 4 . 2 . Single Molecule , Real Time ( SMRT ) sequencing of Xcm strains MS14003 and AR81009 was obtained from DNA prepped using a standard CTAB DNA preparation . Blue Pippin size selection and library preparation was done at the University of Deleware Sequencin Facility . The genomes were assembled using FALCON-Integrate ( https://github . com/PacificBiosciences/FALCON-integrate/commit/cd9e93 ) [71] . The following parameters were used: Assembly parameters for MS14003: length_cutoff = 7000; length_cutoff_pr = 7000; pa_HPCdaligner_option = -v -dal8 -t16 -e . 70 -l2000 -s240 -M10; ovlp_HPCdaligner_option = -v -dal8 -t32 -h60 -e . 96 -l2000 -s240 -M10; falcon_sense_option = —output_multi—min_idt 0 . 70—min_cov 5—local_match_count_threshold 2—max_n_read 300—n_core 6; overlap_filtering_setting = —max_diff 80—max_cov 160—min_cov 5—bestn 10; Assembly parameters for AR81009: length_cutoff = 8000; length_cutoff_pr = 8000; pa_HPCdaligner_option = -v -dal8 -t16 -e . 72 -l2000 -s240 -M10; ovlp_HPCdaligner_option = -v -dal8 -t32 -h60 -e . 96 -l2000 -s240 -M10; falcon_sense_option = —output_multi—min_idt 0 . 72—min_cov 4—local_match_count_threshold 2—max_n_read 320—n_core 6; overlap_filtering_setting = —max_diff 90—max_cov 300—min_cov 10—bestn 10 . Assemblies were polished using iterations of pbalign and quiver , which can be found at https://github . com/PacificBiosciences/pbalign/commit/cda7abb and https://github . com/PacificBiosciences/GenomicConsensus/commit/43775fa . Two iterations were run for Xcm strain MS14003 and 3 iterations for AR81009 . Chromosomes were then reoriented to the DnaA gene and plasmids were reoriented to ParA . The assemblies were checked for overlap using BLAST , and trimmed to circularize the sequences [72] . TAL effectors were annotated and grouped by RVD sequences using AnnoTALE [41] . Homologous regions among plasmids that are greater than 1 kb were determined using progressiveMauve [39] . Genomic comparisons between the MS14003 and AR81009 chromosomes were visualized using Circos [73] . Single-copy genes on each of the chromosomes were identified and joined using their annotated gene IDs . Lines connecting the two chromosomes represent these common genes and their respective positions in each genome . A sliding window of 1KB was used to determine the average GC content . Methylation was determined using the Base Modification and Motif Analysis workflow from pbsmrtpipe v0 . 42 . 0 at https://github . com/PacificBiosciences/pbsmrtpipe . Western Blot analysis of Transcription Activator-Like ( TAL ) effectors was performed using a polyclonal TAL specific antibody [40] . Briefly , bacteria were suspended in 5 . 4 pH minimal media for 4 . 5 hours to induce effector production and secretion . Bacteria were pelleted and then suspended in laemmli buffer and incubated at 95 degrees Celsius for three minutes to lyse the cells . Freshly boiled samples were loaded onto a 4–6% gradient gel and run for several hours to ensure sufficient separation of the different sized TAL effectors . Susceptible cotton were inoculated with Xcm using a needleless syringe at an OD600 of 0 . 5 . Infected and mock-treated tissue were collected and flash frozen at 24 and 48 hours post inoculation . RNA was extracted using the Sigma tRNA kit . RNA-sequencing libraries were generated as previously described [74] . Raw reads were trimmed using Trimmomatic [63] . The Tuxedo Suite was used for mapping reads to the TM-1 NBI Gossypium hirsutum genome [75] , assembling transcripts , and quantifying differential expression [27] . Read mapping identified several mis-annotated SWEET genes that skewed differential expression results . The annotations of SWEET genes Gh_A12G1747 , Gh_D07G0487 , and Gh_D12G1898 were shortened to exclude 20-30kb introns . Two exons were added to Gh_D05G1488 . The 2 . 7kb scaffold named Scaffold013374 was also removed from analysis because its gene Gh_Sca013374G01 has exact sequence homology to Gh_A12G1747 and created multi-mapped reads that interfered with expression analysis . Homeologous pairs were identified based on syntenic regions with MCScan [76] . A syntenic region was defined as a region with a minimum of five genes with an average intergenic distance of two and within extended distance of 40 . All other values were set to the default . Comparisons between homeologs was performed by examining cuffdiff differential expression and classifying them according to the sub-genome expression pattern . Genes considered up or down regulated meet both differential expression from mock significance of q-value < 0 . 05 and the absolute value of the log2 fold change is greater than 2 . Bioinformatic prediction of TAL effector binding sites on the G . hirsutum promoterome was performed using the TAL Effector-Nucleotide Targeter ( TALEnt ) [50] . In short , the regions of the genome that were within 300 basepairs of annotated genes were queried with the RVD’s of MS14003 and AR81009 using a cutoff score of 4 . Promiscuously binding TALs 16 from MS14003 and 16a from AR81009 were removed from analysis .
|
Cotton bacterial blight ( CBB ) , caused by Xanthomonas citri pv . malvacearum ( Xcm ) , significantly limited cotton yields in the early 20th century but has been controlled by classical resistance genes for more than 50 years . In 2011 , the pathogen re-emerged with a vengeance . In this study , we compare diverse pathogen isolates and cotton varieties to further understand the virulence mechanisms employed by Xcm and to identify promising resistance strategies . We generate fully contiguous genome assemblies for two diverse Xcm strains and identify pathogen proteins used to modulate host transcription and promote susceptibility . RNA-Sequencing of infected cotton reveals novel putative gene targets for the development of durable Xcm resistance . Together , the data presented reveal contributing factors for CBB re-emergence in the U . S . and highlight several promising routes towards the development of durable resistance including classical resistance genes and potential manipulation of susceptibility targets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"&",
"methods"
] |
[
"taxonomy",
"phylogenetics",
"data",
"management",
"fiber",
"crops",
"genome",
"analysis",
"crops",
"plants",
"flowering",
"plants",
"bacteria",
"genomic",
"libraries",
"computer",
"and",
"information",
"sciences",
"crop",
"science",
"gene",
"expression",
"evolutionary",
"systematics",
"cotton",
"agriculture",
"eukaryota",
"genetics",
"biology",
"and",
"life",
"sciences",
"xanthomonas",
"genomics",
"evolutionary",
"biology",
"computational",
"biology",
"organisms"
] |
2017
|
Genomics-enabled analysis of the emergent disease cotton bacterial blight
|
The premotor theory of attention postulates that spatial attention arises from the activation of saccade areas and that the deployment of attention is the consequence of motor programming . Yet attentional and oculomotor processes have been shown to be dissociable at the neuronal level in covert attention tasks . To investigate a potential dissociation at the behavioral level , we instructed human participants to move their eyes ( saccade ) towards 1 of 2 nearby , competing saccade targets . The spatial distribution of visual attention was determined using oriented visual stimuli presented either at the target locations , between them , or at several other equidistant locations . Results demonstrate that accurate saccades towards one of the targets were associated with presaccadic enhancement of visual sensitivity at the respective saccade endpoint compared to the nonsaccaded target location . In contrast , averaging saccades , landing between the 2 targets , were not associated with attentional facilitation at the saccade endpoint . Rather , attention before averaging saccades was equally deployed at the 2 target locations . Taken together , our results reveal that visual attention is not obligatorily coupled to the endpoint of a subsequent saccade . Rather , our results suggest that the oculomotor program depends on the state of attentional selection before saccade onset and that saccade averaging arises from unresolved attentional selection .
To process information from our rich visual environment , we evolved with attentional mechanisms allowing us to discriminate which flow to account for and which to ignore [1 , 2] . For example , we can extract salient saccade targets from a cluttered visual scene to later examine their contents with precise foveal vision [3–6] . This link between attention and saccadic eye movements led researchers to propose that spatial visual attention is directly dependent on the oculomotor system [7 , 8] , introducing what they called the “premotor theory of attention . ” This influential theory relies on 2 main hypotheses . The first hypothesis states that visual attention is operated by the oculomotor system itself . Indeed , overlapping neuronal activations have been observed in visual attention tasks involving the deployment of attention with ( overt ) or without ( covert ) eye movements in functional magnetic resonance imaging ( fMRI ) [9] . These activations include cortical and subcortical areas such as the Frontal Eye Field ( FEF ) , the parietal cortex , and the Superior Colliculus ( SC ) . At the behavioral level , there is indeed evidence for a concurrent encoding of spatial attention and saccade programming [10] . For example , various studies demonstrated that visual attention , measured as a local improvement in visual sensitivity , is allocated to the saccade target before the eyes start to move [11 , 12] . Nevertheless , some other studies suggested that saccade preparation does not necessarily entail a shift of attention towards the saccade goal , casting some doubt in regard of the coupling between attention and oculomotor control [13–16] . The second hypothesis of the premotor theory of attention implies that the deployment of visual attention is always preceded by an activation of the oculomotor system . Under this hypothesis , covert attention involves the preparation of a saccade that is canceled before the eyes move . In line with this hypothesis , subthreshold microstimulation of the FEF or the SC , which did not systematically lead to a saccade , resulted in attentional benefits measured both behaviorally and electrophysiologically at the stimulated movement field position [17–20] . However , because microstimulation effects cannot be solely restricted to the motor cells within the stimulated areas , these results did not demonstrate that the deployment of visual attention is preceded by a premotor activation alone . Instead , it was shown that motor cells within FEF or SC stayed completely silent during a covert attention task [21–23] , while visual and visuomotor cells displayed sustained attentional effects . In other words , attention is not always preceded by motor activity , at least not within these recorded oculomotor centers . To shed light on this controversy and to test this second hypothesis at the behavioral level , one can imagine measuring visual sensitivity at the intended saccade goal and at the endpoint of the saccade . Under such conditions , measured sensitivity should correlate with the activity of both the visual and motor cells within oculomotor centers . Taking advantage of the fact that saccades tend to undershoot the target , Deubel and Schneider [12] found that attention was restricted to the intended saccade goal rather than to the saccade endpoint . However , using saccadic adaptation to decrease the saccadic gain , some authors found the exact opposite effect , with attention allocated to the adapted saccade endpoint rather than to the intended saccade goal [24 , 25] . Knowing that oculomotor centers have several overlapping large receptive fields within the range of these effects [26 , 27] , it is hard to link these contradictory behavioral findings to the neurophysiology described above . Here , we thus propose to use a paradigm leading to a larger spatial dissociation between the intended saccade goal and the saccade endpoint , such as the global effect [28–31] . Indeed , the global effect is associated with systematic and large saccade endpoint deviations towards the center of gravity of 2 saccade targets [28 , 32 , 33] , or of a saccade target and a distractor [34 , 35] , shown at 2 positions separated by up to 60° of rotation [34] . Although the global effect was originally described as reflecting a low-level averaging of neuronal activity ( and therefore respective saccades are often called averaging saccades ) within the oculomotor centers [28 , 36 , 37] , different behavioral observations later suggested a dependency on higher-level attentional processes . First , it was shown that averaging saccades can be elicited by second- and third-order saccade targets [38 , 39] , suggesting that the global effect cannot merely reflect low-level oculomotor processes . Next , it was shown that specifying the location [40 , 41] , the identity [42 , 43] , or the probability of a saccade target to appear at a certain location relative to a distractor [44] systematically reduced the occurrence of averaging saccades . Monkeys make averaging saccades when the FEF or the SC are simultaneously microstimulated at 2 sites [45–48] and when 2 targets are shown in close proximity [49 , 50] . At the neuronal level , it was first proposed that a single peak of motor cell activity associated with saccades ending in between 2 targets precedes an averaging saccade [51 , 52] . Later work suggested instead that averaging saccades follow 2 peaks of activity associated with saccades directed towards the 2 saccade targets [53 , 54] . Recently , Vokoun and colleagues [55] used voltage imaging of slices of rat SC to record population dynamics in response to dual-site electrical stimulation . They observed that the simultaneous stimulation of 2 nearby sites in the intermediate layers led to a merged peak centered in between them in the superficial layers . Moreover , they proposed that such merged activation feeds back into the visual system , leading to the perception of a target at the averaging saccade endpoint . If this proposal of a feedback of merged activation from the superficial layers of the SC into the visual system was true , we would expect to find a presaccadic enhancement of attention at the endpoint of averaging saccades , a result that would be in line with the premotor theory of attention . Van der Stigchel and de Vries [56] directly tested this proposal , instructing participants to move their eyes towards a saccade target presented simultaneously with a distractor and measuring presaccadic attention at these positions as well as in between them . They observed both averaging saccades as well as saccades directed towards the target and the distractor , allowing them to compare the deployment of attention at the intended saccade goal and at the saccade endpoint . Unfortunately , they reported no main effect of the saccade landing direction as well as no interaction between the saccade landing direction and the position of their attention probes when analyzing visual discrimination performance as a function of the saccade endpoint . Therefore , contrary to many reports [11 , 12] , the saccade landing position had no significant effect on the deployment of attention in their paradigm , preventing any conclusion about whether or not attention is deployed at the endpoint of averaging saccades . Other studies suggested that attention is not necessarily allocated to the saccadic endpoint [11 , 44] or argued that saccades towards the center of gravity within extended target configurations are based on the computation of a central reference point via spatial pooling [57 , 58] . However , none of these studies measured visual attention at the averaging saccade endpoint to determine whether averaged oculomotor programs are associated with attentional averaging . Here , we measured visual attention at various locations in space , including the averaging saccade endpoint , in a free-choice saccade task that entailed the presentation of 2 nearby saccade targets . Our design therefore allowed us to investigate whether attention is allocated at the endpoint of averaging saccades . More specifically , given the spatial resolution of our design , we could distinguish the following 3 possible outcomes related to the deployment of visual attention before averaging saccades: ( a ) attention is deployed at the exact location of the saccade endpoint , ( b ) attention spreads across an extended area including the saccade endpoint , and ( c ) attention is deployed at 2 discrete saccade target areas flanking the saccade endpoint but not at the endpoint itself . We observed a presaccadic enhancement of visual sensitivity at the endpoint of accurate but not averaging saccades , ruling out an obligatory coupling of attention to the endpoint of a subsequently executed saccade ( against [a] ) . Contrary to the idea of an extended spread of attention around the center of gravity , averaging saccades were associated with moderate enhancement of visual sensitivity at the 2 saccade targets ( against [b] ) . Our results instead suggest that the oculomotor program depends on the state of attentional selection before saccade onset , with attention being deployed at the 2 discrete targets ( favoring [c] ) and saccade averaging resulting from uncompleted attentional selection .
Our goal was to determine whether the presaccadic deployment of attention is obligatorily coupled to the saccade endpoint . To do so , we probed visual attention at various locations while participants prepared a saccade towards 1 of 2 potential saccade targets , presented either transiently or continuously and separated by an intertarget angular distance of either 90° or 30° ( Fig 1A ) . Just before the saccade , a discrimination target was shown randomly across trials at 1 of the 2 potential saccade targets ( ST1 and ST2 ) , at the position in between the saccade targets ( BTW ) , or at 1 of 21 equidistant control positions ( CTRL ) . Fig 1B shows the normalized frequency of saccade landing endpoints observed across participants within the 90° and 30° condition , irrespective of the duration of the saccade targets ( i . e . , transient and continuous combined ) . While saccades were equally distributed over the 2 saccade targets in the 90° condition ( Fig 1B , top ) , a substantial proportion of saccades ended in between them in the 30° condition ( Fig 1B , bottom ) . To further analyze our data , we looked at the distribution of saccade landing directions either binned in evenly distributed angular sectors of 5° ( Fig 2A and 2B ) or 15° ( centered on the 24 stimuli streams , Fig 2C and 2D ) . In the 90° condition ( Fig 2C ) , 41 . 0% ± 1 . 0% of the saccades ended within the sector including ST1 ( most counterclockwise saccade target ) and 41 . 8% ± 1 . 9% within the sector including ST2 ( most clockwise saccade target ) . Note that an average of 4 . 0% ± 0 . 9% of saccades ended within the sectors adjacent to the saccade targets . In the 30° condition ( Fig 2D ) , 33 . 6% ± 2 . 4% of the saccades ended within the sector in between the 2 saccade targets ( BTW ) , while 29 . 95 ± 1 . 6% of the saccades ended within the sector of ST1 and 32 . 0% ± 1 . 8% within the sector of ST2 . Therefore , when participants had to select between 2 equidistant saccade targets separated by an angular distance of 30° , they executed an averaging saccade ( ending in the BTW sector ) in about one-third of the trials . For further inspection , saccade endpoint distributions as a function of saccade latency are provided for each participant in S1 Fig . In order to determine potential differences between the 2 intertarget angular distance conditions ( 90° and 30° ) , we first looked at saccade latencies and amplitudes . We found slightly longer saccade latencies ( 90°: 192 . 2 ± 1 . 7 ms versus 30°: 188 . 2 ± 2 . 2 ms; p = 0 . 0012 ) and larger amplitudes ( 90°: 10 . 0 ± 0 . 1° versus 30°: 9 . 7 ± 0 . 1°; p = 0 . 0002 ) in the 90° as compared to the 30° condition . Saccade latency did not differ as a function of the saccade landing position ( ST1 , ST2 , or BTW ) both in the 90° and 30° condition ( all p > 0 . 05 , Fig 2E and 2F ) . In the 90° condition , amplitudes of saccades towards ST1 ( 10 . 1 ± 0 . 1° ) and ST2 ( 10 . 0 ± 0 . 1° ) did not differ significantly from each other ( ST1 versus ST2: p = 0 . 7902 ) , whereas amplitudes of saccades towards BTW ( 7 . 9 ± 0 . 2° ) were significantly smaller than those of saccades towards ST1 and ST2 ( both p < 0 . 0001 ) ( see Fig 2G ) . In the 30° condition , amplitudes of saccades towards ST1 ( 9 . 7 ± 0 . 1° ) and ST2 ( 9 . 8 ± 0 . 1° ) , as well as towards ST1 and BTW ( 9 . 7 ± 0 . 1° ) , did not differ significantly from each other ( ST1 versus ST2: p = 0 . 2216; ST1 versus BTW: p = 0 . 5998 ) , whereas amplitudes of saccades towards ST2 were significantly larger than those of saccades towards BTW ( ST2 versus BTW: p = 0 . 0118 ) ( see Fig 2H ) . Note that the proportion of averaging saccades did not vary as a function of saccade latency . Comparing trials of the 30° condition separated in 2 equal groups of early ( 167 . 1 ± 1 . 8 ms ) and late ( 209 . 3 ± 3 . 2 ms ) saccade latencies , we found a comparable proportion of averaging saccades ( early BTW: 35 . 1 ± 3 . 0% versus late BTW: 32 . 1 ± 2 . 2%; p = 0 . 1632 ) . This effect is most likely the consequence of the instruction given to the participants to saccade as fast as possible , such that early and late averaging saccade latencies differed by less than 40 ms ( early BTW: 168 . 2 ± 2 . 0 ms versus late BTW: 207 . 4 ± 3 . 1 ms; p < 0 . 0001 ) . However , we found that the mean absolute saccade endpoint deviation relative to the BTW location slightly increased as a function of saccade latency ( see A-B in S2 Fig and A-B in S2 Fig for individual participant data for both the 90° and 30° conditions ) . Thus , saccade averaging was more pronounced for short-latency saccades . Overall , for each intertarget angular distance , we observed either no differences or only some nonsystematic differences of a few milliseconds and a few minutes of arc . Although saccade latencies and amplitudes did not differ much between these conditions , the saccade landing-direction distributions reflect 2 distinct oculomotor modes as a function of the intertarget angular distance . Saccades were mostly accurate in the 90° condition , whereas we observed both accurate and averaging saccades in the 30° condition . Our paradigm allowed us to measure both the oculomotor behavior and the presaccadic allocation of attention through the presentation of a discrimination target at 1 of 24 possible positions . We first verified that the presentation of the discrimination target itself did not systematically influence oculomotor behavior . We did not find any differences with respect to saccade latency and amplitude when comparing trials with and without the presentation of a discrimination target ( 3 . 5% of trials were without discrimination target , both p > 0 . 05 ) . This result validates that the distractor streams and , in particular , the presentation of a discrimination target did not bias the deployment of attention . Fig 3A and 3B shows visual sensitivity as a function of the discrimination target position rotated as to align the 2 saccade targets around the geometrical angle zero in both the 90° ( Fig 3A ) and 30° ( Fig 3B ) condition . Irrespective of the duration of the saccade targets , we found higher sensitivity for discrimination targets shown at the saccade targets than at the control positions ( corresponding to the average across all positions except for ST1 , ST2 , and BTW ) in both the 90° ( ST1: d’ = 2 . 2 ± 0 . 3 versus CTRL: d’ = 0 . 3 ± 0 . 1 , p < 0 . 0001; ST2: d’ = 2 . 2 ± 0 . 4 versus CTRL , p < 0 . 0001; ST1 versus ST2 , p = 0 . 8964; Fig 3A ) and the 30° ( ST1: d’ = 2 . 2 ± 0 . 3 versus CTRL: d’ = 0 . 3 ± 0 . 1 , p < 0 . 0001; ST2: d’ = 2 . 1 ± 0 . 3 versus CTRL , p < 0 . 0001; ST1 versus ST2 , p = 0 . 6026; Fig 3B ) condition . These effects contrast with the low sensitivity observed for discrimination targets shown in between the saccade targets ( BTW ) in the 90° ( BTW: d’ = 0 . 2 ± 0 . 1 versus ST1 , p < 0 . 0001; BTW versus ST2 , p < 0 . 0001 ) and especially in the 30° ( BTW: d’ = 0 . 6 ± 0 . 2 versus ST1 , p < 0 . 0001; BTW versus ST2 , p < 0 . 0001 ) condition . Thus , despite the fact that saccades landed in between the saccade targets in a third of the trials in the 30° condition , the overall sensitivity at this position stayed rather low . One should , however , note that sensitivity was still increased at this position compared to the control positions in the 30° condition ( 30°: BTW versus CTRL , p = 0 . 0010 ) , whereas this was not the case in the 90° condition ( 90°: BTW versus CTRL , p = 0 . 7732 ) . On the other hand , such slight facilitation observed in between the saccade targets in the 30° condition relative to the control positions was only observed for trials in which the targets were shown transiently ( BTW: d’ = 0 . 8 ± 0 . 2 versus CTRL: d’ = 0 . 3 ± 0 . 1 , p < 0 . 0001 ) but not continuously ( BTW: d’ = 0 . 5 ± 0 . 2 versus CTRL: d’ = 0 . 3 ± 0 . 0 , p = 0 . 10880 ) . It is important to note that the discrimination target temporally overlapped with the saccade targets in the continuous but never in the transient condition . The observed difference between the 2 conditions therefore suggests that the appearance of a discrimination target at BTW was masked by the continuous presentation of the saccade targets . Altogether , the results above demonstrate that presaccadic attention was mainly allocated towards the saccade targets , and to a much smaller extent towards the position in between . This last result , however , cannot be attributed to a large spread of attention extending to more than 1 of the tested directions because we did not observe a consistent benefit at the 2 other positions adjacent to the saccade targets in the 30° condition ( ST1 + 15°: d’ = 0 . 4 ± 0 . 1 versus CTRL: d’ = 0 . 3 ± 0 . 1 , p = 0 . 0914; ST2 − 15°: d’ = 0 . 4 ± 0 . 1 versus CTRL , p = 0 . 0336; here , CTRL excludes ST1 + 15° and ST2 − 15° , respectively , in addition to ST1 , ST2 , and BTW ) nor at the 4 adjacent positions of the saccade targets in the 90° condition ( ST1 ± 15°: d’ = 0 . 3 ± 0 . 1 versus CTRL: d’ = 0 . 2 ± 0 . 1 , p = 0 . 5742; ST2 ± 15°: d’ = 0 . 3 ± 0 . 1 versus CTRL , p = 0 . 3200; here , CTRL excludes ST1 ± 15° and ST2 ± 15° , respectively , in addition to ST1 , ST2 , and BTW ) . At that stage , one cannot exclude the possibility that attention is always drawn towards the saccade endpoint before both accurate and averaging saccades because we found higher sensitivity for both the saccade targets—and , in the 30° condition , also for the position in between them—compared to the control locations . Although we found higher sensitivity at the saccade targets than in between them , this may just reflect the combined effect of the saccade preparation and the presence of visual cues ( the saccade targets themselves ) . To estimate the effect of saccade preparation , we thus needed to specify our results depending on where the saccade ended within each trial . To do so , we redefined the position of the discrimination targets relative to the saccade direction . Fig 3C and 3D shows visual sensitivity as a function of the discrimination target position relative to the saccade direction . We found higher sensitivity for discrimination targets shown at the saccade targets when compared to the position in between them or to the control positions in both the 90° and 30° conditions , for trials in which accurate saccades were made towards ST1 ( all p < 0 . 0001 ) or ST2 ( all p < 0 . 0001 ) . The same effects were found for averaging saccades in the 30° condition ( all p = 0 . 00010 ) . In addition to the facilitation effect of the saccade target presentation , we found that , irrespective of the intertarget distance ( 90° or 30° ) , sensitivity at ST1 was improved when an accurate eye movement was made towards ST1 ( 90°: ST1: d’ = 3 . 2 ± 0 . 5 versus ST2: d’ = 1 . 7 ± 0 . 4 , p < 0 . 0001 [see blue lines and bars in Fig 3C and 3D]; note that in the 30° condition , sensitivity at ST1: d’ = 2 . 9 ± 0 . 4 was only marginally superior to those observed at ST2: d’ = 2 . 1 ± 0 . 5 , p = 0 . 0740 ) . The same selective improvement was observed at ST2 before the execution of accurate saccades towards it ( 90°: ST2 versus ST1 , p < 0 . 0001; 30°: ST2 versus ST1 , p = 0 . 0002 [see red lines and bars in Fig 3C and 3D] ) . In particular , preparing an accurate eye movement towards 1 of the 2 saccade targets improved sensitivity when comparing trials in which the discrimination target was shown at the saccaded location ( e . g . , DT at ST1 and saccade made towards ST1 ) to trials in which the discrimination target was shown at the same position when it was not the saccaded position ( e . g . , DT at ST1 and saccade landing at ST2 or BTW ) in both the 90° ( Fig 3E; ST1+2 saccaded: d’ = 3 . 0 ± 0 . 4 versus ST1+2 nonsaccaded: d’ = 1 . 7 ± 0 . 4 , p < 0 . 0001 ) and the 30° ( Fig 3F; ST1+2 saccaded: d’ = 2 . 7 ± 0 . 4 versus ST1+2 nonsaccaded: d’ = 2 . 0 ± 0 . 3 , p = 0 . 0080 ) condition . Crucially for averaging saccade trials , for which the intended saccade goal ( ST1 or ST2 ) and the saccade endpoint ( BTW ) were dissociated ( see green lines and bars in Fig 3D ) , we found a rather low sensitivity for discrimination targets shown in between the saccade targets ( BTW: d’ = 0 . 4 ± 0 . 2 ) , highly reduced when compared to discrimination targets shown at the saccade targets ( ST1: d’ = 2 . 2 ± 0 . 4 and ST2: d’ = 2 . 2 ± 0 . 4 , both p < 0 . 0001 ) . Furthermore , and contrary to above ( Fig 3B ) , it was not different from the sensitivity gathered across the control locations ( CTRL: d’ = 0 . 3 ± 0 . 1 , p = 0 . 4026 ) , both when the saccade targets were shown transiently or continuously ( both p > 0 . 05 ) . Thus , contrary to accurate saccades , the execution of averaging saccades did not lead to any improvement at the saccade endpoint . Moreover , a visual inspection of sensitivity as a function of the saccade latency shows a relative independence of these measures , suggesting that , irrespective of the saccade latency , attention was not deployed at the averaging saccade endpoint ( see C-D in S2 Fig and A-B in S4 Fig for individual participant data in the 90° and 30° conditions ) . Visual sensitivity was significantly reduced at the intermediate location ( BTW ) before averaging saccades compared to saccades that landed at 1 of the saccade targets ( Fig 3F; BTW saccaded: d’ = 0 . 4 ± 0 . 2 versus BTW nonsaccaded: d’ = 0 . 7 ± 0 . 2 , p < 0 . 0001 ) . This sensitivity reduction can , however , be mainly attributed to a masking effect of the continuous presentation of the saccade targets ( BTW saccaded: d’ = 0 . 3 ± 0 . 3 versus BTW nonsaccaded: d’ = 0 . 7 ± 0 . 2 , p = 0 . 0088 ) because it was not found for saccade targets presented transiently ( BTW saccaded: d’ = 0 . 7 ± 0 . 2 versus BTW nonsaccaded: d’ = 0 . 7 ± 0 . 3 , p = 0 . 9664 ) . These findings demonstrate , contrary to what is predicted by the premotor theory of attention , that the preparation of averaging saccades does not lead to a deployment of attention at the corresponding saccade endpoint . Instead , we found that averaging saccades were associated with an equal distribution of attention towards the 2 saccade targets ( ST1: d’ = 2 . 2 ± 0 . 4 versus ST2: d’ = 2 . 2 ± 0 . 4 , p = 0 . 8402 ) . One interpretation of these effects could be that averaging saccades result from an unsuccessful or at least uncompleted presaccadic attentional selection among the 2 saccade targets , with resources equally distributed between them . On the other hand , it is possible that , despite landing in between the targets , presaccadic attentional selection was successful before averaging saccades but directed half of the time towards the most clockwise saccade target and half of the time towards the most counterclockwise saccade target . If this were the case , across trials , one would also expect to find an equal and moderate enhancement of sensitivity for discrimination targets shown at the saccade targets . To disentangle these 2 interpretations , we analyzed trials in which a corrective saccade followed the execution of an averaging saccade . We reasoned that if averaging saccades resulted from a successful trial-by-trial presaccadic attentional selection of 1 of the 2 saccade targets , they should be followed by corrective saccades directed equally often towards both targets . Moreover , they should be associated with an attentional benefit at the goal of the corrective saccades . Contrary to these predictions , we observed corrective saccades in only 48 . 1% ± 5 . 8% of the averaging saccade trials . Corrective saccades were not all clearly directed towards the saccade targets ( see A-B in S5 Fig ) , ending either in the angular sector of the most counterclockwise saccade target ( ST1: 48 . 3% ± 3 . 1% of all the corrective saccades following an averaging saccade ) , the most clockwise saccade target ( ST2: 38 . 3% ± 2 . 5% ) , or in between them ( BTW: 11 . 9% ± 2 . 8% ) . They were , moreover , not equally often directed towards each of the saccade targets ( ST1 versus ST2 , p = 0 . 0288 ) , probably reflecting a bias of our participants . As shown in C in S5 Fig , we did not find any significant benefit at the endpoint of the corrective saccades following an averaging saccade , when comparing trials in which discrimination targets were shown at the endpoint of the corrective saccade ( ST1+2 correctively saccaded: d’ = 2 . 8 ± 0 . 5 ) to trials in which a discrimination target was shown at the same position when it was not the endpoint of the corrective saccade ( ST1+2 correctively nonsaccaded: d’ = 2 . 5 ± 0 . 8 , p = 0 . 68300 ) . Moreover , no significant benefit could be found when the corrective saccades following an averaging saccade ended still in between the saccade targets ( BTW correctively saccaded: d’ = 0 . 7 ± 1 . 1 versus BTW correctively nonsaccaded: d’ = −0 . 1 ± 0 . 6 , p = 0 . 4698 ) . Taken together , these results suggest that averaging saccades result from an unsuccessful or uncompleted presaccadic attentional selection among the 2 saccade targets . Finally , we wanted to exclude the possibility that the poor discrimination performance at the endpoint of averaging saccades was a result of the rather coarse saccade direction binning used in our analysis ( ±7 . 5° of rotation around ST1 , BTW , ST2 , and the distractor locations ) . We chose this binning procedure to end up with 24 equal saccade direction bins centered on the locations at which we measured visual sensitivity . Nevertheless , one might argue that we thereby classified a substantial proportion of saccades as averaging saccades ( landing within the BTW bin ) despite the possibility that they were actually biased towards 1 of the saccade targets and landed in the outer areas of the bin . To validate our analysis , we analyzed visual sensitivity as a function of the saccade direction using smaller bins ( ±2 . 5° ) . As evident in S6 Fig , in which we contrast the data for these 2 binning procedures , the smaller binning did not systematically alter our results . Crucially , we still found low visual sensitivity at BTW even for the proportion of saccades landing precisely at the most central bin ( i . e . , within ±2 . 5° around the center of BTW ) .
We observed a clear oculomotor dissociation between trials in which 2 equidistant saccade targets were shown at 2 different angular distances from each other . While only accurate saccades were found for an intertarget angular distance of 90° , we observed both accurate and averaging saccades when the same targets were separated by 30° . Combined with a measure of presaccadic visual sensitivity , this dissociation allowed us to determine the influence of saccade preparation on the deployment of attention when the intended saccade goal and the saccade endpoint were spatially associated ( accurate saccades ) or clearly dissociated from each other ( averaging saccades ) . Accurate saccades were associated with a strong and systematic presaccadic enhancement of visual sensitivity at the saccade endpoint when compared to the nonsaccaded locations for intertarget angular distances of both 90° and 30° . In contrast , we did not observe a presaccadic enhancement of visual sensitivity at the endpoint of averaging saccades . Rather , averaging saccades were associated with an equal deployment of attention at the 2 saccade target locations . Our corrective saccade analysis indicated that this result cannot be explained by a trial-by-trial presaccadic attentional selection of 1 of the 2 saccade targets . Overall , these effects rule out the proposal that the deployment of attention is strictly derived from the upcoming oculomotor program . Rather , they reflect a spatial dissociation between the deployment of visual attention and the averaging saccade endpoint . More specifically , these results rule out an account in which attention is precisely allocated to the saccade endpoint ( alternative [a] in Introduction ) or spreads over an extended region including the saccade endpoint before averaging saccades ( alternative [b] in Introduction ) . Our data instead favor an account in which attention is equally allocated at 2 discrete saccade target locations before averaging saccades ( alternative [c] in Introduction ) . Contrary to the idea that the activation of the oculomotor system precedes spatial attention , we propose that the oculomotor program depends on the state of attentional selection before the saccade , with averaging saccades arising from an uncompleted attentional selection process . Findlay [28] referred to the "global effect" as the phenomenon of directing the eyes towards the center of gravity of 2 presented targets [29] . To his view , this phenomenon reflects a coarse or global processing of a visual scene before rapidly generated eye movements . His account thus predicts that in our experiment , visual sensitivity should be coarsely distributed over the 2 saccade targets as well as over their adjacent locations before the execution of averaging saccades . Our precise measure of presaccadic visual sensitivity allowed us to determine the spatial specificity of attentional deployment during saccade preparation . Contrary to the notion of a global processing ( including the locations at the saccade targets and in between ) before averaging saccades , we observed a precise allocation of attention limited only to the saccade targets ( limited to at least approximately 2 . 6° , the distance between 2 of our adjacent stimuli ) . Therefore , before an averaging saccade , the visual system indeed seems to have precise access to the saccade target configuration , reflecting an enhancement of local rather than global visual information processing [59] . Such a discontinuous deployment of attention was also found in various tasks entailing the presentation of multiple targets [60–62] . Our results can also rule out other models of averaging saccades based solely on low-level oculomotor processing [36 , 37 , 63] . We report here that when an accurate saccade is prepared towards 1 of 2 identical saccade targets , the subsequent movement correlates with an attentional benefit at the saccade endpoint , whereas averaging saccades resulted in the absence of a selective attentional benefit at 1 of the 2 targets as well as in between them ( i . e . , at the saccade endpoint ) . In this regard , our results match with previous studies showing a reduction in the occurrence of averaging saccades when attentional selection of the saccade goal is made easier by specifying its location or its identity [40–44] . Similarly , a model relying on attentional selection could also explain why averaging saccades are less often observed in delayed saccade tasks [40 , 64] , as they also give more time for the attentional selection to complete [43] . Early studies have often reported that averaging saccades are associated with faster saccade latencies as compared to accurate saccades [28 , 34] . Yet , recently , Weaver , Zoest , and Hickey [65] proposed that the spatial and temporal components of saccade programming are relatively independent from each other . They argued that attentional mechanisms can affect oculomotor behavior only when acting upon it before the onset of the movement . It might well be that our instructions to saccade as fast and as accurately as possible reduced the saccade latency range and thereby reduced potential differences between the latencies of accurate and averaging saccades . Furthermore , given that participants were engaged in a dual task , the attentional task might have slowed down saccade execution , leading to averaging saccades even at longer latencies . We propose that the type of saccade executed on a given trial was determined by the speed at which attentional selection was processed . Accordingly , accurate saccades were presumably executed whenever attentional selection of a target was readily resolved before saccade onset . Another account of the global effect is that averaging saccades reflect a time-saving strategy [40] , in which an averaging saccade followed by a correction movement allows for faster oculomotor action than a deliberately delayed accurate saccade . Given that participants saccaded accurately towards one of the targets with a similar latency as found for averaging saccades in two-thirds of the trials in our paradigm , our results speak against such a strategy . Although we observed some corrective saccades that ended nearby the saccade targets and therefore increased the accuracy of initial averaging saccades , they came with a cost of about 200 ms , rendering such strategy inefficient . Moreover , if participants would have strategically planned 2 successive saccades ( an averaging saccade followed by a corrective saccade ) , we would expect to find attentional benefits at both saccade endpoints as reported in sequential saccade tasks [62 , 66] . Contrary to this prediction , we found neither an attentional enhancement at the endpoint of averaging saccades nor at the endpoint of corrective saccades compared to the positions not reached by corrective saccades . Therefore , our results argue against earlier accounts of the global effect and propose that averaging saccades reflect a compromise between the dynamics of attentional selection and the instructions to move the eyes as fast as possible . Our proposal is based on the results of a combined measure of visual attention and averaging saccades . Similar to a previous report [56] , we found an overall enhancement of visual sensitivity at the 2 saccade targets , when the data were not split depending on the saccade direction . In order to conclude on the deployment of attention before averaging saccades , however , one needs to specify visual sensitivity depending on the saccade direction . Crucially , and contrary to Van der Stigchel and de Vries [56] , we indeed found an influence of the saccade direction ( i . e . , endpoint ) on the allocation of attention when taking into account saccade direction . Within a paradigm producing both accurate and averaging saccades , we observed a presaccadic shift of attention [11 , 12] , reflected by selectively enhanced sensitivity at the endpoint of accurate saccades . The replication of this presaccadic attention effect comes as a prerequisite to drawing conclusions on the effect of averaging saccades , for which , instead , we found no attentional benefit at the saccade endpoint . Van der Stigchel and de Vries [56] concluded that there is no attentional shift towards the endpoint of averaging saccades . However , they also reported no main effect of the saccade landing direction as well as no interaction between the saccade landing direction and the position of their attention probes when they analyzed their data as a function of the saccade endpoint . Their results are therefore inconclusive , or even speak in favor of an attentional global effect . Moreover , when we combined all trials irrespective of the saccade direction , we found a slight increase of sensitivity at the position in between the 2 potential saccade targets when they were presented transiently but not when they were presented continuously . Because Van der Stigchel and de Vries [56] used a continuous presentation of a saccade target and a distractor , their results most likely reflect a masking effect of their stimuli on the discrimination target rather than an absence of attentional modulation . Here , we clearly dissociated attention allocated to the intended saccade goal from attention allocated to the endpoint of the saccade and found no benefit at the averaging saccade endpoint . This result is theoretically consistent with the idea that attention is not restricted to the endpoint of a saccade [11 , 44] and provides behavioral evidence against the main hypothesis of the premotor theory of attention , which postulates that the deployment of visual attention is derived from oculomotor programming [7 , 8] . We illustrate our results in a theoretical framework ( Fig 4 ) , inspired by both behavioral and neurophysiological findings , linking visual attention and oculomotor programming [67] . This theoretical framework neither provides a strict model nor a computational framework . It aims at putting our results in the context of the current view on saccade programming and yielding new testable predictions . We propose that our attentional effects rely on a top-down modulation [5 , 19] of feature-selective areas of the visual cortex by the priority maps [68] . Initially , the onsets of the saccade targets strongly activate neurons with corresponding receptive fields within columns of the feature and priority maps ( Fig 4A ) . Their activity will then decay until the saccade target-selection process begins . We propose that , before an accurate saccade , one of the saccade targets is selected , such that oculomotor cells centered on the saccaded location become more active in comparison to those encoding the nonsaccaded target location ( Fig 4B ) . Because our 2 targets were physically identical , saccade target selection probably occurs within the priority maps and propagates via a top-down mechanism to the corresponding feature map columns [5 , 69–71] . Oculomotor cells within the priority maps are connected to the areas of the brainstem circuitry controlling the horizontal ( e . g . , pons and medulla ) and vertical ( e . g . , rostral midbrain ) components of an eye movement [72 , 73] . Given that only 1 saccade can be executed at a time , a winner-takes-all integration of the motor output [47 , 74 , 75] from the priority maps is typically assumed such that the most active population will determine the subsequent saccade vector . The exact nature of this integration is , however , beyond the scope of this study . Thus , in our framework , an accurate saccade towards the selected saccade target ( i . e . , the saccade target that is represented as the most active population at the level of the priority maps ) is triggered by the saccade generator , and the activity state within the feature maps leads to higher sensitivity at the saccade endpoint before the eyes start to move ( Fig 4B ) . Following the same rationale , we propose that averaging saccades arise from an unresolved saccade target-selection process . Given the behavioral nature of our data , we can only speculate about the neural correlates of averaging saccades at the level of the priority maps in this experiment . We will , however , discuss our results in the light of 2 alternative accounts concerning the representation of averaging saccades at the level of the SC . While Edelman and Keller [54] found evidence for a bimodal distribution of collicular activity before averaging saccades at express latencies , an earlier study by Glimcher and Sparks [52] argued for an intermediate unimodal distribution in case of regular-latency averaging saccades . Because averaging saccades were executed at regular latencies in this experiment , they might indeed have been associated with a unimodal distribution of activity at an intermediate collicular site ( early oculomotor selection—Fig 4C ) at saccade onset . According to this view , averaging saccades were initially reflected by 2 equally enhanced collicular populations coding for the 2 saccade targets . This bimodal distribution of activity propagates to the feature maps , leading to an equal enhancement of visual sensitivity at the 2 saccade targets . However , the initial bimodal collicular activity distribution then progresses into a unimodal distribution centered at an intermediate collicular site to subsequently allow for the execution of a single saccade . Such a scenario is in line with evidence from a recent study performing dual-site electrical stimulation in the intermediate layers of the SC [55] . If the absence of attentional deployment at the averaging saccade endpoint observed here was indeed associated with a single active population located at an intermediate site of the SC , our results would clearly refute the premotor theory of attention . Alternatively , averaging saccades may result from a bimodal collicular activity distribution at saccade onset ( late oculomotor selection—Fig 4D ) . In this case , the collicular sites of enhanced activity would match with the observed attentional benefits at the 2 saccade targets , and oculomotor averaging across the active collicular populations would be achieved by integration downstream of the SC . This conception could be considered compatible with a weak version of the premotor theory of attention because one could argue that the output from the SC—which is likely the last node for visuomotor transformation—is simultaneously recruited to guide attention and eye movements . However , while the final oculomotor program was averaged , attention clearly was not in this experiment . Thus , attentional and oculomotor programming are necessarily dissociable at some processing level . One possible option to account for the observed dissociation at the behavioral level is to assume that the brainstem circuitry and the attentional system deploy different algorithms to read out the collicular code . Disentangling the 2 options discussed above ( early versus late oculomotor selection ) would constitute an important step in the understanding of the link between attention and action and would require simultaneous behavioral and neural recordings . In regard to the neural recording , one should , however , carefully distinguish between the different classes of neurons ( fixation , visual , motor , and visuomotor ) , which appear to reside along a continuum with variable response properties depending on the experimental conditions [76] . According to our view , attentional selection is not completed at the onset of averaging saccades , as reflected by the equal and moderate attentional benefits at the saccade targets . This proposal is supported by electrophysiological recordings showing that averaging saccades are associated with 2 distinct peaks within the intermediate layers of the SC [53 , 54] . A similar , general conception of oculomotor programming was expressed by He and Kowler [44] , who proposed a 2-stage process in which a single mechanism resolves attentional selection before the oculomotor program is computed at a later stage based on attentional weighting . Our results , moreover , go against a recent proposal that a merged activation within the superficial layers of the SC would feed back into the visual system [55] because this should have led to some attentional enhancement in between the saccade targets before an averaging saccade . Our framework leads to some predictions in regard to the global effect . First , it predicts that any experimental manipulation modifying the difficulty of saccade target selection will directly impact the occurrence of averaging saccades . For example , specifying the location , the identity , or the probability of a saccade target appearing at a certain location will decrease the task difficulty , thereby increasing the speed of the attentional selection process and reducing the occurrence of averaging saccades [40–44 , 77] . Also , it predicts that , at a given latency , an easy saccade task should lead to fewer averaging saccades as compared to a more difficult one . Using a simple 2-saccade target task , it was shown that monkeys make averaging saccades only for express but not for normal saccade latencies [50] , whereas they execute averaging saccades even for normal saccade latencies in a task rendered harder by a visual search display [49] . Similarly , Viswanathan and colleagues [78] showed that—at a saccade latency for which no consistent global effect was found with a distractor shown nearby a prosaccade target—a clear global effect was evident with the same distractor shown nearby an antisaccade target . These results are in line with our first prediction , as antisaccades are associated with a slower attentional selection [79] . Second , our framework predicts that one should not find any incremental presaccadic attentional benefit at one of the competing saccade targets before an averaging saccade , irrespective of the observed saccade latency . Future studies could directly test this prediction by measuring neuronal activity associated with the saccade targets before an averaging saccade . Third , we proposed 2 alternative explanations that could account for the observed behavioral dissociation between attention and the saccade endpoint before averaging saccades at the neuronal level . Both accounts question the validity of the premotor theory of attention in a saccade task rather than in a covert attention task [21–23] . Combining a measure of presaccadic visual sensitivity with a free-choice saccade task , we spatially dissociated attention allocated to the intended saccade goal from attention allocated to the saccade endpoint . We report here that attention is not obligatorily coupled to the endpoint of the oculomotor program , providing evidence against the strict view that oculomotor processes precede attention . Instead , we propose that saccadic responses depend on the state of attentional selection at saccade onset .
This experiment was approved by the Ethics Committee of the Faculty for Psychology and Pedagogics of the Ludwig-Maximilians-Universität München ( approval number 13_b_2015 ) and conducted in accordance with the Declaration of Helsinki . All participants gave written informed consent . Thirteen participants ( aged 20–28 , 7 females , 12 right-eye dominant , 1 author ) completed the experiment for a compensation of 50€ . The study was run over 2 experimental sessions ( on different days ) of 12 blocks of approximately 150 minutes each ( including breaks ) . All participants except for 1 author ( LW ) were naive as to the purpose of the study , and all had normal or corrected to normal vision . Participants sat in a quiet and dimly illuminated room , with their head positioned on a chin and forehead rest . The experiment was controlled by an Apple iMac computer ( Cupertino , CA ) . Manual responses were recorded via a standard keyboard . The dominant eye’s gaze position was recorded and made available online using an EyeLink 1000 Desktop Mount ( SR Research , Osgoode , Ontario , Canada ) at a sampling rate of 1 kHz . The experimental software controlling the display and the response collection as well as the eye tracking were implemented in Matlab ( The MathWorks , Natick , MA ) , using the Psychophysics [80 , 81] and EyeLink toolboxes [82] . Stimuli were presented at a viewing distance of 60 cm , on a 24-in Sony GDM F900 CRT screen ( Tokyo , Japan ) with a spatial resolution of 1 , 024 × 640 pixels and a vertical refresh rate of 120 Hz [83] . Each trial began with participants fixating on a central fixation target forming a black ( approximately 0 cd/m2 ) and white ( approximately 57 cd/m2 ) “bull’s eye” ( 0 . 4° radius ) on a gray background ( approximately 19 . 5 cd/m2 ) . When the participant’s gaze was detected within a 2 . 0°-radius virtual circle centered on the fixation point for at least 200 ms , the trial began . At that time , 24 distractor streams appeared equally distributed along a 10°-radius imaginary circle centered on the fixation target ( see Fig 1A ) . Distractor streams consisted of flickering stimuli ( 40 Hz ) , alternating every 25 ms between a vertical Gabor patch ( frequency: 2 . 5 cpd; 100% contrast; random phase selected each stream refresh; SD of the Gaussian window: 1 . 1°; mean luminance: approximately 28 . 5 cd/m2 ) and a Gaussian pixel noise mask ( made of approximately 0 . 22°-width pixels with the same Gaussian envelope as the Gabors ) . After a random fixation period between 300 and 600 ms ( in steps of 1 screen refresh: approximately 8 ms ) , the fixation target switched off together with the onset of 2 saccade targets . Saccade targets , ST1 and ST2 , were gray circles ( approximately 39 cd/m2; 1 . 1° radius; 0 . 2° width ) surrounding 2 randomly chosen streams with an intertarget angular distance of 90° or 30° . They were either presented transiently ( 50 ms ) or continuously ( until the end of the trial ) . When presented transiently , the saccade targets had always disappeared from the screen at the time the discrimination target appeared on the screen . When presented continuously , on the other hand , the saccade targets always temporally overlapped with the presentation of the discrimination target . Our motivation to include these 2 saccade target durations was to check for a potential masking effect of the saccade targets on the discriminability of a discrimination target . Participants were instructed to select 1 of the saccade targets by moving their eyes towards it as fast and as accurately as possible . In 96 . 5% of all trials , between 75 and 175 ms after the saccade target onset ( a time determined to maximize discrimination target offsets in the last 200 ms before the saccade ) , 1 of the 24 distractor streams was replaced by a discrimination target stream in which a tilted Gabor was played ( 25 ms , rotated clockwise or counterclockwise by 12° relative to the vertical ) . The discrimination target could appear at any of the 24 distractor streams with equal probability , and subjects were explicitly informed about this fact at the beginning of the experiment . In 3 . 5% of all the trials , we did not present any discrimination target , in order to evaluate its influence on saccade metrics ( note that all other analyses are based on the discrimination-target-present trials ) . At 500 ms after the saccade target onset , all stimuli disappeared , and participants were instructed to report the orientation of the discrimination target using the keyboard ( right or left arrow key ) . Incorrect responses were followed by a negative feedback sound . On trials in which no discrimination target was shown , participants’ responses were followed by a random feedback sound . Three participants were excluded from the analysis because their performance stayed at chance level irrespective of the position of the discrimination target . The remaining 10 participants completed between 6 , 972 and 7 , 055 trials of the saccade task . Correct fixation within a 2 . 0°-radius virtual circle centered on the fixation point was checked online . Trials with fixation breaks were repeated at the end of each block , together with trials during which a saccade started ( i . e . , crossed the virtual circle around the fixation target ) within the first 50 ms or after more than 350 ms following the saccade target onset ( participants repeated between 46 to 395 trials across all blocks ) . In our experiment , we did not indicate the location of the discrimination target . Therefore , the perceptual task required participants to base their decision on multiple potential locations . One might therefore argue that the low sensitivity at the intermediate location BTW was observed because participants did not take the intermediate location into account as a decision variable for the perceptual task . In order to validate that our results reflect attentional effects and were not selectively biased by varying decision criteria across the different locations , we ran a control experiment , in which the position of the discrimination target was revealed by the presentation of a report cue at the end of each trial . Consequently , participants knew which location to base their discrimination judgment upon in this control experiment , which was—except for the presentation of the report cue—identical to the main experiment . Participants were instructed to give their discrimination judgment only after the report cue had appeared . The report cue ( a black circle; approximately 0 cd/m2 ) was presented right after the offset of the distractor streams and stayed on the screen until the trial end . Overall , we tested 8 participants ( 4 participated in the main experiment ) on an equal amount of blocks and trials as in the main experiment . S7 Fig shows the results of this control experiment in the same format as those of the main experiment ( see Fig 3 ) . Before proceeding to the analysis of the behavioral results , we scanned offline the recorded eye-position data . Saccades were detected based on their velocity distribution [84] using a moving average over 20 subsequent eye-position samples . Saccade onset and offset were detected when the velocity exceeded or fell below the median of the moving average by 3 SDs for at least 20 ms . We included trials if a correct fixation was maintained within a 2 . 0° radius centered on the fixation target , if a correct saccade started at the fixation target and landed at a distance between 7° and 13° from the fixation target ( ±30% of the instructed saccade size ) , and if no blink occurred during the trial . Finally , only trials in which the discrimination target offset was included in the last 200 ms preceding the saccade onset were included in the analysis ( mean ± SEM discrimination target offset relative to the saccade onset for the selected trials: −50 . 2 ± 1 . 3 ms ) . In total , we included 53 , 117 trials in the analysis ( 78 . 2% of the online-selected trials; 75 . 7% of all trials played ) corresponding to an average of 106 . 0 ± 2 . 1 trials ( 115 . 9 ± 3 . 3 no-discrimination-target trials ) and 105 . 3 ± 1 . 8 trials ( 125 . 0 ± 4 . 4 no-discrimination-target trials ) per discrimination target location and participant , in the 90° and 30° conditions , respectively . Corrective saccades were defined as the saccades directly following the offline-selected main saccades sequence and landing at a distance between 7° and 13° from the fixation target . Corrective saccades were included only if they started before the participant’s behavioral response and within the first 500 ms following the main saccade sequence . In total , we obtained 14 , 714 corrective saccade trials in the analysis ( 21 . 7% of the online-selected trials; 21 . 0% of all trials played ) . Before proceeding to any behavioral analysis , we first rotated the trial configuration as to align the 2 saccade target locations ( ST1: +45° , ST2: −45° and ST1: +15° , ST2: −15° for the conditions in which they were separated by 90° and 30° , respectively ) symmetrically around the geometrical angle 0 ( BTW ) . We then determined the sensitivity to discriminate the orientation of the discrimination targets ( d’ ) : d’ = z ( hit rate ) − z ( false alarm rate ) . To do so , we defined a clockwise response to a clockwise discrimination target ( arbitrarily ) as a hit and a clockwise response to a counterclockwise discrimination target as a false alarm . Corrected performance of 99% and 1% were substituted if the observed proportion correct was equal to 100% or 0% , respectively . Performance below the chance level ( 50% or d’ = 0 ) were transformed to negative d’ values [83] . We analyzed sensitivity as a function of the discrimination position in space irrespective of the saccade landing direction ( Fig 3A and 3B ) but also as a function of the discrimination target position relative to the saccade landing direction ( Fig 3C–3F ) . To do so , we redefined the position of the discrimination target relative to the saccade direction binned across 24 even , angular sectors of 15° ( ±7 . 5° from each distractor stream center angle ) . This binning was chosen to match with the locations at which we tested visual attention . We initially computed single-subject means and then averaged these means across participants for each of the compared conditions to get the presented results . For all statistical comparisons , we drew ( with replacement ) 10 , 000 bootstrap samples from the original pair of compared values . We then calculated the difference of these bootstrapped samples and derived 2-tailed p-values from the distribution of these differences . Individual raw data and averaged processed data can be found in the Open Science Framework ( OSF ) online repository at https://osf . io/762up/ .
|
The premotor theory of attention postulates that spatial visual attention is a consequence of the brain activity that controls eye movement . Indeed , attention and eye movement share overlapping brain networks , and attention is deployed at the target of an eye movement ( saccade ) even before the eyes start to move . But is attention always deployed at the endpoint of saccades ? Here , we measured visual attention before accurate saccades and before saccades that landed in between 2 targets ( averaging saccades ) . While accurate saccades were associated with a selective enhancement of visual sensitivity at their endpoint , no such enhancement was found at the endpoint of averaging saccades . Rather , visual sensitivity was evenly distributed across the 2 saccade targets , suggesting that saccade averaging arises from unresolved attentional selection . Overall , our results reveal that attention is not always coupled to the endpoint of saccades , arguing against a simplistic view of the premotor theory of attention at the behavioral level . Instead , we propose that saccadic responses depend on the state of attentional selection at saccade onset .
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2018
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Visual attention is not deployed at the endpoint of averaging saccades
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Lectin-like bacteriocins consist of tandem monocot mannose-binding domains and display a genus-specific killing activity . Here we show that pyocin L1 , a novel member of this family from Pseudomonas aeruginosa , targets susceptible strains of this species through recognition of the common polysaccharide antigen ( CPA ) of P . aeruginosa lipopolysaccharide that is predominantly a homopolymer of d-rhamnose . Structural and biophysical analyses show that recognition of CPA occurs through the C-terminal carbohydrate-binding domain of pyocin L1 and that this interaction is a prerequisite for bactericidal activity . Further to this , we show that the previously described lectin-like bacteriocin putidacin L1 shows a similar carbohydrate-binding specificity , indicating that oligosaccharides containing d-rhamnose and not d-mannose , as was previously thought , are the physiologically relevant ligands for this group of bacteriocins . The widespread inclusion of d-rhamnose in the lipopolysaccharide of members of the genus Pseudomonas explains the unusual genus-specific activity of the lectin-like bacteriocins .
The ability to target a subgroup of pathogenic bacteria in a complex bacterial community has potential applications in medicine and agriculture where the maintenance of a ‘normal’ microbiome is beneficial . For example , the use of broad spectrum antibiotics to treat bacterial infections is known to cause a range of complications associated with collateral damage to the microbiome , including antibiotic associated diarrhea and Clostridium difficile infection [1] , [2] . In addition , there is growing evidence to suggest that microbial dysbiosis may play a role in a range of chronic diseases such as inflammatory bowel disease , diabetes , obesity and rheumatoid arthritis [3] , [4] , [5] , [6] . Indeed , for Crohn's disease , where the link with dysbiosis is well established , the administration of multiple courses of antibiotics is associated with an increased risk factor for the development of this chronic form of inflammatory bowel disease [7] , [8] , [9] . In contrast to the broad spectrum antibiotics that are widely used in medicine and agriculture , protein antibiotics known as bacteriocins often target a specific bacterial species or a group of closely related bacterial species [10] , [11] , [12] , [13] . Well characterised bacteriocins include the S-type pyocins from P . aeruginosa and the closely related colicins of E . coli [12] , [13] . The colicin-like bacteriocins form a diverse family of multidomain protein antibiotics which share similar mechanisms of uptake and kill cells through either a pore-forming activity , a specific nuclease activity against DNA , tRNA or rRNA or through inhibition of cell wall synthesis [14] , [15] , [16] , [17] . In the case of S-type pyocins it is thought that their activity is limited to strains of P . aeruginosa , whereas colicins show activity against E . coli and some strains of closely related bacteria such as Salmonella spp . [18] . In the case of colicins and S-type pyocins , killing specificity is primarily determined by the presence of a specific outer membrane receptor on the cell surface . For example , the well characterised E group colicins utilise the TonB-dependent BtuB receptor , which has a normal physiological role in vitamin B12 uptake [19] . Colicin-like bacteriocins have also been shown to have a potent antibiofilm activity , indicating their potential as useful therapeutics for the treatment of chronic biofilm mediated infections [20] , [21] . In the case of the opportunistic human pathogen P . aeruginosa there is an urgent requirement for the development of novel therapeutic options since its ability to form drug-resistant biofilms in combination with the presence of an outer membrane that is highly impermeable to many classes of antibiotics can make this pathogen essentially untreatable in some groups of patients . This is exemplified in cystic fibrosis patients where chronic lung infection with P . aeruginosa is the leading cause of mortality [22] . An interesting addition to this group of protein antibiotics is the recently discovered lectin-like bacteriocins that contain two carbohydrate-binding domains of the monocot mannose-binding lectin ( MMBL ) family [23] , [24] , [25] , [26] , [27] . Lectin-like bacteriocins from P . putida ( putidacin L1 or LlpABW ) P . syringae ( LlpAPss642 ) and P . fluorescens ( LlpA1Pf-5 ) have been characterised and have the unprecedented ability to kill strains of a broad range of bacterial species within the genus Pseudomonas , but are not active outside this genus [24] , [26] , [27] . Similarly the lectin-like bacteriocin LlpAXcm761 from Xanthomonas citri pv . malvacearum LMG 761 has the ability to kill various species within the genus Xanthomonas [24] . The molecular basis of this unusual genus specific activity has not been explained . Lectins are a structurally and evolutionarily diverse class of proteins produced widely by prokaryotes and eukaryotes and are defined by their ability to recognise and bind carbohydrates . This binding is generally highly specific and mediates a range of diverse functions , including cell-cell interaction , immune recognition and cytotoxicity [28] , [29] MMBLs represent a structurally conserved lectin subclass , of which the mannose-binding Galanthus nivalis agglutinin ( GNA ) was the first to be characterised [30] . The MMBL-fold consists of a three sided β-prism; each face of which contains a sugar binding motif with the conserved sequence QxDxNxVxY [31] . While originally identified in monocots like G . nivalis or Allium sativum , it is now recognised that proteins of this class are distributed widely throughout prokaryotes and eukaryotes , where they have evolved to mediate diverse functions [30] , [32] , [33] , [34] . Structural and biochemical analysis of MMBLs has shown that they are generally translated as a single polypeptide chain containing tandem β-prism domains that are then proteolytically processed into monomers . These domains often form homo- or hetero-dimers by strand exchange and π-stacking [35] . The lectin-like bacteriocins are not proteolytically processed and thus consist of a single peptide chain , containing tandem β-prism domains . Sequence alignments of members of this class from Pseudomonas spp . show complete conservation of two sugar binding motifs on the C-terminal domain and partial conservation of two sites on the N-terminal domain [23] . Recent work by Ghequire et al [23] on the characterisation of putidacin L1 shows these motifs to be important for cytotoxicity . Mutagenesis of the first C-terminal motif has the most dramatic effect on activity , while mutagenesis of the second C-terminal and first N-terminal sugar binding motifs leads to a synergistic reduction in activity . This study also showed low-affinity binding between putidacin L1 and methyl-α-d-mannose or a range of mannose containing oligosaccharides . However , Kds for these protein-carbohydrate complexes were reported in the range from 46 mM for methyl-α-d-mannoside to 2 mM for a mannose containing pentasaccharide [23] . An extensive search for high affinity carbohydrate binding through the use of glycan arrays failed to detect high affinity carbohydrate binding for this lectin-like bacteriocin [23] . Despite progress in our understanding of the structure and host range of MMBL-like bacteriocins , the mechanism by which these bacteriocins target susceptible strains and exert their antimicrobial effects is unknown . Here we report on the discovery of a novel member of this family , pyocin L1 from P . aeruginosa , and show that it utilises lipopolysaccharide ( LPS ) as a surface receptor , specifically targeting the common polysaccharide antigen ( CPA ) that is a conserved homopolymer of d-rhamnose . Structural and biophysical analysis shows that the C-terminal carbohydrate binding motifs are responsible for d-rhamnose recognition and that these sites are specific for this sugar over d-mannose . Further to this , we show that the previously described putidacin L1 also selectively binds LPS from susceptible , but not from resistant , P . syringae isolates and shows selectivity for d-rhamnose over d-mannose . This work shows that the physiologically relevant ligand for the QxDxNxVxY carbohydrate binding site of the lectin-like bacteriocins is indeed d-rhamnose and not d-mannose as previously thought . As such , the genus-specific activity of lectin-like bacteriocins from Pseudomonas spp . can be attributed to the widespread inclusion of the rare d-rhamnose in the LPS of members of the genus Pseudomonas .
As part of a wider project , aimed at identifying bacteriocins that could be used as novel therapeutics in the treatment of P . aeruginosa infections , we searched the genomes of 10 recently sequenced clinical and environmental isolates of P . aeruginosa for genes with homology to known bacteriocins . One putative bacteriocin gene identified in strain C1433 , an isolate from a patient with cystic fibrosis , encodes a protein with 31% identity to the lectin-like bacteriocin LlpA1Pf-5 , from P . fluorescens . This protein , designated pyocin L1 , contained 256-amino acids with a predicted molecular mass of 28413 Da . Alignment of the pyocin L1 protein sequence with other lectin-like bacteriocins , LlpA1Pf-5 , LlpAPss642 , putidacin L1 ( LlpABW ) , LlpAAu1504 from Burkholderia cenocepacia and LlpAXcm761 from Xanthomonas citri pv . malvacearum shows that pyocin L1 contains tandem MMBL domains with three conserved QxDxNxVxY MMBL sugar-binding motifs ( Figure S1 ) . Two of these motifs are located in the C-terminal domain of the protein and one in the N-terminal domain . Comparison with the sequences of other lectin-like bacteriocins shows that the C-terminal QxDxNxVxY motifs are highly conserved , with only LlpAXcm761 lacking one C-terminal motif . In contrast the N-terminal sugar-binding motifs are less well conserved with only LlpAAu1504 possessing two fully conserved QxDxNxVxY motifs ( Figure S1 ) . In order to determine the killing spectrum of pyocin L1 we cloned the pyocin L1 open reading frame into the pET21a vector and expressed and purified the protein by nickel affinity , anion exchange and size exclusion chromatography . Purified pyocin L1 was tested for its ability to inhibit the growth of 32 environmental and clinical isolates of P . aeruginosa using an overlay spot plate method on LB agar [36] . Under these conditions , pyocin L1 showed killing activity against nine of the P . aeruginosa strains tested . Strain E2 , an environmental isolate from a tomato plant for which the genome sequence is available , and strain P8 , a clinical isolate from a cystic fibrosis patient , showed the greatest sensitivity to pyocin L1 with killing observed down to concentrations of 27 nM and 7 nM , respectively . Pyocin L1 also showed activity against 5 of the 11 P . syringae strains tested , although the effect was much weaker , with cell killing observed at high µM concentrations . In order to gain insight into the bacteriocidal activity of pyocin L1 , we subjected P . aeruginosa E2 to high concentrations of recombinant protein and recovered mutants with greatly increased tolerance to pyocin L1 ( Figure 1A ) . The genomes of two of these mutants were sequenced and comparative analysis with the genome of wild-type E2 revealed a dinuclear deletion , C710 and T711 , of the 1146-bp wbpZ gene . This deletion was common to both mutants . wbpZ encodes a glycosyltransferase of 381 amino acids that plays a key role in lipopolysaccharide synthesis , specifically in the synthesis of the common polysaccharide antigen ( CPA ) also known as A-band LPS [37] , ( Figure S2 ) . Most strains of P . aeruginosa produce two distinct LPS-types that differ in their O-antigen , but share the same core oligosaccharide . The CPA is predominantly a homopolymer of d-rhamnose and the O-specific antigen contains a heteropolymeric repeating unit that varies widely among strains [38] . Consistent with mutation of wbpZ , we found that production of CPA , as determined by immunoblotting with a CPA-specific monoclonal antibody [39] , in both M4 ( E2 ) and M11 ( E2 ) was reduced to undetectable levels ( Figure 1B ) . Visualisation of LPS from these strains was performed via silver staining and comparable quantities of LPS were shown to be present . These observations suggest that pyocin L1 may utilise CPA as a cellular receptor . To test this idea further , we obtained two transposon insertion mutants of P . aeruginosa PAO1 , which is sensitive to pyocin L1 , with insertions in the genes responsible for the transport of CPA to the periplasm [40] . These two genes , wzt and wzm , encode the ATP-binding component and membrane component of a CPA dedicated ABC transporter [38] . Pyocin L1 , which shows good activity against PAO1 showed no activity against strains with insertions in wzm and wzt ( Figure 1C ) and immunoblotting with a CPA-specific antibody confirmed the absence of the CPA in these pyocin L1 resistant strains ( Figure 1D ) . Thus , the presence of CPA on the cell surface is required for pyocin L1 killing . In order to determine if the requirement for CPA is due to a direct interaction with pyocin L1 we purified LPS from wild-type PAO1 and from the pyocin L1 resistant , wzm and wzt mutants ( which produce no CPA but do produce the O-specific antigen ) and analysed the pyocin-CPA interaction by isothermal titration calorimetry ( ITC ) . Titration of pyocin L1 into isolated LPS-derived polysaccharides ( a mixture of CPA and the O-specific antigen containing polysaccharides ) from PAO1 gave rise to strong saturable exothermic heats of binding ( Figure 2A ) , whereas no binding was detected on titration of pyocin L1 into an equivalent concentration of LPS carbohydrates from PAO1 wzt , which produces the O-specific antigen but not the CPA ( Figure 2B ) . These data show that pyocin L1 binds directly to the CPA and that this interaction is required for killing . The CPA is therefore likely to be the cellular receptor for pyocin L1 . The evolutionary relationships between MMBL-like bacteriocins and the originally identified mannose-binding members of this protein family , led to the assumption that carbohydrate binding of polysaccharides by the lectin-like bacteriocins is primarily mediated through binding of d-mannose at one or more of their conserved QxDxNxVxY carbohydrate binding motifs . Indeed , the recent structures [23] of putidacin L1 bound to mannose-containing monosaccharides adds weight to this idea , although measured affinities between polysaccharides and putidacin L1 are weak ( mM ) and so may not be physiologically relevant . However , the strong interaction between pyocin L1 and CPA , is incompatible with this and suggests that d-rhamnose and not d-mannose is the likely physiological substrate for the QxDxNxVxY carbohydrate binding motifs . To determine the affinity of pyocin L1 for d-rhamnose and d-mannose , isothermal titration calorimetry ( ITC ) was performed . Titration of pyocin L1 into d-rhamnose gave rise to weakly saturable heats of binding that are significantly larger than the heats observed on titration of pyocin L1 into an identical concentration of d-mannose ( Figure 3 ) . From this experiment an apparent Kd of 5–10 mM was estimated for the interaction of pyocin L1 with d-rhamnose with apparently weaker binding for d-mannose , Kd>50 mM . The interaction between pyocin L1 and these monosaccharides was also probed using NMR with 15N labelled pyocin L1 , monitoring changes to its 15N-heteronuclear single quantum correlation ( 15N-HSQC ) spectra on addition of d-rhamnose or d-mannose . In the absence of added monosaccharide 15N-HSQC spectra of pyocin L1 , which should contain one crosspeak for each non-proline amide NH as well as peaks for the NH groups in various side chains , were well resolved and dispersed , indicative of a folded protein . Chemical shift perturbation monitored by 15N-HSQC allows the mapping of changes to a protein that occur on ligand binding . Addition of either d-rhamnose or d-mannose up to a concentration of 100 mM did not give rise to large or global changes in chemical shifts ( Figure S3 ) . On addition of d-rhamnose significant chemical shift changes were observed for a discrete subset of peaks including some in the amide side chain region of the spectra , while changes of a smaller magnitude were observed on the addition of equal concentrations of d-mannose ( Figure S3 ) . Fitting the chemical shift changes that occur on addition of d-rhamnose , for peaks showing strong shifts , to a single site binding model indicates a Kd for the pyocin L1- d-rhamnose complex in the range of 5–20 mM ( Figures 3C–F ) . These data correlated well with the ITC sugar binding data , with low mM binding of pyocin L1 to d-rhamnose and much weaker binding to d-mannose . In an attempt to determine the location of the pyocin L1 d-rhamnose binding site ( s ) and the structural basis of the d-rhamnose specificity of pyocin L1 we determined the X-ray structures of pyocin L1 with bound d-mannose , d-rhamnose and in the unbound form ( Table 1 ) . Pyocin L1 , as predicted by sequence homology to MMBL proteins , consists of two tandem β-prism domains characteristic of MMBLs , connected by antiparallel strands propagating from the end of each MMBL domain and lending a strand to the reciprocal β-prism . The strands contain a tryptophan residue which forms π-stacking interactions with two other tryptophans in the β-prism to stabilise the structure ( Figure 4A ) . This interaction is conserved throughout MMBLs , with most members of the class utilising it to form either homo- or hetero-dimers of single MMBL subunits . However , in pyocin L1 , as with the recently described structure of putidacin L1 , both domains are from a single polypeptide chain [23] . Other structural elements are also common between the two bacteriocins , namely a C-terminal extension of 30 amino acids and a two-turn α-helix insertion into loop 6 of the N-terminal MMBL domain ( Figure 4B ) . The overall root mean square deviation ( rmsd ) of backbone atoms for pyocin L1 and putidacin L1 is 7 . 5 Å , which is relatively high due to a difference in the relative orientation of the two MMBL domains . In contrast , the relative orientation of the tandem MMBL domains of pyocin L1 matches those of the dimeric plant lectins very closely , with alignment of pyocin L1 with the snowdrop lectin homodimer ( pdb ID: 1MSA ) giving an rmsd of 4 . 81 Å . Comparison of the respective N- and C- terminal domains from pyocin L1 and putidacin L1 shows they possess very similar folds with rmsds of 2 . 77 Å and 2 . 02 Å , respectively ( Figures 4C–D ) . The higher value for comparison of the N-terminal domains is due to the presence of a 2-strand extension to β-sheet two of the putidacin L1 N-terminal MMBL domain , which is absent from pyocin L1 and other MMBLs . In order to identify protein structures which share a similar fold to pyocin L1 we submitted the structure of the DALI server ( http://ekhidna . biocenter . helsinki . fi/dali_server/start ) . The DALI server searches the protein data bank ( PDB ) to identify proteins structurally related to the query structure [41] . Significant structural homology was only identified for putidacin L1 and other proteins previously characterised as containing a MMBL fold such as the snowdrop lectin . MMBL dimers of plant origin often form higher order structures , however small angle X-ray scattering of pyocin L1 showed it to be monomeric in solution ( Figure S4 ) . Electron density maps , derived from both d-mannose and d-rhamnose soaked crystals show clear density for sugar moieties in both sites , C1 and C2 ( Figure 5 ) . The sugars refined well in these densities at full occupancy , giving B-factors comparable to the surrounding protein side chains . The canonical MMBL hydrogen bonds observed for both d-mannose and d-rhamnose were the same: Gln to O3 , Asp to O2 , Asn to O2 and Tyr to O4 . In addition , O6 of d-mannose forms a hydrogen bond with Tyr169 in C1 and His194 in C2 . As d-rhamnose is C6 deoxy d-mannose , it lacks these interactions ( Figure 6 ) . The fact that d-mannose forms an additional hydrogen bond is counter-intuitive given that pyocin L1 has a significantly stronger affinity for d-rhamnose , however Val154 , Val163 and Ala166 of C1 and Val184 and Ala191 of C2 form a hydrophobic pocket to accommodate the C6-methyl group of d-rhamnose ( Figure S5 ) . Weak density was observed for both sugars at site N1 , however given the high concentrations used in the soak and the overall low binding affinity of pyocin L1 for monomeric sugars , it is unlikely that N1 represents a primary binding site for d-rhamnose ( Figure S5 ) . The conserved residues in site N2 form interactions with the C-terminal extension of the protein and as such are inaccessible . Weak density was also observed adjacent to the binding site C1 of mol B in both the soaks and in mol A of the d-rhamnose form . This density may correspond to a peripheral binding site utilised in binding to the carbohydrate chain of LPS , as is observed in the structure of putidacin L1 bound to oligosaccharides [23] . To test the idea that the observed binding of d-rhamnose to sites C1 and C2 is reflective of CPA binding and that this binding is critical to pyocin L1 cytotoxicity , we created pyocin L1 variants in which the conserved aspartic acids of the QxDxNxVxY motifs of the C1 and C2 sugar binding sites were mutated to alanine and compared their cytotoxicity and ability to bind the CPA by ITC with the wild-type protein . Titrations with wild-type pyocin L1 and the D150A ( C1 ) and D180A ( C2 ) variants were performed by titrating protein at a concentration of 100 µM into a solution of LPS-derived polysaccharide ( 1 mg ml−1 ) from strain PAO1 ( Figure 7 ) . Under these conditions we were able to generate binding isotherms that enabled us to accurately determine an apparent Kd of 0 . 15 ( ±0 . 07 ) µM for the wild-type pyocin L1-CPA complex . For both the D150A ( C1 ) and D180A ( C2 ) variants , affinity for CPA was reduced . For the pyocin L1 D150A-CPA complex a Kd of 1 . 52 ( ±0 . 51 ) µM was determined , a 10-fold increase in Kd relative to the wild-type pyocin L1-CPA complex . However , CPA binding to the D180A variant was severely weakened and although heats of binding were still observed the Kd for this complex , which could not be accurately determined , is likely >500 µM . We also produced a double mutant in which both D150A and D180A mutations were present . For this double mutant , no binding to CPA was observed by ITC . These data show that both the C1 and C2 sugar binding motifs are required for full CPA binding , but that the C2 binding site is the major CPA binding determinant . The killing activity of these sugar binding motif variants showed a good correlation with their ability to bind the CPA . Both the D150A and D180A variants showed reduced cytotoxicity against PAO1 relative to pyocin L1 , with the D150A showing a greater reduction in activity and for the D150A/D180A variant very low levels of cytotoxicity were observed ( Figure 7 ) . Pyocin L1 targets sensitive strains of P . aeruginosa through binding to LPS and utilises this as a cell surface receptor . To determine if LPS binding is common to the homologous and previously characterised lectin-like bacteriocin putidacin L1 , we purified this protein and determined if the susceptibility of a number of strains of P . syringae correlated with the ability of putidacin L1 to bind to LPS-derived carbohydrates from these strains . From the five strains of P . syringae tested , LMG 5456 and LMG 2222 were found to be highly susceptible to putidacin L1 with killing down to concentrations of 0 . 3 and 7 . 6 nM respectively . DC3000 and NCPPB 2563 showed complete resistance and LMG 1247 was highly tolerant ( killing down to 0 . 6 µM ) . Binding of putidacin L1 to the isolated LPS-derived polysaccharides of the above mentioned strains was tested by ITC . Large saturable heats of binding were observed for putidacin L1 and the LPS-derived polysaccharides from LMG 5456 and LMG 2222 , while no binding was observed between putidacin L1 and the LPS-derived polysaccharides from LMG 1247 , 2563 or DC3000 ( Figure 8 ) . Thus , there is excellent correlation between putidacin L1 cell killing and the binding of LPS-derived polysaccharide indicating that like pyocin L1 , putidacin L1 utilises LPS as a surface receptor . Although P . syringae O-antigens are diverse relative to CPA , the incorporation of d-rhamnose is widespread and seemingly almost universal in strains of this species [42] , [43] . Interestingly , in cases where d-rhamnose is not a component of P . syringae LPS , l-rhamnose is present [42] . As with pyocin L1 we utilised ITC and NMR to characterise the binding affinity of putidacin L1 for d-rhamnose , in comparison with d-mannose and l-rhamnose . Putidacin L1 exhibited an affinity of 5–10 mM for d-rhamnose , which is comparable to that of pyocin L1 , and approximately 10-fold stronger than its affinity for d-mannose ( Figure S6 ) . Interestingly , no binding of l-rhamnose to putidacin L1 or pyocin L1 was observed ( Figure S7 ) . It is interesting to note that in the strains of P . syringae we have tested , the killing spectrum ( but not the potency ) of pyocin L1 and putidacin L1 is identical . This observation combined with the specificity of putidacin L1 for d-rhamnose , strongly suggests that it also binds to a d-rhamnose containing O-antigen . Indeed branched d-rhamnose O-antigens are common in P . syringae [42] , [43] . Our data for both pyocin L1 and putidacin L1 indicate that d-rhamnose containing O-antigens are utilised as surface receptors for lectin-like bacteriocins from Pseudomonas spp . This is an attractive hypothesis since the inclusion of d-rhamnose in the lipopolysaccharides from members of this genus is widespread and could form an important component of the genus specific activity of this group of bacteriocins .
In this work we have shown that pyocin L1 targets susceptible cells through binding to the CPA component of LPS and that primary recognition of CPA occurs through binding of d-rhamnose at the conserved QxDxNxVxY sugar binding motifs of the C-terminal lectin domain . The ability of both pyocin L1 and putidacin L1 to recognise d-rhamnose containing carbohydrates is an important component of their ability to target sensitive strains of Pseudomonas spp . The use of the O-antigen as a primary receptor differentiates the lectin-like bacteriocins from other multidomain bacteriocins such as colicins and S-type pyocins ( colicin-like bacteriocins ) which utilise outer membrane proteins as their primary cell surface receptors [44] . The colicin-like bacteriocins also possess a flexible , or natively disordered N-terminal region that is thought to pass through the lumen of a coreceptor and interact with the periplasmic Tol or Ton complexes that mediate translocation of the bacteriocin across the outer membrane [11] , [44] . The lack of such a flexible N-terminal region in the lectin-like bacteriocins suggests that either they do not need to cross the outer membrane in order to mediate their cytotoxicity or they do so by a mechanism that is fundamentally different to the diverse family of colicin-like bacteriocins . Given the extensive structural homology between the lectin-like bacteriocins and plant lectins it seems likely that these bacteriocins share a common ancestor with plant lectins and from an evolutionary perspective are unrelated to the colicin-like bacteriocins . In addition to O-antigen recognition , additional factors , as yet to be determined , are clearly also important in strain and species specificity among the lectin-like bacteriocins . Indeed , recent work from Ghequire et al . has shown through domain swapping experiments that for putidacin L1 ( LlpABW ) and the homologous lectin-like bacteriocin LlpA1Pf-5 from Pseudomonas fluorescens , species specificity is governed by the identity of the N-terminal lectin domain [23] . Thus , in view of these data and our own data it seems likely that the C-terminal lectin domain of this class of bacteriocins plays a general role in the recognition of d-rhamnose containing O-antigens , with the N-terminal domain interacting with species-specific factors and thus determining the precise species and strain specificity of these bacteriocins . Although there are few clues as to how the lectin-like bacteriocins ultimately kill susceptible cells , we have established a clear role for the C-terminal MMBL domain of these proteins . The roles of the N-terminal MMBL domain and the C-terminal extension remain to be discovered [23] . However , from the previous work of Ghequire et al , it is clear that all three of these regions are required for killing of susceptible cells . Interestingly , although rhamnose is frequently a component of plant and bacterial glycoconjugates , such as the rhamnolipids of P . aeruginosa [45] and pectic polysaccharides of plant cell walls [46] , it is generally the l-form of this sugar that is found in nature . Although otherwise rare , d-rhamnose is found frequently as a component of the LPS of plant pathogens and plant associated bacteria such as P . syringae [42] , [43] , P . putida [47] , Xanthomonas campestris [48] and Burkholderia spp . [49] , but is a relatively rare component of the O-antigens of animal pathogens such as E . coli , Salmonella and Klebsiella . It is interesting to speculate that since d-rhamnose is a common component of the LPS of bacterial plant pathogens , that some of the many lectins produced by plants may have evolved to target d-rhamnose as part of plant defence to bacterial pathogens . The specificity of lectin-like bacteriocins suggests that these protein antibiotics may be useful in combating plant pathogenic bacteria , either through the use of bacteriocin expressing biocontrol strains or by the production of transgenic plants engineered to express these proteins . The specific targeting mechanism described here , binding of d-rhamnose containing polymers , indicates that the lectin-like bacteriocins would not interact with either plant or animal cells , since these lack d-rhamnose containing glycoconjugates . In addition , these narrow spectrum antibiotics would leave the majority of the soil microbiome and the gut microbiome of plant-eating animals intact and so would be likely to have minimal environmental impact and minimal impact on animal health . This latter property and the potency of these protein antibiotics could also make the use of lectin-like bacteriocins in the treatment of chronic multidrug-resistant P . aeruginosa infections in humans an attractive proposition .
Strains and plasmids utilised in this study are presented in Supplementary Table S1 . Strains of P . aeruginosa were grown in LB at 37°C , P . syringae were grown in King's B Media ( KB ) ( 20 g peptone , 10 g glycerol , 1 . 5 g MgSO4 , 1 . 5 g K2HPO4 per liter adjusted to pH 7 . 5 ) at 28°C . Pyocin L1 was amplified from the genomic DNA of the producing strain P . aeruginosa C1433 [50] by PCR using primers designed to introduce an NdeI site at the start of the pyoL1 gene ( ACA GAT CAT ATG AAG TCT CCA AAC AAA AGG AGG ) and an XhoI site at the end of the gene ( ACA GAT CTC GAG GAC CAC GGC GCG CCG TCG TGG ATA GTC GTG GGG CCA A ) . The PCR product was ligated into the corresponding sites of the E . coli expression vector pET21a to give pETPyoL1 which encodes pyocin L1 with a C-terminal His6 tag separated from the C-terminus of pyocin L1 by a 6 amino acid linker ( RRRAVV ) . Pyocin L1 was overexpressed from E . coli BL21 ( DE3 ) pLysS carrying the plasmid pETPyoL1 . Five litres of LB broth was inoculated ( 1∶100 ) from an overnight culture and cells were grown at 37°C in a shaking incubator to an OD600 = 0 . 6 . Protein production was induced by the addition of 0 . 3 mM isopropyl β-d-1-thiogalactopyranoside ( IPTG ) , the cells were grown at 22°C for a further 20 hand harvested by centrifugation . Cells were resuspended in 20 mM Tris-HCl , 500 mM NaCl , 5 mM imidazole ( pH 7 . 5 ) and lysed using an MSE Soniprep 150 ( Wolf Laboratories ) and the cell debris was separated by centrifugation . The cell-free lysate was applied to a 5-ml His Trap HP column ( GE Healthcare ) equilibrated in 20 mM Tris-HCl , 500 mM NaCl , 5 mM imidazole ( pH 7 . 5 ) and pyocin L1 was eluted over a 5–500 mM imidazole gradient . Pyocin L1 containing fractions were identified by SDS PAGE , pooled and dialyzed overnight into 50 mM Tris-HCl , 200 mM NaCl , pH 7 . 5 and remaining contaminants were removed by gel filtration chromatography on a Superdex S75 26/600 column ( GE Healthcare ) equilibrated in the same buffer . The protein was concentrated using a centrifugal concentrator ( Vivaspin 20 ) with a molecular weight cut off of 5 kDa and stored at −80°C until required . The putidacin L1 open reading frame was synthesised ( DNA 2 . 0 ) and cloned into pET21a via 5′ NdeI and 3′ XhoI restriction sites . The stop codon was removed in order to utilise the pET21a C-terminal His6 tag . Purification of putidacin L1 was performed as for pyocin L1 . Constructs to express the pyocin L1 mutants D31A , D97A , D150A and D180A were created using the QuikChange Site Directed Mutagenesis Kit ( Stratagene ) utilising pETPyoL1 as a template . The primers used were CAA ATT GGT CAT GCA AGC GGC TGG CAA CTT GGT CCT TTA CG and CGT AAA GGA CCA AGT TGC CAG CCG CTT GCA TGA CCA ATT TG for D31A , GCG TAC CTG AAT CTT CAA GAT GCT GGG GAC TTC GGT ATA TTT TC and GAA AAT ATA CCG AAG TCC CCA GCA TCT TGA AGA TTC AGG TAC GC for D97A , CGC CTA GCG TTT CAG GGA GCT GGC AAC CTA GTG ATC TAT C and GAT AGA TCA CTA GGT TGC CAG CTC CCT GAA ACG CTA GGC G for D150A and GAT AGA GCA GTA GTG CAA GAG GCT GGA AAT TTT GTT ATC TAC AAA G and CTT TGT AGA TAA CAA AAT TTC CAG CCT CTT GCA CTA CTG CTC TAT C for D180A . Mutant proteins were purified as described above for wild-type pyocin L1 . Soft agar overlay spot plates were performed using the method of [35] . 150 µl of test strain culture at OD600 = 0 . 6 was added to 6 ml of 0 . 8% soft agar and poured over an LB or KB agar plate . 5 µl of bacteriocin at varying concentrations was spotted onto the plates and incubated for 20 h at 37 or 28°C . 1 . 5 ml of a culture of P . aeruginosa E2 ( OD600 = 0 . 6 ) was centrifuged and resuspended in 100 µl of LB , to which 100 µl ( 8 mg ml−1 ) of purified pyocin L1 was added . The culture was grown for 1 h , plated onto a LB agar plate and incubated for 20 h at 37°C . Isolated colonies were identified as P . aeruginosa using 16S PCR as described previously [51] . The genomes of P . aeruginosa E2 and derived pyocin L1 tolerant mutants were sequenced at the Glasgow Polyomics Facility , generating paired-end reads on an Illumina MiSeq Personal Sequencer . Reads were mapped to the previously sequenced parent genomes of P . aeruginosa E2 using the CLC genomics workbench , MAUVE and RAST to create an ordered annotated genome . The CLC genomics workbench was used for genome comparisons and the identification of SNPs/INDELs . LPS was purified from 1 litre cultures of P . aeruginosa and P . syringae strains as described previously , with modifications including the omission of the final trifluoroacetic acid hydrolysis and chromatography steps [52] . Cells were grown for 20 h at 37°C and 28°C for P . aeruginosa and P . syringae respectively , pelleted by centrifugation at 6000 g for 20 min , and resuspended in 50 mM Tris , pH 7 . 5 containing lysozyme ( 2 mg ml−1 ) and DNase I ( 0 . 5 mg ml−1 ) . Cells were lysed by sonication and the cell lysate was incubated at 20°C for 30 min before EDTA was added to a final concentration of 2 mM . An equal volume of aqueous phenol was added and the solution was heated at 70°C for 20 min , with vigorous mixing . The solution was then cooled on ice for 30 min , centrifuged at 7000 g for 20 min and the aqueous phase extracted . Proteinase K was added to a final concentration of 0 . 05 mg ml−1 and dialysed for 12 h against 2×5 L H2O . LPS was pelleted by ultracentrifugation at 100 , 000 g for 1 h , resuspended in H2O and heated to 60°C for 30 min to remove residual proteinase K activity . LPS-derived carbohydrates were isolated by heating LPS in 2% acetic acid for 1 . 5 h at 96°C . Lipid A was removed by centrifugation at 13 , 500 g for 3 min followed by extraction with an equal volume of chloroform . The aqueous phase was then lyophilised . Purified LPS from wild-type and mutant samples were resolved by electrophoresis on 12% SDS-polyacrylamide gels . The LPS banding patterns were visualised by the Invitrogen ultrafast silver staining method . For immunoblotting LPS was transferred onto nitrocellulose membranes and western immunoblotting was performed as previously described using the CPA-specific monoclonal antibody N1F10 and alkaline phosphatase-conjugated goat anti-mouse Fab2 as the secondary antibody [39] . The blots were developed using SIGMAFAS BCIP/NBT tablets . ITC experiments were performed on a VP-ITC microcalorimeter ( MicroCal LLC ) . For monosaccharide binding , titrations were carried out at 299 K with regular 15 µl injections of ligands into 60–100 µM pyocin L1 or putidacin L1 at 300 s intervals . 50 mM d-rhamnose , d-mannose or l-rhamnose were used as titrants and reactions were performed in 0 . 2 M sodium phosphate buffer , pH 7 . 5 . d-rhamnose ( >97% ) was obtained from Carbosynth Limited ( UK ) and d-mannose and l-rhamnose ( >99% ) from Sigma-Aldrich ( UK ) . For O-antigen-pyocin L1 binding reactions , pyocin L1 or pyocin L1 variants were used as titrant at 100 or 150 µM with cleaved O-antigen sugars dissolved at 1 mg ml−1 in the chamber . For curve fitting we estimated the molar concentration of LPS-derived CPA containing carbohydrate chains at 20 µM based on an estimated average molecular weight of 10 kDa for CPA containing polysaccharides and estimating the percentage of total LPS represented by CPA containing carbohydrates as 20% of the total by weight [53] . This value may not be accurate and as such the stoichiometry implied by the fit is likely to be unreliable . However , the use of this estimated value has no impact on the reported parameters of ΔH , ΔS and Kd . For O-antigen-putidacin L1 binding reactions , O-antigen was used as the titrant at 3 mg ml−1 with 60 µM putidacin L1 in the chamber . Reactions were performed in 20 mM HEPES buffer pH 7 . 5 . All samples were degassed extensively prior to the experiments . Calorimetric data were calculated by integrating the area under each peak and fitted with a single-site binding model with Microcal LLC Origin software . The heats of dilution for each titration were obtained and subtracted from the raw data . NMR chemical shift perturbation analysis of sugar binding by pyocin L1 and putidacin L1 was carried out at 305 K and 300 K respectively . Fast-HSQC spectra [54] were recorded using 15N labelled proteins ( 0 . 1–0 . 2 mM ) and unlabelled ligands , d-rhamnose and d-mannose ( 100 mM ) , on a Bruker AVANCE 600 MHz spectrometer . Protein samples were prepared with and without the sugars present and volumes were exchanged at fixed ratios , making sure the protein concentration remained unchanged . The spectra were processed with Topspin and analysed with CCPNmr analysis [55] . Purified pyocin L1 at a concentration of 15 mg ml−1 was screened for crystallisation conditions using the Morpheus and PGA crystallisation screens ( Molecular Dimensions ) [56] . Screens were prepared using a Cartesian Honeybee 8+1 dispensing robot , into 96-well , MRC-format , sitting drop plates ( reservoir volume of 80 µl; drop size of 0 . 5 µl of protein and 0 . 5 µl of reservoir solution ) . Clusters of needle shaped crystals grew in a number of conditions in each screen over 3 to 7 days . Two of these conditions , condition 1 ( 20% v/v ethylene glycol , 10% w/v PEG 8000 , 0 . 03 M CaCl2 , 0 . 03 M MgCl2 , 0 . 1 M Tris/Bicine , pH 8 . 5 ) and condition 2 ( 20% PEG 550 MME , 20% PEG 20 K , 0 . 03 M CaCl2 , 0 . 03 M MgCl2 0 . 1 M MOPS/HEPES , pH 7 . 5 ) from the Morpheus screen were selected for optimisation by vapour diffusion in 24 well plates ( reservoir volume 500 µl , drop size 1 µl protein and 1 µl reservoir solution ) . Clusters of needles from these trays grew after 3–7 days and were mechanically separated . The un-soaked crystals were from condition 1 , while soaked crystals were from condition 2 . Un-soaked crystals were looped and directly cryo-cooled to 110 K in liquid nitrogen; D-mannose and D-rhamnose soaked crystals were soaked for 2–12 min in artificial mother liquor containing 4 M d-mannose or 2 M d-rhamnose , before cryo-cooling to 110 K . X-ray diffraction data were collected at the Diamond Light Source , Oxfordshire , UK at beam lines I04 , I04-1 and I24 . Automatic data processing was performed with Xia2 within the EDNA package [57] . A dataset from an un-soaked pyocin L1 crystal was submitted to the Balbes pipeline along with the amino acid sequence for pyocin L1 [58] . Balbes produced a partial molecular replacement solution based on the structure of Galanthus nivalis agglutinin ( PDB ID: 1MSA ) . Initial phases from Balbes were improved via density modification and an initial model was built using Phase and Build from the Phenix package [59] . The model was then built and refined using REFMAC5 and Coot 0 . 7 [60] , [61] . Validation of all models was performed using the Molprobity web server and Procheck from CCP4-I [62] , [63] . Two structures of sugar soaked pyocin L1 were solved by molecular replacement using Phaser [64] , with the sugar-free pyocin L1 as the search model . Additional electron density corresponding to bound sugars , was observed in both 2Fo-2Fc and Fo-Fc maps [65] . Sugars were fitted and structures refined using Coot 0 . 7 and REFMAC5 . β-d-mannose ( PDB ID: BMA ) corresponded best to the density of bound d-mannose . The density in the d-rhamnose complex best corresponded to α-d-rhamnose , for which no PDB ligand exists; a model for α-d-rhamnose was prepared by removing the oxygen from carbon 6 of α-d-mannose and submitting these PDB coordinates to the Prodrg server , which generated the model and modeling restraints [65] . The resultant α-d-rhamnose was designated with the PDB ID: XXR . SAXS was carried out on the X33 beamline at the Deutsches Elektronen Synchrotron ( DESY , Hamburg , Germany ) . Data were collected on samples of Pyocin L1 in the range of 0 . 5–5 mg ml−1 . Buffer was read before and after each sample and an average of the buffer scattering was subtracted from the sample scattering . The data obtained for each sample were analysed using PRIMUS [66] , merging scattering data at low angles with high angle data . The distance distribution function , p ( r ) , was obtained by indirect Fourier transform of the scattering intensity using GNOM [67] . A Guinier plot ( ln I ( s ) vs s2 ) was used to calculate the molecular weight at I ( 0 ) and radius of gyration , Rg , of PyoL1 . Ab initio models of the protein in solution were built using DAMMIF [68] , averaged with DAMAVER [69] and overlaid with the available crystal structure using SUPCOMB [70] .
|
Due to rapidly increasing rates of antibiotic resistance observed among Gram-negative pathogens , such as Pseudomonas aeruginosa , there is an urgent requirement for novel approaches to the treatment of bacterial infections . Lectin-like bacteriocins are highly potent protein antibiotics that display an unusual ability to kill a select group of bacteria within a specific genus . In this work , we show how the lectin-like protein antibiotic , pyocin L1 , can kill Pseudomonas aeruginosa with extraordinary potency through specific binding to the common polysaccharide antigen ( CPA ) of P . aeruginosa lipopolysaccharide . The CPA is predominantly a homopolymer of the sugar d-rhamnose that although generally rare in nature is found frequently as a component of the lipopolysaccharide of members of the genus Pseudomonas . The targeting of d-rhamnose containing polysaccharides by pyocin L1 and a related lectin-like protein antibiotic , putidacin L1 , explains the unusual genus- specific killing activity of the lectin-like bacteriocins . As we learn more about the link between changes to the microbiome and a range of chronic diseases there is a growing realisation that the ability to target specific bacterial pathogens while maintaining the normal gut flora is a desirable property for next generation antibiotics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"microbial",
"mutation",
"protein",
"interactions",
"microbiology",
"bacterial",
"biochemistry",
"genome",
"sequencing",
"protein",
"classes",
"protein",
"structure",
"bacterial",
"pathogens",
"plant",
"microbiology",
"proteins",
"microbial",
"pathogens",
"biology",
"recombinant",
"proteins",
"biochemistry",
"protein",
"chemistry",
"gram",
"negative",
"genomics",
"defense",
"proteins"
] |
2014
|
Lectin-Like Bacteriocins from Pseudomonas spp. Utilise D-Rhamnose Containing Lipopolysaccharide as a Cellular Receptor
|
Eukaryotic DNA replication origins differ both in their efficiency and in the characteristic time during S phase when they become active . The biological basis for these differences remains unknown , but they could be a consequence of chromatin structure . The availability of genome-wide maps of nucleosome positions has led to an explosion of information about how nucleosomes are assembled at transcription start sites , but no similar maps exist for DNA replication origins . Here we combine high-resolution genome-wide nucleosome maps with comprehensive annotations of DNA replication origins to identify patterns of nucleosome occupancy at eukaryotic replication origins . On average , replication origins contain a nucleosome depleted region centered next to the ACS element , flanked on both sides by arrays of well-positioned nucleosomes . Our analysis identified DNA sequence properties that correlate with nucleosome occupancy at replication origins genome-wide and that are correlated with the nucleosome-depleted region . Clustering analysis of all annotated replication origins revealed a surprising diversity of nucleosome occupancy patterns . We provide evidence that the origin recognition complex , which binds to the origin , acts as a barrier element to position and phase nucleosomes on both sides of the origin . Finally , analysis of chromatin reconstituted in vitro reveals that origins are inherently nucleosome depleted . Together our data provide a comprehensive , genome-wide view of chromatin structure at replication origins and suggest a model of nucleosome positioning at replication origins in which the underlying sequence occludes nucleosomes to permit binding of the origin recognition complex , which then ( likely in concert with nucleosome modifiers and remodelers ) positions nucleosomes adjacent to the origin to promote replication origin function .
All DNA transactions in living cells occur in the context of a highly regulated and dynamic chromatin structure . Not surprisingly , there is considerable evidence of functional relationships between nucleosomes , which are the basic repeating unit of chromosome structure , and origins of DNA replication . These relationships have been studied most extensively in the budding yeast Saccharomyces cerevisiae , largely due to the presence of well-defined replication origins in this organism , many of which have been identified on the basis of their ability to support plasmid maintenance in vivo . These sequences have been termed autonomously replicating sequences , or ARSs and many function as origins of replication in their chromosomal context . Budding yeast ARSs consist of an essential element , the ARS consensus sequence ( ACS ) as well as three elements that , while non-essential , contribute to origin function [1] . The ACS contains the binding site for the origin recognition complex ( ORC ) , a six-member protein complex that is essential for the initiation of DNA replication [2] . A number of studies have sought to identify which ARSs function as bona fide replication origins in the chromosomal context in vivo . These include approaches in which the genomic location of newly-replicated DNA is identified using high resolution tiling microarrays [3] , [4] , and studies in which binding sites for ORC or other critical replication factors are mapped across the genome [5]–[7] . The most comprehensive annotation of functional replication origins currently available combines these datasets with phylogenetic analysis and functional analysis to define 228 functional ARSs , and to locate the ACS within each of these [8] , [9] . Analysis of the canonical budding yeast replication origin ARS1 shows that this origin is flanked by two positioned nucleosomes and that the ACS is located in a nucleosome-depleted region ( NDR ) [10] . Mutations in the origin which cause the ACS to become occupied by a nucleosome compromise origin function [11] , presumably by occluding the ORC binding site . Mutations in the ORC binding site in both ARS1 and ARS307 allow nucleosomes to encroach upon the origin , indicating a role for ORC in maintaining a NDR at origins [12] . Interestingly , positioning nucleosomes away from the ORC binding site also compromise ARS1 function without affecting ORC binding [12] . Together , these studies with single origins indicated that nucleosomes can have both a negative and a positive role in regulating origin function , and that ORC is important for positioning nucleosomes that flank the origin . The extent to which these properties are generalizable across all replication origins remains unclear . Here we ask if the predictive power of newly available genome-wide datasets can address the extensibility of these findings to each well-defined origin . The availability of genome-wide maps of nucleosome positions in budding yeast has made it possible to investigate the relationship between replication origin function and nucleosome positioning on a global scale . The construction of these maps relies first on traditional nucleosome mapping tools whereby nucleosomes are cross-linked to DNA in vivo , followed by digestion with micrococcal nuclease ( MNase ) to degrade the linker DNA between nucleosomes . The mononucleosomal DNA , corresponding to the DNA contained within individual nucleosomes , is then hybridized to a high-resolution tiling microarray [13] , [14] or sequenced either directly or after antibody immunoprecipitation [15]–[17] to identify the regions of the genome that are occupied by nucleosomes . Average views of such data across large numbers ( 82 to 248 ) of annotated replication origins [15]–[17] or views of several individual replication origins [8] largely confirm the single-origin view derived from studies of ARS1: replication origins tend to contain a nucleosome-depleted region ( NDR ) flanked by nucleosomes . Here we use a comprehensively curated set of functional replication origins from budding yeast [8] combined with nucleosome maps constructed from tiling array hybridization of mononucleosome DNA [13] to analyze the chromatin structure at replication origins genome-wide . We find that the average view of chromatin organization at origins hides a surprising degree of diversity at individual origins . Since these origins are active in the chromosomal context , it suggests that functional origins can be built with a wide range of nucleosome positions relative to the ORC binding site . Genetic perturbation of ORC function caused origins to become more nucleosome-occupied and changed the phasing of the flanking nucleosomes . However , ORC-depleted origins did not become fully occupied by nucleosomes , likely because the underlying sequence at replication origins is resistant to nucleosome occupancy . Together these data provide a comprehensive view of the diversity of chromatin structure at replication origins , and suggest a model of nucleosome positioning at replication origins in which the underlying DNA sequence occludes nucleosomes to create a permissive environment for ORC binding , after which ORC positions nucleosomes in regular arrays on both sides of the ACS .
Considerable insight into chromatin structure at promoter elements has been gleaned from recent analyses of whole-genome nucleosome maps in the budding yeast S . cerevisiae . These analyses are facilitated by the ability to align all of the promoters in the yeast genome centered on a single functional element , the transcription start site ( TSS ) . Although some analysis of the nucleosome structure at DNA replication origins has been performed [8] , [15]–[17] , current views have not benefited from a systematic alignment of replication origins by a single functional element , analogous to the TSS for promoter analysis . Consequently , in the absence of such a fiduciary mark , these studies lack resolution . The most obvious feature with which to align replication origins is the ARS consensus sequence ( ACS ) , a 15 bp motif present in all budding yeast origins characterized to date . Additionally , replication origins have an intrinsic asymmetry , with the B1 element positioned 3′ of the ACS when the ACS is oriented with the T-rich strand as the 5′ to 3′ strand . We used a comprehensively curated set of 228 ACSs [8] , plus 50 ACSs annotated in the Saccharomyces Genome Database to generate ACS-aligned nucleosome maps for 222 ARSs . ARSs containing more than 9 duplicated microarray probes in the 800-bp region centered on the ACS where not included in our analysis . The nucleosome maps were aligned by the T residue at position 1 of the ACS and were oriented in the same direction . Although not all of these ACSs have been confirmed experimentally , those that have not are derived from the integration of three independent datasets: mapping of nascent replicating DNA [4] , [18] , genome-wide binding profiles of the essential initiation factors ORC and MCM complex [6] , and evolutionary conservation among the sensu stricto yeast species [8] , and so represent a high-quality dataset with extremely low levels of false-positive ACSs predicted . We first applied this alignment to high-resolution nucleosome maps derived from microarray analysis of nucleosomal DNA [13] . We compared this ACS-centered view of 222 replication origins to a TSS-centered view of 222 randomly selected promoters ( Figure 1 ) . As expected , the aggregate ACS-centered view , presented as an average plot in Figure 1A revealed a significant nucleosome depleted region ( NDR ) centered 36 bp to the right of the ACS . We measured the peak-to-peak distance between the nucleosomes flanking the NDR in the average plots ( Figure 1C ) . When the ACS-centered average of the origins was compared to the TSS-centered view of 222 promoters ( Figure 1B ) several differences were apparent . The NDR for origins is , on average , narrower than that for promoters ( ∼276 bp vs ∼312 bp ) , and dramatically narrower than that previously reported for origins ( 500 bp ) when origins were analyzed without the benefit of ACS alignment and without being oriented with respect to the T-rich strand [17] . The size of the average NDR that we measured contains ∼146 bp of DNA sequence that would be within the two flanking nucleosomes . Therefore ∼130 bp of DNA is free of nucleosomes at the average replication origin in budding yeast . This is significantly larger than the length of DNA that is protected by ORC [2] , [19] . The ACS-centered average nucleosome map also reveals the presence of arrays of positioned nucleosomes extending away from the origin in both directions . The presence of a positioned nucleosome on each side of an NDR at replication origins has been previously noted [16] , but the phased arrays of nucleosomes that are apparent in our analysis have not been described . By analogy with TSSs , we refer to the upstream flanking nucleosome as -1 , and the downstream flanking nucleosome as +1 . Although both origins and promoters are flanked by positioned nucleosomes , they differ in their spacing . The linker between the first two nucleosomes flanking ACSs ( +1 and +2 or −1 and −2 ) is larger than that between the nucleosomes downstream of TSSs ( Figure 1C ) . Additionally , the asymmetric organization of nucleosomes surrounding TSSs , with more discrete positioning of nucleosomes downstream of the TSS than upstream , is not apparent around ACSs , which have phasing that is equally discrete both upstream and downstream . This symmetrical arrangement of nucleosomes at origins might be functionally relevant , given that origins act in a symmetrical fashion in establishing bi-directional replication forks . A decay of the nucleosome phasing is apparent as one moves away from the ACS in both the “+” and “−” directions . This is similar to the decay of phasing seen at TSSs [13]–[15] , [20] and , as proposed for TSSs [16] , [21] , suggests the nucleosomes upstream and downstream of the +1 and −1 flanking nucleosomes are statistically positioned . It is of interest that despite the propensity for replication origins to be located within intergenic regions [8] , they do not adopt a nucleosome structure that is similar to that of the average promoter region . Finally , we plotted the data as bivariate histograms to display the diversity in the data that is not reflected in the average plot ( Figure 1A and 1B ) . The considerable scatter in these plots suggests that there are substantial differences in nucleosome structure among the 222 ARSs analyzed . Current models of nucleosome positioning suggest that nucleosome occupancy patterns are the combined result of contributions of DNA sequence , including periodic dinucleotide patterns and other structural and sequence features of DNA , and of protein factors , including chromatin remodeling factors and other DNA binding proteins [22] . For example , regions of high AT content are known to exclude nucleosomes [23] and poly ( dA∶dT ) tracts correlate with exclusion of nucleosomes at replication origins [17] . We sought to identify sequence features that correlate with the average nucleosome occupancy pattern at origins of replication . As expected we found that GC content was highly correlated with nucleosome depletion at the ACS , but described a much larger NDR and did not recapitulate the phasing adjacent to the ACS ( Figure 2A ) . We compared 103 different dinucleotide properties [24] and found a number of dinucleotide properties that correlated with the average replication origin nucleosome occupancy pattern . The dinucleotide properties were grouped using k-means clustering ( Figure S1 ) to show 6 general patterns . The average dinucleotide profile of each group is shown in Figure 2B , compared to the ACS-centered average nucleosome occupancy . Features in group I , which contains DNA structural features such as twist+rise and minor groove distance , describe the NDR width accurately , describe the positioned nucleosomes flanking the NDR , and to a lesser extent describe the positions of nucleosomes flanking the +1 and −1 nucleosomes . Dinucleotide features in group II anti-correlate with the NDR but do not describe the flanking nucleosomes . Group III and V features , such as melting temperature and free energy , tend to describe a more extensive NDR than observed in our average nucleosome map . Finally , groups IV and VI contain dinucleotide features that anti-correlate with the NDR , with the +1 and −1 nucleosomes , and to a lesser extent the flanking nucleosomes . We conclude that DNA sequence features contribute to nucleosome occupancy patterns at replication origins , particularly with respect to the NDR . To categorize replication origins across the genome and to visualize the diversity suggested by the bivariate histograms , we used k-means clustering to group origins by the similarities of their nucleosome occupancy patterns surrounding the ACS . Analysis using 2 to 7 groups revealed several patterns of nucleosome occupancy surrounding the ACS ( Figure S2 ) . To highlight some of the diversity in individual origin profiles we produced a heatmap of origins assembled into 4 groups ( Figure 3A ) . This grouping was chosen because it had low average inter-group correlations ( all were below 0 . 7 ) indicating that the identified groups are relatively distinct . In the heat map , blue regions correspond to the NDR and linker regions while yellow regions are occupied by nucleosomes . Considerable diversity is apparent: the extent of the NDR varies as does the length of the linker 3′ to the flanking nucleosome , some origins have a second NDR either one or two nucleosomes 5′ of the major NDR , and some origins lack a clear NDR . Although replication origins occur most often in intergenic regions , the different nucleosome patterns could represent the influence of other chromosomal features . We mapped the positions of nearby TSSs and translation stop sites ( gene ends ) for each of the nucleosome occupancy patterns ( Figure 3B ) . For the two groups of origins ( groups 3 and 4 ) that contain a second NDR to the left of the ACS there is a peak of TSS elements immediately 5′ to the second NDR . The NDR associated with TSSs is typically centered −50 to −100 bp relative to the TSS [13]–[17] . Therefore the position of the TSS elements 5′ to the second NDR of the origin profile indicates that the transcription units are oriented away from the ACS , as would be expected for origins positioned within intergenic regions . This orientation ensures that replication and transcription are co-directional . The NDR at the ACS for replication origins in groups 1 and 2 is associated with a peak in gene ends , again consistent with the intergenic location of most origins . Gene ends ( i . e . 3′UTRs ) are associated with low nucleosome occupancy [16] , [25] , which could contribute to the propensity of the region surrounding the ACS to remain unoccupied by nucleosomes . Together these data suggest that nucleosome occupancy at replication origins reflects proximity to TSSs and to gene ends . We did not detect a relationship between nucleosome occupancy and proximity to other prominent chromosomal features such as centromeres , telomeres , and adjacent ARSs . The diverse nature of nucleosome positions was also apparent when individual origins were analyzed ( Figure 4 and Figure S3 ) . In this analysis , the log2 ratios from the microarrays were used to determine the position of each individual nucleosome midpoint ( indicated by broken lines , Figure 4 , and red squares , Figure S3 ) . Replication origins such as ARSVII–112 show a pattern similar to the average pattern . Origins such as ARSII–170 and ARSIV–1166 have a second NDR adjacent to the ACS . Some origins such as ARSX–737 lack a clear NDR . Since essentially all of the origins in this study are considered to be efficient , our data indicate that active , functional replication origins can be built with a variety of nucleosome occupancy patterns . Our nucleosome mapping data indicated that there is a preferred arrangement of TSSs or gene ends with respect to a subset of origins . Analysis of individual origins ( Figure S3 ) also suggested that there is variation in NDR width among replication origins . Accordingly , we asked whether there was any relationship between TSS and gene end locations or NDR width and the timing of replication origin firing , using genome-wide datasets that quantify origin timing in vivo [3] , [4] , [26] . Using the dataset of Feng et al . in which origin firing in the presence of HU ( one definition of early origins ) was determined by mapping the location of nascent ssDNA genome-wide [26] we found that TSS-proximal origins , those origins with a TSS within 800bp of the ACS , had a greater proportion of early origins , 0 . 47 ( N = 107 ) , than the entire ACS-containing origin data set , 0 . 39 ( N = 222 ) . This difference is significant as this proportion occurs relatively rarely , in the upper tail of the timing distribution ( i . e . , 98 . 3–99 . 1% of 10000 re-samples of 107 origins from the set of 222 origins have a lower proportion of early origins ) . For these 107 origins with a TSS within 800 bp of the ACS , we used a moving sum to describe the distribution of TSSs ( Figure 5A , pink triangles ) . The TSS distribution was non-uniform , with peaks occurring at −276bp , +112bp , and +328bp relative to the ACS . We then determined the proportion of early origins [26] across the TSS distribution ( Figure 5A , green diamonds ) . Local maxima in HU timing overlap the peaks in TSS distribution: origins with a TSS at these positions tend to fire early . A similar trend is apparent in the dataset of Raghuraman et al , which is derived from mapping of newly-replicated DNA using microarrays [3] . The TSS peaks overlap with the earliest replication times ( local minima ) in the timing dataset ( Figure 5B ) . Interestingly , this trend was not evident in the final genome-wide replication timing dataset [4] . This may reflect the different methodologies used to determine replication timing . Indeed , the correlation among the different timing datasets is low . We conclude that the location of TSS elements relative to the ACS can influence replication timing . In particular , origins with a TSS ∼46 bp or ∼380 bp to the right of the ACS have a higher than average proportion of early origins and an earlier mean replication time . A similar analysis was performed for gene ends ( Figure 5C and 5D ) . As was the case for TSSs , the distribution of gene ends with respect to the ACS was non-uniform ( Figure 5C , pink triangles ) . The clearest trend in our comparison to the Feng et al dataset [26] was that origins with a gene end positioned at the ACS tended to be late firing ( a local minimum in the proportion of early origins; Figure 5C , green diamonds ) . This pattern was also observed when the Raghuraman et al dataset [3] was analyzed ( Figure 5D ) . Thus , the location of gene ends relative to the ACS also influences replication timing , and in particular origins with a gene end at the ACS tend to fire late in the cell cycle . Finally , we examined the distribution of NDR widths for the 222 origins . The NDR width distribution was divided into 7 quantiles ( Figure 5E ) and the proportion of early origins [26] was calculated for each . The quantile representing the narrowest NDRs ( 128 to 236 bp ) had a low proportion of early origins ( i . e . , these origins tended to be late firing ) . Similarly , the origins with the widest NDRs ( 324 to 580 bp ) also tended to be late firing . The earliest origins were found to have NDR widths between 303 and 324 bp . These data suggest that there is an NDR width that is optimal for early origin firing . One reasonable candidate for a barrier element that establishes nucleosome positioning at replication origins is the binding of the origin recognition complex ( ORC ) . To genetically perturb ORC function we took advantage of a GAL1 promoter-driven orc2-1 allele [27] . This allele produces Orc2 with a very short half-life [28] that is rapidly depleted when the GAL1 promoter is repressed by the addition of glucose to the culture medium [27] . Depletion of Orc2 in mitosis greatly reduces ORC function , as the depleted cells accumulate in late G1 phase of the subsequent cell cycle , unable to initiate DNA replication [27] ( Figure S4 ) . We isolated nucleosomal DNA from Orc2-depleted and control cells , hybridized this DNA to tiling microarrays , and generated nucleosome occupancy maps ( Figure 6A and 6B ) . As expected , the control nucleosome map is highly similar ( correlation of 0 . 998 ) to that shown in Figure 1 and shows a similar NDR width distribution ( Figure S5 ) . By contrast , nucleosome positioning is altered when Orc2 is depleted ( Figure 6B ) . To highlight the differences between WT and the Orc2 depletion strain we compared the nucleosome profile of the control cells to that of the Orc2-depleted cells across the 222 replication origins analyzed ( Figure 6C , green line ) . The primary effect of Orc2 depletion was a shift of nucleosomes inward towards the ACS and an accompanying increase in nucleosome occupancy at the ACS . To quantify this change in NDR width , the microarray log2 ratios were used to determine the location of nucleosome midpoints . The nucleosome calls for each origin in the Orc2 depletion strain and wild-type ( Figure S6 ) give an indication of nucleosome occupancy changes at each individual origin . Using these nucleosome calls we analyzed the influence of Orc2 depletion on the distance between the two nucleosomes flanking the ACS for each origin ( Figure 6D ) and determined that NDR width was reduced in a large fraction of origins . On average , the NDR was reduced from 276 bp in wild-type to 228 bp upon Orc2 depletion . The peak-to-trough height of the nucleosomes flanking the ARS was also slightly reduced , indicating that the nucleosomes became more delocalized upon Orc2 depletion . Together , these observations suggest that ORC contributes to the establishment of nucleosome positioning at replication origins . As a control we compared TSS-centered nucleosome maps of GAL:orc2-1 and wild-type and found the maps to be almost identical ( Figure S7 ) . Although there was small decrease in nucleosome occupancy at TSSs ( an effect opposite to that seen at ACSs ) , the positions of the nucleosomes flanking TSSs were unchanged , indicating that the effect of ORC depletion is specific to replication origins . We noted that upon Orc2 depletion the ACS did not , on average , become completely nucleosome occupied . Although this could in part be due to incomplete inactivation of ORC , it is also possible that even in the complete absence of ORC the ACS would not become nucleosome-bound . This extreme case of complete ORC depletion is difficult to achieve in vivo because ORC genes are essential . We turned instead to analysis of maps of nucleosomes assembled on S . cerevisiae genomic DNA in vitro in the complete absence of non-histone proteins [29] . The average ACS-centered view of 174 ARSs in this dataset is shown in Figure 7A . When nucleosomes are assembled in the complete absence of ORC a large NDR remains at the ACS , indicating that the underlying sequence of the origin is a critical element that specifies the low nucleosome occupancy at the ACS , and offering an explanation for the persistent NDR we observed after ORC depletion . This is also consistent with our observation that a number of DNA sequence properties correlate with the low occupancy at the NDR ( Figure 2B and Figure S1 ) . It is worth noting , however , that the NDR in the in vitro map is substantially larger than those in the in vivo maps ( 445 bp vs 276 bp ) , similar to the case for promoters in this dataset . Interestingly , a number of dinucleotide sequence parameters also described NDRs larger than in the in vivo map ( Figure 2B and Figure S1 ) . One reasonable possibility is that the sequence surrounding the ACS occludes nucleosomes over a wider region than that observed in the in vivo maps , but the contributions of other proteins in vivo likely results in a denser nucleosome packing than is achieved in the in vitro reconstitutions [29] , resulting in a greater encroachment of nucleosomes into the ACS region . Lastly , in the in vitro data , there was a complete absence of phasing of the nucleosomes adjacent to the ACS , indicating that while sequence plays a large role in preventing nucleosome formation at the ACS , the assembly of a phased array of positioned nucleosomes at replication origins likely requires the contribution of non-histone protein factors or higher histone density than was achieved in vitro .
We have produced a comprehensive nucleosome map of DNA replication origins in S . cerevisiae . Our analysis is distinct from previous genome-wide views of nucleosome position at replication origins [15]–[17] in that we combined a comprehensively curated set of origins in which the ACS element was accurately mapped [8] with the most comprehensive genome-wide nucleosome maps . In this manner , we detected the NDR flanked by nucleosomes that was evident in previous views ( derived without critical alignment parameters [15]–[17] ) . But more importantly , we extend this view by detecting phased arrays of positioned nucleosomes extending from either side of the origin NDR . Considerable diversity was evident in the replication origin nucleosome maps , reinforcing the notion that the average view does not reflect the different nucleosome occupancy patterns that exist at active , functional replication origins . We found that adjacent genomic features , most notably TSS elements and gene ends , can influence the nucleosome patterns at replication origins . In particular , the presence of an adjacent TSS can result in a second NDR in addition to the NDR at the ACS . We found that TSSs are distributed asymmetrically at replication origins and that maxima in the TSS distribution correlate with early origin firing . The presence of a second NDR could improve the accessibility of the replication origin for ORC or the proteins that are recruited by ORC , or factors bound at the promoter element within the second NDR could play a direct role in recruiting replication proteins to the pre-initiation complex . In either case the activity of the replication origin would be promoted , consistent with increased likelihood that these origins will be active in early S phase . We also found that extremes of NDR width , either narrow or wide , were characteristic of late origins . For example , origins with the narrowest NDR have higher than average occupancy at the ACS . This architecture could lead to a competition between nucleosomes and ORC for binding at the ACS , resulting in a reduced efficiency of origin firing , as previously suggested [11] , [17] , [30] . We conclude that functional replication origins can be built with different chromatin architectures , and that adjacent genomic features can influence the timing of replication origin firing . Unfortunately , due to a lack of appropriate genome-wide datasets we were unable to test more sophisticated measures of origin robustness . Origin efficiency , or the likelihood that a given origin will fire in a given cell cycle , is an important parameter to test with respect to origin nucleosome architecture . This parameter is quite complex , however , encompassing not simply the intrinsic efficiency of an origin , but also the time during S phase when it fires ( as later firing origins are more likely to be replicated passively from a neighboring origin ) , as well as the proximity of other origins , which also have unique efficiencies . As genome-wide origin efficiency datasets become available in S . cerevisiae our classification of different nucleosome patterns at replication origins will be an important tool for further investigating the impact of nucleosome structure on origin function . Accordingly , we expect the analysis presented here to represent a benchmark for future large-scale studies . One attractive model of nucleosome positioning posits that uniformly-spaced arrays of nucleosomes , such as those seen downstream of TSSs , are the result of nucleosome packing adjacent to a barrier element [16] , [20] , [21] , [31] , [32] . This uniform spacing decays further away from the barrier element , and this decay is seen as a decrease in the peak to trough height . Our data suggests that , on average , replication origins conform to this statistical positioning model . The average ACS-centered view of replication origins revealed strongly positioned +1 and −1 nucleosomes flanked by arrays of phased nucleosomes in which the uniform spacing decays as one moves away from the ACS . As is the case with the +1 nucleosome at TSSs [21] , the key to understanding nucleosome positioning at replication origins likely lies in understanding the elements responsible for positioning the +1 and −1 nucleosomes that flank the ACS . Analysis of the underlying sequence at replication origins gave conflicting results . On one hand , assembly of nucleosomes in vitro ( in the complete absence of ORC ) resulted in a larger NDR at the ACS than that observed in vivo , indicating that the intrinsic sequence preference of histones does not accurately describe the positions of the +1 and −1 nucleosomes . However , this large NDR is likely the result of lower nucleosome density ( approximately 50% of the in vivo density ) in the chromatin assembled in vitro [29] , which might prevent the more dramatic encroachment of nucleosomes towards the ACS that is observed in vivo . Analysis of dinucleotide patterns revealed some sequence properties that predicted both an NDR of the expected size , and the positions of the +1 and −1 nucleosomes , indicating a role for sequence in positioning these critical nucleosomes . Perhaps the most compelling evidence that DNA sequence alone does not position the +1 and −1 nucleosomes at replication origins comes from genetic perturbation of the origin recognition complex . Upon depletion of ORC we found that most origins displayed a change in the position of the nucleosomes flanking the ACS , with nucleosomes shifting inwards towards the ACS . In addition , in many cases the flanking nucleosomes became delocalized . These changes result in a shift in the phasing of adjacent nucleosomes and in delocalization of adjacent nucleosomes . Thus , when ORC binding is compromised the position of the +1 and −1 nucleosomes is altered , consistent with ACS-bound ORC serving as a barrier element component . However , the nucleosome-free region that we observe in vivo when ORC is present is , at ∼130 bp , considerably larger than both the in vitro binding footprint of purified ORC [2] and the ORC footprint seen in vivo [33] , [34] , suggesting that bound ORC is not the sole barrier element . We propose that ORC , in concert with additional protein factors recruited by ORC , positions the nucleosomes that flank the NDR at origins of replication . Together our data suggest a model of nucleosome assembly at replication origins ( Figure 7B ) in which the NDR is specified by the DNA sequence of the ARS . This NDR is narrower in vivo than in vitro due to the presence of chromatin remodeling and modifying activities , yet wider than the ORC binding site . This sequence-specified NDR creates a chromatin environment that is permissive for ORC binding to the ACS . Binding of ORC , and perhaps recruitment of chromatin remodelers and modifiers by ORC ( such as Rpd3 , Sir1 , Hat1 , and Hat2 [35]–[38] ) specifies the position of the +1 and −1 nucleosomes , resulting in arrays of phased nucleosomes on either side of the ACS . These positioned nucleosomes then become important for the assembly of the pre-replicative complex of replication initiation proteins [12] prior to origin firing . One particularly attractive feature of this model is that it is consistent with the suspected role of chromatin structure in regulating replication origins in metazoans [39]–[42] . Perhaps in the more complex replication origins of higher eukaryotes the role of DNA sequence recognition by ORC has been partially replaced by a more direct interplay between nucleosomes and the origin recognition complex . That the bromo-adjacent homology domain , which interacts with nucleosomes in some contexts [43] , facilitates the binding of human ORC with chromosomes [44] suggests a mechanism by which this could be achieved . It will of course be of tremendous interest to test whether nucleosome positioning at DNA replication origins is dictated by a combination of DNA sequence and ORC binding in other organisms , as appears to be the case in yeast . Note added in proof: While this manuscript was under review a similar study was published [45] . Although the studies utilized different ( but largely overlapping ) origin/ACS lists and methodologies ( sequencing vs . microarray hybridization ) , they reached complementary conclusions .
In this section , wild-type refers to the S288C nucleosomal dataset ( http://chemogenomics . stanford . edu/supplements/03nuc/files/analyzed_data_complete_bw20 . txt ) [13] . The tiling array coordinates within this dataset refer to a February 2006 genome release . Nieduszynski et al . , proACS coordinates for 228 origins refer to an October 2003 release [8] . To locate these ACSs within the February 2006 genome ( http://hugheslab . ccbr . utoronto . ca/supplementary-data/tillo/nucleosomes/ ) , the 15bp proACS for each origin was used to search the corresponding chromosomal sequence in order to find its location ( s ) . In cases where more than one match was found ( N = 8 origins ) , the closest ACS to the described ACS was chosen as the 2006 proACS . A coordinate was assigned to each ACS , as the minimum of its start/end proACS coordinates . Using SGD chromosomal features from February 2006 , 65 ACSs were located . SGD proACS calls are 11bp long . To locate the 15bp proACS , the minimum of ACS start/end sites were subtracted by 2 . These ACSs were annotated with their ORIdb identifier , and the entire list of Nieduszynski et al . , and SGD ACSs was filtered for duplicate origin calls . This resulted in a list of 278 ACS calls ( 228 Nieduszynski + 50 SGD ) . This list was then filtered based on the criteria that at least 800bp of flanking sequence is located on either side of the ACS to give a list of 255 ACSs . The final list was obtained after origins which contained more than 9 duplicated probe sequences were removed . Duplicated sequences were identified from the tiling array BPMAP file using the R affy package to parse the BPMAP file . The coordinates and identities of origins are summarized in Table S1 . The ACS coordinates can be used to extract nucleosome position information for individual origins from the web-accessible compendium of nucleosome positions at http://refnucl . atlas . bx . psu . edu [46] . ACS proximal probes , all probes within 800bp of the ACS were localized and converted to a text file where each position 0 , represents the nearest ACS probe . When a probe is not located within a 4bp window , the value was assigned as NA . The orientation of the ACS , which strand is the T-rich strand , was taken into account by flipping the entire list of extracted ( − ) -sense , T-rich strand on the C strand , log2 values . This list ( Table S2 ) was imported into R ( R Development Core Team , Vienna , Austria; http://www . r-project . org/ ) , and LOESS-smoothed using a span that encompassed 36 probes . Using R , the mean-ACS centered ACS profile was generated and overlaid onto a bivariate histogram , generated using the R hexbin package . The hexbin serves as a two-dimensional error bar for each point within the mean ACS profile . As a comparison , a random subset of coding genes was obtained using a random number generator to pick 222 origins from a list of 5015 coding genes [13] . To calculate the average size of nucleosomes NDRs in ARSs and coding gene profiles , the locations of nucleosome midpoints , peak log2 values , were visually selected using R and the distance between points was determined . A list of 103 DNA dinucleotide properties were obtained from the DiProDB website [24] . The sequence of 222 oriented origins was used to count dinucleotides within 75bp windows using the count function of the Seqinr package [47] . At each window , the dinucleotide counts were multiplied by the corresponding property value , summed for all dinucleotides and divided by the total number of dinucleotides in the window . This value was then assigned to the central probe . In order to cluster the data the average DNA dinucleotide profile was rescaled , a linear conversion of a set of numbers so that the values lie in the range of -1 to 1 , and LOESS-smoothed using a span of 76bp . Using the average and scaled DNA dinucleotide properties , the values between −372 to +424 around the ACS were clustered into 6 groups using the R-implementation of k-means clustering with 10000 iterations . The data were visualized using a heatmap in which each average DNA dinucleotide property is sorted by correlation with its k-means assigned subcluster average DNA dinucleotide property . The ∼800-bp region ( −372 to 424bp ) which on average encompasses the region containing two nucleosomes surrounding the ACS was clustered using the R-implementation of k-means clustering with 10000 iterations . The heatmap was constructed using the heatmap . 2 function of the R gplots package . Subclustered nucleosome occupancy patterns are based on the per-position average log2 value of origins within a particular cluster . The genomic context of each origin in our dataset ( N = 222 ) was determined by comparing the location of the ACS against a list of genomic features ( Table S3 ) : coding gene start/end sites ( http://chemogenomics . stanford . edu/supplements/03nuc/files/clusters/polyA_segments_verified_coords . txt ) , telomeres and centromeres ( http://downloads . yeastgenome . org/chromosomal_feature/archive/SGD_features . tab . 200602 . gz ) , and the locations of all ARSs ( http://www . oridb . org ) localized to the February 2006 genome release using BLAT ( http://genome-test . cse . ucsc . edu/~kent/exe/ ) . Genomic context was analyzed for each origin by determining the location of the closest centromere ( CDEII element ) , telomeric region , origin region ( ORIdb ) , gene start and gene end sites with respect to the ACS ( Table S4 ) . The orientation of genomic features was taken into account by determining the orientation of each genomic feature with respect to aligned origins ( T-rich side of the ACS on the Watson strand ) . For each subcluster , the locations of gene ends and TSSs within 800bp of the ACS were determined using a moving sum count in which the number of TSSs or gene ends were counted within a 25 probe window . The moving sum distribution was LOESS-smoothed using a span encompassing 26 probes . Replication timing from Raghuraman et al . ( N = 170 ) as well as origin activity in hydroxyurea from Yabuki et al ( N = 222 ) and Feng et al . ( N = 222 ) [3] , [4] , [26] were obtained from OriDB [9] . In order to compare the replication timing of all 222 origins , replication data ( http://www . sciencemag . org/feature/data/raghu1064351/PooledHLData/pooledHLdata . html ) was used to identify the nearest replication time data point closest to the ACS location . One caveat of this approach is the differences in genome builds between the ACS coordinates and the Raghuraman et al . data . The influence of genome build differences was not strong because replication timing data was smoothed in a 10-kb window: 159 of 170 origins were assigned replication times identical to those assigned by ORIdb and the remaining 11 origins differ by only ∼2 . 3 minutes . The replication timing and origin activity in HU data was used to determine the average replication timing within 25 probe windows of TSSs or gene ends distributed within the 800bp region surrounding the ACS . The proportion of early origins and replication time was determined when a region of 25 probes contained more than 4 origins with either a TSS or a gene end . The early origin proportion distribution was LOESS-smoothed using a span which encompassed 26 probes . NDR width was determined using microarray log2 ratios to determine the location of nucleosome midpoints . The nucleosome midpoint was defined in the 800bp region surrounding the ACS by determining the correlation of 26 probe windows against the 26 probes which encompassed the average log2 maxima on either side of the ACS . The local maxima which passed a correlation cutoff of 0 . 45 were defined as nucleosome midpoint locations . The ACS-proximal nucleosome calls on either side of the ACS were used to calculate the NDR width . The width distribution was determined using a moving sum with a window of 35bp . The proportion of early origins within each 35bp window was determined using the Feng et al . dataset . The NDR widths were divided into 7 quantiles in order to highlight changes in replication timing for different NDR widths . The proportion of early origins was found for each NDR width group and the P-value was determined by resampling 10 , 000 groups of identical size and determining how many samples contained early origin proportions that were less extreme . Nucleosomal DNA was obtained as described via micrococcal nuclease digestion [13] with the following modifications: increasing the size of cultures from 50mL to 200mL and modifications to nucleosomal DNA purification . Single colonies of either W303-1A or GAL:orc2-1 [28] were inoculated into 25mL of YPAG and grown overnight ( ∼20h ) at 30°C . The cultures were diluted to an OD ∼0 . 1/mL in a final volume of 200mL YPAG in a baffled 1L flask . Cultures were grown until an OD600 ∼0 . 6/mL and then blocked with nocodazole ( Sigma ) at a final concentration of 5µg/mL with a final concentration of 1% DMSO . Cells were blocked for 90 minutes , collected and resuspended in 200mL YPAD containing 5µg/mL nocodazole and 1% DMSO . Cells were blocked in YPAD for 60 minutes , collected and released into YPAD . Samples were collected every 15 minutes from 30 minutes to 2 hours after the release from the nocodazole , and analyzed by flow cytometry using a Guava EasyCyte ( Massachusetts , US ) following sample preparation as described [48] . The final sample , at 2 hours post-release , was cross-linked using methanol-free formaldehyde at a final concentration of 2% . After the formaldehyde was quenched using 125 mM glycine for 5 minutes , the cells were collected , washed with 1× PBS , collected into a 50 mL Falcon tube , frozen using liquid N2 and stored at −80°C . Following spheroplasting and micrococcal nuclease digestion , nucleosomal or genomic DNA was isolated using a phenol-extraction , followed by a phenol-chloroform extraction [13] , followed by ethanol precipitation and resuspension in 50µL of dH2O and 4µL 10 mg/mL RNase A . RNA was digested for 3h at 37°C followed by ethanol precipitation and resuspension in 45µL H2O . The quality of DNA was assessed using 2% w/v agarose gels and an Agilent BioAnalyzer . DNA labeling and hybridization to 4bp resolution Affymetrix tiling arrays was as described [13] . Two biological replicates of GAL:orc2-1 and W303-1A nucleosomal DNA microarrays were obtained along with one biological replicate of W303-1A genomic DNA ( http://www . ebi . ac . uk/microarray-as/ae/ , Accession Number: E-MEXP-2369 ) . To get a view of nucleosome positioning within GAL:orc2-1 or W303-1A the nucleosomal DNA CEL files were compared against the CEL file of W303-1A genomic DNA as described [13] . The text files from TAS were parsed in a similar manner as the Lee et al . wild-type data: the 1600bp window-centered on the ACS was extracted and oriented based on which strand contained the T-rich ACS sequence ( Tables S5 , S6 , S7 ) . To highlight differences between GAL:orc2-1 and W303-1A origins , the text file obtained by comparing nucleosomal arrays GAL:orc2-1 vs W303-1A was LOESS-smoothed and nucleosome locations were determined using the same criteria used to identify nucleosomes in the Lee et al . 2007 dataset . The normalized genome-wide locations of nucleosomes assembled onto naked yeast genomic DNA data file ( http://genie . weizmann . ac . il/pubs/nucleosomes08/nucleosomes08_data . html ) [29] was parsed to obtain the normalized log2 value of the 1600bp surrounding the ACS start coordinate . This dataset is missing values that are present in the tiling array data . Thus , origins which had at most 40 missing calls in the 800 bp region ( N = 801 calls ) surrounding the ACS ( N = 174 ) were used to construct the average ACS profile of in vitro nucleosomes and bivariate histogram as for the wild type profile .
|
Eukaryotic DNA replication begins at specific sites in the genome called replication origins , which are bound by the proteins that comprise the origin recognition complex ( ORC ) . In budding yeast , there are more replication origins available than are used in any particular cell division cycle . Each origin has a characteristic time during the cell division cycle when the DNA replication machinery is assembled at a particular origin and begins to replicate DNA . Previous studies have indicated that differences in replication timing and origin use/availability may be a consequence of the chromatin structure surrounding an origin . Here we present a genome-wide analysis of nucleosome architecture of replication origins aligned by their ORC-binding site . We find that origins can be built with a variety of nucleosome occupancy patterns , and that these patterns are influenced by adjacent genomic features . Finally , we determined the genome-wide consequences of ORC depletion on nucleosome architecture at origins . ORC depletion allowed encroachment of flanking nucleosomes towards the origin and changed the nucleosome phasing , indicating that ORC acts as a barrier to position and phase nucleosomes . Our analysis provides a comprehensive , genome-wide view of replication origins that reveals a previously unappreciated diversity in origin structure .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry/replication",
"and",
"repair",
"genetics",
"and",
"genomics/chromosome",
"biology"
] |
2010
|
Diversity of Eukaryotic DNA Replication Origins Revealed by Genome-Wide Analysis of Chromatin Structure
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Sex chromosomes evolve distinctive types of chromatin from a pair of ancestral autosomes that are usually euchromatic . In Drosophila , the dosage-compensated X becomes enriched for hyperactive chromatin in males ( mediated by H4K16ac ) , while the Y chromosome acquires silencing heterochromatin ( enriched for H3K9me2/3 ) . Drosophila autosomes are typically mostly euchromatic but the small dot chromosome has evolved a heterochromatin-like milieu ( enriched for H3K9me2/3 ) that permits the normal expression of dot-linked genes , but which is different from typical pericentric heterochromatin . In Drosophila busckii , the dot chromosomes have fused to the ancestral sex chromosomes , creating a pair of ‘neo-sex’ chromosomes . Here we collect genomic , transcriptomic and epigenomic data from D . busckii , to investigate the evolutionary trajectory of sex chromosomes from a largely heterochromatic ancestor . We show that the neo-sex chromosomes formed <1 million years ago , but nearly 60% of neo-Y linked genes have already become non-functional . Expression levels are generally lower for the neo-Y alleles relative to their neo-X homologs , and the silencing heterochromatin mark H3K9me2 , but not H3K9me3 , is significantly enriched on silenced neo-Y genes . Despite rampant neo-Y degeneration , we find that the neo-X is deficient for the canonical histone modification mark of dosage compensation ( H4K16ac ) , relative to autosomes or the compensated ancestral X chromosome , possibly reflecting constraints imposed on evolving hyperactive chromatin in an originally heterochromatic environment . Yet , neo-X genes are transcriptionally more active in males , relative to females , suggesting the evolution of incipient dosage compensation on the neo-X . Our data show that Y degeneration proceeds quickly after sex chromosomes become established through genomic and epigenetic changes , and are consistent with the idea that the evolution of sex-linked chromatin is influenced by its ancestral configuration .
Sex chromosomes have originated independently many times from ordinary autosomes in both plants and animals [1] . A common feature of heteromorphic sex chromosomes is that while X chromosomes maintain most of their ancestral genes , Y chromosomes often degenerate due to their lack of recombination , with only few functional genes remaining ( for a recent review see [2] ) . The loss of gene function is often accompanied by an accumulation of repetitive DNA on ancient Y chromosomes , and a switch of chromatin structure from euchromatin to genetically inert heterochromatin [2 , 3] . Loss and silencing of Y-linked genes drives the evolution of dosage compensation on the X chromosome , which is often mediated by chromosome-wide epigenetic modifications . Drosophila males , for example , acquire a hyperactive chromatin conformation of their single X , while one of the two X’s in female mammals becomes heterochromatic [4 , 5] . Studies of young sex chromosomes have improved our understanding of the genomic and epigenomic mechanisms driving the divergence between X and Y [6–9] . Neo-sex chromosomes of Drosophila are formed by chromosomal fusions between the ancestral sex chromosomes and ordinary autosomes . The neo-Y , which is the autosome that became linked to the Y , entirely lacks recombination since it is transmitted through males only , which in Drosophila do not undergo meiotic recombination . Consistent with theoretical predictions that selection is ineffective on non-recombining chromosomes [10] , neo-Y chromosomes in several Drosophila taxa have undergone chromosome-wide degeneration , and the extent of gene loss roughly corresponds to the age of the neo-Y . In particular , the very recently formed neo-Y of D . albomicans ( <0 . 1 million year old ) still contains most of its protein coding genes with <2% being putatively non-functional [11] , but a large fraction of neo-Y genes ( roughly 40% ) are down-regulated [9] , suggesting that transcriptional silencing might be initiating Y degeneration . The older neo-Y chromosome of D . miranda ( 1 . 5 million years old ) has acquired stop codons and frame-shift mutations in almost half of its genes , shows a dramatic accumulation of transposable elements ( between 30–50% of its DNA is composed of TEs ) [12 , 13] , and most neo-Y genes are expressed at a lower level than their neo-X homologs [11] . These changes at the DNA sequence level are accompanied by a global change in chromatin structure , and the D . miranda neo-Y is adopting a heterochromatic appearance marked by histone H3 lysine 9 di-methylation ( H3K9me2 ) [3] . The neo-X of D . miranda , in contrast , has maintained most of its ancestral genes but is evolving partial dosage compensation , by co-opting the canonical dosage-compensation machinery of Drosophila ( the MSL-complex ) . This complex is targeted to the ancestral X of Drosophila species , and up-regulates gene expression through changes of the chromatin conformation of the X , mediated by histone H4 lysine 16 acetylation ( H4K16ac ) [14 , 15] . The neo-sex chromosome shared by members of the D . pseudoobscura species group was formed about 15 million years ago , and has evolved the typical properties of old sex chromosomes: the neo-Y is completely degenerate and heterochromatic , while the neo-X is fully dosage compensated by the MSL machinery [3 , 16] . Well-studied neo-sex chromosome systems are all derived from euchromatic autosomes , and studying a neo-sex chromosome that originated from an autosome with some features similar to heterochromatin may allow a more general understanding of the evolutionary principles of chromatin formation on sex chromosomes . Here , we collect data on the genome , transcriptome and epigenome of D . busckii , a species with a poorly characterized neo-sex chromosome derived by a fusion ( and supposedly followed by a pericentric inversion on the X ) between the ancestral sex chromosomes and the “heterochromatic” dot chromosome ( Fig 1A ) [17 , 18] . The age , and the extent of sequence , expression and epigenetic divergence of the neo-sex chromosomes of D . busckii are unknown , but the dot chromosome has an unusual evolutionary history and a unique chromatin structure . It was a sex chromosome in an ancestor of higher Diptera , and only reverted to an autosomal inheritance in the ancestor of the Drosophilidae family [19 , 20] . Studies on the assembled distal arm ( ~1 . 2Mb ) of the D . melanogaster dot chromosome have revealed several features that distinguish it from other autosomes: it has a very low recombination rate and a high repeat content [21–23] , harbors less than 100 genes [24] that have low codon usage bias[25] and which show evidence of reduced levels of positive and purifying selection [26] . Genes on the dot chromosome are embedded into a unique heterochromatin-like milieu that is regulated differently from canonical pericentric heterochromatin [21 , 27] . Both dot-linked genes and genes located in pericentric heterochromatin are enriched for the ‘silencing’ histone marks H3K9me2 and H3K9me3 and the heterochromatin protein HP1a relative to euchromatin , but show a depletion of these marks at the transcriptional start sites of active genes . In addition , expression of dot-linked genes ( but not genes in pericentric heterochromatin ) requires binding of the chromosomal protein Painting of Fourth ( POF ) and the histone methyltransferase EGG , and the gene bodies of transcribed genes show an enrichment of the histone modification H3K9me3 ( but not H3K9me2 ) that is not observed at active genes located in pericentromeric heterochromatin . Genes on the dot chromosome that are not expressed and repetitive regions on the dot chromosome probably adopt a more general POF/EGG independent mechanism of heterochromatin packaging that is shared with pericentromeric regions [28] . Intriguingly , in three Drosophila species including D . busckii , POF was found to bind the X chromosome specifically in males [29] . This mimics the localization of the MSL complex , the canonical dosage compensation machinery of Drosophila , but unlike in other Drosophila species , immunostaining to polytene chromosomes detected no binding of the MSL complex on the X chromosome of D . busckii [16 , 29] . The phylogenetic position of D . busckii is uncertain , and some early studies placed it as a sister to all other Drosophila species [16 , 30] . These findings , together with the discovery that the dot was actually the ancestral sex chromosome in Diptera led to the hypothesis that D . busckii might harbor a more ancestral mechanism of dosage compensation mediated by POF [31] , which may have been derived from a dosage compensation system in an ancestor of Drosophilidae where the dot was the X chromosome [19] . Here , we collect DNA sequence , transcriptome and chromatin data characteristic of dosage compensation and heterochromatin together with immunostaining of polytene chromosomes , to characterize the formation of a sex chromosome from a heterochromatic ancestor , and also to disentangle the relationship between POF and MSL .
We sequenced the D . busckii female genome to an extremely high sequencing coverage ( >150 fold , S1 Table ) with libraries spanning a gradient of insert sizes ( up to 10kb ) to produce a highly continuous de novo assembly ( scaffold N50: 946kb , average scaffold size: 60 . 8kb ) with a total assembled length of 152 . 7Mb . Orthologous Drosophila chromosomes show high conservation ( >95% ) in their gene content [32] , and we assign the chromosomal locations of D . busckii genome scaffolds based on their alignments with D . melanogaster chromosomes . 89% of the sequences could be assigned to individual linkage groups , and we further tested our chromosomal assignments by sequencing the male genome . The ancestral X chromosome is hemizygous in males , and mapped male read depth is indeed only half of the female read depth along the entire X chromosome , while read depths are very similar between sexes on autosomes ( median log10 coverage value of male vs . female: 3 . 50 vs . 3 . 46; P>0 . 05 , Wilcoxon test ) ( Fig 1B ) . Interestingly , coverage in both sexes is also very similar along the dot chromosome and only slightly reduced in males ( median of male vs . female: 3 . 42 vs . 3 . 44 ) , implying that the neo-X and neo-Y still share considerable sequence homology . This suggests that the age of the neo-sex system of D . busckii is younger than that of D . miranda , which shows significantly reduced male read depth ( by about 25% ) along its neo-sex chromosome due to neo-X/Y divergence [11] . We annotate 13 . 1% of the assembled genome as consisting of repetitive elements , with the dot chromosome containing the highest repeat content ( 17 . 3% ) among all chromosomes . We also produce transcriptomes of male and female D . busckii third instar larvae and adults , and integrated them during gene annotation . A total of 12 , 648 protein-coding genes were annotated using D . melanogaster proteins as query , 11 , 859 ( 93 . 6% ) of which have one-to-one D . melanogaster orthologs . We find a higher proportion of annotated genes actively expressed in male than in female ( 69 . 4% vs . 53 . 8% ) with a normalized expression level RPKM ( average RNA-seq reads per kilobase of gene per million fragments mapped ) higher than 5 , and also a generally lower male expression level on the X chromosome relative to autosomes ( Wilcoxon test , P<0 . 05 , S1 Fig ) , in both developmental stages . These patterns are consistent with sex-biased expression patterns found in D . melanogaster [33 , 34] , and a similar ‘demasculinization’ found on the X chromosomes in other Diptera [19 , 20] . The phylogenetic relationship of D . busckii within the Drosophila genus is unclear . Some studies placed it as a sister to all other Drosophila species [16 , 30] , while others put it within the Drosophila subgenus [35] . This uncertainty in the phylogenetic position of D . busckii could have resulted from the small number of genes that were previously investigated , and we use whole-genome sequence alignments of representative Drosophila species and other Drosophilidae , to generate a phylogenomic tree . Our alignments include D . melanogaster , D . pseudoobscura and D . willistoni from the Sophophora subgenus; D . albomicans [11] , D . grimshawi and D . virilis from the Drosophila subgenus , D . busckii and two recently sequenced Diptera species within the Drosophilidae family: Scaptodrosophila lebanonensis [36] and Phortica variegata [19] as outgroups to the Drosophila genus [35–37] . In total , we aligned CDS sequences of 6189 orthologous genes spanning a total of 19 . 1Mb from each species and acquired a consensus tree with high bootstrapping values ( Fig 2 ) . D . busckii consistently clusters with the Drosophila subgenus species ( D . albomicans , D . grimshawi and D . virilis ) rather than being placed at the base of all Drosophila . This phylogenetic analysis suggests that D . busckii is not a member of an early divergent Drosophila lineage , but originated within the Drosophila subgenus . We assembled and mapped a total of 1 . 17Mb ( with 6 . 9% of the sequence as gaps ) of dot chromosome sequence in D . busckii , in comparison to 1 . 35Mb of assembled dot sequence in D . melanogaster . The D . busckii dot chromosome overall shows more than 10 times higher levels of heterozygosity ( 1 . 56 SNPs per 100bp on average ) in male than in female , predominantly due to nucleotide sequence divergence between the neo-X and neo-Y chromosomes ( Fig 1B ) . The median level of pairwise divergence at silent sites between neo-X and neo-Y alleles is 0 . 84% , which is about 3 times lower than synonymous divergence between neo-sex-linked genes of D . miranda ( 2 . 8% ) [11] . Assuming a mutation rate of 5 x 10−9 per bp ( as estimated in D . melanogaster ) [38] and 10 generations a year , this indicates that the D . busckii neo-sex chromosomes originated only about 850 , 000 years ( 0 . 85 MY ) ago . Note that while the fixation of ancestral polymorphisms can contribute to the neo-X/Y divergence , the low level of silent site diversity on the dot [23] implies that ancestral polymorphism is expected to have very limited impact on our estimate of the age of the neo-sex chromosomes of D . busckii . The recent formation of the D . busckii neo-sex chromosome is consistent with the similar level of read depth observed between sexes along the neo-X chromosome , suggesting this system is still at an initial stage of differentiation ( Fig 1B ) . We annotate a total of 86 neo-sex linked genes ( vs . 80 protein-coding genes on the D . melanogaster dot chromosome , see notes in Materials and Methods ) , all of which show the same level of read depth between sexes ( S2 Fig ) . Thus , unlike on the older neo-Y chromosome of D . miranda [11] , none of the protein-coding genes has yet been deleted from the neo-Y of D . busckii . However , we find male-specific SNPs or indels ( i . e . , mutations on the neo-Y ) that cause premature stop codons and/or frameshift mutations in 50 neo-sex linked genes , implying that there is a large number of genes on the neo-Y that supposedly have lost their normal functions ( Fig 3A ) . The proportion of putative non-functional genes ( 58 . 2% ) is much higher on the neo-Y of D . busckii than on that of D . miranda ( 34 . 2% ) [11] . This is unexpected , since there has been less time for degeneration on the younger neo-Y chromosome of D . busckii . In addition , the much smaller size of the dot chromosome predicts weaker effects of Hill-Robertson interference [10 , 39] and thus a lower rate of degeneration on the D . busckii neo-Y . However , simulation results have shown that the effects of interference asymptote quite fast with the number of genes [40] . Several other factors could help to explain the large fraction of non-functional genes on the recently formed neo-Y of D . busckii . First , genes located on the dot generally show lower levels of evolutionary constraint [41 , 42] . Consistent with reduced levels of purifying selection on dot-linked genes , we find that the neo-X alleles show a significantly lower level of codon usage bias than genes on autosomes and the X chromosome ( Wilcoxon test , P<0 . 05; S3 Fig ) . Note that it is possible that selection for optimal codon usage has become more efficient for dot-linked genes on the neo-X since the dot/X fusion , which may have placed them within a more highly recombining environment , as has been observed for D . willistoni [43] . In this case , ancestral levels of codon usage bias may have been even lower for dot-linked genes . Further , the median rate of protein evolution ( as measured by the ratio of nonsynonymous vs . synonymous substitutions using PAML ) at the ancestral branch before the neo-X/Y divergence is higher than that of other autosomes ( median Ka/Ks = 0 . 082 vs . 0 . 075 ) , and non-functional genes show a higher ancestral rate of protein evolution than genes with a functional copy on the neo-Y ( median Ka/Ks = 0 . 086 vs . 0 . 068; S4 Fig ) . Although both differences are not statistically significant , probably due to the low number of genes on the dot chromosome , these results are consistent with the idea that genes under lower selective constraints are becoming pseudogenized more quickly on a degenerating neo-Y , as observed on the neo-Y of D . miranda [11 , 44] . In addition , the gene content of the dot chromosome appears feminized / demasculinized , that is , dot genes in Drosophila and in other Diptera species are over-expressed in ovaries , and under-expressed in testis [19] . Genes with female function are under less purifying selection on the male-limited neo-Y chromosome , which may contribute to accelerated rates of pseudogenization . Neo-X homologs of neo-Y genes that are functional are expressed at a significantly higher level in both male larvae ( S5B Fig ) and adults ( Fig 3B ) than neo-X homologs of neo-Y genes that have become pseudogenized ( Wilcoxon test , P = 0 . 0087 ) . This indicates that the loss of functional Y-linked genes preferentially starts from lowly-expressed genes with less selective constraints , consistent with our findings on the neo-Y of D . miranda [44] . Finally , hemizygosity of dot-linked genes is generally tolerated in D . melanogaster [42] , and null mutations at dot-linked genes may have a negligible effect on fitness if heterozygous . Thus , lower levels of evolutionary constraints , an excess of female-biased genes , and general haplosufficiency of dot genes may contribute to their rapid degeneration on the neo-Y of D . busckii . In addition to functional decay in protein coding sequences , we also found a chromosome-wide expression bias for neo-sex linked genes ( Fig 3C ) : 75 genes ( 88% ) display significantly higher expression from the neo-X chromosome relative to the neo-Y in male adults ( Fisher’s exact test , P<0 . 05 , see Methods ) , and a similar pattern was observed in male larvae ( S3A Fig ) . Putative pseudogenes on the neo-Y tend to show a slightly more severe ( but not statistically significant ) expression bias than functional genes ( median log2 ratio of neo-X vs . neo-Y expression: 1 . 80 vs . 1 . 71; Wilcoxon test , P = 0 . 41 ) . This chromosome-wide expression bias for neo-sex linked genes could be caused by down-regulation of neo-Y alleles and/or up-regulation of neo-X alleles ( i . e . , dosage compensation ) . Although many genes ( 77 . 9% ) show a similar level of expression for male ( with neo-X/Y gene expression levels combined ) and female ( less than 1 . 5 fold difference; Fig 3D ) , genes with lower relative expression from the neo-Y tend to be more female-biased ( Fig 3E , blue line , Spearman’s rank correlation coefficient: -0 . 47 , P = 1 . 04e-5 ) . This suggests that neo-X-biased expression is partly due to down-regulation of neo-Y linked genes . The single neo-X chromosome in males is transcribed at a higher level than a single neo-X chromosome in females ( Fig 3F ) , which suggests that some form of dosage compensation has evolved on the neo-X . However , there is no significant correlation between down-regulation of neo-Y genes ( i . e . neo-X vs . neo-Y expression bias ) , and up-regulation of neo-X genes in males ( i . e . expression of the neo-X in males vs . females , Fig 3E , orange line; F-statistic P>0 . 05 ) . This may suggests that dosage compensation is not gene-specific , but could also reflect a lack of statistical power due to the low number of genes on the dot . The neo-Y chromosome of D . miranda has become partially heterochromatic within 1 . 5 million years . It is enriched for the silencing histone modification H3K9me2 relative to the neo-X and other chromosomes [3] , and expression of neo-Y genes is down-regulated chromosome-wide . To investigate whether an accumulation of silencing histone marks may cause down-regulation of neo-Y linked gene expression in D . busckii , we obtained ChIP-seq profiles of both H3K9me2 and H3K9me3 from male larvae . The two histone modification marks are strongly correlated with each other and HP1a in pericentric heterochromatin , but have distinctive distributions on the dot chromosome of D . melanogaster: H3K9me3 shows an unusual correlation with POF over actively transcribed gene bodies , while H3K9me2 strongly associates with silenced genes [27 , 45] . We analyzed the distribution of H3K9me2 and H3K9me3 at active and silent genes ( expression status defined from S1 Fig ) , and find that both marks are significantly enriched on the dot chromosomes of D . busckii relative to autosomes ( Wilcoxon test , P<0 . 05; see Methods , Fig 4A and 4D ) . H3K9me3 shows a similar level of enrichment between the neo-Y and the neo-X ( Wilcoxon test , P>0 . 05 , Fig 4D ) , and enrichment tends to be higher at active relative to silent genes on both the neo-X and neo-Y ( Wilcoxon test P>0 . 05; Fig 4D–4F ) . In contrast , H3K9me2 levels are significantly increased at neo-Y genes relative to their neo-X homologs ( Wilcoxon test , P = 0 . 000637 , Fig 4A ) , particularly on those that are transcriptionally silenced ( Wilcoxon test , P = 0 . 000381 , Fig 4A–4C ) , and non-functional neo-Y genes show a significant increase in H3K9me2 binding relative to their neo-X homologs ( Wilcoxon test , P = 0 . 0001494; S6 Fig ) . The H3K9me2 enrichment level of silent neo-Y genes is higher than that of active neo-Y genes ( median value: 0 . 79 vs . 0 . 47 , Wilcoxon test P = 0 . 089 , Fig 4A ) , and the enrichment level of H3K9me2 , but not H3K9me3 , is negatively correlated with the gene expression level of neo-Y but not neo-X alleles ( S7 Fig , Spearman’s rank correlation coefficient -0 . 23 , P = 0 . 04 ) . We further analyzed metagene enrichment profiles , and find both H3K9me2 and H3K9me3 to be enriched at gene bodies relative to their flanking regions . The increase of H3K9me2 enrichment on silent neo-Y genes is not restricted to gene bodies but extends into flanking regions as well ( Fig 4C ) . These results suggest that down-regulation of neo-Y gene expression may be caused by H3K9me2 modification , but it is also possible that some genes are first silenced through mutations in their regulatory region , and then preferentially become targeted by H3K9me2 . Overall , our results provide robust evidence that the neo-Y chromosome of D . busckii is becoming more heterochromatic , mediated by H3K9me2 enrichment , which further contributes to the degeneration of neo-Y genes . Most genes on the ancestral X of D . busckii are expressed at similar levels in males and females , i . e . they are dosage compensated ( S8 Fig ) . The molecular mechanism of dosage compensation in D . busckii has been unclear , and in situ hybridization experiments to polytene chromosomes to stain for components of the MSL machinery , using antibodies derived from D . melanogaster , have previously failed to identify MSL binding on the ancestral X chromosome of D . busckii [16] . Instead , an antibody designed against the POF protein in D . melanogaster was found to coat the entire X chromosome of D . busckii in males only [29] , and to co-localize with H4K16ac , a histone marker for dosage compensation in Drosophila [46] . This has led to the proposal that D . busckii does not utilize the MSL machinery to compensate its X chromosome , but instead is using a regulatory mechanism that involves POF [47] . However , it is unclear whether the MSL antibodies tested are just too diverged to produce a reliable hybridization signal , or if MSL-dependent dosage compensation is indeed absent in D . busckii . To evaluate the mechanism of dosage compensation in D . busckii , we utilized both bioinformatics and experimental approaches . First , we annotated the intact open reading frames and gene expression patterns of the key MSL complex proteins and non-coding RNAs , as well as the POF protein and a duplicated copy of POF found in D . busckii . Transcriptome profiling revealed that MSL-2 , POF , roX-1 and roX-2 non-coding RNA all exhibit male-biased expression patterns ( S9 Fig ) , similar to their orthologs in D . melanogaster . We further performed immunostaining with a new D . melanogaster MSL-2 antibody , and find weak but male-specific staining of the X chromosome in D . busckii ( Fig 5A ) . In D . melanogaster , the MSL complex catalyzes the deposition of the activating histone mark H4K16ac , and ChIP-seq profiling in D . busckii clearly reveals that H4K16ac is significantly enriched on the ancestral male X relative to autosomes and the neo-sex chromosomes ( Wilcoxon test , P<2 . 2e-16 , Fig 5B ) . This is consistent with MSL-dependent dosage compensation in D . busckii , and orthologous X-linked genes show a significant correlation in their enrichment levels of H4K16ac between larvae samples of D . busckii and D . melanogaster ( Spearman’s rank correlation coefficient: 0 . 36 , P<2 . 2e-16; Fig 5C ) , suggesting that a similar set of genes is being targeted by the dosage compensation complex on the X in both species . Finally , our metagene analysis of the H4K16ac mark reveals a distinctive 3’ bias specifically over active X-linked gene bodies ( Fig 5D ) , consistent with the pattern mediated by the MSL complex in D . melanogaster [46 , 48] . Taken together , these results suggest that D . busckii shares the same mechanism of dosage compensation for the ancestral X chromosome as D . melanogaster , despite their distant phylogenetic relationship ( Fig 2 ) and their different sex chromosome karyotype . Degeneration and down-regulation of neo-Y genes should select for the acquisition of dosage compensation on the D . busckii neo-X . If the MSL-complex were co-opted on the neo-X in D . busckii to achieve dosage compensation , we would expect similar enrichment of H4K16ac along the neo-X in males . Instead , we find that neo-X linked genes are significantly depleted for H4K16ac relative to autosomes ( Wilcoxon test , P<2 . 28e-13 ) ( Fig 5B ) , similar to the H4K16ac depletion patterns on the dot in D . melanogaster ( S10 Fig ) . This indicates a lack of MSL-dependent dosage compensation on the D . busckii neo-X chromosome , in contrast to other neo-sex chromosome systems where a substantial fraction of the neo-Y has become pseudogenized [3 , 16] . Instead , it suggests that an ancestrally repressive chromatin structure , as is the case for the dot , may severely constrain the evolution of hyperactive chromatin , despite rampant Y degeneration .
We have performed a detailed investigation of the genomic and epigenomic evolution of the young neo-sex chromosomes of D . busckii . All previously studied neo-sex chromosome systems are derived from euchromatic autosomes , but the D . busckii neo-sex chromosome originated from the dot chromosome and its unique , more heterochromatic conformation is probably dictating its unusual patterns of chromatin evolution . We found that both the neo-X and neo-Y chromosome are enriched for both H3K9me2/3 relative to other chromosomes , but only H3K9me2 was reported to have a silencing function on the heterochromatic dot chromosome in D . melanogaster [45] . Consistent with the idea that increased heterochromatin formation may contribute to the observed down-regulation of neo-Y gene expression ( Fig 3B ) , we find that H3K9me2 is enriched at silenced neo-Y linked genes relative to their neo-X homologs , and these genes also tend to become pseudogenized more quickly on the neo-Y . This is consistent with our results in D . miranda , and suggests that genes on the neo-Y under lower selective constraints are more likely to become heterochromatic and non-functional early on [3 , 44] . In contrast , the H3K9me3 mark is not associated with silent chromatin on the dot chromosome of D . melanogaster , and instead enriched along actively transcribed genes on the dot chromosome [45] . We found that neo-Y linked genes show a similar level of H3K9me3 enrichment relative to their neo-X homologs , and no difference between active and silenced genes , suggesting that H3K9me3 does not contribute significantly to expression differences between the neo-X and neo-Y of D . busckii . One important caveat in the above analysis is that we can only measure relative expression or histone modification changes on the neo-sex chromosomes , but cannot distinguish whether those changes occurred on the neo-X or neo-Y . It is formally possible that the neo-X has evolved reduced levels of H3K9me2 ( but not H3K9me3 ) , relative to the neo-Y . No close relatives of D . busckii that lack the neo-sex chromosome fusion are known , preventing us from directly distinguishing between those possibilities . Two chromosome-wide regulatory systems have been characterized in D . melanogaster: one that is mediated by the MSL complex and that targets the male X chromosome; and the other that is mediated by POF and that targets the dot chromosome in both sexes . POF has been shown to bind the nascent RNA of actively transcribed genes on the dot chromosome , and increases levels of expression of these genes [49] . Since some studies placed D . busckii as a sister to all other Drosophila species , an apparent lack of MSL-binding to the X chromosome [29] has led to the intriguing hypothesis that POF may represent an ancestral dosage compensation system . However , our phylogenomic analysis demonstrates that D . busckii in fact belongs to the Drosophila subgenus ( Fig 2 ) , and we show that MSL-dependent dosage compensation appears to be conserved in D . busckii . The MSL complex is present in D . busckii males and its components show similar male-biased expression patterns as found in D . melanogaster ( S9 Fig ) , it binds the X chromosome of D . busckii males ( Fig 5A ) , and the H4K16ac dosage compensation mark is found along actively transcribed X-genes in D . busckii ( Fig 5B ) . This calls for a re-examination of the proposed role of POF in dosage compensation in D . busckii [29] . Despite clear evidence for dosage compensation of the ancestral X chromosome of D . busckii by the MSL complex , we found no signs of MSL-mediated dosage compensation on its neo-X . Rampant neo-Y degeneration ( i . e . almost 60% of neo-Y genes have frameshift mutations or stop codons ) should in principle select for the evolution of dosage compensation on the neo-X of D . busckii . Indeed , in other Drosophila species with neo-sex chromosomes , dosage compensation was found to evolve rapidly after their formation and degeneration of neo-Y genes , by co-opting the ancestral MSL machinery . In D . miranda , the neo-X chromosome has evolved partial dosage compensation through the acquisition of novel MSL-binding sites that recruit the MSL-complex to the neo-X [14 , 15] , and MSL-binding was found to be associated with H4K16ac enrichment . Even older neo-X chromosomes , like the one shared by members of the D . pseudoobscura subgroup , have evolved full MSL-mediated dosage compensation [3 , 16] . In contrast , we did not detect any enrichment of the H4K16ac modification on the neo-X of D . busckii . This is probably due to the younger age of the D . busckii neo-X chromosome , the fact that the dot chromosome contains only few genes and flies with a single copy of the dot chromosome are fully viable in D . melanogaster ( due to compensation mediated by POF [50] ) , and/or the difficulty of evolving a hyper-active chromatin structure for dosage compensation from an ancestrally more heterochromatic background . Our previous work in D . miranda showed that dosage compensation preferentially evolves in chromatin regions that are ancestrally active [3] , probably due to an antagonism between forming repressive , condensed heterochromatin and hyperactive , open chromatin resulting in dosage compensation [50] . Despite down-regulation of neo-Y genes and a lack of MSL-mediated dosage compensation of neo-X genes , we find that transcription of the single neo-X chromosome in males is not simply half that in females , and neo-sex linked genes do not exhibit strong sex-biased expression patterns . This suggests that the down-regulation of neo-Y linked genes is either at least partially compensated by transcriptional buffering mechanism [47] , which may play an important role during early sex chromosome differentiation , before the establishment of global dosage compensation on young X chromosomes . Alternatively , a POF-mediated regulatory mechanism might compensate for reduced gene dose of neo-Y linked genes . It will be of great interest to further investigate the evolutionary and functional relationship between these two chromosome-wide compensatory mechanisms that have been described in Drosophila .
An iso-female line of D . busckii provided by J . Larsson and originally caught in Tallinn , Estonia in the year 2000 was used for this study . About 50 virgin adult male and female were used for genomic DNA extraction using Puregene Core Kit A ( Qiagen , Inc ) . Total RNA from about 50 larvae and virgin adult flies of each sex were extracted by RNAeasy Mini Kit ( Qiagen , Inc ) . Library preparation and genomic or poly-A selected transcriptome sequencing were then performed at Beijing Genomic Institute or UC Berkeley Sequencing facility following the standard Illumina protocol . We sequenced the libraries by paired-end sequencing with 90bp read length for all the RNA-seq libraries and most of the genomic libraries , and 50bp for long-insert libraries . Female DNA was sequenced to very high coverage ( 172 fold , S1 Table ) for de novo assembly of a reference genome , and male DNA was sequenced to medium coverage ( 27 fold ) . We assembled the reference genome by ALLPATHS-LG [51] with standard parameters . The output scaffold sequences were aligned to D . melanogaster chromosomal sequences ( v5 . 46 ) downloaded from FlyBase by LASTZ ( http://www . bx . psu . edu/~rsharris/lastz/ ) using a nucleotide matrix for distant species comparison . Alignment results were filtered using a cutoff of at least 30% of the entire scaffold aligned with 50% sequence identity . We wrote customized perl scripts to build pseudo-chromosomal sequences of D . busckii . We further used RepeatMasker and RepeatModeler ( http://www . repeatmasker . org ) to annotate the repeat content of the D . busckii genome . RNA-seq reads from both sexes were separately aligned to the chromosome sequences of D . busckii by tophat [52] . The alignments were then provided to cufflinks [53] for transcriptome annotation . We integrated the annotation results from cufflinks and used a non-redundant protein sequence set of D . melanogaster ( v5 . 46 ) to annotate the D . busckii genome using the MAKER pipeline [54] . We annotated 79 out of 80 dot-linked D . melanogaster protein-coding genes in the D . busckii genome . 71 of them are located on the dot chromosome of D . busckii , and the remaining 8 genes are located on the X chromosome or other autosomes , including 4 genes whose D . virilis orthologs also map to other chromosomes [55] . The additional 15 genes annotated on the D . busckii dot chromosome are either predicted by a combination of RNA-seq evidence and de novo open reading frame annotation , or have a D . melanogaster ortholog located on another chromosome . We compared the distributions of normalized expression level ( measured by Reads Per Kilobase per Million , RPKM ) in gene regions and intergenic regions , and used the value where the two distributions separate as a cutoff ( log10 RPKM = 0 . 65; S1 Fig ) to define genes that are transcriptionally active or not . We analyzed the codon usage bias of all annotated D . busckii genes by CodonW ( http://codonw . sourceforge . net/ ) . We used the standard GATK pipeline [56] for calling SNPs in male and female DNA samples . In brief , sequencing reads were aligned to the D . busckii genome with bowtie2 [57] and PCR duplicate reads were removed using the Picard tool ( http://broadinstittute . github . io/picard ) . We used UnifiedGenotyper for calling variants , and discarded SNPs/indels with low qualities ( Quality<30 ) , low coverage ( Depth<5 ) , strand biases or clustering patterns for initial SNP filtering . To account for the different sequencing coverage of the male and female samples , we further plot the distributions of variant qualities of male and female SNPs to determine a different variant quality cutoff for the second round of filtering . We identified a total of 16977 heterozygous SNP sites from the male sample and only 496 female heterozygous sites on the dot chromosome . After excluding the sites that are shared by both sexes , we used the quality-filtered male-specific SNPs/indels as the putative fixed neo-X/Y divergence sites , and introduced the alternative nucleotides to the reference neo-X genome to produce the reference genomic sequence of the neo-Y chromosome . Note that only individuals from a single inbred line were sequenced; this means that some of the fixed differences between the neo-X and neo-Y are not actually fixed in the population but may be segregating on either chromosome . Based on the female-specific heterozygous sites , we estimated that only 1 . 5% of the divergence sites maybe derived from segregating polymorphic sites . We then used GeneWise [58] and annotated the non-functional genes of the neo-Y using the proteins annotated from the female reference genome as query . To analyze neo-X and neo-Y allele-specific gene expression and histone profiles ( see below ) , we aligned the male RNA-seq or ChIP-seq reads against the female reference genome and specifically collected reads that overlapped the male-specific SNP sites . These reads encompass informative neo-X/Y divergence sites , and we used customized perl scripts to assign their linkage to either the neo-X or neo-Y , dependent on whether the SNP is male-specific or not . To correct for potential mapping biases , we normalized the count of RNA-seq reads against the DNA-seq reads from males , whose ratios between neo-X and neo-Y alleles are expected to be 1 . To test the significance of biased gene expression between neo-X/Y alleles , we used Fisher’s tests with the allelic-specific DNA-seq read count and allelic-specific RNA-seq read count of the neo-X or neo-Y allele for the 2×2 table . This should account for potential mapping biases of neo-X and neo-Y derived reads , and their ratio is expected to be similar between neo-X/Y alleles if they are transcribing at a similar level . Since the enrichment of ChIP-seq profiles is calculated by normalizing against the input DNA-seq control , we did not do any further correction . When comparing the binding level between the neo-X/Y alleles or different chromosomes , we calculated the ratio of aligned read numbers of ChIP experiment vs . input DNA control , spanning the gene body and 1 . 5 kb flanking regions . We collected CDS sequences from D . pseudoobscura ( v3 . 1 ) , D . virilis ( v1 . 2 ) , D . willistoni ( v1 . 3 ) , D . grimshawi ( v1 . 3 ) and D . albomicans from FlyBase , and two Diptera species Scaptodrosophila lebanonensis and zoophilic fruitfly ( Phortica variegata ) whose genomes have been recently produced in our lab [19 , 36] . Orthologous relationships of genes between species were determined through reciprocal BLAST or precomputed annotation from FlyBase . We aligned all the orthologous sequences for the same gene by translatorX [59] , a program that performs codon-based nucleotide sequence alignment and removed low-quality alignment regions by Gblock [60] . The alignments were then concatenated and provided to RAxML for constructing maximum-likelihood trees with the GTRCAT algorithm , with P . variegate assigned as an outgroup to all Drosophila species . We bootstrapped the tree 1 , 000 times and calculated confidence values for each node as described in the manual of RAxML [61] . Branch-specific evolutionary rates were calculated for the resulting high-confidence tree using the PAML package [62] . To collect data for as many genes as possible , we only used D . melanogaster and D . virilis as outgroups of D . busckii in the input tree . We calculated lineage specific synonymous or nonsynonymous substitution rates using codeml under the ‘free-ratio’ model , which assumes each phylogenetic branch has a different rate of evolution . Polytene chromosomes were dissected from male third instar larvae and processed for immunostaining with primary MSL-2 antibody ( Santa Cruz Biotechnology , sc-32458 , dilution ratio: 1:10 , room temperature , overnight ) and secondary fluorescence antibody Alexa Fluor 555 Dye ( Life Technologies , room temperature , 2 hours ) . Approximately 5g of male third instar larvae were used for chromatin extraction . Chromatin was cross-linked with formaldehyde and sheared by sonication . Chromatin pull-down with IgG agarose beads ( Sigma , A2909 ) was performed as described previously [63] . We used the following antibodies for ChIP-seq experiments: ( 1 ) H3K9me3 ( Abcam ab8898; 3 μl/IP ) ( 2 ) anti-H4K16ac ( Millipore 07–329; 5 μl/IP ) ( 3 ) H3K9me2 ( Abcam ab1220; 3μl/IP ) . Immunoprecipitated and input DNAs were purified and processed according to the standard paired-end Solexa library preparation protocol . Paired-end 100-bp DNA sequencing was performed on the Illumina Genome Analyzer located at UC Berkeley Vincent J . Coates Genomic Sequencing Facility . ChIP-seq and input control reads were aligned to the D . busckii genome by bowtie2 [57] . The resulting alignments were filtered using a cutoff for mapping quality higher than 30 , and provided to MACS [64] to call peaks of enrichment along the chromosomes . We use MEME [65] to identify targeting sequence motifs within peak regions . For metagene analyses , we first determine a cutoff to define ‘bound’ or ‘unbound’ states of certain chromatin marks within each scaled bin of genes or flanking regions , by comparing the distribution of their normalized enrichment levels between chromosomes ( S11 Fig ) . Then for each bin , we calculated the average bound level across all the studied genes , after dividing them into different groups of chromosomes and active/silent genes . ChIP-seq data of male D . melanogaster is downloaded from NCBI SRA database ( accession#: PRJEB3015 ) [66] and orthologous relationship between D . busckii and D . melanogaster genes was determined using reciprocally best BLAST searches .
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DNA is packaged with proteins into two general types of chromatin: the transcriptionally active euchromatin and repressive heterochromatin . Sex chromosomes typically evolve from a pair of euchromatic autosomes . The Y chromosome of Drosophila is gene poor and almost entirely heterochromatic; the X chromosome , in contrast , has evolved a hyperactive euchromatin structure and globally up-regulates its gene expression , to compensate for loss of activity from the homologous genes on the Y chromosome . The evolutionary trajectory along which sex chromosomes evolve such opposite types of chromatin configurations remains unclear , as most sex chromosomes are ancient and no longer contain signatures of their transitions . Here we investigate a pair of unusual young sex chromosomes ( termed ‘neo-Y’ and ‘neo-X’ chromosomes ) in D . busckii , which formed through fusions of a largely heterochromatic autosome ( the ‘dot chromosome’ ) to the ancestral sex chromosomes . We show that nearly 60% of the neo-Y genes have already become non-functional within only 1 million years of evolution . Gene expression is lower on the neo-Y than on the neo-X , which is associated with a higher level of binding of a silencing heterochromatin mark . The neo-X , on the other hand , shows no evidence of evolving hyperactive chromatin for dosage compensation . Our results show that the Y chromosome can degenerate quickly , but the tempo and mode of chromatin evolution on the sex chromosomes may be constrained by the ancestral chromatin configuration .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Ancestral Chromatin Configuration Constrains Chromatin Evolution on Differentiating Sex Chromosomes in Drosophila
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HIV-1 infection is characterized by a chronic activation of the immune system and suppressed function of T lymphocytes . Regulatory CD4+ CD25high FoxP3+CD127low T cells ( Treg ) play a key role in both conditions . Here , we show that HIV-1 positive patients have a significant increase of Treg-associated expression of CD39/ENTPD1 , an ectoenzyme which in concert with CD73 generates adenosine . We show in vitro that the CD39/adenosine axis is involved in Treg suppression in HIV infection . Treg inhibitory effects are relieved by CD39 down modulation and are reproduced by an adenosine-agonist in accordance with a higher expression of the adenosine A2A receptor on patients' T cells . Notably , the expansion of the Treg CD39+ correlates with the level of immune activation and lower CD4+ counts in HIV-1 infected patients . Finally , in a genetic association study performed in three different cohorts , we identified a CD39 gene polymorphism that was associated with down-modulated CD39 expression and a slower progression to AIDS .
HIV-1 infection is characterized by chronic immune activation which , in combination with the progressive depletion of CD4+ T cells , profoundly perturbs antigen-specific T cell responses [1] . The population of CD4+CD25high FoxP3+ regulatory T cells ( Treg ) suppresses antigen-specific T cell responses and controls inappropriate or exaggerated immune activation induced by pathogens , thereby influencing the outcome of various infections [2] , [3] . In particular , these cells suppress in vitro HIV-1-specific CD4+ and CD8+ effector T-cell responses [2] , [4] . We , and others , have reported an HIV-1-driven expansion of Treg expression in chronic and acute HIV-1 infection [5] , [6] , including a relationship between the expansion of Treg , the level of cellular immune activation and the depletion of CD4+ T cells in acute HIV infection [5] . The molecular mechanisms by which Treg mediate their suppressive activity remain poorly understood . In humans , the Treg population exhibits considerable diversity . Phenotypically and functionally distinct subsets of Treg can mediate suppression through distinct mechanisms from secretion of IL-10 , TGF-ß , IL-35 , Granzyme B , perforin , to CTLA-4 and GITR interactions [7] , [8] , [9] . Recently , it has been reported that CD39 is expressed on human and murine Treg , while CD73 is found only on the surface of murine Treg [10] , [11] , [12] . CD39 , a member of the ectonucleotidase triphosphate diphosphohydrolase family ( ENTPD ) , also referred to as ENTPD-1 ( EC 3 . 6 . 1 . 5 ) , is the dominant immune system ectonucleotidase that hydrolyses extracellular ATP and adenosine diphosphate ( ADP ) into adenosine monophosphate ( AMP ) at the sites of immune activation . CD73 is an ecto-5′-nucleotidase ( 5′NT ) that exists in a soluble or membrane-bound form and catalyzes the dephosphorylation of AMP to adenosine [13] , [14] , [15] . Adenosine is a critical regulator of innate and adaptive immune responses [16] , [17] , inhibiting T lymphocyte proliferation and the secretion of inflammatory cytokines including IL-2 , TNFa , and IFN-γ [13] , [14] , [15] . These effects are mediated through A2A receptors stimulating the generation of cAMP , and are mimicked by adenosine agonists [18] . CD39 has also been described as an activation marker of lymphoid cells [19] . Therefore , the CD39/Adenosine pathway may be important to the balance between activation and regulation of effector immune responses . Here we tested the hypothesis that the CD39/adenosine pathway is involved in the pathogenesis of HIV-1 disease . First , we investigated the phenotype and the function of Treg-expressing CD39 molecules in a cohort of chronically HIV-positive patients and determined whether these characteristics are associated with clinical outcomes . Second , to assess our hypothesis in an in vivo context , we investigated whether CD39 genetic polymorphisms were associated with rates of HIV-1 disease progression in three independent cohorts .
In order to discriminate between Treg and activated T cells , we further characterized Treg population as gated T cells expressing CD4+CD25high FoxP3+high and CD127low ( gating strategy is shown in Fig . S1 ) . These cells are designated thereafter as Treg cells while CD4+CD25lowCD127high T cells are designated as activated CD4+CD25low T cells ( T act ) . First , we confirmed a significant increase in the percentages of Treg cells in a cohort of HIV-positive individuals , receiving either a combination of antiretroviral drugs ( c-ART+ , n = 39 ) or not ( c-ART− , n = 39 ) , as compared to healthy controls ( n = 25 ) ( mean 5 . 8% and 6 . 2% respectively vs 2 . 4% , P<0 . 0001 ) ( Fig . 1a ) . As shown in Fig . 1b and 1c , percentages of Treg expressing CD39+ ( Treg CD39+ ) were significantly higher in both c-ART+ and c-ART− patients , as compared to healthy controls ( mean 2 . 79% and 2 . 26% vs 0 . 97% , P<0 . 001 , Fig . 1b ) . Moreover , Treg from both c-ART− and c-ART+ subjects expressed a higher density of CD39 molecules as compared to those from HIV-1 negative controls ( mean fluorescence intensity ( MFI ) 1327 and 1203 , respectively , vs . 652 , P<0 . 001 and P<0 . 01 ) ( Fig . 1c ) . Phenotypic analyses were performed in 16 HIV-1 positive patients before and 12 months following c-ART initiation . Among them , 9 patients experienced a good response to c-ART ( group A; undetectable plasma viral load at month 12 ) , while in 7 patients ( group B ) viral replication remained detectable ( above 50 copies/ml ) . No significant decrease of CD39 expression was observed in group A: % Treg CD39+ ( mean ± SD ) : 2 . 4±1 . 2 vs . 1 . 8±1 . 0 at baseline; TregCD39+ MFI ( mean ± SD ) : 1557±360 vs . 1261±656 at baseline , ( P>0 . 05 for both ) . Moreover , in patients with on-going viral replication %Treg CD39+ increased significantly in spite of ART ( 6 . 1±2 . 4 versus 3 . 4±2 . 3 at baseline; P = 0 . 043 ) . CD39 has also been described as an activation marker of lymphoid cells [19] . Therefore , we looked at the percentages of Tact in HIV-1 positive patients and controls . As expected , the frequency of activated CD4+CD25low T cells was significantly higher in both populations of patients as compared to controls ( Fig . S1b ) . Consequently , percentages of CD4+CD25lowCD39+ were significantly higher in HIV-1 positive patients as compared to controls ( Fig . S1c ) . In contrast to Treg , CD4+CD25− T cells from both HIV-positive subjects and controls did not express CD39 ( not shown ) . Thus , an expansion of CD39+CD4+ T cells in both Treg and T act T cell populations , which persist in patients with controlled viral load under c-ART , is observed in HIV-1 positive patients . In HIV-positive subjects and in HIV-negative controls , Treg cells were mostly of CD45RA−CD28+ memory phenotype ( mean 75% ) . CD45RA−CD28+ Treg contained a higher percentage of CD39+ cells as compared to CD45RA+CD28+ Treg cells ( mean 65% vs . 28% , respectively , P<0 . 05 ) ( Fig . S2 ) . We next investigated whether down-modulation of the CD39 enzyme can impact Treg function . First , by exposing cells to a blocking anti-CD39 ( BY40 ) mAb , we induced a down-modulation of CD39 expression at the surface of the YT2C2 NK line cells ( Fig . S3a ) . Next , BY40 mAb down-modulated the expression of CD39 on ex-vivo purified peripheral blood Treg from HIV-negative controls as compared to untreated cells or cells treated with an IgG1 control mAb ( % of positive cells ( mean ± SD ) : 32±11% vs 44±13% , and 42±14% , respectively ) ( Fig . 2a , b ) . In these experiments , CD39 expression following in vitro incubation with BY40 mAb was assessed using a commercial PE anti-CD39 ( clone TU66 ) which has been previously checked to be non-competitive with BY40 ( Fig . S4 ) . Finally , we found that this down modulation effect of BY40 was associated with decreased CD39 ATPase activity on primary monocytes ( Fig . S3b ) . The functional consequences of CD39 down-modulation were investigated in co-culture assays developed to evaluate the suppressive effects of Treg on T cell proliferation [5] , [6] , [20] . As shown in Fig . 3a and b ( for one representative experiment and pooled data from 6 HIV-positive subjects ) , the Treg-mediated inhibition of anti-CD3 induced CD8 T cell proliferation was significantly higher in HIV-positive subjects ( n = 6 ) as compared to HIV-negative controls ( n = 6 ) , ( mean inhibition 56% vs 22 . 5%; P<0 . 01 ) ( Fig . 3b ) . Pre-incubation with anti-CD39 BY40 mAb reversed by ∼50% the suppressive effect of Treg from HIV-positive subjects ( average suppression rate of 28% in the presence of Treg pre-treated with BY40 as compared to 56% and 57% for Treg pre-treated or not with IgG1 control mAb , ( P = 0 . 01; one-way ANOVA and paired T-test P = 0 . 01 for group by group comparisons ) . Interestingly , although the suppression mediated by Treg from HIV-negative controls was less significant , a similar effect of anti-CD39 BY40 mAb was noted ( average inhibition 12 . 3% as compared to 22 . 5% , one-way ANOVA P<0 . 01 and paired T-test P<0 . 01 ) . These results are in accordance with the higher density of CD39 molecules expressed by Treg from HIV-positive subjects and indicate that this enzyme is involved , at least in part , in the Treg-mediated inhibition of CD8+ T cell proliferation . Next , we evaluated the effects of Treg on the cytokine production of CD8 T cells in response to HIV-1 antigens . Cytokine production ( IFN-γ , TNFα and IL-2 ) of CD8-gated T cells was analyzed by intra cytoplamic staining and flow cytometry after overnight stimulation with a pool of whole Gag 15mer peptides ( 2 µg/ml ) . As shown in Fig . 4 , the percentages ( mean ± SD ) of CD8+ Cytokines+ T cells were 2 . 1+/−0 . 7% vs . 3 . 3%+/−1% ( n = 5 ) in the presence of Treg and CD4+CD25− respectively ( P = 0 . 05 ) . Pre-incubation of Treg with anti-CD39 mAbs , but not with isotype control , relieved this suppressive effect: 3 . 2+/−0 . 8% , ( P = 0 . 05 ) . Together , these results indicate that CD39 enzyme participates in the Treg-mediated suppression on CD8 T cell proliferation and responses to HIV peptides . To further investigate the involvement of CD39/adenosine in the Treg-mediated inhibition of CD8+ T cell proliferation in HIV-1 positive subjects , we studied the effects of the A2AR agonist CGS21680 on proliferation of anti-CD3 stimulated T cells . The mean ( ±SD ) inhibition of CD4+ T cells was 47% ( ±11 ) and 57% ( ±8 . 3 ) in the presence of 0 . 1 and 1 mM of CGS , respectively in c-ART− HIV positive patients . Similarly , the same doses of CGS inhibited by 47% and 65% the proliferation of anti-CD3 activated CD8+ T cells from c-ART− HIV-positive subjects ( P<0 . 05 ) ( Fig . 5a , b ) . In contrast , the proliferation of CD4+ and CD8+ T cells from HIV-negative controls and c-ART+ HIV-positive subjects was much lower and below 20% at the highest dose of CGS21680 ( 1 mM ) ( Fig . 5a , b ) ( P = 0 . 015 and P = 0 . 027 respectively; one-way ANOVA and P<0 . 05 unpaired T-test for comparison between c-ART−HIV-positive patients and the two other groups ( Fig . 5a , b ) . In accordance with this , we found that both CD4+ and CD8+ purified T cells from c-ART− HIV-positive subjects ( n = 7 ) expressed a significantly higher level of A2AR mRNA than c-ART+ subjects ( n = 5 ) or HIV-negative controls ( n = 6 ) ( Fig . 5c ) . Since the HIV-positive subjects we studied were heterogeneous in terms of disease duration and clinical stage , we assessed whether CD39 expression correlated with established markers of disease progression . The frequency of the Treg CD39+ subset correlated directly with plasma HIV-1 viral load in the group of c-ART− subjects ( P<0 . 05 , R = 0 . 45 ) ( Fig . 6a ) . Moreover , the percentage of Treg CD39+ subset correlated directly with the activation of CD4+ T cells in c-ART− subjects , assessed by the percentage of CD4+HLA-DR+ ( P<0 . 05 , R = 0 . 66 ) ( Fig . 6b ) . Finally , the percentage of Treg CD39+ cells and CD39 MFI correlated inversely with absolute CD4+ T cell count in c-ART− subjects ( P<0 . 001 , R = −0 . 51 and P<0 . 001 , R = −0 . 57 , respectively ) ( Fig . 6 c , d ) as well as in c-ART+ subjects ( P<0 . 001 , R = −0 . 57 and P<0 . 01 , R = −0 . 43 ) ( Fig . 6 e , f ) . The independent prognostication value of CD39 expression on Treg for CD4 T cell counts was studied in c-ART− and in c-ART+ patients , using multiple linear regression models ( SPSS v . 17 . 0 ) . The frequencies of Treg , Treg CD39+ , Tact , Tact CD39+ , and viral load ( for c-ART− group only ) were included as predictors of CD4 absolute count . For c-ART+HIV-positive patients , in a full model ( R2 = 0 , 398 , ANOVA P = 0 . 02 ) the percentage of Treg CD39+ had the most important partial predictive effect ( partial correlation coefficient −0 . 479 ) , confirmed by sequential multiple regression analysis of the same set of variables ( partial correlation coefficient −0 . 612 vs . 0 . 360 for Tact , ANOVA P = 0 . 001 ) . For c-ART−HIV-positive patients , in a full model ( R2 = 0 , 392 , ANOVA sig . = 0 . 045 ) , again the percentage of TregCD39+ had the most important contribution as predictor for CD4 absolute count , followed by CD39Tact ( partial correlation coefficients −0 . 375 , and 0 . 265 respectively ) . These results indicate that the frequency of Treg CD39+ is an independent predictive factor for CD4 cell count variability . Our results highly suggest that the frequency of Treg CD39+ cells , as well as the density of the enzyme molecule at the surface of those cells , predict disease progression . Recently , CD39 gene polymorphisms associated with the level of enzyme expression have been shown to be associated with susceptibility to Crohn's disease [21] . In order to assess the role of CD39 on HIV-1 disease progression , we investigated whether CD39 gene polymorphisms could be associated with clinical outcomes . For that , we exploited the GRIV cohort , comprising subjects exhibiting extreme profiles of AIDS progression ( LTNP , long-term non-progressors and RP , rapid progressors ) [20] , [22] , [23] . We thus performed a genetic case-control association study on the candidate gene CD39 using the genotype data collected from our previous genome-wide association studies [22] , [23] ( see Methods ) . Fourteen SNPs were identified in the CD39 gene . No polymorphism was significantly associated with rapid progression , whereas four SNPs were significantly associated with LTNP: rs10882665 ( P = 1 . 33×10−2 ) , rs3181123 ( P = 1 . 38×10−2 ) , rs1933166 ( P = 1 . 76×10−2 ) , and rs11188513 ( P = 3 . 60×10−2 ) ( Fig . S5 ) . Of note , rs10882665 and rs3181123 are in full linkage disequilibrium ( r2 = 1 ) . To eliminate a potential association with HIV-1 infection rather than with LTNP , we compared the allelic frequency of each of these SNPs in the RP population . The frequency observed in the RP group was similar to the frequency observed in the control group , confirming that this was an association with LTNP . To confirm these results , we used two additional independent Caucasian cohorts that examined AIDS progression phenotype: the ACS and the MACS cohorts ( see Methods ) . The rs11188513 SNP ( whose frequency in LTNP and control groups were , respectively , 39% and 34% , P = 3 . 60×10−2 , ( Fig . 7a ) was the only polymorphism also associated with disease progression both in ACS ( P = 2 . 64×10−2 ) and MACS ( P = 2 . 07×10−2 ) ( Fig . 7b , c and Table S1 ) . The P values compute the probability that an association is due to chance and the combined P value for rs11188513 over the three cohorts was significant after Bonferroni corrections , P = 6 . 11×10−3 . Importantly , as shown in Fig . 7 , the rs11188513-C allele favoured slower progression of HIV infection in all three cohorts . This association was independent from the CCR5 polymorphisms ( P1 and Delta32 ) also located in chromosome 3 , since the p value was not modified by using the CCR5 variants as covariates . To further explore this association , we examined the Genevar [24] and the Dixon [25] mRNA expression databases , and found a correlation ( P = 3 . 26×10−5 and P = 1 . 9×10−14 , respectively ) between the rs11188513-C allele and lower expression of the CD39 gene . Thus , the genetic association study combined with the mRNA expression database information demonstrate that the rs11188513-C allele is associated both with a slower progression to AIDS and with a lower expression of CD39 gene .
We show here the involvement of the CD39/adenosine pathway in the Treg-mediated suppressive effect on HIV-1-infected subjects' T cell functions . We demonstrate that HIV-positive subjects exhibit both a higher frequency of Treg CD39+ and a higher in vitro sensitivity of effector T cells to the suppressive effect of adenosine , due to a higher expression of its predominant A2A receptor . Expansion of Treg CD39+ correlates inversely with CD4 T cell counts in HIV infection independently of plasma viral loads and T cell activation . Finally , in a genetic association study conducted in three different HIV-positive cohorts we show that the level of CD39 gene expression can indeed impact the course of disease progression . Recent data have shown that mouse Treg constitutively express CD39 [26] , while the proportion of Treg CD39+ cells appears highly variable in healthy human controls [10] . Therefore , in contrast to mice , CD39 expression might delineate a subpopulation of human Treg [10] , [27] . However , studies on human Treg CD39+ cells are scarce . Few studies have analyzed the expression of CD39 in HIV disease [28] . Leal et al . have shown an increased nucleotidase activity related to enhanced CD39 expression on lymphocytes of HIV-positive subjects [28] . More recently , and in accordance with results presented here , an increase in the frequency of Treg expressing CD39 has been shown in different cohorts of HIV infected patients [29] . However , these observations warrant further investigations on the role of CD39 and the clinical relevance of these findings . Our results reinforce these observations and provide new insights about the biological mechanisms involving the CD39/adenosine axis . The demonstration that blocking of CD39 with BY40 mAb relieved , although not completely , the suppressive effect of Treg on effector T cells opens the way to new therapeutic interventions aimed to modulate Treg functions [29] . Moreover , we found that Treg CD39+ inhibit cytokine production by HIV-specific CD8 T cells , an effect partially relieved by pre-incubation of Treg CD39+ with anti-CD39 mAb . These results demonstrate that CD39 enzymatic pathway is responsible , at least in part , for the inefficiency of CD8 T cells responses in chronic HIV-1 infection . In contrast , the CD39 pathway seemed to be less predominant in coculture studies performed with cells purified from HIV negative controls . However , we cannot rule out that down-modulation of CD39 enzymatic activity may also interfere with other suppressive pathways . Our results are similar to those reported in cancer and HIV patients in whom the purified Treg CD39+ subset mediated a higher suppression as compared to control patients [27] . From a clinical stand-point , it is interesting to note the persistence of a higher frequency of Treg CD39+ cells in HIV-positive subjects with controlled viral load , as compared to HIV-negative controls . Likely , this may reflect ongoing chronic immune activation . We show here that the frequency of TregCD39+ is correlated positively to the percentages of activated CD4+ T cells expressing HLA-DR ( Fig . 6b ) and a higher frequency of conventional T cells ( CD4+CD25− ) expressing CCR5 ( not shown ) which may partly explain CD4+ T cell depletion . Alternatively , since the Treg CD39+ subset is mostly confined to the memory CD4 T cell compartment , this population may represent HIV-inducible Treg , as previously reported [5] , [6] . Recently , an expansion of suppressive FoxP3+CD39+ CD8 regulatory T cells associated with poor antiviral response has been reported in HIV-infected patients [30] . In our study , we have checked that expression of CD39 molecule on other blood subsets ( B , NK and monocytes ) did not vary significantly between patients' groups ( Fig S6 ) . Altogether these results support the conclusion that the Treg subset expressing a high density of both CD25 and CD39 molecules represents a highly-enriched population of suppressor T cells in HIV-1 infected patients . Adenosine is formed in tissue microenvironments under inflammatory insult [16] , [31] , [32] , [33] . Several studies have shown that adenosine plays an important non-redundant role in the regulation of T cell activation [18] , [34] , [35] . Using the dose-dependant inhibitory effect of the adenosine receptor agonist CGS21680 [18] , we confirmed the involvement of CD39/adenosine pathway in the Treg-mediated inhibition of T cell proliferation in HIV-1 infected patients . It is noteworthy that CD39/adenosine inhibition affected both CD8 and CD4 T cells , and was significantly more important in c-ART-naïve HIV positive subjects . This latter difference was due to a significantly higher level of A2AR expression . We found that CGS21680 did not inhibit the proliferation of T cells from c-ART treated patients . However , as we did not evaluate CGS21680 effects on other T cell functions , we cannot rule out that A2AR agonists may also impair T cell cytotoxicity and production of cytokines such as IL-2 and IFN-g rather than cell proliferation , as recently demonstrated [36] , [37] . Our data provide clues to the suppressive mechanisms of Treg in the context of chronic immune activation . CD39 expression by Treg is important for the extracellular removal of ATP and allows Treg infiltration of inflamed tissues , resulting in an increase of local extracellular adenosine concentration by ATP catabolism [11] , [38] . Extracellular ATP depletion may also increase Treg survival and favour the local accumulation of Treg , since high levels of ATP have been shown to be a pro-apoptotic factor [39] , [40] . On the other hand , this microenvironment represents a self-protective mechanism against immune attacks [16] , [41] by inducing a rapid tolerization of activated cells , as demonstrated in cancer models [42] . Recent data in a mice model has shown that tissue-derived adenosine promotes peripheral tolerance by inducing T cell anergy and Treg differentiation [37] . Altogether , these studies show that initiation of T cell activation in inflamed tissue and/or tumour microenvironments might result in the induction of T cell unresponsiveness by an A2AR-dependent mechanism . These observations may explain the reports of HIV infection in which Treg coexist in tissues infiltrated with HIV-specific T cells that are poorly capable of controlling local HIV replication [43] , [44] . Of note , our study was limited to peripheral blood . Whether , the involvement of CD39/adenosine pathway plays also a key role in secondary lymphoid organs or in mucosa deserves further studies . Treg CD39+ expansion may help establish the relationship between immune activation and Treg-mediated suppression in HIV-1 infection . Increased ATP and adenine nucleotides in inflamed sites may serve as substrates for Treg-expressed nucleotidases but also may exert direct Treg-activating effects [45] . Thus , the ATP-Treg balance might be crucial for the regulation of inflammation . However , in the long term , CD39-mediated inhibition of T cell proliferation might exert an adverse effect not only on the immediate generation of T-cell immune responses , but also on the maintenance and restoration of the T-cell pool , thus contributing to disease progression . We also showed that despite efficient c-ART , the percentage of Treg CD39+ remains higher in c-ART+ HIV-1 subjects as compared to controls . Although T cells from these individuals express low levels of A2AR , we found that Treg still exert a significant inhibitory effect that was relieved by anti-CD39 blocking antibodies . This observation corroborates the observation of an inverse relationship between the frequency of Treg CD39+ and CD4+ T cell counts in patients ( Fig . 6 ) . Although the role of Treg in HIV-1 infection remains unclear , the identification of a novel Treg subset participating in Treg suppression may be useful to discriminate between a “friend or foe” role of Treg in HIV-1 infection . Through a candidate gene association study , we identified a CD39 gene variant associated with down-modulation of CD39 expression that impacts the course of disease progression , a finding that was replicated in three different cohorts . Such high P values for the association of this variant and CD39 expression in both Genevar and Dixon databases are extremely rare . Since the SNP identified is in high linkage disequilibrium ( r2>0 . 9 ) with several other SNPs within the CD39 gene , further studies are warranted to determine which of them is a causal variant . It is important to note that , according to the HapMap database , this SNP exists at a allelic frequency of ∼30% in the African population and at ∼70% in the Asian population , suggesting that this genetic variant may be an important determinant of disease progression in both populations . Overall , the genetic association study confirms in vivo the hypotheses put forward by our experimental work: subjects carrying the CD39-C allele are likely to exhibit a lower CD39 expression , which could impact the control of T cell immune responses , and in turn slow down HIV-1 disease progression . Our data show that the CD39/adenosine axis might be a novel pathway involved in the Treg-mediated suppression in HIV infection through both an expansion of Treg strongly expressing the ectonucleotidase CD39 , and an increased sensitivity of patients' T cells to adenosine . In this context , the possibility to revert Treg-mediated inhibition using CD39-blocking mAb or by modifying the adenosine turnover with specific drugs seems an attractive approach for the design of novel treatments to enhance T lymphocyte restoration and effector T cell responses .
Blood samples were collected from HIV-1-positive subjects either naive from treatment ( c-ART– , n = 39 , CD4+ T cells counts ( mean ± SD ) : 387±242 cells/µl; viral load ( mean ± SD ) : 4 , 2±1 , 1 log HIV RNA copies /ml or stable under c-ART for more than 6 months ( c-ART+ , n = 39 , CD4+ T cells counts ( mean ± SD ) : 485±440 cells/µl ; viral load <1 , 6 log copies /ml ) , at the Hospital of Infectious Diseases , Sofia , Bulgaria and Henri Mondor Hospital , Créteil , France . Blood from 25 HIV-negative donors was obtained at the Regional Blood Transfusion Centre , Creteil , France . CD8+ and CD4+ T cells were purified using RosetteSep enrichment antibody cocktails ( StemCell Technologies , Vancouver , BC , Canada ) according to the manufacturer's instructions . CD4+CD25hi cells were further isolated with CD25 magnetic beads and two passages on MS columns ( Miltenyi Biotec , Bergisch-Gladbach , Germany ) . The positive fraction contained >80% Treg expressing high levels of FoxP3 transcription factor as verified by flow cytometry ( data not shown ) . CD8+ T cells were stained with 0 . 5 mM CFSE ( Molecular probes , Eugene OR , US ) as previously described [46] . CFSE-labelled CD8+ T cells were cultivated in 96-well U-bottom plates , coated with 5 mg/mL anti-CD3 mAb ( UCHT1; Beckman Coulter , Villepinte , France ) in the presence or absence of Treg ( total cell concentration 1 . 25×105/ml and final volume 200 ml and the Treg/Effector ratio was 1/4 as determined in previous studies [43] , [44] ) . In some experiments , Treg were pre-incubated with 10 µg/ml of anti-CD39 ( BY40 , IgG1 ) or isotype control mAb for 15 min at 37°C , and added to CD8+ T cells without a washing step . The effects of BY40 mAb on CD39 expression and inhibition of ATPase activity were evaluated using YT2C2 NK cell line ( flow cytometry ) and fresh monocytes using malachite green phosphate detection kit ( R&D System , Minneapolis , USA ) , according to manufacturer's instruction ( See methods in the legend of Fig . S3 ) . To assess the effect of adenosine analogue CGS 21680 , PBMC were pre-incubated for 1 h with different concentrations of either CGS 21680 ( Sigma-Aldrich , Lyon , France ) or DMSO as control . Cells were then stimulated with anti-CD3 for 5 days as described above . At day 2 of culture , DMSO and CGS 21680 were added in identical concentrations . For intracellular staining ( ICS ) , CD8+ T cells were stimulated in the presence or absence of Treg ( Treg/effector ratio:1/4 ) overnight with a pool of whole Gag 15-mer peptides ( 2 µg/ml ) supplemented with anti-CD28 and anti-CD49d antibodies ( 1 µg/ml of each ) . Brefeldine A ( 10 µg/ml ) was added 1 h after the peptide stimulation . Cells were surface stained with anti-CD8 mAb and ICS was performed with PE-Cy7-conjugated IFN-γ , TNFα and IL-2 antibodies . When indicated , Treg were pre-incubated with 10 µg/ml of anti-CD39 mAb or isotype control for 15 min at 37°C , and added to CD8+ T cells without a washing step . Total RNA was isolated from purified CD4+ and CD8+ T cells and RT-PCR was performed by the ABI Prism 7500 Sequence Detection System ( Applied Biosystems , Courtaboeuf , France ) in 50 µL reaction with Platinum SYBR Green qPCR SuperMix-UDG w/ROX ( Invitrogen ) and 0 . 2 µM of each primer . S14 mRNA which expression was found to be stable among the different group of patients was used as control to normalize each sample . Sequences of the A2AR- and S14-specific primers were forward: CGAGGGCTAAGGGCATCATTG , reverse: CTCCTTTGGCTGACCGCAGTT ) and forward: GGCAGACCGAGATGAATCCTCA , reverse: CAGGTCCAGGGGTCTTGG TCC . The relative levels of A2AR mRNA were calculated using the 2−ΔΔCT method . Anti-CD39-PE ( clone TU66 ) , anti-CD25-PC7 , anti-CD4-FITC or Pac . blue , anti-CD8-PerCP , anti-CD3-APC , and CD28-PerCP-Cy5 . 5 , were products of BD Biosciences ( Le Pont de Claix , France ) , CD45RA-ECD from Beckman Coulter ( Villepinte , France ) , and CD127-Biot/ strepta-APCCy5 . 5 , FoxP3-Alexa 488 , CCR7-APC-Alexa 750 from ebiosciences ( Montrouge , France ) . Blocking anti-CD39 mAb ( BY40 ) was produced in one of our laboratories ( A . B ) by immunizing mice with the YT2C2 NK cell line . BY40 is IgG1 monoclonal antibody , which is with BY12 mAb unique regarding its epitope mapping as we previously reported [47] . BY40 is not cytotoxic and it inhibits directly ATPase activities mediated by cell membrane anchored CD39 ( AB personal data and this paper Fig . S3 ) Cells were analysed by LSR II ( BD Immunocytometry systems ) . At least 20 000 CD4 or CD8-gated events were collected for cell surface studies . Statistically significant differences were assessed by one-way ANOVA , followed by paired t-samples T-test , or by unpaired T-test assuming independent samples where appropriate . Correlations were assessed using Spearman's rank order test ( GraphPad° Prism 5 . 0 statistical software ) . The independent prognostication value of CD39 expression on Treg was evaluated in multiple linear regression models ( SPSS v . 17 . 0 ) . ( For more details , see previously published works [23] , [50] , [51] ) . For the GRIV ( cases and controls ) and ACS analyses , the CD39 genotyping data were obtained using the Illumina Infinium II HumanHap300 BeadChips , when for the MACS analysis , they were obtained using the Affymetrix GeneChip Human Mapping 500K Array . In each study , quality control filters ( e . g . missingness , low minor allele frequency , Hardy-Weinberg equilibrium deviation ) were applied to ensure reliable genotyping data as previously described [23] , [50] , [51] . In each cohort , potential population stratification was also considered using the Eigenstrat software [52] . First , to confirm continental ancestries , the genotypes of each participants group were combined with the genotypes from the three HapMap reference populations . Among the initial ACS group , 13 subjects were thus excluded from further analyses ( n = 404 ) to avoid spurious associations resulting from a non-European ancestry . Then , in each study group of European descent , the top ten most significant principal components were identified and included as covariates in the regression models described below . The rs11188513 SNP untyped in the MACS group was imputed using Impute software [53] and the HapMap release 21 phased data for the population of European descent ( CEU ) as the reference panel . We first performed a genetic case-control association analysis in the GRIV cohort using a logistic regression and an additive model , including as covariates the 10 principal components identified by Eigenstrat . All SNPs found to be significant in the GRIV cohort were tested for replication in ACS and MACS cohorts . The SNP rs11188513 was the only polymorphism exhibiting a significant p-value both in ACS and MACS . For the replication in the ACS and MACS groups , we performed Kaplan-Meier survival analysis and regression -Cox proportional regression and linear regression for ACS and MACS respectively- in an additive model including as covariates the 10 principal components identified by Eigenstrat . The significant associations ( P<0 . 05 ) were also retested using age , sex , and CCR5-P1 and D32 polymorphisms as covariates and yielded identical results . To evaluate the combined p-value obtained over the 3 cohorts for each SNP , we used the classical Fisher method [54] . Approval and written informed consent from all subjects were obtained before study initiation . The study was approved by the following ethical committees : Hospital of Infectious Diseases , Sofia , Bulgaria and CCP IX Ile de France - Henri Mondor Hospital , Créteil , France . Ethic statements for GRIV , MACS ACS cohorts have been already reported [23] , [50] , [51] .
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HIV-1 infection is characterized by a chronic activation of the immune system . Regulatory T cells ( Treg ) represent a population of lymphocytes that controls inappropriate or exaggerated immune activation induced by pathogens , thereby influencing the outcome of various infections . Several studies have shown that Treg are expanded in HIV infected patients . However , the mechanisms of Treg immune-modulator functions are not clearly known . CD39 is an ectonucleotidase which converts the proinflammatory ATP signal into AMP and the immunosuppressive adenosine in concert with CD73 . A critical role of CD39 has been described for Treg in general but few studies have analyzed its role in HIV infection . We report here an expansion of Treg expressing CD39 in a cohort of HIV-infected patients . In vitro these cells exerted a strong suppressive effect on the effector CD8 T cells . Treg inhibitory effects were relieved by CD39 down-modulation using an anti-CD39 monoclonal antibody . Treg suppressive effects were reproduced by an adenosine agonist in accordance with a higher expression of the adenosine A2A receptor on patients' T cells . From a clinical stand point , we show also a correlation between Treg CD39+ expansion and both immune activation and CD4+ T cell depletion in patients . Finally , by genetic analysis of three different cohorts of patients , we found that a CD39 gene polymorphism associated with a lower CD39 expression correlated with a slower progression to AIDS . Thus , our results contribute to elucidate the mechanisms by which Treg suppression occurs during HIV infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine"
] |
2011
|
CD39/Adenosine Pathway Is Involved in AIDS Progression
|
So far , the computational identification of transcription factor binding sites is hampered by the complexity of vertebrate genomes . Here we present an in silico procedure to predict target sites of a transcription factor in complex genomes using its binding site . In a first step sequence , comparison of closely related genomes identifies the binding sites in conserved cis-regulatory regions ( phylogenetic footprinting ) . Subsequently , more remote genomes are introduced into the comparison to identify highly conserved and therefore putatively functional binding sites ( phylogenetic filtering ) . When applied to the binding site of atonal homolog 5 ( Ath5 or ATOH7 ) , this procedure efficiently filters evolutionarily conserved binding sites out of more than 300 , 000 instances in a vertebrate genome . We validate a selection of the linked target genes by showing coexpression with and transcriptional regulation by Ath5 . Finally , chromatin immunoprecipitation demonstrates the occupancy of the target gene promoters by Ath5 . Thus , our procedure , applied to whole genomes , is a fast and predictive tool to in silico filter the target genes of a given transcription factor with defined binding site .
To understand regulatory networks , it is important to unravel the direct interactions of its transcriptional regulators . For this , the corresponding transcription factor binding sites in the upstream region of the respective target genes have to be identified . However , available approaches have not been able to overcome problems related to the fact that the transcription factor binding sites are short ( 6–20 bp ) and consequently are found very frequently , spread all over the genome . These motifs are functional in only a small fraction of their instances [1] . It has been suggested that epigenetic processes , in particular histone modifications , permit or prevent the access to chromatin [2] . Cooperative binding of multiple transcription factors to combinations of motifs also account for the high selectivity in vivo . Combinations of transcription factor binding sites have therefore been used to computationally predict regulatory modules [3–6] . Comparative genomic approaches applied methods commonly termed “phylogenetic footprinting” [7] . These techniques are based on the fact that functional genomic regions are under selective pressure , resulting in the evolutionary conservation of the respective sequences . Phylogenetic footprinting identifies conserved stretches of noncoding DNA in sequence alignments of related species with limited complexity . To apply this approach to complex genomes , the complexity can be reduced by focusing on the sequences flanking identified genes . In closely related species , neutrally evolving sequences , as well as functionally relevant and therefore conserved sequences , result in an alignment . Consequently , functional motifs are masked by the high degree of overall sequence similarity . On the other hand , if the genomes are too diverged , sequence comparison may fail to detect short conserved functional motifs due to the lack of significant alignment . Thus , the evolutionary distance of the genomes analyzed has to be considered . To overcome these problems , we developed a novel evolutionary filtering approach that takes advantage of the increasing number of sequenced vertebrate genomes . In a first step , we limited the complexity of closely related genomes by restricting the analysis to the upstream region of annotated genes . Considering only those genes that contain a transcription factor binding site in this region , we subsequently performed alignments with their orthologs from closely related genomes . In the second step , the regions of their orthologs in more diverged genomes were scanned for the presence of the motif . This evolutionary double filtering allowed to identify—in the large number of occurrences of a short motif—the small number of evolutionarily conserved transcription factor binding sites . We benchmarked this procedure using the available dataset for the transcription factor E2F by comparing the results with the existing chromatin immunoprecipitation ( ChIP ) on chip [8] . Eighty-five percent of our in silico predicted targets contained in the ChIP on chip dataset were experimentally validated . This demonstrates the predictive power of the procedure in the context of the complex human genome . We next used our procedure to de novo identify of a set of Ath5 target genes . The basic helix loop helix ( bHLH ) transcription factor Ath5 is a key regulator of vertebrate retinal development . Ath5 is required for the differentiation of retinal ganglion cells ( RGCs ) , which provide the axonal link of the retina to the respective visual centers [9–11] . Loss of ath5 function results in the absence of RGC formation in vertebrates [12–14] . Conversely , gain of ath5 function by overexpression in the retina promotes RGC formation [15 , 16] . So far , only a few Ath5 target genes have been identified , including Ath5 itself [17 , 18] and its binding site is only poorly defined . We show that Ath5 interacts with its own promoter and autoregulates its own expression via binding to an extended E-box motif ( CCACCTG ) containing the consensus site recognized by bHLH transcription factors [19] . Using this motif , we predict by phylogenetic double filtering a conserved set of target genes and experimentally validate a number of those targets in vivo .
We first experimentally defined an Ath5 binding site to be used as a signature for the computational prediction of its conserved target genes . Ath5 had been shown to control its own expression in a conserved positive regulatory feedback loop [17 , 18] . Since our aim is to identify conserved target genes , we also searched for motifs within the Ath5 regulatory region that are conserved throughout vertebrates . In a comparative approach using promoterwise ( http://www . ebi . ac . uk/~birney/wise2/ ) we identified two evolutionarily conserved ( from teleosts to mammals ) extended E-box motifs ( CCACCTG ) within 2 kb of upstream sequences that in medaka fish embryos faithfully recapitulate ath5 expression in a reporter construct ( Figure 1A–1C ) . To test the interaction of Ath5 with these conserved CCACCTG motifs , electrophoresis mobility shift assays ( EMSAs ) were performed with oligos containing the two wild-type motifs or different variants in which the motif was altered with or without affecting the E-box consensus ( see Materials and Methods ) . We found that the presence of at least one E-box was sufficient to allow binding of Ath5 . Binding was only abolished if the consensus E-box in both motifs was changed ( Figure S1A ) . Furthermore , only those oligos in which one of the E-boxes was preserved competed with the wild-type probe when added in excess ( Figure S1B ) . Those results confirm the specificity of the interaction and indicate a high affinity of Ath5 for the conserved CCACCTG motif . To investigate the ability of Ath5 to activate its own promoter , we used cos7 cells in a luciferase transcription assay . As previously demonstrated for chick Ath5 [17] , the medaka 2-kb Ath5 promoter is also strongly activated by Ath5 in a dose-dependent manner ( Figure 1D ) . Our mutational analysis revealed that changing one of the motifs while preserving the E-box consensus results in reduced transcriptional activation ( 2-fold versus 6 . 5-fold of the wild-type promoter; Figure 1D ) . No activation was observed in all the other variants tested . Furthermore , embryos injected with corresponding GFP reporter constructs , in which the E-box consensus in the two conserved motifs is disrupted , failed to express GFP in the endogenous domain ( unpublished data; see also Materials and Methods ) . This indicates that only the identified CCACCTG motifs are efficiently recognized and bound by Ath5 . To identify functional target sites , and , consequently a conserved target gene set of a given transcription factor in a genome-wide manner , we devised a multistep procedure that relies on the evolutionary conservation of functionally relevant transcription factor binding sites . First , we reduce the complexity by limiting the search space to the region upstream of annotated human genes . We subsequently search for the presence of the motif corresponding to the transcription factor binding site in a conserved region with rodents ( see Materials and Methods for the definition of conservation ) . In a last step , we scan orthologous regions in more diverged species for the presence of the motif . This additional filtering step is independent of any alignment , i . e . , the motif does not have to lie in a conserved stretch . All the genes with an upstream region that passes the last filter are defined as the predicted target genes of the analyzed transcription factor ( see Text S1 for details ) . To assess the performance of our in silico procedure , we benchmarked it using the binding site of the transcription factors E2F ( Transfac , Jaspar [20 , 21] ) by comparing our dataset with that obtained by ChIP [8] . The details of the benchmarking procedure are described in Text S1 . Of the 1 , 342 genes with Ensembl identification numbers that were tested by Ren et al . [8] , we predict 14 to be bound by E2F , of which 12 ( 85 . 7 % ) are correct . This is a significant improvement over a control where genes are randomly sampled ( p-value < 0 . 00001 ) . We note , however , that our stringent conservation requirement misses 89% of the bound genes . Low sensitivity is , at this point , an unavoidable consequence of comparative studies that aim at high specificity using evolutionarily distant species . Using the defined Ath5 binding site , we applied our evolutionary double filtering procedure to identify conserved Ath5 binding sites and , by this , potential Ath5 target genes . In previous studies , the majority of conserved regulatory regions had been found within 5 kb upstream of genes [22] . Therefore , in our search for the Ath5 binding site , we concentrated on the 5-kb upstream sequence of all annotated genes in the vertebrate genomes analyzed . Candidate genes were thus identified by the presence of the conserved CCACCTG motif or its corresponding reverse complement within this region . Our procedure ( Text S1 ) filtered the number of occurrences of the 7-bp Ath5 binding site from about 324 , 000 instances in the entire human genome ( Ensembl v42 , repeat masked sequences ) to 166 evolutionarily conserved sites and the corresponding genes ( Table S1 ) . We noted that the majority of these sites are found within the first 2 kb upstream of the annotated transcriptional start site ( Figure S2 ) . This is in contrast to the random distribution of Ath5 motifs present in the 5 kb upstream sequences of all annotated human genes and further confirms previous studies on the position of relevant regulatory elements [23] relative to the gene start . We compared the gene ontology annotation ( GO ) [24] of the identified gene set to that of the entire annotated human genome ( Figure S2 ) . We found an enrichment of the cellular component “nucleus” ( p = 1 . 2e−04 ) , the biological process “transcription factor activity” ( p = 1 . 40e−08 ) , and the biological function “development” ( p = 7 . 02e−12 ) . We organized the set of predicted target genes of Ath5 into functional categories: transcription factor , neuronal function , axon guidance and growth , cell cycle and signaling , development , and others ( n = 166; Table S1 and references therein ) . We analyzed the expression pattern of thirty predicted target genes within relevant categories ( see Table S1 ) in the medaka fish retina in comparison to ath5 expression ( Figures 2 and 3; unpublished data ) by whole mount in situ hybridization and found retinal expression for 19 of them ( Figures 2 and 3; unpublished data ) . At the onset of retinal differentiation ( stage 27 ) [25] , all the target genes expressed in the retina show an expression overlapping with that of ath5 in the central retina ( Figure 2; unpublished data ) . At subsequent retinal differentiation stages , the expression patterns of the different target genes can be classified into three major groups . In the first group , the expression pattern remains entirely overlapping with that of ath5 ( Figure 2A–2J ) . The second group is composed of genes expressed late in mature RGCs in the central retina , abutting the ath5 expression domain ( Figure 2K–2O ) . In the third group , in addition to the GCL , late expression is also found in neurons of the inner nuclear layer ( Figure 2P–2T ) . Analyzing the predicted target genes with respect to the GO categories we found Ath5 among the transcription factors , in agreement with its autoregulatory function , as well as a number of factors that have been implicated to function in RGC differentiation , including Brn3C ( POU4F3 , Figure 3A ) , Gfi-1 ( GFI1 , Figure 3D ) , Irx5 ( IRX5 , Figure 3E ) , Dlx2 ( DLX2 , Figure 3H ) , Dlx1 ( DLX1 ) , and Tbx2 ( TBX2 Figure 3K ) [26] . In some cases , their involvement in differentiation and/or survival of RGCs has been well documented , such as for Brn3C and Dlx1/Dlx2 ( Figure 3A and 3H ) [16 , 27 , 28] . The majority of the genes in the category “neuronal function” are ion channels such as the voltage dependent anion channel Vdac-2 ( Figure 3L ) . This category also contains the RNA binding protein ELAVL3 ( HuC , ElavC ) , which has been shown to function in RGC development ( Figure 3G ) . The category “axon guidance” contains the cell adhesion molecules CD166 ( ALCAM , Figure 3B ) , MCAM , Slit-1 ( SLIT1 , Figure 3N ) and integrin alpha-6 ( Int-α6 , ITGA6 , Figure 3O and 3P ) that play a role in axonal guidance ( see Table S1 ) . Furthermore , this category contains genes that were not previously shown to be expressed in RGCs . Our analysis confirmed expression in RGCs for ADAM11 ( Figure 3C ) and NN1 ( NAV1 , Figure 3J ) . The last category includes genes involved in cell cycle regulation and cell signaling ( RAB25 , Figure 3M and MNT/ROX , Figure 3I ) . Some of those genes , e . g . , NDRG1 and NDRG2 , play a role in cell differentiation , whereas others , e . g . , CABLES1 and CABLES2 stimulate neurite outgrowth [29] . In conclusion , we analyzed 30 putative target genes by whole mount in situ hybridization in medaka fish and found retinal expression for 19 of them ( Figure 3; unpublished data ) . The remaining 11 genes either showed no expression or a pattern that was not consistent with regulation by Ath5 . Furthermore , retinal expression had already been shown in other species for five additional predicted target genes ( Table S1 ) . Thus , out of these 35 genes analyzed , 24 ( 63% ) are expressed in a pattern consistent with their regulation by Ath5 ( Figures 2 and 3 ) . We used ectopic Ath5 expression in the developing medaka embryo to examine the transcriptional regulation of the target genes . To monitor ectopic Ath5 expression , a plasmid expressing Ath5 under the control of a strong and ubiquitous promoter was injected into one-cell stage embryos together with the 2kb Ath5::GFP reporter . This results in a mosaic distribution of the cosegregating plasmids in the injected embryo [30] . Cells expressing Ath5 ( as visualized by GFP ) also ectopically express the putative Ath5 target gene HuC , as visualized by fluorescent in situ hybridization ( Figure 4A ) . Similar results were obtained for other target genes such as Brn3C and CD166 ( unpublished data ) . Control embryos coinjected with the empty expression vector and the Ath5::GFP reporter did not show any colocalization of ectopic GFP with any of the target genes analyzed ( Figure 4B ) . Ectopic overexpression of the related bHLH transcription factors Xath3 ( Xenopus NeuroM , Neurod4 ) or Xash1 ( Xenopus Ash1 ) did not result in ectopic activation of these Ath5 targets genes ( Figure S3; Table S2; Text S1; unpublished data ) . Taken together , these experiments show that the expression of HuC , Brn3C , and CD166 is specifically activated by Ath5 . We next analyzed whether Ath5 binds to the promoters of the predicted target genes using ChIP on chick retinal chromatin preparations [18] . We concentrated on the chick orthologs of the target genes Dlx2 , HuC , Nn1 , and Int-α6 ( Table S1 ) [31] . Ath5 in vivo occupancy of target sequences was found in all cases tested ( Figure 5 ) . As a negative control , in the same extracts we found no Ath5 occupancy of the neuroM promoter , a gene also expressed in the retina but not activated by Ath5 [18] . In addition , no occupancy of the Ath5 target sequences was detected in extracts from the optic tectum , where ath5 is not expressed ( Figure 5 ) . Our results show that our procedure efficiently identifies novel transcriptional targets of Ath5 . Out of 35 predicted genes analyzed , 24 are expressed in a pattern consistent with regulation by Ath5 . When tested for ectopic induction by Ath5 , in fish embryos three out of three tested genes were directly activated by ectopic Ath5 . Finally , ChIP showed the occupancy by Ath5 of all four ( out of four ) target loci tested . Some of these target genes have been implicated to function in RGC differentiation . We demonstrate that Ath5 regulates the transcription of these genes and furthermore is bound to their promoter during retinogenesis .
In the work presented here we describe an approach for the identification of relevant target genes that relies on a novel computational procedure . This in silico procedure provides predictions for functionally relevant instances of transcription factor binding sites . This is achieved by a phylogenetic double filtering process that relies on the use of evolutionarily diverged genomes , reducing the large number of spurious motif matches , thereby selecting for the putative functional instances of the motif . Hence , our procedure predicts only evolutionarily conserved targets . Of crucial importance for the efficiency of the procedure is the second filtering step , where diverged genomes are analyzed for the presence of the motif in an alignment-independent way . Recently , a comprehensive list of putative regulatory motifs was identified using annotated vertebrate genomes [23 , 32] , but this work did not identify the direct target genes linked to the motifs . Our benchmarking analysis demonstrates that our method significantly enriched ( p < 0 . 00001 ) for true target genes of a transcription factor when compared to an experimental data set . Thus , our procedure provides a list of putative targets that have a high probability of being relevant . This list , as illustrated by our Ath5 target gene prediction , represents a valuable starting point for a downstream analysis of this transcriptional network . The use of distantly related fish species for the filtering procedure also implies that the list of predicted targets contains only genes from which the regulation through the transcription factor studied has been retained from mammals to fish . Considering the entire target gene set of a given transcription factor in one species , the nonconserved target genes will be missed using this procedure . This loss and the apparently low sensitivity ( 89% for the benchmarking using E2F ) are intended , and are an unavoidable consequence of comparative studies aiming at high specificity using evolutionarily distant species . With the addition of more entirely sequenced genomes resulting from the ongoing sequencing efforts of many vertebrate species , the sensitivity issue will be improved while retaining similar specificity [33] . This will also allow clade-specific innovations to be addressed , rather than just conserved functions . A prerequisite for using this procedure is an established binding site for the transcription factor studied . We experimentally identified an Ath5 binding site , relying on the direct Ath5 autoregulation , which is necessary for the upregulation of its expression in RGC precursors [34 , 35] . Based on this 7-bp Ath5 binding site , we identify 73 putative Ath5-regulated target genes . A recent microarray study on Ath5-regulated genes [26] compared wild-type and Ath5 mutant mouse retinae . The significant ( p = 5 × 10−5 ) but limited ( nine genes ) overlap between our data set and the microarray study is not surprising , given the different approaches used . While our approach predicts direct targets of a given transcription factor , the microarray analysis does not distinguish between direct and more indirect responses and provides a more global view of the transcriptional differences . This is well supported by our benchmark analysis . More recently , in a candidate gene approach , a number of transcriptional targets shared by the transcription factors Ath5 and NeuroD in Xenopus was reported [36] . Three out of the four Xenopus Ath5 target genes with clear orthologs in other vertebrate species were also identified by our procedure , further supporting the significance of our results . Ath5 is one of the earliest transcription factors specifically expressed in terminally differentiating RGCs , suggesting its key position in the underlying regulatory network . The fact that within the target genes we find a strong enrichment of the GO term “transcription factor activity” is in good accordance with this and provides further evidence for the significance of our results . Within the predicted target genes , we find a strong enrichment of genes acting in cell cycle control , axonal guidance , and neuronal function . Considering that Ath5 is required for the differentiation of neurons that provide axonal connectivity , this finding is in good agreement with the developmental role of Ath5 . For example , Ath5 is upregulated shortly before final mitosis [35] , and cell cycle exit is a prerequisite for neuronal differentiation . The suggested role of Ath5 in this process is underscored by the enrichment of target genes acting in cell cycle control . Our target gene validation by whole mount in situ hybridization revealed a coexpression with Ath5 in 63% of the cases analyzed . Furthermore , we show in vivo activation of targets by Ath5 . This activation is specific for Ath5 , whereas other related bHLH transcription factors fail to activate these targets . This strongly suggests that the identified Ath5 binding site is specifically recognized by Ath5 to activate transcription . Furthermore , in ath5 mutant lakritz embryos the expression of all predicted target genes analyzed is absent from the retina , demonstrating their dependence on Ath5 function ( Figure S4; unpublished data ) . Finally , target gene promoters are occupied in vivo by Ath5 at the time of retinal differentiation , as has been shown for the single established Ath5 target gene , NachR [18] . In summary , we present a novel in silico approach that predicts target genes of a given transcription factor . Our benchmarking and experimental application and validation on a novel binding site shows the high predictive power to identify in vivo relevant target genes .
The 5 kb upstream ( 1 , 000 bp upstream regions for the E2F benchmarking ) of all annotated genes were retrieved in Homo sapiens , Mus musculus , Rattus norvegicus , Takifugu rubripes , and Danio rerio using Ensembl version 17 . The sequences were repeat masked and exon masked ( for possible annotated exon , upstream of the annotated gene start ) . The gene start was considered to be the annotated start of the longest transcript for each gene . Orthologous gene pairs were taken from the Compara database ( version 17 ) and all the possible pairs were considered; best reciprocal hits as well as Reciprocal Hit based on Synteny around . For each human upstream sequence retrieved , the 5-kb orthologous regions in rat and mouse were identified using the downstream gene orthology mapping described above . Pair-wise alignments between human and mouse and human and rat were done using Promoterwise [32] . A conserved region is defined as a region with significant alignments . A significant alignment is defined has having a promoterwise hit higher than 25 bitscore . See [32] for the justification of such a cutoff . A conserved site between human and mouse ( or rat ) is defined as a sequence that satisfies the motif description in both species in one position of the significant alignment . A conserved site between human and a fish is defined as a sequence that satisfies the motif description in both species' 5-kb ( or 1-kb for the E2F benchmarking ) orthologous region but is not necessarily located in a significant alignment between these two species . The motif description can either be a discrete motif or a position weight matrix ( PWM ) . Both the forward and reverse strand were analyzed . The ChIP data from [8] was used to benchmark the computational procedure . From the ChIP data , we used the 130 genes described in Table 3 in [8] as the positive set . The corresponding Ensembl identification numbers were retrieved from the gene annotation ( 113 genes ) . The total set corresponds to the entire array used in the experiment ( 1 , 449 genes from Table S1 , of which 1 , 342 have an Ensembl identification number ) . The E2F PWMs ( M00516 , M00050; Transfac [20] , ) were used to search E2F target genes as described in the filtering procedure section of the Materials and Methods . The sites were located using the perl module TFBS::pwm [37] with variable score cutoff ( ranging from 75% to 100% ) . The sensitivity and specificity for each PWM hit cutoff was calculated by comparing the result obtained from the filtering approach to the reference data from [8] . with TP ( true positive ) being the number of genes overlapping between the positive gene set in [8] ( 113 genes ) and the gene set from the filtering procedure ( x genes depending on the matrix cutoff ) . FN ( false negative ) is the number of genes in the positive gene set in [8] ( 113 genes ) minus the TP . FP ( false positive ) is the number of genes overlapping among the gene sets from the filtering approach and the negative set of [8] ( 1 , 342 − 113 ) and TN ( true negative ) is the the negative set minus FP . Randomization: A set of genes was randomly sampled from the genes analyzed by [8] . The number of genes in that random set corresponds to the real number of genes found by the computational procedure to overlap with the set of genes analyzed by [8] . The overlap between the random set and the positive set of [8] was assessed and compared with the real overlap obtained using the computational procedure . This randomization procedure was repeated 100 , 000 times . For example , the filtering dataset using the PWM M00516 with a cutoff of 85% gave 38 candidate genes , out of which 14 overlapped with the 1 , 342 genes studied by [8] and 12 overlapped with the positive set ( 113 genes , POS ) . We randomly picked 14 genes from the genes set studied by [8] ( 1 , 342 genes , ALL ) and calculated the overlap of this random set with the positive set ( 113 genes ) . The procedure was repeated 100 , 000 times . The average overlap and maximum overlap was assessed . For each GO term identification number ( from cellular component , molecular function , biological process ) , we calculated the number of genes annotated with the GO identification number in the positive set ( 166 predicted target genes of Ath5 ) and in the entire human gene set ( Ensembl version 17 ) . The enrichment of each GO term identification number was evaluated using hypergeometry distribution [38] . Only GO categories with more than three genes in the positive set were further analyzed . The positions of the Ath5 motif ( CCACCTG ) and its reverse complement motif are located on the upstream sequences of the human genes and the distance relative to the annotated start site is calculated ( in bp from the longest transcript , Ensembl version 17 ) . The distribution of these relative positions is then analyzed for all the annotated genes in the human genome and compared with the same distribution obtained using only the 166 predicted target genes of Ath5 ( see Table S1 ) . The Cab strain of wild-type Oryzias latipes from a closed stock at EMBL-Heidelberg was kept as described [39] . Embryos were staged according to Iwamatsu [25] . Zebrafish lak mutants were obtained by crosses of heterozygous lakth241 carriers . A fragment of about 60 bp encoding medaka ath5 homolog was amplified from a 3-d-old embryo cDNA library using degenerate PCR primers ( forward ATGCARGGIYTNAAYACNGC , reverse TSICCCCAYTGIGGNACNAC ) . The PCR conditions were: 5 cycles at 95 °C for 1 min , 50 °C for 1 min and 72 °C for 1 min , followed by 30 annealing cycles at 55 °C . The PCR product was cloned into TOPO TA vector ( Invitrogen ) and sequenced . Based on this sequence , we designed specific primers for amplifying the full-length cDNA using standard PCR techniques . Full-length ath5 sequence was cloned in the eukaryotic expression vector pCS2+ for overexpression , in vitro translation , and fluorescein-labeled probe synthesis ( see below ) . The medaka ath5 cDNA was used to screen a medaka genomic cosmid library . The 5 kb of ath5 genomic sequence immediately 5′ of the coding region was then cloned into pGL3 ( Promega ) or into a promoterless GFP reporter ( F . Loosli and J . W . , unpublished results ) . The second vector contains recognition sequences for I-SceI meganuclease for efficient transgenesis [40] . Deletion constructs containing 4 , 3 , 2 , or 1 . 5 kb of 5′ ath5 genomic region were created by PCR ( primer sequences are available upon request ) . Point mutations in the two Ath5 binding motifs were generated using the QuickChange XL kit ( Stratagene ) . Primer sequences are as follows: WT ( GGGGGCGGGCCTCCACCTGCTGCCACCTGTTTGTCTGCTGCG ) , M ( GGGGGCGGGCCTCCAATTGCTGCCACCTGTTTGTCTGCTGCG ) , N ( GGGGGCGGGCCTCCACCTGCTGCCATATGTTTGTCTGCTGCG ) , NM ( GGGGCGGGCCTCCAATGCTGCCATATGTTTGTCTGCTGCG ) , H ( GGGGGCGGGCCTCAAGCTTCTGCCACCTGTTTGTCTGCTGCG ) , P ( GGGGGCGGGCCTCCACCTGCTGCCGATCGTTTGTCTGCTGCG ) , and HP ( GGGGGCGGGCCTCAAGCTTCTGCCGATCGTTTGTCTGCTGCG ) . See also Table S3 . The Tbx2 and Dlx1 fugu 5′ genomic regions were identified in Ensembl . Two PCR products of 2 . 6 and 2 . 3 kb , containing the Ath5 binding motif , were amplified from fugu genomic DNA ( Medical Research Council , Rosalind Franklin Centre for Genomics Research ) using specific primers ( Tbx2 forward GAA CCT CAC GGT GTT GCT CAA AGG CAC and reverse CCT GTT TAT TTG GAC CCG AAA CGA GCG; Dlx1 forward TTG AAT GTG GTG ACC TTT CTG CAG AAG and reverse GGA CGG CTC CCA ATT TAA GTC GAA CTG ) and cloned into pGL3 . All constructs were verified by sequencing . Transgenic fish embryos were generated as previously described [40] . As previously reported , due to the early integration of the reported construct , we observed a very low or null degree of mosaicism in the injected fish allowing the direct analysis of F0 embryos . Identical patterns of expression were maintained in the following generations ( up to F2 ) . Injection of reporter constructs differing in the e-box consensus led to different transgenesis efficiencies . WT: 44/110 embryos reproduce endogenous GFP expression pattern , 40% maximum reachable transgenesis efficiency . Variant H: 44/115 , 38%; variant P: 26/107 , 24%; and variant HP: 9/111 , 8% . See Table S3 for primer sequences . To test the activity of Xenopus Ath5 promoter , the pG1X5 3 . 3-kb construct [34] was injected into embryos at the one-cell stage at a concentration of 20 ng/μl , and embryos were scored 4 d later for GFP expression . Double whole mount in situ analysis on medaka embryos was performed using a fluorescein probe for ath5 , revealed with fast red ( Roche ) . Digoxygenin probes for the other target genes were revealed with the NBT/BCIP substrate ( Roche ) using standard protocols . The sequences of the medaka homologs of the genes were obtained by blasting the fugu coding region on the medaka genome sequence at http://medaka . utgenome . org/ . Partial cDNA sequences were amplified by PCR from a cDNA library and cloned with TOPO TA vector kit ( Invitrogen ) . All the clones where confirmed by sequencing and submitted to the European Molecular Biology Laboratory ( EMBL ) database . Primer sequences are available upon request . Embedding and sectioning was performed according to standard procedures as described previously [41] . Zebrafish in situs were performed using standard protocols . The sequences for Zebrafish Brn3C , Gfi-1 , CD166 , and Adam11 orthologs were retrieved from Ensembl , sequences were amplified using standard PCR reactions from zebrafish 72 h-post-fertilization cDNA , and partial coding sequences were cloned into pCRII-TOPO vector ( Invitrogen ) , following manufacturer's instructions . Primers and constructs sequences are available upon request . Injection of expression plasmids into one-cell stage fish embryos leads to mosaic distribution and expression with cosegregation of different constructs [29] . Medaka embryos at the one-cell stage were injected with a solution containing 50 ng/μl of the 2 kb ath5 5′ genomic region driving GFP expression and 50 ng/μl of either the ath5 , Xath5 , Xath3/NeuroM , or Xash1 coding region in pCS2+ or else the pCS2+ empty vector [42] . A medaka ath5 morpholino oligonucleotide ( TCG ACG GGA CTT CAT GGT TTC TGT G ) was coinjected at a concentration of 0 . 1 mM as indicated . We checked the specificity and efficacy of this morpholino oligo in standard control experiments [43 , 44] . At the tested concentrations , the morpholino injections faithfully phenocopied the zebrafish lak/ath5 mutant phenotype . As judged by histolgical criteria and molecular marker analysis , no signs of ganglion cell differentiation were detected after up to 5 d of development ( unpublished data ) . No additional morphological abnormalities were observed . Injected embryos were allowed to develop until stage 22 ( 2 d , [25] ) before fixation and in situ hybridization followed by fluorescent fast red detection ( probes used are indicated in the figure legend and in the main text ) . GFP was detected using anti-GFP antibody ( rabbit polyclonal , Molecular Probes ) at 1:250 dilution detected with anti-rabbit secondary antibody Alexa-488 conjugated ( Molecular Probes , 1:500 ) . Nuclei were counterstained with DAPI and embryos analyzed using confocal microscopy ( Leica TCS-SP ) . ChIP has been performed on chick dissected retina and optic tectum as previously described [18] . Primers and genomic sequences are available upon request . EMSA and luciferase assays were performed using standard protocols . Briefly , each reaction contained 1μg of salmon sperm DNA , 1μg of poly ( dC-dI ) , and ∼1ng of DNA probe ( see Table S3 for sequences ) end-labeled using T4 polynucleotide kinase with [γ-32P]dATP . Ath5 was in vitro translated ( Promega TnT sp6 coupled reticulocyte lysate system ) according to the manufacturer's specifications . of The Ath5 transcription translation reaction ( 5 μl ) or mock reaction was added to each sample in 20 μl of total volume in water . Competition was performed with 10 , 100 , or 1 , 000-fold molar excess cold competitor DNA added to the reaction on ice 10 min before the radiolabeled DNA was added for an additional 20 min . The 20-μl reaction was run on a 5% nondenaturing polyacrylamide gel in 0 . 5× TBE buffer , at 250 V for 4z6 h at 4 °C . After electophoresis , the gel was dried and visualized by autoradiography . Luciferase transcription assay was performed using the Dual-Luciferase reporter system ( Promega ) according to the manufacturer's specifications . Cos7 cells were plated on 24-well plates and transfected at 50% confluence using Fugene6 ( Roche ) . Each well received 20 ng of Ath5-pGL3 reporter vector DNA or mutant constructs and 5 ng of pRL DNA . In addition , 0 ng , 3 ng , 10 ng , 30 ng , 100 ng , or 300 ng of Ath5pCS2+-expressing vector was added . Total DNA transfected was kept constant by adding the appropriate amounts of pCS2+ empty vector . Cells were lysed after 24 h and lysates were then assayed for luciferase activity . Tbx2 and Dlx1 promoter assays were performed using 200 ng or 40 ng of reporter pGL3 vector and lysed after 24 h or 48 h , respectively . Each experiment was performed in quadruplicate and results were confirmed at least in two independent experiments . Results were independently reproduced in BHK21 cells ( unpublished data ) .
The Ensembl ( http://www . ensembl . org/ ) accession number for medaka ath5 is ENSORLG00000013722 .
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To establish regulatory gene networks that drive key biological processes is of crucial importance to identify the genes that are directly controlled by transcriptional regulators . Ideally , this can be accomplished by identifying the direct transcription factor binding site in the cis-regulatory regions of the respective target genes . However , problems related to the fact that the motifs recognized and bound by transcription factors are short ( 6–20 bp ) and consequently found very frequently and spread all over the genome , have limited this approach . The transcription factor Ath5 is involved in the specification and differentiation of retinal ganglion cells in the developing vertebrate eye . We show that Ath5 directly regulates its own expression by binding to a small region of its proximal promoter that contains two identical motifs . Using this motif description , together with conservation across large evolutionary distances , we then searched in the genome for other target genes of Ath5 and predicted 166 direct target genes . We then validated a subset of these predictions both in vitro and in vivo . Our analysis therefore provides an example of computation prediction of transcriptional target genes . At the same time , the genes identified represent the most comprehensive list of effectors mediating the role of Ath5 during eye development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"developmental",
"biology",
"chicken",
"mammals",
"medaka",
"in",
"vitro",
"computational",
"biology",
"teleost",
"fishes",
"genetics",
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"genomics"
] |
2007
|
In Vivo Validation of a Computationally Predicted Conserved Ath5 Target Gene Set
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Oropouche ( ORO ) virus , a member of the Simbu serogroup , is one of the few human pathogens in the Orthobunyavirus genus in the family Bunyaviridae . Genetic analyses of ORO-like strains from Iquitos , Peru , identified a novel reassortant containing the S and L segments of ORO virus and the M segment of a novel Simbu serogroup virus . This new pathogen , which we named Iquitos ( IQT ) virus , was first isolated during 1999 from a febrile patient in Iquitos , an Amazonian city in Peru . Subsequently , the virus was identified as the cause of outbreaks of “Oropouche fever” during 2005 and 2006 in Iquitos . In addition to the identification of 17 isolates of IQT virus between 1999 and 2006 , surveys for neutralizing antibody among Iquitos residents revealed prevalence rates of 14 . 9% for ORO virus and 15 . 4% for IQT virus . Limited studies indicate that prior infection with ORO virus does not seem to protect against disease caused with the IQT virus infection . Identification of a new Orthobunyavirus human pathogen in the Amazon region of Peru highlights the need for strengthening surveillance activities and laboratory capabilities , and investigating the emergence of new pathogens in tropical regions of South America .
Viruses in the family Bunyaviridae are classified into five genera: Orthobunyavirus , Hantavirus , Nairovirus , Phlebovirus and Tospoviruses . The orthobunyaviruses are enveloped , negative sense RNA viruses whose genome comprises three segments: small ( S ) , medium ( M ) and large ( L ) . The S segment encodes for the nucleocapsid and a nonstructural protein , NSs . The M segment encodes for the glycoproteins Gn and Gc , whereas the L segment encodes the viral polymerase . Some members of the Orthobunyavirus genus are known to cause clinical disease in humans , including Oropouche ( ORO ) virus , a member of the Simbu serogroup , which causes a febrile disease often associated with headache , dizziness , weakness , myalgias and arthralgias [1] . Bunyamwera virus , which is considered the prototype member of the family , causes a febrile illness with headache , arthralgias , rash and infrequent central nervous system involvement [2] . Hemorrhagic manifestations associated with some orthobunyavirus infections have also been reported recently [3] , [4] . Genetic reassortment among members of the same serogroup within the Orthobunyavirus genus occurs in nature and has led to the emergence of new viruses , occasionally with increased pathogenicity . This appears to be the case with Ngari virus , which has been associated with hemorrhagic fever in Kenya and Somalia [3] , [5] . This virus is comprised of the S and L segments of Bunyamwera virus and the M segment of Batai virus [4] . On the basis of genetic and antigenic analyses , we previously reported that Jatobal virus ( JAT ) , a member of the Simbu serogroup , is a reassortant virus that contains the S segment of ORO virus and the M and L segments of a still unrecognized Simbu serogroup virus [6] . Within the Orthobunyavirus genus , 18 serogroups have been recognized on the basis of the results of cross-hemagglutination inhibition ( HAI ) and antibody neutralization relationships [7] . The Simbu serogroup contains at least 25 members , and recent phylogenetic analyses demonstrated that the genetic relationships amongst these viruses are consistent with the results of serological relationships [7] , [8] . ORO virus was originally isolated in 1955 from blood of a febrile forest worker who lived in Vega de Oropouche , Trinidad [9] . Outbreaks involving thousands of human cases continue to be reported in Brazil [10] , [11] , [12] , [13] , and circulation of the virus has been reported in Panama , Peru , and Trinidad [14] , [15] , [16] , [17] . Based on the S segment , three genotypes of ORO virus were distinguished phylogenetically: genotype I includes the isolates from Brazil and Trinidad , genotype II includes isolates from Brazil and Peru , and genotype III is represented by the isolates from Brazil and Panama [18] , [19] . ORO virus has been isolated from mosquitoes ( Coquillettidia venezuelensis in Trinidad [9]; Ochlerotatus serratus and Culex quinquefasciatus in Brazil [20] ) and frequently from the midge Culicoides paraensis [9] , [21] , [22] . High population densities of this midge have been found during epidemics of ORO virus [23] . Transmission of the virus has been demonstrated via C . paraensis , from infected to susceptible hamsters , and from infected humans to susceptible hamsters [20] , [24] . ORO virus has also been isolated from sloths ( Bradypus tridactylus [20] ) , and from a monkey ( Callithrix sp . ) in Brazil [10] . In 1995 , the U . S . Naval Medical Research Unit Six ( NAMRU-6 ) in Lima , in collaboration with the Ministry of Health of Peru , initiated a passive surveillance study to investigate etiology of febrile illnesses . As part of these surveillance activities , several ORO-like strains were obtained from febrile patients in Iquitos , Peru . In this study , we describe the identification of a novel reassortant virus which we named Iquitos ( IQT ) based on the location of the isolation of the virus . Specifically , we: 1 ) demonstrated that IQT virus causes clinical disease in humans similar to ORO fever , 2 ) described the genetic relationship of IQT virus to other members of the Simbu serogroup , 3 ) identified the risk factors associated with human infection by IQT virus in an urban setting of the Amazon region of Peru , and 4 ) describe the clinical manifestations associated with infection . Significantly , immunity to ORO virus does not appear to protect against infection by IQT virus , and both ORO and IQT viruses appear to have similar antibody prevalence in Iquitos over the past 10 years .
The IQT1690 ORO strain used in this study was isolated in 1995 from a 50 year-old male resident of Iquitos , Peru . The first strain of IQT virus ( IQT9924 ) was isolated in Iquitos from a 13 year-old boy ( Table 1 ) . The origin of the ORO strains BeH379693 , BeH544552 , MD023 , GML444477 and GML 445252 were previously described [18] , [19] . Iquitos is a city of about 380 , 000 inhabitants located 120 meters above sea level in the Amazon Basin in northeastern Peru . The human use study protocols were approved by the Ministries of Health of Peru and by the NAMRU-6 Institutional Review Board ( protocol NMRCD . 2000 . 0006 ) . The study subjects were patients ( ≥ 5 years of age ) who presented with a diagnosis of an acute , febrile undifferentiated illness at military or civilian outpatient clinics in Iquitos . The criteria for inclusion in the program was fever ≥ 38°C of no more than five days duration , headache , myalgia and other nonspecific symptoms . Demographic and clinical information were obtained from each patient at the time of voluntary enrollment and a signed consent form was obtained from patients 18 years of age and older . In addition , written assent was obtained from patients between 8 and 17 years of age . For patients younger than 18 years , written consent was obtained from parents or legal guardians . Paired-blood samples were collected , one during the acute phase of illness and a second sample 2-4 weeks after onset of symptoms . Acute serum samples were tested for virus by cell culture , and both acute and convalescent serum samples were assayed for IgM antibody to a variety of arboviruses by an enzyme-linked immunosorbent assay ( ELISA ) , as described previously [25] . For virus isolation attempts , serum samples were diluted 1∶5 in Eagle's minimum essential medium ( EMEM ) supplemented with 2% fetal bovine serum , 200 µg of streptomycin , and 200U/ml of penicillin . Two hundred µl of the diluted samples were inoculated into flasks containing either confluent monolayers of African green monkey kidney ( Vero ) cells or Aedes albopictus mosquito ( C6/36 ) cells . Vero and C6/36 cell cultures were maintained at 37°C and 28°C , respectively , and were examined daily for 10 days for evidence of viral cytopathic effects ( CPE ) . Spot-slides of C6/36 and Vero cells were subsequently prepared on days 10 post-inoculation of the samples ( or sooner if CPE developed ) and an immunofluorescence assay ( IFA ) was performed using polyclonal antibody against arboviruses endemic to Peru , including ORO virus [17] , [25] , [26] , [27] , [28] , [29] , [30] . A total of 1037 human serum samples collected in Iquitos during 2006 were tested for IgG antibody to the ORO strain IQT1690 and IQT9924 virus using a previously described ELISA [25] . The samples were collected as part of a cross-sectional antibody prevalence study conducted in Iquitos after an outbreak of febrile illness associated with Venezuelan equine encephalitis virus ( VEEV ) infection ( protocol PJT . NMRCD . 001 ) . Three neighborhoods where VEE cases were reported and a control neighborhood where VEE cases were not reported were included in the study [31] . Serum samples from a subset of the original study participants who agreed to future use of their samples were tested by ELISA IgG antibody . ELISA positive samples were further tested using an 80% plaque reduction neutralization assay ( PRNT ) each for the ORO strain IQT 1690 and IQT9924 virus . Briefly , sera were heat-inactivated at 56°C for 30 minutes and 2-fold serum dilutions were prepared and mixed with 100 plaque forming units ( PFUs ) of each virus and incubated at 4°C overnight . The virus-serum dilutions mixtures were inoculated into confluent monolayer of Vero cells propagated in microplates and incubated at 37°C for 1 hour before adding an overlay of 0 . 4% of agarose in EMEM . After 72 hours of incubation at 37°C , the plates were stained with 0 . 25% crystal violet in 20% methanol and plaques were counted . All IgG antibody positive samples were tested at an initial concentration of 1∶20 and all positive samples were further titrated to determine endpoint titers . Neutralization titers were considered as the highest serum dilution that reduced plaque formation by ≥ 80% . Viral RNA was extracted using the QIAamp viral RNA mini kit ( Qiagen , Valencia , CA ) or Trizol reagent ( Invitrogen , Carlsbad , CA ) following the manufacturer's protocols . The reverse transcription reaction ( RT ) was done using 1X RT buffer , 0 . 2 mM dNTPs , 1 µM of primers , 80 units of RNAsin ribonuclease inhibitor ( Promega , Madison , WI ) , 1mM of dithiothreitol , 200U of SuperScript reverse transcriptase ( Invitrogen ) and 5 µl of RNA . The reactions were incubated at 42°C for 1 hour . The PCR included 1X PCR buffer , 0 . 25 mM dNTPs , 1 µM of primers , 3 mM of MgCl2 , 2 . 5 units of GoTaq DNA polymerase ( Promega , Madison , WI ) and 5 µl of cDNA . The conditions for the PCRs included incubation at 94°C for 2 minutes , 35 cycles of 94°C for 30 seconds , 50°C for 1 minute , 72°C for 1 . 5 minutes and a final extension of 72°C for 10 minutes to ensure complete double-stranded DNA synthesis . The primers used for the PCR amplification have been previously described and included ORO N3 ( GTGAATTCCCACTATATGCCAATTCCGAATT ) and ORO N5 ( AAAGAGGATCCAATAATGTCAGAGTTCATTT ) that amplifies the S segment , M14C ( CGG AAT TCA GTA GTG TAC TACC ) and M619R ( GAC ATA TG ( CT ) TGA TTG AAG CAA GCA TG ) that amplifies the M segment and M13CBunL1C ( TGTAAAACGACGGCCAGTAGTGTACTCCT ) , and BunL605R ( AGTGAAGTCICCATGTGC ) that amplifies the L segment [3] , [10] , [19] . Partial sequences of the S , M , and L segments were obtained and compared to published ORO virus sequences . Purified PCR products were sequenced directly and sequencing analyses of the PCR products were performed using an Applied Biosystems Prism automated DNA sequencing kit ( Foster City , CA ) according to the manufacturer’s’ protocols . Sequences were aligned using the Clustal program in the Mac Vector software package ( MacVector Inc . , Cary , NC ) and phylogenetic analyses were performed using the maximum parsimony , neighbor joining , and maximum likelihood methods implemented in the PAUP software [32] , [33] . For the neighbor joining analyses , the HKY85 distance was used . Bootstrap values , to place confidence values on grouping within trees , were calculated based on 500–1000 replicates . To prepare ORO virus hyperimmune ascitic fluid for classical cross-neutralization tests , mice received 4 weekly intraperitoneal ( IP ) injections of virus-infected newborn mouse brain suspension with Freud's adjuvant . To investigate antigenic differences among the viruses , cross-neutralization assays were then performed , using a previously described PRNT [34] . To further evaluate the antigenic differences between the ORO strain IQT1690 and IQT9924 virus , acute and convalescent sera from patients infected with these viruses were tested against both strains using PRNT . Three to 4 week-old golden Syrian female hamsters were inoculated ( IP ) with serial 10-fold dilutions of virus . The LD50 was calculated by the Reed and Muench method [35] . To determine viremia levels in infected patients , serum samples were prepared in 10-fold dilutions and 100 µl of each dilution was inoculated onto confluent monolayers of Vero cells in 12 well-plates . Viruses were adsorbed to the monolayers for 1 hour at 37°C . A 3-ml overlay consisting of EMEM with 0 . 4% agarose was added , and the cells were incubated at 37°C for 72 hours . Agar plugs were removed , and the cells were stained with 0 . 25% crystal violet in 20% methanol . The sensitivity of the assay corresponded to a detection limit of 100 PFU/ml . The analysis was performed using SPSS 17 . 0 for Windows ( SPSS Inc , Chicago , IL , USA ) . The proportions of positive results ( PRNT for IQT1690 and IQT9924 antibody ≥ 20 ) were calculated with their respective 95% confidence intervals ( 95% CI ) . The proportions were compared using the Pearson Chi-Square and Fisher exact tests . A 2-tailed p-values < 0 . 05 was used for all statistical analyses . Multivariate analysis was performed using logistic regression ( enter method ) adjusting by gender , age ( adult , child ) , occupation , type of house , contact with domestic animals , travel history and neighborhoods .
In 1999 , IQT virus strain IQT9924 was isolated in Iquitos , Peru , from a 13-year-old boy who had an illness that included symptoms of fever , headache , eye pain , body pain , arthralgias , diarrhea , and chills . These clinical symptoms were typical of ORO virus infections in Peru [17] . The strain was provisionally identified as ORO virus based on results of serological reactivity in an indirect immunofluorescence test with virus-infected cells . Sixteen additional IQT9924 virus isolates ( provisionally identified as ORO virus ) were obtained from febrile patients living in Iquitos: 1 in 2003 , 7 in 2005 , and 8 in 2006 . Of the 17 IQT 9924 virus positive patients , detailed clinical information was available for 16 ( Table 2 ) . The most common general symptoms were: chills ( 16 ) , headache ( 15 ) , arthralgia or loss of joint function ( 15 ) , general malaise ( 14 ) , diminished appetite ( 13 ) , myalgias ( 13 ) , retro-orbital pain ( 11 ) , bone pain ( 9 ) , and pallor ( 7 ) . Gastrointestinal symptoms were very common , and 12 patients had at least one of the following symptoms: nausea ( 10 ) , abdominal pain ( 7 ) , vomiting ( 4 ) , or diarrhea ( 2 ) . No patients had jaundice , hepatomegaly , splenomegaly , abdominal distension , or ascites . Six patients had at least one respiratory symptom: cough ( 6 ) , rhinorrhea ( 3 ) , pharyngeal congestion ( 2 ) , or expectoration ( 1 ) ; no patient had dyspnea . Only one patient complained of a rash , and this was described as erythematous and affecting both the central trunk and distal extremities . With the exception of one patient who had petechiae , none of the patients had hemorrhagic manifestations , including epistaxis , bleeding gums , melena , hematochezia , vaginal bleeding , petechiae , purpura , and ecchymosis . All patients had a fever as this was one of the inclusion criteria . Based on virus isolation and serological analyses , the peak incidence for ORO virus was reported in Iquitos as occurring in 2004 , 2005 and 2006 [30] . Because the circulation of IQT9924 virus only was detected serologically and genetically ( see below ) during that time period , we believe that the study most likely reported the incidence of IQT9924 virus . Genetic analyses of the S , M , and L segments were conducted to determine the relationships of the isolates to ORO virus and other members of the Simbu serogroup . Phylogenetic trees were constructed for both the S- and M-RNA segments based on members of the Simbu serogroup that included an example from each of the three known ORO genotypes [19] . The phylogenetic tree based on the S segment placed the S-RNA of IQT9924 virus among isolates of ORO virus genotype II ( Fig . 1 ) , while the M-RNA phylogenetic tree ( Fig . 2 ) indicated that the strain IQT9924 had an M-RNA that was unique from any other Simbu serogroup virus identified to date , but was most closely related to ORO virus . Significantly , IQT9924 virus had an M-RNA distinct from JAT virus , a reassortant with a non-ORO virus M-RNA [6] . The L-RNA phylogenetic tree showed that IQT9924 virus had an L-RNA of ORO virus ( data not shown ) . The M segment fragment sequence ( ∼590 nucleotides ) of IQT9924 displayed only 68% nucleotide and 65% amino acid identity to the prototype ORO strains BeAn 19991 ( Brazil ) and Tr9760 ( Trinidad ) whereas the S and L segment fragment sequences exhibited 95% nucleotide and 96% amino acid identity to ORO virus . Based on partial sequences of the S , M and L segments , all ORO-like viruses isolated from patients living in Iquitos after 1999 had similar genetic characteristics and would appear to be examples of IQT9924 virus . Genetic analyses of ORO virus isolates obtained in Peru before 1999 , did not reveal the circulation of IQT9924 virus prior to 1999 [19] . We investigated the serological relationships of IQT9924 virus using cross-neutralization tests . Mouse antisera were prepared against ORO virus strain Trinidad55 , IQT9924 virus , and JAT virus . These antisera displayed a 4-fold or greater difference in neutralization titer among these viruses ( Table 3 ) , indicating that IQT9924 virus is serologically distinct from ORO and JAT viruses . Next , convalescent sera from patients in Iquitos who had been infected with either OROV or IQT9924 virus were assayed by PRNT to confirm the antigenic differences . A 4-fold or greater difference in neutralization titer was observed between these viruses ( Table 4 ) corroborating our initial findings with mouse antisera . Of particular importance , neutralizing antibody to ORO virus were detected in the acute serum samples of 2 febrile patients from whom the IQT9924 virus had been isolated ( Table 5 ) . Furthermore , a boost in ORO neutralizing antibody titers was observed in both patients after infection with IQT9924 ( Table 5 ) . Overall , the results suggest that prior ORO virus infection does not protect against febrile disease caused by the new reassortant virus ( IQT 9924 ) and is consistent with the cross-neutralization data indicating that IQT9924 and ORO are distinct viruses , and that there is limited cross-protective immunity such that individuals can be infected and have clinical diseases caused by both viruses . The hamster is used as an animal model of ORO virus infection [36] . Thus , we evaluated the hamster virulence phenotype of IQT9924 virus and compared the results to those obtained with representatives of the three ORO genotypes . The IQT 9924 virus was found to be poorly virulent with a LD50 of 4220 PFU and was similar to two Panamanian strains ( GML 444477 and GML445252 ) belonging to ORO genotype III that were non-lethal at the highest doses inoculated ( >2 , 500 and >2 , 000 PFU , respectively ) ( Table 6 ) . In comparison , ORO genotype II strain MD023 from Madre de Dios , Peru , was virulent with LD50 of 45 PFU . Finally , two genotype I strains , BeH379693 and BeH544552 from Brazil , displayed different virulence phenotypes ( >7 , 000 PFU vs 1 PFU , respectively ) . Since Simbu serogroup viruses are arboviruses , we undertook preliminary studies with a limited number of serum samples to determine viremia titers among ORO and IQT9924 virus-infected patients . Serum infectivity titers were obtained from a total of 19 patients . Two ORO virus-infected patients had viremias of 6×103 and 7×105 PFU/ml whereas 17 patients infected with IQT9924 virus had levels that were below the limit of detection of the assay ( <100 ) to 1 . 8×105 PFU/ml ( Table 1 ) . Examination of 1037 human serum samples from acute febrile infections in Iquitos revealed that the overall neutralizing antibody prevalence to ORO virus was 14 . 9% ( 154/1037 ) ( 95% CI 12 . 8–17 . 1 ) whereas prevalence to IQT9924 virus was 15 . 4% ( 160/1037 ) ( 95% CI 13 . 3–17 . 7 ) . Only 3 . 4% ( 35/1037 ) of the serum samples had neutralizing antibodies ( >20 ) to both viruses . Neutralizing antibody prevalence to both ORO and IQT9924 viruses was higher among females than males ( 17 . 3% vs 10 . 2% and 17 . 5% vs 11 . 3% , respectively; p<0 . 05 ) . The neutralizing antibody prevalence to ORO virus in persons living in the neighborhoods of San Juan ( 24 . 1% ) and Bellavista Nanay ( 16 . 5% ) was higher compared to other neighborhoods ( Fig . 3 ) . Similarly , the PRNT antibody prevalence to IQT9924 virus was also higher in the neighborhoods of San Juan ( 33 . 9% ) and Bellavista Nanay ( 12 . 2% ) . In San Juan , PRNT antibody prevalence to IQT9924 virus was significantly higher than to ORO virus ( p<0 . 05 ) . Neutralizing antibody prevalence to both viruses in the study population increased with age after adulthood ( 0% in 5–9 years old to 32 . 3% in 40-49 years old for ORO virus and 3 . 6% in 5–9 years old to 25% in >70 years old for IQT9924 virus ) ( Table 7 ) . The antibody prevalence to ORO and IQT9924 viruses in adults was 20 . 1% and 18 . 8% , respectively , compared to 2 . 8% and 7 . 9% in the younger group ( <20 years old ) . The univariate analysis did not detect an association between ORO or IQT9924 virus antibody prevalence and occupation , type of housing , travel or contact with chickens or rodents . Logistic regression models were used to test the significance of each factor . The variables predictive of ORO virus neutralizing antibody prevalence in the model were gender , age and neighborhood , whereas factors such as gender , age , neighborhood , and contact with pigs were predictive of IQT9924 virus antibody prevalence .
The first confirmed ORO fever cases in Peru were reported in 1992 involving a small outbreak of eight febrile cases living in Iquitos [37] . Subsequently , only sporadic cases of ORO fever were reported in Peru , mainly in the Amazon region . The situation in Peru differs from Brazil , where ORO fever outbreaks are usually associated with hundreds of human cases [11] , [18] , [38] . The reasons for this apparent difference in ORO transmission rates remain unknown . To date , three ORO genotypes have been described based on the nucleoprotein gene ( S segment ) : genotype I among viruses circulating in Brazil and Trinidad , genotype II among viruses from Brazil and Peru , and genotype III among viruses from Brazil and Panama [10] , [18] , [19] . In this study , we sought to genetically characterize strains from Peru that were provisionally identified as ORO virus . Sequence analyses based on the S and L segments placed the strain IQT9924 , isolated in Iquitos in 1999 , within ORO genotype II while phylogenetic analyses of the M segment revealed that IQT9924 virus contained a M-RNA segment of a still unidentified Simbu-serogroup virus . Thus , IQT9924 was identified as a Simbu serogroup reassortant virus . Serological characterization of IQT9924 virus confirmed that the virus was distinct from ORO virus . More importantly , we obtained serological evidence that prior ORO virus infection does not protect against clinical disease caused by this new reassortant virus , providing additional confirmation that ORO and IQT9924 are two distinct viral entities . Because most arbovirus laboratories in South America identify ORO virus without detailed serological or genetic characterization , it is uncertain whether IQT9924 circulates in other South American countries . Further genetic and antigenic characterization of ORO isolates from South America is necessary to fully determine the geographic distribution of this newly identified human pathogen . Studies conducted in Brazil reported viremia titers higher than 3log10 suckling mice LD50 ( SMLD50 ) /ml among ORO- infected patients , including approximately 10% of the patients who developed viremia titers ranging from 5 . 0 to 5 . 3 log10 SMLD50/ml during the first two days of illness [20] . These levels of viremia were high enough to infect C . paraensis and transmission was demonstrated to susceptible hosts . Consequently , it has been postulated that humans are the primary amplifying host during ORO fever epidemics [20] , [24] . In contrast , viremia levels lower than 5 . 3log10 SMLD50/ml were not sufficient to infect C . paraensis [24] . In this study , we measured viremia by plaque assay ( instead of SMLD50 ) and found that the average viremia levels detected among ORO and IQT9924 virus-infected patients were 4 . 8±0 . 7 log10 PFU/ml and 3 . 4±1 . 1 log10 PFU/ml , respectively with at least one ORO-infected patient developing a viremia titer of 5 . 9 log10 PFU/ml ( Table 1 ) . It is not known if C . paraensis is the vector of IQT9924 virus . However , studies conducted in and around Iquitos during 1996–1997 identified C . paraensis as the most common biting midge of all host-seeking ceratopogonids at 16 sites and C . insinuatus being the second most common biting midge [39] , [40] . Subsequent studies conducted in 2001 , 2002 , and 2003 showed that the peaks in biting activities in the Punchana district , located near Iquitos , occurred between October and December for C . paraensis . Likewise , peaks in biting activities were observed between October and April for C . insinuatus in Santa Clara , near Iquitos [40] . It has not been possible to determine peak incidence rates of ORO fever due to lack of identification of cases . In contrast , data from our febrile surveillance study suggest that the number of IQT9924 virus-infected patients peak from December to April indicating a possible overlap with the biting activities of C . insinuatus [30] . Felippe-Bauer et al [41] recently identified two new morphological species in the C . paraensis complex from the Department of Amazonas and Loreto , Peru . Thus , future studies should investigate the role of this midge in ORO and IQT9924 virus transmission . In Iquitos , the overall neutralizing antibody prevalence for ORO virus was 14 . 9% whereas prevalence for IQT9924 virus was 15 . 4% . These numbers are lower than those from previous studies that reported antibody prevalence to ORO virus ( based mostly on IgG antibodies ) as high as 35% in certain neighborhoods near Iquitos [14] , [15] . Thus , it would appear that ORO virus prevalence varies among neighborhoods and this was demonstrated in our study where neighborhoods , such as San Juan and Bellavista Nanay , have higher ORO virus prevalence rates . Antibody prevalence to ORO and IQT9924 viruses was higher among females and is consistent with previous studies on ORO virus prevalence [15] , [42] . Another study carried out in Brazil after an ORO virus outbreak also detected higher antibody prevalence among women [43]; however , a subsequent study in Brazil failed to detect gender differences in attack rates [20] . Our clinical data indicate that the IQT9924 virus shares many of the same clinical manifestations as ORO virus . Headache and chills affected the vast majority of IQT9924-infected patients , similar to previous studies of ORO virus infection [18] , [42] . Bone , muscle , and joint pain also were observed commonly for both viruses . Rash and hemorrhagic manifestations have not been classically described with ORO virus infection and were only found in one of 16 IQT9924-infected patients in this study . Interestingly , respiratory complaints—primarily cough—were found in 38% of our IQT9924-infected patients , a finding not commonly reported with ORO virus infection . Among the Orthobunyavirus genus , studies have shown that the pathogenicity of these viruses is multigenic with the M segment being a major determinant [44] . Thus , it is very likely that the donor of IQT9924 virus M segment may also cause human illness in the Amazon region of Peru . It is worth noting that this new virus was first isolated in 1999 and , subsequently in 2005 and 2006 , when it was the cause of outbreaks of febrile illness in Iquitos . Some of the patients infected with the IQT9924 virus in 2005 and 2006 resided within Quistococha and Zungarococha , which are considered rural areas near Iquitos . Thus , it is possible that a spill-over of cases occurred from rural to urban areas as was suggested during the 2005-2006 VEEV outbreaks in Iquitos [31] . Unusual high annual river levels occurred in early 2006 , which may had an impact on arthropod density and geographic distribution leading to the observed VEEV and IQT9924 virus outbreaks . Finally , it appears that this IQT9924 virus has emerged and possibly replaced ORO virus in Iquitos because ORO virus cases have not been identified in the area since 1999 . In summary , this study identified a new pathogen , IQT9924 a reassortant of ORO virus that was associated with febrile illness in the Amazon region of Peru . We propose the name Iquitos virus for this newly identified Orthobunyavirus member of the Simbu-serogroup because there have been cases of disease caused by this virus in the Iquitos area over several years . While reassortment among members of the same serogroup of the Bunyaviridae has been identified , few reassortants have been associated with human disease . It will be important to determine the geographic distribution of Iquitos virus and to evaluate its potential as a major public health problem as ORO virus has been done in Brazil .
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Oropouche ( ORO ) virus is one of the few human pathogens in the Orthobunyavirus genus in the family Bunyaviridae . Phylogenetic analyses of ORO-like strains isolated from febrile patients in Iquitos , Peru , identified a novel ORO reassortant virus , which we named Iquitos ( IQT ) virus based on the location of the isolation of the virus . This novel pathogen was first isolated during 1999 from a 13-year-old boy who had an illness that included symptoms of fever , headache , eye pain , body pain , arthralgias , diarrhea , and chills . Subsequently , the virus was identified as the cause of outbreaks of “Oropouche fever” during 2005 and 2006 in Iquitos . Limited serological studies indicate that prior infection with ORO virus does not seem to protect against disease caused with the IQT virus infection . In summary , we identified a new Orthobunyavirus human pathogen in the Amazon region of Peru; these results highlight the need for strengthening surveillance activities and investigating the emergence of new pathogens in tropical regions of South America .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"virology",
"emerging",
"viral",
"diseases",
"neglected",
"tropical",
"diseases",
"biology",
"microbiology",
"arboviral",
"infections"
] |
2011
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Iquitos Virus: A Novel Reassortant Orthobunyavirus Associated with Human Illness in Peru
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Signaling via pattern recognition receptors ( PRRs ) expressed on professional antigen presenting cells , such as dendritic cells ( DCs ) , is crucial to the fate of engulfed microbes . Among the many PRRs expressed by DCs are Toll-like receptors ( TLRs ) and C-type lectins such as DC-SIGN . DC-SIGN is targeted by several major human pathogens for immune-evasion , although its role in intracellular routing of pathogens to autophagosomes is poorly understood . Here we examined the role of DC-SIGN and TLRs in evasion of autophagy and survival of Porphyromonas gingivalis in human monocyte-derived DCs ( MoDCs ) . We employed a panel of P . gingivalis isogenic fimbriae deficient strains with defined defects in Mfa-1 fimbriae , a DC-SIGN ligand , and FimA fimbriae , a TLR2 agonist . Our results show that DC-SIGN dependent uptake of Mfa1+P . gingivalis strains by MoDCs resulted in lower intracellular killing and higher intracellular content of P . gingivalis . Moreover , Mfa1+P . gingivalis was mostly contained within single membrane vesicles , where it survived intracellularly . Survival was decreased by activation of TLR2 and/or autophagy . Mfa1+P . gingivalis strain did not induce significant levels of Rab5 , LC3-II , and LAMP1 . In contrast , P . gingivalis uptake through a DC-SIGN independent manner was associated with early endosomal routing through Rab5 , increased LC3-II and LAMP-1 , as well as the formation of double membrane intracellular phagophores , a characteristic feature of autophagy . These results suggest that selective engagement of DC-SIGN by Mfa-1+P . gingivalis promotes evasion of antibacterial autophagy and lysosome fusion , resulting in intracellular persistence in myeloid DCs; however TLR2 activation can overcome autophagy evasion and pathogen persistence in DCs .
Antimicrobial autophagy or xenophagy plays an important role in controlling bacterial infection and promoting innate immunity . Recent evidence has revealed critical roles for autophagy in the ability of immune cells to recognize and selectively target microbes for elimination . [1–4][1] . Dendritic cells ( DCs ) are innate immune cells that serve as a bridge to the adaptive immune response . DCs capture a wide variety of microbes in the peripheral tissues for which they are equipped with broad spectrum of pattern recognition receptors ( PRRs ) . The major classes of PRRs expressed by DCs include Toll-like receptors ( TLRs ) , NOD-like family receptors , CARD helicases and C-type lectin receptors [5 , 6] . Many of the PRRs come equipped with unique phagocytic machinery evolved for efficient antigen processing and presentation [7–9] . Phagocytosis relies on a network of endocytic vesicles such as early endosomes and/or autophagosomes , which fuse with lysosomes for degradation [10] . Intracellular vesicle maturation does not necessarily proceed through similar steps in different phagocytic cells [11] . Moreover , different phagosomal maturation pathways have been reported in the same cell type [11 , 12] . These different pathways are dictated primarily by the initial recognition step by PRRs and by the cargo contained in the vesicle [13] . Hence the immune cell type , the PRRs engaged and the properties of the microbe seem to be crucial for microbial clearance by autophagy . DC-SIGN ( DC specific ICAM-3 grabbing non-integrin ) is a C-type lectin receptor involved in pathogen uptake , signaling and antigen presentation in DCs [14–16] . For uptake DC-SIGN contains internalizing motifs in its cytoplasmic tail [17] . Interestingly , DC-SIGN has been implicated in immune suppression and regulation in certain contexts [17–19] . Most notably DC-SIGN is targeted for immune escape by several pathogens such as HIV , hepatitis C virus , herpesvirus 8 ( HHV-8 ) , Mycobacterium tuberculosis , Helicobacter pylori and Streptococcus pneumonia [17 , 20–22] . Recently , we reported that DC-SIGN engagement by the minor fimbriae ( Mfa1 ) of Porphyromonas gingivalis yields weak DC maturation and an immunosuppressive cytokine profile . In the absence of Mfa1 , P . gingivalis yields a very different DC response with high levels of IL-23 and IL-6 as well as induction of a Th1/Th17 type response [14 , 23] . Furthermore , this study demonstrated that the anaerobe P . gingivalis survives within DCs in an aerobic atmosphere , while it dies rapidly in the absence of DCs [14] . Early study of the relationship of fimbrial strain differences to alveolar bone loss , showed that this Mfa1+Pg strain ( DPG3 ) induced higher bone loss than Pg381 strain in a periodontitis mouse model [24] . The destruction induced by Mfa1+Pg was similar to wild type strain P . gingivalis ATCC 53977 that has been reported to be invasive in the abscess model [25] . P . gingivalis expresses a number of virulence factors that bind to and signal through PRRs . The adhesion proteins , known as fimbriae , on P . gingivalis signal through PRRs , and facilitate invasion of host cells . P . gingivalis expresses both minor ( MFa1 ) and major fimbriae ( FimA ) which are highly regulated depending on growth conditions [26 , 27] . We have previously shown that expression of Mfa1 is involved in targeting DC-SIGN while other studies have shown expression of FimA targets a non-DC-SIGN route , mostly through TLR2 [28 , 29] . The engagement of DC-SIGN and TLRs activates distinct signaling pathways [6 , 30] and we propose that differential signaling through these distinct PRRs results in differential intracellular routing and processing of P . gingivalis within DCs . TLRs are essential for phagosome maturation and subsequent bacterial clearance [31 , 32] . TLR signaling is also involved in the maturation of autophagosomes [33] . The ability of P . gingivalis to manipulate DC-SIGN and TLR signaling through differential fimbrial expression [26 , 34] , could have profound effects on bacterial survival[26] . However , the role of P . gingivalis major and minor fimbriae in DC-SIGN-TLR2 crosstalk and its influence on survival of P . gingivalis within DCs has not been examined . In the present study , a combination of approaches was used to address the role of DC-SIGN and TLRs in intracellular routing and survival of P . gingivalis , including blocking PRRs and autophagy , siRNA gene silencing and activation of autophagy in monocyte derived DCs ( MoDCs ) , To address the role of fimbriae in this regard we utilized defined bacterial mutants , that solely express minor fimbriae ( Mfa1+Pg ) , major fimbriae ( FimA+Pg ) or are deficient in both fimbriae ( MFB ) [35] ( Table 1 ) . Our results indicate that engagement of DC-SIGN by MFA-1 allows P . gingivalis to evade autophagy and lysosome fusion , resulting in pathogen persistence and survival within DCs . In contrast , activation of autophagy or of TLR2 by P . gingivalis expressing FimA results in autophagy mediated killing of this pathogen within DCs . Collectively , our studies reveal a novel mechanism that enables this pathogen to evade host detection and clearance and which could have profound implications for the treatment of other diseases involving low-grade chronic infection .
At 2 , 12 and 24h after bacterial co-culture with MoDCs , the MoDCs were imaged for intracellular P . gingivalis by epifluorescence microscopy and transmission electron microscopy ( TEM ) . All P . gingivalis strains except the double fimbriae negative P . gingivalis strain MFB ( Table 1 ) were taken up by MoDCs . There were marked differences in the P . gingivalis content of MoDCs at 2 , 12 and 24 hours , particularly when comparing double fimbriae positive strain Pg381 to Mfa1+Pg ( Fig . 1A ) . We observed a higher number of Mfa1+Pg within MoDCs ( Fig . 1A ) ( S1 Fig . ) This difference was most apparent after 24 hours , with large numbers of intra-and extra-cellular bacteria present . In contrast , MoDCs infected with Pg381 showed minimal bacterial content after 24 hours . Survival of intracellular bacteria was then assessed quantitatively by lysing MoDCs and growing bacteria in broth cultures and on anaerobic blood agar plates . Mfa1+Pg was recovered at higher numbers from MoDCs lysates in broth and on blood agar compared to Pg381 Fig . 1B ) . No significant difference was detected in the growth or death patterns of all strains in the media under anaerobic conditions in the absence of DCs ( Fig . 1C ) . To determine whether expression of DC-SIGN [14] was altered by P . gingivalis infection , MoDCs were infected with all the strains at different multiplicities of infection ( MOIs ) and gene expression of DC-SIGN was quantified at 2 , 6 , 12 and 24 hours ( Fig . 2A ) ( S2 Fig . ) . At 12 hours , a distinct pattern of DC-SIGN expression was detected in MoDCs infected with Mfa1+Pg compared to Pg381 and FimA+Pg . Infection with Mfa1+Pg up-regulated DC-SIGN mRNA at 1 and 10 MOIs in a dose dependent manner ( p<0 . 01 ) ( Fig . 2A ) ( Table 2 ) . In contrast , we observed decreased expression of DC-SIGN when MoDCs were incubated with Pg381 ( MOI-10 ) down-regulated DC-SIGN mRNA expression significantly ( p<0 . 05 ) at 12 hours ( -4 . 55 fold ) . Fold regulations were calculated relative to un-infected MoDCs ( Fig . 2A ) ( Table 2 ) . We also examined DC-SIGN expression on MoDCs by immunoelectron microscopy ( Fig . 2B ) and flow cytometry ( Fig . 2C ) . The results confirm a difference in DC-SIGN expression in MoDCs as a function of P . gingivalis strain . Mfa1+Pg induced higher positive immuno-labeling for DC-SIGN in MoDCs relative to Pg381 ( Fig . 2B ) . These results correlated well with our initial results from the mRNA analysis . DC-SIGN was detected on the membrane but also in the cytoplasm of MoDCs infected with Mfa1+Pg . The presence of cytoplasmic staining is consistent with previous evidence for Mfa1+Pg localization to DC-SIGN positive intracellular compartments [14] and the possibility of receptor recycling to the cell membrane after the phagocytic process . The cells infected with Pg381 showed minimal staining for DC-SIGN at the cell membrane and no cytoplasmic staining was detected at any of the time points ( Fig . 2B ) . Stably transfected DC-SIGN positive and negative Raji cells served as positive and negative controls respectively for DC-SIGN expression by immuno-electron microscopy ( S3 Fig . ) . We quantitatively assessed the increase of DC-SIGN in MoDCs infected with MFa1+Pg by flow cytometry analysis ( Fig . 2C ) ( S4 Fig . ) . We also monitored the expression of other C-type lectins and TLRs on the infected MoDCs by flow cytometry . Although P . gingivalis up-regulated the expression of TLR2 and CXCR4 , we did not observe strain specific differences in expression of these receptors ( S4 Fig . ) . Moreover , there were no changes in the expression of DCIR and mannose receptor ( MMR ) upon P . gingivalis infection . We did observe increased expression of Dectin receptors , but only on Mfa1+Pg infected MoDCs ( S4 Fig . ) . To analyze the phagosomal machinery involved in uptake and routing of different P . gingivalis strains by MoDCs , we assessed levels of the GTPase Rab family of proteins at early ( 2 hours ) through late ( 24hours ) stages of infection . Furthermore , we monitored co localization of the P . gingivalis strains with Rab proteins at these time points . Although both Pg381 and Mfa1+Pg were taken up at 2 hours , only Pg381 was associated with Rab5 at significantly higher levels as compared to Mfa1+Pg ( Table 3 ) ( S5 Fig . A and B ) . Association of Pg381 with Rab5 within MoDCs was detected up to 12 hours . After this point , detectable bacteria and Rab5 signals significantly decreased at 24 hours . The Rab5 signal was weak at all time points in MoDCs infected with Mfa1+Pg . In addition , Mfa1+Pg were more apparent than within MoDCs at 24 hours ( S5 Fig . A and B ) . MoDCs generally showed weak staining for Rab7 following infection either with Pg381 or Mfa1+Pg up to 24 hours ( S5 Fig . C and D ) . Since Rab7 was not detected during intracellular processing of any of the P . gingivalis strains examined , we investigated whether anti-bacterial autophagy may be involved in killing of Pg381 but not Mfa1+Pg . To investigate the role of autophagy as a putative survival mechanism utilized by Mfa1+Pg , the viability of P . gingivalis strains in MoDCs was monitored after induction of autophagy by the mTOR inhibitor , Rapamycin . Initial studies established that the viability of MoDCs and of P . gingivalis alone were not Rapamycin-sensitive at the concentrations used . Entry into MoDCs resulted in a significant increase in survival of Mfa1+Pg at 24 hours ( p<0 . 001 ) ( Fig . 3A ) . Rapamycin treatment of Mfa1+Pg-infected MoDCs ( Mfa1+Pg+Rapa ) significantly decreased P . gingivalis survival by ~48% ( p <0 . 001 ) ( Fig . 3A ) . Increased autophagy induction was confirmed by immuno-labeling of LC3-II in MoDCs treated with Rapamycin for 11 hours ( 1 hour after P . gingivalis infections ) . Rapamycin treatment increased the LC3-II signal in cells infected with all fimbriated strains as well as in un-infected ( Cont . ) ( Fig . 3B and C ) . To determine the kinetics of survival of P . gingivalis within MoDCs , as well as the involvement of autophagy , intracellular bacteria were monitored after 6 , 12 , 24 and 48 hours of incubation with MoDCs with or without Rapamycin ( Fig . 4 ) . The levels of P . gingivalis Mfa1+Pg within MoDCs ( Mfa1+Pg+DC ) were the highest at all time points except at 6 hours when we observed similar levels to that observed with Pg381+DCs ( Fig . 4A ) . Levels of Pg381 and FimA+Pg within MoDCs ( Pg381+DCs and FimA+Pg+DCs ) were nonetheless significantly higher than bacteria without MoDCs , until 12 hours , at which point we observed a significant decrease in survival of both strains at 24 and 48 hours . Moreover , the highest level of Mfa1+Pg was observed at 24 hours , with the numbers of P . gingivalis increasing within MoDCs at 6 , 12 and 24 hours ( Fig . 4A ) . Activation of autophagy with Rapamycin significantly inhibited Mfa1+Pg survival at 6 , 12 and 24 hours within MoDCs ( Mfa1+Pg+DCs+Rapa ) ( Fig . 4B ) . However , the numbers of Mfa1+Pg detected within MoDCs treated with Rapamycin ( Mfa1+Pg+DCs+Rapa ) were still higher than bacteria alone ( Mfa1+Pg ) at 12 and 24 hours ( Fig . 4B ) . For Pg381 , rapamycin also significantly decreased intracellular survival , with no significant differences detected relative to bacteria alone except at 12 hours ( Fig . 4C ) . FimA+Pg exhibited significant intracellular survival ( FimA+Pg+DCs ) only at 12 hours , which was significantly inhibited with rapamycin treatment ( Fig . 4D ) . The analysis of the data using three-factor repeated measures ANOVA showed that both time and intracellular environment were significant factors ( P<0 . 05 ) in P . gingivalis survival , with rapamycin significantly impairing P . gingivalis survival with MoDCs . Due the marked difference in Rab5 induction in MoDCs infected with Pg381 and Mfa1+Pg , and the important role of Rab5 in regulation of subsequent autophagy [36 , 37] we tracked the LC3-II signal in MoDCs infected with labeled P . gingivalis strains between 2 to 24 hours ( S6 Fig . A and B ) . LC3-II is the active form of cytosolic LC3 that associates with the autophagosome until cargo degradation [38] . Pg381 infection resulted in significant increases of LC3-II within MoDCs at 2 , 6 and 12 hours ( p = 0 . 0317 , 0 . 008 and < 0 . 001 , respectively ) . In contrast , LC3-II remains unchanged in MoDCs infected with Mfa1+Pg during 24 hours of infection . The highest level of LC3-II within Pg381-infected MoDCs was evident at 12 hours ( mean fluorescent intensity = 2360 . 06 ±251 . 72 ) ( S6 Fig . A and B ) . Hence , further analysis of the uptake of different P . gingivalis strains by MoDCs and the expression of LC3-II were carried out at the 12 hr time point ( Fig . 5A and B ) . Infection with Pg381 and FimA+ Pg , but not Mfa1+Pg increased LC3-II levels in MoDCs during the first 12 hours . ( Fig . 5A ) ( S6 Fig . A and B ) . Although there was generally a low level of co-localization between all P . gingivalis strains with LC3-II , strains Pg381 and FimA+Pg showed higher Pearson’s correlation than Mfa1+Pg with LC3-II . Moreover , quantification of the LC3-II signals in infected MoDCs revealed significant increases in cells infected with Pg381 and FimA+Pg ( P<0 . 001 for both strains compared to Mfa1+Pg ) . Cells infected with Mfa1+Pg , in contrast , showed decreased levels of LC3-II compared to un-infected cells ( Cont . ) ( Fig . 5B ) . Uptake of Pg381 and all mutant strains except MFB was confirmed by intensity quantification of CFSE ( Fig . 5C ) . The highest uptake was detected in cells infected with Mfa1+Pg yet these cells had the lowest level of LC3-II signal ( Fig . 5B and 5C ) . Infection of MoDCs by Mfa1+Pg has previously been shown to depend on engagement of DC-SIGN [14] . We confirmed this result by blocking DC-SIGN with HIV gp120 , prior to infection ( Fig . 6A and 6B ) . Blocking DC-SIGN increased the LC3-II signal in MoDCs prior to addition of Mfa1+Pg ( Fig . 6B ) . Moreover , blocking DC-SIGN restored the basal expression of LC3-II in MoDCs ( Fig . 6B ) . To confirm the HIV gp120 blocking experiments , we additionally knocked down DC-SIGN using siRNA . DC-SIGN knockdown inhibited uptake of Mfa1+Pg but not Pg381 ( Fig . 6C and 6D ) and restored LC3-II signals in MoDCs ( Fig . 6C and 6D ) . Furthermore , DC-SIGN knockdown significantly decreased survival of Mfa1+Pg in MoDCs ( Fig . 6E ) . A scrambled sequence control did not inhibit uptake or effect LC3-II signal . To confirm the contribution of actin-mediated endocytic trafficking in LC3-II induction , MoDCs were treated with cytochalasin-D ( CytD ) prior to infection . CytD significantly inhibited intracellular localization of both Pg381 and Mfa1+Pg by MoDCs and restored the LC3-II signals to the basal level in MoDCs ( S7 Fig . C and D ) . We further confirmed LC3-II conversion in MoDCs by Western blot analysis ( Fig . 7 ) . Pg381 and FimA+Pg increased LC3-II expression in MoDCs . In contrast , Mfa1+Pg-infected MoDCs showed no significant difference in LC3-II compared to the uninfected control ( Cont . ) or MFB treated MoDCs ( Fig . 7A and 7B ) . To determine if increased LC3-II signal in MoDCs infected with Pg381 was indeed due to increased induction , as opposed to accumulation from lack of autophagosomal—lysosome fusion , the latter was inhibited by Bafilomycin in the flux test as reported [39] . Dose response of Bafilomycin was confirmed in MoDCs , with the highest LC3-II accumulation observed between 3–4nM ( S7 Fig . A and B ) . Bafilomycin treatment further increased LC3-II by Pg381 , indicating an increase in autophagy by Pg381 rather than a block in autophagosome-lysosomal fusion ( Fig . 7C and 7D ) . To establish the role of TLR signaling in autophagy induction leading to DC maturation and intracellular killing of P . gingivalis , we activated TLR pathways using specific agonists for TLR1/2 ( Pam3csk4 ) and TLR4 ( E . coli LPS ) . The results demonstrate that TLR1/2 activation was highly potent in stimulating CD83 expression and intracellular LC3-II within MoDCs infected with P . gingivalis ( Fig . 8A and 8B ) . In addition , we observed a significant reduction in the Mfa1+Pg counts within MoDCs treated with Pam3csk4 after 24 hours of infection ( Fig . 8C ) . The initial interaction of P . gingivalis with the outer membrane of MoDCs was visualized by scanning electron microscopy ( SEM ) after 2 hours of infection ( Fig . 9A and 9B ) . Both strains ( Pg381 and Mfa1+Pg ) were able to engage the MoDC surface at 2 hours ( Fig . 9A and 9B ) . To directly visualize formation of double membrane autophagosomes , TEM analysis was performed in MoDCs infected with all fimbriated strains . Tracking of Pg381 and FimA+Pg within MoDCs after 12 hours of infections demonstrated that the majority of bacteria were contained in double membrane structures . In contrast , greater numbers of Mfa1+Pg were consistently detected within single membrane vesicles in the cytoplasm of MoDCs ( Fig . 9C ) . Quantification of the double membrane structures that contained bacteria in were carried out in three randomly selected EM grids of each sample . The ratio of Mfa1+Pg trapped in double membrane relative to the total intracellular bacteria was significantly lower than Pg381 and FimA+Pg ( Fig . 9D ) . Due to the important role of lysosome fusion in autophagosome maturation [40] , CFSE-labeled P . gingivalis and LAMP1+ lysosomes were tracked in MoDCs by epifluorescence microscopy and P . gingivalis-LAMP1 co-localization was quantified between 2 and 24 hours . The uptake of all three fimbriated P . gingivalis strains by MoDCs at 2 , 12 and 24 hours relative to uninfected controls was confirmed . ( Fig . 10A and 10B ) ( S8 Fig . ) . At 24 hours , Pg381 was mostly undetectable compared to Mfa1+Pg within MoDCs ( S8 Fig . ) . Uptake of Pg381 was associated with increased LAMP1 at 2 hours , while minimal LAMP1 was detected in cells infected with Mfa1+Pg at all time points ( S8 Fig . ) . The highest LAMP1 signal in Pg381 and FimA+Pg infected MoDCs was observed at 12 hours , yet there was no increase in LAMP1 signal within Mfa1+Pg infected MoDCs ( Fig . 10A ) . Quantification of LAMP1 intensities within Pg381 and FimA+Pg -infected MoDCs was higher than Mfa1+Pg-infected and un-infected MoDCs ( p<0 . 001 ) ( Fig . 10B ) . In addition , co-localization was higher in Pg381 and FimA+Pg with LAMP1 at 12 hours ( Fig . 10C ) ( Table 4 and 5 ) .
Our results indicate that canonical autophagosomal and lysosomal clearance of P . gingivalis within DCs is dependent on initial routing to early endosomes , followed by autophagosomal and lysosomal routing . We show that P . gingivalis is able to survive within DCs by subversion of this canonical pathway via its Mfa-1 fimbriae . DCs are unlike macrophages and neutrophils in that the lysosomal machinery is specialized for efficient epitope preservation , rather total degradation [41] . Nevertheless , DCs must have mechanisms to control and inhibit the growth of intracellular pathogens; otherwise DCs could serve as a significant niche for pathogen dissemination to distant organs as recently suggested [42] . Autophagy has been widely recognized as an antibacterial lysosomal mechanism with an immune regulatory component [2 , 4 , 43][1] . Although the configuration of autophagy apparently serves DCs well in most situations , very little is known about its role in elimination of specific intracellular pathogens . More specifically , it is unclear how engagement of different PRRs on human myeloid DCs influences the induction of and maturation of antimicrobial autophagosomes . The minor Mfa1 fimbriae are 67-kDa glycoproteins that target DC-SIGN for entry into MoDCs ( and Raji cells ) [14] . As this strain does not express FimA , a TLR2 agonist [28] expressed by all the other fimbriated strains , the present work represents a unique opportunity to study the role of PRRs on DCs in the context of antimicrobial autophagy . DC-SIGN is a type II transmembrane receptor that recognizes a wide range of pathogens through internal mannose branched structures and terminal di-mannose n-oligosaccharides [44] . Three internalization motifs ( di-leucine motif , tri-acidic cluster , ITAM motif ) on its cytoplasmic tail facilitate pathogen uptake [45] . Despite the diverse immune functions and regulations mediated by DC-SIGN [15 , 23 , 46 , 47] , the immune escape mechanisms associated with DC-SIGN engagement by pathogens remain largely “unexplained” [17 , 18 , 20 , 21] . Our results are consistent with a role for DC-SIGN in routing P . gingivalis into distinct intracellular vesicles that escape early autophagosomal recognition and subsequent vesicle maturation and degradation . This DC-SIGN dependent re-routing seems to begin at the stage of early phagosomal formation , whereas DC-SIGN independent routing ‘traps’ P . gingivalis within Rab5 rich vesicles immediately after uptake . Rab5 has been suggested as an early stage initiator of autophagy and facilitator of subsequent lysosomal fusion [37][40] . In addition , Rab5 is reported to enhance autophagy by inhibition of mTORC1 ( mTOR complex ) . The results of the current study suggest that when TLR signaling is weak ( e . g . in the absence of FimA on the Mfa1+Pg strain ) engagement of DC-SIGN dominates the response . This was clarified by adding exogenous TLR1/2 agonist , which inhibited Mfa1+Pg survival in parallel with higher LC3-II signals and stronger DC maturation . We surmise that lacking a strong signal for autophagy activation such as a TLR2 agonist , pathogens internalized by DC—SIGN are preferably routed to non-autophagosomal , non-lysosomal compartments where they survive in DCs . One of the striking observations of the present work was the preferential routing of Mfa1+Pg to single-membrane intracellular vesicles , while the other strains were predominantly contained within characteristic double membrane phagophores . Classically , autophagy has been identified by the formation of double membrane vesicles that ultimately fuse with the lysosome for degradation of its intracellular components [48] . However , recently an alternative pathway known as non-canonical autophagy or LC3 associated phagocytosis ( LAP ) has been described [49] , which involves the formation of single membrane vesicles . Here , DC-SIGN engagement appears to facilitate containment within single membrane vesicles; thus allowing P . gingivalis to evading lysosomal fusion . Some reports suggest that certain pathogens use ( single membrane ) autophagosomes for replication by blocking autophagosomal maturation [2 , 50 , 51] . Whether P . gingivalis utilizes DC-SIGN to enter a non-canonical pathway of autophagy for bacterial survival in DCs remains to be determined . One of the earliest studies describing P . gingivalis evasion of the conventional endocytic pathway and rerouting to ( canonical ) autophagosomes was in human aortic endothelial cells in [50] . Endothelial cells have been reported to express DC-SIGN [52] but this was not addressed in the previous study [50] . DCs , in contrast to endothelial cells , are highly migratory professional antigen presenting cells . Most reports of autophagy as an anti-microbial mechanism are from studies of immune cells [1 , 2 , 50 , 51 , 53] . In the present study of myeloid DCs , P . gingivalis appears to use an alternative tactic to evade autophagic capture through early engagement with DC-SIGN receptor and through non-canonical autophagy . Analyses of intracellular survival of the different P . gingivalis strains suggest that DCs can use autophagy and lysosomal fusion to clear this intracellular pathogen . Pg381 , which expresses both Mfa1 and FimA , engages both DC-SIGN and TLR2 . It appears that DC-SIGN dependent routing is overruled by a strong TLR signal since Pg381 activates the autophagic process and is subsequently cleared , although this strain still survives longer inside MoDCs than outside in an aerobic atmosphere . Indeed , TLR signaling is a well-accepted inducer of autophagy as well as of phagosomal maturation [31–33] . However , the majority of autophagy studies that addressed TLRs were conducted in macrophages and neutrophils , both of which are naturally equipped with strong intracellular killing arsenal and higher expression of TLRs . DC-SIGN expression is considered restricted to immature DCs with few exceptions in endothelium and specific macrophage subpopulations [54 , 55] . Our current data shows that DC-SIGN engagement may cause a positive feedback loop that increases the receptor expression resulting in subsequent bacterial uptake and survival . Increases in DC-SIGN+DCs in tissues of patients with periodontitis has previously been reported [56] . This could further enhance the pathogenicity of P . gingivalis and other DC-SIGN targeting pathogens . In the case of P . gingivalis , its ability to regulate fimbrial expression in different environmental cues such as pH , temperature and hemin content [26 , 27 , 34] may aid in its pathogenicity during chronic inflammation by regulation of DC-SIGN engagement ( and expression ) . Current efforts in our laboratory are directed to identifying the levels of expression of Mfa1 and FimA in clinical samples of disease vs . healthy patients . Phagosomes are known to have a high degree heterogeneity and individuality depending on host cell types , microbe captured and pattern recognition receptors engaged [11] . When Staphylococcus aureus and Salmonella typhimurium engage TLRs , for example , they are delivered to lysosomes at an inducible rate manifest by increased clearance and phagolysosomal fusion . However , slower phagolysosme maturation results from engagement with members of C-type lectin family and scavenger receptors [57] . DCs infected through DC-SIGN maintain an immature state and are more resistant to apoptosis [58] . Typically , immature DCs have a short life and active apoptosis is initiated shortly after maturation to avoid immune overstimulation [59 , 60] . Dominant DC-SIGN engagement alters such homeostatic balance and hinders the intracellular resistance to such infection . The DC-SIGN route may be a hallmark of chronic inflammation in response to low grade infection as it provides a protective niche for microbial persistence within the host . It is also influential in depolarizing the immune effector response [14] . Several lines of evidence have emerged linking autophagy and chronic inflammatory diseases [1 , 61 , 62] . In immature primary DCs , autophagy induction by NOD2 was essential for routing bacteria to lysosome and MHC presentation [61] . Early recognition of the microbe , here by DC-SIGN , could be crucial in driving ‘normal’ versus ‘up-normal’[1] autophagy and affect the fate of inflammatory process in chronic periodontitis . The relevance of this work from a clinical standpoint comes from evidence that Mfa1+ P . gingivalis strains infect myeloid DCs in oral mucosal tissues and in blood of humans with periodontitis , wherein it is disseminated to distant sites of angiogenesis [42] . We conclude that this intracellular pathogen can survive within DCs by evasion of autophagy through coordinated regulation of Mfa1 and FimA expression . This may also facilitate its dissemination [42] . Potential therapeutic tactics for resolving chronic inflammatory diseases by forced autophagy to activate a strong immune response is also suggested by these studies .
Human monocytes were isolated from mononuclear fractions of peripheral human blood by Human monocyte enrichment technique . After incubating the blood with the enrichment kit ( RossetteSep , Cat . no . 15028 ) for 20 minutes , monocyte separation was carried out using medium density Ficoll ( GE Healthcare , Cat . no . 17–1440–03 ) . Cells were seeded in the presence of GM-CSF ( 1000 unit/ml , Gemini Bio-Product , Cat . no . 300–124P ) and IL-4 ( 1000 unit/ml , Gemini Bio-Product , Cat . no . 300–154P ) at a concentration ( 3–4 x 105 cells/ml ) for 5–6 days . Flow cytometry analyses were carried out to verify the immature DC phenotype ( CD1a+ , CD83- , CD14- , DC-SIGN+ ) . Cell surface markers of DCs were evaluated by four-color immunofluorescence staining with the following antibodies: CD1a-PE ( Miltenyi , Cat . no . 120–000–889 ) , DC-SIGN-FITC ( Miltenyi , Cat . no . 130–092–873 ) , CD14-PerCP ( Miltenyi , Cat . no . 130–094–969 ) and CD83-APC ( Miltenyi , Cat . no . 130–094–186 ) . After 30 min at 4°C and washing with staining buffer ( PBS pH 7 . 2 , 2 mM EDTA , and 2% FBS ) , cells were fixed in 1% paraformaldehyde . Positive marker expression was calculated as a percentage of total DCs by forward scatter and side scatter characteristics [14 , 23] . To corroborate the immunoelectron microscopy staining of MoDCs for DC-SIGN , stably transfected DC-SIGN-positive ( Raji-DCs ) and negative Raji cells ( Raji ) were be obtained and the phenotype verified by flow cytometry[47] . The cells were cultured in 10% heat-inactivated FBS ( Gemini , Cat . no . 100–500 ) , RPMI 1640 with L-glutamine , and NaHCO3 ( Cornning , Cat . no . 10041CM ) in a 5% CO2 incubator at 37°C . Cells were centrifuged into a pellet and prepared for transmission electron microscopy sectioning . MoDCs were pre-incubated with HIV-1 gp120 Chiang Mai ( CM ) envelope protein ( GP120 ) for 30 min at 37°C . GP120 protein was obtained through the National Institutes of Health AIDS Research and Reference Reagent Program , Division of AIDS , National Institute of Allergy and Infectious Diseases , National Institutes of Health ( Cat . no . 2968 ) For actin polymerization inhibition , MoDCs were treated with cytochalasin D at 0 . 5 μM , the minimal concentration needed to arrest cytoskeletal rearrangements in Raji cells [14] . Cells were then washed 2 times with PBS and co-cultured with CFSE- stained P . gingivalis for 2 , 12 and 24 hours at 37°C . Cells were fixed with 1% paraformaldehyde and prepared for immunofluorescence staining and epifluorescence microscopy . Cells were incubated with predesigned Siliencer Select siRNAs for DC-SIGN ( Cat . No . 4392420 Ambion ) for 24 hours at 10 nM concentration . 12 ul of lipofectamine 2000 Reagent ( Cat . No . 11668–500 invitrogen ) were used with Opti-MEM medium ( Cat . No . 11058–021 LifeTechnologies ) were used for siRNA delivery . Flow cytometry analysis was performed in control and infected MoDCs to confirm inhibition of DC-SIGN . MoDCs were incubated with Rapamycin ( Cat . no . Tlrl-rap , InvivoGen , San Diego , CA ) at 200nM one hour after P . gingivalis infections . Induction of autophagy was confirmed by fluorescence staining of LC3-II within MoDCs after 2 and 6 hours . E . coli 026:B6 LPS ( L2654 , 2 . 5% protein , 1 , 500 , 000 EU/mg LPS , Sigma-Aldrich , St . Louis , MO ) . For stimulations , cells were treated with LPS at 1000 u/ml ( 200 ng/ml ) . For TLR1/2 stimulation Pam3CSK4 ( Synthetic triacylated lipoprotein ) ( Cat . No . tlrl-pms , InvivoGen , San Diego , CA ) were used at 1ug/ml . Four P . gingivalis strains were used in this study; 1 ) Pg381 , which expresses both minor ( Mfa1 ) and major ( FimA ) fimbriae , 2 ) isogenic minor fimbria-deficient mutant ( FimA+Pg ) , which expresses only the major fimbriae , 3 ) isogenic major fimbria-deficient mutant ( Mfa1+Pg ) , which expresses only the minor fimbriae and 4 ) the double fimbriae mutant ( MFB ) ( Table 1 ) . P . gingivalis strains were maintained anaerobically in ( 10% H2 , 10% CO2 , and 80% N2 ) in a Forma Scientific anaerobic system glove box model 1025/1029 at 37°C in Difco anaerobe broth MIC [63] . Mutant strains were maintained using erythromycin ( 5 μg/ml ) for mutant Mfa1+Pg , tetracycline ( 2 μg/ml ) for mutant FimA+Pg and both erythromycin and tetracycline for double fimbriae mutant MFB . Bacteria suspensions were washed five times in PBS and re-suspended for spectrophotometer reading at OD 660 nm of 0 . 11 , which previously determined to be equal to 5 x 107 CFU [64] . For bacterial CFSE staining , the suspension were washed ( 3 times ) and re-suspended in 5μM of CFSE in PBS . The bacteria were incubated for 30 min at 37°C in the dark [14 , 65] . MoDCs were pulsed with Pg381 , Mfa1+Pg , FimA+Pg and MFB at 0 . 1 , 1 and 10 MOI and incubated with the MoDCs for 2 , 6 , 12 and 24 hours and each experimental condition were performed in triplicate . After 24 hours of MoDCs infection with P . gingivalis strains , cells were washed three times in PBS and re-suspended in sterile water on ice for 20 min to lyse the cells . Lysates were re-suspended in anaerobe broth for 3 days . After broth incubation , bacterial suspensions were washed three times in PBS and re-suspended for spectrophotometer reading at OD 660 in triplicate . Viable counts ( CFU ) were calculated based on a plate count serial dilution versus OD readings . For confirming the identity of the P . gingivalis ( black pigmented Gram negative coccobacilli ) suspensions were cultured on 5% blood agar plates in triplicate under anaerobic conditions ( 10% H2 , 5% CO2 in nitrogen ) . Plates were incubated in anaerobic conditions at 35°C for 14 days until black colonies were detected and select colonies gram-stained . After MoDCs were infected with the P . gingivalis strains for 2 , 6 , 12 , 24 and 48 hours , cells were washed three times in PBS and re-suspended in sterile water on ice for 20 min to lyse the cells . Bacterial suspensions were washed three times in PBS and re-suspended for spectrophotometer reading at OD 660 in triplicate . Corresponding CFU counts were calculated based on a linear regression of plate count in serial dilution versus OD readings . Black colonies were confirmed in blood agar plate under anaerobic conditions ( 10% H2 , 5% CO2 in nitrogen ) . After MoDCs fixation , the procedures were carried out at the Electron Microscopy and Histology Core , Department of Cellular Biology and Anatomy , Georgia Regents University . The cells were fixed in 2% glutaraldehyde in 0 . 1 M sodium cacodylate ( NaCac ) buffer , pH 7 . 4 , postfixed in 2% osmium tetroxide in 0 . 1 M NaCac , stained en bloc with 2% uranyl acetate , dehydrated with a graded ethanol series and embedded in Epon-Araldite resin . Thin sections were cut with a diamond knife and stained with uranyl acetate and lead citrate . Cells were observed in transmission electron microscope ( JEM 1230—JEOL USA Inc . ) at 110 kV and imaged with a CCD camera and first light digital camera controller ( Gatan Inc . ) . The procedures were carried out at the Electron Microscopy and Histology Core , Department of Cellular Biology and Anatomy , Georgia Regents University . Staining for DC-SIGN using anti-CD209 ( mouse monoclonal , R&D Systems , # MAB161 ) , was carried out to identify the P . gingivalis-containing vesicles . After DCs were pulsed with different P . gingivalis strains for the 2 , 12 and 24 hours , cells were centrifuged into a pellet . Cells were be fixed in 4% formaldehyde 0 . 2% glutaraldehyde in 0 . 1 M sodium cacodylate ( NaCac ) buffer , pH 7 . 4 , dehydrated with a graded ethanol series through 95% and embedded in LR White resin . Thin sections were cut with a diamond knife on a Leica EM UC6 ultramicrotome ( Leica Microsystems Inc . ) and collected on nickel grids . Sections were incubated in blocking buffer ( 5% BSA , 3% normal serum , 0 . 05% Tween-20 in Tris-buffered saline , pH 7 . 4 ) at room temperature in a humid chamber for 2 hours and with primary antibody diluted in blocking buffer overnight at 4°C . Grids were washed and incubated with gold-labeled secondary antibody for 2 hours at room temperature then washed and stained with 2% alcoholic uranyl acetate and 0 . 08% alkaline bismuth subnitrate . Cells were observed in a JEM 1230 transmission electron microscope ( JEOL USA Inc . ) at 110 kV and were imaged with an UltraScan 4000 CCD camera & First Light Digital Camera Controller ( Gatan Inc . ) For RNA isolation , direct lysis of the cell suspensions were achieved by RNeasy kit ( Cat . no . 74104 , Qiagen ) by adding 300 μl of Qiagen’s buffer RLT per sample . The lysates were collected and pipetted directly into the Qiashredder spin column . Ethanol ( 70% ) was added and then samples were transferred to RNeasy spin columns . The samples were washed with buffer RW1 , RBE , then the RNA samples were collected and stored at -80°C . RNA quantity and integrity were tested and only ratios of absorbance at 260 and 280 nm of 1 . 8–2 . 0 , were included in the study . One-step qrt-PCR were performed using Express qPCR SuperMix ( Cat . no . A10312 , Invitrogen ) . Pre-formulated individual TaqMan gene expression primers ( Applied Biosystems ) were used for DC-SIGN mRNA detection ( Hs . 01588349_m1 ) . For qrt-PCR reactions , 5μl of the RNA sample , 25μl PCR master mix ( 2x ) and 2 . 5μl TaqMan gene expression assay were used per reaction . All PCRs were performed in triplicate and were carried out on a real-time PCR , StepOne ( Applied Biosystems ) . For calculations and statistical analysis , fold changes were calculated using ( 2-ΔΔCT ) method in the experimental samples [66] . Statistical analysis for gene expression was performed using the one sample t-test , which estimates the calculated difference ( in fold-regulation ) between experimental and control samples . A p value of <0 . 05 is the cut-off for significant differences . Cells were fixed with 4% paraformaldehyde , blocked and counterstained with fluorescent-labeled antibodies against Anti-Human CD206 ( MMR ) ( Cat . No . 53–2069–41 ) , Anti-Human DCIR ( Clec4A ) ( Cat . No . 12–9875–41 ) , Anti-Human CD209 ( DC-SIGN ) ( Cat . No . 45–2099–41 ) , Anti-Human Dectin-1 ( Cat . No . 46–9856–41 ) , Anti-Human CD284 ( TLR4 ) ( Cat . No . 12–9917–41 ) , Anti-Human CD184 ( CXCR4 ) ( Cat . No . 15–9999–41 ) , Anti-Human CD282 ( TLR2 ) ( Cat . No . 17–9922–41 ) , Anti-Human CD286 ( TLR6 ) ( Cat . No . 13–9069–80 ) ( all ebioscience , USA ) . All markers were measured against isotype controls . The markers were measured as MFI using the Accuri C6 Flow Cytometry system . MoDCs were infected with P . gingivalis prelabeled with carboxyfluorescein succinimidyl ester ( CSFE ) fluorescence . Cells were fixed with 1% paraformaldehyde , washed with PBS twice and permeabalized with 0 . 5% saponin . MoDCs were incubated with LC3-II antibody ( ab51520 ) for 2 hours and then washed with PBS . Pellets were res-suspended in cytospin fluid ( Cat . no . 6768315 , Shandon ) centrifuged at 400 rpm for 4 minutes . Slides were mounted with anti-fade reagent ( Invitrogen , P36931 ) and dried for microscopic analysis . Microscopic images were obtained with epifluorescence microscope ( Nikon E600 ) then analyzed by image enhanced fluorescence microscopy aided by deconvolution analysis . Quantifications of the fluorescent intensities and co-localization within infected cells were done by NIS-Elements BR and AR software . Three randomly selected regions of interest were selected for each field to quantify fluorescence dye intensities . Cells were centrifuged and washed twice with PBS . After washing , cells were lysed by addition of cell lysis buffer ( Cell signaling Cat . no . 9803S ) and incubated for 20 minutes on ice . Samples were centrifuged and the supernatant was collected and stored at -80°C . Proteins were denatured at 70°C for 10 minutes immediately prior to loading . For immunoblotting , 50 μg of total cellular protein per lane were separated by blot 4% to 12% Bis Tris Plus gradient gel and transferred to PVDF ( polyvinylidine difluoride ) membranes using iBlotting dry transfer system ( Lifetechnologies , Cat . no . IB1001 ) . The membranes were incubated with primary antibody LC3B ( Abcam , Cat . No . ab48394 ) or GAPDH ( Meridian life science , Cat . No . H86504M ) and secondary antibody peroxidase-conjugated goat anti-rabbit or goat anti-mouse IgGs in iBind solution for 2 . 5 hours ( iBind western system , life technologies ) . The specific protein signals were visualized using chemiluminescent peroxidase substrate and exposing the membranes to the high performance chemiluminescene film for detection . Protein loading was verified by detection of GAPDH using mouse anti-GAPDH monoclonal antibody . Monocytes were transduced with CellLight BacMam 2 . 0 ( lifetechnologies ) to visualize lysosomal marker LAMP1 ( C10504 ) , early endosome Rab5 ( C10587 ) and late endosome Rab7 ( C10589 ) . Transduction was performed simultaneously with differentiation of MoDCs at the 5th day . Cells were transfected with 30 PPC ( particle per cell ) for 24 hours . At the 6th day MoDCs were harvested and immature phenotype ( CD1c+DC-SIGN+CD83-CD14- ) by flow cytometry . Quantifications of the fluorescent intensities and co-localization within infected cells were done by NIS-Elements BR and AR software . These studies were determined by the Human Assurance Committee at Georgia Regents University to be human subject exempt , due to the use of anonymized peripheral blood samples for monocytes .
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Among the most successful of human microbes are intracellular pathogens . By entering the intracellular milieu , these pathogens are protected from harsh environmental factors in the host , including the humoral and cellular immune responses . Porphyromonas gingivalis is an opportunistic pathogen that colonizes the oral mucosa and accesses the bloodstream and distant sites such as the blood vessel walls , brain , placenta and other organs . Still unclear is how P . gingivalis traverses from oral mucosa to these distant sites . Dendritic cells are highly migratory antigen presenting cells that “patrol” the blood , skin , mucosa and all the major organ systems . Capture of microbes by dendritic cells activates a tightly regulated series of events , including directed migration towards the secondary lymphoid organs , where processed antigens are ostensibly presented to T cells . Autophagy is now recognized as an integral component of microbial clearance , antigen processing and presentation by dendritic cells . We report here that P . gingivalis is able to subvert autophagic destruction within dendritic cells . This occurs through its glycoprotein fimbriae , called Mfa-1 , which targets the C-type lectin DC-SIGN on dendritic cells . The other major fimbriae on P . gingivalis , FimA , targets TLR2 , which promotes autophagic destruction of P . gingivalis . We conclude that DC-SIGN-TLR2 crosstalk determines the intracellular fate of this pathogen within dendritic cells , and may have profound implications for the treatment of many chronic diseases involving low-grade infections .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Porphyromonas gingivalis Evasion of Autophagy and Intracellular Killing by Human Myeloid Dendritic Cells Involves DC-SIGN-TLR2 Crosstalk
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Monocyte phenotype and output changes with age , but why this occurs and how it impacts anti-bacterial immunity are not clear . We found that , in both humans and mice , circulating monocyte phenotype and function was altered with age due to increasing levels of TNF in the circulation that occur as part of the aging process . Ly6C+ monocytes from old ( 18–22 mo ) mice and CD14+CD16+ intermediate/inflammatory monocytes from older adults also contributed to this “age-associated inflammation” as they produced more of the inflammatory cytokines IL6 and TNF in the steady state and when stimulated with bacterial products . Using an aged mouse model of pneumococcal colonization we found that chronic exposure to TNF with age altered the maturity of circulating monocytes , as measured by F4/80 expression , and this decrease in monocyte maturation was directly linked to susceptibility to infection . Ly6C+ monocytes from old mice had higher levels of CCR2 expression , which promoted premature egress from the bone marrow when challenged with Streptococcus pneumoniae . Although Ly6C+ monocyte recruitment and TNF levels in the blood and nasopharnyx were higher in old mice during S . pneumoniae colonization , bacterial clearance was impaired . Counterintuitively , elevated TNF and excessive monocyte recruitment in old mice contributed to impaired anti-pneumococcal immunity since bacterial clearance was improved upon pharmacological reduction of TNF or Ly6C+ monocytes , which were the major producers of TNF . Thus , with age TNF impairs inflammatory monocyte development , function and promotes premature egress , which contribute to systemic inflammation and is ultimately detrimental to anti-pneumococcal immunity .
Monocyte phenotype and function change with age but whether these changes contribute to susceptibility to infectious disease is unclear . In mice , monocytes can be subdivided based on their expression of the Ly6C antigen into Ly6Chigh ( Ly6Chigh , CCR2high , CX3CR1low ) and Ly6Clow ( Ly6Clow , CCR2low , CX3CR1high ) monocytes [1 , 2] . In humans , the functional equivalents are CD14++CD16-/+ and CD14+CD16++ monocytes , respectively [1 , 3] . Ly6Chigh monocyte output from the bone marrow to the blood increases in a CCR2-dependent manner early during infection [4 , 5] , and they become the dominant monocyte subtype in the blood , preferentially homing to sites of inflammation[6] . Ly6Chigh monocytes produce high levels of inflammatory cytokines[4 , 5 , 7]; thus , they are often called “inflammatory monocytes” . In the elderly , numbers of circulating CD14++CD16+ and CD14++CD16- monocytes , are significantly higher[8] . CD14++CD16+ monocytes derived from elderly individuals are more senescent ( i . e . have shorter telomeres ) than other monocyte subsets and produce more pro-inflammatory cytokines ( IL6 , TNF , IL1β , IL12p70 ) and have higher levels of some chemokine receptors ( e . g . CCR2 , CCR5 , CCR7 , CX3CR1 ) [9 , 10] . Due to their ability to produce large amounts of pro-inflammatory cytokines , Ly6Chigh monocytes contribute to the pathology of several models of chronic inflammation [11 , 12 , 13 , 14 , 15 , 16 , 17] . During chronic inflammatory conditions , the number of circulating Ly6Chigh monocytes increase progressively[18] and their ablation is an effective strategy for decreasing pathology [16 , 17 , 19 , 20] . Whether Ly6Chigh monocytes contribute to chronic age-associated inflammation and increased susceptibility to infection is not known and is the focus of this study . Aging is accompanied by an increase in the levels of pro-inflammatory cytokines such as tumour necrosis factor ( TNF ) and interleukins 1β ( IL1β ) and 6 ( IL6 ) in the serum and tissues , a phenomenon that has been termed “inflamm-aging”[reviewed in[21 , 22]] . This age-associated , systemic state of chronic , low-grade inflammation ( defined as “para-inflammation” by Medzhitov[23] ) is well-documented although its cellular source has yet to be definitively identified . Adipose tissue[24] , CD4+ T cells or macrophages[25 , 26] have all been proposed to contribute . Increases in serum cytokines ( particularly IL6 and TNF ) are generally thought to be a pathological consequence of aging , as they correlate with risk of classical “diseases of age” such as dementia[27] , stroke[28] , cardiovascular disease[29] as well as frailty[30 , 31] and all-cause mortality[32 , 33] . Conversely , lower than average levels of age-associated inflammation predict good health in age[34] . Furthermore , most chronic inflammatory conditions are characterized by increased numbers of CD14++CD16+ and/or CD14++CD16- monocytes [35 , 36 , 37 , 38 , 39 , 40 , 41] . Herein , we investigate the role of monocytes , which are known to be critical mediators of chronic inflammation , contribute to age-associated inflammation . Inflamm-aging contributes to susceptibility to infectious disease , and particularly pneumonia , which is a major cause of death in the elderly[42] . Susceptibility to pneumonia correlates with increased levels of IL6 and TNF before an infection [43 , 44 , 45] . When young mice are infused with TNF , they become as susceptible to experimental infection with Streptococcus pneumoniae as old mice[46] . Using a mouse model of pneumococcal colonization , we investigated whether changes in monocyte phenotype adversely affect host defense towards S . pneumoniae . We show that with age that there is an in increase in circulating Ly6C+ monocytes during the steady state due to increased expression of CCR2 . Using heterochronic bone marrow chimeras , we demonstrate that the aging microenvironment , rather than intrinsic changes in myeloid progenitors , drives changes in monocyte phenotype , including decreased expression of F4/80 ( a marker of maturity ) , and increased expression of CCR2 ( required for monocyte mobilization ) . We demonstrate that age-associated increases in TNF are the driving factor behind changes in monocyte phenotype , as TNF deficiency or treatment with anti-TNF antibodies normalizes expression of CCR2 on Ly6C+ monocytes . Decreased CCR2 expression results in decreased numbers of monocytes in the circulation and reduced production of TNF and IL6 . Finally , we demonstrate that , although TNF levels and the recruitment of Ly6C+ monocytes are increased in old mice during nasopharyngeal S . pneumoniae colonization , this , counterintuitively , results in diminished bacterial clearance . To our knowledge , this is the first mechanistic study that investigates the role of Ly6C+ monocytes as central mediators of inflamm-aging and demonstrates that TNF is a key contributor to age-associated defects in myeloid phenotype and anti-bacterial function . These data indicate that Ly6C+monocyte frequency and increased production of pro-inflammatory cytokines contributes to both age-associated inflammation and declining anti-bacterial immunity .
It has been reported that with age the proportion of myeloid cells and cytokines in the blood is increased . We quantitated circulating leukocyte populations in old ( 18–22 mo ) mice and found that , consistent with previously published data[47 , 48] , there was a decrease in the percentage of T cells and an increase in the number of myeloid cells when compared with young ( 10–14 wk ) mice ( Fig 1A & S1A Fig ) . Analysis of monocyte subsets indicated that the absolute number of both Ly6Chigh and Ly6Clow monocytes was increased with age ( Fig 1A ) . An increase in Ly6Chigh monocyte frequency within the blood of old mice was paralleled by a similar increase in the bone marrow ( Fig 1B ) , suggesting that increased myelopoiesis within the bone marrow may precede increased numbers of these cells in the blood . Consistent with this , we also found that the number of M-CSF responsive cells ( myeloid precursors and monocytes capable of differentiating into bona fide macrophages ex vivo ) in the bone marrow was significantly increased with age ( S1C Fig ) . The C-C chemokine receptor type 2 ( CCR2 ) is expressed at high levels on Ly6Chigh monocytes and is essential for their entry into the blood in response to the production of CCL2[49] . Since CCR2 is required for monocytes , and especially Ly6Chigh monocytes , to leave the bone marrow and enter the blood , we hypothesized that enhanced CCR2 expression on Ly6Chigh monocytes could prompt their premature emigration from the bone marrow and could explain the increased number of Ly6Chighmonocytes seen with age . CCR2 expression was measured on Ly6Chigh monocytes in the blood and bone marrow of old mice and found to be dramatically increased ( Fig 1C ) . Consistent with previous research[1] , CCR2 expression was more pronounced on Ly6Chigh monocytes ( S1E Fig ) . As Ly6Chigh monocytes represent an intermediate stage in monocyte-to-macrophage differentiation , we investigated their maturity using the monocyte/macrophage maturity marker , F4/80 . Interestingly , we found that there was an inverse relationship between CCR2 expression and F4/80 expression on Ly6Chigh monocytes in the blood of old mice . With age , these cells showed significantly decreased levels of F4/80 ( Fig 1D ) , suggesting that their increased CCR2 expression may prompt these cells to enter the circulation in an immature state . When CCR2 expression was measured on myeloid precursors undergoing M-CSF-stimulated differentiation into macrophages , increased CCR2 expression occurred during an intermediate stage of differentiation ( day 5 ) on cells from old mice ( S1D Fig ) . To determine whether increased CCR2 expression was sufficient to increase Ly6Chigh monocyte egress , we intraperitoneally injected young and old mice with 100 nM of CCL2 and measured Ly6Chigh monocyte recruitment after 4 hours . We found that despite administering an equivalent dose of CCL2 , Ly6Chigh monocyte recruitment to the peritoneum was increased ~5-fold in old mice relative to young mice ( Fig 1E ) . A less dramatic increase in Ly6Clow monocytes was also observed ( Fig 1E ) , consistent with previous studies . Since we found that there was an expansion of monocytes with age and these cells are known to be potent producers of pro-inflammatory cytokines , we postulated that they might contribute significantly to age-associated inflammation . To determine whether the increased numbers of monocytes with age contributed to age-associated increases in IL6 production , we targeted this cell population using carboxylated polystyrene microparticles ( PS-MPs ) , which have been shown by others to lead to a reduction of primarily Ly6Chigh monocytes in the blood[50] . We found that when circulating monocytes were decreased in old mice ( Fig 2A ) , this reduced circulating levels of IL6 ( Fig 2B ) . In humans , CD14++CD16+HLA-DR+/intermediate monocytes are the biggest producers of inflammatory cytokines under a variety of stimulation conditions [3] . Intracellular cytokine staining reveals that of the three human monocyte populations ( classical , intermediate , non-classical ) intermediate monocytes are the major producers of TNF ( Fig 3A ) and IL6 ( Fig 3B ) after stimulation with LPS or S . pneumoniae and older donors ( 63–70 yrs ) produce more cytokines than younger donors ( 26–52 yrs ) . Additionally , CD14+ monocytes isolated from PBMCs from older donors produced more TNF ( Fig 3C ) and IL6 ( Fig 3D ) in response to LPS than did younger donors . As in mice , the numbers of intermediate monocytes may be influenced by levels of age-associated inflammation since the frequency of intermediate monocytes , are positively correlated with plasma TNF ( Fig 3E ) as has been shown to occur in other chronic inflammatory conditions [51] . A weaker correlation ( p<0 . 02 ) was observed between TNF levels and the numerically dominant classical monocytes and no correlation was found between non-classical monocytes and TNF ( p = 0 . 2 ) . To determine whether age-related changes in Ly6Chigh monocyte numbers , phenotype and inflammatory capacity were caused by changes in the aging bone marrow microenvironment or due to intrinsic changes in the myeloid precursors themselves , we created heterochronic bone marrow chimeras . When young bone marrow was transferred to old recipient mice the number of Ly6Chigh and Ly6Clow monocytes was increased to levels comparable to old mice ( Fig 1A ) or old recipient mice who had received old donor marrow ( Fig 4A ) . In contrast , young recipient mice that had received old donor marrow had Ly6Chigh and Ly6Clow monocyte numbers comparable to young mice ( Fig 1A ) or to young recipient mice that had received young donor bone marrow ( Fig 4A ) . In addition , the increase in CCR2 expression observed on circulating monocytes from old mice ( Fig 1C ) was also observed in circulating monocytes from old recipient mice who had received young donor marrow but not on young recipient mice who received old donor marrow ( Fig 4B ) . These data demonstrate that increases of Ly6C+ monocytes and increased CCR2 expression occur in a manner entirely dependent on the bone marrow microenvironment . Since TNF is one of the central cytokines associated with inflamm-aging , we investigated whether TNF was sufficient to drive expansion of the Ly6Chigh monocytes . We aged TNF knockout ( KO ) mice ( 18–22 mo ) and quantified Ly6Chigh monocytes in their blood . We found that , unlike their WT counterparts , old TNF KO mice did not have higher numbers of circulating Ly6Chigh monocytes ( Fig 4C ) , but did have an increase in bone-marrow Ly6Chigh monocytes compared to their young counterparts ( Fig 4D ) . Surface expression of CCR2 on Ly6Chigh monocytes in both the blood ( Fig 4E ) and the bone marrow ( Fig 4F ) of old TNF KO mice was comparable to the levels seen in young mice . Similarly there were no changes in Ly6Clow monocytes in aged TNF KO mice ( S1D Fig ) . These data suggest that increased production of Ly6Chigh monocytes in the bone marrow occur independent of TNF , but that increases in CCR2 expression on these cells in the bone marrow , and their subsequent mobilization to the blood is TNF-dependent . Consistent with our observation that Ly6C+monocytes contribute to elevated levels of circulating cytokines with age ( Fig 2 ) , old WT mice produced more IL6 than young mice following 24 hour stimulation of whole blood with either PBS or LPS ( Fig 4G ) . In comparison , old TNF KO mice , which did not have an increase of Ly6C+monocytes in the blood did not have an age-associated increase in IL6 in whole blood in response to PBS or LPS ( Fig 4G ) . We investigated whether it was chronic or acute exposure to TNF that mediated age-related increases in serum IL6 and changes in monocyte phenotype and function . We first sought to determine whether increases in circulating Ly6C+ monocytes were inducible after administration of TNF . TNF ( 5ng/g ) was administered intraperitoneally for 3 weeks , a time point chosen because it would allow for multiple cycles of monopoiesis and complete turnover of pre-formed monocytes [52] . Young mice showed a large increase in Ly6Chigh monocytes in the blood and a less dramatic increase of Ly6Clow monocytes ( Fig 5A ) . This was accompanied by a significant increase in serum IL6 in TNF-treated , but not vehicle control mice ( Fig 5B ) . We next asked whether blocking TNF could reduce numbers of Ly6C+ monocytes in old animals . Young and old WT mice were administered Adalimumab ( HUMIRA ) , a human monoclonal antibody specific for TNF , or an IgG isotype control at a dose of 50 ng/g for a period of three weeks via intraperitoneal injection . Anti-TNF therapy reduced the levels of plasma TNF from an average of 1 . 5 pg/ml to below the level of detection ( LOD = 0 . 25pg/ml ) in old mice and decreased the number of circulating Ly6Chigh but not Ly6Clow monocytes in the blood to levels similar to young mice ( Fig 5C ) . Anti-TNF therapy also reduced CCR2 expression on Ly6Chigh monocytes in the blood of old mice to levels that are equivalent to those seen in young mice ( Fig 5D ) and reduced the percentage of monocytes that stained positive for IL6 or TNF by ICS after LPS stimulation ( Fig 5E ) . Anti-TNF treatment reduces IL6 levels in the circulation of old mice ( Fig 5F ) and when blood from young and old mice treated with anti-TNF or IgG controls was stimulated with LPS , IL6 levels were lower in old mice treated with anti-TNF compared to those that were treated with IgG ( Fig 5G ) . In order to determine if age-related changes in Ly6Chigh monocyte numbers or maturity might play a role in defective anti-bacterial immunity with age , we investigated the trafficking of these cells following nasopharyngeal colonization of young and old mice with the bacterial pathogen , S . pneumoniae . We selected this pathogen specifically because of the high burden of disease caused by S . pneumoniae in elderly individuals and because it has been previously demonstrated that its clearance from the nasopharynx is largely dependent on recruited monocytes/macrophages[53 , 54] . Following intranasal delivery of S . pneumoniae , we found that old mice had defects in clearance of the colonization . By Day 21 most of the young mice had cleared the bacteria , while old mice still harbored high bacterial loads ( Fig 6A ) . Old mice were also more susceptible to bacterial invasion to the lungs at day 3 ( Fig 6B ) and mortality throughout the course of colonization ( Fig 6C ) . Although serum production of CCL2 in old mice was comparable to that of young mice ( Fig 6D ) , old mice had increased Ly6Chigh but not Ly6Clow monocyte numbers in the circulation during colonization ( days 3 , 7 , 14 , 21 ) ( Fig 6E ) . We next investigated whether the monocytes/macrophages recruited in the context of age had maturity defects ( as measured by F4/80 expression ) . In old mice , circulating Ly6Chigh monocytes had decreased expression of F4/80 during colonization ( Fig 6F ) , suggesting that the decreased F4/80 expression seen in the bone marrow during the steady state ( Fig 1D ) perpetuates following their egress during infectious challenge . Despite their inability to control bacterial loads in the nasopharynx , old mice also had a significant increase in the expression of CCL2 in the nasopharynx during colonization ( Fig 6G ) , and had higher numbers of recruited Ly6Chigh monocytes ( Fig 6H ) and macrophages ( Fig 6I ) to the nasopharynx compared to young mice . Although resident macrophages from young and old mice present in the nasopharynx during the steady state expressed equal levels of F4/80 , monocytes/macrophages recruited to the nasopharynx during S . pneumoniae colonization showed decreased expression F4/80 ( Fig 6J ) , similar to that seen in their counterparts in the blood ( Fig 6F ) . In order to determine whether bacterial binding and internalization was different between monocytes derived from young and old mice we compared bacterial binding ( measured at 4°C ) and internalization/killing ( measured at 37°C ) . Although there was a significant decrease in bacterial binding between young and old mice , this did not appear to affect internalization or bacterial killing ( Fig 6K ) . Although trafficking of blood monocytes was not impaired with age , old mice nonetheless displayed impaired clearance of S . pneumoniae . To explain this , we hypothesized that high levels of recruited but developmentally immature Ly6Chigh monocytes could , in fact , have negative consequences for clearance . Interestingly , TNF , which we showed caused increased numbers of Ly6Chigh monocytes in the blood ( Fig 4A ) , was increased with age during S . pneumoniae colonization in the nasopharynx ( Fig 7A ) and blood ( Fig 7B ) . We next compared nasopharyngeal bacterial loads in WT and TNF KO mice , to determine whether TNF production affected bacterial clearance . Although TNF had no effect on clearance of colonization in young mice we found that old TNF KOs had significantly fewer CFUs in the nasopharnyx compared to their old WT counterparts at day 3 ( Fig 7C ) . Old TNF KO mice also had decreased recruitment of Ly6Chigh monocytes in the blood ( Fig 7D ) , confirming that TNF can regulate mobilization of these cells during infection as well as in the steady state . To determine whether the decreased recruitment of Ly6Chigh monocytes we observed was responsible for improved bacterial clearance in old TNF KO mice , we preferentially targeted this cell population using negatively-charged polystyrene microparticles ( PS-MPs ) ( Fig 8A ) . We observed that there were also decreases in monocytes in the lungs , but not neutrophils with this treatment ( S2 Fig ) . Old mice were given PS-MPs on day prior to and every 3 days during the course of S . pneumoniae colonization and bacterial loads were measured at day 7 . PS-MP-treated old mice had increased survival ( Fig 8B ) , less weight loss ( Fig 8C ) and lower bacterial loads in the nasopharynx ( Fig 8D ) , lungs ( Fig 8E ) and spleen ( Fig 8F ) compared to old control mice . Similar results were observed with Gr-1 antibody , which reduces numbers of monocytes and neutrophils . These data confirm that increased trafficking of this cell type during S . pneumoniae colonization impairs host defense .
Epidemiological data strongly suggests that there is a reciprocal link between pneumonia and age-associated inflammation . Older adults who have higher than age-average levels of the cytokines TNF and IL6 in their circulation have a much higher risk of acquiring pneumonia than their peers who have lower than age-average levels[55] . Although a robust inflammatory response is generally thought to be protective against infection , in the elderly , high levels of circulating inflammatory cytokines during pneumonia are associated with more severe disease and higher mortality[56 , 57] . Similarly , having a chronic inflammatory disease such as dementia , diabetes , or cardiovascular disease is strongly associated with susceptibility to acquiring pneumonia [58 , 59 , 60] . Conversely , having a pneumonia in mid- to late-life can often exacerbate or accelerate sub-clinical or existing chronic inflammatory conditions and can be the harbinger of declining health and decreased quality of life[58 , 59] . Although descriptions of this reciprocal relationship between chronic , age-associated inflammation and pneumonia , especially that caused by S . pneumoniae , are strong , the mechanistic explanations are weak . Herein we demonstrate that monocytes , both contribute to age-associated inflammation and are impaired by chronic exposure to the inflammatory cytokine TNF , and this ultimately impairs their anti-pneumococcal function . Our data using aged TNF KO mice or anti-TNF therapy indicate that the increased levels of TNF that occur with age impair monocyte development and ultimately anti-bacterial immunity . Although macrophages have previously been shown to promote inflamm-aging[61] , our data suggest that this may begin earlier in myelopoesis since monocytes produce more inflammatory cytokines such as TNF and IL6 with age and ablation of monocytes reduces levels of serum cytokines . The increase in circulating monocytes did not occur in old TNF KO mice . Furthermore , by treating young WT mice with a low-dose regime of TNF delivered intraperitoneally , we found that Ly6C+ monocytes were increased in the blood in a manner similar to old mice , demonstrating that TNF is sufficient to increase numbers of circulating Ly6C+ monocytes . Monocytes appear to be both highly responsive to increased levels of TNF but also seem to be a major source of age-associated TNF . Our observational studies in humans imply that the numbers of intermediate ( CD14++CD16- ) monocytes , which we have previously shown express higher levels of CCR2 with age [62] , correlate with increased levels of TNF and contribute to hyper-inflammatory responses to bacterial infection . Studies in patients on anti-TNF therapy for rheumatoid arthritis validate our observations that TNF drives increases in inflammatory monocytes . In these patients anti-TNF therapy decreases the levels of circulating CD14++CD16- monocytes in the blood and synovial fluid as well as decreases CCR2 expression on peripheral blood mononuclear cells and thus is consistent with our data demonstrating that TNF-mediated changes in CCR2 expression are sufficient to alter the numbers of Ly6Chigh monocytes in the circulation [63 , 64] . Interestingly , decreases in CD14++CD16- monocytes correlate with a positive prognostic response for patients , but whether this is because they contribute directly to disease progression or the inflammatory tone of rheumatoid arthritis is not known [63] . Increases in Ly6Chigh monocytes are associated with defects in maturity . Interestingly , our chimera data demonstrate that phenotypic changes in monocytes ( i . e . CCR2 and F4/80 expression ) were not due to intrinsic defects in myeloid precursors but rather the influence of the bone marrow microenvironment , and , since these changes did not occur in TNF KO mice , TNF produced in the context of the microenvironment . Although F4/80 levels were equivalent on blood monocytes during the steady state , they were lower on Ly6Chigh monocytes/differentiating macrophages recruited during nasopharyngeal S . pneumoniae colonization in old mice . These changes had functional significance; despite robust Ly6Chigh monocyte recruitment and TNF production in old mice , bacterial clearance was significantly impaired . In fact , our data suggest that TNF is detrimental to clearance of S . pneumoniae from the nasopharynx with age , as old TNF KO mice had lower bacterial loads compared to their WT counterparts . Although TNF is often thought of as a key anti-bacterial cytokine , mouse studies have demonstrated that TNF is required for control for S . pneumoniae bacteremia but not for survival in lung infection[65] . In our study , old TNF KO mice recruited fewer circulating Ly6Chigh monocytes during S . pneumoniae colonization compared to old WT mice and counter-intuitively , this appeared to be protective against infection as when we depleted circulating Ly6Chigh monocytes using carboxylated polystyrene microparticles colonization , bacterial loads in the nasopharynx decreased . These data are consistent with the clinical observation that rheumatoid arthritis patients ( who have high levels of circulating TNF ) are at increased risk of pneumonia but that there is no increase in risk of pneumonia for patients on anti-TNF therapy [66] . Whether pneumonia risk is decreased with anti-TNF therapy is not known; however , patients on anti-TNF therapy do live slightly longer than their untreated counterparts , despite an increased risk in re-activation of chronic infections[67 , 68] . These observations have important therapeutic significance , since the belief that host responses to bacteria are impaired with age due to poor innate cell recruitment has been the foundation of two large clinical trials testing the use of cytokines ( G-CSF ) to mobilize myeloid cells as an adjunct to antibiotics and one clinical trial testing GM-CSF as an adjuvant for pneumococcal vaccination . Although mouse models ( tested in young mice ) showed promise , these strategies all failed when tested in populations where the median ages were 59 , 61 and 68 , respectively [reviewed in[69] and[70]] . Our data suggests that use of G-CSF , GM-CSF or other myeloid chemoattractant-based therapies in older adults would enhance recruitment of a population that is fundamentally immature and predisposed towards TNF and IL6 production that provides no functional benefit to the host for clearance and may even exacerbate infection . In summary , our data suggest that monocytes are both contributors to age-associated inflammation and have altered anti-pneumococcal function as a result of age-associated inflammation . Lowering levels of TNF may be an effective strategy in improving host defence against S . pneumoniae in older adults . In fact , it has been shown that immunosuppressive steroid use in combination with antibiotics reduces pneumonia mortality in the elderly[71 , 72 , 73 , 74] , although uptake for this therapy has been limited . Although it may be counterintuitive to limit inflammatory responses during a bacterial infection , the clinical observations and our animal model indicates that anti-bacterial strategies need to be tailored to the age of the host .
All experiments were performed in accordance with Institutional Animal Utilization protocols approved by McMaster University’s Animal Research Ethics Board ( #13-05-13 and #13-05-14 ) as per the recommendations of the Canadian Council for Animal Care . Participants or Power of Attorney for participants were approached to determine interest in the study . Informed written consent was obtained from the participant or their legally authorized representative approved by the Hamilton Integrated Research Ethics Board ( #09–450 ) . Female C57BL/6J mice were purchased from Jackson Laboratories and aged in house . Colonization was performed as previously described[75] . To protect from age-related obesity aging mice are fed with a low protein diet Teklad Irradiated Global 14% protein Maintenance Diet and provided with an exercise wheel , as were young controls . The average weight of a young mouse is this study is 20g+/-1g and the old mice are on average , 27g+/-2 . 5g . TNF knockout mice ( KO ) mice ( C57BL/6J background ) were bred in the barrier unit at the McMaster University Central Animal Facility ( Hamilton , ON , Canada ) as previously described[76] . All mice were housed in specific pathogen-free conditions . Continual monitoring of the health status of mice was performed . Monocyte frequency was measured in whole blood according to staining procedures described in [62] . Briefly , intermediate monocytes were positive for the expression of HLA-DR and CD16 , stained brightly for CD14 , and were negative for lymphoid and neutrophil markers ( CD2 , CD3 , CD15 , CD19 , CD56 , and NKp46 ) . They are presented as cells per microlitre of whole blood , which was measured using CountBright Absolute Counting Beads ( Life Technologies , CA , USA ) . Serum TNF was measured in elderly donors ( 61–100 yrs ) using the Milliplex High Sensitivity ELISA kit ( Millipore , ON , CA ) . For intracellular cytokine staining , described in [62] , the production of TNF and IL-6 was measured in classical ( CD14++ ) , intermediate ( CD14++CD16+ ) and non-classical ( CD14+CD16+ ) monocytes after a 6 hour incubation period in the presence of 50 ng/ml LPS and 5 x 106 CFU of heat-killed S . pneumoniae . For cytokine secretion , CD14+ monocytes were isolated from PBMCs of young ( 26–52 yrs ) and older ( 63–70 yrs ) by positive selection procedure ( Stemcell , BC , CAN ) and stimulated for 22 hours in the presence of 50 ng/ml LPS . TNF and IL-6 were measured by ELISA ( eBioscience , CA , USA ) . Monoclonal antibodies with the following specificities were used in this study: F4/80 ( APC ) , Ly6C ( FITC ) , CD45 ( eFluor 450 ) , CD11b ( PE-Cy7 or PerCPCy5 . 5 ) , MHC II ( PerCP eFluor 710 ) , CD3 ( Alexa Fluor 700 ) , CD4 ( Alexa Fluor 605NC ) , Ly6G ( PE ) , Ter119 ( PE ) , B220 ( PE ) , NK1 . 1 ( PE ) , CCR2 ( PE ) , IL6 ( PE ) or TNF ( PECy7 ) . Blood and single cell suspensions of lung were stained according to previously published procedures [75] . Total cell counts were determined using CountBright Absolute Counting Beads ( Life Technologies ) . To attain a single-cell suspension of mouse lung tissue , half a lung was collected from each S . pneumoniae-colonized mouse and kept on ice . Immediately following , each lung was mechanically dissociated and enzymatically degraded using a Miltenyi Biotec Lung Dissociation Kit ( Cat#: 130-095-927 ) along with the gentleMACS Octo-Dissociator with Heaters ( Cat#: 130-096-427 ) . Following dissociation as per protocol , cell suspensions were filtered ( 70 μM cell filter ) and centrifuged at 300 x g for 10 min . Subsequently , single-cell suspensions were re-suspended in phosphate-buffered saline & processed for flow cytometry . A gating strategy for distinguishing Ly6Chigh and Ly6Clow monocytes is presented in S3 Fig . 100 nM of recombinant murine CCL2 ( endotoxin-free , eBioscience ) was diluted in sterile saline and administered intraperitoneally . Recruited cells were isolated via peritoneal lavage and quantitated using flow cytometry . Murine recombinant TNF ( eBioscience ) diluted in sterile saline was administered intraperitoneally every other day for 3 weeks at a dose of 5 ng per gram of body weight . Adalimumab ( HUMIRA , Abbott Laboratories ) , a humanized anti-TNF antibody , or the human IgG isotype control diluted in sterile saline were administered intraperitoneally at a dose of 50 ng per gram of body weight for a period of 3 weeks . FITC Fluoresbrite 500 nm carboxylated polsytrene microparticles ( PS-MPs ) were obtained from Polysciences . PS-MPs were injected via tail vein at 4 x 109 particles in 200 μl as previously described[50] . Monocyte depletion was confirmed by flow cytometry . Serum TNF and CCL2 was measured using high-sensitivity ELISA as per manufacturer's instructions ( Meso Scale Discovery ) . For quantitative PCR analysis , RNA Lysis Buffer ( Qiagen ) was used to collect nasopharyngeal RNA via nasal lavage . RNA was extracted using an RNAqueous Micro Kit ( Ambion ) , reverse-transcribed to cDNA using M-MULV reverse transcriptase ( New England Biolabs ) and qPCR was performed using GoTaq qPCR Master Mix ( Promega , WI , USA ) and the ABI 7900HT Fast Real-time PCR System ( Applied Biosystems , CA , USA ) all to manufacturer’s instructions . Cycle threshold ( Ct ) values relative to the internal reference dye were transformed by standard curve , followed by normalization to the housekeeping gene GAPDH . Normalized results are presented as relative to an internal calibrator sample . 100μL samples of peripheral blood , were incubated with TRITC-labeled S . pneumoniae ( MOI 20 ) resuspended in 100μL of complete RPMI at 4°C to allow binding , but not uptake . After 30 min of incubation , cells were stained for flow cytometry . Following RBC lysis ( 1x 1-step Fix/Lyse Solution eBioscience; ref: 00-5333-57 ) for 10min , cells were washed 2x with PBS to remove excess stain and non-adherent bacteria , and re-suspended in FACS wash ( 10% fetal bovine solution in PBS ) . Flow cytometry was performed and the amount of S . pneumoniae bound by Ly6Chigh monocytes was quantitated based on the mean fluorescent intensities of TRITC . Adalimumab ( HUMIRA , Abbott Laboratories ) , a humanized anti-TNF antibody , or the human IgG isotype control diluted in sterile saline were administered to mice . A dose of 50 ng per gram of body weight was given intraperitoneally in a volume of 200 μl every other day , for a period of 3 weeks to young and old WT mice . Unless otherwise mentioned in the figure legend , statistical significance was determined by two-tailed Mann-Whitney-Wilcoxon tests , one-way analysis of variance or two-way analysis of variance with Fischer’s LSD post-tests where appropriate .
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As we age , levels of inflammatory cytokines in the blood and tissues increase . Although this appears to be an inevitable part of aging , it ultimately contributes to declining health . Epidemiological studies indicate that older adults with higher than age-average levels of inflammatory cytokines are at increased risk of acquiring , becoming hospitalized with and dying of Streptococcus pneumoniae pneumonia but how age-associated inflammation increased susceptibility to was not entirely clear . We demonstrate that the increase in the inflammatory cytokine TNF that occurs with age cause monocytes to leave the bone marrow prematurely and these immature monocytes produce more inflammatory cytokines when stimulated with bacterial products , thus further increasing levels of inflammatory cytokines in the blood . Furthermore , although old mice have higher levels of these inflammatory monocytes arriving at the site of S . pneumoniae , they are not able to clear the bacteria . By pharmacologically or genetically removing the inflammatory cytokine TNF or reducing the number of inflammatory monocytes we were able to restore antibacterial immunity in aged mice . Thus we demonstrate that monocytes are both influenced by and contributors to age-associated inflammation and that chronic exposure to age-associated inflammation increases susceptibility to S . pneumoniae due to altering monocyte maturity and function .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2016
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TNF Drives Monocyte Dysfunction with Age and Results in Impaired Anti-pneumococcal Immunity
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Zika virus ( ZIKV ) is an emergent threat provoking a worldwide explosive outbreak . Since January 2015 , 41 countries reported autochthonous cases . In Brazil , an increase in Guillain-Barré syndrome and microcephaly cases was linked to ZIKV infections . A recent report describing low experimental transmission efficiency of its main putative vector , Ae . aegypti , in conjunction with apparent sexual transmission notifications , prompted the investigation of other potential sources of viral dissemination . Urine and saliva have been previously established as useful tools in ZIKV diagnosis . Here , we described the presence and isolation of infectious ZIKV particles from saliva and urine of acute phase patients in the Rio de Janeiro state , Brazil . Nine urine and five saliva samples from nine patients from Rio de Janeiro presenting rash and other typical Zika acute phase symptoms were inoculated in Vero cell culture and submitted to specific ZIKV RNA detection and quantification through , respectively , NAT-Zika , RT-PCR and RT-qPCR . Two ZIKV isolates were achieved , one from urine and one from saliva specimens . ZIKV nucleic acid was identified by all methods in four patients . Whenever both urine and saliva samples were available from the same patient , urine viral loads were higher , corroborating the general sense that it is a better source for ZIKV molecular diagnostic . In spite of this , from the two isolated strains , each from one patient , only one derived from urine , suggesting that other factors , like the acidic nature of this fluid , might interfere with virion infectivity . The complete genome of both ZIKV isolates was obtained . Phylogenetic analysis revealed similarity with strains previously isolated during the South America outbreak . The detection of infectious ZIKV particles in urine and saliva of patients during the acute phase may represent a critical factor in the spread of virus . The epidemiological relevance of this finding , regarding the contribution of alternative non-vectorial ZIKV transmission routes , needs further investigation .
Zika virus ( ZIKV ) is an emerging mosquito-borne virus of the family Flaviviridae and genus Flavivirus [1] . ZIKV was first reported in 1947 after isolation from a febrile sentinel rhesus monkey [2] . Since then , serologic evidence of human ZIKV infection in Africa and Asia was detected , but until 2005 only few human cases were reported [3] . The first well-described outbreak outside these geographic regions happened in 2007 in Micronesia , more specifically in Yap State , when the majority of the population was affected with Zika fever [4] . Intriguingly , the local mosquito vector was not confirmed by neither viral isolation nor molecular methods [4] . On October 2013 , a second intense outbreak in Oceania occurred in French Polynesia ( 2013/2014 ) , and soon after spread over to New Caledonia ( 2014 ) , Cook Islands , ( 2014 ) and Easter Island , 2014 [5 , 6] . In these outbreaks , approximately 80% of ZIKV infections were asymptomatic [4 , 7] . Commonly , Zika is considered to be a mild disease lasting one week with symptoms including fever , rash , conjunctivitis , arthralgia , myalgia , headache and malaise . However , during the French Polynesian epidemic , its association with severe neurological complications , the Guillain-Barré syndrome ( GBS ) was reported for the first time [8] . In April 2015 , the first autochthonous cases in the Americas were identified in Brazil [9 , 10] . At present , Brazil is suffering from an explosive outbreak of ZIKV . Hence , in February 2016 , Brazilian Ministry of Health ( MoH ) appraised the incidence of greater than one million cases of ZIKV disease cases [11] . Notably , in addition of an increase of GBS cases as occurred in the French Polynesia outbreak , the MoH of Brazil described a rise of microcephaly occurrence . Between 22 October 2015 to 5 March 2016 , 6158 cases of microcephaly and/or central nervous system malformation were noticed in contrast to the estimated average number of 163 annual cases [12] . So far , 745 suspected cases of microcephaly have been confirmed as ZIKV-associated microcephaly in a total of 1927 investigated cases [11–13] . More recently , a case of ZIKV infection with vertical transmission demonstrated the association of severe fetal brain injury with fetal infection with ZIKV [14] . Moreover , ZIKV nucleic acid was detected in amniotic fluid of two pregnant women , whose fetuses were diagnosed with microcephaly , corroborating vertical transmission possibility [15] . Other abnormalities such as placental insufficiency , fetal growth restriction , CNS injury , and fetal death have also been reported in association with ZIKV infection [16] . This scenario of ZIKV infection linked to severe neurological complications as well as the establishment of ongoing ZIKV outbreaks in several countries in Latin America led to the WHO to declare ZIKV an international public health emergency [11 , 17 , 18] . The transmission of ZIKV has been associated with several Aedes mosquito species belonging to subgenus Stegomyia , notably Ae . aegypti [19 , 20] and Ae . albopictus [21] . However , a recent study proposes that although susceptible to infection , Ae . aegypti and Ae . albopictus from the Americas display an unexpectedly low vector competence for a fifth-passage ZIKV strain from New Caledonia [22] , suggesting other factors such as the large naïve population for ZIKV and the high densities of human-biting mosquitoes contribute to the rapid spread of ZIKV during the current outbreak . Nonetheless , perinatal transmission [23] and potential risk for transfusion-transmitted ZIKV infections has also been demonstrated [24] . Most remarkably , ZIKV can be likely disseminated by sexual contact , due to its presence in semen [25 , 26] . In addition , it was demonstrated that ZIKV exists in urine [27 , 28] , breast milk [29] and saliva [30] . Indeed , ZIKV was more frequently detected in urine and saliva than in blood using ZIKV RT-PCR tests for diagnosis . It was considered that patients exhibit the highest concentrations of ZIKV in saliva at disease onset [30] while in urine , ZIKV possibly remains detectable for longer periods [27] . In this study , we demonstrate that it is possible to recover infective ZIKV from both saliva and urine of acute phase patients by means of viral isolation in Vero cells . This achievement suggest that ZIKV may be transmitted between humans by infected saliva and urine .
The Acute Febrile Illnesses Laboratory and Molecular Biology of Flavivirus Laboratory conducted this study at Oswaldo Cruz Foundation , Rio de Janeiro . The institutional review boards at Fundação Oswaldo Cruz ( Fiocruz ) approved the study protocol . All subjects provided written , informed consent before participation , and a medical assistant filled a standardized medical questionnaire form , during an interview with the participants . In this study , most enrolled patients ( 6 out of nine ) were selected from the cohort of pregnant women with rash [16] , with exception of two men and one woman who went to the consultation in the non-pregnant branch of the Acute-Fever Illnesses Clinic of Fiocruz [31] . The age distribution of the nine patients is consistent with the overall age profile of the clinic . The inclusion criteria was based on the presence of pruritus/itching rash as they were identified as symptoms that can potentially help in distinguishing ZIKV from other arboviral infections [31] . A standard case report form was utilized to record information about demographics and clinical features . Urine and saliva samples were asked for all the enrolled patients , but four patients did not managed to collect saliva . Numbers of days from the first reported symptom ( days after symptoms onset ) and main signs and symptoms were recorded . Urine and saliva samples investigated in this study were collected from January 14th to February 2nd , 2016 . Saliva and urine specimens were collected in 50 mL sterile certified , DNase-/RNase-free tubes , and after collection , in some cases , the pH was measured by a digital pH meter , in order to investigate the relevance of the pH for viral infection . Twenty five millimeter diameter sterile syringe filters with a 0 . 22 μm pore size were used to filter the specimens . The samples were aliquoted for subsequently analysis and assays , as infection in Vero cell culture and RNA isolation . The African green monkey kidney ( Vero ) cell line ( ATCC- CCL81 ) was grown in 37°C , under an atmosphere containing 5% CO2 , in Earle’s 199 medium supplemented with 5% fetal bovine serum ( FBS ) and 40 μg/ml of gentamicin . The Vero cells were seeded at a density of 40 , 000 cells/cm2 in 25 cm2 culture flasks 24 hours before inoculation . The urine and saliva samples were diluted in Earle’s 199 medium supplemented with 5% FBS ( 1:2 and 1:4 ) , and 1 mL of each dilution was inoculated onto Vero cells monolayer . After 1 h incubation at 37°C , the inoculum was removed and replaced by 10 mL culture medium in the presence of 40 μg/ml of gentamicin . As negative control for each experiment , Vero cells seeded in one culture flask were mock inoculated with culture media . The presence of infectious viral particles was controlled by observation of cytopathic effects ( CPE ) . Vero cells were seeded at a density of 40 , 000 cells/cm2 in 6-well plates 24 h before inoculation . Dilutions of the biological specimens ( 1:2 , 1:4 and 1:8 ) in culture media were used to infect monolayers ( 200 μL/well ) . After 1 h incubation at 37°C , the inoculum was removed and replaced by 3 mL of 2 . 4% CMC ( carboxymethyl cellulose ) in Earle’s 199 medium . After 7 days incubation at 37°C , cells were fixed with 10% formaldehyde , washed , and stained with 0 . 4% crystal violet for visualization of plaques . Viral RNA was isolated from 140 μL of each biological specimens and cell culture supernatant using the QIAamp Viral RNA Mini Kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s recommendations . RNA was eluted in 60 μl of AVE buffer and stored at -80°C until use . The concentration and purity of each RNA sample were measured by Thermo Scientific NanoDrop 8000 Spectrophotometer and Agilent 2100 Bioanalyzer using the Agilent RNA 6000 Nano Kit according the manufacturer’s instructions . The viral RNA was reverse transcribed applying the Superscript IV First-Strand Synthesis System ( Invitrogen ) using random hexamers according to the manufacturer’s recommendations . The reverse transcription reaction was carried out at 23°C for 10 min , 55°C for 10 min and 80°C for 10 min . Further , the viral RNA was amplified by conventional PCR using GoTaq Green Master Mix ( Promega ) according to the manufacturer’s recommendations . The set of primers utilized in this procedure were: ZK3F , 5' GCTACTGGATTGAGAGTGAGAAG 3' , and ZK2R , 5' CTCAGAGATGGTCCTCTTGTTC 3´ for ZIKV; CHIK E1 F , 5´TACCCATTCATGTGGGGC3´ and CHIK E1R , 5´GCCTTTGTACACCACGATT 3´ [32]; and DEN F , 5´ TCAATATGCTGAAACGCG CGAGAAACCG 3´ and DEN R , 5´ TTGCACCAACAGTCAATGTCTTCAGGTTC3´ for DENV [33] . The thermocycling program set up in a Veriti 96 Well thermocycler ( Applied Biosystem ) was 1 cycle of 95°C for 5 min; 40 cycles of 95°C for 40 sec , 50°C for 40 sec , 72°C for 30 sec; 1 cycle of 72°C for 10min and hold of 4°C . 10 ml of Amplified products were detected by electrophoresis on a 2% agarose gel , visualized by ethidium bromide staining UV . To discard co-infection of ZIKV with dengue and/or chikungunya viruses , we analyzed the urine , saliva samples and the viral strains isolated from Vero cell using he NAT- Dengue , Zika and Chikungunya discriminatory kit ( Instituto de Biologia Molecular do Paraná and Fundação Oswaldo Cruz , Brazil ) . To measure genomic ZIKV load , viral RNA was reverse transcribed and amplified using the TaqMan Fast Virus 1-Step Master Mix ( Applied Biosystems ) in an Applied Biosystems StepOnePlus Instrument . For each reaction we used 400 nM forward primer ( 5’-CTTGGAGTGCTTGTGATT-3’ , genome position 3451–3468 ) , 600 nM reverse primer ( 5’-CTCCTCCAGTGTTCATTT-3’ , genome position 3637–3620 ) and 250 nM probe ( 5’FAM- AGAAGAGAATGACCACAAAGATCA-3’TAMRA , genome position 3494–3517 ) . The sequences of this primer set were kindly provided by Isabelle Lepark-Goffart ( French National Reference Centre for Arboviruses , IRBA , Marseille , France ) . Samples were run in duplicate . The reverse transcription was performed at 50°C for 5 minutes . The qPCR conditions were 95°C for 20 seconds , followed by 40 amplification cycles of 95°C for 15 seconds and 60°C for 1 minute . Copy numbers of ZIKV genomic RNA were calculated by absolute quantitation using a standard curve for each run . To construct a standard curve , we cloned an amplicon comprising the genomic region 3085–4032 of the isolate Rio-U1 using pGEM-T Easy Vector ( Promega ) to serve as a template for in vitro transcription . The RNA transcript was made with mMessage mMachine High Yield Capped RNA Transcription Kit ( Invitrogen ) using T7 enzyme and purified using MEGAclear Kit ( Ambion ) according to manufacturer’s instructions . The purity of the transcript was verified using NanoDrop 8000 Spectrophotometer ( Thermo Scientific ) , the integrity was analyzed using 2100 Bioanalyzer ( Agilent ) using the RNA 6000 Nano Kit ( Agilent ) , and the concentration of the RNA was accessed using Qubit 2 . 0 Fluorometer ( Invitrogen ) . The standard curve was generated by a ten-fold dilution ( ranging from 10 to 109 copies/reaction ) of the transcript . The limit of detection under standard assay conditions was approximately 40 viral RNA copies/mL . Viral RNA samples were obtained from the first passage of Vero cell isolates from urine of patient 1 and saliva of patient 6 . Double-stranded cDNA libraries were prepared using the TruSeq Stranded mRNA LT Sample Preparation Kit ( Illumina , San Diego , CA , USA ) . Briefly , the polyA containing mRNA purification step was not performed and the protocol was started with 25–35 ng of RNA in 5 ul of molecular biology grade water to which were added 13 ul of Fragment , Prime , Finish Mix . The remaining steps of the protocol were carried out without any modifications . Library quality control was performed using the 2100 Bioanalyzer System with the Agilent DNA 1000 Kit ( Agilent , Santa Clara , CA , USA ) . The libraries were individually quantified via qPCR using a KAPA Library Quantification Kits for Illumina platforms ( KAPA Biosystems , Wilmington , MA , USA ) . The libraries were pooled together in equimolar quantities and sequenced . Paired-end reads ( 2 × 75 bp ) were obtained using a MiSeq Reagent Kits v3 ( 150-cycles ) in a MiSeq sequencing system ( Illumina ) . A total of 17 , 413 , 830 reads was generated for Rio-U1 sample and 21 , 734 , 486 for Rio-S1 sample . Related reads to Chlorocebus sabaeus have been filtered using Bowtie2 and Samtools , remaining 12 , 614 , 062 reads of Rio-U1 and 12 , 943 , 134 of Rio-S1 . Both genomes were assembled using Ray 2 . 20 ( k = 31 ) . The completed genome of Rio-U1 has 10 , 795bp ( Accession number KU926309 ) and Rio-S1 has 10 , 805bp ( Accession number KU926310 ) . Gene prediction was performed by GenemarkS 4 . 17 . Mature peptides were identified by blastp against the protein annotated in reference sequence NC_012532 . Nucleotide sequences encoding the precursor polyprotein of 39 ZIKV strains and 1 of DENV 4 were aligned using the Clustal W [34] . Evolutionary analysis was performed as described elsewhere [15] . Phylogenetic studies were carried out using the Maximum Likelihood method based on the General Time Reversible model [35] of the MEGA7 software [36] . Evolutionary history of these sequences was represented by bootstrap consensus tree ( from 1000 replicates ) , in a traditional branch style . Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed . Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood ( MCL ) approach , and then selecting the topology with superior log likelihood value . A discrete Gamma distribution was used to model evolutionary rate differences among sites ( 5 categories; +G , parameter = 0 . 9645 ) . The rate variation model allowed some sites to be evolutionarily invariable ( [+I] , 37 . 8665% sites ) . The analysis involved 40 nucleotide sequences . All positions with less than 95% site coverage were eliminated . There were 10247 positions in the final dataset . Timetree was inferred by Reltime method [37] from MEGA , using GTR ( G+I , 5 categories ) , partial deletion with site coverage cutoff of 95% .
We examined nine enrolled patients suspected of ZIKV infection . The initial medical support and collection of urine and saliva samples were performed from January 14th to February 2nd 2016 . Out of seven women , six were pregnant with gestational ages varying from 18 to 33 weeks , median value of 20 . 5 ± 5 . 8 weeks ( Table A and B in S1 Text ) . The female patient ages ranged from 20 to 42 years old ( median value of 28 . 5 ±7 . 4 years ) and the male patient ages were 24 and 68 years old . All the patients live in the metropolitan area of Rio de Janeiro ( Table C and D in S1 Text ) . The most frequent sign of ZIKV disease was pruritic maculo papular rash which lasted in average 4 days ( Table A and B in S1 Text ) . However , other clinical symptoms were also prevalent , such as low-grade fever ( < 38°C ) , headache , myalgia and arthralgia of large and small joints , present in 5 out of 9 patients . We collected and analyzed urine from patients 1 to 4 and both urine and saliva samples from patients 5 to 9 . Vero cells cultures were inoculated at the same date of sample collection and then daily observed through inverted microscopic examination until the appearance of cytopathic effect ( CPE ) . Within one week of incubation , only two samples exhibited CPE ( 2 out of 14 ) , the urine sample of patient 1 with CPE detected at 4th day of post-inoculation ( 1 out of 9 ) and the saliva sample of patient 6 at 5th day post-inoculation ( 1 out of 5 ) . In this last infection , we recognized small foci of rounded and refractive cells detaching from the monolayer ( Fig 1A and 1B ) . After one-week incubation , we proceeded to split cells from negative cultures by means of trypsinization when monolayer was confluent . This procedure was repeated for three consecutive times . Nevertheless , it was not possible to isolate ZIKV in these samples , neither by detecting CPE in Vero cell monolayers or ZIKV genome by RT-PCR . We also analyzed these samples by plaque forming assay as a way to detect infectious virus particles . Unfortunately , we did not perform this analysis with urine of patient 1 , because we received a small aliquot of this specimen . Nevertheless , we detected viral plaques from samples of patient 6 ( Fig 1C and 1D ) , in which the dilution 1:2 of saliva originated in 8 PFU resulting in an original viral concentration of 80 PFU/ml in saliva of patient 6 . Interestingly , only one viral plaque was visualized by means of this methodology in urine sample of this patient 6 , resulting in a titer of 10 PFU/ml . Furthermore , we analyzed all urine and saliva specimens by RT-PCR to confirm the detection of ZIKV ( Fig 2A and 2B ) . In addition , we included RNA samples of ZIKV isolated from patient 1 and 6 in Vero cells . The set of samples of patient 1 and 6 were all positive and an expected-amplicon band of around 300 bp was seen in electrophoretic analyses , demonstrating the presence of ZIKV genome in these samples ( Fig 2A and 2B ) . We also observed a faint band from urine and saliva of patient 9 ( Fig 2B ) . The ZIKV specificity of this approach was confirmed when we tested this protocol in RNA samples of Chikungunya ( CHIKV ) , dengue ( DENV ) and yellow fever ( YFV ) viruses ( Fig 2C ) . Notwithstanding , it was mandatory to confirm the result of Zika virus infections in patients and isolations in Vero cells , since ZIKV , DENV and CHIKV are co-circulating in Brazil and the diseases caused by them exhibit similar symptoms . So , each sample was tested for the presence of these three viruses by the ZIKV nucleic acid testing ( NAT ) of samples which was established to be routinely used in Brazil as diagnosis test since December 2015 . ( Table 1 ) . All patients included in this study were negative for DENV and CHIKV ( Ct > 40 . 0 ) . Patient 1 was positive for ZIKV in urine ( Ct of 30 . 02 ) and patient 6 in urine ( Ct of 25 . 56 ) and saliva ( Ct of 30 . 27 ) and the viral isolates derived obtained from specimens of these patients were also positive and presented Ct of 12 . 62 and Ct of 20 . 88 , respectively . Patient 9 was also positive for ZIKV in urine specimen ( Table 1 ) , whereas urine from patient 7 presented amplification in a late cycle and , therefore , this result was considered inconclusive . To validate negative results , the ribosomal 18S RNA was detected in all samples showing that there was no inhibition of the RT-PCR . Viral loads of these samples were then measured by a RT-qPCR assay resulting in data consistent with those obtained by the diagnosis assay kit ( Table 1 and Fig 3 ) . Accordingly , the highest viral loads were obtained from those specimens that allowed us to isolate ZIKV by Vero cell infections . The urine of patient 1 exhibited a ZIKV-genomic RNA copies of 2 . 68 x 103 per ml whereas the patient 6 displayed 2 . 53 x 105 ZIKV RNA copies per ml in urine and 7 . 44 x 104 ZIKV RNA copies per ml in saliva . As expected for isolated viral samples , we observed an increase of genomic ZIKV RNA copies in Vero-cell- isolated samples , in which the isolated from patient 1 presented 1 . 24 x 1010 copies/ml and patient 6 , 2 . 88 x 109 copies/ml ( Fig 3 ) . Furthermore , we confirmed positivity of the urine from patient 7 ( 102 copies/ml ) and the positive detection of ZIKV RNA in saliva ( 40 copies/ml ) and urine ( 431 copies/ml ) of patient 9 , although this established value is borderline localized in the limit of detection . The genomic sequences of Vero cell isolates ZIKV Rio-U1 strain ( KU926309 ) , isolated from urine and Rio-S1 ( KU926310 ) strain , isolated from saliva , were then determined . The comparison between Rio-U1 and Rio-S1 yielded 99 . 61% identity , displaying six amino acid variations in the viral proteins ( Table 2 ) . For phylogenetic analysis , we used nucleotide sequences coding the complete ZIKV polyprotein . We observed that all sequences sampled in the Americas form a robust monophyletic cluster ( bootstrap score = 97% ) within the Asian genotype and share a common ancestor with the ZIKV strain that circulated in French Polynesia in November 2013 and remained genetically isolated from African clusters ( Fig 4 ) . Phylogenetic analysis of the isolated viruses exhibiting the highest identity of ZIKV strain Rio-U1 with KU501216 . 1 and KU501217 . 1 both from Guatemala ( 99 . 7% identity ) , isolated also related with the first reported autochthonous transmission of ZIKV in Brazil [38] . Whereas Rio-S1 presented 99 . 7% of identity with KU527068 . 1 , isolated in Brazil from a Zika-associated microcephaly case [14] .
Infective ZIKV particles exists in urine and saliva of patients . We reported this evidence for the first time in a worldwide press release on February 5 , 2016 . We communicated our data before the Carnival in Brazil ( from February 6th to 10th ) , because we were very concerned about the risk of pregnant women to be exposed to ZIKV in an event involving crowds and also considering the global emergency declared by WHO . Part of our discovery , the viral isolation from saliva , was further confirmed in case report study of a patient who developed a febrile illness after returning from the Dominican Republic to Italy [39] . In our study , we also demonstrate the occurrence of infectious Zika viral particles in urine besides of saliva of patients . Moreover , we also showed that the saliva of an acute phase patient may have a viral concentration of 80 PFU/ml . The isolation of two ZIKV samples from urine and saliva was associated with ZIKV load in infected patients during the acute phase . Actually , the presence of ZIKV genome in urine is not a novelty . Hence , former studies preconized the use of urine and saliva for ZIKV RNA detection and diagnosis [27 , 30] , since ZIKV genome was more frequently identified in saliva and urine compared to blood . Furthermore , the finding of flaviviral genome in urine was earlier described in Dengue [40] , Yellow Fever [41] , St . Louis Encephalitis [42] , Japanese Encephalitis [43] , and West Nile viruses [44] . Dengue genome was also detected in saliva of infected patients [40] . Interestingly , the existence of excreted-infectious West Nile particles in the urine of acute phase patients was earlier described in conjunction with their isolation in Vero E6 and in BHK21 cells [45] . Particularly , ZIKV isolation was approached by many groups utilizing Vero cells ( GeneBank: KJ776791; JN860885; KU647676 ) . Therefore , we adopted this cell model to detect , amplify and quantify viable ZIKV straight from patient’s samples of urine and saliva . The recovery of ZIKV from these urine and saliva was effective in two of nine patients whose viral load were clearly detectable . Interestingly , despite the fact that the viral load found in the urine of patient 1 was considerably lower , around one hundred times , than the equivalent sample in patient 6 , we only recovered virus from urine of the former ( Rio-U1 strain ) . On the other hand , recovering of infective ZIKV from patient 6 , the Rio-S1 strain , was successful using the saliva sample , but not with urine one , even though the highest number of copies has been established in urine . Concordantly , we detected in this analysis a superior number of plaques in plaque assay of saliva . Viral detection and recovery from urine and saliva of ZIKV patients might be firstly related to the severity of infection as well as the period of specimen collection after the onset of Zika symptoms . The detection of ZIKV RNA in saliva improved the diagnosis in the first week from the disease onset [30] . But ZIKV viruria persists for longer periods after disease beginning and , in some cases , for longer than two weeks from Zika onset [27] , as described in the two recently reported cases of Guillain–Barré syndrome occurred in Martinica [46] . However , it is necessary to perform additional clinical studies associating disease onset , severity of symptoms and viral persistence in urine and saliva to better clarify this point . Another aspect in viral recovering deals with the physiological pH found in saliva and urine . Hence , pH in urine varies from 4 . 5 to 8 . 0 while saliva assumes values near neutral pH . It is well known that the flavivirus envelope protein E undergoes irreversible conformational changes at a mildly acidic pH ( below 6 . 5 ) , a process naturally occurring in the viral membrane fusion in endosomes [47] . These structural changes are irreversible , and outside of cellular environment , provoke loss of infectivity and hemagglutination activity as well as virus aggregation due to increased hydrophobicity [48] . Thereby , we suggest that the failure of recovering ZIKV strain in Vero cells propagation from the urine of patient 6 would be due to the inactivation of most ZIKV due to exposition of the acidic pH value of 5 . 6 of this urine specimen . The infectious virus number was lower , at least proportionally to the viral RNA copies presented in this fluid , when compared to saliva of the same patient . We do not establish the pH of patient´s 1 urine , due to volume sample limitations . The importance of ZIKV in urine for human transmission is unexplored , but the effect of acidic pH on viral viability might represent a serious restriction for viral spreading . In West Nile Virus when a similar urine excretion occurs , it is considered that the presence of infectious particles would represent a real risk for inter human transmission through kidney transplantation [45] . In reference to the occurrence of viable ZIKV in saliva , a large range of viruses can be identified in this specimen , such as Cytomegalovirus , Ebola virus , Enteroviruses , Hepatitis B virus , Hepatitis C virus , Human herpesviruses , HIV , Human papillomavirus , Influenza virus , Measles virus , Rhinoviruses and Rubella virus [49 , 50] . As previously mentioned , Zika and dengue virus were also discovered in saliva [30 , 40] . Although , the presence of intact viral particles in saliva do not distinguish viable virus from noninfectious virus . However , for the first time , we could well identify ZIKV plaque forming units from saliva of an infected man in Vero cell monolayers with a titer corresponding to 80 PFU per ml . Essentially , another important subject is that the existence of viable virus in oral fluid samples does not always indicate that the virus can be transmitted orally and become epidemiologically relevant . Actually , viral infections of the oral cavity are relatively rare , since saliva contains antiviral molecules and is relatively hypotonic being capable of lysing enveloped viruses [51] . Perhaps , the established proportion of approximately 1 PFU to 1 , 000 ZIKV RNA copies in saliva of one patient was modulated by these host factors . Although saliva functions as a protective barrier for virus entry , some studies have shown that a disruption in oral mucosa or periodontal disease can facilitate virus entry [52] . Since previous studies detected Flaviviruses as Dengue [53 , 54] and Zika [30] virus in saliva , and our study have demonstrated possible infectious ability of Zika viral particles in saliva , a potential person-to-person Zika virus infection through this specimen , using a disrupted oral mucosa or periodontal pockets as virus entry , should be considered and investigated . ZIKV is an emergent vector-borne disease , but fast growing evidence points to an increased relevance of its non-vector ways of transmission , as perinatal and transplacental transmission occurs from mother to child [14 , 23] . Additionally , ZIKV genome was also detected in breast milk , followed by viral isolation of infective viral particles [29] . Moreover , cases of probable sexual transmission have been reported with association of ZIKV in semen [25 , 26] . In addition , viral contamination linked to blood transfusion and organ transplantation have been previously discussed [55] . Furthermore , reports of laboratorial infection or bites of animals was associated to the transmission [56] . Finally , evidence of vertical and/or venereal transmission between mosquitoes was supported by the detection of ZIKV natural infection in males Ae . furcifer [19] . We compared the complete coding sequences obtained in this study with public sequence data from Zika virus representative of the isolates from three distinct genotypes in Asian , West African , and East African in addition to isolates from recent outbreak in Americans . Similarly to the sequences described in the recent widespread epidemic of ZIKV in the Americas , the sequences Rio-S1 and Rio-U1 from ZIKV isolated in this study clustered with the Asian clade , covering sequences from New World , Pacific , Micronesian and Malaysian strains . Since surveillance programs have reported periodic circulation of the ZIKV virus since 1968 , with high frequency activity varying an interval of 1–2 years added to fact that RNA virus evolve fast , their host and vector broad range , non-vector transmission , and particularly risk of neurotropic and teratogenic outcomes , the molecular epidemiologic vigilance is crucial to solve this questions . In conclusion , the detection of infective ZIKV in saliva and urine of patients deserves a more detailed study to establish whether or not these fluids contribute to viral transmission . Surely , these findings will be extremely relevant to prevent and control ZIKV transmission .
|
The American continent has recently been the scene of a devastating epidemic of Zika virus and its severe manifestations , such as microcephaly in newborns and Guillain-Barré Syndrome . Zika virus , first detected in 1947 in Africa , only from 2007 started provoking outbreaks . Zika , dengue and chikungunya viruses are primarily transmitted by Aedes mosquitoes . Dengue has been endemic in Brazil for almost 30 years , and the country is largely infested by its main vector , Aedes aegypti . Chikungunya virus entered the country in late 2014 and Zika presence was confirmed eight months later . Nevertheless , Zika notifications multiplied and spread across the country with unprecedented speed , raising the possibility of other transmission routes . This hypothesis was strengthened by some recent reports of Zika sexual transmission in Ae . aegypti-free areas and by the description of a low transmission efficiency to Zika virus in local Ae . aegypti . We found Zika active particles in both urine and saliva of acute phase patients , and a finding that was promptly announced by Fiocruz via Press Conference on February 5 , 2016 . In this work , we bring up the potential alternative person-to-person infection routes beyond the vectorial transmission , that might have epidemiological relevance .
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2016
|
Isolation of Infective Zika Virus from Urine and Saliva of Patients in Brazil
|
Mass anthelmintic drug administration is recommended in developing countries to address infection by soil-transmitted helminthiases ( STH ) . We quantified the public health benefit of treatment with mebendazole in eight million Vietnamese children aged 5–14 years from 2006 to 2011 . This was compared to the environmental impact of the pharmaceutical supply chain of mebendazole , as the resource use and emissions associated with pharmaceutical production can be associated with a public health burden , e . g . through emissions of fine particulate matter . Through Markov modelling the disability due to STH was quantified for hookworm , Ascaris lumbricoides and Trichuris trichiura . For each worm type , four levels of intensity of infection were included: none , light , medium and heavy . The treatment effect on patients was quantified in Disability-Adjusted Life Years ( DALYs ) . The public health burden induced by the pharmaceutical supply chain of mebendazole was quantified in DALYs through Life Cycle Assessment . Compared to ‘no treatment’ , the modelled results of five-year treatment averted 116 , 587 DALYs ( 68% reduction ) for the three worms combined and largely driven by A . lumbricoides . The main change in DALYs occurred in the first year of treatment , after which the results stabilized . The public health burden associated with the pharmaceutical supply chain was 6 DALYs . The public health benefit of the Mass Drug Administration ( MDA ) averted substantially more DALYs than those induced by the pharmaceutical supply chain . These results were verified in a sensitivity analysis . The starting prevalence for each worm was the most sensitive model parameter . This methodology is useful for policymakers interested in a holistic approach towards the public health performance of MDA programs , enveloping both the treatment benefit received by the patient and the public health burden associated with the resource consumption and environmental emissions of the pharmaceutical production and supply chain .
Every year , millions of children from developing countries receive medicines donated through the World Health Organization ( WHO ) . In 2016 , 1 . 3 billion tablets were shipped for the treatment of lymphatic filariasis , soil-transmitted helminthiases ( STH ) and schistosomiasis [1–3] . This study focuses on STH , which comprises four nematode infections: Necator americanus , Ancylostoma duodenale , Ascaris lumbricoides ( roundworms ) and Trichuris trichiura ( whipworms ) . The first two are frequently combined and referred to as hookworms . In 2010 , STH affected 1 . 45 billion people worldwide and is associated with high morbidity due to abdominal pain , anaemia and malabsorption of nutrients [4] . Children from the poorest developing countries are the most impacted by this disease , and 875 million children were reported to require annual treatment in 2012 [5] . In 2012 , certain pharmaceutical companies , non-governmental organizations ( NGOs ) , governments and banks signed the London Declaration on Neglected Tropical Diseases , committing to supply the necessary drugs to achieve control of STH by 2020 [6] . The global target is “to eliminate morbidity due to soil-transmitted helminthiases in children by 2020 , by regularly treating at least 75% of the children in endemic areas” [7 , 8] . In areas with high ( >50% ) prevalence of STH , the WHO recommends anthelmintic drug treatment every six months for school-aged children [7] . The effect of these treatments on the prevalence of STH is reported in multiple studies and the global public health impact of STH has been mapped in the Global Burden of Disease ( GBD ) [1 , 9–12] . The pharmaceutical production , distribution and disposal of these anthelmintic drugs requires significant resources and causes emissions of hazardous compounds , which are associated with an effect on global human health , e . g . through emissions of fine particulate matter , which can be seen as a public health burden and quantified through Life Cycle Assessment ( LCA ) [13–15] . This environmental impact is currently not assessed along with the Mass Drug Administration ( MDA ) programs . However , the WHO recognizes that environmental factors ( e . g . air pollution and Climate Change ) are responsible for 22% of all global mortality and morbidity [16] . A holistic evaluation should compare both the public health benefit of MDA programs and the contribution to the public health burden attributable to the environment associated with the pharmaceutical production , distribution and disposal of the medicines [17] . The aim is to quantify and compare the public health benefit and burden for mebendazole MDA in Vietnam by using a common metric: the Disability-Adjusted Life Year ( DALY ) [18] . A DALY is equivalent to one healthy life year lost . This study focuses on Vietnam , building on a previously published model of STH prevalence progression after treatment with anthelmintic drugs , which are supplied to Vietnam through the MDA program [9 , 19] . While multiple anthelmintic drugs are donated for the treatment of STH , data from the pharmaceutical supply chain was only collected for mebendazole . This demonstration study aims to provide a first insight on both the public health benefit for patients and public health burden attributable to the environment of MDA programs .
The mebendazole MDA program , including treatment with mebendazole every six months for five years was compared with a ‘no treatment’ group where patients were not treated and worm prevalence and morbidity were assumed to stay constant over time on a population level [2] . The morbidity in the treated group was calculated on a yearly basis . The number of children infected by each separate worm type and intensity of infection at each year were multiplied with the disability associated with that specific infection . The prevalence and number of infected children were able to change over time , but the disability associated with each infection state is fixed . The public health burden of mebendazole Mass Drug Administration ( MDA ) was analysed through Life Cycle Assessment ( LCA ) methodology , which considered the full cradle-to-grave impact of the pharmaceutical supply chain . The resource use and emissions associated with the production of the medicine are linked to environmental cause-effect chains , enabling the quantification of the environmental impact [61–63] . For example , burning natural gas leads to an emission of CO2 ( kg ) , which is associated with a radiative forcing in the atmosphere ( W/m2 ) , causing a global mean temperature increase ( °C ) . The latter may lead to disability through direct effects of ( extreme ) heat or cold , foodborne and waterborne diseases , vector-borne diseases , natural disasters and risk of malnutrition [64 , 65] . In this study we focus on the environmental impact which causes a global Human Health burden [17 , 66] .
The modelled public health outcomes of five years mebendazole MDA compared to the baseline are listed in Table 6 . The DALYs for the A . lumbricoides and T . trichiura treatment groups decreased over five years , while the DALYs of the hookworm treatment group increased and the DALYs of the ‘no treatment’ group stayed constant over time . This way mebendazole MDA averted 119 , 088 DALYs ( 89 . 32% reduction ) for A . lumbricoides and 10 , 575 DALYs ( 70 . 46% reduction ) for T . trichiura compared to the ‘no treatment’ group . For hookworm , there was an increase in DALYs of 13 , 076 ( 55 . 02% increase ) , which is a counterintuitive result as mebendazole is known to have efficacy against hookworm in terms of egg reduction rate , and thus morbidity [25 , 77–79] . In total 116 , 587 DALYs are averted for the three worms combined , which is a reduction of 67 . 74% . The treatment effect was the most pronounced in the first year . After an initial drop in disability the values stabilized over time because an equilibrium with reinfection was reached [26] . Fig 3 and Fig 4 provide more detail and display respectively the STH prevalence and DALYs attributable to both intensity of infection and cause of disability over five years of mebendazole MDA . Fig 3 shows that compared to the initial baseline values before treatment , the total prevalence absolutely decreased with 8 . 61% for hookworm , 56 . 18% for A . lumbricoides and 21 . 04% for T . trichiura . The prevalence of heavy infection in all three worms reached 0% after one year of treatment . From the top part of Fig 4 it is notable that for A . lumbricoides and T . trichiura all DALYs are attributable to moderate and heavy infection at baseline , while after the first year of treatment all DALYs are attributable to moderate infection . For hookworm light infection is responsible for 83 . 79% of disability at baseline , while for later treatment years light infection and moderate infection are each responsible for around 25% and 75% of the DALYs , respectively . These trends can also be seen in the bottom part of Fig 4 , where the DALYs attributable to mild abdominopelvic problems and symptomatic infection correspond with the public health burden of respectively moderate and heavy infection . For hookworm however , the largest share of DALYs at baseline can be attributed to anaemia , while after the first year of treatment mild abdominopelvic problems are responsible for the largest amount of DALYs . At year 5 , the fractions of DALYs averted from moderate infection compared to baseline were 88 . 15% and 68 . 77% for A . lumbricoides and T . trichiura , respectively . For hookworm , the DALYs from moderate infection increased with 687 . 2% . After the first year of treatment , 100% of the DALYs due to heavy infection are averted for all three worms . The results of the scenario analysis on the percentage of anaemia attributable to hookworm are displayed in Table 7 . In the most conservative scenario where 0% of anaemia is attributable to hookworm , the DALYs due to hookworm increased with 19 , 726 ( negative DALYs averted ) over five years of mebendazole MDA . The public health burden associated with the pharmaceutical supply chain is provided in Table 8 . A public health burden of 6 . 46 DALYs was caused by the production , distribution and disposal of 64 million mebendazole tablets . API synthesis represented the largest impact ( 86 . 41% ) with electricity and chemical reagents as the main drivers . Formulation of the tablets was responsible for the second largest impact , mainly due to electricity use . A table with results on midpoint impact categories can be found in Table E in S1 Text . When comparing the public health benefit to the burden , even when including the theoretical increase in DALYs from hookworm infection , 18 , 035 times more DALYs were averted than created . A one-way and probabilistic sensitivity analysis were carried out , to be found in Table F and Figure F , G , H and I in S1 Text . The conclusion did not change; in all cases the public health benefit because of mebendazole Mass Drug Administration was larger than the public health burden associated with the pharmaceutical supply chain . The starting prevalence for each worm was the most sensitive model parameter .
The treatment of eight million Vietnamese children with mebendazole for five years substantially decreased STH prevalence and averted 116 , 587 DALYs for the children compared to a ‘no treatment’ group , which is a reduction of 67 , 74% . To do this , 64 million mebendazole tablets are required , creating 6 . 46 DALYs associated with the pharmaceutical supply chain . A factor 18 , 035 more DALYs were averted than created . The increase in DALYs from hookworm infection is considered a counterintuitive result . This is one of the first attempts to compare the public health benefit of health care MDA programs to the public health burden associated with resource use and emissions of the pharmaceutical supply chain , using a common metric . From an LCA perspective , there is a growing interest to quantify the benefit or handprint of products , rather than focus only on the environmental burdens [80 , 81] . Simultaneously there is a willingness to include environmental assessments in health care decision making . A recent survey indicates that 71% of health care decision makers think the criteria of environmental impact should be considered when making decisions on health care interventions [82] . These developments suggest that there is support for a more holistic approach of health care interventions , which we aimed for in this study . We aimed towards a transparent calculation of DALYs , using a previously published Markov model . The model was then linked with literature on the disability associated with STH . The cradle-to-grave scope of the Life Cycle Assessment included the full pharmaceutical supply chain . The public health burden was based on multiple primary data sources . This holistic approach , combining different fields of research , could allow program managers to estimate the net public health performance of MDA programs . The following limitations of the study should be noted . The modelling approach that was taken in this study was based on imperfect information . We aimed to provide the data inputs for the model through literature review , but given the paucity of certain data regarding STH some inputs were adopted from sources or settings that differ from the one in this study . For example , the Markov model transition probabilities were based on a study in Pemba island , Tanzania , instead of Vietnam . A full overview of model inputs , assumptions and limitations can be found in Table B , C and D in S1 Text . The transition probabilities of the Markov model adopted in this study cause the prevalence of light , moderate and heavy infection to reach steady state values over five years treatment , regardless of the initial setting , i . e . starting prevalence [83] . In the case of moderate hookworm infection the starting prevalence ( 0 . 67% ) is lower than the steady state prevalence ( 5 . 28% ) , causing a theoretical increase in both prevalence and DALYs , rather than a decrease . We consider this a counterintuitive result and the main limitation of the Markov model: it reports a decrease in health status , rather than an increase , when the starting prevalence is lower than the steady state value reached over time . The relatively high steady state value after treatment for moderate hookworm may have been influenced by the high untreated hookworm prevalence in the population on which the TPMS 4 transition probabilities were based . In that study , the initial prevalence of moderate hookworm was 20 . 02% , with a total hookworm prevalence of 70 . 02% in 1324 children , which is 45 . 42% higher than the total hookworm prevalence in this study ( A . Montresor , personal communication ) . These findings were also confirmed in a 1994 study in 3595 children from the same Pemba island , Tanzania , which reported moderate hookworm infection at 13 . 30% , with a total hookworm prevalence of 93 . 73% [37] . For A . lumbricoides and T . trichiura the initial prevalence in the population on which the model was based was 75 . 00 and 26 . 96% , respectively . These values are respectively 12 . 40 and 0 . 96% higher than the initial prevalence from Vietnam used in this study . It should also be noted that the source for STH prevalence in this study is an average of multiple provinces in Vietnam , which ignores the high variability across regions . From the one-way and probabilistic sensitivity analysis in S1 Text it can be seen that a 50% relative change in starting prevalence can have a marked influence on the results . A what-if analysis showed that , all other values held constant , increasing the starting prevalence of moderate hookworm from 0 . 67% to 3 . 15% causes the total DALYs averted for hookworm to become 0 . Further increasing said starting prevalence leads to positive DALYs averted . If the initial untreated prevalence is lower than the steady state values , it could be argued to keep the values constant from a model perspective , rather than let them increase . However , for the sake of transparency , we did not adapt the model . Another peculiar part of the transition matrix is the fact that for all three worms the probability of going from heavy infection to no infection is 1 , instead of the more gradual decrease from heavy to moderate and then light infection that could be expected . Aside from model considerations , the efficacy of mebendazole to treat hookworm has been reported as highly variable by Keiser et al . ( 2008 ) and more recently by Moser et al . ( 2017 ) . As a result , combining hookworm data from different sources can possibly lead to higher prevalence after treatment compared to the baseline . Considering the limitations of the model , the results for hookworm are unlikely and should be conservatively interpreted . The counterintuitive result for hookworm may lead to certain skepticism regarding the results for A . lumbricoides and T . trichiura . In this case , the cause for said counterintuitive result is a methodological weakness of the Markov model concept . The transition probabilities of a Markov model that predicts a treatment effect are always influenced by the pre-treatment situation , in this case the prevalence . The Markov model we used in this study was based on children in Tanzania , and while the starting prevalence for A . lumbricoides and T . trichiura in Vietnam were in line with the values from Tanzania , as discussed earlier , those for hookworm were not . Therefore the results for hookworm should be interpreted conservatively , but that does not follow for A . lumbricoides and T . trichiura . We applied a static transmission model ( constant probabilities ) , which has limitations compared to dynamic transmission modelling when considering communicable diseases [84] . While the linear transition probabilities in the Markov model allowed the prevalence of STH to reach a steady state value after year 2 , MDA may actually lead to a complete elimination of the disease , which could be captured with the non-linear structure of a dynamic transmission model . Dynamic models may also include the indirect effects of treatment that arise from averted infections: while this study focuses on the treatment of children aged 5–14 , it is possible that as a result younger children or adults have a reduced risk of infection because a lower fraction of the population is infected . Because of that , our results may be an underestimation of the treatment effect . Considering the specific case of comparing the public health benefit and burden of MDA programs , our findings clearly indicate that the benefit outweighs the burden . In that regard , applying a dynamic model may not be necessary [84] . An exception is probably the specific case of hookworm , where the quantification of DALYs over time may have benefited from a dynamic transmission model . We aimed to use data from populations of Vietnamese children as much as possible . However , available published data was limited and in particular for the transition probabilities and assumptions regarding the prevalence of anaemia , data input originated from studies outside Vietnam: Brazil , Tanzania , Zimbabwe and Malaysia . There exist significant regional differences in the prevalence of anaemia , and although a general trend was observed across studies , Vietnamese data could have increased the validity of the outcomes . The Life Cycle Assessment excluded any health care related resource use or emissions outside the pharmaceutical supply chain , e . g . the treatment of any co-morbidities in hospitals . Mebendazole MDA might increase the overall health of children , reducing the need for hospitalization and its associated resource use . However , the direct link between STH infection and hospitalization is not clear . This potential consequential reduction in environmental impact was not taken into account , which is considered conservative . There is a real possibility that a fraction of the tablets are lost before administration to the children in Vietnam ( Janssen Pharmaceutica , personal communication ) . These losses were not taken into account , but would probably not have changed the main conclusions . Multiple anthelmintic drugs are donated for the treatment of STH , but data from the pharmaceutical supply chain was only collected for mebendazole . Because of that , other anthelmintic drugs such as Albendazole were not considered in this simulation . In Life Cycle Impact Assessment multiple methods exist [63] . We adopted the grouping of environmental midpoint ( effect ) categories to calculate results on endpoint ( damage ) given the opportunity to directly compare the public health benefit and burden of MDA programs [85] . A recent study by Montresor et al . quantified the DALYs that were averted from 2010 to 2015 by anthelmintic treatment , compared to the baseline morbidity present in 2010 due to the lack of large-scale treatment [86] . Similar to this study , the calculation of the averted DALYs is also based on the reduction in STH prevalence after treatment . The varying national coverage over the years was taken into account , contrary to the assumption in this study that the coverage remains constant . However , a more linear approach is used , linking the reduction in prevalence to a reduced morbidity through the elimination of cases of moderate and heavy intensity of infection , as defined by Marocco et al . [87] . The averted DALYs are quantified from a top-down approach adopting data from the WHO , rather than quantifying them separately for each intensity of infection [88] . The estimated fraction of averted DALYs for children aged 5–14 in the South East Asia Region is 64% , including widely varying treatment coverages . For the same age group and region , Montresor et al . estimate that 84% of DALYs could be averted by 2020 if 75% coverage is reached . The estimations in our study are more conservative and are more in line with the general South East Asia Region . The fact that hookworm theoretically increases DALYs instead of averting them in this study adds to the conservative nature of our estimate . We compared the estimated DALYs from the Global Burden of Disease ( GBD ) report due to STH in Vietnam for children aged 5–14 with the results of this study . Although it is unclear to which degree the baseline STH prevalence in Vietnam of the GBD study compares to that of our study , the results from the GBD in 2005 and 2010 are generally consistent with our Markov simulation from 2006 to 2011 for A . lumbricoides and T . trichiura . For hookworm the large decrease in DALYs from the GBD is not visible in our study , as discussed earlier . We identified one prior study that reports both patient outcomes and environmental impact for an , albeit different , pharmaceutical treatment [89] . However , as patient benefit and environmental burden are reported in two different metrics , the results of that study are not directly comparable to the outcomes of this study . The literature retrieved in this study supports the claim that hookworm infection was associated with higher anaemia prevalence . We assumed the anaemia prevalence would then decrease with deworming . However , this was not confirmed by all reviews identified in the literature . While Gulani et al . and Smith et al . report increased haemoglobin after deworming , Hall et al . reported no effect . Taylor-Robinson et al . , state that there was insufficient evidence to know whether deworming effects haemoglobin , although their approach diverges from the other reviews in that mainly the effect of MDA on an unscreened population was quantified . Due to the high number of individuals that are either not infected or have a light infection , the treatment effect on the smaller fraction of moderate and heavily infected individuals may have been diluted [30 , 36 , 57 , 58 , 90–92] . As shown in the scenario analysis , the conclusion of the study holds even with no reduction of anaemia prevalence due to treatment . For wasting there is more agreement that deworming increases weight and height , supporting the assumptions of this study . Hall et al . state that deworming leads to significant extra gains in weight and height if STH prevalence is above 50% [90] . Taylor-Robinson et al . report that treating infected children with a single deworming dose may increase weight gain over the next six months [57] . While Welch et al . note little to no improvement on weight or height 12 months after mass deworming , a subgroup analysis on children with STH prevalence >20% does suggest weight gain [56] . The results of this study suggest that MDA of mebendazole for the treatment of STH substantially averts disability , based on limited evidence for children aged 5–14 in Vietnam , even if the public health burden of the pharmaceutical supply chain is fully considered . The public health burden associated with the pharmaceutical supply chain of mebendazole is negligible when compared to the public health benefit and expressed in a common metric . This methodology may be useful if future policy would place a heavy emphasis on environmental considerations . For example , we could consider a future where the effectiveness of the treatment would not only be compared to the monetary costs ( cost-effectiveness ) but also to the environmental impact . Furthermore , consider a scenario where the limiting factor of the total national health care budget is accompanied with a limited total environmental impact e . g . through managed decline of emissions [93] . Next to cost-effectiveness , this would require a framework to simultaneously evaluate the environmental burden and the effectiveness of pharmaceutical treatments . We aimed to propose such a framework in the current study . The main limitation of this study is related to the transition probabilities of the Markov model , which reach a steady state value after several years of treatment , allowing DALYs to increase if the initial prevalence is below the steady state value , as is the case with hookworm . While the Markov model has advantages with respect to transparency and reproducibility , the latter issue could be addressed in future research by developing transition probabilities that adapt to the starting prevalence . A future study with a singular focus on the treatment benefit over time ( and not including the burden through Life Cycle Assessment ) may apply a dynamic transmission model to address the same previously mentioned issue . Quantitative evidence on the long-term influence of STH infection on developmental and cognitive abilities is required to include this disability in future models . The influence of treatment of co-morbidities , both for the patient and the environment , should be included to capture the broader public health impact of STH infection . The influence of Water , Sanitation and Hygiene ( WASH ) programs on the health of children infected with STH could be included in future studies next to deworming . Health care utilization other than deworming , e . g . hospitalization , could be included if there is a clear link with STH infection . The results of this study and their generalizability should be validated with research on other disease areas , countries , health care settings and standards . The environmental part of this study is limited to impacts on public health . Other environmental Areas of Protection ( AoP ) , such as depletion of natural resources and damage to ecosystems may be considered for inclusion in future studies to capture all environmental aspects . We considered the specific case of mebendazole , and many challenges still remain to capture the full impact of STH infection . However , this study provides a first insight on the public health benefit and burden of MDA programs , evaluated with a holistic approach that includes both the treatment benefit for the patient and the public health burden induced by the pharmaceutical supply chain . Such a methodology might be useful for policymakers interested in a holistic approach towards the two scientific fields that are described .
|
Millions of children from developing countries are infected by soil-transmitted helminthiases ( STH ) , an infection of intestinal worms that cause abdominal pain , bad absorption of nutrients from food and a decrease in the amount of red blood cells . This disease can be treated with anthelmintic medication , such as mebendazole , that decreases the intensity of infection and leads to a public health benefit . Because reinfection often occurs within months , regular treatment ( every six months ) is advised . Given the number of people that are infected with this disease , numerous tablets are required each year to facilitate treatment . However , the industrial production of these tablets can have a negative effect on global human health , e . g . through emissions of fine particulate matter , which should be considered as a public health burden . Our findings suggest that the public health benefit of treating STH with anthelmintic medication is 18 , 035 times larger than the public health burden associated with pharmaceutical production . However , the conclusion that the health benefits for the patients outweigh the health damage due to resource use and emission from industry may not hold for every medical treatment , therefore we propose a more holistic evaluation of health care programmes , including a broader approach towards human health .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"invertebrates",
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"helminths",
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"hookworms",
"parasitic",
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"anemia",
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"lumbricoides",
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2018
|
The public health benefit and burden of mass drug administration programs in Vietnamese schoolchildren: Impact of mebendazole
|
Mucosal associated invariant T cells ( MAIT ) are innate T lymphocytes that detect a large variety of bacteria and yeasts . This recognition depends on the detection of microbial compounds presented by the evolutionarily conserved major-histocompatibility-complex ( MHC ) class I molecule , MR1 . Here we show that MAIT cells display cytotoxic activity towards MR1 overexpressing non-hematopoietic cells cocultured with bacteria . The NK receptor , CD161 , highly expressed by MAIT cells , modulated the cytokine but not the cytotoxic response triggered by bacteria infected cells . MAIT cells are also activated by and kill epithelial cells expressing endogenous levels of MRI after infection with the invasive bacteria Shigella flexneri . In contrast , MAIT cells were not activated by epithelial cells infected by Salmonella enterica Typhimurium . Finally , MAIT cells are activated in human volunteers receiving an attenuated strain of Shigella dysenteriae-1 tested as a potential vaccine . Thus , in humans , MAIT cells are the most abundant T cell subset able to detect and kill bacteria infected cells .
Detection of bacterial infections occurs through detection of microbial compounds by the innate immune receptors [1] , [2] . As the infection progresses , the adaptive immune system respond to compounds produced by these pathogens in a process that requires priming of naïve cells and subsequent proliferation and differentiation . Innate like T cells bridge these two systems by providing immediate effectors functions in response to the infection [3] , [4] . Indeed , in contrast to conventional T cells that express a very diverse T cell receptor ( TCR ) repertoire and are restricted by polymorphic MHC molecules , innate like T cells display semi-invariant TCRs and are restricted by non-polymorphic MHC-Ib molecules . In humans , they represent large oligoclonal expansions with immediate effector properties . Within the innate-like T cells , Mucosa-Associated Invariant T ( MAIT ) cells are restricted by the evolutionarily conserved MHC related molecule , MR1 [5] , [6] . In humans , MAIT cells are abundant in peripheral blood and liver , are uniformly memory and display a tissue-targeted phenotype [7] , [8] . MAIT cells express transcription factors associated with specific effector activities such as RORγt and ZBTB16 [7] , [8] . Accordingly , they express at their cell surface high levels of cytokine receptors for IL-18 , IL-12 and IL-23 [8] , [9] . MAIT cells functions are probably related to their capacity to secrete TNF-α , IFN-γ , IL-17 as well as Granzyme B [8] , [10] , the latter suggesting cytotoxic capability . MAIT cells are identifiable by the high expression of CD161 and the detection of the Vα7 . 2 TCRα segment [8] , [9] . CD161 is a C-type lectin-like membrane receptor and is also known as NKR-P1A . The ligand of CD161 is the lectin-like transcript 1 ( LLT1 ) [11] , which is detected on activated B cells and TLR-activated pDC and cDCs [12] . Whether CD161 triggering has activatory or inhibitory effects is still not clear [12] , [13] and its impact on MAIT cell functions is not known . MAIT cells detect highly conserved compounds derived from bacteria and yeasts , which confer them with a wide specificity to microbes [10] , [14] , [15] . These compounds have been recently identified as derivatives of riboflavin precursors synthesized by most microbes [15] . The MR1 molecule presenting these coumpounds is ubiquitously expressed [16] , hence many cell types could have the capacity to activate MAIT cells including non-phagocytic epithelial cells . Bacterial pathogens induce their own uptake in these cells , providing a way to enter the host organisms through epithelial surfaces [17] . For example , Shigella flexneri ( Sf ) , Salmonella enterica serovar Typhimurium ( ST ) and Listeria monocytogenes ( Lm ) are intestinal pathogens , which inject effector proteins that induce internalization of the bacteria through a phagocytic-like mechanism [17] . While ST mainly remains in a vacuole that does not fuse with the lysosomal compartment , Sf and Lm escape to the cytoplasm and then to neighboring cells [17] . As the MAIT specific ligand belongs to the riboflavin metabolic pathway [15] , which is present in Enterobacteriacea [15] , commensal species such as Escherichia coli as well as pathogens like Shigella and Salmonella can provide the MAIT specific ligand . However , Listeria species do not have this metabolic pathway , providing an explanation for their lack of MAIT stimulatory potential [10] , [14] , [15] . Although , these pathogens are known to induce T cell responses when presented by hematopoietic cells , the question remains whether MAIT cells sense their presence in epithelial cells . In this study , we show that MAIT cells can kill epithelial cells presenting a bacterial ligand on MR1 . Interestingly , the NK receptor molecule , CD161 modulates the cytokine response after triggering but does not abrogate the cytotoxic activity of MAIT cells . MAIT cells recognize and effectively lyse epithelial cells infected by Sf , in a process requiring only endogenous levels of MR1 . In contrast , MAIT cells do not sense ST-infected epithelial cells . Moreover , MAIT cells become activated and decreased in numbers in the blood of human volunteers who had been orally vaccinated with an attenuated strain of Shigella dysetenriae-1 as a candidate vaccine . Therefore , MAIT cells are abundant innate like T cells that can detect and clear infected cells early in bacterial infections .
The expression of CD8 coreceptors ( αβ heterodimer or αα homodimer ) by MAIT cells [9] suggests the expression of a differentiation program with cytotoxic potential . This is confirmed by the secretion of granzyme A and B after TCR triggering [8] . Accordingly , May-Grunwald-Giemsa-staining of FACS sorted MAIT cells revealed basophilic granules that are very similar to those found in non-MAIT memory CD8+ T cells whereas these granules were absent from memory CD4+ T cells ( Figure 1A ) . Additionally , intra-cytoplasmic staining of MAIT cells for cytotoxic molecules , granulysin and perforin was positive at steady state in comparison with isotype control ( Figure 1B ) . Upon stimulation with anti-CD3 -CD28 beads , staining for perforin increased whereas granulysin levels were barely modified ( Figure 1B ) . MAIT degranulated upon stimulation as surface staining for CD107a ( LAMP1 ) increased . Taken together , these observations show that MAIT cells express the necessary molecules to be cytotoxic . However , this cytotoxic potential has not been tested to date . We used epithelial cell line , overexpressing human MR1 , as target cells ( Hela-hMR1 ) . Hela-hMR1 cells express at their cell surface a high level of MR1 , which is normally more present in the endosomal compartment [18] . We cultured these cells and the parental cell lines ( Hela ) with increasing multiplicity of infection ( MOI ) of the non-invasive bacteria , Escherichia coli ( Ec ) . After washing , FACS-sorted MAIT cells ( Vα7 . 2+ CD161hi T cells ) were seeded on these presenting cells . After overnight incubation , MAIT cells activation was assessed by upregulation of CD69 and CD25 at their surface by FACS analysis . Ec cultured with Hela-hMR1 activated MAIT cells in a dose dependent manner ( Figure 2A–B ) . This activation was significantly lower with endogenous level of MR1 in parental Hela cells and was only visible at high MOI ( Figure 2A–B ) . These results were repeated in another cell type , HT-1080 with similar results ( Figure S1 ) . In the conditions where MAIT cells are highly activated , CD107a presence at the cell surface was detected ( Figure 2C ) , indicative of degranulation processes in these cells ( Figure 2C ) . To test the specific killing of target cells by MAIT cells in presence of bacteria , we quantified the release of lactate dehydrogenase ( LDH ) , a cytoplasmic protein , in the culture supernatant . We observed a MOI-dependent increase of LDH in the culture supernatant of Hela-hMR1 cells in the presence of MAIT cells and bacteria ( Figure 2D ) . The LDH release also depended on the effector/target ratio as more MAIT cells per Hela-hMR1 cells increased LDH amounts ( Figure 2D ) . The induction of LDH release in parental Hela cells was limited and the cell death induced by Ec in the absence of MAIT cells was negligible ( Figure 2D ) . To visualize the killing events , we performed time-lapse microscopy . Hela and Hela-hMR1 cells were cultured with FACS sorted MAIT cells , in the presence of Ec lysates or not , and frame-by-frame analysis was performed overnight . We observed that Ec lysates induced killing events observed when the lymphoid smaller cells entered in contact with bigger epithelial Hela cells . These killing events were numerous in conditions with Hela-hMR1 and the lysate , but very rare with Hela cells and totally absent in conditions without of the bacterial lysate ( Figure 2E , Movie S1 , S2 , S3 , S4 ) . Taken together , these results clearly demonstrate that MAIT cells are cytotoxic , killing target cells in a MR1 and bacterial ligand dependent manner . MAIT cells express high levels of CD161 ( NKRP1A ) and NKG2D [8] , [9] . Ligation of CD161 induces inhibitory signals on NK cells [19] whereas it can be co-stimulatory on T cells [12] . Triggering NKG2D induces effector functions in T cells [20] . Hence , we tested the modulation of MAIT cell responses towards bacteria infected epithelial cells in the presence of specific antibodies against these receptors . In presence of anti-NKG2D antibody , the MAIT cell response to Hela-hMR1 cells cultured with increasing amounts of Ec was unaffected , with upregulation of CD69 and CD25 comparable to control . The presence of anti-MR1 antibody abrogated this response . Surprisingly , in the presence of anti-CD161 antibody , the upregulation of CD25 was reduced . However , levels of CD69 were similar to the control conditions , indicating that MAIT cell response was not fully inhibited ( Figure 3A ) . In the same conditions , as estimated by LDH release , anti-NKG2D antibody did not change the cytotoxic capacities of MAIT cells . Interestingly , anti-CD161 antibody did not inhibit the MAIT dependent killing of Hela-hMR1 infected cells either . However , anti-MR1 antibody clearly reduced LDH release from target cells ( Figure 3B ) . These results indicate that modulation of CD25 upregulation did not impair the capacity of MAIT cells to be cytotoxic . Taken together , these observations suggest that the ligation of CD161 modulates the MAIT cell response . We hypothesized that CD161 cross-linking might modify the cytokine response of MAIT cells induced by TCR triggering . To test this hypothesis in an analytical setting , we FACS sorted MAIT cells gating on the Vα7 . 2+ , CD4 negative and IL-18Rαhi cells as an alternative sorting strategy to avoid CD161 triggering prior to the experiments [8] . We then stimulated MAIT cells with increasing doses of plastic-coated anti-CD3 and anti-CD28 in the presence of increasing amounts of soluble anti-CD161 antibody . The production of cytokines was assessed after 24 hours in the culture supernatant . The presence of anti-CD161 antibody reduced the amounts of IFN-γ , TNF-α and IL-2 in a dose dependent manner ( Figure 3C ) . Interestingly , Granzyme B secretion was not modified by CD161 triggering at high concentration of anti-CD3+CD28 antibodies ( Figure 3C ) . As controls , no difference was observed with an irrelevant antibody ( anti-NKG2D ) but increase amounts of IFN-γ and TNF-α were detected in presence of soluble IL-12 ( data not shown ) . Taken together , these data suggest that CD161 cross-linking modulates the cytokine but not the cytotoxic response of MAIT cells . We then tested the potential relevance of the cytotoxic ability of MAIT cells by studying an infection with the invasive bacteria Salmonella enterica Typhimurium ( ST ) . Previous works have used ST to study the MAIT cell response to infected cells , however these studies relied on cell lines over-expressing hMR1 [15] , [21] . As we have shown previously [10] and above , MR1 over-expressing-cells can present the MAIT cell specific ligand from non-invasive bacteria such as Ec when used at high MOI . Hence , we tested if ST detection by MAIT cells required epithelial cell invasion . We first confirmed the presence of the MAIT ligand in this Salmonella strain by feeding PFA-fixed ST to monocytes . By fixing the bacteria , monocytes were less sensitive to the cell death induced by the pathogenic bacteria , but the capacity of the bacteria to activate MAIT cells was preserved [10] . Autologous MAIT cells cultured in the presence of these ST-infected monocytes overnight displayed CD69- CD25-upregulation , indicative of a strong activation ( Figure 4A ) . This activation was blocked by the addition of the anti-MR1 antibody ( Figure 4A ) , confirming the specificity of the reaction . These results confirm the presence of the MAIT specific ligand in ST . We then performed a gentamicin treatment assay with the invasive strain of ST on Hela cells over-expressing or not hMR1 . After 30 minutes of infection in absence of serum and antibiotics to ensure bacterial fitness and allow invasion , cells were washed extensively and placed in medium supplemented with serum and gentamicin , which kill extracellular bacteria . Magnetic sorted Vα7 . 2 positive T cells were then added for overnight culture . Surprisingly , ST-infected Hela cells failed to induce a strong response from MAIT cells , only inducing upregulation of CD69 and CD25 at high MOI , to levels comparable to Ec ( Figure 4B ) . As previously shown [15] , [21] , Hela-hMR1 cells in the presence of ST activated MAIT cells but at similar levels than Ec ( Figure 4B ) . The lower activation observed at high MOI with ST is probably related to a cytotoxic effect of ST . We checked by cell lysate plating on LB agar plates and by immunofluorescence that ST was properly invasive ( data not shown and Figure S2 ) . These results suggest that ST-infected epithelial cells are not more potent MAIT cell activator than Ec-infected cells . Salmonella entry is mediated by the injection of effector proteins through a molecular syringe , all of which are encoded by a region of the bacterial chromosome , Salmonella pathogeny island 1 , Spi1 [22] . We compared ST deficient for the Spi1 locus , which do not enter epithelial cells , to the wild type strain for their capacity to activate MAIT cells . We did not find any difference in the upregulation of activation markers after overnight culture of MAIT cells with wild-type- or ΔSpi1-infected Hela or Hela-hMR1 cells ( Figure 4B–C ) . On the other hand some virulence factors critical for ST pathogeny are encoded by a second locus , Spi2 . This mutant is not virulent in vivo and is defective for its intracellular trafficking in epithelial cells [23] , hence these bacteria could allow for MAIT cell dependent sensing . However , no difference was observed when comparing the activation of MAIT cells by wild-type- or ΔSpi2-infected Hela or Hela-hMR1 ( Figure 4B–C ) . Taken together , these results show that intracellular Salmonella does not induce stronger activation of MAIT cells than non-invasive bacteria . Salmonella invasion of epithelial cells shares many features with Shigella mechanisms of entry , however the following steps of infection differ [17] . While Sf escape to the cytoplasm , ST reside in a vacuole preventing its fusion with the lysosomal compartment . We therefore tested the capacity of MAIT cells to detect Sf-infected cells . We first checked the presence of the MAIT specific ligand in Sf . To do so , we used monocytes cultured in presence of PFA-fixed Ec or Sf . After overnight co-culture with autologous MAIT cells , we observed increased CD69 and CD25 expression by these T cells ( Figure 5A ) . The Ec- and Sf-dependent activations were MR1 dependent as the addition of an anti-MR1 antibody abrogated the upregulation of CD69 and CD25 ( Figure 5A ) . These results clearly indicate that Sf expresses the MAIT specific ligand . We then studied the response of MAIT cell towards epithelial cell expressing physiological levels of MR1 in the context of Sf invasion . We infected non-transfected Hela cells with increasing doses of Sf or Ec , washed and co-cultured with Vα7 . 2 positive sorted cells . MAIT cell activation was assessed by upregulation of CD69 and CD25 by Vα7 . 2+CD161hi cells . Sf-infected Hela cells activated MAIT cells in a MOI dependent manner ( Figure 5B–C ) and significantly more than Ec-infected cells ( Figure 5B ) . This activation was specific to MAIT cells , as the Vα7 . 2+CD161neg T cells cultured in the same conditions were not activated ( Figure 5C ) . This activation was blocked by the addition of the anti–MR1 antibody ( Figure 5D ) , indicating that endogenous level of MR1 present in the parental Hela cell line is sufficient for MAIT cell activation . Finally , we compared wild type Sf with an uninvasive mutant , ΔMxiD . This virulence protein controls the secretion of the effector proteins necessary for Sf entry into epithelial cells [24] . The ΔMxiD Sf mutant induced significantly less MAIT cell response as compared with wild type Sf ( Figure 5E ) . The response induced was comparable to the activation observed with Ec ( Figure 5B ) . These results indicate that bacterial invasiveness is required for the strong MAIT cells activation induced by Sf . Taken together , these results show that MAIT cells detect Shigella-infected epithelial cells in a manner requiring cell invasion and presentation of the specific ligand on endogenous MR1 . The capacity of MAIT cells to lyse Sf-infected epithelial cells was then tested in the same conditions . MAIT cells stimulated by Sf-infected Hela cells degranulated upon activation as the CD107a marker was increased at the cell surface as compared with the uninfected condition ( Figure 6A ) . The quantification of LDH release in the supernatant of overnight-infected cells revealed , as expected , that Sf infection induced cell death in a MOI dependent manner as compared to uninfected cells ( Figure 6B ) . However , infected Hela cells cultured in the presence of MAIT cells showed increased LDH release compared with Hela cells alone , suggesting that , indeed , MAIT cells could lyse Sf-infected cells ( Figure 6B ) . This increased LDH release was abrogated by the addition of the anti-MR1 antibody ( Figure 6B ) , further supporting the hypothesis of a MAIT cell-dependent killing . To determine the contribution of the MAIT-induced cell death compared to the Sf-triggered cytotoxicity , we added to the classical gentamicin treatment , chloramphenicol , a cell-permeant antibiotic , which kill intracellular bacteria . Sf-dependent cell death was reduced in the gentamicin plus chloramphenicol treatment condition as compared with gentamicin only . The level of LDH release was similar to the uninfected control even with high Sf MOI ( Figure 6C ) , indicating that bacterial viability is necessary for Sf-induced cell death . When MAIT cells were added in the same conditions , higher LDH activity was found in the supernatants of the infected cells in both types of antibiotic treatment as compared with Hela cells without T cells ( Figure 6C ) . In the two conditions ( gentamicin and gentamicin plus chloramphenicol ) , MAIT cell activation was similar ( data not shown ) . These results indicate that MAIT cells lyse Sf-infected epithelial cells in an endogenous MR1-dependent manner . We then studied the relevance of enteric invasive bacterial pathogen detection by MAIT cells in vivo . To do so , we analyzed MAIT cell numbers and phenotype in the blood of volunteers that participated in a clinical trial testing the efficacy of an attenuated strain of Shigella dysenteriae 1 ( SD1 ) , SC599 , as an oral vaccine [25] . In this study , healthy volunteers received orally 105 or 107 live bacteria . SD1-LPS-specific antibody secreting cell ( ASC ) response ( IgA , IgG , IgM ) as well as SD1-specific antibody response were measured in the blood and serum of these subjects and compared to individuals receiving a placebo . The vaccinated group could be divided into 2 subgroups: responders ( R ) who showed specific IgA response above the threshold of 20 ASC per 106 PBMC and non-responders ( NR ) who did not [25] . We measured the number of MAIT cells in the PBMCs from these volunteers at day 7 , 9 and 11 ( D7 , D9 and D11 ) after vaccine administration as compared to baseline levels ( BL ) ( Figure 7A ) . A significant reduction of the percentage of MAIT cells normalized to BL was observed at D11 in subjects receiving the bacteria as compared with the placebo group ( Figure 7B ) . No differences were detected in other T cell subsets such as CD161+ T cells or Vα7 . 2+CD161lo cells ( Figure S3 ) . These results suggest that circulating MAIT cell numbers are specifically modified after an oral experimental infection with Shigella . When MAIT cells were analyzed separately in the R and NR groups , only a trend towards an increased MAIT number at BL was detected in the responding subjects ( Figure 7C ) . However , in the responder group the MAIT cells were more activated at day D11 as shown by the significant upregulation of the activation marker HLA-DR in comparison with the BL or the non-responder group ( Figure 7C ) . This activation state was not observed in the non-responder group ( Figure 7C ) . Although one subject in the placebo group displayed MAIT cells with increased HLA-DR expression at D11 the difference was not significant for the group as a whole ( Figure 7C ) . No significant expression of HLA-DR was detected in the CD161+ or Vα7 . 2+CD161lo T cell subsets ( Supplementary Figure S3 ) . Taken together , these results strongly suggest that MAIT cells are specifically activated during the course of an enteric bacterial infection with a Shigella dysenteriae strain in human .
MAIT cells are microbial reactive CD8 T cells that produce effector molecules such as TNF-α , IFN-γ and IL-17 [8] , [10] . However , their cytotoxic capacity has not been assessed to date . Our study demonstrates that MAIT cells can lyse MR1 expressing epithelial cells in the presence of bacteria or bacterial ligand . We show that the NK receptor CD161 , which is highly expressed by MAIT cells , modulates the cytokine but not the cytotoxic response of this T cell subset . Furthermore , epithelial cells infected with the invasive bacteria Shigella flexneri , but not Salmonella enterica Typhimurium , are excellent targets for MAIT cell dependent cytotoxicity in a process depending on physiological levels of MR1 . Finally , we show that MAIT cells are activated by the oral administration of Shigella dysenteriae in human volunteers that mounted a B-cell response towards the vaccine strain . The cytotoxic capacity of MAIT cells , a large T cell population with a wide microbial reactivity , could have major impacts on many infectious diseases caused by pathogens expressing MAIT specific ligands [10] , [15] . The lysis of infected cells could participate to the immune control of bacterial infections by limiting the spreading of the bacteria into the organisms . MAIT cells also secrete cytokines such as IFN- γ and TNF- α that could also be involved in this antibacterial response . This secretion was reduced in the presence of anti-CD161 antibody . The effect of CD161 triggering on NK or T cell effector activities is still controversial [19] , [26] . A recent report suggests that CD161 has stimulatory function on IL-17 producing CD4 T cells [27] . However , an inhibitory effect was previously shown on NK cells and CD8 T cells [19] , [27] , [28] . Our data confirm this latter finding using a homogenous T cell population . This modulation of the cytokine response contrasting with an unaltered cytotoxic response could be an important mechanism to control MAIT cell response . We propose a model in which MAIT cell detect and lyse infected epithelial cells , preventing early propagation of the bacterial infection without inducing a strong inflammatory response . However if the bacterial infection goes further , phagocytic cells would pick up bacteria or infected cell remnants . In this case , activation of MAIT cells by these professional presenting cells producing IL-12 and IL-23 would induce the secretion of cytokines such as IFN-γ and IL-17 , which increases the adaptive and innate immune response to the infection . The known CD161 ligand , LLT1 , is expressed by activated antigen presenting cells . The expression of LLT1 by epithelial cells both in inflammatory and steady-state conditions remains to be elucidated and could have a significant impact on MAIT stimulation . Salmonella block the fusion of the bacteria containing vacuole with the endosomal and lysosmal compartments . By mediating this inhibition , Salmonella could block the mechanisms that are necessary for efficient loading of the ligand into the MR1 groove . Interestingly , it was recently shown that Salmonella activate MAIT cells when cultured with non-phagocytic cells [15] , [21] , however in these studies , the presenting cells overexpress MR1 and the necessity for bacterial entry was not assessed . Additionally , no differences were observed between Salmonella and other non-invasive bacteria such as Escherichia coli , Pseudomonas aeroginosa or Klebsiella pneumoniae [21] . This discrepancy with our study could be explained by the overexpression of MR1 . Our results show that the active entry into epithelial cells of Salmonella does not enhance MAIT activation . We can speculate that some Salmonella virulence factors are responsible for the absence of MR1 loading . As the immune system and pathogens co-evolve , one has to always consider that the pathogen could use the immune response to its benefit . Considering the intracellular life-cycle of Shigella , we can hypothesize that the escape of the bacteria to the cytoplasm leads to an efficient loading of the MAIT ligand on MR1 . The cellular and molecular mechanisms by which the MAIT specific ligand is loaded on MR1 need to be determined . However the recent determination of the nature of this compound and the use of intracellular bacterial pathogens should provide the tools to study these processes . The results we report herein in vivo in human volunteers show that MAIT cells are activated after ingestion of the attenuated strain of Shigella dysenteriae 1 , SC599 [25] . This activation was observed in the individuals that mounted a detectable B-cell response against the bacteria and not in the non-responders . The original study of this clinical trial [25] applied two doses of bacteria with no change in the antibody response , suggesting that the lack of immunogenicity is not due to a lack of antigen availability . However , shedding of the vaccine strain was detectable in only 20 to 30% of the feces of volunteers , suggesting that these results might be explained by the absence of sufficient uptake of the bacteria in the intestine . It remains to be assessed whether differences in MAIT cell response to the bacteria are implicated in the antibody response observed in this study [25] . In summary , our study shows that MAIT cells efficiently kill bacterially infected non-phagocytic cells . Modulation of the cytokine response of these bacteria specific T cells could be achieved by CD161 triggering . Finally , we show that MAIT cells participate to the immune response against an enteric infection in humans .
Blood samples were obtained from healthy donors from the blood bank ( Etablissement Français du sang , site de Crozatier ) in accordance with institutional regulations . PBMCs were obtained using a standard Ficoll gradient according to the manufacturer protocol ( GE healthcare ) . Monocytes were isolated by adherence on plastic culture plates . MAIT cells were isolated by MACS sort using the biotinylated anti-Vα7 . 2 ( 3C10 ) antibody and anti-biotin magnetic beads ( Miltenyi ) according to the manufacturer specifications . In some experiments , the positive fraction was subsequently , stained with anti-CD3-A700 ( HIT3a , BioLegend ) , anti-CD161-APC ( 191B8 , Miltenyi Biotec ) , and streptavidin phycoerythrin ( PE ) -Cy7 ( BD Biosciences PharMingen ) . MAIT cells were fluorescence-activated cell sorter sorted on a BD Aria II . This randomized , double-blind , placebo-controlled Phase 2 trial was conducted in healthy volunteers recruited into two vaccine trial centres , the “CIC de Vaccinologie Cochin-Pasteur” in Paris , France , and the St George's Vaccine Institute in London , United Kingdom in 2006 . The protocol was approved by Local Ethics Committees and competent authorities , and a written informed consent was obtained from all volunteers . This study is registered at ClinicalTrials . gov with the identifier NCT00210288 . Fasting volunteers ingested 120 ml of 2% sodium bicarbonate buffer , followed 5 min later by 30 ml 2% ( w/v ) bicarbonate solution containing the assigned vaccine dose or no vaccine ( placebo ) . SC599 vaccine was an attenuated Shigella dysenteriae strain , described elsewhere [29] . Quantification by Elispot of circulating IgA-ASC ( antibody secreting cells ) was performed on days 0 , 7 , 9 and 11 after vaccination and responders to the vaccine were defined as exhibiting ≥20 LPS-specific IgA ASC/106 PBMC . Complete details of the clinical trial has been published [25] . Cryopreserved PBMC from subjects included either in the placebo group , or in the group vaccinated with 107 CFU were tested for peripheral MAIT cells frequency , as detailed below . Escherichia coli , Dh5α ATCC strain was used as uninvasive bacteria . Shigella flexneri strain M90T and MxiD mutants were used throughout the study and was provided by Pr . Sansonnetti ( Institut Pasteur , France ) . Salmonella enterica Typhimurium SL1344 , ΔSpi1 and ΔSpi2 mutants were provided by Dr . Tedin ( University of Berlin , Germany ) . Bacteria were cultured overnight in Luria broth at 37°C , then diluted 1/100 for a 3 hour subculture with shaking for Shigella , without for Salmonella , washed in PBS and diluted according to needs . Where indicated , bacteria were fixed in 1% PFA for 5 min and then washed extensively before use . For in vitro infection , cells were washed and put in DMEM without supplement . Dilutions of bacteria in DMEM were added , spun down and left at 37°C for 30 minutes . Then cells were washed 3 times in complete medium and put back at 37°C for 2 hours in complete medium supplemented with 100 µg/mL Gentamicin ( and 10 µg/mL Chloramphenicol where indicated ) . At this point , purified T cells were added for an overnight co-culture . Then cells were harvested and stained for FACS analysis . The bacterial lysate was prepared by growing Dh5α to saturation then pellet the bacteria and resuspension to induce the release of the MAIT ligand ( C . Soudais et al . , in preparation ) . Supernatants of cell culture were harvested and cytotoxic activity was assessed by quantification of the lactate dehydrogenase ( LDH ) activity . A commercial kit containing a colorimetric substrate was used according to the manufacturer specifications ( Promega ) . Flow cytometry was performed with directly conjugated antibodies according to standard techniques with analysis on a FACS Aria and LSRII flow cytometers ( Becton Dickinson ) . DAPI and a 405 nm excitation were used to exclude dead cells . The following antibodies , from BD Pharmingen , eBiosciences or Biolegend , for human cell staining , were used: anti-CD45RO-FITC ( UCHL1 ) , anti-CD4-APC-Cy7 ( RP4-T4 ) , anti-CD3ε-Alexa 700 ( UCHT1 ) , anti-CD8β-PE Texas Red ( 2ST8 . 5H7 ) , anti-CD161-APC , -PE or -FITC ( DX12 ) , anti-CD69-APC or -PECy5 ( CH/4 ) and the antibody anti-Vα7 . 2-biot , -PE or APC ( 3C10 ) has been described elsewhere . For quantification of cytokines , CBA ( BD biosciences ) technology was used according to the manufacturer specifications . Hela cells , over-expressing MR1 or not , were cultured in presence of bacterial lysate , with enriched MAIT on 35 mm dishes ( Fluorodish ) placed into a chamber on the videomicroscope at 37°C in a 5% CO2 atmosphere . Time-lapse images were acquired on a Nikon Ti microscope equipped with a CCD camera ( Roper Scientific ) , and a piezoelectric motor ( LVDT , Physik Instrument ) , by using a dry 20× objective 1 . 40NA . Acquisition was done with the Metamorph software ( MDS AZ ) . Stacks of z-planes ( step 1 µm ) were acquired every 3 min . Movies show the projection of the z stacks . All quantitative data were analyzed on Prism software using two-way ANOVA , testing the significance of the presence of bacteria ( MOI ) and the different conditions ( Hela vs Hela-MR1; Sf vs Ec; or Ctr vs anti-MR1 ) ; * represents p<0 . 05 for both parameters . In figure 7 , paired and unpaired nonparametric t-test was applied; * represents p<0 . 05 .
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Human Mucosa-Associated Invariant T cells ( MAIT ) detect microbe-derived compounds presented by the MHC-like molecule , MR1 . These foreign antigens are produced by a wide variety of microbes , including commensal and pathogenic bacteria or yeasts . MAIT cells expend shortly after birth and constitute the major antibacterial T cell subset described and , hence , could play important roles in infectious diseases . Here we show that MAIT cells recognize epithelial cells infected by the intestinal pathogen Shigella flexneri in a process requiring endogenous MR1 , while the closely related bacterium Salmonella Tyhpimurium is not . Upon recognition , infected epithelial cells are efficiently lysed by MAIT cells . We also show that the triggering of CD161 , a natural killer receptor highly expressed by MAIT cells , can modulate the cytokine but not the cytotoxic function of these cells . Finally , we provide evidence that MAIT cells are activated during the course of an experimental enteric infection in humans . Our study provides important insight on the antibacterial function of MAIT cells and their interaction with pathogenic bacterial species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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MAIT Cells Detect and Efficiently Lyse Bacterially-Infected Epithelial Cells
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The anthrax toxin is a tripartite toxin , where the two enzymatic subunits require the third subunit , the protective antigen ( PA ) , to interact with cells and be escorted to their cytoplasmic targets . PA binds to cells via one of two receptors , TEM8 and CMG2 . Interestingly , the toxin times and triggers its own endocytosis , in particular through the heptamerization of PA . Here we show that PA triggers the ubiquitination of its receptors in a β-arrestin-dependent manner and that this step is required for clathrin-mediated endocytosis . In addition , we find that endocytosis is dependent on the heterotetrameric adaptor AP-1 but not the more conventional AP-2 . Finally , we show that endocytosis of PA is strongly dependent on actin . Unexpectedly , actin was also found to be essential for efficient heptamerization of PA , but only when bound to one of its 2 receptors , TEM8 , due to the active organization of TEM8 into actin-dependent domains . Endocytic pathways are highly modular systems . Here we identify some of the key players that allow efficient heptamerization of PA and subsequent ubiquitin-dependent , clathrin-mediated endocytosis of the anthrax toxin .
Bacterial toxins endowed with enzymatic activity generally have targets , or require co-factors , that reside in the cytoplasm of the target cell . Such is the case for the anthrax toxin produced by Bacillus anthracis . It is composed of three independent polypeptide chains , 2 of which have an enzymatic activity–edema factor ( EF ) and lethal factor ( LF ) –and one , the protective antigen ( PA ) , which has the ability to interact with the target cell . EF is a calmodulin dependent adenylate cyclase that must thus reach the cytoplasm to become active . LF is a metalloprotease that cleaves MAP kinase kinases , all of which are cytoplasmic [1] , [2] . EF and LF reach their final destination through the help of PA . PA binds specifically to two identified anthrax toxin receptors , tumor endothelial marker 8 ( TEM8 , also called ANTXR1 ) and capillary morphogenesis 2 ( CMG2 , or ANTXR2 ) , two type I membrane proteins that share extensive sequence similarity both in their extracellular and intracellular domains [3] , [4] . PA , which is produced by the bacterium as an 83 kDa form ( PA83 ) , is processed into an 63 kDa form , by host enzymes such as the endoprotease furin [5] . The thus generated PA63 has the capacity to heptamerize ( PA7mer ) into a ring-like structure , which serves as the receptor of EF and LF [6] . The hetero oligomeric complex–i . e . PA7mer-EF/LF and receptors–is then internalized by the cell and delivered to early endosomes , where PA7mer undergoes a conformational change that leads to its membrane insertion and pore-formation ( pPA7mer ) [2] . EF and LF are also sensitive to the acidic pH of endosomes , which leads to their partial unfolding , allowing them to be translocated through the lumen of the PA channel to the other side of the membrane [7] . Considering that PA7mer is the receptor for EF and LF , one crucial point is that PA should not undergo endocytosis in its monomeric form , i . e . the enzymatic subunits would fail to be delivered to the cytoplasm . This is indeed what has been observed by us and others [8] , [9] leading to the notion that the anthrax toxin times its entry into the cells , in particular by triggering the activation of src-like kinases [10] . We have previously shown that processing of PA leads to a relocalization of the toxin from the glycerolipid region of the plasma membrane to lipid rafts [8] , where the receptors encounter the E3 ubiquitin ligase Cbl that modifies a juxtamembranous lysine of the cytoplasmic tail of the receptors [11] . Ubiquitination in turn promotes endocytosis of the toxin through a mechanism that requires the large GTPase dynamin [8] , [12]–involved in vesicles fission , but not the caveolar proteins caveolin-1 , pointing towards a role for clathrin in anthrax toxin endocytosis [8] . We here sought to identify other molecular players involved in anthrax toxin uptake . We show that clathrin is , as predicted , involved in endocytosis but that the anthrax toxin follows a non-canonical clathrin-dependent route that depends of β-arrestins and the heterotetrameric adaptor complex AP-1 . Moreover we show that endocytosis is strongly actin dependent , in contrast to endocytosis of another bacterial toxin , diphtheria toxin . Interestingly we found that actin also promotes the heptamerization of PA63 , but only when it is bound to TEM8 , not when bound to CMG2 .
Previous studies of our laboratory indicated that anthrax toxin endocytosis in HeLa cells is independent of caveolin-1 but was affected by the overexpression of dominant negative mutants of dynamin 2 or of the accessory protein Eps15 [8] . Here we sought to confirm these findings using an independent method , namely gene silencing by RNAi , to more precisely define the molecular players involved in anthrax toxin endocytosis . As mentioned , PA63 heptamerizes at the cell surface in to PA7mer , which is an SDS-sensitive complex . Upon arrival in endosomes , PA7mer converts to an SDS-resistant form ( pPA7mer ) , which is transmembrane . Although formation of pPA7mer is a late event , which occurs only after sorting of the toxin-receptor complex into the intraluminal vesicles of multivesicular endosomes [13] , monitoring formation of pPA7mer as a function of time is a convenient read-out to identify factors that affect toxin endocytosis . As shown in Fig . 1A , RNAi against either CHC or dynamin 2 strongly delayed the formation of the pPA7mer in HeLa cells , whereas RNAi against Eps15 had no effect , despite efficient silencing of protein expression ( Fig . S1A ) . We believe that the discrepancy between our present findings based on RNAi and our previous conclusions based on over-expression of dominant negative Eps15 is due to the fact that inhibition of either transferrin or PA endocytosis required strong over-expression , which might have been somewhat toxic [8] . Moreover , the effect of Eps15 over-expression was by no means as strong as the one observed for dynamin , in parallel experiments [8] . Hela cells express mainly TEM8 . The above experiments therefore indicate that PA is internalized in a clathrin dependent manner when bound to TEM8 . To investigate whether CMG2-mediated PA uptake is also clathrin dependent , we first made use of Baby Hamster Kidney ( BHK ) cells–which strongly express CMG2 ( Fig . 1B ) but have undetectable levels of TEM8 messenger ( not shown ) –and for which a cell line is available that allows the inducible expression of clathrin heavy chain ( CHC ) antisense RNA [14] . Formation of pPA7mer was strongly delayed in CHC antisense RNA-expressing BHK cells ( Fig . 1C ) . Since formation of pPA7mer is a rather late event , we used a recently established FACS-based assay to monitor the initial step of endocytosis of the toxin [10] . PA was pre-bound to BHK cells at 4°C . Cells were then incubated at 37°C for different times prior to PA labeling at 4°C and FACS analysis . Incubation at 37°C led to a decrease of surface fluorescence for control cells , indicating that PA had been internalized but not for the CHC antisense RNA-expressing BHK cells ( Fig . S1B ) . To independently confirm the involvement of clathrin in CMG2-mediated PA endocytosis , we ectopically expressed CMG2 in Hela cells and performed RNAi against CHC . As shown in Fig . 1D , ectopic expression of CMG2 markedly accelerated and increased the formation of pPA7mer , indicating that the transfected receptor was mediating PA uptake . This was confirmed by the fact that immunoprecipitation of V5-tagged CMG2 led to the co-immunoprecipitation of PA63 and pPA7mer ( Fig . S1C , left panel ) . We next performed RNAi against CHC in these cells and found that formation of pPA7mer was abolished during the first 45 min ( Fig . 1E ) , indicating that CHC was implicated not only in TEM8 mediated PA uptake but also CMG2-mediated uptake . Clathrin coated vesicles are composed of three layers: the inner layer consists of the cargo , the outer layer is composed by clathrin and the middle layer is composed of adaptor and accessory/regulatory molecules that link the cargo to the clathrin coat [15] . We here investigated the possible involvement of the heterotetrameric adaptor complex AP-2–arguably the most widely used adaptor in the formation of clathrin-coated vesicles at the plasma membrane [16]– , of AP-1–which mostly operates on the Trans-Golgi Network and endosomes– , and the adaptor proteins Grb2 , Dab2 and β-arrestins . β-arrestins have been shown to act as adaptors , linking cargo receptors to clathrin , either directly , or via AP2 [17] . Their involvement has been shown in particular during the endocytosis of G-coupled receptors but also single-transmembrane receptors , such as the tyrosine kinase IGF-1R [18] or the Drosophila Notch receptor [19] . Expression of these different adaptors/accessory proteins was silenced by RNAi , and formation of pPA7mer , the SDS-resistant heptameric PA pore , was monitored as a function of time . As a second read-out , we also monitored the cleavage of the LF target MEK1 , which reveals the delivery of LF to the cytoplasm . RNAi against CHC and the E3 ligase Cbl were used as positive controls . All RNAi duplexes were efficient at lowering the expression levels of the corresponding proteins ( Fig . S1A ) . Due to the high degree of similarity between β-arrestin-1 and 2 , silencing one gene led to a decrease in the levels of the other . Our studies can therefore not discriminate between the two forms . Silencing of Grb2 , Dab2 and the µ subunit of the AP-2 complex had no effect on anthrax toxin entry into Hela cells ( Fig . 2AB ) . The lack of involvement of AP-2 was somewhat surprising , raising the possibility that AP2 was insufficiently knocked down to see an effect on endocytosis . We therefore monitored the entry of a second bacterial toxin , namely diphtheria toxin , which has also been shown to enter cells via clathrin-mediated endocytosis [20] as confirmed here ( Fig . S2A ) . Diphtheria toxin is an ADP-ribosylating toxin that modifies elongation factor 2 thus leading to the inhibition of protein synthesis [21] . In contrast to the anthrax toxin , entry of diphtheria toxin was delayed in AP-2 , but not AP-1 , RNAi treated cells , when compared to cells treated with an irrelevant RNAi ( Fig . S2B ) , indicating that silencing of AP-2 was efficient . It has been previously reported for the EGF-receptor that the temperature at which the ligand is added to the cells affects the apparent AP-2 dependence of the process , i . e . EGF-R endocytosis in AP-2 RNAi treated cells still occurred when the ligand was pre-bound at 4°C , but not when the ligand was added to the cells at 37°C [22] . We therefore repeated our knockdown experiments , omitting the pre-binding of PA63 at 4°C and adding it directly to cells at 37°C . In contrast to what was observed for EGF receptor , we still found that AP-2 depletion had no effect on the formation of pPA7mer ( Fig . S3A ) . In marked contrast to the knock down of AP-2 , silencing the µ subunit of the AP-1 adaptor drastically delayed the formation of the SDS-resistant pPA7mer and LF-mediated MEK1 cleavage in Hela cells Fig . 2AB . This was not due to an off-target effect of the RNAi duplexes , since a similar effect was observed upon silencing of the γ subunit of the AP-1 complex ( Fig . S3B ) . The lack of pPA7mer formation was not due to an effect of AP-1 silencing on endosome acidification–due for example to altered trafficking of vATPase components–since diphtheria toxin , which as the anthrax toxin requires transport to an acidic compartment for cytoplasmic delivery of the enzymatic subunit , retained full activity in AP-1 depleted cells ( Fig . S2B ) . In addition to a requirement for AP-1 , we found that silencing β-arrestins strongly delayed formation of pPA7mer Fig . 2AB . Knowing that Hela cells mainly express TEM8 , the above experiments indicate that TEM8-mediated uptake depends on AP1 and β-arrestins but not on AP-2 . To investigate whether CMG2-mediated uptake had the same requirements , we repeated the above experiments in Hela cells transfected with CMG2 . As shown in Fig . 2C , formation of pPA7mer was not detected within the first 45 min when silencing AP-1 or β-arrestin , but occurred normally when silencing AP-2 , showing that both receptors have the same adaptor requirements . We next investigated whether the delay in pPA7mer formation observed in Hela cells depleted in AP-1 or β-arrestins was due to an effect on the initial step of uptake from the plasma membrane . We included the Cbl silencing in this analysis since the role of ubiquitination in the initial uptake of plasma membrane proteins is somewhat unclear in mammalian cells [23] . Using our FACS based endocytosis assay , PA was found to remain at the cell surface upon silencing of the µ subunit of AP-1 , of β-arrestins as well as of Cbl , while silencing of the µ subunit of AP-2 had no effect ( Fig . 3A ) . As has been well established , endocytosis of PA requires heptamerization [1] , [9] . Therefore the lack of endocytosis of PA upon RNAi silencing of genes could be due either to an effect on heptamerization or on endocytosis itself . Heptameric PA at the cell surface is sensitive to SDS and thus migrates as a monomer on SDS gels . It can however be converted to an SDS resistant form by submitting cell extracts to a pH 4 . 5 treatment . Using this treatment , we could rule out that silencing of CHC , β-arrestins or AP-1 affected formation of surface PA7mer in Hela cells ( Fig . S3C ) , altogether showing that the endocytosis process itself was affected upon silencing of these genes . While the role of AP adaptor complexes in recruiting clathrin to cargo molecules has been well established , the role of β-arrestins is less clear [17] . β-arrestins have been proposed to be adaptors either for clathrin or for E3 ubiquitin ligases to the cargo receptor [24] . We therefore investigated whether silencing β-arrestins would affect ubiquitination of TEM8 or CMG2 . Two isoforms of TEM8 have been reported , TEM8-1 , which has a long 200 amino acid cytoplasmic tail , and TEM8-2 which has a short truncated tail ( Fig . S7A ) . The long form is thought to be the more ubiquitous form . While silencing of AP-1 had no significant effect on TEM8-1 ubiquitination ( Fig . S4 ) , silencing β-arrestins strongly diminished ubiquitination ( Fig . 3B , note that the apparent lack of ubiquitination in the 40 min is not due to the lower about of TEM8 , since even at higher levels of TEM8 , as shown in Fig . S4 , no ubiquitination signal can be detected upon β-arrestin silencing ) . That β-arrestins are involved in recruiting Cbl to the anthrax receptors was further supported by immunoprecipitation experiments . Immunoprecipitation of endogenous β-arrestin indeed led to the co-immunoprecipitation of Cbl and of TEM8-1-HA , when transfected ( Fig . 3C ) . Cbl was not detected in immunoprecipitations against an irrelevant protein ( not shown ) . It is worth mentioning that another E3 ligase known to be involved in endocytosis of certain transmembrane proteins such as the epithelial sodium channel [25] , namely Nedd4 , is not involved in anthrax toxin endocytosis ( Fig . S3B ) , underlying the specificity of the E3 ligase Cbl . We similarly found that silencing of β-arrestin , prevented PA induced ubiquitination of CMG2 upon ectopic expression in Hela cells ( Fig . 3D ) . To further dissect the mechanisms governing anthrax toxin endocytosis , we investigated the role of actin . It has previously been reported that production of cAMP triggered by anthrax edema toxin is actin dependent [26] . More recently , Mogridge and colleagues have shown that TEM8-1 , but not TEM8-2 , interacts with actin and have proposed that interaction with actin modulates the affinity for PA , TEM8-2 having a higher affinity for PA than TEM8-1 [27] . During the course of the present studies , Garlick et al . identified a 33 residue stretch in the cytoplasmic tail of TEM8-1 responsible for actin binding and found that a synthetic peptide encompassing this region could trigger actin bundling in vitro [27] . We here sought to investigate what the exact role of actin in anthrax toxin endocytosis could be . Using our FACS based PA internalization assay , we monitored the effect of the G-actin sequestering drug Latrunculin A . Whereas PA was rapidly internalized in controls cells , endocytosis was completely blocked in latrunculin treated Hela cells ( Fig . 4 ) . This was confirmed by the fact that the SDS-resistant pPA7mer failed to form and MEK1 remained intact ( Fig . 5A and Fig . S5 ) . As mentioned above , endocytosis of PA requires heptamerization [1] . To test whether latrunculin treatment affected heptamerization of PA at the cell surface , cell extracts were submitted to a pH 4 . 5 treatment , prior to SDS-PAGE . To our surprise , heptamerization was severely inhibited in latrunculin treated Hela cells , indicating that the drug had blocked the oligomerization process ( Fig . 5B ) . This was not due to an effect of latrunculin on PA heptamerization per se , as will become apparent later . The involvement of actin in PA63 heptamerization at the surface of Hela cells was further confirmed by treating cells with blebbistatin , an inhibitor of myosin II ATPase activity [28] . Although the effect was not as strong as with latrunculin , blebbistatin significantly inhibited both the appearance of endosomal pPA7mer ( SDS resistant ) and the surface PA7mer ( converted to SDS resistant by an acid treatment ) ( Fig . S6 ) . The above experiments show that the actin cytoskeleton promotes toxin oligomerization in Hela cells . We next wished to confirm the findings of Go et al . on the differential ability of TEM8 isoforms 1 and 2 to interact with actin [29] . Actin could be readily detected upon immuno-precipitation of HA-tagged TEM8-1 expressed in Hela cells . We could also co-immunoprecipitate the actin nucleating protein talin , its interacting protein vinculin ( Fig . 6A ) and the blebbistatin sensitive myosin II heavy chain 9 , MyH9 [30] ( Fig . S7B ) . Since the interaction of TEM8-1 with actin appears to be direct [27] , talin , vinculin and myosin II might be involved in regulating the dynamics of the process , as suggested by the here-observed effects of blebbistatin . This will however require further investigation . In contrast to the observations reported by Go et al . [29] , we could also detect actin in TEM8-2-HA immunoprecipitates , albeit at far lower levels than for TEM8-1-HA . In agreement with Go et al . [29] , the Y383C mutation , which mimics a mutation found in Hyaline fibromatosis syndrome [31] , [32] , affected actin binding , but in our hands did not abolish it ( Fig . 6C ) . We were however unable to detect any co-immunoprecipitation of actin with CMG2 ( Fig . 6B ) . This was somewhat surprising considering the high degree of similarity between the cytoplasmic tails of CMG2 and TEM8-1 ( Fig . S7A ) . Since the tail of TEM8-1 is some 50 residues longer than that of CMG2 , we generated a truncated TEM8-1 matching the length of CMG2 , and found that the ability to immunoprecipitate actin was unaffected ( not shown ) . We also constructed chimeric proteins in which the transmembrane and/or cytosolic tail of CMG2 were replaced by that of TEM8-1 and found that the cytosolic tail of TEM8-1 is sufficient to confer actin-binding ability to the proteins ( Fig . 6B ) . Garlick et al . have narrowed down the actin interacting domain of TEM8-1 with actin to residues 379 to 411 ( DASYYGGRGVGGIKRMEVRWGEKGSTEEGAKLE ) [27] , a stretch that is absent in TEM8-2 but fully conserved , with the exception of a single residue ( bold underlined ) , in CMG2 . Combined , the observations of Mogridge and ours thus suggest that direct binding of the 379–411 amino acid stretch to actin must be prevented in CMG2 , through regions that differ from the tail of TEM8-1 ( Fig . S7A ) . The observation that TEM8-2 can , albeit to a far lesser extend , also interacts with actin suggest that TEM8 might be able to interact with actin by more than one way , possibly directly and indirectly via the interaction with talin , vinculin and myosin II , an interaction that would also be prevented in CMG2 . The effect of latrunculin A on PA heptamerization prompted us to evaluate the effect of PA binding on the interaction of TEM8 with actin . Interestingly , when TEM8-1 ( WT or Y383C mutant ) was immuno-precipitated from PA83 treated cells , the interaction with actin , talin and vinculin was strongly diminished Fig . 6AC . The decreased interaction with actin did not require the oligomerization of PA63 since it was equally observed when treating cells with a mutant of PA that is resistant to furin cleavage and thus remains monomeric ( Fig . 6C ) [8] . These observations show that TEM8-1 , as well as TEM8-2 albeit to a far lesser extent , interact with the actin cytoskeleton , but that binding of PA83 releases this interaction . These findings provide a mechanistic explanation for the observations of Mogridge and colleagues that the association of TEM8 with the cytoskeleton correlates with weakened binding to PA [27] , [29] . They also provide the first evidence that binding of the toxin to TEM8-1 on the outside of the cell leads to a conformational change on the cytosolic side , corresponding to a form of outside-in signaling . Go et al . recently described inside-out signaling where by actin might govern the affinity of TEM8 for its ligand [29] . Thus TEM8 is , as integrins , capable of both inside-out and outside-in signaling . Using fluorescence recovery after photobleaching ( FRAP ) , Go et al . [29] have shown that the motilities of TEM8-1 and TEM8-2 at the cell surface differ , TEM8-1 being less mobile with a 25% immobile fraction . The difference between the two isoforms was however abolished after latrunculin treatment [29] . We made the same observations after expression of GFP-fusions of these proteins in Hela cells and FRAP analysis ( Fig . 7A for TEM8-1; TEM8-2 not shown ) . More specifically , the recovery half-life ( t1/2 ) of TEM8-1 was 27 . 4±5 . 4 s in control cells , with an immobile fraction of 30±9% . After latrunculin treatment , the immobile fraction dropped to 16±7% and t1/2 decreased to 12 . 2±4 . 9 s . We extended these analyses to CMG2 . As predicted from the biochemical observations , we found no effect of latrunculin A treatment on the mobility of CMG2 using FRAP: t1/2 was 13 . 3±4 . 2 s with 9±9% immobile fraction in control cells vs . t1/2 = 11±4 . 2 s in latrunculin treated cell , p = 0 . 21 ( Fig . 7B ) . FRAP analysis also confirmed the observation that PA binding alleviates the interaction with actin . When cells were incubated with the PA , the latrunculin A dependence of t1/2 for TEM8-1 was indeed strongly reduced ( Fig . 7C ) and in certain experiments even abolished . These experiments were performed using the mutant PA that cannot heptamerize , in order to focus on the effect of toxin binding , without the complications of oligomerization and internalization . The residual effect of latrunculin A could be due to the fact that not all TEM8-1 molecules are occupied by PA , and this may also account for the variability between experiments performed on transiently transfected cells , where expression levels are not tightly controlled . PA binding had not effect on the latrunculin A dependence of CMG2 mobility as expected from the biochemical observations ( Fig . 7D ) . The differential abilities of TEM8-1 , TEM8-2 or CMG2 to interact with actin suggested that the requirement for actin in toxin uptake might depend on the receptor used . To test this , we made use of a CHO mutant cell lines that is devoid of anthrax toxin receptors ( CHOΔATR ) and thus defective in toxin binding [8] . These cells were recomplemented with TEM8-1 , TEM8-2 or CMG2 and the formation of the SDS-resistant pPA7mer was monitored . To follow the heptamerization process , we also , in a parallel set of gels , submitted cell extracts to the pH 4 . 5 treatment mentioned above , that converts all PA7mer to an SDS-resistant form . As expected , recomplementation with TEM8-1 led to the same observation as in HeLa cells: latrunculin A treatment prevented heptamerization of PA , thus even after acid treatment , heptamers could not be detected ( Fig . 8A ) . In cells expressing TEM8-2 , formation of pPA7mer was also completely blocked by latrunculin A . When monitoring the heptamerization process however , it became apparent that latrunculin A affected the process , but much less than for TEM8-1 ( ≈40% residual oligomerization , Fig . 8B ) . Finally , in cells recomplemented with CMG2 , formation of pPA7mer was again drastically affected by latrunculin , but in striking contrast to the observations made for TEM8 , surface heptamerization was unaltered ( indicating that latrunculin does not inhibit the heptamerization process per se ) ( Fig . 8C ) . The absence of pPA7mer , despite normal heptamerization , indicates that latrunculin A blocked the endocytic process . To test this directly , we monitored endocytosis of PA in cells recomplemented with CMG2-GFP using our FACS assay , and found that indeed latrunculin A blocked endocytosis ( Fig . S8 ) . Altogether these observations show that 1 ) actin is required for efficient heptamerization of PA when bound to TEM8-1 , to a lesser extent when bound to TEM8-2 , but not when bound to CMG2; 2 ) actin is required for the endocytosis of heptameric PA , probably irrespective of the receptor , as observed for CMG2 and TEM8-2 . While actin was shown to play an active role in the formation of endocytic vesicles in yeast [33] , the situation is less clear in mammalian cells . To test whether actin is systematically required for clathrin-mediated endocytosis , we investigated the effect of latrunculin on diphtheria toxin entry . As shown in Fig . S2C , latrunculin had no effect on the kinetics of diphtheria mediated ADP-ribosylation of EF-2 , illustrating that clathrin-dependent endocytosis can be actin independent .
Our previous findings that anthrax toxin endocytosis is dependent on dynamin , while independent of caveolin [8] , pointed towards a requirement for clathrin . We now show that silencing of CHC inhibits endocytosis of the toxin . Clathrin-mediated endocytosis however does not correspond to a unique entry route but encompassed a collection of internalization pathways that are linked by the use of the clathrin coat protein . The last years have indeed revealed a highly flexible system where each membrane cargo protein recruits , via “sorting signals” in its cytoplasmic domain , a specific set of adaptors and accessory proteins that then interact with clathrin [15] . In addition , some “add on” modules [15] maybe allow and modulate interactions with the actin cytoskeleton ( see below ) . We had previously shown that ubiquitination of anthrax toxin receptors is necessary for the formation of pPA7mers in endosomes [11] . We had however not determined the step at which ubiquitination is required , i . e . removal from the plasma membrane or sorting into intraluminal vesicles of multivesicular endosomes . Here we show that ubiquitination by Cbl is at least required for the initial step of endocytosis , much like what has been observed in yeast . Interestingly , we found that ubiquitination of the receptors depends on β-arrestins . These adaptor molecules have been implicated in the endocytosis of G coupled receptors , as well as single-transmembrane receptors , such as the tyrosine kinase receptor IGF-1R [18] , the Drosophila Notch receptor [19] and others ( for review see [34] ) . Since the tails of β-arrestins contain clathrin and AP-2 interaction motifs , they have been proposed to act as adaptors to bring clathrin to the cargo receptor [17] . Recent studies in yeast identified a family of arrestin related proteins–so called ART ( arrestin-related trafficking adaptors ) proteins–that mediate endocytosis of specific plasma membrane proteins . Instead of acting as an adaptor between the receptor and AP-2 , ARTs were shown to act as adaptors for the Nedd4-like E3 ubiquitin ligase Rsp5 [24] , [35] . Such an adaptor function of arrestins to recruit ubiquitin ligases has also been proposed for the β2-adrenergic receptor [17] . It is likely that β-arrestin is important for recruitment of RING domain containing ligase Cbl to the cytoplasmic tail of anthrax toxin receptors . β-Arrestin antibodies indeed precipitated both TEM8-1 and Cbl and depletion of β-arrestin prevented the Cbl-mediated ubiquitination of TEM8-1 . Combined with the observations in yeast , our findings illustrate that β-arrestins are able to recruit E3 ligases both of RING ( here ) and HECT ( in yeast ) domain containing E3 ligases . Both TEM8-1 and CMG2 contain two YXXF motifs ( F: bulky hydrophobic , http://elm . eu . org/ ) in their cytoplasmic tails that are potential interaction sites with heterotetrameric clathrin adaptors . The surprise was to find a requirement for AP-1 , but not AP-2 . The best-described role of AP-1 is in budding of clathrin coated vesicles from the Trans-Golgi network and endosomes [36] . Therefore the apparent requirement of AP-1 in anthrax endocytosis could be due to an indirect effect , such as the surface delivery of a required component . AP-1 silencing however had no effect on the level of the anthrax receptors at the cell surface–toxin binding was unaffected– , nor on endocytosis of diphtheria toxin or acidification of endosomes . Moreover , this is not the first report for a role of AP-1 at the cell surface . AP-1 was found to be required for phagocytosis in macrophages and in Dictyostelium , and was detected on nascent phagosomes [37] possibly to deliver membrane . Similarly a requirement for AP-1 , but not AP-2 , was found for clathrin-mediated entry of the human bacterial pathogen Listeria monocytogenes and AP-1 localized to the entering bacterium [38] again possibly to deliver membrane . Finally , AP-1 was recently found to be able to functionally compensate for AP-2 in mediating the recycling of synaptic vesicles [39] . Although we cannot fully exclude an indirect effect of AP-1 silencing , our data combined with that of recent literature do suggest that AP-1 could play a role in specific types on endocytosis . When analyzing the role of actin in anthrax toxin endocytosis , we found that TEM8-1 driven heptamerization of PA was strongly affected by actin depolymerizing drugs or inhibitors of the myosin II motor . Intriguingly , whereas TEM8-1 was found to interact with actin in control cells , this interaction was lost upon toxin binding . Our interpretation of these findings is that the cortical actin cytoskeleton actively organizes TEM8-1 at the cell surface , in a manner that favors the oligomerization process . Similarly Mayor and coworker recently found that actin actively organizes GPI-anchored proteins into domains [40] . We are not insinuating that GPI-anchored proteins and TEM8-1 reside in similar domains , was it only because GPI-anchored are well established to associate with lipid rafts , which is not the case for TEM8-1 at steady state [8] , [11] . The similarities between TEM8-1 and GPI-anchored proteins in terms of actin dependence do however illustrate the capacity of the cortical cytoskeleton to organize protein domains within membranes . A second surprising observation was that whereas latrunculin led to an increase in the 2 dimensional diffusion coefficient of TEM8-1 in the plasma membrane ( FRAP experiments ) , it inhibited heptamerization . The efficiency of oligomerization depends on the collision probability between receptor bound PA monomers . Therefore , one would expect that increased motility would lead to accelerated oligomerization . This was however not the case , indicating that the actin dependent localization/organization of TEM8-1 on the membrane is more important for efficient oligomerization than the ability of this receptor to rapidly diffuse at the cell surface . Despite the high degree of similarity between the cytoplasmic tail of CMG2 and that of TEM8 , and in particular the conservation of the various potential motifs for binding of actin or actin interacting proteins ( multiple potential SH3 binding motifs , profiling binding motifs and possibly a distant WASP interacting motif , Fig . S7A ) , we could not detect any interaction of CMG2 with actin using three totally independent methods ( immunoprecipitation , FRAP and PA heptamerization experiments ) . Since CMG2 also contains the stretch of residues found by Garlick et al . [27] to mediate direct binding of TEM8-1 to actin , it appears that regions of the cytoplasmic tail of CMG2 , that are not conserved in TEM8-1 , might be involved in preventing actin binding at steady state , possibly to ensure high ligand binding affinity . The observation that actin promotes PA heptamerization when the toxin is bound to TEM8-1 , but not when bound to CMG2 provides an possible explanation for an issue that has remained mysterious to us . In vitro studies have shown that the affinity of PA for the von Willebrand domain of CMG2 is some 3 orders of magnitude higher than that of PA for the von Willebrand domain of TEM8 [3] , [41] . Interestingly , Liu et al . recently reported that the apparent functional affinity of PA for its receptor is only 10 times higher for CMG2 than TEM8 [42] . These functional affinities were obtained by performing toxicity tests . It is interesting to note that in these assays performed on living cells , the apparent difference in binding is 100 lower than expected from experiments on purified von Willebrand domains . Finally when analyzing endocytosis of the toxin in cellular systems–i . e . in the absence of competition with an inactive PA mutant , we have not observed any drastic difference in the kinetics of PA heptamerization or of MEK1 cleavage between cells expressing TEM8 vs . CMG2 . One speculation to explain these anomalies is that actin promoted oligomerization partially compensates for the lower affinity of PA for the von Willebrand domain of TEM8 . In addition to its role in PA heptamerization , we found that actin is required for the endocytic process , irrespective of the receptor usage . For large cargoes such as the Vesicular Stomatitis virus , it has been proposed that since the transport vesicles is only incompletely coated by clathrin , actin is required to complete the process [43] . Considering the small size of the anthrax toxin , even taking into account heptamerization of PA and binding of EF and LF , partial coating seems somewhat unlikely . In this case , actin could accelerate the pinching off and detachment of the clathrin coated vesicle as observed in yeast [15] . Clarifying the role of actin in clathrin mediated endocytosis in mammalian cells will clearly require further studies using multiple systems and cargoes . Combined with previous reports , our present findings provide the following sequence of events leading to endocytosis of the anthrax toxin ( Fig . 9 ) . In the absence of toxin , TEM8-1 interacts with the actin cytoskeleton . This reduces its mobility and leads to some form of surface organization , all of which is not observed for CMG2 . Upon secretion by the bacterium and diffusion towards target cells , PA binds to the receptors with an affinity that depends of the receptor identity ( higher affinity for CMG2 than TEM8 ) and on the inside-out signaling , mediated by the actin cytoskeleton , that affects the conformation of the ectodomain of TEM8-1 [29] , similar to what is observed for integrins . Upon toxin binding , interaction between TEM8 and the actin cytoskeleton is released . Despite this lost interaction , the actin dependent surface organization/clustering of TEM8-1 favors the heptamerization of PA63 . Heptamerization leads to the activation of src-like kinases which in turn phosphorylate the cytoplasmic tail of CMG2 [10] , as step that is required for the β-arrestin mediated Cbl-dependent ubiquitination of the receptors . The modifications finally allow the recruitment of AP-1 and clathrin , leading to clathrin coated pit formation , which could pinch off and detach through the action of the GTPase dynamin and of actin .
Hela cells were grown in complete Modified Eagle's medium ( MEM ) ( Gibco ) supplemented with 10% fetal calf serum ( FCS ) , 2 mM L-glutamine , penicillin and streptomycin . The anthrax toxin receptor–deficient CHO ( here designated as CHOΔATR ) cells were grown in F12 medium as described previously [44] , [45] . Stable BHK21-tTA/anti-CHC cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) containing 200 ng ml–1 puromycin and 2 µg ml–1 tetracycline [46] . To induce CHC antisense RNA expression , tetracycline was removed from the medium for 48 hours . Anthrax toxin subunits and diphtheria toxin were a gift from S . Leppla , prepared as described [47] , including wild type PA , PA U7 in which the furin cleavage site RKKR is changed to PGG , LF . PA63 corresponds to trypsin-nicked PA83 [8] . Antibodies against anthrax PA were from the Leppla laboratory; the aerolysin mutant ( G202C-I445C ) named ASSP was produced in our lab as described [48] . The rat anti-mouse CMG2 was generated by genetic immunization with the mouse CMG2 construct ( outsourced to Genovac ) . The antibody against the N-terminal peptide of MEK1 was produced in our laboratory; anti-HA , anti-GFP monoclonals and anti-HA-agarose conjugated beads from Roche ( Applied Science , IN ) ; anti-tubulin , anti-AP1 , anti-vinculin , anti-talin , anti-MYH9 and anti-β arrestin from Sigma; anti-actin from Millipore; anti-EF2 , anti-Cbl , anti-AP2 , anti-caveolin , anti-Ubiquitin from Santa Cruz; anti-GRB2 from BD Transduction Laboratories; anti-Dab2 from Abcam laboratories , anti-CHC from Affinity Bioreagents , anti-transferrin receptor from Zymed , anti-caveolin from Santa Cruz , anti-rab 5 was kindly provided by J . Gruenberg . Mouse and rabbit HRP secondary antibodies were from Pierce , IL , rat HRP secondary antibodies were from Sigma and Alexa-conjugated secondary antibodies from Molecular Probes . Latrunculin A was purchased from Invitrogen and used at a final concentration of 0 . 4 µg/ml for 45 min in medium without serum at 37°C . Blebbistatin was purchased from Sigma and used at a final concentration of 50 µM for 1 hr in medium without serum at 37°C . Human TEM8-HA/1 , TEM8-HA/2 and CMG2-4-HA were cloned in pIREShyg2 as described [11] . The human TEM8/1-GFP ( isoform 1 ) and the human TEM8/2-GFP ( isoform 2 ) were cloned in pHS003-EGFP , kindly provided by J . Young . Human TEM8-HA/1 Y383C was generated by mutagenesis using Quickchange ( Stratagene ) reagent with the following primers: 5′- G GTA GAC GCC TCT TAT TGT GGT GGG AGA GGC GTT GG-3′ . The human and mouse CMG2 ( isoform 4 ) gene tagged with a V5 epitope was cloned in pcDNA3 . 1/V5-HIS-TOPO expression vector . The human CMG2-GFP ( isoform 1 ) was cloned in pHS003-EGFP as described [49] . Synthetic genes were synthesized by Geneart: CTT for extracellular part of CMG2 ( amino acids 1 to 318 ) and transmembrane and cytosolic regions of TEM8 ( amino acids 321 to 573 ) , CCT for extracellular and transmembrane regions of CMG2 ( amino acids 1 to 341 ) and cytosolic part of TEM8 ( amino acids 344 to 573 ) and cloned in pIREShyg2 with a HA tag at the C-terminus . The human Eps15 gene was cloned in pEGFP-C2 , kindly provided by A . Dautry-Varsat ( Pasteur Institute ) . Plasmids were transfected into Hela cells for 48 or 72 hrs ( 2 µg cDNA/9 . 6 cm2 plate ) using Fugene ( Roche Diagnostics Corporation ) . siRNA target sequences were the following: human CHC: GGCCCAGGT GGTAATCATTTT , Grb2: AAGTTTGGAAACGATGTGCAG , NEDD4: ATGGAGTTGATTAGATTACAA , Dynamin II: CTGCAGCTCATCTTCTCAAAA , EPS15: GTGGACCAACATAATATTAAA , AP1 µ subunit: AAGGCATCAAGTATCGGAAGA , AP1 γ subunit: AACGAATGTTCGGATGACTTT , AP2 µ subunit: GGAAAACATCAAGAACAATTT , β-arrestin 1: AAAGCCTTCTGCGCGGAGAAT , β-arrestin 2: AAGGACCGCAAAGTGTTTGTG , DAB2: AAGGTTGGCCTTAGTAGTCAA , were purchased from Qiagen . Cbl siRNA was purchased from Santa Cruz ( sc-29242 ) . As control siRNA we used the following target sequence of the viral glycoprotein VSV-G: ATTGAACAAACGAAACAAGGA . To do silencing , Hela cells were transfected for 72 hours with 100 pmol/9 . 2 cm2 dish of siRNA using oligofectamine ( Invitrogen ) transfection reagent . Hela cells were harvested , washed with PBS and homogenized by passage through a 22G injection needle in HB ( HB: 2 . 9 mM imidazole and 250 mM sucrose , pH 7 . 4 ) containing the Roche mini tablet protease inhibitors cocktail following manufacturer's instructions . To convert surface PA7mer to an SDS-resistant form , cell extracts were incubated at room temperature for 10 min with 145 mM NaCl and 20 mM MES-Tris , pH 4 . 5 . Protein quantification was done with Pierce BCA kit . Proteins were loaded at 40 µg prot/lane and separated on a 4–20% acrylamide precast Novex gel ( Invitrogen ) under reducing conditions for PA and native condition for diphtheria toxin and transferred to nitrocellulose membranes ( Schleicher and Schuell ) . For immunoprecipitations , cells were lysed 30 min at 4°C in IP buffer ( 0 . 5%NP40 , 500 mM NaCl , 500 mM Tris-HCl pH 7 . 4 , 20 mM EDTA , 10 mM NaF , 2 mM benzamidine , and a cocktail of protease inhibitors , Roche ) , centrifuged 3 min at 2000 g and supernatants were incubated 16 h at 4°C with antibodies and beads . To follow protein Ubiquitination , 1 mM of NEM was added in the lysis buffer described above . BHK or Hela cells were incubated one hour at 4°C with 1 ug/ml PA63 , washed and incubated different times at 37°C , washed at 4°C and incubated 5 min on ice with cold trypsin . Loosely attached cells were harvested by pipetting and stained for 30 min on ice with anti-PA antibodies , followed stained for 30 min on ice with secondary fluorescent antibodies , washed in PBS+1%FCS and then evaluated on a FACSCalibur™ ( Becton Dickinson ) . FACS data were analyzed using FlowJo software ( FlowJo , LLC ) . HeLa cells were seeded and transfected with GFP-tagged receptors in 35 mm glass bottom dishes ( MatTek ) . Samples were analyzed on a Leica SP2 confocal scanning microscope using a 63x oil immersion objective . After 10 scans of a chosen region a rectangle of 1×3 µm on the edge of a cell was irreversibly bleached using the full power of the 488 , 458 and 476 nm laser lines . The bleaching resulted in a ∼80% depletion of fluorescence . Recovery of fluorescence was monitored over 80 seconds with a 2 second interval by scanning the region with a low 488 nm laser power to minimize photobleaching during sampling . The fluorescence of the bleached area was normalized at each time point using a non-bleached control area . Recovery kinetics were fitted to an exponential function .
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Bacillus anthracis is the bacterium responsible for the anthrax disease . Its virulence is mainly due to 2 factors , the anthrax toxin and the anti-phagocytic capsule . This toxin is composed of three independent polypeptide chains . Two of these have enzymatic activity and are responsible for the effects of the toxin . The third has no activity but is absolutely required to bring the 2 enzymatic subunits into the cell where they act . If one blocks entry into the cells , one blocks the effects of these toxins , which is why it is important to understand how the toxin enters into the cell at the molecular level . Here we identified various molecules that are involved in efficiently bringing the toxin into the cell . First , we found that the actin cytoskeleton plays an important role in organizing one of the two anthrax toxin receptors at the cell surface . Second , we found a cytosolic protein , β-arrestin , that is required to modify the intracellular part of the toxin receptor , to allow uptake . Finally , we directly show , for the first time , that anthrax toxin uptake is mediated by the so-called clathrin-dependent pathway , a very modular entry pathway , but that the toxin utilizes this pathway in an unconventional way .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/membranes",
"and",
"sorting",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2010
|
Endocytosis of the Anthrax Toxin Is Mediated by Clathrin, Actin and Unconventional Adaptors
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One of the major mechanisms driving the evolution of all organisms is genomic rearrangement . In hyperthermophilic Archaea of the order Thermococcales , large chromosomal inversions occur so frequently that even closely related genomes are difficult to align . Clearly not resulting from the native homologous recombination machinery , the causative agent of these inversions has remained elusive . We present a model in which genomic inversions are catalyzed by the integrase enzyme encoded by a family of mobile genetic elements . We characterized the integrase from Thermococcus nautili plasmid pTN3 and showed that besides canonical site-specific reactions , it catalyzes low sequence specificity recombination reactions with the same outcome as homologous recombination events on DNA segments as short as 104bp both in vitro and in vivo , in contrast to other known tyrosine recombinases . Through serial culturing , we showed that the integrase-mediated divergence of T . nautili strains occurs at an astonishing rate , with at least four large-scale genomic inversions appearing within 60 generations . Our results and the ubiquitous distribution of pTN3-like integrated elements suggest that a major mechanism of evolution of an entire order of Archaea results from the activity of a selfish mobile genetic element .
Large-scale genomic rearrangements allow organisms to evolve much more rapidly than through random mutation alone . Rearrangements can result in the movement of genes within genomes , changes in coding strand use , loss of nonessential functions and the incorporation of foreign DNA . As a result , the organization , content and processing of genetic information can be deeply altered . In all three domains of life , chromosomal reorganization is mainly promoted by recombination between homologous sequences , for example between redundant ribosomal operons [1 , 2] or integrated copies of mobile elements ( ME ) such as prophages [3 , 4] , transposons [5 , 6] and insertion sequences ( IS ) [7] . Such recombination can result in the DNA inversions readily observed in closely related genomes [8 , 9] . In addition to homologous recombination , chromosomes can undergo rearrangement through retrotransposon-associated non-homologous recombination [10] . Other elements like integrons confer rapid adaptation to bacteria in changing environments by shuffling cassette arrays encoding a variety of functions , a process involving a site-specific recombinase and two types of attachment sites [11] . Further genomic rearrangement/reorganization can occur through the acquisition of new genetic material , predominantly by lateral gene transfer . Such gene transfer occurs in all organisms through infection by mobile elements such as viruses or plasmids , or through the uptake of free or encapsulated DNA from the environment [12 , 13] . Genomes can acquire novel genes in a fashion ranging from transient to permanent depending on the type of element and the physiological conditions of the host . When ME succeed in stably inserting their genome , the inserted DNA is then replicated as part of the host chromosome . The transactions between ME DNA and host genome are catalyzed by recombinases typically encoded by the elements themselves . These recombinases rank in different classes based on their enzymatic activity and the specificity of their DNA targets . The smallest ME are insertion sequences ( IS ) composed of a short DNA segment encoding only the enzymes involved in their transposition which can occur at many different genomic locations [14] . The related transposons are larger DNA segments which can be transposed by two flanking IS and frequently carry additional genes such as antibiotic resistance determinants [15] . The most frequent IS recombinases are DDE transposases which do not form covalent transposase-DNA intermediates during transposition [16] . Other and typically larger ME such as plasmids and viruses encode recombinases promoting DNA transactions with a stronger DNA sequence specificity . Such site-specific recombination is not only used for mobile element integration and excision in bacteria but also in the spread of antibiotic resistance by transposable elements , the control of plasmid copy number , regulation of gene expression and the resolution of concatenated chromosomes [17] . Site-specific recombinases can be categorized into the serine recombinases and tyrosine recombinases ( Y-recombinases ) ; which , in contrast to DDE transposases , form covalent enzyme-DNA intermediates during recombination , albeit with markedly different mechanisms of action . Before religation of the two recombining DNA strands , serine recombinases generate breaks in all strands while Y-recombinases produce two sequential single-strand breaks [17] . As a rule , site-specific integration/excision reactions promoted by Y-recombinases occur via a synaptic complex composed of two DNA duplexes carrying the specific sites bound by four recombinase protomers [17] . The two-recombinase pairs are activated sequentially , allowing one strand from each duplex to be exchanged at a time via two consecutive and symmetrical Holliday junctions . A notable exception is Vibrio cholerae phage CTX . Not only does this phage integrate into its host genome in single stranded form where two sites fold into a hairpin structure , mimicking a recombination target for the cellular XerCD chromosome resolvase; but also only requires XerC for integration [18] . One of the best-studied Y-recombinases is the integrase of phage λ . The primary function of this enzyme is the integration of phage DNA into the chromosome of its bacterial host ( and its excision ) . This function is achieved by promoting site-specific recombination between the phage attachment site attP and its chromosomal counterpart attB [19] . Under particular circumstances , the integrase of the lambdoid phage HK022 is capable of generating inversions between attP and a secondary attachment site in the HK022 left operon [3] . Similarly , the primary function of the yeast FLP protein is the control of the 2μ plasmid copy number [20] by DNA inversion between two divergent 34bp FRT sites located on the plasmid [21] . FLP recombinase activity has also been successfully used for integration and excision of synthetic DNA in mammalian genomes [22] . The recombination activities of both λ integrase and FLP recombinase are summarized as shown in S1 Fig . Historically , this reciprocal and conservative recombination between two stringently defined double-stranded DNA sequences in each chromosome was denominated the Campbell model [23] . The sequences of a considerable number of Y-recombinases have been compared to reveal the position of conserved residues and infer the location of the catalytic active site [24] . They share in their C-terminal moiety a rather well conserved region of ~120 amino acids containing up to six nearly invariant amino acids R . . K . . HxxR . . [W/H] . . Y forming the active site [25 , 26] . A small number of Y-recombinases have been characterized biochemically in Archaea , for example the XerA recombinase of the hyperthermophilic euryarchaeon Pyrococcus abyssi which exhibits a perfect active site consensus [27] . Sequence alignments have revealed that other archaeal active sites diverge slightly from the bacterial consensus R . . HxxR . . Y [28] . The integrases of viruses SSV1 isolated from the hyperthermophilic crenarchaeon Sulfolobus shibatae [29] and SSV2 from Sulfolobus islandicus [30] share the consensus R . . KxxR . . Y while the plasmidic integrase of Sulfolobus sp . NOB8H2 displays R . . YxxR . . Y [28] . Mobile elements therefore contribute to genome evolution through both site-specific and homologous recombination , which usually operate by distinct mechanisms and enzymatic activities . Homologous recombination is also known to occur frequently between multiple IS copies resulting in large scale archaeal genomic rearrangements , as observed in both Crenarchaeota e . g . Sulfolobus islandicus [31] and Euryarchaota e . g . Pyrococcus abyssi [32] . The distribution of archaeal ISs is patchy not only at the phylum level but also at genus level [9] . Interestingly , genome shuffling occurs in Thermococcus [33] even if ISs are seldom found in this genus suggesting that alternative recombination mechanisms are capable of producing large-scale genomic rearrangements . If site-specific recombination only requires specific nucleotide sequences targeted by a dedicated recombinase , homologous recombination on the other hand is a much more complex process . In all organisms , homologous recombination constitutes one of several pathways to repair double-strand breaks . In addition to DNA synthesis , it requires dedicated recombinases and their accessory factors which act on stretches of near-sequence-identical DNA . In eukaryotic and bacterial cells , the enzymes and pathways involved in homologous recombination have been extensively studied ( see [34 , 35] for reviews ) , whereas archaeal homologous recombination is still an active field of investigation . It is known that the initial resectioning step after double-strand break involves the Rad50–Mre11–HerA–NurA complex to generate 3’ single-strand substrates [36 , 37] . The RecA paralog RadA and its accessory functions associate with this ssDNA to constitute the presynaptic filament , which will scan and pair with homologous sequences [38] . In the archaeon Thermococcus kodakarensis , homologous recombination has been detected experimentally between stretches of identical DNA sequences equal to or greater than 500bp [39] . To our knowledge , a direct overlap between site-specific and homologous recombination processes has not been described so far . In the present work , we report the discovery and characterization of a new integrase from the hyperthermophilic archaeon Thermococcus nautili [40 , 41] capable of catalyzing both site-specific recombination and low sequence specificity recombination reactions mimicking homologous recombination . The wide distribution of this particular Y-recombinase among the Thermococcus genus provides a valid rationale for the observed genomic rearrangements in these Archaea .
We compared the chromosomes of the 13 completely sequenced Thermococcus species available to date by dotplot analysis and observed high levels of genome scrambling as shown in Fig 1A . Strikingly , comparison of T . onnurineus and T . sp . 4557 chromosomes by this approach revealed only two large inversions of 139/143Kb and 102/74Kb respectively ( Fig 1B & 1C ) . This relatively small number of inversions facilitated the investigation of the synteny breakpoints bordering both inversions . Using the SyntTax web tool [42] , a composite representation was obtained as shown in Fig 1C . Gene order is conserved immediately upstream and downstream of each inversion border and was used to identify the synteny breakpoints . For each inversion , the breakpoints are located within tRNA gene pairs , transcribed in opposite orientations . Interestingly , T . nautili plasmid pTN3 integrates in the tRNALeu gene BD01_0018 [41 , 43] ( S2 Fig ) and this gene displays over 97% sequence identity with tRNALeu ( GQS_t10759 ) , which borders a large chromosomal inversion between T . onnurineus and T . sp . 4557 ( Fig 1B ) . The concordance between the chromosomal attachment site of the pTN3 integrase ( IntpTN3 ) and the recombination targets bordering each inversion ( in opposite orientations ) led us to define a working model to explain the formation of genomic inversions observed in the Thermococcus genus . We hypothesize that the frequent genomic inversions observed in the evolution of the Thermococcales order are a result of enzymatic activity of the integrase encoded by horizontally mobile elements , such as pTN3 . The integrase of pTN3 shares significant sequence similarity with canonical Y-recombinases and its predicted active site can be defined as R . . K . . AxxR . . Y which only slightly diverges from the consensus ( S3A Fig ) . In addition , IntpTN3 displays a high degree of conservation with two biochemically characterized hyperthermophilic Y-recombinases , the archaeal IntSSV1 [44] and IntSSV2 [30] ( S3B Fig ) . Thus , it seemed worthwhile to compare the enzymatic activities of IntpTN3 to those of other enzymes of the same family such as phage λ integrase and Saccharomyces cerevisiae 2μ plasmid FLP protein and to validate them against the canonical Y-recombinase model . In order to characterize the activities of IntpTN3 , it was necessary to over-produce and purify the enzyme ( S4 Fig ) and to construct DNA substrates carrying appropriate attachment sites ( as determined by sequential deletions ( S5 Fig ) . An integrase variant ( IntpTN3Y428A ) in which the catalytic tyrosine is substituted with an alanine was constructed , purified and tested ( S6 Fig ) . We used these proteins and DNA components in a series of in vitro and in vivo experiments , detailed below , to ascertain the properties of IntpTN3 . The large-scale genomic inversions observed between T . sp . 4557 and T . onnurineus display minor gene order rearrangements near the recombination endpoints indicating that these events are not recent and might have undergone remodeling ( Fig 1C ) . In order to identify more recent rearrangements , we investigated whether large-scale genomic inversions could occur spontaneously under laboratory conditions . T . nautili carrying its natural plasmids was sub-cultured in two independent experiments for 60 and 66 generations ( therefore termed T . nautili 60G and 66G ) in rich liquid medium with intermittent storage at 4°C and the metagenomes of the resulting populations were completely re-sequenced . We observed in both T . nautili 60G and 66G sub-cultures a high proportion of a novel rearranged genome exhibiting four new large-scale chromosomal inversions when compared to the original published T . nautili genome ( GenBank accession NZ_CP007264 ) [41] ( Fig 6A ) . By mapping the frequency of the Illumina reads around the four inversion sites , we measured the incidence of the rearranged genome in the T . nautili 66G population , which was found in most cases to exceed that of the original genome ( S3 Table ) . Both T . nautili 60G and 66G rearranged chromosomes were remarkably similar when compared by dotplot analysis ( S7 Fig ) . Additionally , plasmid pTN3 was largely underrepresented in the T . nautili 66G sub-culture ( S3 Table ) , whereas the smaller pTN1 and pTN2 were conserved . The chromosomally-integrated pTN3 copy carrying the disrupted integrase gene was also retained . The chain of nested inversion events leading to these new recombined genomes could be reconstructed ( Fig 6C ) and allowed us to analyze and precisely map the recombination endpoints . Each of the four genomic inversions occurred between paralogous gene pairs: between tRNAGly genes BD01_1557 and BD01_1976 , between methyl accepting chemotaxis genes BD01_1166 and BD01_1584 , between transposase genes BD01_1317 and BD01_1763 and finally between UDP-glucose-6 dehydrogenase genes BD01_1333 and BD01_1481 . For each pair of paralogous genes , the inversion events always occurred between two inverted segments of DNA sharing extensive sequence identity ( S8 Fig ) . However , we could not detect significant similarity between inverted DNA segments corresponding to different pairs of paralogous genes using BLAST ( e-value ≥ 0 . 075 ) . Furthermore , none of these sequences could be aligned with the original pTN3 attachment site , tRNALeu ( e-value ≥ 10 ) . In a control experiment , in contrast to T . nautili , the genome of a closely related organism , the plasmid-less Thermococcus sp . 5–4 ( GenBank accession CP021848 ) remained stable when sub-cultured for 36 or 66 generations in two separate experiments ( Fig 6B and S7 Fig ) . The remarkable differences in the outcome of T . nautili and T . sp 5–4 sub-culturing experiments and the observation that tRNAGly genes could recombine in these conditions suggested a causal link between IntpTN3 and genome shuffling . To ascertain if the new recombinations in T . nautili 60G and 66G could have been indeed generated by IntpTN3 , we decided to test whether this integrase was able to catalyze in vitro inversions using the sequences detected at the borders of these recombination events . New inversion templates pCB548 and pCB552 were thus constructed respectively carrying sequences encompassing tRNAGly genes BD01_1557 and BD01_1976 or sequence fragments from chemotaxis genes BD01_1166 and BD01_1584 ( S8 Fig ) . To limit the number and size of generated fragments , an in vitro inversion assay was conducted on linear fragments originating from these plasmids and compared to a linear fragment carrying inverted attP sites derived from pCB524 . Inversions could be detected with all three templates albeit with significantly longer incubation times or higher IntpTN3 concentrations for pCB548 and pCB552-derived templates as compared to pCB524 ( Fig 7 ) . To confirm this recombination event , one of the products of the pCB548 template inversion reaction was further characterized by DNA sequencing and corresponded to a bona fide cross-over between BD01_1557 and BD01_1976 ( S9 Fig ) . We conclude that IntpTN3 is able to catalyze low sequence specificity recombination reactions between sites that differ in sequence from its cognate att site , with the same outcome as homologous recombination events . It is to be noted that IntpTN3 catalyzes these two types of reactions with a different efficiency . Site-specific recombination reactions reach the equilibrium within 30 minutes whereas several hours and higher enzyme concentrations are required to detect all low sequence specificity recombinations . The absence of inter-pair DNA similarity observed in T . nautili 60G and 66G chromosomal inversions prompted us to test whether IntpTN3 could catalyze recombination between homologous non-archaeal sequences . The simplest experiment consisted of the incubation of cloning vector pBR322 DNA with the integrase in the same conditions as described above . This recombination reaction promoted by IntpTN3 yielded a ladder of plasmid multimers produced by sequential integration , which could be readily observed by eletrophoretic migration whereas no homologous integration reaction was detected with the mutated IntpTN3Y428A ( Fig 8A ) . Surprisingly , IntpTN3 generated also a double-strand cut at the pBR322 ColE1 origin of replication for which we have no explanation at this stage ( S10 Fig ) . This cleavage does not constitute an intermediate step in the recombination reaction since none of IntpTN3 linear substrates shown in Fig 7 carries the ColE1 origin . In addition to the homologous integration reaction , we investigated the capacity of IntpTN3 to promote inversions between homologous sequences of bacterial origin . Short DNA segments of decreasing length ( 250 , 175 and 100bp , see S11 Fig ) originating from the E . coli lacZ gene were cloned in opposite orientations respective to the lacZα gene of pUC18 to generate plasmids pCB574 , pCB571 and pCB558 , respectively . These templates were linearized , incubated with IntpTN3 and tested by subsequent restriction analysis . In each case , IntpTN3 generated additional bands consistent with homologous inversion reactions displaying efficiencies proportional to the extent of DNA identity ( Fig 8B ) .
The major mechanism producing chromosomal rearrangements is recombinational exchange between homologous sequences [46] . These rearrangements often consist of DNA inversions between IS elements [9 , 46 , 47] . The observation that , in the Thermococcus genus , large chromosomal inversions occur even in the absence of IS elements prompted us to investigate the molecular mechanism behind these rearrangements . The presence of tRNA genes at recombination endpoints in genomes as diverse as plant chloroplasts [48 , 49] and Thermococcales [9] , combined with the fact that integrases often target tRNA genes [50] , lead us to propose a precise molecular model involving IntpTN3 to explain large-scale genomic rearrangements . Using a combination of comparative genomics , in vitro analyses , and serial culturing experiments , we uncovered a mechanism and enzymatic activity responsible for the shuffling-driven chromosomal evolution in Thermococcales . By means of deep comparative genomic analyses , we were able to correlate genome scrambling with the presence of a mobile element . This mobile element has been identified as plasmid pTN3 , naturally present in T . nautili both as an episome and integrated in the genome [41 , 43] . Plasmid pTN3 encodes the IntpTN3 integrase of the Y-recombinase superfamily capable of promoting its site-specific plasmid integration at a tRNALeu gene of its host . Due to perfect DNA conservation between attB and attP attachment sites ( S2B Fig ) , an intact and presumably expressed tRNALeu is reconstituted upon pTN3 chromosomal integration . We successfully reproduced , with high efficiency in a purified in vitro system , the canonical DNA reactions of integration and excision expected from a bona fide integrase . Site-specific mutation of the active site tyrosine to alanine abolished these activities . A positive excision reaction was also obtained in vivo by expressing wild-type IntpTN3 and the catalytic tyrosine mutant IntpTN3Y428A in T . kodakarensis KOD1 cells . The genome of this strain carries the integrated episome TKV4 [45] which is remarkably similar to pTN3 ( Fig 9 ) . Surprisingly , both wild-type and mutant forms of the integrase excised TKV4 in circular form . This suggests that a truncated C-terminal IntTKV4 , presumably impaired in DNA-binding but carrying the catalytic tyrosine , can complement IntpTN3Y428A . A plausible explanation invokes the participation of integrase dimers in the recombination reaction . In this case , only the heterodimeric form would possess an active catalytic site where Tyr428 is provided by the first monomer while the second monomer contributes the remaining conserved residues . This cleavage in trans was initially reported for the FLP recombinase [51 , 52] . Similarly , the complementation of activity between a DNA-binding impaired mutant and a catalytic tyrosine residue mutant has been described for another archaeal integrase , IntSSV1 [44] . The peculiar location of tRNALeu GQS_t10759 at the exact border of a large DNA inversion observed between the genomes of T . onnurineus and T . sp . 4557 suggested that this inversion could have occurred by the recombinase activity of IntpTN3 . In our purified system , we could obtain highly efficient DNA inversions between two inverted copies of GQS_t10759 . Paradoxically , we were unable to promote inversion between tRNALeu GQS_t10759 and tRNAThr GQS_t10745 contrary to what the genomic comparisons between T . onnurineus and T . sp . 4557 suggested . An experiment of prolonged T . nautili cultivation was instrumental in elucidating the large-scale inversion mechanism in Thermococcus . The strain carrying its natural plasmids was cultivated during 60 or 66 generations; total DNA was extracted from this population and sequenced in a manner similar to a metagenome . We observed the high incidence in the resulting populations of a particular recombined genome with four large chromosomal inversions and a very low copy number of plasmid pTN3 encoding active IntpTN3 ( < 2/chromosome ) ( S3 Table ) . This plasmid loss could have contributed to the higher fitness and spread of a particular clone in the population . The four large-scale inversions occurred between four pairs of naturally occurring paralogous genes sharing at least 104bp of sequence identity in inverted orientation ( S8 Fig ) . No significant sequence conservation could be detected between the four pairs . We did not observe chromosomal rearrangements after prolonged incubation of Thermococcus sp . 5–4 , which does not carry plasmids . The potential causal link between pTN3 and a number of unrelated sequence pairs involved in large scale genomic shuffling in T . nautili was difficult to conciliate with the classical site-specific recombination properties we described for IntpTN3 . Remarkably , by in vitro assays with this integrase , we succeeded in producing inversions between several pairs of inverted paralogous genes detected in our T . nautili sub-culturing experiments . These results suggested that the recombination properties of IntpTN3 could be extended to virtually any homologous pair of DNA sequences . Using exogenous pBR322 plasmid DNA or genes segments from bacterial origin , we demonstrated in vitro that IntpTN3 actively promotes low sequence specificity reactions mimicking homologous integration and inversion of any sequence pair as short as 100bp . The catalytic site mutation in variant IntpTN3Y428A abolishes this particular recombination reaction as well . Interestingly , cellular homologous recombination in Archaea operates according to a different pathway with dedicated enzymes [36 , 37] and in Thermococcus kodakarensis has only been reported between DNA segments of 500bp or more [39] . These reactions unveiled a specific IntpTN3-generated double-strand cut at the ColE1 origin of replication carried by pBR322 and its derivatives ( S10 Fig ) . At this moment , we do not have a precise rationale to explain this observation other than a potential distant secondary structure similarity between the small RNAI and RNAII encoded by the ColE1 origin and the tRNALeu encoded by IntpTN3 attB substrate . Biological interactions between tRNAs and ColE1 RNAs have been reported [53] . Clearly , this double-strand cleavage does not participate in any recombination reaction since we demonstrated all in vitro IntpTN3 inversions on linear DNA segments devoid of ColE1 origin . The positive in vitro IntpTN3-promoted low sequence specificity recombination results explain the failure of this enzyme to promote inversion between tRNALeu GQS_t10759 and tRNAThr GQS_t10745 . These sites were initially thought to constitute inversion endpoints between the genomes of T . onnurineus and T . sp . 4557 but do not share sufficient sequence similarity to be efficiently recombined in vitro . The particular positioning of these sequences in opposite orientations could have occurred through previous overlapping inversions between a different set of paralogs or by less frequent native homologous recombination . We observed a similar situation in the sequence of the T . nautili 60G and 66G populations . In several cases , homologous segments were in direct orientation in the original genome but became opposed due to a previous overlapping inversion therefore indicating that T . nautili 60G and 66G inversions occurred sequentially . In order to investigate whether pTN3 could account for large-scale rearrangements in the Thermococcus genus , we examined by synteny analysis the distribution of pTN3-like integrated element among completely sequenced Thermococcales . Out of 17 sequenced Thermococcus , and in addition to the previously reported T . kodakarensis TKV4 element [45] , five isolates were found to harbor a pTN3-related element ( Fig 9 ) . The natural competence for DNA uptake of some Thermococcales such as T . kodakarensis [39] and the capacity of pTN3 to be transferred between cells using membrane vesicles [43] could explain the ubiquitous presence of this mobile element . Protein sequence and structural comparisons between IntpTN3 and other hyperthermophilic archaeal integrases such as that of crenarchaeal virus SSV1 indicate that these proteins are clearly related . However , IntpTN3 possesses several additional interspersed domains relative to SSV1 ( S2 and S12 Figs ) . We surmise that these additional domains contribute to the low sequence specificity recombination reactions akin to homologous recombination events that we have observed . By summing up all direct and indirect evidence reported here , it is very likely that the integrase encoded by pTN3-like plasmids can account for the genomic shuffling observed in the Thermococcus genus . Plasmids of the pTN3 class are genetically closely related to viruses as they encode a capsid protein and a DNA packaging ATPase [43] but pTN3 virions have not be observed to date . It is not clear at this stage whether plasmids or viruses equipped with an IntpTN3-like integrase have a better fitness either due to provirus maintenance or by virion spreading . An integrase mimicking homologous recombination could promote viral integration into the host genome only if both viral and cellular chromosomes share significant DNA similarity . This enzyme however , could facilitate integration of a virus into the genome of a closely related provirus . The question arises whether an enzyme promoting genome shuffling using very short repeated segments as substrates , would be beneficial for a cellular organism . On one hand , ‘wrongly’ recombined genomes would result in suboptimal gene expression programs and cells carrying scrambled genomes would display a reduced fitness and clearly be counter-selected in the population . Interestingly , the presence of a pTN3-specific spacer in a T . nautili CRISPR locus strongly suggests that the presence of this plasmid is deleterious [41] . On the other hand , it is also possible to envision situations where high-level genome shuffling by inversion could be advantageous . Alternate gene expression patterns could increase , for instance , adaptation to rapid environmental changes . In addition , for organisms such as Thermococcales where highly-expressed essential housekeeping genes maintain invariable positions [33] , genome scrambling could be beneficial by relocating “less desirable” integrated elements to chromosomal areas of reduced gene expression , therefore minimizing their impact on cellular physiology .
Escherichia coli strain XL1-Blue was used for cloning , plasmid amplification and site-directed mutagenesis . Overexpression of recombinant wild-type or mutant IntpTN3 was carried out in strain BL21 ( DE3 ) ( Novagen ) . All E . coli strains were grown in Luria-Bertani medium supplemented with 100μg/mL ampicillin or/and 50μg/mL kanamycin when necessary . T . kodakarensis KUW1 ( ΔpyrF ΔtrpE ) was grown anaerobically in ASW-YT medium [54] at 85°C . Long term Thermococcus sub-culturing experiments were carried out in the same conditions by sequential 50x dilutions of stationary phase cultures into fresh media . The number of generations was assessed statistically at each dilution step using a Thoma cell counting chamber under 400x magnification . The plasmids used or constructed in this work are listed in S1 Table . Transformation with pRC524 and pRC526 plasmids ( see below ) was performed following standard protocols [55] . Plasmid-containing KUW1 strains were grown in ASW-CH medium [54] supplemented with uracil ( 10 μg/mL ) . T . nautili sp . 30–1 ( CP007264 ) was grown anaerobically at 85°C in Zillig’s broth [56] . Genomic sequences were compared and aligned by dotplot analysis using Gepard [57] . Conservation of gene order was assessed by synteny analysis using Absynte [58] and SyntTax [42] . The original genome of Thermococcus 5–4 JCM31817 ( GenBank accession CP021848 ) and the genomes of sub-cultured T . nautili 60G and 66G and T . sp . 5–4 36G and 66G were sequenced by Genoscope ( Centre National de Séquençage , France ) , using Illumina MiSeq . Reads were assembled with Newbler ( release 2 . 9 ) and gap closure was performed by PCR , Sanger sequencing and Oxford Nanopore MinION . The primary genomic sequences of rearranged T . nautili 60G , 66G and T . 5–4 36G , 66G are available in S1 , S2 , S3 and S4 Datasets , respectively . These genomic sequences are compared by dotplot analysis in S7 Fig . Genomic regions corresponding to ~2000bp upstream and downstream of inversion break-points were extracted from both the ancestral T . nautili sequence , and the sub-cultured T . nautili 66G sequence . Illumina sequencing reads were mapped to the ancestral sequence , and the pool of unmapped reads were mapped to the 66G sequence ( Geneious 6 . 1 . 8 ) . Two positions close to the break-point which differ in base composition between ancestral and 66G sequences were chosen to classify reads as resulting from original or inverted genome sequences . Bases were enumerated at these positions , and the percentage of reads corresponding to original sequences or inversions were calculated . The prevalence of pTN3 in the population was determined by comparing read depth across the entire T . nautili 66G genome ( excluding the integrated pTN3 region ) to that of pTN3 ( S3 Table ) . The gene encoding the integrase of the plasmid pTN3 of T . nautili 30–1 , ( gene ID: 17125032 ) was codon-optimized for expression in E . coli and synthesized by GenScript . The synthetic gene contained a Strep-Tag at the 5’ end and was cloned into pET26b+ expression vector ( Novagen ) to yield pJO344 . Plasmid pJO496 carrying the mutated IntpTN3Y428A was obtained by site directed mutagenesis of pJO344 with primers Int_A and Int_B ( S2 Table ) using the Agilent QuikChange Lightning Site-Directed Mutagenesis Kit . Wild-type IntpTN3 and mutated IntpTN3Y428A were purified from E . coli BL21 ( DE3 ) strain ( Novagen ) harboring respectively pJO344 or pJO496 by affinity chromatography and gel filtration ( S4 Fig ) . All integrase enzymatic assays were conducted with strep-tagged protein derivatives . Plasmids used for the integrase dimerization assays were constructed as follows . EcoRI and BamHI restriction sites were added respectively at the 5’ and 3’ end of the various oligonucleotides shown in S5 Fig . Each oligonucleotide ( Sigma-Aldrich ) was annealed to its complementary sequence and the resulting double-stranded segments were cloned between the corresponding restrictions sites of pUC18 . To generate plasmid pMC451 , the Leu2-88 fragment was cloned in pBR322 instead of pUC18 . Plasmids pMC477 and pMC479 used respectively for att integration/excision and inversion assays were constructed using pMC451 as backbone . The insertion fragment was amplified with primers Leu43scaI_fw and Leu43scaI_rev using pMC449 plasmid DNA as template . It contains tRNALeu gene ( 2-44bp ) and lacZa gene for blue-white screening . This amplified region was cloned in pMC451 in both possible orientation using ScaI and NruI blunt sites . Plasmid pCB538 was obtained by amplifying with primers LacZ100-Sac1-For and KanR-Xba1-Rev ( S2 Table ) a 1364bp fragment from pUC4K and subsequent cloning between the XbaI-SacI sites of pUC18 . The other plasmids: pCB548 , pCB552 , pCB572 and pCB574 used for non-att inversion assays were generated by Gibson Assembly [59] . Briefly , for pCB548 , the genomic region corresponding to -80 to +245 of BD01_1557 ( T . nautili ) was amplified by PCR ( Phusion Polymerase , ThermoScientific ) using primers 1557_fwd and 1557_rev ( S2 Table ) ; the region from –80 to +245 of BD01_1976 was amplified using primers 1976_fwd and 1976_rev . The KmR gene was amplified from plasmid pUC4K using primers KanR_fwd and KanR_rev . Fragments were assembled into EcoRI + SalI digested pUC18 using the NEBuilder HiFi DNA Assembly Master Mix ( New England Biolabs ) following the manufacturer’s protocols . Similarly , for pCB552 , part of the genes BD01_1166 and BD01_1584 ( S8 Fig ) were amplified by PCR and assembled into EcoRI + SalI digested pUC18 with the KmR gene sequence . To construct pCB538 , a fragment containing KmR and the beginning of the lacZ gene ( lac100 ) was PCR-amplified from pUC4K with the primers LacZ100-Sac1-For and KanR-Xba1-Rev containing the restriction sites for SacI and XbaI , respectively , at the 5’ end . The adequately digested fragment was then ligated into a SacI-XbaI digested pUC18 . For plasmids pCB572 and pCB574 , part of the lacZ gene was amplified from pUC18 and the KmR gene sequence was amplified from plasmid pUC4K . The two fragments were then assembled into the EcoRI digested pUC18 . Purified plasmids pCB548 , pCB552 , were digested using ScaI and EcoRI and plasmids pCB572 and pCB574 were digested using ScaI . The fragments containing the non att-sites were then gel purified using the kit NucleoSpin Gel and PCR Clean-up ( Macherey Nagel ) . All plasmid constructs were confirmed by DNA sequencing ( Beckman Coulter Genomics ) . Standard in vitro integrase assays were performed as follows: 165ng ( 8 . 25ng/μL , 3 . 1pmol ) purified IntpTN3 and 0 . 5μg ( 25ng/μL , 10pmol ) supercoiled plasmid substrates were incubated 30 min at 65°C in a reaction buffer containing 300mM KCl , 27 mM Tris HCl pH8 , 0 . 17mM DTT and 1mM MgSO4 . Depending on the size of the plasmid substrate , the DNA/integrase molar ratio varied from 30 to 60 . For substrates with non-att sites , the integrase concentration was increased up to 50pmol . To assay dimer formation , the reaction products were separated by gel electrophoresis and visualized with ethidium bromide . For the excision and inversion assays , reaction products were purified with the NucleoSpin Gel and PCR Clean-up kit ( Macherey-Nagel ) and digested with appropriate restriction enzymes ( Thermo Scientific ) prior to eletrophoretic separation . In vitro circularization of TKV4 was performed in a standard integrase assay with genomic DNA of T . kodakarensis isolated as described previously [60] . The reaction products were purified using NucleoSpin Gel and PCR Clean-up kit ( Macherey-Nagel ) . Recircularized products were scored by amplifying a reconstituted full-length TKV4 integrase gene . PCR was performed using Phusion Polymerase ( ThermoScientific ) and primers TKV4_FW and TKV4_REV ( S2 Table ) in conditions recommended by the supplier . In vivo circularization of TKV4 was obtained using total DNA from T . kodakarensis KUW1 transformed with plasmid pRC524 or pRC526 . These plasmids express constitutively wild type integrase and mutated IntpTN3Y428A from the PhmtB promoter present in parental pLC70 . DNA extraction and PCR reactions was performed as per the in vitro assay described above . To generate plasmids pRC524 and pRC526 , the IntpTN3 integrase gene was amplified by PCR with primers int_fwd and int_rev ( S2 Table ) , using total T . nautili genomic DNA as a template . The amplification product was cloned into pJET1 . 2 using the CloneJET PCR Cloning Kit ( Thermo Fischer Scientific ) . The Y428A mutation was introduced into the integrase gene using the QuickChange II Site Directed Mutagenesis Kit ( Agilent Technologies ) with primer intY428A_fwd and its reverse complement . Both the wild-type and Y428A alleles were digested from pJET1 . 2 using SalI and NotI and cloned into the corresponding sites of pLC70 . All in vitro and in vivo recombination junctions and plasmid constructs were confirmed by DNA sequencing ( Beckman Coulter Genomics ) .
|
Mobile elements ( MEs ) such as viruses , plasmids and transposons infect most living organisms and often encode recombinases promoting their insertion into cellular genomes . These insertions alter the genome of their host according to two main mechanisms . First , MEs provide new functions to the cell by integrating their own genetic information into the DNA of the host , at one or more locations . Secondly , cellular homologous recombination will act upon multiple integrated copies and produce a variety of large-scale chromosomal rearrangements . If such modifications are advantageous , they will spread into the population by natural selection . Typically , enzymes involved in cellular homologous recombination and the integration of MEs are distinct . We describe here a novel plasmid-encoded archaeal integrase which in addition to site-specific recombination can catalyze low sequence specificity recombination reactions akin to homologous recombination .
|
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2017
|
Flipping chromosomes in deep-sea archaea
|
Human Herpesvirus 6 ( HHV-6 ) is a ubiquitous virus with an estimated seroprevalence of 95% in the adult population . HHV-6 is associated with several neurologic disorders , including multiple sclerosis , an inflammatory demyelinating disease affecting the CNS . Animal models of HHV-6 infection would help clarify its role in human disease but have been slow to develop because rodents lack CD46 , the receptor for cellular entry . Therefore , we investigated the effects of HHV-6 infections in a non-human primate , the common marmoset Callithrix jacchus . We inoculated a total of 12 marmosets with HHV-6A and HHV-6B intravenously and HHV-6A intranasally . Animals were monitored for 25 weeks post-inoculation clinically , immunologically and by MRI . Marmosets inoculated with HHV-6A intravenously exhibited neurologic symptoms and generated virus-specific antibody responses , while those inoculated intravenously with HHV-6B were asymptomatic and generated comparatively lower antibody responses . Viral DNA was detected at a low frequency in paraffin-embedded CNS tissue of a subset of marmosets inoculated with HHV-6A and HHV-6B intravenously . When different routes of HHV-6A inoculation were compared , intravenous inoculation resulted in virus-specific antibody responses and infrequent detection of viral DNA in the periphery , while intranasal inoculation resulted in negligible virus-specific antibody responses and frequent detection of viral DNA in the periphery . Moreover , marmosets inoculated with HHV-6A intravenously exhibited neurologic symptoms , while marmosets inoculated with HHV-6A intranasally were asymptomatic . We demonstrate that a marmoset model of HHV-6 infection can serve to further define the contribution of this ubiquitous virus to human neurologic disorders .
Human Herpes Virus 6 ( HHV-6 ) is a member of the Roseolovirus genus of the β-herpesvirus subfamily [1] . Since its identification in 1986 , two species , HHV-6A and HHV-6B , have been described [2] . Though HHV-6A and HHV-6B share high sequence homology , they differ in cellular tropism and clinical manifestation [3] , [4] , [5] , [6] to the extent that they were recently reclassified as two distinct viruses ( International Committee on Taxonomy of Viruses , 2011 ) . Primary infection with HHV-6B is often associated with febrile illness [7] , and this virus is the etiologic agent of the self-limiting childhood illness roseola infantum [8] . By contrast , the symptoms associated with HHV-6A infection are largely unknown . HHV-6 is acquired during early childhood [7] . The virus has a worldwide distribution , with an estimated seroprevalence of 95% in the adult population [9] , [10] . HHV-6 cell tropism is notably lymphotropic and neurotropic , though it can infect a wide range of human cells in vitro due to the ubiquity of its major receptor , CD46 [11] . Similar to other herpesviruses , HHV-6 can establish lifelong latent , asymptomatic infections [12] . However , the virus may reactivate as a consequence of immunosuppression , manifesting for example as a febrile illness [13] or encephalitis following bone marrow [14] or solid organ [15] transplantation . HHV-6 DNA has been reported in normal brain tissues [16] suggesting that this virus may be a commensal of the brain under some circumstances [17] . However , HHV-6 is also associated with neurologic conditions including encephalitis [18] [19] [20] , temporal lobe epilepsy [21] [22] and multiple sclerosis ( MS ) [23] , [24] , [25] , findings that have been established by assessing both the distribution of viral DNA and serologic responses . HHV-6 DNA is found in MS lesions [26] [27] [28] . Moreover , HHV-6 DNA has been detected in cell-free compartments , such as the sera and urine , of some MS patients [29] , and is detected at higher frequencies during periods of clinical exacerbation relative to periods of remission . As HHV-6 is normally cell-associated , the detection of viral DNA in cell-free compartments suggests an active infection [30] . More recently , significantly elevated serum HHV-6 IgM in MS patients versus controls was reported in an Iranian population [31] , and a positive , dose-dependent correlation of serum HHV-6 IgG titers with MS relapse risk was reported in an Australian MS cohort [32] . Despite the association of HHV-6 with several central nervous system ( CNS ) disorders [33] , [34] , [19] it has been difficult to prove causation in clinical disease . This is partly due to the ubiquity of HHV-6 infection in the general population and also because no animal model exists . Animal models of HHV-6 infection have been difficult to establish because rodents lack the complement regulatory receptor , CD46 , that HHV-6 uses for cellular entry [35] . The common marmoset ( C . jacchus ) is a New World non-human primate that naturally expresses CD46 [36] and is therefore susceptible to infection with HHV-6 . Marmoset models of various neurologic diseases have been developed [37] , including the animal model for MS , experimental autoimmune encephalomyelitis ( EAE ) . EAE is an inflammatory , demyelinating disease of the CNS induced by immunization with myelin antigen ( s ) [38] , [39] , [40] , [41] . It is increasingly apparent that marmoset EAE ( relative to rodent EAE ) has superior translational applicability , due to greater similarities with MS such as CD8 T cell involvement , the presence of both brain and spinal cord lesions and importantly , the ability for MRI analysis of lesions [39] . Marmosets are particularly appropriate for studies involving MRI monitoring because the cerebral organization resembles , but is considerably simpler than that of humans . Moreover , these primates are ideal models for studying the pathogenesis and host response to a human virus in a non-human system due to their genetic and immunologic proximity to humans , in addition to their broad behavioral range [39] . Marmosets have been infected with other human herpesviruses , such as Varicella Zoster virus ( VZV ) [42] , Kaposi's sarcoma-associated herpesvirus ( KSHV ) [43] , as well as non-herpesviruses such as dengue virus ( DENV ) [44] . In this study , marmosets were inoculated with HHV-6A or HHV-6B intravenously , or with HHV-6A intranasally . Intranasal inoculation was examined based on a recent report demonstrating the olfactory pathway as a possible route of HHV-6 entry into the CNS [45] . All resulting infections were monitored clinically , immunologically and by MRI for 25 weeks following the first inoculation . Previous work has demonstrated that following intravenously administered HHV-6-infected cell lysates , marmosets can develop hypotonic paralysis with sensory deficits accompanied by weight loss ( Genain , C . , unpublished data , 6th international conference on HHV-6 & 7 ) . Here we report that marmosets inoculated intravenously with HHV-6A exhibit neurologic symptoms , mount virus-specific IgM and IgG responses and effectively clear the virus from peripheral circulation . By contrast , marmosets inoculated intranasally with HHV-6A are asymptomatic , do not mount virus-specific IgM or IgG responses and fail to clear the virus from peripheral circulation within the 25-week monitoring period of this study . These observations suggest that the route of inoculation is an important determinant for establishing humoral immunity , and that humoral immunity may influence not only the peripheral circulation of viral DNA , but also pathological features such as clinical symptoms and CNS pathology .
Fifteen adult common marmosets ( Callithrix jacchus ) ( Table 1 ) were used in this study . Marmosets were singly housed with a twelve-hour light/dark cycle on a diet of Zupreem canned marmoset food , Purina 5040 biscuits , fruit and vegetable treats and ad libitum unfiltered water and PRANG rehydrator . All marmosets were housed at the National Institutes of Health Intramural Research Program ( PHS Assurance #A4149-01 ) facilities in accordance with the standards of the American Association for Accreditation of Laboratory Animal Care and the National Institute of Neurological Disorders and Stroke's Internal Animal Care and Use Committee ( NINDS IACUC ) . All experiments adhered to a protocol that was reviewed and approved by the NINDS IACUC . HHV-6A ( U1102 ) and HHV-6B ( Z29 ) were separately propagated in the T-lymphoblastoid cell line SupT1 as described previously [46] . The supernatants of infected cells were quantified using real time PCR , with primers to detect the intermediate early U90 region of the HHV-6 genome as described previously [47] . Supernatants were stored at −80°C until use . Marmosets were anesthetized with ketamine ( 10 mg/kg ) prior to viral inoculations . Three groups of marmosets were injected intravenously with HHV-6A supernatants ( 1×109 viral copies of DNA ) ( n = 4; M01–M04 ) , HHV-6B supernatants ( 1×109 viral copies of DNA ) ( n = 4; M05–M08 ) or mock-infected supernatants from uninfected SupT1 cells ( n = 3; M09–M11 ) ( Table 1 ) . Marmosets were re-exposed intravenously once a month for a total of four doses ( 4×109 total viral copies of DNA ) . A fourth group of marmosets was induced with HHV-6A supernatant intranasally ( 2×107 viral copies of DNA ) ( n = 4; M12–M15 ) ( Table 1 ) . Marmosets were re-exposed intranasally once a month for a total of three doses ( 6×107 total viral copies of DNA ) . Following HHV-6 inoculation , all marmosets were monitored and scored daily for signs of disease development . Clinical signs were scored using a previously described semiquantitative scale commonly used to assess marmoset EAE [48] . Briefly , 0: no clinical signs; 0 . 5: apathy or altered walking pattern without ataxia; 1: lethargy or tremor; 2: ataxia or optic disease; 2 . 25: monoparesis; 2 . 5: paraparesis or sensory loss; 3: paraplegia or hemiplegia . Body weights were measured three times per week and prior to each MRI , marmosets were subject to a neurologic exam performed by a neurologist . Marmosets were anesthetized with ketamine ( 10 mg/kg ) intramuscularly prior to blood sampling . Approximately 1 cc blood was drawn from the femoral triangle of each animal prior to HHV-6 inoculation and every two weeks post-inoculation . PBMC were isolated using Lymphocyte Separation Medium ( Mediatech , VA ) and plasma was collected from the isolation . Saliva was collected at the time of blood sampling using gauze to swab the mouth of the animal . Saliva was diluted with PBS and spun out of the gauze for DNA extraction . DNA extraction from PBMC , plasma , saliva and organs collected at euthanasia was performed with the DNeasy Blood and Tissue DNA extraction kit ( Qiagen , CA ) . DNA extraction from 10 µM scrolls from paraffin-embedded brain and spinal cord sections was performed with the QIAamp DNA formalin fixed paraffin embedded ( FFPE ) tissue kit ( Qiagen , CA ) . Nested PCR ( nPCR ) was used to monitor the presence of HHV-6 DNA in the plasma , PBMC and saliva every two weeks , as well as in the organs and paraffin-embedded sections following necropsy . HHV-6 nPCR was performed with primers against the U57 region ( major capsid protein , MCP ) of the viral genome , as described previously [20] . As this method does not distinguish between HHV-6A and HHV-6B , sequencing was conducted at the NINDS DNA sequencing facility ( Bethesda , MD ) to determine the species present in the PCR positive samples . All reactions were performed in triplicate , and PCR positive was defined as a positive result two out of three times . All extracted samples were tested for the presence of amplifiable DNA through the amplification of β-actin using real-time PCR [47] . For the paraffin-embedded CNS tissue , one 10 µM scroll was isolated from each section for DNA extraction . Only samples with amplifiable DNA , as defined by β-actin Ct values ≤35 , were further analyzed for the presence of HHV-6 DNA ( >97% of all samples tested ) . Plasma antibodies against HHV-6 proteins were measured every two weeks for 25 weeks post-inoculation using electrochemiluminescence technology ( MSD , Gaithersburg , MD ) developed in our laboratory [49] . HHV-6A or mock-infected ( SupT1 ) cell lysate , prepared as previously described [24] , was spotted onto high bind plates and allowed to dry overnight at room temperature ( RT ) . Plasma samples were diluted in MSD Antibody Diluent ( final dilution 1∶10 ) and added to plates . Sulfo-Tag-labeled anti-human IgG ( Jackson ImmunoResearch ) was used to detect IgG responses and Sulfo-Tag-labeled polyclonal anti-human IgM ( MSD ) was used to detect IgM responses . Each sample was tested in duplicate , and signal intensity is expressed as light emitting units . Results are corrected for responses to uninfected SupT1 lysates and reported as fold increases over baseline ( before viral inoculation ) . MRI scans of the brain were performed monthly following viral inoculation , and scans obtained during the experimental monitoring period were compared to baseline scans ( conducted before viral inoculation ) . Before each MRI experiment , marmosets were fasted for 12 h , sedated with an intramuscular injection of 10 mg/kg ketamine and orally intubated . Throughout the imaging session , sedated marmosets were mechanically ventilated with a mixture of oxygen and 1 . 25–2% isoflurane , and physiological parameters including end-tidal CO2 , heart rate , and SPO2 were monitored using a capnograph and pulse oximeter ( Surgivet , Waukesha , WI , USA ) . Rectal temperature was also monitored , and maintained at 38 . 5°C with a water heating pad . MRI was performed on a 7 T/30 cm USR/AVIII MRI scanner ( BrukerBiospin Corp . , Ettlingen , Germany ) equipped with a 15 cm gradient set of 450 mT/m strength ( Resonance Research Inc . , Billerica , MA , USA ) . A custom-built , 16-rung , high-pass birdcage radiofrequency coil with a 12 cm inner diameter was used for transmission and a custom-built five-element receive-only phased array equipped with preamplifiers was used for reception . For all marmosets , the MRI protocol included T2-weighted Turbo Spin Echo ( T2w-TSE ) , T1-weighted Magnetization Prepared Rapid Acquisition Gradient Echo ( T1w-MPRAGE ) and T1-weighted Fast Low Angle Shot imaging ( T1w-FLASH ) performed before the injection of contrast agent . A tail vein was cannulated for the administration of a bolus of Gadolinium-Diethylene-Triamine Penta-acetic Acid ( Gd-DTPA; Magnevist ) . Each marmoset received 0 . 3 mMol/kg of Gd-DTPA over three minutes . T1w-FLASH imaging was repeated approximately 20 minutes after the injection of Gd-DTPA . At necropsy , animals were transcardially perfused with cold 4% paraformaldehyde ( PFA ) and whole brain and spinal cord were collected . The brain was placed in 10% neutral buffered formalin ( NBF ) , and the spinal cord was cut into superior and inferior sections and then placed in 4% PFA . Tissue sections were submerged in nonmagnetic oil ( Fomblin ) , and postmortem MR images were recorded in the same magnet using a volume transmit-receive RF coil with 40 mm diameter ( Bruker-Biospin ) . For all marmosets , the post-mortem brain MRI protocol included T2w-TSE and T2*-weighted Multi Gradient Echo imaging ( T2*-MGE ) . The spinal cord post-mortem imaging protocol included T2* weighted Fast Low Angle Shot Imaging ( T2*-FLASH ) . All animals were necropsied within 1 hour of death . Brains and spinal cords were fixed in 10% NBF and 4% PFA , respectively , and subsequently embedded in paraffin , sectioned at 5 µm and stained using hematoxylin and eosin ( HE ) . Two special stains , Bielschowski's method for neurofibrils and luxol fast blue ( LFB ) for myelin , were additionally performed on all sections . Standard immunoperoxidase IHC for ionized calcium binding adapter molecule one ( Iba-1 ) , a macrophage and microglia-specific marker , was also performed . Sections of brain and spinal cord were deparaffinized , rehydrated , and blocked with 3% hydrogen peroxide in PBS . Iba-1 pretreatment involved microwaving for 20 minutes in 0 . 01 citrate buffer , followed by 20 minutes of cooling . Following pretreatment , an avidin-biotin block ( Invitrogen Corporation , Frederick , MD , USA ) and a Dako Protein block ( 10 minutes; Carpineria , CA , USA ) were conducted on all sections . A wash of tris-buffered saline ( TBS ) followed each step . Sections were incubated with Iba-1 ( Wako Pure Chemical Industries , Ltd . , Osaka , Japan; polyclonal ) at a 1∶1000 dilution for thirty minutes at RT . Slides were then incubated with a secondary antibody , biotinylated goat anti-rabbit ( Vector Laboratories , Burlingame , CA , USA ) at a 1∶200 dilution for 30 minutes at RT , followed by 30 minutes incubation at RT with Vectastain ABC Elite ( Vector Laboratories , Burlingame , CA , USA ) . Antigen-antibody complex formation was detected using diaminobenzidine ( DAB; DakoCyomation , Carpinteria , CA , USA ) and counterstained with Mayer's hematoxylin . Irrelevant , isotype-matched primary antibodies were used in place of the test antibody as negative controls . Positive control tissue consisted of rhesus macaque spleen .
In our initial experiment , eight marmosets were inoculated intravenously with HHV-6A ( n = 4 ) or HHV-6B ( n = 4 ) . Each marmoset received four inoculations , and daily monitoring was conducted for 180 days from the first inoculation . Shortly after the second intravenous inoculation of HHV-6A , neurologic symptoms developed in two of the four marmosets , M01 and M04 ( Figure 1 , solid lines ) . M01 presented with sensory and motor impairment in her left arm , characterized by flapping , which persisted for 29 days and then resolved ( clinical score: 2 . 5 ) . M04 first presented with a facial palsy characterized by a droopy lower lip and an inability to blink the eye on the affected side , which persisted for seven days and then resolved ( clinical score: 1 . 5 ) . M04 then presented with motor impairment in his left leg , holding it in retraction while moving . This lasted for 63 days and then resolved , but a recurrence of weakness in this leg was noted on day 167 post-inoculation and persisted through the end of the monitoring period ( clinical score: 2 . 5 ) . Following the third intravenous inoculation of HHV-6A , M03 presented with abnormal sitting behaviors , in which he would keep one or both feet from touching the cage bottom when at rest ( clinical score: 2 . 5 ) . Neurological exams revealed diminished sensation in all extremities , and he failed to respond to hot or cold stimuli . M02 exhibited more minor disease symptoms ( clinical score: 0 . 5 ) ( Figure 1 ) . Though neurologic symptoms were observed in three of the four marmosets inoculated intravenously with HHV-6A , intravenous inoculation with HHV-6B did not result in clinical symptoms , similar to the SupT1 control inoculations . In mouse and marmoset models of EAE , weight loss is a surrogate marker of disease [50] . In our experiment , all marmosets were weighed several times per week , but none exhibited weight loss over the 25-week monitoring period ( Figure 1 , dashed lines ) . All marmosets underwent MRI scans of the brain before viral inoculations , which served as the baseline control for each animal . Following HHV-6 inoculation , all animals were scanned monthly to assess radiologic changes from their baseline scan . As shown in Figure 2 , bilateral , T2-hyperintense lesions were noted in the corpus callosum of one marmoset inoculated with HHV-6A intravenously ( M04 ) . These lesions , absent 82 days post-inoculation ( Figure 2A ) were noted on consecutive slices of scans conducted 173 days ( Figure 2B ) and 194 days ( Figure 2C ) post-inoculation . The lesions had resolved by the time of the post-mortem scan , which was conducted 433 days post-inoculation ( Figure 2D ) . On day 167 post-inoculation , M04 experienced a recurrence of motor weakness in his hind limbs , corresponding to a score of 2 . 5 ( Figure 1 , solid line ) . This deficit was still present on days 173 and 194 , when the brain abnormalities were detected . No MRI-detectable lesions were observed in the brains of the HHV-6B inoculated animals , comparable to the SupT1 vehicle controls . Marmoset plasma was collected prior to HHV-6 inoculation and every two weeks post inoculation , to monitor longitudinal IgM and IgG reactivity to HHV-6 and SupT1 control lysates . Intravenous inoculation of HHV-6A led to a rapid , virus-specific IgM response that was detectable as early as week one post-inoculation ( Figure 3A ) . Over the 25 week monitoring period , three of the four marmosets exposed to HHV-6A intravenously produced HHV-6-specific IgM responses greater than two-fold above baseline ( M01 , M02 and M04 , Figure 3A ) , and all four mounted HHV-6-specific IgG responses ( Figure 3C ) that were detectable as early as week three post-inoculation . By contrast to what we observed in the HHV-6A intravenously inoculated marmosets , none of the marmosets inoculated intravenously with HHV-6B mounted virus-specific IgM responses ( Figure 3B ) , and only two of the four demonstrated virus-specific IgG responses ( Figure 3D ) . M05 mounted a robust virus-specific IgG response , the magnitude of which , in the absence of a detectable IgM response , suggests a previous exposure to this virus . M07 also mounted an IgG response , though lower in magnitude than M05 ( Figure 3D ) . The virus-specific IgM ( Figure 3B ) and IgG ( Figure 3D ) responses of the remaining two HHV-6B intravenously inoculated marmosets , M06 and M08 , increased less than two-fold over baseline , and were therefore considered negative . SupT1 control marmosets did not generate a virus-specific antibody response ( data not shown ) . Collectively , these results demonstrate that among the intravenously inoculated marmosets , animals inoculated with HHV-6A mounted greater IgM and IgG responses compared to animals inoculated with HHV-6B , although these differences did not reach statistical significance ( Figure 4A ) . This may be due to the small numbers of animals per group , a common limitation in NHP studies , or the heterogeneity between marmosets , which is inherent to this outbred model . In marmosets inoculated with HHV-6A or HHV-6B intravenously , viral DNA was detected infrequently in the plasma , PBMC or saliva during the 25-week monitoring period ( Figure 5 ) . Viral DNA was detected in two of the marmosets inoculated with HHV-6A , at three weeks in the plasma and nine weeks in the saliva of M03 , and at 21 weeks in the PBMC of M04 . Viral DNA was also detected in one marmoset inoculated with HHV-6B , at three weeks in the PBMC and plasma of M08 ( Figure 5 ) . None of the vehicle control marmosets tested positive for HHV-6 DNA ( data not shown ) . At euthanasia , the spleen , cervical lymph nodes ( LN ) , olfactory bulb , heart , kidney and liver were collected and analyzed for the presence of HHV-6 DNA . Viral DNA was detected by nPCR in the tissues of two of the four euthanized HHV-6A animals , but none of the three HHV-6B euthanized animals . HHV-6 DNA was detected in the spleen of M02 and in all analyzed tissues of M03 , and confirmed as HHV-6A by sequencing ( data not shown ) . As intravenous inoculation of HHV-6A but not HHV-6B led to neurologic symptoms , we similarly characterized another group of marmosets that we inoculated with HHV-6A intranasally , which represents a more physiologic route of infection . Intranasal inoculation was examined based on the recent report that HHV-6 DNA could be detected in human nasal mucus and olfactory bulb [51] , suggesting the olfactory pathway as a route of transmission for this virus . As salivary glands are a known reservoir of HHV-6 and other herpesviruses [52] , [53] , the nasal cavity may also serve as a reservoir for HHV-6 . Four naïve marmosets were inoculated with HHV-6A intranasally ( Table 1 ) and monitored daily as previously described . During the 25-week study period , none presented with clinical signs of disease , in contrast to marmosets inoculated intravenously with this virus ( Figure 1 ) . Interestingly , unlike marmosets inoculated with HHV-6A intravenously , marmosets inoculated with HHV-6A intranasally failed to generate virus-specific IgM ( Figure 6A ) or IgG antibody responses ( Figure 6B ) ; all detectable serum antibodies were less than two fold above baseline throughout the 25-week study period . The HHV-6-specific IgM and IgG responses of intravenously inoculated marmosets were significantly higher compared to those of intranasally inoculated marmosets ( Figure 4B ) . Unlike marmosets inoculated with HHV-6A intravenously , marmosets inoculated with HHV-6A intranasally routinely tested positive for viral DNA in the saliva , PBMC and plasma ( Figure 5 ) . Viral DNA was detected with increasing frequency as a function of the inoculations . By week 11 ( after the third and final inoculation at week 8 ) , HHV-6A DNA was detected consistently in all marmosets , with an apparent increase during weeks 13–15 and weeks 23–25 , during which viral DNA was detected in multiple compartments of most marmosets ( Figure 5 ) . The frequency of marmosets testing positive over the 25-week period was significantly elevated in the intranasally inoculated group compared to the intravenously inoculated group ( Figures 4C and 4D ) . M15 was sacrificed for analysis as a representative of the HHV-6A intranasal group , but viral DNA was not detected in his spleen , cervical LN , olfactory bulb , heart , kidney or liver ( data not shown ) . Histological evaluation of the spinal cord and brain sections showed limited pathology in M03 and M04 , both of which were intravenously inoculated with HHV-6A . Iba-1 IHC showed multifocal macrophage/microglia nodules in the cervical spinal cord of M03 ( Figure 7A ) and mild , multifocal , gliosis in the thoracic and lumbar spinal cord of M04 ( Figures 7D , 7E ) . Increased expression of Iba-1 is a nonspecific response to tissue injury and indicates activation of macrophages or microglia , the resident macrophages of the CNS . Macrophageal/microglial activation can be induced in the context of viral infection but is generally associated with CNS injury or disease . LFB and Bielschowski's silver stain demonstrated mild myelin abnormalities in M03 , including focal areas of swollen myelin sheaths ( Figures 7B and 7C ) . In M04 , LFB staining of the dorsal root ganglion ( Figure 7F ) showed variation in myelin sheath size and an abnormally high number of cells in the extracellular matrix , suggesting gliosis . The arrow in Figure 7F denotes a region of focal central neuronal chromatolysis , which is indicative of mild reversible damage . Histological staining of other study subjects did not reveal significant pathology . As euthanasia and subsequent histological examination of the tissues was performed between 136 and 433 days post-inoculation , lesions reflective of the clinical signs observed during the study may have evolved and undergone repair and healing by the time of sacrifice . Brains and spinal cords of all euthanized animals ( M01–M04 , M05 , M06 , M07 , M12 ) were embedded in paraffin wax and sectioned , and DNA was isolated and PCR amplified for HHV-6 sequences . Of the 77 total scrolls surveyed , six ( 8% ) were PCR positive for HHV-6 DNA ( Table 1 ) . The positive scrolls were from three of the eight marmosets sacrificed for analysis in this study , one that was inoculated with HHV-6A intravenously ( M04 ) , and two that were inoculated with HHV-6B intravenously ( M05 and M07 ) . One brain region from M04 and one from M05 were positive for viral DNA , while one spinal and three brain regions from M07 were positive . Interestingly , portions of the occipital cortex and cerebellum were positive in all three animals ( Table 1 ) . These results demonstrate that in a subset of HHV-6 intravenously inoculated marmosets , viral DNA can be detected in paraffin-embedded CNS tissue up to 14 months ( in the case of M04 ) following intravenous viral inoculation .
The NCBI reference sequence NC_001664 . 2 was used for HHV-6A . The NCBI reference sequence NC_000898 . 1 was used for HHV-6B . The Entrez gene ID numbers for the genes mentioned in the text are as follows , HHV-6A U90: 1487968; HHV-6B U90: 1497087; HHV-6A U57: 1487939; HHV-6B U57: 1497059 .
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The human herpesviruses HHV-6A and HHV-6B are widely distributed in the human population , but also specifically associated with several central nervous system ( CNS ) diseases . We investigated HHV-6A and HHV-6B infections in the common marmoset , a non-human primate naturally susceptible to infection , unlike rodents . We inoculated marmosets with HHV-6A and HHV-6B intravenously , and with HHV-6A intranasally , to represent a more physiologic route of infection . Following intravenous HHV-6A inoculation , marmosets exhibited clinical symptoms with evidence of spinal cord pathology . Animals inoculated intravenously with HHV-6B were asymptomatic and without detectable CNS pathology . Both groups developed robust anti-viral antibody responses , and we detected viral DNA infrequently in the periphery . By contrast , marmosets inoculated intranasally with HHV-6A were asymptomatic , failed to generate anti-viral antibodies , and we frequently detected viral DNA in the periphery . Interestingly , HHV-6 DNA was detected in brain and spinal cord sections of several intravenously inoculated animals , demonstrating that HHV-6 can gain access to and persist in the CNS . These observations help to define the contributions of ubiquitous herpesviruses to neurologic disease development in a non-human primate . As little is known about the acquisition and host response to HHV-6A , this model may clarify how this virus may trigger or potentiate disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"marmoset",
"viral",
"transmission",
"and",
"infection",
"immunology",
"microbiology",
"neuroscience",
"animal",
"models",
"model",
"organisms",
"neurovirulence",
"neuroimaging",
"viral",
"disease",
"diagnosis",
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"virology"
] |
2013
|
Novel Marmoset (Callithrix jacchus) Model of Human Herpesvirus 6A and 6B Infections: Immunologic, Virologic and Radiologic Characterization
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Motor adaptation paradigms provide a quantitative method to study short-term modification of motor commands . Despite the growing understanding of the role motion states ( e . g . , velocity ) play in this form of motor learning , there is little information on the relative stability of memories based on these movement characteristics , especially in comparison to the initial adaptation . Here , we trained subjects to make reaching movements perturbed by force patterns dependent upon either limb position or velocity . Following training , subjects were exposed to a series of error-clamp trials to measure the temporal characteristics of the feedforward motor output during the decay of learning . The compensatory force patterns were largely based on the perturbation kinematic ( e . g . , velocity ) , but also showed a small contribution from the other motion kinematic ( e . g . , position ) . However , the velocity contribution in response to the position-based perturbation decayed at a slower rate than the position contribution to velocity-based training , suggesting a difference in stability . Next , we modified a previous model of motor adaptation to reflect this difference and simulated the behavior for different learning goals . We were interested in the stability of learning when the perturbations were based on different combinations of limb position or velocity that subsequently resulted in biased amounts of motion-based learning . We trained additional subjects on these combined motion-state perturbations and confirmed the predictions of the model . Specifically , we show that ( 1 ) there is a significant separation between the observed gain-space trajectories for the learning and decay of adaptation and ( 2 ) for combined motion-state perturbations , the gain associated to changes in limb position decayed at a faster rate than the velocity-dependent gain , even when the position-dependent gain at the end of training was significantly greater . Collectively , these results suggest that the state-dependent adaptation associated with movement velocity is relatively more stable than that based on position .
The motor system adapts to movement perturbations , a process largely driven by the error between the executed movement and the predicted consequences of that movement [1–3] . This short-term form of motor learning is a gradual updating of the motor commands required to counteract the movement perturbation . Similar to learning , the decay of adaptation following the removal of the perturbation is typically a gradual process as the motor commands revert back to the state prior to exposure [4 , 5] . Thus , examining and comparing the progression and decay of motor adaptation provides insight into the stability of these updates to the issued motor commands . The decay of motor adaptation has been studied for various behavioral paradigms involving limb movement: prism displacement [6 , 7] , locomotion [8 , 9] , visuomotor alterations [5 , 10 , 11] and force-field perturbations [12–15] In the last case , subjects make reaching movements while interacting with a robotic manipulandum and are exposed to a force perturbation typically dependent upon either a single motion kinematic parameter ( e . g . , changes in position , velocity or acceleration during the movement ) or the combination of these motion states [16] . In response to the movement disturbance , subjects apply an adaptive response based on the temporal characteristics of the limb state . Although previous investigations of force-field adaptation have examined the time course and the factors that influence the stability and retention of these state-dependent compensatory responses [4 , 12 , 14 , 17–23] the relative stability of the different state-dependent components that drive adaptation is not well understood , especially in direct comparison to the initial learning process . Here , we applied a framework developed by Sing and colleagues [16] to compare the progression and decay of state-dependent adaptation in response to different types of novel movement dynamics . Based on this framework , the feedforward motor output in response to the applied force perturbation is the weighted sum of gains assigned to the kinematic parameters of the reaching motion ( changes in limb position and velocity , [16 , 23–25] ) . The model predicted that the changes in these gains during adaptation would follow a different time course than during the adaptation decay , but the authors did not explicitly test this prediction nor the relative stability of the changes based on the kinematic parameters . To assess the difference in the relative stability of the motor memory based on changes in limb position or velocity we first modified the original model proposed by Sing et al . [16] based on observed differences in the retention of adaptation in response to purely velocity- or position-dependent disturbances . The resulting model simulations predicted that when motor learning is based on the combination of position and velocity the decay of adaptation is biased towards velocity , independent of the final adaptation level . That is , the model predicted that the decay of position-based adaptation would occur at a faster rate , even when this learning was significantly higher at the end of training . We tested two additional groups of subjects and found that the behavioral results were in agreement with the predictions of the model . Collectively , our behavioral and simulation results suggest that ( 1 ) the decay of motor adaptation is not merely the reversal of the learning process , but at least a partially distinct process likely involving separate mechanisms , ( 2 ) the velocity-based contribution to updating motor commands is more stable than that based on position , and ( 3 ) a model with asymmetrical retention factors for position- and velocity-based motor primitives can predict the time course of adaptation decay for various state-dependent motor learning goals .
We first trained subjects to make reaching movements in either a position- or velocity-dependent force-field ( pFF and vFF ) ( Fig 1 ) . Each subject experienced only one type of perturbation after an initial baseline period , during which error-clamp trials were used to quantify the feedforward adaptive changes to the motor output ( see Materials and Methods ) . Based on the forces subjects applied during the error-clamp trials , we were able to determine the adaptation coefficient ( the linear regression of the applied lateral force profile onto the ideal compensatory force profile ) and the respective gain of the position-dependent and velocity-dependent force components to the overall force profile ( see Materials and Methods ) . Fig 2A plots the adaptation coefficient as a function of trial number for pFF and vFF training . Similar to previous studies [16 , 21 , 26 , 27] , we observed a fast progression of adaptation early on ( within the first 15 trials ) that plateaued after approximately 75 trials for both force-field types ( Fig 2A ) . An exponential fit of the adaptation curve showed a faster overall adaptation for pFF training ( time constant of 7 . 2 ± 0 . 8 for pFF compared to 12 . 3 ± 5 . 5 trials for vFF . See S1A Fig ) . The amount of adaptation at the end of training was significantly greater than at the beginning , but the adaptation levels were not significantly different between perturbation types ( 2-way ANOVA , P < 0 . 001 for the main effect of training period and P = 0 . 22 for the main effect of perturbation type ) . Specifically , early adaptation levels were not significantly different between pFF and vFF training ( 0 . 38 ± 0 . 03 compared to 0 . 30 ± 0 . 05 , mean ± SEM , P = 0 . 18 , two-tailed t-test ) . There was also no difference in the adaptation level between vFF and pFF late in training where the behavior asymptotes ( 0 . 72 ± 0 . 02 for pFF and 0 . 69 ± 0 . 04 for vFF , P = 0 . 58 , two-tailed t-test ) . ( Early adaptation period was determined over trials 1–15 , while the late/asymptotic adaptation period was trials 150–160 . See Materials and Methods for justification of these ranges . ) Immediately following training , subjects experienced a sequence of consecutive error-clamp trials to determine the decay of feedforward changes to motor output . Following the start of the consecutive error clamps the adaptation coefficient began to decay and reached asymptote by the end of the period . An exponential fit of the adaptation decay curve showed a faster decrease in adaptation for vFF over pFF training ( time constant of 11 . 0 ± 2 . 2 trials for pFF compared to 7 . 1 ± 1 . 0 trials for vFF . See S1B Fig ) . The adaptation coefficient levels at the end of the decay period remained significantly greater than baseline levels ( vFF: 0 . 006 ± 0 . 006 vs . 0 . 26 ± 0 . 05 , pFF:-0 . 004 ± 0 . 004 vs . 0 . 19 ± 0 . 03 , paired two tailed t-test , P < 0 . 001 for both cases ) . Although the pattern of decay was similar for both pFF and vFF training ( Fig 2A ) , there was a slight , but insignificant difference in the adaptation level before the start of decay as noted above . In order to examine the decay with respect to the final adaptation levels , we normalized the decay of adaptation by the initial value of the adaptation coefficient at the beginning of the decay period ( Fig 2B ) . Starting at an adaptation level of 1 . 0 , we analyzed the decay of adaptation in early and late epochs during the decay period . The percentage of adaptation at the beginning of the decay period was significantly greater than at the end , but the adaptation levels were not significantly different between perturbation types ( 2-way ANOVA , P < 0 . 001 for the main effect of decay period and P = 0 . 97 for the main effect of perturbation type ) . In the early epoch , there was not a significant difference in the percentage of adaptation that remained for pFF and vFF training ( pFF: 42 . 3 ± 5 . 2% , vs vFF: 36 . 0 ± 6 . 2%; P = 0 . 41 , post hoc comparisons using Bonferroni correction ) . This was also true for the late epoch ( pFF: 18 . 8 ± 3 . 4% , vs vFF: 25 . 5 ± 5 . 3%; P = 0 . 35 , Bonferroni correction ) . ( The early epoch of decay was over trials 11–20 , while the late epoch was trials 50–60 . See Materials and Methods for justification of these ranges . ) Although the one dimensional adaptation coefficient suggested similar behavior for vFF and pFF training , we were interested in the temporal characteristics of the corresponding force profiles during the adaptation and the decay periods ( see Materials and Methods ) . As in previous studies [16 , 23 , 25] we compared the temporal shape of the force profiles with changes in limb position and velocity ( Fig 2C and 2D ) . As shown previously [16] , early in adaptation the force pattern was dependent on both the position and velocity changes during the movement ( pFF: 21 . 5 ± 2 . 2% for velocity and 78 . 5 ± 2 . 2% for position , vFF: 67 . 8 ± 7 . 3% for velocity and 32 . 2 ± 7 . 3% for position ) ( top panels in Fig 2C and 2D ) . Notably , late in the adaptation period the force pattern was mostly aligned with the appropriate movement parameter for the adaptation . In other words , the force exerted by subjects in the late phase of pFF adaptation was largely aligned with changes in limb position ( 95 . 9 ± 0 . 6% compared to 4 . 1 ± 0 . 6% for velocity ) , and late adaptation to vFF was mostly aligned with changes in movement velocity ( 93 . 2 ± 1 . 6% compared to 6 . 8 ± 1 . 6% for position ) . We also examined the temporal force patterns during the decay period of the respective force-field perturbations . Interestingly , the force profiles remained aligned to the appropriate motion state required to compensate for the perturbation in both the early and late stages of decay ( bottom panels in Fig 2C and 2D ) . In the early phase of the decay of pFF learning ( Fig 2C bottom panel ) , the force profiles mainly consisted of a position-dependent component with a minimal velocity-dependent component ( 90 . 1 ± 7 . 0% compared to 9 . 9 ± 7 . 0% ) . In the late decay phase of pFF learning , the position-dependent component continued to contribute the most to the exerted force while the velocity contribution remained small ( 80 . 4 ± 8 . 0% compared to 19 . 6 ± 8 . 0% ) . Similarly , the force profiles in both the early and late decay phases of vFF learning were mostly dependent on movement velocity , with less contribution of limb position ( early: 76 . 4 ± 7 . 0% compared to 23 . 6 ± 7 . 0% , late: 76 . 0 ± 8 . 2% compared to 24 . 0 ± 8 . 2% ) ( bottom panels in Fig 2D ) . Thus , the comparison of the temporal force profiles suggests that the proportional contributions of limb position and velocity to the overall motor output achieved at the end of training were largely maintained during the decay of the motor learning . Differences in the force profile described above suggest that the gain associated to the respective motion states is different not only between the two types of force-field adaptations , but also between the learning and decay periods . In order to visualize these differences , we examined the changes in the respective gain associated to the motion states for adaptation and decay in a two dimensional gain-space ( see Materials and Methods ) . We parsed the position-dependent and velocity-dependent force components and found a clear separation between adaptation and decay paths for both pFF and vFF training ( Fig 3A and 3B ) . For both types of perturbations , we identified a goal-aligned and a goal-misaligned component . The goal-aligned component for pFF training is parallel to abscissa in gain space and represents the position-dependent force component , whereas the goal-misaligned component is parallel to the ordinate and represents the velocity-dependent force component . These relationships are reversed for vFF training with the goal-aligned and goal-misaligned components represented by the velocity- and position-dependent axes , respectively . In both pFF and vFF training , the goal-aligned force component had a significantly greater contribution to the initial adaptation than the goal-misaligned components ( pFF: goal-aligned ( 0 . 33 ± 0 . 03 ) vs goal-misaligned ( 0 . 21 ± 0 . 02 ) ; vFF: goal-aligned ( 0 . 25 ± 0 . 03 ) vs goal-misaligned ( 0 . 09 ± 0 . 04 ) , P < 0 . 05 in both cases ) . ( Note there is a larger goal-misaligned component for initial pFF adaptation than for initial vFF adaptation . That is , the velocity-dependent contribution to the adaptive response for pFF training was larger than the position-dependent contribution for vFF training . This asymmetry supports an initial adaptation bias towards velocity-dependent learning as discussed below ) . As subjects continued to experience the force-field , contributions from the goal-aligned component increased whereas the goal-misaligned component decreased ( Fig 3A and 3B ) . We examined two time points during training ( points 1 and 2 ) which represent early ( training trials 1–15 ) and late adaptation ( training trials 150–160 ) respectively . By the end of the training period ( the point labeled 2 in Fig 3A and 3B ) , the majority of the compensatory force was due to the contribution of the goal-aligned force component ( vFF: goal-aligned ( 0 . 66 ± 0 . 04 ) vs goal-misaligned ( 0 . 05 ± 0 . 008 ) ; pFF: goal-aligned ( 0 . 69 ± 0 . 02 ) vs goal-misaligned ( 0 . 1 ± 0 . 01 ) , P < 0 . 05 in both cases ) . This decrease in the goal-misaligned component resulted in a curvature in the learning trajectories ( note the difference between points labeled 1 and 2 ) . However , the magnitude of this curvature was not the same for pFF and vFF training; the contribution of the velocity-dependent force for pFF training from early ( point 1 in Fig 3A ) to late adaptation ( point 2 in Fig 3A ) was significantly different in magnitude ( 1st point: 0 . 21 ± 0 . 02 vs . 2nd point: 0 . 10 ± 0 . 01 , P < 0 . 05 ) . Although there was a similar decrease in the contribution of the position-dependent forces from early to late adaptation for vFF training , this decrease in magnitude was not significant ( 1st point: 0 . 09 ± 0 . 04 vs . 2nd point: 0 . 05 ± 0 . 01 , P = 0 . 32 ) . The gain-space trajectories diverge from the initial adaptation path with the start of the decay period ( Fig 3A and 3B gray lines ) . In both cases , the direction of change in gain is toward the origin of the gain-space . However , the gain-space trajectories never return completely to the origin , indicating only partial decay of the force-field adaptation within the period examined . This is in agreement to the asymptotic behavior seen at the end of the decay period for the adaptation coefficient ( Fig 2A and 2B ) . Separation of the adaptation and decay gain-space trajectories for both pFF and vFF training demonstrate a difference in the behavior of the motor system during the decay of adaptation . The change in the goal-misaligned component between adaptation and decay dictates the shape of this separation . Fig 3C shows the gains for both the aligned and misaligned components for pFF training during the adaptation and decay period as a function of trial . The gain applied to the aligned component at the end of the decay period remained significantly greater than baseline levels , but this was not the case for the misaligned gain ( aligned: -0 . 006 ± 0 . 004 vs . 0 . 12 ± 0 . 02 , paired two tailed t-test , P < 0 . 001; misaligned: 0 . 009 ± 0 . 005 vs . 0 . 03 ± 0 . 02 , paired two tailed t-test , P = 0 . 27 ) . In order to capture the changes in the goal-misaligned component , we defined a third point in the gain-space trajectory . This 3rd point was the trial range during the decay period at which the gain of the aligned component was not significantly different from the respective gain during initial learning ( trials 12–14 and 16–18 of the decay period for pFF and vFF , respectively ) . For example , for pFF training , there was no significant difference in the gain for the goal-aligned component between the 1st and 3rd points ( 0 . 33 ± 0 . 03 compared to 0 . 27 ± 0 . 03 , P = 0 . 16 , two-tailed t-test ) . We determined this point in order to isolate changes in the gain of the goal-misaligned component between adaptation and decay , and quantify the trajectory separation . For pFF training the goal-misaligned component was significantly different between the three different points ( ANOVA , P < 0 . 001 for the main effect of period ) . The value of the goal-misaligned component at the 1st point was significantly greater than the respective gain at the 2nd and 3rd points ( 0 . 21 ± 0 . 02 compared to 0 . 09 ± 0 . 01 and 0 . 08 ± 0 . 02 , P < 0 . 05 for both cases , multiple comparisons corrected ) ( Fig 3C ) . The difference between the 1st and 2nd points shows that the early adaptation level is less specific to the goal in comparison to late adaptation . The difference between the 1st and 3rd points further shows that for similar values of the goal-aligned component , adaptation and decay gain-space trajectories are significantly distinct . The goal-misaligned component was significantly different from zero for all 3 points , indicating that both adaptation and decay are confined in the 1st quadrant of the gain-space ( P < 0 . 05 , two-tailed t-test ) . The behavior of the goal-misaligned component was slightly different for vFF training but the overall effect was the same . As above for pFF training , we compared the adaptation and decay gain-space trajectories at points where there was no significant difference in the gain of the goal-aligned component ( between the 1st and 3rd points in Fig 3B , 0 . 24 ± 0 . 03 compared to 0 . 21 ± 0 . 03 , P = 0 . 16 , two-tailed t-test ) . Again , the goal-misaligned component was significantly different between the three different points ( ANOVA , P < 0 . 05 for the main effect of period ) . Unlike pFF training , there was no significant difference in the gain of the goal-misaligned component between the 1st and 2nd points ( 0 . 09 ± 0 . 04 compared to 0 . 05 ± 0 . 01 , P = 0 . 52 , multiple comparisons corrected ) . Additionally , the value of goal-misaligned component at the 3rd point was significantly less than the respective gain at the 1st and 2nd points ( -0 . 02 ± 0 . 01 compared to 0 . 09 ± 0 . 04 and 0 . 05 ± 0 . 01 , P < 0 . 05 for both cases , multiple comparisons corrected ) ( Fig 3D ) . The goal-misaligned component was significantly different from zero for both early and late adaptation indicating that adaptation was confined to the 1st quadrant of the gain-space , but late decay showed a nominal , but negative gain for the position-dependent component . Similar to pFF training , the gain for the goal-aligned component at the end of the decay period remained significantly greater than baseline levels ( 0 . 008 ± 0 . 005 vs . 0 . 16 ± 0 . 03 , paired two tailed t-test , P < 0 . 001 for both cases ) , but the misaligned component was not ( -0 . 003 ± 0 . 003 vs . 0 . 002 ± 0 . 01 , paired two tailed t-test , P = 0 . 75 ) . Although a separation between adaptation and decay was present in both vFF and pFF training gain-space trajectories , the shapes of the trajectories were not the same . We identified three differences between the gain-space trajectories . First , the initial learning for pFF training was less specific compared to vFF adaptation . That is , vFF adaptation was more aligned with the goal ( parallel to the ordinate ) compared to pFF training ( parallel to the abscissa ) . Another way to quantify this difference is to determine the angle between the learning gain-space trajectory and the ideal ( straight ) trajectory to the adaptation goal . For early training ( 1st point ) this angle was significantly greater for pFF training compared to vFF adaptation ( pFF: 31 . 9° ± 2 . 6° vs . vFF: 18 . 6° ± 6 . 9° , P < 0 . 05 , one-tail t-test ) . In other words , initial vFF training was more aligned with the learning goal ( parallel to the velocity-dependent axis ) than initial pFF adaptation ( parallel to the position-dependent axis ) . Second , there was greater change in the learning gain-space trajectory for pFF training—a significantly larger difference was observed along the goal-misaligned gain axis between early and late adaptation for pFF adaptation ( a difference in gain of 0 . 1 ± 0 . 02 for pFF compared to a difference of 0 . 03 ± 0 . 04 for vFF , two-tailed t-test , P < 0 . 05 ) . Finally , although the decay of the goal-aligned component was slightly faster for vFF training ( time constant of 10 . 4 ± 2 . 2 trials for pFF compared to 7 . 9 ± 0 . 9 trials for vFF . See S2A Fig ) , there was a much larger difference in the decay of the goal-misaligned component , with levels for pFF training significantly greater than vFF adaptation throughout the decay period ( S2B Fig ) . In other words , velocity-based learning persisted at a nonzero value during the decay of pFF training . However , any subsequent position-based learning quickly decreased to zero for vFF training . We hypothesized that these asymmetries for pFF and vFF training represent a possible intrinsic bias of the motor system to ( 1 ) associate the imposing perturbation with the kinematics of the movement and ( 2 ) retain the motion based learning . If the association between movement kinematics and the force-field perturbation is biased toward the velocity changes during the movement then adaptation to a force-field perturbation that is equally dependent on both position and velocity should be biased toward the velocity-dependent axis . This effect should also persist during the decay of the adaptation if velocity-dependent learning is more stable than that based on position . Sing et al . [16] previously studied adaptation to different force-field perturbations that were dependent on a combination of changes in limb position and velocity . However , the decay of this adaptation to different learning goals was not examined . Moreover , in their viscoelastic primitive model to describe the adaptation there was the basic assumption that motor learning based on changes in movement position and velocity is symmetric—an assumption challenged by the results described above . We therefore modified this model in order to make predictions about adaptation behavior and decay to novel movement dynamics dependent on different combinations of changes in limb position and velocity . The viscoelastic primitive model proposed by Sing et al . [16] captured the changes in the temporal pattern of force during adaptation to different types of force-field perturbations . The application of this model to the vFF and pFF behavioral data are shown in Fig 4A . In this model the force pattern in each trial is a weighted sum of motor primitives that are differentially tuned to changes in position and velocity during the movement . On each trial the error between the current motor output in the 2D gain-space and the learning goal , combined with a gradient descent rule , determines how the weights of the respective primitives are updated . This model captures the initially similar motor output in response to the vFF and pFF perturbations , as well as the late-learning rotation of the gain-space trajectory toward the relevant motor learning goal ( velocity for vFF training or position for pFF training ) . However , as mentioned above , this model assumes that the decay of the adaptation is the same for both types of motion-based learning ( a symmetric primitive model ) . This similarity in retention results in a decay trajectory that travels directly back to the baseline value towards the origin . Interestingly , this decay structure makes testable predictions for force-field perturbations that combine velocity- and position-based learning ( Fig 4A ) . First , utilizing the parameters determined from the simultaneous fit to the vFF and pFF behavioral data , for an unbiased combination ( ucFF , equally dependent on both motion states ) the learning and decay gain-space trajectories will be similar , with the decay closely following the reverse of the adaptation path . Second , using the same model parameters , the decay for adaptation to a position biased force-field ( pcFF , a greater position and smaller velocity dependence ) will be biased towards the position axis due to the greater representation of position-based learning at the end of training . In our modification to this model we assume , based on the behavioral results above ( see S2 Fig and S3 Fig ) , that the retention of learning based on changes in movement velocity is greater than the retention of learning based on changes in limb position . That is , during adaptation , the portion of the primitive population that encodes velocity information maintains a larger representation of this motion-based learning . We modeled this asymmetry by imposing that each primitive has two decay rates , one for position-based learning and one for velocity ( see Materials and Methods ) . In this case , on each trial the amount of adaptation is scaled with different non-unity factors for position and velocity . We refer to this implementation as the asymmetric primitive model and , similar to the symmetric model , we applied this model to the vFF and pFF behavioral data and made predictions for force-field perturbations that combine velocity- and position-based learning ( see Materials and Methods ) . When determining the values of the respective retention factors , we did not put any constraint on the relationship . Thus , the retention asymmetry could be in either direction , allowing a direct assessment of any difference . This asymmetric model makes similar predictions as the symmetric model for the time course of adaptation for pFF and vFF training ( Fig 4B ) . However , only the asymmetric model captures the small , but distinct separation in the decay of pFF and vFF training ( see S3 Fig ) . In addition , the two models make distinct predictions about the decay for ucFF and pcFF training . As described above , the symmetric primitive model predicts equal adaptation to position and velocity , and decay along a similar trajectory for ucFF learning ( Fig 4A and 4B ) . However , the asymmetric model ( whose parameters are based solely on the vFF and pFF behavioral data ) predicts that the final adaptation to this perturbation is biased toward velocity-based learning , and that the decay lies completely in the portion of the primitive space with more velocity contribution ( αK = 0 . 942 vs αB = 0 . 951 ) . When the two models simulate adaptation to the pcFF perturbation , the symmetric model predicts a decay that remains biased toward position . In contrast , the asymmetric model predicts a shift towards the velocity axis during decay . This is important in the sense that the adaptation endpoint in the primitive gain space imposes distinct decay characteristics under the two models that can directly be tested . In order to further visualize the differences between the decay trajectories under the two models , we normalized the trajectories with respect to the end point of adaptation ( Fig 4C ) . We did this to remove the effect of the adaptation endpoint for each force-field type , and more importantly reveal the difference between the decay rates . Under both ucFF and pcFF , the symmetric primitive model predicts a decay that follows the unity , x = y line . This is expected due to the same decay rates for position- and velocity-based learning . In contrast , the asymmetric model ( whose parameters in this case are based only on the vFF and pFF data ) predicts that the decay will be biased towards the velocity axis . The bias in the decay is predicted by the larger retention rate for velocity state compared to position ( S6 Fig ) . Although both models fit the pFF and vFF data qualitatively , it is important to note that there are aspects of the simulated adaptation that both models fail to capture ( e . g . , differences in the initial adaptation trajectory ( magnitude and direction ) between pFF and vFF , Fig 3A and 3B ) . For additional insight into these differences we focused on the predictions of the asymmetric model simulation , fitting the model separately to the vFF and pFF data in S4 Fig . Note that as in Fig 4 , the values of the respective retention factors were not constrained . Consistent with Fig 4 , in all cases the normalized decay trajectory is above the unity line demonstrating that velocity-based learning is decaying slower than position-based adaptation . Additionally , in S5 Fig and S6 Fig we show the influence of the primitive distribution on the learning trajectory and the influence of the retention rates on the decay trajectory . Finally , based on the same parameters in Fig 4 , we also simulated the decay for adaptation to a velocity biased force-field ( vcFF , a greater velocity and smaller position dependence , S7 Fig ) . Although the learning trajectory mirrors the pcFF simulation , the relative stability of the motion-based adaptation are consistent with Fig 4C . To test the predictions of the symmetric and asymmetric primitive models we trained two additional groups of subjects in force-field perturbations that were unbiased ( ucFF ) and position biased ( pcFF ) combinations of the two motion states in order to further characterize the stability of velocity- and position-dependent learning . Previous studies have shown that the adaptation rate to a force-field with a positive correlated dependence on limb position and velocity is faster than adaptation to a purely position or velocity-dependent force-field [16] . Here , we examined how the ratio of position and velocity dependence influenced the motor adaptation and stability during the decay period . As described for the simulations above , we first examined adaptation and decay in response to a force-field equally dependent on both motion states ( ucFF ) . Following this , we examined learning and the subsequent decay for movements made within a combination force-field with a greater position and smaller velocity dependence ( pcFF ) . We observed that the adaptation to an unbiased combination force-field ( equally dependent on the state of the position and velocity during the movement ) was generally closer to the learning goal by the end of the adaptation period ( Fig 5A ) . The applied gain was significantly different between the late periods of adaptation and decay , and between the two types of motion states ( 2-way ANOVA , P < 0 . 001 for both the main effect of period and the main effect of motion state ) . Subjects initially adapted to the force-field by applying similar state-dependent gains for changes in position and velocity ( position: 0 . 20 ± 0 . 03 , velocity: 0 . 28 ± 0 . 04 , P = 0 . 09 , paired two-tailed t-test ) . However , by the end of the adaptation period , the velocity-dependent gain was significantly greater than the position-dependent gain ( position: 0 . 54 ± 0 . 03 , velocity: 0 . 68 ± 0 . 04 , P < 0 . 05 , paired two-tailed t-test ) . This resulted in a gain-space learning trajectory that was above the unity line and clearly biased towards the velocity-dependent gain axis ( ordinate in Fig 5A ) . To examine the characteristics of this bias , we projected the gain-space trajectory onto the position and velocity gain axes at each point during learning and decay ( Fig 5C ) . As described above , early in adaptation the position- and velocity-dependent gains had similar magnitudes , but the velocity-dependent gain was significantly greater than the position-dependent gain by the end of the adaptation period . This significant difference between the velocity- and position-dependent gains extended throughout the decay period ( Late in decay: position: 0 . 07 ± 0 . 02 compared to velocity: 0 . 18 ± 0 . 03 , P < 0 . 05 for both cases , paired two-tailed t-test , Fig 5C bar graph ) . An exponential fit to the decay of the velocity and position components showed a larger time constant for velocity ( time constant of 9 . 4 ± 1 . 8 trials for position compared to 12 . 1 ± 2 . 0 trials for velocity . See S8A Fig ) . Additionally , the applied gains based on velocity and position at the end of the decay period remained significantly greater than baseline levels ( velocity: 3 . 8 x 10−4 ± 0 . 007 vs . 0 . 18 ± 0 . 03 , position: 0 . 003 ± 0 . 007 vs 0 . 07 ± 0 . 02 , paired two tailed t-test , P < 0 . 001 for both cases ) . This clearly shows that when the force-field is equally dependent on changes in movement position and velocity , the gain of the velocity-dependent force contributed more in both the adaptation and decay periods . This is in agreement with the predictions of asymmetric primitive models , which suggest a bias late in adaptation toward velocity continuing throughout the decay period ( Fig 4B ) . One might suspect that the observed bias in the decay trajectory for the unbiased combination force-field is the consequence of the unbalanced adaptation levels; the final adaptation has a significantly greater velocity-dependent gain compared to position . In order to remove this confound , we normalized the gain-space trajectory during the decay by the position and velocity-dependent gains by the respective values at the beginning of the decay period . Thus , the rescaled initial point of decay in gain-space is located at [1 . 0 , 1 . 0] . If the shape of the decay gain-space trajectory in Fig 5A was the result of unequal learning at the end of adaptation , then the normalized decay should be aligned with the equality line in gain space . However , the normalized trajectory clearly shows that the velocity-dependent gain was always greater than the respective position-dependent gain throughout the decay period ( Fig 5E ) . When we examined the temporal changes of the normalized gains during decay ( Fig 5E ) by projecting the trajectory onto the position and velocity-dependent gain axes , we observed the same effect ( Fig 5G ) . The percentage of adaptation at the beginning of the decay period was significantly greater than at the end , and the percentage of adaptation based on velocity and position was significantly different ( 2-way ANOVA , P < 0 . 001 for the main effect of period and the main effect of motion-based learning ) . For the early and late epochs of decay , the normalized velocity-dependent gain was significantly greater than position ( early epoch: position: 37 ± 7% vs . velocity: 52 ± 6%; Late epoch: position: 12 ± 5% vs . velocity: 25 ± 5%; P < 0 . 05 for all cases , paired two-tailed t-test ) . This is in line with the decay predicted by the asymmetric primitive model and suggests that there is an asymmetry in the retention rates between position- and velocity-based motion-state learning ( Fig 4C ) . As stated previously , a potential confound for the unbiased combination force-field is that the velocity-dependent gain at the end of adaptation was significantly greater than position . This may have influenced the decay and resulted in the velocity contribution being more stable throughout the decay period . We therefore conducted an additional experiment using a position-biased combination force-field ( pcFF ) . As predicted by both models , at the end of training the adaptation gain-space trajectory for this force-field is biased toward the position axis ( abscissa ) as shown in Fig 4A and 4B . However , as the decay period starts , the asymmetric model predicts that the gain-space trajectory will move toward the velocity-dependent gain axis ( ordinate ) and remain above the unity line for the remainder of the decay period ( Fig 4B ) . In contrast , the symmetric model predicts that the adaptation will decay towards the position axis ( Fig 4A ) Fig 5B shows the behavioral results for subjects trained on this combination force field . The decay of adaptation is clearly biased towards the velocity axis , consistent with the predictions of the asymmetric model . This can be seen in the trial-by-trial changes of both gains during adaptation and decay period ( Fig 5D ) . The applied gain was significantly different between the late periods of adaptation and decay , but there was no main effect of motion state ( 2-way ANOVA , P < 0 . 001 for the main effect of period and P = 0 . 27 for the main effect of motion state ) . ( Note that the non-significant effect of motion state is due to significant effects in opposite directions in the late periods of adaptation and decay . See below . ) Similar to the ucFF results , an exponential fit to the decay of the velocity and position components showed a larger time constant for velocity ( time constant of 5 . 7 ± 0 . 6 trials for position compared to 9 . 3 ± 0 . 9 trials for velocity . See S8B Fig ) . In addition , the final gains applied to velocity and position at the end of the decay period remained significantly greater than baseline levels ( velocity: 0 . 001 ± 0 . 007 vs . 0 . 10 ± 0 . 03 , position: 8 . 9 x10-4 ± 0 . 007 vs . 0 . 06 ± 0 . 02 , paired two tailed t-test , P < 0 . 001 for both cases ) . Although the adaptation starts with equal contribution of both motion components , late adaptation is significantly dominated by the position-dependent learning ( Early adaptation: position: 0 . 26 ± 0 . 05 vs . velocity: 0 . 26 ± 0 . 03 , P = 0 . 99 , paired two-tailed t-test; Late adaptation: position: 0 . 61 ± 0 . 03 vs . velocity: 0 . 53 ± 0 . 02 , P < 0 . 05 , paired two-tailed t-test ) . At the start of the decay period there is a rapid drop in the position-dependent gain . However , the decay of the velocity-dependent gain is much slower , resulting in the gain-space trajectory remaining above the unity line throughout much of the decay period ( Late in decay: position: 0 . 06 ± 0 . 02 vs . velocity: 0 . 10 ± 0 . 03 , P < 0 . 05 , multiple comparisons corrected ) . Due to the significant difference in the gain magnitudes at the end of the training , we also examined the normalized decay for pcFF training . Similar to the results for the unbiased combination force-field , we observed that the normalized decay gain-space trajectory was above the unity line for the entire decay period , indicating that the velocity-dependent gain decayed at a slower rate than the position-dependent gain . In addition , the percentage of adaptation at the beginning of the decay period was significantly greater than at the end , and the percentage of adaptation based on velocity and position was significantly different ( 2-way ANOVA , P < 0 . 001 for the main effect of period and the main effect of motion-based learning ) . This effect is strongly present in both early and late epochs of the decay period ( Early , position: 24 ± 5% vs . velocity: 42 ± 6%; Late position: 11 ± 4% vs . velocity: 20 ± 6%; P < 0 . 05 for both cases , paired two-tailed t-test ) ( Fig 5F and 5H ) . This again is in agreement with the simulations from the asymmetric primitive model ( Fig 4B and 4C , and insets in Fig 5A , 5B , 5E and 5F ) . When this model was applied simultaneously to the ucFF and pcFF behavioral data the simulations ( insets in Fig 5A , 5B , 5E and 5F ) predicted adaptation during training to be biased toward the position axis , but with the start of the decay , the gain-space trajectory is biased towards the velocity axis due to an asymmetry in the stability of the motion-state learning ( αK = 0 . 9424 vs αB = 0 . 9654 ) .
The gradual decay of newly formed motor memories has been studied for different contexts and tasks , including: prism [6 , 7] , locomotion [8 , 9] , visuomotor [10 , 11] and force-field perturbations [4 , 12 , 15] . The decay of adaptation in these studies is often at a different rate compared to initial learning suggesting at least partially separate mechanisms [23 , 27] . Recently , Kitago and colleagues [5] examined the decay of visuomotor adaptation for different types of assessments . For all methods examined , there was a decay in the adaptation , but the rate was the fastest when the perturbation was removed and slowest when the errors orthogonal to the ideal movement trajectory were visually clamped to zero ( similar to the error clamps used in the current study ) . ( Note the minimum decrease in adaptation level occurred with the passage of time , but the decay rate is difficult to quantify and compare for this context . ) This suggests that the method in which the decay rate is assessed ( i . e . , the context of the decay period ) potentially has a considerable influence on the decay rate of the motor memory [13 , 14 , 28] . In order to evaluate modifications to the feedforward changes in the motor output , it was necessary to utilize error-clamp trials . Although it is possible that assessing adaptation decay in a different manner ( e . g . , decay trials with perturbation removal ) could influence the reductions in motor output we report , our main interest was how these changes in motor output compared to the initial learning and varied for different learning goals . Our results show clear separation in the applied gains to changes in position and velocity between the initial learning of the novel dynamics and the subsequent decay of the motor adaptation . In all cases , the applied gain did not return to baseline levels . This was true for the goal aligned motion state ( Fig 3C and 3D ) when the perturbation was based on a single state , and when the perturbation had a codependence on both motion sates ( Fig 5C and 5D ) . This behavioral difference mirrors the neuronal retention of learning reported throughout the sensorimotor system following motor adaptation . The activity of a population of premotor , supplementary motor , and primary motor cortex cells is modified during force-field adaptation and these correlated modifications are retained during the decay of the learning , serving as a memory trace of the training [29–33] . The behavioral difference between learning and decay we report may reflect these persistent neural changes throughout the sensorimotor system specifically tuned to the motion kinematics required for force-field compensation . Although the focus in our study was the examination of the decay of the velocity- and position-dependent learning , there are some aspects of the adaptation trajectories that are not captured by the primitive model ( e . g . , differences in the initial adaptation trajectories in Figs 3 and 5 , compared to the simulations in Fig 4 ) . These inconsistencies may be due to several interesting factors resulting from perturbation-dependent changes in the primitive distribution . The supplemental simulations ( S5 Fig and S6 Fig ) suggest that there are possible changes in the primitive distribution ( at least within this computational framework ) occurring during the two types of training ( vFF and pFF ) that influence the learning trajectories; it is possible that the primitive space may rotate during training , but the extent towards a particular axis may have different rates . We plan to address this possibility with future , systematic experiments . Finally , the sensory adaptation that occurs with motor learning may provide an additional measure to assess differences in adaptation retention . Ostry and colleagues [34] demonstrated that following the exposure to a force-field movement perturbation there was an accompanying modification in the perception of limb position . Specifically , the perceptual shift was in the direction of the movement disturbance and learning-dependent; there was no observed sensory modification when the limb was moved passively through the same trajectories experienced during the motor perturbation . Thus , another possible assessment of any asymmetry in the retention of the velocity and position components could be to compare the magnitude of the accompanying perceptual shifts in limb estimation and the degree to which these perceptual modifications persists throughout the decay period . Several studies have suggested that how the perturbation is introduced ( e . g . , abruptly vs . gradually ) and the duration of exposure ( e . g . , long vs . short ) influence the stability and subsequent properties ( transfer , long-term retention , etc . ) of motor adaptation [21 , 35–37] . For example , Huang and Shadmehr [22] showed that when the force-field perturbation was applied for a short duration , the decay of the adaptation was much more rapid than for longer training periods , suggesting less relative stability in the modifications to the motor commands . This is in agreement with recordings in motor cortex [38]; the activity of a subset of neurons is modified dependent on the rate of movement perturbations experienced , indicating that the neural representation of adaptation is influenced by the training schedule . In addition to training schedule , previous studies have examined factors that influence the stability of adaptation retention [12 , 17–21 , 39 , 40] . However , an important distinction of the current study is that we examined the stability of the components of the motor adaptation ( the motion-state based learning ) rather than the long-term stability of the adaptation or stability in competition with the formation of other motor memories . As in Sing et al . [16] , our current observations show that the motor memory in the late stages of training is more specific to the task goal compared to the initial stages due to modification in the gains applied to the goal-aligned and goal-misaligned motion parameters . This specificity in the motor output remains throughout the decay period; there is no reemergence of the initial goal-misaligned learning as the acquired goal-aligned learning gradually decays . Based on the collective work described above , it would be interesting to examine the influence of the ( 1 ) training duration , ( 2 ) introduction rate and ( 3 ) passage of time on the stability of these adjustments to the motion state gains . For example , there is recent evidence that performance becomes more task specific with sufficient breaks after training , suggesting that the passage of time may influence the ability to perform more task-relevant actions [41] . We hypothesize that the well-known savings following a break after initial training ( faster re-adaptation with exposure ) will reflect more goal-aligned movements [10] . That is , savings over multiple days of training should result in adaptation gain-space trajectories closer to the goal-aligned axis ( the motion kinematic of the experienced force-field ) than on the first day of exposure . As demonstrated previously [16 , 24 , 25] , the initial adaptive responses that we observe when learning novel movement dynamics are consistent with motor primitives with correlated position and velocity tuning . This theoretical framework is based on the codependent encoding of these motion states observed throughout the sensorimotor system [42–47] . Our current results suggest that this codependence does not necessarily result in an equal representation of the two motion states , but rather codependent processing biased towards velocity . For example , similar to previous studies [16 , 24 , 25] , initial position-dependent learning is biased towards the velocity-dependent gain; the gain-space trajectory is typically closer to the middle of the gain space than that observed for velocity-dependent learning ( Fig 3A and 3B ) . In addition to initial learning , the decay of the position-dependent gain was relatively faster than the reduction of the velocity-dependent gain for both combination force-fields , suggesting an asymmetry in relative stability ( Fig 5 ) . Why should learning based on movement velocity be more stable than that based on position ? A possible answer may be found in the encoding asymmetries in motor cortex [47] . Velocity tuning among primary motor cortex neurons is more abundant compared to position . Another reason for a velocity bias could be that movement velocity provides substantially more motion information compared to position . For example , during point-to-point movements , there can be a significantly larger variance in the temporal changes in movement velocity for similar movement trajectories [48] . Take for example the force-field perturbations used here; the peak force experienced by the subject can vary broadly when based on movement velocity , whereas this peak is restricted when based on positional changes . If such a coding bias exists throughout the sensorimotor system , this imbalance would support a preference towards velocity-based learning during initial force-field adaptation and an asymmetry during the subsequent decay . Possible support for this bias may be found in a recent study by Rotella and colleagues [49] . The authors asked subjects to produce isometric hand forces which were then mapped to the position or velocity of a virtual cursor . Under these different mappings , they then tested the generalization of adaptation when a visuomotor rotation was applied to the cursor motion . Interestingly , the generalization of adaptation under the velocity mapping was broader , which is aligned with the current implications that movement velocity is a more stable basis for motor learning than changes in position . We investigated the decay of short-term adaptation to motion-dependent perturbations applied to reaching movements . We observed a clear separation between the initial learning and subsequent decay when the motor output was represented as the respective gains subjects applied to changes in position or velocity during movement . When the perturbation was only based on one motion state ( position or velocity ) , this separation was a direct effect of a sustained decrease in the gain of the goal-aligned motion parameter , with no reemergence of the goal-misaligned parameter during the decay period . When exposed to novel dynamics that required a combination of position- and velocity-dependent learning , the applied velocity-dependent gain was relatively more stable during the decay period , even when the gain applied to changes in position was significantly greater at the end of training . This difference in the relative state-dependent learning stability suggests that the motor system has an inherent preference towards adjusting and retaining modifications to motor commands based on movement velocity . A modified model of adaptation that accounts for greater retention of velocity-based learning captures these behavioral results , and importantly predicts the decay behavior for training with novel force-fields that are jointly dependent on the two motion states . Overall our results show that the decay of motor adaptation is not exactly unlearning—the complete reversal of the learning process . Rather , in agreement with previous physiological and behavioral studies , our results suggest that the decay of adaptation likely shares overlapping mechanisms with the learning process , but is a distinct process that reduces the motor memory traces formed over the training period .
The study protocol was approved by the George Mason University Institutional Review Board , and all participants gave informed written consent . Fifty-six healthy subjects ( 37 male and 19 female ) without known neurological impairment were recruited from the George Mason University community to participate in the study . All participants were right-handed and performed the task using their right hand . Each individual participated in only one of the experimental sessions and experienced only one type of force-field ( 14—Velocity-dependent Force-field , 14—Position-dependent Force-field , 14—Unbiased Combination Force-field , and 14—Position-biased Combination Force-field ) . The experimental paradigm was based on the standard force-field adaptation paradigm [26] . The subjects were instructed to move a cursor between two targets located on a screen in the sagittal axis of their body while grasping a robot manipulandum ( KINARM End-Point Lab , Fig 1A ) . The manipulandum measured hand position , velocity , and the force applied by subjects , and its motors were used to apply forces to the hand , all at a sampling rate of 1000 Hz . A semi-transparent mirror was used to project the location of hand and visual targets to the plane of movement while occluding the subject’s view of the hand ( refresh rate of 60Hz ) . During the experiment the subjects reached to circular targets 0 . 6 cm in diameter that were spaced 10 cm apart on the sagittal axis of the body . The subjects were instructed to ‘‘make quick reaching movements to the targets in both the forward and backward directions . ” At the end of each trial , subjects received visual and auditory feedback about the completed movement . If the peak movement velocity was between 0 . 25–0 . 35 m/s and the movement duration was shorter than 750 ms , the reach target ( green target in Fig 1A ) filled green with an auditory reward indicating a movement within the required criteria . If the peak movement speed was below 0 . 25 m/s , the reach target filled yellow to indicate that the movement was too slow . If the peak movement speed was above 0 . 35 m/s , the reach target filled red to indicate the movement was too fast . In both of the latter cases no auditory feedback was given . The endpoint of each movement was used as the start point for the following trial , and movements were made only in these two directions . The subjects received a performance score at the end of each block of movements that indicated the percentage of correct trials only in the trained 270° movement direction . Subjects were asked to maintain the score above 50% throughout the experiment . Only 270° movements with a peak velocity between 0 . 2–0 . 4 m/s were used in the subsequent data analysis . In addition , subjects had to initiate their movement within 75–2000 ms after the reach target appeared on the screen . Otherwise all targets were extinguished and the trial was immediately repeated . Three trial types were used during the experiment: null trials , force-field ( FF ) trials , and error-clamp ( EC ) trials ( Fig 1B ) . Null trials were used for initial practice , during which the motors of the robot manipulandum did not apply any force to the hand . During FF trials , the robot applied a force at the hand that was dependent either on movement position ( with respect to the start location ) , velocity , or a positive combination of limb position and velocity . The force that the robot applied to the hand was always orthogonal to the direction of movement , and had the general form of: [FxFy]=cK . [0−KK0] . [xy]+cB . [0−BB0] . [x˙y˙] , K=45N . sm , B=15Nm ( 1 ) For a position-dependent force-field trial ( pFF ) , cK = ±1 and cB = 0 , where cK = ±1 and cK = −1 correspond to clockwise and counterclockwise direction of the force-field , respectively ( a clockwise force-field is shown in Fig 1B ) . For a velocity-dependent force-field trial ( vFF ) , cK = 0 and cB = ±1 . Unbiased combination force-field trials ( ucFF ) had a force pattern dependent on both the position and velocity , with cK = ±0 . 71 and cB = ±0 . 71 for clockwise and counterclockwise directions [16 , 23 , 24] . Lastly , the Position-biased combination force-field trials ( pcFF ) had a motion dependent force pattern similar to the ucFF . However , the contribution of the position-dependent component was 20% greater and the velocity-dependent component was 25% less , with cK = ±0 . 85 and cB = ±0 . 53 . As in Sing et al . [16] the values for K and B were chosen in order to have approximately equal peak perturbing force for vFF and pFF . Each subject experienced only one type of force-field throughout the experimental session . During error-clamp trials , the robot motors constrained movements in a straight line toward the reach target by counteracting any motion perpendicular to the target direction [21 , 50] . This was achieved by applying a stiff one-dimensional spring ( 6 kN/m ) and a damper ( 150 Ns/m ) in the axis perpendicular to the reach direction . In these trials , perpendicular displacement from a straight line to the reach target was held to less than 0 . 6 mm and averaged about 0 . 2 mm in magnitude . Each subject experienced the same basic experimental paradigm shown in Fig 1C . Subjects performed sets of 90° and 270° movements . Each experiment started with a baseline period , during which subjects completed 360 null trials ( 180 movements in the trained 270° direction ) . These null trials were divided into 4 blocks . The first two blocks had 80 movements each and the last two blocks each required 100 movements . During the last 2 blocks of trials 12 error-clamp trials were pseudo-randomly interspersed for the 270° movement direction in order to measure the baseline levels of forces for each subject . The average lateral forces during these trials were then subtracted from the forces applied on error-clamp trials during the adaptation and decay periods . Following the baseline period , subjects experienced the adaptation transition block ( 124 total movements , 62 in the trained 270° direction ) , during which the force-field environment was suddenly introduced after an initial 30 null movement trials ( 15 in the trained 270° direction ) . We designed the adaptation transition block to capture the immediate changes in the applied force due to initial exposure to the force-field . Once the perturbation was introduced all 90° movements were made under the error-clamp condition , and the force-field was only applied to 270° movements . For the first 10 training trials , the ratio of force-field ( FF ) to error clamp ( EC ) trials was 3 FF: 2 EC which was then reduced to 5 FF: 1 EC for the last 84 trials ( 42 in the trained direction ) . The adaptation transition period was followed by 2 blocks of training ( 96 total trials each ) in which the subjects experienced only one of the four force-field environments ( vFF , pFF , ucFF , or pcFF ) . Similar to baseline period , we pseudo-randomly inserted 16 error-clamp trials in the 270° movement direction in order to measure the adaptation level at different points in training . The ratio of 5 FF: 1 EC was maintained throughout this training period . Only the 270° direction movements were used for analysis of adaptation and decay . The sign ( direction ) of the FF remained constant for each subject , but was counterbalanced between subjects . After the adaptation period , subjects experienced the decay transition block of 146 total trials . This block started with 26 training trials . For the first 12 trials , there was a ratio of 5 FF: 1 EC which increased to 4 FF: 3 EC for the last 14 trials in order to obtain an accurate measure of final adaptation levels . These 26 trials were then followed by 120 consecutive error-clamp trials ( 60 in the trained 270° direction ) . We refer to these 120 error-clamp trials as the decay period , during which the adaptation decayed to the baseline levels prior to experiencing the force-field . Inclusion of the decay period within the transition block effectively masked any possible context dependent changes in the behavior of the subject due to the removal of the force-field [14 , 28] . We used 60 consecutive error clamp trials to measure adaptation decay in order to keep this critical experimental block within a reasonable duration , and avoid breaks and possible cognitive influences during the transition to the decay period . In addition , based on the exponential decay time constants ( S1 Fig , S2 Fig , S8 Fig ) , this number of error clamp trials proved sufficient to observe asymptotic levels of decay . As described previously [16 , 21 , 37 , 50 , 51] we used error-clamp trials to measure the change in feedforward motor output during the adaptation and decay periods . The use of error-clamp trials reduces the lateral errors experienced during the movement that elicit online feedback correction . Given that the lateral force during error-clamp trials reflects the predictive feedforward adaptive response to the force-fields , we limited our analysis to these force patterns . Based on Eq 1 , subjects fully compensate for the force-field when they produce a countering force that is proportional to the movement velocity , position , or the positive combination of the two . We first computed the ideal force pattern by examining the longitudinal movement kinematics ( position , velocity ) during the error-clamp trial movement . The movement and force signals were analyzed within a temporal window of 1500 ms centered on the peak velocity ( ±750 ) . Next we defined the adaptation coefficient by determining the linear regression coefficient between the ideal force and the lateral force applied by the subject during the error-clamp trials [16 , 21 , 37 , 50 , 52] . We computed the adaptation coefficient for each subject during both the adaptation and decay periods and averaged the values over all subjects . In all cases we provide the SEM of this average value . We further characterized the adaptation and decay behavior by projecting the lateral force during each error-clamp trial onto a two-dimensional space that parsed the position-dependent and velocity-dependent components of the applied force [16] . We refer to this two-dimensional space as the gain-space . This gain-space represents complete adaptation to a vFF by the point [0 , 1] , pFF by the point [1 , 0] , ucFF by the point [0 . 71 , 0 . 71] , and a pcFF by the point [0 . 85 , 0 . 53] . Additionally , the abscissa and ordinate of each point in this gain-space corresponds to the position-dependent and velocity-dependent components of the applied force . In order to depict adaptation and decay in gain-space , we first calculated a multiple regression between the lateral force during the error-clamp trials , and both the changes in position and velocity during the movement . We then rescaled the coefficients for the position and velocity components by the 45N/m and 15Ns/m factors , respectively , and projected these coefficients onto the gain-space . For each subject , we performed this analysis and calculated the average gains over all subjects [16 , 23–25] . Similar to Sing et al . [16] , the characterization of position and velocity contributions in the force output produce excellent fits ( R2 values ranging from 0 . 91 to 0 . 99 , see Fig 2 ) . As in this prior study , the inclusion of an acceleration term resulted in highly significant but relatively small improvements in the representation of these force profiles; in the majority of cases for the different types of perturbations ( vFF , pFF , ucFF , and pcFF; early and late ) the acceleration signal’s contribution was significant ( P < 0 . 001 in all cases except early pFF training , P = 0 . 38 ) , but the overall force profile variance accounted for only improved by at most 3% . We therefore elected to focus only on the contributions of the position and velocity state variables . We operationally defined early and late/asymptotic adaptation as the first 15 ( 1–15 ) and last 10 ( 150–160 ) trials of training . Thus , the mean and standard error values for these periods are plotted as a function of the mean trial number within these windows for the adaptation period ( Figs 2 , 3 and 5 ) . The data during the decay periods were normalized by dividing all the subject data by the mean ( across subjects ) of the first decay trial . Due to the increased frequency of EC trials during the decay period , we used a smaller window to assess early and late levels ( trials 11–20 and 50–60 respectively ) . Here we excluded the first 10 trials in the analysis for the early epoch in order to remove the effect of normalization of adaptation gains ( Fig 5G and 5H ) . We initially tested the main effect of group condition on the different epochs of interest with a repeated measures ANOVA and subsequently determined the epoch in which these conditions were significantly different with post-hoc analysis . For example , two-tailed t-tests were performed between different force-field groups to compare the behavior within each epoch . For all tests the significance level was 0 . 05 . In S1 Fig , S2 Fig and S8 Fig we applied a standard exponential model with rate and offset parameters to determine the time constants of learning and decay for the different types of perturbations ( vFF , pFF , ucFF and pcFF ) and learning components ( goal-aligned and goal misaligned , velocity- and position-based ) . We computed the standard deviation of the best-fit parameter values for these model fits by bootstrapping the fits to the data . We made 500 different bootstrap estimates of the fit parameters , each by averaging data from 14 randomly generated choices made from the 14 subject data pool with replacement . We fit the model to each of these bootstrap estimates and determined the standard deviation of each parameter . The viscoelastic primitive model first proposed by Sing et al . [16] consists of N motor primitives , Si = [Ki Bi]T = Rn×2 , which collectively generate the motor output . The primitives are jointly distributed as [KiBi]~N ( μ , Σ ) , μ=[00]; Σ=[σK2ρσKσBρσKσBσB2] In this model these primitives have a similar dependency on position and velocity via σK = σB . Moreover , the correlation between the primitives is determined by ρ . The motor output on each trial is determined by a weighted combination of motor primitives . Each primitive receives input from the changes in position and velocity during the movement and creates a force output: FSi=[KiBi]T[PV] Given that the vector [P V]T is shared between all primitives , we can factor out this vector and simplify the calculation . The final force is a weighted linear combination of the primitive forces: Foutput=∑i=1nwiFSi [KoutputBouput]T[PV]=∑i=1nwi[KiBi]T[PV] [KoutputBouput]=∑i=1nwi[KiBi]T=STW In this equation the W ∈ Rn×1 is a weight vector that drives the learning in the model . The output vector [Koutput Boutput]T represents the gain in position-velocity ( p-v ) primitive gain-space , which we refer to as y , or the current motor adaptation state . The goal of adaptation can be defined as a vector y* ∈ R2×1 . On each trial of adaptation we can project the error vector between the goal and the motor output and use a gradient descent rule to compute the weight change for each primitive Si as dwin=ηSi ( y*−yn−1 ) The weight change can be used to create a new gain state in the p-v primitive gain-space yn+1 yn+1=[αK00αB]yn+STdW For the symmetric model , the retention value for αK and αB are the same . When we applied this model to the vFF and pFF behavioral data ( Fig 3 ) we estimated these parameters to be: αK = αB = 0 . 951 , σK = σB = 0 . 401 , η = 1 . 5 x 10−4 , ρ = 0 . 51 . As with the exponential fits , we made 500 different bootstrap estimates of the fit parameters , each by averaging data from 14 randomly generated choices made from the 14 subject data pool with replacement . For the asymmetric model , the retention values were not constrained and can result in an asymmetry in either direction . When we applied this model to the vFF and pFF behavioral data we estimated these parameters to be: αK = 0 . 942 , αB = 0 . 951 , σK = 0 . 464 , σB = 0 . 379 , η = 1 . 5 x 10−4 , ρ = 0 . 47 . Note that the retention is biased towards velocity primitives , αK < αB . In addition , when asymmetric model is applied simultaneously to the ucFF and pcFF behavioral data these parameters were estimated to be: αK = 0 . 914 , αB = 0 . 958 , σK = 0 . 546 , σB = 0 . 565 , η = 1 . 5 x 10−4 , ρ = 0 . 48 .
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Human motor adaptation of limb movement in response to force perturbations has been shown to be motion-state dependent . That is , the compensatory response to these disturbances is correlated and proportional to the temporal changes in the position , velocity , and acceleration during the motion . Despite a growing understanding of this adaptation process , there is little information on the relative stability of this learning when based on these different temporal features of movement . Here we modified a previous computational model of motor adaptation to predict the decay of the compensatory response associated to different motion states , specifically learning based on temporal variations in limb position and velocity . We confirmed the simulated behavior by examining the decay of the temporal force output after subjects were trained to compensate for movement disturbances based on different combinations and magnitudes of these two motion states . Both simulation and behavioral results show that velocity-based learning decays at a slower rate than position-based , even when learning is significantly biased towards the latter at the end of training . Collectively , these results suggest that motion-state learning based on movement velocity is more stable than that based on limb position .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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2017
|
The decay of motor adaptation to novel movement dynamics reveals an asymmetry in the stability of motion state-dependent learning
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The broadly conserved bacterial signalling molecule cyclic-di-adenosine monophosphate ( c-di-AMP ) controls osmoresistance via its regulation of potassium ( K+ ) and compatible solute uptake . High levels of c-di-AMP resulting from inactivation of c-di-AMP phosphodiesterase activity leads to poor growth of bacteria under high osmotic conditions . To better understand how bacteria can adjust in response to excessive c-di-AMP levels and to identify signals that feed into the c-di-AMP network , we characterised genes identified in a screen for osmoresistant suppressor mutants of the high c-di-AMP Lactococcus ΔgdpP strain . Mutations were identified which increased the uptake of osmoprotectants , including gain-of-function mutations in a Kup family K+ importer ( KupB ) and inactivation of the glycine betaine transporter transcriptional repressor BusR . The KupB mutations increased the intracellular K+ level while BusR inactivation increased the glycine betaine level . In addition , BusR was found to directly bind c-di-AMP and repress expression of the glycine betaine transporter in response to elevated c-di-AMP . Interestingly , overactive KupB activity or loss of BusR triggered c-di-AMP accumulation , suggesting turgor pressure changes act as a signal for this second messenger . In another group of suppressors , overexpression of an operon encoding an EmrB family multidrug resistance protein allowed cells to lower their intracellular level of c-di-AMP through active export . Lastly evidence is provided that c-di-AMP levels in several bacteria are rapidly responsive to environmental osmolarity changes . Taken together , this work provides evidence for a model in which high c-di-AMP containing cells are dehydrated due to lower K+ and compatible solute levels and that this osmoregulation system is able to sense and respond to cellular water stress .
In order to survive and grow , bacteria must be able to sense and respond to a multitude of environmental conditions . Changes in external osmolarity can cause cellular water loss or gain due to uncontrolled osmotic movement across the semipermeable cytoplasmic membrane . Cells can adapt to these changes in order to maintain appropriate cellular volume and solute concentration for metabolism as well as turgor pressure to drive expansion for cell division [1] . In response to an osmotic upshift ( hyperosmotic stress ) , bacteria import potassium ions ( K+ ) which is followed by a secondary response involving uptake or synthesis of compatible solutes such as glycine betaine , carnitine and proline [2] . This allows the cell to limit the loss of water and maintain turgor . During osmotic downshift ( hypoosmotic stress ) , bacteria release K+ and compatible solutes from the cell in order to limit water influx causing cell swelling and in severe cases , lysis . The speed at which cells need to detect and respond to the external osmolarity change is critical and therefore the early responses in many cases involves posttranslational modulation of existing transporter activity , since the synthesis of new proteins can take too long [3] . These transporters include membrane stretch-activated mechanosensitive channels that activate during an osmotic downshift and intracellular ionic strength or K+ activated compatible solute uptake systems that function during an osmotic upshift [4] . The signalling molecule cyclic-di-AMP ( c-di-AMP ) found in many Gram-positive bacteria and some Gram-negative bacteria has been recently demonstrated to play a significant role in regulating K+ and compatible solute ( carnitine ) import systems either via direct binding to transporter protein complexes ( Ktr and OpuCA ) , a regulatory sensor kinase ( KdpD ) or riboswitch upstream of the transporter genes ( ktr and kimA ) [5–9] . A high level of c-di-AMP has been found to repress K+ and carnitine uptake , thereby inhibiting growth under hyperosmotic conditions [5 , 6 , 10 , 11] . Conversely , low/absent c-di-AMP results in cells which have likely uncontrolled K+ and compatible solute uptake , resulting in viability only under conditions where external K+ and compatible solute concentrations are low or when the cells are osmotically stabilised by the addition of high salt [9 , 12] . Whilst the role of c-di-AMP in osmoregulation is becoming evident , the signals which trigger changes in the c-di-AMP pool are still poorly understood [13 , 14] . C-di-AMP is synthesised from two ATP molecules by diadenylate cyclase ( DAC ) enzymes and degraded by phosphodiesterase ( PDE ) enzymes . In most Gram-positive bacteria , only one DAC exists , named CdaA or DacA , and it is localised to the membrane via three transmembrane domains [15] . Most bacteria also contain one PDE , called GdpP , while others contain an additional PDE called PgpH [16] and both of these PDEs are membrane localised [17] . It is through these enzymes , and possibly active c-di-AMP export , that the c-di-AMP pool is regulated . The intracellular level of c-di-AMP is under strict control since both low and high levels have been shown to be detrimental to growth in several Firmicutes [18 , 19] . C-di-AMP levels in bacteria have been found to be responsive to growth phase , acid stress , growth media nitrogen source , ( p ) ppGpp induced by mupirocin , mutations in the peptidoglycan biosynthesis enzyme GlmM or YbbR protein and inactivation of the LiaFSR membrane stress response system [11 , 20–23] . Upregulated CdaA expression and a 2-fold higher level of c-di-AMP was observed in Bacillus subtilis grown in defined media with 5mM K+ compared to cells grown with 0 . 1mM K+ [9] . Recently , it was found that inactivation of the gating component ( CabP ) of the Trk family K+ transporter in Streptococcus pneumoniae reduced the c-di-AMP level [24] . These results suggest that K+ may act as a signal for c-di-AMP level modulation directly or indirectly . In this study , we sought to better understand the cause of high c-di-AMP toxicity and to identify mechanisms by which this toxicity can be averted in bacteria . We used a high c-di-AMP containing Lactococcus lactis ΔgdpP mutant which is hypersensitive to elevated salt in growth media . This strain can accrue suppressor mutations which allows it to grow under such an osmotically stressful condition and previous work has identified changes in the c-di-AMP synthase CdaA and binding partner GlmM which both lowered the c-di-AMP level [11] . Here we expanded the screen to saturation and identified and characterised three different suppressor mutant groups . Two groups rescue osmoresistance through mutations which elevate osmolyte ( K+ or glycine betaine ) uptake while one group activated the expression of a c-di-AMP export system involving a multidrug resistance protein ( MDR ) which lowered intracellular c-di-AMP . In addition we show that c-di-AMP accumulates in response to elevated K+ and glycine betaine uptake , and in several bacteria the c-di-AMP level is rapidly responsive to environmental osmolarity changes , thus allowing it to sense and respond to water stress .
To identify regulators of the c-di-AMP pool , we previously employed a genetic screen to obtain osmoresistant suppressors of the high c-di-AMP Lc . lactis ΔgdpP strain OS2 [11] . In the current study , a further 212 osmoresistant suppressor mutants were obtained and analysed . Numerous cdaA mutations were identified ( n = 184 ) as well as several restorative gdpP mutations ( n = 8 ) . Twenty suppressors which contained identical cdaA and gdpP sequences as the parent strain were subjected to whole genome sequencing and the mutation locations are shown in Table 1 . The 20 suppressors contained mutations in 3 functionally linked gene groups . The first group included 4 independent mutants which possessed changes in the K+ uptake transporter KupB ( Llmg0588 ) . The second group included 11 independent mutants which contained changes in the Eep/PptAB hydrophobic peptide processing and export system ( S1A Fig ) . Eep ( Llmg2413 ) is a transmembrane metalloprotease homolog of RseP [25] and PptAB ( Llmg2271-2270 ) are ATPase and permease homologs of EcsAB [26] . Interestingly one suppressor contained a mutation in pptB and a mutation in busR which encodes a transcriptional repressor of the glycine betaine transporter [27] . The third group included 2 independent mutants which contain deletions in an intergenic region between the ribosomal 50S protein encoding rplL and the MarR family transcriptional regulator encoding rmaX . Osmoresistance of representative strains from these groups as well as a cdaA mutant are shown in Fig 1A . As found in previous work [11] , mutations lowering the c-di-AMP level restored osmoresistance in ΔgdpP and this was found to be the case for the eep , pptB , rpilLΔterm209 and cdaA mutants ( Fig 1B ) . A pptB gene disruption was constructed in the ΔgdpP background strain which , as expected , resulted in restoration of osmoresistance ( S1B Fig ) and a lowering of c-di-AMP ( S1C Fig ) . Strikingly , the kupB mutants were found to have significantly elevated c-di-AMP or at least equivalent levels to the ΔgdpP parent ( Fig 1B ) . This is the only group of osmoresistant suppressors we have identified thus far which do not have lower c-di-AMP levels relative to the ΔgdpP parent . Therefore , they have developed osmoresistance independently of c-di-AMP pool modulation . Importantly suppressors which contain only a single mutation in kupB ( kupBA618V and kupBR508G ) had significantly elevated c-di-AMP ( Fig 1B ) , suggesting that changes in K+ uptake triggers c-di-AMP accumulation . In the two other kupB suppressors , one additional mutation is present in each ( ftsXP249L or pptAS55fs ) which likely caused a lowering of the c-di-AMP level ( Fig 1B ) . KupB contains twelve transmembrane spanning domains and a C-terminal 227 amino acid intracellular domain ( Fig 1C ) . Among the 4 identified KupB missense mutations in the suppressor mutants , one is present in an internal loop region between membrane-spanning domains while three are in the C-terminal intracellular domain . KupB is homologous to a large family of Kup proteins present in several Gram-positive bacteria , Gram-negative bacteria and plants ( Fig 1D ) where their roles in K+ uptake and osmoregulation is well established [28] . Kup proteins possess highly similar transmembrane domains , but differ in their C-terminal intracellular domain ( Fig 1D ) which has been proposed to regulate K+ uptake [29] . Mutations identified in our suppressor screen restore osmoresistance , suggesting that they are gain of function mutations and might result in increased K+ uptake most likely through modification of channel regulation . To determine if the KupB mutations resulted in a gain-of-function activity we overexpressed KupBA618V and wild-type KupB using its native promoter in ΔgdpP and measured their effect on osmoresistance . It was found that overexpression of wild-type KupB increased the osmoresistance of ΔgdpP , suggesting that an increase in kupB copy number can increase K+ uptake ( Fig 2A ) . Overexpression of KupBA618V in ΔgdpP restored osmoresistance to a higher level than both ΔgdpP overexpressing wild-type KupB and the ΔgdpPkupBA618V suppressor mutant ( Fig 2A ) . We next determined the intracellular K+ levels and found that ΔgdpP has lower K+ compared to both the wild-type and the ΔgdpPkupBA618V suppressor mutant ( Fig 2B ) . Overexpression of KupBA618V in ΔgdpP resulted in 43% higher K+ level than ΔgdpP ( Fig 2B ) . Together these results confirm that the A618V mutation in KupB is a gain-of-function mutation which increases osmoresistance by increasing K+ uptake . One possibility was that these mutations prevent c-di-AMP from binding KupB and inhibiting K+ uptake , since it has been shown to bind to and/or regulate K+ transporters in other bacteria such as Ktr , Kdp and KimA . To determine if c-di-AMP binds to KupB we carried out the differential radial capillary action of ligand assay ( DRaCALA ) using the KupB intracellular C-terminal as fusions to hexa-His or MBP tags . This domain could be expressed but both fusion proteins were insoluble and testing of whole cell lysates with radiolabelled c-di-AMP did not reveal any binding ( S2 Fig ) . Previous work has shown that insoluble proteins in whole cell lysates can bind cyclic dinucleotides [8 , 30] . Therefore at this stage the regulation of KupB by c-di-AMP or other signals is not known . Interestingly using quantitative reverse transcriptase PCR ( qRT-PCR ) , it was found that transcription of kupB was slightly higher ( 3-fold; P = 0 . 02; Student’s t test ) in ΔgdpP compared to wild-type . This may be in response to low intracellular K+ levels present in the high c-di-AMP mutant ΔgdpP . The c-di-AMP levels in L . lactis ΔgdpP strains overexpressing wild-type KupB or KupBA618V were determined next . Overexpression of wild-type KupB triggered higher c-di-AMP in ΔgdpP , while the c-di-AMP level in ΔgdpP overexpressing KupBA618V was even higher ( Fig 2C ) . The level of c-di-AMP in these ΔgdpP background strains directly correlated with their osmoresistance level ( Fig 2A ) , with the highest c-di-AMP strains being the most osmoresistant . This result is in stark contrast to previous work , where high c-di-AMP ΔgdpP mutants were found to be osmosensitive due to reduced activity or expression of K+ and/or compatible solute transporters [5 , 7 , 8 , 11] . In the situation here , it appears that increased intracellular K+ due to enhanced K+ import activity not only results in the restoration of osmoresistance in ΔgdpP , but also that intracellular K+ serves either directly or indirectly as a signal for the cell to increase the c-di-AMP level . Indeed the level of K+ in ΔgdpP , ΔgdpPkupBA618V and ΔgdpP-pGh-kupBA618V strains directly correlated with the level of c-di-AMP ( Fig 2B and 2C ) . An increase in the c-di-AMP level can occur via either increased c-di-AMP synthesis and/or decreased c-di-AMP hydrolysis . However it appears that control is mediated by increased c-di-AMP synthesis by CdaA here , as gdpP is defective in these strains . Using qRT-PCR , transcription of cdaA in ΔgdpPkupBA618V was unchanged compared with the parent ΔgdpP ( P = 0 . 96; Student’s t test ) , which suggests that increased c-di-AMP levels are not due to higher cdaA expression . Next we examined if elevated K+ uptake could trigger greater c-di-AMP accumulation in strains other than ΔgdpP . We overexpressed KupB and KupBA618V in wild-type Lc . lactis and related Lactobacillus reuteri which both contain GdpP . In Lc . lactis , the level of c-di-AMP was 7 . 5-fold higher when KupBA618V was overexpressed compared to cells overexpressing KupB or the wild-type ( Fig 2D ) . In wild-type Lb . reuteri , overexpression of KupB and KupBA618V resulted in 1 . 9-fold and 6 . 7 fold c-di-AMP level increases , respectively ( Fig 2D ) . Therefore the c-di-AMP pool size correlates directly with K+ uptake activity in at least two different bacterial genera irrespective of the presence of GdpP . One suppressor mutant contained an in-frame deletion in the busR gene and an inactivating mutation in the pptB gene ( S189fs ) ( Table 1 ) . This busR deletion removed 42 residues ( amino acids 42–83 ) from the N-terminal GntR HTH domain ( Fig 3A ) . BusR is a transcriptional repressor of the BusAA-AB glycine betaine transporter in Lc . lactis [27] . To determine if the 42 amino acid deletion reduces the ability of BusR to repress the busAA promoter , we expressed the wild-type and mutant BusR in E . coli in conjunction with a lacZ fusion to the promoter of busAA . It was found that the busAA promoter activity increased moderately upon NaCl addition , similarly to that described before [31] ( S3A Fig ) . The wild-type BusR repressed expression of busAA more strongly than the mutated BusR under all conditions with or without additional NaCl ( S3A Fig ) suggesting that the 42 amino acid deletion is a loss-of-function mutation . BusR is a member of the family of GntR regulators and homologs are present in a subset of Firmicutes including Clostridium spp . and certain lactic acid bacteria ( S3B Fig ) . These proteins contain a C-terminal TrkA_C domain ( Pfam02080 ) which is also found in gating components of the Trk family of K+ transporters that bind c-di-AMP [6 , 7] . To determine if BusR binds c-di-AMP we expressed both full length and the TrkA_C domain in E . coli and carried out DRaCALA on whole cell lysates . Both full length BusR and the BusR TrkA_C domain bound c-di-AMP ( Fig 3B ) and the purified TrkA_C domain was found to interact with c-di-AMP with a Kd of ~10μM ( Fig 3C ) . We determined the expression of busAA-AB in strains with varying c-di-AMP levels using a plasmid containing the busAA-AB promoter fused to a lacZ reporter . It was found that expression of busAA-AB was absent in the high c-di-AMP strain ΔgdpP compared to wild-type ( Fig 3D ) . All osmoresistant suppressors were found to have greater busAA-AB expression as compared to ΔgdpP ( Fig 3D ) . Suppressors which have lower c-di-AMP including those with mutations in cdaA , rpilLΔterm85 and glmM had restored busAA-AB expression to varying levels ( Fig 3D ) . The suppressor mutant ΔgdpP pptB189fs busRΔ126 containing an inactive BusR showed very high busAA-AB expression , as expected ( Fig 3D ) . Glycine betaine levels in different strains were measured and it was found that ΔgdpP contained ~10-fold less than wild-type ( Fig 3E ) . Inactivation of busR in the wild-type or ΔgdpP background resulted in elevated glycine betaine levels compared to their parent strains ( Fig 3E ) . Glycine betaine levels in osmoresistant suppressor mutants of ΔgdpP ( cdaAD123Y , rpilLΔterm85 , pptB189fsbusRΔ126 ) were all significantly higher ( 14- to 37-fold ) compared with ΔgdpP ( Fig 3E ) . These results suggest that BusAA-AB expression and glycine betaine levels are reduced in strains with high c-di-AMP due to this signalling molecule binding to BusR and enhancing its repression . Next we determined if strong repression of glycine betaine transporter expression by BusR contributes to the osmosensitive phenotype of ΔgdpP . Inactivation of BusR in ΔgdpP restored osmoresistance , indicating that glycine betaine transport allows the ΔgdpP to regain normal turgor pressure under high osmolarity conditions ( Fig 3F ) . Lastly we investigated if increased glycine betaine uptake caused by loss of BusR triggers c-di-AMP accumulation . It was found that the level of c-di-AMP was indeed 3-fold higher in ΔbusR compared to WT ( Fig 3G ) . Taken together these results provide evidence that uncontrolled glycine betaine uptake is sensed by the cell , which in turn responds by elevating the c-di-AMP level in order to enhance BusR mediated repression of busAA-AB . Two independent osmoresistant mutants containing overlapping 209 and 85bp deletions in an intergenic region between rplL and rmaX were obtained ( Figs 4A and S4A ) . This deleted region includes two inverted repeats with free energies of -8 . 1 and -11 . 7 kcal/mol ( S4A and S4B Fig ) . Either both or one are likely to act as transcription terminators for the upstream rplJ-rplL operon . The two different deletion mutants showed equivalent salt resistance and the deletion events were verified by PCR using primers flanking the deletion ( S4C and S4D Fig ) . The operon upstream of the deletion events contains two ribosomal genes rplJ ( 50S ribosomal protein L10 ) and rplL ( 50S ribosomal protein L7/L12 ) ( Fig 4A ) . Downstream of the deletion is a three gene operon composed of rmaX ( MarR transcriptional regulator ) , llmg1210 ( MDR of the EmrB family ) and llmg1211 ( predicted membrane protein of unknown function ) . Due to the deletion of a putative terminator , we hypothesised that transcription of the downstream operon may now be increased due to extension of transcripts from the likely highly expressed ribosomal genes . Using qRT-PCR , it was found that RNA transcripts of rmaX , llmg1210 and llmg1211 were several hundred fold higher in the two intergenic deletion suppressor mutants relative to the parent ΔgdpP ( Fig 4B ) . As expected , expression of the rplJ and llmg1212 were not elevated in the deletion suppressor mutants ( Fig 4B ) . Previous work has demonstrated that c-di-AMP is actively secreted by MDR proteins of the MFS superfamily in L . monocytogenes and B . subtilis [17 , 32–36] . Llmg1210 was found to share 45% amino acid identity with MdrT ( Lmo2588 ) and 36% identity with MdrM ( Lmo1617 ) ( S5 Fig ) , both of which have been shown to export c-di-AMP in L . monocytogenes [32] . We hypothesised that higher MDR expression leads to greater c-di-AMP export resulting in a lowering of the intracellular c-di-AMP . The 2 different deletion suppressor mutants contained reduced intracellular c-di-AMP levels compared to ΔgdpP , which were equivalent to that of eep and pptB suppressor mutants ( Fig 4C ) . However extracellular c-di-AMP levels were found to be disproportionately high in the deletion mutants , between 7 and 45 fold higher compared to the eep and pptB suppressor mutants ( Fig 4C ) . To determine which gene ( s ) within the overexpressed operon is required for c-di-AMP export and osmoresistance , we overexpressed each using the predicted strong rplJ promoter on a plasmid in the ΔgdpP strain . It was found that overexpression of rmaX or llmg1211 did not lower the intracellular c-di-AMP level or restore osmoresistance ( Fig 4D and 4E ) . We were able to clone llmg1210 downstream of the rplJ promoter in E . coli as a host , however upon introduction in Lc . lactis ΔgdpP , mutations in llmg1210 occurred in several independent trials , suggesting that overexpression of this protein by itself is toxic . When we cloned llmg1210 combined with llmg1211 downstream of the rplJ promoter , the plasmid was stable in ΔgdpP and a reduction of intracellular c-di-AMP occurred along with rescue of osmoresistance ( Fig 4D and 4E ) . To further confirm the role of this overexpressed operon in c-di-AMP export , we generated a series of chromosomally integrated mutants throughout the operon in the osmoresistant intergenic deletion suppressor strain ΔgdpPrplLtermΔ85 ( S6A Fig ) . In this strain , the rmaX-llmg1210-llmg1211 operon is highly expressed . It was found that inactivation of expression of the entire operon by plasmid insertion elevated intracellular c-di-AMP and eliminated osmoresistance ( S6B and S6C Fig ) . Interestingly the c-di-AMP level was significantly higher than that in ΔgdpP suggesting that the rmaX operon may be expressed and function to export c-di-AMP in ΔgdpP without the deletion event . Insertion of a plasmid allowing overexpression of rmaX only also resulted in similarly high c-di-AMP level and osmosensitivity . Plasmid insertion allowing overexpression of rmaX-llmg1210 from the genome lowered the c-di-AMP level compared to the two strains with plasmid insertions upstream ( p < 0 . 001 ) , however this was not sufficiently low enough to restore osmoresistance ( S6B and S6C Fig ) . Only the plasmid insertion following llmg1211 , which allows expression of all three genes , lowered the intracellular c-di-AMP level enough to restore osmoresistance in this deletion suppressor mutant ( S6B and S6C Fig ) . Together these results suggest that the MDR llmg1210 is a c-di-AMP export protein , but requires llmg1211 for full activity and/or stability . From the findings above and from other work , it is clear that c-di-AMP is a major regulator of osmoresistance . Therefore the c-di-AMP pool size would be predicted to be responsive to and inversely proportional to the external osmolarity allowing appropriate regulation of K+ and compatible solute transporters to control cell turgor . Therefore cells experiencing high turgor in low osmolarity environments would elevate their c-di-AMP pool , while cells experiencing low turgor in high osmolarity environments would deplete their c-di-AMP pool . Difficulties exist when trying to test this hypothesis however , since wild-type bacteria generally have low levels of c-di-AMP and changes in environmental osmolarity imposed during growth will likely lead to other physiological and gene expression changes which may have indirect influences on the c-di-AMP pool size . In order to separate direct and indirect effects , a simple new method was developed whereby the c-di-AMP level could be monitored in non-growing cells in low and high osmolarity conditions ( Fig 5A ) . In an initial attempt to stimulate c-di-AMP accumulation , we suspended washed Lc . lactis WT cells in a low osmolarity buffer to stimulate increased turgor pressure . However , the c-di-AMP level remained the same as that found in cells grown in culture media and extracted with acetonitrile-methanol ( Fig 5B ) . We hypothesised that washed cells have depleted ATP and therefore no immediate precursor for c-di-AMP synthesis . This is the case for bacterial ATP binding cassette ( ABC ) solute uptake systems which need to be energised by glucose addition to cells during uptake assays [37 , 38] . It was found that energizing the cells by the addition of glucose resulted in a ~10-fold increase in c-di-AMP ( Fig 5B ) . The addition of the closely related analog deoxyglucose , which is unable to initiate glycolysis , did not trigger c-di-AMP accumulation ( Fig 5B ) . Several Gram-positive bacteria with a single DAC domain protein were analysed using this assay and all rapidly increased their c-di-AMP pools under these low osmolarity conditions which would trigger high turgor pressure ( Fig 5C–5F ) . We next examined the effect of an osmotic increase on c-di-AMP levels in several bacteria . These conditions would trigger lower turgor pressure . Ten minutes after glucose addition , either water , NaCl or KCl ( 0 . 1 M or 0 . 3 M final concentration ) was added and cells were harvested after a further 10 minutes . The addition of NaCl or KCl resulted in rapid depletion or a block in c-di-AMP synthesis relative to water treated cells for all bacteria ( Fig 5C–5F ) . We also tested the effect of non-ionic solutes ( sorbitol and sucrose ) on c-di-AMP levels in L . monocytogenes and found that they also stopped c-di-AMP synthesis or at higher levels triggered rapid c-di-AMP degradation ( Fig 5G ) . These results demonstrate that environmental osmolarity changes trigger rapid c-di-AMP level fluctuation . To determine the roles of CdaA and GdpP in c-di-AMP control in the energised cell assay , we examined an osmoresistant suppressor mutant of Lc . lactis ΔgdpP which has a partially defective cdaA ( T273fs mutation ) . The c-di-AMP level did not vary over the 20 minute time course ( Fig 5H ) . This demonstrates that CdaA and GdpP are the main controllers of c-di-AMP pool modulation in this assay .
The primary role of a second messenger is to transduce a signal ( s ) from the environment to effectors within the cell , resulting in a physiological response and ultimately adaptation . It is becoming apparent that a major role of c-di-AMP is in osmoregulation due to its control of osmolyte ( K+ and compatible solute ) transporters and observed osmosensitive and osmoresistant phenotypes of high and low c-di-AMP mutants , respectively . The results presented here reinforce this notion , with suppressor mutations which elevate K+ or glycine betaine transport rescuing osmoresistance in a high c-di-AMP mutant of Lc . lactis . Conversely in suppressor screens with mutants devoid of c-di-AMP , mutations inactivating osmolyte ( peptides and glycine betaine ) uptake [12 , 39 , 40] or increasing osmolyte ( K+ ) export [9] have been found . Osmolyte import and export affects cellular turgor pressure and a recent proposition is that the central role of c-di-AMP is in the regulation of turgor pressure [41] , which is supported by our work presented here . The largest number of binding effectors under the control of c-di-AMP known at present are involved in K+ uptake [6 , 7 , 9] . KupB identified in this work has not been linked to c-di-AMP signalling pathways . It is a member of the Kup/HAK/KT family of K+ importer proteins ( Pfam02705 ) which are widely distributed in bacteria , fungi and plants [28] . In bacteria , Kup homologs are most common in Proteobacteria ( 634 species ) , followed by Actinobacteria ( 91 species ) in the Pfam database . Sixty-one species , mainly lactic acid bacteria , within the Firmicutes also contain Kup homologs . In E . coli , Kup is the major K+ importer under hyperosmotic conditions at low pH and likely functions as an H+—K+ symporter [42 , 43] . Little is known regarding the regulation of Kup family proteins . Deletion of the intracellular the C-terminal domain of Kup in E . coli significantly reduced K+ transport activity [29] which suggests that this domain has a regulatory function . In Firmicutes , K+ transporters KtrAB , KtrCD , KdpABCD and KimA are regulated by c-di-AMP either via direct binding to the transporter or a two-component sensor kinase and/or through gene expression changes . In Lc . lactis MG1363 , homologs of Ktr , Kdp and KimA are absent , and we did not find any evidence for c-di-AMP-mediated regulation of KupB . In lactoccoci , the kup gene has likely undergone a duplication event since immediately upstream there is a highly similar gene encoding KupA ( >70% amino acid identity to KupB ) in the majority of Lc . lactis strains . However , in strain MG1363 this is a pseudogene carrying a stop codon at amino acid 254 . It was found that deletion of kupB did not affect the growth of Lc . lactis MG1363 in chemically defined media with lower K+ concentrations ( S7 Fig ) , so it appears that there is another K+ transport system distinct from other characterised bacterial K+ transporters yet to be identified . C-di-AMP is a significant regulator of compatible solute uptake through binding of the cystathionine-β-synthase ( CBS ) domain in the OpuCA carnitine transporters in L . monocytogenes and S . aureus [5 , 8] . In our screen we identified a deletion event in busR which encodes the repressor of the glycine betaine transporter BusAA-AB [27] . It was found in previous work that busAA-AB expression was reduced in ΔgdpP [10] , however the mechanism was not clear at that time . In this work , BusR was found to bind c-di-AMP and rescue osmoresistance in ΔgdpP , which has also been recently reported for Streptococcus agalactiae BusR [39] . It is likely that this is a conserved function in other Gram-positive bacteria that contain BusR orthologs with the same GntR family domain structure . C-di-AMP binding takes place via the TrkA_C domain with a Kd of 10 μM , which is higher than other c-di-AMP binding proteins analysed using DRaCALA . These include L . monocytogenes pyruvate carboxylase ( 8 μM ) , CbpA ( 2 . 2 μM ) , CbpB ( 1 . 8 μM ) , PstA ( 1 . 3 μM ) , OpuCA ( 1 . 2 μM ) and PgpH ( 0 . 3–0 . 4 μM ) ; S . aureus OpuCA ( 2 . 5 μM ) and KdpD ( 2 μM ) ; and Enterococcus faecalis OpuCA ( 6 μM ) [5 , 8 , 16 , 44 , 45] . The homologous RCK_C domain of KtrA in S . aureus has a Kd of 0 . 4 μM [6] . Therefore it is possible that the affinities for proteins towards c-di-AMP have evolved to transduce the signal at different threshold concentrations of this nucleotide . We found that glycine betaine levels were significantly lower in the high c-di-AMP ΔgdpP mutant , which were restored by inactivation of busR or suppressor mutations which lower the c-di-AMP level . Low glycine betaine levels due to enhanced repression of busAA-AB by c-di-AMP bound BusR is a likely important contributor to osmosensitive phenotype of ΔgdpP . BusR promoter binding has also been found to be influenced by ionic strength in vitro [46] , so it is possible that it senses multiple signals within the cell in order to respond to osmotic challenges . Whilst the kupB and busR mutations resulted in restoration of osmolyte uptake to allow growth of ΔgdpP under high osmolarity , another way to achieve this is for the cell to simply reduce its intracellular c-di-AMP level . During this screen many destructive mutations in cdaA and restorative mutations in gdpP were observed which lowered the c-di-AMP level . Also in previous work a mutation in glmM was found to downregulate CdaA activity and lower the c-di-AMP level [11] . Two independent intergenic deletion mutations identified in the current work provide a straightforward way to lower the intracellular c-di-AMP pool , which is export . By removing a transcription terminator from a highly expressed ribosomal operon upstream , the suppressor mutants evolved to have a large increase in MDR gene expression . We were unable to demonstrate the MDR Llmg1210 alone was responsible for c-di-AMP export , since Llmg1211 co-expression was needed to stabilise the construct . Llmg1211 ( DUF4811 ) has no homology to proteins with known function , but contains 2 N-terminal transmembrane domains and is located adjacent to Llmg1210 MDR homologs in other species suggesting they have a functional linkage . Interestingly L . lactis MG1363 contains another gene encoding a DUF4811 protein ( Llmg1625 ) , which is also located in an operon with MDR and MarR regulator genes upstream . C-di-AMP export has been observed in several pathogens where it triggers an IFN-β innate immune response [33 , 47 , 48] . In L . monocytogenes , overexpression of MDRs were observed following inactivation of their cognate transcriptional repressors , which led to elevated c-di-AMP export [32 , 33] . A strain of L . monocytogenes ( LO28 ) with a naturally occurring mutant tetR exported high levels of c-di-AMP because of strong expression of MdrT [34] . The amino acid identity ( 45% ) between Llmg1210 and MdrT supports its likely function as a c-di-AMP exporter in Lc . lactis , however MdrT has also been shown to act as a bile exporter in L . monocytogenes [49] suggesting it may exhibit broad substrate specificity . Our results demonstrate that active c-di-AMP export is a mechanism by which the cell can modulate intracellular c-di-AMP levels significantly enough to impact cellular physiology , in this case osmoresistance . Several other mutations were identified in the osmoresistance suppressor screen which triggered a lowering of the c-di-AMP level in ΔgdpP . The most common changes were in a peptide cleavage and export system which is broadly conserved in Gram-positive bacteria . Interestingly , loss of function mutations in Eep ( RseP ) and PptAB ( EcsAB ) genes were found during a screen for acid resistant suppressor mutants from a ybbR deletion strain of S . aureus [22] . Like that seen in our study , these mutations lowered the c-di-AMP compared to the parent strain . The connection between peptide processing/export and c-di-AMP level regulation is not clear at present . In B . subtilis , inactivation of EcsAB results in a defect in intramembrane cleavage activity by the Eep ortholog RsaP and it was suggested that peptides not cleared from the membrane may inhibit RsaP activity [50] . Peptides exported by these systems are known to function in cell-to-cell communication as pheromones following re-importation into a neighbouring cell [25 , 51] . We tested the ability of spent supernatants from wild-type and ΔgdpP Lc . lactis to induce salt sensitivity in the eep or pptAB suppressor strains , but no effect was observed . Recent work in L . monocytogenes revealed that mutations in the Opp peptide uptake system can restore growth in a DacA ( CdaA ) mutant [12] . Peptides can function as osmolytes and were shown to be toxic in cells lacking c-di-AMP likely due to an uncontrollable increase in intracellular osmotic pressure [12] . Obtaining osmoresistant suppressor mutants unable to export peptides in ΔgdpP aligns with the hypothesis that elevated osmolyte ( peptide ) levels within a cell with high c-di-AMP can restore normal turgor pressure and allow growth on high salt agar . One additional mutation was also observed in each of two kupB suppressor mutants . These were in ftsX and pptA and at least for the latter , it is most likely this change which caused a lowering of the c-di-AMP level , similar to other single ppt mutants studied here . It remains to be determined if and how changes in FtsX , which regulates cell wall peptidoglycan hydrolase activity [52] affects c-di-AMP levels . This study has identified both cellular and external stimuli which trigger significant variations in the c-di-AMP level in bacteria . It was found that enhanced K+ uptake due to gain-of-function mutations in the KupB transporter or simply overexpression of wild-type KupB resulted in elevated c-di-AMP in Lc . lactis and Lb . reuteri . This result is in agreement with recent work showing inactivation of the Trk K+ transporter gating component CabP which is predicted to lower K+ uptake , triggered a lower c-di-AMP level in S . pneumoniae [24] . Our results show that CdaA is activated as a result of higher K+ uptake during growth ( since gdpP is inactivated in ΔgdpP ) . We also found that inactivation of BusR results in higher c-di-AMP suggesting that elevated glycine betaine uptake triggers c-di-AMP accumulation . In the cell suspension assay , under low osmotic conditions , c-di-AMP accumulation was observed under low osmotic conditions , but the addition of ionic or non-ionic solutes triggered a halt in synthesis or increased degradation of c-di-AMP . It therefore appears that the c-di-AMP level is modulated in response to turgor pressure changes as a result of water migration . Uncontrolled K+ or glycine betaine uptake during growth or low osmolarity conditions will result in water entering the cells causing high turgor pressure . In these cases , c-di-AMP accumulation would occur , allowing the cell to subsequently block uptake of K+ and compatible solutes . This then limits excessive cellular hydration and potentially cell lysis . Upon entry into a high osmolarity environment , cells require greater K+ and compatible solutes in order to prevent cellular dehydration and a fall in turgor pressure , so they therefore lower their c-di-AMP level to achieve this . This feedback loop ensures that the cell can quickly sense turgor pressure , and if need be , change the cell’s physiology to regulate water migration through the c-di-AMP signalling receptor network . These environmental changes are directly received at the protein/enzyme level , since the assay involves non-growing cells . Thus , the enzymes involved in c-di-AMP synthesis and degradation could therefore be considered as osmosensors in addition to their roles as osmoregulators . The mechanisms underlying coordinated synthesis and/or hydrolysis of c-di-AMP in response to increased K+ and glycine betaine uptake , or external osmolarity , are currently unknown . Several possibilities appear possible . Both CdaA and GdpP contain 3 and 2 transmembrane domains which may allow sensing of membrane stretching or curvature which is likely to change under varying turgor pressures . CdaA forms a complex with and is regulated by the membrane bound extracellullar protein CdaR and the peptidoglycan biosynthesis enzyme GlmM in several bacteria [11 , 19 , 21 , 23] . These protein-protein interactions ( or individual activities ) may be affected by changes in turgor pressure or ionic strength within the cell and affect c-di-AMP synthesis . In L . lactis , cdaR is a non-functional pseudogene , however c-di-AMP levels in this strain are responsive to external osmolarity changes , suggesting that this protein is not essential for sensing . Ultimately , identification of the osmo-signal sensing mechanism of the c-di-AMP system will be of significant interest as it is likely to be conserved across many bacteria .
Lc . lactis strains ( S1 Table ) were grown at 30°C in M17 media ( Difco , USA ) supplemented with 0 . 5% ( w/v ) glucose ( GM17 ) . Lb . reuteri BR11 and Lb . plantarum 299v were grown in deMan Rogosa Sharpe ( MRS ) media ( Oxoid , UK ) at 37°C either anaerobically on agar or in static liquid cultures . L . monocytogenes ATCC 19112 and S . aureus IPOOM14235 were grown in Heart Infusion ( HI ) media ( Oxoid , UK ) at 37°C with aeration at 150 rpm . E . coli NEB-5α containing pRV300 derivatives were grown in Luria-Bertani ( LB ) broth containing 100 μg/ml ampicillin at 37°C with aeration at 230 rpm . E . coli NEB-5α containing pGh9 derivatives were grown in HI media ( Oxoid , UK ) containing 150 μg/ml erythromycin at 30°C with aeration at 230 rpm . Osmoresistant suppressor mutants of ΔgdpP strain OS2 were isolated and confirmed as described before [11] . Sanger sequencing of cdaA and gdpP from the suppressors and whole genome sequencing of 20 mutants using the HiSeq2000 platform was carried out at Macrogen ( Seoul , South Korea ) . SNP analysis was carried out using Geneious 8 . 1 . 8 . ( Biomatters Ltd , New Zealand ) as described previously [11] . Plasmids and primers used in this study are shown in S2 and S3 Tables . Lc . lactis was transformed as described previously [10] . Insertional inactivated mutants were made using pRV300 and gene overexpression was done using pGh9 . Lc . lactis transformants were grown at 30°C in the presence of 3 μg/ml erythromycin . Lb . reuteri was transformed as described previously [37] and plasmid containing cells were maintained using 10μg/ml erythromycin . For pGh9-kupBA618V transformations into Lc . lactis 0 . 1–0 . 2M NaCl was added to the agar as a precaution to prevent mutations occurring . Wild-type or 42 amino acid deleted busR and downstream busAA promoter were cloned into pTCV-lac and introduced into E . coli T7 Express LysY ( New England Biolabs ) with selection using kanamycin ( 50 μg/ml ) . For β-galactosidase activity assays , strains were grown overnight in LB broth without NaCl ( 10 g/L tryptone; 5 g/L Yeast extract ) and then diluted 1:100 in the same fresh LB medium and grown at 30°C , aeration 220 rpm to early log phase ( OD600 ~0 . 25 ) where 0 , 0 . 1 , 0 . 2 , 0 . 3 or 0 . 4 M NaCl was added . Following further incubation to OD600 ~0 . 6 , cells were quantified for β-galactosidase as described previously ( Miller , 1972 ) , except chloroform and 0 . 1% SDS were used to permeabilize cells instead of toluene . The busAA promoter ( 252 bp ) was also cloned into pTCV-lac and introduced into different Lc . lactis strains . Promoter activity ( β-galactosidase activity ) in different strains were compared following growth on GM17 0 . 1 M NaCl agar supplemented with 3 μg/ml erythromycin and 80 μg X-gal at 30°C for 2 days followed by storage at 4°C for 2 days for colour development . It should be noted that we have found that the ΔgdpP strain can undergo mutations restoring normal c-di-AMP levels during prolonged subculture even under normal growth conditions and caution should be taken when working with high c-di-AMP strains [53] . The ΔgdpP strains were generated with minimal sub-culturing and the cdaA and gdpP genes were checked for mutations and c-di-AMP levels were checked following mutant construction . Strains were grown to mid-log phase ( OD600 ~0 . 7 ) , then pelleted by centrifugation ( 5 , 000 x g for 10 mins ) , cells then washed 2 times in 1/10 KPM buffer , then re-suspended in 1 . 5 ml ice-cold extraction buffer ( 40:40:20 methanol:acetonitrile:ddH2O v/v mix ) . Lysis was carried out according to that described previously [11] . For determining both extracellular and intracellular c-di-AMP levels , cells were grown in minimal media . First , overnight Lc . lactis cultures were diluted 1:100 into 15ml GM17 broth and incubated at 30°C till OD600 ∼ 0 . 7 . Cells were pelleted by centrifugation at 5000 × g ( Beckman Coulter , USA ) for 10 min at 4°C , then washed 2 times and re-suspended in 15ml minimal media D6046 ( Sigma-Aldrich , St . Louis , MO ) supplemented with KH2PO4 3 . 6mg/ml , K2HPO4 7 . 3mg/ml , histidine 0 . 13mg/ml , arginine 0 . 72 mg/ml , leucine 1mg/ml , valine 0 . 6mg/ml , glucose 0 . 5% , potassium acetate 0 . 9mg/ml , MOPS 13mg/ml , guanine 0 . 05mg/ml , xanthine 0 . 05mg/ml , FeSO4 0 . 10mg/ml , ZnSO4 0 . 1mg/ml , adenine 0 . 2 mg/ml and incubated a further 3 hours . Cultures were centrifuged to separate supernatant and cells and supernatants were subsequently filtered ( 0 . 22 μm pore size ) and used directly to quantify extracellular c-di-AMP . Cells were resuspended in 0 . 5 ml ice-cold extraction buffer and lysed as described previously [11] . C-di-AMP quantification using ultra performance liquid chromatography-coupled tandem mass spectrometry ( UPLC-MS/MS ) was carried out as described previously ( 11 ) using a different column ( HSS PFP 1 . 8μm , 2 . 1 x 100 mm ) and a BEH C18 VanGuard pre-column protector . Eluent A was composed of 0 . 1% of formic acid in water while eluent B was 100% acetonitrile . C-di-AMP was detected using electrospray ionization in a negative ion mode at m/z 657 . 5 → 328 . 26 and the internal standard 625 . 52 → 312 . 26 with collision energy being 30V and 28V , respectively . Overnight cultures were diluted 1:100 into GM17 broth with erythromycin if required and incubated until late log phase ( OD600 ~ 1 ) . Cultures ( 50 ml ) were centrifuged at 5 , 000 x g ( Beckman Coulter , USA ) for 10 mins at 4°C and the supernatants were discarded . A second centrifugation was carried out and all media residue was removed by pipette . Cells were subsequently digested with 500μl of 15% HNO3 at 95°C for 1 hour . After digestion , the mixture was centrifuged at 5000 x g for 30 minutes at 4°C . Thereafter , the supernatant was collected to measure K+ content using Vista-Pro , CCD Simultaneous ICP-OES ( Varian Inc . , USA ) . The argon gas was ionized and used to create plasma at 7 , 000–10 , 000°C and the emission wavelength of 766 . 491 nm was used for measuring K+ . Mean ± SEM were calculated based on three biological replicates . Full length BusR and the BusR C-terminal RCK_C domain were codon optimised for expression in E . coli and cloned into pRSETA as fusions to His-6 tags ( Geneart , Germany ) . The C-terminal intracellular fragment of KupB was also cloned into pRSETA and pMAL-p5X . The resulting constructs were transformed into E . coli BL21 for expression . Briefly , bacterial cultures were grown at 37°C in LB broth with ampicillin ( 100 μg/mL ) to OD ~ 0 . 7 , then induced with 0 . 5 mM IPTG at 37°C for 3 hours . Bacteria were pelleted , resuspended in lysis buffer ( 30mM K2HPO4 pH 8 , 300mM NaCl , 1mM PMSF ) , and lysed by sonication . For purification of the BusR C-terminal domain , after centrifugation the cell lysate was collected and applied to a Ni-NTA resin ( Thermo Fisher ) , washed several times with wash buffer ( 30mM K2HPO4 pH 8 , 300mM NaCl ) , and eluted with elution buffer ( wash buffer + 300mM imidazole ) . The elute from the Ni-NTA resin was exchanged into nucleotide binding buffer ( 50mM Tris HCl pH 7 . 5 , 150mM NaCl , 20mM MgCl2 ) using a PD10 desalting column ( GE Healthcare ) . Radio-labeled c-di-AMP was prepared from 32P-ATP ( Perkin-Elmer ) , and DRaCALA was performed as previously described [16] . Briefly , proteins were incubated with 32P-c-di-AMP for 10 minutes at room temperature , then spotted on a nitrocellulose membrane . Radioactivity was visualized with a Phospho-Imager and a Typhoon imaging system ( GE Healthcare ) . Lc . lactis were grown overnight in GM17 at 30°C , subcultured 1:100 into 30 ml fresh GM17 and incubated at 30°C till OD600 ~ 0 . 9 ( mid-log phase ) . Cells were harvested by centrifugation at 5200 x g for 10 min , and washed twice with 2ml 1/10 KPM buffer [0 . 01M K2HPO4 adjusted to pH 6 . 5 with H3PO4 and 1mM MgSO4 . 7H2O] . Cells were resuspended in 0 . 3 ml 1/10 KPM buffer and 1 . 2 ml extraction buffer ( 40% methanol:40% acetonitrile:20% ddH2O v/v ) . Samples were mixed with 0 . 5 ml equivalent of 0 . 1 mm zirconia/silica beads and disrupted using a Precellys 24 homogenizer ( Bertin Technologies ) three times for 30s each , with 1 min cooling on ice in between . Glass beads were separated by centrifugation at 17000 x g for 5 min . The supernatant was dried under liquid nitrogen before resuspended in 0 . 5 ml MilliQ water before filtered ( 0 . 22 mm pore size ) . Glycine betaine level was measured using UPLC/MS/MS with HSS PFP column ( 1 . 8μm , 2 . 1 x 100 mm; Waters ) and a BEH C18 VanGuard pre-column protector . The method uses was the same with c-di-AMP quantification method described above , with some modifications . Glycine betaine was detected using electrospray ionization in positive ion mode at m/z 118 → 58 with the collision energy being 25eV . The levels were calculated based on a standard curve prepared with glycine betaine from Sigma-Aldrich . Overnight cultures were diluted 1:100 in GM17 and incubated until OD600 ~ 0 . 6 . To 500μL of culture , 1ml RNA protect reagent ( Qiagen , Hilden , Germany ) was added and then tubes were vortexed for 5s and held for 5 minutes at room temperature . Cells were harvested by centrifugation ( 5000 x g for 10min ) . RNA was extracted using the RNeasy minikit ( Qiagen , Hilden , Germany ) with some modifications as previously described [10] . cDNA was synthesized using SuperScript III First-Strand Synthesis SuperMix ( Invitrogen , Carlsbad , CA ) . Platinum SYBR green quantitative PCR ( qPCR ) SuperMix-UDG ( Invitrogen , Carlsbad , CA ) was used for qPCR using the Rotor-gene Q qPCR machine ( Qiagen ) with primers described in S3 Table . Test genes rplJ , llmg1209 , llmg1210 , llmg1211 , llmg1212 , cdaA and kupB and the reference gene tufA were amplified along with no reverse transcriptase and no template controls . Data was analyzed using the comparative CT method [54] from 3 biological replicates . Overnight cultures were diluted 1:100 in fresh media and incubated until OD600~0 . 7 ( mid-log phase ) . Then , 30 ml of the mid-log culture was aliquoted into different tubes for various conditions ( 30 ml of culture was used for a single c-di-AMP measurement for each condition or time-point replicate ) . Next , cells were pelleted by centrifugation at 5 , 000 x g ( Beckman Coulter , USA ) for 10 min at 4°C and washed twice with buffer ( 1/10 KPM [0 . 01M K2HPO4 adjusted to pH 6 . 5 with H3PO4 and 1mM MgSO4 . 7H2O] ) . Cells were resuspended in 1 . 5 ml buffer and the cells were energised by adding 20mM D-glucose and incubated at 30°C ( Lc . lactis ) or 37°C ( Lb . plantarum , L . monocytogenes and S . aureus ) for 10 min to allow ATP production [37 , 38] . D-deoxyglucose ( 20mM ) was also tested as it de-energises cells and blocks ATP synthesis . Samples were either taken at this point for the time-course experiments or additional 90 μl ddH2O or NaCl ( 0 . 1M or 0 . 3M final concentration ) or sorbitol ( 0 . 2M or 0 . 6M ) or sucrose ( 0 . 2M or 0 . 6M ) was added . At the 20 min time point the assay was stopped . To prevent any changes in external osmolarity or effects of centrifugation , at the harvest time point the cell suspensions were mixed directly with 0 . 5 ml equivalent of 0 . 1 mm zirconia/silica beads ( Daintree Scientific , Australia ) and were immediately lysed using a Precellys24 homogeniser ( Bertin Instruments , France ) three times for 30 seconds at 6 , 000 rpm with 1 min cooling on ice in between . Beads were separated by centrifugation at 16 , 873 x g ( Eppendorf , Germany ) for 5 min . The supernatant was mixed with methanol/acetonitrile producing a final v/v ratio of 40:40:20 methanol:acetonitrile:supernatant . The sample was centrifuged at 16 , 873 x g for 5 min and the supernatant was air-dried with nitrogen at 40°C before being resuspended in 0 . 5 ml ddH2O and filtered ( 0 . 22 μm pore size ) . Levels of c-di-AMP were determined with UPLC-MS/MS as above . Overnight cultures of WT Lc . lactis and ΔkupB were diluted 1:100 in fresh GM17 media and grown OD600~0 . 7 . Two millilitres of culture was centrifuged at 11000 x g for 3 mins and the cell pellets were washed 2 times in chemically defined media ZMB1 [55] with some modifications as follows . The potassium salts K2SO4 and KI were omitted and potassium phosphate buffers were replaced with NaH2PO4 ( 2 . 736 g/L ) and Na2HPO4 ( 5 . 23 g/L ) . The washed cell pellets were re-suspended in 2 ml of modified ZMB1 and 5 μL of the cell suspension was inoculated into 5 ml of media with various concentrations of KCl ( 0 . 1 mM , 0 . 5 mM , 1 mM , 5 mM , 10 mM , 20 mM or 50 mM ) . The cultures were incubated at 30°C for 19 hours , at which point the OD600 was determined .
|
Second messengers relay signals received from the environment to intracellular targets that adjust cellular physiology . One widespread bacterial cyclic-dinucleotide signalling molecule , cyclic-di-AMP ( c-di-AMP ) has been shown to regulate a range of cellular processes via binding to protein and riboswitch targets , with most identified thus far being linked to osmoregulation functions . C-di-AMP levels need to be carefully tuned under different environmental conditions to allow optimal growth . Here we show that a Lactococcus lactis GdpP phosphodiesterase mutant with a high intracellular pool of c-di-AMP is able to grow under hyperosmotic conditions after acquiring mutations which increase osmolyte ( potassium [K+] or compatible solute ) uptake or by actively exporting c-di-AMP . Interestingly , elevated K+ or glycine betaine uptake triggered accumulation of c-di-AMP and environmental osmolarity changes were also found to significantly impact c-di-AMP levels in various bacteria . These results support a model in which c-di-AMP negatively impacts osmoresistance through inhibition of the import of osmoprotectants and this system can sense both cellular and environmental changes causing water stress .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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] |
2018
|
Enhanced uptake of potassium or glycine betaine or export of cyclic-di-AMP restores osmoresistance in a high cyclic-di-AMP Lactococcus lactis mutant
|
The rapid proliferation of antibiotic-resistant pathogens has spurred the use of drug combinations to maintain clinical efficacy and combat the evolution of resistance . Drug pairs can interact synergistically or antagonistically , yielding inhibitory effects larger or smaller than expected from the drugs' individual potencies . Clinical strategies often favor synergistic interactions because they maximize the rate at which the infection is cleared from an individual , but it is unclear how such interactions affect the evolution of multi-drug resistance . We used a mathematical model of in vivo infection dynamics to determine the optimal treatment strategy for preventing the evolution of multi-drug resistance . We found that synergy has two conflicting effects: it clears the infection faster and thereby decreases the time during which resistant mutants can arise , but increases the selective advantage of these mutants over wild-type cells . When competition for resources is weak , the former effect is dominant and greater synergy more effectively prevents multi-drug resistance . However , under conditions of strong resource competition , a tradeoff emerges in which greater synergy increases the rate of infection clearance , but also increases the risk of multi-drug resistance . This tradeoff breaks down at a critical level of drug interaction , above which greater synergy has no effect on infection clearance , but still increases the risk of multi-drug resistance . These results suggest that the optimal strategy for suppressing multi-drug resistance is not always to maximize synergy , and that in some cases drug antagonism , despite its weaker efficacy , may better suppress the evolution of multi-drug resistance .
As antibiotic-resistant pathogens become more common , clinicians increasingly turn to multi-drug treatment to control infection [1]–[5] . The inhibitory effect of two drugs in combination can be larger or smaller than expected from their individual effects , corresponding to synergistic or antagonistic interactions between the drugs respectively [6]–[9] . Synergistic interactions are usually thought of as advantageous since , for a given amount of drug , they more effectively inhibit the growth of drug-sensitive pathogens . However , in vitro studies have suggested that , for the same level of inhibition , more synergistic drug pairs may foster antibiotic resistance [10]–[12] . Antagonistic drug combinations , on the other hand , are less effective at inhibiting drug-sensitive pathogens , but can reduce and even invert the selective advantage of single-drug resistant mutants , causing selection against resistance [13] . These recent observations point to a possible tradeoff in the choice of synergistic versus antagonistic drug combinations with respect to their effects on treating infection and suppressing antibiotic resistance . However , while antagonistic drug combinations increase selection against resistance , and should therefore minimize resistance , they also kill the infection more slowly , giving resistance more time to emerge . Antagonism therefore has two contradicting effects on the evolution of resistance: on one hand , it increases the risk of resistance by decreasing antibiotic inhibition and allowing more time for resistance to evolve; on the other hand , it decreases the risk of resistance by decreasing the selective advantage of single drug resistant mutants . We ask which of these opposing effects is stronger , and therefore which type of drug interaction – synergistic or antagonistic – best prevents the overall chance of emergence of multi-drug resistance . We frame this problem in the context of a clinical infection , formalizing the two main factors in the success of an antibiotic treatment as “treatment efficacy” and “prevention of multi-drug resistance . ” Treatment efficacy is the rate at which the infection is cleared by the treatment , and can be defined as the reciprocal of the time , , at which the total infection is eliminated , . Prevention of multi-drug resistance is defined as the reciprocal of the number of double-drug resistant mutants expected to arise during the course of treatment , . In real infections , multi-drug resistance can arise either through a single mutation conferring cross-resistance to both drugs simultaneously , or through the sequential acquisition of mutations conferring resistance to each drug individually [10] , [14]–[16] . Furthermore , resistance to a single drug can develop in several small steps or in one large step [15] , [17]–[19] . For simplicity , and to emphasize the role of drug interactions , we concentrate here on an idealized case in which resistance to the two-drug combination evolves through sequential acquisition of two spontaneous mutations , each conferring strong resistance specific to one of the two antibiotics ( Fig . 1 ) . We asked what level of drug interaction ( ranging from strong synergy to strong antagonism ) maximizes treatment efficacy ( ) and prevention of multi-drug resistance ( ) . Maximizing is straightforward: as more synergistic drug pairs have increased killing potency and clear the infection more quickly , maximally synergistic drug pairs should maximize [4] , [20] . In attempting to maximize , however , the best choice of drug interaction is less clear . Assuming sequential acquisition of resistance to each drug , the rate at which multi-drug resistance arises will depend on the size of the single-drug resistant mutant population . The size of this single-mutant population , in turn , depends on two factors: the rate at which such mutants arise , and their selective advantage over the wild-type . Synergistic drug pairs decrease the first factor because they more quickly kill the source wild-type population from which single mutants arise . However , synergistic drug pairs also increase the second factor: single-drug resistant mutants will have a strong selective advantage in a synergistic treatment because resistance removes both the burden of one drug , and its enhancing effect on the other drug [13] ( Fig . 1B ) . Synergistic drug pairs therefore decrease the rate at which single-drug resistant mutants appear , but increase their selective advantage . Antagonistic drug pairs do the opposite: though they allow a larger number of single-drug resistant mutants to arise , they also diminish the selective advantage of these mutants . The net effect of a given drug interaction on the evolution of multi-drug resistance is therefore not obvious , and requires a quantitative model to determine the overall impact of mutation and selection's countervailing effects . To better understand how drug interactions affect the risk of multi-drug resistance , we used a population genetic model of microbial infection previously applied to predict single-drug resistance in vivo in mice [21] , and modified it to account for the sequential acquisition of mutations leading to multi-drug resistance . We used this model to ask what level of drug interaction maximizes treatment efficacy ( ) and prevention of multi-drug resistance ( ) .
We based our model on work by Jumbe et al . [21] , which investigated a mouse-thigh P . aeruginosa infection model [22]–[24] and provided a mathematical model that quantitatively described the relationship between exposure to the fluoroquinolone antibiotic levofloxacin , and changes in drug-susceptible and drug-resistant bacterial subpopulations over time . This mathematical model was successful both in reproducing the observed changes in drug-susceptible and –resistant subpopulations over time , and in predicting the dose of levofloxacin needed to suppress amplification of levofloxacin-resistant ( efflux-pump-expressing ) mutants . To investigate the effect of antibiotic interactions on treatment efficacy and the prevention of multi-drug resistance in a simple scenario , we modified the Jumbe et al . model in four ways: we include a second antibiotic in our model; we assume a constant antibiotic dose; we assume a low hill coefficient , consistent with the mechanisms of a range of antibiotics [25]; and we assume no cost for antibiotic resistance . The consequences of these assumptions are discussed throughout the text . Our model incorporates treatment with two antibiotics , A and B . It uses a set of ordinary differential equations ( ODEs ) to follow the population sizes of the drug-sensitive wild-type strain ( ) , the total single-drug resistant population ( ; we assume symmetry between drugs A and B such that their respective resistant populations are equal , ) , and the expected number of multi-drug resistant mutants ( ) arising over time ( Fig . 1A; Methods ) : ( 1 ) ( 2 ) ( 3 ) Populations are affected by growth , antibiotic killing and mutation , where , and are the growth rate , antibiotic killing rate and frequency of resistance mutations per generation , respectively , and and are the effective doses of antibiotic felt by the wild-type and single-drug resistant mutant populations . We assume for simplicity that antibiotic resistance imposes no fitness cost , so that the growth rates of the sensitive and resistant populations are the same . To account for competition for resources , we assume this growth rate is given by the logistic equation , ( 4 ) where is the maximal growth rate , is the total population size , and is the maximal carrying capacity ( Fig . S1B ) . This competition for resources was included in the in vivo murine infection model [21] , and has been observed in or inferred from a range of infections [26] , including S . pneumoniae [27] , and Methicillin-Resistant S . aureus ( MRSA ) [28] . While we assume the growth rates of the wild-type and single-drug resistant mutants are the same , the rates at which they are killed by antibiotic are different and depend on the effective antibiotic dose , , felt by each population: ( 5 ) where is the maximal killing rate and is the Hill coefficient , which determines the steepness of the killing rate as a function of drug dose . In contrast to Jumbe et al . , we set , which is representative of many common antibiotics [25] , although different values of give rise to similar overall model behavior ( Fig . S2 ) . The effective drug dose , , depends on the dosage of the two drugs and on their interaction ( Fig . 1B , Text S1 , Fig . S1A ) . For simplicity we assume both drugs are administered at the same dose , , defined in units of their minimum inhibitory concentration ( ) , the single-drug dose at which the wild-type death rate equals its growth rate at resource-unlimited conditions: . For the wild-type , the effective dose is the sum of the dosage of the two drugs plus their level of interaction : . Values of are positive , zero , or negative for synergy , additivity and antagonism , respectively . While in practice the value of is specific to a given drug pair [29] , we treat it as continuous in order to investigate all potential treatment strategies . We assume that single-drug resistant mutants are affected by only one of the drugs , which is reasonable in the case of resistance mechanisms that decrease the intracellular concentration of antibiotic , such as efflux pump expression or enzymatic degradation [11] , [13] , [30] . The effective dose of single-drug resistant mutants is therefore , and is independent of ( Fig . 1B ) . Except where indicated , we set ( general model behavior is robust to changes in drug dosage , Fig . S2A ) , which for an additive drug pair is consistent with the drug dosage used in Jumbe et al . [21] . Mutations from wild-type to single-drug resistance , or from single- to double-drug resistance , arise at a rate per individual per replication , or per individual per unit time . Since in any effective treatment the number of double mutants arising is smaller than 1 , we do not account for the growth or death of this fractional population , but rather define as the integrated number of double mutants generated via mutation during treatment ( Eq . 3 ) . Prevention of multi-drug resistance is then defined as . The model therefore consists of Eqs . 1–5 . Parameter values , following Jumbe et al . [21] , are given in Table S1 . Initial conditions for the model are , , - the population sizes at the onset of treatment . We assume that prior to treatment , the infections have grown from a single cell to the initial population size while mutating; while the overall mutation rate is a function of model parameters , and ( Eqs . 2 , 3 ) , and are functions of alone: , . No double-drug resistant mutants are present: . We integrate the ODEs with these initial conditions ( Methods ) and define as the time at which the total population size drops below one ( is defined as infinity if the population reaches a non-zero steady state ) . To determine the impact of drug interaction on treatment outcome , we first looked at the differences in treatment efficacy ( ) and prevention of multi-drug resistance ( ) over a range of drug interaction values ( ) while holding drug dosage fixed ( Fig . 2 , ) . We observed two distinct and robust ( Fig . S2 ) behaviors , depending on whether falls above or below a critical value , ( Fig . 2; for the parameters used , ) . For , we observed a tradeoff between treatment efficacy and prevention of resistance . In this regime , increasing synergy yields greater ( Fig . 2 , unshaded region ) ; this is expected , as increasing the synergistic interaction between the drugs kills the wild-type more quickly . Despite faster infection clearance , however , greater synergy actually decreases ; namely , it increases the risk of multi-drug resistance . Conversely , more antagonistic drug pairs increase , albeit at the expense of reduced efficacy . This tradeoff between efficacy and prevention of resistance breaks down at a critical threshold , , above which increasing synergy no longer increases , but still decreases ( Fig . 2 , shaded region ) . Above this “synergy ceiling , ” further increasing synergy therefore has only undesirable effects , since it increases the risk of multi-drug resistance without increasing efficacy . Optimal drug pairs for treating the given infection must therefore have a level of drug interaction lower than and , due to the tradeoff between and , the optimal value of will depend on the relative importance assigned to these two conflicting goals . To understand what determines the level of the synergy ceiling , , we asked what causes the transition from tradeoff behavior at , to plateau behavior at ( Fig . 2 ) . Due to the sharp biphasic behavior of efficacy ( ) around , we looked to population time courses to determine how the time of clearance , , was affected by drug interactions below , at or above ( , respectively; Fig . 3A ) . For ( Fig . 3A , top ) , the wild-type subpopulation outlives the single-drug resistant mutants and . Since wild-type killing is stronger for more synergistic drug pairs , increasing decreases , explaining why efficacy increases with in this region ( Fig . 2 , unshaded region ) . For ( Fig . 3A; bottom ) , however , the wild-type is eliminated before the single-mutant population , and . Because the killing rate of the single-drug resistant mutants is independent of , is effectively independent of , causing to plateau for ( Fig . 2 , shaded region ) . is therefore the level of drug interaction for which wild-type and single-mutant populations are cleared simultaneously ( ; Fig . 3A , middle ) . Since represents the level of drug interaction for which , parameters that differentially alter and will alter . While we found that a number of model parameters had some effect on ( Fig . S2 ) , the strongest effect was due to changes in the frequency of resistance mutations , . differentially affects and because , although it has virtually no effect on , the single-mutant population size at the onset of treatment increases linearly with , , thereby increasing . For to match this increase in , the wild-type killing rate must decrease; namely , must be reduced . We therefore expected to decrease with increasing and , indeed , increasing the frequency of resistance mutations gave rise to consistent decreases in ( Fig . 3B ) . Interestingly , for high frequencies of resistance ( ) the synergy ceiling falls below zero ( representing an antagonistic drug interaction ) ; in this case mildly synergistic and even additive interactions fall in the undesirable regime where the risk of multi-drug resistance increases without any corresponding gain in treatment efficacy . Why does synergy , despite clearing the infection faster , increase the risk of multi-drug resistance ( Fig . 2 ) ? Since synergistic drug pairs clear the infection more quickly than antagonistic drug pairs , the rate at which they generate double mutants must also be higher . The overall rate at which double mutants arise , ( Eqs . 3 , 4 ) , is affected by two variables: it increases with the size of the single-mutant population , , and decreases with total population size , , due to the inhibitory effect of resource limitation on growth and mutation ( Fig . 4A ) . The total number of double mutants expected to arise is simply the integral of this instantaneous rate over the treatment course . In order to determine why synergistic treatments increase , we therefore analyzed the trajectories of synergistic and antagonistic treatments through the space of versus ( Fig . 4A; , solid line , , dashed line ) . The initial slopes of these trajectories ( Fig . 4A , arrows ) are determined by the relative fitness of the wild-type and single-drug resistant populations under antibiotic treatment . The synergistic treatment selects strongly against the wild-type population , producing a trajectory with a steep slope that drives treatment into a region of high ( Fig . 4A , red region ) ; this is because the rapid decrease in wild-type population size relieves competition for resources , creating a window of opportunity in which the still-large single-mutant population can rapidly grow and mutate . Conversely , the antagonistic treatment selects only weakly against the wild-type , producing a trajectory with a shallow slope that skirts the high region . Antagonistic drug pairs therefore decrease in a competition-dependent fashion: weak killing of the wild-type maintains competition for resources , limiting growth and mutation of the single-drug resistant population until it is eliminated . It is important to note that resource competition is significant only at the beginning of treatment , when . If competition for resources is required for the advantage of antagonism over synergy in preventing resistance , then we should expect a decrease in initial population size to decrease this advantage . To test this prediction , we looked at the relative ability of our representative synergistic ( ) and antagonistic ( ) drug pairs to prevent multi-drug resistance , , over a range of ( Fig . 4C , circles; sensitivity to other model parameters is minimal , Fig . S2 ) . Indeed , we found that the advantage of antagonistic drug pairs in preventing resistance ( , below dashed line ) was limited to cases where is close to . In fact , for significantly lower than , the trend reverses and synergy better prevents resistance ( , above dashed line ) . This is because , for low population sizes , resource competition effects are negligible; therefore no longer depends on , and becomes a function of alone . Because synergistic drug pairs better limit by quickly killing the wild-type population from which single mutants arise , they therefore also better limit multi-drug resistance in cases of weak competition . Indeed , this advantage of synergy disappeared entirely when we artificially turned off wild-type to single-mutant mutation , allowing only those single mutants present at the start of treatment to contribute to ( Fig . 4C , triangles ) . Importantly , when competition for resources is weak ( significantly less than , ) , the tradeoff between treatment efficacy and prevention of multi-drug resistance no longer exists ( Fig . 4D; compare with Fig . 2 ) . As a result , is no longer useful as a “synergy ceiling” because , although drug pairs with do not further improve , they do improve . For infections with weak competition , the use of maximally synergistic drug pairs therefore represents the best possible treatment strategy .
We used a population dynamic model of bacterial infection to determine what drug interactions best suppress the emergence of multi-drug resistance . Whereas antagonistic drug pairs kill bacterial populations more slowly , and therefore allow more time for resistance to emerge , they also decrease the selective advantage of resistant mutants . Which of these two opposing effects of antagonism dominates in determining its overall impact on the chance of evolving multi-drug resistance ? Framing this problem in the context of a clinical infection , we asked how two measures of treatment outcome , treatment efficacy and prevention of multi-drug resistance , depend on drug interaction . We found that the optimal drug interaction can be determined primarily as a function of two infection parameters: population size at the outset of treatment , and the frequency of resistance mutations ( see summary of our results in Fig . S3 ) . For clinically relevant scenarios where initial population sizes are well below the carrying capacity , competition for resources is weak and synergy , which is typically preferred in clinical settings for its superior treatment efficacy [3] , [4] , [20] , is also expected to best prevent the emergence of multi-drug resistance . Where resource competition is significant , however , strong synergy may not always be the optimal treatment strategy . Real infections frequently exhibit competition , due either to a scarcity of carbon or iron [26] , [31] , [32] , or saturation of available adhesion sites ( e . g . in biofilm formation [33] , [34] ) . In our model , such competition is predicted to give rise to a tradeoff between treatment efficacy and resistance prevention: increased synergy leads to greater efficacy , but at the expense of an increased risk of multi-drug resistance . Importantly , this tradeoff saturates for levels of synergy greater than a critical value , above which greater synergy does not further increase efficacy , but still increases the risk of multi-drug resistance . If the goal is to minimize multi-drug resistance , then choosing drug interactions above this “synergy ceiling” may be counterproductive . This is especially important given the dependence of on the frequency of resistance mutations: our model predicts that for infections where resistance rates are high ( ) may be negative ( antagonistic ) , favoring the use of antagonistic drug pairs over mildly synergistic or even purely additive antibiotic combinations . Indeed , for the modified Jumbe et al . model that we study , is nearly additive; and while the resistance frequency we use may be an overestimate ( Jumbe et al . determined this as the rate of all mutations conferring only a 3-fold increase in the MIC ) , these and higher mutation rates have been identified in human pathogens [35] , [36] . Together , the potential for strong competition and high mutation rates in infection suggest that the tradeoff and synergy ceiling behaviors observed in our model – as well as the ability of antagonistic drug pairs to minimize multi-drug resistance – may describe the properties of some clinical infections . We emphasize that drawing concrete therapeutic conclusions from this study would be beyond its scope . Our model incorporates many simplifying assumptions: we assume to be a fixed value , although it has been observed to change with both the absolute and relative doses of the antibiotics administered [37] , [38]; drug administration and pharmacokinetics are not considered , although they may significantly impact the evolution of resistance [23] , [39]–[42]; resistance mutation rates per generation are assumed to be independent of growth and antibiotic-killing rates; and while we consider an idealized case in which multi-drug resistance arises from strong , sequential mutations conferring resistance to each antibiotic , real mutations may confer cross-resistance to both drugs simultaneously , or only partial resistance to a single drug [10] , [14] , [16] . One consequence of partial resistance is antibiotic killing of drug-resistant mutants for drug interactions above ; while for strong resistance this killing would be minimal , weak resistance may allow enough killing to undermine synergy ceiling behavior ( Fig . S4 ) . Finally , we note that this model does not consider the impact of host immune defenses , which may substantially impact microbial growth and death rates in clinical infections [43] , [44]; whether the influence of host defenses favors the use of some drug combinations over others , however , remains to be seen . While these caveats indicate the limitations of this simple model and suggest important avenues for future study , our results make a number of novel predictions about the relationship between drug interaction and multi-drug resistance: that there exist conditions under which antagonistic drug pairs may better prevent multi-drug resistance despite their weaker efficacy; that there is a synergy ceiling to how much efficacy can be achieved by modulating drug interaction; and that , below this ceiling , changes in drug interaction may produce a tradeoff between inhibition and multi-drug resistance . By basing our model on a previous experimental model of infection [21] , we have identified regions of parameter space in which such behaviors may be relevant in a clinical scenario , and which could be tested in future experimental models of infection . Finally , our model highlights the idea that the optimal choice of drug pair in treating an infection may be contextual: while strongly synergistic drug pairs seem the preferred strategy in scenarios where resource limitation and other forms of competition are negligible , antagonistic drug pairs may best prevent resistance in cases of high mutation rates and strong intra-infection competition . While present therapeutic knowledge generally favors synergistic drug pairs , our work motivates further research into the impact and potential utility of antagonistic interactions both in clinical and in ecological settings .
Our model consists of 3 ODEs ( Eq . 1–3 ) describing the population sizes of the wild-type and single-drug resistant mutants ( , ) , as well as the number of double mutants expected to arise during a treatment course ( ) . Parameter values for this model include first-order maximal growth ( ) and death rate ( ) constants , carrying capacity and mutation rate ( per individual per generation ) , which were taken from the in vivo murine model investigated in Jumbe et al . [21] ( Table S1 ) . Initial population sizes ( ) were determined by assuming that , prior to treatment , the infections grew from a single cell to the initial population size while mutating , such that and ; unless otherwise indicated , . ODEs were solved in MATLAB ( Version 7 . 1 , MathWorks , Natick , MA ) using a built-in , numerical ODE solver ( ODE45 ) . To avoid artifacts associated with using continuous ODEs to describe finite populations , each step was modified with the assumption that the wild-type or single-drug resistant population is eliminated ( size decreases to zero ) if its size drops below one .
|
The use of antibiotics against bacterial infections has led to the emergence of multi-drug resistant pathogens such as tuberculosis and MRSA . In order to control resistance , clinicians have increasingly turned to multi-antibiotic therapies . The common wisdom is to use combinations of drugs that act synergistically to kill the infection , but the impact of drug synergy on the evolution of resistance is unclear . Using mathematical simulations of an in vivo infection model , we asked what level of drug synergy would minimize the risk of multi-drug resistance while preserving the efficacy of treatment . We found that synergy may increase or decrease the risk of multi-drug resistance in a given treatment , depending on infection properties such as mutation rate and the availability of resources . Surprisingly , under conditions of strong competition for resources within the host , we found that maximal synergy—currently favored in clinical settings—can actually increase the risk of multi-drug resistance . Our results identify conditions under which drug synergy exacerbates the problem of multi-drug resistance , and offer guidelines for the selection of drug pairs that suppress it .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/population",
"genetics",
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics",
"ecology/evolutionary",
"ecology",
"pharmacology/drug",
"resistance",
"microbiology/microbial",
"evolution",
"and",
"genomics",
"computational",
"biology/evolutionary",
"modeling",
"infectious",
"diseases/bacterial",
"infections",
"computational",
"biology/systems",
"biology",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance"
] |
2010
|
Optimal Drug Synergy in Antimicrobial Treatments
|
Long noncoding RNAs constitute a major fraction of the eukaryotic transcriptome , and together with proteins , they intricately fine-tune various growth regulatory signals to control cellular homeostasis . Here , we describe the functional characterisation of a novel pair of long intergenic noncoding RNAs ( lincRNAs ) comprised of complementary , fully overlapping sense and antisense transcripts Genomic Instability Inducing RNA ( Ginir ) and antisense RNA of Ginir ( Giniras ) , respectively , from mouse cells . This transcript pair is expressed in a spatiotemporal manner during embryonic development . The individual levels of the sense and antisense transcripts are finely balanced during embryonic growth and in adult tissues . Functional studies of the individual transcripts performed using overexpression and knock-down strategies in mouse cells has led to the discovery that Ginir RNA is a regulator of cellular proliferation and can act as an oncogene having a preeminent role in malignant transformation . Mechanistically , we demonstrate that the oncogenic function of Ginir is mediated by its interaction with centrosomal protein 112 ( Cep112 ) . Additionally , we establish here a specific interaction between Cep112 with breast cancer type 1 susceptibility protein ( Brca1 ) , another centrosome-associated protein . Next , we prove that the mutual interaction between Cep112 with Brca1 is significant for mitotic regulation and maintenance of genomic stability . Furthermore , we demonstrate that the Cep112 protein interaction with Brca1 protein is impaired when an elevated level of Ginir RNA is present in the cells , resulting in severe deregulation and abnormality in mitosis , leading to malignant transformation . Inhibiting the Ginir RNA function in transformed cells attenuates transformation and restores genomic stability . Together , these findings unravel , to our knowledge , a hitherto-unknown mechanism of oncogenesis mediated by a long noncoding RNA and establishes a unique role of Cep112–Brca1 interaction being modulated by Ginir RNA in maintaining mitotic fidelity .
The recent surge of information regarding evolutionary conservation , functionality , and annotation of sequences from the mammalian genome has revealed that a bulk of the transcriptome is noncoding and includes small and long noncoding RNAs ( lncRNAs ) . FANTOM data sets of the human and mouse transcriptomes have emphasised that about 63% of the mammalian genome is pervasively transcribed , even from retrotransposon elements , and amongst them , more than 73% of genes show some form of antisense transcription [1 , 2] . The advances made on the analyses of various transcriptomes followed by identification of several lncRNAs have led to the exposition of their regulatory roles in transcription [3] , posttranscriptional gene silencing ( PTGS ) [4] , organogenesis [5] , pluripotency , reprogramming , differentiation [6] , and also epigenetic regulation [7] . A multitude of lncRNAs have pathological roles in a variety of disease processes [8] . In particular , deregulations of lncRNAs have been correlated to several diseases that include α-thalassaemia [9] , myotonic dystrophy [10] , Alzheimer disease [11] , spinocerebellar ataxia type 8 [12] , and also various cancers that include tumours of the central nervous system ( CNS ) [13] , mammary gland [14] , colon [15] , skin [16] , lung [17] , and many more . Furthermore , the expression profiles of lncRNAs are considered to serve as biomarkers for cancer diagnosis [18] . Close to more than 500 temporally expressed S-phase-enriched lncRNAs have been recently identified from HeLa cells with potential prognostic value and oncogenic potential [19] . The present knowledge about diverse functions mediated by many lncRNAs , especially those implicated in gene regulation , range from them functioning as a decoy like Tsix [20] , to their role in alternative splicing like metastasis-associated lung adenocarcinoma transcript 1 ( MALAT1 ) [21] , to a few acting as a scaffold like P21-associated noncoding RNA DNA damage-activated ( PANDA ) [22] or as a sponge like taurine up-regulated gene 1 ( TUG1 ) [23] . A multitude of lncRNAs regulate transcriptional networks by competing for a limited pool of microRNAs and thereby function as competing endogenous RNAs ( ceRNAs ) like hepatocellular carcinoma up-regulated lncRNA ( HULC ) , cAMP response element binding protein ( CREB ) [24] , differentiation antagonizing non-protein-coding RNA ( DANCR ) [25] , H19 [26] , and many more [27] . Recently , noncoding RNAs have been identified that act as enhancer RNAs , like Bloodlinc [28] , or function as allosteric modulators , like CCND1 [29] . The varied mechanisms through which they mediate gene regulation range from chromatin remodelling by either binding to polycomb repressive complex 1/2 ( PRC1/2 ) [30] or by forming RNA–DNA triplexes [31] , to acting as competitive inhibitors of target protein/RNA interactions [31] . The lncRNAs can either directly interact with proteins or they can sequester proteins that are master regulators of cellular growth . We here report the identification of an acutely transforming cDNA derived from the transcriptome of the mouse melanoma cells—Clone M3 . This cDNA , upon transfection into mouse NIH/3T3 fibroblasts , formed foci of neoplastically transformed cells . The characterisation of the sequence of this acutely transforming gene was found to encode an lncRNA . We named this RNA as Genomic Instability-Inducing RNA ( Ginir ) because it exerted a regulatory role on cell proliferation and maintained genomic stability under conditions of normal cellular homeostasis . An increased expression level of Ginir in mouse fibroblast cells induced genomic instability and oncogenic transformation . Here , we provide a mechanistic insight about the oncogenic role of noncoding RNA Ginir in mouse cells . Our data exemplify that ( i ) Ginir RNA function is modulated by its full-length natural antisense transcript ( NAT; Giniras ) , ( ii ) Ginir RNA targets centrosomal protein 112 ( Cep112 ) and alters its subcellular localisation by binding to it , ( iii ) breast cancer type 1 susceptibility protein ( Brca1 ) and Cep112 proteins interact with each other in the absence of Ginir noncoding RNA , ( iv ) interaction of Cep112 protein with Brca1 protein is impaired in the presence of high levels of Ginir RNA , and ( v ) interference of Cep112–Brca1 interaction due to increased presence of Ginir RNA causes replicative stress and induces mitotic dysregulation , causing genomic instability , and thereby propels cells towards malignant transformation .
This transforming cDNA sequence was unconventional because it encoded three putative ORFs , each with a short sequence of amino acids ( aa ) ( ORF1–46 aa , ORF2–51 aa , and ORF3–104 aa ) ( S1C Fig ) . Of these three ORFs , only ORF1 and ORF2 were expressed as an N-terminal green fluorescent protein ( GFP ) fusion protein of sizes 38 . 1 and 39 . 1 kDa , respectively ( S1D Fig ) . However , the ectopic expression of each these two putative proteins neither yielded any discernible phenotype nor showed any oncogenic transformation in vitro or in vivo , as seen by the tumourigenicity assay in immune-compromised mice ( S1E Fig ) . Moreover , the expression of the sequence of 557 bases as a fusion RNA appended either to the 5′ or 3′ UTR of GFP was highly tumourigenic ( S1E Fig ) . These results together ruled out the possibility that the two distinct ORFs—ORF1 and ORF2—could specify oncoproteins . Instead , it suggested that the sequence of 557 nucleotides itself was functioning as a noncoding oncogenic RNA . The noncoding nature of this sequence was confirmed using software tools like Coding Potential Assessment Tool ( CPAT ) ( S1F Fig ) and Coding Potential Calculator ( CPC2 ) ( S1G Fig ) . The sequence of this transcript was extended using 5′ and 3′ rapid amplification of cDNA ends ( RACE ) in both directions to obtain a 612-nucleotides-long extended sequence ( Fig 1A and 1B ) . The 612-base-sequence data deposited at the National Center for Biotechnology Information ( NCBI ) bear the accession number EF649772 . 1 . The ectopic expression of 612 nucleotides’ RNA in NIH/3T3 cells also caused oncogenic transformation like its 557-nucleotides-long counterpart . BLAST analyses indicated that the 612-nucleotides-long transcript sequence originated from an intronless genomic segment located at position A6q of the X chromosome ( Fig 1C ) , and it was flanked by the genes coding for the proteins Leucine Zipper down-regulated in cancer-1 ( LDOC1 ) and the melanoma-associated antigen-11 on its 5′ and 3′ ends respectively ( Fig 1D ) . Its coordinates on mouse X chromosome were chrX:61 , 982 , 243–61 , 982 , 854 ( https://genome . ucsc . edu; GRCm38/mm10 Assembly ) . The sequence was found significantly conserved in the rat genome ( Fig 1C and 1E ) . Because the 612-nucleotides-long RNA originated from an independent transcription unit having no overlap with these two adjacent protein-coding genes , its identity became obvious as lincRNA . Bioinformatic analyses of Ginir sequence demonstrated its part homology to several unannotated noncoding RNAs ( S2A Fig ) . More significantly , a deregulated overexpression of this lincRNA resulted in genomic instability ( see the section Ginir RNA expression in cells induces dsDNA breaks and activates DNA damage response ( DDR ) pathway proteins ) accompanied by oncogenic transformation , demonstrating that it was functioning as a Ginir . Homology search data to Ginir sequence ( http://www . ensembl . org/; http://blast . ncbi . nlm . nih . gov ) demonstrated that the Ginir sequence was partially homologous to several expressed sequence tags ( ESTs ) apparently relevant to neurogenesis and especially to those that were specifically found during embryogenesis ( Figs 1C and 2A ) . This prompted us to examine if Ginir RNA was being expressed in cultured cells of embryonic origin like in NIH/3T3 cells and was also expressed during mouse embryonic development . When RNase protection assay ( RPA ) using RNA isolated from NIH/3T3 cells was performed using Ginir-specific sense and antisense hybridisation riboprobes , we found a completely overlapping antisense RNA to Ginir ( Giniras ) being transcribed ( Fig 2B ) . Further , employing an orientation-specific polymerase chain reaction ( PCR ) amplification approach in which strand-specific primers for cDNA synthesis were used , we yet again obtained a predominant expression of the Giniras transcript in NIH/3T3 cells ( S2C Fig ) . In contrast , a weaker expression of Ginir RNA was evident under similar culture conditions in NIH/3T3 cells by reverse transcription polymerase chain reaction ( RT-PCR ) ( S2C Fig ) as well as by RPA ( Fig 2B ) . Though not well represented , the RNA sequencing ( RNA-seq ) analysis of total RNA from NIH/3T3 cells generated sequence reads that matched the genomic sequence of Ginir region on the X chromosome ( S1 Table , S2D Fig ) . The poor representation of lncRNA sequences in the RNA-seq data is reported for several lincRNAs , and this is often ascribed to their low expression levels , spatiotemporal expression pattern , and near-universal alternative splicing of noncoding exons [35] . With the finding that the Ginir RNA sequence predominately displays homology to several ESTs that are expressed during mouse embryonic development , it is possible that Ginir RNA expression might also have a characteristic spatiotemporal expression profile as seen with many other lncRNAs [36 , 37] . We therefore isolated RNA from developmentally timed whole mouse embryos and determined the expression of both Ginir and Giniras transcripts in them . The Ginir transcript was detectable during the embryonic developmental stages of 5 . 5 to 9 . 5 days and again at 13 . 5 days post coitum ( dpc ) . By contrast , the Giniras transcript was clearly present in the intervals of 10 . 5 to 12 . 5 dpc ( Fig 2C ) . Instead , a more prominent Giniras RNA expression relative to Ginir RNA expression was evident in some of the adult mouse tissues examined , which included brain , heart , lungs , spleen , pancreas , and kidney ( Fig 2D ) . These findings are consistent with an argument that Ginir RNA expression is more tightly coupled to proliferative stages of cells during embryonic development , whereas predominant Giniras RNA expression is associated with nonproliferative cells of the organs . When fluorescein amidite ( FAM ) -labelled Ginir-specific locked nucleic acid ( LNA ) probe ( FAM-LNA-Ginir ) and Texas Red–labelled Giniras-specific LNA probes ( TxR-LNA-Giniras ) were used on the whole embryos individually or together , it became clear that expression of Ginir , as well as Giniras RNA , was differentially regulated in various differentiating tissues during embryonic development . In 10 . 5- , 13 . 5- , and 14 . 5-dpc mouse embryos ( Fig 2E and 2F ) , the presence of both Ginir and Giniras transcripts was detectable in appreciable amounts , particularly in the developing ventricles of the brain and forelimb , signifying that both these transcripts may have functions in the development of these tissues . Whereas Giniras transcript was found exclusively localised to emerging forelimb buds by stage 10 . 5 dpc , Ginir RNA was differentially localised to the spinal cord ( Fig 2E ) . By stage 13 . 5 dpc , a higher abundance of both Ginir and Giniras transcripts was marked in both brain and developing forelimb buds ( Fig 2F ) ; this colocalised expression pattern of Ginir and Giniras RNA was restricted to specific regions of the forebrain and midbrain and persisted until 14 . 5 dpc ( Fig 2F ) . Taken together , these findings demonstrated that Ginir was specifically expressed during embryonic development of brain , spinal cord , and forelimb buds at early to midgestational stages , whereas the Giniras expression followed temporally to Ginir function in developing tissues and significantly was more prominent in several adult tissues . These observations are in accord with reports that the expression of lncRNAs are spatially precise and are often restricted to particular structures or cell types of the brain [38] . It is well established that the brain is a complex organ and harbours the most transcriptional diversity amongst other somatic tissues [35] . To address the function of the individual transcripts of the noncoding RNA pair Ginir and Giniras , we took advantage of our prior observations ( S1A Fig ) that had demonstrated the foci-forming potential of Ginir RNA upon its ectopic expression in NIH/3T3 cells . To gain a mechanistic insight into their role in cell transformation , we generated stable cell lines of NIH/3T3 cells that overexpressed either the individual transcripts of Ginir or Giniras or both in combination ( Ginir+Giniras ) from the strong cytomegalovirus ( CMV ) promoter . The clone of cells stably expressing Ginir RNA ( NIH/3T3-Ginir ) had 16-fold-higher levels ( P ≤ 0 . 0001 ) of Ginir transcript over endogenous level of expression in NIH/3T3 cells ( S3A Fig ) . Similarly , the stable clone of cells expressing Giniras RNA ( NIH/3T3-Giniras ) had 12-fold-higher amounts of Giniras transcript than its endogenous level of expression in NIH/3T3 cells ( P ≤ 0 . 001 ) . The RPA performed using NIH/3T3-Ginir cells confirmed overexpression of Ginir transcript as compared to Giniras ( S3B Fig ) . We performed multiple transfections ( n = 5 ) to generate several independent stable cell lines of NIH/3T3-Ginir , NIH/3T3-Giniras , and NIH/3T3-Ginir+Giniras overexpressing either transcript or both . For comparison , clones were generated with only the empty vector ( NIH/3T3-EV ) , which served as the control . A few of the multiple independent clones of NIH/3T3-Ginir ( termed here as clones A , B , and C ) were randomly picked up for further studies . The expanded population of cells from these individual clones shared the following properties: they ( i ) appeared highly refractile , ( ii ) had lost contact inhibition ( Fig 3A ) , ( iii ) had a higher proliferative potential as measured by 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) assay ( Fig 3B ) , and ( iv ) showed a higher percent of positivity for the proliferation marker Ki67 as compared to the cells derived from NIH/3T3-EV , NIH/3T3-Giniras , or NIH/3T3-Ginir+Giniras clones ( S3C Fig ) . The NIH/3T3-Ginir+Giniras transfectant clones showed proliferative potential comparable to NIH/3T3-EV cells ( Fig 3C ) . We picked the expanded cells of NIH/3T3-Ginir ( Clone A ) for further analysis in vitro . Fluorescence-activated cell sorting ( FACS ) analysis of propidium iodide ( PI ) -stained cells of NIH/3T3-Ginir ( Clone A ) demonstrated an increased fraction of cycling cells ( S3D and S3E Fig ) as compared to NIH/3T3-EV cells . Clone A transfectant cells expressed higher levels of phosphorylated retinoblastoma protein ( pRb ) , had increased levels of cyclins D1 and E ( S3F Fig ) , showed increased clonogenicity in soft agar ( S3G Fig ) , and displayed higher invasion potential in a Matrigel assay ( S3H Fig ) . The NIH/3T3-Ginir cells also demonstrated an increased migration rate in wound healing assay ( S3I and S3J Fig ) and exhibited a pronounced angiogenic potential in a chicken chorioallantoic membrane ( CAM ) experimental system ( S3K Fig ) . Subcutaneous introduction of NIH/3T3-Ginir ( A ) cells rapidly formed malignant tumours in NOD/SCID mice at the injected sites within 2 weeks of injections ( Fig 3D–3G ) , whereas mice injected with NIH/3T3-EV or NIH/3T3-Giniras cells did not develop tumours up to 95 days post injection ( Fig 3D and 3E ) . When the tumour forming potential of NIH/3T3-Ginir+Giniras cells was examined , they formed tumours of substantially reduced sizes ( P < 0 . 0001 ) and with a delayed onset ( Fig 3F and 3G ) . Kaplan-Meir survival analysis of NIH/3T3-Ginir-induced tumour-bearing mice demonstrated a median survival of 75 days ( S3L Fig ) , whereas mice injected with NIH/3T3-EV and NIH/3T3-Giniras cells survived healthily for prolonged periods ( >95 days; P ≤ 0 . 0001 ) . Collectively , these results indicate that Ginir functions as a dominant oncogene in mouse cells . A number of noncoding RNAs such as urothelial cancer associated 1 ( UCA1 ) , MALAT1 , H19 , and plasmacytoma variant translocation ( PVT1 ) have been implicated in the promotion of metastasis of tumour cells , but evidence for a more direct role of these RNAs in this process is still lacking [39] . Our findings that the NIH/3T3-Ginir cells had an enhanced migration potential in both 2D and 3D matrix assays in vitro led us to investigate whether Ginir RNA–induced tumour cells would also have detectable metastatic potential in a lung colonisation assay . To assay metastasis , we performed a standard tail vein assay [40] . For this , we introduced cells grown out from NIH/3T3-Ginir ( Clones A and B ) and NIH/3T3-Giniras cells through the tail veins of NOD/SCID mice . In addition , we also examined if the same cells could metastasise or colonise to any distant tissue when introduced through subcutaneous injections . These experiments demonstrated that NIH/3T3-Ginir cells generated numerous macroscopic foci of tumour cells in the lungs of the injected mice ( Fig 3H and 3I ) within 6–8 weeks post injection of cells . This was also confirmed by the haematoxylin–eosin ( HE ) staining of the lung tissues of NIH/3T3-Ginir cells ( Fig 3J and 3K ) . These data contrasted with the results obtained from mice injected with similar numbers of NIH/3T3-EV or NIH/3T3-Giniras cells , which did not colonise tumour foci to lungs or any other tissues ( Fig 3H and 3I ) up to 12 weeks . When NIH/3T3-Ginir cells ( from Clones A and B ) were introduced subcutaneously , not only did tumours develop at the injected site but also the colonisation of lungs in the injected mice could be seen by 11 weeks ( S3M Fig ) , which was also confirmed by HE staining of the lung tissues ( S3N Fig ) . These data demonstrated the potential of Ginir RNA to act as a metastasis-promoting noncoding RNA . In summary , our data establish that lncRNA Ginir functions as a metastasis-inducing oncogenic RNA . To uncover differential gene expression , if any , we performed transcriptome sequencing and compared the RNA profiles of NIH/3T3 , NIH/3T3-Ginir ( A ) , NIH/3T3-Giniras , and Clone M3 cell lines . High-quality sequence reads were obtained with all the samples as shown in S4A and S4B Fig , and a heatmap generated from the expression data is shown in S4C Fig . The differential expression of RNA from NIH/3T3-EV cells in comparison with NIH/3T3-Ginir ( A ) cells indicated that 555 genes were significantly ( P ≤ 0 . 05 ) altered ( S4D and S4E Fig ) . Gene Ontology ( GO ) analysis of the gene data sets and heatmap demonstrated that the enrichment terms were mainly concentrated to pathways important for cell cycle progression , cell division , RNA splicing , RNA processing , and cell–cell adhesion ( S4F and S4G Fig ) . An analysis of the cellular components involved in these processes indicated that most of these genes were localised to the centromeric regions of the chromosomes , the kinetochores , and the cell–cell adherens junctions ( S4H and S4I Fig ) . These RNA-seq differential expression data suggested the role of Ginir in cell division and mitosis . Next , we asked if a high level of Ginir expression was continuously required for the induction and/or maintenance of the transformed state or whether it involved a hit-and-run mechanism . To find this , we down-regulated Ginir expression in NIH-Ginir ( A ) and NIH-Ginir ( B ) cells by superimposing the expression of Ginir-specific short hairpin RNAs ( shRNAs ) . The shRNAs were designed to target specifically two different regions of Ginir RNA sequence ( sequences as specified in Materials and methods ) and were termed as shGinir1 and shGinir2 . Stable cell line clones expressing these shRNAs were generated , and expanded cells from them were used for further studies . We found that the Ginir RNA expression level had significantly decreased with shGinir1 and shGinir2 knock-down constructs in NIH/3T3-Ginir cells ( Fig 4A ) . The Ginir RNA–deficient NIH/3T3-Ginir ( A ) cells displayed a reversal of their refractive cell morphology and appeared more like NIH/3T3 cells ( Fig 4B and S5A Fig ) . In addition , the same cells had a reduced proliferative potential in an MTT assay ( Fig 4C ) , a lower fraction of cycling cells in the S+G2/M phase ( S5C Fig ) , a decreased percent of positivity for Ki67 ( Fig 4D and S5B Fig ) , and an impaired 2D cell migration potential in culture ( Fig 4E ) . Importantly with shRNA-mediated Ginir RNA depletion , the cells formed smaller-sized tumours as compared to the parent NIH/3T3-Ginir ( A ) cells in the tumourigenicity assays done in NOD/SCID mice ( P ≤ 0 . 0001 ) ( Fig 4G and 4H ) . The cells derived from the NIH/3T3-Ginir ( B ) clone also showed similar phenotypes and reduced tumourigenicity after the Ginir RNA levels were independently knocked down by introducing Ginir shRNAs 1 and 2 into them ( S5D Fig ) . Thus , when Ginir RNA was down-regulated , it caused significant attenuation of tumour phenotype , indicating that a high level of Ginir RNA expression was continuously required for the maintenance of malignant transformation . Taken together , these data provide unambiguous evidence about the oncogenic nature of Ginir . To address the role of endogenous Ginir RNA , we used two mouse cell lines; one was NIH/3T3 , and the other was B16F10 melanoma cell line . As stated previously , NIH/3T3 cells have basal levels of Ginir RNA expression , whereas B16F10 melanoma cells express high levels of Ginir RNA . We generated Ginir-deficient cell lines of both of them by expressing shRNA1 and shRNA2 in them . The down-regulation of Ginir RNA in NIH-shRNA1 and NIH-shRNA2 cells was confirmed by RT-PCR ( Fig 5A ) . The NIH-shRNA Ginir1 and NIH-shRNA Ginir2 cells demonstrated a lower proliferative rate in MTT assay ( P ≤ 0 . 0001 ) ( Fig 5B ) and had decreased levels of Ki67 antigen expression ( P ≤ 0 . 05 ) as compared to control cells generated by introducing a scrambled shRNA ( NIH-shRNA Control ) ( Fig 5C and S6A Fig ) . Down-regulation of Ginir RNA altered the cell cycle kinetics by decreasing the population of cycling cells in the S+G2/M phase and increasing the proportion of cells in the G0/G1 phase ( Fig 5D ) . These data in combination with the Ginir RNA overexpression data cited earlier provide evidence for a role of Ginir RNA in maintaining homeostasis in the cell proliferation dynamics . Particularly in NIH/3T3 cells , moderate Ginir RNA expression positively regulated cell proliferation , and its reduced level was favourable for slow cycling , whereas its overexpression perpetuated unregulated growth and cell transformation . We also generated Ginir-deficient B16F10 melanoma cells by the same procedure followed for NIH/3T3 cells and confirmed the down-regulation of Ginir RNA in these cells by RT-PCR ( Fig 5E ) . We used the Ginir RNA–deficient B16F10 cells to compare the growth and transforming potential of these cells with their parental B16F10 cells . The Ginir-deficient B16F10 cells exhibited a more differentiated morphology ( Fig 5F ) , showed reduced proliferative potential in MTT assay ( Fig 5G ) , possessed a lower fraction of cells expressing KI67 antigen ( Fig 5H and S6B Fig ) , and demonstrated decreased cell migration in wound healing assay ( Fig 5I and 5J ) . B16F10 cells are highly tumourigenic and metastatic in nature . Consistent with our tumour data obtained with NIH/3T3-Ginir cells and their NIH-GinirshRNA derivative cells displaying contrasting growth kinetics , we found that these experiments with B16F10 melanoma cells showed similar retardation of tumour growth ( Fig 5K and S6C Fig ) . The reduced tumour size could result from a delayed initiation of tumour growth ( S6C Fig ) or could be an outcome of a decreased tumour volume due to an overall reduction in the tumour growth kinetics ( Fig 5L ) . Ginir knock-down in B16F10 cells affected melanin production , resulting in reduced pigmentation ( S6D Fig ) as compared to B16F10 cells . These data make us speculate that besides proliferation , Ginir RNA may also be involved in pigmentation . To understand the function of Ginir RNA in cell transformation , it is important to know its subcellular localisation within the cells . To gain insight into this , we obtained RNA from the nuclear and cytoplasmic compartments of Clone M3 , NIH/3T3-EV , NIH/3T3-Ginir , and NIH/3T3-Giniras cells . By using strand-specific primers for cDNA synthesis followed by RT-PCR , we measured Ginir and Giniras RNA levels in these two compartments . The Ginir RNA was found to be more abundant in the nuclear compartment of Clone M3 ( Fig 6A ) and NIH/3T3-Ginir cells ( Fig 6C ) , whereas it was enriched in the cytoplasm of NIH/3T3-EV ( Fig 6B ) and NIH/3T3-Giniras cells ( Fig 6D ) Furthermore , by in situ hybridisation using two independent Ginir sequence–specific LNA probes , we observed a more prominent partitioning of Ginir RNA to the nucleus ( Fig 6E–6H ) in NIH/3T3-Ginir cells , providing a direct support to the data obtained by RT-PCR experiments . Secondly , the localisation of Ginir RNA seen with the same two fluorescence in situ hybridisation ( FISH ) probes was specific , since a much reduced nuclear staining was observed in the Ginir knock-down cells ( Fig 6E and 6F ) . Cells treated with RNase A lacked any hybridisation fluorescence signal , confirming the specificity of binding of LNA probes to Ginir RNA but not to DNA ( S7A and S7B Fig ) . Like the data obtained with RT-PCR , the Giniras RNA localisation by LNA probe was seen mainly in the cytoplasmic compartment ( S7C Fig ) . In B16F10 melanoma cells , the Ginir RNA showed a prominent localisation to the nuclear compartment ( Fig 6E–6H ) . In summary , we demonstrate that the Ginir and Giniras RNAs are primarily compartmentalised to the cytoplasm in normal cells; however , Ginir RNA is mainly partitioned to the nuclear compartment in transformed cells . As a part of dsDNA damage response , ataxia–telangiectasia-mutated ( ATM ) kinase phosphorylates several downstream target proteins , which include the histone protein H2Ax , converting it to γH2AX [41] . We found that Ginir RNA overexpression either transiently or stably in NIH/3T3 cells increased the levels of γH2AX ( Fig 7A ) ; also evident were high numbers of repair foci in their nuclei ( Fig 7B and 7C ) . In comparison , such repair foci were considerably less in NIH/3T3-Giniras and NIH/3T3-EV cells . The observation that even transient expression of Ginir RNA in NIH/3T3 cells caused an increased expression of γH2AX indicated that the increase was due to a primary effect caused by Ginir expression and was not due to secondary effects of cell transformation . This notion was further supported by the observations that the number of γH2AX repair foci significantly decreased after Ginir expression was knocked down , as evident in NIHGinir-shGinir1 and shGinir2 cells ( Fig 7D–7F ) . Consistent with the increased level of γH2AX foci , NIH/3T3-Ginir ( A ) or NIH/3T3-Ginir ( B ) cells showed activation of member proteins involved in DDR , which included meiotic recombination 11 ( Mre11 ) , Rad52 , p53 binding protein 1 ( 53BP1 ) , ATM/ataxia–telangiectasia and Rad3-related kinase ( ATR ) -Substrate , pATM , p53 , and p21 . ( Fig 7G ) . The DDR elicited by Ginir was further corroborated using a more direct DNA damage assay such as the comet assay , which demonstrated the presence of DNA damage signatures in NIH/3T3-Ginir cells ( Fig 7H ) . A significant finding was that NIH/3T3-GinirA , B , and C independent transfectant lines show lower amounts of Brca1 protein in them compared to NIH/3T3-EV cells ( Fig 7I ) . Also , these cells accumulated a higher level of nuclear p53 and exhibited cytoplasmic expression of p21 protein , which are known to work together to impair DNA repair , compromise the tumour suppressor function of p53 , and promote genomic instability . Thus , a higher Ginir RNA expression led to activation of DDR , resulting in increased cell survival , as no apoptosis was detected , and cells continued cycling with error-prone DNA synthesis . Taken together , our data indicate that Ginir overexpression leads to genomic instability . The NIH/3T3-Ginir cells always showed a preponderance of large numbers of multinucleated giant cells ( Fig 8A ) , which were rare in number in NIH/3T3-EV and NIH/3T3-Giniras cells and were significantly reduced upon Ginir knock-down in NIH/3T3-Ginir cells ( Fig 8A and 8B , and S8A Fig ) . The down-regulation of Ginir RNA in NIH/3T3-Ginir cells led to restoration of phenotype to that exhibited by NIH/3T3-EV cells , highlighting the specificity of giant cell induction by Ginir RNA ( Fig 8A and 8B and S8A Fig ) . The giant cell formation was prominently visible using the antibody directed against kinesin family member 20b ( Kif20b ) , a microtubule-associated protein marker ( Fig 8C ) . In addition , the in situ localisation of Ginir RNA using LNA-FISH probes demonstrated a distinct Ginir RNA compartmentalisation to the nuclei of the transformed cells ( Fig 8D ) as well as to the multinucleated giant cells ( Fig 8E and S8C Fig ) . Strong oncogenic signals are known to induce senescence , and it is possible that the giant cells arose because of Ginir RNA . The giant cell formation in NIH/3T3-Ginir cells was found unrelated to the normal senescence response by the criteria that these cells were not growth arrested and showed the expression of the proliferation marker Ki67 ( Fig 8F ) . Furthermore , defects in spindle formation along with abnormal amplification of centrosome numbers were evident in multinucleated giant cells of NIH/3T3-Ginir cells ( Fig 8G ) . These features appeared specific to high Ginir RNA expression in these cells , since the NIHGinir-shGinir1 and NIHGinir-shGinir2 cells did not exhibit these defects ( S8A Fig ) . A large number ( approximately 40% ) of NIH/3T3-Ginir cells showed greater than 2 centrosomes per cell , and this abnormality in centrosome numbers induced because of Ginir were evident by staining of these cells with centrosomal markers like ɣ-tubulin ( Fig 8H ) and aurora-related kinase 1 ( Ark1 ) ( Fig 8I ) . The numerical effects on centrosome numbers ( >2 centrosomes/cell ) induced by Ginir overexpression were quantified and occurred in a NIH/3T3-Ginir cell population to a larger extent ( approximately 45%–50% ) ( Fig 8J ) as compared to control cells ( P ≤ 0 . 0001 ) . These defects in centrosome numbers were evident in both interphase ( Fig 8H and 8I ) and metaphase cells ( Fig 8K ) . More notably , knock-down of Ginir caused reversal to regulated mitosis manifested in terms of formation of regular bipolar spindles as evident by Ark1 staining ( Fig 8G ) . Thus , high levels of Ginir RNA expression apparently accelerated karyokinesis but retarded cytokinesis , thereby generating multinucleated giant cells . The accelerated karyokinesis that was desynchronised from cytokinesis represented another manifestation of the failure of cell cycle arrest at spindle checkpoint , triggering a premature and abnormal spindle dynamics . A low Ginir RNA level may be necessary to maintain a normal and synchronised spindle dynamics , karyokinesis , and cytokinesis within these cells . Defects in cytokinesis and karyokinesis are known to induce chromosomal and genomic instability [42] . In summary , these data show that excess Ginir RNA expression causes centrosomal defects and genomic instability , leading to mitotic dysregulation . A multitude of lncRNAs are known to mediate their functions by interacting directly with specific protein targets within cells . To determine whether such target proteins could be identified for Ginir RNA , we followed the approach of using biotin-labelled RNA affinity pull-down and RNA-immunoprecipitation ( RIP ) assays . In the pull-down assay , 5′-biotin-labelled full-length Ginir RNA was incubated with protein lysates prepared from various cell types for possible interactions to occur , and then , the proteins bound to Ginir RNA were pulled down by streptavidin capture beads . This strategy is detailed in Fig 9A . The bound proteins recovered from the pull-down complexes were identified by mass spectrometry ( S9A Fig and S2 Table ) . Frequently detected interacting proteins of Ginir RNA obtained from NIH/3T3 , NIH/3T3-Ginir cells , and mouse embryonic brain tissues were shortlisted on the basis of their high confidence index for matrix-assisted laser desorption ionisation time-of-flight mass spectrometry ( MALDI-TOF ) and sorted on the basis of their computed interaction scores using bioinformatics tools like RPISeq [43] and catRAPID [44 , 45] ( S9B Fig and S1 Text ) . The data from multiple independent experiments using cells from different tissue sources and cultured cell lines consistently demonstrated Cep112 , a centrosome-associated protein , as a strong interacting partner for Ginir RNA , as it had the highest interaction score ( S1 Text ) . Cep112 ( the centrosomal protein of 112 kDa ) , also known as Ccdc46 or Macoco , is a centrosomal protein with ATPase domain and has 18 possible splice variants that can give rise to 12 protein isoforms ( S9C Fig ) . We found that NIH/3T3 cells expressed multiple isoforms of Cep112 , and most prominent amongst them were the 112- , 66- , and 28-kDa isoforms ( Fig 9B and S9C Fig ) . This raised the question if some or all the Cep112 isoforms were interacting with Ginir RNA . The biotin RNA pull-down assays identified one prominent interacting isoform of 112-kDa size for Ginir RNA ( Fig 9C and S9D Fig ) . Another isoform of Cep112 was of 66-kDa size , and it also showed interaction with Ginir , though detection of this isoform expression varied with the antibody source used ( S9D Fig ) . The interaction of Cep112 appeared specific to Ginir RNA , as other noncoding RNAs like HOX transcript antisense RNA ( Hotair ) and Giniras failed to interact with the same protein ( Fig 9C and S9D Fig ) . Similarly , another unrelated RNA from Xenopus species , termed as the Xenopus elongation factor ( XEF ) RNA , failed to interact with Cep112 protein ( Fig 9C ) . To eliminate the possibility of Ginir RNA binding nonspecifically to any protein , we performed blotting with β-tubulin antibody and found that there was no interaction of Ginir RNA with β-tubulin ( Fig 9C and S9D Fig ) . This series of pull-down experiments clearly demonstrated the specificity of interaction of Ginir RNA with centrosomal protein Cep112 . The important components of the DNA damage response pathway are the twin repair proteins Brca1 and Brca2 . Loss-of-function mutations in Brca1 and Brca2 result in increased mutation rates and induction of genomic instability [46 , 47] . Low Brca1 protein expression levels are frequently found in various transformed cells or tumour cells . Consistent with this observation , we found a decreased expression level of Brca1 protein in NIH/3T3-Ginir cells as compared to NIH/3T3-EV cells ( Fig 7I ) . We therefore sought to determine whether a low level of Brca1 expression in these cells was induced because of high levels of Ginir RNA expression . For this , we performed biotin RNA pull-down experiments using biotinylated Ginir RNA followed by immunoblotting with Brca1 antibody . In these experiments , a physical interaction of Ginir with Brca1 in vitro could be detected ( Fig 10A and S10A Fig ) in elute II but not in elute I ( Fig 10A ) . The interaction was specific , as it was seen only with Ginir RNA but not with other noncoding RNAs like Hotair and Giniras and mRNA like XEF ( S10A Fig ) . Similarly , with RIP performed using two independent NIH/3T3-Ginir RNA expressing clones A and B , we obtained evidence for a weaker interaction of Ginir RNA with Brca1 as compared to Cep112 ( Fig 10B and 10C ) . The specificity of interaction was further confirmed using U6 small nuclear RNA ( snRNA ) as a control ( Fig 10D ) . Besides its involvement in DNA repair , Brca1 protein has roles in centrosome function , and in cooperation with other centrosomal proteins , it plays a vital role in the nucleation and assembly of microtubules during chromosomal segregation [48] . For instance , Brca1 protein regulates centrosome duplication and cytokinesis by interacting with other centrosomal proteins like Obg-like ATPase 1 ( OLA1 ) [49] , ninein-like protein ( Nlp ) [50] , and γ-tubulin [51] . These reports and our data prompted us to examine if interactions of Ginir RNA occur with either Cep112 and Brca1 proteins independently of each other or whether the Cep112 protein interacts with Brca1 protein . To determine this , we performed protein docking analysis using ZDOCK tool and found significant docking scores suggestive of a stable interaction between the C-terminal of Brca1 protein with a domain near the C-terminal region of Cep112 protein ( Fig 10E ) . Next , to investigate the interaction between Cep112 and Brca1 proteins , we performed coimmunoprecipitation of Cep112 with Brca1 ( Fig 10F ) and vice versa ( Fig 10G ) . Here , we found a strong interaction between these two proteins in NIH/3T3 cells . In another set of experiments , we overexpressed Flag-Cep112 in NIH/3T3 cells and obtained stable clones of cells expressing Flag-Cep112 protein , as was confirmed by western blotting ( Fig 10H and 10I ) . The interaction was further validated in Flag-Cep112-expressing cells , wherein we demonstrated coimmunoprecipitation of Brca1 with Flag tag antibody ( Fig 10J ) and vice versa ( S10B Fig ) . A weaker interaction of Cep112 protein with Brca1 protein was found in NIH/3T3-Ginir ( A ) cells ( Fig 10K ) . In NIHGinir-shGinir RNA knock-down cells , the interaction of Cep112 with Brca1 was significantly restored ( Fig 10K ) . These data strongly indicate that Cep112 and Brca1 proteins interact strongly in NIH/3T3-EV or NIH/3T3-Ginir-shGinir1 knock-down cells , but the interaction is weaker in NIH/3T3-Ginir ( A ) cells , where Ginir RNA expression level is high . The localisation of Brca1 to the centrosomes was distinctly evident by coexpression of Brca1 with centrosomal marker protein γ-tubulin ( Fig 10L ) . Further , we detected interaction of Cep112 protein with Brca1 protein in centrosomes only in the NIH/3T3-EV cells ( Fig 10M ) but not in the NIH/3T3-Ginir ( A ) cells . This could mainly be attributed to the fact that each of these two proteins was mislocalised from the centrosomes in these cells . Besides normal localisation of Brca1 to the nucleus , there was a specific enrichment of Brca1 protein to the centrosomes in NIH/3T3-EV cells . In contrast , in NIH/3T3-Ginir cells , Brca1 was seen mainly in the nucleus but was entirely absent from the centrosomes ( Fig 10N and S10E Fig ) . The bioinformatics tools like CatRAPID also showed high interaction propensity ( = 37 ) as well as high discriminative power ( = 85 ) of Brca1 to bind to Ginir RNA ( S10F Fig ) . By RNA-FISH , we obtained a specific localisation of Ginir RNA to the perinuclear region of the cells that may be centrosome ( Fig 10O ) . These data assume importance and may have significance in proliferation and oncogenesis , as this is the first evidence , to our knowledge , demonstrating interaction of two proteins Cep112 and Brca1 with each other and their dynamic localisation into centrosome , nuclear , and cytoplasmic compartments . Next , we asked ( 1 ) as to how a higher Ginir RNA level induced a lower expression level of Brca1 and Cep112 proteins and ( 2 ) whether interaction of Cep112 protein with Brca1 protein was mediated through Ginir RNA . For this , we prepared cell lysates from NIH/3T3-Ginir cells ( stable cell lines NIH-Ginir A , B , and C ) and divided each of the protein lysates into two parts; one was treated with RNase mix ( A , H , and III ) to deplete RNA from them , and the second part received RNase inhibitor ( RNasin ) to protect the integrity of all RNAs present . When we used a specific Brca1 antibody to pull down Brca1 from each of these two samples , we found that the Brca1–Cep112 protein complex was present in the Rnase-treated cell lysates but was nearly absent in the lysates that were pretreated with RNasin ( Fig 10P and S10C and S10D Fig ) . This demonstrated that binding of an RNA to the Cep112–Brca1 protein complex disrupted their interaction or that this interaction did not occur in the presence of the RNA . In conclusion , we provide a strong evidence that a higher amount of Ginir RNA impairs interaction of Cep112 and Brca1 proteins , leading to their mislocalisation in the cells and thereby resulting in mitotic dysregulation ( Figs 10N and 9G , S9H and S10E Figs ) . To understand as to how Ginir RNA regulates spindle dynamics and to generate independent evidence for the hypothesis of whether a collaboration of Cep112 protein with Brca1 protein is involved in this process , we examined NIH/3T3 cells in which the Cep112 and Brca1 proteins were individually down-regulated using two independent pools of specific small interfering RNAs ( siRNAs ) to each of these proteins ( Fig 11A and 11B ) . Cells that were depleted with Brca1 protein ( NIH-siBrca1 ) showed complete absence of Cep112 protein , whereas cells depleted for Cep112 ( NIH-siCep112 ) showed diminished levels of Brca1 . These observations indicated that both these proteins were involved in their mutual stabilisation , and a knock-down of either proteins affected the cellular level of the other protein ( Fig 11A and 11B ) . Thus , Ginir RNA , by interrupting interaction between these two proteins , destabilised them and decreased their individual levels . A feature that was common to the depletion of these two proteins was that the siBrca1- and siCep112-expressing cells had increased levels of γH2Ax ( Fig 11C ) and had an increased number of repair foci in their nuclei ( Fig 11D–11G ) . Both the NIH-siCep112 ( pool I ) and NIH-siBrca1 ( pool I ) cells , when examined independently , shared a common feature , which was an overactivation of Ark1 , and many cells showed abnormal spindle dynamics , a phenomenon also found in NIH/3T3-Ginir ( A ) cells ( Fig 11H ) . Abnormal effects were also seen on centrosome numbers in NIH-siCep112 ( pool II ) and NIH-siBrca1 ( pool II ) cells , as was evident from ɣ-tubulin staining ( Fig 11I ) . Cells deficient in either Cep112 or Brca1 individually showed an abnormal number of centrosomes . We quantified these data regarding abnormalities in centrosome number and found that almost 40% of the NIH-siCep112 and NIH-siBrca1 cells showed >2 centrosomes ( Fig 11J ) . In summary , we found that Cep112–Brca1 interaction was perturbed upon Ginir RNA overexpression , and this had significant bearing on normal mitotic regulation , a process required for progression of high-fidelity cell division , as depicted in the form of a model in Fig 12 . The same is explained schematically in S11 Fig . The data reported here drive one to recognise the role of Ginir noncoding RNAs in mediating protein–protein interactions and regulating cell division with fidelity . Any dysregulation in these processes has high propensity to culminate in malignant transformation .
Here , we report the identification and functional characterisation of a novel lincRNA , termed Ginir because at high levels of its expression , it induces genomic instability in the target cells , leading to oncogenesis . We also provide mechanistic insight into its role as an RNA-promoting malignant transformation . This lincRNA was identified in Clone M3 mouse melanoma cells by a functional cloning approach , aimed to identify any activated , acutely transforming oncogene that might be functioning in Clone M3 melanoma cells . The expression clone inducing foci of transformed cells after transfection into the indicator NIH/3T3 recipient cells was found to contain a cDNA that was derived from a noncoding RNA named Ginir . Ginir is also expressed in cultured mouse embryonic fibroblast cells such as NIH/3T3 at low levels . However , when expressed at high levels in the same NIH/3T3 cells , it induces genomic instability and promotes cellular transformation . In this respect , it compares favourably with the known acutely transforming oncogenes that code for oncoproteins such as H-ras ( Gly→Val ) , c-myc , et cetera . It is well known that c-myc expression remains at low levels in normal cells , but when the expression level of the c-myc protein is increased because of promotion of its transcription and translation by chromosomal translocations or retroviral promoter integration near to the gene , cellular transformation is promoted [52 , 53] . We found that Ginir RNA coexisted with a full-length antisense RNA ( Giniras ) in normal and malignant cells , suggesting that Giniras RNA may be a natural regulator of Ginir RNA functions in vivo . Ectopic expression of Giniras RNA using strong promoters did not show any special phenotype in NIH/3T3 cells per se , but when its overexpression was engineered in Ginir RNA–induced transformed NIH/3T3 cells , its oncogenic phenotype was blunted , supporting its role as a natural regulator of Ginir RNA function in vivo . We found evidence for Ginir RNA to have normal cellular functions , especially during mouse embryonic development , exhibiting a profile of expression that appeared to be developmentally regulated in a spatiotemporal manner . Since the mammalian transcriptomes are rich in noncoding RNAs because of pervasive transcription , and since only about 2% of the noncoding RNAs are functionally annotated so far , it is very likely that many more lncRNAs will be found to act as protooncogenes . A gap exists in our knowledge about whether the majority of lncRNAs exhibit their biological function by following one or more mechanistic pathways or there are overlaps , or whether they elicit their transforming potential using mechanisms that are entirely distinct from those of protein-coding oncogenes . Hence , it is important to distinguish these RNA-mediated mechanisms from those of protein-mediated mechanisms of oncogenesis . Certainly , the mechanisms through which noncoding RNAs participate in transformation and tumour progression are varied and complex . Therefore , it is important to dissect out whether the lncRNAs regulate gene expression by functioning in cis or whether they act in trans . One of the recently reported Cancer Testis noncoding RNA , Thor , was shown to function as an oncogene by binding to an RNA-binding protein ( RBP ) , insulin-like growth factor 2 mRNA-binding protein 1 ( IGF2BP1 ) , causing stabilisation of its target mRNAs like insulin-like growth factor 2 ( IGF2 ) and CD44 [54] . Another noncoding RNA , hypoxia-inducible factor 1-alpha ( HIF-1a ) coactivating RNA ( HIFCAR ) , was shown to exert its oncogenic role as an HIF-1a coactivator and thereby regulate the HIF-1 transcriptional network , crucial for cancer development [55] . Another noncoding RNA , noncoding RNA activated by DNA damage ( NORAD ) , was induced after DNA damage and was shown to maintain genomic stability by sequestering PUMILIO proteins , which repressed the stability and translation of mRNAs involved in mitosis and DNA repair [56] . Most often , trans-acting lncRNAs function by modulating the activity or abundance of proteins or RNAs to which they directly bind . A key feature of these lncRNAs is that they often require stoichiometric interaction with their target molecules to exert measurable regulatory effects . Therefore , it is essential to carefully quantify the cellular copy number of lncRNAs and their target ( s ) and understand whether an lncRNA is competent to regulate many target genes or whether it is functioning by sequestering some specific abundant proteins . This necessitates the importance of maintaining a defined stoichiometry between the noncoding RNA molecules and their target RNAs or proteins [57] . Yet , it is unclear as to how noncoding RNAs interact with their targets or as to how their expression influences cellular signalling . Although many studies have attempted to predict the putative mechanism of dysfunctional lncRNAs by using the bioinformatics approach , the lack of conserved sequences and the absence of functional motifs have posed complexity in defining their functions—especially so when the secondary or tertiary structures of lncRNAs play significant roles in their biological activity [58] . Ginir belongs to a small group of lincRNAs operating as relatively short-length RNA , in this case having only 612 nucleotides . The Ginir sequence is conserved in rat , implying that the sequence serves some essential function ( s ) . The 557-nucleotides-long sequence isolated initially was later shown to be a subset of the 612-nucleotides-long Ginir RNA sequence , demonstrating that the 557-nucleotide sequence had all the information that was necessary and enough to induce oncogenic transformation in the cells . We used a 3-fold approach—namely , nuclease protection assay , strand-specific primer-mediated reverse transcription followed by PCR , and hybridisation in situ with fluorescently labelled LNA probes to establish that Ginir was transcribed in normal cultured cells as well as during early mouse embryogenesis . Its expression during early development indicated that Ginir transcription was required for normal cellular functions . While examining the relative abundance of Ginir transcripts in normal cultured cells and in tissues during early mouse development , we detected the presence of a complete antisense transcript to Ginir RNA in all of them . Because of its full antisense nature , we designated it as Giniras , which was also a noncoding RNA . During these experiments , we discovered that Ginir and Giniras lncRNAs existed as a pair of sense–antisense overlapping transcripts and that Giniras acted as a NAT to Ginir noncoding RNA . Several NATs are reported in mammals in accordance with the computational studies , which suggest that 15%–25% of mammalian genes are overlapping [59] . Several antisense transcripts to protein-coding genes like antisense intronic noncoding RASSF1 ( ANRASSF1 ) [60] , antisense noncoding RNA in the INK4 locus ( ANRIL ) [61] , keratin type II cytoskeleton 7 antisense RNA 1 ( KRT7-AS ) [62] , or Sirtuin 1 antisense RNA ( Sirt1-AS ) [63] exist wherein they establish complex configurations as RNA–DNA duplexes and triplexes and thereby associate with regulatory proteins to affect the regulation of neighbouring regions . These regulatory mechanisms operate at pretranscriptional and transcriptional levels . A much less studied category is the class of natural double-stranded RNAs ( ndsRNAs ) that are expressed from interspersed genomic locations . Our data indicate that the Ginir/Giniras noncoding RNA pair is a validated example of ndsRNA . From a 500-Kb region located in chromosome 8 , several hundreds of sense–antisense pairs are known to arise . For example , one pair from this region is shown to be expressed from chromosome 8q24 . 21 , and it forms a stable ndsRNA molecule ( nds-2a ) that binds to regulator of chromosome condensation 1 ( RCC1 ) and ras-related nuclear protein ( RAN ) and through the latter , with the mitotic RANGAP1-SUMO1-RANBP2 complex [64] . A similar phenomenon is reported in chick embryo development , wherein the stoichiometry in the expression of lncRNA male hypermethylated ( MHM ) , and its antisense RNA is shown to be important for development of organs like gonads , limbs , heart , branchial arch , and brain [65] . With a major number of noncoding RNAs not yet identified , it is possible a vast repertoire of ndsRNA exist , many of which could also be involved in regulating embryonic development and cell growth . We provide evidence that the Ginir/Giniras pair exhibits contrasting effects on cell growth , like the other noncoding RNA pair X-inactive specific transcript ( Xist ) /Tsix that has opposing effects on X chromosome inactivation [66] . This is like another lncRNA , ubiquitin carboxy-terminal hydrolase L1 ( Uchl1 ) , whose antisense Uchl1-AS expression is dependent on the embedded short interspersed nuclear element ( SINEB2 ) ( in its genomic loci ) [67] . Instead , a natural antisense RNA to MALAT1 is known to exhibit a feed-forward positive regulatory loop to MALAT1 by promoting its 3′ end cleavage and transcript maturation [68] . Moreover , like the majority of lncRNAs , Ginir is also transcribed from a deserted region of the genome . Although these regions are largely considered as generating transcriptional noise , it is now known that these regions are rich in repeat elements , and they actively produce noncoding transcripts [69] . Although the factors governing evolution and the origin of noncoding RNAs are enigmatic , one possible factor driving lincRNA evolution and their biological functions is transposable element ( TE ) insertions [70] . A significant fraction of lincRNAs , almost to an extent of 83% in humans and about 66% in mouse , contain an inserted TE [71] . Rather , most of the mammalian genomes that include mouse and human are comprised of repetitive sequences such as TEs , tandem repeats ( TRs ) , and local repeats ( LRs ) [70] . The locus for noncoding RNA FIRRE , which is required for pluripotency and adipogenesis , on the X chromosome is comprised of numerous LRs [72] . Analysis of the genomic locus of Ginir using genome browsers has revealed that the Ginir locus resides in the LINE1 region . It is recently shown that almost 50% of noncoding RNAs originate near long interspersed nuclear elements ( LINE/L1 ) or SINE/Alu , and fewer than 15% originate in long terminal repeat ( LTR ) /endogenous retroviruses ( ERVs ) [73] . This is a minor disagreement from a report that has demonstrated significant enrichment of LTR/ERVs and depletion of LINE L1 and SINE/Alu at the start site of all lncRNAs [71] . It is considered that LINE/L1 sequences are enriched in brain-specific transcripts as compared with non-tissue-specific transcripts [73] , yet a statistical evaluation is lacking , and the data mining and analyses from different data sets for low-expression transcripts show variability . Hence , a detailed study is warranted about the presence of these repeats in noncoding RNAs . The fact that Ginir and Giniras are mainly expressed during embryonic development , are found enriched in the developing brain , and are present in other adult tissues ( prominently Giniras is expressed ) makes any kind of generalisation about a tissue-specific expression pattern or developmental presence of LINE1-encoded lncRNAs difficult . A new class of lncRNAs , termed chromatin-enriched RNAs ( cheRNAs ) , that are tightly associated with chromatin is identified , and their presence is strongly correlated with expression of nearby genes . One of the members of this class of cheRNA is hemin-induced cheRNA downstream of foetal haemoglobin ( HIDALGO ) , which is involved in erythroid differentiation . Werner and colleagues showed that these cheRNAs reside within class I TEs and are cell type specific and play important roles in lineage specification and differentiation [74] . We found that during early embryonic development , Ginir transcripts were more abundant than the antisense Giniras transcripts . As the development progresses and organ development and differentiated tissues become more prominent , an up-regulation of Giniras expression is evident . Thus , it is plausible that a fine balance between the expression of Ginir and Giniras lncRNAs may be amongst the important molecular events during tissue morphogenesis in mouse embryos . LncRNAs like XIST [75] , H19 [76] , HOTAIR [77] , and regulator of reprogramming ( RoR ) [78] are described as vital players during embryonic development , but most of these RNAs are shown to be crucial during initial stages of development . XIST and H19 regulate processes like X chromosome inactivation and genomic imprinting , whereas HOTAIR and ROR affect the epigenetic changes by interacting with PRC2 complex and thereby determine embryonic stem cell fates . Except for some preliminary reports about lncRNAs like UCA1 [79] and some recently reported ones like Pnky [80] , FOXF1-adjacent noncoding developmental regulatory RNA ( Fendrr ) [81] , and AB063319 [82] , there are not many reports exemplifying the role of lncRNAs during tissue morphogenesis . One of the lncRNAs , termed lnc myogenic differentiation ( MyoD ) , is encoded next to the MyoD gene and is directly activated by MyoD , thereby inhibiting proliferation and creating a permissive state for skeletal muscle differentiation [83] . Since the pace of cell division decreases and the state of differentiation increases during the later stages of embryonic development , we speculate that Ginir RNA modulates the pace of cell proliferation during development . Since Giniras is complementary to the entire length of Ginir , it can eclipse the entire sequence of Ginir by forming an RNA: RNA hybrid that thereby masks the functioning of important regulatory elements required for RNA: RNA or RNA: Protein interactions . More than 20 noncoding RNAs are linked to cancer . These include but are not restricted to lncRNAs like HOTAIR [84] , Pvt1 [85] , RoR [86] , prostate cancer–associated transcript 1 ( PCAT1 ) [87] , ANRIL [88] , H19 [89] , nuclear enriched abundant transcript 1 ( NEAT1 ) [90] , UCA1 [91] , lung adenocarcinoma transcript 1 ( LUADT1 ) [92] , colorectal cancer–associated lncRNA ( CCAL ) [93] , and many more . Certain lncRNAs like HOTAIR [30] , MALAT1 [94] , and UCA1 are implicated in metastasis . Here , the involvement of most of them is more of an association than a cause of malignant transformation . For example , genomic loci encoding some of these lncRNAs are hotspots of epigenomic alterations , mutations , single-nucleotide polymorphisms , and somatic copy-number alterations , and thereby their expression levels are found enhanced in cancer . Expressions of some of these lncRNAs are indicators of good or bad prognostic factors , determinants of metastasis , and predictors of therapeutic responsiveness . However , Ginir RNA is distinct from all of them in that it is one of the unique lincRNAs that we unequivocally demonstrate to function singularly as an acutely transforming oncogene . Using several in vitro and in vivo assays , we have demonstrated that Ginir at high expression levels is tumourigenic and effectively potentiates migration , invasion , and metastasis and is also proangiogenic . Analyses of Ginir transcript abundance in oncogenically transformed cells show that it is more abundant in pathological cells compared to normal cells . Down-regulation of Ginir function by expressing Giniras RNA or using sequence specific shRNAs causes significant attenuation of tumourigenic potential of Ginir RNA–expressing cells . To gain an insight into the mechanistic details of oncogenic transformation brought about by Ginir RNA , we created specific cell lines in which Ginir , Giniras , and Ginir+Giniras transcripts were expressed in abundance , using exogenously introduced transcript constructs . We also created cell lines in which a relatively high level of Ginir RNA was being expressed initially , which was subsequently brought down by coexpressing shRNAs that were specific to Ginir RNA sequences . The results from all these experiments are in accord with a model in which an excess of Ginir RNA was shown to be involved in deregulating a checkpoint that was involved in suppressing cellular proliferation or that was prolonging the cell cycle duration or both ( Fig 12 ) . Our data indicated that when the level of Ginir expression in the tumour cells was down-regulated , or Ginir RNA was sequestered by its antisense partner Giniras , the cells apparently exited the transformed state in that they were no longer able to form credible tumours in mice . A coordinated loss of a set of related properties was indicative of a primary role that the Ginir overexpression had in orchestrating them . Because we detected the differential regulation of Ginir and Giniras transcription , our hypothesis that Ginir promoted proliferation , whereas Giniras retarded proliferation and promoted differentiation as a part of natural regulatory mechanism , becomes supported . Ginir-overexpressing cells showed a higher number of multinucleated giant cells having copious Ginir expression within nuclei . These types of giant cells are also formed during senescence , but they are not always multinucleated , and they are nonproliferative . The giant cells induced by Ginir , by contrast , were proliferative because mitotic and proliferative capacity was present in them , indicating that the nuclear divisions had been desynchronised from the cytokinesis . Thus , a specific property associated with Ginir-transformed tumour cells was that the mitotic dynamics persisted in the absence of cytokinesis , giving rise to giant multinucleated cells [95] . Such polyploid giant cells have also been cited as a repertoire of cancer stem cells and are likely responsible for their continuous generation [96] . In some cases of lncRNAs , an epigenomic alteration results from its nuclear localisation . We have demonstrated here that the multinucleated giant cells have an overabundance of Ginir RNA in their nuclei ( more than the NIH/3T3-Ginir nuclei ) , suggesting that there may have been reprogramming of its epigenome . Thus , the giant multinucleated cells may represent cells qualified for dedifferentiation and most appropriate to acquire stem cell–like properties , which are hallmarks of high-grade tumours that were observable in the tumours induced by NIH/3T3-Ginir cells growing as xenografts in mice . Our studies show that knocking down endogenous Ginir in normal cells diminished their proliferation . The mouse melanoma cells like B16F10 , when made deficient for Ginir RNA , led to attenuation in their transformation-related properties in vitro and to tumourigenicity in vivo . These results underscore that Ginir and Giniras RNAs must be included in those control mechanisms for which proliferation needs to be modulated , and accordingly , the Ginir RNA and its negative modulator Giniras RNA expression during embryonic growth are coordinated . Mutations including deletions or genomic sequence alterations in Ginir or Giniras locus may prove to be embryonic lethal , precluding their isolation to delineate individual functions of these two RNAs using a genetic approach . Our finding of an increased level of γH2Ax foci representing DNA double-stranded breaks being present in the cells with higher levels of Ginir RNA is the fallout of a compromised DDR pathway . A strong oncogenic stimulation apparently forces a cell to undertake a proliferation cycle without adequate preparation , thereby having increased episodes of replication fork stalling or replication fork collapse . Since single-stranded DNA and unreplicated ends accumulate beyond an accepted time delay , the surveillance machinery comprising the checkpoint kinases sense them as dsDNA breaks and activate DDR responses . To make this repair process efficient and error free , BRCA1 protein function is critically required . DNA damage after ionising radiation treatment results in accumulation of γH2Ax foci , which also activates the repair response , but an important difference is that growth arrest sets in immediately after the DNA damage . In the case of Ginir RNA overexpression , however , the γH2Ax foci accumulate in the nuclei without the onset of growth arrest . BRCA1 protein has multiple roles in cell physiology , and it interacts with a variety of proteins and noncoding RNAs in both cytoplasmic and nuclear compartments . During cell division , it performs a centriolar role in collaboration with the centrosome proteins like the Cep proteins . These proteins have been identified as components of the centrosome complex using a proteomics approach [97] . One of the members of the Cep family , Cep112 , is demonstrated by us as a strong interactor of Ginir RNA . Our data demonstrate clearly that Brca1 stabilises the Cep112 protein by directly interacting with it and localising it to the centrosomal compartment . In the presence of an overabundant supply of Ginir RNA , both Brca1 and Cep112 proteins remain bound to the Ginir RNA and consequently Brca1:Cep112 protein–protein interaction is impaired . The disruption of this interaction also mislocalises the Cep112 and Brca1 proteins into different cellular compartments , thereby inhibiting their natural functions . We have provided convincing evidence in support of direct Cep112–Brca1 protein interaction , and further , we have demonstrated that interaction of Ginir RNA with Cep112 and Brca1 proteins deregulates centrosome functions and disrupts spindle assembly , thereby impairing mitotic regulation . Together , our data show that these processes promote defects in centrosome and spindle functions that include a desynchronised karyokinesis from cytokinesis , giving rise to multinucleated giant cells . All these effects together contribute to an increased genomic instability . Indeed , Brca1 is also known to interact with another lncRNA—DNA damage–sensitive RNA1 ( DDSR1 ) —that is induced after DNA damage , wherein it promotes homologous recombination by regulating recruitment of DNA repair factors to double-stranded breaks after DNA damage [98] . Several other lncRNAs like the recently identified NORAD [56] , another lncRNA-JADE , an activator of Jade1 [99] , and RoR [100] are critically involved in maintenance of genomic stability . Brca1 , when recruited to centrosome , interacts with a plethora of centrosomal proteins like γ-tubulin [51] , Nlp [50] , OLA1 [49] , and KIAA0101 [101] , ensuring error-free centrosomal duplication and cytokinesis . Most importantly , a recent finding demonstrates binding of a Cep family protein ( Cep72 ) to Brca1 , thus disturbing centrosomal regulation by Brca1 and consequently triggering genomic instability [102] . According to a recent report , Brca2 functions with Cep55 to regulate cytokinesis and ploidy [103] . Recently , another member of the Cep family , Cep192 , is shown to be important in the interphase-organising microtubules and cytoskeleton . Defects in targeting Cep192 for proteasome-mediated degradation by a member of the Fbxo family , F-box only protein 13 ( Fbx13 ) , was shown to cause accumulation of CEP192 and ɣ-tubulin at the centrosomes with the consequence of defects in cell motility [104] . Here , we provide data on a novel interaction between Cep112 and Brca1 in NIH/3T3 cells , which is abrogated upon Ginir overexpression . Although interaction of Brca1 protein to other centrosomal proteins is known , this is a first report , to our knowledge , demonstrating interaction of Brca1 protein to a novel centrosomal protein Cep112 . This interaction is important for stabilisation of the interacting proteins as revealed by our data ( Fig 9I ) , and thus , any disruption in their interaction causes a decrease in the abundance of these proteins . Additionally , a high level of expression of Ginir RNA apparently affects the intracellular localisation of these proteins , thereby placing them away from their normal functional sites . We found that Cep112 , which is a cytoplasmic protein with prominent localisation in the centrosome , was enriched in the nucleus in response to an elevated expression of Ginir RNA . The nuclear localisation of Cep112 could be an attribute of a decoying function of Ginir . In these cells , Brca1 protein was down-regulated and was nearly absent from the nucleus with a much-diminished presence in the centrosome in Ginir RNA–induced transformed cells . Thus , mislocalisation of these two proteins from their functional sites caused mitotic dysregulation and thereby served as crucial contributor to genomic instability . Taken together , Ginir may be considered as an oncofoetal molecule like H19 , as it shares a similar expression pattern with it during mouse embryonic development followed by its activation during adult tumourigenesis [105] . While our study establishes the effects of disequilibrium between Ginir/Giniras RNAs in cell growth regulation , still several questions remain unanswered , like the physiological roles of Ginir/Giniras RNAs during development . Undoubtedly , a detailed understanding of their biology would have tremendous ramifications in investigating the role of lncRNAs in fine-tuning gene expression and regulating tissue homeostasis . This could pave the way for designing novel therapeutic strategies for proliferative disorders like cancer . A detailed understanding of Ginir–Giniras equilibrium could open avenues for utilisation of NATs in disease , therapy , and molecular research .
The use of animals for this study was approved by the Institutional Animal Ethics Committee ( IAEC ) of National Centre for Cell Science ( NCCS ) ; Pune , India ( IAEC/2016/B-263 ) . All animal procedures followed were strictly in accordance with the animal ethics guidelines of the NCCS . All cells were obtained from American Type Culture Collection ( ATCC; Manassas , VA , United States ) . Mouse fibroblast cells NIH/3T3 were maintained in Dulbecco’s Modified Eagle Medium ( DMEM; Invitrogen; Carlsbad , CA , US ) supplemented with 10% bovine calf serum ( Invitrogen ) . Clone M3 , mouse melanoma cells were maintained in Ham’s F10 medium supplemented with 15% horse serum ( Invitrogen ) and 2 . 5% foetal bovine serum ( FBS; Invitrogen ) . B16F10 , mouse melanoma cells were maintained in DMEM ( Invitrogen ) supplemented with 10% FBS ( Invitrogen ) . All the cell lines were maintained in culture in the presence of antibiotics penicillin ( 200 U/ml ) and streptomycin ( 200 μg/ml ) ( Sigma-Aldrich , St . Louis , MO , US ) . The cell cultures were incubated at 37 °C with 5% CO2 in a humidified incubator . All cell lines are tested for mycoplasma contamination regularly . RNA extraction from cell lines was performed using TRIzol RNA Isolation Reagent ( Invitrogen ) by following the manufacturer’s instructions . The RNA was treated with RQ1 Rnase-free DNase ( 1 U/μl , Promega , Madison , WI , US , # M6101 ) for 30 minutes at 37 °C . cDNA was prepared with 1 μg of RNA using oligo-dT and random primers , whereas orientation-specific cDNA was prepared with 3 μg of RNA using Ginir- ( sense; G1F ) or Giniras- ( antisense; G1R ) specific primers along with primers for internal control Gapdh at 42 °C for 90 minutes using a Reverse Transcription System kit ( Promega , # A3500 ) . Semiquantitative PCR was performed using Taq DNA polymerase ( Merck Bioscience; Darmstadt , Germany ) . Strand-specific real-time PCR was performed using Mesa green master mix ( Eurogenetec , Seraing , Belgium ) using ABI 7500 Fast real-time PCR ( Applied Biosystems , Foster City , CA , US ) . Primers used for the experiments are listed in S3 Table . Transfections were done using Lipofectamine 2000 reagent ( Thermo Fisher Scientific , Waltham , MA , US , # 12566014 ) according to the manufacturer’s instructions . NIH/3T3 cells were transfected with pTargetT expression vector ( Promega ) constructs of either Ginir or Giniras cDNAs , and the stable transfectant clones were selected using G418 ( 500 μg/ml , Thermo Fisher Scientific , # 10131035 ) . Cells transfected with only pTargetT expression vector served as control . Additionally , stable clones coexpressing both Ginir ( cloned in pTargetT and Giniras ( cloned in pCEP4; Thermo Fisher Scientific ) were generated in NIH/3T3 cells and selected on both G418 ( 500 μg/ml ) and Hygromycin ( 100 μg/ml ) ( Thermo Fisher Scientific , #10687–010 ) . The overexpression of Ginir and Giniras was determined by strand-specific PCR as described earlier . Only those clones were selected that showed Ginir and Giniras overexpression by ≥2 fold and were propagated as independent cell lines . We performed these transfections thrice , and from each transfection , multiple clones were chosen . We here report detailed characterisation of three independent transfectant clones A , B , and C , and each experiment was performed using at least two independent transfectants at least thrice . siRNA transfections were done in NIH/3T3 cells with two independent siRNA pools of Brca1 and Cep112 . Pool # 1 siRNAs were procured from Santa Cruz , Dallas , TX , US , and pool # 2 siRNAs ( Stealth siRNAs ) were purchased from Thermo Fisher Scientific . For pool # 1 siRNA transfections , 100 nM each of Brca1 siRNA pool ( m ) ( Santa Cruz , #sc-29824 ) , Cep112/Ccdc46 siRNA pool ( m ) ( Santa Cruz , #sc-142115 ) , and control siRNA-A ( Santa Cruz , #sc-37007 ) was used . Cells were assayed after 48 hours of transfection . For pool # 2 transfections , 100 pmol of each of Brca1 siRNA pool ( m ) ( Thermo Fisher Scientific , #1320001 , ID- MSS202430 ) , Cep112 siRNA pool ( m ) ( Thermo Fisher Scientific , #1320001 , ID- MSS293588 ) , and control stealth siRNA was used in 2 ml of complete medium . NIH/3T3-Ginir cells were transfected with shRNAs for Ginir and scrambled shRNAs as control ( TransOMIC technologies , Huntsville , AL , US ) . The transOMIC shRNAs used in this study were designed using Custom shERWOOD Design Service . The sequences for shRNAs ( 1 and 2 ) and control shRNA used are as follows . HEK293T cells grown to 70%–80% confluency in a 60-mm dish were transfected with Lipofectamine 3000 ( Invitrogen ) using the manufacturer’s instructions . Briefly , in two separate tubes—one having 8 μl of Lipofectamine 3000 mixed with 250 μl of Opti-MEM and another with 5 μg of plasmid DNA constructs ( i . e . , packaging plasmid pCMV delta R8 . 2 [2 μg] , envelope plasmid pCMV-VSV-G [1 μg] , and transfer plasmid pZIP-shGinir [1 and 2] [2 μg] ) in 250 μl of Opti-MEM and 10 μl of P3000 Reagent—were incubated at room temperature ( RT ) for 5 minutes . Later , the contents of both the tubes were mixed and further incubated at RT for 30 minutes to form liposome–DNA complexes . Meanwhile , cells were rinsed in plain DMEM and fed with 3 ml of DMEM . The DNA–liposome mix was added dropwise onto the cells , incubated at 37 °C for 6 hours , and fed with complete medium . After 12 hours , the culture medium was replaced with DMEM-F12 medium supplemented with sodium butyrate and incubated at 37 °C for 6 hours . Next , complete media containing high serum ( 20% FBS ) were added to the plate , and cells were further incubated for 24 hours at 37 °C . Later , viral supernatant was collected in a 15-ml tube , and again , fresh media containing high serum ( 20% FBS ) were added . Next day , both the 24-hour and 48-hour viral supernatants are pooled and spun at 2 , 000 rpm at 4 °C for 10 minutes . Pooled viral supplements were used for transduction of target cells . After 48 hours , the cells were selected on puromycin- ( 1 μg/ml ) containing medium for 7–8 days by intermittently replacing them with fresh medium . The puromycin-resistant colonies were pooled and subcultured to develop into cell lines . These were maintained on complete medium supplemented with penicillin and streptomycin and puromycin ( 1 μg/ml ) . The stable cell lines were cryopreserved in liquid nitrogen . Total mRNA was isolated using Oligotex direct mRNA isolation kit ( Qiagen , Hilden , Germany ) using manufacturer instructions . RACE was performed using Marathon RACE cDNA amplification kit ( Clontech , Mountain View , CA , US ) according to the manufacturer’s protocol . The Marathon adapters were ligated using T4 DNA ligase . These adapter-ligated RACE cDNAs were used for primary and nested PCR using Ginir-specific primers and adapter primers ( S3 Table ) . RACE-PCR reactions were performed using Advantage 2 Polymerase Mix ( Clontech ) . The primer sequences used were as follows: Ribonuclease protection assay was performed using HybSpeed RPA kit ( Ambion , # 1412 ) as per the manufacturer’s protocol . Total RNA was isolated from cells and treated with DNase I to remove the traces of DNA contamination . Fifty μg of DNased total RNA and 5 × 106 CPM of Ginir-specific ( sense/antisense ) probes ( 1 × 105 CPM/μg of RNA ) were mixed and coprecipitated with ammonium acetate and ethanol . The pellet was dissolved in 20 μl of HybSpeed hybridisation buffer . The reaction mix was denatured at 95 °C for 5 minutes , hybridised for 3 hours at 68 °C with sense/antisense riboprobes for Ginir , and digested using RNaseA/T1 mix for 30 minutes at 37 °C . The RNA was dissolved in 10 μl RNA gel loading buffer , denatured at 90 °C for 5 minutes , and separated on 10% PAGE with 8 M Urea . The gel was wrapped in Saran wrap and exposed to phosphor screen overnight and scanned using phosphor imager FX ( Biorad , Hercules , CA , US ) . Pregnant female Swiss Webster ( CFW ) females ( 4–6 weeks old ) were used for embryo isolation . Embryos ranging from 5 . 5 dpc to 13 . 5 dpc were isolated by dissecting impregnated mice using dissection microscope and instruments . These embryos were very carefully washed with 1X phosphate-buffered saline ( PBS ) . Half the embryos were fixed in 4% paraformaldehyde ( PFA ) for in situ hybridisation , and the remaining embryos were stored in TRIzol at −80 °C until use . Morphological criteria were used to identify precise embryo staging using ‘Theiler Staging Criteria for Mouse Embryonic Development’ . RNA was isolated from embryos of all the stages using TRIzol reagent ( Invitrogen ) following the manufacturer’s protocol . Strand-specific cDNA for Ginir/Giniras and Gapdh was prepared using Quantitect Reverse Transcription Kit ( Qiagen , # 205311 ) . RNA-FISH was performed on cells and embryo sections using custom-designed Ginir- and Giniras-specific fluorescent LNA probes ( Exiquon , Vedbaek , Denmark ) . Two independent LNA probes targeting different regions of Ginir sequence were used to ensure specificity of the fluorescence . The sequences of LNA probes used were the following: In brief , cells and embryos at various dpc stages , as well as whole-mount embryos , were fixed in 4% PFA followed by permeabilisation in 0 . 5% Triton-X 100 containing RNasin ( 50 U/ul , Thermo Fisher Scientific , #AM2694 ) . Prehybridisation was done at 37 °C for 30 minutes in hybridisation buffer without probe ( 10% dextran sulphate , 0 . 5% BSA , 2X SSC in 50% formamide , RNasin 50 U/μl ) . Simultaneously , cells used for RNase control were treated with RNase A ( 1 mg/ml , Sigma , # R4642 ) in 1X PBS at 37 °C followed by prehybridisation at 37 °C for 30 minutes . Denaturation of LNA probes ( 25 nM/ sample ) was done at 80 °C for 75 seconds in hybridisation buffer containing 500 μg/ml of tRNA and 50 U/μl of RNasin . Hybridisation was performed by inverting the cover slips on paraffin-coated slides and incubating them at 46 °C in a moist hybridisation chamber for 4 hours . Coverslips were then transferred to a 24-well plate for washes—first wash ( 50% formamide in 2X SSC + RNasin 50 U/μl ) for 20 minutes at 37 °C , second wash ( 2X SSC + RNasin—50 U/μl ) for 20 minutes at 37 °C , third wash ( 1X SSC + RNasin—50 U/μl ) for 20 minutes at RT , and final wash ( 4X SSC ) for 2 minutes at RT . Nuclei were stained with DAPI and mounted using mounting medium supplemented with DABCO . Confocal images were collected using Zeiss LSM510 META confocal microscope with an Axiovert 100M imaging system ( Carl Zeiss , Oberkochen , Germany ) . A few of the images were acquired using Leica SP5 II system ( Leica Microsystems , Wetzlar , Germany ) and with Olympus FluoView FV1000Confocal Microscope ( Olympus , Shinjuku , Tokyo , Japan ) . Proliferation potential of cells was determined by using an MTT assay [106 , 107] . Briefly , cells were seeded into 96-well plates ( BD Biosciences , San Jose , CA , US ) at a cell density of 1 × 103 cells/well in growth medium . Cell growth was assayed by addition of 20 μl of MTT ( 5 mg/ml; Sigma-Aldrich ) to each well , and the plate was incubated at 37 °C for 4 hours . The proliferation assay was performed for 3–7 days , and cell growth was assayed at every 24-hour interval . Later , the reaction was stopped by addition of 200 μl dimethyl sulfoxide ( Sigma-Aldrich ) . Optical density was measured at 570 nm with a microplate reader ( Bio-Rad , Hercules , CA , US ) . The clonogenic potential of cells was assessed by soft agar assay by a method described by Anzaono and colleagues [108 , 109] . The assay plates were incubated for 7–10 days at 37 °C to score for colony formation . Each set was plated in triplicates , and the assay was performed at least three times . The colonies were counted under Olympus IX70 inverted microscope using 10× objectives in 10 different fields to acquire the average number of colonies per cell line . Migration of cells in vitro was determined by wound-closure migration assay [110] . A single wound was created in the centre of cell monolayers and later visualised after 6–20 hours ( less than doubling time of the cells ) to detect migration of cells to the created gap . Three independent experiments were performed , and data represent the average ± SEM of the wound gap that remained after 6–20 hours . Alkaline Comet assay was performed as described by Olive and colleagues ( 95 ) . Comet slides were stained with PI at a concentration of 20 μg/ml for 10 minutes . Images were acquired on Leica SP5 II system ( Leica Microsystems ) . The CAM assay for assessing angiogenesis was performed on a patch of chorioallantois exposed by the shell window of fertilised white chicken eggs as per the method described [111 , 112] . For in vivo tumourigenicity assays , female NOD-SCID mice were used . Briefly , 1 × 106 of test and control cells were injected subcutaneously into NOD-SCID mice , and mice were periodically observed for tumour development . Tumour volumes were determined using the formula 1/2 ( Length × Width2 ) . Tumours were formalin fixed and analysed by HE staining . Also , different tissues like lungs were sectioned and stained with HE to analyse the extent of invasion of injected cells . Control NIH/3T3-EV , NIH/3T3-Giniras , and NIH/3T3-Ginir transfectant clones A and B were injected into NOD/SCID mice via tail vein with 1 × 106 cells . The mice were killed at 6 weeks and 8 weeks post injection . Later , the lungs were sectioned and stained with HE for histopathological analysis . Cytoplasmic and nuclear fractions from cells were collected using Nuclear Extraction Kit ( Chemicon International , Billerica , MA , US , # 2900 ) according to the manufacturer’s instructions . Briefly , cells were harvested and resuspended in 5 cell pellet volumes of ice-cold 1X Cytoplasmic Lysis Buffer containing RNasin ( 100 U/ml , Thermo Fisher Scientific ) followed by incubation on ice for 15 minutes . Cells were then pelleted and resuspended in two volumes of ice-cold 1X Cytoplasmic Lysis Buffer ( with RNasin ) . Cells were lysed in the buffer by continuous drawing and ejecting through a 27-gauge needle . Cell suspension was centrifuged , and a supernatant containing a cytosolic portion of the cell lysate was collected . The remaining pellet containing the nuclear portion of the cell lysate was resuspended in two-thirds of the original cell pellet volume of ice-cold Nuclear Extraction Buffer ( with 100 U/ml RNasin ) . The nuclear portion was similarly disrupted using a 27-gauge needle followed by agitation at 4 °C for 30–60 minutes on a rotor at low speed . The suspension obtained was then centrifuged at 16 , 000g for 5 minutes at 4 °C , and the supernatant containing the nuclear fraction was collected . RNA from each fraction was isolated using Trizol Reagent ( Ambion ) . For cell cycle analysis , cells were fixed in chilled methanol at −20 °C for 10 minutes , rehydrated in PBS for 30 minutes , and treated with RNase A ( 100 μg/ml , Sigma-Aldrich ) at 37 °C for 30 minutes . Nuclei were stained with PI ( Invitrogen , # P1304MP ) for 30 minutes . A total of 10 , 000 nuclei were examined by flow cytometry using FACS Calibur ( BD Biosciences , San Jose , CA , US ) , and DNA histograms were analysed using CellQuest Pro FACS analysis software 5 . 2 . 1 ( BD Biosciences ) . Cells were lysed in 1X M-PER Mammalian Protein Extraction Reagent ( Pierce , Rockford , IL , US ) containing 1X protease inhibitor cocktail ( PIC ) ( Sigma-Aldrich ) . The lysates were separated on SDS-PAGE and transferred to Hybond-P PVDF Membrane ( GE Healthcare ) followed by immunoblotting and detection with Super Signaling West Femto Kit ( Pierce ) . Primary antibodies used for immunoblotting were Brca1 ( 1:2 , 000 , # sc-7867 , # sc-646 ) , Cep112 ( 1:1 , 500 , # sc-246162 , # sc-246163 ) , Gapdh ( 1:2 , 000 , # sc-32233 ) , β-actin ( 1:2 , 000 , # sc-81178 ) , PCNA ( 1:1 , 000 , sc-7907 ) , pp21 ( Ser146 ) ( 1:1 , 000 , # sc-12902 ) , and pRb ( 1:1 , 000 , # sc-12901R ) from Santa Cruz Biotechnology; α-tubulin ( 1:10 , 000 , # T8203 ) and RPA32 ( 1:1 , 000 , # R1280 ) from Sigma Aldrich; Phospho-Histone H2A . X ( Ser139 ) /γH2Ax ( 1:4 , 000 , # 2577S ) , pp53 ( Ser15 ) ( 1:1 , 000 , 9284S ) , and Flag ( 1:1 , 000 , # 2638 ) from Cell Signalling ( Danvers , MA , US ) ; and Cep112 ( 1:2 , 000 , # 24928-1-AP ) and Brca1 ( 1:2 , 000 , # 20649-1-AP ) from Proteintech ( Rosemont , IL , US ) . The HRP-conjugated secondary antibodies were Goat anti-Mouse IgG HRP ( 1:2 , 000 , # 31430 ) , Goat anti-Rabbit IgG HRP ( 1:10 , 000 , # 31463 ) , Rabbit anti-Goat IgG ( 1:2 , 000 , # 31433 ) from Pierce , and Donkey anti-Goat HRP ( 1:6 , 000 , Santa Cruz , # sc-2020 ) . Quantification of western blots was done using Image J tool , version 1 . 41 . Cells were grown onto coverslips for 24–48 hours . Later , they were fixed in 4% PFA ( Sigma-Aldrich ) for 10 minutes at RT . For immunostaining of centrosomal proteins , cells were fixed with chilled methanol for 20 minutes at −20 °C . Later , cells were washed with 1X PBS , permeabilised using 0 . 01% Triton X-100 ( Sigma-Aldrich ) for 10 minutes , and blocked with 5% BSA ( MP Biomedicals , Santa Ana , CA , US ) for 30 minutes at RT . This was followed by incubation with primary antibodies Ki67 ( 1:100 , # sc-23900 ) , Cep112 ( 1:50 , # sc-246163 ) , Brca1 ( 1:100 , # sc-646 ) , Ark1 ( 1:50 , # sc-14321 ) , and PCNA ( 1:100 , # sc-7907 ) from Santa Cruz Biotechnology; Cep112 ( 1:100 , # 24928-1-AP ) and γ-tubulin ( 1:500 , # 15176-1-AP ) from Proteintech; γ-tubulin ( 1:500 , # T5326 ) and α-tubulin ( 1:1 , 500 , # T8203 ) from Sigma-Aldrich; 53BP1 ( 1:100 , # 4937 ) , Mre11 ( 1:100 , # 4895 ) , RAD-52 ( 1:100 , # 3425 ) , ATMATR-S ( 1:100 , 2851S ) , pATM ( Ser1981 ) ( 1:100 , # 4526S ) , p53 ( 1:100 , # 2524S ) , and γH2Ax ( 1:500 , # 2577S ) from Cell Signalling; and p21 ( 1:100 , Abcam , Cambridge , UK , # 18209 ) . All antibodies were diluted in 1X PBS and incubated for 1 hour at RT . The cells were then incubated with appropriate species-specific Alexa Fluor–conjugated secondary antibodies ( Molecular Probes , Invitrogen ) diluted in 1X PBS ( 1:100 dilutions ) for 1 hour at RT in dark . Later , cells were incubated with 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Sigma-Aldrich ) for 10 minutes and mounted in medium containing 1% 1 , 4-diazabicyclo ( 2 . 2 . 2 ) octane ( DABCO ) ( Sigma-Aldrich ) . Confocal images were acquired using Zeiss LSM510 META confocal microscope with an Axiovert 100M imaging system . A few of the images were also acquired using Leica SP5 II system and Olympus Fluoview FV1000 Confocal Microscope . The centrosome content was determined using the method described by Starita and colleagues [113] . In brief , the centrosome number was determined for cells in both interphase and metaphase by staining them using ɣ-tubulin-specific antibody followed by Alexa Fluor–conjugated secondary antibody . The cells were visually scored for centrosome content using Leica SP5 II confocal system . At least 150 cells were counted , and cells with normal ( 1 to 2 ) and abnormal ( >2 ) centrosomes were individually scored , and a histogram was plotted as the percentage of cells with normal and abnormal centrosome numbers . The experiment was performed at least 3 times for each cell type . Biotinylated pull-down assay with Ginir/Giniras probes was performed using the method described by McHugh and colleagues [114] . Whole-cell lysates were harvested from 100-mm dishes . Ginir/Giniras specific riboprobes were synthesised using Megascript T7 Kit ( Invitrogen ) as per the manufacturer’s protocol . For preparing Ginir hot probe , 0 . 2 mM of biotinylated CTP ( Biotin-14-CTP ) ( Invitrogen , #19519–016 ) was added to the reaction mix along with the PCR product to be used as the template . In another independent reaction , cold probe was synthesised using one-tenth of Bio-CTP as used in hot probe synthesis . Control unbiotinylated probe was synthesised with no biotin in the reaction mix . Using similar reaction mix , antisense ( Giniras ) and unrelated ( Hotair , XEF ) biotin probes were also synthesised using their respective PCR products as a template . All the tubes were incubated at 37 °C for 4 hours . After RNA synthesis , turbo DNase was added to digest template DNA . RNA was precipitated using the ammonium acetate precipitation method . All the probes along with controls were incubated with cell lysates and passed through streptavidin columns ( μMACS Streptavidin Kit , Miltenyi Biotec , Bergisch Gladbach , Germany ) . Hot-bound , cold-bound , and no-biotin-probe-bound elutes were collected and resolved in SDS-PAGE along with the input fraction . The gel was stained with Coomassie Brilliant Blue . The specific protein bands were then purified by in-gel digestion ( In gel digestion kit , Thermo Scientific ) and analysed by MALDI-TOF . For validation of pull-down , immunoblotting of elutes with specific antibodies for Brca1 ( 1:2 , 000 , Santa Cruz , # sc-7867 ) and Cep112 ( 1:2 , 000 , Proteintech , # 24928-1-AP ) was done . Native RIP was performed as described [115] . Briefly , cells were cross-linked using 1% formaldehyde for 30 minutes at RT and then pelleted . Later , cells were lysed using SDS lysis buffer ( 1% SDS , 10 mM EDTA , 50 mM Tris HCl [pH 8 . 1] ) and RNasin ( 50 U/ml ) . Lysates were sonicated at high power for 4 minutes ( 30 seconds on-and-off cycle ) . After sonication , the insoluble elements were cleared by centrifugation at maximum speed for 10 minutes at 4 °C . The supernatant was then diluted 10-fold with IP buffer ( 0 . 01% SDS , 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 16 . 7mM Tris [pH 8 . 1] , 167 mM NaCl , 1X PIC , RNasin [50 U/ml] ) . Then , 10% of the aliquot was preserved as an input sample and frozen at 80 °C until the reverse cross-linking step . The rest of the lysate was precleared with protein A/G beads and respective Isotype IgG overnight at 4 °C . One-ml aliquots were made of the diluted supernatant , and 0 . 5 μg of respective antibodies ( Brca1 , Cep112 , and Gapdh ) were added to each of the tubes . Respective Isotype IgG was used as negative control . Immune complexes were formed by slow mixing on a rotating platform at 4 °C overnight . Immune complexes were collected by adding 50 μl of protein A/G Agarose beads ( Santa Cruz ) followed by mixing by rotation at 4 °C for 2 hours . The beads were preblocked with yeast tRNA ( Invitrogen , # AM7179 ) ( 50 μl of beads with 2 mg tRNA ) by rotation mixing for 90 minutes at 4 °C . Immune complexes were then washed for 5 minutes each with low-salt buffer ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris HCl [pH 8 . 1] , 150 mM NaCl ) , high-salt buffer ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris HCl [pH 8 . 1] , 500 mM NaCl ) , and LiCl buffer ( 0 . 25 M LiCl , 1% NP40 , 1% deoxycholate , 1 mM EDTA , 10 mM Tris-HCl [pH 8 . 1] ) . This was followed by two washes with TE buffer ( Tris-EDTA [pH 8 . 0] ) . Immune complexes were then eluted in elution buffer ( 1% SDS , 0 . 1 M NaHCO3 , RNasin—50 U/μl ) . Reverse cross-linking was done with 200 mM of NaCl at 65 °C for 2 hours . RNA isolation was done from elutes and input samples using TRIzol reagent ( Invitrogen ) . Strand-specific cDNAs were prepared after DNase treatment followed by PCR amplification with gene-specific primers . Cells were lysed in IP lysis buffer ( Pierce ) containing 1X PIC ( Sigma-Aldrich ) . To evaluate RNA–protein interactions , 500 μg of lysate was treated with RNasin ( 200 U/ml ) , whereas the other 500 μg was subjected to treatment with RNase A ( 200 μg/ml , Sigma , # R4642 ) , RNase III ( 0 . 05 U/μl , Invitrogen , #AM2290 ) , and RNase H ( 5 U/μl , Invitrogen , # 18021–014 ) at 37 °C for 1 hour . The lysates were further used for immunoprecipitation using Dynabeads protein-A immunoprecipitation kit ( Invitrogen , # 10006D ) , and the manufacturer’s protocol was followed . Two μg of antibodies to Brca1 ( Santa Cruz , # sc-646 ) , Cep112 ( Santa Cruz , # sc-246163 ) , Flag ( Cell Signalling , # 2638 ) , Rabbit IgG ( Santa Cruz , # sc-2027 ) , and Goat IgG ( Santa Cruz , # sc-2028 ) were added to 50 μl of protein-A beads in 200 μl of antibody binding and washing buffer and incubated with rotation mixing at 4 °C for 6 hours . Antibodies were cross-linked to protein-A beads by incubating with dimethyl pimelimidate dihydrochloride ( DMP ) ( Sigma , # D8388 ) in cross-linking buffer ( 0 . 2 M Triethanolamine [pH 8 . 2] ) for 1 hour and 30 minutes at RT . Beads cross-linked with antibodies were then incubated with test lysates along with 1X PIC at 4 °C for 2 hours . Three brief washes in washing buffer were followed by elution in low-pH elution buffer ( 0 . 1 M glycine [pH 2 . 5] ) . The elutes were then run in SDS-PAGE along with input fractions and immunoblotted with respective antibodies . Total RNA was isolated from NIH/3T3 , NIH/3T3-Ginir , NIH/3T3-Giniras , and Clone M3 cells using TRizol reagent according to the manufacturer’s protocol . RNA-seq libraries were generated using the Illumina TruSeq kit ( version 2 ) following the manufacturer’s instructions . The libraries were sequenced on a HiSeq 2000 system ( Illumina ) , and the RNA-seq reads ( read length 100 bp ) were analysed with Basespace’s RNA Express pipeline ( RNA Express Legacy version: 1 . 0 . 0 ) , which encompasses alignment using HISAT2 [116] . Quantification and differential analyses were done using String Tie-Ballgown protocol . Identification of GO terms enriched in the genes up-regulated in NIH/3T3-Ginir was performed using online tool DAVID ( https://david . ncifcrf . gov/summary . jsp ) . Noncoding potential of Ginir was validated using prediction tools like test code , CPC program , and phylo CSF . ORF finder was used to predict the probable ORFs in Ginir sequence . Chromosomal localisation and other gene-based predictions in relation to Ginir sequence were carried out using the UCSC genome browser . Computational prediction of Ginir interaction to probable proteins was done with tools like catRAPID and RPIseq . We used Ensembl and Uniport databases to validate Cep112 isoforms . Protein docking analysis for Brca1 and Cep112 was done using ZDOCK tool . All statistical analyses were performed using Graphpad Prism software . Results are presented as mean ± SEM . We used one-way or two-way ANOVA for comparison between two experimental groups in different experiments , as described in the figure legends . Additional statistical information has also been provided in the figure legends . P ≤ 0 . 05 was considered as statistically significant for all the experiments , and values were assigned accordingly ( *P ≤ 0 . 05 , **P ≤ 0 . 001 , ***P ≤ 0 . 0001 ) .
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The growth of multicellular organisms is tightly regulated by cellular homeostasis mediated by cell division . This is achieved with the help of various proteins acting in a highly coordinated manner via intricately woven intercellular signalling pathways , which regulate cell division . Here , we identify a long noncoding RNA pair , which we named Genomic Instability Inducing RNA ( Ginir ) /antisense RNA of Ginir ( Giniras ) , and explore its function in cellular homeostasis . We show that this RNA pair is expressed in a spatiotemporally regulated manner during development and is enriched in the brain . We find that Ginir acts as a dominant oncogene when Ginir transcript levels are overexpressed in mouse fibroblasts and that centrosomal protein 112 ( Cep112 ) is its interacting protein partner . We also report that Cep112 interacts with breast cancer type 1 susceptibility protein ( Brca1 ) , a protein well known for its role in genome surveillance . Our data reveal that interactions between these two proteins are perturbed in the presence of excessive levels of Ginir RNA , which results in aberrant mitosis and drives the cells towards neoplastic transformation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"non-coding",
"rna",
"sequences",
"centrosomes",
"protein",
"interactions",
"gene",
"regulation",
"cancer",
"risk",
"factors",
"long",
"non-coding",
"rnas",
"oncology",
"mammalian",
"genomics",
"medical",
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"cellular",
"structures",
"and",
"organelles",
"small",
"interfering",
"rnas",
"genome",
"complexity",
"antisense",
"rna",
"proteins",
"epidemiology",
"gene",
"expression",
"animal",
"genomics",
"biochemistry",
"rna",
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] |
2018
|
Noncoding RNA Ginir functions as an oncogene by associating with centrosomal proteins
|
The adjuvanticity of bacterial adenylate cyclase toxins has been ascribed to their capacity , largely mediated by cAMP , to modulate APC activation , resulting in the expression of Th2–driving cytokines . On the other hand , cAMP has been demonstrated to induce a Th2 bias when present during T cell priming , suggesting that bacterial cAMP elevating toxins may directly affect the Th1/Th2 balance . Here we have investigated the effects on human CD4+ T cell differentiation of two adenylate cyclase toxins , Bacillus anthracis edema toxin ( ET ) and Bordetella pertussis CyaA , which differ in structure , mode of cell entry , and subcellular localization . We show that low concentrations of ET and CyaA , but not of their genetically detoxified adenylate cyclase defective counterparts , potently promote Th2 cell differentiation by inducing expression of the master Th2 transcription factors , c-maf and GATA-3 . We also present evidence that the Th2–polarizing concentrations of ET and CyaA selectively inhibit TCR–dependent activation of Akt1 , which is required for Th1 cell differentiation , while enhancing the activation of two TCR–signaling mediators , Vav1 and p38 , implicated in Th2 cell differentiation . This is at variance from the immunosuppressive toxin concentrations , which interfere with the earliest step in TCR signaling , activation of the tyrosine kinase Lck , resulting in impaired CD3ζ phosphorylation and inhibition of TCR coupling to ZAP-70 and Erk activation . These results demonstrate that , notwithstanding their differences in their intracellular localization , which result in focalized cAMP production , both toxins directly affect the Th1/Th2 balance by interfering with the same steps in TCR signaling , and suggest that their adjuvanticity is likely to result from their combined effects on APC and CD4+ T cells . Furthermore , our results strongly support the key role of cAMP in the adjuvanticity of these toxins .
Development of an effective humoral immune response is crucially dependent on T cell help . The last step of B cell differentiation , involving immunoglobulin affinity maturation and isotype switching , occurs in peripheral lymphoid organs under the guidance of a specialized CD4+ T cell subset , known as T helper 2 ( Th2 ) . These cells provide both soluble ( IL-4 ) and membrane-bound ( CD40L ) factors essential for terminal differentiation of antigen specific B cells [1] . Th2 cells are characterized by expression of a unique complement of cytokines , including IL-4 , IL-5 , IL-10 and IL-13 , which are expressed through a complex transcriptional program involving chromatin remodelling at the Th2 cytokine locus control region and de novo expression of the lineage specific transcription factors c-maf and GATA-3 [2] . Priming the Th2 differentiation program in naive CD4+ T cells requires essential cues which are provided by antigen presenting cells ( APC ) in the form of cytokines . Engagement of the T cell antigen receptor ( TCR ) on naive T cells in the presence of IL-4 promotes their differentiation to Th2 effector cells , whilst simultaneously antagonising committment to the alternative Th1 lineage , which controls cell mediated immunity [1] , [2] . Additional factors present during T cell priming may profoundly affect the developmental program of helper T cells . Among these , of paramount importance is the second messenger cAMP , which is produced by cellular adenylate cyclases in response to heterotrimeric G-protein coupled surface receptors , such as the receptors for prostaglandin E2 , a proinflammatory prostanoid produced by activated APC [3] . cAMP has been shown to favour Th2 cell differentiation and GATA-3 dependent production of IL-4 and IL-5 through a pathway regulated by phosphoinositide-dependent kinase 1 ( PDK1 ) and protein kinase A ( PKA ) [4]–[9] . Suppression of both innate and adaptive immune responses through elevation of intracellular cAMP to supraphysiological levels represents a powerful strategy of immune evasion by many bacterial pathogens . This can be achieved indirectly , as for the bacterial enterotoxins , cholera toxin ( CT ) and E . coli heat-labile enterotoxin ( LT ) , which enhance intracellular cAMP production by activating the Gsα subunit of heterotrimeric G-proteins coupled to cellular adenylate cyclases [10] . Alternatively , bacteria such as B . anthracis or B . pertussis produce and deliver into target cells an adenylate cyclase toxin , the edema factor ( EF ) and CyaA respectively , respectively , which are themselves adenylate cyclases that catalyze the production of large amounts of cAMP [11] , [12] . Notwithstanding their immmunosuppressive activity , when administered to mice at subtoxic concentrations together with antigen these toxins potentiate antibody responses , an effect associated with enhanced generation of antigen specific Th2 cells [13]–[17] . The adjuvanticity of cAMP elevating toxins is believed to result from their capacity to modulate APC differentiation and function . This is exemplified by ET and CyaA , which have been reported to selectively inhibit the production by macrophages and dendritic cells of the master Th1 polarizing cytokine , IL-12 , while upregulating IL-4 and IL-10 production , thereby enhancing the induction of Th2 cells [13]–[15] , [17]–[19] . The finding that both non-hydrolysable cAMP analogues and PGE2 evoke similar effects on APC [18]–[21] strongly supports the notion that the cAMP elevating activity of these toxins largely accounts for their capacity to differentially affect cytokine production by APC . We and others have demonstrated that ET and CyaA potently suppress T cell activation [22]–[24] . This activity results from their capacity to uncouple TCR engagement from activation of the MAP kinase cascade , which is essential for the initiation of the transcriptional program governing T cell activation , proliferation and subsequent differentiation to armed effector cells . Here we have investigated the additional possibility , suggested by the instructive role of the cAMP/PKA axis in Th2 cell differentiation [4]–[9] , that these bacterial toxins might alter TCR signaling to promote naive T cell committment to the Th2 lineage when used at low concentrations . The results show that both ET and CyaA , but not their enzymatically deficient counterparts , directly affect the Th1/Th2 balance by selectively inhibiting TCR dependent activation of the Th1 driving kinase Akt1 while enhancing activation of two essential components of the TCR signaling cascade selectively implicated in human Th2 cell differentiation , the guanine nucleotide exchanger Vav1 and the stress kinase p38 . These data support the notion that the adjuvanticity of these cAMP elevating toxins results from their combined effects on APC and CD4+ T cells .
Both B . anthracis ET and B . pertussis CyaA potently suppress T cell activation through their cAMP elevating activity [22]–[24] . To assess the potential effect of these toxins on CD4+ T cell differentiation , a permissive concentration of ET or CyaA was identified in a T cell proliferation assay . Genetically inactivated mutants of ET and CyaA , EL1 [25] and CyaA-E5 [26] respectively , were included as controls . Both ET and CyaA inhibited T cell proliferation in a concentration-dependent manner ( Figure 1A ) . Conversely , neither EL1 or CyaA-E5 affected T cell proliferation ( Figure 1B ) . The toxin concentration selected for the polarization experiments , 0 . 11 nM and 0 . 28 nM for ET and CyaA respectively , resulted in ∼40% inhibition in the proliferation assays ( see arrow in Figure 1A ) . A time course analysis of intracellular cAMP production in purified peripheral blood T cells showed that these concentrations of ET and CyaA induced a detectable accumulation of intracellular cAMP , albeit at much lower levels as compared to that obtained with high toxin concentrations ( Figure 2A , top ) . The kinetics of cAMP production by ET and CyaA were significantly different . A fully immunosuppressive ET concentration resulted in a slow increase in intracellular cAMP beginning from 2 h , with a further progressive rise up to 8 h ( Figure 2A , top left ) . On the other hand , a fully immunosuppressive concentration of CyaA evoked a rapid rise of cAMP to plateau levels beginning from the earliest time point analyzed , and the levels of cAMP remained high up to 8 h ( Figure 2A , top left ) . High cAMP concentrations were still measurable after 24 h ( data not shown ) . The kinetics of cAMP production by immunosuppressive concentrations of ET and CyaA were largely reproduced by the low toxin concentrations selected for the studies on T cell polarization ( Figure 2A , top right , and data not shown for 24 h ) . No increase in cAMP was elicited by EL1 and CyaA-E5 , even at the highest concentration used ( Figure 2A , bottom left ) . To understand whether the modest increase in the levels of cAMP catalyzed by low concentrations of ET or CyaA was sufficient to elicit a biological response , we measured the activity of PKA , one of the major cellular targets of cAMP . As a readout of PKA activation we used an antibody specific for the phosphorylated PKA consensus , R-X-X-pT-X-X/R-R-X-pS-X-X , which recognizes phosphorylated PKA substrates . The increase in intracellular cAMP following T cell treatment with high concentrations of ET or CyaA resulted in a strong potentiation of PKA activity , as shown by the qualitative and quantitative changes in the phosphoprotein pattern in lysates from toxin-treated cells compared to untreated cells ( Figure 2B , top panel ) . A similar enhancement in PKA activity was also observed in cells treated with low concentrations of ET or CyaA , despite the smaller increase in intracellular cAMP measured under these conditions ( Figure 2B , top panel ) . Interestingly , notwithstanding the different interacellular localization of the two adenylate cyclase toxins , there was a general overlap in the phosphoprotein pattern observed in cells treated with ET or CyaA . Consistent with the agonistic activity of ET and CyaA on PKA , analysis of the phosphorylation state of the transcriptional activator CREB , a specific PKA substrate , showed that low toxin concentrations induced CREB phosphorylation , albeit to a lesser extent compared to high toxin concentrations ( Figure 2B , middle panel ) . The agonistic effect of the toxins was abrogated to a significant extent when cells were pretreated with pharmacological PKA inhibitors ( H89 or KT5720 ) ( Figure 2C and data not shown ) . Moroever , no CREB phosphorylation was observed in T cells treated with the adenylate cyclase defective ET or CyaA mutant ( Figure 2C ) , supporting the notion that the effects of the toxins are mediated by the cAMP/PKA axis . Of note , maximal activation of both PKA and CREB was observed in cells stimulated by TCR/CD3 cross-linking ( Figure 2B ) , consistent with the potent agonistic activity of the receptor on cAMP production ( Figure 2A , bottom right ) . However , as opposed to the long-lasting increase in cAMP elicited by the toxins , TCR engagement resulted in a transient increase in intracellular cAMP ( Figure 2A , bottom right ) . No further significant enhancement in TCR-dependent PKA or CREB activation was observed in T cells treated with ET or CyaA ( Figure 2B and respective legend ) , despite the increase in cAMP production observed under these conditions ( Figure 2D ) , indicating that PKA and CREB activation reaches plateau levels in response to the cAMP burst elicited by the TCR . To assess the impact of low concentrations of ET and CyaA on human helper T cell polarization , enriched human CD4+ T cells from healthy donors were exposed to ET or CyaA and subsequently primed by TCR/CD3 cross-linking using immobilized anti-CD3 mAb . After 10 days , cells were washed , and restimulated for 24 h or 48 h using the same anti-CD3 mAb . The identity of the Th subset into which cells had differentiated was determined by ELISPOT analysis of cytokine production . As shown in Figure 3A , priming of cells that had been exposed to low concentrations of ET or CyaA resulted in a dramatic increase in production of the Th2 cytokines , IL-4 and IL-13 , to levels close to those measured in cells primed to differentiate to the Th2 subset ( TCR/CD3 cross-linking in the presence of IL-4 ) . Conversely , consistent with the mutual antagonism of the Th1/Th2 differentiation programs , ET or CyaA had a modest inhibitory effect on production of the Th1 cytokines IFNγ and TNF-α ( Figure 3B ) . No significant enhancement in Th2 cytokines above the levels produced by T cells primed in neutral conditions ( TCR/CD3 cross-linking alone ) was observed when cells were pretreated with the adenylate cyclase defective EL1 or CyaA-E5 mutants ( Figure 3A ) , indicating that the Th2 driving activity of ET and CyaA is dependent on their capacity to produce cAMP . Differentiation of helper T cells to the Th2 subset is crucially dependent on expression of the lineage specific transcription factors c-maf and GATA-3 , which are essential for transcriptional regulation of the Th2 cytokine control locus [2] . To understand whether ET and CyaA promote production of IL-4 and IL-13 by shaping the transcriptional program triggered by the TCR in naive T cells , resulting in expression of lineage specific transcription factors , the levels of c-maf and GATA-3 mRNA in T cells primed in the presence of either toxin were measured by real-time RT-PCR . Both ET and CyaA potently upregulated expression of c-maf and GATA-3 to levels comparable or higher than those detectable in T cells primed in the presence of IL-4 ( Figure 4A ) . Conversely , expression of the Th1 lineage specific transcription factor T-bet was not significantly affected in T cells primed in the presence of either CyaA or ET ( Figure 4B ) . TCR signaling is initiated by Lck , a T cell specific Src family protein tyrosine kinase which is responsible for phosphorylation of the ITAMs within the ζ chain of the CD3 complex . As all Src kinases , Lck is negatively regulated by a C-terminal tyrosine residue , Y505 , which , when phosphorylated , establishes an intramolecular interaction with the SH2 domain , resulting in a close , inactive conformation . The inhibitory tyrosine residue is phosphorylated by Csk , which in resting cells is maintained close to Lck in lipid rafts through interaction with the PAG adaptor and whose activity is potentiated by PKA dependent phosphorylation of a serine residue at position 364 [27] . By elevating intracellular cAMP , ET and CyaA have therefore the potential to antagonize TCR signaling beginning from the earliest step . Analysis of the phosphorylation state of Y505 on Lck using a phosphospecific antibody revealed that high concentrations of ET or CyaA effectively block TCR dependent dephosphorylation of Lck ( Figure 5A and 5B ) . This activity was not reproduced by the respective adenylate cyclase defective mutants ( Figure 5B ) , supporting the notion that the suppressive effect of ET and CyaA on TCR dependent Lck activation is mediated by cAMP . No enhancement in Lck kinase activity in response to TCR engagement was moreover observed when cells were pretreated with high concentrations of ET or CyaA , as assessed by measuring Lck autophosphorylation in in vitro kinase assays ( data not shown ) . Consistent with the failure of the TCR to trigger activation of Lck in the presence of either toxin , both TCR dependent CD3ζ phosphorylation and activation of the effector kinase ZAP-70 , which occurs following recruitment to the phosphorylated ITAMs of CD3ζ , were found to be inhibited by ET or CyaA ( Figure 5A and 5B ) . In agreement with previous reports [22]–[24] , activation of the MAP kinase cascade , which couples these early signaling events to gene transcription , was found to be impaired by immunosuppressive concentrations of ET or CyaA , as assessed using as a readout phosphorylation of Erk1/2 ( Figure 5A and 5B ) . Conversely , neither CD3ζ and ZAP-70 phosphorylation , nor Erk1/2 phosphorylation , were affected when the TCR was stimulated in cells exposed to low , Th2 polarizing concentrations of ET or CyaA ( Figure 6A and 6B ) . To understand whether Th2 polarizing concentrations of ET and CyaA could selectively affect downstream components of TCR signaling specifically implicated in Th lineage committment , we focused on two molecules in the TCR signaling cascade , the Rac/Cdc42 specific guanine nucleotide exchanger Vav1 and the stress-activated kinase p38 , which have been implicated in human Th2 cell differentiation [28]–[32] . Furthermore , we assessed the effect of the toxins on the serine/threonine kinase Akt1 , which has been associated to Th1 cell differentiation [9] . Strikingly , analysis of Akt1 activation using phosphospecific antibodies which recognize two critical residues , T308 and S473 , showed that low , Th2 polarizing concentrations of ET or CyaA were sufficient to potently impair TCR dependent Akt phosphorylation ( Figure 7A and data not shown ) . Conversely , both basal and TCR dependent Vav1 phosphorylation on Y174 , which positively regulates Vav1 activity , was potentiated by low concentrations of ET or CyaA ( Figure 7B ) . A similar enhancement was observed for p38 ( Figure 7B ) , consistent with the capacity of PKA to act as an agonist of this kinase [31] , [33] . The phosphodiesterase inhibitor , IBMX , further potentiated the agonistic activity of the toxins on p38 activation ( data not shown ) , further supporting the notion that the effects of the toxins are mediated by cAMP . Hence ET and CyaA alter the Th1/Th2 balance at least in part by antagonizing the Akt1 dependent pathway leading to Th1 cell differentiation and by potentiating the Vav1 and p38 dependent pathway ( s ) leading to Th2 cell differentiation . Of note , TCR dependent Vav1 and p38 activation was not impaired , but actually enhanced , when cells were pretreated with high concentrations of ET or CyaA ( Figure 7B ) , despite their potent inhibitory activity on initiation of TCR signaling , suggesting that an Lck independent pathway triggered by the TCR , which can be potentiated by cAMP , may contribute to a significant extent to their activation .
The B . anthracis ET and B . pertussis CyaA adenylate cyclase toxins act as potent suppressors of T cell activation and proliferation in the 10−9–10−6 molar range of concentrations [22]–[24] . In the absence of systemic intoxication , these high concentrations are likely to be reached only locally through accumulation of the toxins at the primary site of infection . However , there are anatomical districts and localized infections ( e . g . cutaneous anthrax ) where low amount of toxins may be released and might modulate the host immune response . We found that both ET and CyaA are potent promoters of naive CD4+ T cell differentiation to Th2 effectors when used at subnanomolar concentrations ( 0 . 1–0 . 3 nM ) . Interestingly , distinct effects of high vs low concentrations of CyaA have also been observed in neutrophils and other phagocytes , ranging from cytolysis to apoptosis to impairment of effector functions [34] , suggesting the biological outcome of host cell exposure to the toxin is likely to be dictated by its proximity to the bacterium . The sensitivity of T cells to such low ET concentration can be accounted for by the fact that human leukocytes express the high affinity CMG2 receptor for protective antigen ( PA ) , the receptor binding subunit of ET [35] . Furthermore , although T cells lack CD11b/CD18 , the only known CyaA receptor , CyaA can effectively insert into cell membranes or artificial lipid bilayers in the absence of CD11b/CD18 , albeit with a reduced efficacy [36] . The presence on T cells of a putative alternative CyaA receptor cannot however be ruled out . The immunosuppressant activity of high concentrations of ET and CyaA is fully consistent with the known inhibitory effects of cAMP on T cell activation . In physiological conditions cAMP production by a TCR-coupled adenylate cyclase is part of a negative feed-back loop which ensures extinction of TCR signaling through PKA dependent activation of Csk , a kinase that inhibits Lck by phosphorylating its C-terminal tyrosine residue [27] . This feed-back loop does not become immediately operational because cAMP production is counterbalanced by TCR dependent recruitment of PDE-4 to lipid rafts , where also the activated TCR localizes , thereby allowing the protein tyrosine kinase cascade to start [37] . Once PDE-4 dissociates from lipid rafts , the feed-back loop can terminate the signal . Since cAMP production and PKA activation are TCR-dependent , cAMP returns to basal levels after signal extinction . Alterations in this finely regulated cAMP balance by adenylate cyclase agonists , such as PGE2 receptors , result in impaired TCR signaling and T cell activation [27] . The inhibitory activity of high concentrations of ET and CyaA on Lck activation and CD3ζ phosphorylation indicates that these toxins preventing firing of the TCR signaling cascade by altering the cAMP balance through the massive and sustained production of cAMP . In this context , it should be underlined that TCR engagement results in a rapid elevation in the levels of cAMP , which elicit a potent enhancement in PKA activation , comparable to the one observed in cells exposed to high toxin concentrations . Nevertheless , under these conditions the TCR triggers a productive signaling cascade , as opposed to cells pre-exposed to high concentrations of ET or CyaA , supporting the importance of the spatiotemporal regulation of cAMP in TCR signaling . Low toxin concentrations on the other hand , do not inhibit initiation of TCR signaling . The intracellular concentration of cAMP measured under these conditions , which is very modest compared to the burst of cAMP evoked by the TCR , may be locally and transiently neutralized by PDE-4 . At variance with their inability to impair initiation of TCR signaling , low concentrations of ET or CyaA were found to selectively affect specific downstream nodes -Akt1 , Vav1 and p38 activation- crucial to Th1/Th2 lineage committment . This activity is likely to result from their PKA dependent modulation of intracellular signaling mediators implicated in Th2 cell differentiation downstream of signal initiation . Akt1 has been reported to favour Th1 cell differentiation by providing the CD28 costimulatory signal required for expression of the Th1 cytokines IL-2 and IFN-γ [9] . Although our study was carried out on T cells stimulated by TCR/CD3 cross-linking in the absence of CD28 costimulation , the results show that Akt1 is effectively phosphorylated in response to TCR engagement and that this event is potently inhibited by low concentrations of ET or CyaA . The negative regulation of Akt by PKA [38] , [39] is likely to underlie this inhibitory , TCR-distal effect of the two toxins , which would moreover favour differentiation to the Th2 lineage by potentiating the PDK1/PKA pathway coupling the TCR to IL-4 gene transcription [9] . Under the same conditions , both toxins enhance TCR dependent phosphorylation of the guanine nucleotide exchanger Vav1 and activation of the stress kinase p38 , which participate in Th2 lineage commitment . A skewing of the Th1/Th2 balance to Th1 , as well as defects in Th2 dependent B cell responses , have been indeed observed in Vav1−/− mice [28] , [40] . Furthermore , p38 has been implicated in human Th2 cell differentiation , at least in part through its capacity to promote activation of GATA-3 [29]–[32] . The similar enhancement of TCR-dependent Vav and p38 activation in the presence of high toxin concentrations , which block signal initiation , supports a local effect on a specific signaling module independent of the TCR proximal , Lck-dependent signaling cascade . While there is evidence for an agonistic role of cAMP in p38 activation [31] , the potential function of cAMP in the modulation of Vav1 activity has not been directly addressed . We have previously characterized a Fyn dependent , Lck independent pathway linking the TCR to Vav1 phosphorylation and p38 activation which could be potentiated by PGE2 [41] . Together with the evidence implicating Vav1 and p38 in Th2 cell differentiation , the Th1 bias observed in Fyn−/− mice [42] , [43] may suggest a potential involvement of this cAMP sensitive pathway in the Th2 promoting activity of ET and CyaA . It is noteworthy that the effects of ET and CyaA are almost undistinguishable , notwithstanding their differences in stucture , mode of cell entry and intracellular localization . ET is an A–B type toxin , consisting of a cell binding component , PA , which targets cells via the receptors TEM8 or CMG2 , and a toxin component , EF . ET enters the cell by receptor mediated internalization and is transported to the endosomes , wherefrom it is released into the cytosol [11] . CyaA is a single polypeptide which binds to target cells both directly and through a membrane receptor , which in macrophages and other APC is the integrin CD11b/CD18 [44] . Following binding , the adenylate cyclase N-terminal domain is translocated accross the plasma membrane of target cells [12] . Hence EF and CyaA produce cAMP not only with different kinetics , which is delayed for ET probably due to the multistep mechanism of delivery into host cells , but also at different subcellular locations , in the cytosol with a prevalent perinuclear localization , and close to the plasma membrane , respectively [45]–[47] . The role of AKAPs in segregation of PKA pools at specific subcellular localizations and dynamic recruitment of phosphodiesterases underscores the importance of the spatiotemporal control of cAMP signaling [48] . By focalizing cAMP production at distinct sites within the cell , EF and CyaA could differentially affect early and late events in TCR signaling . Our findings indicate that the critical targets of the cAMP dependent Th2 polarizing activities of ET and CyaA can be activated independently of the subcellular site of cAMP production . It is likely that the sustained cAMP production overrides the negative local feedback mechanisms , resulting in loss of compartmentalization of cAMP dependent signaling . It should be however underlined that low ET concentrations are almost as effective as high concentrations in triggering PKA activation , suggesting that ET may modulate other functions , such as CREB mediated gene expression [45] , [47] , through activation of specific PKA pools . Both ET and CyaA have been reported to potentiate antibody responses and development of antigen specific Th2 cells in mice when coadministered with antigen [13]–[17] . This adjuvant activity had been related to their capacity to modulate cytokine production by dendritic cells and macrophages in vitro , resulting in reduction in IL-12 and enhancement in IL-10 and IL-4 expression [13]–[16] , [18] , [19] . Our finding that ET and CyaA directly affect the Th1/Th2 balance by favouring Th2 cell development suggests that their adjuvanticity is also due to their effects on CD4+ T cells . This activity appears mediated by cAMP , as it cannot be reproduced by the respective enzymatically deficient mutants . The Th2 polarizing effects of these toxins resulting from their modulation of cytokine expression by APC have also been related to their cAMP elevating activity [15] , [18] , [19] , [49] . Consistent with these findings , both non-hydrolysable cAMP analogues and PGE2 have been reported to favour Th2 cell development both by directly affecting CD4+ T cell differentiation [4] , [6] , [8] and by shaping the pattern of cytokine production by APC [19]–[21] . The Th2 driving activity of ET may be very relevant to cutaneous anthrax , where there is a limited toxin production and where resolution of infection has been causally linked to the development of an antibody response against the toxin [50] , [51] . As opposed to the clear-cut role of cAMP in both the direct and the APC dependent Th2 driving activity of ET or CyaA in vitro , the role of cAMP in the adjuvanticity of the toxins in vivo is more controvertial . While the adjuvant activity of adenylate cyclase deficient ET mutants has as yet not been tested in vivo , there are discrepancies as to the adjuvanticity of catalytically inactive CyaA mutants , which have been proposed to result from a number of factors , including the genetic background of the mouse strain , the route of antigen delivery , the dose of CyaA mutant and the vaccination schedule [14] , [15] , [52] , [53] . Genetically detoxified mutants of other cAMP elevating toxins such as CT or LT-I , or their individual B ( binding ) subunits , have been demonstrated to retain adjuvant activity [10] , indicating that both cAMP production and toxin binding to specific receptors contribute to their adjuvanticity . This possibility has been ruled out for ET , as PA does not harbour any activity either on APC or T cells in vitro nor acts as an adjuvant in vivo [16] . On the other hand , at variance with other reports [15] , [53] , an enzymatically deficient CyaA mutant , highly purified to rule out a contamination by LPS , has been reported to display adjuvant properties comparable or even superior to wild-type CyaA [14] , [52] . A potential implication of CD11b/CD18 in the adjuvanticity of CyaA appears unlikely , as this integrin suppresses cytokine production by dendritic cells [54] , [55] , and moreover does not account either for the immunodeviating activity of CyaA on APC in vitro , which is cAMP dependent [15] , [18] , [19] , or for the conflicting results obtained by different groups in vivo [14] , [15] , [52] , [53] . An integrated and detailed analysis of the structural and functional interaction of adenylate cyclase toxins with the different cellular components which together orchestrate the immune response is expected not only to clarify their mechanism of adjuvanticity but also to lead to the development of more specific and effective adjuvants .
Peripheral blood mononuclear cells were purified from buffy coats from anonymous healthy donors ( collectively ∼30 , available from authorised blood banks ) by density gradient centrifugation on Ficoll-Paque ( Amersham Biosciences , Buckinghamshire , UK ) , using a Beckman GS-6R tabletop centrifuge ( Beckman Coulter SpA , Milan , Italy ) . Cells were washed 2× in phosphate buffered saline ( PBS ) , resuspended in RPMI 1640 ( Invitrogen Ltd , Paisley , UK ) ( buffered with sodium bicarbonate to pH 7 . 2 ) supplemented with 7 . 5% fetal calf serum ( FCS ) ( Hyclone , Thermofischer Scientific Inc , SouthLogan , UT ) , plated in plastic flasks ( Sarstedt AG , Numbrecht , Germany ) and incubated overnight at 37°C in a humidified atmosphere with 5% CO2 . Non-adherent cells , which consisted principally of peripheral blood lymphocytes ( PBL ) and of which >90% were T cells ( CD3+ ) , were centrifuged at 800×g for 5 min at room temperature in Beckman GS-6R tabletop centrifuge and resuspended in fresh RPMI 1640 supplemented with 7 . 5% FCS . For cAMP measurement , T cells were purified from peripheral blood mononuclear cell suspensions using the StemSep Human T cell enrichment kit ( Voden Medical Instruments SpA , Milan , Italy ) . Phosphospecific antibodies recognizing the phosphorylated active forms of CD3ζ , ZAP-70 ( Y493 ) , Vav1 ( Y160 ) , Akt1 ( T308/S473 ) , Erk1/2 ( T202/Y204 ) , p38 ( T180/Y182 ) and CREB ( S133 ) , as well as an antibody recognizing phosphorylated Y505 on Lck , were from Cell Signaling Technology ( Beverly , MA ) , Santa Cruz Biotechnology ( Santa Cruz , CA ) and Biosource Europe SA ( Nivelles , Belgium ) . An antibody against the phosphorylated PKA consensus phosphorylation site , R-X-X-pT-X-X/R-R-X-pS-X-X , was purchased from Cell Signaling Technology . Anti-CD3ζ , -Lck , ZAP-70 , -Vav , -Erk2 , -p38 , -CREB and anti-actin antibodies were from Santa Cruz Biotechnology , Upstate Biotechnology ( Dundee , UK ) and Cell Signaling Technology . A mAb suitable for immunoprecipitation of tyrosine phosphorylated CD3ζ was kindly provided by M . Banyiash . Fluorochrome-labeled anti-CD3 mAb were obtained from Becton Dickinson Biosciences ( Milan , Italy ) . Unlabeled secondary antibodies were purchased from Cappel ( ICN Pharmaceuticals Inc , CA ) and peroxidase labeled antibodies from Amersham Biosciences . IgG antibodies from OKT3 ( anti-CD3; American Type Culture Collection , Manassas , VA ) hybridoma supernatants were purified on Mabtrap ( Amersham Biosciences , Inc ) and titrated by flow cytometry . CyaA and the enzymatically inactive variant CyaA-E5 ( resulting from a Leu-Gln dipeptide insertion between D188 and I189 in the catalytic core of the enzyme ) were expressed in E . coli and purified to near homogeneity by previously established procedures modified as described [56] in order to eliminate most of the contaminating endotoxin . The specific activity of CyaA , measured as described in Ladant et al . [26] was higher than 500 µmol cAMP/min . mg whereas CyaA-E5 had no detectable enzymatic activity . In both preparations the endotoxin content , determined using a LAL assay ( QCL-1000 kit from Lonza ) , was below 0 . 5 EU/µg protein . PA , LF and EL1 were expressed in E . coli and purified as described [25] , [45] , [57] . H89 , KT5720 , IBMX and 8-CPT were purchased from Sigma-Aldrich ( Milan , Italy ) and Calbiochem ( Merck Biosciences GmbH , Schwalboch , Germany ) . For immunoblot analyses cells were plated at 5×106 cells/ml in plastic flasks in RPMI 1640 supplemented with 7 . 5% FCS and 2 mM CaCl2 ( required for CyaA entry into the cells ) , added with CyaA/CyaA-E5 , and incubated at 37°C in a humidified atmosphere with 5% CO2 for 2 h before activation . Alternatively , cells were plated as above , added with ET ( ratio PA∶EF 1 . 6 ) , and incubated at 37°C for 6 h before activation . Activations by TCR/CD3 cross-linking were performed by incubating PBL with saturating concentrations of anti-CD3 mAb ( as assessed by flow cytometry ) and 50 µg ml−1 secondary antibodies ( goat anti-mouse immunoglobulin Ig ) in RPMI 1640 for 1–5 min at 37°C as previously described [58] . None of the above mentioned treatments affected cell viability , as assessed by Trypan blue exclusion ( data not shown ) . When required , cells were pretreated with the PKA inhibitors , H89 ( 20 µM ) and KT5720 ( 56 nM ) , or with the PDE inhibitor , IBMX ( 0 . 5 mM ) , for 1 h before addition of ET or CyaA . Alternatively , cells were treated for 30 min with 8-CPT ( 100 µM ) . Cells were recovered by centrifugation at 16 , 000×g for 30 sec at 4°C in an Eppendorf 5415R microcentrifuge ( Eppendorf srl , Milan , Italy ) , washed 2× in PBS and lysed in 1% ( v/v ) Triton X-100 in 20 mM Tris-HCl pH 8 , 150 mM NaCl ( in the presence of 0 . 2 mg/ml Na orthovanadate , 1 µg/ml pepstatin , leupeptin , and aprotinin , and 10 mM phenyl methyl sulfonyl fluoride ) . To normalize for variations in protein content among samples , equal amounts of proteins from each sample ( measured using a kit from Pierce , Rockford , IL ) were resolved by 12% SDS-PAGE and transferred to 0 . 45-µm nitrocellulose filters Whatman GmbH , Dassel , Germany ) . Prestained molecular mass markers ( Invitrogen ) were included in each gel . Immunoblots were carried out using primary antibodies and peroxidase-labeled secondary antibodies according to the manufacturers' instructions and a chemiluminescence detection kit ( Pierce ) . Blots were scanned using a laser densitometer ( Duoscan T2500 Agfa , Milan , Italy ) and quantified using the ImageQuant 5 . 0 software ( Molecular Dynamics , Sunnyvale , CA ) . Data were normalized to loading controls . For proliferation assays , cells ( 2×105/sample ) were plated in 96-well plates in RPMI 1640 supplemented with 7 . 5% FCS ( and 2 mM CaCl2 for CyaA treatments and respective controls ) , added with CyaA/CyaA-E5 ( 0 . 07–45 nM ) or ET/EL1 ( 0 . 01 pM–1 . 1 nM ) , and incubated at 37°C in a humidified atmosphere with 5% CO2 for 2 h ( CyaA ) or 6 h ( ET ) before activation . Cells were activated by CD3 cross-linking on secondary antibody-coated plates as described [58] and processed 16–48 h after activation . [3H]-thymidine ( 1 mCi ) was added to each microtiter well ( 96-well plates ) for the last 18 h of culture . After harvesting the cells with an automatic harvester ( Micromate 196 , Canberra Packard , Meriden , CT ) , proliferation was determined by measuring the [3H]thymidine ( Amersham , Buckinghamshire , UK ) incorporation in a β-counter ( Matrix 9600 , Canberra Packard , Meriden , CT ) . To measure Th cell differentiation , enriched human CD4+ T cells were activated by immobilized anti-CD3 mAb ( purified from OKT3 hybridoma supernatants on Mabtrap , Amersham Biosciences Europe ) as described [59] , in the absence or presence of recombinant hIL-12 ( 2 ng/ml , Sigma-Aldrich Milan , Italy ) to promote Th1 differentiation , or recombinant hIL-4 ( 10 ng/ml , Sigma-Aldrich Milan , Italy ) to promote Th2 differentiation . Recombinant hIL-2 ( kindly provided by Eurocetus Milan , Italy ) was added to the cultures on day 4 and 7 . After 10 days , cells ( 1×106 ) were washed , stimulated for 24 h or 48 h using anti-CD3 mAb and the levels of IL-4 , IL-13 , IFNγ and TNF-α were measured by ELISPOT as described [59] . Intracellular cAMP was quantitated by enzyme-linked immunoassay kit ( Biotrak EIA , Amersham Biosciences ) according to the manufacturers' instructions . For these experiments , cells ( 1×106 plated in 96-well plates in 200 µl RPMI 1640/7 . 5% FCS ) were treated with CyaA/CyaA-E5 or ET/EL1 as described above for 30 min to 24 h in a humidified atmosphere with 5% CO2 . At the end of the treatment , cells were washed 2× in PBS and lysed in the lysis reagent included in the kit . Total RNA was extracted from Th cells , polarized as described above , using Tri Reagent ( Ambion , Austin , TX ) . Reverse transcription-polymerase chain reaction ( RT-PCR ) was carried out on 400 ng total RNA using ImProm-II™ reverse transcriptase and and oligo-dT ( Promega Italia srl , Milan , Italy ) as first strand primer . Real-time quantitative PCR was performed using SYBR Green I SensiMix™ dT Kit ( Quantace , Watford , UK ) according to the manufacturer's instructions , in an Opticon 2 Continuous Fluorescence Detection System ( MJ Research , Bio-Rad Laboratories , Waltham , MA ) . All samples were run in duplicate on 96-well optical PCR plates ( Roche Diagnostics , Milan , Italy ) . The specific primers used to amplify cDNA fragments corresponding to c-maf , GATA-3 , T-bet and GAPDH were: 5′-TGGAGTCGGAGAAGAACCAG-3′ ( sense ) , 5′-GCTTCCAAAATGTGGCGTAT-3′ ( antisense ) for c-Maf; 5′-GAAGGAAGGCATCCAGACCAG-3′ ( sense ) , 5′-ACCCATGGCGGTGACCATGC-3′ ( antisense ) for GATA-3; 5′-TAATAACCCCTTTGCCAAAGG-3′ ( sense ) ; and 5′-TCCCCCAAGGAATTGACAGT-3′ ( anti-sense ) for T-bet and 5′-TGCACCACCAACTGCTTAGC-3′ ( sense ) and 5′-GGCATGGACTGTGGTCATGAG-3′ ( anti-sense ) for GAPDH . After an initial denaturation for 10 min at 95°C , denaturation at the subsequent 40 cycles was performed for 15 s at 95°C , followed by 15 s primer annealing at 60°C and a final extension at 72°C for 30 s . The ΔΔCT method [60] was applied as a comparative quantification method . The specificity of the amplified fragment was demonstrated by the melting curve , where a single peak was observed for each sample amplified with c-maf , GATA-3 , T-bet and GAPDH primers . c-maf , GATA-3 and T-bet mRNA levels were normalized to GAPDH , used as a housekeeping gene . Mean values , standard deviation values and Student's t test ( unpaired ) were calculated using the Microsoft Excel application . A level of P<0 . 05 was considered statistically significant .
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Colonization by pathogens requires keeping at bay the host immune defenses , at least at the onset of infection . The adenylate cyclase ( AC ) toxins produced by many pathogenic bacteria assist in this crucial function by catalyzing the production of cAMP , which acts as a potent immunosuppressant . Nevertheless , at low concentrations , these toxins act as adjuvants , enhancing antibody responses to vaccination . We have investigated the molecular basis of the immunomodulatory activities of two AC toxins , Bacillus anthracis edema toxin and Bordetella pertussis CyaA . We show that high toxin concentrations inhibit activation of T lymphocytes , which orchestrate the adaptive immune response against pathogens , whereas low toxin concentrations promote differentiation of helper T lymphocytes to Th2 effectors , which are required for development of antibody-producing cells . Both the immunosuppressant and Th2–driving activities of the toxins are dependent on cAMP . The results demonstrate that , dependent on their concentration , the AC toxins of B . anthracis and B . pertussis evoke distinct responses on target T lymphocytes by differentially modulating antigen receptor signaling , resulting either in suppression of T cell activation or Th2 cell differentiation . These results are of relevance to the evolution of disease in infected individuals and provide novel mechanistic insight into the adjuvanticity of these toxins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"infectious",
"diseases",
"immunology/immunomodulation",
"immunology/immune",
"response",
"infectious",
"diseases/bacterial",
"infections",
"immunology/immunity",
"to",
"infections",
"immunology/leukocyte",
"activation"
] |
2009
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The Adenylate Cyclase Toxins of Bacillus anthracis and Bordetella pertussis Promote Th2 Cell Development by Shaping T Cell Antigen Receptor Signaling
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Nelson Bay orthoreoviruses ( NBVs ) are members of the fusogenic orthoreoviruses and possess 10-segmented double-stranded RNA genomes . NBV was first isolated from a fruit bat in Australia more than 40 years ago , but it was not associated with any disease . However , several NBV strains have been recently identified as causative agents for respiratory tract infections in humans . Isolation of these pathogenic bat reoviruses from patients suggests that NBVs have evolved to propagate in humans in the form of zoonosis . To date , no strategy has been developed to rescue infectious viruses from cloned cDNA for any member of the fusogenic orthoreoviruses . In this study , we report the development of a plasmid-based reverse genetics system free of helper viruses and independent of any selection for NBV isolated from humans with acute respiratory infection . cDNAs corresponding to each of the 10 full-length RNA gene segments of NBV were cotransfected into culture cells expressing T7 RNA polymerase , and viable NBV was isolated using a plaque assay . The growth kinetics and cell-to-cell fusion activity of recombinant strains , rescued using the reverse genetics system , were indistinguishable from those of native strains . We used the reverse genetics system to generate viruses deficient in the cell attachment protein σC to define the biological function of this protein in the viral life cycle . Our results with σC-deficient viruses demonstrated that σC is dispensable for cell attachment in several cell lines , including murine fibroblast L929 cells but not in human lung epithelial A549 cells , and plays a critical role in viral pathogenesis . We also used the system to rescue a virus that expresses a yellow fluorescent protein . The reverse genetics system developed in this study can be applied to study the propagation and pathogenesis of pathogenic NBVs and in the generation of recombinant NBVs for future vaccines and therapeutics .
Members of the genus Orthoreovirus belonging to the family Reoviridae are nonenveloped viruses . Their genomes contain 10-segmented double-stranded RNA ( dsRNA ) divided into three classes based on their sizes: large ( L1–L3 ) , medium ( M1–M3 ) , and small ( S1–S4 ) . The orthoreoviruses are classified into fusogenic and nonfusogenic subgroups based on their ability to induce cell-to-cell fusion during cell culture [1] . The fusogenic subgroup comprises the avian orthoreovirus ( ARV ) , baboon orthoreovirus ( BRV ) , reptilian orthoreovirus , Broome reovirus ( BroV ) , and Nelson Bay orthoreovirus ( NBV ) , whereas the nonfusogenic subgroup comprises the prototypical mammalian orthoreovirus ( MRV ) [1–3] . Nonfusogenic MRVs are quite common and generally asymptomatic in humans . Although natural infections involving fusogenic orthoreoviruses cause severe diseases in infected animals , infections involving these orthoreoviruses in humans have not been reported . However , in 2006 , the Melaka ( Mel ) virus , a new fusogenic orthoreovirus , was isolated from a patient with acute respiratory tract infection in Malaysia [4] . This newly isolated virus is genetically related to the NBV strains Nelson Bay ( NB ) and Pulau , which were isolated from fruit bats in Australia and Malaysia , respectively [5 , 6] . Subsequently , other related NBV strains have been isolated from patients with severe respiratory illness in Malaysia and Hong Kong [7–10] . Recently , we reported an imported case of a respiratory tract infection associated with NBV strains in a patient who returned to Japan from Bali , Indonesia , in 2007 and termed the strain Miyazaki-Bali/2007 ( MB ) [11 , 12] . Although there is no evidence for direct human-to-human or bat-to-human transmission of MB virus , possible bat-to-human or human-to-human transmissions have been reported in human infections by the Mel , Kampar , and Sikamat viruses [4 , 7 , 9 , 11] . A more recent epidemiological study in Malaysia detected NBVs in the oropharyngeal swab samples of 34 of 200 patients with acute upper respiratory tract infections [13] . These isolates have given rise to increasing concerns about the zoonotic transmission of bat-borne orthoreoviruses in humans . For the most part , the gene segments of orthoreoviruses are monocistronic and encode a single unique translation product . However , the S1 segments of ARV and NBV encode tricistronic mRNAs containing three partially overlapping open reading frames ( ORFs ) that are translated into two nonstructural proteins , p10 and p17 , and one structural protein , σC [14 , 16] . The p10 protein is a fusion-associated small transmembrane ( FAST ) protein that has been shown to induce cell-to-cell fusion and the syncytium-inducing properties of fusogenic orthoreoviruses [17] . The p17 protein , which is encoded by the second ORF of the S1 segment , has no sequence similarity to the known proteins . Previous studies revealed that the ARV p17 is a CRM-1-independent nucleocytoplasmic shuttling protein that plays an important role in the nuclear process comprising gene transcription and cell growth regulation [18 , 19] . The σC protein is encoded by the third ORF of the ARV S1 segment [14] . It is an elongated trimeric minor outer capsid protein and is responsible for cell attachment [20 , 21] . In previous studies , the ARV σC induced high levels of type-specific neutralization antibodies , and the attachment of ARV to permissive cells can be inhibited by pretreatment using recombinant σC protein expressed in Escherichia coli , demonstrating an important role of σC as a receptor binding protein [22 , 23] . Junctional adhesion molecule-A ( JAM-A ) , which interacts with σ1 , a functional and structural homolog of fusogenic orthoreovirus σC proteins , has been identified as a serotype-independent receptor for MRVs [24] . Although the cellular receptor for ARV σC has not been identified , structural studies indicated that the C-terminal globular head domain of σC protein , with a similar overall topology compared to that of MRV σ1 , is involved in receptor binding [25] . The third ORF of the S1 segment of NBV is predicted to encode the homologous cell attachment protein σC when compared with the sequences of other reovirus cell attachment proteins [16] . However , the precise functions of NBV σC in the viral life cycle are poorly defined . A reverse genetics system to engineer viable viruses that contain specific sequence modification is a powerful approach for studying viral replication and pathogenesis and developing vaccines and viral vectors . Although the development of a reverse genetics system for the Reoviridae family has lagged behind that for other RNA virus families because of technical complexities associated with the manipulation of multiple-segmented dsRNA genomes , in recent years , reverse genetics systems have been developed for the genus Rotavirus , rotavirus; genus Orbivirus , bluetongue virus , African horse sickness virus , and epizootic hemorrhagic disease virus; and genus Orthoreovirus , MRV [26–33] . Partial plasmid-based reverse genetics systems that are limited to helper virus-dependent and single-gene modification methods have been developed for rotavirus [27 , 30] . However , reverse genetics systems that are helper virus-independent have been established for orbivirus and orthoreovirus . Bluetongue virus , African horse sickness virus , and epizootic hemorrhagic disease virus can be rescued by the transfection of in vitro-transcribed RNAs into permissive cell lines [29 , 31–33] . An entirely plasmid only-based reverse genetics system has been developed for MRV , and viable viruses can be recovered from cloned cDNAs of each of the 10 viral gene segments [28 , 34] . More recently , a plasmid only-based system has been established for bluetongue virus [35] . In contrast to reverse genetics systems established for nonfusogenic MRVs , the reverse genetics approach has not been developed for fusogenic orthoreoviruses . This technological issue is perhaps the single most important limitation to studies of the aforementioned fusogenic orthoreoviruses . In this study , we developed an entirely plasmid-based reverse genetics system for NBV associated with acute upper respiratory tract infection in humans . This is the first genetic manipulation for the fusogenic subgroup of orthoreoviruses using a strategy that does not require a helper virus and selection system . Deletions and point mutations introduced into viral minor outer capsid σC protein were used to define the function of this protein in the viral life cycle . We found that NBV σC is not required for propagation in murine fibroblast L929 cells , but it plays a critical role in the attachment to human lung epithelial A549 cells . We ascertained that the C-terminal globular head domain of the σC protein is involved in binding selectively to the surface of A549 cells and that σC is a determinant of viral virulence in infected mice . We generated a recombinant virus that expresses a yellow fluorescent protein ( ZsYellow ) by replacement of the σC ORF with ZsYellow . The reverse genetics system can be employed for studies on the replication and pathogenesis of this important group of pathogenic orthoreoviruses .
To generate the recombinant strain ( rs ) MB from cloned cDNA , L929 cells were infected with the attenuated vaccinia virus ( rDIs-T7pol ) , which expresses T7 RNA polymerase , 1 h prior to transfection with plasmids encoding cDNAs corresponding to each of the 10 viral gene segments . Each plasmid contains a full-length MB gene-segment cDNA flanked by the T7 RNA polymerase promoter and the hepatitis delta virus ( HDV ) ribozyme sequences . Transcription using T7 RNA polymerase generates nascent transcripts corresponding to viral full-length ( + ) -sense RNAs containing the native viral 5′-end . Self-cleavage by the HDV ribozyme generates the native viral 3′-end . The vaccinia virus rDIs-T7pol employed for rescue experiments replicates permissively in chick embryo fibroblasts ( CEF ) , but is incapable of replicating in most mammalian cells [36] . Following incubation , the cells were collected at 24 and 48 h post transfection , and the viral titers were determined by a plaque assay using L929 cell monolayers . Viral titers following transfection with the 10 MB plasmids were 3 . 6 × 104 and 2 . 8 × 105 plaque-forming units ( PFU ) /ml at 24 and 48 h , respectively , whereas recombinant viruses were not rescued from cells transfected with nine MB plasmids that did not include a plasmid encoding S3 segment ( Fig 1A ) . To confirm whether the rescued virus was generated using the cloned cDNA , a unique EcoRV site was created in the S3 segment by the introduction of a silent point mutation , A to C , at nucleotide position 640 in the S3 plasmid ( Fig 1B ) . The viral dsRNA was extracted from MB and rsMB virions . The full-length S3 segment ( 1192 bp ) was amplified using RT-PCR . The amplified S3 gene fragment derived from MB was not digested using EcoRV , whereas the rsMB S3 gene fragment was digested to produce 635- and 557-bp fragments ( Fig 1C ) . The sequence was determined by direct sequencing of the PCR fragment , and the sequence analysis confirmed the expected A to C substitution as a genetic marker in the S3 segment of rsMB ( Fig 1D ) . These results indicate that rsMB originated from the cloned cDNA . To develop a vaccinia virus-free reverse genetics system , we transfected BHK/T7-9 cells stably expressing T7 RNA polymerase with the 10 MB plasmids and determined viral titers . Viral titers following transfection were 9 . 1 × 102 PFU/ml at 48 h ( Fig 1E ) . These results demonstrate that BHK/T7-9 cells can be used as an alternative to L929 cells infected with rDIs-T7pol for entirely rescuing the infectious virus from cloned DNA . To confirm whether rsMB generated from cloned cDNA reflects the characteristics of the parent MB strain , we first investigated the replication kinetics of MB and rsMB in L929 and monkey kidney epithelial Vero cells . Growth kinetics for the two viruses was virtually identical at all the time points ( Fig 2A ) . The fusogenic NBV strain MB exhibits large syncytium formation in infected cells based on the fusogenic ability of FAST protein encoded by the S1 segment [11 , 17] . To determine whether rsMB forms a syncytium in a manner similar to native MB , cells were infected with MB and rsMB and processed 12 h post infection for image analysis by Giemsa staining . Both MB and rsMB formed morphologically indistinguishable large syncytia in infected Vero cells ( Fig 2B ) . Furthermore , to confirm that rsMB contains the correct pattern of gene segments , the viral genomic dsRNAs were resolved using sodium dodecyl sulfate ( SDS ) -polyacrylamide gel electrophoresis . The electropherotype of rsMB was indistinguishable from that of MB ( Fig 2C ) . Collectively , these results demonstrate that the replication characteristics of rsMB are indistinguishable from those of native MB . Gene segment reassortment can occur among orthoreovirus species with segmented genomes during coinfection of cells with different strains [37–40] . This event can result in the generation of new viruses with altered virulence . However , the NBV assembly process has not yet been fully resolved and the genome reassortment among NBV strains occurring in nature has not been elucidated . To assess whether genome reassortment can occur among NBV strains , we attempted to generate recombinant monoreassortant viruses , which possess the S1 segment of strains NB or Mel and the other nine segments of strain MB . To rescue S1 monoreassortant viruses , the S1 plasmid , which encodes the full-length S1 segment of both the strains NB and Mel , was cotransfected into rDIs-T7pol-infected L929 cells with other nine plasmids from strain MB , and the viruses were rescued using a plaque assay 48 h post transfection . The electrophoretic pattern of the monoreassortant rsMB/NB-S1 , which contains the NB S1 segment in an otherwise MB background , clearly reveals the comigration of S1 RNA with native NB ( Fig 3A ) . The pattern of the monoreassortant rsMB/Mel-S1 , which contains the Mel S1 segment in an otherwise MB background , reveals a different electropherotype of S1 RNA with native MB and rsMB ( Fig 3A ) . To assess the replication kinetics of the reassortant viruses in L929 cells , the cells were infected with rsMB , rsMB/NB-S1 , or rsMB/Mel-S1 at a multiplicity of infection ( MOI ) of 0 . 1 or 0 . 01 PFU/cell . The recombinant monoreassortant viruses exhibit replication kinetics similar to the native rsMB ( Fig 3B and 3C ) . These results suggest that genome reassortment events can occur among NBV species when the cells are coinfected with different NBV strains , and the recovery of S1 monoreassortant viruses shows the utility of the rescue system to potentially generate reassortant viruses with any desired genetic combination between different NBV strains . In most orthoreoviruses , the S1 segment is functionally polycistronic and encodes σ1/σC , an outer fiber protein of the virions responsible for attachment to the host cell membrane [20 , 21 , 41] . Although sequence analysis of NBV polycistronic S1 segments revealed that σC is encoded in the third ORF of the S1 segment [16] , structural and functional analysis of NBV σC in viral replication has not been performed . In addition , unlike other orthoreoviruses , BRV and BroV isolated from the Australian fruit bat , which are divergent from the NBV species , were found to lack a σ1/σC homolog in their polycistronic S class gene segments [3 , 42] . These reports suggest that BRV and BroV may utilize distinct strategies to infect cells compared to those employed by other orthoreoviruses . To define the importance of NBV σC in the viral life cycle , we used a reverse genetics system to generate recombinant viruses incapable of expressing σC protein . Viable σC-null viruses were rescued from cells transfected with cDNAs of nine gene segments and S1 cDNA featuring deletion of most of the nucleotide sequence spanning 707–1450 corresponding to the σC ORF or disruption/insertion of the σC translational start codon/stop codon ( rsMB/σC-del and rsMB/σC-ACG , respectively; Fig 4A ) . Electropherotype analysis of the rescued viruses demonstrated that rsMB/σC-del displayed the expected migration of the S1 segment compared with that of rsMB ( Fig 4B ) . The mutations in the σC ORF of the S1 segment from rsMB/σC-del and rsMB/σC-ACG were confirmed by direct sequencing of viral genomic RNA . To confirm that defective mutations in the σC ORF of the S1 segment from the rsMB/σC-del and rsMB/σC-ACG viruses prevent σC protein synthesis , we assessed the expression of σC protein by immunoblotting with σC-specific antiserum in L929 cells infected with the recombinant viruses . The expression of σC protein was detected in cells infected with the wild-type virus and transfected with FLAG-tagged fusion σC protein expression plasmid but not in cells infected with rsMB/σC-del and rsMB/σC-ACG viruses ( Fig 4C ) . Immunoblotting with NBV-specific antiserum demonstrated equivalent expression levels of other NBV proteins ( μB/μNS and σB ) in cells infected with the wild-type and σC expression-defective viruses , suggesting a similar level of infection by the wild-type and σC-deficient viruses ( Fig 4C ) . We also confirmed that σC proteins were included in purified wild-type virions ( S1A Fig ) . To test the generalizability of virus recovery in the absence of σC protein expression among other NBV strains , we attempted to generate monoreassortant viruses containing the NB or Mel S1 segments lacking σC protein expression in the genetic background of strain MB ( S1B Fig ) . The expected alteration in the S1 segment from each monoreassortant virus was confirmed by electrophoretic mobility using viral dsRNA ( S1C Fig ) . Based on these results , we conclude that recombinant NBVs that lack σC protein can be rescued by reverse genetics as a strain-independent generalizability . To determine whether σC protein influences NBV growth in cell culture , the viral titers of rsMB/σC-del and rsMB/σC-ACG were determined following infection of L929 cells at an MOI of 0 . 1 PFU/cell . The replication kinetics and yields of infectious progeny for the rsMB/σC-del and rsMB/σC-ACG viruses were indistinguishable from those of the wild-type viruses ( Fig 5A ) . To further analyze the characteristics of σC-deficient viruses , the infectivity of these viruses to L929 cells was determined using an indirect immunofluorescence assay . The cells were infected with wild-type , rsMB/σC-del , or rsMB/σC-ACG viruses at an MOI of 30 PFU/cell . At 12 h post infection , the infected cells were detected using NBV-specific antiserum . The infectivity did not differ significantly among wild-type , rsMB/σC-del , and rsMB/σC-ACG viruses ( 27 . 0% , 24 . 3% , and 26 . 9% , respectively ) , indicating that σC protein is not indispensable for viral replication and infectivity in L929 cells ( Fig 5B and 5C ) . Previous studies have shown that ARV σC and MRV σ1 proteins play roles in regulating apoptosis in infected and transfected cells [43–46] . Therefore , to investigate the role of NBV σC in apoptosis , L929 cells were infected with wild-type and σC-deficient viruses , and apoptosis was quantitated using a caspase 3/7 activity assay ( S2 Fig ) . Wild-type and σC-deficient viruses induced equivalent levels of apoptosis in comparison with those of mock and MRV strain rsT3D [28] , which was used as a positive control for this assay ( S2 Fig ) . This result indicates that NBV can induce apoptosis , but σC protein is not required for apoptosis induction in L929 cells . If NBV can bind to cell-surface receptors through σC , cell lines with significantly reduced susceptibility to infection by σC-deficient viruses may exist . To test this hypothesis , several cell lines were infected with the σC-deficient viruses and viral infectivity was assessed by an indirect immunofluorescence assay using NBV-specific antiserum . We found that the infectivity of rsMB/σC-ACG markedly decreased in comparison to that of the wild-type virus in A549 cells ( Fig 6A and 6B ) . Following infection of other cell lines , such as hamster kidney fibroblast BHK-21 , hamster ovary CHO-K1 , bat kidney DemKT1 [47] , and Vero cells , the viral infectivity of rsMB/σC-ACG and wild-type viruses was not found to differ ( S3 Fig ) . To further investigate whether σC plays a critical role in viral infectivity , we determined whether σC-specific antiserum could inhibit NBV infection in A549 cells . The wild-type virus was treated using σC-specific antiserum at various concentrations prior to infection . The number of infected A549 cells significantly decreased in a concentration-dependent manner ( Fig 7A and 7B ) . In contrast , the wild-type virus efficiently infected L929 cells , which are susceptible to wild-type and σC-deficient viruses , regardless of the presence or absence of σC-specific antiserum ( Fig 7C and 7D ) . To determine whether wild-type and rsMB/σC-ACG viruses can bind to A549 cells , A549 cells were incubated with wild-type or rsMB/σC-ACG virions and scored for virus binding using flow cytometry . Wild-type virions bound to A549 cells , but rsMB/σC-ACG virions were incapable of binding to A549 cells ( S4A Fig ) . When wild-type virus was treated with σC-specific antiserum prior to infection , the amount of virus binding was significantly inhibited ( S4A Fig ) . To further investigate the importance of σC protein during the infection of A549 cells , we performed a cell-surface binding assay using soluble σC protein with 3 × FLAG-tagged epitopes ( 3 × FLAG-MB-σC ) . Recombinant soluble MRV strain T3D σ1 protein ( 3 × FLAG-T3D-σ1 ) , which is required for cell attachment of the MRV infection , was used as a positive control for the binding assay . Immunoblotting using FLAG antibody demonstrated that both proteins were expressed and purified from the soluble fraction ( S4B Fig ) . Binding of soluble σ1 was observed for A549 and L929 cells but not for CHO-K1 cells , which reflects the cellular tropism of MRVs ( Fig 8 ) . When the A549 cells were incubated with 3 × FLAG-MB-σC , the fluorescence intensity increased compared with that of mock-treated cells ( Fig 8 ) . In contrast , binding of σC protein was not observed for L929 and CHO-K1 cells ( Fig 8 ) . In addition , binding of σC protein was not observed for other cell lines such as BHK-21 , DemKT1 , and Vero cells ( S4C Fig ) . These results provide evidence that σC protein is required for efficient infection in A549 cells and that this protein plays an important role in viral attachment to host cells . Orthoreovirus cell attachment proteins ( ARV σC and MRV σ1 ) form an elongated homotrimeric fiber topped with a globular head [23 , 48 , 49] . The C-terminal head domain of cell attachment proteins has the same topology in both viruses and plays a key role in receptor recognition [24 , 25 , 50–52] . Although an interaction between the ARV receptor and the C-terminal head domain has not yet been identified , the MRV receptor has been identified as JAM-A [24] . Automated comparative protein structure modeling using SWISS-MODEL ( http://swissmodel . expasy . org/ ) showed that the NBV σC is composed of three domains , the N-terminal tail , body , and C-terminal head regions , similar to ARV and MRV ( Fig 9A ) . Thus , to define the structural function of NBV σC in cell attachment , truncated σC proteins , namely , 3 × FLAG-Fd-σC-T encoding amino acid residues 1–145 corresponding to the predicted tail domain and 3 × FLAG-Fd-σC-BH encoding amino acid residues 146–331 corresponding to the predicted body and head domains , were purified from transfected cells . The foldon sequence of T4 phage fibritin was inserted at the N-terminus of σC protein to facilitate stabilization of the trimer structure [53] . Expression of truncated σC protein was confirmed by immunoblotting using σC-specific antiserum ( Fig 9B ) . When A549 cells were incubated with soluble 3 × FLAG-Fd-σC-BH , the fluorescence intensity significantly increased , although the intensity was lower than that of wild-type 3 × FLAG-MB-σC ( Fig 9C ) . In contrast , 3 × FLAG-Fd-σC-T failed to bind A549 cells ( Fig 9C ) . These results suggest that the C-terminal head domain of σC protein participates in cell attachment of A549 cells . To further investigate the role of the head domain of σC protein in viral infection , we used reverse genetics to generate reoviruses expressing truncated σC . A stop codon was inserted into the σC ORF at amino acid position 197 to yield viruses lacking the head domain of σC protein ( rsMB/σC-Head-del ) . The infectivity of rsMB/σC-Head-del was significantly decreased in A549 cells in comparison with that of wild-type ( Fig 9D ) . In contrast , infectivity did not differ significantly in L929 cells between the wild-type and rsMB/σC-Head-del viruses ( Fig 9E ) . Overall , these results suggest that the C-terminal head region of σC protein is required for efficient infection in A549 cells . Although σC is not required for viral replication and infectivity in several cell cultures ( Fig 5 and S3 Fig ) , it is possible that σC contributes to viral virulence in vivo . Therefore , to determine whether σC influences viral pathogenesis , we inoculated 4-week-old C3H mice intranasally with 4 × 105 PFU of rsMB or rsMB/σC-ACG and assessed the daily disease progression in the mice . A significant decrease in body weight was observed in mice infected with rsMB at 4 to 7 days post infection compared to that of mice infected with rsMB/σC-ACG ( Fig 10A ) . Eighty percent of the mice infected with rsMB died within 14 days post infection ( Fig 10B ) . In contrast , all the mice in the group infected with rsMB/σC-ACG survived ( Fig 10B ) , suggesting that σC plays an important role in viral pathogenesis . The analysis of the σC-deficient viruses demonstrated that σC was not essential for viral replication in L929 cells and led us to postulate that the σC ORF could be replaced by a foreign gene . To test this hypothesis , we introduced sequences encoding ZsYellow into the σC ORF of the MB S1 plasmid ( Fig 11A ) . ZsYellow is expressed as a fusion protein with amino acids 1–143 of σC at the N-terminus . RT-PCR analysis using specific primers for MB S1 and ZsYellow genes confirmed incorporation of a recombinant ZsYellow gene in the S1 gene segment of the resultant virus , rsMB/σC-ZsY ( Fig 11B ) . The replication-competent rsMB/σC-ZsY was capable of replicating in L929 cells , similar to the wild-type virus ( Fig 11C ) . Expression of ZsYellow fused with amino acids 1–143 of σC and NBV antigens was clearly observed in the syncytia of Vero cells infected with rsMB/σC-ZsY ( Fig 11D ) . These results demonstrate that NBV can be engineered to express a foreign gene by replacing the σC ORF .
Although the nonfusogenic MRVs are relatively mild or asymptomatic , the fusogenic orthoreoviruses are pathogenic and cause various severe symptoms in vertebrates [2 , 3 , 54–56] . Several NBVs have recently been identified as causative agents of severe respiratory tract infections in humans [4 , 7–11] , suggesting that infections by bat-borne orthoreoviruses may represent potential threats such as emerging zoonosis . To date , no reverse genetics system has been developed for fusogenic orthoreoviruses; therefore , progress in understanding fusogenic orthoreovirus biology and disease has been restricted by this technological barrier . In this study , we established a plasmid-based reverse genetics system for fusogenic NBV strain MB isolated from a patient with acute respiratory infection based on the reverse genetics systems previously developed for MRV [28] . This system permits the selective introduction of desired mutations into the viral genomes using a strategy that does not require a helper virus and selection system . Growth kinetics , cell-to-cell fusion ability , and genomic electrophoretic profiles were indistinguishable between rsMB and native MB ( Fig 2 ) . These results demonstrate that the replication characteristics of rsMB generated from cloned cDNA reflect native MB and provide an important advance as a new tool to investigate the molecular biology of NBV propagation and pathogenesis . In previous studies , viral titers following transfection with 10 plasmids encoding the genomes of the MRV strains T1L and T3D were ~10 PFU/ml and below the limit of detection at 24 h post transfection , respectively [28 , 34] . In contrast , in the newly developed NBV reverse genetics system , the viral titer ( ~10 , 000 PFU/ml ) following transfection with the 10 MB plasmids was markedly higher at 24 h post transfection ( Fig 1 ) . The peak titer ( ~107 PFU/ml ) of the NBV strain MB is at a similar level or lower in comparison to those of the MRV strains T1L and T3D in L929 cells employed for virus rescue [28 , 34] . A potential explanation may be that the NBV strain MB exhibits more efficient replication over the MRV strains during early replication events including dsRNA genome synthesis and core assembly following transfection with NBV cDNA into L929 cells . The reverse genetics system for NBV developed in this study may have a significant advantage to promote the rescue of highly attenuated viruses and viral vectors that have been difficult to recover using current rescue methods for the family Reoviridae . In addition , the higher efficiency of the NBV reverse genetics system compared to other Reoviridae rescue systems may allow us to progress studies of the common replication pathway shared by the family Reoviridae , which is poorly understood including dsRNA genome synthesis , gene segment packaging , and virion assembly . We also developed a vaccinia virus-free reverse genetics system using BHK/T7-9 cells . Although the rescue efficiency of the vaccinia virus-free system was lower than that of the rDIs-T7pol-based system , the elimination of the vaccinia virus makes this reverse genetics system an alternative and is a simple approach for the recovery of recombinant viruses ( Fig 1 ) . The reverse genetics for multiple-segmented viruses appears to be inefficient because of the limitation of the transfection efficiency of the target cells . Previous studies have shown that the efficiency of virus rescue using a strategy in which the number of required plasmids was reduced from 10 to 4 was substantially increased in comparison to the first-generation system for the MRV strains T1L and T3D [34] . Thus , further refinement of the rescue system of NBV with a reduction in the total number of plasmids will expand the utility of reverse genetics for studies of NBV biology . Viruses have an extraordinary ability to adapt and evolve . For segmented viruses including reovirus family viruses , genome reassortment events occur in cells coinfected with more than two different virus strains and play a key role in introducing genomic and phenotypic changes leading to virus diversity . In the genus Orthoreovirus , reassortment events have been reported for ARVs and MRVs [37–40] . Although phylogenic analysis based on nucleotide sequences of S class gene segments from different NBV strains suggests the possibility of genome gene reassortment among these viruses , there is no direct evidence whether gene reassortment occurs during mixed infection in culture cells or animal hosts . We used the plasmid-based reverse genetics system to generate monoreassortant viruses containing the S1 segment from strains NB ( isolated from bats ) or Mel ( isolated from humans ) in an otherwise MB background . As with the other nine NBV gene segments , S1 segments from strains MB , NB , and Mel possess conserved nucleotide sequences that are identical for the NBV species at the 5′- ( 5′-GCUU-3′ ) and 3′-ends ( 5′-UCAUC-3′ ) ; however , the S1 gene segment ( σC protein ) from strain MB shows significant sequence diversity in terms of nucleotides [66 . 0% ( Mel ) and 57 . 9% ( NB ) ] and amino acid [57 . 3% ( Mel ) and 43 . 6% ( NB ) ] , in comparison with the other nine gene segments among these strains isolated from humans and bats [1 , 4 , 11 , 12 , 16 , 57] . We demonstrated the generation of S1 monoreassortant viruses in experimental culture conditions using the reverse genetics system , suggesting that reassortant events may occur among different NBV strains in nature and that the development of a reverse genetics system may allow us to generate any desired combinatorial exchange for the systematic characterization of gene segments with phenotypic differences among various NBV strains . Genetic experiments using recombinant reassortant viruses will provide key insights into viral replication and pathogenesis . Growth kinetics for the S1 monoreassortant viruses was virtually identical to that of the wild-type viruses ( Fig 3 ) . Previous reports using reassortant viruses to understand viral pathogenic mechanisms demonstrate that the orthoreovirus S1 segments influence strain-specific differences in viral replication in tissues and the pathway of spread in the host [39 , 58–63] . Thus , it may be possible that monoreassortant viruses containing S1 segments derived from different NBV strains promote distinct viral growth characteristics in certain cell or tissue types to promote spread within or between hosts . We used the NBV reverse genetics system to introduce mutations in σC protein , which probably forms part of the viral minor outer capsid and is considered essential for virus infection . In other orthoreoviruses , MRV σ1 , which is analogous to σC protein , functions as the viral cell attachment protein [41] and is the primary virulence determinant through the cellular receptor binding for cell attachment or through the intracellular signal transduction pathways mediated by receptor binding [64–66] . Conversely , BRV and BroV do not exceptionally encode homologs of the cell attachment proteins σ1 and σC in their polycistronic S gene segments , suggesting that a σ1/σC homolog-independent cell attachment pathway to bind to the cell-surface receptors may also exist in orthoreovirus entry [3 , 42] . Therefore , we applied our reverse genetics system to study the σC protein to understand the mechanism by which this protein mediates critical steps in viral replication . The resulting σC-deficient viruses are viable ( Fig 4 and S1 Fig ) , and the caspase 3/7 activity assay using cells infected with wild-type or σC-deficient viruses indicated that the σC protein is not required for apoptosis induction in L929 cells ( S2 Fig ) . The single-cycle replication kinetics and infectivity of σC-deficient viruses indicate that σC protein is dispensable for reovirus propagation in several cell lines including L929 cells ( Fig 5 and S3 Fig ) . However , the virus infectivity of σC-deficient virus was significantly diminished in A549 cells ( Fig 6 ) , and pretreatment of a wild-type virus with σC-specific antiserum blocked infection in A549 cells ( Fig 7 ) , suggesting that σC functions in viral cell attachment of A549 cells . Flow cytometry confirmed that recombinant soluble σC protein binds the surface of A549 cells , which is nonpermissive to σC-deficient virus infection , but not the surfaces of L929 , CHO-K1 , BHK-21 , DemKT1 , or Vero cells , which are permissive to σC-deficient virus infection ( Fig 8 and S4 Fig ) . These results provide direct evidence that NBV uses σC protein for cell attachment and infection in A549 cells and suggest that NBV uses σC and different capsid proteins to bind distinct cell-surface components , engaging independent receptors to facilitate virus infection . Our findings lead us to question regarding which other NBV proteins function as virus ligands for cell attachment in cell lines that are permissive to σC-deficient virus infection . Based on the structural analysis of ARV and MRV virions assumed to be similar to NBV virions [67 , 68] , a candidate is the major outer capsid protein σB , which is considered to be present in 600 copies and form 200 heterohexameric complexes with a more internal layer composed of 600 copies of μB protein per virion . Recently , Konopka-Anstadt et al . demonstrated that MRV uses the Nogo receptor NgR1 for entry into neurons [69] . Further experiments revealed the possibility that MRV uses σ3 , a functional homolog of σB , to attach to NgR1 , but more studies are required to identify the binding partner for NgR1 [69] . Another candidate involved in cell attachment for σC-deficient viruses is λC , which probably forms a pentameric turret at the virion fivefold symmetry axes , providing a total of 60 copies per virion and serving as the insertion site for the attachment protein σC [67 , 68] . MRV λ2 , which is a λC homolog , contains conserved integrin binding sequences , suggesting that λ2 mediates the internalization of MRV to enter cells through an interaction with integrins [70] . However , direct binding between NBV proteins such as λC and integrins has not been reported . Although there are no reports of cellular receptors for ARV and NBV σC proteins , the MRV receptor JAM-A has been identified as a binding partner of σ1 , which is the structural and functional homolog of σC protein , for MRV entry [24] . The C-terminal globular head domain is predicted to play a key role in receptor recognition from previous MRV σ1 studies on cell attachment [24 , 50–52] . Therefore , we investigated and demonstrated the importance of the C-terminal head domain of NBV σC in cell attachment of A549 cells ( Fig 9 ) . However , NBV σC probably binds to distinct but as yet unknown receptor molecules because recombinant σC protein binding was not observed for L929 cells , which express JAM-A and are susceptible to MRV infection . ( Fig 8 ) . Although it is known that the nonfusogenic MRV cell attachment protein σ1 is a major determinant of reovirus disease [58–60 , 62] , the contribution of fusogenic NBV σC to viral virulence has not been elucidated . In this study , we demonstrated the importance of σC in viral pathogenesis using a mouse model of NBV infection ( Fig 10 ) . These results suggest that the cell attachment function of σC may contribute to viral infection and pathogenesis in vivo , even though it is not required for cell binding in various cell lines . The alternative usage of receptors for entry by NBVs in vivo may be potentially significant . NBVs may use multiple independent viral ligands and cellular receptors to internalize into different cell types , as governed by the expression patterns of the entry receptors . Alternatively , NBVs may use distinct ligands and receptors for entry into the same cell type under different cellular conditions . In addition to the selective engagement of viral receptors by NBVs , post-entry signaling events may contribute to disease pathogenesis . Thus , future studies should focus on understanding the mechanism by which selective receptor recognitions and post-binding signaling pathways of cell attachment proteins contribute to viral replication and pathogenesis . We generated a replication-competent NBV expressing ZsYellow and demonstrated that the S1 gene segment encoding σC was suitable for the insertion of a foreign gene ( Fig 11 ) . A replication-competent NBV expressing a reporter gene will allow novel approaches for the study of NBV replication and pathogenesis both in vitro and in vivo and will provide a powerful system for examining NBV entry and for the identification of NBV receptors . We have established a reverse genetics system that allows the recovery of the pathogenic NBV strain MB from cloned cDNA . Recombinant σC mutant viruses provide new insights for understanding the viral entry machinery and orthoreovirus evolution through the gain or loss of cell attachment proteins . Unlike nonfusogenic MRVs , fusogenic orthoreoviruses encode two novel nonstructural viral proteins , FAST and NSP ( p16 or p17 ) , in polycistronic S gene segments [2 , 3 , 15–19 , 42 , 71 , 72] . However , the precise functions of these viral proteins in the viral life cycle are poorly understood . We expect that recombinant NBVs in which FAST , NSP , and other viral proteins are systematically altered using this reverse genetics will provide new insights into the viral replication machinery and establish a platform to advance basic and applied research for the family Reoviridae .
A549 , CHO-K1 , L929 , Vero , and human embryonic kidney 293T cells were obtained from the American Type Culture Collection and were grown in Dulbecco’s modified Eagle’s medium ( DMEM; Nacalai Tesque ) supplemented with 5% fetal bovine serum ( FBS; Gibco ) , 100 units/ml penicillin , and 100 μg/ml streptomycin ( Nacalai Tesque ) . BHK/T7-9 cells , a derivative of BHK cells , were grown in DMEM supplemented with 5% FBS , 10% tryptose phosphate broth , 100 units/ml penicillin , and 100 μg/ml streptomycin [73] . NBV strain MB was isolated from a patient with acute respiratory tract infection in Japan in 2007 [11] . NBV strain NB was isolated from the heart blood of a flying fox collected in Australia in 1968 [5] . Virus titers were determined using a plaque assay with L929 cell monolayers as previously described with slight modification [74] . The attenuated vaccinia virus rDIs-T7pol expressing T7 RNA polymerase was propagated in CEF [36] . CEF cells were prepared from 11-day-old embryonated eggs following standard procedures . The genomic viral dsRNA was extracted from purified virions using Sepasol-RNA I Super reagent ( Nacalai Tesque ) . cDNA corresponding to each MB gene segment were amplified from viral dsRNA via the full-length amplification of cDNA as previously described [75] . Briefly , a self-priming anchor-primer was ligated to the 3′-ends of viral dsRNAs using T4 RNA ligase ( Thermo scientific ) . The adaptor-ligated NBV dsRNAs purified by agarose gel electrophoresis were reverse transcribed using Superscript III reverse transcriptase ( Invitrogen ) . PCR amplification was performed using the KOD-Plus-NEO polymerase ( Toyobo ) with a single primer complementary to the anchor primer . Amplified full-length viral cDNA was blunt-end ligated into the EcoRV site of pBluescript KS ( + ) vector . Primer sets were designed on the basis of the nucleotide sequences of the NBV strain Kampar [7 , 57] . The viral sequences were determined using the ABI 3130 genetic analyzer ( Life Technologies ) . To generate the rescue plasmids pT7-L1MB , pT7-L2MB , pT7-L3MB , pT7-M1MB , pT7-M2MB , pT7-M3MB , pT7-S1MB , pT7-S2MB , pT7-S3MB , and pT7-S4MB encoding the full-length cDNA of each gene segment derived from MB , viral cDNA-containing fragments were subcloned into pT7-L1T3D , which encodes the full-length cDNA of the L1 segment of MRV strain T3D [28] . Viral cDNA fused at their native 5′-termini to the T7 promoter were inserted into pT7-L1T3D by complete replacement of plasmid sequences encoding T3D L1 , resulting in ligation of native 3′-termini to the HDV ribozyme sequence . pT7-S3MB , encoding the entire MB S3 gene , has an EcoRV restriction site as a genetic marker created by introducing a single nucleotide change at position 640 in the S3 gene using a KOD-Plus-Mutagenesis kit ( Toyobo ) . To generate the rescue plasmids pT7-S1NB and pT7-S1Mel , encoding strains NB and Mel S1 genes , respectively , the full-length cDNAs of NB S1 and Mel S1 genes were synthesized by gene synthesis services ( Eurofins Genomics ) based on the nucleotide sequences of NB S1 ( GenBank accession number: AF218360 ) and Mel S1 ( GenBank accession number: EF026043 ) , respectively . The artificial synthetic NB S1 and Mel S1 cDNAs were inserted into pT7-L1T3D , thereby replacing the T3D L1 cDNA and generating pT7-S1NB and pT7-S1Mel , respectively . To generate constructs for the rescue of σC mutant viruses , pT7-S1MB was altered using a KOD-Plus-Mutagenesis kit . pT7-S1MB-σC-del contains a deletion of the S1 nucleotide sequence 707–1450 within the σC ORF . To generate pT7-S1MB-σC-ACG , the start codon and five other downstream AUG codons in the σC ORF were disrupted ( AUG–ACG ) at nucleotide positions 572–574 , 581–583 , 653–655 , 716–718 , 1010–1012 , and 1121–1123 , and the five stop codons were inserted into the σC ORF . The coding sequence of the overlapping p17 ORF was not affected . To generate pT7-S1MB-Head-del , lacking the C-terminal region of σC protein , a stop codon ( UCA–UGA ) was introduced into the S1 cDNA at the nucleotide position 1160–1162 . To generate constructs for the rescue of monoreassortant viruses that contain NB or Mel S1 segments lacking σC protein expression , a KOD-Plus-Mutagenesis kit was used . Rescue plasmids pT7-S1NB-σC-del and pT7-S1Mel-σC-del contain deletions of the S1 nucleotide sequences 700–1466 and 709–1454 within the σC ORF , respectively . pT7-S1NB-σC-ACG and pT7-S1Mel-σC-ACG contain an AUG–ACG modification of the σC translation initiation codon at S1 nucleotide positions 611–613 and 584–586 , respectively , and two stop codons were inserted into the σC ORF . To generate pT7-S1MB-σC-ZsY , S1 the nucleotide sequence 1001–1516 within pT7-S1MB was replaced with the ZsYellow ORF . To generate the mammalian expression vector pCAG-MB-σC-FLAG encoding the MB σC protein fused to a copy of FLAG epitope tag at the C-terminus , σC cDNA fused to the FLAG epitope tag was cloned into the EcoRI site of the pCAGGS vector [76] . To generate the mammalian expression vectors p3×FLAG-MB-σC and p3×FLAG-T3D-σ1 encoding the MB σC and MRV T3D σ1 proteins , respectively , fused to three copies of FLAG epitope tag at the N-terminus , MB σC and T3D σ1 cDNAs fused to the FLAG epitope tags were cloned into the EcoRV site and KpnI site , respectively , of the p3×FLAG-CMV-10 expression vector ( Sigma ) . To generate p3×FLAG-Fd-σC-T and p3×FLAG-Fd-σC-BH encoding the N-terminal predicted tail domain ( MB S1 nucleotide sequences 572–1006 ) and the C-terminal predicted head–body domains ( MB S1 nucleotide sequences 1007–1567 ) of σC protein , respectively , partial σC cDNA fragments fused to a foldon sequence ( GYIPEAPRDGQAYVRKDGEWVLLSTFL ) , derived from the T4 phage fibritin , at the N-terminus to stabilize the protein trimer were cloned into the EcoRV and KpnI sites of the p3×FLAG-CMV-10 expression vector [53] . The nucleotide sequences of the plasmids were confirmed by DNA sequencing . The primer sequences used for plasmid construction are available upon request . Monolayers of L929 cells ( 8 × 105 cells ) in six-well plates ( Corning ) were infected with rDIs-T7pol at an MOI of ~3 TCID50/cell . At 1 h post infection , cells were cotransfected with plasmids encoding each of the 10 gene segments of strain MB ( pT7-L1MB , 0 . 66 μg; pT7-L2MB , 0 . 66 μg; pT7-L3MB , 0 . 66 μg; pT7-M1MB , 0 . 58 μg; pT7-M2MB , 0 . 58 μg; pT7-M3MB , 0 . 58 μg; pT7-S1MB , 0 . 5 μg; pT7-S2MB , 0 . 5 μg; pT7-S3MB , 0 . 5 μg; and pT7-S4MB , 0 . 5 μg ) using 2 μl of TransIT-LT1 transfection reagent ( Mirus ) per microgram of plasmid DNA . Following 1–2 days of incubation , recombinant virus was isolated from transfected cells by plaque purification using L929 cell monolayers . To establish the vaccinia virus-free reverse genetics system , monolayers of BHK/T7-9 cells ( 8 × 105 cells ) seeded in six-well plates were cotransfected with the 10 MB plasmids . The amount of each plasmid used for transfection was identical to that described for vaccinia virus-based reverse genetics system . To generate viruses containing engineered changes in σC , cells were cotransfected with nine plasmids from strain MB in combination with pT7-S1MB-σC-del , pT7-S1MB-σC-ACG , pT7-S1NB-σC-del , pT7-S1NB-σC-ACG , pT7-S1Mel-σC-del , pT7-S1Mel-σC-ACG , pT7-S1MB-Head-del , or pT7-S1MB-σC-ZsY . The mutations in the S1 segment from recombinant viruses were confirmed by nucleotide sequence analysis using extracted dsRNA genome from the virions . Monolayers of L929 or Vero cells ( 2 × 105 cells ) in 24-well plates ( Corning ) were infected with viruses at an MOI of 0 . 1 PFU/cell . After 1 h of incubation , the cells were washed using phosphate-buffered saline ( PBS ) twice and incubated with maintenance medium . Cultures were harvested at various intervals for virus titration . Monolayers of Vero cells ( 8 × 105 cells ) in six-well plate were infected with the viruses at an MOI of 0 . 1 PFU/cell . After 1 h of incubation , the cells were washed using PBS twice and incubated with maintenance medium for 12 h . The cells were fixed with methanol and stained with Giemsa’s Stain Solution ( Nacalai Tesque ) . Viral dsRNAs were extracted from virions and mixed with equal volume of 2 × sample buffer ( 125 mM Tris–HCl pH 6 . 8 , 10% 2-mercaptoethanol , 4% SDS , 10% sucrose ) . The dsRNAs were separated using a 10% precast polyacrylamide gel ( Atto ) and visualized by ethidium bromide staining . To generate antiserum against strain MB σC , the σC coding region of the MB S1 gene was cloned downstream of sequences encoding poly-histidine ( His ) tag in the pTrcHisA vector ( Life Technologies ) . The His-σC fusion protein expressed in BL21 cells ( Takara ) was purified from the soluble fraction using His-Select R Nickel Affinity Gel ( Sigma ) according to the manufacturer’s instructions . The His-σC fusion protein was mixed with Alhydrogel adjuvant 2% ( InvivoGen ) according to the manufacturer’s instructions , and ICR mice ( CLEA Japan ) were immunized and boosted with the protein-adjuvant mixture to generate σC-specific serum . Antiserum was obtained 4 weeks after administration of the last booster . To generate antiserum against strain MB , virions were mixed with Alhydrogel adjuvant 2% according to the manufacturer’s instructions , and mice were immunized and boosted with the virus-adjuvant mixture . Antiserum was obtained 4 weeks after administration of the last booster . The cells were lysed in buffer consisting of 25 mM Tris–HCl pH7 . 4 , 150 mM NaCl , 1% NP-40 , 1% sodium deoxycholate , and 0 . 1% SDS . After centrifugation , the soluble protein fractions were size fractionated using SDS-polyacrylamide gel electrophoresis and electroblotted onto polyvinylidene difluoride membranes ( Millipore ) . Viral proteins were detected using Chemi-Lumi One Ultra ( Nacalai Tesque ) following incubation with antiserum-specific for σC or NBV at a dilution of 1:2000 and HRP conjugated anti-mouse IgG secondary antibody ( Sigma ) at a dilution of 1:2000 . Monolayers of cells were seeded onto glass coverslips ( As One ) and infected with the viruses . After incubation of 12 h for L929 cells or 6 h for A549 cells , cells were fixed with PBS containing 4% paraformaldehyde , washed with PBS , and incubated with NBV-specific antiserum . After three washes with PBS , cells were incubated with CF488 Goat Anti-Mouse IgG second antibody ( Biotium ) or Alexa Fluor 633 Goat Anti-Mouse IgG second antibody ( Invitrogen ) at a dilution of 1:1000 . Cells were also incubated with 4′ , 6-diamidino-2-phenylindole ( DAPI ) to label nuclei and then washed three times using PBS . For infection inhibition assay using σC-specific antiserum , the antiserum was added to virus stock at various concentrations and incubated for 1 h prior to infection . The images were acquired with a FluoView FV1000 laser scanning confocal microscope ( Olympus ) . The number of nuclei in the image was counted using ImageJ software [77] . The infectivity rate of the viruses was expressed as the ratio of the number of infected cells to the total number of cells in the image . To express the cell attachment proteins MRV σ1 and NBV σC in mammalian cells , 293T cells were transfected with p3×FLAG-T3D-σ1 or p3×FLAG-MB-σC using 1 mg/ml polyethyleneimine solution ( Cosmo Bio ) . After 48 h of incubation , the cells were collected and lysed in buffer containing of 50 mM Tris–HCl pH 7 . 4 , 150 mM NaCl , and 1% Triton X-100 . The recombinant proteins were purified from the soluble fraction using ANTI-FLAG M2 Affinity Gel ( Sigma ) according to the manufacturer’s instructions . The purified proteins were competitively eluted using 3 × FLAG peptide ( Sigma ) . The proteins were dialyzed using Vivaspin 6 ( Sartorius ) and used for cell-surface binding assays . The cells were detached using Cell Dissociation Solution Non-enzymatic ( Sigma ) . In total , 5 × 105 cells were incubated with purified soluble recombinant proteins at 4°C for 1 h . The cells were washed three times using PBS and incubated with monoclonal anti-FLAG-M2 antibody ( Sigma ) at a dilution of 1:500 at 4°C for 1 h . After three washes with PBS , the cells were incubated with CF488 Goat Anti-Mouse IgG second antibody at a dilution of 1:500 at 4°C for 1 h . The cells were incubated with PBS containing 10 μg/ml propidium iodide solution ( Sigma ) to stain dead cells . The signal intensity of living cells was quantified using a FACSCalibur ( Becton Dickinson ) . Data were analyzed using FlowJo software . To evaluate the role of σC protein in viral pathogenesis , a mouse model of NBV infection was used ( Y . Kanai and T . Kobayashi , manuscript in preparation ) . Four-week-old male C3H mice were purchased from CLEA , Japan . The mice were intranasally infected with 20 μl ( 4 × 105 PFU ) of purified virus diluted with PBS and their body weight changes and survival were monitored for 14 days . Mice were euthanized when moribund . Each data point is expressed as the mean of triplicate samples . Error bars indicate the standard deviation . The significance of differences was determined via Student’s t-test or ANOVA or log rank test using Prism software ( GraphPad Software , Inc . ) . p values < 0 . 05 were considered statistically significant . The mouse experiments were conducted following the approval of the Animal Research Committee of Research Institute for Microbial Diseases , Osaka University and the guidelines for the Care and Use of Laboratory Animals of the Ministry of Education , Culture , Sports , Science and Technology , Japan .
|
Nelson Bay orthoreoviruses ( NBVs ) are members of the fusogenic orthoreoviruses that have various host species , including reptiles , birds , and mammals . Recently , several NBV strains have been isolated from patients with acute respiratory tract infections . Isolation of these pathogenic reoviruses raises concerns about the potential emerging infections of bat-borne orthoreoviruses in humans . The development of an entirely plasmid-based reverse genetics system for double-stranded RNA viruses has trailed other systems of major animal RNA virus groups because of the technical complexities involved in the manipulation of genomes composed of 10 or more segments . In this study , we developed a plasmid-based reverse genetics system for a pathogenic NBV strain . We used this system to generate viruses incapable of expressing the cell attachment protein σC and to rescue a replication-competent virus that expresses a yellow fluorescent protein . Our studies using σC-deficient viruses suggest that NBVs may engage multiple independent viral ligands and cellular receptors for efficient cell attachment and viral pathogenesis , thus providing new insight into the biology of orthoreoviruses . The reverse genetics approach described in this study can be exploited for fusogenic orthoreovirus biology and used to develop vaccines , diagnostics , and therapeutics .
|
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2016
|
Reverse Genetics for Fusogenic Bat-Borne Orthoreovirus Associated with Acute Respiratory Tract Infections in Humans: Role of Outer Capsid Protein σC in Viral Replication and Pathogenesis
|
Ribosome biogenesis is a global process required for growth and proliferation of all cells , yet perturbation of ribosome biogenesis during human development often leads to tissue-specific defects termed ribosomopathies . Transcription of the ribosomal RNAs ( rRNAs ) by RNA polymerases ( Pol ) I and III , is considered a rate limiting step of ribosome biogenesis and mutations in the genes coding for RNA Pol I and III subunits , POLR1C and POLR1D cause Treacher Collins syndrome , a rare congenital craniofacial disorder . Our understanding of the functions of individual RNA polymerase subunits , however , remains poor . We discovered that polr1c and polr1d are dynamically expressed during zebrafish embryonic development , particularly in craniofacial tissues . Consistent with this pattern of activity , polr1c and polr1d homozygous mutant zebrafish exhibit cartilage hypoplasia and cranioskeletal anomalies characteristic of humans with Treacher Collins syndrome . Mechanistically , we discovered that polr1c and polr1d loss-of-function results in deficient ribosome biogenesis , Tp53-dependent neuroepithelial cell death and a deficiency of migrating neural crest cells , which are the primary progenitors of the craniofacial skeleton . More importantly , we show that genetic inhibition of tp53 can suppress neuroepithelial cell death and ameliorate the skeletal anomalies in polr1c and polr1d mutants , providing a potential avenue to prevent the pathogenesis of Treacher Collins syndrome . Our work therefore has uncovered tissue-specific roles for polr1c and polr1d in rRNA transcription , ribosome biogenesis , and neural crest and craniofacial development during embryogenesis . Furthermore , we have established polr1c and polr1d mutant zebrafish as models of Treacher Collins syndrome together with a unifying mechanism underlying its pathogenesis and possible prevention .
Ribosomes are large ribonucleoprotein complexes that translate mRNA , thus synthesizing all the proteins within a cell . The process of making ribosomes , which is known as ribosome biogenesis , takes place within the nucleolus and begins with the transcription of ribosomal RNAs ( rRNAs ) by RNA Polymerases I and III ( RNA Pol I and III ) . RNA Pol I transcribes the 47S precursor rRNA which is subsequently processed into 18S , 5 . 8S , and 28S rRNAs , while RNA Pol III transcribes the 5S rRNA [1] . Transcription of the 47S rRNA is one of the rate-limiting steps of ribosome biogenesis , and accounts for about 60% of all cellular transcription in eukaryotes [2] . Ribosome biogenesis is a complex and metabolically expensive endeavor that universally governs the quality and quantity of all cellular proteins in all cells , and is therefore highly regulated by , and integrated with , cell growth , proliferation and differentiation [3 , 4] . Disruptions in ribosome biogenesis often result in disorders of embryonic development or adult homeostasis , which are collectively termed ribosomopathies [5] . Given the ribosome’s universal importance in all cells , it is surprising that ribosomopathies exhibit very specific clinical phenotypes which may include defects in the craniofacial , axial , and/or limb skeleton as well as in hematopoiesis or organogenesis . In addition , considerable variability exists within the phenotypic spectrum of individual ribosomopathies , which presents a considerable challenge to our understanding of the etiology and mechanistic pathogenesis of these conditions . Treacher Collins syndrome ( TCS , MIM 154500; TCS2 , MIM 613717; TCS3 , MIM 248390 ) is a rare congenital disorder of craniofacial development . TCS is characterized by hypoplasia of the facial bones , particularly the mandible and zygomatic complex , together with cleft palate , downward slanting of the palpebral fissures , and anomalies of the external and middle ear . Interestingly , there is a considerable degree of phenotypic variability in the severity and combination of these characteristic anomalies both between and within families [6 , 7] . TCS occurs with an estimated incidence of 1:50000 live births and is primarily associated with autosomal dominant mutations in TCOF1 [8] . TCOF1 encodes a putative nucleolar phosphoprotein termed treacle , which functions in the initiation of transcription by RNA Pol I as well as in rRNA processing [9 , 10] . Mice with heterozygous mutations in Tcof1 phenocopy the cranioskeletal anomalies observed in humans with TCS including retrognathia , micrognathia and cleft palate [11] . Furthermore , Tcof1 has been shown to play a critical role in the survival and proliferation of neuroepithelial and neural crest progenitor cells , which generate most of the craniofacial skeleton [11 , 12] . Collectively these results imply that ribosome biogenesis may be dynamically or spatiotemporally regulated and furthermore that neural crest cell progenitors exhibit a specific threshold sensitivity to deficiencies in ribosome biogenesis . Recently , mutations in POLR1C and POLR1D were also found to underlie the etiology of TCS [13] . The mutations in POLR1C were autosomal recessive , while mutations in POLR1D were either autosomal dominant or autosomal recessive [13 , 14] . POLR1C and POLR1D encode subunits common to RNA Pol I and RNA Pol III , which transcribe rRNAs [2] , however the precise functional roles for POLR1C and POLR1D in ribosome biogenesis and embryonic development , as well as in the pathogenesis of TCS , remain to be determined . In order to understand the roles of POLR1C and POLR1D , we characterized the spatiotemporal activity of polr1c and polr1d during zebrafish embryogenesis and investigated the phenotype of polr1c and polr1d homozygous mutant zebrafish with a particular emphasis on craniofacial development . We discovered that polr1c and polr1d are spatiotemporally and dynamically expressed , particularly during craniofacial development , and consistent with this pattern of activity , polr1c and polr1d homozygous mutant zebrafish exhibit cartilage hypoplasia and cranioskeletal anomalies characteristic of TCS . Mechanistically , we discovered that polr1c and polr1d loss-of-function perturbs ribosome biogenesis , resulting in Tp53-dependent neuroepithelial cell death and a deficiency of migrating neural crest cells , which underpins the cranioskeletal defects . More importantly , we show that genetic inhibition of tp53 can suppress neuroepithelial cell death and ameliorate the cranioskeletal anomalies in polr1c-/- and polr1d-/- mutants , providing a potential avenue to prevent the pathogenesis of TCS . Our work has therefore revealed tissue specific roles for polr1c and polr1d during embryogenesis and more specifically in craniofacial development . Furthermore , we have established polr1c-/- and polr1d-/- mutant zebrafish as models of TCS , while also unifying the underlying biochemical and cellular disease mechanisms as well as avenues for possible prevention .
To begin to understand the roles of polr1c and polr1d in craniofacial development , we characterized the activity of these genes during zebrafish embryogenesis ( Fig 1 ) . polr1c and polr1d are maternally expressed at stages <1 hour post fertilization ( hpf ) and remain ubiquitously expressed through gastrulation ( 6 hpf ) and early neurulation ( 11 hpf ) ( Fig 1A–1F ) . A more dynamic pattern of polr1c and polr1d expression emerges by 24 hpf , with enriched domains of activity in the eye , midbrain , and central nervous system ( Fig 1G and 1H ) . In 36 hpf zebrafish embryos , elevated levels of expression persist within the eye and discrete regions of the brain . In addition , enriched expression is evident in the pharyngeal arches , which will eventually give rise to the craniofacial cartilages ( Fig 1I and 1J ) . At 48 hpf , polr1c and polr1d continue to be expressed at very low levels broadly throughout the embryo , however high levels remain in the lens and tectum ( Fig 1K and 1L ) . These analyses collectively demonstrate that RNA Pol I and III subunits such as polr1c and polr1d , exhibit surprisingly dynamic spatiotemporal patterns of activity during embryogenesis . This suggests there may be tissue-specific threshold requirements for rRNA transcription during development and furthermore that polr1c and polr1d may play functional roles in craniofacial morphogenesis . To test our hypothesis that RNA polymerase subunits exert tissue-specific roles during embryogenesis , we characterized the phenotype of two mutant zebrafish lines: polr1chi1124Tg and polr1dhi2393Tg hereafter referred to as polr1c-/- and polr1d-/- respectively . These zebrafish lines were generated by insertion mutagenesis which disrupts the transcription of each gene [15] . The mutation in polr1c lies in exon 2 while the mutation in polr1d is located in the first intron ( http://web . mit . edu/hopkins/ ) . These mutations dramatically reduce the levels of polr1c and polr1d transcripts during embryogenesis ( S1 Fig ) . Homozygous polr1c-/- and polr1d-/- mutant embryos are phenotypically distinguishable from their control siblings at least as early as 24 hpf by their smaller eyes , disrupted midbrain-hindbrain boundary and necrotic cranial tissue ( Fig 2A–2D ) . Interestingly , these affected structures are consistent with the tissue domains where polr1c and polr1d are primarily expressed , supporting an autonomous role for RNA Pol I . At 3 dpf , polr1c-/- and polr1d-/- mutants are distinguishable from control siblings by their smaller heads , microphthalmia , and hyploplastic jaws ( Fig 2E–2G ) . By 5 days post fertilization ( dpf ) , the craniofacial anomalies in polr1c-/- and polr1d-/- mutants become more pronounced ( Fig 2H–2J ) . Although overall body size is comparable between mutant embryos and control siblings , polr1c-/- and polr1d-/- mutants present with a considerably smaller head together with mandibular hypoplasia and microphthalmia . polr1c-/- and polr1d-/- mutant embryos develop pericardial edema , and fail to inflate their swim bladders . Subsequently , both polr1c-/- and polr1d-/- mutant embryos die between 9–10 dpf . To further characterize the extent of craniofacial defects in polr1c-/- and polr1d-/- mutant embryos , we stained their cartilage with Alcian blue . In 5 dpf mutant embryos , the craniofacial cartilages are severely hypoplastic ( Fig 3A–3C ) . Consistent with the morphology of a smaller jaw , hypoplasia of individual cartilage elements such as the palatoquadrate and Meckel’s cartilage was also observed ( Fig 3D–3F ) , mimicking characteristic features of TCS in humans . The ceratohyal was similarly hypoplastic and exhibited reversed polarity in polr1c-/- and polr1d-/- mutants . Furthermore , the posterior pharyngeal arch derived ceratobranchials that comprise part of the viscerocranium exhibit very little Alcian blue staining , which is further evidence for cartilage hypoplasia ( Fig 3G–3I ) . In the neurocranium , the ethmoid plate is smaller in mutant embryos compared to controls , however the parachordal cartilages appear to be of normal size . ( Fig 3J–3L ) . By 9 dpf , all the craniofacial cartilage elements in polr1c and polr1d mutant embryos appear hypoplastic compared to controls ( S2 Fig ) . Collectively , these craniofacial anomalies mimic the primary characteristic features of TCS in humans . This establishes polr1c-/- and polr1d-/- mutant zebrafish as potential models for understanding the pathogenesis of TCS , while also providing evidence for tissue-specific roles of RNA polymerase I and III subunits during embryogenesis . The craniofacial skeleton in zebrafish is derived from both neural crest cells ( NCC ) and mesoderm [16 , 17] . Our observations indicate that NCC-derived structures of the viscerocranium and neurocranium are malformed in polr1c-/- and polr1d-/- mutants . In contrast the parachordal cartilages , which are of mesoderm origin were unaffected . We therefore hypothesized that polr1c and polr1d loss-of-function may specifically affect NCC development and thus underpin the cellular pathogenesis of craniofacial anomalies in polr1c-/- and polr1d-/- mutant zebrafish . To test our hypothesis , we initially investigated whether the neural plate , the progenitor tissue from which NCC are derived , was specified properly in polr1c-/- and polr1d-/- mutant embryos . Using sox2 as a marker of definitive neural plate formation and specification , we observed similar sox2 expression and patterning of the neural plate in 11hpf polr1c-/- and polr1d-/- mutant embryos compared to controls ( S3 Fig ) . This suggests that polr1c and polr1d are not necessary for neural plate formation . To identify anomalies in early NCC development , we examined premigratory and migratory NCC through in situ hybridization with sox10 and foxd3 respectively , which are genes known to play important roles in NCC formation , survival , migration , and fate determination [18 , 19] . The spatiotemporal patterns of sox10 ( Fig 4A–4H ) and foxd3 activity ( Fig 4I–4P ) in premigratory and migratory NCC were very similar in polr1c-/- and polr1d-/- mutants compared to control siblings . However , using dlx2 as a marker of mature cranial NCC as they colonize the pharyngeal arches and complete their migration [20] , we observed smaller domains of expression particularly with respect to the caudal-most pharyngeal arches ( Fig 4Q–4X ) . Although the expression levels of sox10 , foxd3 , and dlx2 appeared to be normal in polr1c-/- and polr1d-/- mutants , indicating that the specification and migration of NCC occurred properly , we hypothesized that smaller territories of dlx2 expression were indicative of reduced numbers of migrating NCC colonizing the pharyngeal arches . Furthermore , we posited that reduced numbers of migrating NCC could account for the cranioskeletal hypoplasia observed in 5 dpf polr1c-/- and polr1d-/- mutants ( Fig 3 ) . To further validate our hypothesis that a deficiency in migrating NCC and pharyngeal arch hypoplasia underpins the cranioskeletal malformations in polr1c-/- and polr1d-/- mutants , we investigated the structure and composition of the pharyngeal arches . Endodermal pouches are known to play an important role in cranioskeletal patterning and differentiation [21] . To rule out the possibility that a defect in endodermal pouch patterning was responsible for the phenotype in polr1c-/- and polr1d-/- mutant embryos , we bred fli1a:egfp , which labels post-migratory NCC that colonize the branchial arches , into the background of polr1c-/- and polr1d-/- mutant zebrafish and immunostained with Zn-8 , which marks the endodermal pouches [22 , 23] . We observed no alteration in the formation or segregation of the endodermal pouches in 36 hpf polr1c-/- and polr1d-/- mutants as evidenced by a normal pattern of Zn-8 activity ( Fig 5A–5C ) . In contrast , fli1a:egfp labeling of post-migratory NCC in combination with volumetric rendering revealed a significant reduction in the size of the pharyngeal arches in polr1c-/- and polr1d-/- mutant embryos ( Fig 5D–5F ) . polr1c-/- embryos exhibited an average volume of 3 . 55 x 105 μm3 in contrast to 4 . 47 x105 μm3 in control siblings ( p = 0 . 0088 , t-test; Fig 5G ) . Similarly , polr1d-/- embryos exhibited a volume of 2 . 09 x 105 μm3 in contrast to 2 . 49 x 105 μm3 in control siblings ( p = 0 . 022 , t-test; Fig 5H ) . Thus the volume of pharyngeal arches 1 and 2 in polr1c-/- and polr1d-/- mutant embryos was reduced by approximately 20% compared to controls . Moreover , this is consistent with the apparently smaller domains of dlx2 expression , which was also indicative of reduced pharyngeal arch size ( Fig 4Q–4X ) . Since the specification and migration of NCC appears to occur normally in polr1c-/- and polr1d-/- mutant embryos as evidenced by sox10 and foxd3 expression , this implies that pharyngeal arch hypoplasia is the result of an overall reduction in the number of NCC colonizing the pharyngeal arches . Consequently , we hypothesized that increased apoptosis and/or decreased proliferation might account for these reduced cell and tissue populations . In order to validate our hypothesis and determine the mechanistic basis underlying the apparent reduction of NCC in polr1c-/- and polr1d-/- mutant embryos , we tested whether polr1c and polr1d played functional roles in cell survival and/or proliferation . Using TUNEL staining as a marker of apoptosis , we observed increased cell death in 24 hpf polr1c-/- and polr1d-/- mutant embryos , particularly in the cranial region and along the dorsal aspect of the embryo ( Fig 6A–6C ) . Transverse sections of TUNEL stained embryos revealed that the majority of cell death was localized within the neural tube or neuroepithelium , the dorsal-most regions of which contains NCC progenitors and pre-migratory NCC ( Fig 6D–6F ) . Thus the reduced NCC population and subsequent pharyngeal arch hypoplasia observed in polr1c-/- and polr1d-/- mutant embryos , occurs at least in part due to pre-migratory NCC progenitor apoptosis . However , it was important to determine whether cell death was also occurring in migrating NCC , which could also contribute to pharyngeal arch hypoplasia . Therefore , we bred sox10:gfp which labels migratory NCC , into the background of polr1c-/- and polr1d-/- mutant zebrafish and stained for apoptosis with TUNEL . We observed no significant co-localization of TUNEL with sox10:gfp at 24 or 48 hpf ( S4 Fig ) . These results demonstrate that polr1c and polr1d loss-of-function specifically affects the viability of neuroepithelial cells in 24 hpf embryos . Thus , elevated apoptosis diminishes the pool of pre-migratory NCC , which leads to a reduced population of migrating NCC , resulting in pharyngeal arch hypoplasia and consequently cranioskeletal anomalies . p53 is a well-known mediator of cell death underlying the pathogenesis of neurocristopathies and ribosomopathies [12] . We therefore hypothesized that the neuroepithelial apoptosis observed in polr1c-/- and polr1d-/- mutant embryos would also be p53-dependent . Quantitative RT-PCR ( qPCR ) revealed a significant increase in tp53 transcript levels in 36 hpf mutant embryos ( Fig 6G ) . polr1c-/- embryos exhibited an approximately 6-fold higher level of tp53 compared to control siblings while polr1d-/- embryos displayed an approximately 4-fold higher level . In addition , Western blot analysis also revealed a substantial increase in the levels of Tp53 in 5 dpf mutant embryos compared to controls ( Fig 6H ) . Collectively , these results suggested that the diminishment of migrating NCC in polr1c-/- and polr1d-/- mutant embryos , which occurs as a consequence of neuroepithelial cell death , was Tp53-dependent . Cell and tissue hypoplasia can occur in response to decreased proliferation as well as increased apoptosis . Hence , as a further step towards understanding the roles of polr1c and polr1d during embryogenesis , it was important to determine whether cell proliferation was also affected in polr1c-/- and polr1d-/- mutant embryos . Therefore , we examined control , polr1c-/- and polr1d-/- mutant zebrafish in which migrating NCC were labeled with sox10:gfp and performed co-staining with the mitotic marker phospho-histone H3 ( pHH3 ) to label proliferating cells . While overall pHH3 staining appeared to be similar between control and mutant embryos at 24 hpf ( S5A–S5C Fig ) and 36 hpf ( S5D–S5F Fig; quantification in J ) , the proportion of proliferating cells within the NCC-derived pharyngeal arch 1 and 2 mesenchyme was considerably reduced in polr1c-/- and polr1d-/- embryos ( S5G–S5I Fig and S5K Fig ) . Indeed only 6 . 7% of sox10:gfp labeled NCC in polr1c-/- embryos co-labelled with pHH3 compared to 14% in control siblings . Thus , quantification of pHH3 positive NCC within the pharyngeal arches revealed that proliferation in polr1c-/- embryos was reduced by as much as 50% compared to controls ( S5K Fig ) . Furthermore , the rates of proliferation were similar between polr1c-/- and polr1d-/- embryos . Thus polr1c and polr1d loss-of-function diminishes the proliferation capacity of migrating NCC that colonize the pharyngeal arches . Taken together , our analyses demonstrate that Tp53-dependent apoptotic elimination of pre-migratory NCC , combined with decreased NCC proliferation , collectively results in fewer migrating NCC in polr1c-/- and polr1d-/- embryos compared to control siblings . This reduction in the number of migrating NCC and ensuing smaller pharyngeal arches can account for the hypoplasia of craniofacial cartilages observed in 5 dpf polr1c-/- and polr1d-/- mutant zebrafish . rRNA transcription accounts for up to 60% of all cellular transcription in eukaryotes and is a considered a rate-limiting step of ribosome biogenesis [2] . Furthermore , deficient ribosome biogenesis and nucleolar stress is associated with p53-dependent apoptosis [24] . Therefore we hypothesized that polr1c and polr1d loss-of-function should lead to diminished rRNA transcription and perturbed ribosome biogenesis underpinning the activation of Tp53-dependent apoptosis in polr1c-/- and polr1d-/- mutant embryos . Ribosome biogenesis begins with transcription of the 47S precursor rRNA by RNA Pol I and 5S rRNA by RNA Pol III . The 47S rRNA contains a 5’ externally transcribed sequence ( ETS ) and two internally transcribed sequences ( ITS1 and ITS2 ) , which separate the 18S , 5 . 8S , and 28S rRNA sequences . The 5’ETS , ITS1 , and ITS2 are subsequently cleaved from the 47S transcript as part of the processing that generates the mature 18S , 5 . 8S , and 28S rRNAs during ribosome biogenesis . The 5’ETS , ITS1 , and ITS2 transcripts can be used as an estimate of 47S transcription [25] , and may provide a more sensitive indicator of perturbations in rRNA synthesis than the steady-state levels of mature 18S or 28S rRNAs [26] . To validate our hypothesis , we evaluated rRNA transcription by quantifying the levels of 5’ETS , ITS2 , and 18S rRNAs by qPCR . We observed a significant reduction of the 5’ETS , ITS2 , 18S rRNA transcripts in polr1c-/- and polr1d-/- mutant embryos compared to control siblings ( Fig 7A and 7B ) . At 36 hpf , the 5’ETS is reduced by 38% in polr1c-/- mutants and 32% in polr1d-/- mutants whereas ITS2 is reduced by 23% in polr1c-/- mutants and 25% in polr1d-/- mutants , relative to control embryos . The levels of 18S rRNA , which reflect the activity of both the precursor 47S transcript as well as the processed fully mature 18S rRNA , were also considerably diminished in polr1c-/- and polr1d-/- mutant embryos . In fact , polr1c-/- mutant embryos exhibited a 58% reduction in the levels of 18S compared to controls . polr1d-/- mutant embryos exhibited a similar reduction of about 39% compared to controls . To understand the impact of polr1c and polr1d mutations on RNA Pol III function in ribosome biogenesis in addition to their function as a part of RNA Pol I , we investigated the levels of 5S rRNA . No significant changes were observed in the levels of 5S rRNA in polr1c-/- and polr1d-/- mutant embryos ( S1 Fig ) . Taken together , our data demonstrates as predicted , that 47S rRNA transcription is reduced in polr1c-/- and polr1d-/- mutant embryos and furthermore , that the disruption of rRNA synthesis primarily occurs as a result of perturbed RNA Pol I function . Given that 47S rRNA transcription is considered a rate-limiting step during ribosome biogenesis [2] , we hypothesized that reduced rRNA transcription in polr1c-/- and polr1d-/- mutant embryos would result in an overall reduction in ribosome biogenesis . We evaluated ribosome biogenesis in 3 dpf polr1c-/- and polr1d-/- mutant embryos and control siblings through polysome profiling ( Fig 7C and 7D ) . The polysome profiles revealed similar sized 40S ( small subunit ) and 60S ( large subunit ) peaks in mutant embryos compared to controls . This indicates that the ratio of small and large subunit production was not affected in polr1c-/- and polr1d-/- mutant embryos . However , there were reductions in the 80S peak in both polr1c-/- and polr1d-/- mutant embryos compared to controls , which is indicative of a deficiency in the production or assembly of functional 80S ribosomes . The polysome peaks were shorter and slightly broader in mutant embryos compared to controls , highlighting an overall decrease in ribosome biogenesis in polr1c-/- and polr1d-/- mutant embryos , which is consistent with diminished production of 47S rRNA ( Fig 7A and 7B ) . Collectively these data suggest that decreased rRNA transcription and perturbed ribosome biogenesis contribute to the Tp53-dependent neuroepithelial apoptosis and diminished pool of migrating NCC that occurs in association with craniofacial cartilage hypoplasia in polr1c-/- and polr1d-/- mutant zebrafish . Our data demonstrated a correlation between deficient ribosome biogenesis , Tp53-dependent cell death , and craniofacial anomalies characteristic of TCS in polr1c-/- and polr1d-/- mutant zebrafish . Consequently , we hypothesized that inhibition of Tp53 would suppress neuroepithelial apoptosis and prevent the pathogenesis of craniofacial anomalies . To test our hypothesis , we crossed the tp53M214K/M214K allele into the background of polr1c-/- and polr1d-/- mutant zebrafish in an effort to inhibit Tp53 function . The tp53M214K/M214K allele , hereafter referred to as tp53-/- , carries a mutation within the DNA-binding domain of tp53 [27] that disrupts its ability to initiate the transcription of downstream target genes . Consistent with our prediction , TUNEL staining revealed reduced levels of cell death in 24 hpf polr1c-/-; tp53-/- and polr1d-/-; tp53-/- embryos compared to polr1c-/- and polr1d-/- siblings ( S6 Fig; S7 Fig ) . Transverse sections further demonstrated that the specific reduction of cell death within the neuroepithelium of polr1c-/- and polr1d-/- embryos was dose-dependent for tp53 ( S6E–S6H Fig; S7E–S7H Fig ) . Consistent with these results , we also observed a similar tp53 dose-dependent rescue of cranial cartilage formation in polr1c-/- ( S8 Fig ) and polr1d-/- mutant embryos ( Fig 8 ) . Removal of one copy of tp53 improved jaw development ( Fig 8F and 8G ) and patterning of the viscerocranium including the ceratohyal and ceratobranchial cartilages ( Fig 8J and 8K ) in polr1c-/- and polr1d-/- embryos . For example , although the ceratohyal remained smaller compared to control siblings , its polarity was restored to normal . Similarly , the ceratobranchials were larger and displayed more organized stacking . Removal of both copies of tp53 , and thereby complete inhibition of Tp53 , rescued the craniofacial phenotype to an even greater degree ( Fig 8H and 8L ) . For example , the ceratohyal was more elongated in polr1d-/-; tp53-/- embryos compared to polr1d-/-; tp53+/- embryos . To provide a more detailed analysis of the ability of Tp53 inhibition to prevent the pathogenesis of craniofacial anomalies in polr1c-/- and polr1d-/- mutant embryos , we classified the cranial cartilage phenotypes as severe , mild , or wild-type , and quantified the proportion of embryos commensurate with each category ( S9 Fig ) . The severe category included mutants with a hypoplastic ceratohyal of reversed polarity , whereas mild mutants exhibited a forward projecting but still hypoplastic ceratohyal . The wild-type category denoted embryos with a ceratohyal of relatively normal size and polarity , commensurate with wild-type embryos . Loss of one copy of tp53 prevented craniofacial anomalies in 19% of polr1c-/-; tp53+/- and 24% of polr1d-/-; tp53+/- embryos as each of these embryos developed with a wild-type phenotype . At the same time we observed a concomitant reduction in the number of polr1c-/-; tp53+/- and polr1d-/-; tp53+/- embryos that were classified as severe to 32% and 22% respectively , with the remainder presenting with a mild phenotype . Consistent with tp53 dose-dependency , removal of both copies substantially improved the efficacy of rescue . The percentage of polr1c-/-; tp53-/- and polr1d-/-; tp53-/- embryos that exhibited a wild-type phenotype dramatically increased to 62% and 35% respectively . Furthermore , the percentage of polr1c-/-; tp53-/- and polr1d-/-; tp53-/- embryos that exhibited the severe phenotype was concomitantly reduced to 9 . 5% and 0% respectively , with the remainder displaying a mild phenotype . Despite the considerable improvement in formation and patterning of the cranial cartilages in polr1c-/-; tp53-/- and polr1d-/-; tp53-/- embryos , the skeletal elements generally remained slightly smaller overall relative to control siblings . Furthermore , although Tp53 inhibition was sufficient to suppress neuroepithelial cell death and dramatically prevent cranioskeletal anomalies in polr1c-/- and polr1d-/- mutant embryos , this was still insufficient to rescue their long-term viability . polr1c-/-; tp53-/- and polr1d-/-; tp53-/- mutant zebrafish die around 10 dpf , which is very similar to polr1c-/- and polr1d-/- mutant zebrafish . Nonetheless , our results demonstrate that polr1c and polr1d play critical roles in rRNA transcription and ribosome biogenesis during embryogenesis and particularly in craniofacial development . Furthermore , we have established polr1c-/- and polr1d-/- mutant zebrafish as new models of TCS . polr1c and polr1d loss-of-function perturbs ribosome biogenesis which leads to Tp53-dependent neuroepithelial apoptosis , a diminished population of migrating NCC with reduced proliferation capacity , pharyngeal arch hypoplasia , and consequently craniofacial anomalies . Tp53 inhibition can suppress neuroepithelial apoptosis and substantially rescue cranioskeletal development in polr1c-/- and polr1d-/- mutant embryos providing a potential avenue for the therapeutic prevention of TCS .
Congenital craniofacial anomalies account for approximately one-third of all birth defects in newborn babies [28] and to date more than 700 distinct syndromes have been reported [29] . Craniofacial disorders are typically described and classified according to the extent of alterations to the craniofacial skeleton , which is derived primarily from NCC [17] . Most craniofacial anomalies are therefore attributed to defects in NCC development . In order to develop therapeutic avenues for minimizing or preventing craniofacial anomalies , it is essential to understand the precise etiology and pathogenesis of individual malformation syndromes . This requires a thorough understanding of ( i ) the normal signals and mechanisms that regulate NCC formation , survival , migration and differentiation; and ( ii ) the functional developmental roles played by genes that are mutated in association with the etiology of specific disorders . Facial dysostosis describes a set of clinically and etiologically heterogeneous congenital craniofacial anomalies that encompass maxillary , malar and mandibular hypoplasia , together with cleft palate , and/or ear defects [30] . Facial dysostosis can be subdivided into acrofacial dysostosis and mandibulofacial dysostosis . Acrofacial dysostosis presents with similar craniofacial anomalies to those observed in mandibulofacial dysostosis but with the addition of limb defects . Several distinct mandibulofacial dysostosis syndromes have been documented , with the most well-known and best understood being TCS [31] . TCS , which is also known as mandibulofacial dysostosis and Franschetti-Zwahlen-Klein syndrome [32] , is characterized primarily by hypoplasia of the facial bones , particularly the maxilla , mandible and zygomatic complex . In addition , the palate is often high-arched or frequently cleft [33 , 34] . TCOF1 encodes a nucleolar phosphoprotein called Treacle , which promotes rDNA transcription via direct binding of upstream binding factor ( UBF ) and RNA Pol I in the nucleolus . Tcof1 is broadly expressed throughout the mouse embryo during embryogenesis with elevated levels of activity in the neuroepithelium where it plays a vital role in cell survival . Analyses of a Tcof1+/- mouse model of TCS determined that this disorder arises through extensive p53-dependent neuroepithelial apoptosis , together with a deficiency in the generation and proliferation of NCC , which are the precursors of the craniofacial skeleton [35–37] . Furthermore , Tcof1 haploinsufficiency leads to deficient ribosome biogenesis [38] which provides the trigger for induction of p53-mediated apoptosis [39] . Consistent with this mechanism , genetic and pharmacological inhibition of p53 can suppress neuroepithelial apoptosis in Tcof1+/- embryos and prevent the pathogenesis of craniofacial anomalies characteristic of TCS [37] . TCS is therefore considered to be both a neurocristopathy and ribosomopathy disorder . However , mutations in TCOF1 do not account for all individuals diagnosed with TCS . Whole exome sequencing of individuals with TCS that lacked a mutation in TCOF1 , subsequently revealed causative mutations in POLR1C and POLR1D [40] . In contrast to TCOF1 , the mutations identified to date in POLR1C are all autosomal recessive [40] . However , similar to TCOF1 , mutations in POLR1C perturb its function as a part of RNA Pol I [41] . Analyses in HeLa cells revealed that RNA Pol I targeting to the nucleolus was reduced in association with POLR1C mutations in the pathogenesis of TCS . In contrast , no effect on the assembly or function of Pol III was observed . More recently , recessive mutations in POLR1C have been found to cause leukodystrophy , or degeneration of white matter in the brain , and interestingly , these mutations alter POLR1C function specifically as a part of RNA Pol III . Leukodystrophy associated mutations in POLR1C perturb Pol III assembly and occupancy at Pol III promoters but not Pol I assembly or occupancy at the rDNA promoter . With respect to POLR1D , at least 17 distinct mutations have been described , and similar to TCOF1 , they mainly elicit their effect in an autosomal dominant manner [7 , 40 , 42 , 43] . However , similar to POLR1C , recessive mutations in POLR1D have also been identified in association with TCS , but to date none of these have been linked to leukodystrophy . In contrast to our understanding of the role of TCOF1 during embryogenesis and in the etiology and pathogenesis of TCS , there is a paucity of information about POLR1C and POLR1D . Therefore , we set out to explore the functional roles of polr1c and polr1d during embryogenesis and more specifically in craniofacial development in an effort to better understand the pathogenesis of TCS and the tissue-specificity of this ribosomopathy . We discovered that polr1c and polr1d are dynamically and spatiotemporally expressed during zebrafish embryogenesis . In particular , polr1c and polr1d exhibit elevated levels of expression in specific craniofacial tissues from as early as 24 hpf . Consistent with this pattern of activity , zebrafish with mutations in polr1c and polr1d present with cranioskeletal hypoplasia that mimics TCS in humans . Our studies also showed that reduced rRNA production in polr1c-/- and polr1d-/- mutant embryos led to induction of Tp53 dependent neuroepithelial apoptosis . This in turn resulted in fewer migrating NCC , which exhibited decreased proliferation capacity during colonization of the pharyngeal arches . Consequently the pharyngeal arches in 36 hpf polr1c-/- and polr1d-/- mutant zebrafish were hypoplastic , and this manifested as small and often malformed craniofacial cartilages in 5 dpf fish . Consistent with all of this data , genetic inhibition of tp53 ameliorated the cranioskeletal malformations in polr1c-/- and polr1d-/- mutant embryos in a dose-dependent manner . Collectively , our data demonstrates that polr1c and polr1d are spatiotemporally expressed and play critical roles in rRNA transcription and ribosome biogenesis during zebrafish embryogenesis . Furthermore , polr1c and polr1d are essential for neuroepithelial cell survival and NCC proliferation during zebrafish craniofacial development . Moreover , these data are consistent with analyses of craniofacial development in Tcof1+/- mouse embryo models of TCS . Thus , TCS is caused by mutations in three distinct genes involved in rRNA transcription: TCOF1 , POLR1C , and POLR1D [10–13 , 30] . Here we have identified a common unifying cellular and biochemical mechanism underpinning the pathogenesis of TCS irrespective of whether its cause is associated with mutations in TCOF1 , POLR1C , or POLR1D . Mutations in TCOF1 account for about 80% of patients with TCS , while mutations in POLR1C and POLR1D account for only about 2% of the patients sequenced to date . This suggests that mutations in additional genes may also be causative for TCS . Other subunits of RNA Pol I , or factors that interact with TCOF1 , POLR1C , and POLR1D , make ideal candidates for an association with the etiology of TCS . Consistent with this idea , we recently identified mutations in the RNA Pol I subunit , POLR1A , in association with another ribosomopathy disorder , Acrofacial dysostosis , Cincinnati type [44] . POLR1A is the largest subunit of RNA Pol I and contains the active site of the polymerase [45 , 46] . Acrofacial dysostosis , Cincinnati type is characterized by hypoplasia of the zygomatic arches , maxilla , and mandible , with or without limb skeletal defects . Acrofacial dysostosis comprises a subgroup of facial dysostosis , and the craniofacial phenotype overlaps considerably with mandibulofacial dysostosis of which TCS is a prime example . Similar to polr1c and polr1d , polr1a is also dynamically expressed during zebrafish embryogenesis , particularly with respect to craniofacial development [44] . Furthermore , polr1a loss-of-function also leads to perturbed rRNA transcription , decreased ribosome biogenesis and Tp53-dependent cell death , resulting in a deficiency of NCC derived skeletal precursor cells and consequently craniofacial anomalies . Thus the tissue-specific phenotypes that result from alterations in rRNA transcription and ribosome biogenesis , exhibit a common underlying mechanism . This is true not just for TCS and Acrofacial dysostosis , Cincinnati type , as deficient ribosome biogenesis induced p53-dependent cell death , and rescue by p53 inhibition , has also been observed for Diamond Blackfan anemia ( DBA ) and 5q- syndrome [12 , 47–51] . The spatiotemporally dynamic expression of polr1a , polr1c , and polr1d during zebrafish embryogenesis , particularly in craniofacial tissues , is consistent with their loss-of function phenotypes as well as with the etiology and pathogenesis of Acrofacial dysostosis , Cincinnati type and TCS , respectively . However , the tissue-specific activity and function of these RNA Pol I subunits is surprising given that rRNA transcription is considered one of the rate-limiting steps of ribosome biogenesis , and furthermore that ribosome biogenesis is a tightly regulated global process , thought to be integral to all cell growth and proliferation . The similarity in dynamic expression and function for polr1a , polr1c , and polr1d lends support to the idea that different tissues have different threshold requirements for RNA Pol I activity and thus ribosome biogenesis during development . Consistent with this idea , cells with higher rates of ribosome biogenesis prior to RNA Pol I perturbation have been found more likely to undergo p53-induced apoptosis [52] . In contrast , cells with lower rates of ribosome biogenesis undergo cell cycle arrest . This raises the interesting possibility in the context of Tcof1 , polr1a , polr1c , and polr1d mutant embryos , that neuroepithelial cells have a higher rate of rRNA transcription and ribosome biogenesis and are thus more likely to undergo apoptosis than other cell types with lower rates of rRNA transcription and ribosome biogenesis . Our data suggests that there may well be differential or tissue specific levels of rRNA transcription during embryogenesis and/or that individual tissues may require distinct threshold levels of ribosome biogenesis for normal development and function . In the future , it will be important to quantify the relative levels of rRNA transcription and ribosome biogenesis in specific tissues during embryogenesis to determine whether there is an association between threshold levels and the effect of perturbation . Further evidence of tissue specific requirements for ribosome biogenesis comes from other ribosomopathies . Diamond-Blackfan anemia ( DBA ) for example is a ribosomopathy characterized primarily by disruptions of the erythroid precursor population but can also occur together with craniofacial and digit anomalies in some individuals . The majority of DBA cases result from mutations in ribosomal protein genes that encode protein constituents of either the 40S or 60S ribosomes [53] [54] [48 , 55 , 56] . The subtle differences in polr1a , polr1c , and polr1d expression and ensuing loss-of-function phenotypes raises the intriguing possibility that perhaps the subunit composition of RNA Pol I may be spatiotemporally dynamic , or alternatively , that translation may occur in a tissue specific manner in the form of specialized ribosomes . In agreement with this idea , Rpl38-/- mouse embryos exhibit tissue defects in cranial and axial skeleton development [57] . These defects were found to be specifically associated with altered Hox gene expression . Thus , mutation of Rpl38 did not affect global protein synthesis but rather specifically impacted the translation of Hox genes , implying a tissue specific role for Rpl38 in ribosome biogenesis and translation . Bent Bone Dysplasia syndrome ( BBDS ) provides further evidence for both tissue threshold sensitivity as well as differential control of ribosome biogenesis by lineage-specific factors [58] . BBDS is characterized by bent long bones , underdeveloped clavicles and pubic bone together with poor mineralization of the skull , and is associated with mutations in FGFR2 . The FGFR2 mutations in BBDS activate rDNA transcription and alter osteoblast differentiation . In preosteoblasts , nucleolar FGFR2 represses RUNX2 , which functions as a transcription factor to promote osteoblast differentiation by repressing rDNA transcription at rDNA promoters [59] . These studies suggest not only a specific link between ribosome biogenesis and bone formation but also that ribosome biogenesis may be a mechanism for coordinating proliferation and cell fate . It will be important to explore whether polr1c and polr1d also interact with factors such as Runx2 and function specifically in osteoblast differentiation . Numerous rRNA cleavage and processing events together with ribosome co-factors collectively offer considerable opportunities for the differential or spatiotemporally specific regulation of ribosome biogenesis during embryogenesis . The similarity of expression patterns for polr1a , polr1c , and polr1d lends strong support to the idea that particular tissues such as neuroepithelial cells and neural crest cell progenitors require high levels of RNA Pol I activity during embryogenesis . However , the roles of polr1c and polr1d as a part of RNA Pol III remain to be fully investigated . Our results showed that 5S rRNA transcription is not affected in the polr1c-/- mutant zebrafish . Furthermore , other studies have revealed that mutations in POLR1C that are associated with the etiology and pathogenesis of TCS do not impact RNA Pol III function [41] . Nonetheless , RNA Pol III performs several important functions in cells including transcription of the 5S rRNA as well as tRNAs and ncRNAs [60] , and yet the roles of RNA Pol III during embryogenesis remain poorly understood . In the future , it will therefore be important to determine whether specific mutations exert distinct effects on RNA Pol I versus RNA Pol III function in different tissues and in association with the different phenotypes characteristic of TCS and leukodystrophy . It is also important to be cognizant of other possibilities such as functions for RNA Pol I and III subunits in processes other than rRNA transcription and ribosome biogenesis as has recently been shown for TCOF1/Treacle [61 , 62] . In summary , we described the spatiotemporal activity and functional roles of polr1c and polr1d during embryogenesis and particularly in craniofacial development . We discovered that polr1c and polr1d play important functions in rRNA transcription and furthermore that polr1c and polr1d loss-of-function results in tissue-specific phenotypes , including craniofacial cartilage anomalies that mimic TCS in humans . Moreover , inhibition of Tp53 function was able to ameliorate cranioskeletal anomalies in polr1c-/- and polr1d-/- mutant zebrafish . Collectively our data provides a unifying cellular and biochemical mechanism underlying the pathogenesis of TCS irrespective of whether TCOF1 , POLR1C , or POLR1D is mutated . The tissue-specific phenotypes we observed in polr1c-/- and polr1d-/- mutant zebrafish augment a growing body of work suggesting that rRNA transcription and ribosome biogenesis are dynamically regulated during embryogenesis . Our results therefore provide new insights into the tissue specific roles of RNA Pol I during development and in the etiology and pathogenesis of TCS .
Adult zebrafish ( Danio rerio ) were housed and maintained in the Stowers Institute Zebrafish Facility according to IACUC standards and as detailed in Protocol # 2015–0138 which was approved on 6/24/2015 . Zebrafish embryos were raised at 28 . 5°C and staged according to Kimmel et al . , 1995 [63] . When necessary , 1-Phenyl-2-thiourea ( 0 . 002% ) was added to the embryo media to prevent pigment development . polr1chi1124Tg , and polr1dhi2393Tg zebrafish were maintained as heterozygotes and incrossed to generate homozygous mutant embryos . The polr1c and polr1d heterozygous mutant lines were crossed with additional available reporter lines including Tg ( fli1a:egfp ) , referred to as fli1a:egfp , and Tg ( 7 . 2kb-sox10:gfp ) , referred to as sox10:gfp , as well as the tp53M214K line . PCR was conducted on polr1c and polr1d adults and embryos for the presence of the insertional mutation . The polr1c wild type allele was detected using the primers forward 5’-CTATTGCTTTTGTCGCATAAAGCG-3’ and reverse 5’-CTCCAGTGTGTTTTCATCTGAAC-3’ . The polr1c mutant allele was detected using the primers forward 5’-CTATTGCTTTTGTCGCAT AAAGCG-3’ and reverse 5’-GCTAGCTTGCCAAACCTACAGGT-3’ . The polr1d wild type allele was detected using primers forward 5’-CAGTCACAACGTGCGACATGC-3’ and reverse 5’-GGTAAACGAGTTGATTTACGCATTG-3’ and the mutant allele detected using forward 5’-CAGTCACAACGTGCGACATGC-3’ and reverse 5’-GCTAGCTTGCCAAACCTACAGGT-3’ .
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Ribosomes synthesize all proteins , and are therefore critical for cell growth and proliferation . Ribosome biogenesis , or the process of making ribosomes , is one of the most energy consuming processes within a cell , and disruptions in ribosome biogenesis can lead to congenital disorders termed ribosomopathies . Interestingly , individual ribosomopathies are characterized by tissue-specific phenotypes , which is surprising given the universal importance of ribosomes . Treacher Collins syndrome ( TCS ) for example , is a ribosomopathy characterized by anomalies of facial bones , palate , eyes and ears . Mutations in TCOF1 , POLR1C , and POLR1D are associated with the underlying etiology of TCS . TCOF1 plays an important role in the synthesis of ribosomal RNA , one of the rate-limiting steps of ribosome biogenesis . Consequently , TCOF1 is essential for the survival and proliferation of neural crest cell progenitors , which are the precursors of craniofacial bone , cartilage and connective tissue . In contrast , the functions of POLR1C and POLR1D , which are subunits of RNA Polymerases I and III remain unknown . Here we examined the function of polr1c and polr1d during zebrafish development and discovered that these genes display dynamic spatiotemporal activity during embryogenesis with enriched expression in craniofacial tissues . Furthermore , we observed that polr1c and polr1d loss-of-function zebrafish exhibit anomalies in craniofacial cartilage development , which reflects the characteristic features of TCS . An examination of polr1c-/- and polr1d-/- mutants revealed that diminished ribosome biogenesis results in neuroepithelial cell death and a deficiency of migrating neural crest cells , which are the progenitors of the craniofacial skeleton . Moreover , the cell death observed in polr1c-/- and polr1d-/- mutants is Tp53-dependent , and inhibition of tp53 is sufficient to repress cell death and rescue cranioskeletal cartilage formation in polr1c-/- and polr1d-/- mutant embryos . These studies provide evidence for tissue-specific functions of polr1c and polr1d during embryonic development , while also establishing polr1c and polr1d loss-of-function zebrafish mutants as models of Treacher Collins syndrome .
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2016
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The Roles of RNA Polymerase I and III Subunits Polr1c and Polr1d in Craniofacial Development and in Zebrafish Models of Treacher Collins Syndrome
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Classical and Connectionist theories of cognitive architecture seek to explain systematicity ( i . e . , the property of human cognition whereby cognitive capacity comes in groups of related behaviours ) as a consequence of syntactically and functionally compositional representations , respectively . However , both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality ( e . g . grammars , networks ) that do not account for systematicity . By analogy with the Ptolemaic ( i . e . geocentric ) theory of planetary motion , although either theory can be made to be consistent with the data , both nonetheless fail to fully explain it . Category theory , a branch of mathematics , provides an alternative explanation based on the formal concept of adjunction , which relates a pair of structure-preserving maps , called functors . A functor generalizes the notion of a map between representational states to include a map between state transformations ( or processes ) . In a formal sense , systematicity is a necessary consequence of a higher-order theory of cognitive architecture , in contrast to the first-order theories derived from Classicism or Connectionism . Category theory offers a re-conceptualization for cognitive science , analogous to the one that Copernicus provided for astronomy , where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them .
To further clarify what is required of a theory to explain systematicity [1] , [3] , Aizawa [2] presents an explanatory standard for systematicity and the problem of ad hoc assumptions , which we follow , by analogy with the Ptolemean ( geocentric ) versus Copernican ( heliocentric ) explanations for the motions of the planets ( see [10] for a review ) . The geocentric explanation for planetary motion places the Earth at the center of the other planets' circular orbits . Although this theory can roughly predict planetary position , it fails to predict periods of apparent retrograde motion for the superior planets ( i . e . Mars , Jupiter , etc . ) across the night sky without the assumption of epicycles ( i . e . , circular orbits with centers that orbit the Earth ) . This additional assumption is ad hoc in that it is unconnected with the rest of the theory and motivated only by the need to fit the data—the assumption could not be confirmed independently of confirming the theory . The heliocentric explanation , having all planets move around the Sun , eschews this ad hoc assumption . Retrograde motion falls out as a natural consequence of the positions of the Earth and other planets relative to the Sun . Tellingly , as more accurate data became available , the geocentric theory had to be further augmented with epicycles on epicycles to account for planetary motion; not so for the heliocentric theory . The theory of planetary motion , of course , does not end there . The heliocentric theory , with its circular orbits , cannot explain the elliptical motion of the planets without further assumptions , and so was superseded by Newtonian mechanics . Newtonian mechanics cannot explain the precession of planetary orbits , and was in turn superseded by Einstein's theory of relativity . In each case , the superseding theory incorporates all that was explained by the preceding theory . Evaluating competing theories in this manner has an extensive history in science , and so one may expect it to be a reasonable standard for an explanation of systematicity in cognitive science . Aizawa [2] notes that although philosophers of science may not have a precise definition for the concept of an ad hoc assumption , one can nonetheless usefully characterize the idea by analogy with generally accepted examples , such as the assumption of epicycles , which we just mentioned . Another example Aizawa uses is the Creationist versus Darwinian theory of speciation , where the appeal to a supernatural being to explain the existence of different species is an ad hoc assumption . The general sense in which a theory fails to provide a satisfactory explanation by its appeal to ad hoc assumptions is when those additional , so called auxiliary , assumptions are unconnected to the core assumptions and principles of the theory , motivated only by the need to fit the data , and cannot be confirmed independently of confirming the theory . In this sense , the core theory has no explanatory power for the particular phenomenon of interest . Note that an auxiliary assumption is not necessarily ad hoc , nor is it precluded from subsequent inclusion into the set of core assumptions of the modified theory . Orthogonal experiments may provide confirmatory data for an auxiliary assumption , independent of the theory in question . Observations of the Jovian moons would have been the sort of independent confirmatory evidence for epicycles , had such data been available at the time , to justifiably include it as one of the core assumptions . However , the assumption that all heavenly bodies are governed this way ultimately proved untenable . The kind of theory sought here is one where systematicity necessarily follows without requiring such ad hoc assumptions . This characterization guides our analysis of the problem posed by the systematicity property , and our explanation for it . The problem for Classical and Connectionist theories is that they cannot explain systematicity without recourse to their own ad hoc assumptions [2] . For Classicism , having a combinatorial syntax and semantics does not differentiate between grammars such as G1 and G2 . For Connectionism , a common recourse to learning also does not work , whereby systematicity is acquired by adjusting network parameters ( e . g . , connection weights ) to realize some behaviours—training set—while generalizing to others—test set . Learning also requires ad hoc assumptions , because even widely used learning models , such as feedforward [11] and simple recurrent networks [12] , fail to achieve systematicity [13]–[17] when construed as a degree of generalization [18] , [19] . Hence , neither Classical nor Connectionist proposals satisfy the explanatory standard laid out by Fodor and Pylyshyn [1] and Fodor and McLaughlin [3] ( see also [20] , Appendix ) , and further articulated by Aizawa [2] . Ironically , failure to meet this criterion was one of the reasons Classicists rejected Connectionist explanations for systematicity . The import of Aizawa's analysis is that the same shortcoming also befalls Classicism , and so an explanation for systematicity is still needed . In this regard , it would appear that the 90s were also the “lost decade” for cognitive science . In hindsight , the root of the difficulty that surrounds the systematicity problem has been that cognitive scientists never had a theory of structure to start with ( i . e . one that was divorced , or at least separated from specific implementations of structure-sensitive processes ) . In fact , such a theory has been available for quite some time , but its relevance to one of the foundational problems of cognitive science has not previously been realized . Our category-theory based approach addresses the problem of ad hoc assumptions because the concept of an adjunction , which is central to our argument , ensures that the construct we seek not only exists , but is unique . That is to say , from this core assumption and category theory principles , the systematicity property necessarily follows for the particular cognitive domains of interest , because in each case the one and only collection of cognitive capacities derived from our theory is the systematic collection , without further restriction by additional ( ad hoc ) assumptions .
A category consists of a class of objects ; a set of morphisms ( also called arrows , or maps ) from to where each morphism has as its domain and as its codomain , including the identity morphism for each object ; and a composition operation , denoted “” , of morphisms and , written that satisfy the laws of: The most familiar example of a category is , which has sets for objects and functions for morphisms , where the identity morphism is the identity function and the composition operation is the usual function composition operator “” . Another example , where continuity is important , is the category of metric spaces and continuous functions . Certain morphisms have important properties that warrant giving them names . Two such morphisms , which we will refer to later , are called isomorphisms and homomorphisms . A morphism is an isomorphism if there exists a morphism , such that and . If exists , then it is the inverse of , also denoted as . Homomorphisms pertain to categories whose objects have additional internal structure , such as groups . For example , the category has groups for objects , and the morphisms are group homomorphisms . A group consists of a set of elements , and an associative binary operation , satisfying identity and inverse axioms . That is , has an identity element , and for each , an inverse element , such that and . A group homomorphism is a morphism , such that , for all . Homomorphisms in other categories ( e . g . , graph homomorphisms ) are defined analogously . A product of two objects and in a category is an object together with two morphisms and , such that for any pair of morphisms and , there is a unique morphism , such that the following diagram commutes: ( 1 ) where a broken arrow indicates that there exists exactly one morphism making the diagram commute . To say that a diagram commutes is to mean that the compositions along any two paths with the same start object and the same finish object are the same . So , in this diagram , and , where and are sometimes called projection morphisms . A product object is unique up to a unique isomorphism . That is , for any other product object with morphisms and there is one and only one isomorphism between and that makes a diagram like this one commute . Hence , is not unique , only unique with respect to another product object via isomorphism . This characteristic has an important consequence for our explanation of systematicity , which we present in the Results section . An essential characteristic of a product object is that the constituents and are retrievable via the projection morphisms . is also written , and since is uniquely determined by and , is often written as , and the diagram used in defining a product then becomes ( 2 ) In , is ( up to isomorphism ) the Cartesian product ( , , ) , where , , and is the product function , sending to , so that and . The “maps to” arrow , , indicates the action of a function on a domain element , so is equivalent to . ( refers both to a general product in any category with products and the more specific Cartesian product in the category . ) The categorical concept of product is a very general notion of combinatoriality . Not surprisingly , then , Classical and Connectionist notions of combinatoriality can be seen as special cases of categorical products . A grammar like G1 ( Introduction ) , for instance , can be used to realize the Cartesian product of the set of agents and the set of patients ( i . e . by employing the first production without the loves symbol ) . A categorical product can also be realized by including suitable rules for inferring the agent and patient from this Cartesian product . ( A grammar like G2 cannot realize a Cartesian product , or categorical product; in fact , it realizes a union of two partial products . ) Similarly , a Connectionist method such as the outer product of two vector spaces with suitable projections from the outer product space to the original vector spaces also realizes a categorical product . However , an explanation for systematicity requires more than just realization , and as we shall see , additional category theory concepts are needed . A functor is a structure-preserving map between categories and that associates each object in to an object in ; and each morphism in to a morphism in , such that for each object in ; and for all morphisms and for which compositions and are defined in categories and , respectively . The following diagram shows the details of a functor: ( 3 ) where dashed rectangles encapsulate the categories , and arrows between morphisms are omitted . The object and morphism components of a functor are sometimes explicitly distinguished as and , respectively . Otherwise , the functor component is implicitly identified by its argument . Functor composition and isomorphism are defined analogously to morphisms ( above ) . That is , the composition of functors and is the functor , sending all objects in to objects in ; and morphisms in to morphisms , such that identity and composition are respected . That is , ; and . A functor is an isomorphic functor , if and only if there exists a functor such that and , where and are the identity functors sending objects and morphisms to themselves in the respective categories . Theories of cognition employ some form of representation . Functors provide a theoretical basis for constructing representations . For example , computational systems often employ lists of items , such as numbers . In category theory , lists can be modeled as monoids from the category whose objects are monoids , and morphisms are monoid homomorphisms [28] . A monoid is a set , with an associative binary operation , and an identity element , such that for all . A list monoid [28] is the set of all ordered lists constructed from set by concatenation operator , where the identity element is the empty list ( so that , e . g . , ) . ( It is worth noting that strings , e . g . , lists of characters , of length 2 over the set are denoted , and strings of length denoted . In computer science , often means “match anything” , hence the notation can be read as strings of any length . ) Lists can be constructed from sets by the functor , as indicated in the example diagram ( 4 ) where is the object part of ( i . e . , ) and is the morphism part ( i . e . , ) , so that , e . g . , ( i . e . , morphism is mapped to monoid homomorphism , which we will refer to as ) . ( For simplicity , we have omitted composition with a second morphism in each of the categories and functor mappings , as was shown in Diagram 3 . ) So , for example , . The examples pertaining to lists were adapted from [28] ( Chapter 2 ) , where in [28] corresponds to our . We choose to label the object component of the functor rather than to emphasize the fact that the functor constructs a set of lists of numbers from a set of numbers , not just a single list containing those numbers . The two different sorts of arrows in Diagrams 3 and 4 highlight the constructive nature of functors . The objects are ( co ) domains with respect to the morphisms within categories , but are themselves elements of larger objects ( in general , the class ) with respect to the morphisms between categories . In programmer parlance , was “lifted” from being a function over numbers to become a function over lists of numbers . In this way , functors provide a means for constructing new representations and processes from existing ones in a structurally consistent manner . Notice that the definition of functor does not dictate a particular choice for monoid homomorphism as part of the definition of . A natural choice is to define so that functions applied to one-item lists result in one-item lists ( i . e . , ) . Another choice that turns out to also respect the definition of a functor includes two copies of each transformed element ( i . e . , ) . In this case , So , and in particular are monoid homomorphisms . In fact , there are many possible monoid homomorphisms that could be chosen to define this functor . Consequently , in the case of an architectural component of a cognitive system , there are many possible ways of constructing structurally consistent representations and processes from existing ones . We need to find a principled way to choose the “right” monoid homomorphism . In the context of explaining systematicity , a similarly principled choice is necessary . To narrow the choice down to a particular monoid homomorphisms , and hence a particular representational scheme , we need two additional category theory concepts: natural transformation and adjunction . A natural transformation is a structure-preserving morphism from domain functor to codomain functor that consists of for each object in , such that , as indicated by the commutative diagram in the category ( 5 ) Again for expository purposes , we include the source category and functor arrows , which are usually left implicit in such diagrams . When a transformation is natural in the technical sense it seems natural in the intuitive sense , for mathematicians . In fact , category theory was founded in an attempt to formalize such intuitions [24] . We will return to this point about naturality , in the Discussion , as it pertains to an explanation of systematicity without reliance on ad hoc assumptions . A natural transformation is a natural isomorphism , or natural equivalence if and only if each is an isomorphism . That is , for each there exists a such that and . Natural transformations also compose , and the composition of two natural transformations is also a natural transformation . Just as there are identity morphisms mapping objects to themselves , and identity functors mapping categories to themselves , there are also identity natural transformations , , mapping functors to themselves . And , so , the composition of a natural isomorphism ( isomorphic natural transformation ) , , with its inverse , , is an identity natural transformation , i . e . , . Functors preserve structure between categories; natural transformations identify the similarities between functors . For our purposes , functors construct new representations and processes from existing ones; natural transformations identify the similarities between constructions . A simple example that is closely related to the functor example , illustrating this perspective , involves list reversal as indicated by the commutative diagram ( 6 ) where the domain and codomain objects of each morphism are sets of lists , such as ; and is essentially with ( co ) domain the set instead of the monoid . As the diagram illustrates , squaring a reversed list is the same as reversing a squared list . So , there is a non-trivial ( i . e . non-identity ) relationship between the list monoid construction functor ( ) and itself . The functor constructing the lists in Diagram 6 is closely related to in that the returned object is just the underlying set of the monoid , forgetting the binary operation and the identity element . The underlying set can also be extracted by a functor from the category , as we will see in the next section . This example shows how two ways of constructing individual lists , via the functor , are related by the list reversal natural transformation , . Although their associated diagrams look similar , there is an important difference between functor and natural transformation pertaining to the equality constraint that defines the relationships between object elements . For a functor , the equality constraint is local to the codomain of the transformation , i . e . the relationships between object elements within the constructed category . And so , the elements of the objects in the new category are only indirectly related to the elements in the corresponding objects of the source category by the categories' common external structure ( i . e . inter-object relationships ) . For a natural transformation , the equality constraint spans the transformation , involving object elements mapped by both domain and codomain functors . And so , the two functors are directly related to each other by the internal structure of their associated objects ( i . e . the relationships between object elements within an object ) . As part of a theory of cognitive architecture , there is a tension between the freedom afforded by functorial construction on the one hand—allowing an architecture to transcend the specific details of the source elements to realize a variety of possible representational schemes for those elements—and the need to pin down such possibilities to specific referents on the other . This tension is resolved with adjunctions . An adjunction consists of a pair of functors , and a natural transformation , such that for every and there exists a unique , such that , indicated by the following commutative diagram: ( 7 ) where the functors are implicitly identified by ( co ) domain categories ( left subdiagram ) and ( right subdiagram ) . The two functors are called an adjoint pair , , where is the left adjoint of , and is the right adjoint of ; and natural transformation is called the unit of the adjunction . The left and right functors of an adjoint pair are like “inverses” of each other , but unlike an isomorphic functor whose composition with its inverse sends all objects and morphisms to themselves , the returned objects and their elements of a composition of left and right adjoints are related to the argument ( source ) objects and their elements by a natural transformation . For categories and , the adjoint pair , consisting of functor that constructs the free monoid on the set , and then “forgetful” functor returns the underlying set of monoid , are related by an injection . The injection is called an insertion of generators , whose component at , , sends each element of to the corresponding element ( one-item list ) in . The elements together generate the set ( i . e . is the alphabet from which the set of all “words” is constructed where each is mapped to ) . In this context , is the unit of this adjoint pair . The effect of on objects has just been given; the effect on morphisms is as follows: if is a function , then is defined as follows: ( cf . [25] , p . 111–112 ) . Note that is the functor defined in the Functors section . Monoid is “free” in the informal sense that there are no missing or extra bits in the construction used to satisfy commutativity . The precise definition of free is as follows . Given the forgetful functor , and an object of , is free on if there is a morphism such that for any morphism , there exists a unique morphism such that , indicated in the following commutative diagram: ( 8 ) However , not just any monoid generated from a set is a free monoid . For instance , the monoid ( i . e . addition modulo 2 ) in the diagram ( 9 ) is not the free monoid on any set , because the only homomorphism , , maps 0 and 1 to , which does not make the diagram commute for . That is , . ( It is easy to show that the free monoid on the empty set is . So is not the free monoid on the empty set , either . ) Other free objects , such as the free group on a set are defined analogously ( see [21] ) . A simple example of a free monoid as may be employed by a cognitive system is a primitive form of counting , where is the free monoid counter , having elements , on singleton set . This monoid is isomorphic to addition over the natural numbers , i . e . the monoid . From free objects we get an alternative ( equivalent ) definition of adjunction: consider functor from the original definition . If for every object , is free on with morphism , then functor , with morphism mappings defined so that , is the left adjoint of , and is the right adjoint of [31] . Yet another ( equivalent ) definition of adjunction , favoured by category theorists for its conceptual elegance , highlights the symmetry between a pair of adjoint functors: a bijection ( one-to-one correspondence ) between the set of morphisms from object to in category and the set of morphisms from object to in category . So , identifying the unique morphism in one category means that it is associated with one and only one morphism in the other category . In the list construction example , the unit of the adjunction is the injection sending each element in the set to the one-item list in the set of all lists constructed from , as shown in the following diagram: ( 10 ) where the left adjoint , , constructs the free monoid on the set ; and the right adjoint , , returns the underlying set , , of a list monoid , as mentioned earlier . In this way , given , the only homomorphism in the constructed category making the diagram commute is . The definition for arrow is essentially the same as , except that its ( co ) domain is a set , not a monoid . Other monoid homomorphisms that could have been chosen as part of the functor definition , such as , are excluded by and the commutativity property of the adjunction , because . Since this arrangement works for any morphism in , it can also be used to define a particular list length function from a family of analogous “length” functions as indicated in the following commutative diagram: ( 11 ) where monoid is the set of non-negative integers with addition as the operator and 0 as the identity element; is a constant function sending every element to the number 1; and / are functions returning the number of items in a list . As in the previous example , the definition of functor affords other choices for “length” , such as , where is a list . This arrow is also a monoid homomorphism , since , where and are the lengths of lists and , respectively . Again , however , the morphism and the commutativity property force the usual choice for length function ( i . e . ) , and excludes others such as , because . A general pattern emerges from this use of adjunction . Functor construction may afford multiple choices for particular morphisms ( processes ) in the constructed category , but a principled choice is obtained through the commutativity property of the adjunction . This arrangement means that we are not committed a priori to a particular representational scheme; i . e . , we do not have to make an ad hoc assumption about what that representational format should be . Given that an architecture has the capacity for an instance of the group of computations under consideration , then necessarily it applies to all other computations in that group . In the case of list length , for example , may indeed be the “correct” choice when we require the length of a list of characters in number of bytes for characters that are 2-byte unicodes ( i . e . the characters appearing in the extended set that includes other special symbols and language scripts requiring two bytes for unique identification ) . So , to paraphrase , a computational architecture with the capacity to count the length ( in bytes ) of some lists of 2-byte unicodes necessarily has the capacity to compute byte lengths for all other unicode lists . In this way , the explanation for the “systematicity of list length” has two parts: existence is afforded by the possible list length functions; and uniqueness is afforded by the commutativity property of the adjunction . Without the adjunction , the choice of construction is by ad hoc assumption . Our explanation for the systematicity of human cognition follows this pattern .
For expository purposes , we develop our adjoint functors explanation from its components . One may wonder whether a simpler category theory construct would suffice to explain systematicity . For this example domain , the components of this adjoint have some systematicity properties , but in and of themselves do not explain systematicity—just as for Classicism and Connectionism , having a property is not the same as explaining it . This bottom-up approach motivates the more complex category theory construct from which the systematicity properties necessarily follow . Our approach has three steps . First , we show a categorical product that has the systematicity of representation and systematicity of inference properties . However , a product of two objects may afford many isomorphic product objects that do not also have the compositionality of representation property . Second , we show that the product functor provides the principled means for constructing only those products that also have the compositionality of representation property . There may , however , be several products that have the compositionality property , but which differ in semantic content by having different orders between identical sets of constituents . So , a principled choice is needed to determine the product . So , third , we show that the diagonal functor , which is left adjoint to the product functor , provides that principled choice by the commutativity property of the ( diagonal , product ) adjoint functor pair . For concreteness , we refer to the category , but our explanation does not depend on this category . ( If we require an explanation of systematicity with respect to ternary relational propositions , then a ternary product is employed . The explanation for systematicity extends analogously , where the diagonal and product functors involve object triples . We may also need to explicitly represent a symbol for a relation , such as Loves . In this case , an object representing the relation symbol is paired with the product object representing the related entities . We address this situation in the next section . For present purposes , we omit relation symbols , since the relation is constant across the instances considered here and nothing essentially changes by its omission . First , suppose objects ( say , agents ) and ( patients ) are sets containing representations of John and Mary , denoted as . Although and are the same set of members , we maintain distinct names to keep track of the distinction between member pairs . ( The assignment of elements to objects is itself an assumption , but not an ad hoc one for our theory , as explained in the next section and in the Discussion . ) A categorical product of these two sets is the Cartesian product of and , which is the set of all pairwise combinations of elements from and , together with projections and for retrieving the first and second constituents in each case . That is , , , and . By definition , the Cartesian product generates all pairwise combinations of elements from and , therefore this Cartesian product has the systematicity of representation property . Moreover , by definition , the categorical product affords the retrieval of each constituent from each representation ( otherwise it is not a product ) , therefore the categorical product also has the systematicity of inference property . In this case , from the categorical product definition takes the role of input , so in terms of Diagram 2 inferring John as the lover from John loves Mary is just , where JM is the input and is the input-to-product object map , whose unique existence is guaranteed by definition . The Cartesian product , however , is not the only product object that satisfies the definition of a categorical product of and . An alternative product has as the product object , and and as the projections . Indeed , for this example , any four-item set together with the appropriate projections for retrieving the constituents would suffice . However , these alternatives do not have the compositionality of representation property: the semantic contents of these representations , whatever they may be , are not systematically related to each other , or the semantic content of John , or Mary . Hence , categorical products , in themselves , do not necessarily provide an explanation of systematicity . Second , for any category that has products ( i . e . every pair of objects in has a product ) , one can define a product functor ( or , , in the ternary case ) , that is from the Cartesian product of categories , , itself a category , to , where , , as indicated by the following diagram: ( 12 ) recalling that our functor diagrams explicitly identify the object component , , but not the morphism component , , of the functor . In this case , the semantic contents of these elements are systematically related to each other and their constituents John and Mary . This categorical construction is an instance of Classical compositionality , whereby the constituents , are tokened wherever the compositions are tokened . As such , it has the compositionality of representation property . Although the product functor has the compositionality of representation property , it introduces a different problem: , where and is also a valid product , but the semantic content of is not the same as . That is because they have different order relationships between their constituents even though the corresponding constituents are identical . Thus , a principled choice is required to determine whether , for example , John loves Mary should map to ( John , Mary ) , or ( Mary , John ) . Otherwise , one can define an architecture that does not have the systematicity of inference property by employing both products to correctly infer Johnas the lover in John loves Mary via , yet incorrectly infer John as the lover in Mary loves John via , where position within the product triple identifies the relevant projection . The assumption that architectures employ only the first product is ad hoc just like the assumption that Classical architectures employ grammars such as G1 , but not G2 . So , a principled choice is needed to determine the product . Third—final step , this problem brings us to the second aspect of our explanation foreshadowed in the Introduction ( i . e . uniqueness ) . Again , as we saw with lists , a particular construction is specified through the left adjoint functor . The left adjoint to the product functor is the diagonal functor ( or , , in the ternary case ) , where , as indicated by the following diagram: ( 13 ) The ( diagonal , product ) adjoint pair is indicated by the following commutative diagram: ( 14 ) ( see [28] Example 2 . 4 . 6 ) . In this manner , the John loves Mary family of cognitive capacities is specified by the commutative diagram ( 15 ) where and are the agent and patient maps from the set of proposition inputs into the set containing all the possible constituent representations . Here , we explicitly consider the case of equality , so that . When , and have different codomains , since , so the conflict between these products does not come into play , therefore the adjunction is not required and the product functor is sufficient . With the understanding that sets and are equal , we maintain the notational distinction for clarity in the subsequent text . Given as the morphism used by the architecture to map proposition inputs to their corresponding internal representations , then the definition of an adjunction guarantees that is unique with respect to making Diagram 15 commute via . That is , , where is the input for proposition John loves Mary . The alternative construction is excluded because . Having excluded by the commutativity property of the adjunction , the only two remaining ways to map the other inputs ( i . e . and ) are equal . So , given that the architecture can represent John loves Mary as via and infer John as the lover via from the product , then necessarily it can represent Mary loves John and infer Mary as the lover using the same morphisms . That is , , or . This explanation works regardless of whether proposition John loves Mary is represented as ( John , Mary ) via , or ( Mary , John ) via . In the latter case , the adjunction picks out just the construction , and hence , because it is the one and only one that makes the following diagram commute: ( 16 ) That is , , but . Given that the architecture can represent John loves Mary as via and infer John as the lover via from the product , then necessarily it can do so for Mary loves John using the same morphisms . That is , , or . If we need to explicitly represent a symbol for a relation , such as Loves , the product object is paired with an object , say , representing the context in which the entities are related . The object representing the relation in this case is . This situation may arise where we need an explanation for systematicity that involves multiple similar relations , e . g . , loves , likes , dislikes , and hates , where the capacity for instances of each of these relationships is co-extensive . That is , if one can represent John loves Mary and John likes Mary , then one can also represent the other six combinations , such as Mary loves John and Mary likes John . If one can represent John loves Mary , but not John likes Mary , then one can represent Mary loves John , but not Mary likes John . In this case , there is a category of relation symbols whose objects , , are symbols referring to each relation ( e . g . , loves , likes , etc . ) , and whose morphisms , , are just the identity morphisms for each object . ( Such a category is called a discrete category . ) Each relation , in this case , is a pair . Hence , the capacity to represent instances of the loves and likes relations extends to the other instances for both relations . For these situations , the diagonal and product functors have extensions . The extension to the diagonal functor is: , such that and . The product functor is: , such that and . The adjunction , which is an extension of the one shown in Diagram 15 , is shown in the following commutative diagram: ( 17 ) In this situation , provides the explicit context in which entities are related . Under the assumption that these relation symbols belong to a different category , then cases such as loves loves loves cannot be generated . Note that supposing different objects for these entities is not an ad hoc assumption for our theory . does not contain members such as John or Mary , and likewise ( or , ) does not contain relation symbols , because they refer to different types of entities with respect to the theory—Loves refers to a relation , which is at the level of objects in our theory , whereas John and Mary refer to entities in a relationship , which are members of objects . In summary , products may have the systematicity of representation and inference properties ( see also Discussion ) , but may not have the compositionality of representation property . Product functors construct products that have the compositionality property , but there may be more than one product with this property . The possible presence of multiple products requires a principled choice for fixing the product . That choice is provided by the ( diagonal , product ) adjoint functor pair . Importantly , the unit of the adjunction , , is not a free parameter of the explanation , it defines the specific adjunction in part; and there is no choice in representational format ( i . e . left-right , or right-left constituent order ) —the given capacity to represent a proposition fixes the same order for all the other propositions . The same situation also applies for the explicit ( multiple ) relational propositions domain . Hence , systematicity is a necessary consequence of this ( extended ) adjoint pair without recourse to ad hoc assumptions , and so meets the explanatory standard set by Aizawa [2] , and Fodor and Pylyshyn [1] , for this domain . Another domain in which humans exhibit systematicity is relational schema induction . This domain is more complex than the previous one in that the intrinsic connection is between relations , rather than within one . In the relational schema induction paradigm [32] , participants are required to do cue-response prediction over a set of stimuli , such as letters and shapes , whose relationships conform to a group-like structure . For example , participants are shown ( trigram , shape ) pairs generated from a set of four trigrams ( e . g . , NEJ , POB , KEF , BEJ ) and two shapes ( e . g . , square , circle ) , and are required to predict the response trigram , also from the same trigram set . Suppose , for example , a participant is presented with NEJ and square . After making a prediction , the correct response trigram is presented . This procedure is repeated with a new cue-response trial . The first two responses are not predictable prior to the feedback provided by the correct trigram . Hence , the first two trials are regarded as “information” trials . Each block of eight trials ( i . e . all possible trigram-shape combinations ) is repeatedly presented until a certain criterion level of correct performance is reached ( e . g . , correct responses to all eight trials in a block ) . Each set of eight cue-response pairs ( i . e . , four trigram times two shapes ) constitutes a task instance . Once participants reach criterion a new task instance of eight cue-response pairs was randomly generated from a larger pool of possible trigrams and shapes ( task instance examples are shown in Tables 1 and 2 ) . The crucial data for this paradigm are the performances on subsequent task instances . When subsequent task instances conformed to the same structure , albeit with different stimuli , mean response error over the 48 participants was at or near optimal level: 2 . 00 errors per eight trials for the sequence of task instances conforming to the Klein group , and 2 . 67 for task instances conforming to the cyclic-4 group—two information trials are needed to determine the assignment of novel stimuli to structural elements [32] . The results provide another example of systematicity of human cognition: given that a person can correctly do one task instance and the information trials from the new task instance , then necessarily they can predict trials of all others , with the usual provision for a distinction between competence and performance . This task is modelled as the category of sets with actions , ( cf . [25] , 6 . 3 . 1 , and [33] Definition 5 . 2 ) , that has objects for task instances , where is a set of states indicated by trigrams , is a set of “actions” indicated by shapes , and specifies the action of a shape on a trigram resulting in a trigram . The morphisms in this category consist of pairs of maps and , such that the following diagram commutes: ( 18 ) where the identity morphism is the pair of identity maps , and compositions are defined component-wise . In our example , the set consists of four elements representing the four trigrams , and the set consists of two elements representing the two shapes . For the purpose of finding a suitable adjoint , we need to see how is naturally embedded in a monoid . Recall that a monoid consists of a set and a binary associative operator that satisfies closure: i . e . , for all , whenever is defined , and there is an identity element , such that . In terms of our ASets ( i . e . objects in ) , the monoid identity corresponds to a “shape” whose action is to do nothing at all to the trigrams on which it acts: it leaves them unchanged . ( However , this shape was not included in the experiments [32] . ) The adjoint functor pair used for this domain consists of the forgetful functor , which returns the underlying sets , i . e . and , and its left adjoint , the free functor , which constructs ASets . The ( free , forgetful ) adjoint is shown in the following commutative diagram: ( 19 ) where and , for the instance of interest to us , and are the ( trigram , shape ) pairs of sets for the first and second tasks ( respectively ) , as defined for example in Tables 1 and 2 so that , , etc . Full details and a proof that is an adjoint functor pair are provided in Text S1 . Our explanation for systematicity in this domain follows the now familiar pattern , where monoids model the relationships between actions in each task instance . ( Though our argument employs monoids , nothing essential changes if instead we use semigroups , or groups , where for example each task instance is extended with two additional shapes , one explicitly corresponding to the identity element , and the other to the remaining element in the Klein , or cyclic-4 group . For these cases , the proofs of adjointness can be extended to involve free semigroups and free groups , respectively . ) Given an ASet modelling the first task instance and an ASet modelling the second task instance , there is more than one homomorphism from the first to the second , only some of which afford the correct responses to the stimuli in the second task instance . For example , one homomorphism has the following trigram and shape mappings: , , , , , and . Basically , the <1 ? show=[to] ? >first table collapses to a table with one row and two columns . It is straight forward to check that it is indeed a homomorphism , for example , . However , this homomorphism does not yield the correct responses to some of the stimuli in the second task instance . For example , all predictions to trigrams REZ and JOQ are no longer possible . Thus , a principled choice is required to select only those homomorphisms that indeed result in models for the second task instance . That choice is determined by and the commutative property of the adjunction . That is , having obtained the first task instance , and given the two information trials of the second task instance that identify the correspondences between task stimuli , then there is one and only one homomorphism making the diagram commute , so that correct responses are obtained from the remaining trials of the second task instance . And so , systematicity is a necessary consequence of this adjunction . Some readers may be interested in developing alternatives , or extensions to existing theories to address the systematicity problem in light of our explanation , so it is worth formally characterizing how our approach differs from previous ones . The difference between our category theory explanation and Classical/Connectionist approaches to systematicity may be characterized as higher-order versus first-order theories . Category theory also provides a formal basis for this distinction in terms of more general n-category theory ( see , e . g . , [34] ) . Though the concerns of n-category theorists go way beyond what we need here , some elementary aspects of the theory are used to formalize the difference between why our adjoint functors explanation addresses the systematicity problem and why the Classical or Connectionist approach does not . Notice that the definitions of functor and natural transformation are very similar to the definition of a morphism . In fact , functors and natural transformations are morphisms at different levels of analysis: a natural transformation is a morphism one level above functors as we shall see . For n-category theory , a category such as is a 1-category , with 0-objects ( i . e . sets ) for objects and 1-morphisms ( i . e . functions ) for arrows . A functor is a morphism between categories . The category of categories , , has categories for objects and functors for arrows . Thus , a functor is a 2-morphism between 1-objects ( i . e . 1-categories ) in a 2-category . A natural transformation is a morphism between functors . The functor category , of functors from to , has functors for objects and natural transformations for arrows . Thus , a natural transformation is a 3-morphism between 2-objects ( i . e . functors ) in a 3-category . ( A 0-category is just a discrete category , where the only arrows are identities , which are 0-morphisms . ) In this way , the order of the category provides a formal notion of explanatory level . Classical or Connectionist compositionality is essentially a lower-levels attempt to account for systematicity . For the examples we used , that level is perhaps best described in terms of a 1-category . Indeed , a context-free grammar defined by a graph is modelled as the free category on that graph containing sets of terminal and non-terminal symbols for objects and productions for morphisms [31] . By contrast , our category theory explanation involves higher levels of analysis , specifically functors and natural transformations , which live in 2-categories and 3-categories , respectively . Of course , one can also develop higher-order grammars that take as input or return as output other grammars . Similarly , one can develop higher-order networks that take as input or return as output other networks ( e . g . , networks whose connectivity is dynamic , such as cascade correlation [35] ) . However , the problem is that neither Classical nor Connectionist compositionality delineates those ( higher-order ) grammars or networks that have the systematicity property from those that do not . Likewise for our category theory explanation , not just any functor , nor just any natural transformation accounts for systematicity . If the explanation was left at either of these levels , then our approach would also succumb to the same problem that befalls Classicism and Connectionism—i . e . the problem of having to stipulate , ad hoc , just which functors or natural transformations account for the systematicity property . Rather , it is a natural transformation between an identity functor and a composition of two other functors ( ) that defines the adjunction that accounts for systematicity relative to the particular domain of interest . In this formal sense , a crucial difference is that there is also a between-levels aspect to our explanation .
Our adjoints explanation of systematicity has essentially two parts: ( 1 ) existence , showing how a particular connection between cognitive capacities is possible from a functorial specification of the architecture; and ( 2 ) uniqueness , explaining why that particular connection is necessary because it is the one and only one that satisfies the commutativity property of the adjunction . In contrast , the Classical and Connectionist explanations only provide an account of existence , but not uniqueness . That is , some grammars/networks afford the required intrinsic links between capacities and some do not , just like some functorial constructions do and some do not; but , for Classicism or Connectionism , there is no further explanation determining only those grammars or networks yielding systematicity ( other than by ad hoc assumption ) , whereas for the category theory explanation the adjunction specifies only the systematic functors . So , our explanation meets the explanatory standard laid out by Aizawa . To be regarded as a theoretical explanation for systematicity , such an explanation should be potentially falsifiable . Our explanation could be challenged by an alternative theory that accounts for systematicity ( without ad hoc assumptions ) in a way that does not require , or implement an adjunction . This possibility would not falsify our explanation as such , but may provide an alternative theory that is preferred on other grounds . Alternatively , there may exist a domain in which humans exhibit systematicity but for which there does not exist a relevant adjunction . Hence , the category theory approach we have put forward is in principle falsifiable . The unit of an adjunction is a natural transformation between functors . The sense in which a transformation is natural is that the transformation does not depend on a particular “basis” . A mathematician's example is to contrast the dual of a vector space with the , natural , double dual ( dual of the dual ) of a vector space—the former depends on a specific set of basis vectors chosen ad hoc , the latter does not . The analogue , here , is that our explanation of systematicity is natural in that it does not depend on a particular representational scheme ( i . e . , constituent order for relational propositions ) . Hence , the explanation does not depend on ad hoc assumptions about internal representations . Contrast this explanation with the Classical one , which must assume a particular grammatical form ( e . g . , G1 over G2 ) to fit the data . In addition to explaining systematicity , our category theory approach has further implications . According to our explanation , systematicity with respect to binary relational propositions requires a category with products . A category theory account has also been provided for the strikingly similar profiles of development for a suite of reasoning abilities that included Transitive Inference and Class Inclusion , among others [30]—all abilities are acquired around the age of five years . The difference between the difficulties of younger children and the successes of older children ( relative to age five ) across all these reasoning tasks was explained as their capacity to compute ( co ) products . ( A coproduct is related to a product by arrow reversal—see , e . g . , [28] for a formal definition . ) Therefore , our explanation implies that systematicity is not a property of younger children's cognition . Some support for this implication is found on memory tasks that require binding the background context of memorized items [36] , though further work is needed to test this implication directly . Our explanation for systematicity in regard to binary relational propositions does not depend on , it only requires a category with products . For example , the categories of topological spaces and continuous mappings , and of vector spaces and linear mappings [21] could also be used . These possibilities imply that an explanation of systematicity does not depend on a particular ( discrete symbolic , or continuous subsymbolic ) representational format . Thus , a further benefit is that our approach opens the way for integration of other ( sub/symbolic ) levels of analysis . Though some effort is needed to provide a category theory explanation for systematicity , even for a relatively simple domain such as relational propositions , the potential payoff is that our explanation generalizes to other domains where an appropriate adjunction is identified . This sort of tradeoff has been noted elsewhere in the context of a category theory treatment of automata [25] . We sketch one possibility in the domain of context-free grammars . Languages conforming to context-free grammars can be modelled as the free category on the directed graph that defines the grammar , whose vertices are sets of terminal and non-terminal symbols , and edges are transitions [31] . The left adjoint is the functor from the category of directed graphs and graph homomorphisms to the category of categories and functors ( category homomorphisms ) . The right adjoint is the forgetful functor , which returns the underlying graph ( i . e . the arrows , forgetting their compositions ) . The explanation here is analogous to our explanation for relational schemas . The problem Aizawa raised with respect to Classicism is avoided here because systematicity is not derived from individual grammars , but homomorphic relationships between grammars . Having provided an explanation of systematicity in terms of the rather abstract category theory concept of adjoint functors , one may wonder what this explanation means for a more typical conception of cognitive architecture in terms of internal representations and processes , and their realization in the brain . Human cognition is remarkable in that it affords the ability to think about things that have no sensory access ( e . g . , “a dog that is one lightyear long …” ) ; yet reason about such entities as if they were grounded in our everyday experience ( “… is smaller than a dog that is two lightyears long” ) . However , these two aspects must be reconciled: unbridled abstraction means that one can no longer determine what a particular internal representation is supposed to refer to; yet blinkering the system with over-narrowly defined representations curtails one's ability to think outside the box . These aspects appear in the form of functors and natural transformations in category theory . The adjunction is the category theory way of bringing them into precise “synchrony” , or co-ordination , so that we may think abstractly about very specific things . The realization of computational processes in the brain is classically conceived as a physical instantiation mapping from computational states to brain states , where the syntactic relationships between computational states correspond to physical relationships between brain states via such maps ( see [1] , p13 ) . Category theory affords a similar , but more general and formal treatment in terms of functors . Diagrams of categories are formally defined as functors that map graphs ( i . e . the shape of the diagram ) to categories ( see , e . g . , [37] ) . Analogously , a categorial cognitive system would involve a functor from a categorial computational model to a brain system . Up to this point , we have not considered the relatively new Bayesian approach to cognitive modelling ( see , e . g . , [38] , [39] for summaries ) because , to our knowledge , a Bayesian explanation for systematicity has not yet been articulated . Nonetheless , the hierarchical Bayesian approach offers a significant advance with the ability to learn a diverse range of structures , such as lists , trees , and other ( acyclic or cyclic ) graphs , from data [40] . An important aspect of this approach is that structural form ( or the type of structure ) is encoded as prior beliefs by hyperparameters in the higher layers , and instances of those structures are encoded as parameters in the lower layers in so far as they conform to the constraints imposed by the data ( environment ) . In this way , the architecture is not required to presume one particular structure to induce a group of behaviours from data . The hierarchical Bayesian approach affords the sort of higher-order theory that our analysis in the previous section implies . However , the question for the Bayesians is essentially the same as for the Classicists and Connectionists: that is , to articulate the Bayesian architectural principles from which systematicity necessarily follows . As the approach currently stands , systematicity depends on a number of factors including the available data , network connectivity , and optimization parameters . A Bayesian network with independently modifiable parameters for representing the distributions of constituents in each argument position of a relation may not have the systematicity property in the absence of data with , say , Mary in the patient position ( so called strong systematicity [18] ) , simply because there may be no ( prior ) information available to determine the value of the associated parameters . Hyperparameters may enable a dependency between lower level parameters so that the acquisition of one entails the acquisition of another . Still , systematicity may not necessarily follow from hyperparameters alone: for example , one can envisage a network where partial hyperparametrization links some but not all behaviours within the group , analogous to the problem that was raised with respect to classical compositionality . All theories make certain assumptions . The question is whether those assumptions are extrinsic to the theory and carry the essential explanatory burden ( i . e . they are ad hoc ) . In our case , one may question whether supposing that an object contains representations of John and Mary is not itself an ad hoc assumption , for the Cartesian product does not necessarily represent all possible combinations of mental representations [41] ( e . g . , generates representations corresponding to John loves Mary and Mary loves Mary , but not John loves John ) . Our explanation for systematicity of binary relational propositions is a consequence of the ( diagonal , product ) adjoint ( Diagram 15 ) , not a specific categorical product . Though the categorical product is a component of the explanation , the particular product is derived from the adjunction , not chosen independently of it . Where the constituent entities are of the same sort , and so belong to the same object ( ) in our theory , the diagonal functor generates the object pair , and the product functor takes and generates the product object , hence cases like cannot occur in this formulation . The assumption that relation symbols belong to a different category than the related arguments precludes the generation of intrinsically unconnected cases , such as loves loves loves . Typing , in this sense , shares some of the explanatory burden , but types are not extrinsic to our theory . An element cannot exist without belonging to an object ( its type ) in a category , by definition . Hence , types are intrinsic to the theory . Moreover , the explanatory burden is also born by the adjunction in our example domains . Even with typing , there must still be a principled choice for the order of those constituents , when they involve the same objects , which is provided by the adjunction . And , given that adjunctions are central to category theory , neither the assumption of types , nor our use of adjunction can be regarded as ad hoc for the purpose of explaining systematicity in these domains . Classicism also makes a distinction between atomic and molecular representations , as a core assumption [1] . However , even under core assumptions that are equivalent to ours—John and Mary belong to the same word classes , which differ from loves—systematicity does not necessarily follow , as exemplified by grammar G2 . Hence , the critical difference between our explanation of systematicity and the Classical approach is the adjunction . This assumption of typing , though , is acute for quasi-systematic domains , where cognitive capacity may extend to some but not all possible constituent combinations , which appear to be particularly prevalent in language ( see [41] ) . For these cases , we would also need category theory-derived principled restrictions to products . Equalizers and pullbacks ( see [30] for an application to cognitive development ) are two ways to restrict ( product ) objects , in the same arrow-theoretic style . Products , pullbacks and equalizers are all instances of the general , formal concept of a limit in category theory . The existence of adjoint functors is closely linked to the existence of limits in the respective categories ( cf . adjoint functor theorems [21] , p210–214 ) , which suggests that an appropriate adjunction can also be found for domains that require an explanation for quasi-systematicity . Needless to say , our category theory explanation is not the final word on a theory of cognitive architecture . For our approach ( and Classicism ) , where the assignment of elements to objects ( and , words to word classes ) is asserted , there is also the broader question of why they get assigned in a particular way . This question pertains to the acquisition of representations , whereas the systematicity problem pertains to their intrinsic connections . Incorporating category theory into the Bayesian approach may provide a more integrative theory in this regard . A connection between category theory and probability has been known for some time ( see [42] ) , and category theory concepts have been incorporated into the development of probabilistic functional programming [43] . A potentially fruitful line of future research , then , may be to identify a suitable adjunction with respect to , say , a category of Bayesian models , if such a category exists . From a category theory perspective , we now see why cognitive science lacked a satisfactory explanation for systematicity—cognitive scientists were working with lower-order theories in attempting to explain an essentially higher-order property . Category theory offers a re-conceptualization for cognitive science , analogous to the one that Copernicus provided for astronomy , where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them .
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Our minds are not the sum of some arbitrary collection of mental abilities . Instead , our mental abilities come in groups of related behaviours . This property of human cognition has substantial biological advantage in that the benefits afforded by a cognitive behaviour transfer to a related situation without any of the cost that came with acquiring that behaviour in the first place . The problem of systematicity is to explain why our mental abilities are organized this way . Cognitive scientists , however , have been unable to agree on a satisfactory explanation . Existing theories cannot explain systematicity without some overly strong assumptions . We provide a new explanation based on a mathematical theory of structure called Category Theory . The key difference between our explanation and previous ones is that systematicity emerges as a natural consequence of structural relationships between cognitive processes , rather than relying on the specific details of the cognitive representations on which those processes operate , and without relying on overly strong assumptions .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"mathematics",
"neuroscience/psychology",
"computational",
"biology"
] |
2010
|
Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition
|
Tomato Yellow Leaf Curl Virus Disease incited by Tomato yellow leaf curl virus ( TYLCV ) causes huge losses in tomato production worldwide and is caused by different related begomovirus species . Breeding for TYLCV resistance has been based on the introgression of multiple resistance genes originating from several wild tomato species . In this study we have fine-mapped the widely used Solanum chilense–derived Ty-1 and Ty-3 genes by screening nearly 12 , 000 plants for recombination events and generating recombinant inbred lines . Multiple molecular markers were developed and used in combination with disease tests to fine-map the genes to a small genomic region ( approximately 70 kb ) . Using a Tobacco Rattle Virus–Virus Induced Gene Silencing approach , the resistance gene was identified . It is shown that Ty-1 and Ty-3 are allelic and that they code for a RNA–dependent RNA polymerase ( RDR ) belonging to the RDRγ type , which has an atypical DFDGD motif in the catalytic domain . In contrast to the RDRα type , characterized by a catalytic DLDGD motif , no clear function has yet been described for the RDRγ type , and thus the Ty-1/Ty-3 gene unveils a completely new class of resistance gene . Although speculative , the resistance mechanism of Ty-1/Ty-3 and its specificity towards TYLCV are discussed in light of the function of the related RDRα class in the amplification of the RNAi response in plants and transcriptional silencing of geminiviruses in plants .
Plant pathogens are a major limiting factor for agricultural productivity worldwide . Viruses are among these and cause large yield losses in a variety of economically important crops . Although most viruses have small genomes and code for a very limited amount of proteins , they can cause a variety of disease symptoms , and the mechanisms underlying these are still mostly unknown . Plants utilize several lines of defense mechanisms to protect themselves from pathogen invasion . The mechanism that has been studied the most is resistance ( R ) gene-mediated resistance , which relies on the ability of a plant to recognize a pathogen and consequently trigger the hypersensitive cell death response ( HR ) [1] . Meanwhile , a large number of R genes have been identified , including ones responsible for the ( in ) direct recognition of viruses , such as Sw-5 for tospoviruses in tomato [2] , Rx2 for Potato virus X [3] and the I locus for Bean common mosaic virus [4] . In addition to these dominant R genes , a second type of resistance gene is inherited recessively , which is more common in resistances to viruses compared with resistance to fungi or bacteria [5]–[6] . Most of these genes are linked to the eukaryotic translation initiation complex and negatively affect the viral RNA replication cycle [7] . RNA silencing ( also called RNA interference , RNAi ) , is a conserved eukaryotic gene regulation mechanism that involves the biogenesis of small ( s ) RNA molecules of ∼21–26 nucleotides in size from perfect or imperfect long double stranded ( ds ) RNA molecules by an enzyme designated Dicer ( mammals , insects ) , or Dicer-like protein ( DCL ) ( plants ) [8] . One strand of these sRNA molecules is incorporated into an RNA-induced silencing complex ( RISC ) and enables the latter to sense and target RNA molecules with sequence complementarity to the uploaded RNA strand for degradation or translational arrest by means of the core Argonaute ( AGO ) protein . In recent years , RNA silencing has become known as an antiviral defense mechanism in plants and insects in which viral double-stranded RNA replicative intermediates or secondary RNA folding structures are cleaved into primary , small-interfering ( si ) RNA molecules . In plants , the viral primary siRNA molecules also act as primers for the host RNA-dependent RNA polymerases ( RDR ) to convert ( aberrant ) RNA target sequences into new long dsRNAs . These in turn become processed into secondary siRNAs . This not only leads to an amplification of the siRNA signal , but also results in a distributional spread of siRNA molecules from the entire RNA target sequence , referred to as transitive silencing [9] . The amplification of siRNAs is required to mount a strong antiviral RNAi response . Arabidopsis RDR1 , 2 and 6 , and orthologs of these genes , have been demonstrated to be involved in this amplification and plants from which these genes have been knocked-out exhibit higher susceptibility to various plant viruses [10]–[14] . The whitefly transmitted tomato yellow leaf curl disease ( TYLCD ) is one of the most devastating diseases of tomato ( Solanum lycopersicum ) and is caused by several species of the Begomovirus genus ( Geminiviridae ) [15] . Tomato yellow leaf curl viruses ( TYLCV ) are the most widespread and currently rank 3rd among the economically and scientifically most important plant viruses worldwide [16] . They have a single-stranded circular bi-directionally organized DNA genome with six partially-overlapping open reading frames [17] . Because of their limited coding capacity they rely , like most viruses , not only on their own proteins but also on the host cell machinery for their infection cycle [18] . Since the whitefly insect vector is hard to control , breeding TYLCV resistant tomato cultivars provides an attractive strategy to manage TYLCV . All domesticated tomatoes are susceptible to TYLCV , but high levels of resistance were found in several related wild tomato species . Genetic studies have led to the mapping of five TYLCV resistance/tolerance genes which are being exploited for resistance breeding . These genes have different origins: Ty-2 was introgressed from S . habrochaites , Ty-5 ( ty-5 ) was introgressed from S . peruvianum while Ty-1 , Ty-3 and Ty-4 all originated from different S . chilense accessions [19]–[24] . So far , none of these genes have been cloned and the underlying resistance mechanisms are still unknown . In contrast with classical R-genes none of the resistances to TYLCV described so far are associated with a HR . Moreover , in almost all TYLCV resistant materials , viral replication occurs [25]–[28] . This also holds true for Ty-1/Ty-3 , where in the donors ( S . chilense LA1969/LA1932 ) as well as in a commercial line with a Ty-1 introgression ( 3761 , A . B . Seeds , Ness Ziona , Israel ) TYLCV is replicating and detectable [29]–[31] , although the level does not exceed more than 10% of that in susceptible tomato cultivars . Though many loci ( i . e . Ty-1 to Ty-5 ) for TYLCV resistance have been described , the genes conferring resistance have not been identified . Recently , several papers have reported on host genes in a gene network contributing to the resistance originating from S . habrochaites [32]–[34] . By differential cDNA library comparisons of susceptible and resistant tomato lines before and after TYLCV inoculation , approximately 70 genes were found to be preferentially expressed in a tomato line with a resistance introgressed from S . habrochaites . For three of those , a lipocalin-like protein ( SlVRSLip ) , a Permease I-like protein and a hexose transporter LeHT1 , it was shown that their silencing ( partly ) compromised resistance . In our previous study we found that Ty-1 and Ty-3 map closer than previously reported and that they might be allelic [35] . In the present study Ty-1 and Ty-3 are fine mapped , and using a Tobacco Rattle Virus ( TRV ) induced silencing approach , the genes have been identified and found to be allelic . They code for an RNA-dependent RNA polymerase ( RDR ) of the γ class , a class of RDRs for which no function is yet described . The role of this new class of resistance genes will be discussed in light of the TYLCV infection cycle .
Previously , we mapped Ty-1 in the interval between MSc05732-4 and MSc05732-14 [35] . To fine-map Ty-1 , markers T0774 and SL_2 . 40ch06_30 . 891 , which flank this interval , were used to screen an F2 population derived from a cross between the susceptible Fla . 7776 and a recombinant inbred line ( RIL ) carrying the S . chilense Ty-1 introgression . Approximately 2 , 000 F2 plants were screened , 13 recombinants were identified , and RILs were developed for each of these ( designated R1 to R13 ) . Four RILs ( R1 , 4 , 12 and 5 ) containing the S . chilense introgression between markers Hba0161K22 and WU_M31 were resistant , while eight RILs that lacked this interval were susceptible ( Figure 1A ) . R7 , which resulted from a recombination event between markers WU-M27 and UF_TY3-P19 , showed an intermediate response . These results were confirmed for the three most informative recombinants ( R7 , R8 and R11 ) ( Table S1 ) using agroinoculation and show that Ty-1 is located between HBa0161K22 and WU_M31 , an interval of approximately 70 kb . The Ty-3 gene was previously mapped between T0774 and T1079 [21] . By screening an F2 population ( n = 717 ) from a cross between the susceptible line Fla . 7781 with the resistant line Fla . 8680 ( carrying the Ty-3 introgression from S . chilense LA2779 ) , 30 recombinants were identified . RILs of these recombinants were generated and tested with TYLCV . Results mapped Ty-3 to the interval between T0774 and P6-25 ( Table S2 ) . To further narrow down the Ty-3 interval , RILs of two key recombinants were used to generate three F2 sub-populations , A , B and C . Screening more than 10 , 500 individuals of these sub-populations with markers Mi23 and P6-25 ( sub-population A and B ) and markers T0774 and T0834 ( sub-population C ) identified 309 recombinants ( Table S3 ) . Cuttings of these recombinants were evaluated for TYLCV disease severity ( Table S3; control experiments , Table S4 ) and interval QTL mapping confirmed the location of Ty-3 between markers T0774 and P6-25 , with a LOD of over 50 in an interval between markers SL_2 . 40ch06_30 . 696 and cLEG-31-P16 ( Figure S1 ) . Recombinants in this interval were further analysed by testing their RILs with TYLCV and by saturating this region with additional molecular markers ( Figure 1B , Table S5 ) . RILs carrying the S . chilense LA2779 introgression between markers UF_TY3_P1 and UF_TY3_P23 were resistant ( recombinant class C to I , Figure 1B ) , while RILs with introgressions that did not span this region were susceptible; these results map Ty-3 to a region of approximately 71 kb that overlaps the region containing Ty-1 ( Figure 2 ) . According to the ITAG2 . 3 release of the tomato genome , the region to which Ty-1/Ty-3 mapped was predicted to contain five genes; Solyc06g051160 ( 408 bp ) , Solyc06g051170 ( 1728 bp ) , Solyc06g051180 ( 438 bp ) , Solyc06g051190 ( 957 bp ) and Solyc06g051200 ( 843 bp ) [36] ( Figure 2 ) . While gene Solyc06g051160 has an unknown function and Solyc06g051200 encodes a predicted ribosomal protein , the other three genes are each predicted to encode ( parts of ) an RNA-dependent RNA polymerase ( RDR ) . Arabidopsis thaliana RDRs in general are approximately 3 kb in size , but these three predicted genes are all much shorter . Since the genes only share low sequence similarity they likely are not paralogous . Interestingly , the crossing-over event in the intermediate resistant R7 occurred within the candidate gene Solyc06g051190 . After amplification and sequence analysis of this gene from R7 and subsequent alignment to the corresponding regions of a Ty-1 line and a ty-1 line , the recombination site in R7 could be pinpointed between two SNPs . This region covered less than 100 base pairs in which the recombination point mapped to the last part of predicted exon number 4 ( Figures S2 and S3 ) . Plants of R7 thus contained a chimeric predicted gene Solyc06g051190 . To identify the Ty-1 gene from the five candidate genes predicted in the Ty-1 interval , a Tobacco Rattle Virus ( TRV ) based Virus Induced Gene Silencing ( VIGS ) approach was applied . For three out of five genes a VIGS construct could be made; TRV2-160 for Solyc06g051160; TRV2-180 for Solyc06g051180 and TRV2-190 for Solyc06g051190 . The two VIGS vectors , TRV2-180 and TR2-190 , are specific and both are assumed to target an individual RDR , due to low sequence similarity between Solyc06g051180 and Solyc06g051190 . Several attempts to make a VIGS construct for Solyc06g051170 and Solyc06g051200 failed so experiments were done with the available constructs . When plants containing Ty-1 were agroinfiltrated with empty vector control ( EV , TRV2 without an insert ) or TRV2-160 , and two weeks later superimposed with a TYLCV challenge , the plants maintained resistance to TYLCV . However , when either TRV2-180 or TRV2-190 was used , the resistance was compromised as observed by the appearance of TYLCV disease symptoms ( Figure 3 ) . Repeated analysis confirmed these results , which , together with the fact that both Solyc06g051180 and Solyc06g051190 are predicted RDRs located in close proximity to one another within the Ty-1/Ty-3 region , suggest that Solyc06g051180 and Solyc06g051190 might belong to one and the same gene . Our initial mapping studies indicated that Ty-1 and Ty-3 could be alleles of the same gene [35] , and the fine mapping of both genes to a similar marker interval strengthened this hypothesis . To test this , the Ty-1 VIGS approach was again applied to compromise TYLCV resistance in plants carrying the Ty-3; as a control , plants with resistance based on Ty-2 were included . As in the Ty-1 plants , resistance in the Ty-3 lines was compromised by TRV2-180 and TRV2-190 , but not by TRV-160 ( Figure 3 ) . On the other hand , plants containing Ty-2 remained fully resistant against TYLCV after silencing with all three constructs . Altogether these data indicate that Ty-1 and Ty-3 indeed are allelic , while Ty-2 belongs to another class of resistance genes . To test the hypothesis that Solyc06g051170 , Solyc06g051180 and Solyc06g051190 were part of the same gene , and to clone the entire Ty-1 gene , several primer pairs were designed to enable RT-PCR amplification of the exons from the three predicted genes , and tested on cDNA of Ty-1 lines and TYLCV susceptible cv . Moneymaker . Primers designed on the start and stop codons of the three predicted genes did not amplify any products . However , when primers were used that were located a bit downstream of the start codon or upstream of the stop codon products were amplified , indicating that the predicted start and stop codons were wrong . To test whether the initially predicted genes were all part of one RDR-encoding ORF other primer pairs were tested . When primers targeting Solyc06g051170 were combined with Solyc06g051190 ( Figure S4 , F6-R4 ) surprisingly a product of approximately 700 bp was amplified indicating that all three predicted genes were indeed not paralogous but part of one and the same RDR gene . This was confirmed by sequence analysis of all overlapping PCR fragments obtained ( Figure S4 ) . Using a GeneRacer ( Invitrogen ) approach the genuine start and stop codons of the RDR gene were identified . Based on these sequences new primers ( Table S6 , Ty-F7-CACC and Ty-R5 ) were designed that supported the amplification of a product of approximately 3 . 1 kb from cDNA of a Ty-1 line , a Ty-3 line and from cv . Moneymaker . Sequence analysis of the amplified Ty-1/Ty-3 gene products revealed that the gene contained 19 exons . Compared with the three predicted genes the first predicted exon of Solyc06g051190 was not expressed , nor was the last exon containing the stop codon ( Figure 2 ) . For Solyc06g051180 the first exon started earlier than predicted , the last exon was shorter than predicted , again losing the stop codon . Finally for Solyc06g051170 the first predicted exon was not expressed . Alignment of the amino acid ( aa ) sequences of Ty-1 , Ty-3 and ty-1 ( the susceptible allele from tomato cv . Moneymaker ) revealed high sequence identity between all alleles , with only small differences . The most significant difference was a four aa deletion in the N-terminal domain of the susceptible allele . In addition , 20 aa changes were observed , with only small differences between Ty-1 and Ty-3 . Multiple sequence alignment with the six RDRs identified in A . thaliana ( Figure S5 and S6 ) showed a high sequence similarity to RDR3 , RDR4 , and RDR5 and the presence of the atypical DFDGD catalytic motif of these genes in both Ty-1 and Ty-3 alleles ( Figure 4A ) . The homology inferred from the sequence similarity was supported by a phylogenetic analysis using an unrooted neighbor joining tree , in which Ty-1 and Ty-3 grouped in the clade containing RDR3 , 4 and 5 ( Figure 5 ) . Interestingly , although the ty-1 allele ( Moneymaker ) appeared in the same clade , it showed less similarity to RDR3/4/5 then the Ty-1/Ty-3 allele ( Figure 4B ) . Considering a potential role of the Ty-1 encoded RDR in mounting a strong antiviral RNAi response , its transcriptional expression level was analyzed . To this end , a time-series experiment was performed during which expression of the resistant Ty-1 and the susceptible ty-1 allele was quantified upon TYLCV-challenge via agroinoculation in both tomato lines . The expression level of the specific allele was measured by qPCR at several time points ( Figure 6 ) . The results showed that at all time points the basic transcription level of the Ty-1 allele was significantly higher compared to the ty-1 allele . In the resistance line , no significant difference was observed for the Ty-1 expression between mock and TYLCV inoculated plants at all time points . However , in the susceptible Moneymaker line , the expression of the ty-1 allele was induced upon TYLCV inoculation at 12 and 19 days . Compared with day 0 of resistant and susceptible lines , the respective expression of Ty-1 and ty-1 was decreased at day 5 and increased at day 19 .
Nowadays many dominant and recessive virus resistance genes are well characterized and used in breeding of various crops . Most of these genes either do not allow/prevent viral replication or limit this to the first cells of entry in the host . The TYLCV resistance genes Ty-1 and Ty-3 are different from these because they lead to a level of virus tolerance ( rather than immunity ) . Plants carrying these genes and challenged by the virus still show low levels of viral replication and systemic spread but with moderate ( as with Ty-3 ) or no ( as with Ty-1 ) visual symptoms . Recently we observed that the S . chilense LA1969 derived Ty-1 and the S . chilense LA2779 derived Ty-3 map close to each other and that they might be allelic [35] . Here we show by fine mapping and functional analysis that Ty-1 and Ty-3 are alleles of the same gene and code for RNA-dependent RNA polymerases from a class of functionally unknown RDR genes . Sequence data shows that most of the SNPs that are present in Ty-1 are also present in Ty-3 , which is logical since both alleles originate from S . chilense accessions . The most striking difference between Ty-1/Ty-3 and the ty-1 allele is a deletion of 4 amino acids in the first amino-terminal part of the protein . However , it is not likely that this deletion solely causes a functional loss , since recombinant R7 contains a chimeric RDR – with the N-terminal part of ty-1 , and still confers partial resistance to TYLCV . Recently , three genes have been reported which are involved in different networks related to TYLCV resistance introgressed from S . habrochaites [32]–[34] . Of the three identified genes , SlVRSLip functions downstream LeHT1 within the same network , while Permease I-like protein functions in a different network [32]–[34] . In another study , 18 host genes with a potential role in Tomato Yellow Leaf Curl Sardinia Virus ( TYLCSV ) infection were identified . Interestingly , almost half of these genes had a role in posttranslational modifications [37] . Whether RDRs encoded by Ty-1 and Ty-3 play a role in one any of these networks remains to be analysed . RDRs are defined by a conserved catalytic domain and are found in RNA viruses and multicellular organisms ( plants , fungi and invertebrate animals ) , but so far are not described in vertebrates and insects . For RNA viruses , the RDR is required to enable replication of its RNA genome to render viral progeny [38] . In multicellular organisms , three major classes of eukaryotic RDRs have been described and some of their functions have been unraveled . The first class is presented by RDRα and members of these are found in plants , animals and fungi . The class of RDRβ genes has been found only in animals and fungi while RDRγ members are only found in plants and fungi [39] . In the model plant A . thaliana a total of six RDRs have been identified [40] . Three of them belong to the RDRα type , i . e . RDR1 , RDR2 and RDR6 , and are characterized by a catalytic DLDGD motif . The other three belong to the RDRγ class of genes and are denoted RDR3 , RDR4 and RDR5 ( also referred to as RDR3a , RDR3b and RDR3c , respectively ) . Members of this class have an atypical DFDGD motif in the catalytic domain [40] . The RDRα genes are all known to be involved in RNA silencing , specifically in the amplification of the siRNA signal and resulting in transitive silencing . RNA silencing is generally accepted as a defense system against viral invasion , and is induced by viral dsRNA replicative intermediates or folding structures [41] . Geminiviruses are also targeted by RNAi , as observed by the synthesis of geminivirus-specific siRNAs , ( small-RNA directed ) viral DNA methylation and post-transcriptional gene silencing of the protein-coding genes [42]–[45] . Although geminiviruses contain a single stranded DNA genome , siRNAs have been observed to originate from the entire virus genome although their distribution was not always equal . The siRNAs are postulated to originate in two ways; 1 ) as a result of DCL processing from dsRNA molecules that are generated by RDR from bidirectional geminivirus transcripts with overlapping 3′ ends , and 2 ) mRNA folding structures [42]–[43] , [45]–[46] . It is proposed that plants employ silencing of DNA by RNA-directed methylation as a strategy to repress geminivirus replication/transcription [47] . This is supported by two major observations; methylation of geminivirus DNA greatly reduces its ability to replicate in protoplasts [48] , and the identification of geminivirus RNA silencing suppressor proteins ( RSS ) C2 , C4 and V2 that exert their activity by interference in the process of DNA methylation and transcriptional gene silencing [49]–[56] . Antiviral RNAi defense against geminiviruses thus seems to mostly rely on a methylation-based defence , a process that involves the action of siRNA-directed methylation pathway component Ago4 . Although several studies have pointed towards the involvement of RDR1 and RDR6 in the biogenesis of geminivirus-specific siRNAs , the involvement of other antiviral RDRs in this cannot yet be excluded [10] , [57] . Besides their role in RNAi , several studies have described other ( endogenous ) functions of the RDRα ( 1 , 2 and 6 ) genes [58] , e . g . being involved in herbivore resistance ( RDR1 ) [59] , female gamete formation ( RDR2 and 6 ) [60] or in developmental timing ( RDR6 ) [61] . While a knockdown of RDR from the RDR1/2/6 class renders plants highly susceptible to many different viruses [11] , their transcriptional up-regulation has been observed to lead to ( elevated ) resistance levels against different plant viruses [62] . Viruses are able to counteract RNAi by coding for viral RSS proteins , and many of these have been shown to sequester siRNAs and prevent their uploading into RISC [63] . The presence of a viral RSS , however , does not seem to enable viruses to overcome elevated levels of resistance caused by transcriptional up-regulation of the RDR1/2/6 class of genes . For RDR3 , RDR4 , and RDR5 a function has not yet been described [64] . How to explain the resistance mechanism of the Ty-1/Ty-3 encoded RDRs remains speculative at present . The resistance spectrum of these alleles is not well studied; Ty-3 also provides resistance to the bipartite Tomato mottle virus ( ToMoV ) , but studies describing disease tests with other geminiviruses on Ty-1/Ty-3 carrying lines are not available [21] . These genes act specifically on geminiviruses; what then is the identity of the ( conserved ? ) Avr protein , and what are the characteristics of resistance breaking isolates ? Considering the role of the DLDGD type of RDRs ( 1 , 2 and 6 ) in the generation of secondary siRNAs , irrespective of the RNA virus involved , it is tempting to propose a role of the DFDGD type of RDRs ( 3 , 4 and 5 ) , and thus of Ty-1/Ty-3 , in the formation of dsRNA too . Since Ty-1/Ty-3 lines are resistant to TYLCV , but still allow for a symptomless ( Ty-1 ) or an almost symptomless ( Ty-3 ) infection with low titres of TYLCV , a resistance strategy as earlier described for the RDRα ( 1 , 2 and 6 ) genes could be possible , where transcriptional up regulation provides ( elevated ) resistance levels against different plant viruses . In light of this , transcriptional expression analysis of Ty-1 showed elevated expression levels in resistant lines compared to those in susceptible lines , even without TYLCV challenging . Whether differences in the Ty-1 vs . ty-1 protein or just those in transcriptional expression levels , or even a combination of both , are the cause of resistance remains to be investigated . However , since we did not observe hyper-susceptibility in tomato Moneymaker after silencing of the susceptible allele , as what is observed for Potato Virus X ( PVX ) and potato potyvirus Y ( PVY ) after silencing of Nicotiana benthamiana RDR6 [14] , a function of ty-1 in resistance is highly unlikely . The functionality and transcriptional upregulation of Ty-1 thus seems the most plausible reason to explain the resistance . To solve this issue , transgenic tomato lines ( over ) expressing either the resistant allele or the susceptible allele will be made . Analysis of the expression level and protein sequence of Ty-1/ty-1 in other resistant/susceptible tomato varieties and wild species will additionally be informative and experiments for these are currently being prepared . The observed resistance specificity of Ty-1/Ty-3 against TYLCV does seem to contradict the idea that its transcriptional up regulation provides ( elevated ) resistance levels against other geminiviruses unless people have somehow overlooked a partial resistance to other , distinct geminiviruses . Furthermore , it is possible that the RDRγ ( 3 , 4 and 5 ) class of genes may be involved in the generation of siRNAs that will mainly direct methylation of DNA and thereby support transcriptional silencing of geminivirus DNA genomes . If this hypothesis is true , this could explain why these genes will not confer ( partial ) resistance to most other plant viruses , of which ∼75% harbours an RNA genome and thus cannot be transcriptionally silenced by the siRNA-directed DNA methylation pathway . The possibility of an alternate route for dsRNA formation during geminivirus infections , besides the one involving RDR1/2/6 , is being supported by the observations that mutants lacking RDR1 , RDR2 and RDR6 still revealed basal levels of RNA silencing and siRNA biogenesis , and plants infected with TYLCV only showed a moderate increase in susceptibility to geminiviruses in plants deficient in RDR2 and 6 [11] , [47] . Whether the Ty-1/Ty-3 encoded RDR represents a player in this , and how the resistance mechanism acts , will be a challenge to investigate in the near future .
For fine-mapping Ty-1 from S . chilense accession LA1969 , a TYLCV-resistant commercial hybrid Tygress with an introgression between markers Mi23 and P6-25 , reflecting the same interval as described by Verlaan et al . ( 2011 ) , was used . This Ty-1 introgression was done by Jaap Hoogstraten of the Royal Sluis Seed Company , and it is different from the LA1969 Ty-1 introgression that was done in Israel [19] . This hybrid was self-pollinated to produce F2 progeny . Through two cycles of selection for recombination in this F2 population , two recombinants were identified and used to generate RILs by selfing and selection with marker genotyping for homozygous introgressions . The first recombination event resulted in a resistant RIL containing a S . chilense introgression flanked by markers Mi23 and HBa0045I03 and was used as a control ( named as Ty-1 RIL , Figure 1 ) in all Ty-1 experiments . Another recombination event resulted in a resistant RIL containing a S . chilense introgression between markers T0774 and HBa0045I03 . The susceptible Fla . 7776 was crossed to this inbred and an F2 population was generated . Approximately 2000 F2 plants were screened for recombination between the markers T0774 and SL_2 . 40ch06_30 . 891 and 13 recombinants were identified . These recombinants were selfed to develop F4 RILs as described before . RILs were evaluated , along with the controls Fla . 7776 , Tygress and the Ty-1 RIL in fall 2011 . Four week-old seedlings were inoculated with TYLCV for 11 days then transplanted to the field on 4 October in a non-randomized trial with two replications of 4-plant plots . TYLCV disease severity was evaluated on each plant 6 weeks after exposure to whiteflies . For the three most informative recombinants ( R7 , R8 and R11 ) results were confirmed in the greenhouse using agroinoculation as described below . Fla . 8680 , which contains Ty-3 within an approximately 27 cM introgression from the S . chilense accession LA2779 , was crossed to the susceptible breeding line Fla . 7781 to produce an F2 population . F2 plants ( n = 717 ) were individually screened in fall 2006 for recombination between the molecular markers C2_At2g39590 and T0834 , located near the distal ends of the introgression . Recombinants selected from this F2 population were used to develop RILs as described above . The F4 and F5 RILs were evaluated for resistance in fall 2007 and spring 2008 , respectively , in a randomized complete block design with three blocks and 12-plant plots . To further fine-map the Ty-3 locus , three F2 sub-populations were developed using two key recombinants , i . e . 554 and 157 ( Table S3 ) . Sub-population A was an F4 generated by self-pollinating F3 progeny of recombinant 554 which were heterozygous for the introgression; sub-population B was an F2 derived from a cross between the susceptible breeding line Fla . 7776 and the F5 RIL of recombinant 554 ( RIL 554 ) . Sub-population C was also an F2 developed from a cross of Fla . 7776 and the F5 RIL of recombinant 157 ( RIL 157 ) . Seeds of all three sub-populations were sown and leaf tissue was collected from each plant at approximately 5 weeks after sowing . Sub-populations A and B were screened with the markers Mi23 and P6-25 , and the markers T0774 and T0834 were used to screen sub-population C . Recombinants were transplanted to the field , along with controls , in early to mid-March , 2009 . Controls included the TYLCV resistant commercial hybrids Tygress and SecuriTY 28 , the resistant inbreds Fla . 8680 and Fla . 8602 , the susceptible inbreds Horizon and Fla . 7776 , RILs 554 and 157 and their F1 hybrids with Fla . 7776 . One month after transplanting to the field , 6–8 cuttings were taken from each plant , rooted in a 1∶1 perlite , fine vermiculite media under mist for 2 weeks , then inoculated with whiteflies viruliferous for TYLCV for 11 days . Inoculated cuttings were transplanted to the field on 11 May in a non-randomized design with 3 replications of 2-plant plots , with the exception that only 2 replications were planted for recombinants having cross-overs outside the T0774 to P6-25 interval . TYLCV disease severity was evaluated on each plant at 5–6 weeks after exposure to whiteflies . Self-pollinated seed was harvested from all original recombinant plants , and progeny were grown out in summer 2009 from 26 individuals with recombination between markers SL_2 . 40ch06_30 . 696 and cLEG-31-P16 . Plants homozygous for the recombined introgression were selected for producing RILs . These RILs were grown in spring 2010 , along with the controls Fla . 7776 , Fla . 8680 , the F1 hybrids between Fla . 7776 and each of RILs 554 and 157 , and the commercial hybrid Tygress . Three week-old seedlings were inoculated with TYLCV for two weeks then transplanted to the field on 23 March in a randomized complete block design with three blocks and six-plant plots . TYLCV disease severity was evaluated on each plant at seven weeks after exposure to whiteflies . Whitefly mediated inoculation: Plants were inoculated with whiteflies viruliferous for the TYLCV-IL strain according to the method of [65] with some modifications . Briefly , plants were exposed to viruliferous whiteflies in growth chambers for the specified period of time . After inoculation , the whiteflies were killed by treating plants with an insecticidal soap and with Admire ( imidacloprid ) , and the plants were then transplanted to the field . Plants were rated for disease severity on a 0 to 4 disease severity index scale as described by Scott et al . ( 1996 ) , where 0 = no symptoms and 4 = severe symptoms and stunting . Intermediate scores such as 1 . 5 , 2 . 5 , etc . were incorporated to allow for more precise disease severity ratings . Agrobacterium mediated inoculation: An infectious TYLCV-IL clone ( pTYCz40a ) was used for agroinoculation using the method as described in [35] . Briefly , A . tumefaciens LBA4404 was transformed , cultured in LB , pelleted and resuspended in infiltration medium at an OD600 of 0 . 5 . Three week old seedlings were infiltrated by pressure inoculation in the leaves with a needle-less syringe . For the VIGS experiments the agro infiltration was done two weeks after TRV inoculation . DNA was extracted from young leaves using the cetyltrimethyl ammonium bromide ( CTAB ) protocol of [66] with minor modifications as described by [67] . Molecular markers used in this study were either publicly available , or were designed using the software Primer3 [68] from Ty-3-region BAC-end sequences , FOS-end sequences , the draft tomato genome available through the Sol Genomics Network ( SGN; http://solgenomics . net/ ) [36] , or from a private database of S . lycopersicum sequences . Polymerase chain reaction ( PCR ) parameters , primer sequences , restriction enzymes , and detection methods were described by [69] or [35] . Additional molecular markers designed are described in Table S5 and Figure S3 , and used the same PCR parameters described by [69] . Analyses of variance , se calculations , and Duncan's multiple range tests were performed in SAS ( Version 9 . 1; SAS Institute , Cary , NC ) . Mapping and interval analysis of Ty-3 was performed in Windows QTL Cartographer 2 . 0 ( 2007 , N . C . State University ) using mean disease severity of the cuttings for each recombinant and a subset of molecular markers specific to the Ty-3 region . For gene silencing , the TRV based VIGS system as described in [70] was used . Briefly , fragments of approximately 350 base pairs of Solyc06g051160 , Solyc06g051180 and Solyc06g051190 were amplified from Ty-1 cDNA using primers compatible with the Gateway system ( Table S6 ) . After cloning to pENTR the inserts were sequenced to confirm their identity . Positive clones were selected for further processing of the inserts into the TRV2 vector and subsequently transformed to Agrobacterium tumefaciens strain GV3101 . A 3 ml culture of A . tumefaciens strain GV3101 containing the TRV replicons was grown overnight at 28°C , 200 RPM in appropriate selective LB medium . Cultures were transferred to 20 mL LB containing proper selection pressure , 10 mM MES and 200 µM acetosyringone , and further grown overnight in a 28°C shaker . A . tumefaciens cells were pelleted , and resuspended in infiltration buffer ( 20 g/L sucrose , 5 g/L MS salts ( no vitamins ) , 10 mM MES ) to a final OD600 of 1 . Agro infiltration was performed on cotyledons of 10 day old seedlings using pressure inoculation with a 2 , 5 mL syringe without a needle . A neighbour joining tree with a bootstrap value of 1000 was generated using MEGA version 5 [71] . Arabidopsis RDR sequences were downloaded from The Arabidopsis Information Resource ( www . arabidopsis . org ) [72] . For gene expression analysis , 17 day old seedlings were agroinoculated as described above . For the mock treatment infiltration buffer without bacteria was used . Top leaves of plants were harvested 0 , 5 , 12 and 19 days after TYLCV inoculation and grinded in liquid nitrogen using mortar and pestle . Total RNA was extracted by using the RNeasy Plant Mini Kit ( Qiagen ) as described by the manufacturer . One µg RNA was digested using DNase I ( Amp . Grade ) following the manufacturers protocol ( Invitrogen ) and cDNA was synthesized using the iScript cDNA Synthesis Kit following the protocol ( Bio-Rad ) . Quantitative Real-Time PCR was performed in 10 µl reactions in a Bio-Rad iCycler iQ5 using SYBR Green Supermix ( Bio-Rad ) according to the protocol provided by the manufacturer . For quantitative RT-PCR of Ty-1/ty-1 the forward primer 180-F1 ( 5′-GGCAAAATATGCAGCCAGGCTTTCC-3′ ) and the reverse primer 180-R1 ( 5′-TCAGTATGTATACGAGGTTCGCCGT-3′ ) were used . As a reference the ACT gene was used as described by [73] with primers: ACT-F ( 5′-GAAATAGCATAAGATGGCAGACG-3′ ) and ACT-R ( 5′-ATACCCACCATCACACCAGTAT-3′ ) . Gene expression levels were calculated using the ΔΔCt method as described by [74] .
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Tomato yellow leaf curl virus and related begomoviruses cause major economic damage to tomato production in tropical and subtropical regions around the world . Because cultivated tomato is inherently susceptible to these viruses , breeders have incorporated several resistance alleles from wild tomato relatives . Among these are the commercially important alleles , Ty-1 and Ty-3 , which were introgressed from the wild tomato relative Solanum chilense . These genes were originally mapped to different regions on chromosome 6 , but recent findings suggest they may rather be alleles of the same gene . Here , we describe the precise mapping of Ty-1 and Ty-3 to a common chromosomal region , and we show that Ty-1 and Ty-3 are alleles that code for an RNA–dependent RNA polymerase of a class for which no function had been described before . Thus , Ty-1/Ty-3 unveils a completely new class of resistance genes . These results will be useful to breeders who utilize these genes in their breeding programs , and further studies should shed new light on the mechanism by which this gene functions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"rna",
"interference",
"plant",
"biology",
"crop",
"genetics",
"gene",
"function",
"plant",
"science",
"crops",
"plant",
"pathology",
"vegetables",
"gene",
"expression",
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"diseases",
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] |
2013
|
The Tomato Yellow Leaf Curl Virus Resistance Genes Ty-1 and Ty-3 Are Allelic and Code for DFDGD-Class RNA–Dependent RNA Polymerases
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In developing strategies to control malaria vectors , there is increased interest in biological methods that do not cause instant vector mortality , but have sublethal and lethal effects at different ages and stages in the mosquito life cycle . These techniques , particularly if integrated with other vector control interventions , may produce substantial reductions in malaria transmission due to the total effect of alterations to multiple life history parameters at relevant points in the life-cycle and transmission-cycle of the vector . To quantify this effect , an analytically tractable gonotrophic cycle model of mosquito-malaria interactions is developed that unites existing continuous and discrete feeding cycle approaches . As a case study , the combined use of fungal biopesticides and insecticide treated bednets ( ITNs ) is considered . Low values of the equilibrium EIR and human prevalence were obtained when fungal biopesticides and ITNs were combined , even for scenarios where each intervention acting alone had relatively little impact . The effect of the combined interventions on the equilibrium EIR was at least as strong as the multiplicative effect of both interventions . For scenarios representing difficult conditions for malaria control , due to high transmission intensity and widespread insecticide resistance , the effect of the combined interventions on the equilibrium EIR was greater than the multiplicative effect , as a result of synergistic interactions between the interventions . Fungal biopesticide application was found to be most effective when ITN coverage was high , producing significant reductions in equilibrium prevalence for low levels of biopesticide coverage . By incorporating biological mechanisms relevant to vectorial capacity , continuous-time vector population models can increase their applicability to integrated vector management .
Malaria is a major contributer to the global disease burden , and disproportionately affects low-income countries with climates suitable for transmission [1]–[3] . Vector control strategies have proven effective in reducing malaria transmission and prevalence [4]–[6] , and are a key element of current malaria control initiatives [7]–[9] . Indoor residual spraying ( IRS ) and insecticide-treated bednet ( ITN ) interventions have been and remain the dominant methods of controlling malaria vectors [4] , [9]–[12] , but problems of public health and insecticide resistance associated with chemical insecticides have increased interest in alternate methods , including novel biological methods [13] , [14]–[16] . Because the incubation period of the malaria parasite is relatively long in comparison to the average adult mosquito lifespan , biological methods of vector control that have sublethal and lethal effects at different points in the mosquito life cycle may substantially reduce the potential for malaria transmission [17]–[21] . Such methods may be most effective when combined with established methods in a strategic manner [8] , [22] , [23] . In order to impact on malaria prevalence it is necessary to reduce transmission to very low levels [24] , [25] . Vector management strategies that combine multiple mosquito control interventions would therefore benefit from tactical design to alter mosquito life history in ways that are likely to maximise the impact on malaria transmission , given the resources available . This paper presents an age-structured model that explores the impact of interventions that affect multiple gonotrophic and demographic processes in the mosquito on malaria transmission and prevalence . As a case study , the combined use of fungal biopesticide and ITN interventions is considered . Biopesticides containing spores of entomopathogenic fungi are a novel strategy for controlling malaria vectors that have shown potential to cause substantial reductions in malaria transmission in laboratory and field studies [17]–[20] . The biopesticide targets adult mosquitoes , infecting them with a fungal pathogen that does not kill instantly , and can generate a wide range of mortality patterns , some early-acting while others showing a distinct delay [17] , [26] , [27] . Fungal pathogen-induced mortality rates typically increase with the fungal infection age , with the average times to death due to fungal infection less than 10 days [17]–[21] . This slow-acting mortality suggests that high fungal infection rates in adult mosquitoes would be required to affect malaria prevalence , however sublethal effects of fungal infection on mosquitoes have been observed which may considerably reduce their transmission potential [21] . Fungal infection can cause a reduction in the blood feeding rate and lifetime fecundity [26] . There is also evidence that co-infection with the fungal pathogen and the malaria parasite can cause greater than additive mortality and reduced transmissibility of the malaria pathogen [15] , [17] . In contrast to fungal biopesticides , ITNs are an established and widely used vector control method [6] , [28]–[30] that has proven successful in reducing malaria transmission and prevalence in situations where high levels of community-wide ITN coverage are achieved [6] , [29]–[31] . They therefore have a focal role in current vector control initiatives [7] , [10] , [12] . ITNs work by targeting the adult host-seeking mosquito population , increasing the time taken for mosquitoes to find a blood meal and increasing the mortality risk while host-seeking . Both factors interact positively to reduce the likelihood that mosquitoes live long enough to contract and transmit malaria . The effect of ITNs on mosquito mortality rates depends on levels of insecticide resistance [14] , [32]–[34] , the persistence of the insecticide treatment , and the excito-repellency properties of the insecticide [35] , [36] . This study considers mechanisms by which ITN and fungal biopesticide interventions may affect mosquito populations at the scale of the gonotrophic cycle and at within-gonotrophic cycle time scales . The gonotrophic cycle in female adult mosquitoes is often conceptualised in terms of a host-seeking stage , during which mosquitoes actively search for a blood meal , and a non-host-seeking stage , during which blood from a recent blood meal is digested , oocytes are developed and eggs are oviposited , after which host-seeking activity begins again [37] , [38] . While the ITN intervention reduces the rate of host-seeking success throughout the adult mosquito population as a whole , the fungal biopesticide may also extend the host-seeking stage in fungal pathogen-infected mosquitoes due to a deterioration in flight and blood-feeding capabilities [26] . The non-host-seeking stage in fungal pathogen-infected mosquitoes may also be protracted due to impaired metabolic functioning [26] . Within a given gonotrophic cycle , the period during which mosquitoes are exposed to a risk of fungal infection may not necessarily correspond to a particular gonotrophic cycle stage . For biopesticide application as a residual treatment in and around domestic dwellings , the fungal infection risk would conceivably be higher whilst mosquitoes are host-seeking , and also for some time after they obtain a blood meal when they often rest on nearby surfaces for a period of less than 24 hours [39] , [40] . In fungal pathogen-infected mosquitoes , fungal infection age , and the corresponding risk of fungal pathogen-induced mortality , increases continually , with the mortality risk for all mosquitoes being augmented during the host-seeking stage by the presence of ITNs . Similar to [21] , the population dynamic model presented here is an age-structured Susceptible-Exposed-Infectious ( SEI ) model based on integral equations , considering fungal pathogen-induced age-dependent mortality in adult mosquitoes that can contract the fungal infection at any point in their adult life . This model reformulates that of [21] to explicitly incorporate gonotrophic cycles in the adult mosquito population by defining a recursive series of host-seeking and non-host-seeking classes of mosquitoes . The model thus retains the properties of existing continuous [21] , [41]–[43] and discrete feeding cycle approaches [31] , [44] , [45] . Equilibrium analysis is used to validate the model for limiting cases similar to those represented by existing continuous-time models [21] , [43] . Cases where the risk of fungal infection varies throughout the gonotrophic cycle and where fungal infection causes within-population variation in the lengths of both host-seeking and non-host-seeking stages are explored numerically . The model is parameterized with literature data on mosquito-malaria interactions , and the effects fungal biopesticides and ITNs on mosquito populations . A series of questions relevant to mosquito control by fungal biopesticides , ITNs and both interventions in combination are explored . How do sublethal effects of fungal infection on rates of finding and processing blood meals affect the impact of biopesticides on malaria transmission rates ? How is the fungal biopesticide performance affected by variation in the period of biopesticide exposure within a given gonotrophic cycle ? How does the performance of fungal biopesticide and ITN interventions combined compare with that of each single intervention for varying levels of transmission intensity and insecticide resistance ? Mechanisms important to the performance of fungal biopesticides , ITNs and both interventions combined are identified and discussed .
The blood-feeding patterns of adult female mosquitoes are assumed to follow a gonotrophic cycle whereby mosquitoes repeatedly seek a blood meal , obtain a single blood meal , and then stop seeking blood for a fixed time period after which they resume host-seeking activity and the gonotrophic cycle begins again . The model accordingly divides each gonotrophic cycle into two stages: a host seeking stage of variable duration , during which the mosquito is searching for a blood meal , and a non-host-seeking stage of fixed duration , during which the mosquito does not seek blood meals ( Figure 1 ) . During the host-seeking stage , mosquitoes are assumed to feed soley on humans at a rate f ( Table 1 ) . This rate may be intuitively interpreted by the corresponding daily probability of finding a blood meal given that the mosquito does not die , ( Table 1 ) . During the host-seeking stage the mosquito mortality rate due to all mortality sources other than fungal biopesticide and ITN interventions is . Upon finding a blood meal mosquitoes enter the non-host-seeking stage , which lasts for days . During the non-host-seeking stage the mosquito mortality rate due to all mortality sources other than fungal biopesticide and ITN interventions is . For each stage of the malaria infection process ( susceptible , exposed and infectious ) , the maximum number of gonotrophic cycles completed , including host-seeking and non-host-seeking stages , is nS , nE and nI respectively ( Table S3 ) . In describing the model below , the rate of fungal infection in adult mosquitoes , F , is assumed to be a constant . This rate may be intuitively interpreted by the corresponding daily probability of fungal infection given that the mosquito does not die , ( Table 1 ) . However fungal infection risk may differ at different points in the gonotrophic cycle and so a version of the model where F varies throughout the gonotrophic cycle is analysed numerically in the Results . The mortality rate of mosquitoes that are infected with the fungal pathogen is increased by an amount that may vary with u , the time that has elapsed since infection [21] . As in [21] , , is modelled using the Weibull model , where and β are the rate and shape parameters respectively ( Table S3 ) . The measure of fungal pathogen virulence is the expected time until death due to fungal infection given no other mortality , , given by ( 1 ) [21] . Sublethal effects of fungal infection on mosquitoes are also considered , including a reduction in the host-seeking success and an increase in the time to the next host-seeking period following a blood meal in fungal pathogen-infected mosquitoes . This is modeled by a reduced blood feeding rate and an increased duration of the non-host-seeking stage in fungal pathogen-infected mosquitoes ( Table 1 ) . Similar to [45] , an ITN intervention is assumed to affect two gonotrophic cycle parameters , the mortality rate of host-seeking mosquitoes , , and the rate at which host-seeking mosquitoes take blood meals , fF or f for mosquitoes with and without fungal infection respectively . The blood-feeding rates fF and f are multiplied by a factor , where is the fraction of humans protected by ITNs [45] . This assumes that mosquitoes do not bite ITN users or non-human hosts . The parameter is referred to as the ITN coverage , in line with ITN literature [6] , [31] , [45] . The mortality rate is assumed to be directly proportional to such that when ( Table 1 ) . The effect of ITNs on mosquito mortality can be reduced by insecticide resistance in the mosquito population [32] , [34] , limited persistence of the insecticide treatment , or excito-repellency properties of the insecticide [36] . Therefore , the limiting case in which the ITN intervention has no effect on mosquito mortality is also considered . The model is described by a system of integral equations for the mosquito density , defined as the number of mosquitoes per human [21] , [43] , as a function of time in each of a series of 12 stages . The stages correspond to the mosquito's malaria infection status ( susceptible , exposed , infectious ) , gonotrophic cycle stage ( host-seeking , non-host-seeking ) , and fungal infection status ( infected , uninfected ) . Equations for stages of mosquitoes without fungal infection ( Text S1 ) are considered separately from stages of fungal pathogen-infected mosquitoes ( Text S2 ) . A series of expressions are defined , , which give the probability that a mosquito remains in the same stage over different periods of time . These are described in more detail in Text S1 and Text S2 and are listed in Tables S1 and S2 . Similar to [21] , [43] , the model assumes that mosquitoes are recruited to the adult population at a constant rate ε , that a constant fraction , x , of the human population is infected with malaria , the probability of transmission from an infected human or an infected mosquito is b , and that it takes the Plasmodium exactly TE days to mature in mosquito and become infectious ( Table 1 ) . The equilibrium daily EIR , denoted , is given by ( 2 ) where and are the equilibrium densities of infectious , host-seeking mosquitoes with and without fungal infection respectively . To qualitatively estimate the relationship between the model-derived equilibrium daily EIR and the malaria prevalence in the human population , denoted , the best fit model obtained by [25] was used , with no change to the best fit parameters . This provides a conservative estimate of the prevalence , because the equilibrium EIR estimates from this model do not take into account reductions in prevalence that may occur as a result of the decrease in EIR resulting from fungal biopesticide and ITN interventions . Expressions are derived for the equilibrium density of susceptible , exposed and infectious host-seeking mosquitoes for the limiting case in which there are no sublethal effects of fungal infection on mosquito feeding biology ( , ) and the shape parameter of the fungal pathogen-induced mortality function ( Text S1 and Text S2 ) . The analytically derived equilibrium EIR agrees well with the equilibrium obtained by simulating equations ( S1 . 1 ) – ( S1 . 17 ) and ( S2 . 1 ) – ( S2 . 18 ) through time using a simulation algorithm coded in C++ ( Figure 2 ) . The simulation algorithm is used to obtain the equilibrium for the general case in which , and . The pattern in Figure 2 is similar to that produced by simpler models [21] , whereby increasing the shape parameter β above 1 reduces the equilibrium malaria transmission rate . The ITN intervention corresponds to a limiting case of the model in which the fungal infection rate F is zero . For this case , the dynamic system is much simpler , requiring only equations ( S1 . 1 ) – ( S1 . 17 ) , and solution of the equilibrium EIR is much simpler , given by equations ( S1 . 18 ) – ( S1 . 27 ) . The ITN intervention produces a rapid decline in the equilibrium EIR as the ITN coverage ( ) increases , but does not have a strong impact on the equilibrium malaria prevalence in humans until the level of ITN coverage is moderate to high ( Figure 3 ) . These patterns are similar to those produced by [31] , [45] . Figure 3 also shows the case where the ITN intervention has no effect on mosquito mortality , and thus affects only the blood feeding rates fF and f . This may represent widespread insecticide resistance in the mosquito population . The aim of the models developed here is to explore generic issues relating to fungal biopesticide and ITN interventions rather than to parameterise a specific case . In order to use biological relevant parameters , a survey of the literature was conducted ( Table 1 ) . Using these parameters , the model gives an equilibrium annual EIR in the absence of the fungal biopesticide and ITNs of 47 . 8 , which consistent with a number of regions in Africa with moderate to high malaria prevalence [24] , [25] . This is similar to the value of 45 given by the continuous time models of [21] , [43] with a human biting rate of and other parameters as in Table 1 . Similar to [21] , the equilibrium baseline EIR scales linearly with the recruitment rate ε .
Fungal infection can substantially reduce mosquito blood-feeding activity [26] . Here , two possible effects of fungal infection on mosquito blood-feeding biology are considered , including a reduction in the blood-feeding rate in host-seeking mosquitoes , , and an increase in the duration of the non-host-seeking stage , ( Table 1 ) . Even when these effects act simultaneously , they have less potential to produce very low equilibrium EIR than decreasing the average time to death from fungal infection , ( Figure 4 ) . The strongest sublethal effects shown in Figure 4 represent more than a four fold increase in the average gonotrophic cycle length in fungal pathogen-infected mosquitoes , . For moderate to high daily probability of fungal infection , this has a similar effect to a 25% reduction in the average time to death from fungal infection ( Figure 4B and C ) . When the daily probability of fungal infection is low , reductions in the equilibrium EIR obtained by either increasing the fungal pathogen virulence ( by reducing ) or increasing the sublethal effects are considerably less , and the impact of strong sublethal effects on the EIR is of similar magnitude to that produced by strong reductions in ( Figure 4A ) . For a fungal biopesticide applied in and around human settlements , mosquitoes may not be exposed to a risk of fungal infection for their entire gonotrophic cycle . They may be most likely to contract the fungal pathogen when they are host seeking and for a time period directly after blood feeding . A shorter period of exposure to a risk of fungal infection within a given gonotrophic cycle may lead to lower fungal infection levels in the mosquito population , with implications for fungal biopesticide performance . Here , the effect of varying fungal infection risk throughout the gonotrophic cycle is explored by varying the fungal infection rate , F , such that non-host-seeking mosquitoes experience a constant fungal infection rate for an initial proportion of the non-host-seeking stage , denoted α , and a fungal infection rate of zero for the remainder of the non-host-seeking stage ( Table 1 ) . The corresponding daily probability of fungal infection during the period of biopesticide exposure is ( Table 1 ) . Host-seeking mosquitoes are assumed to experience fungal infection rate ( and daily probability of fungal infection CE ) throughout the entire host-seeking stage . The equilibrium daily EIR is marginally higher if mosquitoes are exposed to fungal infection risk for half of the non-host-seeking stage ( ) in comparison to exposure for the full stage duration ( ) ( Figure 5A , C , E ) . However , if mosquitoes are only exposed to fungal infection risk when they are host-seeking ( ) , the equilibrium EIR is considerably higher . The corresponding estimate of the equilibrium malaria prevalence in humans varies more between the three exposure periods than the equilibrium EIR ( Figure 5B , D , F ) , because it is sensitive to changes in EIR at low EIR values . The effect of varying the period of biopesticide exposure on prevalence is greater for more virulent biopesticides , which benefit more from the very low transmission levels achieved for longer duration of exposure to fungal infection risk ( Figure 5A , B ) . Similar to [21] , if mosquitoes are always exposed to fungal infection risk , there is a threshold level of the daily probability of fungal infection above which additional reductions in equilibrium EIR and prevalence are marginal . This is not the case if mosquitoes are only exposed to fungal infection risk when they are host-seeking , where prevalence continues to decline steadily as the daily probability of fungal infection is increased to high values ( Figure 5 ) . ITNs can be an effective means of malaria control if high levels of community-wide ITN coverage can be achieved [31] . Fungal biopesticides may be used in combination with ITN interventions at varying levels of coverage to give greater reductions in malaria transmission and human prevalence . To quantify this effect , the impact of biopesticide application on the equilibrium EIR and human malaria prevalence is explored for fixed levels of ITN coverage . The daily probability of fungal infection during the period of biopesticide exposure ( CE ) is referred to as the fungal biopesticide coverage throughout this section . The conservative assumptions that mosquitoes are only exposed to a risk of fungal infection during the host-seeking stage ( ) , at a constant rate , and that there are no sublethal effects of fungal infection on mosquito feeding biology ( ) are adopted ( Table 1 ) . Low values of equilibrium prevalence are not obtained by the fungal biopesticide intervention alone , or by the ITN intervention alone unless ITN coverage is high ( Figure 6A ) . When both interventions are used in combination , low prevalence is obtained with moderate coverage of each intervention . The proportional reduction in equilibrium EIR obtained by the combined interventions found to be very close to the multiplicative effect of both interventions , or the product of the proportional reductions given by each intervention acting alone . The difference between the equilibrium prevalence obtained from the combining the two interventions , , and the prevalence corresponding to the multiplicative effect of the two interventions on the equilibrium EIR , denoted , is calculated as . As is small in this case ( Figure 7A , open circles ) , there is negligible redundancy in combining both interventions , and also negligible synergistic effects , or no increase in the impact of one intervention on malaria transmission due to the presence of the other intervention . The mortality in host-seeking mosquitoes caused by the ITN intervention may be reduced by the development of insecticide resistance in the mosquito population . To give a conservative estimate of intervention performance , the limiting case in which the ITN intervention causes no increase to mosquito mortality is examined . In this case the ITN intervention affects only the blood-feeding rate of host-seeking mosquitoes , by providing a physical barrier between mosquitoes and the human hosts that are protected . For this scenario , low values of equilibrium prevalence are still be obtained by the ITN and fungal biopesticide interventions combined , although higher coverage of each intervention is required ( Figure 6B ) . The baseline equilibrium prevalence is reduced by approximately 50% by the two interventions combined with moderate coverage of each intervention , whereas a similar reduction requires high coverage of each intervention acting alone ( Figure 6B ) . When the ITN intervention has no mortality effect on mosquitoes , the reduction in equilibrium prevalence obtained by the interventions combined is greater than the multiplicative effect of both interventions ( Figure 7A , crosses ) . The deviation from the multiplicative effect generally increases with increasing ITN coverage ( Figure 7A ) , although not with increasing fungal biopesticide coverage ( results not shown ) . Thus when ITN coverage is high , the addition of the fungal biopesticide has a large impact on prevalence even at low biopesticide coverage ( Figure 6B ) . The combined effect of the two interventions , being greater than multiplicative , is indicative of synergistic interactions between the interventions . This synergism results from the increase in the average time required for mosquitoes to find a blood meal due to the presence of non-lethal bednets , which increases the period of exposure to the fungal biopesticide in a given gonotrophic cycle , and improves the performance of the fungal biopesticide intervention . Variation in malaria transmission intensity will affect the efficacy of vector control strategies and may alter the appropriate choice of strategy . The case of high malaria transmission intensity is considered here by increasing the recruitment rate ε to give an annual EIR of 412 . This value is similar to levels of transmission measured in the high transmission season in the Garki district of Nigeria , an area where malaria control has proven difficult [46] . When transmission intensity is high , low values of equilibrium prevalence are not obtained by either the fungal biopesticide or the ITN intervention acting alone , but are obtained when ITN and fungal biopesticide interventions are combined if ITN coverage is high and fungal biopesticide coverage is moderate ( Figure 8A ) . When ITN coverage is high , fungal biopesticide application is again more effective , producing a sharp decline in prevalence even at low biopesticide coverage . For high transmission intensity , the combined effect of the two interventions is similar to the multiplicative effect of both interventions ( Figure 7B , open circles ) As above , the ITN intervention is now considered to have no effect on mosquito mortality , representing widespread insecticide resistance . In this case , considerable reductions in prevalence are only obtained when ITN and fungal biopesticide interventions are combined ( Figure 8B ) . Low equilibrium prevalence is still obtained by the combined interventions for high levels of coverage of each intervention . When the ITN intervention does not affect mosquito mortality , the combined effect of the two interventions exceeds the multiplicative effect of both interventions , to a greater extent than when transmission intensity was lower ( Figure 7B , crosses ) . This demonstrates a case of considerable synergism between the two interventions , whereby the ITN intervention improves the performance of the fungal biopesticide intervention , particularly when ITN coverage is high ( Figure 8B ) . Low transmission intensity is simulated by decreasing ε to give an annual EIR of 1 . 5 , a value in the lower part of the range reported in [25] . In this case , low values of equilibrium prevalence are obtained by the combined use of fungal biopesticide and ITN interventions for low to moderate levels of coverage of each intervention . If the interventions act alone the level of coverage required to achieve low prevalence is considerably higher , though still moderate ( Figure 9 ) .
This study models the effect of fungal biopesticide interventions on rates of malaria transmission with greater biological detail than [21] . While the models are not formally equivalent , the results of this model are consistent with those of [21] for the limiting case in which mosquitoes are continually exposed to a constant risk of fungal infection and there are no sublethal effects of fungal infection on the mosquito blood-feeding biology . The baseline EIR ( in the absence of the interventions ) is also similar for the two models . The same assumptions about mosquito-fungus interactions described in [21] apply to this model with two exceptions . Firstly , by incorporating gonotrophic structure , this model can consider the effects of fungal infection on host-seeking and non-host-seeking stages of the gonotrophic cycle . Secondly , this model allows the fungal infection rate to vary throughout the gonotrophic cycle . Firstly , the model results indicate that high virulence will be important to the success of the fungal biopesticide intervention even if fungal infection has a strong effect on both the average time taken for host-seeking mosquitoes to find a blood meal and the time to the next period of host-seeking activity following a blood meal . Secondly , assuming realistically that mosquitoes are not exposed to the fungal biopesticide for their entire gonotrophic cycle , the results indicate that a high daily probability of fungal infection during the period of biopesticide exposure will be important to the success of the biopesticide intervention . This implies an important role for strategies designed to prolong the period of biopesticide exposure , such as the use of African water storage pots sprayed with the biopesticide [20] . Consistent with the goals of Integrated Vector Management frameworks [7] , [8] , [10] , the model explores the total impact of multiple mosquito control interventions used in combination . The principle and practise of combining interventions to give substantial impacts on malaria transmission and manage insecticide resistance is not new [5] , [8] , [41] , [47] , [48] . This study explores interactions not previously considered , namely those between ITNs , an widely-used method of controlling malaria vectors , and fungal biopesticides , a novel method of biocontrol that has slow-acting and potentially complex effects on the mosquito life cycle . In the absence of field data on the combined application of fungal biopesticides and ITNs , conservative assumptions were adopted . Fungal infection was assumed to affect only mosquito mortality , and the exposure of mosquitoes to both interventions was restricted to the host-seeking stage of the gonotrophic cycle . Similar to [31] , [49] , the equilibrium EIR estimates given by this model are conservative in that they do not take into account changes in human malaria prevalence that may result from the interventions . This assumption may be accurate in the short term given that the lifespan of the vector is considerably shorter than the duration of malaria infection in humans [25] . This SEI model could be extended incorporate human prevalence dynamics [50] , however the relationship between human prevalence and rates of malaria transmission is known to be complex and heterogeneous [25] . Low estimates of the equilibrium prevalence were obtained by combining fungal biopesticide and ITN interventions even for scenarios where the impact of each intervention acting alone was relatively small , indicating that the combination is effective . In general , the impact of combining the two interventions on malaria transmission was at least as good as the multiplicative effect of both interventions , which intuitively demonstrates that the combination is efficient . This need not be the case , for example , if large numbers of fungal pathogen-infected mosquitoes are killed by ITNs , the effective coverage of the fungal biopesticide would be reduced . Figure 7 shows that the impact of the combined interventions is sometimes slightly less than multiplicative , which is indicative of this effect . A spatially heterogeneous process , whereby locations that are sprayed with the fungal biopesticide are also those that are best protected by ITNs , may lead to correlation between the probability of fungal infection and the probability of encountering an ITN . This may result in greater redundancy in the combined effect of both interventions compared to the multiplicative effect . However , as conditions for malaria control become more difficult due to increasing transmission intensity or the development of insecticide resistance , interactions between the two interventions become increasingly synergistic , in that the performance of the fungal biopesticide is enhanced by the ITN intervention . This allowed low prevalence to be obtained for the combined interventions even for high transmission intensity and widespread resistance of mosquitoes to mortality from ITNs . The mechanism underlying this synergism , namely the protraction of the period of exposure to the fungal biopesticide due to the presence of non-lethal ITNs , may be more robust to spatial heterogeneity in the application of both interventions . Deflection of mosquitoes by non-lethal bednets may increase their likelihood of encountering the biopesticide whether it is sprayed at the same location or at another location within the range of mosquito diffusion . The model results suggest that combining fungal biopesticide and ITN interventions can allow each intervention to be used at lower coverage to maintain a given level of malaria control . This may increase the persistence of each intervention , as lower coverage of each intervention may reduce the selection pressure for the evolution of resistance . However , cautious interpretation is again necessary . The mechanisms behind the success of high community-wide ITN coverage observed in the field may be more complex than those represented by homogenous models of ITN interventions , including the model presented here . Evidence that malaria transmission in human populations is highly heterogenous supports this suggestion [25] . It is encouraging , however , that if high ITN coverage is achieved , the model results indicate that fungal biopesticide application can be very effective even at low biopesticide coverage , particularly when transmission intensity is high and insecticide resistance is widespread . The model presented here could also be extended to incorporate additional gonotrophic processes with important malaria transmission implications . Firstly , multiple blood feeding within a single gonotrophic cycle could considerably alter transmission patterns , depending on the mosquito life-stages in which it occurs . Multiple blood meals per cycle may be more common in newly emerged Anopheles mosquitoes [51] , but can also be more prominent in mosquitoes harbouring infectious sporozoites [52] , with the later having the most serious epidemiological implications . Secondly , the time required for blood meal digestion and oocyte development in mosquitoes decreases with increasing temperature , as does the Plasmodium incubation period , leading to substantial variation in these parameters across different field locations [38] , [53] . Covariation in duration of the non-host-seeking stage and the Plasmodium incubation period may have a stronger effect on malaria transmission compared to varying each parameter in isolation . By quantifying the impact of the combined use of fungal biopesticide and ITN interventions on malaria transmission and prevalence , the model indicates that these interventions combined may considerably improve malaria control even in situations each single intervention would have a relatively low impact . Modelling is no substitute for field studies , and attempts to make generalizations about vector biology need to be cautiously interpreted [37] . Recent vector control initiatives encourage the development of models that have the capacity to use field data to guide decision making [7] . This study demonstrates that biological mechanisms relevant to vectorial capacity can be built into existing continuous-time , population-level frameworks to allow direct parameterization from field and laboratory data on both established and novel interventions . This is a means by which models can increase their applicability to integrated vector management strategies .
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It has recently been proposed that mosquito vectors of malaria may be controlled by biopesticide sprays containing spores of fungi that are pathogenic to mosquitoes , causing reduced blood feeding activity and eventual death . This technique has been shown to have strong potential to reduce malaria transmission rates , and may be most effective when combined with other interventions as part of an integrated vector management strategy . I develop a model to quantify the total impact of combined interventions that can affect mosquitoes at different ages and stages in their lifecycle . As a case study , I consider the combined use of fungal biopesticides and insecticide- treated bednets ( ITNs ) , a widespread and important vector control method . The model demonstrates that these interventions combined can have strong effects on malaria transmission even in situations where each intervention acting alone has relatively little impact . In situations difficult for malaria control due to high transmission intensity and widespread insecticide resistance , the performance of the combined interventions is improved by synergistic interactions between the interventions , whereby the ITN intervention improves the performance of the fungal biopesticide intervention . The results suggest that the combined use of ITNs and fungal biopesticides may be an efficient and effective method of malaria control .
|
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"Abstract",
"Introduction",
"Models",
"Results",
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2009
|
Combining Fungal Biopesticides and Insecticide-Treated Bednets to Enhance Malaria Control
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Congenital toxoplasmosis is a serious but preventable and treatable disease . Gestational screening facilitates early detection and treatment of primary acquisition . Thus , fetal infection can be promptly diagnosed and treated and outcomes can be improved . We tested 180 sera with the Toxoplasma ICT IgG-IgM point-of-care ( POC ) test . Sera were from 116 chronically infected persons ( 48 serotype II; 14 serotype I-III; 25 serotype I-IIIa; 28 serotype Atypical , haplogroup 12; 1 not typed ) . These represent strains of parasites infecting mothers of congenitally infected children in the U . S . 51 seronegative samples and 13 samples from recently infected persons known to be IgG/IgM positive within the prior 2 . 7 months also were tested . Interpretation was confirmed by two blinded observers . A comparison of costs for POC vs . commercial laboratory testing methods was performed . We found that this new Toxoplasma ICT IgG-IgM POC test was highly sensitive ( 100% ) and specific ( 100% ) for distinguishing IgG/IgM-positive from negative sera . Use of such reliable POC tests can be cost-saving and benefit patients . Our work demonstrates that the Toxoplasma ICT IgG-IgM test can function reliably as a point-of-care test to diagnose Toxoplasma gondii infection in the U . S . This provides an opportunity to improve maternal-fetal care by using approaches , diagnostic tools , and medicines already available . This infection has serious , lifelong consequences for infected persons and their families . From the present study , it appears a simple , low-cost POC test is now available to help prevent morbidity/disability , decrease cost , and make gestational screening feasible . It also offers new options for improved prenatal care in low- and middle-income countries .
Toxoplasmosis , a disease caused by the Apicomplexan parasite Toxoplasma gondii , remains a major source of morbidity and mortality in the United States and globally . It causes a wide range of clinical manifestations , varying from a self-limited minor illness to devastating eye disease , congenital infection , and meningoencephalitis [1–4] . Treatment is currently imperfect , with available medicines effective against the active tachyzoite stage but not the slower-growing bradyzoite stage . In 2012 , an estimate of annual morbidity from congenital toxoplasmosis was made [5] . If this estimate is applied to a 10-year time period , there would be 1 . 9 million new cases of congenital toxoplasmosis globally , causing 12 million disability-adjusted life years ( DALYs ) [5] . There is renewed interest in this organism in light of the more widespread recognition that 2–3 billion people are infected with this parasite with the potential to reactivate and cause life-threatening disease when significant immunosuppression occurs in these individuals . In addition , there is increasing evidence about its role in chronic inflammatory changes , epilepsy , and possibly neurodegenerative and neurobehavioral disease in some cases . Recent , new compounds with documented efficacy against the latent , encysted bradyzoite life-stage , as well as effective vaccines for prevention of disease in mice , have potential to reach human trials in the not-so-distant future [6–10] . However , current patients continue to suffer profound effects from this parasitic infection . It has been well-documented that early treatment of infected mothers decreases risk of transmission to the fetus and severity of clinical disease and therefore improves clinical outcomes [11] . In several countries , including France , Austria , and Uruguay , mandatory screening for this infection during gestation has saved lives and reduced morbidity and mortality [11–14] . However , while serologic screening has been demonstrated to be cost saving in countries that constrain costs of testing and medications , there is no screening program in the United States [15 , 16] . Definition of better strategies to prevent this neglected , rarely diagnosed , and thus often untreated or mistreated disease is needed . As a result , we reasoned that development and validation of accurate , easy-to-use , and inexpensive point-of-care ( POC ) testing could help solve this problem . A novel POC test , the Toxoplasma ICT IgG-IgM ( LDBIO Diagnostic , Lyon , France; LDBIO ) presents a unique opportunity , as it has been found to be accurate for detection of infection in France [17] , and it is both economical , at ~US$4–8 per test , and rapid , with results available within 20 minutes , and requires no machinery . The cost we were charged was $4 and the list price was $8 . $4 is used in our considerations from here on . It has been found to be reliable with low antibody titer sera and reliable when testing sera during seroconversion of pregnant women . For example , it was sensitive and specific for sera from a pregnant woman who initially was seronegative , then had only IgM , and lastly had IgG and IgM specific for T . gondii . The test is being used in France and has been studied with a French cohort in Lyon , where it was found to be both sensitive and specific . However , as has been documented previously , the population of European T . gondii is more genetically uniform , with predominance of Type II strains , which is distinct from the parasites found in the Western Hemisphere . Congenital Toxoplasma infection in the United States is caused by a more eclectic group of parasites , with Type II , Type I-III , and Atypical parasites ( non-II ) causing disease . Given the genetic diversity that characterizes the U . S . population of T . gondii , we felt it was important to assess ease of performance , sensitivity , specificity , and positive and negative predictive value of the test for U . S . patients . The National Collaborative Chicago-Based Congenital Toxoplasmosis Study ( NCCCTS ) cohort is well characterized , with sera stored from these patients evaluated across many decades . We tested these samples for parasite serotype [18] . Herein , we present the application of this novel POC testing to distinctly American sera with the goal of preliminary validation of its use for patients in the United States .
Serologic samples were obtained from those in the NCCCTS cohort , as well as from volunteers . All seropositive persons had serum tested using gold-standard serologic testing in the Palo Alto Reference Laboratory previously with Sabin-Feldman Dye Test and IgM ELISA , with the exception of some seronegative volunteers , who were tested in the University of Chicago Hospitals CLIA-approved Clinical Laboratory , which currently uses a Bio-Rad assay [18 , 19] . Mothers of congenitally infected children in the NCCCTS also had their sera tested with a peptide ELISA to determine II , NE-II ( I/III ) or I-IIIa , or Atypical ( response to II = NE/II ) serotype , as described earlier [18] . Among our samples , there were 13 sera obtained from mothers within 2 . 7 months of birth of their congenitally infected child . Seronegative persons were laboratory personnel and fathers or adoptive mothers of congenitally infected children ( 18 women , 33 men ) . The POC testing materials were purchased from LDBIO in Lyon , France ( http://www . ldbiodiagnostics . com ) . Additional details concerning this test are described in Chapey , Wallon , and Peyron [17] . Sera had been stored at −20 or −80°C prior to testing and were thawed completely . Using a micropipette , 30 μL of serum was removed and placed into the well of the POC test . Three drops of eluent ( provided in a dispensing bottle for drops in the kit ) were then placed in the well . All POC tests were interpreted after 20 minutes . Results were interpreted by two individuals blinded to whether the serum was known to be positive or negative or of a certain parasite serotype . They determined whether the test was positive ( as indicated by a pink , positive line and a blue , positive control line on the test ) or negative ( as indicated by the absence of the aforementioned pink , positive line and the presence of the blue , positive control line ) . This confirmed interpretation . Once the samples had been interpreted to be positive or negative , the data were compared to records of serologic testing maintained for the NCCCTS cohort for the purposes of statistical analysis . Earlier serological testing had been performed and interpreted by the Palo Alto Medical Foundation Toxoplasma Serology Laboratory ( PAMF-TSL ) [20] . A standard formula for sensitivity , specificity , positive predictive value , and negative predictive value was used to determine test characteristics for these samples [21 , 22] . We calculated the cost of monthly prenatal screening throughout gestation for toxoplasmosis in the U . S . using the Toxoplasma ICT IgG-IgM test as a POC test in the OB/GYN healthcare provider office ( that costs $4-8/test ) versus a commercially available Toxoplasma IgG and IgM test ( that costs $650 for the Toxoplasma IgG and IgM ) . The NCCCTS was/is conducted with ethical standards for human experimentation established in the Declaration of Helsinki , with prior University of Chicago Institutional Review Board approval ( University of Chicago IRB Protocols 8793 , 8796 , 8797 , 15408A , and 16708A ) and in accordance with Health Insurance Portability and Accountability Act regulations . It was reviewed regularly by a Data Safety Monitoring Board . Informed consents were obtained from subjects for all aspects of the NCCCTS , including collection of serum samples , in accordance with University of Chicago Institutional Review Board and National Institutes of Health guidelines . All adult subjects provided informed consent . For any participant under the age of 18 years old , a parent or guardian provided informed consent on behalf of the subject . All consent was written , except for some volunteers who were not part of the NCCCTS , who provided oral consent . These volunteers who provided oral consent did so decades ago , at a time when obtaining written consent was not a usual part of obtaining a serum sample . Oral consent was witnessed . All serum samples were obtained with IRB approval . ANOVA was used to assess differences within groups with respect to time from the birth of an infected baby to the collection of serum samples . Given the tendency for duration data to be skewed , the results of ANOVA were confirmed with Kruskal-Wallis . P < 0 . 05 was considered to be significant for time from birth to sample obtained .
Results are from the following 116 serum samples from chronically infected persons tested: 48 samples from patients infected with Type II parasites , 14 samples from patients infected with Type I-III parasites , 25 samples from patients infected with Type I-IIIa parasites , 28 samples from patients infected with Atypical-type parasites , and 1 sample from a patient who was not typed . Thirteen additional samples were from acutely infected persons ( less than 2 . 7 months since birth of their congenitally infected child ) . These 13 acutely infected persons with serum tested at the time of their primary infection included: 5 samples from patients infected with Type II parasites , 1 sample from a patient infected with Type I-III parasites , 4 samples from patients infected with Atypical-type parasites , and 3 samples from patients infected with parasites of unknown serotype . 51 samples from seronegative persons also were tested . Fig 1 and S1 Table show the time from the birth of a T . gondii-congenitally infected child to when the sample was obtained , organized by parasite serotype . S1 Table has the serologic test results for the mother at the time of diagnosis of her child that documents her seropositivity . This includes children diagnosed at birth . There were no false positives or false negatives . Test characteristics , including sensitivity , specificity , positive likelihood ratio , and negative likelihood ratio , are in Tables 1–3 . The Toxoplasma ICT IgG-IgM test proved highly sensitive ( 100% ) and specific ( 100% ) in testing human sera from patients with infections with T . gondii strains circulating in the United States . There was no significant difference between samples based on serotype with respect to the time from the birth of an infected baby to sample collection by ANOVA ( p = 0 . 59 ) and Kruskal-Wallis ( p = 0 . 52 ) . Table 4 summarizes cost calculations for prenatal screening in the U . S . using the Toxoplasma ICT IgG-IgM POC test . In this table , we also contrast cost for standard anti-T . gondii IgM and IgG testing in a commercial laboratory at a university hospital . In Table 4 , we considered what a person with indemnity insurance that paid in full in one U . S . hospital is charged . This information was provided by the hospital laboratory ( Table 4 ) . This is contrasted with reagent costs and the time for a medical assistant or technician to perform the Toxoplasma ICT IgG-IgM test . This analysis was with serum , or cost for whole blood collected from finger prick . This includes costs for materials , reagents , time for performing and interpreting the test and entering the result into the patient’s medical record ( Table 4 ) . We have previously shown that for the United States , at a cost of $12 per test and prevalence as low as 1/10 , 000 live births , monthly prenatal screening with treatment was found to be cost-saving [15] . This was without addressing costs of quality of life or suffering for families and patients , the prevention of which confers separate and profound benefit for a program with testing , accurate diagnosis , and treatment . A $4 point-of-care test ( $4 was the charge we paid; $8 is listed on the website ) increases the cost-savings over what was demonstrated in detailed cost-benefit studies for the United States and Austria [12] . In terms of charges actually billed , if the cost were $650 for testing once , as it is at present in a university hospital , then ten tests ( that would be needed for monthly testing during pregnancy ) would cost $6 , 500 for one pregnancy . This far exceeds the capacity of an obstetrician to function within his or her $1 , 000 capitation for providing care per pregnancy . However , at $40 per pregnancy for ten tests ( that would be needed for monthly testing during pregnancy ) , this becomes a feasible opportunity to prevent , diagnose , and treat accordingly this infection and its consequences .
Herein , this novel Toxoplasma ICT IgG-IgM POC test for Toxoplasma IgG and IgM has proven very effective at identifying that sera of U . S . patients with known T . gondii infection are seropositive , and distinguishes them from those without serologic evidence of infection . It is also capable of identifying sera of acutely infected patients with high accuracy [17] . This new test has proven to be an effective screening method; it is accurate , rapid , and inexpensive . We were charged US$4 per test kit . The novel POC test , the Toxoplasma ICT IgG-IgM test ( LDBIO Diagnostic , Lyon , France; LDBIO ) , is already commercially available in France and represents a unique opportunity , as it has already been found to have a very good diagnostic performance when tested for detection of Toxoplasma infections from T . gondii strains circulating in France [17] . Moreover , it is both economical , at US$4–8 per test , and rapid , with results available within 20 minutes , and requires no large equipment . In the prior validation study in France , the Toxoplasma ICT IgG-IgM test was tested with 400 samples ( 99 positive for IgG and/or IgM and 301 negative for IgG/IgM when tested with the reference , widely commercially used , Architect automated chemiluminescence test ) . There were zero false negative Toxoplasma ICT IgG-IgM test results , while 13 false-positive Toxoplasma ICT IgG-IgM test results were identified among 301 seronegative samples . The Toxoplasma ICT IgG-IgM test correctly identified 21 positive sample that had only low IgG titers and also was reliable when testing sera during seroconversion of 5 pregnant women . Specifically , the Toxoplasma ICT IgG-IgM correctly characterized a pregnant woman who initially was seronegative by the Architect test ( Architect negative; Toxoplasma ICT IgG-IgM negative ) , then had only IgM ( Architect IgM positive , IgG negative; Toxoplasma ICT IgG-IgM positive ) , and lastly had IgG and IgM specific for T . gondii ( Architect IgG and IgM positive; Toxoplasma ICT IgG-IgM positive ) [13] . We did not encounter in testing the U . S . sera the few false-positive results found in France in the prior validation of the Toxoplasma ICT IgG-IgM test in France . A total of 580 sera were tested using the Toxoplasma ICT IgG-IgM POC test , i . e . , in the present Toxoplasma ICT IgG-IgM validation study in the U . S . ( 180 sera ) , and in the prior Toxoplasma ICT IgG-IgM validation study in Lyon , France , by Chapey et al . ( 400 sera ) [17] . This included in total 228 positive samples and 353 negative samples by reference T . gondii serologic testing . In the U . S . , there was 100% sensitivity and specificity , with perfect correlation with serologic status . In France , performance was also excellent , with 97% sensitivity and 96% specificity and documentation that seroconversion was detected in pregnant women in this commercially available test , already approved for clinical use in France . In France , there were 13 false positive sera among 109 true negative sera . There were 3 false positive nonspecific IgMs in true negative sera in France for these three reference Architect test results . In subsequent testing , these 3 women did not develop T . gondii-specific IgG . Thus , this test appears to be comparable to other standard , commercially available , conventional serologic tests , and it appears to detect infection with parasites found in France and in the U . S . Practically , for this combined IgG/IgM testing , a positive result would precipitate confirmatory testing [23] . Therefore , if any false-positive results did occur , they would be detected with the follow-up testing and not result in harm to the patient . As this test is used , it is a screening test , and patients should be informed that confirmatory testing will be necessary for a positive result . Testing with such a POC test like Toxoplasma ICT IgG-IgM test could bring with it , especially in low- and middle-income settings , the incentive for ideal , more frequent monthly obstetrical care for pregnant women , improving maternal and child health . Additional cost savings and maternal-fetal health benefit could occur with multiplexed testing at the beginning and end of gestation . This could screen the patient not only for Toxoplasma infection , as with the Toxoplasma ICT IgG-IgM POC testing , but also for HIV , syphilis , hepatitis B , CMV IgG and IgM , herpes zoster , and immunity to rubella [24 , 25] . Inclusion of other pathogens , such as Zika virus or Trypanosoma cruzi , the causative agent of Chagas disease , could also be beneficial in areas where those infections are prevalent . This multiplexed testing at the beginning and end of gestation , in combination with Toxoplasma ICT IgG-IgM POC testing on a monthly basis between the two multiplexed tests , could be remarkably cost-saving for patients and healthcare systems more broadly . It could facilitate profound improvement in quality of life and care for whole families . The considerations outlined in Fig 2 demonstrate many potential benefits from an inexpensive POC-based prenatal screening program and the “spillover” improvements in health care and outcomes that can result . Of note , the test was not effective in the analysis of saliva and we were advised by the manufacturer that , in its present iteration , it was not reliable for testing whole blood when patients had low levels of antibodies , as the pink indicator was obscured by a yellow color from the whole blood ( D . Limone , personal communication to R . McLeod , 2016 ) . Preliminary tests showed it could detect antibodies in a few persons . However , further work to create a test with a dark-colored indicator is underway with future studies planned similar to those described initially by Chapey et al . for the Toxoplasma ICT IgG-IgM test when first tested in France ( FP , PL ) [17] . POC tests function well for other infections such as HIV . POC testing for T . gondii based on testing saliva would reduce the need for venipuncture or fingerstick . The data herein with sera from U . S . patients infected with Type II , I/III , I/IIIa , and Atypical parasites demonstrate that the pink line indicator works well . The black indicator kit utilizes the same antigen and test strip so detection of infections with U . S . parasites would be comparable . We also wanted to develop a method whereby this test , proven herein to be very high-functioning , could be made a true point-of-care test with serum in a setting without use of a central laboratory . Thus , we developed a simple , practical method whereby this could be safely utilized in an obstetrical outpatient setting . Although more complicated than testing whole blood from a fingerprick , this approach makes the test feasible at very low cost following a small initial investment . This approach of obtaining serum in the clinic and testing is likely to be replaced in some settings with a test for whole blood currently in development , should it prove to be as sensitive and specific . Whole blood obtained by fingerstick , or saliva , is useful when processing of sera in a central laboratory is costly or inconvenient . Further , more extensive testing is needed for confirmation that this test with the dark colored indicator can be used with whole blood . This would not require electricity , centrifuges , or anything beyond a capillary tube , lancet , and the test kits at point of care . Despite these present limitations , this test has the potential for true clinical utility in identifying those who need further serologic testing and separating them from those for whom no further screening during pregnancy would be required . In a hypothetical clinical scenario , all pregnant women could be screened at the outset of their visits with an obstetrician or midwife , perhaps even using a multiplexed test for multiple congenital pathogens and T . gondii IgG and IgM separately [24] . Should the patient’s serum screen positive for Toxoplasma IgG/IgM , she could subsequently have confirmatory serologic testing at a reference laboratory for Toxoplasma infection performed . Ideally , this would begin at 11 weeks gestation or earlier . If the patient were truly acutely infected in this gestation , based on the results of confirmatory testing at a reference laboratory , she could receive appropriate therapy to reduce the risk of transmission to her fetus as well as the severity of potential clinical disease in the fetus [12 , 13] . Should she be identified as chronically infected , she would no longer need further screening for the rest of her gestation . On the other hand , should a patient be negative for both IgG and IgM with the POC test , she could undergo testing on an ongoing , monthly basis during her visits with her obstetric care provider and one month post-partum ( to allow also for detection of infections acquired very late in gestation , which nevertheless could have clinical implication for the management of the newborn infant ) . This will allow rapid detection of seroconversion , as shown with samples obtained in France during seroconversion [17] . Results of the Toxoplasma ICT IgG-IgM POC test will progress from negative ( Fig 3 , left ) to positive ( Fig 3 , right ) . This test system will facilitate early identification , diagnosis , and treatment of congenital toxoplasmosis in a cost-saving manner . This approach has been demonstrated to reduce the risk of vertical transmission , as well as the severity of clinical disease in the fetus , and has been demonstrated to be robustly cost-saving in Austria [3 , 11–13 , 16] . This testing has the capacity to reduce human suffering , be cost-saving for patients and health care systems , and thereby may overcome previous objections to the implementation of screening for congenital toxoplasmosis during pregnancy . It is critical to test such point-of-care tests carefully , as we ( FP ) also tested another licensed test system , which did not function as well . All rules and regulations pertaining to POC testing will have to be complied with . There is an extremely promising new technique utilizing printed strips for multiplexed tests that can cost US$0 . 01 ( http://med . stanford . edu/news/all-news/2017/02/scientists-develop-lab-on-a-chip-that-costs-1-cent-to-make . html ) . There are ongoing studies being performed by our research group , comparing the test studied herein with other POC tests . Some of these point-of-care tests provide separate IgG and IgM results . If some or all of these tests work as well as the Toxoplasma ICT IgG-IgM , any , or all , could progress to FDA or other country’s formal approvals and CLIA , or equivalent , certification . Where serologic screening is standard , as it is now in France and Austria , there are economies of scale and possibly other models for testing sites that lower costs . Further , with competing tests , market forces or regulation reduce costs . One cost- benefit analyses provides broadly applicable equations with sensitivity analyses based on outcomes and cost data in the U . S . [15] . Another cost-benefit analysis provides precise , detailed , patient specific , currently accurate data in the very recent analyses from Austria [12] . These types of analyses are complex and beyond the scope of this present work which evaluates the performance of the Toxoplasma ICT IgG-IgM with sera from the U . S . and considers the cost and feasibility of implementing such testing . In countries where obstetrical visits are less frequent than once monthly , monthly gestational testing could incentivize marked improvements in maternal gestational health care and , when multiplexed [24] , improve diagnosis of other congenital infections . The information in Fig 2 presents an analysis of possible life and cost savings in a developing country where such screening is implemented monthly during gestation and where urine glucose and blood pressure are also tested during those visits .
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Toxoplasmosis , a disease caused by the parasite Toxoplasma gondii , presents a major health burden in both the developed and developing world . Untreated congenital toxoplasmosis causes damage to the eye and brain , but early detection and treatment reduce transmission and disease . Fetal infection can be promptly diagnosed and treated and outcomes can be improved . Gestational screening for toxoplasmosis has international precedent . In this paper , we demonstrated that the new Toxoplasma ICT IgG-IgM test had 100% sensitivity and specificity in detecting Toxoplasma infection ( N = 180 U . S . sera from uninfected persons and those with varying parasite serotypes ) . The use of an inexpensive , easy-to-use point-of-care test facilitates screening of pregnant women for T . gondii infection . In turn , this facilitates prompt treatment for the infection and thereby reduces the health burden caused by this disease . This provides an opportunity to improve maternal-fetal care by using approaches , diagnostic tools , and medicines already available .
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2017
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Point-of-care testing for Toxoplasma gondii IgG/IgM using Toxoplasma ICT IgG-IgM test with sera from the United States and implications for developing countries
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Although a vaccine could be available as early as 2016 , vector control remains the primary approach used to prevent dengue , the most common and widespread arbovirus of humans worldwide . We reviewed the evidence for effectiveness of vector control methods in reducing its transmission . Studies of any design published since 1980 were included if they evaluated method ( s ) targeting Aedes aegypti or Ae . albopictus for at least 3 months . Primary outcome was dengue incidence . Following Cochrane and PRISMA Group guidelines , database searches yielded 960 reports , and 41 were eligible for inclusion , with 19 providing data for meta-analysis . Study duration ranged from 5 months to 10 years . Studies evaluating multiple tools/approaches ( 23 records ) were more common than single methods , while environmental management was the most common method ( 19 studies ) . Only 9/41 reports were randomized controlled trials ( RCTs ) . Two out of 19 studies evaluating dengue incidence were RCTs , and neither reported any statistically significant impact . No RCTs evaluated effectiveness of insecticide space-spraying ( fogging ) against dengue . Based on meta-analyses , house screening significantly reduced dengue risk , OR 0 . 22 ( 95% CI 0 . 05–0 . 93 , p = 0 . 04 ) , as did combining community-based environmental management and water container covers , OR 0 . 22 ( 95% CI 0 . 15–0 . 32 , p<0 . 0001 ) . Indoor residual spraying ( IRS ) did not impact significantly on infection risk ( OR 0 . 67; 95% CI 0 . 22–2 . 11; p = 0 . 50 ) . Skin repellents , insecticide-treated bed nets or traps had no effect ( p>0 . 5 ) , but insecticide aerosols ( OR 2 . 03; 95% CI 1 . 44–2 . 86 ) and mosquito coils ( OR 1 . 44; 95% CI 1 . 09–1 . 91 ) were associated with higher dengue risk ( p = 0 . 01 ) . Although 23/41 studies examined the impact of insecticide-based tools , only 9 evaluated the insecticide susceptibility status of the target vector population during the study . This review and meta-analysis demonstrate the remarkable paucity of reliable evidence for the effectiveness of any dengue vector control method . Standardised studies of higher quality to evaluate and compare methods must be prioritised to optimise cost-effective dengue prevention .
Dengue is a viral infection transmitted between humans by Aedes mosquitoes . With an estimated 390 million dengue infections occurring every year , and almost half the world’s population exposed to infection with dengue viruses , it is the most widespread mosquito-borne arboviral disease today , affecting 128 countries worldwide [1–3] . The dramatic increase in dengue over the past 50 years can be attributed to a number of factors , ranging from increased urbanization , in-country and international population movement , erratic water supplies and ineffective or unsustainable vector control [4 , 5] . The human and economic cost of frequent dengue outbreaks is high , though current Figs are almost certainly underestimates [6–9] . Dengue is showing signs of emergence in more temperate latitudes [10–13] and is a potential threat to many of the international mass-gatherings that are a feature the modern era , such as the FIFA World Cup and the Olympics , or religious gatherings like the Hajj , although their contribution to global spread has never been proven [14 , 15] . Until recent advances in vaccine development [16–17] , and the approval and potential availability of the first product in 2016 [18] , dengue has been unique among the major vector-borne diseases , in that prevention from infection could only be attempted by reducing or eliminating bites by infected vector mosquitoes [19 , 20] . Dengue viruses are transmitted primarily by Aedes aegypti , a cosmotropical mosquito that thrives in urban environments . It is highly anthropophilic and breeds in small bodies of fresh water , most commonly in the numerous containers found around the home , ranging from water storage drums and overhead tanks to bottles , buckets and discarded waste items [4] . Between blood feeding and oviposition , adult female mosquitoes rest within or close to human dwellings [19] . A second vector , Aedes albopictus , was originally confined to Asia , but in recent decades has expanded its global range and contributed to the spread of the chikungunya virus , as well as dengue [21–24] . Control of dengue vectors can be directed against the immature aquatic stages ( larvae and pupae ) or the adult mosquitoes , with a number of methods available for each approach . Described in detail elsewhere [19 , 25] , they can be grouped according to whether they target the vector directly ( i . e . aim to kill mosquitoes using insecticides or natural enemies or prevent them from biting using repellents ) or indirectly ( e . g . environmental modification or sanitation improvements that reduce potential larval development sites , or house improvements that prevent mosquito entry ) . Some approaches require skilled staff and/or dedicated resources ( e . g . specialised spraying equipment , insecticides , transport ) in order to be delivered effectively in a vertical approach . For others , affected communities , empowered through education and advocacy , can mobilize and mount effective control operations relatively independently via horizontal or community-based efforts . Hence , space-spraying and larviciding require trained personnel to deliver potentially toxic insecticides using specialized equipment and are dependent on vertical municipality-driven programs . In contrast , reductions in potential larval development sites can be achieved with householders and communities taking responsibility , supported by education and social mobilization [19] . In dengue-affected communities worldwide , immature vector populations are targeted through the reduction or elimination of potential larval development sites , typically by collection of purposeless or discarded containers in ‘clean-up’ or environmental management campaigns; functional or useful sites are either covered ( water storage containers ) , drained ( gutters or channels ) or treated with an appropriate insecticide ( usually referred to as ‘larviciding’ ) or biological control agent ( predatory copepods or fish ) . Identification of , and targeted action towards , ‘productive’ container types ( i . e . those that are assessed as contributing the greatest burden of pupae , relative to other containers in the area ) can potentially enable more cost-effective larval control [26 , 27] . The typical response to dengue outbreaks is to target the adult mosquito population by space-spraying or fogging with insecticide , delivered outside or inside the home , with the aim of severely reducing the vector population at the time of delivery . This method is not designed to deliver persistent insecticide residues on treated surfaces and if the outbreak continues , it must be repeated at intervals that coincide with the vector life cycle [19] . Previously , Erlanger et al . ( 2008 ) [28] reviewed data on the effectiveness on vector indices of all vector control methods and concluded that integrated vector control was the most effective , while environmental management had minimal impact . Notably , the evidence for impact of outdoor space spraying was limited , though only 1 of the studies included was less than 30 years old ( dated from 2015 ) . Two subsequent reviews [29 , 30] focused on peri-domestic space spraying and concluded that there was no evidence to support its use in dengue outbreak control , either as a standalone intervention or in combination with other interventions . Horstick et al . ( 2010 ) [31] also found no evidence for a demonstrable effect of vector control on entomological indices and identified specific weaknesses in funding , management , staffing and community engagement , all of which conspired to lower operational standards and ultimately restrict any likelihood of success . Recent reviews have examined the evidence for the effectiveness of individual methods , including copepods , fish and temephos [32–34] . Today , dengue outbreaks occur at an increasing frequency and intensity in affected communities worldwide and the need for evidence-based selection of the most appropriate interventions has never been greater . What are the best currently available dengue vector control tools , as measured by their impact on dengue infections , and not simply on vector populations ? Are previous dengue control failures the result of low operational and management strategies , or are the available tools simply not effective ? What evidence exists to provide a basis for evaluating dengue vector control today ? To answer these questions and to provide guidance on the most effective strategies currently available to combat dengue , we report here on a systematic review and meta-analysis of the evidence .
To systematically review randomized and non-randomized studies to evaluate the evidence of the effectiveness of vector control interventions in reducing a ) Aedes sp . vector indices and b ) human DENV infection and/or disease . The original search was conducted in April 2012 and updated in December 2013 and on 10th January 2015 . Table 1 displays the eligibility criteria . Only studies that presented data for a minimum duration of 3 months were included ( regardless of the frequency of treatments undertaken within that period ) , as this was considered the minimum period required to demonstrate a sustained impact on the vector population and/or impact on dengue transmission . In addition , only studies published since 1980 were considered eligible for inclusion , for a number of reasons . The period after 1980 saw the expansion in urban populations worldwide , notably in the less developed countries where the ratio of populations in urban and rural regions began to change dramatically [35 , 36] . This also was the beginning of the ‘globalization’ era , as characterized by steep increases in trans-national and international movement of humans and merchandise , and the time when all four dengue serotypes were reported in every continent , leading to an increase in the frequency and magnitude of dengue outbreaks [5 , 37 , 38] . We are familiar with the achievements prior to 1970 , such as the ambitious yellow fever programs when Aedes aegypti populations were significantly diminished , and indeed eliminated from many cities and large geographic areas throughout Latin America [1 , 4 , 5 , 39] . On balance , it was concluded that the control tools available before the 1980s ( e . g . the highly persistent insecticide DDT ) and the settings in which they were carried out , were not pertinent to the challenge of dengue control in urban environments of the 21st century , based on the significant logistical , sociological and epidemiological changes , and the rise in insecticide resistance in vector populations [40 , 41] that have occurred in many of those countries during the past 35 years . The primary outcome was dengue incidence ( any reported case data; clinical or lab-confirmed/ serologically positive cases ) ; secondary outcomes were a range of vector indices: Breteau Index ( BI ) , House Index ( HI ) , Container Index ( CI ) , tank positivity , number of mosquito adults , pupae per person index ( PPPI ) , presence of Aedes immatures and ovitrap positivity rates . All methods were pre-specified in the review protocol . PRISMA Group guidelines were followed as standard methodologies [42 , 43] . The databases WHOLIS , MEDLINE , EMBASE , LILACS and Science Citation Index were searched using the Medical Subject Heading ( MeSH ) “dengue” followed by the Boolean operator “and” combined with the following ‘free text’ terms “epidemic” and further combined in succession with: ‘threshold’ ‘sentinel’ ‘early warning’ ‘case management’ ‘vector control’ ‘DDSS’ ‘space spraying’ ‘indoor residual spraying’ ‘fogging’ ‘integrated vector management’ ‘IVM’ ‘source reduction’ ‘container’ ‘larvicide’ ‘repellent’ ‘insecticide’ ‘adulticide’ ‘fumigant’ ‘aerial spraying’ ‘dengue decision support system’ . The reference list of each of the included studies was also searched , and ‘‘grey literature” ( cited unpublished documents ) were sought by communication with authors . No limits were placed on year of publication status or language . Search results were imported into EndNote ( EndNote X5 , Build 7473 ) . LRB and PJM independently assessed the title and abstract of each record ( or the corresponding full article ) retrieved by the search for eligibility; any discrepancies were discussed . The full article was retrieved for each eligible study . The study’s investigators were contacted if eligibility was unclear , additional data were unpublished or the article was inaccessible . Each article was scrutinized to detect multiple publications from the same trial; such publications were included as a single study . LRB and PJM independently extracted data according to an agreed checklist and differences were discussed . Trial characteristics and risk of bias information were extracted along with outcome data ( S1 Table ) . For each randomized controlled trial , we extracted the number of individuals randomized and the number of individuals analysed for each treatment group . For dichotomous outcomes , we extracted the number of individuals experiencing the event in each treatment group for each study . For continuous outcomes we extracted means and standard deviations ( where presented ) or medians , interquartile ranges , and ranges . When such data were not reported , we extracted narrative information and tabulated results . For non-randomized studies , we extracted measures of effect , as well as treatment group data . Using a pre-piloted form , LRB and PJM independently assessed risk of bias and discussed any differences ( S2 and S3 Tables ) . For randomized controlled trials we used the Cochrane risk of bias tool and addressed: random sequence generation; allocation concealment; blinding; incomplete outcome data , selective outcome reporting , and other biases [43] . For each component of each trial , a judgment of high , low , or unclear risk of bias was made and the rationale for the judgment was given ( S2 Table and S1 Fig ) . For non-randomized studies , LRB and PJM used the Quality Assessment Tool for Quantitative Studies [44] ( S3 Table ) . This ensured that each study could be ranked according to inherent study design limitations , which included but were not limited to , bias , confounding and blinding . Analyses were performed in Review Manager ( RevMan Version 5 . 2 . Copenhagen: The Nordic Cochrane Centre , 2012 ) . We extracted the measure of effect and CI from the study reports . Where possible , we stratified analyses by intervention , outcome , measures of effect and study design . For multi-arm trials , data from numerous intervention groups were pooled . We calculated trial-level results ( i . e . MD , RR or OR and standard error [SE] ) and pooled them using random-effects inverse-variance meta-analysis to account for large variability present between studies . Results were visualised in forest plots . Sub-group analyses were used to stratify studies that used different and/ or combination interventions . Heterogeneity was assessed using the I2 test statistic , the chi-squared test ( P<0 . 01 indicated possible significance ) and by visual inspection of the forest plots to identify overlapping confidence intervals . Studies that could not be visualised in forest plots were presented in tables . When heterogeneity was detected , possible causes were explored using subgroup analyses and predefined covariates . Subgroup analyses were planned to explore potential sources of heterogeneity ( i . e . effects of seasonality , mosquito species , duration of intervention , coverage ) , but analyses were not carried out because of the low number of studies available for analysis . For the same reason , sensitivity analyses that excluded studies with a high risk of bias were pre-planned to assess the robustness of results , but were not carried out . Hence , the planned funnel plots were not constructed to explore possible publication biases .
A total of 960 potentially relevant studies were identified using systematic searches of the databases , grey literature and their cited reference lists and 19 more were identified from other sources ( Fig 1 ) . After removing duplicates , 582 citations were screened , of which 480 were excluded . The full texts of the remaining 102 records were assessed and 61 articles were excluded . The reasons for exclusion were: incomplete outcome data ( 18 studies ) ; study was a review , a non-peer reviewed report or a mathematical model ( 14 studies ) ; no intervention was carried out ( eight studies ) ; undefined or inadequate dengue case definition ( three studies ) ; intervention or outbreak duration was less than 3 months ( 10 studies ) ; study included only one required outcome ( three studies ) ; study preceded 1980 ( three studies ) ; time series data collection not reported ( two studies ) . Forty-one studies were included in the review [45–85] ( S1 Table ) , nineteen of which reported sufficient data for inclusion in meta-analyses [46–48 , 52 , 54 , 55 , 58 , 59 , 66 , 69 , 73 , 74 , 76 , 77 , 80–83 , 85] . The main characteristics of included studies are summarised in S4 Table . Of the 41 included studies , geographic study locations comprised: SE Asia ( n = 11 ) or Central America ( 10 ) , South Asia ( 8 ) , Australasia ( 4 ) , South America ( 5 ) and North America ( 3 ) . All studies were published between 1986 and 2014 , and 2009 was the median year of publication . Grouped by study design , the studies comprised: 9 randomised controlled trials ( i . e . 7 cluster-randomized and 2 randomized controlled trials ) and 32 non-randomised studies ( i . e . 8 controlled trials , 7 longitudinal studies , 4 interrupted time series studies , 5 before and after studies , 2 observational studies , 1 case-control study , 1 cross sectional study , 1 retrospective observational study , 1 ecological study and 2 models ) ( S4 Table ) . Vertical and community-led interventions were used exclusively in 20 and 10 studies respectively , and 11 studies used a combination of both . Combination interventions ( 23 studies ) were more common than single interventions ( 18 studies ) . Study duration ranged from 5 months to 10 years; 16 studies were less than 1 year , 12 took place over 1–3 years and 7 studies were 8 or more years in duration . Fig 2 ( top ) summarises the frequency of vector control tools by study design . The most frequently evaluated interventions were clean-up programs ( n = 19 ) , of which 4 were cluster randomised controlled trials . Outdoor fogging ( 9 ) , education ( 11 ) , larviciding ( 7 ) water jar covers ( 7 ) also were the subject of multiple studies . All studies presented data on Aedes aegypti; four presented additional data on Aedes albopictus ( S4 Table ) . Nineteen studies reported dengue incidence , 17 studies reported BI , 16 studies reported HI , 11 studies reported CI , 1 study reported tank positivity , 3 studies reported number of mosquito adults , 6 studies reported pupal indices , 3 studies reported ovitrap data . Fig 2 ( bottom ) summarises the reported reduction in outcome at a statistically significant level ( p<0 . 05 ) . Of note was the observation that in studies where it was an outcome , dengue incidence was not reduced in either of 2 randomised study designs , although 8 of 14 studies with other experimental designs reported a statistically significant reduction . Nineteen studies [46–48 , 52 , 54 , 55 , 58 , 59 , 66 , 69 , 73 , 74 , 76 , 77 , 80–83 , 85] provided sufficient data to allow their inclusion in meta-analyses . The results of those analyses are presented here stratified by reported outcome , either the impact on dengue incidence or on vector indices .
The dramatic growth in dengue over the past 35 years has been a remarkable epidemiological event and , as evidenced by its continued global spread , a challenge for which the public health community was not prepared . It is not surprising that 24 of the 41 studies included in this review were published in the past 7 years , reflecting the increase in attention and resources devoted to devising effective control strategies as recognition of the dengue pandemic grew . However , the fact that the global increase in focus on dengue control generated so few studies performed at a standard required for inclusion in this review , indicates that the magnitude of the response to the dengue pandemic has not been sufficient . Moreover , most of these studies investigated the impact of interventions on dengue vector indices alone , rather than dengue incidence . This also is discouraging , as the limitations of the Stegomyia larval indices , primarily their poor correlation with dengue transmission , are well known [86] . Finally , the inadequacy of the response to global dengue threat is demonstrated by the identification of thirteen studies that measured the impact of vector control on dengue incidence in the past 35 years , and that only six of these were suitable for inclusion in a meta-analysis . Simply stated , we do not have a clear understanding of which of the currently available interventions actually work , where or when they succeed or might work best , and the reasons why they succeed or fail . Nowhere is the inadequacy more apparent than in the absence of appropriately designed trials to evaluate insecticide fogging or space-spraying for the prevention of dengue transmission . Although space spraying is the standard public health response to a dengue outbreak worldwide , and is recommended by WHO for this purpose [19] , our study revealed the scant evidence available from studies to evaluate this method sufficiently . Earlier reviews also noted this serious omission from the literature published before 1980 [29 , 31] . Remarkably , no randomised controlled trials have been undertaken to evaluate the effectiveness of space-spraying or fogging to reduce dengue transmission or dengue incidence , anywhere in the past 35 years . We identified only one study [74] suitable for inclusion in a meta-analysis that demonstrated a significant impact of outdoor fogging on dengue vector populations . Without adequate evidence , it is impossible to determine how effective space-spraying programs , whether indoor or outdoor , have been . It may be the case that outdoor fogging has the potential to impact on dengue vector populations sufficiently to impact transmission , but the minimum treatment frequency and geographic area requiring treatment remain unknown . The most encouraging report comes from a recent longitudinal study analysing twelve years of data from the city of Iquitos in Peru [84] , which concluded that dengue cases could be reduced if intensive city-wide space-spraying ( outdoor fogging ) was conducted early in the transmission season . Given the cost implications of delivering a similar scale treatment in an even larger city , possibly with the need to do so in advance of an outbreak crisis , further studies to demonstrate the potential benefits are essential . Of those that could be assessed adequately , the method with the most evidence supporting effectiveness in preventing dengue transmission was house screening . Data from cross-sectional [52] and case-control studies [59] in Australia , and a case-control study in Taiwan ( 69 ) were included in a meta-analysis that indicated a significant protective effect of window and door screens on dengue transmission as detected by serology ( ELISA or HIA ( haemagglutination inhibition assay ) ) ( Fig 3 ) . Although the weaker study designs limited the power of this result , the results are encouraging . Aedes aegypti exhibit predominantly indoor resting and blood feeding behaviour ( termed endophagic and endophilic behaviour , respectively ) [87] , and barriers to access would be expected to impact on this species . Malaria vector mosquitoes and other arthropods of medical importance are also active indoors and can be targeted in the same way , increasing the likelihood of perception of benefit and adoption by householders . “Mosquito-proofing” houses was first considered over a century ago , and its potential as a sustainable and effective tool for malaria control has been evaluated in randomized controlled trials in recent years [88–90] . New investigations of screening for dengue prevention are also underway . Recent studies in a high-risk dengue setting in Mexico reported that window and door screens were a popular and widely-adopted intervention that significantly reduced domestic infestations of Aedes aegypti [91 , 92] . House screening is not included in the current WHO dengue guidelines , but given its potential and wide ranging benefits , it is a strong candidate for randomised controlled trials to evaluate its effectiveness in preventing dengue . Two observational studies reported on the impact of indoor residual spraying IRS , with contradictory results and while one of these reported a positive significant reduction in the odds of ( secondary ) incidence [66] , the second study reported an insignificant increase [69] . Consequently , the pooled odds ratio showed no statistically significant effect between intervention and control groups . While indoor residual spraying can target Aedes aegypti , such methods have rarely been used , nor are currently recommended [19 , 93 , 94] . Yet IRS is already used widely to control a number of other vector-borne diseases in various settings worldwide and , as it allows the delivery of a range of different insecticide classes , it can be an important tool for managing insecticide resistance [95–98] . The possibility that existing IRS programs might be expanded with minimal change to include dengue is an attractive prospect . Probably the most widespread practices to suppress dengue vector populations are clean-up campaigns , typically community-driven and in tandem with education and health promotional campaigns as well as numerous additional approaches . Efforts promoting environmental and peri-domestic clean-up to reduce vector larval development sites have been routine practices in many dengue-endemic localities for decades and as shown in Fig 2 , they were the most common intervention evaluated in the reviewed studies . However , clean-up campaigns were evaluated only as one element within multiple interventions or they continued to be promoted as a background across all the arms within a study . Thus , source reduction or clean-up campaigns were applied in some way in 20 studies but were associated with interventions ranging from fogging or water container covers targeting adult mosquitoes to larviciding and copepods for control of immatures ( S3 Table ) . Hence it is not possible to dissect their specific contribution to reducing vector populations or their impact on dengue transmission . Of these , the strongest evidence ( Fig 3 ) was from Cuba [58] where results indicated that community working groups ( CWGs ) , initially set up some years earlier , in a preceding study [71] promoting environmental management , conversion of garbage zones into gardens , water pipe repairs and the use of water container covers not only reduced vector indices , but also impacted dengue transmission , significantly more than the routine A . aegypti control programme . Although WHO recommends community participation as an essential element of sustainable dengue prevention [99] , there is little evidence that it can impact on dengue transmission [100] . A number of randomised controlled studies have demonstrated significant impacts on vector indices [47 , 48 , 73 , 83 , 101] ( Fig 5 ) even though the methods of intervention varied considerably between the studies . Results from a cluster randomised controlled trial in Nicaragua and Mexico [102] reported reductions in dengue sero-conversion rates and self-reported dengue cases as well as vector indices , following community mobilisation to deliver pesticide-free vector control . Clearly further evidence is needed . It remains to be determined how best practice is defined in any setting ( i . e . which tools or methods the community should employ ) , and what coverage is necessary in order to not simply reduce mosquito indices , but to impact on dengue virus transmission . The use of fish and crustaceans as biological control agents that prey on or compete with the immature vector stages may have potential in certain contexts , but we identified only three studies that evaluated copepods ( aquatic Crustaceans ) [78 , 79 , 103] . In all cases , the crustaceans were used together with clean-up programs , obscuring the impact of each method , and none of the reports provided sufficient data to be included in a meta-analyses . Consistent with earlier specific reviews [32 , 34] , there remains little evidence to suggest that biological control has widespread potential . A substantial number of reports demonstrated impacts on vector indices of insecticide-treated materials ( ITMs ) , deployed as window or door curtains [54 , 75 , 77 , 82 , 104 , 105] , although they were effective only where houses with fewer and smaller windows and doors [75–77 , 104] and where coverage of the intervention was particularly high [77] . Hence , in the meta-analyses , no significant impact on vector populations was indicated and the heterogeneity between the studies was high ( Fig 4 ) . Effects on dengue incidence of ITMs used as vertical window or door screens or as horizontal covers for water containers , need to be quantified in locations and contexts where housing conditions indicate suitability . ITMs , used as curtains hung or fixed tightly across external windows and doors , function in a similar way to mesh screens , and potentially could provide enough protection without the need for insecticide , as suggested by a study in Mexico , where ITMs reduced vector populations even though the targeted population was highly resistant to the insecticide used [90] . There was no evidence of any impact on dengue infection risk by insecticide-treated bed nets [52 , 69] , mosquito traps [69 , 81] or mosquito repellents [52] . Ongoing studies are investigating a range of novel trap designs for Aedes spp . surveillance and control [106–108] but to date , evidence of traps preventing any mosquito-borne disease remains elusive . Both opinion and evidence are weighed against the use of skin repellents for prevention of vector-borne diseases [109] , and attention has moved towards a new generation of spatial repellents , to be deployed within or close to houses to prevent mosquito entry , possibly in combination with attractant lethal traps in what is termed a ‘push-pull’ strategy [108 , 110] . The significant negative associations found between the use of insecticide aerosols [52] and mosquito coils [52 , 69] and higher odds of dengue incidence have a number of possible explanations . These tools may have been purchased in response to an actual increase in mosquito numbers , or a dengue case in the home or a neighbour’s house , during a period of dengue transmission . Alternately , householders using aerosols or coils may have relied solely on these anti-mosquito devices and not have adopted any other more effective preventative measures . Approaches involving the use of genetically modified ( GM ) mosquitoes or the intracellular symbiont Wolbachia [111] are recent advances in insect control and only one field trial , demonstrating impact on the vector population only [85] , was included in this review . An increase in the numbers of reports from ongoing new trials can be expected , although the use of GM mosquitoes for dengue control will have to confront or overcome additional regulatory or ethical challenges and requirements prior to field tests and eventual deployment [112–116] . Regarding trials of methods that require the use of insecticides , we noted that while 23/41 studies examined the impact of insecticide-based tools , only 9 of these cited recent information on insecticide resistance or referred to an evaluation of the susceptibility status of the target vector population at any stage of the study . Resistance to DDT , pyrethroids and other insecticides has been documented widely in dengue vectors , and continues to emerge , potentially impacting on intervention effectiveness [40 , 117–119] . Clearly , insecticide susceptibility testing must be an integral part of any trial where insecticide-based interventions are under evaluation , as recommended by the World Health Organisation [4] . Today , there is a widespread perception that Aedes aegypti control ‘has failed’ or that existing methods will not reduce dengue transmission , and that this is why we should abandon existing approaches and invest in or pursue alternative strategies [111 , 120 , 121] . As we have shown in this review and meta-analysis , this is incorrect . In reality , there is very little reliable evidence from appropriately designed trials to reach a conclusion about any of the control methods available . That this also applies to insecticide space-spraying or fogging illustrates clearly the urgent need for such fundamental trials . Care in designing studies is critical . Randomized controlled trials are the most robust design for evaluating the effectiveness of any intervention [122] . In our review , only eight of the nineteen reports included in the meta-analysis ( 7 CRCTs , 1 RCT ) were randomised , none of which reported a significant impact on dengue incidence . In contrast , eight other studies that reported a positive reduction in dengue incidence at p<0 . 05 , were not derived from randomised controlled trials , but from weaker experimental designs ( see Fig 3 ) . Weakness in the designs of trials investigating vector control tools have been recognised , and expert guidance , identification of challenges and pitfalls and clear recommendations for improvement are available [123 , 124] . Also apparent from this review is the large number of studies investigating impacts on the vector population alone , with no measures of the effectiveness of the intervention on dengue transmission . We recognise that detecting dengue viruses or confirming current , recent or historic dengue infections are not simple routine or inexpensive tasks , requiring skills and equipment that are not available without considerable investment . However , without this additional investment , the value of many studies that are limited to evaluating impacts on the vector alone is seriously reduced . Demonstration of impact on vector populations is achievable and often reported but is no guarantee that an intervention will translate into a reduction in dengue transmission [125 , 126] . This is particularly true for dengue , where the complex relationship between vector abundance , virus transmission and human infection rates are far from clear [86 , 127 , 128] . As well as their role in dengue transmission , Aedes aegypti is the main urban vector of yellow fever in Africa and South America , and this species and Aedes albopictus variously are vectors of the Chikungunya and Zika viruses , two emerging human pathogens that constitute a new global threat [129–132] . Despite the fears surrounding these threats , the urge to respond must be tempered by reality , and based on sound evidence . In the large urban zones where these vectors proliferate , to simply continue to use what has always been used , for that reason alone , or to pursue new approaches without sound supporting evidence would be wrong , and potentially a profligate waste of resources . Hence , there is an argument for instituting a global independent advisory body to guide decisions regarding the selection of approaches and tools for control or prevention of infections transmitted by urban Aedes sp . vector populations , and the design of appropriate multi-centre trials to evaluate their effectiveness . With this in mind , we hope that the findings of this review and meta-analysis will contribute to the sound evidence base on which that approach would be founded .
|
Dengue fever has increased dramatically over the past 50 years and today is the most widespread mosquito-borne arboviral disease , affecting nearly half the world’s population in 128 countries . Until the arrival of a vaccine , control of its Aedes vectors has been the only method to prevent dengue infection . With dengue outbreaks occurring at increasing frequency and intensity , we undertook a systematic review and meta-analysis of the literature , to evaluate the evidence for effectiveness of vector control strategies currently available . Forty-one studies ( from 5 months to 10 years duration ) were included in the review . Most studies investigated combinations of approaches but only 9 studies were randomized controlled trials ( RCTs ) . Remarkably , no RCTs evaluated effectiveness against dengue of insecticide space-spraying ( outdoor fogging ) , the main response to dengue outbreaks used worldwide . Nevertheless , there was limited evidence indicating that house screening and to a lesser extent , community-based environmental management with water container covers could reduce risk of dengue infection . However , skin repellents , bed nets and mosquito traps had no effect while insecticide aerosols and mosquito coils were associated with higher dengue risk . However , the quality of the few studies eligible for inclusion was poor overall , and the evidence base is very weak , compromising the knowledge base for making recommendations on delivery of appropriate and effective control . Given this paucity of reliable evidence , standardised studies of higher quality must now be a priority .
|
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2016
|
Is Dengue Vector Control Deficient in Effectiveness or Evidence?: Systematic Review and Meta-analysis
|
Site-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs . Such experiments , as well as structure-activity relationships for series of ligands , are usually interpreted with computationally derived models of ligand binding modes . However , systematic approaches for accurate calculations of the corresponding binding free energies are still lacking . Here , we report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations . A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 receptor and series of eight analogous antagonists . The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data . We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones .
G-protein coupled receptors ( GPCRs ) are an important group of membrane proteins that mediate physiological signals from the outside to the inside of cells . They are targets for approximately 30% of all prescribed drugs and of major interest to the pharmaceutical industry [1] . The understanding of GPCR structure , function and ligand binding has traditionally advanced through a combination of biochemical experiments and computationally generated 3D structure models [2] . Common experimental approaches include site-directed mutagenesis , generation of chimeric receptors and the substituted-cysteine accessibility method , while 3D models are used for design and interpretation of such experiments . In recent years , the field has benefitted enormously from breakthroughs in membrane protein crystallography , with a steadily increasing number of GPCR crystal structures determined since 2007 [3] . These structures not only enable structure-based drug design for crystallized targets but also make modelling of homologous GPCRs for the same purpose feasible [4] . Computational modelling is of optimal use in combination with site-directed mutagenesis data and structure-activity relationships for series of ligands [5] , but requires careful validation . Reliable free energy calculations based on molecular dynamics ( MD ) simulations can provide the missing links between experimental binding affinities and 3D structures of protein-ligand complexes [6] . In particular , approaches based on the free energy perturbation ( FEP ) method enable the evaluation of relative binding free energies between different ligands binding to a given receptor as well as to mutant versions of it [7] , [8] . These techniques can yield accurate and convergent results provided that the complexes compared are not too dissimilar [9] , [10] . However , when ligands differ by larger substituents , or receptors differ by more drastic mutations ( e . g . , tryptophan to alanine ) , the methodology becomes considerably less reliable due to convergence and sampling problems associated with the simulations . Hence , reliable FEP schemes for the systematic prediction of ligand binding and mutagenesis effects are rather scarce , and particularly so in the field of GPCRs where they would have a large impact [11] . The basic problem with applying free energy calculations to complexes that differ substantially in chemical structure is both that numerical instabilities can arise and that conformational sampling becomes more critical , when large groups of atoms vanish or appear during the computational “alchemical” transformations used [8] . To overcome this limitation , we present here a new FEP scheme for accurate calculation of the energetics of alanine scanning , which is applied to characterize the binding of antagonists to the human neuropeptide Y ( NPY ) receptor type 1 GPCR . The NPY system is comprised in mammals by three neuronal and endocrine peptides ( NPY , peptide YY and pancreatic polypeptide ) which activate receptors belonging to the rhodopsin-like ( class A ) GPCRs . Four functional receptors named Y1 , Y2 , Y4 and Y5 exist in humans and are all expressed in the peripheral and central nervous system . The NPY system has broad biological functions , including involvement in control of feeding behavior , cortical neural activity and emotional regulation . As a consequence , this system has been implicated in several human diseases such as obesity , alcoholism and depression [12] . However , until now no effective drugs have been developed for the NPY system , an area that would definitely benefit from structural insights into receptor-ligand interactions . With no crystal structures yet determined for any of the Y receptors , homology modelling in combination with site-directed mutagenesis has proven extremely useful for characterization of receptor-ligand interactions [13] . BIBP3226 is a competitive and Y1-selective antagonist which is widely used as a pharmacological tool for studying the physiological role of the Y1 receptor . For therapeutic application , however , the compound has drawbacks with regard to toxicity as well as low oral availability and brain penetration [14] . There is extensive experimental data available in the literature for this particular receptor-ligand pair , with binding studies for BIBP3226 to both wild-type ( wt ) and alanine mutants of Y1 [15] , [16] , as well as Y1 wt binding data for numerous BIBP3226 analogs [17] , [18] . We apply our new free energy perturbation scheme to a combined data set of alanine scanning for thirteen amino acids in the binding site region of Y1 and the binding of seven analogs of BIBP3226 , and show how this methodology can be efficiently used to validate structural models of the hY1-BIBP3226 complex . The structural insights obtained further demonstrate the applicability of the approach in ligand design projects aimed at structure-based development of new GPCR ligands .
In this work thirteen amino acids in the binding site region of Y1 are mutated to alanine using the free energy perturbation technique , namely Y2 . 64 , N3 . 28 , S4 . 57 , F4 . 60 , Y5 . 38 , T5 . 39 , Q5 . 46 , W6 . 48 , T6 . 52 , N6 . 55 , T6 . 56 , F6 . 58 and D6 . 59 ( Figure 1 and Table S1 , Supporting Information ) . Experimental relative binding free energies for the hY1 mutants compared to the wt receptor were derived from BIBP3226 Ki values [15] , [16] , whereas relative binding free energies between the reference compound BIBP3226 and the seven analogs ( Figure 1 , Table S2 ) were estimated from experimental IC50 values [17] , [18] for wt hY1 ( Methods ) . The hY1-BIBP3226 complex that was used as starting structure for all FEP calculations is shown in Figure 1A . The system was generated by homology modelling of hY1 with the program Modeller [19] , followed by insertion of the model in a lipid bilayer and refinement by MD equilibration using GROMACS4 . 0 . 5 [20] , as implemented in the GPCR-ModSim web server [21] . Then both automated docking with Glide [22] and mutagenesis-guided docking of BIBP3226 into the hY1 model were carried out , and the resulting complexes were subject to a final round of MD equilibration using a spherical simulation system using the program Q [23] , which also allows for very efficient FEP calculations [6] . Based both on structural stabilities of the wt hY1− BIBP3226 complexes and subsequent free energy calculations , the mutagenesis-guided docking approach was found to provide the best starting model ( see below ) . In this complex BIBP3226 is positioned at the bottom of the hY1 orthosteric binding cavity . The deep pocket between F4 . 60 and W6 . 48 is occupied by the phenol moiety of BIBP3226 , which places the hydroxyl group at hydrogen bond distance to both Q5 . 46 and N6 . 55 . The guanidinium group of the ligand forms a salt bridge with the key NPY receptor residue D6 . 59 [15] , [16] , [24] and hydrogen bonds to N6 . 55 . The pocket between transmembrane ( TM ) helices TM2 , TM3 and TM7 and extracellular loop 2 accommodates the biphenyl moiety of BIBP3226 . The position of the ligands and their interactions with the receptors were generally very stable throughout the MD simulations . As an example , the BIBP3226 heavy atom RMSD was only 0 . 3 Å between the initial structure and the average wt structure from a total of ( 13+7 ) ×6 = 120 independent equilibration runs ( 60 ns ) for this complex . Analogously , the RMSD of the side chain heavy atoms belonging to the binding site ( defined as all residues within 5 Å of the ligand ) was also very low ( RMSD = 0 . 5 Å ) . The only exceptions to this stability were two types of mutations . The first includes the N6 . 55A and D6 . 59 receptor mutations which both involve the deletion of a key polar interaction with the D-arginine moiety of BIBP3226 , thereby rendering the ligand more flexible and shifting its position somewhat in the binding pocket . The second type is ligand modifications that remove the hydroxyl group from BIBP3226 , which provides the hydrogen bonds responsible for attachment to both N6 . 55 and Q5 . 46 . Free energy simulations of single point mutations where larger residues are mutated to alanine ( alanine scanning ) involve the annihilation of a substantial number of atoms . The conformational states of the native ( wt ) protein and a given alanine mutant are then often too dissimilar for standard FEP protocols to yield accurate and convergent results . The most common ways to computationally transform the protein from wt to mutant is either to simultaneously change both electrostatic and van der Waals interaction potentials or to do it separately in two stages . It has been established that in the annihilation of repulsive atomic centers , an intermediate stage with so-called soft-core potentials ( that avoid singularities ) is beneficial for convergence [25] . However , the main problem with these approaches is still that the transformation between each stage is carried out via linear combinations of the end state potentials for all atoms involved . To overcome this problem , we instead constructed a smooth scheme based on successive fragment annihilation , which is illustrated for the case of a Tyr→Ala mutation in Figure 2 . The basic idea is to divide the whole transformation into a series of smaller “subperturbations” between a number of additional intermediate states , which are designed to be similar enough to ensure convergent free energy differences . Each subperturbation is as usual divided into a series of even finer grained FEP windows , yielding a total number of perturbation steps of several hundred ( Figure 3 ) . This strategy is not to be confused with the nowadays outdated “slow growth” method [26] in which only the two end states are used together with a transformation potential that changes in every MD step . In our scheme we defined groups of atoms in the wt residue ( Figure 2 shows the Tyr example ) , based on their distance to the Cβ atom . Each group will undergo three consecutive types of transformations during its annihilation: charge annihilation , regular van der Waals ( Lennard-Jones ) potential transformation to soft-core and , finally , annihilation of the soft-core potential . In the Tyr→Ala case five atom groups are defined and eight independent subperturbations are used ( Figure 2 ) . For cases where new atoms are instead created , as in the BIBP3226 ligand perturbations discussed below , the scheme is simply reversed and annihilation and creation of groups can also , of course , be treated simultaneously . We assessed the precision of our method for every protein and ligand mutation from six independent MD/FEP simulations , each corresponding to a total length of 4–6 ns including all subperturbations . Besides the precision , a critical convergence measure is the hysteresis resulting from applying the FEP formula ( see Methods section ) in the forward and reverse summation direction for each individual simulation . The average hysteresis obtained in this way from the six replicate trajectories for each alanine scan FEP calculation was in the range 0 . 0–0 . 5 kcal/mol , with an overall average for all mutations of 0 . 25 kcal/mol . The corresponding hysteresis range for the BIBP3226 ligand mutations was 0 . 0–0 . 1 kcal/mol , with an average over all ligands of 0 . 06 kcal/mol . These hysteresis errors are , in fact , remarkably small and clearly demonstrate the efficiency of our FEP scheme . As an illustration , Figure 3A shows the forward and reverse progression of the free energy change for a Tyr→Ala mutation in the hY1 apo structure corresponding to the upper row of the thermodynamic cycle in Figure 2 . Furthermore , the precision of the different free energy calculations , in terms of standard errors of the mean ( s . e . m . ) based on the six independent trajectories , is very satisfactory and typically about 0 . 5 kcal/mol for the different protein simulations and ≤0 . 2 kcal/mol for the BIBP3226 mutations in water ( Table 1 and Table S3 ) . The above results can be compared to those of less intricate reference protocols as shown in Figure 3 . The first of these ( Figure 3B ) transforms electrostatic and van der Waals parameters simultaneously with no extra intermediate states . The second reference scheme utilizes intermediate soft-core [25] van der Waals interactions and separate transformations of electrostatic and van der Waals potentials , but performs the operations on the entire sidechain simultaneously ( Figure 3C ) . Intermediate states with soft-core potentials clearly reduce the hysteresis error to some extent ( Figure 3C ) , but it is evident that the stepwise elimination of atoms , with many extra intermediate states , is key to the superior performance of our method ( Figure 3A ) . As an additional control , Figure 4 shows analogous FEP curves for our scheme and the second reference protocol , extracted from a transformation where one phenyl group is created and one simultaneously annihilated in water . This is a useful benchmark since the correct free energy change is exactly zero and both hysteresis errors and accuracy ( in this case based on ten independent simulations ) can be assessed . The result of the FEP calculations utilizing our new method is ΔG = −0 . 06±0 . 07 kcal/mol with an average hysteresis error of 0 . 13 kcal/mol ( Figure 4A ) . Hence , convergence ( hysteresis ) , precision and accuracy are all excellent . In contrast , the performance of the reference protocol is considerably worse with ΔG = 3 . 8±0 . 2 kcal/mol with a hysteresis of 0 . 4 kcal/mol ( Figure 4B ) . The relative binding free energies calculated from the MD/FEP simulations are generally in good agreement with experimental values , thus supporting the validity of the underlying structural model . For the alanine mutations the mean unsigned error with respect to experimental BIBP3226 binding free energies is 0 . 9 kcal/mol and the method is generally successful in discriminating mutations that have large effects on ligand binding from those that have only minor effects ( Figure 1C ) . If only the data from Sjödin et al . is considered , which has smaller relative experimental errors [16] , the performance of the FEP calculations improves ( <|error|> = 0 . 6 kcal/mol ) and better agreement is observed in this case for the two independently measured mutations [15] , [16] F4 . 60A and T5 . 39A ( Figure 1C ) . Moreover , for the six mutations for which has been determined with an uncertainty of less than 0 . 2 kcal/mol , the mean unsigned error of the calculations is only 0 . 5 kcal/mol ( Table 1 ) . Comparison of binding free energy differences between calculations and experiment can thus be used to validate the structural model . Here , the agreement is very good in most instances indicating that this GPCR-antagonist model has a close resemblance to the correct structure . The binding pocket between TM3 , TM4 , TM5 and TM6 and its interactions with the 4-hydroxybenzylamine and D-arginine groups of BIBP3226 are the part of the structure that is most thoroughly validated . In our structure , six of the thirteen mutated amino acids - F4 . 60 , T5 . 39 , Q5 . 46 , W6 . 48 , N6 . 55 and D6 . 59 - line the wall of this subpocket and the ligands differ only in this region ( Figure 1A ) . The FEP calculations reproduce the large positive ΔΔGbind associated with mutating D6 . 59 , N6 . 55 and Q5 . 46 to alanine ( Figure 1C ) . In the hY1 structure these three residues have ionic and polar interactions with the guanidinium and hydroxyl groups of the ligand ( Figure 1A ) . It can be clearly seen from the FEP calculations that the large ΔΔGbind is primarily due to considerably more favourable electrostatics for the D6 . 59 , N6 . 55 and Q5 . 46 sidechains in the holo structure compared to the apo structure ( ΔΔGFEP1 in Table 1 ) . Further , the large effect of the W6 . 48A mutation is also well reproduced by the simulations . When this tryptophan residue is mutated to alanine a cavity is created deep in the binding site and gradually filled with water , with the total change in binding free energy accumulating gradually over the series of smaller perturbations ( Table 1 ) . As mentioned , the experimental data for the two mutants F4 . 60A and T5 . 39A is ambiguous . One report indicates that F4 . 60 has a significant effect on BIBP3226 binding but that T5 . 39A has a negligible effect [15] . In contrast , the higher precision data say the opposite [16] which is also supported by the present FEP calculations ( Figure 1C ) . In the structural model of the hY1 complex both of these residues are in contact with the ligand . Residues Y2 . 64 and N3 . 28 face another part of the binding cavity , namely the pocket between TM2 , TM3 and TM7 ( Figure 1A ) . Y2 . 64 contacts one of the phenyl groups of the ligand and the FEP calculations yield a lower binding affinity for Y2 . 64A to BIBP3226 in accordance with experimental measurements . N3 . 28 , on the other hand , is not in direct contact with the ligand and the calculations in this case predict no change in affinity of N3 . 28A for the antagonist , again in agreement with experiment . The five remaining mutated residues are situated in interfaces between TM helices . Among these , S4 . 57A , T6 . 52A and T6 . 56A were shown in the experimental assays to bind BIBP3226 with essentially wt affinity [15] . The FEP calculations reproduce this pattern for S5 . 47A and T6 . 56A , while the binding free energy difference for T6 . 52A is overpredicted by 2 . 7 kcal/mol ( Figure 1C ) . This is the only real outlier among the 13 alanine mutations examined , which might indicate that the conformation of this sidechain and/or its interaction network is not properly modeled . Finally , the calculations also reproduce the detrimental effect on BIBP3226 binding affinity for alanine mutations of the two aromatic residues F6 . 58 and Y5 . 38 . The overall results of the simulations for the relative binding free energies of the BIBP3226 ligand series are remarkably good , with a mean unsigned error of 1 . 2 kcal/mol . Moreover , the method is clearly successful in discriminating the best binders from the low affinity ligands ( Figure 1D ) . The calculations closely reproduce the weaker affinity of the dehydroxylated analog ( 2 ) as well as the larger effect of the combined dehydroxylated and ( S ) -methylated compound ( 9 ) . Although ΔΔGbind for the ( R ) -enantiomer of the latter compound ( 8 ) is somewhat underestimated by the FEP simulations , it is noteworthy that the structural model still correctly discriminates between the two enantiomers ( 8 vs . 9 ) . Furthermore , the enantiomeric compounds 11 and 12 , which differ in the stereochemistry of their hydroxymethyl substituent at the same chiral center , are both correctly ranked and predicted to be low affinity ligands , in agreement with the experimental binding data . From the FEP calculations it is also clear that the low affinity of the hydroxymethyl compounds 11 and 12 is due to unfavorable desolvation in the hY1 binding pocket ( see corresponding ΔΔGFEP4 values in Table S3 ) . The calculations further yield diminished affinities for both the pyridine analog ( 18 ) and the tertiary amide compound ( 25 ) . As a useful control of the ability of the free energy calculations to discriminate against suboptimal structural models , all of the above FEP simulations were also carried out for the top-ranked solution resulting from the automated docking of BIBP3226 to the hY1 model ( Figure S1 ) . This docking solution essentially has the ligand rotated 180° around its arginine sidechain thereby interchanging the binding cavities for the phenol and biphenyl groups . The conformation is intuitively unrealistic since it places the biphenyl moiety in the vicinity of a number of polar groups . With this ligand orientation the correlation with the experimental binding data for the series of analogs is completely lost , indicating that the substituted phenol moiety must be in the wrong place . Also the alanine scanning results deteriorate although the effect is not as pronounced , probably due to the fact that the ligand is still occupying the same cavities even though it is flipped . It is , however , noteworthy that both the N6 . 55A and Q5 . 46A mutations now become outliers , most likely because the hydrogen bonding interactions with the phenol have been lost . Although the prediction for T6 . 52A mutation is actually better for this model this probably just reflects our suspicion that this receptor sidechain is in the wrong conformation , as discussed above .
Thermodynamic cycle free energy perturbation methods , or alchemical free energy calculations as they are sometimes called , have been around for quite some time [27] and were early applied to biochemical problems such as ligand binding [28] , [29] , protein stability [30] and enzyme catalysis [31] . These applications were clearly of more exploratory character and it is only recently that more systematic use of the FEP technique has been made , particularly in studies of aqueous solvation [32] , [33] , but also for ligand design purposes [34] and other key biochemical problems dealing with molecular recognition [6] . However , reliable computational schemes for systematically quantifying the effects of protein mutations on ligand binding have largely been lacking . In particular , the feasibility of carrying out larger scale computational alanine scanning simulations would be of great importance in connection with such mutagenesis experiments , as these are one of the major experimental routes for probing protein-ligand interactions in the absence of 3D structures . This is especially true for membrane protein interactions with ligands , such as ion channel blocking and ligand binding to GPCRs , given the limited availability of structural information for these systems . The free energy calculation scheme developed here turns out to be very efficient for systematically modelling the effect of single-point alanine mutations on protein-ligand binding , even for the complex case of a membrane receptor . The smooth stepwise transformation procedure overcomes the long-standing convergence problem with FEP simulations that involve the creation or annihilation of many atoms [9] , [10] . When applied to the hY1-BIBP3226 system , the agreement between calculated and experimental binding free energies is remarkably good for the thirteen alanine mutations and the series of eight receptor antagonists . These results thus serve to validate the 3D model of the complex and , conversely , a severely erroneous model could immediately be identified as such based on the loss of correlation between calculations and experiment . It is also noteworthy that even for the most complex Trp→Ala mutation , which involves the annihilation of a complete indole ring , a precision within 1 kcal/mol can be attained with only about 35 ns simulation time for each of the holo and apo states . A key aspect with regard to efficiency when dealing with many mutants and/or ligand molecules is also the size of the simulation system . Hence , while the common practice in MD studies of membrane proteins is to set up large simulation systems encompassing lipid bilayer patches with lateral dimensions of a hundred Å or more and a large number of solvent molecules [11] , [35] it is not clear that this strategy is optimal for doing many independent free energy calculations . After all , the goal in this case is not to simulate conformational changes distal to the binding site but to obtain as reliable free energy estimates as possible at a computational cost that allows many mutants or ligands to be evaluated . In this respect , reduced models that still yield correct local structural fluctuations of the binding site [36] may be significantly more efficient than larger scale models , precisely because they do not sample large scale conformational motions that require much longer timescales for convergence . A case in point here is large ribosome complexes where reduced models allow for extensive free energy calculations [6] at a low computational cost . As far as GPCRs are concerned there has been considerable recent progress with virtual screening strategies using homology models , as exemplified by the D3 dopamine [37] and A2A adenosine [38] receptors . These cases seem particularly favorable in terms of availability of experimental data . The D3 receptor both has structural templates with high homology and the existence of well-defined dopamine anchoring points , which is true for aminergic receptors in general . The A2A homology model , on the other hand , was validated using a unique proprietary technology to generate and characterize hundreds of mutants in vitro [5] , together with large amounts of available binding data . For systems that are structurally less well characterized it is questionable to what extent virtual screening based on docking to homology models is really meaningful . In this respect , the combination of experimental and computational alanine scanning , as well as free energy calculations of structure-activity relationships for a series of ligands , can provide the necessary validation needed for model refinement and subsequent virtual screening efforts . We have shown here that a computationally derived model of the Y1-antagonist complex , obtained from homology modeling and docking simulations , rationalizes the existing mutagenesis and binding data while a suboptimal model of the same complex clearly fails to do so .
Experimental relative binding free energies for the hY1 mutants compared to wt hY1 were derived from BIBP3226 Ki values as . For the F4 . 60A and T5 . 39A mutants there are two sets of experimental values available from independent reports [15] , [16] , resulting in values that differ by at least 1 . 4 kcal/mol between the two sources . In these cases we used the average of the two measurements to assess the errors between the calculations and experiment . Further , mutations that have a BIBP3226 Ki value outside the concentration interval screened in the binding assay were not considered when calculating mean unsigned errors . Relative wt hY1 binding free energies between the reference compound BIBP3226 and the seven analogs were estimated from experimental IC50 values [17] , [18] as . The sequence of the hY1 receptor ( Swiss-Prot accession number: P25929 ) was aligned with a multiple sequence alignment of all the inactive-like GPCRs of known structure using the GPCR-ModSim ( http://gpcr . usc . es ) web-server [39] . The human C-X-C chemokine receptor type 4 ( hCXCR4 ) was considered the best template for modeling of the hY1 receptor because it is a peptide binding GPCR with high homology to hY1 in the C-terminal part of extracellular loop 2 . This loop segment ( Cys5 . 25-Ser5 . 31 in hY1 ) constitutes part of the orthosteric binding cavity wall and is often involved in ligand binding . Further , the hCXCR4 structures are determined in the inactive state in complex with antagonists [40] . This is important since BIBP3226 binds inactive state hY1 . The sequence identity between hCXCR4 and hY1 in the transmembrane region is 29% . A chimeric template receptor was assembled making use of the structural alignment of the X-ray structures available from GPCR-ModSim . The chimeric template consisted mainly of hCXCR4 in complex with a cyclic peptide antagonist [40] ( PDB entry 3OE0 ) , but with some poorly defined intracellular parts extracted from two alternative templates: the intracellular loop 1 and the N-terminal end of TM6 from the hCXCR4 structure in complex with a small antagonist [40] ( PDB entry 3ODU ) while TM8 and the C-terminal end of TM7 were adopted from the hA2AR in complex with ZM241385 [41] ( PDB entry 3EML ) . This chimeric structure was used as template for homology modeling of the hY1 receptor using the program Modeller 9 . 9 [19] . The hY1-hCXCR4 sequence alignment was manually refined in the longer loop regions and the N-terminus was discarded from hY1 modeling due to lack of sequence similarity . Five hundred homology models of the hY1 receptor were generated and the best candidate model was selected on the basis of low DOPE-HR assessment score [42] and orientation of Asp6 . 59 towards the binding crevice , a residue shown by mutagenesis to be important for both agonist and antagonist binding [15] , [16] , [24] . The hY1 model was treated with the membrane insertion and equilibration protocol implemented in the GPCR-ModSim web-server [21] ( Figure S2A ) . Briefly , the system is embedded in a pre-equilibrated POPC ( 1- palmitoyl-2-oleoyl phosphatidylcholine ) membrane model so that the TM bundle is parallel to the vertical axis of the membrane . The system is then soaked with bulk water and inserted into a hexagonal prism-shaped box of dimensions 118×121×100 Å , consisting of slightly more than 60 . 000 atoms . The system is energy minimized and equilibrated for 5 ns in a MD simulation with periodic boundary conditions ( PBC ) using GROMACS4 . 0 . 5 [20] . In the equilibration , a first phase of 2 . 5 ns where positional restraints for the protein atoms are gradually released is followed by 2 . 5 ns where positional restraints are only applied to the α-carbons [43] . The OPLS all-atom ( OPLS-AA ) force-field [44] was used with Berger united-atom parameters for the POPC lipids [45] . The binding mode of the antagonist BIBP3226 in the equilibrated homology model of hY1 was explored with two alternative docking strategies . First , automated docking with Glide SP ( Glide , version 5 . 7 , Schrödinger , LLC , New York , NY , 2011 ) was carried out , using default settings and a grid dimension of 30 Å×30 Å×30 Å centered on a point in the binding cavity halfway between T2 . 61 and S5 . 39 , where the top ranked binding mode by GlideScore [22] was selected . Second , mutagenesis-guided docking was performed with PyMOL ( Version 1 . 4 . 1 , Schrödinger LCC , New York ) , using the extensive mutagenesis and structure-activity relationship data available [15]–[18] to guide placement of the ligand in the binding site . Here , we particularly required a salt bridge between D6 . 59 and the D-arginine moiety of BIBP3226 as well as hydrogen bonds between the ligand and the two residues Q5 . 46 and N6 . 55 , as the experimental data indicate these interactions to be important . Briefly , the manual docking started from a lower ranked docking solution from Glide which had these polar contacts with the receptor . Manual adjustments of torsion angles and translation displacement of the ligand were performed in PyMOL to enhance the hydrogen bonds . The structural stability of the obtained ligand-receptor complex was evaluated using the MD equilibration protocol described below . The final mutagenesis-guided docking pose was generated after two iterative rounds of MD and manual adjustments . BIBP3226 binding modes from both strategies were further evaluated using MD and FEP calculations . The hY1-BIBP3226 system was further equilibrated using the MD software Q [23] . A 40 Å radius spherical system was used , containing the predicted receptor-ligand complex with surrounding lipids and water molecules extracted from the equilibrated PBC simulation system described above ( Figure S2B ) . Water molecules with oxygen atoms within 2 . 6 Å of any ligand heavy atom were removed . This spherical GPCR system was equilibrated for 2 . 1 ns using the MD settings described in detail below . From the final structure of this equilibration a 24 Å radius spherical simulation system was extracted and used as starting structure for all free energy calculations . MD simulations were carried out using Q with the OPLS-AA force-field [44] . Simulations of the holo and apo states of the hY1 receptor as well as free BIBP3226 in water were conducted with spherical systems with a radius of 24 Å ( Figure S2C ) . The GPCR simulation systems were centered on a point in the orthosteric binding site situated approximately between T2 . 61 and T5 . 39 . Ionizable residues near the edge of the spherical system were neutralized to avoid artifacts due to missing dielectric screening [46] and each system was solvated with TIP3P water [47] . For the holo state of hY1 the water configuration from the end point of the homology model equilibration was used as solvent starting structure . The starting structure for the apo state of hY1 was generated from the holo structure by replacement of BIBP3226 with water molecules . The free state of BIBP3226 was generated by solvation of the ligand with a 24 Å radius spherical water grid . For the solvated GPCR systems , all atoms outside the 24 Å sphere were tightly restrained to their initial coordinates and excluded from non-bonded interactions . Further , a restraint of 10 kcal mol−1 Å−2 to the initial coordinates was applied to solute atoms within the outer 3 Å shell of the spherical systems . Water molecules at the sphere surface were subjected to radial and polarization restraints according to the SCAAS model [23] , [48] . For the free ligand in water , a weak harmonic restraint was applied to the geometrical center of the solute to prevent it from approaching the sphere edge . The SHAKE algorithm [49] was applied to constrain solvent bonds and angles . A direct non-bonded interaction cutoff of 10 Å was used for all atoms except those that undergo parameter changes during the FEP calculation ( for which no cutoff was applied ) , and long-range electrostatic interactions beyond the cutoff were treated with the local reaction field approximation [50] . In all simulations the system was slowly heated from 1 to 298 K while restraints on the solute coordinates to their initial position were gradually released . This was followed by 0 . 5 ns of unrestrained equilibration and 4–6 ns of FEP data collection ( simulation time depending on the number of subperturbations ) at 298 K , using an MD time step of 1 fs . In our FEP scheme , we divide the whole transformation into a series of smaller subperturbations between additional intermediate states , which are designed to be similar enough to ensure convergent free energy calculations . Each subperturbation is as usual divided into a series of even finer grained FEP windows , yielding a total number of perturbation steps of several hundred . The free energy difference associated with each subperturbation was calculated using Zwanzig's exponential formula [51] ( 1 ) where Um denotes the effective potential energy function of a particular FEP window and n is the number of intermediate states . Um is constructed as a linear combination of the initial ( A ) and final ( B ) potentials of the subperturbation ( 2 ) where the coupling parameter is stepwise incremented from 0 to 1 . The subperturbations are defined by grouping atoms are based on their distance ( number of bonds ) to a fragment common to both the start and end state of the overall transformation . In the case of alanine scanning , the groups are thus defined by the distance to the Cβ atom . The annihilation of groups involve intermediate transformations of the regular van der Waals ( Lennard-Jones ) potentials transformation to soft-core interactions 25 which are given in Q [23] as ( 3 ) where Aij and Bij are the Lennard-Jones parameters for the interaction between atoms i and j , rij the distance between them and αij is a constant that is set here to yield an energy of 20 kcal/mol at rij = 0 . The special case of D6 . 59A , which involves deletion of a charged sidechain , was treated by simultaneous charging of a chloride ion inside the water droplet about 20 Å from the position of the BIBP3226 positive charge in the holo structure ( ΔΔGFEP1 in Table 1 ) . As a test of this procedure , an alternative strategy where the missing ligand positive charge in the apo simulations was compensated by a K+ ion ( thereby yielding the same net charge in the holo and apo states ) was also examined . This gave an essentially identical result for D6 . 59A ( kcal/mol ) but with a slightly lower precision . Each subperturbation comprised 51 intermediate steps and at each step the system was simulated for 10–30 ps . Potential energies were collected every 21 fs and the first 1 ps of sampling in each state was discarded for equilibration . With eight subperturbations for the Tyr→Ala mutation ( Figure 2 ) , the total calculation thus involves about 400 intermediate states and a total data collection MD simulation of 5 . 6 ns . Six replicate FEP MD simulations with different initial atomic velocities were conducted for each mutation , where the initial state was the wt hY1 complex with BIBP3226 . The relative binding free energy for each calculation is taken as an average of applying the FEP formula ( eq . 1 ) in the forward and reverse directions , and all errors are reported as standard errors of the mean ( s . e . m . ) . The hysteresis of a FEP calculation is defined here , for the whole transformation , as ( 4 ) Here , and denote averages over the six independent simulations for applying eq . 1 in the forward and reverse summation directions , respectively . The total hysteresis is thus accumulated as the sum of the hysteresis associated with each subperturbation involved in the entire transformation . In addition to the FEP calculations described above and in Figure 2 , reference calculations were performed for Y2 . 64A in the hY1 apo structure using two less intricate FEP protocols . In the first control protocol Tyr was transformed into Ala using 49 intermediate states . Electrostatic and van der Waals parameters were altered simultaneously and no soft-core van der Waals potentials were utilized . The second control protocol consisted of a series of four FEP calculations using 199 intermediate states between Tyr and Ala . First , electrostatic parameters were changed to zero for all charge groups containing atoms to be annihilated . Second , van der Waals parameters were changed to soft-core van der Waals for all atoms not present in the end state . Third , the soft-core parameters were changed to the van der Waals parameters of the end state , which included annihilation of several atoms . Fourth , electrostatic parameters were changed from zero to the parameters of the end state . The number of replicate simulations , total MD simulation time and all settings were equal in all protocols . To further benchmark our FEP scheme , the phenyl to phenyl transformations of Figure 4 were performed utilizing both the main protocol and the second reference protocol described above . The MD simulations was carried out using the same settings as for the free ligands in water , with the exception that the phenyl molecule was solvated with a 18 Å radius spherical water grid . Ten replicate MD simulations of 3 . 57 ns each were conducted for both protocols .
|
G-protein coupled receptors constitute a family of drug targets of outstanding interest , with more than 30% of the marketed drugs targeting a GPCR . The combination of site-directed mutagenesis , biochemical experiments and computationally generated 3D structural models has traditionally been used to investigate these receptors . The increasing number of GPCR crystal structures now paves the way for detailed characterization of receptor-ligand interactions and energetics using advanced computer simulations . Here , we present an accurate computational scheme to predict and interpret the effects of alanine scanning experiments , based on molecular dynamics free energy simulations . We apply the technique to antagonist binding to the neuropeptide Y receptor Y1 , the structure of which is still unknown . A structural model of a Y1-antagonist complex was derived and used as starting point for computational characterization of the effects on binding of alanine substitutions at thirteen different receptor positions . Further , we used the model and computational scheme to predict the binding of a series of seven antagonist analogs . The results are in excellent agreement with available experimental data and provide validation of both the methodology and structural models of the complexes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"membrane",
"proteins",
"biomacromolecule-ligand",
"interactions",
"biochemistry",
"biochemical",
"simulations",
"transmembrane",
"proteins",
"cell",
"biology",
"proteins",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"cellular",
"structures",
"and",
"organelles",
"cell",
"membranes"
] |
2014
|
Computational Prediction of Alanine Scanning and Ligand Binding Energetics in G-Protein Coupled Receptors
|
The majority of mammalian microRNA ( miRNA ) genes reside within introns of protein-encoding and non-coding genes , yet the mechanisms coordinating primary transcript processing into both mature miRNA and spliced mRNA are poorly understood . Analysis of melanoma invasion suppressor miR-211 expressed from intron 6 of melastatin revealed that microprocessing of miR-211 promotes splicing of the exon 6–exon 7 junction of melastatin by a mechanism requiring the RNase III activity of Drosha . Additionally , mutations in the 5′ splice site ( 5′SS ) , but not in the 3′SS , branch point , or polypyrimidine tract of intron 6 reduced miR-211 biogenesis and Drosha recruitment to intron 6 , indicating that 5′SS recognition by the spliceosome promotes microprocessing of miR-211 . Globally , knockdown of U1 splicing factors reduced intronic miRNA expression . Our data demonstrate novel mutually-cooperative microprocessing and splicing activities at an intronic miRNA locus and suggest that the initiation of spliceosome assembly may promote microprocessing of intronic miRNAs .
Most eukaryotic primary transcripts undergo nuclear splicing , which removes introns and joins exons in a process catalyzed by a multi-megadalton complex called the spliceosome . Many protein-encoding and non-coding genes host small non-coding RNAs , among them hundreds of miRNAs [1] . In fact , most mammalian miRNAs are expressed from introns of protein-encoding and non-coding genes [2] , [3] . MiRNA-containing hairpins are cropped from primary miRNA transcripts ( pri-miRNAs ) by the Microprocessor , a protein complex minimally containing the nuclear RNase III enzyme Drosha and DGCR8 [4]–[7] . Molecular mechanisms coordinating the activities of the spliceosome and the Microprocessor on primary transcripts generating both mature mRNAs and miRNAs are obscure . Pri-miRNA processing may be physically coupled to transcription and/or splicing [8] , [9] , [10] . Pri-miRNA processing is more efficient if pri-miRNAs are retained at the transcription site [11] and clearance of introns following microprocessing of pri-miRNAs may enhance splicing efficiency [12] , suggesting that microprocessing precedes the completion of splicing [2] , [13] . Related , the processing of other classes of intronic small RNAs ( such as snoRNAs ) supports a model of cross-talk between small RNA processing and host gene splicing [14] . Additionally , splicing mutants in fission yeast reduce processing of centromeric transcripts into siRNAs and impair centromere silencing [15] , suggesting that the spliceosome provides a platform that promotes siRNA biogenesis . The melanocyte-specific gene melastatin and its hosted miR-211 gene located in intron 6 are robustly reduced in invasive human melanomas [16] , [17] . Reconstitution of miR-211 but not melastatin suppressed melanoma invasion , implying distinct biological functions for these gene products expressed from a common primary transcript [16] . Surprisingly , we detected increased formation of exon 6-exon 7 junction relative to other melastatin exon-exon junctions which lack intronic miRNAs . Here we demonstrate that microprocessing of miR-211 promotes splicing of the exon 6-exon 7 junction of melastatin , that knockdown of Drosha and its binding partner DGCR8 reduces exon 6-exon 7 junction formation , and that the RNase III activity of Drosha is required to promote exon 6-exon 7 junction formation . We also report that splicing at intron 6 of melastatin promotes microprocessing of miR-211 . Mutations in the 5′SS of intron 6 or knockdown of splicing factors interacting with the 5′SS reduced miR-211 biogenesis and Drosha recruitment to intron 6 . Our analysis of miR-211 biogenesis from intron 6 of melastatin provides a mechanism for exon 6-exon 7 5′SS splice site recognition promoting miR-211 microprocessing , and miR-211 microprocessing promoting exon 6-exon 7 splicing .
To examine the effects of intronic miRNA microprocessing on host gene splicing in a biologically-relevant context , we compared spliced exon-exon junctions of miR-211 host gene melastatin ( Figure 1A ) in human primary melanocytes , human melanoma patient samples , and human melanoma cell lines ( Figure 1B and Figure S1A ) . Consistent with previous reports [16] , [17] , [18] , [19] , melastatin was reduced in melanoma patient samples and melanoma cell lines compared to primary melanocytes . Surprisingly , we did not detect uniformly reduced exon-exon junctions across melastatin . In melanomas , splicing of the exons that flank the miR-211-containing intron 6 ( exon 6-exon 7 ) was increased by 20–100 fold relative to other exon-exon junctions . The increased frequency of exon 6-exon 7 splicing was Microprocessor-dependent because knockdown of Microprocessor components Drosha and DGCR8 ( Figure S1B ) decreased exon 6-exon 7 junction formation by 2–100 fold but did not decrease ( and in some cases increased ) formation of other exon-exon junctions ( Figure 1B ) . Consistent with these results , exon 6-intron 6 and intron 6-exon 7 junctions were selectively decreased relative to other exon-intron junctions of melastatin ( Figure S1C ) , implying increased splicing efficiency at miR-211-containing intron 6 . The Microprocessor-dependent two-fold increase in exon 6-exon 7 junctions relative to other exon-exon junctions was also observed in primary melanocytes ( Figure 1B ) . We note that melastatin mRNA levels in primary melanocytes are 10–10 , 000 fold higher than in melanomas , suggesting that splicing of low-abundance primary transcripts is more sensitive to positive effects of hosted intronic miRNAs than splicing of high-abundance primary transcripts . The increased frequency of exon 6-exon 7 junctions likely was not due to an alternative transcription start site because neither upstream nor downstream exon-exon junctions were increased ( Figure 1B and Figure S1A ) and neither upstream nor downstream exon-intron junctions were decreased ( Figure S1C ) , consistent with our previously-reported chromatin immunoprecipitation ( IP ) and micrococcal nuclease protection assays showing that melastatin and miR-211 are regulated by a common promoter [16] . These data suggest that microprocessing of miR-211 selectively increased the frequency of splicing at intron 6 of melastatin . To directly test whether microprocessing of miR-211 promoted splicing of melastatin exon 6-exon 7 junction , we constructed a melastatin mini-gene encompassing part of exon 6 , entire intron 6 , and part of exon 7 , with either wild-type ( WT ) miR-211 or a scrambled sequence ( SCR ) that does not form an RNA hairpin ( Figure 2A ) . We used HeLa cells for these experiments because HeLa cells express robust Microprocessor [5] and spliceosome [20] activities and do not express melastatin or miR-211 [21] , [22] , enabling precise control of experimental conditions . For all mini-gene vector transfections , we assessed the levels of miR-211 by qRT-PCR and Northern blotting , the levels of spliced mini-gene by exon 6-exon 7 qRT-PCR , the levels of unspliced mini-gene by exon 6-intron 6 qRT-PCR , and the steady-state mini-gene levels by exon 6 qRT-PCR ( Table S1 ) . To minimize detection of transfected plasmid DNA with primers intended for amplification of unspliced melastiatin mRNA , we treated RNA samples with DNaseI prior to RT reactions . We consistently detected four Ct value difference between +RT and the control –RT reactions , indicating that there was ∼16 fold less plasmid DNA than unspliced melastatin mRNA in our samples . Therefore , we do not believe that residual contaminating plasmid DNA influenced our quantitation of unspliced melastatin mRNA . Importantly , the primers used to detect spliced exon 6-exon 7 junctions gave no signal ( Ct values ∼36–40 ) in –RT control reactions . To control for transfection efficiency , all mini-gene experiments were normalized to vector-expressed neomycin . Transfection of the WT mini-gene led to production of both pre-miR-211 and mature miR-211 as well as spliced exon 6-exon 7 junctions ( Figure 2A , Figure S2A and Figure 3B ) . However , transfection of the SCR mini-gene abolished miR-211 production , reduced spliced exon 6-exon 7 junctions by two fold , and modestly increased unspliced exon 6-intron 6 junctions by up to 1 . 5 fold ( Figure 2A , Figure S2A and Figure 3B ) . No difference was detected in the steady-state levels of mini-gene transcripts between the WT and SCR constructs ( Figure S2A ) . Consistent with results in human melanocytes and melanomas , knockdown of Drosha and DGCR8 ( Figure S2B ) decreased WT mini-gene exon 6-exon 7 junction formation by three fold but did not affect SCR mini-gene exon 6-exon 7 junction formation ( Figure 2A ) . Thus microprocessing of miR-211 from intron 6 promoted splicing of exon 6-exon 7 junctions of melastatin . To rule out intron-specific effects of miR-211 on splicing , we cloned miR-211 or a SCR sequence into another melastatin mini-gene containing entire exon 20 , entire intron 20 , and entire exon 21 ( Figure S2C ) . Consistent with intron 6 results , microprocessing of miR-211 from intron 20 increased exon 20-exon 21 splicing by 1 . 4 fold relative to the endogenous or SCR-containing mini-genes , suggesting that positive effects of miR-211 microprocesing on splicing are intronic context-independent . Next , to rule out miRNA-specific effects , we replaced miR-211 in intron 6 with another miRNA not expressed in HeLa cells , miR-124 ( Figure S2D ) . Consistent with our results for the miR-211-expressing mini-gene , miR-124 microprocessing from intron 6 increased exon 6-exon 7 junctions by 1 . 4 fold and decreased unspliced exon 6-intron 6 junctions by 1 . 6 fold compared to the SCR construct . Together , these data show that increased splicing of at least two different host introns was dependent on the presence and microprocessing but not on the identity of a miRNA . To distinguish whether binding or RNase III catalytic activity of Drosha promoted splicing of melastatin exon 6-exon 7 , we knocked down endogenous Drosha in HeLa cells and tested the effects of ectopically-expressed siRNA-resistant WT or RNase III mutant Drosha on melastatin mini-gene splicing ( Figure 2B ) . In these experiments , Drosha knockdown decreased WT mini-gene splicing by up to 1 . 3 fold . These smaller effects ( compared to a three-fold decrease in Figure 2A ) might be due to the absence of DGCR8 knockdown and/or the timing of the reconstitution experiments , in which endogenous Drosha was knocked down for 48 hrs before reconstitution ( instead of 72 hrs as in Figure 2A ) . Rescue of endogenous Drosha knockdown with ectopic WT Drosha restored miR-211 microprocessing and exon 6-exon 7 junction formation . In contrast , ectopic expression of Drosha RNase III mutants ( E1045Q , which abolishes endonuclease activity at 3′ strands of miRNA hairpins; E1222Q , which abolishes endonuclease activity at 5′ strands of miRNA hairpins [6]; or combined E1045 , 1222Q ) failed to restore miR-211 microprocessing from the melastatin mini-gene and also failed to rescue decreased exon 6-exon 7 junction formation after endogenous Drosha knockdown . Importantly , these RNase III mutants do not affect the pri-miRNA binding activity of Drosha [6] . These results demonstrate the requirement for Drosha RNase III activity to promote splicing at the miR-211-containing intron 6 of melastatin . Because abolishing Drosha endonuclease activity at either 5′ or 3′ strands of miRNA hairpins failed to promote exon 6-exon 7 junction formation , our data imply that completion microprocessing precedes completion of splicing , consistent with previous reports [2] , [13] . Positive and negative effects of spliceosome-interacting proteins on miRNA biogenesis suggest that primary transcript splicing may affect microprocessing of hosted intronic miRNAs [8] , [9] . For instance , the KH-type splicing regulatory protein ( KSRP ) is an AU-rich element binding protein [23] that interacts with Drosha to promote biogenesis of a subset of miRNAs by binding to G-rich stretches in terminal loops of miRNA precursors [24] , [25] . Additionally , heterogeneous nuclear ribonucleoprotein A1 ( hnRNP A1 ) , which binds to nascent transcripts and couples transcription and splicing with mRNA export , has been shown to antagonize KSRP-mediated biogenesis of certain miRNAs [26] and promote biogenesis of other miRNAs [27] . The terminal loop of miR-211 possesses a G-rich stretch and intronic sequences surrounding miR-211 possess AU-rich elements , suggesting that splicing might affect miR-211 microprocessing by recruitment of KSRP or hnRNP A1 . To directly test the effects of splicing on intronic miRNA biogenesis , we introduced point mutations in the consensus sequences of the 5′SS , 3′SS , branch points , or polypyrimidine tract of the miR-211-containing melastatin mini-gene ( Figure 3A ) . When transfected into HeLa cells , these mutations reduced fully-spliced mini-gene RNA levels by 2–100 fold and increased unspliced mini-gene RNA levels by up to 2 . 5 fold ( Figure 3B ) . Interestingly , only mutations in the 5′SS reduced the levels of pre-miR-211 and mature miR-211 . In contrast , mutations in the 3′SS or polypyrimidine tract modestly increased miR-211 levels , while branch point mutations had no effect on miR-211 levels . Neither miR-211 nor any mini-gene sequences were detected in HeLa cells transfected with an empty control vector , as expected . Thus 5′SS recognition facilitates miR-211 microprocessing from intron 6 of melastatin , while 3′SS recognition may have a slight inhibitory effect . To rule out cell line-specific effects , we also transfected WT and mutant mini-gene constructs into two other human cancer cell lines , kidney HEK293T and lung A549 ( Figure S2E ) . Consistent with data form HeLa cells , miR-211 microprocessing from constructs containing 5′SS mutation ( 5′SS and 5′+3′SS mutants ) was strongly reduced in both cell lines , as assessed by Northern blotting for pre-miR-211 and miR-211 . Also in agreement with HeLa experiments , we observed decreased spliced exon 6-exon 7 junction formation when miR-211 was replaced by a SCR sequence , as assessed by qRT-PCR . These data suggest that the cooperativity between splicing and microprocessing is cell type-independent . Next , to rule out miRNA-specific effects of splicing on microprocessing , we introduced 5′SS , 3′SS , or 5′+3′SS mutations into the mini-gene containing miR-124 in intron 6 ( Figure S2D ) . Consistent with miR-211 results , miR-124 microprocessing was significantly reduced in constructs containing 5′SS mutation . However , in contrast to miR-211 results , 3′SS mutation reduced miR-124 microprocessing to the same degree as 5′SS mutation , and combined 5′+3′SS mutation abolished miR-124 microprocessing . These data suggest that 5′SS recognition complex binding promotes microprocessing of intronic miRNAs in a miRNA-independent manner , and that the effects of the 3′SS recognition complex on microprocessing are miRNA-dependent . Thus , the molecular mechanisms of positive effects of splicing on microprocessing may be miRNA-dependent . To confirm that decreased miR-211 biogenesis after 5′SS mutation was due to reduced spliceosome activity rather than to an artifact of mini-gene sequence alteration , we knocked down splicing factors that function in different steps of spliceosome assembly . Because the spliceosome is a dynamic multi-megadalton complex which exhibits redundancy [28] , knockdown of individual splicing factors did not significantly affect splicing of the miR-211-containing melastatin mini-gene ( data not shown ) . We therefore knocked down the central splicing factor PRP8 in combination with either a splicing factor unique to U1 ( SNRNP70 ) which binds 5′SS , U2 ( U2AF65 ) which binds the branch point , polypyrimidine tract and 3′SS , or U4/5/6 ( PRP4 ) which bridges U1 and U2 and eventually rearranges the spliceosome for catalysis of exon joining and lariat intron release ( reviewed in [28] ) . Knockdown of U1- , U2- and U4/5/6-specific factors ( Figure S3A ) increased the levels of the unspliced mini-gene by up to two fold and decreased the levels of the spliced mini-gene by 1 . 6 fold ( Figure 3C ) . Importantly , steady-state mini-gene levels were not altered in the knockdowns ( Figure S3B ) . Consistent with our mutational analysis of the melastatin mini-gene splice sites , only knockdown of SNRNP70 , but not U2AF65 or PRP4 , reduced miR-211 biogenesis from the WT mini-gene by up to 1 . 5 fold ( Figure 3C ) . The reduction in miR-211 levels upon knockdown of 5′SS interacting factors is smaller compared to the reduction in miR-211 levels after 5′SS mutation likely because 5′SS mutation completely abolished splicing ( Figure 3B ) while SNRNP70 and PRP8 knockdown decreased splicing only by 1 . 6 fold , indicating incomplete depletion and functional redundancy . These data further demonstrate that 5′SS recognition by U1 precedes and promotes microprocessing of miR-211 and that microprocessing and splicing of miR-211 are mechanistically-coupled processes . To test whether microprocessing and splicing of miR-211 are coupled through direct protein-protein interaction or secondarily through simultaneous interaction with a common primary transcript , we performed co-IP analyses in the presence and absence of RNase A ( Figure S3C ) . Transfection of FLAG-Drosha followed by anti-FLAG IP identified association of U1 , U2 , U4 , U5 , and U6 snRNAs even at high concentrations ( 60 ng/mL ) of RNaseA . These data demonstrate that Drosha can directly interact with the spliceosome independently of contacts with primary transcripts , as suggested previously [5] , [9] , [13] . Therefore , one possible explanation for reduced microprocessing of intronic miR-211 from the melastatin mini-gene after perturbation of 5′SS recognition ( either by the 5′SS mutation or knockdown of U1-specific SNRNP70 ) is that Drosha interaction with intronic miR-211 is stabilized by the spliceosome complex formed at the 5′SS . To assess whether perturbation of the spliceosome assembly at the 5′SS affects Drosha binding to miR-211-containing intron , we analyzed the association of WT and mutant melastatin mini-genes with Drosha ( Figure 3D ) . Anti-FLAG-Drosha IPs were assessed by qRT-PCR for spliced and unspliced mini-gene RNA . We calculated IP efficiency using the formula: ( mini-geneIP/GAPDHIP ) / ( mini-geneINPUT/GAPDHINPUT ) . Thus , a value of one indicates no enrichment of that RNA in Drosha IP despite background detection of both mini-gene RNA and GAPDH in IP , which we minimized through extensive washing and gentle elution with FLAG peptide . Consistent with decreased miR-211 microprocessing from 5′SS mutant mini-gene , 5′SS mutation decreased Drosha association with the unspliced mini-gene RNA by two fold . Also consistent with modestly increased miR-211 microprocessing from 3′SS mutant mini-gene , 3′SS mutation increased Drosha association with the unspliced mini-gene RNA by three fold . Additionally , only knockdown of SNRNP70 , but not U2AF65 or PRP4 , significantly reduced the association of the WT mini-gene with Drosha by 1 . 5 fold as assessed by anti-FLAG immunoprecipitation after splicing factor depletions ( Figure 3C ) . As expected , no enrichment of the spliced mini-gene RNA in anti-FLAG-Drosha immunoprecipitates was observed for all mini-gene constructs . Thus , the 5′SS recognition complex assembly promotes the association of Drosha with miR-211-containing intron 6 of melastatin , increasing microprocessing . A previously-proposed model suggested that splicing and microprocessing of intronic miRNAs were functionally-independent processes [2] . These studies demonstrated that intronic miRNAs can be processed from unspliced introns in cells and that microprocesing occurs before splicing completion in vitro . These studies also showed that the presence of an intronic miRNA did not affect ( and in some cases modestly decreased ) splicing efficiency , while spliceosome assembly modestly increased microprocessing . Our data is consistent with the model of microprocessing preceding splicing completion . Specifically , we demonstrate that U1 recognition of the 5′SS precedes and promotes Drosha binding to and microprocessing of miR-211 or miR-124 , which precedes and promotes completion of splicing in an intronic context-independent manner . Moreover , we identified novel , interdependent , mutually-cooperative Microprocessor and spliceosome activities at the miR-211 locus that are directly coupled through protein-protein interactions [2] , [13] . Consistent with a positive effect of splicing on microprocessing , introducing a miRNA hairpin within a synthetic intron improves the silencing efficiency of RNAi vectors [29] . It is possible that sequence determinants ( e . g . miRNA hairpin loop , miRNA hairpin flanking sequences , exonic or intronic splicing enhancers and silencers , or other contextual parameters ) affects coupling between microprocessing and splicing of intronic miRNAs . The only evolutionarily-conserved portion of intron 6 of melastatin corresponds to the miR-211 hairpin , arguing against the presence of conserved regulatory sites in this intron . Still , cryptic or unknown regulatory elements may be present in intron 6 or intron 20 and thus our observations may be a unique to miR-211 and melastatin . To test whether the effects of splicing on miR-211 and miR-124 microprocessing can be generalized to other intronic miRNAs , we knocked down the 5′SS recognition factor SNRNP70 with PRP8 in two human melanoma cell lines ( 451LU and 501mel ) and performed miRNA microarray . Of the 192 intronic and 190 intergenic miRNAs detected ( Figure 4A and Table S2 ) , 18 intronic but only six intergenic miRNAs were down-regulated by more than two fold after U1 knockdown ( p<0 . 05; Figure 4B and Table S3 ) . Reduced levels of these intronic miRNAs were independently validated by qRT-PCR ( Figure S4 ) . As expected , intronic miR-211 levels decreased after U1 knockdown by 1 . 2 fold in both melanoma cell lines ( Table S2 and Figure S4 ) . For all miRNA/miRNA* pairs detected in the most highly down-regulated group ( four intronic miRNAs and four intergenic miRNAs ) , when the miRNA strand was reduced by more than two fold , the miRNA* strand was also reduced ( Table S3 ) . Similarly , when the miRNA* strand was reduced by more than two fold , the miRNA strand was also reduced , supporting miRNA duplex biogenesis defect upon U1 depletion . Importantly , the majority of intronic miRNAs that were reduced by more than two fold in one cell line were also reduced ( by less than two fold ) in the other cell line ( Table S3 ) . In contrast , the majority of intergenic miRNAs that were reduced by more than two fold in one cell line were increased in the other cell line , indicating a universal mechanism for the U1 splicing complex promoting biogenesis of intronic but not intergenic miRNAs . Thus intronic miRNAs were preferentially reduced upon U1 depletion relative to intergenic miRNAs . These findings suggest that 5′SS recognition complex may globally promote microprocessing of intronic miRNAs , consistent with out detailed analyses of miR-211 and miR-124 microprocessing from intron 6 of melastatin . Here we demonstrate a feed-forward loop between microprocessing and splicing , whereby 5′SS recognition by the U1 complex promotes microprocessing of intronic miR-211 by Drosha ( possibly through recruitment of factors that promote microprocessing , such as KSRP and hnRNP A1 ) , and microprocessing of miR-211 promotes splicing at its host melastatin intron 6 ( Figure 5 ) . Disruption of 5′SS recognition both in cis ( mutations of 5′SS splice site ) and in trans ( knockdown of U1 splicing factors ) decreased processing of miR-211 and , conversely , inhibition of the Microprocessor activity reduced splicing of intron 6 of melastatin . Because RNase III-deficient Drosha was unable to promote exon 6-exon 7 junction formation , our model implies that rapid Microprocessor cropping promotes splicing , possibly by enabling intronic RNA degradation in preparation for splicing , as suggested previously [12] . We note that the biogenesis of mirtrons [30] , [31] is mechanistically distinct from the class of intronic miRNAs described here . Mirtrons are expressed from very short introns in which splicing substitutes for microprocessing and thus mutually-cooperative activities between splicing and microprocessing do not exist . It is notable that the debranched intron lariat possesses a phosphorylated 5′ end that is recessed relative to the overhanging 3′ hydroxylated end [30] , [31] , enabling mirtron recognition by exportin 5 and participation in cytoplasmic miRNA pathways . At one level , therefore , splicing appears to have co-evolved with microprocessing , at least in the case of the mirtron class of intronic miRNAs .
Primary melanocytes , melanoma patient samples , melanoma cell lines , HeLa , HEK293T , and A549 cells were cultured in DMEM supplemented with 10% fetal bovine serum and 1% Penicillin-Streptomycin-Glutamine ( Invitrogen ) . Total RNA was extracted with Trizol ( Invitrogen ) according to manufacturer's instructions . For qRT-PCR analysis of miRNAs and RNU58b , 10 ng total RNA was treated with RNase-free DNase ( Qiagen ) , reverse-transcribed and quantified with TaqMan microRNA assay kit with supplied primers ( Applied Biosystems ) , according to manufacturer's instructions . For qRT-PCR analysis of the melastatin mini-gene cassette , 100 ng of total RNA was treated RNase-free DNase ( Qiagen ) , reverse-transcribed using Quantitect kit ( Qiagen ) , and quantified using iQ SYBR-Green Supermix ( Biorad ) . MiRNA expression profiling was performed using TaqMan Low Density Array ( Applied Biosystems ) . SiRNAs were transfected using HiPerFect ( Qiagen ) according to manufacturer's instructions . Vectors were transfected using Lipofectamine2000 ( Invitrogen ) according to manufacturer's instructions . All siRNAs were purchased from Ambion and had the following target sequences: PRP8 ( 5′ CCCUACAUGUGAACAACGATT 3′ ) ; U2AF65 ( 5′ CCAACUACCUGAACGAUGATT 3′ ) ; SNRNP70 ( 5′ GGUCUACAGUAAGCGGUCATT 3′ ) ; Drosha ( 5′ GACCAGACUUUGUACCCUUTT 3′ ) ; DGCR8 ( 5′ GGAUCAUGACAUUCCAUAATT 3′ ) ; PRP4 ( 5′ UCAUGGCGCUUAUGGGAUU 3′ ) ; Scr control siRNA ( Ambion ) . A fragment of melastatin containing the 3′ end of exon 6 , entire intron 6 , and the 5′ end of exon 7 was amplified from the BAC clone RP11-348B17 ( Children's Hospital Oakland Research Institute ) . Sizes: intron-2783 nt; exon 6–151 nt ( full-size-172 nt ) ; exon 7–108 nt ( full-size-175 nt ) ; pre-miRNA-110 nt; intron upstream of pre-miRNA-934 nt; intron downstream of pre-miRNA-1739 nt . The fragment was digested with HindIII and BamHI restriction enzymes , and inserted into pcDNA3 . 1 vector ( Invitrogen ) . Site directed mutagenesis was performed using the quick change method from Stratagene according to the manufacturer's protocols . miR-211 was replaced by miR-124 in intron 6 by introducing AgeI and SacII or AgeI and PmlI restriction sites around miR-211 , and ligating pre-miR-124 using annealed DNA oligos ( IDT ) with AgeI and SacII or PmlI overhangs . A fragment of melastatin containing entire exon 20 , entire intron 20 , and entire exon 21 was amplified from the BAC clone RP11-348B17 ( Children's Hospital Oakland Research Institute ) . AgeI and EcoRI restriction sites were introduced in intron 20 , and either SCR or pre-miR-211 sequences were ligated using annealed DNA oligos ( IDT ) with AgeI and EcoRI overhangs . Five micrograms of total RNA were resolved on a 12% Urea–Polyacrylamide gel ( BioRad ) and transferred to a Hybond-N+ membrane ( Amersham ) . The membrane was dried , UV crosslinked , pre-incubated with ULTRAhyb-Oligo Hybridization Buffer ( Ambion ) for 1 h , and incubated overnight at 42°C with an antisense probe directed against mature miR-211 , miR-29a , or tRNA ( miR-211 probe: 5′ rArGrGrCrGrArArGrGrArUrGrArCrArArArGrGrGrArA 3′; miR-29a probe: 5′ rArArCrCrGrArUrUrUrCrArGrArUrGrGrUrGrCrUrArG 3′; tRNA DNA probe: 5′ TGGTGGCCCGTACGGGGATCGA 3′ ) . Probes were 5′ end-labeled with PNK ( New England Biolabs ) or using mirVana Probe and Marker kit ( Ambion ) . The membrane was washed for 10 min at 42°C in 2× SSC , 0 . 1% SDS , and for 10 min at 42°C in 0 . 2× SSC , 0 . 1% SDS , exposed and scanned using a Storm PhosphorImaging system ( Molecular Dynamics ) . Sizes of mature miRNAs were confirmed using a labeled small RNA ladder ( Ambion ) . The following antibodies were used: Drosha 07-717 ( Upstate ) , Actin 13E5 #4970 ( Cell Signaling ) , FLAG 2368 ( Cell Signaling ) , and GAPDH 2118 ( Cell Signaling ) . HeLa cells were lysed in RIPA buffer containing 10 mM Tris pH 8 . 0 , 150 mM NaCl , 1% Triton X-100 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 1 mM EDTA , 0 . 5 mM EGTA , 0 . 4 U/uL RNase inhibitors , and a protease inhibitor cocktail tablet , and centrifuged at 12 , 000 g for 15 min at 4°C . Mouse IgG agarose ( Sigma ) and anti-FLAG M2 agarose ( Sigma ) were washed in RIPA . After pre-clearing lysates for 1 hr at 4°C with mouse IgG agarose , IP was performed for 1 hr at 4°C with anti-FLAG agarose pre-blocked with BSA and tRNA . After washing the beads five times with RIPA , complexes were eluted with 150 ug/ml FLAG peptide in lysis buffer by shaking for 30 min at 4°C .
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MicroRNA ( miRNA ) genes are transcribed as long primary RNAs containing local hairpins that are excised by the Microprocessor complex minimally composed of Drosha and DGCR8 . Most mammalian miRNAs reside in introns of protein-encoding and non-coding genes , but it is unclear how microprocessing of an intronic miRNA and splicing at the host gene intron affect each other . We recently reported that in melanoma , a miRNA expressed from intron 6 of melastatin ( miR-211 ) assumes the tumor suppressive function of its host gene . In our current work , we detected elevated melastatin exon 6–exon 7 junctions relative to other exon-exon junctions that lack intronic miRNAs , suggesting that microprocessing promotes splicing . We show that microprocessing of miR-211 precedes completion of splicing of the exon 6–exon 7 junctions and that Drosha's endonuclease activity is required to facilitate exon 6–exon 7 junction formation . Additionally , we found that the first step of spliceosome assembly , recognition of the 5′ splice site by the U1 snRNP complex , promotes microprocessing of miR-211 and other intronic but not intergenic miRNAs . Our findings reveal a mutually cooperative , physical , and functional coupling of intronic miRNA biogenesis and splicing at the host intron , and they suggest a global positive effect of spliceosome assembly on intronic miRNA microprocessing .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"genetics",
"biology",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2011
|
Feed-Forward Microprocessing and Splicing Activities at a MicroRNA–Containing Intron
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Soil-transmitted helminth ( STH ) infections are among the most prevalent neglected tropical diseases ( NTD ) worldwide . Since the publication of the WHO road map to combat NTD in 2012 , there has been a renewed commitment to control STH . In this study , we analysed the geographical distribution and effect of community type on prevalence of hookworm , Trichuris and Ascaris in south Asia and south east Asia . We conducted a systematic review of open-access literature published in PubMed Central and the Global Atlas of Helminth Infection . A total of 4182 articles were available and after applying selection criteria , 174 studies from the region were retained for analysis . Ascaris was the commonest STH identified with an overall prevalence of 18% ( 95% CI , 14–23% ) followed by Trichuris ( 14% , 9–19% ) and hookworm ( 12% , 9–15% ) . Hookworm prevalence was highest in Laos , Vietnam and Cambodia . We found a geographical overlap in countries with high prevalence rates for Trichuris and Ascaris ( Malaysia , Philippines , Myanmar , Vietnam and Bangladesh ) . When the effect of community type was examined , prevalence rates of hookworm was comparable in rural ( 19% , 14–24% ) and tribal communities ( 14% , 10–19% ) . Tribal communities , however , showed higher prevalence of Trichuris ( 38% , 18–63% ) and Ascaris ( 32% , 23–43% ) than rural communities ( 13% , 9–20% and 14% , 9–20% respectively ) . Considerable between and within country heterogeneity in the distribution of STH ( I2 >90% ) was also noted . When available data from school aged children ( SAC ) were analysed , prevalence of Ascaris ( 25% 16–31% ) and Trichuris ( 22% , 14–34% ) were higher than among the general population while that of hookworm ( 10% , 7–16% ) was comparable . Our analysis showed significant variation in prevalence rates between and within countries in the region . Highlighting the importance of community type in prevalence and species mix , we showed that tribal and rural communities had higher hookworm infections than urban communities and for ascariasis and trichuriasis , tribal populations had higher levels of infection than rural populations . We also found a higher prevalence of ascariasis and trichuriasis in SAC compared to the general population but comparable levels of hookworm infections . These key findings need to be taken into account in planning future MDA and other interventions .
Soil-transmitted helminth ( STH ) infections are among the most prevalent neglected tropical diseases ( NTD ) with an estimated 1 . 45 billion people infected with at least one species worldwide [1 , 2] . This number , however , likely remains an underestimate due to the lack of high quality epidemiological data from most geographical regions . Infection by the STH human hookworm ( Ancylostoma duodenale and Necator americanus ) , Trichuris trichiura ( Trichuris ) and Ascaris lumbricoides ( Ascaris ) usually do not result in mortality , but instead lead to chronic infections and extended morbidity . Chronically infected children , for example , have been shown to experience malnutrition , stunting and cognitive deficits [3 , 4] , while pregnant women develop STH-induced anemia [5 , 6] . The “hidden” morbidity of STH infection not only takes a huge toll on the health of an individual , but has also been shown to affect economic development [7 , 8] . In 2012 , the World Health Organization ( WHO ) published a comprehensive roadmap to combat NTDs by 2020 , and for STHs , had set the goal of achieving 75% mass drug administration ( MDA ) coverage in all endemic countries [9] . This WHO initiative was further strengthened by the London declaration with a commitment from pharmaceutical companies and other organizations to support global efforts to control or eliminate 10 specific NTDs including STHs . In order to ensure optimal use of these resources and achieve transmission interruption of STHs , it will be critical to target the right communities in these countries for intervention . A recent review on the global burden of STH infection revealed that nearly 70% of the infections occur in Asia [2] . In the same study , it was found that one quarter ( 26 . 4% ) of the Asian study population hosted at least one STH species . The high STH burden in Asia is probably due to the moist and tropical climatic conditions , scarcity of safe drinking water , inadequate sanitation , and poor hygiene practices , all of which facilitates worm survival and transmission [10 , 11] . What remains unclear , however , is the extent of geographic variability in the prevalence of STH infections between and within countries . Earlier studies have shown that the prevalence of STHs may vary considerably between countries , and also between rural and urban communities [12–14] . Furthermore , since treatment efficacy and reinfection rates are different for each species of STH , in regions with ongoing MDA programs , this would also influence the prevalence and species distribution [15 , 16] . In this study , we aimed to assess the geographical distribution of hookworm Trichuris , and Ascaris in south Asia and south east Asia and to understand the effect of community type , namely urban , rural and tribal on prevalence of STHs .
We searched PubMed Central using the keywords soil transmitted helminth , nematode , Necator , Ancylostoma , Ascaris , Trichuris , hookworm , roundworm , and whipworm . The search was further narrowed using the keywords aborigine , aboriginal , tribe , tribal , indigenous , rural , village , town , city , and slum . To supplement our PubMed Central search , we accessed the Global Atlas of Helminth Infection ( GAHI—http://www . thiswormyworld . org/ ) database for all mapped and unmapped studies of STH infection . For India-specific articles , we hand searched India specific journals from the Christian Medical College ( CMC ) , Vellore library . The articles collected through the searches were evaluated for inclusion in the meta-analysis based on the following criteria: i ) only studies containing primary data on STH infection were included ( any reviews and meta-analyses that were acquired during our search were separately catalogued ) , ii ) only studies conducted in the eligible countries mentioned above were included and studies conducted in other Asian countries were excluded and iii ) as we also planned to determine the prevalence of STH as a function of the type of community environment , we only included studies that clearly defined the study population as rural , tribal , or urban . Based on the description of the study population by the author we categorized the studies as urban /rural /tribal . "Suburban" areas and "towns" were also considered urban . A semi-urban slum was considered "urban" , while a rural slum was considered "rural" . We excluded studies under "unclear whether rural/urban/tribal" category as we could not find the description of the study population in the article . If a given reference had data from more than one community type , the data was collected and analysed as separate datasets . Studies measuring STH infection in hospital inpatients or outpatients , immunocompromised individuals , or individuals with underlying disease condition ( s ) were excluded from this analysis . For data extraction , two reviewers used a standardized excel data extraction form based on the PRISMA guidelines to determine inclusion eligibility for the articles . For each article , the researchers independently attempted to extract the following data: reference name , country , study year ( publication year if study year was not available ) , location , study population , community type , age group , school aged children ( SAC ) or not , study design , number of stool samples collected per person , reported prevalence ( Ascaris , Trichuris and hookworm ) , and sample size . A single reviewer was responsible for comparing the two independent extractions and compiling a list with any discrepancies . These discrepancies were then presented to and resolved by a third reviewer . Data from a particular reference was added to the final STH infection database once both the independent reviewers ( three in cases of discrepancy ) were in agreement . We calculated the prevalence by dividing the number of STH positive individuals by the total number of participants . We then applied a logit transformation to obtain normality and weighted by inverse variance of logit-transformed prevalence . The I2 statistic was used to assess heterogeneity between studies [18] . We subsequently performed random-effects model to account for heterogeneity in prevalence estimates . Because infection is highly correlated with community type and age , we stratified the analyses both according to community type ( urban , rural and tribal ) and whether or not the study included SAC . Data analysis was carried out using R statistical software v . 2 . 8 . 1 [19] . A total of 4182 articles were available based on the search strategy given above and when selection criteria were applied , 3854 records were excluded . Of the remaining 328 full text articles assessed for eligibility , 165 articles were further excluded and 174 articles from 14 countries met the inclusion criteria ( Fig 1 ) . Among the 14 countries included in this study , India ( 46 publications ) , Malaysia ( 21 publications ) and Thailand ( 19 publications ) had the most contributions . On the other hand , there were no studies from Bhutan , Maldives and Singapore that met the inclusion criteria . The only study from Afghanistan that met the inclusion criteria was carried out on 239 urban SAC with a 57% prevalence of Ascaris and 13% prevalence of Trichuris and no hookworm; this was excluded from the country specific analysis . A list of all 174 references included in the analysis is provided in S1 Table . When prevalence of STH in the south Asia and south east Asia region was estimated , Ascaris was the commonest STH identified with a prevalence of 18% ( 95% CI , 14–23% ) followed by Trichuris ( 14% , 9–19% ) and hookworm ( 12% , 9–15% ) . The country-specific prevalence of hookworm , Trichuris and Ascaris have been represented in Figs 2–4 . Considerable heterogeneities between and within countries were noticed in the prevalence of STH for all species limiting further statistical analysis ( I2>90% ) . Among the countries included in our analysis , Laos , which contributed 19 studies and 27 , 087 participants had the highest proportion of individuals infected with hookworm ( Fig 1 ) ( 30% , 17–48% ) , closely followed by Vietnam ( 16 studies , 12049 participants , 29% , 14–52% ) and Cambodia ( 13 studies , 14461 participants , 28% , 18–42% ) ( Fig 2 ) . The countries with the lowest proportion of individuals infected with hookworm were Pakistan ( 4 studies , 2209 participants , 2% , 1–7% ) , Bangladesh ( 7 studies , 2886 participants , 3% , 1–17% ) and Myanmar ( 4 studies , 1000 participants , 4% , 1–18% ) . The wide heterogeneity in prevalence of hookworm infection between countries is reflected on the I2 statistic value of 99 . 6% . The highest proportion of individuals infected with Trichuris was found in Philippines ( 4 studies , 788 participants , 76% , 45–93% ) and Malaysia ( 21 studies , 7907 participants , 72% , 59–83 ) ( Fig 3 ) . These prevalence rates were much higher than those found in other countries . The lowest prevalence of Trichuris was found in Pakistan ( 4 studies , 2209 participants , 1% , 0–1% ) . The between-country I2 statistic value for Trichuris prevalence was 99 . 7% . Based on the results of our analysis , Ascaris is quite widespread throughout south Asia and south east Asia ( Fig 4 ) . Myanmar ( 5 studies , 3497 participants , 55% , 35–71% ) and the Philippines ( 4 studies , 788 participants , 59% 46–72% ) were found to have the highest proportion of individuals with ascariasis . Thailand had a notably lower proportion of Ascaris compared to the other countries in south east Asia ( 15 studies , 8312 participants , 1% , 0–6% ) . The between-country I2 statistic value for Ascaris prevalence was 99 . 7% . An important objective of our study was to observe the effect of community setting on the prevalence of STH infection . Studies from rural communities ranged from 84 ( with 76 , 580 participants ) for Trichuris to 102 ( 88 , 790 participants ) for hookworm while studies from tribal communities ranged from 28 for Trichuris ( 17 , 287 participants ) to 31 ( 17257 participants ) for Ascaris . Studies from urban areas ranged from 53 for Ascaris ( 38 , 001 participants ) to 42 ( 30 , 494 participants ) for hookworm . When the effect of community type was examined for the different STH species ( Fig 5 ) , prevalence rates of hookworm seen in rural ( 19% , 14–24% ) and tribal communities ( 14% , 10–19% ) were comparable . Tribal communities , however , showed a higher prevalence of Trichuris ( 38% , 18–63% ) and Ascaris ( 32% , 23–43% ) than rural communities ( 13% , 9–20% and 14% , 9–20% respectively ) . Urban communities had much lower rates of hookworm ( 3% , 2–6% ) . We also interrogated the studies in our database to determine the prevalence of STH infection among SAC . The prevalence of Ascaris ( 25% 16–31% ) and Trichuris ( 22% , 14–34% ) were slightly higher among the SAC than among the general population . On the other hand , the proportion of SAC infected with hookworm ( 10% , 7–16% ) was comparable to what was observed in the general population . When analysed by the community setting , the relative proportion of SAC infected with hookworm in the different community settings mirrored the proportions for all individuals living in those settings with higher prevalence seen in rural ( 21% , 14–31% ) and tribal ( 16% , 11–23% ) communities than urban ( 4% , 2–7% ) . For Trichuris , SAC showed a comparable prevalence in urban ( 13% , 6–28% ) and rural communities ( 18% , 10–31% ) and much higher prevalence in tribal communities ( 83% , 53–95% ) while for Ascaris , prevalence was similar in all 3 communities ( Fig 6 ) .
Given that STH infections are highly prevalent across south Asia and south east Asia [2] , and that several large initiatives have been launched to interrupt transmission [20] , we conducted a systematic review of STH infection to identify the communities in south Asia and south east Asia that will benefit the most from intervention programs . Although Ascaris was the most prevalent STH in this region , our country specific analysis suggests considerable geographical variation in the distribution of STH . A meta-analysis from South America showed a similar overall high burden of ascariasis [21] . However , in a meta-analysis study from sub-Saharan Africa , hookworm was found to be the commonest STH infection with ascariasis a distant second [22] . Geographical variation in STH infection reflects potential differences in the prevailing climatic and environmental conditions that facilitate transmission of one helminth species over the others . Earlier studies have shown that the abundance and species mix of STH infection can be affected by environmental parameters including surface temperature , rainfall , altitude and soil-type [10 , 23–25] . Ascaris and Trichuris have most often been found in urban and peri-urban communities whereas hookworm are found more often in rural communities [26] . In our analysis , Ascaris was found to be the most predominant species in the urban areas as expected , however , in rural areas , Ascaris , Trichuris and hookworm were present in near equal proportions both in the general population and SAC , suggestive of a greater heterogeneity in the distribution of STH species . Tribal communities , had the highest prevalence of Ascaris and Trichuris while prevalence of hookworm was comparable with that of the rural population . This high worm burden in the tribal communities reflects their poor and marginalized status , which makes them more vulnerable to STH infections [27] . The current WHO recommendation for STH control is to treat pre-SAC and SAC as they are considered to be at the highest risk of infection [9] . In our analysis , as expected , the prevalence of ascariasis and trichuriasis was higher among the SAC than among the general population , reflecting the age-intensity profile of infection , whereas for hookworm infections , the prevalence among SAC was comparable to that of the general population , suggesting the presence of adult reservoirs of infection [28] . In the absence of improvements in water quality , sanitation and hygiene ( WASH ) practices , infected and untreated adults are more likely to sustain the community transmission of STH and thereby reduce the overall impact of targeted school based treatment . A modelling-based study on STH transmission has demonstrated that impact of MDA is highly sensitive to the continuous presence of infective larvae in the environment [29] . The non-availability of high quality epidemiological data on STH due to the lack of nationally-representative sample surveys , as well as differences in study design and sampling strategy , have greatly restricted our ability to understand the true prevalence [30] . The wide heterogeneity between studies has restricted our ability to perform more in-depth comparisons . While countries like India had sufficiently large sample sizes , estimates for countries like Afghanistan , Philippines and Myanmar may not be generalizable to the whole country . Moreover , it is quite possible that many of the studies included here were done in high-risk communities , thereby inflating the country-specific prevalence estimates . The unexpectedly high Trichuris burden observed in the tribal communities is probably an artifact as a large number of tribal studies were from the Orang Asli community in Malaysia with a very high prevalence of Trichuris ( 9 of the 28 tribal studies were from this community ) . A proper assessment of the risk of STH will require robust country-specific data , preferably from nationally-representative epidemiological surveys in various communities [30] . In this analysis , we included studies published between 1990 and 2015 –a period spanning 25 years ( S1 Fig ) . Due to changes in social and environmental conditions over time , the prevalence estimates from earlier studies may not be reflective of the current infection status . Additionally , we included only the studies published in PubMed Central or those available through the GAHI database . As a result , we inevitably have missed some studies , especially those published in non-indexed journals , although it is unlikely , given the paucity of data , that their inclusion would have significantly altered the overall findings . The estimates of STH burden can be affected by the choice of the diagnostic test [31] as well as the number of samples tested per individual [32] . We were not able to adjust for either in the analysis . Moreover , as most studies reported prevalence data , we were only able to estimate the prevalence and not the intensity of infection in these communities . Given the non-linear relationship between the worm aggregation and prevalence [33] , this may not be a good indicator of the true disease burden . Also , the impact of treatment is better measured through a reduction in intensity rather than the prevalence of infection [34 , 35] . We urge that most future studies publish information on both prevalence and intensity of infection for a better assessment of the community disease burden . Despite these limitations , this systematic review provides valuable insight into the epidemiology of STH infections in south Asia and south east Asia . The geographical diversity in species distribution demonstrates the need for a flexible approach to effectively control STH infection in this region . While communities with high ascariasis could continue with the school-based deworming approach , those with predominant hookworm infection may have to adopt a population-based deworming approach . Additionally , Trichuris endemic communities would require dual therapy [36] . Given the region’s high prevalence of Ascaris and Trichuris–both of which are transmitted directly by the fecal-oral route [37]–long-term control of STH in these communities will require a multi-faceted approach that also involves improvements in water supply and sanitation .
|
Soil transmitted helminth infections ( hookworms , Trichuris and Ascaris ) are highly prevalent across south Asia and south east Asia and recently several large initiatives have been launched to control or interrupt transmission . We conducted a systematic review of STH infections to identify the communities in south Asia and south east Asia that will benefit the most from intervention programs . Our analysis showed that Ascaris is the most prevalent STH in the region but there was considerable geographic variation in the region . We found that tribal and rural communities in these countries had higher prevalence of STH compared to urban populations . We also found a higher prevalence of Ascaris and Trichuris in school aged children compared to the general population but comparable levels of hookworm infections . These key findings are important for future planning of intervention strategies for control of STH .
|
[
"Abstract",
"Introduction",
"Methods",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"helminths",
"population",
"dynamics",
"tropical",
"diseases",
"hookworms",
"geographical",
"locations",
"parasitic",
"diseases",
"animals",
"ascaris",
"neglected",
"tropical",
"diseases",
"population",
"biology",
"trichuris",
"people",
"and",
"places",
"helminth",
"infections",
"eukaryota",
"asia",
"trichuriasis",
"nematoda",
"biology",
"and",
"life",
"sciences",
"soil-transmitted",
"helminthiases",
"organisms",
"geographic",
"distribution"
] |
2018
|
Geographical distribution of soil transmitted helminths and the effects of community type in South Asia and South East Asia – A systematic review
|
miRNAs are small regulatory RNAs that , due to their considerable potential to target a wide range of mRNAs , are implicated in essentially all biological process , including cancer . miR-10a is particularly interesting considering its conserved location in the Hox cluster of developmental regulators . A role for this microRNA has been described in developmental regulation as well as for various cancers . However , previous miR-10a studies are exclusively based on transient knockdowns of this miRNA and to extensively study miR-10a loss we have generated a miR-10a knock out mouse . Here we show that , in the Apcmin mouse model of intestinal neoplasia , female miR-10a deficient mice develop significantly more adenomas than miR-10+/+ and male controls . We further found that Lpo is extensively upregulated in the intestinal epithelium of mice deprived of miR-10a . Using in vitro assays , we demonstrate that the primary miR-10a target KLF4 can upregulate transcription of Lpo , whereas siRNA knockdown of KLF4 reduces LPO levels in HCT-116 cells . Furthermore , Klf4 is upregulated in the intestines of miR-10a knockout mice . Lpo has previously been shown to have the capacity to oxidize estrogens into potent depurinating mutagens , creating an instable genomic environment that can cause initiation of cancer . Therefore , we postulate that Lpo upregulation in the intestinal epithelium of miR-10a deficient mice together with the predominant abundance of estrogens in female animals mainly accounts for the sex-related cancer phenotype we observed . This suggests that miR-10a could be used as a potent diagnostic marker for discovering groups of women that are at high risk of developing colorectal carcinoma , which today is one of the leading causes of cancer-related deaths .
A growing number of studies show the importance of aberrant miRNA expression in cancer . Although miRNA profiling studies have proven useful in defining signatures of cancer-deregulated miRNAs with diagnostic and/or prognostic value [1] , [2] , establishing casual relationships is not always possible . Altered miRNA expression in cancer can arise from genomic abnormalities but also by alteration of upstream regulators of miRNA expression and/or maturation , including epigenetic silencing [3] . The miR-10 miRNA family members are encoded in evolutionarily conserved loci within the Homeobox ( Hox ) gene clusters of developmental regulators [4] , [5] . Co-expression of miR-10 and Hox genes during development [6] , [7] and experimental evidence of miR-10 targeting of HOX transcripts [8]–[10] has suggested a role for this miRNA family in development . Mammalian miR-10a and miR-10b are located upstream from HoxB4 and HoxD4 respectively and they present a very high degree of sequence conservation , differing at their eleventh nucleotide only ( U and A respectively ) , which thermodynamically enables them to target a fully overlapping set of mRNAs [11] , . Importantly , both up- and downregulation of miR-10 has been reported in several cancers and although the number of studies where such deregulation was causally linked to the pathogenesis of cancer remains scarce ( for a review , see [4] ) , some miR-10 targets have been demonstrated to be mechanistically linked to metastasis , invasion and migration as well as cell proliferation [9] , [10] , [13]–[16] . Colorectal cancer is the second most commonly diagnosed cancer in women and it is one of the leading causes of cancer-related deaths in the world [17] . Colon cancer arises from the epithelial cells of the lumen of the colon where benign adenomatous polyps are established as an initial step . These further progresses into more advanced adenomas showing high-grade dysplasia and can ultimately evolve into invasive cancer . One of the most studied causes of colon cancer is aberrant signaling of the evolutionary conserved Wnt pathway , which is tightly regulated during development and crucial for adult tissue homeostasis in the intestinal tract [18] . The tumor suppressor Adenomatous polyposis coli ( Apc ) gene is an essential negative regulator of the Wnt pathway and loss of function of this gene is associated with a great majority of colorectal cancers [19]–[21] . Clinically , colon cancer is categorized in four stages ( I to IV ) corresponding to its degree of progression [22] , [23] . Chromosomal instability , DNA-repair and aberrant DNA methylation have been shown to be important for the development and progression of colon cancer ( for a review , see [22] ) . Interestingly , miR-10a was previously found to be moderately up regulated in solid tumors and stage II but not in stage I cancers in the colon [24] , [25] . Here we have generated a miR-10a knock out ( KO ) mouse and crossed it with the ApcMin colon cancer mouse model of familial adenomatous polyposis . Interestingly , only in female mice the specific lack of miR-10a sensitized the intestinal epithelium to increased tumor development . Lactoperoxidase ( Lpo ) was strikingly deregulated between miR-10a KO and WT intestines and our data suggests that LPO is an indirect target of miR-10a , being directly regulated by the transcription factor and primary miR-10a target KLF4 . Compellingly , Lpo has previously been reported to have the capacity to oxidize estrogenic substrates into potent depurinating mutagens , which are known to contribute to the initiation of cancer .
To assess the physiological role and pathophysiological significance of miR-10a , we generated a null allele of miR-10a by gene targeting . The targeted locus consisted of a loxP-flanked neo selection cassette , which replaced the 70 central nucleotides of the pre-miRNA sequence of miR-10a ( Figure 1A ) . The targeting vector was introduced into embryonic stem ( ES ) cells , selected with G418 and correctly targeted clones with the genotype miR-10a+/neo were identified by Southern blotting ( data not shown ) . Chimeric mice were generated that transmitted the mutated allele through the germ line . All offspring were genotyped and verified by PCR ( Figure 1B ) . Breeding of miR-10a+/neo mice to a mouse strain holding an ubiquitously expressed Cre recombinase transgene [26] resulted in deletion of the miR-10a genomic sequence and its replacement by a residual LoxP site , yielding mice with the miR-10a+/− genotype ( Figure 1B ) . Mice carrying the miR-10a floxed allele ( miR-10a− ) were intercrossed with C57BL/6 mice for at least 7 generations before generating experimental cohorts . Interbreeding of heterozygous miR-10a+/− mice produced homozygous null ( miR-10a−/− ) offspring at the expected Mendelian ratios ( Figure S1A ) . These mice were indistinguishable from littermate controls in terms of growth and development ( Figure S1B ) and did not show decreased survival or an increased incidence of spontaneous tumor development compared to WT mice by 2 years of age . Likewise , gross pathological examination of the major organs revealed no differences and analyses of embryo fibroblast cultures did not show differences in proliferation rates or time to replicative senescence ( data not shown ) . To confirm that the mutant allele was null , quantitative RT-PCR was performed on RNAs extracted from the intestines of WT and homozygous ( miR-10a−/− ) mutant mice ( Figure S1C ) . qRT-PCR using specific primers for miR-10b on the same RNA showed no significant difference in the level of this close member of the miR-10 family , suggesting no occurrence of dose-dependent compensation via trans-regulation of miR-10b in the absence of miR-10a ( Figure S1D ) . HoxB4 is located 992 nucleotides downstream from the miR-10a gene and the transcription of both genes has been proposed to be co-regulated [6] , [7] . Deletion of miR-10a did not interfere with transcription of HoxB4 since similar levels of HoxB4 mRNA were detected by quantitative RT-PCR in intestinal samples ( Figure S1E ) . Since miR-10a inactivation alone did not give rise to increased spontaneous tumor formation , we evaluated if the lack of this miRNA could modify tumor formation upon an additional oncogenic injury . Profiling of WT mouse tissues for miR-10a and miR-10b revealed that miR-10a was relatively highly expressed in the mouse intestinal tract ( Figure S2 ) . Furthermore , profiling studies have shown that miR-10a expression is deregulated in human colon cancer [9] , [24] , [25] . Therefore , the ApcMin mouse model [27] was chosen to evaluate the role of miR-10a in intestinal neoplasia development . ApcMin mice carry a mutation in the murine homolog of the human APC gene [28] and develop multiple intestinal tubular adenomas similar to those found in patients with the familial adenomatous polyposis syndrome . Furthermore , the ApcMin mouse model is frequently used to evaluate the significance of genetic modifiers [29]–[32] . To examine the impact of disrupting miR-10a in ApcMin mice , the miR-10a KO and ApcMin mouse strains ( both in a C57BL/6 background ) were intercrossed . Mice were sacrificed at around 140 days of age for tumor burden evaluation . Strikingly , the mean tumor multiplicity in small intestines of miR-10a−/−;ApcMin female mice ( 79 . 33 , n = 15 ) was almost twice as high as in corresponding miR-10a+/+;ApcMin age matched controls ( 41 . 95 , n = 22 ) ( p = 0 . 0042 ) ( Figure 2A ) . Tumor multiplicity ( TM ) and tumor incidence ( TI ) in the large intestine were also higher in the female mice lacking miR-10a ( TM = 2 . 40 , TI = 72 . 2% ) , compared to control ( TM = 0 . 82 , TI = 54 . 2% ) but those differences were less significant or not significant , respectively ( pTM = 0 . 014; pTI = 0 . 38; Figure 2B ) . Noteworthy , the miR-10a genotype did not affect tumor multiplicity in ApcMin male mice irrespective of the anatomic location ( psmall intestine = 0 . 61 , plarge intestine = 0 . 16; Figures 2A and 2B ) . The incidence of polyps in the large intestine of male mice also remained unaffected ( p = 0 . 30 ) . To examine the effect of miR-10a deficiency on tumor size , the flat adenomas of the small intestine were measured at their largest diameter , and this measure was used as indicator of tumor size . No significant difference was observed in mean tumor diameters between miR-10a−/−;ApcMin and miR-10a+/+;ApcMin control mice irrespective of gender ( p = 0 . 614 for males and p = 0 . 071 for females; Figure 2C ) . Entire intestinal tracts were paraffin-embedded as “Swiss rolls” and hematoxylin and eosin ( H&E ) stained sections were examined microscopically based on pathological criteria . Qualitatively , compared to miR-10a+/+;ApcMin mice intestines , samples from miR-10a−/−;ApcMin mice presented more frequently adenomas with high-grade dysplasia and a higher incidence of tubulo-villous adenomas , these differences were more evident in female mice ( Figure 2D ) . However , no invasive carcinomatous processes were observed in any of the analyzed samples . The increased tumor multiplicity and colonic epithelial dysplasia along with unaffected adenoma sizes in the absence of miR-10a , suggest that miR-10a is involved in the tumor initiation/promotion steps but not in enhancing cell proliferation in the ApcMin model of intestinal neoplasia [33] , [34] . However , due to ethical constraints , the mice are sacrificed at a relatively young age and we cannot formally rule out that an effect on tumor progression would be discernable in the end-stage tumors . miRNA exert their biological functions primarily by regulating the translation and stability of targeted mRNAs [35] , [36] . Microarray analysis of deregulated transcripts upon alteration of individual miRNAs in cells and tissues has been proven as a useful tool for identifying direct and indirect miRNA targets [37] , [38] . Therefore , colon mRNA expression was analyzed in miR-10a KO and WT female mice using Affymetrix microarrays . Although 452 transcripts were significantly deregulated in the miR-10a KO samples compared to WT ( P≤0 . 05 ) , the levels of up- or downregulation were modest and only three protein coding genes had false discovery rates ( FDR ) lower than 15% ( Table S2 ) . In addition , we did not detect any enrichment for predicted miR-10a targets among the deregulated transcripts . Although adaptation to loss of miR-10a or functional redundancy by the remaining miR-10b could account for the invariable levels of miR-10 intestinal targets in the absence of miR-10a , low levels of mRNA deregulation upon miRNA alterations have been previously observed [39] , [40] . Furthermore , the variation inherent to tissue samples may shadow a high deregulation within a specific cell type of the tissue . With the exception of Lpo , qRT-PCR measurement of selected transcripts , using independent sample sets , did not show any consistent deregulation of genes identified by the microarray as variant between genotypes . Transcript abundance of selected oncogenes and tumor suppressors , relevant in intestinal tumorigenesis or previously predicted as miR-10a targets but not detected in the microarray , were also unchanged in miR-10a deficient compared to WT intestines ( Figure S3 ) . Interestingly , Lpo was identified as exceptionally highly upregulated in the intestines of miR-10a KO female mice , displaying a 9 . 44 fold increase in expression compared to WT ( p = 1 . 1e-6; adjusted for multiple testing ) . Lactoperoxidase normally plays a role in antimicrobial defense and removal of toxic hydrogen peroxide [41] , [42] . However , this enzyme has also been shown to catalyze the activation of endogenous and xenobiotic compounds , such as estrogens and arylamines , into potent depurinating mutagens [43]–[46] . By increasing genome instability , LPO has been proposed to exert a pro-oncogenic role in tissues like the mammary gland [43] , [47] . Given the importance of genome stability in the initiation and progression of intestinal tumorigenesis [48] , a similar mechanism might be involved in the phenotype observed in miR-10a−/−;ApcMin female mice , i . e . the upregulation of Lpo in miR-10a KO mice would enhance estrogen oncogenic activation , leading to a highly instable genomic environment . qRT-PCR confirmed Lpo overexpression by 29-fold in female miR-10a KO intestines compared to WT ( Figure 3A ) . Similar degrees of Lpo upregulation were obtained in male miR-10a KO mice ( data not shown ) . Accordingly , analysis of protein extracts from miR-10a KO and WT intestines equally revealed a strong induction of Lpo in miR-10a deficient samples ( Figure 3B ) . In agreement with the analysis of Lpo mRNA and Lpo protein level , a clear difference in both intensity and distribution area of Lpo staining was observed between miR-10a KO and WT mice ( p≤0 . 006 , Pearson chi-square test with exact probability ) . Consistently , all stained WT samples had faint Lpo signal in a limited area of the intestine thus scoring low expression while the majority of miR-10a KO tissue samples had a significantly more intense Lpo signal and a more widespread Lpo expression pattern , thus scoring medium to high ( Figure 3C and 3D ) . No bona fide miR-10a binding sites could be identified in the 3′ UTR of Lpo but cryptic sites in the 5′ UTR and coding sequence ( CDS ) carried significant complementarity to miR-10a ( Figure S4A ) . To determine whether Lpo was a direct target of miR-10a , via the putative binding sites identified in the 5′ UTR and the CDS of the gene , luciferase reporters holding the 5′ UTR , the entire CDS or a fragment of the CDS containing the most potent binding site were constructed . However , none of the reporters were affected by co-transfection with a miR-10a duplex ( Figure S4B ) , suggesting that the identified sites were not functional miR-10a targets in this set-up . Further qRT-PCR analysis of Lpo transcripts in intestinal samples using primers in intronic and exonic sequence elements , revealed that the primary transcript of Lpo was upregulated in miR-10a KO samples to similar levels as the Lpo mRNA ( Figure S4C and S4D ) , indicating that Lpo deregulation is transcriptional . Altogether , these results suggest that Lpo is not directly regulated by miR-10a via cognate interaction with target sites in the mRNA but instead that Lpo is regulated at the transcriptional level , probably by one or several primary targets of miR-10a . We hypothesized that one or more transcription factors under miR-10a regulation could be responsible for enhancing LPO transcription . To identify such transcription factors , we scanned a 2 kb region upstream of the LPO transcription start site ( TSS ) using Consite and Transfac databases for transcription factor binding site motifs . From the obtained lists of transcription factors , we extracted those that were predicted as putative miR-10a targets by TargetScan [49] and pursued the analysis of one interesting candidate: Krüppel-like factor 4 ( Klf4 ) . Klf4 , a zinc finger-type transcription factor primarily expressed in the gastrointestinal tract , is an important regulator of differentiation and cell growth arrest of the colonic epithelium and was previously shown to be regulated by miR-10a [50] , [51] . Upregulation of KLF4 has formerly been observed in early stages of colon carcinoma compared to normal mucosal levels [52] . Using an in vitro setup , in the epithelial-like colon carcinoma cell line HCT-116 , we demonstrated a 50% reduction of KLF4 mRNA abundance upon transfection with miR-10a ( Figure 4A ) . Importantly , miR-10a mediated repression of this target was also observed at the protein level ( Figure 4B ) . These results confirm KLF4 as a target of miR-10a as previously described [50] . To investigate the link between KLF4 and LPO we cloned a 1 kb fragment upstream of the TSS of LPO , holding core promoter elements , in front of a luciferase reporter . Co-transfection of this reporter vector with a KLF4 overexpression plasmid in HCT-116 cells resulted in a robust upregulation of luciferase activity , demonstrating the capacity of KLF4 to regulate LPO ( Figure 4C and 4D ) . Using siRNA-mediated knockdown of KLF4 we further linked KLF4 to the regulation of LPO . Although these assays are complicated by a cell density-dependent expression of LPO , we found that knockdown of KLF4 in HCT-116 cells markedly reduced LPO mRNA levels ( Figure 4E ) . Primary miRNA targets commonly show a relatively low degree of regulation on mRNA level after depletion of the miRNA . It can particularly be difficult to detect these changes in tissue samples that already present a considerable variation in mRNA expression between individuals and within different cell types of the tissue . In these types of experiments it is therefore crucial to use a sufficiently large population to reach statistical significance . In our microarray experiment we used intestinal RNA from only 6 animals ( miR-10a KO; n = 3 and WT; n = 3 ) and we hypothesized that the limited population could account for the lack of detectable Klf4 de-regulation . To increase the statistical power we measured intestinal Klf4 mRNA levels from 16 miR-10a KO and 13 WT mice by qRT-PCR and used four housekeeping genes for normalization . The results showed a significant increase of Klf4 mRNA levels in the miR-10a KO mice compared to the WT ( Figure 5A ) . Consistently , Klf4 stainings demonstrated a marked increase of protein distribution and intensity in the mouse intestines of miR-10a KO compared to WT ( Figure 5B and 5C ) . Specificity of Klf4 staining was verified by an independent antibody ( Abcam; ab151733 ) , which gave a similar expression pattern but resulted in an overall weaker staining ( data not shown ) . Hence , our in silico , in vitro and in vivo results support the existence of a regulatory network linking miR-10a to LPO via KLF4 and potentially other transcription factors .
The enormous gene regulatory potential of miRNAs is well demonstrated by many studies showing that perturbed miRNA expression is capable of affecting diverse cellular functions and could ultimately cause disease , including cancer . However , it has been suggested that miRNAs are primarily important in fine-tuning mRNA expression and regulation executed by single miRNAs are in most cases not sufficient to account for pathological phenotypes [40] , [53] . In line with this , KO of individual miRNAs in other animal models have revealed that most are devoid of obvious phenotypes in the absence of additional lesions or stresses [54] , [55] . Particular interest in the miR-10 family members arises from their conserved genomic location in Hox clusters and the increasing amount of evidence for their implication in vertebrate biology and human disease [4] . Here we addressed the question of the direct causal effect of miR-10a in mammalian homeostasis , with a special focus on tumor development , by generating miR-10a null mice . Despite the body of evidence suggesting a role for miR-10 in Hox regulation , the miR-10a−/− mice showed an absence of major developmental defects in the posterior trunk . Nevertheless , these results are in agreement with the virtual lack of phenotypic differences upon inhibition or overexpression of miR-10 during zebra fish development [7] . In the case of miR-10a−/− mice , the lack of appreciable phenotypes could be explained by redundancy and functional compensation by miR-10b , which levels remained unaffected in miR-10a KO mice . A double inactivation of miR-10a and miR-10b would allow disambiguation of the miR-10 role in mammalian development . Interestingly , one gene , Lactoperoxidase , consistently showed an exceptionally high degree of deregulation in the intestines of miR-10a deficient mice and to our knowledge this is the first report correlating this gene in to a specific miRNA deficiency . Lpo is mainly described as an antibacterial agent [41] , [56]–[58] exclusively found in mucosal surfaces , including colon epithelium [59] and exocrine secretions , like milk , tears , and saliva [60] . Importantly , apart from its antimicrobial and hydrogen peroxide detoxification activities , the role of LPO in carcinogenesis , particularly of the mammary gland , has been intensively studied [43]–[45] , [61] , [62] . In the reduction of peroxides , peroxidases can co-catalyze the oxidation of aromatic and heterocyclic amines into electrophilic metabolites with DNA binding capacity . In this way , Lactoperoxidase can catalyze the mutagenic activation of diverse endogenous and xenobiotic carcinogens , including natural [44] , [62] and synthetic estrogens [63] as well as other synthetic or environmental arylamines [43] , [45] . Lpo-mediated activation of such compounds to the derivative quinones and semiquinones , has been shown to induce the formation of the depurinating adducts N3Ade and N7Gua in vitro and in vivo [44] , [45] , [61]–[63] . Subsequent error-prone base excision repair mechanisms may lead to mutations that can be initiating events in breast , prostate and other types of cancer [47] . Furthermore , estradiol and its catechol metabolites have been shown to induce deletions and loss of heterozygosity in epithelial breast cells resulting in an oncogenic transformed phenotype [64] . Here we showed that Lpo is constitutively upregulated in the intestinal epithelium of miR-10a−/− mice and that when these mice were crossed with ApcMin mice , females displayed a significantly increased tumor burden in their intestinal epithelium . We therefore propose that , in our set-up , upregulated Lpo induces a mutagenic environment by increasing the oxidation of endogenous estrogens into their mutagenic derivatives , which subsequently leads to tumor formation . Consistently , this effect would be sex-dependent , since estrogens are intrinsically at higher concentrations in female relative to male mice . Similar to our observations , other genetic studies with ApcMin mice have shown that mutation of genes important for genome stability maintenance , generally lead to an increase in adenoma multiplicities [65]–[68] . Regarding the regulatory mechanism , the Lactoperoxidase mRNA does not contain a miR-10a target site in its 3′UTR . Functional interactions between microRNAs and target sites in other locations than the 3′UTR have been described before , including for miR-10a [69]–[74] . We therefore tested the functionality of putative miR-10a binding sites in the 5′UTR and coding region of LPO , however , our results led us to exclude the possibility of a direct posttranscriptional regulation of LPO by miR-10a . Instead we obtained evidence supporting a model where LPO expression is regulated by the primary miR-10a target KLF4 , which is indeed over-expressed in the intestines of miR-10a KO mice . Putative binding sites for this transcription factor are present in the promoter of LPO , and transcriptional activation of LPO could be reproduced in vitro by over-expressing KLF4 . Moreover , siRNA knockdown of KLF4 in HCT-116 cells resulted in downregulation of LPO expression . Of notice , although not tested experimentally , our bioinformatics approach identified other transcription factors representing primary miR-10a targets with putative binding sites in the LPO promoter , suggesting that additional factors may participate in the indirect regulation of LPO by miR-10a . This could , in part , explain the lack of detected variation in the expression levels of direct miR-10a targets ( including Klf4 ) in our microarray experiments , since the occurrence of small degrees of deregulation of primary targets could have a measurable effect only upon convergence in a secondary node . Alternative explanations could be evoked due to the large variation inherent to tissue samples and the fact that the colon tissue comprises a variety of cell types , such as epithelial , luminal and muscular cells , which all have specific genetic programs , thus very different transcriptomes . A change in mRNA expression in one cell type could therefore be masked by the lack of change in another , which could be due to alternative miRNA-independent regulations or a potential rescue by miR-10b . Nevertheless , transcriptomic , bioinformatics and biochemical evidence allowed us to reveal a regulatory network where miR-10a can indirectly alter the levels of LPO through KLF4 . Interestingly , intestinal miR-10a expression has previously been shown to be downregulated by microbiota in mice [75] , and considering the antibacterial functions of Lpo in innate immunity , it is alluring to suggest that miR-10 could function as the sensor of immune stimuli in this environment where its downregulation would induce Lpo as an antibacterial mechanism in normal epithelium . However , having such a defense program constantly activated in the presence of estrogen , as would be the case in the miR-10a−/− female mice , would ultimately be damaging for the cells . Our results are in contrast with reports of miR-10a upregulation in colon cancer samples [24] , [25] . Such upregulation could correspond to a consequence rather than a cause of oncogenic transformation . This is enforced by the observation by Monzo et . al . showing an upregulation of miR-10a in stage II but not in stage I colon cancer samples [24] , though the pathogenic role of miR-10a upregulation in advanced colon cancer remains to be elucidated . Moreover , Klf4 has been described to inhibit cell growth and play a tumor suppressive rather than an oncogenic role in colorectal cancer [51] . However , as suggested in our study , oncogenic downregulation of miR-10a might be a very early event promoting cellular transformation by a similar mechanism to the one previously described for Lpo in mammary carcinoma [44] . Furthermore , and as mentioned above , not only Klf4 but likely other primary targets of miR-10a are also involved in the regulation of Lpo . In summary , here we present evidence that miR-10a , through a complex regulatory network involving the transcription factor Klf4 , can contribute to tumor formation in female mice . By the indirect upregulation of Lpo levels in the intestinal epithelium , miR-10a deficiency in these mice creates an environment where estrogen could be transformed into potent depurinating mutagens that can ultimately lead to the initiation of cancer and tumor formation . Therefore we suggest that miR-10a may serve as a potential diagnostic marker for identifying groups of women that are at high risk of developing colorectal cancer .
The miR-10a targeting plasmid was constructed using standard recombineering techniques . R1-129 mouse ES cells were electroporated with the linearized targeting plasmid . G418 clones were selected and grown independently . Each clone was genotyped by southern blotting using the probes 5′/PacI and 3′/NsiI ( Table S1 ) and PacI- or NsiI-digested DNA to verify 5′and 3′recombination respectively . All mice were handled according to good animal handling practices and the animal experiments approved by the Danish Animal Experiments Inspectorate . miR-10a KO mice were generated as follows . ES cells from one of the three screened and correctly targeted clones were microinjected in C57BL/6 blastocyst and implanted in foster mothers . The resulting germline chimeras were bred with C57BL/6 ( B6 ) to generate heterozygous mice for the targeted allele . F1 miR-10aneo/+ males were crossed to a ubiquitously expressing Cre mouse line to eliminate the neo resistance cassette by Cre-LoxP recombination . Mice carrying the miR-10a floxed allele ( miR-10a− ) were intercrossed with B6 mice for at least 7 generations before generating experimental cohorts of homozygous mutant and control mice . All mice were genotyped by PCR using tail-tip DNA and primers described in Figure 1A and Table S1 , PCR conditions were 95°C for 5 min followed by 35 cycles of denaturation at 95°C for 30 sec , annealing at 56°C for 30 sec , extension at 72°C for 1 min and a final extension step at 72°C for 7 min . ApcMin mice were obtained from The Jackson Laboratories and are described elsewhere [27] , [28] . Only males were used for breeding with miR-10a+/+ or miR-10a−/− mice . Genotyping of the Min allele was performed by PCR using the primers APC . fw , APC . rw and APC . Min under the same conditions described above . All primer sequences are shown in Table S1 . Mice had free access to food and water . HCT-116 cells were maintained in McCoy's 5A medium ( Gibco ) with 10% fetal bovine serum ( Thermo Scientific ) and 1% penicillin/streptomycin ( Invitrogen ) , incubated at 37°C in 5% CO2 . Total RNA was isolated from mice tissues with TRIzol ( Invitrogen ) , as specified by the manufacturer , followed by DNase I treatment with the DNA-free kit ( Applied Biosystems ) . HCT-116 cells were seeded in 6-well plates and reversely transfected with 50 nM of Allstars negative control ( Qiagen; cat:1027281 ) , a miR-10a duplex ( Ambion; AM17100 , PM10787 ) or siRNAs against KLF4 ( pool of two siRNAs with sequences: CCUUACACAUGAAGAGGCA[dT][dT] , GUGGAUAUCAGGGUAUAAA[dT][dT] , 25 nM each ) using Lipofectamine2000 ( Invitrogen ) . Cells were harvested 48 h or 72 h post-transfection for total RNA extraction using the miRNeasy Kit ( Qiagen; 217004 ) . Mature miRNA specific qRT-PCR were performed using the TaqMan miRNA Assays ( Applied Biosystems ) for mmu/hsa-miR-10a and mmu/hsa-miR-10b; U6 small nuclear B non-coding RNA ( RNU6B ) and miR-184 were used as endogenous controls for normalization . For mRNA quantifications , first strand cDNA was synthesized from total RNA with Multiscribe Reverse Transcriptase ( Applied Biosystems ) and random hexamers . qRT-PCR was performed with the Fast SYBR Green master mix ( Applied Biosystems ) for KLF4 and Lpo mRNA and gene specific primers are described in Table S3 , ACTB , Ubc , 36b4 and Hprt was used for normalization in mRNA relative quantifications . qRT-PCR for human LPO was performed with the TaqMan Gene Expression Assays ( Applied Biosystems; AssayID: Hs00413417_m1 ) and ACTB ( AssayID: Hs03023943_g1 ) was used for normalization . All PCR reactions were done in an Applied Biosystems 7900HT Fast Real-Time PCR System using SDS software . The comparative CT method was used for relative quantification of all RNA species evaluated . Mice were killed by CO2 asphyxiation or cervical dislocation . Then entire intestinal tract was removed and gently washed with cold PBS using a syringe before infusing them with 10% formalin; samples were kept at 4°C before being analyzed . After washing in PBS , intestines were opened lengthwise and examined under a dissection microscope equipped with a graduated ocular graticule at 20× magnification for polyp counting and measurement . The entire intestine was rolled and embedded in paraffin for further histopathological and histochemical evaluation . Paraffin embedded tissues were sectioned , rehydrated and stained using a standard H&E staining protocol . H&E stained sections were blindly examined for scoring of adenoma types and degree of dysplasia based on pathological criteria . For immunohistochemical detection of LPO a polyclonal rabbit-anti-human LPO antibody ( Thermo Fisher Scientific , Rockford , USA , PA1-46353 ) was used diluted 1∶200 where chromogen staining was achieved using the EnVision+ system ( Dako , Glostrup , Denmark , K4003 ) in combination with NovaRED HRP substrate ( VWR international , Herlev , Denmark , SK-4800 ) . Stained sections were scanned using a NanoZoomer-2 . 0HT ( Hamamatsu , Denmark ) using 40× magnification . Scanned sections were evaluated , blinded in respect to genotype of mice , for low , medium or high Lpo expression . Comparison of Lpo expression between miR-10a+/+ ( n = 5 ) and miR-10a−/− ( n = 10 ) mice was done by Pearson Chi-square test with exact probability and the analysis revealed a significant scoring difference ( P≤0 . 006 ) between the two genotypes . For immunohistochemical detection of Klf4 a polyclonal goat-anti-mouse Klf4 antibody ( R&D; AF3158 ) was used diluted 1∶80 ( 2 . 5 µg/ml ) where chromogen staining was achieved using the EnVision+ system ( Dako , Glostrup , Denmark , K4003 ) in combination with NovaRED HRP substrate ( VWR international , Herlev , Denmark , SK-4800 ) , only 1/3 of normal hematoxylin stain was used to prevent a too strong blue nuclear signal that could bias the analysis . Stained sections were scanned using a NanoZoomer-2 . 0HT ( Hamamatsu , Denmark ) using 40× magnification . To quantify the expression level of Klf4 , whole scanned sections were analyzed by the staining analysis software VisiomorphDP , which is part of the Visiopharm software package ( Visiopharm , Hørsholm , Denmark ) . The formula Apositive/ ( Apositive+Anegative ) * ( 255-Imean ) was used as a measure of the Klf4 expression . Apositive = area of positive cells nuclei , Anegative = area of Klf4 negative cell nuclei and Imean = the mean intensity value ( 0–255 , where the darkest colors have the lowest value ) of the separating color band ( ref . ) . Comparison of Klf4 expression between miR-10a+/+ ( n = 5 ) and miR-10a−/− ( n = 8 ) mice was done by Students t-test and the analysis revealed a significant scoring difference ( p = 0 . 019 ) between the two genotypes . Total RNA was extracted from colon samples of 4 months old , WT and miR-10a−/− female mice . Organs were collected as described in the previous section but after PBS washing , samples were frozen in liquid nitrogen and kept at −80°C until RNA was extracted . Four biological replicates of each genotype were analyzed on Affymetrix microarrays ( GeneChip Mouse Gene 1 . 0 ST Array ) at the Microarray Centre , Rigshospitalet , Copenhagen University Hospital as previously described [76] . Affymetrix probe set intensity of miR-10a KO and WT samples were preprocessed using the aroma package in BioConductor , including steps of background correction , normalization , and summarization by RMA ( Robust Multichip Average ) method . We then applied a non-specific filtering step to exclude those genes showing low overall expression levels , as these genes were unlikely to show down- or upregulation after miRNA transfection . To do this , we required the interquartile range of probe set expression levels to be greater than the first quartile value of the interquartile range of expression levels for all probe sets . The duplicated probe sets mapped to the same genes and the probe sets without entrez gene annotation were also removed by this step . The remaining probe sets were subsequently mapped to gene symbols using the Affymetrix mogene10sttranscriptcluster . db annotation Package . Differentially expressed genes were identified by a moderated t-test with P-value less than 0 . 05 ( 452 transcripts and annotation as listed in Table S2 ) , using Limma [77] package in Bioconductor . We defined three datasets: upregulated set ( 296 transcripts ) with P-values≤0 . 05 and log FC>0 , upregulated set ( 156 transcripts ) with P-values≤0 . 05 and log FC<0 , and no change set containing 323 transcripts from the gene set with P-value near 1 . HCT-116 cells were seeded in 6-well plates and reverse transfected with 50 nM Allstars negative control ( Qiagen; Cat: 1027281 ) or a miR-10a duplex ( Ambion; AM17100 , PM10787 ) using Lipofectamine2000 ( Invitrogen ) . Cells were harvested 72 h post-transfection for protein extraction . Cells or tissues were lysed and homogenized in RIPA buffer ( 150 mM NaCl , 0 . 5% DOC , 0 . 1% SDS , 1% Igepal , 50 mM Tris-HCl pH 8 , 2 mM EDTA ) containing 1 mM Pefabloc ( Roche Applied Science ) and 1× Complete Mini protease inhibitor mixture ( Roche Applied Science ) . 20 µg of protein per lane from was separated on a 4–12% NuPAGE Bis-Tris gel ( Invitrogen ) for cell culture experiments and a 3–8% Tris-Acetate gel ( Invitrogen ) for mouse tissues , followed by transfer to a nitrocellulose membrane . Membranes were blocked in 5% milk for 40 min at room temperature and incubated over night with primary antibody at 4°C . Antibodies used were purchased from Santa Cruz: LPO ( ( H-60 ) sc-134848 ) , KLF4 ( ( H-180 ) : sc-20691 ) . A 1 kb sequence upstream the transcription start site of the LPO gene was cloned into pGL4-luc2 ( Promega ) using the following primer sequences ( restriction sites NheI and XhoI are shown in lowercase letters ) : Fwd: 5′-TCgctagcTTTGCCTGGATTCATCAC-3′ , Rev: 5′- TGctcgagCCTGAGCACATTTGTCCC-3′ . The KLF4 over expressing vector was purchased from Addgene ( pcDNA3 . 1-HA-KLF4-FL; plasmid #34593 ) . HCT-116 cells were seeded in 96-well plates ( 15000 cells/well ) and transfected the next day with 50 ng of pGL4-luc2-LPO promoter or pGL4-luc2 ( empty ) and 50 ng of pcDNA3 . 1-HA-KLF4-FL or pcDNA3 . 1+ ( empty ) using Lipofectamine2000 ( Invitrogen ) . Luciferase expression was measured 24 h after transfection using the Dual-Glo luciferase assay ( Promega , E2940 ) ) . For investigating the potential miR-10a binding sites part of the 5′UTR of Lpo was cloned into psi-CHECK-2 ( Promega ) using primers ( restriction sites NheI is shown in lowercase letters ) : Fwd-5′UTR: 5′-GACgctagcACATCAACTGCTCCCTGACATCCT-3′ , Rev-5′UTR: 5′-CGTgctagcTAAAGGACACACACACTCAGGCTCA-3′ , and either the whole coding sequence ( CDS ) or a 609 bp sequence harboring the best miR-10a CDS binding site was cloned into a Firefly luciferase fusion vector pPK-CMV-F4 ( PromoKine ) using primer sequences ( restriction sites XhoI and HindIII are shown in lowercase letters ) : Fwd-CDS: 5′-GTGctcgagACCATGGTTAAAGTGCTTCTGCATCTCC-3′ and Rev-CDS: 5′-CACaagcttTCCTTCACTGAGGCCCAGGGTG-3′ and Fwd-10a site: 5′- GTGctcgagACCATGGTTATCTGTCAGATTATCTCAAGC-3′ and Rev-10a site: 5′- CACaagcttTCCCCTGAGTCTTTAATTTAGGGTCACC-3′ . HCT-116 cells were seeded in 96-well plates ( 15000 cells/well ) and transfected the next day with 30 nM of miR-10a mimic ( Applied Biosystems; AM17100 , PM10787 ) or control ( Qiagen; Allstars neg control: 1027281 ) and 100 ng of psi-CHECK-2-Lpo-5′UTR alone , or 100 ng of pPK-CMV-Lpo-CDS or pPK-CMV-Lpo-10a-site together with 10 ng pRL-TK ( Promega; Renilla vector used for normalization ) using Lipofectamine2000 ( Invitrogen ) . Luciferase expression was measured 24 h after transfection using the Dual-Glo luciferase assay ( Promega , E2940 ) ) .
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Posttranscriptional regulation by microRNA molecules constitutes an important mechanism for gene regulation and numerous studies have demonstrated a correlation between deregulated microRNA levels and diseases , such as cancer . However , genetics studies linking individual microRNAs to the etiology of cancer remain scarce . Here , we provide causal evidence for the involvement of the conserved microRNA miR-10a in the development of intestinal adenomas in the face of activated Wnt signaling . Interestingly , we find that loss of miR-10a mediates an increase in intestinal adenomas in female mice only and delineate the pathway to involve aberrant upregulation of the miR-10a target Klf4 and subsequent transcriptional activation of the Lpo gene encoding the antibacterial protein Lactoperoxidase . Lpo , in turn , has previously been demonstrated to oxidize estrogens into DNA-damaging mutagens .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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Loss of miR-10a Activates Lpo and Collaborates with Activated Wnt Signaling in Inducing Intestinal Neoplasia in Female Mice
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Animals are constantly exposed to the time-varying visual world . Because visual perception is modulated by immediately prior visual experience , visual cortical neurons may register recent visual history into a specific form of offline activity and link it to later visual input . To examine how preceding visual inputs interact with upcoming information at the single neuron level , we designed a simple stimulation protocol in which a brief , orientated flashing stimulus was subsequently coupled to visual stimuli with identical or different features . Using in vivo whole-cell patch-clamp recording and functional two-photon calcium imaging from the primary visual cortex ( V1 ) of awake mice , we discovered that a flash of sinusoidal grating per se induces an early , transient activation as well as a long-delayed reactivation in V1 neurons . This late response , which started hundreds of milliseconds after the flash and persisted for approximately 2 s , was also observed in human V1 electroencephalogram . When another drifting grating stimulus arrived during the late response , the V1 neurons exhibited a sublinear , but apparently increased response , especially to the same grating orientation . In behavioral tests of mice and humans , the flashing stimulation enhanced the detection power of the identically orientated visual stimulation only when the second stimulation was presented during the time window of the late response . Therefore , V1 late responses likely provide a neural basis for admixing temporally separated stimuli and extracting identical features in time-varying visual environments .
The primary visual cortex ( V1 ) has been used as an experimental model to study cortical responses to sensory input . V1 receives direct synaptic inputs from the lateral geniculate nucleus ( LGN ) of the thalamus and provides the output of its computation to higher-order cortical areas [1 , 2] . This route , commonly known as the feed forward pathway , contributes to the hierarchical neural processing of specific visual features , such as orientation , direction , color , and motion . Classical visual processing models consider V1 as a passive relay station for visual information; that is , V1 encodes instantaneous information by transiently responding to the present stimulus feature . However , recent evidence has demonstrated that V1 activity persists over time [3–7] and even propagates throughout the V1 network [8 , 9] . This complex activity is likely associated with the representation of reward timing [4 , 5] , iconic memory [10 , 11] , and working memory [12–14] . Indeed , reverberatory neuronal activity within neocortical circuitry has been proposed as a potential mechanism for short-term storage of information [15 , 16] . How does V1 encode the external world while under a constant flow of visual stimuli ? The measurement of cortical dynamics has revealed that V1 response tuning evolves with time [17] , during which it may interfere with later V1 information [18] . Indeed , preceding visual stimuli are reported to modulate visual perception after brief stimulus-onset asynchrony ( SOA ) [19–22] . Therefore , poststimulus V1 activity appears to intermingle with the subsequent visual information , which produces a complex output [23–25] . In this study , we discovered a novel V1 activation pattern in nonanesthetized mice; in virtually all V1 neurons , an oriented flashing light–induced biphasic membrane voltage ( Vm ) response that consisted of an early , transient depolarization and a late , slow depolarization . The late response exhibited high orientation selectivity , which indicates that V1 maintains the information of a recent stimulus with high fidelity for some time . Flash-induced late response was also observed using electroencephalogram ( EEG ) recordings in humans , suggesting that a long-delayed V1 reactivation prevails in mammals . To understand the effect of the late response on the upcoming visual input , we paired a flashing stimulus to another visual stimulus with a time lag . Flashes modulated the V1 response to the subsequent input in an orientation-selective manner . The flash-induced selective modulation was also replicated in the psychophysical parameters of mice and humans .
We monitored the spiking activity of V1 layer ( L ) 2/3 neurons of P35–P44 mice using the cell-attached recording technique ( Fig 1A ) and applied a brief flashing stimulus ( 17–50 ms ) of a full-field grayscale sinusoidal grating with one of four orientations ( 0° , 45° , 90° , and 135° ) to the eye contralateral to the recording site . As previous reports have demonstrated that L2/3 neurons fire sparsely [26–30] , 56 . 5% of V1 neurons ( 43 of 76 cells ) exhibited a significant increase in their firing rates in response to the grating flashes ( defined by a criterion of p < 0 . 05 versus the baseline firing rates , Z test for comparison of two counts [31] ) . The responses were classified into two types; the first type of responses was spikes immediately ( < 0 . 3 s ) after the stimulus onset ( early spiking , Fig 1A top ) , whereas the second type was spikes with latencies longer than 0 . 4 s ( late spiking , Fig 1A bottom ) . In the pooled data , the population firing rates exhibited two distinct peaks that corresponded to the first and second types of spikes; for individual responsive neurons , the mean firing rates during the early and late responses were 1 . 27 ± 0 . 91 Hz and 0 . 28 ± 0 . 19 Hz , respectively ( mean ± standard deviation [SD] of 11 and 36 neurons ) . Late-spiking neurons were numerically dominant ( Fig 1B , inset ) . Thus , we defined the early and late responses as activity that occurred between 0–0 . 3 s and 0 . 4–2 s , respectively . To investigate the subthreshold Vm dynamics that underlie the biphasic spike responses , we conducted whole-cell current-clamp ( I = 0 ) recordings from V1 neurons ( S1A and S1B Fig ) . In the typical neuron shown in Fig 1C , a grating flash reliably induced early and late depolarization responses . Remarkably , we observed similar biphasic Vm responses in all 28 recorded neurons ( S1C Fig ) , irrespective of their firing types , including nonspiking neurons ( S1D Fig ) . The early depolarization was transient and peaked at latencies of < 0 . 3 s , whereas the late depolarization was more persistent and peaked at approximately 0 . 4−2 . 0 s . On average , the peak amplitudes of the early and late depolarizations were 6 . 7 ± 4 . 2 and 6 . 4 ± 4 . 4 mV ( mean ± SD of 28 cells ) , respectively , and were correlated with each other ( S1C Fig left ) . The area under curves of individual Vm traces during a late period of 0 . 4–2 . 0 s ( late area ) was correlated with their peak amplitudes ( S1C Fig middle ) . Therefore , we quantified both early and late responses using their peak amplitudes in the following analyses . The areas of late responses were not correlated with their peak latencies ( S1C Fig right ) . Thus , the latencies did not affect the magnitude of late responses . This fact also validates our choice of the time window for late Vm responses ( 0 . 4–2 . 0 s ) . The fact that late depolarizations occurred in all recorded neurons suggests that late visual responses represent a global phenomenon that involves the entire V1 cortex . To confirm this possibility , we recorded local field potentials ( LFPs ) , which reflect the compound activity of multiple neurons surrounding the tip of a recording electrode [32] . We found that LFPs in V1 L2/3 responded reliably to a grating flash with biphasic negative fluctuations ( Fig 2A ) . The response signal , if any , was less evident in LFPs recorded from the retrosplenial cortex , a more anterior brain region . We also recorded voltage dynamics of the neocortical surface . We loaded the cerebral surface with RH-1692 , a voltage-sensitive dye ( VSD ) , and monitored the spatiotemporal patterns of flash-evoked activity [33] . As expected by the LFP data , early cortical VSD responses were observed in V1 ( S2 Fig ) . Then , the VSD signal decreased transiently , producing a transitional period . After approximately 0 . 4 s , the late VSD responses also arose at V1 . Therefore , similar to Vm responses in patch-clamp recordings , the VSD signal in V1 was biphasic . We extended the field potential work to visual responses in humans . We recorded EEG from 10 adult participants and measured visual event-related potentials ( ERPs ) at O1 and O2 , according to the international 10/20 coordinate convention [34] . Human ERPs in response to grating flashes were also biphasic; an early and late negative reflection peaked around 0 . 15 s and 0 . 7 s , respectively , after a grating flash ( Fig 2B ) . Previous studies have also reported a specific form of late , slow activation of the rat V1 [4 , 5] and the mouse primary somatosensory cortex [35]; however , these responses emerged as a result of sensory reinforcement learning and were not observed in naïve animals . There is also a study that has reported biphasic responses in naïve cat visual cortex [36]; however , the latency and the duration of this late response was much shorter . By contrast , our flash-evoked late V1 responses occurred in naïve animals and had a much longer latency and duration . Therefore , they represent novel V1 dynamics . This discrepancy most likely occurs as a result of the difference in the features of visual stimuli . Indeed , the durations of flashes were critical [7]; we failed to observe evident long-delayed LFP activity at flash durations of more than 200 ms ( Fig 2C ) . Moreover , we used full-field flashes , which might recruit synaptic inputs from both classical and nonclassical visual receptive fields . It should also be noted that flash-induced late response has a much longer duration than the well-known OFF response that has been described in other studies [37] . The amplitudes of both early and late responses increased at higher contrasts of flash gratings ( S3 Fig ) . Thus , it is feasible that the late responses encode the orientation of flashing stimuli [36] . We measured the orientation selectivity , which is a characteristic of V1 neuron responses [38–41] . Grating flashes with various orientations induced different changes in the late spike rates ( Fig 3A and S4A Fig ) . We calculated the orientation selectivity index ( OSI ) for each late-spiking neuron . On average , the OSIs were 0 . 37 ± 0 . 25 ( mean ± SD of 36 cells ) . To evaluate the statistical significance of OSIs , we compared them with the chance distribution obtained from the trial-shuffled surrogate data ( Fig 3B ) . Overall , the OSIs exhibited significantly higher values than chance , which indicates that the late-spiking responses were orientation-selective ( p = 3 . 3 × 10−3 . D = 0 . 29 , n = 36 cells , Kolmogorov-Smirnov test ) . Late subthreshold Vm responses were also significantly orientation-selective ( Fig 3C and 3D , p = 2 . 7 × 10−9 , D = 0 . 66 , n = 34 cells , Kolmogorov-Smirnov test ) . Their OSIs were lower compared with the late spike responses ( S4B and S4C Fig , p = 5 . 0 × 10−3 , t19 = 3 . 17 , n = 20 cells , paired t test ) , consistent with many previous reports about orientation selectivity of Vm responses [42–44] . Because the early responses were also orientation-selective , we focused on the tuning properties of the early and late responses . We computed the correlation coefficients between the early and late Vm tuning curves of each cell and compared the pooled data to the chance-level distribution of the correlation coefficients in their trial-shuffled surrogates . The correlation coefficients were significantly higher compared with chance , which indicates that the early and late Vm responses of each neuron had a similar orientation tuning ( Fig 3E , p = 0 . 014 , D = 0 . 27 , n = 34 cells , Kolmogorov-Smirnov test ) . Moreover , the OSIs of late responses were positively correlated with the OSIs of early responses ( Fig 3F , R2 = 0 . 61 , p = 1 . 2 × 10−4 , t17 = 4 . 94 , t test for a correlation coefficient ) . Note that neither early nor late OSIs depended on firing rates ( S4A Fig , p = 0 . 490 , R2 = 0 . 01 ) . We thus conclude that late responses conveyed selective information of visual stimuli . We further confirmed flash-induced responses using two-photon calcium imaging . We loaded V1 L2/3 neurons with Fura 2 by pressure-applying its acetoxymethyl ester ( AM ) derivative ( S5A Fig ) . The amplitude of a spike-elicited calcium elevation ( |ΔF/F| ) was nearly linear with the number of action potentials involved in the calcium event ( S5B Fig ) . Note that our imaging system was able to resolve two action potentials at an interspike interval of less than 400 ms ( S5C Fig ) , allowing us to classify early and late spiking neurons . We imaged spike-triggered calcium events en masse from 64 . 6 ± 6 . 04 neurons per video ( mean ± SD of nine videos from nine mice ) with a single-cell resolution at five frames per s ( S5D Fig ) . In the example neuron shown in S5E Fig , the amplitudes of the ΔF/F responses evoked by grating flashes exhibited orientation selectivity . Of the 581 neurons , 323 ( 56% ) neurons were responsive to flashes , and the preferred orientations were uniformly distributed ( S5F Fig ) . Because early spiking responses occurred around 0 . 1–0 . 2 s after a flash , they would be reflected in a rapid ΔF/F increase in the first video frame ( 0 . 2 s ) after the stimulus . According to this definition , we estimated that early spiking neurons contributed 10 . 0% ( 58 out of a total of 581 cells ) , consistent with patch-clamp recording data showing that the majority of flash-responsive neurons are of the late-spiking type ( Fig 1B inset and S1D Fig ) . Therefore , we assumed that most ΔF/F responses reflected putatively late spikes . Although they may overlap with the early-spiking component , the orientation tuning properties were approximately congruent between the early and late responses ( see Fig 3E ) , and thus , the ΔF/F response tuning is still thought to reflect the late-spiking tunings . Consistent with this notion , the distribution of OSIs in the ΔF/F responses was similar to the late-spiking responses obtained by patch-clamp recordings ( S5G Fig , p = 0 . 497 , D = 0 . 15 , Kolmogorov-Smirnov test ) and was higher than that of their surrogate data ( p = 2 . 3×10−6 , D = 0 . 15 , n = 323 cells ) . Because the late response has a long latency , it may interact with a subsequent visual stimulus . We tested this idea by recording the ΔF/F responses to grating stimuli that moved for 2 s toward one of eight directions ( 0° , 45° , 90° , 135° , 180° , 225° , 270° , and 315° ) , which were presented alone ( Drift-only trials ) or 0 . 5 s after grating flashes ( Flash+Drift trials ) . To minimize photobleaching and phototoxicity , we did not test all possible combinations of the flash orientations and the drifting grating directions; instead , we fixed the grating flash orientation to 0° ( vertical orientation; vFlash ) and reduced the total imaging period ( S6A Fig ) . We compared the ΔF/F responses between Flash+Drift and Drift-only trials and examined how the preceding vFlash ( prime ) modulated the ΔF/F responses to subsequent drifting gratings ( target ) . The combinational pattern of a vFlash stimulus and a drifting grating was described as a Δorientation , which represents the orientation difference between vFlash and the drifting gratings and comprised a value of −45° , 0° , 45° , or 90° ( = −90° ) . In Drift-only trials , Δorientation indicates the difference between 0° and the orientations of drifting gratings ( i . e . , the absolute orientation ) . S6B Fig summarizes the data from a representative neuron . For each Δorientation in Drift-only and Flash+Drift trials , we statistically judged whether the neuron responded , i . e . , whether the ΔF/F amplitude was significantly higher compared with the baseline ΔF/F fluctuation ( p < 0 . 05 , n = 10−18 trials , paired t test ) . The significant responses are marked by dark red boxes below the tuning plot . Three other examples are shown in S6C Fig . We pooled the data from the 581 neurons ( S6D Fig ) . For each Δorientation , we compared the number of cells that exhibited significant ΔF/F in Drift-only trials to the number of significant cells in Flash+Drift trials . Notably , the number of significantly responsive cells increased at Δorientation = 0° , where the orientations of vFlash and drifting gratings were matched . The number of responsive cells did not increase at the other Δorientations . Thus , two sequential stimuli with the same orientation activated V1 neurons more efficiently compared with stimuli with different orientations . By focusing on individual cells that were activated under the iso-orientation condition , we analyzed their intrinsic orientation preferences . Flash-induced response enhancement was more evident in cells whose preferred orientations were different from the stimulus orientation ( S6E Fig ) . These data indicate that a flash recruited otherwise irresponsive cells ( due to their cross orientation preferences ) to a subsequent stimulus with the same orientation as the flash . Previous studies have reported that paired visual stimuli lead to a functional adaptation of neuronal responses to the target [45 , 46] . In other words , visual cortical neurons decrease their responsiveness to repeated stimuli . Calcium imaging did not allow us to strictly quantify the response amplitude , and we could not determine whether the observed changes are adaptation ( desensitization ) or priming ( sensitization ) . To quantify the effect of flashes in more details , we returned to patch-clamp recordings of subthreshold Vm responses . In these experiments , the drifting grating orientation was fixed to vertical ( 0° , 180°; vDrift ) , and the orientations of the preceding flashes varied across four orientations ( 0° , 45° , 90° , or 135° ) in a pseudorandom order ( Fig 4A ) . First , the SOA was set to be 0 . 5 s ( Fig 4B ) . We compared the amplitudes of Vm responses to a combination of flash and vDrift stimuli ( Flash+vDrift ) with those of the responses to vDrift alone ( vDrift-only ) . On average , the absolute amplitudes of Flash+vDrift responses were larger than those to vDrift-only responses ( p = 0 . 012 , t51 = 2 . 60 , paired t test ) ; however , for individual neurons , the amplitude relations depended on the amplitudes to responses to Flash alone ( Flash-only , Fig 4C ) . That is , when a neuron exhibited a large depolarization in Flash-only trials ( >2 mV ) , then the depolarization in Flash+vDrift trials was more increased compared to vDrift-only responses . On the other hand , when a neuron exhibited a small depolarization in Flash-only trials ( <2 mV ) , the Flash+vDrift response amplitude was nearly comparable to the vDrift-only response amplitude . To further examine this effect , we employed a new analysis in which we compared Flash+vDrift responses with the linear summation of the Flash-only response and the vDrift-only response ( Fig 4D ) . We found that this augmentation occurred below the value of simple arithmetic summation of two responses . That is , individual responses to Flash-only and vDrift-only stimuli were sublinearly integrated in Flash+vDrift trials ( Fig 4D ) . In our experimental conditions , therefore , a flash facilitated the vDrift responses through a sublinear integration of Vm depolarizations . Notably , their sublinearity differed depending on the orientations of flash gratings and was smaller at Δorientation = 0° than at 90° ( Fig 4D ) . In other words , when two orientations of flash gratings and drifting gratings were matched , the combined responses were less sublinear , thereby exhibiting apparently larger response amplitudes , which is consistent with the flash-induced enhancement in the calcium imaging experiments . This Δorientation-dependent difference was not found at SOAs of 0 . 05 or 3 s ( Fig 4D ) , suggesting the involvement of the orientation selectivity of flash-induced late responses . We replotted these sublinear behaviors ( SOA = 0 . 5 s ) as a function of the difference between their intrinsic orientation preferences and the orientation of the grating stimuli . Flash-induced response sublinearity was the largest in cells whose preferred orientations were identical to the stimulus orientation ( Fig 4E ) . This was also consistent with the results in calcium imaging . Flash-induced modulation of V1 neuronal activity prompted us to evaluate its behavioral consequences . We first measured the visual performance of mice using a virtual optomotor test , which can assess the visual detection ability of naïve mice without behavioral training [47] . A freely moving mouse was placed on the circular platform surrounded by four computer screens on which vertically orientated gratings moved leftward or rightward for 2 s ( Fig 5A; vDrift ) . As a visuomotor reflex , the mouse turned its head in the same direction as the vDrift movement , a behavior that is called a tracking response . The ratio of trials with the tracking responses to the total trials was calculated as the tracking rate and was used as a quantitative measure of visual function . Under the baseline conditions ( i . e . , vDrift-only trials ) , the mean tracking rate was 74 ± 13% ( mean ± SD of 10 mice ) . This ratio increased to 86 ± 10% when vertical flashes were presented 0 . 5 s before vDrift ( Fig 5B , Δorientation = 0°; p = 0 . 037 , t9 = 2 . 45 , paired t test ) . This increment was not observed when horizontal flashes ( Δorientation = 90° ) were coupled ( Fig 5B; p = 0 . 92 , t9 = 0 . 10 ) or when vertical flashes were presented at an SOA of 3 s ( Fig 5C; p = 0 . 69 , t10 = 0 . 41 ) . In mice that received local injection of 10 μM tetrodotoxin into the V1 , flash-induced responses in V1 LFP disappeared ( S7A Fig ) . In these mice , the tracking rate for the vDrift-only trials was reduced to 18 ± 16% ( n = 4 mice , p = 0 . 026 versus naïve mice , t3 = 4 . 13 , Student’s t test ) and was not increased by vertical flashes ( S7B Fig ) . Thus , flash-induced increases in the tracking rates likely depend on V1 late responses . Finally , we conducted a psychophysical test in humans . The participants were asked to report the motion directions of 0 . 25-s drifting gratings ( 0° , 90° , 180° , or 270° ) by flicking a computer mouse toward the same direction within 0 . 70 s ( Fig 5D ) . In Flash+Drift trials , grating flashings at orientations of 0° , 45° , 90° , or 135° were presented 0 . 5 s before the drifting gratings . The correct response ratio was approximately 100% and was not modulated by grating flashes with either Δorientation ( Fig 5E; p > 0 . 05 , n = 11 humans , n = 486−500 trials each , Student’s t test ) . However , the latency of the flicking response was significantly shortened at Δorientation = 0 ( Fig 5F; Drift-only: 357 . 3 ± 54 . 6 ms versus 0°: 347 . 8 ± 56 . 0 ms , mean ± SD; P = 0 . 007 , t993 = 2 . 71 ) . We did not think that this effect was due to illusory motion perception , because the grating phase of a flash stimulus and the first frame of the following drifting stimulus were identical . However , to examine the possible involvement of motion illusion , we presented two successive flashes at an SOA of 0 . 5 s with various combinations of the grating phases and asked participants to answer the "felt" motion direction ( S8 Fig ) . Each stimulus condition was repeated for 80 times . As a result , the participants were not able to distinguish the motion direction; the responses were approximately 50% ( = the chance level ) . Thus , two consecutive grating stimuli at an SOA of 0 . 5 s per se did not induce a motion perception .
We discovered that a brief flashing light evokes long-delayed , slow activation of the mouse V1 network . The late response was observed using different techniques , including patch-clamp recording , LFP recording , VSD recording , and EEG recording , which exclude the possibility of our recording artifact . Importantly , the late response actively interacted with subsequent visual input . This novel phenomenon was heretofore overlooked , probably because past studies tended to record visual responses for shorter terms ( up to a few hundreds of milliseconds ) than our work , and because we used a short flash of full-field gratings , a stimulus pattern that is not very common in vision research . Another reason for the overlook of the late responses may be a consensus that visual responses occur within a few hundred milliseconds after the onset of the visual stimulus , which might have prevented an attempt to record visual responses for seconds . There are mainly three candidates for the initiation site of the late response . First , the late activation of V1 circuit might be generated through reverberation of the recurrent circuit within the V1 . Theoretically , cortical activity is sustained by local reverberation within a recurrent network [15 , 16] . Anatomically , L2/3 is enriched with horizontal synaptic connections [48 , 49] and provides the structural basis of a recurrent circuit . Although V1 L2/3 neurons receive synaptic inputs with various orientation preferences [50] , the synaptic connection probability is biased toward a similar orientation preference [51 , 52] . Recent studies have demonstrated that neurons derived from the same precursor cells are more likely connected and share the same orientation preference [53–55] . These observations suggest the existence of fine-scale subnetworks dedicated to process specific information [56] . We determined that the tuning properties were significantly correlated between the early and late responses . Hence , the neuron population activated by a grating flash is preferentially reactivated at the late phase . The visual cortex may filter visual input information through its specifically wired , reverberatory network [57] and may offer a high orientation tuning during the late response . The second possibility is that the V1 rebound activity arose from subcortical regions , including the lateral geniculate thalamus and the superior colliculus ( and even the retina ) . The lateral geniculate thalamus is anatomically eligible for generating rebound activation , because it contains a recurrent network and receives feedback projections from V1 [37 , 58] . This anatomy might have led to the reliable observation of late response even in the LFP recording . Finally , top-down inputs from higher-order cortices may also have the ability to induce late responses , as recently reported in the hindlimb somatosensory cortex [59] . However , the latency of the late response in the visual cortex was much longer than that observed in the study , suggesting a more complex mechanism than a simple top-down feedback process . We speculate that reverberatory activity in V1-recurrent circuits admixes with late-coming feed forward V1 activity . Recent studies have demonstrated that costimulation of the thalamocortical and cortical pathways efficiently depolarizes cortical neurons through nonlinear summation [60 , 61] . Although a single L2/3 neuron receives variously tuned synaptic inputs irrespective of the orientation preference in the cell’s spike output [50] , synaptic inputs over dendritic trees are nonrandomly distributed and are often spatially clustered [62–64] . Thus , synaptic inputs from flashing and drifting gratings may be locally converged and may lead to nonlinear dendritic boost [61 , 65] when two orientations are matched . At the network level , a grating flash enhanced ( or sublinearly integrated ) the V1 responses to subsequent drifting gratings in an orientation-selective manner . In these experiments , we used an SOA of 0 . 5 so that drifting gratings arrived during the period of flash-evoked late responses . Calcium ΔF/F responses to the drifting gratings were enhanced only when their orientations were identical to the preceding flashes . The flash-induced facilitation can be explained by two possibilities . First , the priming effect may facilitate the responses to sequential stimuli [66 , 67] . However , flash-induced response enhancement is not a normal form of priming because it was not a simple mixture of membrane-potential depolarizations . Flash-induced late response and the response to drifting grating were integrated in a sublinear fashion , but more linearly at Δorientation = 0° , suggestive of the partial existence of priming . It also differed depending on preferred orientations of the neurons . The second possibility is that the facilitation occurred through top-down neural processing [68] , especially feature-based attention [69 , 70] . It is well known that attention modulates the responsiveness of neurons that have receptive fields within the attentional loci [71–73] , enhancing task performance on late-coming target stimuli [70 , 74] . Moreover , it is important to note that feature attention in humans is effective at an SOA of approximately 0 . 5 s [69] , consistent with our findings . Developing a psychophysical method to measure the attentional effect in mice may help verify the second possibility . Focusing on individual neurons and their orientation preferences , a flash recruited neurons with shifted-orientation preferences at the Δorientation = 0° condition . In other words , neurons with cross orientated preferences to the flash orientation were less subject to the sublinearity when the responses were integrated . Consistent with this notion , at Δorientation = 90° , neurons with cross orientated preferences to a flash ( i . e . , iso-orientated with regard to the orientation of the drifting stimulus ) exhibited the minimal sublinear property . Thus , flash-induced late responses might function to recruit neurons that are otherwise irresponsive , leading to stronger activation of the V1 . We found that ongoing visual processing and perception were both affected by the immediately preceding visual information in a feature-specific manner; however , we could not directly show the causal contribution of flash-induced delayed depolarizations per se to subsequent visual perception . Optogenetic prevention of the delayed responses [35] is not applicable to our cases; that is , even if optogenetic manipulation is performed only during the delayed activity period , it inevitably affects both flash-induced delayed responses and drifting grating-evoked activity and cannot isolate the effect of the flash responses on visual perception . Therefore , we need to seek a way to specifically diminish the delayed activity without affecting drifting grating-evoked activity . In this study , we regarded the featured flashes as a model of the initial visual scenes and aimed to separate the effect of suddenly coming and subsequently continuing visual scenes . Hence , we think that , under natural conditions , the pattern-selective late responses observed here may work to facilitate the responses to the passing object , possibly linking our findings to studies on trans-saccadic integration [75–77] . However , two major concerns remain unresolved . First , the late response occurred to flashes with durations of less than 50 ms , whereas natural saccades usually last about 300 ms . Thus , we cannot rule out the possibility that the late response we found is involved in other visual processes than trans-saccadic integrations . Second , although we obtained the behavioral correlates of flash-induced effects on visual function , flashes recruited neurons that were otherwise irresponsive because of the nonpreferred orientation . Therefore , flashes may increase the overall activity level of V1 and diminish the selective responsiveness of individual neurons . According to this notion , the facilitation of V1 activity would decrease the discrimination acuity of the animal , but at the same time , it could increase the sensitivity per se by lowering the visual detection threshold . This possibility must be clarified using a new behavioral paradigm that can distinguish visual detection from visual discrimination .
Animal experiments were performed with the approval of the animal experiment ethics committee at the University of Tokyo ( approval number: 21–6 ) and according to the University of Tokyo’s guidelines for the care and use of laboratory animals . In human studies , the experimental protocol was approved by the Human Research Ethics Committee of the University of Tokyo ( approval number: 24–3 ) and the Center for Information and Neural Networks ( approval number: 1312260010 ) . All participants were provided oral and written informed consents , and they signed the consent forms prior to each experiment . Postnatal days ( P ) 28–35 male C57BL/6J mice ( Japan SLC , Shizuoka , Japan ) were used in the animal experiments as previously described in detail [78 , 79] . The animals were housed in cages in standard laboratory conditions ( a 12-h light/dark cycle , free access to food and water ) . All efforts were made to minimize the animals' suffering and the number of animals used . The animals were anesthetized with ketamine ( 50 mg/kg , i . p . ) and xylazine ( 10 mg/kg , i . p . ) . Anesthesia was confirmed by the lack of paw withdrawal , whisker movement , and eye blink reflexes . The head skin was then removed , and the animal was implanted with a metal head-holding plate . After 2 d of recovery , the head-fixation training on a custom-made stereotaxic fixture was repeated for 1−3 h per d until the implanted animal learned to remain quiet . During and after each session , the animal was rewarded with free access to sucrose-containing water . During the final three sessions , sham experiments were conducted to habituate the animal to the experimental conditions and noise . On the final 2−3 d , the animal was maintained virtually immobile , i . e . , quiet but awake , for more than 2 h . After full habituation , the animals were anesthetized with ketamine/xylazine . A craniotomy ( 1 × 1 mm2 ) , centered at 3 . 5 mm posterior to the bregma and 2 . 0 mm ventrolateral to the sagittal suture , was performed , and the dura was surgically removed . The exposed cortical surface was covered with 1 . 7–2 . 0% agar at a thickness of 0 . 5 mm . Throughout the experiments , a heating pad maintained the rectal temperature at 37°C , and 0 . 2% lidocaine was applied to the surgical region for analgesia . For patch-clamp recordings , the recorded area was confirmed by posthoc imaging of the intracellularly loaded Alexa 594 , which was dissolved at 50 μM in patch-clamp solution . For calcium imaging , pressure-injected SR101 , which was dissolved at 0 . 1 mM in Fura 2-containing solution , was imaged posthoc to confirm the recorded area . Recordings were initiated after recovery from anesthesia , which was confirmed by spontaneous whisker movements and touch-induced eye blink reflexes . The total periods of recording were restricted to less than 1 h to minimize stress in the animals . Visual stimuli were generated in custom-written MATLAB routines ( The MathWorks , Natick , MA , USA ) with Psychtoolbox extensions . A 17-in TN-LCD monitor ( refresh rate = 60 Hz ) was placed 30 cm away from the right cornea , so that it covered 38 . 8° horizontally and 29 . 6° vertically of the mouse visual field . For flash stimulation , sinusoidal gratings ( spatial frequency: 0 . 16 cpd; temporal frequency: 2 Hz; contrast: 100% ) were presented in four evenly spaced orientations ( 0° , 45° , 90° , and 135° ) . The flash duration was set to range between 17–50 ms . Measurement using a high-speed CMOS camera ( ORCA-Flash2 . 8 , Hamamatsu , imaged at 2 , 000 Hz ) revealed that a flashing light on the TN-LCD monitor decayed with a time constant τ1/2 = 5 . 5 ms , and thus , the afterglow was virtually ignorable . For each orientation , the gratings were presented at 2–4 spatial phases , and the responses were averaged to remove the effects of spatial phases . Flash stimuli were intervened with a gray screen for intervals of 8–10 s . In each set , stimuli with four orientations were presented in a pseudorandom order , and the set was repeated 10–40 times . For drifting grating stimulation , sinusoidal gratings ( spatial frequency: 0 . 12 cpd; temporal frequency: 2 Hz; contrast: 100% ) moved toward eight evenly spaced directions ( 0° , 45° , 90° , 135° , 180° , 225° , 270° , and 315° ) for 1 . 5 s at intervals of 8–10 s for electrophysiology and for 2 s at an interval of 6 s for calcium imaging . A gray screen was shown during the interval period . In each set , drifting stimuli with eight directions were presented in a pseudorandom order , and the set was repeated 10–40 times . In the Flash+Drift trials , each flash stimulus was followed by a drifting grating stimulus at an SOA of 0 . 5 s . In S6 Fig , the flash stimuli were fixed at the vertical orientation ( 0° , vFlash ) , whereas in Fig 4 the drifting gratings were fixed at the vertical orientation ( 0° , 180° , vDrift ) and moved rightward or leftward . The procedures for in vivo VSD imaging have been previously described in detail [33 , 80] . The dye RH-1692 ( Optical Imaging , New York , NY ) [81] was dissolved in 4- ( 2-hydroxyethyl ) -1-piperazineethanesulphonic acid ( HEPES ) -buffered saline solution ( 0 . 6 mg ml−1 ) and applied to the exposed cortex for 60–90 min , which stained all neocortical layers . Imaging was initiated approximately 30 min after washing the unbound dye . To minimize movement artifacts because of respiration , the brain was covered with 1 . 5% agarose made in HEPES-buffered saline and sealed with a glass coverslip . For data collection , 12-bit images were captured at 6 . 67-ms temporal resolution with a charge-coupled device camera ( 1M60 Pantera , Dalsa , Waterloo , ON ) and an EPIX E4DB frame grabber with XCAP 3 . 1 imaging software ( EPIX , Inc . , Buffalo Grove , IL ) . RH-1692 was excited with red LEDs ( Luxeon K2 , 627-nm center ) and excitation filters of 630 ± 15 nm . Images were obtained with a microscope composed of front-to-front video lenses ( 8 . 6 × 8 . 6 mm field of view , 67 μm per pixel ) . The depth of field of our imaging setup was 1 mm . RH-1692 fluorescence was filtered through a 673-to-703-nm band-pass optical filter ( Semrock , New York , NY ) . Visual responses were averaged from 40–80 trials of stimulus presentations . Responses to flashes were expressed as the percent change in RH-1692 fluorescence relative to the baseline fluorescence intensity ( ΔF/F0 × 100% ) . Gating flashes were applied to the retina at a distance of approximately 10 cm from the cornea contralateral to the recording site to cover the entire optic angle . Stimulation was repeated every 10 s . The signal was amplified with a MultiClamp 700B , analyzed with pCLAMP10 . 1 ( Molecular Devices , Union City , CA , USA ) and digitized at 20 kHz . The data were reduced to 2 kHz and off-line analyzed using custom-written MATLAB routines . Patch-clamp recordings were obtained from L2/3 neurons at depths of 150–350 μm from the V1 surface using borosilicate glass electrodes ( 3 . 5–6 . 5 MΩ ) that were pulled with a P-97 puller ( Sutter Instruments , Novato , CA , USA ) . The electrode tips were lowered perpendicularly into the V1 with a DMX-11 electric manipulator ( Narishige , Tokyo , Japan ) or obliquely ( at 30° ) with a PatchStar micromanipulator ( Scientifica , Uckfield , UK ) . For cell-attached recordings , pipettes were filled with aCSF . For whole-cell recordings , the intrapipette solution consisted of the following ( in mM ) : 130 K-gluconate , 10 KCl , 10 HEPES , 10 Na2-phosphocreatine , 4 Mg-ATP , 0 . 3 Na2GTP , 0 . 05 Alexa-594 hydrazide , and 0 . 2% biocytin , adjusted to pH 7 . 3 . For morphological reconstruction of the recorded cells , mice were perfused transcardially with 4% paraformaldehyde , and their brains were coronally sectioned at a thickness of 200 μm using a DTK-1500 vibratome ( Dosaka , Kyoto , Japan ) . The sections were incubated with 0 . 3% H2O2 for 30 min and permeabilized with 0 . 2% Triton X-100 for 1 h . Then , the sections were processed with ABC reagent at 4°C overnight and developed with 0 . 0003% H2O2 , 0 . 02% diaminobenzidine , and 10 mM ( NH4 ) 2Ni ( SO4 ) 2 . Experiments in which the series resistance exceeded 70 MΩ or changed by more than 15% during the recording session were discarded . For each neuron , spike responses to a brief inward current were examined , and regular spiking neurons were selected as putative pyramidal cells for the subsequent analyses . LFPs were recorded at a depth of 300 μm from the V1 surface , which corresponded to L2/3 , using borosilicate glass pipettes ( 1−2 MΩ ) filled with aCSF . Traces were band-pass filtered between 1 and 250 Hz . Ten healthy adults ( four males and six females , 25 . 9 ± 5 . 4 ( mean ± SD ) years old ) with normal or corrected-to-normal vision participated in our EEG experiments . The EEG experiment was conducted in a dark room to explore early and late components of the visually evoked ERPs for brief exposures to high-contrast grating stimulus flashes . Visual stimuli were generated on a computer using Psychophysics MATLAB toolbox [82] . The stimuli were presented using a gamma-corrected [83] LCD display ( EIZO FlexScan S2243W , EIZO corporation , Ishikawa , Japan ) whose spatial resolutions were 1 , 920×1 , 200 pixels , and the refresh rate was 60 Hz . Participants viewed the stimuli at a 55-cm distance from the display . The experiment contained two stimulus conditions ( vertical and horizontal gratings ) , and the EEG signals for each of the stimuli were acquired 200 times ( 100 for the horizontal grating and 100 for the vertical grating ) . In each trial , the start of the trial was informed by the change of the color of the central fixation point ( from gray to white ) . After 3–4 s ( randomly jittered to exclude participant’s expectation effect on the EEG signals ) of the fixation color change , a high-contrast ( 100% from the gray background ) grayscale sinusoidal grating ( 1 . 03 cycles per degree ) pattern ( 35 . 2 × 24 . 4° in visual angle ) was flashed for 50 ms . The background brightness was 17 . 80 cd/m2 , which corresponds roughly to 4 . 88 lux , and the grating brightness ranged from 0 . 26 cd/m2 ( 0 . 07 lux ) to 35 . 62 cd/m2 ( 9 . 77 lux ) . Then , participants were asked to keep fixating the central fixation for 4 s without blinking as much as possible . After the 4-s fixation period , the central fixation color changed from white to gray to inform the end of a trial . The task start was initiated by a button press by a participant . The participants could take breaks between trials as they liked , and they could proceed the experiments at their own paces . The stimulus presentation order was pseudorandomized for each participant . One EEG session took about 2 h . The human visual ERPs at O1 and O2 ( following the international 10/20 coordinate convention ) for the two stimulus configurations were collected at 1 kHz ( the left earlobe was used as a reference ) with a wireless EEG system ( Polymate Mini AP108 , Miyuki Giken Co . , Ltd , Tokyo , Japan ) with pasteless dry electrodes ( National Institute of Information and Communications Technology , Japan ) [84] . Electrode impedances for O1 and O2 were kept below 5 kΩ at the beginning of the measurements . Eye movements and blinks were simultaneously recorded with an electrode put on a left eye lid . The onset of the visual stimulus presentation and the EEG measurements were synchronized using a customized photo-trigger detection system ( C6386 , Hamamatsu Photonics K . K . , Shizuoka , Japan ) . The recorded EEG and eye blink–related signals were saved on a computer using in-house MATLAB subroutines after each trial through a Bluetooth wireless connection . The ERP time series were analyzed using EEGLAB MATLAB toolbox ( [85] , http://sccn . ucsd . edu/eeglab/ ) and in-house subroutines written in MATLAB . The EEG signals were aligned offline so that we could evaluate the time series from −200 ms to 3 , 000 ms relative to the grating stimulus onset . The raw data were preprocessed offline by a linear trend removal and a band-pass filtering ( 0 . 5 to 100 Hz ) . Additionally , EEG epochs that contained large potentials exceeding the threshold ( 40 μV ) and abnormal spike or drifting components were excluded by EEGLAB’s automatic outlier detection utilities and visual inspections . These noisy epochs were generally derived from eye movements and blinks . The signal amplitudes were recomputed carefully by taking the mean of −200 to 0 ms ( relative to the stimulus onset ) samples as the baseline for each epoch . The recorded signals from two electrodes were similar and hence averaged for each participant . Finally , the ERPs averaged over 10 participants were given as the final visual event-related time series . The statistical tests to explore whether the signals were higher or lower than the baseline were evaluated by the standard two-tailed t test at each sampling point ( p < 0 . 05 without corrections of multiple comparisons ) . The OSI was defined according to the following equation: OSI= ( ∑Rθsin2θ ) 2+ ( ∑Rθcos2θ ) 2∑Rθ where Rθ is the mean response amplitude to a grating with direction θ [86] . Note that this equation defines the normalized norm of the averaged vector [86] and may give a value that is different from OSI used in other reports [41] . The similarity of the tuning curves between the early and late responses was evaluated using the correlation coefficient ( R ) of the amplitudes of the responses: R=∑ ( Rθ_early−R¯θ_early ) ∑ ( Rθ_late−R¯θ_late ) ∑ ( Rθ_early−R¯θ_early ) 2∑ ( Rθ_late−R¯θ_late ) 2 where Rθ_early and Rθ_late are the amplitudes of early and late responses , respectively , to a grating with direction θ . R-θ_early and R-θ_late represent the mean of the response amplitudes Rθ_early and Rθ_late across all eight θs . For each cell , the OSI and R were compared with their chance levels , which were estimated using a conventional random resampling method in which 1 , 000 surrogates were generated by randomly shuffling all trials irrespective of θ . The mouse was placed in a stereotaxic frame and then on the stage of an upright microscope ( BX61WI; Olympus ) . Cortical neurons were loaded with Fura 2 , a calcium-sensitive fluorescent dye , under online visual guidance with a two-photon laser scanning microscope ( FV1000; Olympus ) . Fura 2 AM was dissolved at 10 mM in DMSO with 10% pluronic acid and diluted at the final concentration of 1 mM in aCSF that contained 0 . 1 mM SR101 . This solution was pressure-injected ( 50–100 mbar for 10 s ) into V1 at a depth of 150–250 μm from the surface through a glass pipette ( tip diameter: 10–30 μm ) . The pipette was carefully withdrawn , and the craniotomized area was sealed with 2% agar and a glass cover slip . After 50–70 min , which enabled the dye loading to the neuronal soma and the washout of extracellular dyes , the Fura-2 fluorescence was two-photon imaged from V1 L2/3 neurons . Neurons and astrocytes were discriminated based on astrocyte-specific staining with SR101 [87] . Fura 2 and SR101 were excited by a mode-locked Ti: sapphire laser at wavelengths of 800 nm and 910 nm , respectively ( 100 fs pulse width , 80 MHz pulse frequency; Maitai HP; Spectra Physics ) [88] . Fluorescent light was corrected by a water-immersion objective lens ( 20× , numerical aperture 0 . 95; Olympus ) . Videos were taken from a 320×320-μm area at five frames per s using FV10-ASW software ( version 3 . 0; Olympus ) . Neurons that exhibited significant visual responses above the baseline ( p < 0 . 05 , paired t test ) in any recording session were selected for analysis . The apparatus was located in a dark , soundproofed room . The room temperature was maintained at 25°C during the experiment . A virtual cylinder comprising a vertical sinusoidal grating ( 0 . 17 cpd , 10%–40% contrast ) was displayed in three-dimensional coordinate space on four 24-in monitors ( refresh rate: 60 Hz ) that were arranged in a quadrangle arena . The images on the monitors were extended by two mirrors on the top and bottom of the arena . A platform ( a white acrylic disc; ϕ = 6 . 0 cm ) was positioned 13 . 5 cm above the bottom mirror . In each experiment , a single male P28–35 C57BL/6J mouse was placed on the platform and was allowed to move freely . The behavior of the mouse was monitored through a camera ( Logicool HD Webcam C615; Logitech , Tokyo , Japan ) that was attached over a small hole of the top mirror . Vertical gratings that drifted leftward or rightward ( temporal frequency: 0 . 5 Hz ) were presented simultaneously on all four screens for 2 s with a random interval between 2–4 s . From the animal’s point of view , the virtual cylinder appeared to rotate around the platform at an angular velocity of 5° per s ) . The mice normally tracked the grating with reflexive head movements in concert with the rotation direction . The drifting directions were randomly alternated , and the rotations were repeated 120 times in one session that took approximately 10 min . In some trials , either a vertical or horizontal grating ( 0 . 17 cycles per degree , 100% contrast ) was flashed 0 . 5 or 3 s before a drifting grating . Animals were habituated to the system prior to the first behavioral test by experiencing at least one full session . When the mice slipped or jumped down from the platform during the test , they were manually returned to the platform , and the test was resumed . If the animal’s head tracked a cylinder rotation , the trial was counted as a “success . ” Manual counting was checked by two independent trained researchers who were blind to the experimental conditions . Through computer-generated order randomization of the stimulation conditions , the experimenters were also blind to the treatment . The trials in which a mouse was grooming or made large movements were excluded from the analyses ( invalid trials ) . The success rate was calculated as a ratio of the successful trials to the total valid trials . Tetrodotoxin was dissolved at 10 μM in aCSF and directly applied to the cortical surface 15 min prior to the behavioral sessions . The exposed cortices were covered with the craniotomized bone segments and mounted with dental cement . The effects of tetrodotoxin were confirmed by flash-induced LFP responses in V1 L2/3 . Eleven healthy right-handed individuals ( three females ) with normal or corrected-to-normal vision participated . The ages ranged from 22 to 42 years , with 26 . 5 ± 5 . 1 years ( mean ± SD ) . The participants performed tasks using a computer mouse with their right hands . A 24-in monitor was placed at a distance of 0 . 5 m from the participants’ eyes in a dark , pseudosoundproofed room . The participants were instructed to report the motion direction of drifting gratings presented on the screen . A 2 × 2 cm2 open square was displayed at the center of the screen against a gray background ( 60 cd/m2 , 5 lux ) . Each trial was initiated when a participant clicked the computer mouse on the square . Then , the square was filled in black , and after a random time interval between 1–3 s , a sinusoidal drifting grating ( spatial frequency: 0 . 12 cpd; temporal frequency: 1 Hz; contrast: 40% ) was presented for 0 . 25 s in one of four movement directions ( 0° , 90° , 180° , and 270° ) . A 50-ms beep tone was presented 0 . 5 s before a drifting grating stimulus . In some trials , a 50-ms grating flash ( spatial frequency: 0 . 12 cpd; contrast: 100% ) was displayed simultaneously with the tone . A full gray screen was displayed during all interstimulus intervals . After each stimulus , the participants were asked to move the mouse cursor in the same direction as the grating motion as rapidly as possible . When the mouse cursor traversed the edge of the square , the square became blank , which cued the trial completion . Incorrect motion reports or failures to respond within 600 ms ( misses ) from stimulus onset were considered errors and were indicated to the participants through a 200-ms peep tone . Each participant performed 160–244 trials per session .
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Animals are constantly exposed to a visual world that varies over time . To examine how the visual cortex integrates visual information that is temporally spaced , we monitored neuronal activity of the primary visual cortex ( V1 ) using single- and multicell recording techniques . We discovered that a brief visual stimulus induced an early , transient activation as well as a delayed reactivation of V1 neurons in mice and humans . Notably , this reactivation of visual cortex conveyed information about stimulus orientation: presentation of a second visual stimulus during this reactivation enhanced the V1 response specifically when the orientations of the two stimuli were identical . Behavioral tests in mice and humans revealed that the ability to detect visual stimuli was also enhanced when the second stimulus was presented during the time window of V1 reactivation . Because animals extract visual information from an environment in constant change , the modulation of visual responses through cortical reactivation might be a strategy commonly used in the visual system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Neocortical Rebound Depolarization Enhances Visual Perception
|
Many normal adult tissues contain rare stem cells with extensive self-maintaining regenerative potential . During development , the stem cells of the hematopoietic and neural systems undergo intrinsically specified changes in their self-renewal potential . In the mouse , mammary stem cells with transplantable regenerative activity are first detectable a few days before birth . They share some phenotypic properties with their adult counterparts but are enriched in a subpopulation that displays a distinct gene expression profile . Here we show that fetal mammary epithelial cells have a greater direct and inducible growth potential than their adult counterparts . The latter feature is revealed in a novel culture system that enables large numbers of in vitro clonogenic progenitors as well as mammary stem cells with serially transplantable activity to be produced within 7 days from single fetal or adult input cells . We further show that these responses are highly dependent on novel factors produced by fibroblasts . These findings provide new avenues for elucidating mechanisms that regulate normal mammary epithelial stem cell properties at the single-cell level , how these change during development , and how their perturbation may contribute to transformation .
The regenerative properties of individual cells within the mammary gland were first indicated by the retrovirally marked clonal outgrowths shown to develop from mouse mammary tissue fragments transplanted into the cleared mammary fat pad [1] , [2] . More recently , we and others have demonstrated that individual cells isolated from the adult mammary gland are capable of regenerating a complete new gland when transplanted in the same type of assay and most of these are confined to a distinct subset of cells with basal ( CD24+/EpCAM+CD49f+ ) features [3]–[5] . The regenerated mammary glands thus produced contain the same spectrum of cell types that are present in the adult mammary gland . These include progenitor cells ( referred to as colony-forming cells , or CFCs ) with a luminal ( CD24++/EpCAM++CD49flow/− ) phenotype and other cells with either a luminal or basal phenotype that are considered to be differentiated because they lack proliferative ability . In addition , the structures produced in vivo contain cells with the same transplantable regenerative activity as the original parental input cell . The latter are thus referred to operationally as mammary repopulating units , or MRUs , based on the method used to detect them . MRUs can be quantified by limiting dilution analysis ( LDA ) of their ability to regenerate large branched glandular structures when transplanted into the cleared fat pad of prepubertal mice [3] , [4] . This MRU assay has now been widely used to investigate mechanisms that regulate normal adult mammary stem cell differentiation and growth control [6] , as well as the effects of various mutations that contribute to the genesis of breast cancer [7] . Previous studies of the development of the mouse mammary gland have shown that the first elements appear on embryonic day 11 ( E11 ) as placodes of specified ectoderm . The cells in these placodes then expand in number and invaginate into the underlying mesenchyme to develop primordial branched structures that , just before birth , are found to contain cells detectable as individually transplantable MRUs [8] , [9] . Interestingly , these MRUs , like their adult counterparts , belong to a subset of cells that are CD49f+ but also have phenotypic and transcriptional differences [8] . However , whether fetal and adult MRUs have different growth and self-renewal properties , as described for stem cell populations in some other tissues [10] , [11] , is not known . The higher self-renewal activity characteristic of these fetal tissues has been attributed to intrinsic molecular mechanisms operating within the stem cells , themselves , albeit in response to environmental cues , and include transcriptional regulators such as Sox17 [12] and Lin28 [13] , [14] in hematopoietic stem cells , and Hmga2 in neural [15] and hematopoietic stem cells [14] . The investigation of such differences requires the availability of a robust system in which the maximum self-renewal/regenerative activity of the stem cells of interest can be elicited and quantified . Such a system has not yet been developed and validated for mammary epithelial stem cells , although a variety of candidate elements have been reported . One of these is Matrigel , a laminin-rich tumor extract that has been widely used to support mammary epithelial cell growth in vitro [3] , [8] , [16]–[18] . Interestingly , adult basal mammary cells have been found to display increased clonogenicity in Matrigel in the presence of Wnt3a and also retain some MRU activity when serially passaged under these conditions [18] . There is also evidence that the number of MRUs detected in transplants of both basal cells and luminal cells of mouse origin is enhanced when Matrigel is injected together with the cells [5] , [19] . Some latent activity or enhanced detection of MRUs in basal and even luminal mammary cells has also been revealed in cells exposed to a Rho-associated kinase inhibitor ( ROCKi ) [20] . We now report the superior ability of fetal mammary epithelial cells to produce MRUs and CFCs as compared to their adult counterparts when they are activated in a Matrigel-based clonal culture system that requires distinct , but as yet unidentified factors produced by fibroblasts .
In a first series of experiments , we determined the number and phenotype of MRUs and CFCs in the mammary rudiment present in the E18 . 5 female C57Bl/6 ( B6 ) embryo . E18 . 5 was chosen because it is the earliest time during the development of the mammary gland when MRUs are reproducibly detected in numbers sufficient for their characterization [8] . In these experiments we also found that antibody staining of EpCAM+ and CD49f+ cells in enzymatically dissociated viable single-cell suspensions of dissected E18 . 5 fetal glands [from which the associated endothelial ( CD31+ ) and blood ( CD45+ and Ter119+ ) cells had been excluded] revealed the presence of three major subpopulations ( Figure 1A ) . These consisted of an EpCAM++CD49f+ fraction and an EpCAM+CD49f+ fraction of the fetal mammary epithelial cells and an EpCAM− fraction that is variably CD49f+ and contains the associated stromal cells . Assessment of the MRU and CFC content of the fetal mammary gland showed that the majority of both of these functionally defined primitive cell types are present in the EpCAM++CD49f+ subset ( Figures 1B and 2A , Tables S1 , S2 , S3 , S4 ) , as determined using optimized assay conditions for both of these ( Figure S1A–D ) . Interestingly , the fetal gland appears to lack entirely the large population of EpCAM++CD49flow/− luminal cells present in the mammary gland of 8–12-wk-old adult virgin female mice ( Figure 1A ) . In addition , the majority of the CD49f+ cells in the fetal gland are EpCAM++ , whereas the majority of the CD49f+ cells in the adult gland are EpCAM+ ( Figure 1B and D ) . Calculation of MRU and CFC frequencies as a function of the number of EpCAM+ cells tested allows comparisons to be made that relate exclusively to the gland itself or derived populations , even when unseparated fractions are being evaluated . Such a comparison of the MRU and CFC frequencies and content in the E18 . 5 fetal ( Figure 1B ) and adult ( Figure 1D ) mammary gland showed that the ratio of their numbers relative to one another is already set at approximately the same value as that characteristic of the adult virgin gland , although the total size of these populations as well as the size of the gland as a whole ( total number of EpCAM+ cells ) undergo an expansion of ∼100-fold over the intervening 4-mo period ( Figure 2B ) . To compare the regenerative properties of fetal and adult mammary epithelial cells ( Figure 3A ) , we first undertook a preliminary survey of culture conditions that would optimize the production over a period of 7 d of adult CFCs measured in secondary 2D CFC assays . These experiments showed superior outputs of CFCs were obtained when cells were incubated in 3D Matrigel cultures as compared to 2D adherent CFC assay conditions ( unpublished data ) . Additional experiments showed that reduced O2 conditions had no effect on CFC production , regardless of the input cell source ( Figure S2A ) and added ROCKi had a slight enhancing effect only in cultures initiated with adult luminal cells ( Figure S2B ) . Cultures initiated with varying inputs of unseparated adult cells showed a marked cell dose–response relationship with linearly increasing outputs of CFCs at cell input numbers below 103 EpCAM+ cells per 250 µl . But cultures initiated with more than 5×103 EpCAM+ cells became inhibitory ( Figure 3B ) . In addition , we found that the addition of irradiated 3T3 cells to cultures initiated with low numbers of adult EpCAM+ cells greatly enhanced the number of CFCs produced in the 7-d Matrigel cultures ( ∼1 , 000-fold ) . Fetal mammary cells placed in the same culture conditions showed a similar input cell dose–dependent increase in CFC output , but much higher CFC outputs were obtained ( ∼50- to 100-fold ) from the same number of EpCAM+ input cells as compared to their adult counterparts ( Figure 3C ) . We then assessed the ability of this culture system to support the production of MRUs . When nonsaturating numbers of input fetal or adult mammary cells were used to initiate the cultures , MRU numbers were also increased ( Figure 3D , Tables S5 and S6 ) . Moreover , the output of MRUs from the same number of EpCAM+ cells was again higher ( ∼4-fold ) for the fetal as compared to adult cells . These findings indicate the ability of fibroblast-containing Matrigel cultures to support the generation within 7 d of expanded populations of MRUs and CFCs and show that the output of both these cell types is greater for fetal mammary epithelial cells than for their adult counterparts . Experiments using a transwell system suggested that the enhancing effect of the added 3T3 cells is mediated , at least in part , by soluble factors ( unpublished data ) . Subsequent experiments showed that the effects of the irradiated 3T3 cells could be partially replaced by addition of 80% 3T3-cell conditioned medium ( CM ) ( Figure 4A and B ) . However , the activity in the CM could not be replaced by the addition of various reported single “niche” elements , including basic fibroblast growth factor ( bFGF ) , colony-stimulating factor 1 ( CSF-1 ) , or hepatocyte growth factor ( HGF ) tested at concentrations previously found to stimulate mammary cells or other cell types ( 16 ng/ml bFGF [21] , 16 ng/ml CSF-1 [22] , and 40 ng/ml HGF [23] , [24] , Figure 4A and C ) . The activity produced by the 3T3 cells also appears to be different from Wnt3a [18] , as neither 160 ng/ml of Wnt3a alone or plus 400 ng/ml R-spondin 1 [25] , [26] even partially mimicked the effect of 3T3 cells in our cultures ( Figure 4A ) . In addition , the effect of the added 3T3 cells could only be minimally inhibited , and only on adult basal cells ( p = 0 . 04 ) , by the addition of one of two Wnt pathway inhibitors [18] , [27] tested ( XAV939 at 0 . 8 µM , Figure 4B ) . The above findings showed that readily detectable numbers of CFCs had been generated over a period of 7 d in cultures initiated with suspensions estimated to contain a single EpCAM+ cell ( Figure 3B and C ) . In such cultures , the discrete structures formed were readily visualized and those derived from fetal cells appeared consistently larger than those derived from adult cells . This difference in overall size reached after 7 d was confirmed in new series of experiments in which only one test cell of a defined phenotype and origin was added to each of a series of fibroblast-containing Matrigel cultures ( Figure 5A ) . Immunohistochemical staining of the structures derived from single cells from the adult MRU-enriched EpCAM+CD49f+ fraction ( blue gate in Figure 1C ) showed that these consisted primarily of K5+ , K14+ cells , and some p63+ cells ( basal markers ) , with very few K8+ or K18+ cells ( luminal markers ) ( Figure 5B ) . In contrast , we found single cells from the adult luminal progenitor-enriched EpCAM++CD49flow/−CD61+ fraction ( solid red gate in Figure 1C ) [28] generated structures that contained a mixture of cells expressing luminal and basal markers . By comparison , single cells from the fetal MRU-enriched EpCAM++CD49f+ fraction ( Figure 1A ) produced structures that most closely resembled those derived from the adult MRU-enriched EpCAM+CD49f+ cells in their predominant K5+ , K14+ , and p63+ cell content with a few progeny expressing the luminal markers , K8 and K18 . Flow cytometric analysis of cell suspensions prepared individually from several of these structures showed that they were all similarly composed of homogeneous populations of EpCAM+CD49f+ cells with slightly higher levels of EpCAM expression in the structures derived from the originally EpCAM++CD49flow/−CD61+ adult ( luminal progenitor-enriched ) cells ( Figure 5C ) . In summary , all clonally generated structures examined after 7 d of growth consisted of cells with a “primitive” ( CD49f+ ) phenotype and those derived from fetal MRU-enriched cells most closely resembled those derived from a functionally similar adult subset . To determine the frequency of fetal and adult cells with structure- , CFC- , and MRU-generating activity and to compare their average outputs of CFCs and MRUs , we examined another series of 7-d Matrigel cultures initiated with single fetal and adult cells . In total , 163 cultures were initiated with single fetal ( EpCAM++CD49f+ ) cells , 124 with single adult EpCAM+CD49f+ ( basal ) cells , and 100 with single adult EpCAM++CD49flow/−CD61+ ( luminal ) cells and visible structures were seen in 43% , 30% , and 18% of these , respectively ( Table 1 ) . The proportion of cultures initiated with a single fetal or adult basal cell that contained CFCs was very similar to the proportion that contained a visible structure ( Table 1 ) , consistent with the likelihood that all of the CFCs detected were present in these clonally derived structures . In contrast , we found CFCs in approximately twice as many cultures initiated with single adult luminal cells as contained a visible structure ( 41% versus 18% ) , consistent with the generally smaller size of these latter structures ( Figure 5A ) . Notably , for all three input cell types , the frequency of cells that generated progeny with CFC activity in secondary assays was always higher ( 1 . 5- to 3-fold ) than the frequency of input cells that displayed CFC activity directly ( Figure 1B and 1D , Table 1 ) . Measurement of the total number of cells as well as the total number of CFCs that had been produced in each of these clonal cultures showed that these output values were highly variable and correlated ( Figure 6A and B ) for all three sources of input cells . The highest CFC outputs were present in clones derived from fetal cells ( up to >104 CFCs per culture , median value ∼10-fold higher than for the basal cell clones , and almost 100-fold higher than for the luminal cell clones , Figure 6B ) . We also identified MRUs in the cultures initiated with single cells . The glands produced by these culture-generated MRUs were indistinguishable in morphology to those generated from freshly isolated cells ( Figure 7 ) . Most of the visible structures derived from the adult basal cells and some of those derived from the luminal progenitors also contained MRUs ( 86% and 33% , respectively , Table 1 ) . If it is assumed that all MRUs produced in these cultures are associated with the formation of a visible structure , the frequency of adult mammary cells that can produce MRUs is at least 50-fold higher for the basal population ( 26% versus 0 . 3% ) and >500-fold higher for the luminal cells ( 6% versus 0 . 01% ) than the frequency of MRUs in the respective input populations , ( Figure 1B and 1D , Tables 1 and S2 ) . The proportion of clonal structures produced from fetal cells that contained MRUs was even higher ( 95% , Table 1 ) . This corresponds to a minimal frequency of fetal EpCAM++CD49f+ cells that can generate MRUs in our 7-d culture system that is 20-fold higher than the frequency of these initially isolated fetal cells that can be immediately detected as MRUs ( 41% versus 2% , Tables 1 and S4 ) . Comparison of the clones generated from single fetal and adult cells showed that the average output of MRUs was significantly higher from the fetal cells ( ∼4-fold , p<0 . 01 , Figure 6D and Table S7 ) . Importantly , the MRUs generated from either single fetal or adult cells in this culture system not only regenerated normal-appearing glands in the cleared fat pad assay ( Figure 7 ) , the regenerated glands thus produced contained progeny MRUs that could be serially transplanted into secondary fat pads ( 6/7 positive fat pads from single fetal cells , and 9/11 positive fat pads from adult cells , Table S8 ) . As a first step toward elucidating the mechanism ( s ) contributing to the more potent MRU-generating activity of E18 . 5 fetal mammary cells as compared to their closest adult counterparts ( the basal subset ) , we obtained global RNA profiles on both of these cell populations using Agilent arrays and compared them . A total of 2 , 262 genes ( probes ) showed significantly different levels of expression ( ≥2-fold , p≤0 . 05 ) , with 1 , 206 genes up-regulated and 1 , 056 genes down-regulated in the fetal population ( see Materials and Methods , Figure 8A and Table S9 ) . Importantly , 937 ( 41% ) of these differentially expressed transcripts overlapped with those identified in a previously published comparison of E18 . 5 fetal and adult MRU-enriched populations isolated using similar phenotypic markers [8] , and with >90% concordance in the directionality of the differences seen . The top 1% of differentially expressed genes ( probes ) that were higher in the fetal dataset and the top 1% of differentially expressed genes ( probes ) that were higher in the adult basal dataset are shown in Figure 8B . The most significant of these is Sox11 , also noted by Spike et al . 2012 [8] , and identified in a recent comparison of E12 . 5 mammary rudiments with adult mammary cells [29] . Additional genes of interest that we found are up-regulated in the E18 . 5 fetal cells include two that encode insulin growth factor 2 mRNA-binding proteins ( Igf2bp1 and Igf2bp3 ) , which are let-7 miRNA targets broadly implicated in many tissue growth and metabolism networks operative during development [30] . Also in this group of highly up-regulated genes in fetal cells is Elf5 , which encodes a member of the ETS family of transcription factors and has been reported to play a role in alveolar cell differentiation during pregnancy [31] , [32] . Another is Aldh1a3 , recently shown to be most highly expressed in the luminal progenitor compartment of normal adult human mammary cells [33] . Interestingly , we also confirmed Ezh2 to be up-regulated in the fetal cells although to a slightly lower degree . Genes that were up-regulated in adult basal cells include Mmp3 , previously shown to be involved in mammary gland morphogenesis [34] , and Myh11 , a myoepithelial cell-specific gene . Also in this latter group is Itgα1 , an integrin α1 subunit , which forms a heterodimer with β1 and functions as a receptor for collagen [35] .
A key assumption in demonstrating an induction or activation of growth and differentiation activities not initially detectable is that the conditions used to elicit this activity are not simply suboptimal . Therefore , an important component of the present studies was an examination of the conditions used to detect CFC and MRU to ensure these were optimized . Current methods widely used to detect mammary progenitors that form colonies within 7 d in low-density 2D cultures at high efficiency include the addition of irradiated 3T3 cells [3] . We also adopted the use of a low O2 atmosphere [36] , [37] and the addition of ROCKi [38] , which we confirmed to be important modifications that selectively enhance the detection of adult basal CFCs and have a comparable effect on fetal CFCs . Similarly , several recent studies have indicated that the frequency of both adult and fetal mammary cells detectable as MRUs is enhanced if Matrigel is co-injected with the test cells [8] , [19] , [20] , [39] . We confirmed this effect on both fetal and adult MRUs from C57Bl/6 mice and also implanted the recipients with E/P pellets to replicate the less consistently attainable enhancing effect of pregnancy on stimulating expansion of in vivo regenerated glands to enable their more facile visualization [21] . Importantly , even using these optimized assay conditions , we found that the frequency of fetal and adult mammary cells that could generate progeny with CFC and/or MRU activity in fibroblast-containing Matrigel cultures was always higher than the frequency of freshly isolated cells with directly demonstrable CFC and/or MRU activity . Moreover , the “induction/activation” process thus inferred could be elicited from all EpCAM+ subsets of cells examined and was true of fetal as well as adult mammary subsets . Particularly intriguing is the observation that MRUs could be derived from adult luminal progenitors , since these were previously thought to have irreversibly lost the bipotent , gland-generating activity of MRUs . However , it is interesting to note that evidence of persisting self-renewal of luminal cells has recently been reported based on the demonstrated presence in the adult of luminal cells derived from cells marked from postnatal day 1 by expression of the luminal marker , K8 , although initiating the trace in cells expressing the basal marker , K14 , in cells at E17 did result in the parallel detection of both basal and luminal phenotypes [40] . Similar studies using a different reporter , Axin2 , have suggested that outcomes of such lineage-tracing experiments may vary depending on the type of gene used to “tag” different cell subsets [41] , and have raised questions as to the unknown specificity in the embryo of lineage markers established in adult mammary epithelial tissue . Adult ER+ or ER− luminal cells were also recently shown to ( re ) activate bipotent features when “passaged” in collagen gels under the kidney capsule of transplanted mice [5] . The structures produced contained cells with basal features ( p63 and smooth muscle actin ) and MRU activity detected in subsequently injected cleared fat pads . Specific ( HGF-mediated ) activation of Met [23] and expression of Slug and Sox9 [20] have also been found to confer MRU activity on luminal cells . Slug alone was active on CD61+ luminal progenitors , whereas Slug in combination with Sox9 converted both CD61+ and CD61− ( differentiated luminal cells ) into MRUs . Thus it is tempting to speculate that the acquisition of a cell surface luminal phenotype may sometimes precede the irreversible molecular “shut-down” of bipotency and self-renewal mechanisms that operate in MRUs that have a basal phenotype , and which can then be reactivated by factors produced by fibroblasts . Indeed evidence for such a model of alternative/latent stem cell populations in the skin [42] and crypt of the small intestine [43] , [44] that can be reactivated under defined conditions has recently been reported . Our finding that the addition of fibroblasts to Matrigel cultures strongly promotes the regenerative activity of cells in all subsets of adult and fetal mammary cells with MRU and CFC activity raises the important question as to the molecular mediators involved and the downstream mechanism by which the biological response is elicited . Recent reports have shown that induced Wnt signalling stimulates or is involved in qualitatively similar responses by developing [41] or adult [18] mammary or adult intestinal epithelial stem cells [26] . Our CM experiments showed that some of the effects of the added 3T3 fibroblasts in our cultures could be elicited by soluble factors that they release , but this could not be mimicked by the addition of Wnt3a , bFGF , HGF , or CSF-1 . On the other hand , a contribution of Wnt signaling cannot be entirely ruled out . A minor effect was in fact obtained using one of two Wnt inhibitors , although this latter experiment could also reflect nonspecific effects of the inhibitor or a role of Wnt signaling in the production or release of different factors that may mediate the effects obtained or cooperate with other factors . Additional experiments to elucidate all of these possibilities and define the numbers and type of factors involved as well as their mode of action will clearly be of great interest and of potential relevance to understanding mammary cell oncogenesis . The availability of a rapid and robust clonal assay to discriminate active agents as now described should greatly accelerate such future investigations . Our findings also provide the first evidence , to our knowledge , of a greater induction of MRU activity in fetal as compared to adult mammary epithelial cells demonstrable at the single-cell level . A higher self-renewal activity of fetal stem cells has been well-documented in the hematopoietic system [45] , [46] and in the neural system [11] , [15] . Several transcription factors and chromatin regulators that have been implicated in maintaining the unique properties of these fetal stem cells include Sox17 , Ezh2 , Lin28 , and Hmga2 [11]–[13] , [15] , [47] . We predict that similar intrinsic programs may be operative in fetal mammary cells , given the increased potency in the regenerative behaviour of fetal cells compared with adult mammary cells as assessed under identical conditions . Reported evidence of differences in gene expression of fetal and adult mouse mammary cells that are enriched in MRUs [8] , [48] are corroborated by our own analysis of the gene expression profiles of fetal ( EpCAM++CD49f+ ) and adult basal ( EpCAM+CD49f+ ) B6 populations that are likely largely overlapping and shown here to be enriched in inducible as well as directly detectable MRUs . Indeed , our analyses show that ∼40% of the differentially expressed genes were also identified in the datasets analyzed by Spike et al . 2012 [8] . These include molecular regulators such as Sox11 , Igf2bp1-3 , Elf5 , and Ezh2 . Taken together , our observations also appear relevant to growing evidence that some breast cancers may originate in luminal cells [23] , [49] , or expand from cells that have or may acquire luminal features [39] . The ease and rapidity with which this enormous proliferative potential of many normal mammary epithelial cells can be activated in vitro suggests that the mechanisms involved may also be targets of transforming events and act as covert contributors to the process of oncogenesis and clonal evolution in nascent breast cancers .
C57Bl6/J mice were used for all experiments and procedures approved by the Animal Care Committee of the University of British Columbia . Mice were considered E0 . 5 on the day of observed plug . All procedures involving mice were approved by the Animal Care Committee of the University of British Columbia . Mammary glands from 8–12-wk adult female and E18 . 5 fetal C57Bl6/J mice were digested overnight ( adult ) or for 1 . 5 h ( fetal ) at 37°C in DMEM/F12 medium containing 1 mg/ml collagenase A ( Roche Diagnostics ) and 100 U/ml hyaluronidase ( Sigma ) and single-cell suspensions obtained as described [3] . Mammary cells and irradiated 3T3 fibroblasts were cultured for 6–7 d in media consisting of DMEM/F12 ( 3∶1 , STEMCELL Technologies ) , 10% fetal bovine serum , 10 ng/ml EGF ( Sigma ) , 1 . 8×10−4 M adenine ( Sigma ) , 5 µg/ml insulin , 0 . 5 µg/ml hydrocortisone , 10−10 M cholera toxin ( Sigma ) , and 10 µM Y-27632 ( Reagents Direct ) . Cultures were incubated at 5% O2 unless indicated otherwise . These were performed as described [4] with the following modifications . We added 25% Matrigel ( BD Biosciences ) to the cell innoculum , and unless indicated otherwise , a silicone elastomer pellet containing 2 mg 17β-estradiol and 4 mg progesterone ( E/P , Sigma ) was implanted subcutaneously 3–4 wk posttransplant . Another 3–4 wk later , glands were fixed and stained . Outgrowths that contained a multiply branched structure were scored as positive . All MRU frequencies were calculated using ELDA software ( http://bioinf . wehi . edu . au/software/elda/ ) [50] . We combined 2 . 5×104 irradiated 3T3 fibroblasts with the mammary cells in 200 µl of the same medium used for CFC assays , and the suspension was then placed on top of 50 µl of solidified ( 100% ) Matrigel ( Cat . No . 354234 , BD Biosciences ) previously added to each well of a 96-well plate . These cultures were then incubated at 37°C for 7 d in a humidified atmosphere containing 20% O2 without further medium addition or exchange . When other constituents were included , these were incorporated into the medium in which the test cells were initially suspended and , in all cases , the medium was not changed throughout the 7-d culture period . Additives tested were Wnt3a ( R&D Systems ) , R-Spondin 1 ( R&D Systems ) , XAV939 ( Cellagen ) , mDKK1 ( R&D Systems ) , bFGF ( STEMCELL Technologies ) , mouse CSF-1 ( STEMCELL Technologies ) , HGF ( PeproTech ) , and 3T3 cell CM obtained by incubating the 3T3 cells in the 2D CFC assay medium for 48 h . To obtain a single-cell suspension from the 7-d cultures , 5 mg/ml dispase was first added for 1–1 . 5 h at 37°C and subsequently 0 . 25% trypsin/EDTA ( both from STEMCELL Technologies ) for 3–4 min at the end of which the cells were readily dissociated by pipetting . Transwell experiments were performed with scaled cultures in 24-well tissue culture plates with 1 . 0 µm pore size inserts . Blocking of nonspecific antibody binding was performed by incubating cells for 10 min on ice in rat serum ( Sigma ) and anti-mouse CD16/32 Fc-gamma III/II Receptor antibody ( Clone 2 . 4G2 , STEMCELL Technologies ) . Mammary cells were depleted of hematopoietic , endothelial , and stromal cells using biotinylated antibodies to CD45 ( clone 30-F11 , Biolegend ) , erythroid cells ( clone TER-119 , Biolegend ) , CD31 ( clone MEC 13 . 3 , BD Pharmingen ) , and for adult cells only , also to BP-1 ( clone 6C3 , eBioscience ) , followed by streptavidin-eFluor780 ( eBioscience ) or streptavidin-phycoerythrin ( PE , BD Pharmingen ) . Anti-CD49f-fluorescein isothiocyanate ( FITC , clone GoH3 , BD Pharmingen ) and anti-CD326 ( EpCAM ) -AlexaFluor 647 ( clone G8 . 8 , Biolegend ) , and anti-CD61-PE ( integrin β3 ) ( clone 2C9 . G2 , BD Pharmingen ) were used to isolate the fractions described . Cells were then exposed to 4′ , 6-diamidino-2-phenylindole ( DAPI ) or propidium iodide ( PI ) to eliminate dead ( DAPI+ or PI+ ) cells . The CD61+ fraction was isolated using fluorescence-minus-one controls , sorted from the adult luminal ( CD45−CD31−Ter119−BP1−EpCAM++CD49f+ ) fraction . Cell sorting was performed using a FACSAria II or Influx II cell sorter ( BD Biosciences ) . Single-cell derived structures in 96-well plates were fixed in 10% buffered formalin ( Fisher ) and subsequently washed in 70% ethanol . Structures were then individually removed and pooled for embedding in paraffin . We prepared 4 µm sections using Target Retrieval solution ( DAKO ) , blocked using Cleanvision solution ( Immunologic ) , and stained with an anti-cytokeratin 5 antibody ( Clone AF138 , Covance ) , anti-cytokeratin 8 antibody ( ab 59400 , Abcam ) , anti-cytokeratin 18 antibody ( Clone E431-1 , Millipore ) , anti-cytokeratin 14 antibody ( LL002 , Novocastra ) , and an anti-p63 antibody ( Clone 4A4 , BioCare Medical ) , and developed using the UltraVision ONE detection system ( Fisher Scientific ) . RNA was extracted using the Absolutely RNA Nanoprep kit ( Agilent ) from three biological replicates of purified CD31−CD45−Ter119−EpCAM++CD49f+ E18 . 5 fetal mammary cells and CD31−CD45−Ter119−BP-1−EpCAM+CD49f+ adult basal mammary cells . Total RNA quality was assessed with the Agilent 2100 bioanalyzer prior to microarray analysis . Samples with a RIN value of greater than or equal to 8 . 0 were deemed to be acceptable for microarray analysis . Samples were prepared following the Agilent One-Color Microarray-Based Exon Analysis Low Input Quick Amp WT Labeling v1 . 0 . cRNA products were generated and hybridized to the Agilent SurePrint G3 Mouse GE 8x60K . Arrays were scanned with the Agilent DNA Microarray Scanner at a 3 µm scan resolution , and data were processed with Agilent Feature Extraction 11 . 0 . 1 . 1 . Green processed signal was then quantile normalized with Agilent GeneSpring 12 . 0 and deposited at the gene expression omnibus ( GSE46357 ) . To minimize multiple testing in comparing the data for the two sources of cells , we first eliminated probes that showed no/low activity in both adult basal and fetal cell datasets using a threshold for their elimination that was established by running a one-dimensional k-means algorithm ( k = 3 ) on mean expression values for each probe in each dataset ( three replicates each ) . The probes that fell in the lowest mean expression cluster in both datasets were thus identified and removed . This filtering left a total of 24 , 066 probes . Differential expression between the two datasets was then determined using the “lmFit” function in the R package “limma” and the Holm method to correct for the multiple testing methodology ( R scripts available as Text S1 ) . We thus identified 2 , 236 probes as showing a ≥2-fold difference between the two datasets using an adjusted p value of ≤0 . 05 .
|
Many adult tissues are maintained by a rare subset of undifferentiated stem cells that can self-renew and give rise to specialized daughter cells that have a more limited regenerative ability . The recent identification of cells in the fetal and adult mammary gland that display the properties of stem cells provides a foundation for investigating their self-renewal and differentiation control . We now show that these stem cell properties can be elicited from single mouse mammary cells placed in 3D cultures if novel factors produced by fibroblasts are present . Moreover , a comparison of the clonal outputs of fetal and adult mammary cells in this in vitro system shows that the fetal mammary cells have superior regenerative activity relative to their adult counterparts . The ability to activate and quantify the regenerative capacity of single mouse mammary epithelial cells in vitro sets the stage for further investigations of the timing and mechanisms that alter their stem cell properties during development , the potential relevance of these events to other normal epithelial tissues , and how these processes might be involved in the genesis of breast cancer .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"stem",
"cells",
"developmental",
"biology",
"biology",
"adult",
"stem",
"cells"
] |
2013
|
Developmental Changes in the in Vitro Activated Regenerative Activity of Primitive Mammary Epithelial Cells
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Circadian clocks have evolved as internal time keeping mechanisms that allow anticipation of daily environmental changes and organization of a daily program of physiological and behavioral rhythms . To better examine the mechanisms underlying circadian clocks in animals and to ask whether clock gene expression and function during development affected subsequent daily time keeping in the adult , we used the genetic tools available in Drosophila to conditionally manipulate the function of the CYCLE component of the positive regulator CLOCK/CYCLE ( CLK/CYC ) or its negative feedback inhibitor PERIOD ( PER ) . Differential manipulation of clock function during development and in adulthood indicated that there is no developmental requirement for either a running clock mechanism or expression of per . However , conditional suppression of CLK/CYC activity either via per over-expression or cyc depletion during metamorphosis resulted in persistent arrhythmic behavior in the adult . Two distinct mechanisms were identified that may contribute to this developmental function of CLK/CYC and both involve the ventral lateral clock neurons ( LNvs ) that are crucial to circadian control of locomotor behavior: ( 1 ) selective depletion of cyc expression in the LNvs resulted in abnormal peptidergic small-LNv dorsal projections , and ( 2 ) PER expression rhythms in the adult LNvs appeared to be affected by developmental inhibition of CLK/CYC activity . Given the conservation of clock genes and circuits among animals , this study provides a rationale for investigating a possible similar developmental role of the homologous mammalian CLOCK/BMAL1 complex .
Circadian clocks are internal daily time keeping mechanisms that allow organisms to anticipate daily environmental rhythms as well as efficiently organize behavioral and physiological functions in a daily schedule . The molecular mechanisms that form the basis for circadian rhythmicity in animals involve interlocked feedback loops controlling gene expression as well as post-translational activities [1] , [2] . In both insects and mammals a circadian transcription complex of two basic helix-loop-helix PAS domain transcription factors promotes the rhythmic expression of several of its negative feedback regulators . The fruit fly Drosophila melanogaster has emerged as a model system for animal circadian clocks that is both successful and representative . In the clock-bearing cells of Drosophila CLOCK/CYCLE ( CLK/CYC ) acts as the central circadian transcription complex and induces peak expression of a set of transcripts including those for the negative feedback regulators period ( per ) , timeless ( tim ) , vrille ( vri ) , and clock work orange ( cwo ) just after dusk [3]–[9] . PER and TIM proteins form a complex with the casein kinase 1ε ortholog DOUBLETIME ( DBT ) , in which TIM helps protect PER from destabilization by DBT-mediated phosphorylation [10]–[12] . PER-containing complexes enter the nucleus around midnight and trigger repression of CLK/CYC activity [5] , [13]–[16] , VRI acts as a transcriptional repressor for the Clk gene [9] , [17] , and CWO reduces CLK/CYC activity by competitively binding CLK/CYC-regulated promoter elements [4] , [7] , [8] . The circadian clock circuits are linked to synchronizing input pathways as well as output pathways that signal time-of-day information to downstream biological functions . The extensive interconnectedness of the molecular circadian cycle complicates identification of the order of its events . We reasoned that the development of transgenic flies with conditional circadian clock function , in which the circadian cycle could be arrested or started at will , would help distinguish direct from indirect effects and determine sequential steps in circadian pathways . Moreover , transgenic flies with conditionally titratable transcription of a clock component would allow molecular , cellular , and behavioral circadian phenotypes to be determined over a range of expression levels . Finally , flies with conditionally controlled clock function would allow separation of developmental and adult functions of clock genes . Based on these arguments we created conditionally rhythmic transgenic Drosophila strains . In the present study , we describe the generation of transgenic flies in which clock function becomes conditional on account of temperature-dependent rescue of the per01 or cyc01 mutations or temperature-dependent mis-expression of per . Moreover , we made use of these flies to experimentally determine the developmental requirements for a functional circadian clock as well as the individual clock components PER and CYC . We confirmed and extended previously published observations [18] , [19] indicating that developmental rescue of arrhythmia in per01 mutants is not needed for restoration of circadian rhythms in adults . However , developmental mis-expression of per or failure to developmentally rescue the cyc01 mutation led to persistent adult arrhythmia . In particular , CLK/CYC function during the pupal and pharate adult stages was associated with adult clock function . Our results suggest two distinct mechanisms underlying the developmental requirement for CLK/CYC function: ( 1 ) cyc expression contributes cell-type-autonomously in the ventral lateral neurons ( LNvs ) to the formation of peptidergic dorsal projections containing the neuropeptide PIGMENT DISPERSING FACTOR ( PDF ) , which are thought to be important for adult circadian behavior and ( 2 ) CLK/CYC activity during development enables normal clock gene expression rhythms in the adult LNvs .
We made use of the temporal and regional gene expression targeting ( TARGET ) system [20] to create transgenic flies in which the essential clock components CYC and PER were expressed conditionally in relevant spatiotemporal patterns . The TARGET system combines the binary GAL4/UAS system [21] that allows transgenic expression to be directed spatiotemporally by a promoter of interest via the intermediate regulator GAL4 with a ubiquitously expressed GAL80ts gene , which encodes a temperature sensitive inhibitor of GAL4 . As a result , the TARGET system permits GAL4-mediated transgenic expression at high temperatures ( e . g . , 29°C ) , but progressively restricts it at lower temperatures . First , we generated transgenic flies that conditionally rescued the arrhythmic per01 phenotype [22] by introducing a GAL4-driver transgene directing expression in all clock-bearing cells ( tim ( UAS ) -Gal4 ) [9] along with a GAL4-responsive per cDNA expression construct ( UAS-per ) [23] and a transgene ubiquitously expressing GAL80ts ( tubP-Gal80ts ) [20] in a per01 genetic background [22] ( see Figure 1A ) . The resulting genotype is abbreviated , here , as per01[timP>per]ts . As expected , clock-controlled phenotypes such as behavioral rhythmicity , relative rhythmic power and period length were readily and significantly modulated by environmental temperature in these flies ( Figure 1B–1D , Figures S1 and S2 ) . Robust circadian rhythms in locomotor activity were virtually absent at a restrictive temperature ( 18°C ) , but rescued to varying degrees over a range of higher temperatures ( 21–29°C ) ( Figure 1B–1D , Figures S1 and S2 ) . The circadian period length observed at 29°C was significantly longer than those at 25°C , 27°C , and 28°C for females and those at 23°C , 25°C and 28°C for males ( Figure 1D , Figure S2A , S2C; Welch test and post-hoc Games-Howell analysis ) . It is noteworthy that the decrease in rhythmicity and relative rhythmic power and the increase in circadian period length found at the highest experimental temperature of transgenic induction ( 29°C ) were also observed as a result of transgenic per over-expression in a wild-type background ( see below ) . In comparison with wild-type controls per01[timP>per]ts flies were much less rhythmic at 18°C ( or 29°C ) , but at 25°C both genotypes showed comparable percentages of rhythmic , weakly rhythmic , and arrhythmic flies ( Figure S3 ) . At permissive temperatures the most consistent difference in the behavior of per01[timP>per]ts flies relative to wild-type controls was a significantly longer circadian period length increased by 2 h or more ( Figure S3B–S3D ) . Next , we examined clock-controlled molecular responses in per01[timP>per]ts flies released from restrictive ( 17 or 18°C ) to permissive conditions ( 25°C ) . As expected , transgenic per expression was strongly induced in adult fly heads following this transition ( Figure S4A , S4B ) . In addition , the Clk , tim , vri , cwo , Par-domain Protein 1 ( Pdp1 ) , and Slow-poke binding protein ( Slob ) clock-controlled transcripts showed relative expression responses that appeared consistent with their circadian phase relationships in wild-type heads . Nevertheless , the amplitude of the observed expression responses in clock-controlled genes was reduced relative to previously reported amplitudes of circadian oscillation in wild-type heads [4] , [7]–[9] , [24]–[27] . Thus , upon transfer to permissive conditions , rescue of molecular circadian oscillations in adult per01[timP>per]ts heads , unlike behavioral rhythms , appeared to be incomplete . This discrepancy might be explained by a selective restoration of high-amplitude clock gene expression rhythms in clock neurons . We , therefore , examined circadian transcript responses in dissected adult brains of per01[timP>per]ts flies released under permissive conditions . However , molecular amplitudes in adult brains were comparable to those previously seen in adult heads ( cf Figure S4A and S4C ) suggesting incomplete restoration of molecular circadian rhythms in both peripheral clocks and the neural clock circuit . Since different time points in the Northern and Quantitative Reverse Transcriptase PCR ( qRT-PCR ) experiments of Figure S4 come from different samples of individual flies incomplete synchrony in the experimental population may have also contributed to the detection of relatively shallow transcript rhythms . To test if developmental expression of per in clock-bearing cells was required for adult clock function , we raised per01[timP>per]ts flies at 17°C in constant light ( LL ) until adulthood and examined behavioral rhythms at restrictive ( 17°C ) and subsequent permissive ( 25°C ) conditions in constant darkness ( DD ) . Consistent with the hypothesis that rescue of circadian clock function in per01 flies can be achieved when per expression is restricted to clock-bearing cells in the adult , we observed restoration of circadian locomotor rhythms immediately following transition to permissive conditions ( Figure 2A , Figure S5 ) . Although adult per01[timP>per]ts flies failed to show strongly rhythmic locomotor behavior at the restrictive temperature in DD , a subset of individual flies did exhibit residual weak rhythms under these conditions ( Figure S2 ) . Nevertheless , we do not believe that the observed behavioral rescue in adults depends on residual clock function during the prior exposure to the restrictive temperature for two reasons: ( 1 ) The phase of the restored rhythms is determined by the phase of the prior switch from restrictive to permissive conditions rather than the phase of the light/dark transition associated with the start of the behavioral experiment ( Figure 2 ) and ( 2 ) our experiments included developmental exposure to LL , which is associated with both behavioral and molecular arrhythmia as well as severely reduced PER expression levels [13] , [28] . Therefore , we conclude that there is no developmental requirement for either a functioning clock mechanism or expression of per in the clock-bearing cells in order to allow circadian clock function in adult flies . Given the ability to restore adult clock function from a circadian cycle arrest due to PER depletion , we were wondering whether circadian cycle arrests associated with excess PER expression were equally reversible . This question was addressed experimentally with the help of transgenic flies , in which per was conditionally over-expressed to high levels in clock-bearing cells due to the introduction of the tim ( UAS ) -Gal4 , UAS-per , and tubP-Gal80ts transgenes in the presence of a wild-type per gene . Flies homozygous for the autosomal tim ( UAS ) -Gal4 and UAS-per insertions with a single X-chromosomal tubP-Gal80ts transgene ( abbreviated as [timP>per]ts ) showed conditional clock function with robust rhythms , relative rhythmic power , and only marginally increased period lengths at permissive ( 17°C ) conditions and behavioral arrhythmia ( females ) or dramatically reduced rhythms ( males ) at the restrictive ( 29°C ) conditions ( Figure 3 , Figure 4 , Figures S6 and S7 ) . Loss of behavioral rhythms during prolonged exposure of adults to the restrictive condition could be reversed by returning the flies to the permissive ( 17°C ) condition ( Figure 3B , Figure S7 ) . However , comparable exposure to restrictive conditions during development resulted in irreversible adult arrhythmia ( Figure 3B , Figure 4 , Figures S6 and S7 ) for both genders . To identify the developmental phase of sensitivity to PER over-expression flies were transferred from a permissive ambient temperature ( ∼23°C ) to 29°C or vice versa at different points during development and then analyzed for behavioral rhythmicity as adults . When exposure to restrictive conditions occurred prior to the pupal stage it did not obviously affect the percentages of flies exhibiting rhythmic , weakly rhythmic or arrhythmic adult behavior or the relative power of the detected rhythms ( Figure 4 , Figure S6 ) . However , when flies were exposed to the restrictive temperature throughout the pupal and pharate adult stages , adult locomotor rhythms were clearly inhibited ( Figure 4 , Figure S6 ) . Therefore , it appears that per over-expression in pupal/pharate adult clock cells irreversibly affects adult circadian behavior . One possible explanation for the observed effect of developmental per mis-expression on adult behavior might be the persistence of abnormally high levels of PER protein into adulthood . However , PER is known to be an unstable protein and even a 7-d exposure to 12-h light/12-h dark/ ( LD ) cycles at the permissive ( 17°C ) temperature did not allow subsequent restoration of behavioral rhythms in DD . Moreover , immunofluorescence analyses of clock neurons exposed to developmental PER over-expression did not reveal a continued increase in adult PER expression . Instead , the persistent behavioral arrhythmia of [timP>per]ts flies raised at 29°C and exposed to 17°C LD for 7 d as adults appeared to be matched by blunted circadian rhythms of PER expression in the PDF-expressing ventral lateral neurons ( Figure 5 ) . No gross morphological defects in clock neurons ( including LNv , LNd , and DN cell bodies and LNv projections ) were apparent in these experiments ( see Figure S8 ) . Thus , our results indicate that excess PER activity in clock cells during metamorphosis negatively affects both adult circadian locomotor activity and molecular rhythms in adult clock neurons . Based on PER's known function as a negative regulator of CLK/CYC circadian transcription complexes the adult phenotypes associated with developmental PER over-expression are likely attributable to inhibition of CLK/CYC activity . We tested this hypothesis by determining whether adult circadian locomotor behavior required prior developmental expression of the essential clock component CYC . To this aim we generated transgenic flies that conditionally expressed cyc in postmitotic neurons by combining the elavC155::Gal4 driver element [29] with UAS-cyc [30] , and tubP-Gal80ts transgenes in a cyc01 background . The resulting flies , here referred to as cyc01 [elav>cyc]ts , showed conditional rescue of rhythmic adult locomotor activity when raised at the permissive temperature for cyc01 rescue ( 29°C ) ( Figure 6 , Figure S9 ) . Ambient temperature ( ∼23°C ) , which acted as a mostly permissive condition for per01 [timP>per]ts flies ( see Figure 1D , Figure S2 , above ) represented a restrictive condition for the cyc01 [elav>cyc]ts strain . This discrepancy is likely attributable to differences either in the amount of GAL4 protein produced in the relevant clock neurons in each of these strains or the level of GAL4-directed transgenic expression that is required to achieve behavioral rescue . Exposure of cyc01 [elav>cyc]ts flies to the restrictive temperature during metamorphosis , severely affected adult behavioral rhythms at the permissive temperature ( Figure 6B , Figure 7 , Figure S10 ) . Therefore , depletion of cyc expression during metamorphosis , indeed , phenocopies the adult behavioral defects of per mis-expression during metamorphosis . To further explore the role of CLK/CYC expression in the PDF-positive LNvs in ensuring normal adult circadian behavior and neuro-anatomy we created flies in which rescue of cyc01 in postmitotic neurons ( by elavC155::Gal4 and UAS-cyc ) was selectively blocked in the PDF-expressing LNvs with the help of a Pdf-Gal80 transgene [31] that expresses the GAL4 inhibitor GAL80 specifically in these cells . The behavioral phenotype of the resulting transgenic flies , indicated as cyc01 ( elav-Pdf ) >cyc , consists of an altered daily locomotor activity profile in the presence of light/dark cycles that includes extended activity in anticipation of lights-on , but reduced activity in anticipation of lights-off ( Figure 8A , Figure S11A ) and a loss of sustained rhythmicity in constant darkness ( Figure 8A–8C; Figure S11 ) . In contrast , control cyc01 rescue flies lacking the Pdf-Gal80 element ( cyc01 elav>cyc ) showed strong behavioral rhythms in constant darkness as well as evening activity in anticipation of the lights-off transition ( Figure 8A–8C; Figure S11 ) . The cyc01 ( elav-Pdf ) >cyc phenotype is clearly different from that of flies with ablated PDF-expressing LNvs or defective expression of the PDF neuropeptide [32] , which also lack consolidated rhythms in constant darkness but show the opposite effect on anticipation of the lights-on and lights-off transitions . The persistence of morning anticipation , which is thought to be attributable to PDF signaling from the s-LNvs [33] suggests that residual PDF expression and function persisted in s-LNvs with a cyc01 circadian cycle arrest . Moreover , it is insightful to compare the behavior of cyc01 ( elav-Pdf ) >cyc flies to previously published observations for flies , in which rescue of the per01 mutation in clock-bearing cells was blocked in the PDF-expressing clock neurons ( per01 ( elav-Pdf ) >per ) [31] . The behavior reported for per01 ( elav-Pdf ) >per flies resembles that of our cyc01 ( elav-Pdf ) >cyc flies with respect to the persistence of morning anticipation as well as reduced rhythmicity in constant darkness [31] . However , loss of evening anticipation appears to be unique to the cyc01-based as opposed to the per01-based arrest of the LNvs . Thus , cyc-depleted LNvs seemed to delay the generation of an evening activity signal by the neural clock circuits . It is possible that a slow rhythmic component in the disorganized clock circuit of cyc01 ( elav-Pdf ) >cyc flies is responsible for this delay in evening activity . Consistent with this notion , the few ( weakly ) rhythmic cyc01 ( elav-Pdf ) >cyc flies represented in Figure 8 and Figure S11 exhibited residual long period rhythms ( τ for females: 25 . 1±1 . 24 h , n = 5; τ for males 24 . 4±0 . 83 h , n = 17 ) . Next , we compared molecular and neuro-anatomical phenotypes of the PDF-expressing LNvs in cyc01 ( elav-Pdf ) >cyc flies with those of controls lacking either the Pdf-Gal80 ( cyc01 elav>cyc ) or the UAS-cyc transgenes ( cyc01 ( elav-Pdf ) >- ) . Flies of these three genotypes were raised at ambient temperature and entrained as adults to LD cycles at 25°C . Brains were harvested 2 h prior to lights-on ( ZT22 ) and stained using antibodies against PDF and the PDP1 . The Pdp1 gene is a direct target gene for CLK/CYC and mutations in Clk or cyc strongly affect PDP1 protein expression in larvae and adults [26] . Indeed , cyc01 ( elav-Pdf ) >- flies , which completely lack CLK/CYC function , exhibited greatly reduced PDP1 expression in their LNvs at ZT22 . Consistent with previous studies [34] , s-LNvs in cyc01 ( elav-Pdf ) >- brains also showed a reduced PDF signal in the s-LNvs and mostly abnormal or missing PDF-positive dorsal projections ( Figure 9 ) . These phenotypes were to a large extent rescued in cyc01 elav>cyc flies , which not only exhibited PDP1 expression in virtually all PDF-expressing LNvs ( and other clock neurons ) at ZT22 , but also presented with normal PDF-expression levels and dorsal LNv projections ( Figure 9 ) . The additional introduction of Pdf-Gal80 in the cyc01 ( elav-Pdf ) >cyc genotype , resulted in cell-type-specific phenotypes that included down-regulation of PDP1 in virtually all LNvs with detectable PDF expression as well as a reduction in the number of s-LNvs with detectable PDF expression ( see Figure 9A , 9B ) . Moreover , PDF-positive sLNv dorsal projections were either abnormal or missing from most cyc01 ( elav-Pdf ) >cyc brains ( see Figure 9C ) , although there is a formal possibility that PDF-negative sLNv projections , which would not have been detectable in these experiments , still extended to the dorsal protocerebrum . For other clock neurons no obvious differences were detected in numbers and PDP1 expression levels between the cyc01 ( elav-Pdf ) >cyc brains and the rescued cyc01 elav>cyc controls .
We created transgenic flies with conditional clock function , in which expression of the essential clock components CYC and PER was induced or repressed in relevant spatiotemporal patterns . In per01 [timP>per]ts flies , which conditionally rescue the per01 mutation , clock function was conditional and readily reversible . Moreover , adult circadian behavior was restored in flies raised under restrictive conditions . In earlier studies conducted by Ewer and colleagues widespread transgenic expression of per under control of a heat-shock protein 70 ( hsp70 ) promoter was shown to partially rescue the per01 mutation resulting in restoration of behavioral rhythms at an abnormally long period length . These long period rhythms could be generated in a conditional manner even when induction was restricted to the adult phase [18] , [19] . In the present study we targeted expression of transgenic per specifically to clock-bearing cells and achieved a more complete conditional rescue of the per01 phenotype that did not require developmental per expression . Although circadian behavior of per01 [timP>per]ts flies at 25°C showed rhythmicity comparable to that observed for wild-type flies , period lengths were at least 2 h longer than those of wild-type flies and molecular rhythms showed a relatively low amplitude . One key difference between the molecular clock circuits in per01 [timP>per]ts at the permissive temperature and those of wild-type flies is the constitutively high level of per mRNA expression in the transgenically rescued flies , which could contribute to the increased circadian period length and blunted molecular rhythms in per01 [timP>per]ts flies . Wild-type flies exhibit a trough in per transcript levels in the early morning that may facilitate subsequent down-regulation of PER protein levels and optimal induction of CLK/CYC-regulated genes [35] . The lack of a trough in per mRNA expression in the conditionally rescued flies could account for a delay in the turnover of PER protein in the morning and , therefore , a lengthened period and blunted CLK/CYC activity . This hypothesis also explains apparent discrepancies with previous reports , in which increased per gene dosage was associated with a shortened circadian period length [36] and decreased per dosage or expression resulted in longer circadian period lengths [22] , [37] , [38] . As long as per expression shows strong circadian regulation the timing of PER nuclear entry and PER-mediated transcriptional repression is predicted to be advanced by the introduction of one or two additional copies of the wild-type per gene and delayed by a reduction in per dosage , while neither manipulation is predicted to strongly affect subsequent PER turnover . Adult circadian behavior was also conditional and reversible in [timP>per]ts flies , which exhibit temperature-dependent over-expression of per . However , developmental over-expression of per during metamorphosis was associated with irreversible behavioral arrhythmia in adults . Likewise , depletion of cyc expression during the metamorphosis in cyc01 [elav>cyc]ts flies resulted in disruption of adult circadian locomotor behavior under permissive conditions . Both increased levels of PER and decreased levels of CYC negatively regulate CLK/CYC activity . The CLK/CYC heterodimer functions as the central transcriptional regulator in the Drosophila clock and its activity critically depends on the presence of both CLK and CYC [3] , [5] , [6] . Loss of functional cyc expression in the cyc01 mutant results in both molecular and circadian arrhythmia and constitutively low expression levels for CLK/CYC-regulated target genes [6] , whereas PER acts as a negative regulator of CLK/CYC activity by binding and inactivating the CLK/CYC complex [5] , [15] , [39] . The arrhythmic locomotor behavior and molecular arrhythmia in the clock neurons observed as a result of per over-expression [23] , [40] are , therefore , interpreted to result from constitutive inhibition of CLK/CYC . Adult behavioral arrhythmia in [timP>per]ts or cyc01 [elav>cyc]ts flies raised under permissive conditions was reversible ( see Figure 3B , Figure 6C , 6F , 6G , Figures S7 , S9C , S9D , above ) . However , exposures to restrictive conditions of comparable duration resulted in long-term after-effects only when they occurred during development and , particularly , during the pupal and pharate adult stages . We , therefore , attribute the effects of circadian arrests during development in [timP>per]ts or cyc01 [elav>cyc]ts flies on adult circadian behavior to a developmental requirement for CLK/CYC function beyond its immediate role in maintaining daily time keeping . The requirement for CLK/CYC activity , but not clock function per se may indicate that one or more transcriptional CLK/CYC targets play a role in enabling adult circadian locomotor behavior . Such targets would likely be expressed constitutively along with other CLK/CYC-regulated genes in conditionally arrested per01 [timP>per]ts flies , but constitutively down-regulated in circadian arrests due to low CLK/CYC activity . A central question that remains is what mechanism links developmental CLK/CYC activity to adult circadian behavior . Our experiments indicate that both clock neuron anatomy and the molecular oscillator itself may be involved . Previously published studies of constitutively arrhythmic alleles of the Clk and cyc genes have documented a reduction in PDF expression as well as neuro-anatomical defects in the LNvs [34] that could be associated with a developmental role for the CLK/CYC transcription factor . By selectively blocking transgenic rescue of cyc01 in the PDF-expressing clock neurons we show , here , that the reduction of PDF expression and PDF-positive dorsal projections from the s-LNvs is a cell-type specific phenotype . PDF is known to play an important role in mediating clock-controlled behavior in both LD and DD conditions . The PDF-producing s-LNvs project towards the dorsal protocerebrum as do DN1 , DN2 , DN3 , and LNd clock neurons , suggesting that the dorsal s-LNv projections may play an important part in signaling across the neural clock circuits [41] . In this context , it may be relevant that expression of the PDF RECEPTOR in ‘E’ cells , a subset of clock neurons including DN1s and LNds [31] , has been associated with circadian control of locomotor activity [42] . Moreover , the axonal terminals of the dorsal s-LNv projections undergo clock-controlled rhythms in remodeling that may play a role in circadian signaling [43] . Nevertheless , the observed developmental requirement for CLK/CYC activity also appears to involve mechanisms other than PDF-mediated signaling for the following reasons . First , developmental over-expression of PER resulted in persistent adult arrhythmia , but did not lead to a loss of PDF-positive dorsal projections from the s-LNvs ( see Figure S8 ) . Second , while developmental suppression of CLK/CYC activity uniformly affected the behavior of adult flies ( Figure 4 , Figure 7 , Figures S6 , S7 , S10 ) constitutive depletion of CYC from the PDF-expressing neurons resulted in a variable phenotype in the s-LNv dorsal projections ( see Figure 9C ) . Third , the light/dark activity pattern of cyc01 ( elav-Pdf ) >cyc flies ( Figure 8A , Figure S11A ) was strikingly different from that of Pdf01 flies or flies from which the PDF-expressing cells have been ablated [32] , suggesting that PDF signaling persisted in cyc-depleted LNvs in spite of the defects in PDF-positive dorsal projections . In principle , neuro-anatomical defects affecting intercellular connectivity rather than cell-autonomous clock function could lead to behavioral phenotypes due to asynchrony among the clock neurons or the loss of output signals . Indeed , apparent separation of molecular and behavioral phenotypes has been reported previously for genetic manipulation of CLK/CYC function [44] , [45] . It may be particularly relevant that rescue of the per01 phenotype in the PDF-expressing clock neurons restores rhythmic behavior [46] , while rescue of cyc01 in the same cells restores molecular , but not behavioral rhythms [45] . However , our experimental results also provide support for developmental phenotypes at the level of the adult molecular clock circuits . Our immunofluorescence expression analyses indicated that the molecular clock circuits in the adult PDF-expressing clock neurons were affected by developmental over-expression of PER . PDF-expressing LNvs in adults that were behaviorally arrhythmic due to developmental PER over-expression exhibited adult PER expression with an altered daily profile , but not necessarily at excessively high levels . Future studies may determine the degree to which neuro-anatomical and molecular phenotypes are linked and help determine the effect of intercellular connectivity in the neural clock circuit on the function of molecular circadian rhythms in individual clock neurons .
Flies were raised on standard yeast cornmeal agar food either at ambient temperature ( observed to range between 22°C and 24°C ) or other experimental temperatures as specified . The conditional per01 rescue flies indicated as per01 [timP>per]ts in Figure 1 , Figure 2 and Figures S1 , S2 , S3 , S5 consisted of male and female y per01 w; tim ( UAS ) -Gal4/tubPGal80ts; UAS-per/+ offspring from a cross between stable lines y per01 w; tim ( UAS ) -Gal4 and y per01 w; tubPGal80ts; UAS-per . These stocks were created by combining the previously described per01 [22] , tim ( UAS ) -Gal4 [9] , tubPGal80ts [20] , and UAS-per [23] genetic elements . Flies with conditional over-expression of per in clock-bearing cells ( [timP>per]ts in Figure 3 , Figure 4 , Figure 5 , and Figures S6 , S7 , S8 ) were obtained from a genetically stable y tubPGal80ts w/FM7c; tim ( UAS ) -Gal4; UAS-per stock as females heterozygous for FM7c and non-FM7c males . The insertion site of the tubPGal80ts transgene in this stock appears to be associated with homozygous female lethality . An X-chromosomal period-lengthening allele present in the genetic background of the original tubPGal80ts stocks was avoided during the creation of the [timP>per]ts stock by recombination with a control y w chromosome . Flies with conditional rescue of cyc01 ( cyc01 [elav>cyc]ts in Figure 6 , Figure 7 and Figures S9 , S10 ) were obtained from a stable elavC155::Gal4; UAS-cyc/CyO; cyc01 tubPGal80ts stock as males and females heterozygous for CyO . The elavC155::Gal4 [29] , UAS-cyc [30] , cyc01 [6] , and tubPGal80ts [20] elements used to create this stock had al been described previously . The cyc01 rescue line elavC155::Gal4; UAS-cyc/CyO; cyc01 ( cyc01 elav>cyc in Figure 8 , Figure 9 and Figure S11 ) was created as a stable stock , whereas the selective cyc01 rescue genotype elavC155::Gal4; UAS-cyc/Pdf-Gal80; cyc01 and the unrescued control genotype elavC155::Gal4; CyO/Pdf-Gal80; cyc01 ( respectively , cyc01 ( elav-Pdf ) >cyc and cyc01 ( elav-Pdf ) >- in Figure 8 , Figure 9 and ) were obtained in offspring from a cross of elavC155::Gal4; UAS-cyc/CyO; cyc01 flies with elavC155::Gal4; Pdf-Gal80; cyc01 flies . The Pdf-Gal80 element used in the latter two genotypes has also been characterized previously [31] . Using previously described protocols [47] , locomotor activity was monitored for individual adult flies of both genders in glass tubes on standard sugar agar media including 0 . 07% Tegosept ( Genesee Scientific ) using the Drosophila Activity Monitoring System ( TriKinetics ) . Experiments were conducted in incubators kept at 70% relative humidity in 12 h L∶ 12 h D or DD conditions using white fluorescent light with an approximate intensity of 450 µW/cm2 during the L condition . Due to lack of space only analyses for female flies are shown in Figure 1BC , Figure 4 , Figure 6D–6G , Figure 7 , and Figure 8; the corresponding analyses for male flies are found in Figures S1 , S6 , S9 , S10 , and S11 , respectively . Individual , experimental average , and experimental median activity records , as well as periodic activity profiles , and chi-square periodograms were generated using ClockLab Software ( ActiMetrics ) . Actograms ( Figure 1BC , Figure 2A , Figure 3B , Figure 6B and 6C , Figure 8A , Figures S1 , S3A–S3C , S11A ) were double-plotted with a resolution of half-hour intervals . Each row represents a 2-day interval of Zeitgeber Time ( ZT , with ZT0 as the time of lights-on; during LD ) or Circadian Time ( CT; during DD ) , of which the second day is repeated as the first day on the next row . Chi-square periodograms ( Figure 1BC , Figure 3B , Figure 6B and 6C , Figures S1 and S3A–S3C ) were used to represent the experimental signal ( amplitude ) observed for a range of period lengths ( τ , x-axis ) relative to threshold values associated with a p<0 . 01 significance ( red line ) . Analyses of the percentages of rhythmic , weakly rhythmic , and arrhythmic flies ( Figure 1D , Figure 4A , Figure 6D and 6F , Figure 7A , Figure 8B , Figures S2AC , S3D , S5AC , S6A , S7A , S7B , S7D , S7E , S9A , S9C , S10A , S11B ) were based on chi-square periodogram statistics for locomotor activity rhythms of individual flies . For period lengths in the circadian range ( ∼15–36 h ) detected with a significance of p<0 . 01 relative rhythmic power was calculated by dividing the detected peak amplitude by the significance threshold value at the same period length . Flies were classified based on their values of relative rhythmic power as rhythmic ( >1 . 5 ) or weakly rhythmic ( [1 , 1 . 5] ) and flies without significant periodicity in the circadian range were considered arrhythmic . Chi-square analyses for association of genotype or experimental protocol with the relative distribution of rhythmic , weakly rhythmic , or arrhythmic behavior were conducted using Microsoft Excel ( Microsoft ) . Next , statistical analyses were performed using SPSS software ( IBM ) to detect associations between the relative rhythmic power values of rhythmic and weakly rhythmic flies with experimental conditions ( Figure 4B , Figure 6E and 6G , Figure 7B , Figures S2BD , S5BD , S6B , S7CF , S9BD , S10B ) or genotypes ( Figure 8C , Figure S11C ) . In virtually all cases Levene's test indicated that homogeneous variances could not be assumed . Therefore , the Welch test statistic with Games-Howell post-hoc analysis was used to test for significant differences in relative rhythmic power among different genotypes and treatments . When only two conditions were compared the non-parametric Mann-Whitney rank-sum test was performed . Average ( Figure 2A , Figure 6B and 6C , Figure S3A , S3B , S3C ) and median ( Figure 1B and 1C , Figure 3B , Figure 8A , Figures S1 , S11A ) activity records that emphasize reproducible features of rhythmic locomotor activity measured in individual flies were created without prior normalization from the raw individual activity records on a point-by-point basis . For representation in illustrative double-plotted actograms we generally used median activity records , which are less susceptible to skewing by outliers and show discrete numbers of events per half-hour bin , but when a better resolution of data with relatively low activity counts was preferred average activity records were used instead . Average daily or circadian activity profiles representing records of median or average activity ± the Standard Error of the Mean ( SEM ) were generated across included days under entraining ( Figure 8A , Figure S11A ) or free running conditions ( S3 , for phase determination in Figure 2B ) , respectively . The phase of the offset of circadian activity ( Figure 2B ) was determined from the activity profiles by interpolation as described previously [47] . Error bars throughout the manuscript represent SEM , except in cases where less than three observations were made . The parentheses surrounding individual error bars in Figure 1B ( right-hand panel ) , Figure S7F , and Figure S9B indicate that these represent the range of two observations , instead . Extraction of total RNA from approximately 100 µl adult heads per time point using the guanidinium thiocyanate/cesium chloride method and subsequent Northern analysis were conducted according to previously published protocols [48] , [49] . Quantitative analysis of the radioactive signals on the blots was conducted with a Storm 840 Phosphorimager ( GE healthcare ) and the resulting data was graphed using Microsoft Excel ( Microsoft Corporation ) . Five independent time course experiments were conducted addressing the transcript responses observed in the adult head upon transfer of per01 [timP>per]ts flies from restrictive to permissive conditions . A representative example is shown in Figure S4A . Flies for the conditions of interest were harvested onto ice , and either adult heads or brains were dissected on a chilled platform and transferred to guanidinium thiocyanate buffer . DNAse I-digested total RNA was obtained from the heads or brains using the RNAqueous4PCR kit ( Ambion ) . Aliquots of the RNA samples were then analyzed with the SuperScript III Platinum SYBR Green One-Step qPCR Kit ( Invitrogen ) using experimental primer pairs designed to specifically amplify fragments of the circadian per , tim , vri , and cwo transcripts , the transgenic UAS-per transcript or the rp49 control transcript . Expression levels measured on a SmartCycler system ( Cepheid ) relative to rp49 were determined using the comparative Cycle threshold ( Ct ) method [50] . Adult brains were dissected , fixed , and stained for immunofluorescence analysis according to standard protocols [51] . Imaging was conducted with a spinning disk confocal microscope . Brains from [timP>per]ts flies raised under restrictive versus permissive conditions were probed with primary antibodies against PDF ( mouse monoclonal; DSHB ) as well as PER ( rabbit polyclonal; [52] ) , whereas brains from cyc01 elav>cyc and cyc01 ( elav-Pdf ) >cyc flies were stained with antibodies to PDF as well as PDP1 ( rabbit polyclonal;[26] ) and then visualized with fluorescently labeled secondary antibodies ( Alexa-488 for PDF , Alexa-568 for PER or PDP1 ) .
|
The fruit fly Drosophila melanogaster is an excellent model system for studying the internal circadian clocks that animals use for daily time keeping . Since clocks exist and function in animals not only in adults , but also during prior development , the question arises if and how adult circadian rhythms depend on developmental clock circuits and components . To address this question we created transgenic flies in which the essential clock components CLOCK/CYCLE ( CLK/CYC ) and PERIOD ( PER ) can be manipulated via environmental temperature . Stopping the clock during development by depleting the negative regulator PER did not prevent restoration of circadian time keeping in the adult . However , a developmental arrest of the clock due to either depletion of the positive regulator CYC or overproduction of PER resulted in a persistent loss of clock-controlled behavior function in adults . Taken together , these observations indicate that adult clock function developmentally requires activity of the CLK/CYC transcription complex rather than a ticking clock . Based on the behavioral , molecular , and anatomical consequences of inhibiting CLK/CYC in circadian pacemaker neurons , we propose that the developmental requirement maps to these cells . It will be interesting to determine whether there is a comparable developmental requirement for the equivalent clock genes in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"regulation",
"anatomy",
"and",
"physiology",
"neuroscience",
"gene",
"function",
"animal",
"models",
"physiological",
"processes",
"developmental",
"biology",
"drosophila",
"melanogaster",
"model",
"organisms",
"organism",
"development",
"chronobiology",
"molecular",
"genetics",
"biology",
"cellular",
"neuroscience",
"neuronal",
"morphology",
"physiology",
"genetics",
"metamorphosis",
"behavioral",
"neuroscience",
"genetics",
"and",
"genomics"
] |
2011
|
Adult Circadian Behavior in Drosophila Requires Developmental Expression of cycle, But Not period
|
Down syndrome ( DS ) , commonly caused by an extra copy of chromosome 21 ( chr21 ) , occurs in approximately one out of 700 live births . Precisely how an extra chr21 causes over 80 clinically defined phenotypes is not yet clear . Reduced representation bisulfite sequencing ( RRBS ) analysis at single base resolution revealed DNA hypermethylation in all autosomes in DS samples . We hypothesize that such global hypermethylation may be mediated by down-regulation of TET family genes involved in DNA demethylation , and down-regulation of REST/NRSF involved in transcriptional and epigenetic regulation . Genes located on chr21 were up-regulated by an average of 53% in DS compared to normal villi , while genes with promoter hypermethylation were modestly down-regulated . DNA methylation perturbation was conserved in DS placenta villi and in adult DS peripheral blood leukocytes , and enriched for genes known to be causally associated with DS phenotypes . Our data suggest that global epigenetic changes may occur early in development and contribute to DS phenotypes .
Genomic copy variations ranging from copy number variations to chromosome aneuploidies offer biological diversity and are also a common cause of genetic disorders . Down syndrome ( DS ) , caused by triplication of chromosome 21 ( chr21 ) , is characterized by over 80 clinically defined phenotypes of different penetrance and expressivity affecting many different organs such as the central nervous system , heart , gastrointestinal tract , and immune system [1] . Since the genetic basis for DS is clearly caused by an extra copy ( occasionally a partial extra copy ) of chr21 , many studies focused on genes located on chr21 . Many , but clearly not all , genes located on chr21 are expressed at higher levels in individuals with DS or mouse models [2]–[4] . Meanwhile , many genes on other chromosomes were also dys-regulated [5]–[7] . How an extra chr21 causes global gene expression dys-regulation and how such dys-regulation contributes to DS phenotypes remain to be addressed . Epigenetic regulation of gene expression is one important mechanism in development and disease . In the nervous system , many key enzymes such as DNMT1 , DNMT3A , and TET1 for epigenetic regulation are abundantly expressed [8] , [9] . Epigenetic alternations are frequently observed in intellectual disability syndromes [10] . For example , Rett syndrome may be caused by mutations in MECP2 [11] . In psychosis , DNA hypermethylation was observed , presumably due to elevated levels of methyl donor S-adenosylmethionine ( SAM ) , and DNMT1 over-expression [12] . In DS , genes such as DYRK1A located on chr21 are potential candidates causing disorders in the nervous system [13] . Homocysteine metabolism is perturbed in children with DS , resulting in lower levels of SAM and S-adenosylhomocysteine ( SAH ) [14] . Small-scale DNA methylation analyses were performed to study potential DNA methylation perturbations in DS [15]–[18] . Intriguingly , promoter hypermethylation was observed in DS [18] , despite of lower levels of SAM . To understand , at epigenome level , the potential perturbations associated with DS , and whether such perturbations are functionally relevant to DS , we quantified CpG methylation at single base resolution in 17 placenta villi samples ( 11 DS and six normal samples ) with an improved version of reduced representation bisulfite sequencing ( RRBS ) . We further quantified the transcriptome in placenta villi ( four DS and five normal samples ) . A global hypermethylation in all genomic regions and all autosomes were observed in DS samples , with genes with promoter hypermethylation enriched for functions relevant to DS phenotypes . Our data suggest epigenetic perturbation may be one important mechanism linking the most common chromosomal aneuploidy and its phenotypes .
RRBS was used to quantify DNA methylation . On average , about 1 . 7 million CpG sites with a sequencing depth ≥10 ( minimum sequencing depth of 10 is used in all subsequent analyses , unless specified otherwise ) in each of 17 placenta villi samples ( 11 DS and six normal samples ) ( Table S1 and Figure S1A-S1B ) . Principal component analysis revealed separation of samples based on disease status ( normal or DS ) , but not on gender ( Figure S1C ) . Assayed CpG sites represent about 3 . 0% of all CpG sites in the human genome ( on both the forward and the reverse strands ) ( Figure 1A ) , spreading across regions that are CpG rich ( CpG islands , 731 , 924 CpGs ) , CpG medium rich ( CpG island shores , defined as 2-kb upstream or downstream of CGIs , 218 , 659 CpGs ) , and other genomic regions ( 738 , 598 CpGs ) ( Figure 1B ) . The covered CpGs were distributed in promoters ( defined as −1000 bp to +500 relative to a transcription start site , 407 , 052 CpGs ) , intragenic regions ( 665 , 138 CpGs ) , intergenic regions ( 626 , 087 CpGs ) and transcription termination regions ( TTRs , defined as −500 to +500 relative to a transcription termination site , 37 , 225 CpGs ) ( Figure 1B ) . On average , 20 , 808 CGIs , 25 , 029 CGI shores and 23 , 061 promoters ( Figure 1A ) were covered for each individual sample , representing 75 . 1% , 50 . 8% and 51 . 9% of all such regions in the human genome , respectively [19] . Two technical replicates for one sample ( sample T3 in Table S1 ) with independent bisulfite conversions were reproducible ( r = 0 . 957 , Figure S2A ) . We also compared our data with a published report using Illumina HumanMethylation27K BeadChip [18] and identified 2 , 894 CpGs that were analyzed by both data sets . Good correlation was observed for both normal ( r = 0 . 929 ) and DS samples ( r = 0 . 913 ) ( Figure S2B–S2C ) . The methylation levels of the CpGs showed a bimodal distribution pattern with ∼30% of the CpGs at 0–5% methylation , and ∼10% of the CpGs at 95–100% methylation ( Figure S3A ) , consistent with earlier large-scale DNA methylome studies in other cell types [20]–[25] , although the proportion of fully methylated CpGs was substantially lower in this study due to the intentional RRBS design to remove repetitive sequences . The distributions of methylation levels for CpGs from different functional locations ( promoters , TTRs , intragenic , and intergenic regions ) were dramatically different ( Figure S3B–S3E ) . CpGs in the promoters were much more enriched in the 0–5% methylation level while very few CpGs were methylated at levels higher than 20% . Higher proportions of CpG sites were partially methylated ( 30–70% methylation level ) in non-promoter regions , an observation also made by others [24] . We next assessed the inter-individual variability in CpG methylation [26] in the five normal samples with male fetuses . We selected partially methylated CpGs ( average methylation 30–70% in the five samples ) since these CpGs were likely to be most variable . At a minimum sequencing depth cut-off of 10 , 20 , or 50 , the overall variability levels measured by standard deviations in the five samples for each partially methylated CpGs were relatively low ( Figure S4 ) , typically below 10% . Interestingly , a number of CpGs were highly variable among the five normal samples . We observed a global DNA hypermethylation in DS samples . Earlier reports showed that hypermethylated promoters outnumbered hypomethylated promoters in DS chorionic villus samples and leukocytes [18] , [27] . In our study , dominance of hypermethylation over hypomethylation in DS was seen in all genomic regions ( promoters , intragenic regions , intergenic regions and transcription termination regions , Figure 2A–2F , Table S2 ) , and in all autosomes ( Figure 2G ) . Such dominance of hypermethylation was most pronounced in promoter regions , particularly promoters overlapping with CGIs ( hypermethylated CpG number/hypomethylated CpG number: 56 . 2 ) . The average CGI methylation levels in individual DS samples were also higher than those of normal samples ( p<0 . 002 , Wilcoxon rank-sum test , two-sided ) ( Figure 2H ) . Global hypermethylation in DS ( not limited or even enriched in chr21 ) is different from X chromosome-specific DNA hypermethylation in females as hypermethylation in the latter is largely confined to the X chromosome . Studies on differential DNA methylation have traditionally been focused on CGIs and promoters . In our study , differential DNA methylation ( hypermethylated and hypomethylated CpGs ) was most frequent in intergenic and TTR regions , followed closely by intragenic regions . Promoters , particularly those overlapping with CGIs , were the least likely to be differentially methylated due to DS ( Table S2 ) , consistent with recent genome-wide DNA methylation studies [24] , [25] . Differential DNA methylation in DS showed conservation in different tissues and across the life course . Out of the nine genes with differential DNA methylation between peripheral blood leukocytes ( PBLs ) from DS adults and karyotypically normal controls reported by Kerkel et al . [27] , three genes ( TCF7 , FAM62C , and CPT1B ) were also similarly differentially methylated in the placenta villi in this study ( p<1 . 8×10−9 , see methods ) . Differential DNA methylation of these genes was further validated by the EpiTYPER assays using gestational age matched samples ( 14 normal and 17 DS samples , Table S3 , Figure S5A–S5B ) . The placenta is of extraembryonic origin while the PBLs are derived from the embryo proper . Significant conservation in DNA methylation perturbation in these two samples of different developmental origins suggests that DNA methylation perturbation in DS may occur very early in development . We next performed RNA-Seq analysis in five normal and four DS placenta villi samples ( Table S1 ) . Genes located on chr21 were up-regulated by an average of 53% in DS ( Figure 3A ) , consistent with previous reports [2]–[4] . _ENREF_10_ENREF_10Many well-studied genes such as BACH1 , SOD1 , TIAM1 , ITSN1 , DSCR1/RCAN1 , and DYRK1A located on chr21 were up-regulated ( Table S4 ) . A total of 589 genes across all autosomes were hypermethylated in the promoters in DS . Out of the 589 genes , 207 genes passed the expression threshold ( reads per kilobase per million mapped reads , RPKM≥0 . 5 ) and are located on autosomes other than chr21 . Significant down-regulation of gene expression was observed for the 207 genes ( p<0 . 05 , Wilcoxon rank-sum test , two-sided ) . Interestingly , the association between promoter hypermethylation and gene expression repression was more pronounced for promoters with lower DNA methylation in the normal samples , suggesting that increased methylation in originally unmethylated promoters is likely to have a bigger impact on gene expression ( Figure 3B ) . We further validated four genes ( CES1 , TFAP2E , CDH13 , NDN ) that showed increased promoter methylation and decreased gene expression , with EpiTYPER assays and quantitative real-time PCR , with a new set of gestational age matched samples ( Table S3 , Figure S6A–S6B ) . An overall DNA hypermethylation in DS is intriguing since reduced levels of SAM ( a primary methyl donor ) and SAH were observed in the plasma of individuals with DS [14] , suggesting enzymes regulating DNA methylation , instead of the availability of methyl donor molecules , are involved . To explore potential pathways leading to global DNA hypermethylation in DS , we investigated the expression changes for several groups of genes involved in epigenetic regulation ( Table S4 ) . The TET family genes ( TET1 ( chr10 ) , TET2 ( chr4 ) , and TET3 ( chr2 ) ) involved in DNA demethylation [9] , [28]–[30] were all down-regulated in DS . TET1 and TET2 down-regulation was further validated with quantitative real-time PCR on a new set of gestational age matched samples ( Table S3 , Figure S7A–S7B ) , while TET3 down-regulation was not statistically significant ( Figure S7C ) . Global DNA hypermethylation was previously observed in TET1 knockdown mouse ES cells [31] . TET1−/− mice were viable , with deficiency in adult neurogenesis ( Cui Q . Y . et al . , manuscript under review ) and smaller body size [32] , phenotypes also observed in DS [33]–[37] . Notably , CpG hypermethylation in DS was indeed 50% more frequent in TET target regions enriched for 5′-hydroxylmethylcytosine [38] . We carried out pathway and process network analyses for 598 genes with differential methylation in their promoters ( hypermethylation: 589 , hypomethylation: 9 ) in DS with a commercial database ( MetaCore from GeneGo Inc . ) . The three significantly enriched ( Hypergeometric p<0 . 05 , corrected for multiple testing ) pathway maps were “Immune response_Lectin induced complement pathway [39]” , “neurophysiological process Dopamine D2 receptor signaling in CNS” and “cytoskeleton remodeling Neurofilaments” ( Figure S8A–S8D ) . Each pathway contained five differentially methylated genes without overlapping genes among the pathways . The three significantly enriched process networks were “Inflammation Complement system” , “Signal transduction Neuropeptide signaling pathways” , “Developmental Neurogenesis Axonal guidance” ( Figure S8E ) . Both analyses pointed to perturbations in the physiology and activity of the neurons , consistent with cognitive impairment and neuronal degeneration being the most prevalent DS phenotypes , and perturbations in the immune system . In addition , eight of the 598 differentially methylated promoters were included in the GeneGO list with causal association to DS ( Table S5 ) . This represents a significant enrichment for DS causally associated genes ( p<0 . 05 , permutation test , 1000 permutations , assuming a universe of 15 , 203 background genes ) . Genes targeted by repressor element 1 silencing transcription factor ( REST ) , aka NRSF , were found to be enriched for differential promoter methylation in DS ( Figure S9 , Table S6 ) . REST is a transcriptional and epigenetic regulator in both neuronal and non-neural cells ( e . g . heart ) [40] . Decreased REST mRNA levels were found in cultured fetal DS brain cell-derived neurospheres [41] . In the placental villi , we also found a down-regulation of REST gene expression in DS samples ( Table S4 ) by RNA-Seq , and quantitative real-time PCR on a set of gestational age matched samples ( Figure S7D , p<0 . 05 , t-test , two-sided ) . Recent work by Stadler et al . demonstrated that REST binding to its target regions was sufficient and necessary to maintain DNA hypomethylation in what they called low-methylated regions [24] . In REST −/− cells DNA hypermethylation was observed [24] . Down-regulation of REST in DS may lead to reduced binding of REST to its target genes , resulting in DNA hypermethylation in the target regions ( Figure 2I ) . REST target genes were marginally up-regulated ( Figure 3A , p = 0 . 06 , Wilcoxon rank-sum test , two-sided ) , consistent with REST being largely a repressor in gene expression .
We propose that epigenetic regulation is one possible mechanism connecting Trisomy 21 and DS phenotypes ( Figure 4A ) . A persistent epigenetic perturbation may occur in DS embryos early in development , as supported by three out of the nine genes being similarly differentially methylated in the placenta villi in early gestation and peripheral blood leukocytes in adulthood . Such early perturbation may confer certain survival advantages , while leaving individuals with DS suffering from developmental defects and elevated risks to certain diseases . Additional epigenetic perturbations may occur later in development , further contributing to various DS phenotypes . Data from other groups and this study also provided two possible pathways leading to global DNA hypermethylation in DS . Down-regulation of the TET family genes may lead to hypermethylation of their target regions through decreased DNA demethylation ( Figure 4B ) . Elevated expression of DYRK1A , a gene located in the DS critical region on chr21 , may induce global epigenetic changes via down-regulating REST expression to cause hypermethylation of REST target genes ( Figure 4C ) . DYRK1A mediates down-regulation of REST and interacts with the REST–SWI/SNF chromatin remodeling complex in mouse Trisomy 21 models [37] , [42] . Global hypermethylation may also be mediated by other enzymes involved in epigenetic regulation of histone modifications . Cautions should be taken for interpreting DNA methylome data derived from the placenta tissues as there are multiple confounding factors such as gestational age of the placenta [43] , gender , and potentially different cell type mixtures from different samples . For both DNA methylation and gene expression , we validated a number of genes using a new set of gestational age matched samples ( normal and DS ) , with EpiTYPER ( for DNA methylation ) and quantitative real-time PCR ( for gene expression ) . We also excluded the X and Y chromosomes from differential DNA methylation analysis since the female X chromosome is known to be hypermethylated compared with the male X chromosome . A few issues remain to be addressed in our model . First , how are the TET genes down-regulated in DS . To our knowledge , regulation of TET genes is not yet well understood . Are chr21 genes directly involved in the down-regulation , or is it an indirect effect ? Segmental trisomies [44] , [45] may be useful in mapping chr21 genes involved in TET genes regulation . Second , bisulfite sequencing does not distinguish between 5-hydroxylmethylcytosine ( 5hmC ) and 5-methylcytosine ( 5mC ) . Is there a concurrent decrease in 5hmC level for the hypermethylated regions in DS ? Third , the functional roles of the two pathways in our model need further characterization , possibly in cell lines or tissues relevant to specific DS phenotypes . Fourth , other potential pathways with epigenetic perturbations in DS remain to be further elucidated . It would be interesting to ask whether epigenetics plays a role for these genes to affect phenotypes . Additionally , it should be noted that although some epigenetic perturbations may be conserved in different tissues , the functional effects of epigenetic perturbations are likely to be temporal and spatial specific . To decipher the exact mechanisms for various DS phenotypes , studies on other tissues at different developmental stages may be necessary , possibly using murine models . Hopefully , a better understanding of the molecular and cellular abnormalities associated with DS may lead to new therapies for the sequela of DS , such as cognitive and developmental defects [46] , [47] .
Informed consent was obtained under the ethics approval from the SingHealth CRIB Committee . Women with euploidy and Down syndrome ( DS ) pregnancies who attended KK Women's and Children's Hospital , Singapore , were recruited . Chorionic villus samples from subjects carrying a normal or DS fetus at the first or second trimesters of pregnancy were collected by chorinic villus sampling ( CVS ) . Placenta villi samples ( fetal side ) from DS fetuses were collected from termination of pregnancy ( TOP ) . All tissue samples were washed with diethylpyrocarbonate ( Sigma-Aldrich , USA ) treated water . For DNA analysis , tissues were stored at −80°C . For RNA analysis , tissues were incubated with RNAlater ( Life Technologies , USA ) at 4°C overnight , and then stored at −80°C . Genomic DNA extraction from tissues was performed with QIAamp DNA Mini Kit ( QIAGEN GmbH , Germany ) , according to manufacturer's instructions . Total RNA was extracted from frozen tissues using TRIZOL protocol ( Life Technologies ) . Six DNA samples from normal pregnancies and 11 samples from pregnancies carrying DS fetuses were chosen for DNA methylation analysis by RRBS ( Table S1 ) . Briefly , 1–5 µg of high molecular weight ( >10 kb ) genomic DNA was used for each library preparation . Each DNA sample was sequentially digested by MspI ( New England Biolabs , USA ) ( 150 Units , two hours , 37°C ) and TaqαI ( New England Biolabs ) ( 150 Units , two hours , 65°C ) . The digested product was purified with the QIAquick PCR Purification Kit ( QIAGEN GmbH ) , and was end-repaired , 3′-end-adenylated , and adapter-ligated using ChIP-Seq Sample Preparation Kit ( Illumina , USA ) . Illumina's RRBS for Methylation Analysis protocol was followed , except that 10 µL of the methylation adapter oligonucleotides were used and the ligation was performed for 15 min at 20°C in the adapter-ligation step . Two different sizes of fragments ( 150–197 bp and 207–230 bp ) were selected by gel electrophoresis with a 3% agarose gel . The purified fragments were then bisulfite treated using the EZ DNA Methylation-Gold Kit ( Zymo Research , USA ) . The converted DNA was amplified using HotStarTaq DNA Polymerase Kit ( QIAGEN GmbH ) , with 1× reaction buffer , 1 . 5 mM of additional MgCl2 , 300 µM of dNTP mix , 500 nM each of PCR primer PE 1 . 0 and 2 . 0 , and 2 . 5 U of HotStarTaq DNA polymerase . The thermocycling condition was 15 min at 94°C for heat activation , and 8–12 cycles of 20 sec at 94°C , 30 sec at 65°C and 30 sec at 72°C , followed by a 5 min final extension at 72°C . The amplified fragments were purified by gel electrophoresis and further quantified by the Agilent 2100 Bioanalyzer ( Agilent Technologies , USA ) . Each DNA library was analyzed by two lanes of paired-end sequencing ( 2×36 bp ) read on an Illumina Genome Analyzer IIx . Sequencing data were deposited into the GEO database with accession numbers GSE42144 . The paired-end 36 bp reads were filtered based on their Phred scores , using a cutoff of 30 which indicates a base calling error probability of 0 . 001 . All reads were then converted in silico based on the C/G base count ratios . Two reference genomes were created , obtained by either converting all cytosine to thymines ( C2T converted genome ) , or all guanines to adenosines ( G2A converted genome ) . The converted reads were aligned to both genomes using the Bowtie program [48] . Bisulfite conversion rate was calculated by:Where non-CpG C→T indicates successful conversion of C to T in non-CpG sites , and non-CpG C→C indicates failed conversion of C to T in non-CpG sites . Polymorphisms overlapping with CpGs may introduce abnormalities . In this regard , CpG sites with percentage of dinucleotide ‘XY’ other than ‘CG’ or ‘TG’ greater than 20% of all reads were deemed to be polymorphic for the sample and were excluded for further analysis . Differential DNA methylation between normal and DS samples were analyzed at single CpG level and at genomic region ( CGI and promoters ) levels . A total of 1 , 562 , 872 CpGs covered in at least 3 normal samples and at least 6 DS samples were used for further analysis . CpGs on the chromosomes X and Y were excluded . A CpG was considered as differentially methylated when 1 ) methylation difference between average DS and average normal samples was at least 10%; and 2 ) p<0 . 05 , Wilcoxon rank-sum test , two-sided . For genomic regions , at least 6 CpGs in each genomic region were required . A genomic region was considered as differentially methylated when 1 ) methylation difference between average DS and average normal samples was at least 10%; and 2 ) p<0 . 05 , Wilcoxon rank-sum test , two-sided . Probability density function ( PDF ) for methylation differences between DS and normal samples were calculated and plotted with the R package . Five RNA samples from normal pregnancies and 4 samples from pregnancies carrying DS fetuses were chosen for mRNA-seq analysis ( Table S1 ) . Briefly , 2–5 µg of total RNA was used for each library preparation . Each RNA sample was treated with DNase I ( Life Technologies ) . Messenger RNA purification and fragmentation , complementary DNA synthesis , end-repair , 3′-end-adenylation , and adapter-ligation were performed using Illumina's mRNA-Seq Sample Preparation Kit . Manufacturer's instructions were followed , except that the SuperScript III First-Strand Synthesis SuperMix ( Life Technologies ) was used for first strand cDNA synthesis . Adapter-ligated cDNA fragments were size-selected using a 3% agarose gel ( 200±25 bp ) . The DNA samples were then amplified by PCR for 15–16 cycles . The PCR products were purified using 3% agarose gels and further quantified by the Agilent 2100 Bioanalyzer ( Agilent Technologies ) . Each library was analyzed by one lane of either 36 bp single read or 2×36 bp paired-end sequencing on an Illumina Genome Analyzer IIx . RNA-Seq data were analyzed using Illumina RNA-Seq pipeline , CASAVA software version 1 . 7 . The high quality reads were aligned step-wise to three reference files , mitochondrial DNA ( chrM ) that makes up the contaminant reference , hg19 genome assembly , and splice junction set created using the refFlat file , using default parameters . All the reference sequences were downloaded from UCSC website ( http://hgdownload . cse . ucsc . edu/goldenPath/hg19/chromosomes/ ) . The expression level for each gene was represented by the reads per kilobase per million mapped reads ( RPKM ) value , using the formula below:Average RPKM values for each gene in each sample group ( normal and DS ) were calculated . When the average RPKM for a gene is less than 0 . 5 , the value was set as 0 . 5 . A gene was considered to be differentially expressed between normal and DS samples when: 1 ) Binomial test with a Benjamini-Hochberg corrected p value of less than 0 . 01; and 2 ) the ratio of ( Average DS/Average normal ) ≥1 . 25 or ≤0 . 8 . We used the R package to calculate the PDF distributions for various gene groups with regard to the expression changes represented by log2 ( Average DS/Average normal ) . Given that only 108 of the 14 , 000 ( 0 . 77% ) genes and 598 out of 16 , 821 ( 3 . 6% ) genes were significantly differentially methylated in the Kerkel study and this study respectively , three out of the nine genes sharing similar differential methylation are statistically significant ( p<1 . 8×10−9 ) for three or more genes shared between two datasets , based on a combined probability of 0 . 77%×3 . 6% under the null hypothesis that the occurrence of differentially methylated genes were independent in the two tissues . Gestational age matched normal ( n = 14 , gestational age: 17 . 41±3 . 77 weeks ) and DS ( n = 17 , gestational age: 17 . 70±3 . 77 weeks ) placenta villi samples were used for differential DNA methylation validation using the EpiTYPER assays . Unless specified , all reagents and equipment were from Sequenom ( San Diego , California , USA ) . Briefly , bisulfite conversion was performed on 1 µg genomic DNA with the EZ DNA Methylation-Gold Kit ( Zymo Research , USA ) . The converted DNA was amplified using HotStarTaq DNA Polymerase Kit ( QIAGEN GmbH ) , with 1× reaction buffer , 1 . 5 or 2 . 5 mM of additional MgCl2 , 200 µM of dNTP mix , 200 nM each of forward and reverse primers ( Table S7 ) , and 1 unit of HotStarTaq DNA polymerase . The thermocycling condition was 15 min at 94°C for heat activation , and 50 cycles of 20 sec at 94°C , 30 sec at 50 or 55°C and 1 min at 72°C , followed by a 3 min final extension at 72°C . The PCR products were then treated with shrimp alkaline phosphatase , and subsequently with the T-cleavage transcription/RNase A cocktail from EpiTYPER Reagent Kit ( Sequenom ) . The reaction products were subjected to conditioning with Clean Resin , and the fragments were analyzed by the MassARRAY system . Data were analyzed using EpiTYPER 1 . 2 software ( Sequenom ) . DNA methylation level for each sample was determined by averaging all analyzed CpGs within the target amplicon . Gestational age matched normal ( n = 8 , gestational age: 19 . 18±3 . 56 weeks ) and DS ( n = 10 , gestational age: 18 . 37±2 . 70 weeks ) placenta villi samples were used for differential gene expression validation . All reagents and equipment involved were from Life Technologies . DNase I treated total RNA samples ( 0 . 5 to 1 µg total RNA ) were subject to first strand DNA synthesis by SuperScript III First-Strand Synthesis SuperMix Kit . Quantitative real-time PCR was performed with Applied Biosystems 7900HT Fast Real-time PCR system with 384-well block module . Each reaction contained 1× Power SYBR Green Master Mix , 100 nM each of forward and reverse primers ( Table S8 ) and cDNA template equivalent to 18 . 2 ng of total RNA in a 10 µL reaction . The thermocycling condition was 10 min at 95°C , and 40 cycles of 15 sec at 95°C and 1 min at 60°C , followed by melting curve analysis . Duplicate reactions were performed for each assay , and the average Ct value was obtained using SDS version 2 . 3 software . GAPDH was used for normalization , with the following formula:
|
Down syndrome ( DS ) occurs in approximately one out of 700 live births . DS is caused by an extra copy of chromosome 21 . Although over 80 clinically defined phenotypes are identified for DS , each affected individual may only show some of the disease phenotypes . Understanding how the extra chromosome 21 causes various disease phenotypes can lead to better management and over the long term , treatment of the individuals with DS to improve outcome . In this study , we looked into DNA methylation changes associated with DS placenta villi tissues . We found genes with perturbed DNA methylation in promoters are functionally relevant to DS phenotypes . Through gene expression analysis , we identified genes ( TET1 , TET2 , REST ) that may contribute to the perturbed DNA methylation in DS .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"aneuploidy",
"dna",
"modification",
"chromosomal",
"disorders",
"genetics",
"epigenetics",
"biology",
"human",
"genetics",
"down",
"syndrome"
] |
2013
|
Global DNA Hypermethylation in Down Syndrome Placenta
|
Dengue virus ( DENV ) is a flavivirus of worldwide importance , with approximately 4 billion people across 128 countries at risk of infection , and up to 390 million infections and 96 million clinically apparent cases estimated annually . Previous in vitro studies have shown that lipids and lipoproteins play a role in modifying virus infectivity . However , the relationship between development of severe dengue and total cholesterol , high-density lipoprotein cholesterol ( HDL-C ) , and low-density lipoprotein cholesterol ( LDL-C ) , respectively , is unclear . We analyzed data from 789 laboratory-confirmed dengue cases and 447 other febrile illnesses ( OFI ) in a prospective pediatric hospital-based study in Managua , Nicaragua between August 2005 and January 2013 , using three different classifications of dengue severity: World Health Organization ( WHO ) 1997 , WHO 2009 , and standardized intervention categories . Total serum cholesterol and LDL-C levels decreased over the course of illness and were generally lower with increasing dengue severity , regardless of classification scheme . Greater decreases in LDL-C than HDL-C were observed among dengue-positive patients compared to patients with OFI and among severe dengue compared to mild dengue cases . Furthermore , daily cholesterol levels declined with daily albumin blood levels . To examine the effect of cholesterol at presentation on subsequent risk of development of severe dengue , relative risks and 95% confidence intervals were calculated using multivariable modified Poisson models . We found that lower total serum cholesterol and LDL-C levels at presentation were associated with subsequent risk of developing dengue hemorrhagic fever/dengue shock syndrome using the WHO 1997 dengue severity classification , and thus that the reduction in LDL-C is likely driving the decreases observed in total serum cholesterol levels among dengue-positive patients . Our results suggest that cholesterol blood levels are important correlates of dengue pathophysiology and should be explored as part of a prognostic biomarker panel for severe dengue .
Dengue virus ( DENV ) is a flavivirus of worldwide importance , with approximately 4 billion people across 128 countries at risk of DENV infection [1] . Of the estimated 390 million annual DENV infections , 96 million are symptomatic , and a subset of individuals develop severe forms of the disease , which consist of hemorrhagic manifestations and vascular leakage , leading to hypovolemic shock [2 , 3] . In vitro studies of the pathophysiology of DENV and other flavivirus ( e . g . , Japanese encephalitis virus , West Nile virus ) infections suggest that lipids and lipoproteins may play a role in modifying virus infectivity of target cells . Cholesterol-rich lipid rafts have been shown to be required for flavivirus entry [4–6] , and the related hepatitis C virus enters host cells via low-density lipoprotein ( LDL ) receptors [7] . The addition of cholesterol during viral adsorption blocks Japanese encephalitis virus and DENV infectivity [4] . Further , lovastatin , an inhibitor of cholesterol synthesis , also inhibits DENV replication [8 , 9] and is currently in clinical trials as a potential dengue therapeutic [10] . After infection , DENV , West Nile virus and Japanese encephalitis virus mimic or hijack lipid metabolic pathways [9 , 11–15] by increasing lipid raft formation , intracellular levels of total cholesterol , and LDL receptors on the surface of infected cells [15] . Together , these studies suggest that cholesterol is beneficial for DENV replication and that DENV infection modulates cholesterol metabolism . Previous clinical studies have generally shown lower levels of plasma and serum cholesterol among severe dengue ( dengue hemorrhagic fever/dengue shock syndrome [DHF/DSS] ) cases compared to less severe dengue cases or healthy controls [16–20] , possibly driven by a reduction in LDL cholesterol ( LDL-C ) [20] . Total cholesterol is comprised of LDL-C , high-density lipoprotein cholesterol ( HDL-C ) , and triglycerides . However , the relationship between severe dengue and total cholesterol , HDL-C , and LDL-C , respectively , is unclear . In the two studies that used multivariable models to examine the relationship between cholesterol and severe dengue , HDL-C and LDL-C were associated with severe dengue outcome in one of these studies [18] , but not the other [19] . Total serum cholesterol was not associated with severe dengue outcome [19] or was not separately analyzed [18] . However , neither of these studies fully accounted for the time ordering of cholesterol level in relation to development of severe dengue outcome . Without time ordering , it is impossible to determine whether cholesterol level affects development of severe dengue or is a result of developing severe dengue . In this study , we sought to delineate the trajectories of cholesterol levels over time by DENV infection status in order to understand the effect of the response to DENV infection on cholesterol levels . We also sought to delineate their trajectories by dengue severity to understand how cholesterol levels change over time among patients who develop severe dengue . Lastly , we aimed to assess the association of cholesterol level at presentation with development of severe dengue . To address these questions , we analyzed data from a prospective hospital-based study of pediatric dengue cases in Managua , Nicaragua , between August 2005 and January 2013 . Because different classifications of dengue severity are used in the literature , we performed analyses using three different classifications of severity: the World Health Organization ( WHO ) 1997 classification criteria [21] , the WHO 2009 classification criteria [22] and standardized intervention categories [23 , 24]
A prospective study was conducted from 2005 to the present in the Infectious Disease Ward of the Hospital Infantil Manuel de Jesús Rivera in Managua , Nicaragua , to study clinical , immunological and viral risk factors for severe dengue . This hospital is the National Pediatric Reference Hospital and treats the vast majority of children seeking tertiary care in Managua and referred from around the country [25] . Infants and children between six months and 14 years of age with fever or history of fever <7 days and one or more of the following signs and symptoms: headache , arthralgias , myalgias , retro-orbital pain , positive tourniquet test , petechiae , or signs of bleeding were eligible to participate in the study . Patients with a defined focus of infection other than dengue or who were actively enrolled in the concurrent Pediatric Dengue Cohort Study [26] were excluded . Children weighing <8 kg , children <6 months of age , and children ≥6 years of age displaying signs of altered consciousness at the time of recruitment were also excluded . For the current analysis , we also excluded children <1 year of age , due to the possible presence of maternal antibodies , as well as children with nephrotic syndrome or obesity ( body mass index ( BMI ) ≥32 ) , due to abnormally high cholesterol levels . Both inpatients and outpatients were enrolled each year during the peak of the dengue season ( August 1 to January 31 ) and followed clinically through the acute phase of illness . Upon enrollment , a medical history was taken and a complete physical exam was performed . Clinical data , including vital signs , signs and symptoms , and fluid balance and treatment , were recorded twice daily on standardized data collection forms during hospitalization or through daily ambulatory visits by the same team of study physicians and nurses responsible for care of hospitalized study participants . A blood sample was also collected daily for three days for complete blood counts with platelets , blood chemistry , and serological , virological and molecular biological tests for DENV infection . Additional samples for platelet count and hematocrit measurements were collected as necessary . A convalescent serum sample ( 14–21 days post-onset of illness ) was also collected for paired serological testing . Participants were hospitalized if they presented any of the following warning signs: abdominal pain or tenderness; persistent vomiting; clinical fluid accumulation; mucosal bleeding; lethargy/restlessness; liver enlargement; or increase in hematocrit concurrent with rapid decrease in platelet count . The protocol for this study was reviewed and approved by the Institutional Review Boards of the University of California , Berkeley , and the Nicaraguan Ministry of Health . Parents or legal guardians of all participants provided written informed consent , and participants 6 years of age and older provided assent . This study conforms with the STROBE reporting guidelines ( see S4 Table ) . All information was collected every 12 hours for inpatients and every 24 hours for outpatients who were asked to return on a daily basis on Case Report Forms ( CRFs ) designed to follow patients’ progress , with vital signs and fluid intake/output recorded more often as appropriate . Each CRF was completed by an infectious disease pediatrician and reviewed independently by a second physician . Following this review , the CRF information was entered into an Access 2003 database by double-data entry and was systematically monitored by weekly quality control checks . For inpatients , a non-fasting blood sample was obtained each morning to measure serum lipids . For outpatients , a non-fasting blood sample was obtained at each follow-up visit . Total serum cholesterol , HDL-C ( direct ) and LDL-C ( direct ) were measured using the CHOD-PAP method ( CHOD: cholesterol oxidase; PAP: phenol plus aminophenazone ) . Total serum cholesterol and HDL-C were measured throughout the study; LDL-C was measured from August 2007 until the present . From August 2005 to July 2007 , the BioCon kit was used for cholesterol measurements , and reactions were read in a spectrophotometer . From August 2007 to the present , cholesterol was measured using the same CHOD-PAP method , but using the Cobas Integra 400 platform and the corresponding cholesterol kit ( Roche Diagnostics ) . The National Reference Laboratory of the Nicaraguan Ministry of Health performed all assays throughout the study . Two quality control systems were used . For the first , the kit’s internal controls and calibration curve were processed for each run . For the second , cholesterol measurements were processed together with two control sera with normal and pathological values for each run ( Precinorm U and Precipath U , Roche Diagnostics , respectively ) . The machine automatically checks for the values of the two control sera and validates the run if these values are within the expected range . Moreover , the laboratory participates every 6 months in an external quality assessment program run by the United Kingdom National External Quality Assessment Service , Clinical Chemistry ( Birmingham ) , a WHO Collaborating Centre . In each assessment , blinded samples are sent by UK NEQAS for processing in the laboratory and results on 25 parameters , including cholesterol , are sent back for review . To date , all reviews have been successful . A case was considered laboratory-confirmed dengue when acute DENV infection was demonstrated by: detection of DENV RNA by RT-PCR; isolation of DENV; seroconversion of DENV-specific IgM antibodies observed by MAC-ELISA in paired acute- and convalescent-phase samples; and/or a ≥4-fold increase in anti-DENV antibody titer measured using Inhibition ELISA in paired acute- and convalescent-phase samples [27–30] . DENV serotypes were identified by RT-PCR and/or virus isolation [31 , 32] . Patients who tested negative for DENV infection were considered patients with other febrile illness ( OFI ) . Laboratory-confirmed dengue cases were classified by severity using a computerized algorithm that compiled all clinical data meeting each criterion for each of the disease classifications as detailed in the WHO Guidelines ( S1 Table ) . Dengue Fever ( DF ) , DHF and DSS were defined according to the 1997 WHO classification criteria ( S2 Table ) [21] . Laboratory-confirmed dengue cases were also classified according to the 2009 revised WHO classification criteria ( Dengue with and without Warning Signs , Severe Dengue; S2 Table ) [22] and the three standardized clinical intervention levels that were established in the DENCO study sponsored by the WHO Special Programme for Research and Training in Tropical Diseases ( Categories 1 , 2 , and 3; S2 Table ) [23 , 24] . The time-point at which the outcome occurred was defined as the day on which the patient’s cumulative signs and symptoms met the WHO or standardized intervention criteria . Dengue cases were defined as primary DENV infections if the convalescent antibody titer was <2 , 560 , and as secondary infections if the convalescent antibody titer was ≥2 , 560 , as determined by Inhibition ELISA [33] . A case was considered indeterminate if RT-PCR yielded negative results , no DENV was isolated , and a convalescent sample could not be obtained . Data from August 1 , 2005 , through January 31 , 2013 , were used for analysis . To delineate the trajectories of cholesterol by DENV infection status , we used repeated measures linear regression ( an exchangeable , working-within-subject correlation model via a generalized estimating equation [34] ) to estimate population average rates of change in levels of total serum cholesterol , LDL-C and HDL-C . Time-varying cholesterol was treated as the outcome and modeled by age , gender , DENV infection status , day of illness and an interaction term for DENV infection status and day of illness in the regression . The day of fever onset was defined as day 1 of illness . Only data from days 2 to 8 of illness were included in the analysis because the counts before and after this period did not allow for meaningful comparisons . It was necessary to use a statistical method that accounts for repeated measures because cholesterol levels measured on the same subjects over time are expected to be correlated . The generalized estimating equation approach corrects for within-subject correlations over time and provides robust standard error estimates that are resistant to model misspecification [35] . After fitting the regression models , we predicted the marginal mean cholesterol levels for each day of illness separately by DENV infection status , weighted by the distribution of age and gender in the study population using a marginal standardization approach . Marginal standardization is a method that estimates predicted probabilities after models are adjusted for confounders , allowing inference to the overall population from which the study data were drawn [36] . The 95% confidence intervals ( CIs ) were calculated for the marginal means using the delta method , and a global test for multiple comparisons was performed across all time-points using the margins command with the contrast option in STATA 13/SE ( StataCorp LP , College Station , TX ) [37] . This global test assesses whether there is an overall difference between groups and was used instead of Bonferroni , which would have likely been an overcorrection [38] . The marginal means and their 95% CIs were then plotted by DENV infection status and day of illness . We repeated this analysis to delineate the trajectories of cholesterol by dengue severity . For each dengue severity classification , time-varying cholesterol was treated as the outcome and modeled by age , gender , severe dengue outcome , day of illness and an interaction term for severe dengue outcome and day of illness in the regression . We restricted the analysis to patients who were classified as mild dengue at presentation and , if they developed severe dengue , developed severe dengue >12 hours after presentation . For the WHO 1997 classification , mild dengue was defined as dengue fever ( DF ) and severe dengue was defined as dengue hemorrhagic fever or dengue shock syndrome ( DHF/DSS ) . For the WHO 2009 classification , mild dengue was defined as dengue with or without warning signs ( DWS ) and severe dengue was defined verbatim ( SD ) . For the standardized intervention categories , mild dengue was defined as intervention category ( IC ) 1/IC 2 care and severe dengue was defined as IC 3 care . To examine the effect of cholesterol level at presentation on subsequent risk of development of severe dengue , relative risks ( RRs ) and 95% CIs were calculated using modified Poisson models with robust standard errors . The modified Poisson approach uses a robust error variance procedure to provide a consistent and efficient estimate of the relative risk [39] . We again restricted the analysis to patients who were classified as mild dengue at presentation and , if they developed severe dengue , developed severe dengue >12 hours after presentation ( n = 108 using the WHO 1997 classification , n = 101 using the WHO 2009 classification , and n = 164 using standardized intervention categories ) to ensure that we had appropriate time ordering of the exposure ( cholesterol ) before the outcome ( severe dengue ) . We constructed a directed acyclic graph [40] to characterize the pathways through which cholesterol at presentation could be associated with development of severe dengue ( see S1 Fig ) and adjusted models for the following confounders based on this working diagram: age ( years ) , gender , immune response ( secondary vs . primary ) and malnutrition . For children ≥2 years of age , malnutrition was defined as less than the third BMI-for-age percentile according to growth charts by the Centers for Disease Control and Prevention [41]; for children <2 years of age , malnutrition was defined as a deficit of ≥10% of the ideal weight based on the Gómez classification [42] . We repeated this analysis for each dengue severity classification . Lastly , we examined the association of cholesterol levels with albumin levels ( as a measure of vascular permeability ) using repeated measures linear regression , adjusting for DENV infection status and day of illness . Median total serum cholesterol levels were then plotted against median albumin levels by DENV infection status and day of illness . All analyses were performed using STATA 13/SE .
Of the 1 , 440 patients in the dengue hospital study , 69 patients <1 year of age , 11 patients with nephrotic syndrome , and 9 patients with obesity ( BMI ≥32 ) were excluded from the analysis ( see Fig 1 ) . An additional 69 patients missing all cholesterol measurements , 2 patients with inadequate samples for laboratory testing , and 44 patients with an indeterminate result of dengue testing were excluded , leaving 1 , 236 patients available for analysis . Of the 1 , 236 patients , 789 ( 64% ) were laboratory-confirmed as DENV-positive . Among the dengue cases , 149 were classified as DHF and 48 as DSS using the 1997 WHO classification , and 66 were classified as Dengue without Warning Signs , 466 as Dengue with Warning Signs , and 257 as Severe Dengue using the 2009 WHO classification . Of the 257 with Severe Dengue , 94 had hypotensive shock , 180 had compensated shock , 74 had fluid accumulation with respiratory difficulty , 2 had alteration in AST or ALT , 9 had change in CNS , and 52 had suspected myocardiopathy . The remaining 447 patients ( 36% ) tested negative for DENV and were classified as OFI . The characteristics of the 1 , 236 study participants included in the analysis are summarized in Table 1 . Overall , similar proportions of males and females were classified as different disease severity categories in both WHO 1997 and 2009 classification schemes and the IC classification scheme . Compared to other age groups , children aged 9 to 12 years were more likely to be classified as DHF , DSS , SD , and IC 3 . Dengue cases were more likely to have a secondary immune response , and approximately half were DENV-3 serotype . Most patients ( 82% ) were hospitalized . Patients generally presented on day 4 or 5 of illness and were hospitalized for a median of 3 days . Patient counts by day of illness and WHO classification are shown in S3 Table . Median cholesterol levels by day of illness and disease severity classification are shown in S2 Fig . We delineated the trajectories of cholesterol levels by DENV infection status ( Fig 2 ) and found that total serum cholesterol levels were significantly lower in dengue-positive patients compared to dengue-negative patients on days 3–8 of illness ( p<0 . 05 ) . Among dengue-positive patients , total serum cholesterol levels decreased from day 2 to 6 of illness , and then increased from day 6 to 8 of illness . However , among dengue-negative patients , total serum cholesterol levels gradually increased from day 2 to 8 of illness . Trajectories of LDL-C levels were similar to those of total serum cholesterol levels . LDL-C levels were significantly lower in dengue-positive patients compared to dengue-negative patients on day 2 ( p<0 . 05 ) and days 4–8 of illness ( p<0 . 001 ) . In contrast , HDL-C levels were significantly lower only on days 5–7 of illness ( p<0 . 001 ) . HDL-C levels followed a similar trajectory among both dengue-positive and dengue-negative patients , decreasing from day 2 to day 6–7 of illness before stabilizing on day 7–8 of illness . For all cholesterol trajectories , a global test for multiple comparisons across all time-points showed that the differences remained significant at the p<0 . 05 level . We also examined the trajectories of cholesterol by dengue severity ( Fig 3 ) . Here the analysis was restricted to patients who were classified as mild dengue at presentation and , if they developed severe dengue , developed severe dengue >12 hours after presentation . Total serum cholesterol levels were significantly lower in patients who developed severe dengue compared to patients with mild dengue on days 5–7 of illness using the WHO 1997 classification ( p<0 . 001 ) , on days 4–8 of illness using the WHO 2009 classification ( p<0 . 05 ) , and on days 5–8 of illness using standardized intervention categories ( p<0 . 05 ) . LDL-C levels were significantly lower in patients who developed severe dengue compared to patients with mild dengue on days 5–7 of illness using the WHO 1997 classification ( p<0 . 01 ) , days 4–8 of illness using the WHO 2009 classification ( p<0 . 05 ) , and on day 2 and days 5–7 of illness using standardized intervention categories ( p<0 . 05 ) . HDL-C levels were significantly lower in patients who developed severe dengue disease compared to patients with mild dengue on days 5–8 of illness using the WHO 1997 classification ( p<0 . 01 ) , days 3–8 of illness using the WHO 2009 classification ( p<0 . 01 ) , and days 4–8 of illness using standardized intervention categories ( p≤0 . 05 ) . Overall , regardless of dengue outcome , both total serum cholesterol and LDL-C levels decreased from day 3–6 and increased from day 6–8 of illness . Similarly , HDL-C levels decreased from day 2–7 of illness before increasing slightly on day 8 of illness . Furthermore , we examined the association of cholesterol levels with albumin levels ( a marker of vascular leakage ) , adjusting for DENV infection status and day of illness . Total serum cholesterol , LDL-C and HDL-C levels were each positively correlated with albumin levels ( p<0 . 0001 for each comparison ) . As shown in S3A Fig , median total serum cholesterol levels followed a similar pattern to median albumin levels , decreasing over the course of illness . Overall , dengue-positive patients had lower median total cholesterol and albumin levels than patients with OFI on all days of illness . In addition , median total cholesterol and albumin levels were lower in severe dengue cases than in mild dengue cases over the course of illness ( S3B–S3D Fig ) . This was observed in all disease severity classification schemes , with the steepest decline apparent in DHF/DSS cases on days 4–6 ( S3B Fig ) . Finally , we constructed multivariable models to examine the effect of cholesterol level at presentation on subsequent risk of development of severe dengue as defined by the three classification schemes . Using the WHO 1997 disease severity classification , we found that for each 10 mg/dl decrease in total serum cholesterol and LDL-C at presentation , risk of development of DHF/DSS increased by 9% ( 95% CI: 0–19% ) and 12% ( 95% CI: 0–26% ) , respectively ( Table 2 ) . A 10 mg/dl decrease in HDL-C at presentation was not significantly associated with risk of development of DHF/DSS . We also examined the effect of total serum cholesterol , LDL-C and HDL-C at presentation on risk of development of severe dengue as defined by the WHO 2009 classification and standardized intervention categories , but none of the findings were statistically significant . Abbreviations: WHO , World Health Organization; LDL-C , low-density lipoprotein cholesterol; HDL-C , high-density lipoprotein cholesterol; DHF/DSS , dengue hemorrhagic fever/dengue shock syndrome; DF , dengue fever .
While other studies have examined cholesterol levels during a particular phase of dengue illness ( acute , critical or convalescent ) [19 , 20] or on day of admission to the hospital [17 , 18] , ours is the first study of which we are aware to analyze changes in cholesterol levels by day of illness in dengue patients , and to use time-ordered analysis that enables prediction of disease severity level based on cholesterol levels at presentation . We found that total serum cholesterol , LDL-C , and HDL-C levels were significantly lower in dengue-positive patients compared to dengue-negative patients , and that LDL-C levels showed greater decreases and thus appeared to drive the reduction in total cholesterol . Total , LDL-C , and HDL-C levels were lower in severe compared to mild dengue during the course of illness regardless of severity classification scheme . Finally , we found that lower total serum cholesterol and LDL-C levels at presentation were associated with subsequent development of DHF/DSS . Liver damage caused by DENV infection could be contributing to the lower cholesterol levels we observed in dengue patients . Liver damage is a well-established characteristic of dengue patients [21] , particularly severe cases , and higher liver enzyme levels ( aspartate aminotransferase and alanine aminotransferase ) have been associated with increasing dengue severity across different classification schemes [43] . The liver is a major site of cholesterol synthesis in humans , and the rate of cholesterol production depends on the cellular level of cholesterol , for which LDL and HDL , among other lipoproteins , are responsible through their roles in cholesterol transport [44] . When we further examined lower levels of LDL-C and HDL-C during DENV infection , we found that although HDL-C and LDL-C both decreased over the course of dengue illness , there were greater decreases in LDL-C among dengue-positive patients compared to patients with OFI , suggesting that the reduction in LDL-C is likely driving the decrease in total serum cholesterol . This finding is supported by that of Seet and colleagues , who found greater decreases in LDL-C compared to HDL-C among DF cases during the acute and critical phases of dengue compared to levels during convalescence [20] . In vitro studies have shown that the related hepatitis C virus can enter cells via LDL receptors [7] , and increased expression of LDL receptors in Huh-7 cells has been reported to alter DENV infection , triggering increased uptake of LDL particles in infected compared to non-infected cells [15] . Our finding of lower HDL-C levels in severe dengue cases compared to mild dengue cases is intriguing . An in vitro study by Li and colleagues has shown that ApoAI , a major HDL apolipoprotein , binds to DENV and is associated with enhanced virus infection [45] . Therefore , the decrease in HDL-C observed among severe cases may be due to lack of available ApoAI , possibly in addition to reduced ApoAI production due to liver dysfunction . We also found that lower total serum cholesterol and LDL-C levels at presentation were associated with subsequent risk of developing severe dengue using the WHO 1997 dengue severity classification . Suvarna and colleagues similarly showed that LDL-C levels were associated with higher odds of DHF [18] . However , while previous studies have shown differences in total serum cholesterol levels by dengue severity using basic statistical tests , they did not find statistically significant associations between lower total cholesterol and severe dengue using multivariable models [19] . A major strength of our study is that we had prospective follow-up of dengue patients over the course of their illness , which allowed us to account for time ordering of the exposure ( cholesterol ) before the outcome ( severe dengue ) . By restricting our analyses to patients who were classified as mild dengue at presentation and , if they developed severe dengue , developed severe dengue >12 hours after presentation , we were able to assess the effect of cholesterol at presentation on development of severe dengue without confounding it with the effect of severe dengue on cholesterol . The ability to account for time ordering is likely the reason for the differences in our results compared to those of other studies . In our study , we analyzed severe dengue using three different dengue severity classifications . Although the WHO released a revised classification scheme in 2009 [22] , it has been somewhat controversial with some arguing that it may result in the misclassification of mild dengue cases as severe or overburden medical centers in dengue-endemic countries [33 , 46 , 47] . In one study , Narvaez and colleagues show that while the revised scheme had higher sensitivity and specificity to identify cases in need of intensive clinical intervention , it was less specific to a particular pathophysiology ( e . g . , vascular leakage leading to shock ) than the traditional WHO 1997 classification scheme [24] . The primary difference between the two classification schemes is the centrality of the vascular leakage syndrome in the WHO 1997 classification , whereas the 2009 WHO classification includes other pathophysiological mechanisms associated with severe dengue disease . In our study , using the WHO 1997 classification , total serum cholesterol and LDL-C were associated with risk of development of severe dengue , whereas HDL-C was not . Interestingly , using the WHO 2009 classification and standardized intervention categories , none of the cholesterol types were associated with risk of development of severe dengue . These results suggest that the association of cholesterol with severe dengue outcome is specific to the pathophysiology of DHF/DSS and not just the pathophysiology of severe illness . Although previous studies have associated low cholesterol with critical illness [48] , meningococcal sepsis [49] , and more hospital admissions for infectious disease [50] , it is possible that not accounting for time ordering or confounders may explain these associations or that other infectious diseases may share similar pathogenic pathways . The increased endothelial permeability that is associated with severe dengue disease [51] could potentially enable leakage of cholesterol molecules , resulting in lower cholesterol levels measured within the circulatory system [52] . Our results showing daily cholesterol levels decreasing alongside daily albumin levels in dengue-positive patients , especially among patients with severe dengue , supports this idea . However , LDL-C has a substantially larger molecular weight than albumin [53] . Nonetheless , studies have shown that cholesterol in the form of LDL can be transported across the microvascular endothelial barrier [54] , and in the form of oxidized LDL can promote vascular leakage [55 , 56] . Other potential explanations for the decrease of serum cholesterol in dengue , and in particular severe dengue , include accumulation in the liver where hepatic steatosis is observed [57] , uptake by monocyte-derived macrophages [58] , or uptake by DENV-infected cells , as cholesterol is involved in flavivirus entry and replication [4–6 , 8 , 9] . More research is needed to specifically investigate the mechanisms that account for the decreased levels of circulating cholesterol and the association of lower cholesterol levels with severe dengue outcome . A large number of severe dengue cases was available for analysis due to the patient population of children , who bear the burden of DHF/DSS in Nicaragua; furthermore , the study was based at the National Pediatric Reference Hospital , which treats the majority of pediatric dengue cases in Nicaragua . Interestingly , a substantial proportion of the DHF/DSS cases were in primary infections; this occurred because a large number of dengue cases were due to DENV-3 since it was the dominant serotype circulating during several years of the study , and DENV-3 is known to cause more DHF/DSS in primary infections than other serotypes ( particularly compared to DENV-2 and DENV-4 ) [28 , 59–61] . Our study had several methodological strengths . We used a directed acyclic graph to guide the construction of our statistical models to ensure that we only adjusted for plausible confounders , thereby avoiding bias . Although our directed acyclic graph is considered a working diagram and therefore could be modified in the future , it is a transparent approach to model-building that relies on our current knowledge rather than the statistical significance of covariates , which may , in fact , reflect relationships with parameters other than the outcome . We also used statistical methods that enabled calculation of the cumulative incidence ratio ( relative risk ) rather than the odds ratio , which may overestimate the relationship between cholesterol and dengue outcome when disease is common [62] . In addition , our study design compared the trajectories of cholesterol levels in dengue cases to those in patients with OFI rather than to those in healthy controls . Patients with OFI , not healthy individuals , are the individuals who present as suspected dengue cases to clinics and hospitals and therefore are the more relevant comparison group for dengue cases . Finally , we accounted for time-ordering in our analyses , which ensured that the exposure preceded the outcome . Our study did have some limitations . It would have been interesting to examine changes in cholesterol levels from baseline values in individuals with dengue over the course of illness . While pre-infection levels would be very difficult to obtain , convalescent samples could have provided a reasonable estimate of baseline values . Unfortunately , cholesterol measurements were not routinely performed on these samples . Prospective studies would enable capture of baseline cholesterol values , with cholesterol measured at convalescence as well . LDL-C was not measured in the first two of the nine years of the hospital study , so we had somewhat fewer measurements available for analysis , and triglycerides were not measured . As the third component of total serum cholesterol in addition to HDL-C and LDL-C , triglyceride levels would have helped us to understand whether LDL-C alone was driving the reduction in total cholesterol levels or whether triglycerides also contributed . In one study , triglycerides <150 mg/dl were estimated to increase the odds of DSS by 41% , although this association was not significant [18] . Although of potential concern , the fact that we obtained non-fasting cholesterol measurements should not have affected our findings . Recent studies have found the impact of eating on cholesterol measurements to be very limited [63 , 64] . According to Langsted and colleagues , “Lipid profiles at most change minimally in response to normal food intake in individuals in the general population” [64] . In their study , the maximum changes in lipid profiles after normal food and fluid intake from fasting levels were 0 . 2 mmol/L for total cholesterol , 0 . 2 mmol/L for low-density lipoprotein cholesterol , and 0 . 1 mmol/L for HDL cholesterol [64] . In addition , eating should not influence the relationship between cholesterol and severe dengue outcome because eating was not a confounder in our directed acyclic graph ( see S1 Fig ) . Nutritional status , however , was considered a confounder , as some studies have shown less severe dengue among malnourished children [65–67] , so it was included in our graph . In summary , our results show that lower total serum cholesterol and LDL-C levels at presentation were associated with subsequent risk of developing severe dengue using the WHO 1997 dengue severity classification and suggest that the reduction in LDL-C is likely driving the decreases observed in total serum cholesterol levels among dengue-positive patients . In addition , they indicate that cholesterol level at presentation may serve as a potential predictor of severe dengue . The burden of dengue is expected to continue to increase in the future due to climate change , globalization , travel , trade , urbanization , socioeconomics , viral evolution and other factors [68] . Therefore , time is of the essence for developing better diagnostic and prognostic tools to identify severe dengue cases for the provision of appropriate supportive care and hopefully , one day , specific therapeutics . Cholesterol and other routine laboratory markers should be explored as a lower cost and more sustainable approach to developing biomarker panels as prognostic markers of severe dengue .
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Dengue is a viral infection of worldwide importance with up to 96 million cases annually . Cholesterol , a type of lipid , may play a role in dengue virus infectivity and severity , but changes in cholesterol levels over the course of illness are not well-understood . To investigate the relationship between development of severe dengue and total cholesterol , high-density lipoprotein cholesterol ( HDL-C ) , and low-density lipoprotein cholesterol ( LDL-C ) , we analyzed data from a prospective hospital-based study of pediatric dengue in Managua , Nicaragua ( August 2005–January 2013 ) . Because different classifications of dengue severity are used , we performed analyses using three different classifications . Cholesterol decreased over the course of illness and differed across disease outcome , regardless of classification scheme . Greater decreases in LDL-C than HDL-C were observed among dengue-positive patients versus patients with other febrile illnesses and among severe versus mild dengue cases . In multivariate models , lower total serum cholesterol and LDL-C levels at presentation were associated with subsequent risk of developing dengue hemorrhagic fever/dengue shock syndrome ( World Health Organization 1997 classification ) . Our results suggest that reduction in LDL-C is likely driving decreases observed in total serum cholesterol levels among dengue-positive patients and should be explored as part of a prognostic biomarker panel for severe dengue .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Lower Low-Density Lipoprotein Cholesterol Levels Are Associated with Severe Dengue Outcome
|
As circulating monocytes enter the site of disease , the local microenvironment instructs their differentiation into tissue macrophages ( MΦ ) . To identify mechanisms that regulate MΦ differentiation , we studied human leprosy as a model , since M1-type antimicrobial MΦ predominate in lesions in the self-limited form , whereas M2-type phagocytic MΦ are characteristic of the lesions in the progressive form . Using a heterotypic co-culture model , we found that unstimulated endothelial cells ( EC ) trigger monocytes to become M2 MΦ . However , biochemical screens identified that IFN-γ and two families of small molecules activated EC to induce monocytes to differentiate into M1 MΦ . The gene expression profiles induced in these activated EC , when overlapped with the transcriptomes of human leprosy lesions , identified Jagged1 ( JAG1 ) as a potential regulator of MΦ differentiation . JAG1 protein was preferentially expressed in the lesions from the self-limited form of leprosy , and localized to the vascular endothelium . The ability of activated EC to induce M1 MΦ was JAG1-dependent and the addition of JAG1 to quiescent EC facilitated monocyte differentiation into M1 MΦ with antimicrobial activity against M . leprae . Our findings indicate a potential role for the IFN-γ-JAG1 axis in instructing MΦ differentiation as part of the host defense response at the site of disease in human leprosy .
When circulating monocytes enter the site of disease , local cues from the tissue microenvironment direct their differentiation into specialized MΦ equipped for diverse tasks [1–3] . While classically activated M1 MΦ with antimicrobial activity promote host defense against intracellular pathogens , alternatively activated ( M2 ) MΦ perform homeostatic functions including phagocytosis critical to tissue remodeling [1–6] . In leprosy , the divergence of MΦ functional programs correlate with the clinical disease spectrum [7–9] . In the self-limited , tuberculoid ( T-lep ) form of leprosy , disease lesions contain well-organized granulomas with M1 MΦ , expressing the MΦ marker CD209 , but negative for the haptoglobin receptor CD163 , yet armed with antimicrobial effector function [7] . By contrast , in the progressive , lepromatous ( L-lep ) form of leprosy , patient lesions are characterized by disorganized granulomas containing MΦ which co-express CD209 and CD163 but lack antimicrobial activity . Instead , these MΦ are programmed with phagocytic function , which results in the accumulation of host-derived lipids and favors mycobacterial growth [10 , 11] , and are therefore referred to as M2 MΦ . These data raise the question regarding the mechanisms by which clues from the microenvironment influence MΦ programming at the site of infection . As a gatekeeper to circulating monocytes that enter disease lesions , the microvasculature is poised to deliver key differentiation cues . The very cells which allow monocytes to exit the blood and enter the site of disease , i . e . EC , were shown to trigger monocyte differentiation into MΦ [12] , specifically of the M2 type [13] . Therefore , unstimulated EC have the ability to instruct M2 MΦ differentiation , yet the conditions that might alter EC to instruct M1 MΦ differentiation are not known . Here , we explore how the EC-monocyte interface can influence M1 MΦ differentiation , including upregulation of antimicrobial activity , in the context of leprosy as a human disease model .
IFN-γ is a potent inflammatory mediator that regulates an extensive gene program in EC [19–21] . We envisioned that small molecules that would facilitate EC-driven M1 MΦ differentiation may do so through convergence upon shared regulatory mechanisms ( Fig 2 ) . From a small molecule library generated by diversity-oriented synthesis ( n = 642 ) [22] , 24 compounds ( 3 . 7% ) were identified which when used to treat EC , promoted M1 MΦ differentiation as measured by cell surface phenotype ( Fig 3A ) . Two structurally distinct families , naphthyridines and tetrahydro-pyrrolo-triazolo-pyridazindiones ( tptp ) accounted for 13 ( 54% ) of the “hits” , and subsequent experiments with compounds from each of these families confirmed that upon treatment of EC , they triggered differentiation of M1 MΦ ( Fig 3B ) . As with IFN-γ , this effect was EC-dependent , since the compounds failed to directly trigger monocytes to become MΦ that express CD209 ( S4 Fig ) . Among 81 naphthyridine analogs [22] , 34 ( 42% ) prompted EC to instruct monocyte differentiation into M1 MΦ ( S5 Fig ) , indicating some specificity among naphthyridines . As with IFN-γ treated EC cultures , M1 MΦ derived from the compound-activated EC cultures were significantly less phagocytic than the M2 MΦ derived from the resting EC cultures ( p < 0 . 0001 ) ( Fig 3C and 3D ) . We chose the most effective compound ( naphthyridine 105A10 ) for further analysis and found that 105A10-treated EC triggered MΦ that were also more responsive to TLR2/1 activation in terms of induction of pro-inflammatory cytokines ( Fig 3E ) . Having identified structurally diverse compounds that mimicked IFN-γ , we sought to use these compounds to explore the mechanisms by which EC trigger this differentiation . Since IFN-γ signaling is primarily through STAT-1 , we sought to determine if active compounds from both families increased the phosphorylation of STAT-1 in treated EC . Active compounds from both tptp and naphthyridine families of compounds failed to induce phosphorylated STAT-1 ( Fig 4A ) . To determine whether the various stimuli induce a common gene signature in EC , we measured the gene expression profiles in EC treated with either IFN-γ , IFN-α or one of four active small molecules ( two naphthyridines: 105A9 , 105A10 and two tptp family members: 104B11 , 104C2 ) . IFN-γ induced a broad profile ( n = 3675 probes >1 . 25-fold induction ) , by comparison , the four compounds induced a more restricted profile , ( range n = 1248–1935 probes >1 . 25-fold induction ) . A high proportion of the genes induced by the four compounds ( 24–29% ) overlapped with the IFN-γ signature ( hypergeometric p values for enrichment: 5 . 35 x 10−32 to 2 . 80 x 10−103 , Fig 4B ) . To identify the genes triggered in activated EC with relevance to leprosy , we overlapped three profiles: i ) induced by IFN-γ in EC , ii ) induced by at least one of the four small molecules in EC; and , iii ) preferentially expressed at the site of disease in the self-limited T-lep vs . the progressive L-lep form of leprosy ( Figs 2 and 5A ) . This analysis identified 166 candidate regulatory genes , of which 50 were induced by at least two of the four compounds ( S1 Table ) . We next tested the role of the 50 common genes in facilitating EC-directed M1 MΦ differentiation . EC were transfected with siRNA against each of these candidate genes , and then treated with IFN-γ to induce the M1 polarizing microenvironment , followed by co-culture with primary human PBMC . In this context , monocyte differentiation into CD163+ MΦ would reflect that the M1 MΦ-polarizing effect of IFN-γ treated EC was being inhibited by the siRNA . Across five separate experiments , eight genes significantly inhibited the effect that IFN-γ exerts on the EC-driven M1 MΦ phenotype ( Fig 5B , S6 Fig ) . In parallel , the gene expression profiles of the 50 common genes were examined in leprosy lesions , with the premise that inverse correlation with CD163 expression may indicate a role in regulating M1 MΦ differentiation at the site of disease . Among the top eight candidate genes , JAG1 demonstrated the strongest inverse correlation with CD163 expression across the spectrum of leprosy lesions ( r = -0 . 834 , R2 = 0 . 6956 , P<0 . 0001 , T-lep lesions n = 10 , L-lep lesions n = 6 ) , with greater expression in T-lep vs . L-lep lesions ( fold change 2 . 2 , p<0 . 0002 ) ( Fig 5B and 5C ) . After confirming that JAG1 is induced on EC following stimulation with IFN-γ ( S7 Fig ) , we then assessed JAG1 expression at the site of disease . JAG1 expression in leprosy lesions was validated by immunohistochemistry , which demonstrated that JAG1 was expressed within the dermis and the granulomas in T-lep , but not L-lep lesions ( Fig 5E , S8 Fig ) . We also noted perivascular labeling of JAG1 in proximity to CD209+ MΦ ( Fig 5F ) , as well JAG1 expression in the microanatomic locations in which M1 MΦ ( CD209+CD163neg ) were found ( Fig 5F ) . In addition , there appeared to be JAG1 staining in the epidermis of both the T-lep and L-lep lesions which is consistent with the known role of JAG1 in keratinocyte differentiation and maturation [23 , 24] . Blinded analysis of JAG1 immunohistochemical staining determined a significant ( p = 0 . 0063 ) increased positive staining in T-lep sections , scores ranged from 0 ( absent ) to 4 ( highly positive ) . Together , these data indicated that JAG1 expression correlated with M1 MΦ accumulation at the site of disease in leprosy . We next investigated whether JAG1 could instruct the differentiation of monocytes into M1 MΦ with antimicrobial function . We found that soluble JAG1 ( sJAG1 ) facilitated EC-driven M1 MΦ differentiation ( Fig 6A and 6B ) . Furthermore , overexpression of JAG1 in EC , as well as addition of a JAG1 agonist peptide to the co-cultures , induced the differentiation of monocytes into the M1 MΦ phenotype ( S9 Fig ) . In contrast , addition of sJAG1 to monocytes alone did not induce MΦ differentiation ( S10 Fig ) . Given that JAG1 is known to activate Notch 1 signaling , we determined whether Notch-downstream genes were upregulated by the addition of JAG1 to the EC/monocyte co-cultures . In comparison to untreated EC , the addition of JAG1 led to the mRNA upregulation of three prototypic Notch-downstream genes in MΦ , HES1 , SOCS3 and RBPJ ( S11 Fig ) . Differentiation of monocytes in the presence of sJAG1 and EC yielded M1 MΦ with decreased phagocytosis ( Fig 6C ) and heightened induction of vitamin D-dependent antimicrobial pathway genes ( Fig 6D ) . To determine whether EC treated by either IFN-γ or JAG1 induced differentiation of monocytes into MΦ with antimicrobial activity , MΦ differentiated in the presence of treated EC were infected with live M . leprae , and the antimicrobial response measured according to the ratio of M . leprae RNA to DNA [25 , 26] ( Fig 6E ) . As compared to MΦ differentiated in the presence of resting EC ( i . e . treated with media ) , the MΦ induced by culture with EC treated with either IFN-γ or sJAG1 showed significant antimicrobial activity . Therefore , when monocytes encounter JAG1 in the context of EC , a differentiation program is triggered , resulting in M1 MΦ , defined by a CD209+CD163neg phenotype and antimicrobial function . The presence of IFN-γ , JAG1-expressing EC and CD209+CD163neg MΦ in the self-limited form of leprosy suggests that the IFN-γ-JAG1-antimicrobial MΦ differentiation pathway contributes to host defense at the site of disease in leprosy .
Our understanding of MΦ immunobiology has been significantly advanced through understanding of the pathways by which microbial ligands and/or cytokines program monocytes to differentiate into M1 and M2 MΦ [27 , 28] . However , it is not clear how local tissue signals can differentially program the MΦ response . Signals from endothelium are involved; this default pathway triggers M2 MΦ differentiation [13] . However , the mechanisms by which monocytes , upon entering the site of disease via the endothelium , are instructed to differentiate into M1 MΦ remain elusive [1–3] . Here , we hypothesized that if EC were to encounter the proper signals , the EC microenvironment would instruct monocytes to differentiate into M1 MΦ , equipped for host defense against intracellular pathogens at the site of disease . By studying leprosy as a model , we provide evidence that upregulation of JAG1 on endothelium instructs monocytes to differentiate into M1 MΦ with antimicrobial activity . Our data indicate that the induction of JAG1 is involved in EC instruction of M1 MΦ differentiation . In addition , the concomitant induction of Notch 1-downstream genes including HES1 , SOCS3 and RBP-J mRNAs was detected in the differentiated M1 MΦ . These findings are consistent with the known ability of JAG1 to signal via Notch 1 receptors [29] , and with reports that Notch 1 signaling , via SOCS3 and RBP-J [30–32] through reprogramming of mitochondrial metabolism [33] , contributes to M1 MΦ differentiation . Nevertheless , since JAG1 is known to signal via several distinct receptors [34 , 35] , further work is necessary to identify the physiologically relevant interactions responsible for EC-driven M1 MΦ differentiation . Not only does IFN-γ induce JAG1 on EC which can influence monocyte differentiation , IFN-γ also augments TLR-induced regulation of JAG1 expression in differentiated MΦ [36] . Further studies will be required to elucidate how JAG1 can contribute to MΦ differentiation , plasticity , function and proliferation at the site of disease [37] . In addition to the role of JAG1 in regulating innate immune responses via MΦ differentiation , evidence suggests a role for JAG1 in regulating adaptive T cell responses . Patients with Alagille syndrome , in which JAG1 mutations result in a multisystem disorder [34 , 38] , can exhibit altered Th1 responses [35] , implicating JAG1 induced signaling in T cell differentiation . In vitro studies have also shown that JAG1 expression on keratinocytes promotes dendritic cell maturation , which could also influence T cell responses [39] . Therefore , the expression of JAG1 by resident cells in tissue can influence both innate and adaptive immune responses . Under resting conditions , EC instruct monocytes to differentiate in M2 MΦ [13] . M2 MΦ are highly phagocytic , and are involved in clearing various biomolecules relevant for tissue repair , removal of excess metabolic products as well as clearance of debris . However , in the context of M . leprae infection , M2 MΦ can phagocytize the bacteria , but are unable to mount an antimicrobial response . Furthermore , these M2 MΦ take up host-derived lipids , providing necessary nutrients for mycobacterial growth [40] . Therefore , the induction of M1 MΦ is required for host defense against this intracellular pathogen , as these MΦ are weakly phagocytic but exhibit a strong antimicrobial response . One direct signal at the site of infection is production of IL-15 , which directly triggers M1 MΦ differentiation . In addition , our data demonstrates that IFN-γ induces JAG1 expression on EC , which also facilitates differentiation of monocytes into M1 MΦ . In the self-limited form of leprosy , JAG1 expression is restricted to microanatomical regions of the granuloma enriched for M1 MΦ . Therefore , our findings support the concept that the IFN-γ-JAG1 axis is involved in the EC instruction of the antimicrobial MΦ response against M . leprae at the site of infection . The ability to model how the microenvironment influences the immune response at the site of disease has become feasible because of advances in analyzing increasingly complex systems . We used a cell co-culture system in which we integrated small molecule screening with gene expression profiles to look for recurrent motifs in gene activation patterns associated with EC-triggering M1 MΦ . Since none of the molecular signals we identified recapitulate the antimicrobial MΦ phenotype on their own , our findings indicate that the emergent properties inherent to more complex heterotypic systems allowed for their discovery [41] . As such , this approach provides a strategy to identify potential drugs or biologic agents that would otherwise not be identified in experiments exploring direct effects on monocyte differentiation into antimicrobial MΦ . The identification of JAG1 and other small molecules that can harness the local microenvironment to augment innate immune responses at the site of disease may hold promise for combating intracellular pathogens .
IFN-γ and IL-4 ( Peprotech ) were used at 10ng/ml . IFN-α ( PBL Interferon Source ) was used at 10ng/ml . IL-15 ( 25ng/ml ) , IL-10 ( 10ng/ml ) , IL-5 ( 10ng/ml ) , fc-JAG1 ( 250ng/ml ) and fc-control ( 250 ng/ml ) were purchased from R&D Systems . JAG1 protein active peptide fragment ( 1μM ) was purchased from Phoenix Pharmaceuticals . Small molecule compound libraries and analogs were synthesized in the Ohyun Kwon laboratory . Compounds were dissolved in DMSO and used at a final concentration of 10 μM . Co-culture experiments were carried out as previously described [22] . In short , Primary human endothelial cells ( EC ) were plated to confluence in a 96 well plate . After adherence , endothelial cells were activated by indicated treatments for a period of 5 hours and subsequently washed 2–3 times to ensure removal of activation treatment . We then added human peripheral blood mononuclear cells ( PBMC ) at a ratio of 3 PBMC to 1 EC . Cultures were incubated at 37°C and 7% CO2 for a period of 48hrs . Human Umbilical Vein Endothelial Cells ( HUVEC ) were purchased from Lonza and used from passages 4–8 . Peripheral blood mononuclear cells were isolated from healthy donors ( UCLA Institutional Review Board # 92-10-591-31 ) using Hypaque Ficoll gradients ( GE Healthcare ) . Samples were retrieved by skin biopsy from patients with leprosy . The designation of tuberculoid leprosy ( T-lep ) and lepromatous leprosy ( L-lep ) was determined according to the criteria of Ridley and Jopling . Patient skin biopsies were performed at the time of diagnosis and subsequently embedded in OCT medium ( Ames , Elkhart , IN ) , snap frozen in liquid nitrogen and stored at -80°C ( 24 ) . HUVEC were stimulated with IFN-γ and compounds ( 104 B11 , 104 C2 , 105 A9 and 105 A10 ) for 15 minutes and then stained according to manufacturer’s protocol for phosphorylated STAT-1 . ( N = 3 ) Cells were harvested after 48 hours incubation at 37°Celsius in 7%CO2 . Surface expression of protein was determined using specific antibodies: CD209 ( Becton Dickinson ) , CD40 ( Becton Dickinson ) , CD1a ( Becton Dickinson ) , CD163 ( R&D systems ) , Jagged1 ( R&D systems ) , CD14 ( Becton Dickinson ) and IgG controls ( Becton Dickinson ) . Phosphorylated STAT-1 levels were determined using Anti-Human phospho-STAT1 ( eBiosciences ) . Cytometric Bead Arrays ( CBA ) were used to characterize TLR2/1R activated CD14+MΦ supernatants . CBAs were performed on 50μL of supernatant that was harvested after 24 hours of incubation . Supernatants were tested for the presence of MIP1-β , IL-6 and TNF-α . CBA Flex kits were obtained from Becton Dickinson and performed according to manufacturer’s recommendations . Samples were acquired using FacsCalibur and FacsVerse flow cytometers and FCS files were analyzed using FlowJo software . PBMC/EC Co-cultures were harvested after 48 hours of incubation and CD14+MΦ were subsequently purified using a CD14 positive selection bead assay ( Miltenyi Biotec ) ( purity > 95% ) . CD14+MΦ from each condition ( DMSO , IFN-γ and 105A10 ) were plated in equal number in 96 well flat bottom plates and stimulated with 10μg/ml TLR2/1 ligand ( EMC Microcollections ) . After 24 hours of stimulation supernatants were harvested and characterized by CBA for production of MIP1-β , TNF-α and IL-6 . cDNA was generated using iScript cDNA synthesis reagent ( Biorad ) following manufacturers guidelines . Primers ( IDT ) were used for determining mRNA expression of CYP27b , CAMP , VDR , and JAG1 . SYBR Green PCR Master Mix ( BioRad ) was used for Real Time PCR reactions and data was normalized to h36B4 gene expression ( IDT ) . Expression values were calculated as previously described [7] . CD14+MΦ from co-cultures were harvested and purified as previously mentioned . After purification , MΦ were plated in 10% FCS with 25-D3 ( 10−8 M ) ( Biomol ) and incubated for 24 hrs . Cells were then harvested and analyzed for CAMP , VDR and Cyp27b1 gene expression by qPCR . Viable bacteria stocks of M . leprae were obtained from Dr . James L . Krahenbuhl of the National Hansen's Disease Programs , Health Resources Service Administration , Baton Rouge , LA . For antimicrobial assays , Endothelial/PBMC co-cultures were set up as previously mentioned . After 48 hours of incubation CD14+ MΦ ( >90% CD209+ ) were isolated from co-cultures for infection with M . leprae . Co-culture conditioned ( Media , IFN-γ and sJAG1 ) CD14+ MΦ were cultured in RPMI with 10% FCS ( Omega Scientific ) in the presence of live M . leprae ( MOI 10:1 ) . Infected cells were subsequently stimulated with IFN-γ ( 10ng/ml ) in the presence of 25-D3 ( 10−8 M ) after 24 hours of infection . To measure antimicrobial activity in M . leprae-infected MΦ ( 5 days post infection ) we followed the protocol as previously described [40 , 42] . In short , qPCR was performed to determine levels of bacterial 16S rRNA and genomic element DNA ( RLEP ) . Expression levels of h36B4 were also evaluated to determine infectivity between all the conditions . The M . leprae 16S rRNA and RLEP primers used were as previously described [25 , 26] . DiI ( 1 , 1′-dioctadecyl-3 , 3 , 3′ , 3′-tetramethylindocarbocyanine perchlorate ) -labeled CuSO4-oxidized low density lipoprotein ( Dil-Ox-LDL ) from Intracel was added to co-cultures after 44 hours and further cultured for 4 hours in the presence of Dil-OxLDL to allow for uptake ( 50μg/ml ) . After incubation , Dil-OxLDL levels were determined within the CD209+ population of our stained cultures . To determine M . leprae uptake ( S12 Fig ) , CD14+ MΦ were harvested from co-cultures as previously mentioned and infected with labeled M . leprae . After 24 hours of infection MΦ were harvested and stained for CD209 , CD14 and CD163 expression . Immunoperoxidase and immunofluorescence labeling were carried out on frozen patient tissue sections . For immunoperoxidase staining , samples were initially blocked with normal horse or goat serum prior to labeling with monoclonal antibodies ( JAG1 ( Abcam ) , vWF ( AbD Serotec ) , CD163 ( AbD Serotec ) and appropriate isotype controls ) . Sections were then labeled with biotinylated horse anti-mouse IgG or biotinylated goat anti-rabbit IgG . After labeling , sections were counterstained with hematoxylin and visualized using the ABC Elite system ( Vector Laboratories ) . In order to determine protein co-localization in tissue sections , two-color immunofluorescence and confocal microscopy were performed . For Immunofluorescence , sections were labeled with rabbit anti-human JAG1 , anti-CD163 ( IgG1 ) , anti-CD209 ( IgG2b ) , anti-vWF ( IgG1 ) and appropriate isotype controls . Subsequently samples were labeled with isotype-specific , fluorochrome ( A488 or A568 ) -labeled goat anti-mouse/rabbit immunoglobulin antibodies ( Molecular Probes ) . Nuclei were stained with DAPI ( 4' , 6'-diamidino-2-phenylindole ) . Double immunofluorescence of skin sections was examined using a Leica-TCS-SP MP inverted single confocal laser scanning and a two-photon laser microscope ( Leica , Heidelberg , Germany ) at the Advanced Microscopy/Spectroscopy Laboratory Macro-Scale Imaging Laboratory , California NanoSystems Institute , University of California at Los Angeles [26] . Blinded review of IHC samples was carried out and positive staining was scored on the scale of 0 ( absent ) to 4 ( highest staining ) relative to isotype controls . Fishers exact test was used to determine significance . siRNA transfections were carried out on 2x104 HUVEC in 96 well plates and 7x103 HUVEC in 384 well plates . siRNA for candidate genes , siControl and siGlow were obtained from Dharmacon as was the transfection reagent Dharmafect 4 . siRNA transfections were performed according to manufacturer’s recommendations using 100nM concentration of siRNA . Decrease in message in transfected cells was confirmed by qPCR and protein expression . Ectopic expression cassettes for JAG1 , GFP and M11-empty vector were obtained from Genecopoeia . Plasmid transfections were carried out on HUVEC that were grown to 80–90% confluence . HUVEC were harvested and transfected with 1μg DNA using the AMAXA transfection device and HUVEC Nucleofect kit ( Lonza ) . To determine transfection efficiency , control cells were characterized for GFP production . In addition , surface expression of transfected JAG1 was confirmed by flow cytometry . For microarrays performed on compound and cytokine treated HUVEC , ECs were seeded in 6 well plates at 1X106 cells/well . Single wells were stimulated for five hours with DMSO , IFN-γ , IFN-α and compounds 104B11 , 104C2 , 105A9 and 105A10 at concentrations noted earlier . After incubation , mRNA was harvested using Trizol ( Invitrogen ) , followed by RNeasy Minelute Cleanup Kit ( Qiagen ) . mRNA samples for all arrays were processed using the Affymetrix Human U133 plus 2 platform and analyzed as previously described [22] . Statistical significance ( < . 05 ) of experimental values was calculated using a paired two-tailed Student’s t-test . Hypergeometric p values were calculated using the online resource ( http://systems . crump . ucla . edu/hypergeometric ) ( Tom Graeber laboratory , UCLA ) . Patient samples were obtained with approval from the IRB of the University of California Los Angeles , the Institutional Ethics Committee of Oswald Cruz Foundation and the University of Southern California School of Medicine . All subjects were legal adults and provided written informed consent before participating in the study [26] .
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Mycobacterial diseases , such as leprosy , continue to be serious causes of mortality and morbidity worldwide . They pose a unique treatment challenge due to their ability to modify the immune response in infected individuals . For example , in leprosy there are two distinct manifestations of the disease , each characterized by the immune response of the individual . One results in a more disseminated and severe form of the disease , lepromatous leprosy , and the other is a more limited form with marked antimicrobial activity , tuberculoid leprosy . These differences in the immune response can be characterized by the phenotype and activation state of the macrophage . We illustrate how the local endothelial microenvironment can “educate” macrophages , identifying Jagged1 and select small molecules that can regulate this pathway . Therefore , these studies identify a potential strategy to intervene in infection and inflammation , by targeting macrophage instruction at the site of disease . Through the integration of in vitro modeling and gene expression profiles at the site of disease , we found that Jagged 1 harnesses the endothelial microenvironment to instruct antimicrobial macrophage responses in leprosy .
|
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2016
|
Jagged1 Instructs Macrophage Differentiation in Leprosy
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Chromosome instability ( CIN ) is observed in most solid tumors and is linked to somatic mutations in genome integrity maintenance genes . The spectrum of mutations that cause CIN is only partly known and it is not possible to predict a priori all pathways whose disruption might lead to CIN . To address this issue , we generated a catalogue of CIN genes and pathways by screening ∼2 , 000 reduction-of-function alleles for 90% of essential genes in Saccharomyces cerevisiae . Integrating this with published CIN phenotypes for other yeast genes generated a systematic CIN gene dataset comprised of 692 genes . Enriched gene ontology terms defined cellular CIN pathways that , together with sequence orthologs , created a list of human CIN candidate genes , which we cross-referenced to published somatic mutation databases revealing hundreds of mutated CIN candidate genes . Characterization of some poorly characterized CIN genes revealed short telomeres in mutants of the ASTRA/TTT components TTI1 and ASA1 . High-throughput phenotypic profiling links ASA1 to TTT ( Tel2-Tti1-Tti2 ) complex function and to TORC1 signaling via Tor1p stability , consistent with the role of TTT in PI3-kinase related kinase biogenesis . The comprehensive CIN gene list presented here in principle comprises all conserved eukaryotic genome integrity pathways . Deriving human CIN candidate genes from the list allows direct cross-referencing with tumor mutational data and thus candidate mutations potentially driving CIN in tumors . Overall , the CIN gene spectrum reveals new chromosome biology and will help us to understand CIN phenotypes in human disease .
Chromosome instability ( CIN ) , involving the unequal distribution of DNA to daughter cells upon mitosis , is observed in the majority of solid tumors . The precise role of CIN in tumor development is uncertain but it may be an important predisposing factor for oncogenic progression by increasing the likelihood of tumor suppressor loss , gene copy number changes or translocations [1] , [2] . Perhaps unsurprisingly , given the shared properties of eukaryotic mitoses , many known CIN genes belong to cellular pathways or structures conserved from yeast to humans ( e . g . BUB1 , MRE11 , Aurora Kinase ) [2] , [3] . Mutations that cause CIN may drive tumor formation and progression [2] . Although high-throughput screens for genome integrity are becoming a reality in human cells , the spectrum of human mutations that lead to CIN in tumors is only partially characterized [4] . An ideal role for model organism genetics then would be to identify all cellular processes whose disruption can lead to a CIN phenotype , thus enabling identification and functional studies of candidate genes to focus on particular mutations among those found in a tumor genome . Most functional genomic screens in yeast have naturally focused on the ∼80% of yeast genes that are non-essential . Indeed , the yeast knockout collection is one of the most valuable genomic resources available . Several collections are now available to assay the functions of essential genes; each allele collection has advantages and disadvantages and only a handful of phenotypic screens have interrogated these collections [5]–[8] . Previous CIN screens of non-essential gene deletions have catalogued the increased frequency of chromosome transmission fidelity ( CTF ) , A-like faker ( ALF ) , Bi-mater ( BiM ) , loss of heterozygosity ( LOH ) , and gross-chromosomal rearrangements ( GCR ) phenotypes [5] , [9]–[13] . All of these phenotypes are considered CIN phenotypes as measured by an increase in the rate of marker loss although the mechanisms predominant in each assay differ . Since non-essential genes have been saturated with genome instability screens , a comprehensive screen of essential genes would create a high quality list of eukaryotic genome integrity pathways . Here we investigate CIN phenotypes in ∼2000 alleles of 1038 essential genes . When combined with published data for non-essential genes this resource defines yeast genome integrity pathways involving 692 genes and 387 enriched gene ontology ( GO ) terms . Using sequence orthology and the enriched GO terms to delineate CIN pathways , our data creates a list of cross species candidate human CIN genes . In principle , the yeast CIN gene catalogue described here comprises all conserved eukaryotic genome integrity pathways . Cross-referencing the derived human candidate CIN gene list with somatic mutations in human cancer reveals hundreds of CIN candidates mutated in tumors . Moreover , since tumor genomes typically contain many mutations this reference list of candidate CIN genes could help prioritize functional testing of novel somatic variants . The CIN gene list also provides biological insights at the level of genome integrity pathways and individual CIN genes . As an example , we conduct a directed secondary screen for telomere length in poorly characterized essential CIN mutant strains . We identify four novel telomere modulators including two subunits of the ASTRA ( ASembly of Tel , Rvb and Atm-like kinase ) complex , TTI1 and ASA1 [14] . ASTRA is an essential seven-subunit protein complex with a proposed role in chromatin biology [14] . Recent work highlights functional interactions among ASTRA subunits in metazoans; namely the TTT complex ( Tel2-Tti1-Tti2 ) and the R2TP ( Rvb1/2 , Tah1 , Pih1 ) complex which together affect biogenesis of phosphoinositide-3 kinase related kinase ( PIKK ) complexes [15]–[18] . Therefore , ASTRA likely represents the interaction between yeast TTT , R2TP ( or at least Rvb1/2 ) and a substrate PIKK . Our phenotypic analysis suggests that Asa1p functions with TTT to direct the biogenesis of PIKKs . Genome-wide phenotypic profiling of double mutants by synthetic genetic array ( SGA ) reveals strong TORC1 defects in TTT-ASA1 mutants which are likely due to reduced TOR-protein levels . Our data suggest that TTT function is conserved in yeast , and that its uncharacterized interacting partner , Asa1p , functions in the TTT pathway .
To approach a complete list of all CIN genes in yeast we performed overlapping genome instability screens on three collections of essential gene alleles representing 90% of essential genes ( Figure 1A ) . The DAmP ( decreased abundance by mRNA perturbation ) collection , which disrupts mRNA stability by inserting KanMX , encoding G418 resistance , into the 3′UTR of 880 essential genes [6] , was screened for CTF [19] and GCR [12] . A collection of 362 ts-alleles created de novo using the diploid-shuffle method [5] , [10] was screened for CTF and ALF phenotypes and a collection of 755 ts-alleles collected from the yeast community and integrated into a standard genetic background ( Z . Li , Charles Boone manuscript in preparation ) was screened for CTF and GCR ( Figure 1A ) . The three CIN assays measure different types of genome instability: CTF measures whole chromosome loss , ALF can detect chromosome loss , gene conversion and chromosome rearrangements ( i . e . deletions or translocations with DNA loss ) , and GCR measures chromosome rearrangements , primarily in the form of terminal chromosomal deletions [12] , [13] . Overall each allele was screened by two CIN assays , linking 257 essential genes to a CIN phenotype , including a large majority not previously associated with CIN ( Table S1 ) . To generate a catalogue of all yeast genes associated with CIN we compiled published genome-wide screens for CTF , ALF , Bi-Mater ( BiM ) , loss of heterozygosity ( LOH ) and GCR as well as chromosomal marker-loss phenotypes reported in the Saccharomyces genome database ( SGD: www . yeastgenome . org ) for individual CIN gene mutants [5] , [9]–[13] , [20] , [21] . When combined with our data , 692 verified yeast ORFs are mutable to a CIN phenotype ( Figure 1B . Table S1 ) . Approximately half of these genes ( 52% ) were either identified by ≥ two independent experiments or showed reproducibly strong CIN phenotypes ( Table S1 ) . Proportionally there are more essential CIN genes ( 323/1156 , 28% ) than non-essential ones ( 369/∼4800 , 7 . 7% ) although the absolute numbers are similar . In general , CIN genes encode nuclear proteins and nuclear localization correlates with a stronger or higher confidence CIN phenotype ( Figure 1B ) . Based on their descriptions in SGD , published associations , and GO terms , the CIN genes can be grouped into a handful of cellular processes ( Figure 1C , Table S1 ) . The breadth of the CIN gene list suggests that many biological processes protect genome integrity . A large proportion of CIN genes function in predictable pathways ( e . g . approximately 40% function in mitosis , DNA replication , repair or modification; Figure 1C ) . Another 20% of CIN genes function on or near DNA in pathways known to impact genome stability ( i . e . transcription , RNA processing , nuclear transport or the proteasome ) . The remaining 40% of CIN genes work in peripheral biological pathways , ( Figure 1C ) some of which have established links to genome integrity ( e . g . iron-sulfur protein biogenesis ) [22] and others with unknown connections to CIN ( e . g . tRNA synthesis , GPI-anchors , secretion ) . The mechanism of CIN for most genes and cellular pathways will require further experiment . However , the entire CIN gene list can also serve as a resource for guiding human CIN candidate gene identification in cancer somatic mutation data . The comprehensive nature of the CIN screens performed allows a description of the cellular pathways that are most readily mutable to CIN . These pathways may represent those most likely to cause CIN when mutated at random in a neoplasia . We calculated enriched GO terms for the entire CIN gene list using all genes screened as a background gene set ( see Materials and Methods ) . This analysis identified 387 GO terms enriched within the CIN gene list ( CIN-GO terms; Figure 2A , Table S2 ) . The enriched terms describe the cellular components ( 79 terms ) , biological processes ( 257 terms ) and molecular functions ( 51 terms ) that define CIN phenotypes in yeast . Figure 2A illustrates the network of enriched cellular pathways in the CIN gene list in the context of the GO hierarchy , colored by the calculated fold-enrichment . Highlighted regions of the CIN-associated GO hierarchy illustrate some of the most enriched clusters of terms , especially DNA replication ( Figure 2A I , II , V and VII ) , DNA repair ( II , IV ) , mitotic chromosome segregation ( III , VI ) and transcription ( VIII ) . Importantly , other less-predictable CIN pathways are also enriched; for example , the mRNA cleavage factor complex ( GO:0005849 , 4 . 3 fold ) , the proteasome ( GO:0031597 , 4 . 4 fold ) , and nuclear import ( GO:0051170 , 3 fold; Table S2 ) . As more tumor genomes are sequenced an important task will be associating particular somatic variants , among all the irrelevant , non-functional variants ( i . e . passenger mutations ) and causative variants ( i . e . driver mutations ) , to particular cancer phenotypes , such as CIN . Since CIN is multigenic ( i . e . mutation in one of many genes can lead to CIN ) and the complete spectrum of human CIN genes is unknown , the CIN gene list described here could direct the search for CIN-associated variants within a tumor genome . To generate a list of human CIN candidates we compiled sequence orthologs , or in some cases functional orthologs , of the yeast CIN genes , providing a list of 485 human CIN candidates ( Table S3 ) . A complementary approach is to define conserved CIN pathways using CIN-associated GO terms ( Figure 2 , Table S2 ) . This approach has the advantage of capturing genes that belong to known CIN pathways but are not necessarily conserved in yeast . While the CIN-GO terms in principle correspond to eukaryotic CIN pathways , analysis of the terms showed a less specific component that represented vague terms with a low total fold-enrichment . To increase the stringency of this approach we set a cut-off of ≥3-fold enrichment for cross-species comparison . In total 2362 human genes were linked to 153 of the CIN-associated GO terms through 4688 associations ( Table S4 ) . The orthology and GO-based human CIN candidate genes represent a partially overlapping sequence space for comparison with somatic mutation data ( Figure 2B ) . As more somatic variants are identified we anticipate the CIN candidate list serving as a filter to direct phenotypic studies , similar to previous candidate gene based studies ( Barber et al . , 2008 ) . To assess the present status of this effort we queried the Catalogue of Somatic Mutations in Cancer ( COSMIC ) and the Cancer Gene Census ( CC ) with each set of CIN candidate genes [23] , [24] . This search identified 136 yeast CIN gene orthologs and 849 CIN-GO based CIN candidate genes with variants in COSMIC and proportionally fewer in the smaller CC dataset ( Figure 2C ) . These mutated cancer genes appear in diverse cellular pathways many of which were not previously associated with CIN . Moreover , the GO-based CIN candidates retrieve significantly more mutated genes than would be expected at random ( p<0 . 01; Figure 2C ) . Figure 2D shows the CIN candidates associated with 10 or more CIN-GO terms illustrating that this approach yields many known cancer genes ( e . g . BLM , WRN , SMC1A , BUB1 , TP53 ) [2] , [3] . The CIN gene catalogue identifies a number of uncharacterized or poorly-characterized essential genes ( Table S1 ) . Since several of these genes have potential connections to telomeres we assayed telomere length in 20 poorly characterized essential CIN mutant strains . Preliminary southern blots of telomere length showed short telomeres for tti1-1 , asa1-1 , and yor060c-1 and long telomeres for yhr122w-1 relative to wildtype ( WT ) ( Figure 3A ) . To confirm these observations we grew these strains at the highest permissive temperature and found reproducibly short or long telomere phenotypes , for the respective strains ( Figure S1 ) . While little is known about TTI1 and ASA1 and they exhibited only weak ALF phenotypes ( i . e . 5- and 3-fold increase over wildtype respectively ) , we chose to focus on these genes because they are physically associated in S . cerevisiae and S . pombe in the context of a putative chromatin related complex called ASTRA [14] . In addition , Tti1p forms part of the TTT complex that , along with Tel2p and Tti2p , participates in a PIKK folding pathway which involves the Hsp90 molecular chaperone and the R2TP/prefoldin-like complex [16] , [18] , [25] . The ASTRA complex contains seven subunits; Tel2p , Tti1p and Tti2p , which comprise the PIKK biogenesis complex TTT; the AAA ATPase complex Rvb1p/Rvb2p; the PIKK Tra1p and Asa1p [14] , [16] , [18] , [25] . Rvb1/2p and Tra1p are highly conserved from yeast to mammals and function in multiple discrete protein complexes [14] , [26] . The remaining ASTRA subunits are weakly conserved ( Figure 3B ) and all but ASA1 function in PIKK biogenesis [16] , [18] . A recent article links the TTT complex , through Tel2p , directly to Rvb1/2 and PIKK biogenesis in the context of the R2TP complex [15] . Therefore , the ASTRA complex may reflect the interaction of TTT , Rvb1/2 and a substrate protein , in this case the PIKK Tra1p [14] . Since the R2TP complex forms a separable functional unit with diverse cellular functions , we chose to focus on the TTT complex and Asa1 . Deletion of TTI2 has not been reported and we found that , like other TTT subunits , it is an essential gene ( Figure S1 ) . Using the diploid-shuffle method we generated tti2-ts alleles to complement our set of TTI1 , TEL2 and ASA1 ts-alleles ( Figure 3C ) [5] . We found that all mutant alleles of TTT components and ASA1 had telomeres as short as a tel1Δ control when passaged at a semi-permissive temperature ( Figure 3D ) . Given the role of the TTT complex in ATM/Tel1p biogenesis we assessed the levels of Tel1p in our mutant strains by western blot ( Figure 3E ) . As expected yeast TTT loss of function mutants lead to Tel1p instability similar to TTT knockdowns in mammalian cells ( Figure 3E ) [16] , [18] . Remarkably , the asa1-1 allele has a similar reduction in Tel1p levels which is consistent with a common function being executed by the TTT complex and Asa1p . TEL1 is non-essential but its conserved paralog MEC1 is essential in yeast carrying a functional SML1 gene . However , deletion of SML1 did not ameliorate the ts-phenotype of the TTT/Asa1 mutants consistent with previous observations for TEL2 mutants ( Figure S1 ) [27] . Overall this data suggests that yeast TTT functions similarly to its mammalian counterpart in PIKK biogenesis and that ASA1 is a putative functional partner of TTT in PIKK biogenesis . To generate an unbiased phenotypic profile of ASTRA mutants we performed synthetic genetic array ( SGA ) screens using tel2-15 , tti1-1 , tti2-1 and asa1-1 as query genes . SGA compares the fitness of arrayed double mutant yeast strains , generated in high-throughput , to the corresponding array single mutants . This technique provides a digital colony size comparison that indicates potential genetic interactions between a query gene ( i . e . an ASTRA ts-allele ) and the arrayed mutant yeast deletion collection , DAmP allele collection or ts-allele collection [6] , [28] , [29] . Each screen yielded hundreds of genetic interactions including approximately 100 shared between at least two of the query genes ( Table S5 ) . The pattern of genetic interactions with the >5000 array mutants represents a phenotypic profile of each query mutant that indicates functional consequences of the query mutation [28] . We used hierarchical clustering of the four SGA profiles to place them within the context of the global yeast genetic interaction network [29] . The TTT-Asa1 mutant SGA profiles clustered together within the global interaction network ( Figure 4A ) . This co-clustering is not an artifact of our in-house SGA screens because other profiles generated in our lab cluster elsewhere in the network according to their biological functions ( data not shown ) . The SGA profiles support a view of TTT and ASA1 as a functionally cohesive unit , consistent with the reported physical assembly of these proteins across species [14] , [18] . Expanding the TTT-Asa1 cluster to show neighboring genes reveals multiple connections to TORC1 including direct components ( e . g . TCO89 , LST8 ) , and downstream effectors ( e . g . DAL81 , URE2 ) . In addition , a recent analysis of physical interactions of yeast kinases and phosphatases place TORC1 at the center of a network involving the RTG1 , 2 , and 3 genes , which control a mitochondria to nucleus signaling cascade called the retrograde response , and components of the chromatin modifying complexes Swi/Snf and SAGA , many of which co-cluster with TTT-Asa1 ( Indicated in Figure 4A ) [30] . Interestingly , another PIKK , Tra1p , whose human ortholog appears to be affected by TTT , is a component of the SAGA complex , suggesting a possible connection between TTT and Tra1p in yeast [15] , [16] , [18] . This convergence of physical and genetic evidence strongly supports a role for TTT-Asa1 in the TORC1 pathway . Like Tel1p and Mec1p , TOR kinases contain the PIKK domain and mTOR is a target of the TTT complex in mammalian cells [15] , [18] , [25] , [31] . Therefore , yeast TTT , and potentially ASA1 , could directly affect TOR stability or function . Consistently , we found that the TTT-Asa1 mutant alleles were hypersensitive to the TORC1 inhibitor rapamycin ( Figure 4B ) and arrested in G1 when grown at a non-permissive temperature ( Figure S2 ) , similar to alleles of the essential TORC1 component KOG1 [32] . Additionally , deletion of TIP41 , a downstream negative regulator of TORC1 signaling , improved the fitness of the sickest representative alleles , tti1-1 and asa1-1 , at 30°C ( Figure 4C ) [33] . Moreover , we observed precocious nuclear localization of Gat1p-GFP , a TORC1 regulated transcription factor , in the TTT , ASA1 and KOG1 mutants under nutrient rich conditions , which maintain Gat1p in the cytosol of WT cells ( Figure 4D ) [34] . Together these data confirm that a defect in TORC1 signaling occurs in TTT and ASA1 mutants . Importantly , we find that the TTT and ASA1 ts-alleles , but not WT or kog1-1 cells , showed decreased levels of Tor1p by western blot ( Figure 4E ) . This data distinguish ASA1 from other TORC1 pathway effectors ( e . g . KOG1 ) and support its function alongside the TTT complex in regulating PIKK stability . Here we identified four telomere length regulators including mutant alleles of the yeast TTT complex and an associated factor , Asa1p , whose mutation phenocopies mutation of TTT [14] , [16] , [18] . Our data link yeast TTT and Asa1p to PIKKs , especially TORC1 , consistent with a role in PIKK stability/biogenesis ( Figure 3 and Figure 4 ) . The data for yeast TTT are consistent with the current model for mammalian TTT function as a PIKK assembly scaffold [15] , while conservation of ASA1 to mammals is unclear ( i . e . the distant ortholog GNB1L is not characterized ) . CIN in the TTT mutants is unlikely to be due to TORC1 defects since other TORC1 components do not appear in the CIN gene list . Instead , the shortened telomeres likely associated with loss of Tel1p , are a probable cause of CIN in the TTT-ASA1 mutants . Alternatively , another PIKK , such as the DNA damage responsive ATR ortholog , Mec1p , or the histone acetyltransferase component Tra1p could be affected by TTT mutation and lead to genome instability . A complete description of the TTT substrate repertoire , its relationship to CIN , its molecular architecture and its interactions with cellular signals , chaperones and PIKKs remains to be elucidated . CIN is an important process in oncogenesis and may represent a weakness relative to normal cells that can be exploited for tumor therapy . Indeed , some current anti-tumor therapies act as DNA damaging agents or mitotic spindle inhibitors which could induce a toxic amount of genome instability in the already sensitized tumor cell . Our goal in this study was to create a framework beginning in a simple model organism that facilitates the search for CIN-associated variants in cancer via cross species candidate genes . The CIN gene catalogue creates complementary lists of human CIN candidate genes based on direct orthology and enriched CIN GO terms . In the coming decade , a huge number of tumor genome sequences will be produced via next-generation sequencing . Functional analyses like the one described here can be continually cross-referenced to mutational data to generate candidate genes which are potentially responsible for CIN in tumors . These candidates already hint at functional relevance for numerous observed somatic mutations in cancer ( Tables S3 and S4 ) . The task of directly testing the function of human variants is immense and will require a large effort from the community .
Table S6 is a list of yeast strains and plasmids used . Yeast was grown in rich media at 30°C unless otherwise stated . Plasmid bearing strains were grown in synthetic complete ( SC ) media lacking the appropriate nutrient . For spot assays , an identical optical density ( OD ) of cells was serially diluted ten-fold and spotted on the indicated plate at the indicated temperature for 72 hours . Growth curves were performed as described [35] . Briefly , logarithmic phase cultures were diluted to and OD of 0 . 05 in a 96-well plate in triplicate and grown for 24 hours in a TECAN M200 plate reader at 30°C . Figure 4C shows the middle curve of the three replicates for each strain . None of the TTT-ASA1 ts-alleles were able to grow at the non-permissive temperature of 37°C , regardless of whether TIP41 was deleted ( unpublished observation ) . SGA was performed using a Singer RoToR essentially as described [28] and was also used to introduce the appropriate chromosomal markers for the CTF ( i . e ade2-101::NatMX and CFIII or CFVII {URA3 , SUP11} ) and GCR ( i . e . pif1Δ::HygMX and hxt13::URA3 ) reporter strains [12] , [13] . Clustering of genetic interaction profiles was done for genes and arrays by average linkage using Cluster 3 . 0 [36] and viewed with Java TreeView . Patches of the CAN1 , URA3 , GCR assay strains were replicated to media containing canavanine and 5′FOA at 30°C to screen for GCR exactly as described [12] . CTF screening of ts-alleles was done by streaking CF containing strains onto SC media with 20% the normal amount of adenine exactly as described in [13] . Due to the temperature sensitivity , CTF assay strains were tested in an iterative fashion with respect to temperature . Beginning at 30°C , strains were deemed CTF , wildtype or inviable . Inviable strains were re-tested at 25°C , CTF strains were put aside as putative hits , and wildtype strains were retested at 34°C . The process was repeated with the 34°C strains at 37°C for wildtype strains and 32°C for inviable strains . All the CTF positive strains from any temperature were retested in three independent experiments and a qualitative strength designation assigned as described [13] . Independently generated fragments of chromosome III and VII were tested for each CTF assay strain [13] , [19] . Screening of DAmP alleles for CTF was conducted at 30°C . The ALF screen was performed as described in [13] except that the 1 cm2 patches of each mutant strain were mated to the MATα test strain at 25°C , 30°C and 34°C to explore a range of semi-permissive temperatures . Enriched GO terms were calculated at ( http://go . princeton . edu/cgi-bin/GOTermFinder ) using the hypergeometric distribution to define enriched terms . Terms ( downloaded September 28th 2010 ) with a p<0 . 05 after Bonferroni correction were considered enriched ( Table S2 ) . Human CIN candidate genes were compared to tumor mutations found in the COSMIC database ( version 49 ) [23] and the Cancer Gene Census [25] . The data was obtained from the Wellcome Trust Sanger Institute Cancer Genome Project web site , http://www . sanger . ac . uk/genetics/CGP . Telomere length was determined essentially as described [37] except that the probes and ladder were labeled with digoxigenin ( DIG ) and detected with anti-DIG antibodies according to manufacturers instructions ( DIG High Prime DNA Labeling and Detection Starter Kit II; Roche ) . Mutants for telomere length analysis were chosen primarily based on www . yeastgenome . org descriptions including “Protein of Unknown Function” , and with “putative” or “potential” functions . Logarithmic Gat1-GFP expressing cultures were grown in SC media and shifted to 37°C for three hours . Live cells were mounted on concanavalin A coated slides and imaged with the GFP filter set ( 500 ms exposure ) using a Zeiss axioscop and Metamorph software ( Molecular Devices ) essentially as described [38] . Images were analyzed using Image J ( http://rsbweb . nih . gov/ij/index . html ) . Experiments were repeated in triplicate and the proportion of cells with nuclear staining was counted for at least 100 cells from each experiment . Logarithmic cultures at 25°C were shifted to the indicated temperature for 5 hours and harvested by centrifugation ( 4000 ×g , 2 min ) . Cell pellets were washed with H2O and resuspended in lysis buffer at 4°C for glass bead lysis ( 50 mM Tris-Cl pH 7 . 8 , 150 mM NaCl , 1 . 5 mM Mg Acetate , 10% Glycerol , 0 . 5% Triton X-100 , 1 mM DTT , 10 mM Na PPi , 5 mM EDTA , 0 . 1 mM NaVO4 , 5 mM NaF , 1× complete protease inhibitor ( Roche ) . Cell lysate was centrifuged ( 10000 ×g , 5 min ) and the supernatant retained . Equal amounts of protein ( quantified by Bradford Assay reagent , Bio-Rad ) were run on SDS-PAGE gels ( 10% for Pgk1p , 6% for Tel1p or Tor1p ) , transferred to nitrocellulose membranes and probed with the indicated antibodies .
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Cancer results from mutations that alter the function of normal genes . The results of these mutations directly lead to the known properties of cancer cells , for example , over-proliferation or resistance to cellular death signals . An additional property of most tumors is chromosome instability ( CIN ) —the unequal distribution of DNA to the daughter cells following cell division . While CIN appears to be important for the formation of many tumors , the mutations that can lead to CIN are not well understood . Here we used a simple model cell , budding yeast , to systematically identify genes whose mutation can lead to CIN . Our data identify previously uncharacterized CIN genes that we show play a role in the stability of important cellular signaling proteins . Moreover , our results directly predict human CIN candidate genes , hundreds of which are mutated in tumors . Together our data create a resource for understanding new chromosome biology and the genetic basis of CIN in human cancers .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
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"gene",
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2011
|
The Complete Spectrum of Yeast Chromosome Instability Genes Identifies Candidate CIN Cancer Genes and Functional Roles for ASTRA Complex Components
|
The molecular nature of biological variation is not well understood . Indeed , many questions persist regarding the types of molecular changes and the classes of genes that underlie morphological variation within and among species . Here we have taken a candidate gene approach based on previous mapping results to identify the gene and ultimately a polymorphism that underlies a trichome density QTL in Arabidopsis thaliana . Our results show that natural allelic variation in the transcription factor ATMYC1 alters trichome density in A . thaliana; this is the first reported function for ATMYC1 . Using site-directed mutagenesis and yeast two-hybrid experiments , we demonstrate that a single amino acid replacement in ATMYC1 , discovered in four ecotypes , eliminates known protein–protein interactions in the trichome initiation pathway . Additionally , in a broad screen for molecular variation at ATMYC1 , including 72 A . thaliana ecotypes , a high-frequency block of variation was detected that results in >10% amino acid replacement within one of the eight exons of the gene . This sequence variation harbors a strong signal of divergent selection but has no measurable effect on trichome density . Homologs of ATMYC1 are pleiotropic , however , so this block of variation may be the result of natural selection having acted on another trait , while maintaining the trichome density role of the gene . These results show that ATMYC1 is an important source of variation for epidermal traits in A . thaliana and indicate that the transcription factors that make up the TTG1 genetic pathway generally may be important sources of epidermal variation in plants .
Understanding the origins , maintenance , and loss of natural variation remain important goals of evolutionary biology; ideally , we should like to know what types of molecular genetic changes generate the variation that natural selection acts on . For most traits , variation is distributed continuously in natural populations , a product of polymorphisms at many loci , environmental effects , and genotype by environment interactions [1] , [2] . Common first approaches to characterizing the genetic bases of natural variation include quantitative trait locus ( QTL ) mapping ( see reviews [3]–[5] ) and , more recently , genome-wide association mapping ( e . g . , [6]–[8] ) . While these methods provide many genetic insights , mapping results largely remain hypotheses regarding the molecular nature of biological diversity . To identify the genes and ultimately the polymorphisms that underlie natural variation still require detailed gene-by-gene analysis [9] . Information about the molecular changes that underlie natural variation within and among species provides important insights into the mechanisms that drive local adaptation , morphological evolution , and speciation . For example , molecular data have revealed a good deal about the evolution of flowering time in Arabidopsis thaliana [10]–[13] , morphology in various groups [14]–[16] , and speciation in Drosophila [17] , [18] . Despite progress for specific traits , few general patterns have emerged regarding the molecular bases of natural variation . For example , King and Wilson [19] proposed the concept of “evolution at two levels” more than three decades ago , yet we still know little about the relative roles of coding versus regulatory mutations in evolution [20] . Such patterns may ultimately prove difficult to identify because they vary according to the taxonomic level of comparison , nature of the trait , or life history , but more data are required . For plants , there are only ∼100 cases where the gene underlying natural variation has been identified and fewer than that for the causal polymorphism ( reviewed in [21] ) . Perhaps further complicating the search for natural molecular evolutionary patterns , these data are heavily biased toward crops; however , roughly a third of the data are reported from work on the model flowering plant Arabidopsis thaliana . Like many model species , A . thaliana has a high degree of intra-specific phenotypic variation ( reviewed in [3] ) and a substantial functional genetic infrastructure [22] , which make it an ideal system for pursuing the genes that underlie natural variation [23] . Indeed , studying highly variable traits with well-described molecular genetic underpinnings may represent our best opportunities to identify genes of interest and ultimately elucidate broad molecular evolutionary patterns . Epidermal cell fate in Arabidopsis thaliana represents one such system . The interaction between an organism and its environment plays a critical role in the evolution of morphology and local adaptation [24] , [25] . For individual plants , which cannot migrate away from sub-optimum conditions , this interaction is all the more important and is mediated by organs such as stomata [26] , [27] , root hairs [28] , [29] , trichomes [30]–[32] , anthocyanin producing cells [33] , [34] , and seed coats [35] . Collectively these organs make up the plant epidermis , an elaborate skin that serves as the interface between the organism and its environment . In A . thaliana , epidermal cell fate is largely regulated by the TTG1 genetic pathway [36] , which is mainly comprised of many pleiotropic and epistatic transcription factors and the scaffold protein , TTG1 . Among the epidermal traits regulated by this pathway , trichome density is known to play a dynamic defensive role in A . thaliana [32]; increased trichome density under herbivorous conditions results in a fitness advantage , but individuals with higher trichome density in the absence of herbivorous insects have been shown to incur a fitness cost . While this suggests that environmental heterogeneity may maintain genetic variation for trichome density ( within or between populations ) , we know little about the molecular nature of this variation . To date , only one QTL for trichome density has been identified [37]; ETC2 encodes a single repeat MYB protein known to be a repressor of the trichome cell fate . This leaves the molecular nature of most trichome density variation within A . thaliana unexplained . Previous QTL mapping results for trichome number [38] , [39] and trichome density [40] have identified multiple QTL in A . thaliana . One QTL mapped by Symonds et al . [40] , TDL5 , was localized to the top of chromosome four independently in each of four Recombinant Inbred Line ( RIL ) populations ( Figure 1 ) . Estimates of the physical position of this QTL and the similar magnitude of effect for TDL5 across mapping populations suggested that the same locus was mapped independently in each population . In an initial screen of the region , no gene with a known trichome phenotype was discovered; however , the search did reveal a bHLH transcription factor , ATMYC1 , three paralogs of which [41] , [42] have reduced trichome density phenotypes when knocked out [43]–[45] . ATMYC1 is expressed in both leaves and seeds [46] but over-expression of the gene has yielded no observable phenotype [44] . More recently , Zimmerman et al . [47] demonstrated protein-protein interactions between ATMYC1 and several R2R3 MYBs with known roles in epidermal cell fate . Here , we present genetic , molecular , and protein-protein interaction data that demonstrate that ATMYC1 is involved in epidermal cell fate and is a Quantitative Trait Gene ( QTG ) that underlies natural variation for trichome density . The results further reveal a complex pattern of protein evolution at ATMYC1 with as yet undetermined origin and effects .
Previous QTL mapping results for trichome density in A . thaliana localized a QTL to the top of chromosome four in four independent mapping populations [40] . Although no known trichome regulator was apparent in this region , ATMYC1 , a paralog of three genes with known roles in trichome initiation was discovered . To determine if ATMYC1 has a role in trichome initiation , we examined TDNA insertion ( knock-out ) lines . A homozygous TDNA insertion line for ATMYC1 ( SALK_057388 ) in a Col-0 background was determined to have a significantly different number of trichomes/first true leaf and trichome density phenotype on fifth true leaves relative to the wildtype Col-0 accession ( Figure 2 ) . The atmyc1 mutant produced fewer trichomes than wildtype on first true leaves and had a lower trichome density on fifth leaves . The trichome phenotype of atmyc1 has since been verified in two additional independent TDNA insertion lines of the gene ( Figure S1 ) . To test for a functional difference between the Col-0 and Ler-0 ( hereafter , Col and Ler ) alleles of ATMYC1 , quantitative genetic complementation analyses were performed by comparing the trichome densities of Col , Ler , a homozygous atmyc1 mutant in a Col background , and pairwise F1s among them ( Figure 2 ) . Germination rates were variable across genotypes in each experiment , resulting in sample sizes ranging from 11–18 and 8–14 for first and fifth leaf phenotypes , respectively . A one-way ANOVA revealed that both traits were found to differ significantly across the compared groups ( first leaf phenotype: F ( 5 , 81 ) = 42 . 455 , p<0 . 001; fifth leaf phenotype: F ( 5 , 56 ) = 20 . 63 , p<0 . 001 ) and Tukey-Kramer post-hoc tests , which account for sample size variation , were revealing in several ways . The test cross of Col x atmyc1 showed little to no evidence of a gene dose effect ( Figure 2 ) . That is , the Col x atmyc1 genotype does not differ significantly from that of the Col wildtype genotype for first and fifth leaf trichome phenotypes , showing that a single Col allele of ATMYC1 is sufficient to complement the reduced trichome phenotype of the mutant to near wildtype levels . In contrast , the Col x Ler genotype has trichome phenotypes significantly higher than the atmyc1 x Ler genotype , showing that a single copy of the Ler ATMYC1 allele does not complement the atmyc1 mutant phenotype , indicating that Ler contains a nonfunctional ( with regard to trichome initiation ) , recessive allele of ATMYC1 . In a screen of 72 A . thaliana accessions , considerable sequence variation was discovered among natural alleles of ATMYC1 with a total of 28 ( inferred ) cDNA haplotypes discovered ( GenBank accession #s: JF801957-JF802028 ) . Median-joining analyses yielded a network that is split into two diverged clusters ( Figure 3 ) ; these have been labeled as Type I and Type II , with 16 and 12 haplotypes , respectively . Alleles of these two Types consistently differ by 25 substitutions , which translate to 17 amino acid replacements . Interestingly , nearly all of this variation ( 24 of 25 substitutions and all 17 replacements ) is in exon six ( Figure S2 ) . Both allele types are at high frequency . Of the 72 accessions for which full-length ATMYC1 sequence was obtained , 31 possess a Type I allele and 41 have a Type II allele; however , no obvious geographic pattern was evident . With regard to the four RIL mapping populations in which TDL5 was mapped previously [40] , it is interesting that the six parental accessions possess five different alleles ( Figure 3; only the allele carried by the four parents of the mapping populations that include Ler as a parent are labeled ) . Perhaps most interesting among these alleles is that which the Ler accession carries , as this allele consistently conferred lower trichome density in previous QTL mapping experiments . Three natural accessions possess this same allele , one of which is La-0 ( cs6765 ) , a wildtype accession from the same region as the progenitor of Ler; the other two are Dra-1 ( cs6686 ) and Sg-2 ( cs6859 ) . An analysis of the 72 A . thaliana alleles of ATMYC1 yielded overall levels of nucleotide diversity and polymorphism ( π & θw; Table 1 ) that are somewhat higher than genome-wide average values reported for A . thaliana genes [48] , [49] . A sliding window analysis revealed high localized levels of nucleotide diversity ( Figure 4 ) , the highest of which was detected within exon six . Because regions of high nucleotide diversity corresponded with divergence between Type I and Type II alleles , we wanted to characterize the nature of this molecular variation . To explore this , we used a sliding window method to study rates of non-synonymous ( KA ) and synonymous ( KS ) divergence between Types I and II alleles across the entire 1 . 58 kb coding region . These analyses revealed evidence of alternative forms of selection that are gene region-specific ( Figure 4 ) . Across most of the gene , it appears that purifying selection has acted to constrain the amino acid sequence ( ratios <<1 ) ; however within exon six , extremely high rates of amino acid replacement are evident between the two Types . As a KA/KS ratio greater than one is often cited as a conservative cut-off for positive selection [50] , [51] , values approaching 30 are exceptional . Even the more rigorous approach using the gene-wide average KS value resulted in a KA/KS ratio greater than eight in this region . Outside of exon six , no other region of ATMYC1 showed evidence of positive selection . Interestingly , most of the divergence between the two A . thaliana ATMYC1 Types falls between two indels that differentiate all A . thaliana alleles from two distant outgroup alleles ( Figure 4 and Figure S2 ) . Of the 93 ecotypes that were screened for trichome density , 50 possessed a Type I allele and 43 possessed a Type II allele . Although broad-sense heritability was relatively high for the experiment ( H2 = 0 . 71 ) , there was no significant difference for trichome density detected between ecotypes carrying the two alternative ATMYC1 allele Types according to a Kruskal-Wallis test ( data not shown ) . Although variation segregating at other loci may overwhelm the effects of alternative ATMYC1 Types , it appears that the observed sequence variation in exon six has little to no effect on trichome density . Given the sample sizes and standard deviations , a power analysis indicated that a trichome density difference of at least three units should have been detectable as significant . Because a QTL was mapped for trichome density near ATMYC1 in the Col x Ler RIL population and quantitative genetic complementation tests revealed a functional difference between the Col and Ler ATMYC1 alleles , we examined polymorphisms between these two alleles . The Col and Ler accessions possess different ATMYC1 Types; however , the variation in exon six that distinguishes the two Types has no detectable effect on trichome density . Therefore , other polymorphisms between the Col and Ler alleles were investigated . The Col and Ler proteins differ at just four other positions: A13T , E83Q , P189A , and P343H ( Col:aa position:Ler ) . Of these polymorphisms , only position 189 is highly conserved across proteins and taxa . Out of 100 homologs , representing monocots and dicots , retrieved through a protein-BLAST search of the Col ATMYC1 protein sequence , all 100 shared the Col state ( proline ) at ATMYC1 amino acid position 189 ( data not shown ) . This position is also of interest as it resides within an undefined , but known MYB interaction domain in the amino end of close paralogs of ATMYC1 [44] , [45] . The other three polymorphic positions were found to be far less conserved . Based on these results , yeast-2-hybrid experiments focused on the highly conserved position 189 and the non-conserved position 13 as a control . We investigated the effects of two Col/Ler ATMYC1 polymorphisms on protein-protein interactions using binding assays with known partners , TTG1 and GL1 [47] , [52] . The results are clear . ATMYC1 encoded by the native Col allele interacts with TTG1 and GL1 and the ATMYC1 protein encoded by the native Ler allele does not . Reciprocal replacements at position 13 for the Col and Ler alleles had no effect on binding , while reciprocal replacements at position 189 qualitatively altered binding for both proteins . Specifically , when the Col allele was changed to match the Ler allele at position 189 , the protein no longer bound to TTG1 or GL1 and when the Ler allele was changed to match the Col allele at position 189 , the resulting protein then bound with TTG1 and GL1 ( Figure 5 ) .
An initial TDNA insertion line of ATMYC1 in the Col background was found to have a reduced trichome density phenotype relative to wildtype Col ( Figure 2 ) ; subsequent examination of additional independent insertion lines have confirmed this trichome initiation phenotype ( Figure S1 ) . The trichome phenotype is consistent with high sequence similarity between ATMYC1 and close paralogs GL3 , EGL3 , and TT8 [41] , [42] , which also have trichome phenotypes upon knock-out [43]–[45] . However , this finding is somewhat surprising , as GL3 and EGL3 have been shown to be sufficient to fulfill the bHLH role in trichome initiation; a gl3/egl3 double mutant is completely glabrous [45] , suggesting that ATMYC1 may be an enhancer of GL3 and EGL3 . While the precise genetic role that ATMYC1 plays in the TTG1 pathway requires more work to elucidate , the trichome cell fate function is clear . Subsequent quantitative genetic complementation tests showed that the Ler allele of ATMYC1 cannot recover the atmyc1 trichome phenotype , while the Col allele recovers it completely ( Figure 2 ) , indicating that natural molecular variation in ATMYC1 alters trichome density . Sequence variation between Col and Ler ( parents of the one of the mapping populations where TDL5 was mapped ) ATMYC1 alleles prompted a broad survey of ATMYC1 from 69 ecotypes and three lab strains of A . thaliana , which revealed a striking pattern of high frequency polymorphism . The coding region of the two primary Types observed consistently differ by 25 substitutions , which translate into 17 amino acid replacements; 24 of the 25 substitutions and all 17 replacements reside within exon six . This amounts to >10% amino acid replacement within exon six; no other region of the gene has a high rate of replacement . Allelic dimorphism has been reported for some , but not all , other loci in A . thaliana ( e . g . , [37] , [53]–[55] ) and is likely the result of diverfgence between two long-isolated populations of A . thaliana with subsequent break-down of population subdivision and admixture . Regardless of the origin of the Types , the ATMYC1 results are striking due to the high frequency of amino acid replacement and because nearly all of the variation resides within one relatively small region of the gene . That the Col and Ler alleles possess different ATMYC1 Types initially suggested that the molecular variation in exon six might explain the functional difference revealed by quantitative complementation tests; however , neither association tests nor yeast 2-hybrid experiments ( Figure 5 ) support this hypothesis . Outside of the block of variation in exon six that differentiates the two Types , only four other changes differentiate the coding regions of the Col and Ler alleles . Yeast-2-hybrid experiments to test the known interactions between ATMYC1 and TTG1 and ATMYC1 and GL1 showed that the P189A ( Col:aa position:Ler ) replacement has a qualitative effect , completely eliminating these interactions for the protein encoded by the Ler allele of ATMYC1 . The proline at this position is conserved , even among distant homologs of ATMYC1 , and likely resides in a known , but undefined , protein-protein interaction domain identified in close paralogs of ATMYC1 [44] , [45] . Indeed , simply replacing the proline for an alanine at this position in the Col allele eliminates the interaction with TTG1and GL1 , while the reciprocal change , replacing the alanine for a proline in the Ler allele , restores these interactions ( Figure 5 ) . Although this may not have been the first or the only mutation in the P189A ATMYC1 allele to eliminate gene function and reduce trichome density , these data show that the P189A replacement is sufficient to explain the functional difference between the Col and Ler alleles , presumably by altering trichome initiation , thereby decreasing trichome density . We conclude that ATMYC1 is a Quantitative Trait Gene ( QTG ) for trichome density in A . thaliana and that the mutation at DNA position 565 is a Quantitative Trait Nucleotide ( QTN ) for the trait . An interesting point to emerge here is that a single base substitution has lead to a qualitative breakdown in protein-protein interaction , which has a quantitative phenotypic effect; based on sequence similarity , this is likely due to functional redundancy between ATMYC1 , GL3 , EGL3 , and TT8 . The nature of the Ler mutation suggests that this was the same QTG mapped for trichome density by Symonds et al . [40] in two other populations that have Ler as a parent: No-0 x Ler and CVI x Ler; CVI and No-0 possess Type I and II ATMYC1 alleles , respectively , but share the proline at amino acid position 189 with Col , further supporting the conclusion that the variation differentiating the Types has little or no effect on trichome density while the replacement at position 189 underlies the mapped effect . Furthermore , QTL mapping results show that the Ler allele at TDL5 consistently confers lower trichome density than the alternative allele from Col , CVI , and No-0 and the additive effect of TDL5 was nearly identical in all three mapping populations [40] . The ATMYC1 allele carried by Ler is shared by three natural ecotypes , La-0 ( Poland ) , Dra-1 ( Czech Republic ) , and Sg-2 ( Germany ) . In our sample , this allele is at a frequency of ∼5% . Because it is unknown if ATMYC1 is pleiotropic , we cannot yet address whether or not the replacement at position 189 affects other traits . However , the 189A allele shows no signs of degradation to pseudogene status in any of the three natural ecotypes . This could be due to one or both of the following: ( 1 ) the protein has other functions that are not affected by the mutation at position 189 and is maintained by purifying selection and ( 2 ) this mutation is relatively recent and there has not been sufficient time for other mutations to accumulate . With regard to the second hypothesis , the 189A allele has at least persisted long enough for migration to increase its presence to multiple populations . Association tests showed no obvious trichome density difference for the two high frequency ATMYC1 Types and yeast-2-hybrid experiments suggest that the divergence between the two Types has no effect on known protein-protein interactions . If the variation in exon six has no effect on trichome density , then what explains the clear signature of divergent selection between the allele Types ? There would seem to be three logical explanations . First , the association test results could reflect confounding factors , such as segregation of variation at other loci that have larger effects on trichome density and essentially swamp out a potential ATMYC1 Types effect . If this were true , the Types effect would have to be in addition to and much weaker than that found for the replacement at position 189 . Second , divergence could have been in response to selection on a trait other than trichome density; indeed , paralogs of ATMYC1 ( GL3 , EGL3 and TT8 ) are all pleiotropic for several epidermal traits [43]–[45] , ATMYC1 has been shown to interact with several MYB transcription factors that coordinate other epidermal fates [47] , and an ATMYC1 homolog from Vitis vinifera ( Vitaceae ) was recently shown to have an epidermal ( anthocyanin ) phenotype [56] . ATMYC1 is most highly expressed in “seeds” [46] , therefore , it may be expected to be involved in testa development as well; however , we have observed no differences in comparisons between a TDNA knock-out line of ATMYC1 in the Col-0 background and Col-0 for three seed coat traits: mucilage production , condensed tannin synthesis , and seed coat cell morphology ( data not shown ) . Finally , the two Types may have evolved independently in response to deleterious indels . Comparisons with outgroup homologs of ATMYC1 show that the divergence between the two A . thaliana allele Types resides between or near two indels ( relative to outgroup taxa ) of 18 and 15 bp after coding DNA positions 705 and 927 ( in Col-0 sequence ) , respectively ( Figure 4 and Figure S2; outgroup sequence data not shown ) . Specifically , rather than diverging from one another , the two A . thaliana Types may have independently diverged away from a common nonfunctional ancestral copy of the gene . Although at this stage we cannot determine the origins of the indels , recombination and transposable elements seem likely candidates . Whatever the origins , in A . thaliana , isolated populations may have acquired independent compensatory mutations that became fixed in each lineage . Because trichome density is dynamic , with the fitness of a given density being relative to the environment [32] , such mutations may persist for long periods , thus allowing time for compensation . All A . thaliana alleles share indel states at these two positions with A . lyrata relative to the more distant outgroups , Capsella bursa-pastoris and Crucihimalaya himalaica . Further functional and analytical tests will be required to resolve the origins and potential effects of the divergence around these indels . Trichome density in A . thaliana is likely to be under alternating forms of selection , depending on the particular environment in which a plant resides . The TTG1 genetic pathway , which contains multiple and various types of transcription factors , many of which are functionally redundant , would seem a likely reservoir of genetic variation for epidermal traits and a prime pathway for “genetic tinkering” [57] with potentially a low risk of permanent unidirectional trait change . Indeed , we have shown here that a low frequency polymorphism that results in a simple amino acid replacement in ATMYC1 reduces trichome density in natural ecotypes of A . thaliana , thereby ascribing the first function to ATMYC1 . Our results also revealed a high frequency block of amino acid replacements in ATMYC1 with as yet unknown effects . ATMYC1 is the second gene in the TTG1 pathway recently identified to affect natural quantitative variation for trichome density; interestingly , for the single-repeat MYB , ETC2 , high frequency polymorphisms do affect trichome density [37] , while a similar pattern in ATMYC1 does not seem to alter trichome density . Clearly patterns that define the types of mutations and classes of genes that underlie natural variation may be difficult to identify; however , the TTG1 pathway is quickly emerging as a good place to search .
A TDNA insertion line ( SALK_057388 ) for the ATMYC1 locus ( At4g00480 ) in the Col-0 background was obtained from The Arabidopsis Biological Resource Center ( ABRC; http://signal . salk . edu/cgi-bin/tdnaexpress ) . The initial batch of seed was screened using a standard PCR protocol to identify a lineage homozygous for the TDNA insertion , which resides in the first exon of the gene . Based on initial observations of a trichome density phenotype for the atmyc1 mutant , first leaf trichome number and fifth leaf trichome density phenotypes were then measured in replicates of Col-0 and atmyc1as described in the following section . To test the hypothesis that variation at the ATMYC1 locus underlies trichome density variation mapped to TDL5 in previous QTL studies [40] , quantitative complementation tests were performed . In the QTL mapping studies , the Ler allele at TDL5 was shown to confer lower trichome density than the alternative parents' ( Col-4 , CVI , and No-0 ) alleles in each mapping population . However , because the available atmyc1 knock-out mutation is in the Col-0 background , the most direct comparison that could be made ( with regard to QTL mapping results ) was between Col and Ler . Crosses were made by transferring pollen from flowers of the Ler accession onto the stigmatic surface of emasculated flowers of the atmyc1 mutant and of Col wildtype . To control for potential cytoplasmic variation among accessions , all crosses were made with Col or atmyc1 as the pollen recipient . Therefore , the resulting F1s differ only at the atmyc1 locus . This allowed for comparisons between individuals with a Col/Ler and an atmyc1/Ler genotype at ATMYC1 , while holding the rest of the genome constant . That is , the only difference between the two sets of progeny is the replacement of a copy of the Col allele with a null ( mutant ) atmyc1 allele . To test for a dosage effect , Col was crossed to atmyc1 , which yields a Col individual with a single functional ATMYC1 allele ( atmyc1/Col genotype ) . Twenty replicates of each F1 genotype , parental accession , and the atmyc1 mutant were potted and the pots were randomized across four flats . All seed were vernalized in the dark for four days at 4°C , and subsequently moved to a fluorescently lit 20°C growth chamber . Sixteen days after emergence the number of trichomes on each of the first two true leaves of each seedling were counted under 50X magnification on a dissecting microscope; this is referred to as the “first leaf” trichome number phenotype . For the “fifth leaf' trichome density phenotype , the same experiment was set up as described above and trichome density was measured on the fifth true leaf at 21 days after emergence , as described by Symonds et al . [40] . The mean for each trait was then calculated from these data for each genotype . The genetic contribution to trichome number and trichome density variation was evaluated for first and fifth leaf phenotypes independently by ANOVA and unplanned pairwise comparisons between genotypes following the Tukey-Kramer method as described by Sokal and Rohlf [58] . DNAs were isolated from 69 natural accessions and three lab strains of A . thaliana acquired from the ABRC ( Table S1 ) , following a modified CTAB method [59] . Primers were designed from the Col-0 ATMYC1 sequence ( GenBank accession #NC003075 ) to PCR amplify the open reading frame plus ∼200 bp up- and down-stream of the start and stop codons , respectively . Primers corresponding with the first and last 21 bp of the Col-0 ATMYC1 cDNA sequence were used to amplify the ATMYC1 homolog from outgroup taxa ( all Brassicaceae ) : Arabidopsis lyrata , Crucihimalaya himalayica , and Capsella bursa-pastoris . All primers were used with manufacturer-supplied 1X Taq buffer , 1U AccuPrime High-Fidelity Taq polymerase ( Invitrogen Inc . ) , and ∼20 ng genomic DNA in 20 uL reactions . PCR samples were checked for amplification success on 0 . 7% agarose gels stained with ethidium bromide , and were subsequently purified in Multiscreen PCR clean-up plates ( #MANU03050 , Millipore ) . Approximately 100 ng of each purified PCR product were then used in each of seven sequencing reactions using primers designed to anneal at staggered internal positions , providing a minimum of two overlapping sequences across the entire gene . Allelic contigs were constructed for each ecotype and sequence editing and validation were performed using sequencher v . 4 . 2 . 2 ( Gene Codes Corp . ) . Full-length genomic sequences of ATMYC1 for all accessions were aligned initially using clustalx v . 1 . 83 [60] , and subsequently corrected by hand . To generate inferred cDNA sequence alignments , introns were identified using the published Col-0 cDNA sequence as template ( Arabidopsis Genome Initiative ) and non-coding DNA sequence was removed from the genomic alignment in bioedit v . 5 . 0 . 9 [61] . Independent cDNA haplotypes were identified using dnasp v . 4 . 00 [62] and exported in rgl format . A haplotype network was constructed in network ( Fluxus Technology , Ltd . ) using the median-joining option and redrawn using indesign ( Adobe , Inc . ) . A high level of divergence between two sets of alleles revealed by the haplotype network was the basis for identifying two Types of ATMYC1 alleles; these Types ( I and II ) are referenced in other sections . To examine nucleotide diversity and molecular evolution of the ATMYC1 locus , the sequence analysis software dnasp v . 4 . 00 [62] was used . The common nucleotide diversity indices , π [63] and θw [64] , were measured across the entire genomic alignment for all sequences and independently for Type I and II data sets . To assess intra-gene variation for nucleotide diversity , a sliding window analysis was run along the full-length ( start to stop ) cDNA sequence alignment of the 72 A . thaliana alleles; window length was set at 45 bp and moved along the alignment at 3 bp intervals . Because of initial observations of high levels of diversity and divergence between two apparent Types of ATMYC1 alleles , we tested the null hypothesis of neutral molecular evolution at this locus by measuring the nonsynonymous substitution rate ( KA ) and the synonymous substitution rate ( KS ) . By examining the ratio of KA/KS , one may identify signals indicative of positive or purifying selection [50] . KA/KS ratios near one are thought to indicate a neutrally evolving gene or region of a gene , values <<1 are expected to be under purifying selection , and values >>1 indicate positive selection . Because different regions of a gene may experience different forms of selection , a sliding window analysis was used to examine sequence divergence ( KA/KS ) between Types I and II ATMYC1 alleles; the window size was set at 45 bp , and was moved in 3 bp increments along the length of aligned ( inferred ) cDNA sequences . These ratio plots were generated in two ways: ( 1 ) using local KA over local KS measures and ( 2 ) using local KA values over the gene-wide KS value . While the former method is the convention , the latter has been suggested as an alternative to deal with false or misleading positives caused by very low local KS values [65] . For each window of sequence the KA/KS ratio was calculated using both methods and the results were plotted using sigmaplot ( Systat Software , Inc . ) . The finding of two highly diverged allele types at the ATMYC1 locus prompted an investigation of the potential effect of this sequence divergence on trichome density . To this end , trichome density was scored on fifth true leaves for a set of 96 ecotypes of A . thaliana ( details on this set of ecotypes can be found in [48] ) following the methods of Symonds et al . [40] . The ecotypes were screened for ATMYC1 Type using a PCR scheme with Type-specific forward and reverse primers that terminate on multiple sites that are polymorphic between the two Types; that is , only one set of primers yields a product for each ecotype , thus distinguishing the two Types . The trichome data were partitioned into the two allele classes and because the data showed a bimodal distribution , a Kruskal-Wallis rank sum test was performed using mapqtl [66] to test for a significant difference in trichome density between the two groups . Although association mapping in A . thaliana ecotype collections is potentially confounded by false positives due to population structure [67] , [68] , we didn't subsequently correct for population structure given our initial negative result . Outside of the variation that distinguishes the two ATMYC1 Types ( Col and Ler possess alternative Types ) , four amino acid replacements differentiate the Col and Ler alleles . To assess the conservation of these four positions , the Col ATMYC1 protein sequence was submitted to a protein BLAST search and the top 100 hits were aligned and conservation at each of the four sites that differ between the Col and Ler alleles was evaluated in this alignment . Based on the placement and conservation of polymorphisms between the Col and Ler alleles , two amino acid positions were selected to test for interaction effects with known partners , TTG1 and GL1: A13T and P189A ( Col:aa position:Ler ) . ATMYC1 cDNAs were cloned by Reverse Transcription and PCR amplified using start to stop gateway primers and recombined into pDONR/Zeo ( Invitrogen ) . These clones were then modified using Stratagene's QuikChange XL Site-Directed Mutagenesis Kit as recommended by the manufacturer . The Col cDNA was modified to make a version with a T13A change , one with a P189A change and one with both changes . The Ler cDNA was modified to make a clone with an A13T change , one with an A189P change and one with both . Each of these clones was then recombined into the yeast two-hybrid DNA binding vector pGBT9 RFB . The WDAD ( TTG1A ) and SRV6 ( pGL1A ) activation domain vectors were described previously [44] . All clones were sequenced in their entirety . The ATMYC1 yeast vectors were transformed into the Y190 yeast strain . WDAD and SRV6 were then transformed into each of the ATMYC1 yeast lines . The yeast two-hybrid assay was performed as previously described [44] using X-gal as a substrate for β-galactosidase activity and growth on histidine dropout media as interaction markers .
|
Among the goals of modern evolutionary biology is to identify the molecular genetic sources of natural variation . Although genetic mapping has led to an increased understanding of the genetic architecture of natural variation , there are surprisingly few cases where the molecular source of the variation has been identified . Here , we utilize previous mapping results to identify the gene and ultimately a polymorphism that underlies natural variation for a dynamic trait in Arabidopsis thaliana: trichome density . We show that plants carrying a knock-out of the bHLH transcription factor ATMYC1 have a reduced trichome density phenotype; this is the first reported function for ATMYC1 . Using traditional and molecular genetic approaches , we identify a single base change in natural alleles of ATMYC1 , which leads to an amino acid replacement that qualitatively alters protein–protein interactions with known partners , presumably altering the trichome cell fate pathway . In a broad screen for molecular variation in ATMYC1 , we identify a dense block of amino acid replacements that differentiates two high-frequency allele types . Although this block of variation does not appear to affect trichome density , based on paralogs of ATMYC1 , we propose that this variation has arisen due to directional selection on another epidermal trait .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"developmental",
"biology",
"plant",
"science",
"plant",
"biology",
"genetics",
"biology",
"population",
"biology",
"evolutionary",
"biology",
"genetics",
"and",
"genomics"
] |
2011
|
Natural Allelic Variation Defines a Role for ATMYC1: Trichome Cell Fate Determination
|
It has been hypothesized that helminth infections increase HIV susceptibility by enhancing systemic immune activation and hence contribute to elevated HIV-1 transmission in sub-Saharan Africa . To study systemic immune activation and HIV-1 co-receptor expression in relation to different helminth infections and in response to helminth treatment . HIV-negative adults with ( n = 189 ) or without ( n = 57 ) different helminth infections , as diagnosed by Kato-Katz , were enrolled in Mbeya , Tanzania . Blinded to helminth infection status , T cell differentiation ( CD45RO , CD27 ) , activation ( HLA-DR , CD38 ) and CCR5 expression was determined at baseline and 3 months after Albendazole/Praziquantel treatment . Plasma cytokine levels were compared using a cytometric bead array . Trichuris and Ascaris infections were linked to increased frequencies of “activated” CD4 and/or CD8 T cells ( p<0 . 05 ) , whereas Hookworm infection was associated with a trend towards decreased HLA-DR+ CD8 T cell frequencies ( p = 0 . 222 ) . In Trichuris infected subjects , there was a linear correlation between HLA-DR+ CD4 T cell frequencies and the cytokines IL-1β and IL-10 ( p<0 . 05 ) . Helminth treatment with Albendazole and Praziquantel significantly decreased eosinophilia for S . mansoni and Hookworm infections ( p<0 . 005 ) but not for Trichuris infection and only moderately modulated T cell activation . CCR5 surface density on memory CD4 T cells was increased by 1 . 2-fold during Trichuris infection ( p-value: 0 . 053 ) and reduced after treatment ( p = 0 . 003 ) . Increased expression of T cell activation markers was associated with Trichuris and Ascaris infections with relatively little effect of helminth treatment .
In 1995 , Bentwich et al . proposed that systemic immune activation associated with chronic helminth infection may be the driving force of HIV transmission in Africa [1] as such infections are common in that environment ( reviewed in [2] ) . Since then , several studies have linked systemic immune activation in African populations to helminth infection [3]–[5] . A series of such studies was conducted in Israel with newly arrived Ethiopian migrants who were characterized by a high prevalence of helminth infections such as Schistosomes , Hookworm , Ascaris lumbricoides ( Ascaris ) or Trichuris trichiura ( Trichuris ) . Compared to Ethiopian migrants that had stayed in Israel for longer periods and had received standard anti-helminthic treatment upon arrival , HLA-DR expression on CD4 and CD8 T cells and lymphocyte apoptosis was substantially higher in the new arrivals [3] . Also , peripheral blood mononuclear cells ( PBMCs ) of these immigrants were highly susceptible to in vitro infection with HIV , which correlated with the state of immune activation [6] . Within a similar study population , the same group also reported higher CCR5 and CXCR4 expression levels in Ethiopians , regardless of the length of their residence in Israel and thus also of the time after anti-helminthic treatment [4] . Contrary to this , a more recent study observed no differences in the T cell immune activation profile of HIV negative subjects between individuals infected with Trichuris and/or Ascaris and non-helminth infected participants , except for a 2-fold increased frequency of CCR5 expression on CD4 T cells in helminth infected subjects [7] . Low systemic immune activation is a correlate of protection against HIV infection [8] , [9] . This has been demonstrated in recent human studies which reported that low immune activation in highly HIV-1-exposed but uninfected individuals contributes to their resistance to HIV infection [9] , [10] . Koning et al . extensively showed that the blood of high risk but persistently seronegative men from the Amsterdam cohort had lower frequencies of co-expression of HLA-DR and CD38 on CD4 T cells , low proportions of cycling T cells as defined by the expression of Ki67 nuclear antigen and low proportion of memory CD4 T cells expressing CCR5 , in comparison to men who were seronegative at the time of analysis but later on became HIV positive [9] . Similarly , Begaud et al . observed significantly lower expression of HLA-DR and CCR5 on CD4 T cells in HIV-1 exposed seronegative heterosexuals from a Central African cohort [10] , suggesting a role of CD4 T cell immune activation in HIV susceptibility . While these studies support a link between systemic T cell activation and HIV susceptibility , it is less clear , whether in populations from endemic areas of sub-Saharan Africa helminth infections in general are associated with systemic T cell activation or whether infections with different helminth species might differ in this regard . In order to elucidate this open question , the present study aimed to investigate systemic T cell activation in relation to infection with different helminth species and to anti-helminthic treatment .
This study was approved by the ethics committees of the Tanzanian National Institute for Medical Research , Mbeya Referral Hospital and Munich University and conducted according to the principles expressed in the Declaration of Helsinki . All participants recruited in the study were adults ( 18–50 years ) who provided written informed consent before enrolment into the study . A total of 386 adult study participants from the “Evaluating and Monitoring the Impact of New Interventions” ( EMINI ) [11] cohort from the Mbeya region in South West Tanzania were enrolled into the prospective Worm_HIV_Interaction_Study ( WHIS ) cohort based on their helminth and HIV infection status about four months after the EMINI field visit . The initial objective was to only include participants with single helminth infection , however , some participants within the HIV negative group turned out to have multiple helminth infections when re-tested after randomization into the WHIS study ( Table 1 ) . 246 HIV negative volunteers were then further stratified according to their helminth-infection status , including 57 helminth negative subjects ( Table 1 ) . Blood , urine and stool specimens were collected from each participant once at baseline and once during the follow up at 1–3 months after helminth treatment irrespective of helminth infection status with a single dose of Albendazole ( 400 mg ) and Praziquantel ( 40 mg/kg ) . Only subjects without detectable helminth infections after treatment were included in the comparison of pre- and post-treatment time points . Helminth diagnosis was performed as described below . HIV status was determined using HIV 1/2 STAT-PAK , ( Chem-bio Diagnostics Systems ) and positive results were confirmed using ELISA ( Bio-Rad ) . Discrepancies between HIV 1/2 STAT-PAK and ELISA were resolved by Western Blot ( MPD HIV Blot 2 . 2 , MP Biomedicals ) . 40 ml of venous blood were drawn from each participant using anticoagulant tubes ( CPDA , EDTA; BD Vacutainer ) . Blood samples were processed within less than 6 hours of the blood draw at the MMRC laboratories . Fresh stool specimens were used for Kato-Katz diagnosis of geohelminth ( Trichuris , Ascaris , Hookworms ) and S . mansoni infections . Briefly , two Kato-Katz thick smears ( 41 . 7 mg each ) were prepared from each fresh stool . Kato-Katz slides were microscopically examined for helminth eggs by experienced technicians within one hour ( for Hookworm eggs ) and within two days ( for other helminth eggs ) after slide preparation . S . haematobium infection was diagnosed by microscopic examination of a filtered urine sample ( 20 ml ) for S . haematobium eggs . Helminth infection was defined as the presence of at least one worm egg in the examined samples . An automated complete blood count machine ( Beckman Coulter ) was used for counting eosinophiles . If eosinophil counts were out of range ( >1 . 0×103/μl ) , determination was performed using the differential blood count . Frequencies of activation ( HLA-DR , CD38 and CCR5 ) and maturation ( CD27 and CD45R0 ) markers were determined in fresh , anti-coagulated whole blood at each of the two time points . Blood samples were incubated for 10 minutes with CCR5 PECy7 followed by 30 minutes incubation using the following fluorochrome labeled monoclonal antibodies for cell surface staining ( mABs ) ; CD3-Pac Blue ( BD ) , CD4 Per-CP Cy5 . 5 ( eBioscience ) , CD8 V500 or CD8 Amcyan , CD27 APC-H7 , CD45RO APC , HLA-DR FITC and CD38 PE ( all from BD ) . Stained cells were finally fixed with 2% paraformaldehyde prior to acquisition . Acquisition was performed on a FACS CANTO II ( BD ) . Compensation was conducted with antibody capture beads ( BD ) stained separately with the individual antibodies used in the test samples . Flow cytometry data was analyzed using FlowJo ( version 9 . 5 . 3; Tree Star Inc . ) . Depending on the expression of CD27 and CD45RO markers on CD4 and CD8 T cells; T cell subsets were defined as follows: naïve ( CD27+CD45RO− ) , “central-like” memory ( CD27+CD45RO+ ) , “effector-like” memory ( CD27−CD45RO+ ) and “terminally differentiated” ( CD27−CD45RO− ) CD4 and CD8 T cells . In addition , total memory CD4 T cells were defined as the sum of central memory , effector memory and terminally differentiated CD4 T cells . We used fresh , anti-coagulated whole blood in order to maximize CCR5 staining sensitivity and minimize staining variability that can arise due to cryopreservation of PBMC . The CCR5 surface density on total memory CD4 T cells was assessed using a strategy that rely on the absence of CCR5 on naïve CD4 T cells . We first standardized all CCR5 median fluorescence intensity ( MFI ) results , in order to compare CCR5 expression on memory CD4 T cells from different subjects and study visits . The CCR5 MFI value specific to CD45RO+ memory CD4 T cells was calculated and standardized by subtracting the CCR5-MFI on CD45RO− naïve CD4 T cells from the same sample ( ΔMFI ) . In addition , CCR5 MFI values specific to HLA-DR+ and HLA-DR− memory CD4 T cells were calculated for each subject . All flow cytometric analyses were blinded for helminth and HIV infection status . Cryopreserved plasma samples from Trichuris infected subjects ( n = 31 ) and randomly selected helminth-negative controls ( n = 27 ) were tested in a single run to determine the concentration of the cytokines IFN-γ , TNFα , IL-1β , IL-2 , IL-4 , IL-5 , IL-6 , IL-9 , IL-10 , IL-12 , IL-13 , IL-17α and IL-22 at baseline using a multiplex cytometric bead array kit ( eBioscience ) as per manufacturer's instructions . Data acquisition was performed on a FACS Calibur ( BD ) . The generated data was analyzed using FlowCytomix Pro 2 . 4 software ( eBioscience ) . Data analyses were performed using Prism version 5 . 0 software ( GraphPad , Inc . ) . Groups were compared using the Mann-Whitney test , paired observations ( before and after helminth treatment ) were compared using the Wilcoxon-matched pairs test and associations were determined by linear regression analysis , with p-values <0 . 05 regarded as significant . Figure and table legends describe which test was used in each case . Helminth specific analyses included all subjects with data who were infected with the respective helminth , meaning that subjects with multiple helminth infection were included in more than one of compared groups .
Table 1 describes the characteristics of the WHIS study population . 246 adults HIV negative volunteers were included in the baseline analysis . 58 . 5% of these were female and the mean age was 33 . 6 years . 159 ( 64 . 6% ) of 246 subjects were infected with a single helminth species and 57 ( 23 . 2% ) had no helminth infection at baseline . The post treatment analysis excluded 69 subjects who were still ( or again ) helminth infected at the post treatment survey ( n = 48 ) or who had no data for this survey ( n = 21 ) resulting in 177 participants whose data were included in the post-treatment analysis . To examine whether different helminth infections modulate systemic immune activation , we first studied the baseline expression of the T cell activation markers HLA-DR and CD38 on total CD4 and CD8 T cells in HIV negative volunteers with ( n = 189 ) and without helminth infection ( n = 57 ) , as determined by the Kato-Katz method . At baseline , the vast majority ( 84% ) of helminth infected individuals were infected with a single worm species as per Kato-Katz test performed during screening . Figure 1A shows a representative zebra plot and the gates that were used to study HLA-DR and CD38 expression on CD4 ( upper panel ) and CD8 T cells ( lower panel ) . Generally HLA-DR expression was confined to the memory population of both CD4 and CD8 T cells , whereas CD38+/HLA DR- CD4 and CD8 T cells consisted predominantly of CD45RO-CD27+ “naïve” T cells and a small proportion of CD27+ memory CD4 T cells ( figure 1B , left panel ) . CD38 expression pattern on different memory CD8 T cell subsets had more inter-individual differences . Analysis of a subset of WHIS volunteers ( n = 19 ) showed that HLA-DR+/CD38+ CD4 T cells were almost exclusively CD45RO+ memory T cells ( median >90% ) and a median of 63% co-expressed CD27 ( supplementary figure S1 ) , indicative of central memory like cells . Similarly , more than 90% of HLA-DR+/CD38+ CD8 T cells were memory T cells , but were distributed roughly equally between CD45RO+CD27+ , CD45RO+CD27− and CD45RO−CD27− cell populations ( supplementary figure S1 ) . Combined as one group , helminth infected subjects had only moderately and mostly insignificant increased frequencies of HLA-DR+ and/or CD38+ CD4 and CD8 T cells ( Table 2 ) when compared to non-infected subjects . Nonetheless , in subjects with helminth infection the median proportion of HLA-DR+/CD38+ CD4 T cells was significantly elevated ( 2 . 16% versus 2 . 63% , p = 0 . 011 ) whereas median HLA-DR+/CD38+ CD8 T cell frequencies were moderately increased ( 5 . 50% versus 6 . 86% , p = 0 . 055 ) . As expected , HIV+ subjects ( n = 77 ) had highly elevated median frequencies of HLA-DR+/CD38+ CD8 T cells ( 25 . 5% ) and CD4 T cells ( 14 . 3% ) compared to all HIV− subjects ( data not shown ) , confirming the validity of our results . CD38+ CD4 and CD38+ CD8 T cell frequencies were also moderately but insignificantly increased ( p<0 . 1 for both ) , although their predominantly “naïve” phenotype is counterintuitive for a T cell activation marker . Thus , taken together as a group , helminth infected individuals had significantly increased frequencies of “activated” HLA-DR+/CD38+ double-positive CD4 , and a trend towards increased frequencies of HLA-DR+/CD38+ and CD38+ CD8 T cells . We next compared these immune activation markers in HIV− study volunteers after further stratification by helminth species: Ascaris lumbricoides ( AL , n = 39 ) , Hookworm ( HW , n = 49 ) , Trichuris trichiura ( TT , n = 33 ) , Schistosoma mansoni ( SM , n = 59 ) and Schistosoma haematobium ( SH , n = 17 ) . We observed substantial differences in the expression of immune activation markers ( HLA-DR and CD38 ) on T cells between different helminth infections . Particularly , subjects with TT and AL infection had significantly increased frequencies of activated T cells in the peripheral blood; In TT infected volunteers median frequencies of HLA-DR+ CD4 T cells ( 9 . 37% versus 7 . 01% , p = 0 . 015 ) and CD8 T cells ( 29 . 30% versus 18 . 44% , p<0 . 0001 ) were increased when compared to helminth negative subjects ( figure 2A ) . Similarly , in AL infected subjects increased median frequencies of HLA-DR+ CD4 and CD8 T cells were also observed ( %CD4 , 9 . 14% , p = 0 . 011; %CD8 , 25 . 4% , p = 0 . 035 ) . SM or HW infections were not associated with substantial increases in HLA-DR+ CD4 T cell frequencies . To the contrary , there was a trend towards lower median frequencies of HLA DR+ CD8 T cells ( 14 . 01% ) in HW infected volunteers compared to helminth negative subjects ( p = 0 . 222 ) . Median frequencies of HLA-DR+CD38+ CD4 T cells were significantly elevated in subjects infected with AL ( 1 . 3-fold , 2 . 92% , p = 0 . 002 ) and SM ( 1 . 2-fold ( 2 . 57% , p = 0 . 025 ) , but not TT infections ( 1 . 3-fold , 2 . 52% , p = 0 . 095 ) when compared to non-infected individuals ( 2 . 16% , figure 2B left panel ) . Median frequencies of HLA-DR+CD38+ CD8 T cells were significantly elevated in subjects infected with TT or AL as compared to non-infected individuals ( figure 2C right panel , 5 . 49% for none-infected , 9 . 96% ( p = 0 . 003 ) for TT and 10 . 18% ( p = 0 . 018 ) for AL ) . SM infected subjects had an insignificant increase in HLA-DR+CD38+ CD8 T cell frequencies ( 1 . 3-fold , 6 . 95% , p = 0 . 115 ) . Worm egg counts are an indicator of parasite burden within the infected host and we thus next wanted to assess whether increases in systemic T cell activation markers correlate with egg counts . Indeed , we observed a weak linear correlation between egg counts and the frequency of HLA-DR+CD38+ CD8 T cells in TT ( p = 0 . 061 , r2 = 0 . 11 , figure 2C left panel ) and SM ( p = 0 . 018 , r2 = 0 . 10 , figure 2C right panel ) infected individuals . In addition , for SM infected individuals , parasite egg counts weakly correlated with the frequency of HLA-DR+CD38+ CD4 T cells ( p = 0 . 071 , r2 = 0 . 05 , supplementary figure S2 ) . No linear relationship was observed for the frequency of HLA-DR+ T cells and parasite egg counts in subjects infected with neither TT nor SM . Interestingly we also did not find a significant linear relationship between egg counts and activated T cells in AL infected subjects ( p>0 . 2 , data not shown ) . In summary , these results show that TT , AL and SM infections are associated with systemic T cell activation . The weak linear correlation between egg counts and CD38+/HLA-DR+ T cells are consistent with a link between parasite burden and immune activation for TT and SM . Nonetheless , other factors might also contribute to immune activation in these individuals . Among the different helminth infections studied , TT infection was most significantly associated with increased systemic T cell activation . TT infection and T cell activation might also be linked to changes in systemic levels of pro-inflammatory cytokines , such as IL-1β , IL-6 or TNFα . We thus next measured plasma levels of 13 different cytokine in TT infected subjects ( n = 31 ) and worm-negative controls ( n = 27 ) simultaneously using a multiplex cytometric bead array for detection of pro-inflammatory cytokines ( IL-1β , IL-6 , TNFα , IL-17α , IFN-γ ) , “TH2” cytokines with anti-helminthic properties ( IL-4 , IL-5 , IL-13 ) and the regulatory cytokine IL-10 . TT infection was linked to increased levels of the pro-inflammatory IL-1β ( median: 3 . 5 pg/ml versus 0 . 0 pg/ml , p = 0 . 021 figure 3A far left panel ) and IL-17α ( median: 75 . 4 versus 0 . 0 pg/ml , p = 0 . 002 , figure 3A far right panel ) , but not to increased levels of IL-6 ( median: 1 . 9 versus 2 . 0 pg/ml , p = 0 . 635 , data not shown ) or TNFα ( median: 1 . 2 versus 2 . 8 pg/ml , p = 0 . 704 , data not shown ) when compared to worm-negative subjects . Furthermore , the majority of TT infected subjects also had elevated levels of IL-13 ( median: 130 . 4 versus 0 . 0 pg/ml , p = 0 . 010 , figure 3A right panel ) , but no detectable differences in IL-4 ( median: 42 . 8 versus 25 . 5 pg/ml , p = 0 . 223 , data not shown ) or IL-5 ( median: 0 . 0 versus 0 . 0 pg/ml , p = 0 . 289 , data not shown ) . Interestingly , plasma levels of the regulatory cytokine IL-10 were also elevated in TT infected subjects ( 8 . 9 versus 0 . 0 pg/ml , p = 0 . 015 , figure 3A left panel ) . Within TT infected individuals , we next compared the plasma concentration of these cytokines with the frequency of HLA-DR+ T cells . Indeed , IL-1β plasma levels correlated positively with the frequency of HLA-DR+ CD4 ( p = 0 . 033 , r2 = 0 . 16 , figure 3B left panel ) and CD8 T cells ( p = 0 . 014 , r2 = 0 . 20 , supplementary figure S3 ) . Similarly , there was a strong correlation between IL-10 plasma levels and HLA-DR+ CD4 ( p = 0 . 025 , r2 = 0 . 17 , figure 3B right panel ) but not with HLA-DR+ CD8 T cells ( p = 0 . 400 , supplementary figure S3 ) . IL-13 and IL-17 concentrations did not correlate with the frequency of HLA-DR+ CD4 or CD8 T cells ( p>0 . 25 ) . These data show that systemic activation of T cells is linked to the pro-inflammatory IL-1β and simultaneously to the regulatory IL-10 . Interestingly , plasma levels of IL-1β , IL-10 , IL-17α and IL-13 closely correlated with each other and could only be detected in a subset of Trichuris infected individuals . For example , subjects with elevated IL-1β levels typically also had elevated IL-10 levels ( p = 0 . 005 , r2 = 0 . 24 , supplementary figure S4 ) , IL-13 ( p<0 . 0001 , r2 = 0 . 41 , supplementary figure S4 ) and IL-17 ( p<0 . 0001 , r2 = 0 . 64 , data not shown ) , suggesting that elevation of pro-inflammatory , anti-helminthic and regulatory cytokines in the plasma is closely linked in TT infected individuals . HIV transmission occurs almost exclusively with CCR5-tropic HIV strains [12] and CCR5-tropic strains also predominate in the majority of individuals during chronic infection [13] . The expression of CCR5 on activated CD4 T cells is likely to contribute to the early selection of CCR5-tropic strains [14] . CCR5 expression is common on memory CD4 T cells in mucosal lymphoid tissues , the mucosa of the reproductive tract and intestine , the lungs and inflamed tissues [15]–[17] ( also reviewed in [18] ) . Generally , CCR5 expression was largely absent from CD45RO− CD27+ ( naïve ) CD4 T cells , whereas less mature CD45RO+CD27+ memory CD4 T cells included substantial proportion of CCR5+ cells ( typically 30–50% ) with a small proportion co-expressing HLA-DR . A representative zebra plot overlay of these T cell subsets delineated by CD27 and CD45RO expression is shown in figure 4A . More mature CD45RO+CD27− memory CD4 T cells contained the largest fraction of CCR5+ cells ( typically 50–80% ) and also HLA-DR+ memory CD4 T cells frequently co-expressed CCR5 . In fact , a higher median density of CCR5 was detected on activated memory ( HLA-DR+ ) CD4 T cells in all studied groups than in non-activated memory ( HLA-DR− ) CD4 T cells ( all: p<0 . 0001 , data not shown ) . For example , the CCR5 median density on HLA-DR+ memory CD4 T cells was more than 3-fold increased compared to HLA-DR− memory CD4 T cells in HIV negative , none-helminth infected subjects ( Medians: 2198 versus 638 respectively , p<0 . 0001 , figure 4B ) . In the present study , we wanted to address the question whether systemic immune activation during chronic infection with different helminth species might also be linked to an increase of CCR5 surface expression on the memory CD4 T cells . In order to compare CCR5 expression density on total memory CD4 T cells from different subjects and study visits , we first determined the CCR5 MFI on CD45RO+ memory and CD45RO− naïve CD4 T cells and standardized CCR5 MFI results for CD45RO+ memory CD4 T cell subset by subtracting CCR5 MFI for CD45RO− naïve CD4 T cells for each sample ( Figure 4C ) . In addition , the frequency of CCR5 expression on activated ( defined by the expression of HLA-DR ) total memory CD4 T cells was studied . None of the helminth infections was associated with substantial changes in the expression of CCR5 on memory CD4 T cells . TT infection was however associated with a moderate but insignificant increase of the ΔCCR5 MFI ( memory-naïve ) as compared to the worm-negative control group ( 1 . 2-fold , medians: 417 versus 339 respectively , p = 0 . 054 , Figure 4D ) . Furthermore , we also observed a trend towards a moderate increased frequencies of CCR5+/HLA-DR+ double positive memory CD4 T cells in the AL infected individuals ( median: 7 . 24% ) compared to the control group ( median: 5 . 70% , p = 0 . 093 , data not shown ) , even though no change in ΔCCR5 MFI could be observed in this group when compared to controls ( median: 381 , p = 0 . 542 , data not shown ) . No significant change in the frequencies of CCR5+/HLA-DR+ memory CD4 T cells could be observed in TT infected group ( median: 6 . 60% ) when compared to the control group ( median: 5 . 70% p = 0 . 204 , data not shown ) . These results suggest that TT infection is associated with a moderately higher density of CCR5 on circulating memory CD4 T cell whereas AL infection is linked to a moderate increase in frequencies of activated CD4 T cells that co-express CCR5 . Whether treatment of helminth infections reduces systemic immune activation in HIV negative individuals has not been explored so far . We only included subjects with no detectable helminth infection post-treatment ( n = 177 ) into this analysis . We first studied the effect of one dose of Albendazole/Praziquantel treatment on eosinophil counts to determine whether helminth treatment has an effect on helminth-induced eosinophilia ( Table 3 ) . At baseline , helminth infection was associated with eosinophilia ( p = 0 . 004 , p-value not shown in Table 3 ) . More specifically , eosinophiles were highest during infections with TT ( median: 400/μl , p = 0 . 009 ) followed by infections with AL ( median 280/μl , p = 0 . 023 ) , SM ( median: 275/μl , p = 0 . 004 ) and HW ( median: 220/μl , p = 0 . 033 , p-values not shown in Table 3 ) . 3 months post treatment eosinophil counts decreased in subjects infected with HW ( p = 0 . 003 ) , SM ( p = 0 . 001 ) and AL ( p = 0 . 115 ) . Only TT infected subjects remained with very high eosinophil counts after treatment ( median: 300/μl vs . 400/μl , p = 0 . 456 ) . Compared to worm negative control subjects , who showed no effect of worm treatment ( p = 0 . 416 ) , the median change in eosinophil counts post treatment differed significantly for SM ( p = 0 . 036 ) infected subjects . Next , we studied the effect of helminth treatment on T cell activation markers . Importantly , flow cytometric analysis of T cell activation markers and CCR5 expression was blinded to helminth infection status at baseline . We compared the frequencies of HLA-DR+ and HLA-DR+/CD38+ on CD4 and CD8 T cells and in addition studied the CCR5 expression density on CD4 T cells at 1–3 months ( Table 3 ) in subjects with and without helminth infection at baseline . Surprisingly , only very minor changes in HLA-DR expression on CD4 T cells could be detected with no substantial differences between helminth infected subjects and the control group . The largest difference between the pre- and post-treatment visit was detected for TT infected subjects from a median of 10 . 71% HLA-DR+ CD4 T cells to a median of 7 . 77% ( p = 0 . 099 ) , but even this change did not differ significantly to that in the control group ( p = 0 . 283 ) . Median frequencies of HLA-DR+ CD8 T cells decreased substantially in TT ( 32 . 76% to 21 . 59% , p = 0 . 003 ) and AL ( 24 . 30% to 22 . 93% , p = 0 . 011 ) infected individuals , whereas it slightly insignificantly increased in HW infected individuals . We also observed a very minor and insignificant increase in HLA-DR+ CD8 T cell frequencies in the control group ( 18 . 62% to 19 . 43% , p = 0 . 127 ) . Compared to the control group , the decrease in HLA-DR expression was more pronounced but still insignificant in TT ( p = 0 . 136 ) and AL ( p = 0 . 091 ) infected subjects . Changes in HLA-DR+/CD38+ CD8 T cells ( data not shown ) were similar to HLA-DR+ CD8 T cells and the biggest declines were observed for TT ( median: 9 . 02% to 6 . 61% , p = 0 . 008 ) and AL ( median: 9 . 96 to 6 . 83% , p = 0 . 128 ) , whereas median frequencies in the control group only declined from 6 . 03% to 5 . 52% ( p = 0 . 161 ) . However differences in HLA-DR expression dynamics between any of the worm infected groups and the control group were insignificant . We next analyzed the effect of helminth treatment on CCR5 density on the cell surface of memory CD4 T cells ( ΔCCR5 MFI , Table 3 ) . A significant decline in CCR5 density was observed in subjects treated for TT ( median: 420 to 282 , p = 0 . 003 ) and AL ( median: 390 to 232 , p = 0 . 003 ) , whereas no significant decline was observed in helminth negatives ( median: 343 to 400 . 5 , p = 0 . 534 ) and in SM infected subjects ( median 289 . 5 to 251 . 5 , p = 0 . 407 ) . Compared to the control group , the treatment induced change in CCR5 density on memory CD4 T cells was significant in TT and AL infected subjects ( p = 0 . 041 and 0 . 026 respectively ) .
It has been hypothesized that systemic immune activation caused by chronic helminth infection contributes to increased HIV transmission in sub-Saharan Africa [1] and therefore to the high HIV prevalence in this region . This hypothesis is supported by observations that low systemic T cell activation is linked to HIV resistance in highly exposed HIV uninfected individuals [8]–[10] . Furthermore , it is well established that T cell activation and proliferation facilitate efficient HIV replication in vivo and in vitro [18]–[20] . Previous studies support the concept that helminth infections are associated with systemic T cell activation [3]–[5] . However , whether helminths are a primary cause of systemic T cell activation in populations from endemic areas of Africa is not entirely clear , because these studies did not specifically investigate immune activation before and after helminth treatment , nor did they differentiate between different helminth species . To fill this gap , we studied systemic T cell activation and HIV co-receptor expression in relation to helminth infection within the large WHIS cohort from Mbeya region , Tanzania , before and after deworming with Albendazole and Praziquantel . Our results show that Trichuris , but also Ascaris and S . mansoni infections are linked to increased frequencies of “activated” CD4 and/or CD8 T cells defined by expression of HLA-DR alone or in combination with CD38 . Of note , increased T cell activation was quite dramatic for CD8 T cells during Trichuris infection , whereas Ascaris infection was rather associated with more activated CD4 T cells . It should nonetheless be noted that frequencies of activated T cells varied greatly between individuals infected with Ascaris or Trichuris , suggesting that causes of systemic T cell activation are multifactorial . Other factors such as additional persistent infections ( as observed during HIV infection ) or host genetic differences are likely to also influence T cell activation status . Hookworm infection was associated with a moderate , but insignificant decrease in the frequency of HLA-DR+ CD8 T cells . Thus , while these results partially agree with previously published data that helminth infections are associated with T cell activation [3] , [4] , our results suggest that not all helminth species are necessarily associated with systemic T cell activation and that Hookworms might even have an opposing effect . Independent of helminth infection status , CD38 expression alone was a characteristic of “naïve” CD27−/CD45RO− CD4 and CD8 T cells , whereas co-expression with HLA-DR was exclusively detected on memory T cells . In our study population it is thus unlikely that CD38 expression on naïve T cells is a marker of T cell activation and we therefore concentrated on HLA-DR expression alone or in combination with CD38 . The etiology of helminth-associated T cell activation is not known . Trichuris and S . mansoni egg counts are positively correlated with the frequency of HLA-DR+/CD38+ CD8 and CD4 T cells ( figures 2D and S2 ) , respectively , suggesting that high parasite burdens contribute to systemic T cell activation . Moreover , Trichuris infection was associated with increased plasma levels of pro-inflammatory ( IL-1β and IL-17α ) , anti-helminthic ( IL-13 ) and regulatory ( IL-10 ) cytokines , which closely correlated with each other; showing a mixed cytokine response to infection with Trichuris . Faulkner et al . also observed a similar mixed cytokine response in the blood of Cameroonian children with Trichuris and Ascaris infections following an exposure to Trichuris antigens [21] . Of interest , IL-1β and IL-10 concentrations in our Trichuris infected volunteers positively correlated with the frequency of HLA-DR+ CD4 and/or CD8 T cells , linking systemic T cell activation to the pro-inflammatory IL-1β and simultaneously to the regulatory IL-10 . It is therefore possible that the immune response to Trichuris infection causes immune activation through the induction of pro-inflammatory cytokines , but also evokes a systemic regulatory and anti-helminthic cytokine response . Our data thus confirm previous reports that Trichuris infections are associated with increased IL-10 levels [21] , [22] and provide a possible link between helminth associated systemic immune activation , hypo responsiveness and anergy [23] , [24] . The differences in T cell activation profile between the different helminth species is intriguing and surprising , particularly for Hookworm versus Trichuris infection . Both species interact closely with the gut epithelium , but only Hookworms feed on blood and thus are probably more exposed to circulating immune cells than Trichuris . Thus it is counterintuitive that Trichuris , but not Hookworms are associated with increased levels of activated , HLA-DR+ T cells . Gaze et al . have demonstrated that experimental human Hookworm infection induces a systemic Hookworm-specific cellular immune responses , which is characterized by production of several TH2 , TH1 and the regulatory cytokines upon re-stimulation of PBMC in vitro [25] , suggesting that Hookworm infection is immunogenic . One potential difference between the two species could be associated plasma levels of the pleiotropic IL-17 . We found increased plasma levels of IL-17α in association with Trichuris infection , whereas George et al . have found decreased levels of this cytokine in Hookworm infected individuals [26] . IL-17 induces IL-1β production in human Macrophages [27] and our results show a close correlation between IL-17α and IL-1β plasma levels . Furthermore IL-1β levels correlated with HLA-DR expression on circulating CD4 T cells in Trichuris infected individuals . Thus , while remaining speculative , differences in the induction of the IL-17 pathway might play a role in the observed difference in systemic T cell activation between Hookworm and Trichuris infection . Very high frequencies of HLA-DR+ ( and CD38+ ) “activated” T cells occur also during HIV infection [28]–[34] and were characteristic for HIV+ WHIS study participants as well ( unpublished data ) . It has previously been suggested that translocation of immunostimulatory microbial compounds , such as Lipopolysaccharide ( LPS ) contribute to systemic immune activation during HIV infection [35] , [36] . Due to the close interaction of Trichuris with the intestinal epithelium we hypothesized that immune activation during Trichuris infection might be caused by microbial translocation and therefore studied LPS levels in subjects with and without Trichuris infection . However , we did not detect increased LPS levels in Trichuris infected subjects ( data not shown ) . Furthermore , in vitro stimulation of PBMCs for 48 h with LPS did not induce an “activated” T cell phenotype , whereas stimulation with the T cell growth factor IL-15 did ( data not shown ) , arguing against this hypothesis . In addition , experimental ( non-productive ) Trichuris infection of Rhesus macaques with inflammatory bowel disease ( IBD ) actually decreases markers ( sCD14 ) of microbial translocation and IBD associated T cell proliferation [37] , further arguing against the notion that microbial translocation is a cause of systemic immune activation in Trichuris infected individuals . Other groups have detected increased plasma levels of LPS in association with S . mansoni and Hookworm infection [26] , [38] . S . mansoni infection indeed correlated with increased levels of HLA-DR+/CD38+ CD4 and CD8 T cells . However , Hookworm infection was not associated with increased , but rather with slightly lower frequencies of “activated” HLA-DR+ CD8 T cells . Thus , while the etiology of T cell activation during helminth infection and its connection to microbial translocation remains to be fully elucidated , it is important to note that despite its reported association with microbial translocation [26] , Hookworm infection was rather linked with a trend to lower frequencies of HLA-DR+ , “activated” CD8 T cells . To determine whether helminth-associated systemic immune activation was primarily caused by helminth infections , we studied the effect of one dose of Albendazole/Praziquantel treatment on reducing systemic immune activation . It is well established that infections with helminths are associated with eosinophilia ( reviewed in [39] , [40] ) . Eosinophils decreased 3 months post treatment in subjects infected with Hookworm , S . mansoni and to a lesser degree Ascaris , but remained exceptionally high in subjects infected with Trichuris , demonstrating a strong effect of Albendazole/Praziquantel treatment on helminth-induced immune system modulation with the exception of Trichuris infections . Having observed this , we studied modulation of activated T cells frequencies post-treatment . HLA-DR+ T cell frequencies most profoundly dropped in subjects infected with Trichuris and Ascaris but increased in those infected with Hookworm , which is consistent with our observations at baseline . Nonetheless , the changes were insignificant when directly compared to the helminth negative control subjects , who were also treated . The relatively minor effect of helminth-treatment in Trichuris infected volunteers on T cell activation and eosinophilia might be explained by the fact that Albendazole treatment might not have completely cleared Trichuris infection . Indeed , it is well known that Abendazole is not fully effective for treating Trichuris infection [41] . Supporting this argument , 30% ( 9 of 30 ) Trichuris infected subjects ( which were excluded in the post-treatment analysis ) had detectable Trichuris eggs post-treatment as per Kato-Katz test and a more sensitive test probably would have detected even more infections . A recent study has demonstrated only 10% cure rate using an identical Albendazole treatment as used during the WHIS study [42] . More effective treatment options [42] could help to clarify the effect of Trichuris eradication on systemic immune activation . However , based on our data , we cannot exclude the possibility that other environmental factors associated with the presence of Ascaris or Trichuris worms also contributed to increased systemic T cell activation in WHIS study volunteers . To our knowledge , only one other longitudinal study has studied the effect of worm treatment on reduction of T-cell activation in HIV negative individuals [43] . Kassu et al . observed no significant changes in the expression of HLA-DR and CD38 on CD4 T cells in HIV negative subjects six months after helminth treatment but a significant decline in frequencies and numbers of HLA-DR+/CD38+ CD8 T cells . This study however did not distinguish between helminth and other intestinal parasites and was limited by a small sample size . Our study therefore provides for the first time extensive evidence on helminth associated systemic T cell activation and the impact of Albendazole/Praziquantel treatment . Is it possible that these activated T cells are helminth-specific ? After Yellow fever ( YF ) vaccination co-expression of HLA-DR and CD38 is characteristic for recently activated , proliferating ( Ki67+ ) YF-specific CD8 T cells during the peak response [44] and thus this is one possible explanation . However , it is counterintuitive that during Trichuris infection such large fractions of CD8 T cells participate in the anti-helminthic immune responses . Is HLA-DR expression a marker of cycling T cells ? It has been documented that HIV associated immune activation defined by HLA DR alone or in combination with CD38 is linked to substantial increases in T cell proliferation [45] . HLA-DR expression on CD25+CD127- CD4 T cells correlate with T cell proliferation during HIV infection [46] and as mentioned above HLA-DR and CD38 is characteristic for recently activated , proliferating ( Ki67+ ) YF-specific CD8 T cells after YF vaccination . Based on these previous findings , we propose that increased frequencies of HLA-DR expressing T cells are a marker of increased systemic T cell proliferation in helminth infected subjects . Although a trend towards increased CCR5 density on memory CD4 T cells and an increased frequency of CCR5+/HLA-DR+ memory CD4 T cells was observed in Trichuris and Ascaris infections respectively , which is in line with previous reports [4] , [7] , these values varied greatly between different individuals , prohibiting conclusions on modulation of cellular susceptibility to HIV infection caused by these helminth species [12] , [47]–[49] . However our data clearly shows that independent of helminth infection , activated HLA-DR+ CD4 T cells express very high levels of CCR5 on their surface potentially facilitating cell entry of HIV . In conclusion , not all studied helminth species modulated the systemic immune system in the same manner . Particularly , Trichuris , Ascaris and S . mansoni infections correlate with increased expression of T cell activation markers with relatively little effect of helminth treatment compared to helminth-negative controls . Contrary , Hookworm infection was associated with slightly decreased frequency of HLA-DR expressing CD8 T cells . Although we fail to demonstrate a strong effect of helminth treatment on T cell activation , the link between parasite burden and activated T cells during Schistosome and Trichuris infection suggest a causal link between the infection and immunomodulation . Because systemic T cell activation potentially contributes to increased HIV transmission risk [8]–[10] through facilitation of early systemic dissemination of the virus , our data support the concept that helminth infections , which are linked to systemic Immune activation and potentially increase CCR5 density on memory CD4 T cells , such as Trichuris infection , could indeed also contribute to increased HIV transmission risk during sexual activity .
|
Helminth infections are common in sub-Saharan Africa where about half of the population may be infected with one or more helminth species . HIV infection is also highly prevalent in this region . Because of the geographic overlap of helminth and HIV infections , it has been hypothesized that helminth infections may increase susceptibility to HIV by increasing systemic immune activation , which has been linked to increased HIV susceptibility . We therefore investigated the profile of T cell activation in individuals infected with different helminth species before and after helminth treatment within the WHIS cohort in Mbeya , Tanzania . Our study shows that systemic T cell activation differs between infections with different helminths . Particularly Trichuris but also Ascaris and S . mansoni infections were linked to increased frequencies of activated , HLA-DR+ T cells with relatively little effect of helminth treatment . Hookworm infection was associated with a trend towards decreased frequencies of activated , HLA-DR+ CD8+ T cells . Our study supports the concept that helminth infections , which are linked to systemic immune activation , could potentially also contribute to increased HIV transmission .
|
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2014
|
Helminth-Associated Systemic Immune Activation and HIV Co-receptor Expression: Response to Albendazole/Praziquantel Treatment
|
This study sought understand how the 2014–2016 EVD Virus Disease ( EVD ) outbreak impacted the nutrition sector in Sierra Leone and use findings for improving nutrition responses during future outbreaks of this magnitude . This qualitative study was iterative and emergent . In-depth interviews ( n = 42 ) were conducted over two phases by purposively sampling both key informants ( n = 21; government stakeholders , management staff from United Nations ( UN ) agencies and non-governmental organizations ( NGO ) ) , as well as community informants ( n = 21; EVD survivors , health workers , community leaders ) until data saturation . Multiple analysts collaborated in a team-based coding approach to identify key themes using Dedoose software . Findings are presented as both quotations and tables/figures . The EVD outbreak effects and the related response strategies , especially movement restriction policies including 21-day quarantines , contributed to disruptions across the food value-chain in Sierra Leone . System-wide impacts were similar to those typically seen in large-scale disasters such as earthquakes . Participants described an array of direct and indirect effects on agricultural production and food storage and processing , as well as on distribution , transport , trade , and retailing . Secondary data were triangulated by interviews which described the aggregate negative effect of this outbreak on key pillars of food security , infant and young child feeding practices , and nutrition . During the humanitarian response , nutrition-specific interventions , including food assistance , were highly accepted , although sharing was reported . Despite EVD impacts across the entire food value-chain , nutrition-sensitive interventions were not central to the initial response as EVD containment and survival took priority . Culturally-appropriate social and behavior change communications were a critical response component for improving health , nutrition , and hygiene-related behaviors through community engagement . Infectious diseases such as EVD have far-reaching effects that impact health and nutrition through interrelated pathways . In Sierra Leone , the entire food value-chain was broken to the extent that the system-wide damage was on par with that typically resulting from large natural disasters . A food value-chain approach , at minimum , offers a foundational framework from which to position nutrition preparedness and response efforts for outbreaks in similar resource constrained settings .
The first three human outbreaks of Ebola Virus Disease ( EVD ) , a zoonotic disease , were recorded between 1976–1979 in the Democratic Republic of Congo and Sudan , with fewer than 400 deaths total . Since the first human case in 1976 until 2013 , just thirteen human outbreaks from various EVD subtypes ( E . Sudan , E . Zaire , E . Ivory Coast ) had occurred in Africa , directly causing an estimated 1 , 300 deaths [1] . Understanding this natural history of EVD underscores the uniquely severe and widespread nature of the 2014–2016 outbreak , whose reported number of deaths surpassed the cumulative sum of EVD outbreaks from the previous 32 years ( 1976–2008 ) [2] . Since December 2013 , when the first case was identified in rural Guinea , until today–years after the final reported case–the 2014–2016 EVD outbreak has had a lasting impact on the population health , livelihoods , and social dynamics of affected countries in West Africa [3] . In Sierra Leone alone , an estimated 14 , 124 cases and 3 , 956 deaths were a direct consequence of the outbreak [4] . And today , thousands of survivors are still coping with the prolonged effects of their own illness episodes [5] . A large proportion of this disease burden may be ascribed to well-documented structural and social challenges that inhibited a timely and effective response at large [6] . Much of it may also be attributable to an infectious disease outbreak in an economically developing country , yet still under resourced with fragile health and nutrition situations . Prior to the outbreak , Sierra Leone was effectively emerging from civil war and unrest , seeing 13 . 5% household income gains since 2001 [2] . There was increasingly strong trade of food commodities: livestock with Guinea and re-imported rice , as well as seasonal livestock with Liberia , for example [7] . From 2008 to 2013 , the proportion of underweight children had decreased from 21 to 16 percent , a key indicator of an improving nutrition situation [8] . Despite such gains , the population majority was living at or below the absolute poverty line and reliant on agricultural livelihoods , even in Freetown where urban and peri-urban agriculture was practiced [2 , 9] . In 2013 , 38% and 9% of children under five years of age remained stunted and wasted , respectively; and only 32% of children under 6 months were exclusively breastfed , including a mere 7% of children under two years who were fed appropriately [8] . The infant and young child nutrition situation , while improving , was sub-optimal even before this outbreak . The relationship between infectious disease and nutritional status is complex and bi-directional: the symptoms of infection contribute to reduced food intake and weakened immune responses , which in turn make it more difficult for the body to fight the disease [10] . EVD and nutrition are no different . At an individual level , EVD patients typically present with symptoms indicative of worsened nutritional status , such as diarrhea , abdominal pain , vomiting , and anorexia [11] . When community members , including heads of households and primary caregivers , become infected , the consequences of infection on nutrition extend beyond that individual to the community and household where typical infant and young child feeding ( IYCF ) practices may be disrupted . This is particularly true during outbreak responses , such as Sierra Leone’s , where quarantines for exposed persons may limit the ability of households to access and utilize food using typical food insecurity coping strategies . Since this outbreak , scholars have proposed solutions to better prepare for , and respond to infectious disease outbreaks at large [12 , 13] . Despite the direct physiological effects of EVD on individual nutritional status , as well as the known complexities surrounding household food and nutrition security during outbreaks [14] , few nutrition-related solutions have been discussed . Research conducted in both Liberia and Guinea suggests that there was a complex interplay of bio-social and cultural factors that contributed to nutrition-related impacts during this EVD outbreak [15 , 16] . It could be surmised then that similar interrelated processes throughout the food system affected nutrition in Sierra Leone as well . Improving response and preparedness options warrants an in-depth reflection on lessons learned from this EVD outbreak and response , including a focus on the non-clinical forces that may have influenced nutrition . In Sierra Leone , research has provided evidence for EVD impacts on market chains and trade , yet questions remain about the downstream effects on the nutrition situation during this outbreak and strategies to mitigate it in the future . Considering the entire food value-chain , from food production to retailing and consumption , may offer some answers [17] . Therefore , we firstly sought to explore how and through what pathways the EVD outbreak impacted nutrition in Sierra Leone . Secondly , we investigated the factors to effective implementation of nutrition response strategies during this outbreak . Thirdly , we aimed to use findings to consider a nutrition preparedness and response framework in planning for future outbreaks of this nature .
This qualitative study collected data over two iterative phases from multiple participant groups using semi-structured , in-depth interviews between September and November 2016 . In phase 1 , we explored the perceptions of policy makers , hospital management , and responding agency staff employed in Sierra Leone during or since the EVD outbreak . In phase 2 , we investigated the community perspectives by interviewing EVD survivors , front-line health workers , and community leaders . Participants were recruited by a local non-government organization ( FOCUS1000 ) with previous experience conducting qualitative research related to nutrition and the EVD response . Using a criterion-based , purposive sampling strategy , participants were initially identified for interviews choosing specific characteristics that would allow for a range of perspectives [18] . Phase 1 participants were sampled based on their professional role representing government , hospital management , NGO , or United Nations organizations involved in the outbreak response at the national level; Phase 2 participants were sampled by their community role ( e . g . EVD survivor , community leader , health worker ) as well as their geographic representation across the four provinces of Sierra Leone . After recruitment , a locally hired data collection team was trained for 40 hours in qualitative research theory and in-depth interviewing skills . Data collectors were university graduates who spoke both English and Krio , as well as Mende or Temne . Training also included field tests and revisions of the interview guides to ensure appropriate content considering multiple languages . The most qualified interviewers were then chosen for fieldwork , during which FOCUS 1000 senior staff supervised the data collection team and oversaw all study procedures . Prior to data collection , FOCUS1000 recruited prospective study participants through phone calls , emails , and letters , as well as sought permissions from local authorities for conducting this work . No notable recruitment challenges were reported . Then , over three months , the team conducted interviews lasting between 45 and 60 minutes , primarily in English and Krio language , and to a lesser extent in Mende and Temne . Two semi-structured guides were used to guide the interviews in subsequent phases , each with similar overall content yet different question types and probes ( Table 1 ) . The primary content domains within the interview guides were chosen a priori , based on a review of the literature , the guiding research questions , and in agreement with UNICEF and FOCUS 1000 team members who had experienced the outbreak . Within those broad domains , though , specific questions and probes , particularly those of the phase 2 informant guide , were derived from phase 1 data in an iterative and flexible research design that allowed for triangulation between phases and participant types . During Phase 1 , 21 interviews were conducted among key informants , government policy makers , as well as hospital management and programme agency staff of UN agencies and non-government organizations , who could speak not only to their own personal experiences but also to those of the organizations they represent . In Phase 2 , 21 additional interviews were conducted among community informants , including EVD survivors , front-line health workers , and community leaders , using a second interview guide which focused on understanding individual lived experiences . Interviews in each phase were conducted with the goal of reaching data saturation , until a repetition of key themes , at which point no additional data collection was thought to yield new information relevant to the research questions [20] . All but one interview was digitally recorded . Each transcript included field notes written by a data collector immediately after interviewing [21] . All digital recordings and corresponding field notes were translated and transcribed into English . Transcripts were continually reviewed and spot checked for accuracy by members of the FOCUS1000 team . Inconsistencies between recordings and transcriptions were resolved by the team prior to analysis . English transcripts were then uploaded into Dedoose qualitative software for data management and analysis [22] . A codebook was also developed based on both the initial study objectives and interview guide content . It served as the analytic framework within Dedoose and contained 46 codes across 7 thematic areas . The coding procedures followed a systematic process in a team-based approach using 3 data analysts [23] . After codebook development , we conducted inter-coder reliability testing within Dedoose to ensure consistency across individual coding efforts [24] . Pooled kappa scores of 0 . 76 and 0 . 71 during reliability testing ensured that we had ‘good’ internal consistency prior to coding the entire data set [25 , 26] . The analytic team also held weekly meetings to address any challenges and questions to ensure continual alignment throughout the coding process . Using 46 codes and sub-codes across 42 transcripts , a combined 1918 code applications were made by the analytic team across the entire data set . Both data-driven and theory-driven codes were identified during this process [27] . Those code applications , which labelled thematic units of text , were then stratified and extracted using Dedoose [22] . Salient themes and sub-themes were identified during interpretation and presented as a combination of tables , figures , and quotations to best illustrate findings [28] . They were then presented to the data collection team , as well as to representatives from government , civil society , and non-government organizations , in a participatory workshop format . This process of ‘member checking’ provided a forum for feedback and discussion around the findings , as well as confirmation that our interpretations of the data accurately represented the nutrition impacts felt during the outbreak [29] . The study protocol was approved by the Office of the Sierra Leone Ethics and Scientific Review Committee . All participants provided oral informed consent prior to interviewing and no identifiers were collected as part of study procedures .
Data suggest that EVD impacted the entire food value-chain , ultimately affecting individual- , household- , and population-level nutritional status . These impacts were multi-factorial: from the clinical manifestation of the disease itself , from the outbreak consequences , and from the containment measures/response strategies . To reduce the spread of EVD in Sierra Leone , the government restricted people's movements by blocking roads and imposing household and community quarantines . However , in doing so , food security and nutrition were negatively impacted due to upstream market chain disruptions ( Fig 1 ) . Fig 1 forms the basis of the subsequent sub sections , which are organized below by emergent themes identified across the food value-chain from Food Production to Food Retailing . The most prominent nutrition-related challenges described by key informants could be organized into three primary categories: a ) food availability and access , b ) capacity , coordination , and logistics , and c ) screening malnutrition cases ( Table 3 ) . Both internal and external inputs were identified to be important coping strategies in the face of those primary nutrition challenges . Data suggest that the level of community acceptability toward the interventions during the nutrition response improved over time as trust was built through enhanced sensitizations and more appropriate social and behavior change communications ( SBCC ) . Specifically , themes around therapeutic foods , including infant formulas , interim care guidelines , and SBCC emerged in this area of inquiry . Finally , comparing the community leaders , survivors , caregivers , and frontline workers who represented the voice of the community members to the Government , United Nations , and NGO staff who provided policy and organizational voices , lessons learned underscore unique perspectives for consideration ( Table 4 ) . Key informants discussed that a key learning ( other than coordination ) was the true importance of community involvement for an effective response . This was not a key lesson learned for community informants who were already aware of their own importance; for them , the key lessons learned focused on timely government support , adequate health worker capacity , and the importance of having a strong health and food system better equipped to absorb such events in the future .
Through in-depth interviews with individuals who directly and indirectly lived through the EVD outbreak in Sierra Leone , we gain a more refined understanding of the potential pathways through which the food system and infant and young child nutrition was impacted . Participants in this study provided multiple perspectives that described humanitarian response strategies which , coupled with the outbreak , disrupted livelihoods limiting food production and trade , weakened typical coping mechanisms , and altered care practices for infants and young children , including orphans . Our qualitative data revealed that the EVD outbreak impacted food security and nutritional status through each level of the food value-chain [33] . The scope of this impact was unique , though , compared to that of other infectious disease outbreaks: it disrupted entire health and food systems . In that sense , its systemic effects were more similar to the 2010 earthquake in Haiti than to the resulting cholera outbreak that same year [34] . Cholera did not have the system-level disruption that EVD did across Sierra Leone , Liberia , and Guinea , despite high morbidity and mortality [35] . In that sense , the EVD outbreak more closely resembled a disaster , such as the earthquakes in Nepal ( 2015 ) and Haiti ( 2010 ) where access to basic needs , including food and nutrition , were a foremost priority [36 , 34] . Outbreaks on par with EVD , while rare , may conceptually draw from disaster responses where many lessons have already been documented , including the need for agile and adaptive systems , enhanced technologies , community engagement , local ownership , and strong coordination mechanisms are paramount [37 , 38 , 39] . To ensure a resilient food value-chain , enhanced safety-net approaches at each stage are critical for ensuring actors can continue pro-nutrition activities at each stage , even during disruptions of this magnitude . Our findings highlight this need not only on the agriculture , food production side of the chain but also downstream at household and individual levels where business incomes and resultant household food expenditures were profoundly hurt [40] . In support of other published work from this event , our findings underscore the importance of trusted community-level nutrition expertise , ideally complemented by capacity in the social and behavioral sciences and community engagement , for improved responses [41 , 42] . Relevant lessons learned can be drawn from other infectious disease outbreaks , such as the HIV/AIDS epidemic that has left a generation of orphaned children facing similar health and social challenges to those felt in the wake of this EVD outbreak [43] . Well-coordinated , interdisciplinary approaches have potential to marry the typically distinct concepts of clinical care and treatment to culturally-appropriate social and behavioral intervention strategies for more appropriate and acceptable implementation both during and after such emergencies . The strong community distrust and social resistance to public health intervention during this response underscore the complexity of such outbreaks and the need for respectful and well-planned response measures [44 , 45] . Regardless of planning , there will always be consequences to response measures during this type of complex emergency . Our findings highlighted some of these unintentional effects , for instance how typical household food insecurity coping strategies during market fluctuations or poor harvests were made difficult by the imposed 21-day quarantines . The quarantines placed additional economic and social burdens that contributed to individual and household diet-related challenges , above those felt by the disease; such lessons should be used to reflect on the viability of alternative approaches , such as Community Care Centers [46] . This finding is particularly important for nutrition in interdependent cultural contexts where food sharing , bartering , and communal agricultural activities are the keystones of rural livelihoods [47] . Our findings suggest that the ‘dynamic complexity’ of food and nutrition insecurity becomes more complex during periods of outbreak [14] , thus requiring careful , proactive preparedness across the value-chain . Such planning among both the organizations already present in a country and those newly arriving in the wake of such a disaster response will present serious coordination challenges that need to account for these new actors , as well as increased sensitization to clearly inform community members what organizations are providing what specific services . Regardless , our findings illustrate why preparations should be made across the food system , including the development of agreed-upon standard operating procedures to guide coordinated food and nutrition activities when movement restrictions are put in place during outbreaks , from production ( e . g . , provision of seeds to households to help during planting when trade is not possible ) through retailing ( e . g . , creation of temporary markets deemed ‘safe’ with provision of food assistance ) . Finally , despite improved coordination efforts , enhanced food assistance , and a more aligned humanitarian response over the course of this outbreak , our data suggest that nutritional challenges were disproportionately felt by infants and young children–an already nutritionally-vulnerable group . Breastfeeding , complementary feeding , and caregiving practices were all impacted in Sierra Leone , both directly by EVD and indirectly through market disruption and due in part to response measures put in place for disease containment . Balancing future responses to both curb contagion while also reducing the potential for negative nutritional consequences likely will remain a challenge considering the close relationship between the two . Utilizing standardized nutrition guidelines , like those provided for treatment and care during this outbreak , may prove to be a useful starting point that can assist front-line workers [48] . Also , by focusing response efforts higher up the food value-chain through both nutrition-sensitive approaches and the implementation of social safety nets , a future response may be able to better avoid some of the IYCF challenges reported in our study . Thus , agricultural activities and nutrition-sensitive intervention approaches that sustain adequate food production [49] , could be considered preparedness strategies . This qualitative study had several important strengths . First , it included both participant and analytic triangulation , a key study aspect designed to improve the data credibility [50] . Second , data collection was iteratively carried out over two phases . This two-phase design allowed for an extended period of data collection where interviewers conducted semi-structured interviews based in part on findings from one participant to the next . Third , this study uniquely considered the perspectives of the interviewers through textual analysis of their detailed field notes following interviews , which is a core aspect of ethnographic research [51] . Fourth , by using ‘member checking’ , whereby we presented final study results to key stakeholders and data collectors in a participatory workshop , we could be more confident that our interpretations of the textual data accurately reflected the accounts within them [29] . However , this research also included some limitations . Many key informants who had worked in Sierra Leone during the EVD outbreak were no longer in those same positions and could not be interviewed . However , in those cases , we recruited participants who were either direct successors to those staff or other key informants who were in similar positions during the outbreak . Similarly , we interviewed participants in 2016 about events from previous years; memories and perspectives of past events can change over time , a primary reason we corroborated findings with multiple participants and using secondary data sources . Further , those people who were interviewed survived the outbreak . While we heard both positive and negative accounts of individual experiences , it is possible that those individuals who did not survive EVD may have had different perspectives . By interviewing family members of those deceased , as well as EVD survivors , we believe that we provided a comprehensive account from multiple angles nonetheless .
This study underscores the magnitude of this EVD outbreak , which considerably impacted Sierra Leone’s food value-chain , negatively affecting food availability and food access across regions . At the policy level , adequate investments in improved emergency outbreak preparedness across the food value-chain may put nutritional health systems in more timely and coordinated positions to address not only the direct threat of an infectious disease but also the indirect toll that poor nutrition takes on community health . At the organizational level , nutrition programme planners should ensure necessary resources and capacity to ensure a combination of nutrition-specific and nutrition-sensitive response options prioritizing community involvement in design , coordination , and implementation across the food value-chain . These important lessons learned should be adapted to other contexts , such as the Democratic Republic of Congo , where a very similar EVD outbreak has been occurring since 2018 [52] . Disasters of this magnitude may unavoidably face food security challenges , yet taking an interdisciplinary approach by convening experts from disciplines across the value chain for preparedness planning , for instance , may help to mitigate such widespread nutrition effects in similar complex emergencies .
|
The 2014–2016 EVD outbreak has greatly impacted the population health and nutrition of affected countries in West Africa , including that of Sierra Leone . Since this recent outbreak , the humanitarian community acknowledges the need for improved solutions to better prepare for , and respond . Despite the importance of nutrition during outbreaks , there has been little systematic research conducted for understanding lessons learned and improving upon the typical nutrition response options currently available . This study used qualitative interviews to collect in-depth narratives from government officials , front-line health workers , non-government organization management , and community members including local leaders and EVD survivors . Findings reveal the unprecedented magnitude of this outbreak , which had systems-wide impacts not dissimilar to those felt by natural disasters . Interviews with people who lived through this event in Sierra Leone described EVD effects which revealed the importance and fragility of multiple , interconnected systems comprising the food value-chain for optimal nutrition in Sierra Leone . Findings across the food value-chain reveal how this interconnected system was impacted at every level with consequences for population-level nutrition . In preparation for future outbreaks of this magnitude , such a framework may prove useful for policy and planning , including improved guidelines development for employing coordinated nutrition-specific and nutrition–sensitive approaches that address immediate and underlying determinants of nutritional status .
|
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"life",
"sciences",
"population",
"groupings",
"mothers",
"cognitive",
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] |
2019
|
A qualitative study to understand how Ebola Virus Disease affected nutrition in Sierra Leone—A food value-chain framework for improving future response strategies
|
Crimean-Congo hemorrhagic fever virus ( CCHFV ) is a zoonotic agent that causes severe , life-threatening disease , with a case fatality rate of 10–50% . It is the most widespread tick-borne virus in the world , with cases reported in Africa , Asia and Eastern Europe . CCHFV is a genetically diverse virus . Its genetic diversity is often correlated to its geographical origin . Genetic variability of CCHFV was determined within few endemic areas , however limited data is available for Kosovo . Furthermore , there is little information about the spatiotemporal genetic changes of CCHFV in endemic areas . Kosovo is an important endemic area for CCHFV . Cases were reported each year and the case-fatality rate is significantly higher compared to nearby regions . In this study , we wanted to examine the genetic variability of CCHFV obtained directly from CCHF-confirmed patients , hospitalized in Kosovo from 1991 to 2013 . We sequenced partial S segment CCHFV nucleotide sequences from 89 patients . Our results show that several viral variants are present in Kosovo and that the genetic diversity is high in relation to the studied area . We also show that variants are mostly uniformly distributed throughout Kosovo and that limited evolutionary changes have occurred in 22 years . Our results also suggest the presence of a new distinct lineage within the European CCHF phylogenetic clade . Our study provide the largest number of CCHFV nucleotide sequences from patients in 22 year span in one endemic area .
Crimean-Congo hemorrhagic fever ( CCHF ) is an acute tick-borne zoonotic disease which is characterized by a fulminant and often hemorrhagic course of disease with the case fatality rate of 10–50% . Causative agent is the Crimean-Congo hemorrhagic fever virus ( CCHFV ) which belongs to the Nairovirus genus in the family Bunyaviridae . CCHF is the most widespread tick-borne disease in the world with cases reported in a number of countries in Africa , Asia , Middle East and southeastern Europe . Geographical distribution is closely linked to the presence of the primary vectors , ticks of the genus Hyalomma [1] . CCHFV genome consists of three single-stranded negative-sense RNA segments: small ( S ) , medium ( M ) and large ( L ) [1] , [2] . Genetic analyses of all three genomic segments have shown that CCHFV exhibits a high level of genetic variability ranging from 20% ( S segment ) , 22% ( L segment ) to 31% ( M segment ) . Genetic variability correlates with the geographical spread of the virus . Namely , phylogenetic analyses of the S segment have shown that geographically separated viral isolates cluster in roughly six clades: two European , three African and one Asian [3] . Genetic variability of CCHFV was also demonstrated within several geographical regions . For example , Ozkaya et al . ( 2010 ) have shown existence of local topotypes of CCHFV in Turkey [4] while Aradaib et al . ( 2011 ) have found the presence of several variants of CCHFV in Sudan [5] . CCHF is endemic in Kosovo . The first reports of CCHF in Kosovo date back to 1957 , when a family outbreak resulting of eight fatal cases , was described [6] . Based on the records of the Institute of Public Health of Kosovo , from 1995 to August 2013 , 228 cases of CCHF have been reported in Kosovo , with the mortality rate of 25 . 5% . There is limited information about CCHFV genetic diversity in Kosovo despite the long presence of CCHFV infections in this area [7] , [8] , [9] . The aim of our study was to investigate the genetic variability of CCHFV from patients in Kosovo in a time span of 22 years in order to determine the spatio-temporal characteristics of CCHFV in this highly endemic area .
For the purpose of the study , we included 89 serum samples of Real-Time RT-PCR confirmed CCHF patients from Kosovo , hospitalized from 1991–2013 . Serum samples were periodically received from the National Institute of Public Health of Kosovo , Republic of Kosovo for confirmatory diagnostics and further analyses . Samples were processed as previously described [10] . The study was retrospective therefore we did not obtain additional informed consent from the patients . Instead , the research was approved by the National Medical Ethics Committee of the Republic of Slovenia . We followed the principles of the Helsinki Declaration , the Oviedo Convention on Human Rights and Biomedicine , and the Slovene Code of Medical Deontology . All human samples were anonymized and no additional sample was taken for the purpose of the study . Total RNA from serum samples between years 1991–2009 was extracted using Trizol LS Reagent ( Invitrogen Life Technologies ) according to the manufacturer's instructions . Total RNA from serum samples between years 2010–2013 was extracted using QIAamp Viral RNA Mini Kit ( Qiagen ) according to the manufacturer's instructions . RT-PCR amplification of the complete S segment was performed as described by Deyde et al . [3] . RT-PCR was performed using the SuperScript III One-Step RT-PCR System with Platinum Taq High Fidelity ( Invitrogen Life Technologies ) according to the manufacturer's instructions . Nested PCR was performed using primer pair CCHF SORF-F ( 5′-GCCATGGAAAACAAGATCGAGG-3′ ) and CCHF SORF-R ( 5′-AGTTCTAGATGATGTTGGCAC-3′ ) , yielding a PCR product of 1 , 456 bp which represents the complete coding region of the CCHF N protein . Nested PCR was performed using KOD Xtreme Hot Start DNA Polymerase ( Novagen , EMD4Biosciences ) according to the manufacturer's instructions . Nested PCR cycling conditions were as follows: initial denaturation at 94°C for 2 minutes , followed by 40 cycles of denaturation at 98°C for 10 seconds , primer annealing at 60°C for 30 seconds and elongation at 68°C for 1 minute and 30 seconds . Additionally , a 536 bp fragment ( primers CCHF F2/R3 ) or a 260 bp fragment ( primers CCHF F3/R2 ) of the S segment was amplified as described by Rodriguez et al . [11] if the amplification of the 1 , 456 bp fragment was not successful . Partial M segment nucleotide sequences were obtained as described previously [12] . PCR products were purified with the Wizard SV Gel and PCR Clean-Up System ( Promega ) , sequenced using the BigDye Terminator 3 . 1 Cycle sequencing kit ( Applied Biosystems ) and analyzed with the 3500 Genetic Analyzer ( Applied Biosystems ) . Nucleotide sequences were assembled and edited using CLC Main Workbench software ( CLC bio , Denmark ) . At least two-fold read coverage was obtained for all sequences . Sequences were aligned in MEGA version 5 [13] using Muscle algorithm . Nucleotide sequences were deposited to the GenBank database ( accession numbers KC477779-837 , KF039932-83 , KF595127-49 ) . Nucleotide substitution model was selected based on Akaike's information criterion ( AIC ) in jModelTest , version 0 . 1 . 1 [14] . The general time-reversible model with gamma-distributed rate variation ( GTR+G ) was employed for phylogenetic analyses of the CCHF S segment . Bayesian phylogenetic analyses were performed in MrBayes 3 . 2 [15] and Tracer version 1 . 5 [16] . Four independent Markov Chain Monte Carlo ( MCMC ) runs of four chains each consisting of 10 , 000 , 000 generations were run to ensure effective sample sizes ( ESS ) of at least 1000 . Phylogenetic analysis of the M segment sequences was performed in MEGA5: Molecular Evolutionary Genetics Analysis [17] . The TN92 model with gamma-distributed rate variation was used for the analysis . Maximum clade credibility trees were depicted using FigTree version 1 . 3 . 1 [16] . Evolutionary rates and calculation of the time of the most recent common ancestor ( tMRCA ) were determined for the larger S segment sequences . We estimated the evolutionary rates using a MCMC method implemented in BEAST 1 . 8 . 0 [16] with a relaxed molecular clock ( under the GTR+G+I model of nucleotide substitution ) and a piecewise-constant Bayesian skyline plot as a coalescent prior . Priors were selected according to Zehender et al . [18] . The chains were conducted until reaching ESS>200 and sampled every 10 , 000 steps . Trees were summarized in a maximum clade credibility tree after a 10% burnin using Tree Annotator 1 . 8 . 0 [16] . Mean evolutionary rates and tMRCA were calculated in TreeStat 1 . 8 . 0 [16] .
We obtained 37 partial CCHFV S segment sequences ( 1019 bp ) from patients hospitalized in 2002 ( n = 3 ) , 2005 ( n = 1 ) , 2010 ( n = 10 ) , 2012 ( n = 11 ) and 2013 ( n = 12 ) . All sequences clustered in the European CCHF genetic lineage V , along with previously published CCHFV sequences from Kosovo ( Figure 1A ) . Overall identity of the sequences ranged from 98 . 8–100% and we detected three amino acid changes; S272N ( present in samples KS153 and KS149 ) , K316R ( present in samples KS208 , KS213 and KS223 ) and V327I ( present in samples KS172 and KS88 ) ( amino acid positions are numbered relative to the nucleoprotein sequence of CCHFV strain Kosovo Hoti , accession number: AAZ32529 ) . CCHFV sequences clustered in roughly three groups designated A1–A3 ( Figure 1A ) . We estimated a mean evolutionary rate of 2 . 76×10−4 substitutions/site/year and the mean tMRCA for the root of 729 . 4 years ago . We then analyzed a shorter fragment of the S segment ( 389 bp ) , because we had more sequences available . We obtained 79 nucleotide sequences from patients hospitalized in 2001 ( n = 15 ) , 2002 ( n = 8 ) , 2003 ( n = 4 ) , 2004 ( n = 7 ) , 2005 ( n = 3 ) , 2006 ( n = 2 ) , 2010 ( n = 10 ) , 2011 ( n = 6 ) , 2012 ( n = 11 ) and 2013 ( n = 13 ) . Overall identity of the sequences ranged from 98 . 5–100% . All sequences clustered in the European genetic lineage V and were distributed in 5 genetic groups ( A1–A5 ) . The latter phylogenetic analysis was comparable to the previous one , although some resolution was lost . Samples KS-154 and KS-165 , which clustered in group A1 in the previous analysis were miss-assigned to group A3 . The most divergent sequences clustered into group A5 . This cluster was also most divergent compared to other sequences in the European genetic lineage V ( maximum nucleotide distance within the European genetic lineage V was 2 . 9 , that is to the Turkish GQ337053 sequence ) . We additionally obtained 4 partial S segment sequences ( 220 bp ) from patients hospitalized in 1991 ( n = 3 ) and 1992 ( n = 1 ) . These sequences were not included in the previous phylogenetic analysis because they were too short . However , clustering into groups A1–A5 can be distinguished by analysis of mutational profiles of four nucleotide changes: 343T/C , 496C/A , 304C/T or 520A/G and 220T/C or 550T/C ( nucleotide positions are numbered relative to the complete S segment sequence of CCHFV strain Kosovo Hoti , accession number: DQ133507 ) . Thereby we were able to assign two sequences from 1991 to group A2 , while the two other sequences could not be definitely assigned ( sequences could be assigned to either group A3 or A4 ) . In order to further support our findings , we sequenced 431 bp of CCHFV M segment . We obtained 50 partial M segment sequences . Overall identity of the sequences ranged from 95 . 2–100% . In general we observed three distinct phylogenetic groups; A1 , A2 and A5 ( Figure 1C ) . Several sequences could not be assigned to any of the observed groups due to the low resolution of the phylogenetic analysis . Despite several attempts we could not obtain longer M segment sequences from these samples due to low sample volumes and low viral loads . Therefore , we could not obtain a phylogenetic tree with higher resolution . Next , we wanted to determine the geographical distribution of the sequences . Each phylogenetic cluster was plotted on the map of Kosovo with respect to the grouping from the 389 bp S segment phylogenetic analysis . As is seen in Figure 2 sequences are evenly distributed throughout the studied area . The two most abundant phylogenetic groups ( A1 and A2 ) are present in almost all studied municipalities . However , sequences from group A1 are present in southern parts in greater abundance than in the northern parts and vice versa for group A2 . The number of sequences we obtained is comparable to the incidence of CCHF in each municipality . On average we sequenced approximately 50% of total confirmed cases in each municipality . Therefore our results portray a realistic picture of the distribution of viral variants in the endemic area . Sequences from the most divergent phylogenetic group ( A5 ) grouped in two neighboring municipalities in central Kosovo . No obvious ecological or geographical barriers are present in this area which could explain the constrained geographical distribution of the variants . We did not observe any temporal correlation to the phylogenetic clustering . From 2001 to 2010 the two major phylogenetic groups ( A1 and A2 ) occurred in similar abundances . However , significant shifts in abundances of the two groups occurred in the following years . In 2011 , 80% confirmed patients were infected with A1 virus variant ( and 20% with A3 ) . On the contrary , in 2012 we detected the A2 virus variant alone ( we sequenced 92% confirmed CCHF cases ) . In 2013 , again both A1 and A2 variants were present ( 9% and 50% confirmed cases , respectively ) .
CCHFV is a genetically diverse virus . It groups into several genetic clades which correlate to the geographic origin to some extent . This correlation is most profoundly seen in the phylogenetic analyses of the viral S segment . The virus groups into seven phylogenetic clades: 2 European , 3 African and 2 Asian [19] . Great genetic diversity of CCHFV has also been shown within each phylogenetic clade in different extents [20] . Several viral variants were detected also within particular endemic areas [4] , [5] , [21] , [22] , [23] , [24] . Furthermore , Ozkaya et al . [4] showed that same viral variants also cluster together geographically . CCHFV is an important causative agent of disease in Kosovo . Due to the high number of CCHF cases in relation to the small size of the endemic area and the long history of CCHF in Kosovo , this area represents an interesting model for studies of viral evolution and genetic variability . The aim of our study was to expand the limited knowledge about the genetic variability of CCHFV in Kosovo . We wanted to obtain partial genome sequences directly from patient serum samples without prior cultivation or cloning in a time span of 22 years . We wanted to determine if there is any geographical clustering of the viral variants and if there were any significant temporal genetic changes . The results of our study revealed that several viral variants are present within the endemic region in Kosovo . Overall nucleotide sequence divergence ( 2% ) is in the scope with previous reports [20] . At least three major phylogenetic groups were formed based on the analysis of a larger portion of the viral S segment . These groups could also be discriminated in the analysis of a smaller S segment fragment . This analysis revealed the presence of 5 distinct phylogenetic clades . Previous report from Turkey described the detection of two genetic variant , or topotypes . Given the fact that the studied area in this report was at least 10 times larger than ours , implies that the overall genetic diversity of CCHFV in Kosovo is very high [4] . This difference can be attributed to several factors . The first is the number of sequenced patients , or rather the proportion of sequenced patients . In our study we sequenced 59% confirmed patients ( a total of 168 confirmed cases from 2001 to 2013 ) , a proportion that is significantly higher than in previous reports . Length of CCHF presence in an endemic area is also important . The first reports of CCHF in Kosovo date back to 1957 , with several sporadic or epidemic years until present . In Turkey however , these reports are scarce and the disease has gained recognition only recently in the last ten years . Our results also suggest that the disease has been present in Kosovo for a long time and that the virus population has been more or less stable during the last 22 years . Variant analysis of nucleotide sequences obtained from patients in years 1991 and 1992 revealed that A2 group has been present throughout the whole period , whilst the existence of A1 group could not be confirmed . We estimated a mean evolutionary rate of 2 . 76×10−4 substitutions/site/year which is in concordance to the estimated evolutionary rate reported in a recent , comprehensive report of whole S segment sequences by Zehender et al . ( 2 . 96×10−4 substitutions/site/year ) [18] . Similarly , we show that the most probable location of the MRCA in Europe was Russia and that the virus was introduced in Kosovo somewhat 50 years ago which coincides with the first reports of the disease in Kosovo in 1957 [25] ( Figure S1 ) . With regard to the temporal changes in virus population we observed changing dynamics of viral variant abundances from 2011 to 2013 . From 2001 to 2011 we steadily detected both major phylogenetic groups ( A1 and A2 ) regardless of the number of cases in each year . However in 2011 we detected only the A1 groups ( out of the two major groups ) and in 2012 we detected only the A2 group . Such a rapid change in relative abundances is somewhat surprising . We could not determine any link with the geographic distribution of the cases nor to any demographic changes in this period . These observations lead us to believe that the underlying cause for the shifts probably lie in the ecology of the disease . There is limited ecological data for Kosovo available , so we could not perform an in-depth analysis . What we have found is that average yearly temperatures in 2010 and 2011 were below average and that average minimum temperatures in 2012 were below average . Data suggest that weather conditions in 2010–2013 changed in relation to previous years . Since climate greatly influences both the vector and the reservoir of the disease , the changing climate patterns could explain the changes in the viral populations . Our results suggest that relative abundances of viral variants are dynamic and are prone to great variations and that ecological factors can play a role in shaping these populations . Of note regarding genetic diversity is also the cluster of three sequences in clade A5 , which is separated from all other sequences present in Kosovo . Furthermore , our results also suggest that this lineage is also significantly different from other sequences in the European CCHFV phylogenetic clade . Spatial analysis of these sequences revealed that all three patients from whom the viral sequences were derived were infected in nearby municipalities , separated no more than 20 km apart . In combination with the temporal analysis it is also evident that the viral variant was present in the area for at least three years . This geographical limitation of the A5 phylogenetic clade is surprising since no obvious ecological and geographical obstacles are present in the area . A greater effort to obtain sequences in this region should be implemented to resolve this issue . Spatial analysis of other phylogenetic clades observed within Kosovo patients did not reveal a clear geographical separation of the major clades . On the other hand , further inspection of the geographical clustering revealed that sequences from the phylogenetic clade A1 clustered more in the southern part of Kosovo , while sequences from clade A2 clustered more in the northern part of Kosovo . Our study provides the first insight into the genetic variability of CCHFV in patients from Kosovo . It provides the largest set of patient derived CCHFV sequences within one geographical area in the span of 22 years . Our results reveal great genetic variability of CCHFV in Kosovo . This diversity is exemplified when we take into account the size of the studied area . Presence of several viral variant and the observed limited evolutionary changes in 22 years suggest that CCHFV has been present in Kosovo for a long time . Our results also suggest that the population of viral variants is prone to significant changes in different endemic years . Further studies are however needed to determine the factors responsible for these changes .
|
Crimean-Congo hemorrhagic fever ( CCHF ) is an acute , tick-borne disease with a case fatality rate of 10–30% . It is geographically the most widespread tick-borne disease in the world . In recent years there has been an increase of the disease incidence in several countries , mainly in the countries of the Balkan . The disease is also endemic in Kosovo . Since CCHF virus is very genetically diverse we aimed to determine the genetic variability of the virus in Kosovo in the span of 22 years . We obtained the largest number of patient derived nucleotide sequences and found great genetic variability which has been more or less stable during the 22 year period . Our results also suggest that significant changes in viral population occur in different years . We show that ecological factors such as temperature could play a role in the composition of the viral population .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"zoonoses",
"crimean-congo",
"hemorrhagic",
"fever",
"neglected",
"tropical",
"diseases",
"biology",
"microbiology",
"evolutionary",
"biology",
"viral",
"diseases"
] |
2014
|
Molecular Epidemiology of Crimean-Congo Hemorrhagic Fever Virus in Kosovo
|
Old world Zoonotic Cutaneous Leishmaniasis ( ZCL ) is a vector-borne human disease caused by Leishmania major , a unicellular eukaryotic parasite transmitted by pool blood-feeding sand flies mainly to wild rodents , such as Psammomys obesus . The human beings who share the rodent and sand fly habitats can be subverted as both sand fly blood resource . ZCL is endemic in the Middle East , Central Asia , Subsaharan and North Africa . Like other vector-borne diseases , the incidence of ZCL displayed by humans varies with environmental and climate factors . However , so far no study has addressed the temporal dynamics or the impact of climate factors on the ZCL risk . Seasonality during the same epidemiologic year and interval between ZCL epidemics ranging from 4 to 7 years were demonstrated . Models showed that ZCL incidence is raising i ) by 1 . 8% ( 95% confidence intervals CI:0 . 0–3 . 6% ) when there is 1 mm increase in the rainfall lagged by 12 to 14 months ii ) by 5 . 0% ( 95% CI: 0 . 8–9 . 4% ) when there is a 1% increase in humidity from July to September in the same epidemiologic year . Higher rainfall is expected to result in increased density of chenopods , a halophytic plant that constitutes the exclusive food of Psammomys obesus . Consequently , following a high density of Psammomys obesus , the pool of Leishmania major transmissible from the rodents to blood-feeding female sand flies could lead to a higher probability of transmission to humans over the next season . These findings provide the evidence that ZCL is highly influenced by climate factors that could affect both Psammomys obesus and the sand fly population densities .
Zoonotic Cutaneous Leishmaniasis ( ZCL ) is responsible of considerable morbidity and disfigurement in the Middle East , Central Asia , Subsaharan and North Africa [1] , particularly in rural areas . In Tunisia , the epidemic emerged since1982 in Kairouan and expanded to governorates of the center and the south ( 15/24 governorates were considered as endemic in 2006 ) . So far , more than 100 , 000 cases were reported mainly from Kairouan , Gafsa and Sidi Bouzid ( population size = 1 , 265 , 424 from the national census of 2004 ) [2] . ZCL is considered as one of the most important compulsory diseases in this region . Most of cases are concentrated in rural area where public health human resources and infrastructure are limited . The etiological agent is an Old world Leishmania species , Leishmania major ( L . major ) , which is transmitted by the sand fly vector , Phlebotomus papatasi . This vector species is highly endophilic ( female adults rest indoors ) and their biting activity occurs in the evening [3] . Rodents are the reservoir for cutaneous leishmaniasis and include Psammomys obesus , Meriones shawi and Meriones libycus . Psammomys obesus , is found in fields of chenopods , a plant of shoals that is its exclusive food source . Transmission is greatest in the summer months ( May to September ) and infected humans who develop disease ( ZCL lesions ) tend to do so between October and May [4] . A previous study conducted in Tunisia [5] indicated the importance of environmental changes caused by the development of agriculture and irrigation projects as risk factors for the emergence of ZCL . Climate variability may influence changes in the vector geographical distribution as well as density of rodents' reservoirs is also highly sensitive to availability of food in the environment . The present study attempted to demonstrate the seasonality of the disease during the same epidemiologic year , to estimate the inter-epidemic interval . The effect of climate factors ( rainfall , temperature and humidity ) on ZCL incidence was quantified . The findings might help the public health policy makers to reduce the burden of leishmaniasis by more appropriate control programs .
This study was conducted in the governorate of Sidi Bouzid ( SBZ ) ( 35°02′00″N , 9°30′00″W ) located in central Tunisia ( Figure 1 ) where zoonotic cutaneous leishmaniasis emerged as an epidemic since 1991 . SBZ is located in central Tunisia in a semi-arid area of 6994 km2 and has an estimated population of 412 , 500 in 2011 . Monthly records of the number of ZCL cases in the whole governorate of SBZ , from January 1991 to December 2007 , were collected from the National Control Program of Leishmaniasis ( NCPL ) of the Regional Directorate of Health of SBZ . The NCPL surveillance strategy relies on passive case detection in 110 health centers as well as active case detection in the schools during epidemics . Case definition is based on parasite confirmation by direct smear and culture in emerging foci and on clinical and epidemiological criteria in old endemic foci . Standardized data collection forms are passed to the NCPL at the regional level on a monthly basis from centers and schools . Data were checked by the data management team at Pasteur Institute of Tunis in collaboration with regional teams , in order to reduce the under reporting bias and the double counts . Queries were sent back to field teams to reconcile inconsistencies and to provide validated forms by consulting the source information . Meteorological data ( monthly average temperature ( in °C ) , average monthly relative humidity ( in % ) and the cumulative monthly rainfall ( in mm ) ) for the governorate of Sidi Bouzid between 1991 and 2007 , were provided by the National Institute of Meteorology ( NIM ) . This data is merged from the meteorological stations of the governorate of Sidi Bouzid by the NIM .
During the study period , January 1991 to December 2007 , the incidence of ZCL ranged widely from 0 to 1608 cases per year in the study area of Sidi Bouzid . Likewise , rainfall showed significant variation while temperature and humidity appeared to be more stable as shown in table 1 . Seasonality of ZCL incidence was significant during the same epidemiologic year defined from the start of vector transmission in May to the end of cases emergence in April the following administrative year ( Figure 2 ) . Indeed , the seasonality test rejected the equality of monthly ZCL incidence mean ( Fisher's ( Fs ) = 19 . 732 , df = 11 , p<0 . 001 and Kruskal-Wallis = 132 . 9 , df = 11 , p<0 . 001 ) . This result demonstrated that the incidence of the disease is significantly higher during the group of months from October to March . The yearly incidence pattern was significantly autocorrelated as shown by the sample autocorrelation function ( Figure 3 ) . The trend of ZCL incidence showed significant peaks in January 1992 , December 1999 , December 2003 and December 2004 as proven by the moving seasonality test ( FM = 5 . 459 , p<0 . 001 ) . This result confirmed the significant variation of ZCL incidence between years ( T = 0 . 8 ) with no evidence of residual seasonality in the entire series ( F = 1 . 59 , p<0 . 001 ) . Therefore , the X-12-ARIMA algorithm was run on the original data for the estimation of a monthly adjusted ZCL incidence distribution . Figure 4 shows the trend of the incidence ( adjusted and not adjusted for seasonality ) of the disease during the whole study period ( 1991–2007 ) revealing an inter-epidemic period ranging from 4 to 7 years . The probability distribution of the ZCL incidence , required for both GAM and GEE models , was over dispersed ( Mean = 135 . 70; Standard deviation , 270 . 16 ) . A negative binomial distribution fitted adequately the data and permitted to estimate the parameter θ by the maximum likelihood technique ( = 0 . 33 , 95% confidence interval ( CI ) : 0 . 28 , 0 . 38 ) . The adjusted relationship between the ZCL incidence and climate variables revealed different patterns . For temperature and humidity ( lagged by 2 months ) , a linear relationship provided the best fit while it was rather parabolic for the rainfall ( lagged by 12 to 14 months ) with a turning point ( TP ) at 37 . 34 mm ( Figure 5 ) . Therefore , in order to quantify and test the effect of these climate variables on ZCL incidence , rainfall was divided in two segments and incorporated into the GEE model using two linear terms as following:Hence the GEE model is described as follows:where ZCL_cases denotes the numbers of ZCL cases on month i ( i from 1 to 12 ) and year j ( j from 1 to 17 ) . It is the same for Rainfall before the turning point ( R1 ) , after the turning point ( R2 ) , humidity ( H ) and temperature ( T ) . Confounders were trend and seasonality variables The results of the GEE model confirmed the significant effect of mean rainfall lagged by 12 to 14 months before the TP ( p = 0 . 02 ) which remained non significant above the TP ( p = 0 . 19 ) . Likewise , humidity lagged by 2 months ( p = 0 . 01 ) exerted a significant effect on ZCL risk in humans . On the other hand , no significant relationship with temperature was detected . The table below provides the percentage of change of ZCL incidence accounted for by unit increase for each climate variable . It shows that the increase by 1 unit in the mean rainfall lagged by 12 to 14 months below the TP contributes to 2 . 0% increase in the incidence of disease ( p = 0 . 02 ) . However above the turning point , the relationship although not significant ( p = 0 . 19 ) , seems to be negatively associated with high rainfall as the curve decreases after the TP . For humidity above 57 . 8% and lagged by 2 months the positive effect is more important because 1 unit increase induced 5% increase of the disease incidence ( p = 0 . 01 ) . Temperature effect was not statistically significant ( p = 0 . 17 ) . Table 2 summarizes the percentage of change in disease incidence attributed to climate variables included in the GEE model .
As the distribution and behavior of vectors and reservoirs are influenced by environmental conditions , climate variability and change became important determinants of the incidence of many vector-borne diseases such as malaria [17]–[19] , dengue [20]–[22] , and leishmaniasis [23]–[28] . In South America , climate variability based on El Niño Southern revealed significant effect on leishmaniasis [29]–[30] while in North America , studies have more focused on the influence of temperature and precipitation on the risk and cycles of leishmaniasis [27]–[28] . A significant relationship was found between Mediterranean visceral leishmaniasis and climatic factors [31]–[32] . Old world cutaneous leishmaniasis caused by L . major is an increasing problem in Maghreb countries and Eastern Mediterranean region [33]–[37] . Its spatial spread was linked to environmental changes , land use and water development projects such as development of dams and wells for agriculture projects [33] . However , this association has never been , to our knowledge , evaluated quantitatively . The present study demonstrated significant seasonality within the same year with a highest peak in December for L . major cutaneous leishmaniasis . The interval between epidemics derived from the time series of adjusted ZCL data has been shown to range from 4 to 7 years . This result would be partially explained by temporal heterogeneity of the force of infection as proposed by Anderson et al . [38] or other factors related to human populations such as seasonal migration and acquired immunity . The longest period between 1992 and 1999 could be explained by the control intervention based on the reduction of the population of reservoir around the city of Sidi Bouzid by mechanical ploughing in order to reduce the transmission to humans [39] . These results confirm the pertinence of environmental changes as a control option for ZCL . It applies in circumstances where high dense human communities such as urban cities or military camps are surrounded by colonies of Psammomys obesus . Unfortunately , this strategy is not feasible in rural area where scattered dwellings are surrounded by heavily infected rodents . Shorter inter-epidemic periods in the following years could be explained by the spread of transmission to surrounding rural area in the governorate of Sidi Bouzid by Meriones movements . Indeed , previous work revealed that in addition to Psammomys obesus , Meriones shawi and Meriones libycus are important reservoirs of L . major leishmaniasis in Tunisia [40]–[42] . In fact , Psammomys obesus , is restricted to fields of chenopods , a plant of shoals that is its primary food source . Phlebotomus papatasi , the vector of cutaneous leishmaniasis finds in the burrows of rodents ideal environment and blood meals to maintain the zoonotic Leishmania transmission cycle . Previous experience showed that human activities that interfere with the ecologic niche of reservoirs such as Psammomys can change the epidemiology of ZCL . Emergence of ZCL epidemics can take place when humans invade the territory of Psammomys [5] or the incidence can be reduced when burrows of rodents and chenopods are properly destroyed . Based on the biologic and epidemiologic reasons [43] , time lags for temperature and humidity were fixed with appropriate data transformation in order to have a valid epidemiologic interpretation of statistical results . Besides , it is not recommended to vary the time lags for temperature , humidity and rainfall as many candidate models will be generated and increase the risk to reject wrongly the null hypothesis which corresponds to the type II error . Previous work showed that both temperature and humidity are proven significant environmental conditions for the density and dynamics of Phlebotomus papatasi during the transmission period ( summer and early autumn ) [44]–[45] which is immediately followed by the season of the disease emergence in humans ( early autumn to winter ) . In the present work , the significant effect of humidity during the transmission season and rainfall of previous year , on incidence of disease was demonstrated and quantified using GAM and GEE . Based on these techniques , we showed for the first time that humidity is more important as a predictor of ZCL incidence than rainfall . Higher rainfall levels would increase the density of chenopods , a halophytic plant , as well as other plants that constitute the food of rodents' reservoirs . Consequently , the reservoir density increases and affects transmission the next season . In fact , rodents benefit from vegetation growth when rainfall is plentiful , but are severely reduced by flooding [46] which might explain the negative association between rainfall above the 37 . 34 mm and ZCL incidence . On the other hand , the humidity in the summer and autumn enhances transmission by increasing vector density during the same season [47]–[48] , a key element in the force of infection through vector capacity , which is analogous to the contact rate in directly-transmitted diseases . Temperature and ZCL incidence were not associated in our model , because most of its effect has been accounted for humidity and rainfall . Despite the new insight provided by this work , some limitations have to be pointed out . The under reporting bias is hard to rule out completely when dealing with surveillance data . However , the surveillance system of leishmaniasis in the area remained the same during the study period . The high awareness among the community , health decision and policy makers were key elements for sustainability of surveillance and control measures in Sidi Bouzid . It is more likely that the trend of incidence used in time series analysis reproduces a valid evolution of the risk through time . Prediction of epidemics and early warning remain a high research priority to improve the response of control programs of cutaneous leishmaniasis and the risk assessment for future land use , in the absence of a safe and efficacious vaccine . The present study established , by appropriate modeling techniques , the relationship between old world L . major cutaneous leishmaniasis and climate factors . The findings support the importance of environmental surveillance to detect expansion of rodents' populations following raining seasons and the monitoring of humidity and vector densities to predict epidemics of cutaneous leishmaniasis .
|
Old world cutaneous leishmaniasis is a vector-borne disease occurring in rural areas of developing countries . The main reservoirs are the rodents Psammomys obesus and Meriones shawi . Zoonotic Leishmania transmission cycle is maintained in the burrows of rodents where the sand fly Phlebotomus papatasi finds the ideal environment and source of blood meals . In the present study we showed seasonality of the incidence of disease during the same cycle with an inter-epidemic period ranging from 4 to 7 years . We evaluated the impact of climate variables ( rainfall , humidity and temperature ) on the incidence of zoonotic cutaneous leishmaniais in central Tunisia . We confirmed that the risk of disease is mainly influenced by the humidity related to the months of July to September during the same season and mean rainfall lagged by 12 to 14 months .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"public",
"health",
"and",
"epidemiology",
"environmental",
"sciences",
"statistics",
"mathematics",
"biostatistics",
"infectious",
"diseases",
"environmental",
"geography",
"epidemiology",
"biology",
"public",
"health",
"confidence",
"intervals",
"ecology",
"earth",
"sciences",
"statistical",
"methods"
] |
2012
|
Temporal Dynamics and Impact of Climate Factors on the Incidence of Zoonotic Cutaneous Leishmaniasis in Central Tunisia
|
Arenaviridae synthesize viral mRNAs using short capped primers presumably acquired from cellular transcripts by a ‘cap-snatching’ mechanism . Here , we report the crystal structure and functional characterization of the N-terminal 196 residues ( NL1 ) of the L protein from the prototypic arenavirus: lymphocytic choriomeningitis virus . The NL1 domain is able to bind and cleave RNA . The 2 . 13 Å resolution crystal structure of NL1 reveals a type II endonuclease α/β architecture similar to the N-terminal end of the influenza virus PA protein . Superimposition of both structures , mutagenesis and reverse genetics studies reveal a unique spatial arrangement of key active site residues related to the PD… ( D/E ) XK type II endonuclease signature sequence . We show that this endonuclease domain is conserved and active across the virus families Arenaviridae , Bunyaviridae and Orthomyxoviridae and propose that the arenavirus NL1 domain is the Arenaviridae cap-snatching endonuclease .
The Arenaviridae family includes 22 viral species into a single genus Arenavirus , with new species awaiting classification [1] , [2] . They cause chronic and asymptomatic infections in rodents , and occasional transmission to man may result in life-threatening meningitis and/or hemorrhagic fever . Lymphocytic choriomeningitis virus ( LCMV ) is the prototypic species and first arenavirus isolated in 1933 . Because its natural host is the common house mouse ( Mus musculus ) , LCMV is the only known arenavirus presumably exhibiting a worldwide distribution . LCMV is a human pathogen of significant clinical relevance , causing central nervous system disease , congenital malformation , choriomeningitis , and systemic and highly fatal infection in immuno-compromised , organt transplant recipient patients [3] , [4] , [5] , [6] . Humans are generally infected through the respiratory tract after exposure to aerosols , or by direct contact with infectious material . Arenaviruses are enveloped viruses with a bisegmented negative single-strand RNA genome . Each RNA segment , called large ( L; ∼7 . 2 kb ) and short ( S; ∼3 . 5 kb ) , contains two open reading frames in mutually opposite orientations and use an ambisense coding strategy to direct the synthesis of two polypeptides [7] . Between the two open reading frames of each segment resides a non-coding intergenic region ( IGR ) , composed of a sequence predicted to form a stable hairpin structure [8] . The S RNA encodes the viral nucleoprotein ( NP; ∼63 kDa ) and glycoprotein precursor ( GPC; ∼75 kDa ) , whereas the L RNA encodes a small RING finger protein ( Z; ∼11 kDa ) and a large protein ( L; ∼250 kDa ) which is the viral RNA-dependent RNA polymerase ( RdRp ) . The two RNA genomes are encapsidated by the NP , which is the most abundant protein in virions and infected cells , and act as templates for two fundamentally different processes , RNA replication and transcription . During RNA replication , the L protein first binds to the 3′-end of RNA templates and reads them from end to end to direct the synthesis of encapsidated full-length anti-genomes . During transcription , the RdRp stops RNA synthesis at a pause site located near the IGR [7] . The newly synthesized mRNA molecules have a non-polyadenylated 3′-end with a heterogeneous sequence mapped within the predicted hairpin in the IGR [9] . Furthermore , non template-directed sequences have been identified at the 5′-end of the subgenomic mRNA [10] . These sequences are variable in length [9] , [10] , [11] and terminate with a 5′-cap structure , which suggests the presence of a cap-snatching mechanism for arenaviruses . In this process , originally described for influenza viruses [12] , [13] and bunyaviruses [14] , the viral RdRp binds cellular mRNAs caps and ‘steals’ them using an endonuclease activity , located in the influenza PA subunit [15] , [16] , and presumably in L protein of bunyaviruses . These short capped RNAs are then used as primers for mRNA synthesis . The arenavirus L protein is an essential element in genome replication and transcription [17] . It is the largest viral protein composed of approximately 2200 amino-acid ( aa ) residues , and sequence analysis using homologous proteins led to the prediction of several conserved domains [18] , [19] . A biological function can be inferred for the L3 domain containing conserved and typical RdRp signature sequence motifs [19] , [20] . For Tacaribe virus , both domains L1 and L3 interact with the Z protein [21] . By analogy with influenza and bunyaviruses , the L protein may also carry activities and domains responsible for a cap-snatching mechanism that would account for the sequence diversity found at the 5′-end of RNA transcripts . The expression and purification of such a large viral polymerase is problematic and has not been documented . We report here the first crystal structure of an Arenaviridae L protein domain at 2 . 13 Å resolution , that of the N-terminus domain of the LCMV L protein . We show that this domain is able to bind nucleotides , with a preference for UTP , and RNA . Structural comparison with the N-terminal part of the influenza virus PA protein characterizes unambiguously the domain as an endonuclease . Sequence and secondary structure analysis of L proteins from various Bunyaviridae family members predict that their N-terminal end carries a similar endonuclease activity , that we demonstrate for Toscana virus ( TOSV ) ( genus Phlebovirus , family Bunyaviridae ) . Activity assays and mutagenesis show that the arenavirus endonuclease exhibits sequence-specificity with a preference for uracil-containing substrates . Lastly , reverse genetics studies correlate expression of endonuclease activity with the selective production of mRNA , making the N-terminus domain of the L protein a likely candidate to be involved in the cap-snatching mechanism of arenaviruses .
Based on aa sequence conservation across arenaviruses and on the presence of a potential nucleotide-binding site , we designed cDNA constructs encoding aa residues 1 to ∼250 for the N-terminal end of four arenavirus ( Pirital virus ( PIRV ) , Lassa fever virus ( LASV ) , Parana virus ( PARV ) , and LCMV ) L proteins . All four domains were expressed as soluble recombinant proteins . We observed a self-limited proteolysis of the Parana arenavirus N-terminus L domain which prompted us to refine boundaries into a shorter 196 aa form , hereafter named “NL1” , fully included in the previously predicted arenavirus L1 domain ( 1–250 aa ) [19] . The construct was expressed in E . coli and purified , but yielded crystals diffracting to 8 Å . However , the homologous 196 residues domain of LCMV yielded well-diffracting crystals . The atomic structure of NL1 was first determined by the SAD technique with seleno-methionylated crystals that diffracted to 3 . 4 Å . The structure was refined using a native data set at 2 . 13 Å resolution ( Table 1 ) . Two NL1 molecules are present within the asymmetric unit . Residues 1–191 are visible for one molecule whilst only 1–175 could be modelled for the other NL1 molecule owing to high mobility of the C-terminal end of helix α7 . The LCMV NL1 monomer structure has approximate dimensions of 59 Å ×37 Å ×27 Å . It features four mixed β-strands forming a twisted plane surrounded by seven α-helices ( Figure 1A ) . The two anti-parallel strands β1 and β2 are connected by helix α4 , whereas the two parallel strands β3 and β4 are connected by the long helix α5 . These two helices run parallel to the central β-sheet and are disposed at the same side of the latter . On the opposite side of the β-sheet , helix α3 is surrounded at its extremity by two N-terminal ( α1 and α2 ) and C-terminal helices ( α6 and α7 ) . A search for similar protein folds using the DALI server [22] returned the PA N-terminal domain structure that was recently identified as a type II endonuclease domain [15] , [16] . The structural match with published molecular structures of the influenza PA N-terminal domains ( PAN ) returns a Z-score of 5 . 7 and an r . s . m . d . of 3 . 9 Å for 121 superposed aa ( PDB code 3EBJ ) and Z-score 5 . 2 , r . m . s . d . 4 Å for 122 aa ( PDB code 2W69 ) . As was the case for PAN , other type II endonuclease proteins are also recovered: the Tt1808 hypothetical protein from Thermus Thermophilus HB88 ( PDB code 1WDJ , Z-score 3 . 8 , r . m . s . d . 3 . 4 Å for 81 aa ) , and the restriction endonuclease SdaI ( PDB code 2IXS , Z-score 3 . 6 , r . m . s . d . 6 . 3 Å for 104 aa ) . The β-sheet forms a negatively charged cavity creating a binding site for divalent cations , whilst above that cavity , the C-terminal end of helix α5 forms a positively charged patch and a concave surface that is likely to accommodate the RNA substrate ( Figure 1B , arrow ) . The PA protein constitutes one subunit that associates with PB1 and PB2 to form the heterotrimeric influenza virus polymerase . Its N-terminal domain PAN hosts the RNA cap-snatching endonuclease activity [15] , [16] . Both NL1 and PAN share a similar core structure . Except for the absence of a fifth β-strand in NL1 , all other secondary structure elements are conserved ( Figure 1C ) and the overall topology of these two structures is very similar ( Figure 1D ) , albeit with interesting differences in the vicinity of the PAN active site ( discussed below ) . At the aa sequence level , NL1 shares the conserved active site sequence motif characteristic of type II endonucleases: PD… ( D/E ) XK . In NL1 , the corresponding residues are P88 , D89…E102 , and either K115 or K122 ( Figure S1A , B ) . The identity of the distal lysine is not certain since it is found at different positions in the primary sequence , as is the case for influenza virus . The influenza PAN domain was crystallized either in the presence of magnesium or manganese ions in the active site which comprises five conserved catalytic residues: H41 , E80 , D108 , E119 and K134 . A structural superimposition of the arenavirus NL1 and influenza PAN active sites shows that the side-chains of three evolutionary-conserved residues within arenaviruses ( P88 , D89 and E102 ) closely superimpose with P107 , D108 and E119 of the influenza virus PAN protein , pointing to a common function for these residues ( Figure 2A and Figure S1B ) . Upon superimposition with PAN , one Mn2+ ion needed for the enzymatic reaction coordinated by D108 in the PAN active site , falls at right distances to be coordinated by the carboxylate side-chains of D89 and E102 . NL1 was crystallized without metal ions and a water molecule is found close to the position that should be occupied by the divalent metal . Interestingly , no close structural match is found neither for H41 nor K134 of the influenza virus PAN . This points to differences between the two active sites since His41 was proposed to play a catalytic role in the influenza PAN . However , we note that another possible contributor could be NL1 C103 main-chain carbonyl as it superimposes quite well with PAN I120 main-chain carbonyl ( Figure 2B ) . The triad made of K115 , D119 , and K122 in NL1 is spatially equivalent to K134 in PAN . In summary , despite no aa sequence homology , the active site structures of the influenza PAN and LCMV NL1 domains are clearly related but not identical ( Figure 1C , 2 ) , strongly suggesting that these two domains exhibit closely related enzymatic activities ( see below ) . In addition to Arenaviridae and Orthomyxoviridae , Bunyaviridae is the other family of virus to possess a segmented negative-strand RNA genome . It contains four genera of animal viruses ( Orthobunyavirus , Phlebovirus , Nairovirus , Hantavirus ) and one genus of plant virus ( Tospovirus ) [23] . Although the genomic organisation differs between these three virus families , Bunyaviridae are also thought to use a cap-snatching mechanism to prime mRNA synthesis [24] . Arenaviruses , and Bunyaviridae share a conserved RdRp motif within their large L protein , as well as a conserved N-terminus domain [18] . Amino-acid sequence alignments , assisted by secondary structured prediction , of the N-terminal part of LCMV and Bunyaviridae L protein reveal that the latter also possesses the conserved active site motifs characteristic of type II endonucleases ( Figure S2A ) . However , we could identify the catalytic motif within the L protein N-terminal end for only four out of the five bunyavirus genera: Orthobunyavirus , Phlebovirus , Hantavirus and Tospovirus . The L protein of Nairovirus is much larger ( ∼4000 aa ) than the L protein of other members of the Bunyaviridae family ( ∼2200 aa ) . The putative endonuclease catalytic motif was located after aa ∼700 , the N-terminal of Nairovirus L protein being assigned as a so-called OTU-like domain [25] . Secondary structure predictions were used to draw the topology diagram of the NL1-like domain for each genera ( Figure S2B ) . As expected from the sequence alignment , each genus seems to share a β-sheet with a variable number of β-strands . Furthermore , the PD catalytic motifs are in each case located in a loop before a β-strand , as expected . The PUMV , HLCV and RVFV NL1-like domains are more closely related to LCMV NL1 than are the TOMV and CCGV . The TOMV NL1-like domain contains 6 β-strands and shares the PD motif just upstream the first β-strand , whereas it is just upstream the second β-strand in the case of NL1 and PAN . Finally , the structural organization of the putative CCGV endonuclease domain seems to diverge even further from the others . Indeed , whereas the conserved lysine is shared by the same helix for all the domains , that of Nairovirus may be located at the end of the β4 strand ( Figure S2B ) . Thus we conclude that the endonuclease motif is conserved across four animal virus genera Orthobunyavirus , Phlebovirus , Nairovirus and Hantavirus . Recent crystal structures of complexes of PAN with three different nucleoside monophosphates show that PAN binds nucleotides [26] . The ability of NL1 to bind nucleotides was investigated using UV-crosslink experiments . We observe that NL1 binds NTPs , preferably UTP and GTP , whereas ATP and CTP show a weaker association ( Figure 3A ) . The PAN structures were determined in complex with ATP , CTP and UTP but not GTP [26] whereas NL1 bind GTP in a stronger fashion than ATP or CTP . The crystal structure relatedness to the endonuclease fold would suggest that the NL1 domain is able to bind RNA rather than nucleotides . We tested RNA binding by NL1 , and found that indeed , NL1 binds RNA ( Figure 3B ) . The band shift assay is also suggestive that the RNA substrate is cleaved under the assay conditions , as judged by degradation products at the bottom of the gel under the labeled RNA oligo ( Figure 3B ) . Therefore , we surmise that nucleotide binding properties observed here reflect the ability of NL1 to bind RNA with some sequence specificity in the cap-snatching pathway ( see below ) . Several synthetic RNA oligonucleotides were used to characterize the endonuclease activity ( Figure 4 ) . NL1 is able to cleave ssRNA having no stable secondary structure at specific sites indicating a preference for the presence of uracil ( Figure 4A , B ) , and adenosine to a lesser extent . Likewise , a moderately stable RNA hairpin containing uracil ( ΔG = −3 . 4 kcal/mole ) is cleaved down to a 14/15-mer product whereas a stable ( ΔG = −14 . 7 kcal/mole ) RNA hairpin devoid of uracil remains unattacked even in its single stranded regions ( Figure 4A , B ) . PolyU RNA is cleaved randomly down to a 8-mer product with a better efficiency than polyA , whereas polyC is not a substrate for NL1 ( not shown ) . A 5′-terminal nucleoside uracil or adenosine 5′-monophosphate is also cleaved and the 5′-monophosphate RNA end apparently competes for internal cleavage . A 5′-capped RNA of 264 nucleotides in length also acts as a substrate . It is cleaved at several specific positions indicated by the sequential appearance of band products over time ( Figure 4B ) . This indicates that the cap structure does not seem to be a direct RNA binding determinant . A Phlebovirus ( Toscana ) virus endonuclease domain was prepared according to bio-informatic predictions described above . Its endonuclease activity was compared to both that of arenavirus NL1 and the influenza H5N1 endonuclease [16] . The enzymes were equally active using short RNA substrates , although it is apparent that sequence-specific cleavage is different for each enzyme: the influenza enzymes prefers cleavage at puric sites , Toscana virus and LCMV enzymes prefer adenosine- and uracil-containing sites ( Figure 4B ) . NL1 is ∼90-fold more active in the presence of Mn2+ than Mg2+ , and shows background activity with Ca2+ and Zn2+ ( Figure 4C and not shown ) . The Mn2+ ion has also a significant stabilizing effect as judged by thermostability studies , whereas Zn2+ has a deleterious effect . Mutagenesis analysis of most residues identified as part of the active site ( Figure 2A ) impaired the endonuclease activity . The most drastic effect was observed for D119 , but residual activity was scored for E51 , D89 , and less for E102 ( Figure 4D ) . As these three residues might coordinate metal ions as proposed above , defective metal-binding due to a point mutation might be compensated by the presence of the remaining two adjacent acidic residues . A double mutant D89A/E102A shows further reduced but not abolished activity . Mutations K115A and K122A generated strongly altered activity , but the similar level of residual activity does not allow the identification of which lysine is predominant in catalysis . The effect of 33 mutations in L1 on virus RNA and protein expression was studied in a cell-based mini-replicon system . The LCMV L protein mediates the synthesis of two RNA species: first , capped mRNA terminating within the intergenic region , and second , antigenomic RNA being a full-length copy of the genomic RNA template [9] , [27] . This dual role in RNA synthesis is recapitulated in the mini-replicon system . It contains all trans-acting factors ( L protein and NP ) required for transcription and replication of a genome analogue containing Renilla luciferase as a reporter gene ( mini-genome ) . Reporter gene expression was measured in luciferase assay ( Table 2 ) , while RNA synthesis was measured in Northern blot ( Figure 5 ) , in which luciferase mRNA and antigenome can easily be distinguished due to their size difference . Wild-type ( WT ) L protein led to expression of high levels of Renilla luciferase ( 2–3 log units signal-to-noise ratio ) as well as Renilla luciferase mRNA and antigenome in a ratio of about 1∶1 . Expression of mutant L protein was verified by immunoblotting ( Figure S3 ) . The phenotype of mutants E41A , E41Q , K44A , S54A , C60A , T108S , F116A , D142N , and W155A is similar to that of wild-type . Mutants E179A , E179Q , E182A , E182Q , and Y183A also express luciferase and RNA at high level , but the steady-state level of mRNA relative to that of antigenome is reduced by about 50% . Mutants F104A , R106A , F112A , K115A , D142A , R144A , R161A , R185A neither express Renilla luciferase nor any RNA species , indicating that global functions of L protein are affected . The most interesting phenotype is observed with mutants D89A , D89N , E102A , E102N , D119A , D119N , K122A , D129A , and D129N . They synthesize antigenome close or equal to wild-type level , but are defective in mRNA and , thus , reporter gene expression ( Figure 5 and Table 2 , shown in boldface ) . A similar phenotype is seen with mutants E51A and E51Q , though associated with reduced antigenome level . These data indicate that residues E51 , D89 , E102 , D119 , K122 , and D129A are essential for viral mRNA synthesis , but not required for expression of uncapped RNA species . With the exception of the D129 residue located at the surface of the protein remote from the endonuclease active site , it is remarkable that these transcription-null mutants form the catalytic site ( Figure 2 ) and match precisely those of the PD… ( D/E ) XK endonuclease type II signature sequence .
The structural and functional results presented here show that the LCMV NL1 domain is an RNA endonuclease . The uncoupling of RNA replication from transcription and selective disappearance of mRNA when NL1 active site residues are mutated strongly suggests that this activity is involved in cap-snatching . The identification of the arenavirus endonuclease is in line with the recent discovery of the PAN endonuclease domain of influenza virus . Whereas the active site of influenza virus features a cluster of three acidic residues , the active site of arenavirus contains four acidic residues ( E51 , D89 , E102 and D119 ) , as well as two important lysine residues K115 and K122 neighboring D119 ( Figure 2A ) . The NL1 active site resembles but is clearly distinct from that of influenza PAN . Indeed , there is no histidine in the catalytic center , and the arenavirus NL1 nuclease has some specific features both upstream and downstream of the PD signature sequence . We define the arenavirus endonuclease motif as E-X38-P-D-X ( 11 , 13 ) -E-X12-K-X3-D-X2-K . The most obvious difference with the only known related RNA endonuclease , that of influenza virus PAN , is a divergence upstream the PD motif in structural elements carrying the E51 residue ( Figure 1C ) , and the presence of a triad K…D…K at the distal side of the latter signature sequence ( Figure 2A ) . Contrary to PAN which shares a conserved and essential histidine involved in the binding of both the metal ion and a nucleotide onto helix α3 [15] , [16] , [26] , NL1 does not possess this conserved histidine residue . Instead , NL1 has a glutamic acid residue E51 , which might reflect a different nucleobase specificity as detected in our nuclease assays ( Figure 4 ) . Likewise , residues downstream the PD motifs are distinct from the consensus sequence , and differently organized into a triad including two lysines . The presence of water molecules and previous structural models for influenza PAN allows to propose putative positions of metal ions , coordinated by D89 and E102 . The first step in the general mechanism for phosphodiester hydrolysis is the preparation of the attacking nucleophile by deprotonation , usually involving a general base deprotonating a water molecule . Lysine is often considered as this general base candidate in endonucleases but is not strictly conserved [28] , [29] . Here , there are no indications against D119 being this general base . Alternately , it could well be either lysine K115 or K122 . Both are oriented towards the active site , and they could well have their pKa lowered by D119 in order to initiate the reaction . Reverse genetic studies provide evidence for K122 , not K115 . Indeed , mRNA production is selectively abolished and clearly uncoupled from RNA synthesis in the case of K122A mutant , while the K115A mutant was completely defective preventing interpretation of its role in the endonuclease catalytic site . Although it is not known if uncapped mRNAs are synthesized and degraded for the transcription-null mutants , the most plausible scenario is that primer shortage prevents significant capped mRNA synthesis . Overall , the replicon data presented here closely match those obtained on the closely related Lassa arenavirus using a similar replicon system [30] . Arenaviruses may thus use two clearly independent and distinct RNA synthesis priming mechanisms: one is dependent on an active endonuclease carried by the N-terminus of the L protein , and the other might be linked to the observation that an extra G residue is found at the 5′-end of arenavirus genomes and antigenomes . The latter G bases would thus reflect a yet-uncharacterized priming mechanism unrelated to the U/A cleavage sequence preference of NL1 . NL1 also binds nucleotides , but the NTP binding site should differ from that of PAN . Indeed , the influenza PAN histidine 41 is involved in binding the nucleobase of the presumed incoming RNA substrate . The NL1 endonuclease does not share the same sequence specificity , and E51 is positioned at a spatially equivalent position . The cap structure does not seem to be a direct RNA binding determinant ( Figure 4B ) , as endonucleolytic cleavage is not directed to cleavage sites preferentially in the vicinity of the cap . We thus infer that an independent cap-binding site way exist elsewhere in viral proteins to bind and select cellular mRNAs , a possibility reminiscent of influenza for which PA carries the endonuclease activity and PB2 the cap binding site [15] , [16] , [31] . Structure and sequence alignment studies show that the N-terminal endonuclease domain of the L protein is also conserved in the Bunyaviridae family , although the Nairovirus endonuclease domain is not located into the N-terminal end of the protein . These findings were confirmed by the endonuclease activity of the N-terminal end of the L protein of TOSV ( Figure 4B ) . Thus , we provide evidence that all three segmented negative single-strand RNA virus species share an endonuclease domain probably involved in the cap-snatching process during the viral life cycle . These data raise the question of a possible common ancestor for these viruses . Indeed , these three virus families use a cap-snatching mechanism involving binding and cleavage of cellular mRNA caps subsequently used by a large primer-dependent RNA-dependent RNA polymerase . It seems more plausible that the L gene has evolved by divergence over time , rather than by multiple acquisitions of several activities converging into a common structure , at least in the case of the endonuclease . Furthermore , our study raises the interesting possibility that other activities involved in RNA replication/transcrition might be discovered by comparative analysis of Orthomyxoviridae PB1 , PB2 , PA and Arenaviridae/Bunyaviridae L proteins . To our knowledge , a single crystal structure of a functional arenavirus protein is currently available , that of the Machupo virus glycoprotein GP1 in complex with its human receptor , TfR1 [32] . Our results provide an arenavirus L domain structure , with a role consistent with the hypothesis of a cap-snatching mechanism suggested for arenaviruses [9] , [10] . The strategy used here to produce individually active domains might be useful to further characterize the Arenaviridae/Bunyaviridae large L protein which had so far resisted all biochemical characterization attempts . The influenza , Arenaviridae and Bunyaviridae endonucleases are so far the only three examples of RNA endonucleases similar to type II DNA restriction endonucleases . The presence of such an endonuclease suggests that it could serve as a fruitful target for antiviral strategies against these two families , since such kind of inhibitors have been reported in the case of the influenza virus [33] , [34] , [35] .
The LCMV NL1 cDNA ( Armstrong strain , aa 1 to 196 ) was cloned into pDest14 with a N-terminus hexa-histidine tag and expressed in E . coli Rosetta ( DE3 ) pLysS ( Novagen ) , at 17°C in 2YT medium overnight after induction with 500 µM IPTG . Cell pellets from harvested cultures were resuspended in 50 mM Tris buffer , pH 8 . 0 , 300 mM NaCl , 10 mM imidazole , 0 . 1% Triton , 5% Glycerol . Lysozyme ( 0 . 25 mg/ml ) , PMSF ( 1 mM ) , DNase I ( 2 µg/ml ) , and EDTA free protease cocktail ( Roche ) were added before sonication . IMAC chromatography of clarified lysates was performed on a 5 ml His prep column ( Akta Xpress FPLC system , GE Healthcare ) eluted with imidazole . Size exclusion chromatography was performed on preparative Superdex 200 column ( GE Healthcare ) pre-equilibrated in 10 mM Imidazole , pH 8 . 0 , 50 mM NaCl , 2 mM DTT . Protein was concentrated ( 28 mg/ml ) using a centrifugal concentrator . For enzymatic studies , WT and mutants were express in the E . coli BL21 star strain ( Invitrogen ) and further purified on HiTrap Q sepharose 1 ml column ( GE Healthcare ) to remove E . coli RNase contaminants . Proteins eluted in a linear gradient from 50 mM to 1 M NaCl in 10 mM Hepes buffer , pH 7 . 5 , 2 mM DTT . A synthetic gene of the H5N1 PAN endonuclease was designed as described [16] . The Toscana virus ( strain France AR_2005 , aa 2 to 233 ) cDNA was obtained from infected cell cultures . Both ORFs were cloned as a N-terminal Thioredoxin-Hexahistidine fusion in pETG20A . The tag was cleaved using TEV protease before a final gel filtration . Crystals grew in LiSO4 250 mM , citrate 50 mM , isopropanol 5 . 5% , using the hanging drop vapor diffusion method in Linbro plates by mixing 1 µl of protein solution with 1 µl of reservoir solution . Crystals were cryoprotected by dipping in a solution containing 65% of crystallization buffer and 35% of a buffer made of size exclusion chromatography buffer/glycerol ( 50/50 ) . Crystals were cryo-cooled in liquid N2 . The crystals belong to space group C2221 and have two molecules per asymmetric unit . Despite repeated attempts , crystal soaked into the above buffer supplemented with various concentrations of MnCl2 yielded crystals diffracting to >4 Å . Diffraction intensities were recorded on the ID14-4 beamline at the European Synchrotron Radiation facility , Grenoble , France . Data were processed and integrated with MOSFLM [36] . Scaling and merging of the intensities was performed with SCALA and programs from the Collaborative Computational Project , No . 4 ( CCP4 ) suite [37] . The structure was determined using SAD data from one selenomethionylated protein crystal diffracting to 3 . 4 Å resolution with SHARP/autoSHARP , followed by density modification with SOLOMON and DM . An initial model was built using BUCCANEER and completed in COOT , followed by refinement using BUSTER ( see Text S1 ) . Details of structure determination are given as supplemental material . Data from a native crystals diffracting to a 2 . 13-Å resolution were collected on an ADSC QUANTUM 315r at a wavelength of 0 . 9835 Å . The structure was refined with BUSTER and COOT using this data set ( Table 1 ) [38] . The atomic coordinates have been deposited at the PDB ( 3JSB ) . A PHI-BLAST search using the sequence corresponding to the L1 domain and the signature of the Arenaviridae endonuclease motif i . e . P-D-x ( 11 , 13 ) -E-x ( 12 ) -K-x ( 3 ) -D-x ( 2 ) -K ; was performed against non-redundant databases [39] . After 3 iterations , Batai and Kairi viruses both belonging to Orthomyxoviridae , appears in the section with an E-Value below threshold . A fourth iteration including these two sequences allows retrieving the entire family of orthomyxoviruses , with E-value comprised between 3e−18 and 2e−4 . A standard CDD search from the sequence of Tensaw virus allows retrieving all the L of the Bunyaviridae family hitting the pfam 04196 [40] . A multiple sequence alignment of the N-terminal end of the L protein from LCMV , HLCV , BUNV , HANV , PUMV , RVFV , TOSV , TOMV , WTMV , CCGV , DUGV , was first performed with the T-coffee algorithm ( http://tcoffee . vital-it . ch/cgi-bin/Tcoffee/tcoffee_cgi/index . cgi ) . Using the secondary structure prediction of the endonuclease domain of L proteins , the putative conserved active site residues were identified and placed correctly in the alignment . 7 µg of purified protein were incubated for 15 min at 25°C , with 0 . 5 µl of the various α-32P NTP ( 0 . 4 µCi/µl ) in 10 µl of reaction buffer containing 10 mM Imidazole , pH 8 . 0 , 50 mM NaCl , 2 mM DTT . The reaction mixtures were then exposed to UV light ( 254 nm ) for 6 min at 5 mm distance . The crosslinked species were separated in a 15% polyacrylamide denaturing gel and visualized by autoradiography using photo-stimulated plates and a Fujilmager ( Fuji ) . The RNA 5′-AUUUUGUUUUUAAUAUUUC-3′ ( Ambion ) was [32P] 5′-end labeled , and 0 . 4 µM of radiolabelled RNA was incubated 20 min at 25°C without and with 1 . 4 µg , 4 . 2 µg and 7 µg of protein in 10 µl of 10 mM Imidazole , pH 8 . 0 , 50 mM NaCl , 2 mM DTT . Reaction mixtures was analyzed by PAGE and visualized by autoradiography . Titration curves with CaCl2 , MnCl2 , MgCl2 and ZnCl2 were performed at 1 mg/ml protein in gel filtration buffer using thermal shift assay . Technical details can be be found in [41] . Endonuclease activity was assayed using 4 different heteromeric RNA substrates: an unstructured 19 mer as described above , a 21 mer stable hairpin ( 5′-UGAGGCCCGGAAACCGGGGCC-3′ ( Ambion ) , ΔG = −14 . 7 Kcal/mole ) , a 22 mer moderately stable hairpin ( 5′- CGCAGUUAGCUCCUAAUCGCCC-3′ ( Ambion ) , ΔG = −3 . 4 Kcal/mole ) , and a long 264 mer RNA corresponding to the SARS-CoV 5′-genome sequence . The latter was radiolabelled with a cap structure at its 5′-end using the ScriptCap m7G Capping System ( Epicentre Biotechnologies ) with [α32P]GTP . Endonuclease assays were carried out using 3 . 3 µM of radio-labeled RNA in a buffer containing 40 mM Tris-base , pH 7 . 5 , 100 mM NaCl , 10 mM β-Mercaptoethanol and 2 mM MnCl2 . Reactions were initiated by the addition of 1 µM of protein and incubated at 37°C , and stopped by the addition of EDTA/formamide . Reactions products were analyzed using denaturing polyacrylamide gel electrophoresis ( 20% polyacrylamide , 7 M urea in TTE buffer ( 89 mM Tris , 28 mM taurine , 0 . 5 mM EDTA ) and analyzed by autoradiography . The LCMV replicon system is based on strain Armstrong clone 13 and has been established in analogy to the Lassa virus replicon described previously [42] . BSR T7/5 cells constitutively expressing T7 RNA polymerase [43] were transiently transfected with T7 promoter-driven expression constructs for L protein , nucleoprotein ( NP ) , mini-genome ( MG ) containing Renilla luciferase reporter gene , and firefly luciferase as a transfection control . L protein mutants were generated as described [44] . One day after transfection , total RNA was prepared for Northern blotting and cell lysate was assayed for firefly and Renilla luciferase activity . Renilla luciferase levels were normalised with firefly luciferase levels resulting in standardized relative light units ( sRLU ) . Northern blot was performed using an antisense 32P-labeled riboprobe targeting the Renilla luciferase gene . Autoradiography was quantified on a PhosphorImager ( Amersham Biosciences ) . To verify protein expression , hemagglutinin ( HA ) -tagged L protein was expressed in BSR T7/5 cells inoculated with modified vaccinia virus Ankara expressing T7 RNA polymerase ( MVA- T7 ) [45] and detected in immunoblot using anti-HA antibody .
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The Arenaviridae virus family includes several life-threatening human pathogens that cause meningitis or hemorrhagic fever . These RNA viruses replicate and transcribe their genome using an RNA synthesis machinery for which no structural data currently exist . They synthesize viral mRNAs using short capped primers presumably acquired from cellular transcripts by a ‘cap-snatching’ mechanism thought to involve the large L protein , which carries RNA-dependent RNA polymerase signature sequences . Here , we report the crystal structure and functional characterization of an isolated N-terminal domain of the L protein ( NL1 ) from the prototypic arenavirus: lymphocytic choriomeningitis virus . The NL1 domain is able to bind and cleave RNA . The 2 . 13 Å resolution crystal structure of NL1 reveals a type II endonuclease α/β architecture similar to the N-terminal end of the influenza virus PA protein . Superimposition of both structures and mutagenesis studies reveal a unique spatial arrangement of key active site residues related to the PD… ( D/E ) XK type II endonuclease signature sequence . Reverse genetic studies show that mutation of active site residues selectively abolish transcription , not replication . We show that this endonuclease domain is conserved and active across the virus families: Arenaviridae , Bunyaviridae and Orthomyxoviridae and propose that the arenavirus NL1 domain is the Arenaviridae cap-snatching endonuclease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"biophysics/structural",
"genomics",
"biochemistry/protein",
"folding",
"virology/emerging",
"viral",
"diseases"
] |
2010
|
The N-Terminal Domain of the Arenavirus L Protein Is an RNA Endonuclease Essential in mRNA Transcription
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Ovulation is essential for the propagation of the species and involves a proteolytic degradation of the follicle wall for the release of the fertilizable oocyte . However , the precise mechanisms for regulating these proteolytic events are largely unknown . Work from our lab and others have shown that there are several parallels between Drosophila and mammalian ovulation at both the cellular and molecular levels . During ovulation in Drosophila , posterior follicle cells surrounding a mature oocyte are selectively degraded and the residual follicle cells remain in the ovary to form a corpus luteum after follicle rupture . Like in mammals , this rupturing process also depends on matrix metalloproteinase 2 ( Mmp2 ) activity localized at the posterior end of mature follicles , where oocytes exit . In the present study , we show that Mmp2 activity is regulated by the octopaminergic signaling in mature follicle cells . Exogenous octopamine ( OA; equivalent to norepinephrine , NE ) is sufficient to induce follicle rupture when isolated mature follicles are cultured ex vivo , in the absence of the oviduct or ovarian muscle sheath . Knocking down the alpha-like adrenergic receptor Oamb ( Octoampine receptor in mushroom bodies ) in mature follicle cells prevents OA-induced follicle rupture ex vivo and ovulation in vivo . We also show that follicular OA-Oamb signaling induces Mmp2 enzymatic activation but not Mmp2 protein expression , likely via intracellular Ca2+ as the second messenger . Our work develops a novel ex vivo follicle rupture assay and demonstrates the role for follicular adrenergic signaling in Mmp2 activation and ovulation in Drosophila , which is likely conserved in other species .
Ovaries in organisms ranging from humans to insects are extensively innervated [1–4] , and neuronal inputs likely play important roles in ovarian physiology [5] . In mammals , ovaries are predominantly innervated by sympathetic fibers from the ovarian plexus nerve and the superior ovarian nerve [6] , which release norepinephrine ( NE ) locally and contribute to follicle development [7] . Deregulation of sympathetic nerve outflow to ovaries is associated with polycystic ovary syndrome ( PCOS ) , a common endocrine disorder leading to anovulatory infertility [8 , 9] . Despite the apparent importance of sympathetic innervation , however , it is not yet clear how the neuronal modulators/transmitters released from nerve termini affect ovulation [10–16] . In Drosophila and other insects , the biogenic monoamines tyramine ( TA ) and octopamine ( OA ) act as functional counterparts to mammalian epinephrine and norepinephrine and regulate a variety of behaviors , including the fight-or-flight response , motivation , aggression , and reproduction [17 , 18] . Analogous to the adrenergic innervation in mammalian ovaries , Drosophila octopaminergic neurons innervate ovaries and the female reproductive tract ( Fig 1A; [3 , 19 , 4] ) . OA released from these neurons is essential for ovulation , as mutations that disrupt the enzymes required for OA synthesis , tyrosine decarboxylase 2 ( Tdc2 ) and tyramine β-hydroxylase ( TβH ) , completely block ovulation [20–22] . Four OA receptors have been identified in Drosophila: Oamb , Octβ1R , Octβ2R , and Octβ3R . Oamb is most closely related to mammalian α-adrenergic receptors , and the other three to β-adrenergic receptors [17 , 23] . Recent work demonstrated that Oamb and Octβ2R are important in egg laying and ovulation [24–26] . Oamb is widely expressed in the female reproductive system , including the ovary , with strongest expression observed in the oviduct [24] . It is currently believed that OA activates receptors in the oviduct , inducing oviduct contraction and secretion , which ultimately regulates ovulation through an unknown mechanism [19 , 27 , 25] . In addition to OA signaling , ovulation in Drosophila is affected by female reproductive gland secretions [28] and by mating , which increases the ovulation rate by stimulating afferent nerve activity in the female reproductive tract [29–33 , 4] . In particular , Ovulin transferred into the female reproductive tract after mating was recently shown to increase octopaminergic signaling and relax oviduct muscle [34] , consistent with the role of OA signaling in regulating muscle contraction . It is , however , not clear whether OA plays any direct roles in the ovary to control ovulation . In addition to above important work on Drosophila ovulation ( also see review [35] ) , recent studies from our lab also showed significant conservation of the basic cellular and molecular mechanisms of ovulation from flies to mammals . Drosophila female contains two ovaries that are connected by the oviduct . Each ovary is organized into ovarioles , which have mature follicles ( stage-14 egg chambers ) at the posterior end toward the oviduct ( Fig 1A; [36] ) . Each mature follicle has one layer of epithelial follicle cells surrounding the oocyte . During ovulation , posterior follicle cells are first trimmed to break the follicle-cell layer and to allow the oocyte to be released into the oviduct . The rest of the follicle cells remain at the end of the ovariole and form a corpus luteum [37] . Similar to vertebrate ovulation [38–40] , the entire follicle rupture requires matrix metalloproteinase 2 ( Mmp2 ) , a proteolytic enzyme expressed in posterior follicle cells of mature egg chambers but only activated during follicle rupture [37] . It is not yet clear what signals control Mmp2 activity , but it is clear that studying this question in Drosophila could yield important insights into the fundamental mechanism of ovulation . Here , we developed the first ex vivo assay for follicle rupture in Drosophila and used it to investigate the role of octopaminergic signaling in this process . We found that OA directly activates its receptor Oamb on mature follicle cells to induce the breakdown of posterior follicle wall and ovulation . In addition , NE could partially substitute for OA , indicating an evolutionary conserved role for follicular adrenergic signaling in ovulation . Finally , we demonstrated that follicular adrenergic signaling activates Mmp2 activity to control ovulation via the intracellular Ca2+ as the second messenger . This is the first demonstration of a direct role of a neuromodulator in the control of follicle rupture during ovulation .
Octopaminergic neurons innervate ovarioles extensively [21] , and OA receptor Oamb is transcribed in mature follicle cells according to in situ hybridization [24] , microarray analysis ( S1 Fig; [41] ) , and the expression of R47A04-Gal4 [42] , an Oamb enhancer element-regulated Gal4 driver , in mature follicle cells [37] . We examined whether OA activates Oamb directly in mature follicle cells to induce follicle rupture . Mature follicles with an intact layer of follicle cells marked by R47A04-Gal4 were isolated from ovaries ( see methods ) and cultured with OA or control media ( Fig 1A ) . After three hours , follicles in control medium maintained an intact follicle-cell layer ( Fig 1B ) . In contrast , about 80% of the follicles cultured with 5 μM of OA had shed their follicle-cell layer to the dorsal appendage at the anterior tip of the oocytes ( Fig 1C ) ; some were completely detached from the oocyte and floating in the medium . This phenomenon of shedding the follicle-cell layer , which we called follicle rupture in our ex vivo culture system , is reminiscent of what occurs during the ovulation process in vivo [37] . The percentage of ruptured follicles with OA stimulation increased dramatically in the first two hours and reached a plateau at about three hours ( Fig 1D ) . Extending the culture period neither increased the percent of ruptured follicles to 100% in the OA medium , nor allowed follicles in the control medium to reach the same level of rupture as OA-stimulated follicles ( Fig 1D ) . To validate that the follicle rupture in our ex vivo assay mimics the in vivo process , we video-recorded the entire rupturing process ( Fig 1E and S1 Movie ) . We found that posterior follicle cells were first trimmed , as we previously observed in vivo [37] . The remaining follicle-cell layer was then squeezed toward the anterior dorsal appendage ( Fig 1E and S1 Movie ) . The entire rupturing process took 13 . 1 ± 5 . 0 minutes ( S1 Table ) , resembling the estimated in vivo ovulation time of 11 . 2 ± 2 . 5 minutes ( Table 1; [37] ) . Each mature follicle initiated the follicle rupture asynchronously , likely reflecting their asynchronous developmental stages; however , the kinetics of all ruptures was similar , with a very slow initial speed ( Fig 1F ) . It took about 10 minutes to rupture through the posterior half of the oocyte , but only four minutes for the rest of the area ( Fig 1E and 1F ) . All data are mean ± 95% confidence interval . Student's T-test was used for egg laying , Chi-square test was used for egg distribution , and Z Score test was used for egg laying time assuming normal distribution To further examine the quality of ex vivo ruptured oocytes , we determined whether these oocytes were activated . Mature oocytes released into the oviduct are activated and resistant to bleach treatment because their egg shells are hardened through cross-linking [43] . This activation process can also be mimicked in vitro by culturing oocytes in hypotonic activation buffer [44 , 45] . Using the established bleach assay ( see methods ) , we found that oocytes from our ex vivo assay dissolved immediately after bleach treatment ( n = 96 ) , indicating that they were not fully activated and their eggshells were not hardened . However , treatment with hypotonic activation buffer for 15 minutes can efficiently activate these ruptured oocytes ( 95% , n = 150; S2A and S2B Fig ) , indicating these oocytes from our ex vivo system are of good quality and responsive to egg activation stimuli . OA-induced follicle rupture is dose-dependent . Stimulation with 1 μM of OA had a minimal effect on follicle rupture , while stimulation with 20 μM of OA reached the maximal effect ( Fig 1G ) , which led us to use 20 μM for all the following experiments . In contrast , stimulation with 20 μM of tyramine ( TA ) , the immediate precursor of OA , had a much weaker effect on follicle rupture ( Fig 1H ) , consistent with a previous report that OA , but not TA , is responsible for inducing ovulation [20] . Since NE is the counterpart of OA in mammals , we tested whether NE can also induce follicle rupture in our ex vivo assay . NE had only a minimal effect at lower doses ( Fig 1I ) . Higher doses of NE could induce follicle rupture ( Fig 1I ) , likely reflecting a differential binding properties of OA and NE to their respective receptors [18] . Nevertheless , these data suggest that OA and NE play a conserved role in regulating follicle rupture . In summary , we developed the first ex vivo assay to study follicle rupture in Drosophila , and our data suggest that OA is sufficient to induce follicle rupture in the absence of the oviduct and muscle function , as these tissues were excluded from our culture assay ( 68 mature follicles examined and none had ovariole muscle; S3A and S3B Fig ) . To identify the receptor responsible for OA/NE-induced follicle rupture , we focused on Oamb , which is essential for ovulation [24] and is the most highly expressed OA receptor in mature follicles ( S1 Fig ) . We verified the requirement of Oamb in ovulation with a new mutant allele ( OambMI12417 ) , in which a MiMIC vector with a splice acceptor [46] was inserted in the coding intron of Oamb gene to disrupt the correct mRNA splicing ( S4 Fig ) . Females bearing this mutant allele laid significantly fewer eggs and took a much longer time to ovulate an egg ( Table 1 ) . We then isolated mature follicles from these females and applied OA stimulation ex vivo . Oamb mutant follicles showed severe defects in OA-induced follicle rupture compared to control follicles ( Fig 2A , 2B and 2E ) . In addition , the Oamb mutation abolished the NE-induced follicle rupture ( Fig 2C–2E ) . The defective response of Oamb mutant follicles to OA/NE stimulation is not likely due to defective OA signaling in the oviduct or other organs , because follicles from TβH or Tdc2 mutant females are fully competent to OA/NE-induced follicle rupture ( Fig 2F and 2G ) . These data indicate that Oamb in mature follicles is likely responsible for OA/NE-induced follicle rupture . To test if Oamb functions directly in mature follicle cells , we knocked down Oamb specifically in these cells with RNA interference ( RNAi ) and then performed OA stimulation ex vivo . Oamb knockdown in mature follicle cells with R47A04-Gal4 severely disrupted OA-induced follicle rupture ( Fig 2H–2J ) . Since R47A04-Gal4 is regulated by an Oamb enhancer element [42] , it could potentially be expressed in other Oamb-expressing cells , which may facilitate follicle maturation and ovulation . To exclude this possibility , we identified another Gal4 driver ( R44E10-Gal4 ) expressed in mature follicle cells ( S5B–S5D Fig ) . Compared to R47A04-Gal4 , which is only expressed in late stage-14 follicles ( S5A Fig ) , R44E10-Gal4 was expressed in all stage-14 follicles , slightly earlier than R47A04-Gal4 . R44E10-Gal4 was not expressed in any tissues in the lower reproductive tract , nor in the neurons innervating the reproductive tract ( S5B , S5E and S5F Fig ) . Like mature follicles isolated using R47A04-Gal4 , follicles isolated using R44E10-Gal4 were also responsive to OA/NE-induced follicle rupture ( S5G and S5H Fig ) . In addition , mature follicles with R44E10-Gal4 driving OambRNAi showed similar unresponsiveness to OA or NE stimulation ( Fig 2K–2M ) . Taken together , these data suggest that follicular Oamb is required for OA/NE-induced follicle rupture ex vivo . To determine whether follicular adrenergic signaling is required for ovulation in vivo , we first analyzed the fecundity of females lacking follicular Oamb . Follicular Oamb-knockdown females with either R47A04-Gal4 or R44E10-Gal4 drivers laid significantly fewer eggs than control flies ( Fig 3A and Table 1 ) . The egg-laying defect is not caused by oogenesis problems , as mature follicles are abundant in these ovaries . In fact , Oamb-knockdown flies generally had more mature follicles in their ovaries ( Fig 3B ) , indicating an ovulation defect . Indeed , Oamb-knockdown flies had a much longer ovulation time compared to control flies but did not show defects in transporting ovulated eggs into the uterus or ejecting them out of the uterus ( Fig 3C and Table 1 ) . These data strongly suggest that follicular Oamb is required for ovulation in vivo . All data are mean ± SD . Student’s T-test was used . Trimming of posterior follicle cells is essential for ovulation and precedes follicle rupture [37] . We investigated the role of follicular adrenergic signaling in this trimming process . Posterior trimmed follicles were readily observed in the ovaries of control females six hours after mating , and they account for 9% of the total mature follicles in each female ( Fig 3D and 3F and Table 2 ) , consistent with our previous analysis [37] . In contrast , the percentage of posterior trimmed follicles was reduced three fold in females lacking follicular Oamb ( Fig 3E and 3F and Table 2 ) , indicating its essential role in follicle trimming . This is consistent with our observation that posterior follicle cells remain intact in Oamb-knockdown follicles even after three hours of OA stimulation ex vivo ( Fig 2I and 2L ) . Furthermore , the percentage of trimmed follicles also decreased in flies that lacked the ability to produce OA; we saw a reduction to 2 . 4% and 0 . 4% in TβH and Tdc2 mutant females , respectively ( Fig 3G–3L and Table 2 ) . This reduction of trimmed follicles was not only observed in mated females , but also in virgin females ( Table 2 ) . Taken together , these data suggest that follicular adrenergic signaling is required for posterior follicle cell trimming . The crucial role of Mmp2 in trimming of posterior follicle cells [37] prompted us to investigate the relationship between follicular adrenergic signaling and Mmp2 activity . It is unlikely that adrenergic signaling regulates Mmp2 expression , as Mmp2 was readily detected in the posterior follicle cells of TβH mutants ( S6A and S6B Fig ) . To test whether OA regulates Mmp2 activity , we examined gelatinase enzymatic activity in the OA-induced ex vivo ovulation assay using in situ zymography [37 , 38] . About 20% of mature follicles cultured in a control medium had gelatinase activity at their posterior end ( Figs 4A , 4C , S6C and S6G ) . In contrast , more than 70% of mature follicles stimulated with OA had gelatinase activity ( Figs 4B , 4C , S6D and S6G ) . The entire eggshells of ruptured oocytes were coated with Mmp-activated gelatin-fluorescein ( Figs 4B and S6D ) , as we observed in vivo [37] . In addition , OA-induced gelatinase activity was blocked in mature follicles with Oamb knockdown or misexpression of Timp , an endogenous inhibitor of Mmp2 [47] , in follicle cells ( S6E–S6G Fig ) . These data indicate that OA-Oamb signaling is sufficient to induce Mmp2 activation . To determine whether Mmp2 activity is required for OA-induced follicle rupture , we isolated mature follicles containing follicle cell-specific Mmp2 knockdown and cultured them in the OA medium . These follicles did not respond to OA stimulation , and their posterior follicle cells remained intact ( Fig 4D–4I ) . In addition , Mmp2 knockdown in follicle cells also abolished the NE-induced follicle rupture ( Fig 4F and 4I ) . Furthermore , misexpression of Timp in mature follicle cells completely prevented follicle rupture ex vivo ( Fig 4F and 4I ) . Therefore , Mmp2 activity in mature follicle cells is essential for OA/NE-induced follicle rupture ex vivo , consistent with its essential role in follicle trimming and ovulation in vivo [37] . To confirm that Mmp2 acts downstream of adrenergic signaling in follicle trimming and rupture , we attempted to rescue the defect of follicle rupture in Oamb mutant flies with ectopic expression of Mmp2 in mature follicle cells . Oamb mutant females had two intact ovaries , which contain a large number of mature follicles ( Fig 4J ) . In contrast , follicular misexpression of Mmp2 in Oamb mutant females caused the breakdown of the ovariole muscle sheath and the release of mature follicles into the abdominal cavity ( Fig 4K ) . Further examination of these released follicles demonstrated that 99% of them ( n = 70 ) had no follicle-cell covering , similar to follicles released upon misexpression of Mmp2 in Oamb heterozygous or wild-type females ( Fig 4L; [37] ) . Therefore , Mmp2 is sufficient to induce follicle rupture in the absence of adrenergic signaling . Together , our data indicate that follicular adrenergic signaling activates Mmp2 to control follicle trimming and ovulation . OA-Oamb interaction can induce transient increase of intracellular Ca2+ concentration ( [Ca2+]i ) [23] . To determine whether OA evokes Ca2+ signaling in mature follicle cells to induce follicle rupture , we first monitored the [Ca2+]i using a genetically encoded calcium sensor ( see methods ) . Fluorescent intensity of the calcium sensor expressed in mature follicle cells rose significantly around six minutes after OA administration in our ex vivo culture system ( S7 Fig and S2 Movie ) . To determine whether Ca2+ is required for OA-induced follicle rupture , we pretreated mature follicles with BAPTA-AM , an intracellular Ca2+ chelator , before OA stimulation . Two hundred μM BAPTA-AM treatment significantly perturbed the OA-induced follicle rupture ( Fig 5A–5C ) . To determine whether Ca2+ is sufficient to induce follicle rupture , we stimulated mature follicles with ionomycin , a potent ionophore for increasing [Ca2+]i . Ionomycin is potent to induce follicle rupture even at 5 μM concentration ( Fig 5D–5F ) , lower than the dose typically used in the field [48] . Taken together these data suggest that the increase of [Ca2+]i is both necessary and sufficient to induce follicle rupture . To further test whether Ca2+ is the second messenger of follicular adrenergic signaling for Mmp2 activation and follicle rupture , we set to examine whether ionomycin is sufficient to induce rupture of follicles lacking follicular Mmp2 or Oamb , which do not respond to OA stimulation . Ionomycin only partially induces follicle rupture when Mmp2 is knocked down in mature follicle cells and is not able to induce any rupture when Timp is overexpressed ( Fig 5G–5I ) . In contrast , ionomycin is able to induce follicle rupture in both control and Oamb mutant follicles at the equal efficiency ( Fig 5J–5L ) . All these data indicate that Ca2+ acts downstream of Oamb but upstream of Mmp2 during follicle rupture . Together , we conclude that follicular adrenergic signaling activates Mmp2 to control follicle trimming and ovulation likely via intracellular Ca2+ ( Fig 5M ) .
Ovulation , an essential step in metazoan reproduction , has been extensively studied in mammals over the past several decades [49–51] . However , progress in the field has been hindered by the limited ability of mammalian model systems to be genetically manipulated . Thus it is still unclear how follicles break their wall in a highly regulated spatio-temporal manner to allow release of oocytes . The model organism Drosophila offers a wealth of tools for genetic manipulation , but to date , few specific readouts for Drosophila ovulation has been developed . Previous studies of Drosophila ovulation have used readouts such as egg laying , percentage of females with eggs in the reproductive tract , or egg retention [21 , 25–27 , 33] . We recently combined these parameters to estimate ovulation time [28 , 37] . In the present study , we developed the first ex vivo follicle rupture assay in Drosophila and demonstrated that OA-induced follicle rupture in this assay is similar to the rupturing process in vivo . This assay gave us the unprecedented ability to visualize the entire process of follicle rupture and quantify its kinetics . Further genetic evidence illustrated that genes required for ex vivo follicle rupture are also required for in vivo ovulation , including Oamb and Mmp2 . Our ex vivo assay represents a simple , specific , and reliable method for measuring rupturing ability of mature follicles . In conjunction with the powerful genetic tools available in Drosophila , this ex vivo assay will allow genetic screens to identify candidate genes involved in follicle rupture , thus opening new avenues for ovulation research . Octopamine , a biogenic amine derived from tyrosine , has been identified as essential for ovulation in Drosophila [20] . The major source of OA is octopaminergic neurons innervating the female reproductive system , and previous studies showed that restoring TβH specifically in these neurons rescues the ovulation defect caused by TβH mutation [21] . Due to its effects on muscle contraction , OA was proposed to regulate ovulation by inducing the contraction of ovarian muscle and relaxation of oviduct muscle [3 , 19 , 25 , 27 , 34] . Ovarian smooth muscle contraction was also proposed to regulate ovulation in mammals in the early 1980’s [11 , 52 , 53] . However , subsequent work suggest that ovulation requires the active proteolytic degradation of the follicle wall rather than passive muscle contraction [54 , 40 , 55] . At least three families of proteolytic enzymes are involved in this process , including matrix metalloproteinases [56 , 57] . Pharmacological blockage of any of these enzymes results in inhibition of follicle rupture . Our recent work suggested that Drosophila also requires proteolysis for breaking the follicle wall and ovulation [37] , and in this way shares similarities with mammalian ovulation at both the cellular and molecular level [28 , 37] . These new insights into Drosophila ovulation process lead to the speculation that octopaminergic signaling may play a direct role on the follicle in controlling ovulation in addition to its role on muscle contraction . Here , we demonstrate that OA-Oamb signaling in mature follicle cells directly regulates follicle wall degradation , follicle rupture , and ovulation by activating key enzyme Mmp2 . Furthermore , our pharmacological data suggest that OA-Oamb signaling likely fulfill these functions via intracellular Ca2+ as the second messenger . However , it is still unclear how OA-Oamb-Ca2+ regulates Mmp2 activity . Lacking a method to detect Mmp2 protein prevents us to test whether OA-Oamb-Ca2+ regulates Mmp2 protein secretion . The Mmp2::GFP fusion allele we previously generated [37] is good to detect Mmp2::GFP expression but not good to track Mmp2 secretion because Mmp2::GFP fusion proteins are not properly processed and secreted to the extracellular space ( S8 Fig ) and Mmp2::GFP homozygous flies are lethal as Mmp2 mutant females do [47] . Alternatively , Ca2+ signaling may indirectly regulate Mmp2 activity via its inhibitor or other regulatory processes . Despite that , it is intriguing that [Ca2+]i also rises after NE and gonadotropin stimulation in human granulosa cells [58] and that perfusion of a Ca2+ chelator in rabbits significantly reduces gonadotropin-induced ovulatory efficiency [59] . Given adrenergic innervation of ovaries observed throughout metazoans , it is plausible to speculate that follicular adrenergic signaling plays conserved roles in regulating Mmp activity and ovulation ( See below ) . Adrenergic innervation of the ovary has long been found in mammals including humans . The role of adrenergic signaling in ovulation has been studied as early as the 1970’s . The neurotransmitter norepinephrine ( NE ) reaches the highest level in peripheral plasma during ovulation [60] and is enriched in the follicular fluid of preovulatory follicles compared to in peripheral plasma in healthy women [16 , 61 , 62] . Functional adrenergic receptors are expressed in mammalian ovarian follicular cells [13 , 58 , 63] . Ovarian perfusion of adrenergic agonists or antagonists influences the ovulation rate in rabbits and rats [12 , 14] . It has been speculated that adrenergic signaling regulates ovulation by stimulating muscle contraction or by increasing production of reactive oxygen species [16 , 53] . In contrast to this view , ovarian sympathetic denervation does not affect ovulation in rabbits and rats [10 , 15]; instead , it rescues ovulation defect in a rat model of PCOS [64 , 65] , which is associated with increased sympathetic inputs to the ovary [8 , 9] . It is not clear why a discrepancy exists between the effects of surgical denervation and of pharmacological agents . Thus , no consensus has been reached in regard to the role of ovarian adrenergic signaling in mammalian ovulation . Instead of regulating ovarian smooth muscle contraction , the results of the present study suggest an alternative pathway for ovarian NE to regulate ovulation . NE likely activates adrenergic receptors in granulosa and theca cells ( equivalent to Drosophila follicle cells ) in mammalian periovulatory follicles , which activates Mmp enzymatic activity at the apex [38] , where mature oocytes rupture through . A surgical denervation may cause tissue damage and activate Mmps directly , bypassing the requirement of follicular adrenergic signaling . Future studies , using both mammalian and Drosophila genetic tools , will identify fundamental mechanisms of adrenergic signaling in ovulation .
Flies were reared on standard cornmeal-molasses food at 25°C unless otherwise indicated . OambMI12417 is a MiMIC line inserted in the coding intron of both Oamb spicing isoforms ( S4 Fig ) [46] , and OambMI12417/Df ( 3R ) BSC141 was used to characterize the Oamb mutant phenotype . TbHM18 [20] and Tdc2RO54 [22] were kindly provided by Dr . Mariana Wolfner . All RNAi-knockdown experiments were performed at 29°C with UAS-dcr2 to increase the efficiency of RNAi . R47A04-Gal4 ( Oamb ) and R44E10-Gal4 ( lilli ) from the Janelia Gal4 collection [42] were used for misexpressing genes or RNAi in mature follicle cells . The following RNAi or overexpressing lines were used: UAS-OambRNAi1 ( V2861 ) and UAS-OambRNAi2 ( V106511 ) from the Vienna Drosophila Resource Center; UAS-OambRNAi3 ( B31233 ) and UAS-OambRNAi4 ( B31171 ) from the Bloomington Drosophila Stock Center; UAS-Mmp2RNAi [66]; UAS-Mmp2 [47]; and UAS-GCaMP5G [67] . UASpGFP-act79B; UAS-mCD8-GFP[37] was used to analyze Gal4 expression in both germline and somatic cells , as well as neurons . UAS-GFPnls and UAS-RFP were used for follicle isolation . Control flies were derived from specific Gal4 drivers crossed to Oregon-R or yv; attP2 ( B36303 ) . The Mmp2::GFP fusion allele in the Mmp2 endogenous locus was used for detecting Mmp2 protein expression [37] . For the ex vivo follicle rupture assay , 4–6-day-old virgin females were used to isolate mature follicles , and follicle cells were fluorescently labeled using R47A04-Gal4 or R44E10-Gal4 . Ovaries were dissected in Grace’s medium and ovarioles were separated from each other using forceps . This process will partially break the ovariole muscle sheath and release mature follicles . Mature follicles with an intact follicle-cell layer and completely dissociated from younger follicles were immediately transferred to new Grace’s medium to minimize their exposure to endogenous biogenic amines during dissection . With this method , we can isolate about 10 mature follicles/female and isolated mature follicles are no longer surrounded by ovariole or oviduct muscle sheaths ( S3 Fig ) . Within one hour , isolated mature follicles were subsequently cultured in culture media ( Grace’s medium , 10% fetal bovine serum , and 1X penicillin/streptomycin ) supplemented with the indicated concentration of OA , TA , NE ( Sigma ) , or ionomycin ( dissolved in ethanol; Cayman Chemical ) . For chelating intracellular Ca2+ , isolated mature follicles were treated with BAPTA-AM ( dissolved in DMSO; Cayman Chemical ) for 30 minutes before OA culture . All cultures were performed at 29°C , the same condition as flies were maintained , to enhance Gal4/UAS expression . About 25–30 follicles were used for each culture group and the percentage of ruptured follicles was then calculated as one data point . Typically three-six replicates were used for each genotype or treatment; data were represented as mean percentage ± standard deviation ( SD ) ; and Student’s T-test was used for statistic analysis . Ruptured follicles were defined as those losing more than 80% follicle-cell covering . With the exception of Fig 1D , all data were collected at the end of the three-hour culture . For Ca2+ imaging and follicle rupture kinetics , video images were captured at 0 . 2 frame/second ( FPS ) with a sCOMS camera ( PCO . Edge ) installed in a Leica MZ10F fluorescent stereoscope . To examine the kinetics of follicle rupture , mature follicles were cultured in 20 μM of OA medium for 20 minutes at 29°C before recording . Unruptured follicles were then transferred into a home-made slide for video recording at room temperature . Each ruptured follicle was analyzed frame-by-frame manually to determine the ruptured distance between the posterior tip of the oocyte and the posterior leading edge of the follicle-cell layer using ImageJ . The percent of ruptured distance was then calculated as the ruptured distance divided by the length of the oocyte from the anterior to posterior tip . Because of the asynchronous onset of follicle rupture , data were normalized at the time point when follicles reach 50% ruptured distance . In situ zymography for detecting gelatinase activity was performed as previously reported with minor modifications [37] . 50 μg/ml of DQ-gelatin conjugated with fluorescein ( Invitrogen ) was added into the culture media with or without OA for three hours . After a quick rinse , mature follicles with posterior fluorescent signal were directly counted . For egg activation , ruptured oocytes were treated with hypotonic activation buffer [45] for 15 minutes and treated with 50% bleach for three minutes . The number of unbroken oocytes was counted . Egg laying , ovulation time , and follicle cell trimming were performed as previously described [28 , 37] . In brief , 4–6-day-old virgin females fed with wet yeast for one day were used . For egg laying , five females were housed with ten Oregon-R males in one bottle to lay eggs on grape juice-agar plates for two days at 29°C . After egg laying , ovaries were dissected and mature follicles in these ovaries were counted . The number of eggs on the plates was then counted , which was used to calculate the average time for laying an egg ( egg-laying time ) . The egg-laying time was partitioned into the ovulation time and the uterus time ( the time egg spent in the uterus and during oviposition ) . The partition ratio was determined based on the percentage of females having eggs in the uterus at six hours after mating . To do so , ten virgins were placed in a vial with 15 Oregon R males for six hours at 29°C , frozen for 4 . 5 minutes at -80°C , and then dissected to examine the reproductive tract . For follicle cell trimming , virgin or mated females were frozen for 4 . 5 minutes at -80°C , and ovary pairs were dissected , fixed , stained with DAPI , and mounted carefully to preserve the posterior end of mature follicles . Trimmed follicles were defined as more than a quarter of oocytes at the posterior end lacking follicle cell covering . Normalized trimming follicles were then calculated by the number of trimming follicles divided by the number of mature follicles in each female . Immunostaining was performed following a standard procedure [68] , including fixation in 4% EM-grade paraformaldehyde for 15 minutes , blocking in PBTG ( PBS+ 0 . 2% Triton+ 0 . 5% BSA+ 2% normal goat serum ) , and primary and secondary antibody staining . For Mmp2::GFP localization , dissected tissues were stained in primary antibodies for 45 minutes at 4°C before the fixation treatment followed with the secondary antibody staining . Mouse anti-Hnt ( 1:75; Developmental Study Hybridoma Bank ) and rabbit anti-GFP ( 1:4000; Invitrogen ) were used as primary antibodies , and Alexa 488 goat anti-rabbit and 546 goat anti-mouse ( 1:1000 , Invitrogen ) were used as secondary antibodies . Images were acquired using a Leica TCS SP8 confocal microscope or Leica MZ10F fluorescent stereoscope with a sCOMS camera ( PCO . Edge ) , and assembled using Photoshop software ( Adobe , Inc . ) .
|
Ovulation is the process of releasing fertilizable oocytes from the ovary and is essential for metazoan reproduction . Our recent work has demonstrated principles governing ovulation process that are highly conserved across species , such that both mammals and Drosophila utilize matrix metalloproteinase ( Mmp ) to degrade extracellular matrix and weaken the follicle wall for follicle rupture . However , a fundamental question remaining in the field is how Mmp activity is precisely regulated during ovulation . This paper reports that Drosophila octopamine ( OA ) , the insect equivalent of norepinephrine ( NE ) , is the signal to induce Mmp activity through activating its receptor Oamb on mature follicle cells and that this may induce ovulation . These findings allow us to develop the first ex vivo follicle rupture assay for Drosophila , which gives us unprecedented ability to characterize the entire follicle rupturing process ex vivo and to identify essential factors for ovulation . Furthermore , we show that NE partially fulfills OA’s role in inducing follicle rupture ex vivo , indicating that follicular adrenergic signal is a conserved signal to regulating Mmp activity and ovulation . Our work not only sheds light on the long-standing question of Mmp regulation , but also may lead to a better understanding of Mmp and NE linked pathological processes including cancer metastasis and polycystic ovary syndrome .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
A Follicle Rupture Assay Reveals an Essential Role for Follicular Adrenergic Signaling in Drosophila Ovulation
|
Antibiotic treatment of Group A Streptococcus ( GAS ) pharyngitis is important in acute rheumatic fever ( ARF ) prevention , however clinical guidelines for prescription vary . GAS carriers with acute viral infections may receive antibiotics unnecessarily . This review assessed the prevalence of GAS pharyngitis and carriage in different settings . A random-effects meta-analysis was performed . Prevalence estimates for GAS+ve pharyngitis , serologically-confirmed GAS pharyngitis and asymptomatic pharyngeal carriage were generated . Findings were stratified by age group , recruitment method and country income level . Medline and EMBASE databases were searched for relevant literature published between 1 January 1946 and 7 April 2017 . Studies reporting prevalence data on GAS+ve or serologically-confirmed GAS pharyngitis that stated participants exhibited symptoms of pharyngitis or upper respiratory tract infection ( URTI ) were included . Included studies reporting the prevalence of asymptomatic GAS carriage needed to state participants were asymptomatic . 285 eligible studies were identified . The prevalence of GAS+ve pharyngitis was 24 . 1% ( 95% CI: 22 . 6–25 . 6% ) in clinical settings ( which used ‘passive recruitment’ methods ) , but less in sore throat management programmes ( which used ‘active recruitment’ , 10 . 0% , 8 . 1–12 . 4% ) . GAS+ve pharyngitis was more prevalent in high-income countries ( 24 . 3% , 22 . 6–26 . 1% ) compared with low/middle-income countries ( 17 . 6% , 14 . 9–20 . 7% ) . In clinical settings , approximately 10% of children swabbed with a sore throat have serologically-confirmed GAS pharyngitis , but this increases to around 50–60% when the child is GAS culture-positive . The prevalence of serologically-confirmed GAS pharyngitis was 10 . 3% ( 6 . 6–15 . 7% ) in children from high-income countries and their asymptomatic GAS carriage prevalence was 10 . 5% ( 8 . 4–12 . 9% ) . A lower carriage prevalence was detected in children from low/middle income countries ( 5 . 9% , 4 . 3–8 . 1% ) . In active sore throat management programmes , if the prevalence of GAS detection approaches the asymptomatic carriage rate ( around 6–11% ) , there may be little benefit from antibiotic treatment as the majority of culture-positive patients are likely carriers .
Acute pharyngitis is a common cause of doctor’s visits across the world . [1] Most pharyngitis episodes ( 40–80% ) are caused by self-limiting viral infections . Group A Streptococcus ( GAS ) infection is the most common bacterial cause of pharyngitis , responsible for approximately15-30% of cases . [2] In a small minority of patients ( 0 . 3–3% ) , untreated GAS pharyngitis may trigger acute rheumatic fever ( ARF ) . [3–5] ARF and its sequela , rheumatic heart disease ( RHD ) , remain important public health problems in low- and middle-income countries , [6–8] and persist in certain ( predominantly Indigenous-minority ) groups in high-income countries . [9 , 10] Indigenous Australians and New Zealand Māori and Pacific populations have among the highest rates of ARF in the world . [1] There is conflicting pressure on clinicians to either prescribe antibiotics to patients with pharyngitis to reduce the risk of ARF , or to withhold prescriptions and minimise antibiotic-related harms . [11–14] Most high-income countries have national clinical guidelines on antibiotic treatment of GAS pharyngitis , but guidelines differ markedly in their recommendations . [15] For example , in North America , Finland and France , throat swabbing and prescribing antibiotics to patients with GAS culture-positive ( GAS+ve ) pharyngitis is recommended . [15–17] Conversely , antibiotic treatment is discouraged in other high-income countries , notably the United Kingdom , Belgium and the Netherlands . [15 , 18] In New Zealand and Australia , clinical guidelines restrict swabbing and treatment to patients at high-risk of ARF . [16 , 17] Specifically , in New Zealand it is recommended that antibiotic treatment begin immediately after a symptomatic high-risk patient presents to a healthcare provider , but be discontinued if GAS is not cultured . In this instance , patients may be exposed to several days of unnecessary treatment while laboratory results are generated . [19 , 20] Despite clinical guidelines , healthcare practitioners sometimes prescribe antibiotics to relieve symptom duration , regardless of the patient’s ARF risk . [21] Accurate diagnosis of true GAS pharyngitis infection remains a major barrier to effective ARF prevention . Some individuals carry GAS in the throat , but have no symptoms of infection nor an antibody response . [11 , 22] The Infectious Diseases Society of America makes a strong recommendation against routine antibiotic treatment of carriers . This recommendation is based on evidence in the literature indicating that carriers are unlikely to transmit GAS pharyngitis , and face little or no risk of developing complications ( including ARF ) . [23] In addition , a previous review estimated the prevalence of asymptomatic pharyngeal GAS carriage at 12% amongst school-aged children . [24] When throat culture alone is used to diagnose GAS pharyngitis , many patients prescribed antibiotics are likely suffering viral pharyngitis with coincidental GAS carriage . [25–27] The reference standard for determining whether true GAS pharyngitis is present requires both throat culture and serological testing to identify GAS+ve patients with elevated antibodies targeting conserved GAS antigens , streptolysin-O and deoxyribonuclease-B . [28 , 29] However , serological testing relies on obtaining patient blood samples and thus is not routinely performed in primary care . This situation has resulted in a major knowledge gap with respect to the prevalence of true GAS pharyngitis . There is an important need to ensure that clinicians target high-risk individuals with effective , evidence-based treatment strategies , particularly in an era of increasing antimicrobial resistance . Accurate prevalence estimates of serologically-confirmed GAS pharyngitis across different geographic and population settings are therefore needed to inform clinical practice and policy . Accordingly , this study aimed to use a systematic literature review to determine: ( i ) the prevalence of GAS culture-positive pharyngitis in different settings and populations; ( ii ) the prevalence of serologically-confirmed GAS pharyngitis in symptomatic GAS+ve individuals; and ( iii ) the prevalence of asymptomatic pharyngeal GAS carriage .
No patient recruitment or other involvement in this study was required and consequently ethics approval was not needed . All data analysed were anonymised . A systematic literature review was conducted and reported in accordance with PRISMA guidelines . [30] No pre-existing review protocols for were identified , but the methodology was loosely based around that of a previously published meta-analysis by Shaikh et al . [31] A total of 17 literature searches on Medline and EMBASE databases were performed to identify articles containing prevalence data on GAS+ve pharyngitis , serologically-confirmed GAS pharyngitis and asymptomatic pharyngeal GAS carriage published between 1 January 1946 and 7 April 2017 . Search terms on Medline included MeSHs: ‘Streptococcal Infections’ AND ‘Pharyngitis’ . Search terms on Embase included MeSHs: ‘Streptococcus Group A’ AND ‘Pharyngitis’ , also ‘Streptococcal pharyngitis’ AND ‘Streptococcus Group A’ . Keyword searches using the terms: ‘GAS pharyngitis’ OR ‘streptococcal pharyngitis’ AND ‘ASO’ OR ‘anti-streptolysin’ OR ‘anti-DNase-B’ were employed on both databases . Keyword searches using the terms: ‘ASOT’ OR ‘ADBT’ OR ‘ADB’ AND ‘streptococc*’ were also performed . Search findings were limited to ‘Humans’ . Further details of the search strategy are listed in the Methodology Appendix . Publications were catalogued using Endnote X7 . A researcher ( JO ) screened the search results and applied the eligibility criteria . Eligible literature , identified in title or abstract screening , was obtained for full screening . Where systematic reviews were identified , prevalence studies they referenced were searched by title on Google Scholar or Medline , if published , and searched by title using the Google search engine if not published . Non-English language papers were screened using Google translator . Explicit a priori inclusion and exclusion criteria were applied to assess article quality and reduce bias . Only studies using throat swab culture with an agar plate and incubator to detect GAS were included . Because we were interested in the point prevalence of GAS , longitudinal studies in which participants were swabbed multiple times had data from the first swab included only . When investigating the prevalence of GAS+ve pharyngitis , all studies that stated participants exhibited symptoms of pharyngitis or upper respiratory tract infection ( URTI ) were included where they presented to health practitioners who decided to obtain a throat swab . GAS pharyngitis demonstrates a wide range of clinical presentations[23 , 32] and we aimed to maximise the number of relevant studies included . For studies investigating serologically-confirmed GAS pharyngitis , the same criteria applied and prevalence data were abstracted where the authors considered their findings provided serological confirmation . Information on the method of confirmation was noted where available . For included studies investigating the prevalence of asymptomatic GAS carriage , the authors needed to state participants did not have symptoms of pharyngitis or URTI when the throat swab was obtained , otherwise were excluded . Only studies that reported the number of participants and the number ( or proportion ) that produced GAS in throat cultures were included ( and where applicable , the number of participants that were serologically-confirmed as having GAS pharyngitis ) . The country or countries recruitment was conducted in was also required to be discernible for inclusion , as was the participant recruitment method ( active/passive ) . Studies were excluded if they were not likely to be representative of the general population , notably those conducted in outbreaks and other distinct settings ( for example , from closed communities such as detention centres ) . We excluded studies that could not be translated to English . Demographic and prevalence data were abstracted and entered on a spreadsheet ( JO ) . This included the study citation , participant age group/s , country study sample was drawn from , number of cases , sample size , and recruitment period ( where available ) . A second reviewer ( EMW ) independently abstracted prevalence data . Where abstracted data differed between the two reviewers , the article was rechecked and remaining differences resolved through discussion between the study authors . Results were stratified using up to five characteristics ( Fig 1 ) : ( a ) clinical outcome measured ( GAS+ve pharyngitis , serologically-confirmed GAS pharyngitis , asymptomatic GAS carriage ) ; ( b ) participant recruitment method ( active or passive recruitment ) ; ( c ) country income level ( OECD or non-OECD member country ) ; ( d ) age group; ( e ) where serologically-confirmed prevalence studies were reported , whether unequivocal case confirmation had occurred or otherwise . ‘GAS+ve pharyngitis’ was considered to occur when an individual with symptoms of pharyngitis or URTI received a throat swab which produced GAS when cultured . ‘Serologically-confirmed GAS pharyngitis’ was considered to occur when an individual with GAS+ve pharyngitis demonstrated an antibody reaction in response to GAS infection . ‘Unequivocal’ confirmation occurred when either a 0 . 2log10 or greater rise in ASO or ADB antibody titres was observed between acute and convalescent serum samples , or a four-fold increase in ASO titre occurred . [29 , 33] ‘Asymptomatic GAS carriage’ occurred when individuals with no symptoms of pharyngitis or UTRI received a throat swab which produced GAS when cultured . ‘Passive recruitment’ was considered to occur where the study population presented to healthcare providers of their own accord and the practitioner obtained a throat swab . ‘Active recruitment’ occurred where a population had been sensitised to reporting pharyngitis or URTI symptoms to healthcare practitioners ( e . g . by being asked about pharyngitis symptoms ) and health services had been aligned to maximise accessibility to the participants ( e . g . in terms of being close-by , involving home visits and/or offering tokens of thanks for study involvement ) . These distinctions were applied to pharyngitis studies . Prevalence studies of asymptomatic pharyngeal GAS carriage require active recruitment , as participants neither require nor present for treatment . National Organisation for Economic Cooperation and Development ( OECD ) membership status was used as a means of classifying populations by socioeconomic position , as member countries tend to have high-income economies and very-high human development indexes[34] ( Fig 1 ) . An ‘all ages’ analysis was performed , which included all studies in each category with no age restrictions applied . Other analyses were restricted to certain age groups: children aged <5 years old; children 5–19 years , all children aged < 20 years ( ‘children’ ) and ‘adults’ ( generally including adults ≥20 years , but also allowing studies where this category started from ≥12 years if that was the adult category the authors used ) . If the study did not state the population age range , it was included in the ‘all ages’ analysis only . Exceptions were when the study population was described using terms such as ‘paediatric’ , in which case it was pooled in the ‘Children’ category . The overall ‘children’ category included more studies than all the child subgroups put together . In order to be included in a more specific age category , the exact age range of the participants was required to be specified , and in many studies participants were simply described using terms such as ‘pediatric’ or ‘children under 16 years-old’ . Similarly , studies which described their populations in such terms as ‘university students’ were grouped in the ‘Adult’ category . Where individual articles did not state case data , but stated the prevalence of GAS and included the number of participants tested , the prevalence percentage was multiplied by the number of participants to find the number of cases . A random-effects meta-analysis was used to produce pooled estimates for all outcome measures . Outcome measures were expressed as summary point prevalence percentages with 95% confidence intervals ( CIs ) . Serologically confirmed GAS pharyngitis prevalence was calculated in two ways: serologically-confirmed patients as a proportion of the total number of symptomatic participants who had throat swabs; and as a proportion of those with GAS+ve throat swabs only . A sub-analysis of serological GAS studies was performed , including only those studies that met the unequivocal criteria . The programme R ( version 3 . 4 . 1 ) was used throughout the analysis with the meta package . [35] To estimate what proportion of total variation across study groups was due to heterogeneity rather than chance , the I2 statistic was used . Heterogeneity in pooled study groups was considered low if I2 <30% , moderate if 30–59% , substantial if 60–75% and considerable if >75% . [36]
In total , 4 , 022 articles were identified and 1 , 076 were selected for further investigation . Exclusion criteria are listed in Fig 2 . Overall , 285 articles that reported prevalence data on GAS were included . Included articles addressed GAS culture-positive pharyngitis ( 254 studies ) , serologically-confirmed GAS pharyngitis ( 21 studies ) and asymptomatic GAS carriage ( 56 studies ) . Of studies that reported on culture-positive pharyngitis , only 22% ( 57 studies ) reported on populations that did not live in OECD countries . Nine of these 57 studies used active recruitment strategies , as did nine OECD studies . All others used passive recruitment . Three-quarters of passive recruitment studies were based in community primary care settings , often general practitioner clinics . One quarter were based in hospital Emergency Departments . Approximately 10% recruited in both hospital and primary care clinics . Considerable heterogeneity was observed within most pooled study groups with some exceptions . Pooled serological studies demonstrated moderate heterogeneity , and low heterogeneity when pooled by age group . Moderate heterogeneity was also observed in the pooled prevalence estimate for GAS carriage in children <5 years old . Further details of abstracted data in this review are presented in the S1 Appendix , with accompanying measures of heterogeneity . A detailed breakdown of prevalence estimates with numbers of included studies and participants is provided in Table 1 . The overall ‘all age’ prevalence of GAS+ve pharyngitis was 22 . 7% ( 95% CI: 21 . 2–24 . 2% ) . Children ( <20 years old ) demonstrated the highest prevalence of culture-positive GAS pharyngitis: 25 . 2% ( 23 . 1–27 . 5% ) . When restricted to children aged <5 years old , a 16 . 6% ( 12 . 6–21 . 6% ) prevalence was estimated . When restricted to children aged 5–19 years old , a prevalence of 24 . 3% ( 19 . 3–30 . 1% ) was found . A prevalence of 13 . 7% ( 11 . 1–16 . 8% ) was identified in adults . GAS+ve pharyngitis was more prevalent in OECD countries: 24 . 3% ( 22 . 6–26 . 1% ) than in non-OECD countries: 17 . 6% ( 14 . 9–20 . 7% ) . Passive recruitment , that is clinical settings where participants self-present to a healthcare provider , generally detected markedly higher prevalence estimates ( overall prevalence: 24 . 1% , 22 . 6–25 . 6% ) than active recruitment ( overall prevalence: 10 . 0% , 8 . 1–12 . 4% ) , where a population was sensitised to reporting a sore throat . This discrepancy was especially marked in 5-19-year-old OECD children ( with a pooled prevalence of 36 . 8% ( 30 . 9–43 . 1% ) in clinical settings , compared with 11 . 6% ( 8 . 3–16 . 1% ) in active sore throat management programmes ) . Similarly , for non-OECD 5-19-year-old children , the prevalence of GAS+ve pharyngitis was much higher: 37 . 4% ( 27 . 7–48 . 2% ) in clinical settings than in active sore throat management programmes: 9 . 2% ( 4 . 9–16 . 6% , Table 1 , Fig 3 ) . This review attached most weight to the pooled prevalence estimate from six reported studies that used the unequivocal criteria for detecting serologically-confirmed GAS pharyngitis . Only 12 of 21 studies investigating serologically-confirmed GAS pharyngitis provided data on the total number of symptomatic individuals swabbed to identify those that were GAS+ve , on whom serological investigation was undertaken ( details in S1 Appendix ) . Of these 12 studies , six reported using the unequivocal criteria–that being a significant titre increase in paired sera . All six were conducted in OECD populations and only one used active recruitment . The overall ‘all age’ prevalence of serologically-confirmed GAS pharyngitis was 9 . 4% ( 5 . 6–15 . 5% ) . Studies using the unequivocal criteria detected a higher pooled prevalence ( with an overall ‘all age’ prevalence of 16 . 4% , 9 . 9–26 . 0% ) . Higher prevalence estimates ( 22 . 6% , 17 . 8–28 . 2% ) were detected when active recruitment was used , however this estimate is based on a single study which included paired serology . By comparison , pooled studies which used passive recruitment with unequivocal confirmation detected an overall prevalence of 15 . 2% ( 8 . 1–26 . 7% ) . Where participants have GAS+ve pharyngitis , the proportion of serologically-confirmed patients is around 50% , and around 60% in 5-19-year-old children ( Table 2 , Fig 3 ) . Table 3 shows the prevalence of GAS carriage as well as numbers of included studies and participants . The overall prevalence of asymptomatic carriage was 7 . 0% ( 5 . 6–8 . 8% ) . When studies were pooled regardless of country income , the highest carriage was observed in children <20 years old ( 8 . 0% , 6 . 6–9 . 7% ) . A slightly lower overall prevalence was detected in non-OECD settings ( 6 . 4% , 4 . 6–8 . 9% , compared with 7 . 5% , 5 . 3–10 . 3% ) in OECD settings ( Table 3 , Fig 3 ) . Pooled prevalence of GAS+ve pharyngitis , serologically confirmed pharyngitis and asymptomatic carriage are shown graphically in Fig 3A–3B for specific age groups and country income levels . GAS+ve pharyngitis was the most prevalent manifestation of GAS . Higher levels were found in OECD countries . The overall prevalence of carriage was similar in high- and low-country income settings , however GAS carriage was twice as prevalent in children from OECD countries compared to children in non-OECD countries ( Fig 3A ) . In passive recruitment OECD studies overall , the sum of the asymptomatic carriage prevalence and the serologically confirmed GAS pharyngitis prevalence approximately equals the prevalence of culture-positive GAS pharyngitis . This relationship was also observed , albeit with less certainty , when restricted to children <20 years old ( Fig 3B ) . This relationship could not be explored in active recruitment settings as only one study in that category examined serologically confirmed GAS pharyngitis .
Due to the collateral damage of antibiotic misuse on human health and the environment , there is a pressing need to target pharyngitis testing and treatment in the most effective and efficient way possible . School-aged children with symptomatic sore throats have a relatively low chance of having serologically-confirmed GAS pharyngitis , particularly in organised sore throat management programmes . ARF prevention programmes need to be carefully designed with this knowledge in mind and targeted to groups at high risk of ARF . Ultimately , reducing ARF is likely to depend on prevention programmes that address the underlying determinants of disease risk , such as income , housing conditions and access to primary healthcare . Further research should validate the main conclusions of this systematic review , particularly through collection of GAS serological data in low- and middle-income countries . It would also be useful to have more studies that measured all three clinically important GAS throat infection outcomes in the same populations at the same time to see how these states are related to one another .
|
Treating sore throats caused by Group A Streptococcus infections ( GAS pharyngitis ) with antibiotics is important for preventing acute rheumatic fever ( ARF ) . It is impossible to distinguish patients with true GAS pharyngitis infections from GAS carriers with pharyngitis caused by viral infections when throat swab culturing alone is used to diagnose GAS pharyngitis . Carriers are not likely to benefit from antibiotic treatment , but may receive treatment unnecessarily . Reported rates of GAS pharyngitis and carriage vary considerably depending on the setting . Thus it is difficult to ascertain which groups are likely to benefit significantly from active sore throat management programmes which treat GAS pharyngitis in order to prevent ARF . We performed a meta-analysis to estimate the prevalence of GAS pharyngitis and asymptomatic carriage in different settings . Approximately 10% of all children swabbed for a sore throat in clinical settings have true GAS pharyngitis , but this increases to around 55% if the children have GAS detected in their throat using swab cultures . In active sore throat management programmes , the prevalence of GAS detection is lower than in clinical settings and if it declines towards 8% ( the asymptomatic carriage level ) , there may be little benefit in treating GAS culture-positive patients with antibiotics .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"antimicrobials",
"medicine",
"and",
"health",
"sciences",
"pathology",
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"laboratory",
"medicine",
"drugs",
"microbiology",
"database",
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"group",
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"streptococcal",
"infection",
"throat",
"bacterial",
"diseases",
"age",
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"streptococcal",
"pharyngitis",
"antibiotics",
"pharmacology",
"pharyngitis",
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"infectious",
"diseases",
"serology",
"streptococcal",
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"population",
"groupings",
"neck"
] |
2018
|
Group A Streptococcus pharyngitis and pharyngeal carriage: A meta-analysis
|
Calmodulin ( CaM ) is a ubiquitous Ca2+ buffer and second messenger that affects cellular function as diverse as cardiac excitability , synaptic plasticity , and gene transcription . In CA1 pyramidal neurons , CaM regulates two opposing Ca2+-dependent processes that underlie memory formation: long-term potentiation ( LTP ) and long-term depression ( LTD ) . Induction of LTP and LTD require activation of Ca2+-CaM-dependent enzymes: Ca2+/CaM-dependent kinase II ( CaMKII ) and calcineurin , respectively . Yet , it remains unclear as to how Ca2+ and CaM produce these two opposing effects , LTP and LTD . CaM binds 4 Ca2+ ions: two in its N-terminal lobe and two in its C-terminal lobe . Experimental studies have shown that the N- and C-terminal lobes of CaM have different binding kinetics toward Ca2+ and its downstream targets . This may suggest that each lobe of CaM differentially responds to Ca2+ signal patterns . Here , we use a novel event-driven particle-based Monte Carlo simulation and statistical point pattern analysis to explore the spatial and temporal dynamics of lobe-specific Ca2+-CaM interaction at the single molecule level . We show that the N-lobe of CaM , but not the C-lobe , exhibits a nano-scale domain of activation that is highly sensitive to the location of Ca2+ channels , and to the microscopic injection rate of Ca2+ ions . We also demonstrate that Ca2+ saturation takes place via two different pathways depending on the Ca2+ injection rate , one dominated by the N-terminal lobe , and the other one by the C-terminal lobe . Taken together , these results suggest that the two lobes of CaM function as distinct Ca2+ sensors that can differentially transduce Ca2+ influx to downstream targets . We discuss a possible role of the N-terminal lobe-specific Ca2+-CaM nano-domain in CaMKII activation required for the induction of synaptic plasticity .
Calmodulin ( CaM ) is a ubiquitous Ca2+ buffer and signaling molecule in cells . In the excitatory synapse of hippocampal CA1 pyramidal neurons , the activation of CaM dependent enzymes results in the induction of synaptic plasticity ( e . g . , long-term potentiation ( LTP ) and long-term depression ( LTD ) ) [1] . The induction of NMDA receptor dependent LTP and LTD require increased Ca2+ and subsequent activation of CaM-dependent downstream enzymes: CaM-dependent protein kinase II ( CaMKII ) and calcineurin . Injection of CA1 pyramidal cells with peptides that block CaMKII activity inhibited the induction [2] , [3] , but not maintenance [4] of LTP , while injection of the activated form of the enzyme also produced LTP-like plasticity [5] , [6] . LTD is also critically dependent on Ca2+ and it appears that the CaM-dependent phosphatase , protein phosphatase 2B ( calcineurin ) is involved in LTD induction [7] . The simplest correlative explanation for these results is that LTD is induced by intermediate levels of Ca2+ that activate CaM and subsequently calcineurin but not CaMKII . Conversely , higher levels of Ca2+ initiate CaM-dependent CaMKII activation and autophosphorylation , leading to LTP induction . However , it is still unknown how Ca2+ and CaM regulate two opposing processes as distinct as LTP or LTD in such a precise and controlled manner . Besides being a major signaling molecule , CaM also functions as a primary Ca2+ buffer in CA1 pyramidal neurons [8] . In fact , most CA1 pyramidal neurons contain CaM but not other EF-hand Ca2+ binding proteins ( e . g . , parvalbumin and calretinin ) ( reviewed in [9] ) . An exception is calbindin-D28K , which is expressed in a subpopulation of CA1 pyramidal neurons but only in rat ( [10] , [11] ) . CaM binds four Ca2+ ions , two in its N-terminal lobe and two in its C-terminal lobe [12] . The binding sites in the N-terminal lobe are lower affinity [13] but exhibit faster kinetics as opposed to the higher affinity , slower kinetics of the C-terminal lobe sites [14] , [15] . Surprisingly little is known as to how such a protein with multiple Ca2+ binding sites influences the diffusion of Ca2+ in the cell . Most pre-existing theories of Ca2+ binding and diffusion assume a fast binding of Ca2+ and single Ca2+ binding site for the buffer ( see reviews by [16] ) . In addition , recent experimental data suggest that each lobe of CaM has different affinity toward its downstream target ( CaMKII and calcineurin ) [17] , [18] , [19] . As each lobe differentially responds to Ca2+ signals and downstream targets , it is possible that these lobe specific properties play distinct biological roles in synaptic spines ( see Discussion for more details ) . This motivated us to dissect the spatial-temporal dynamics of lobe specific Ca2+-CaM interaction in detail at the single molecule level . Many elegant experimental measurements have been made of dendritic spine Ca2+ [20] , [21] , [22] , [23] , [24] . These measurements largely rely on a spatially averaged Ca2+ signal generated from fluorescence imaging of dyes whose quantum efficiency changes upon Ca2+ binding . As such , they contain no direct information relative to the issue of possible micro- or nano-domains of intracellular Ca2+ . The problem is exacerbated by the high diffusion coefficients of free and dye bound Ca2+ which additionally smears the spatial signal in time frames relevant for Ca2+-imaging experiments . These and other caveats related to dye-based Ca2+-imaging experiments were recently reviewed [25] . In addition , we do not have an effective fluorescence reporter to detect and monitor Ca2+ binding to each lobe of CaM at the single molecule level . As such , mathematical models and computer simulations are presently the only tractable means of investigating this critical aspect of synaptic physiology . Furthermore , in a medium size dendritic spine ( i . e . , sphere-shaped spine head of 500 nm diameter ) , the concentration of 1 µM of any chemical species corresponds to ∼40 molecules . The basal ( resting ) level of spine Ca2+ is 50∼100 nM which corresponds to 2∼4 molecules of Ca2+ ions . Under such a circumstance , the behavior of single molecules within synaptic spines is not well described by the concentration-based mathematical approach such as reaction diffusion equation . Here we report the single molecule level analysis of Ca2+-CaM interaction within a dendritic spine using a novel particle-based event-driven Monte Carlo algorithm , which we call Cellular Dynamics Simulator ( CDS , [26] ) . Unlike other commonly used Monte Carlo simulation ( e . g . , MCell , [27] ) , it explicitly takes account of volume exclusion and collision between diffusing molecules in order to accurately simulate chemical reactions in the cellular interior . Using this simulator and first passage time theory , we dissect the mechanisms that influence the dynamics of Ca2+-CaM interaction at the single molecule level . We use a model of CaM built upon detailed kinetic data and ask if the lobe specific spatial-temporal micro-domain of Ca2+-CaM activation can exist and if so how it is biophysically regulated in a small sub-cellular compartment like dendritic spines . We employ a statistical spatial point pattern analysis [28] to understand the spatial profile of Ca2+-CaM interactions . The combination of spatial point pattern analysis and particle based Monte Carlo simulation is a unique computational strategy used in this study . Our analysis shows a higher sensitivity of the N-terminal lobe to the location and influx rate of Ca2+ from typical receptor/channel sources . Each lobe of CaM functions as distinct Ca2+ sensors and responds differentially to Ca2+ influx both in space and in time . Coupled with the experimental knowledge that different enzymes bind preferentially to either the N- or C-lobes of Ca2+ saturated CaM , we propose a possible explanation for how two opposing Ca2+/CaM-dependent enzymes can be differentially activated .
Fig . 1A illustrates the Ca2+ binding and unbinding pathway for each lobe of CaM . As shown , Ca2+ binding to the N-terminal lobe and the first Ca2+ binding event to the C-terminal lobe are diffusion limited while the second Ca2+ binding to the C-terminal lobe is the rate-limiting step in achieving the fully Ca2+-saturated state . If this Ca2+ binding step at the C-terminal lobe is much slower than the diffusion of Ca2+ , the majority of Ca2+ ions that entered the spine head will have moved away from the channel without saturating local CaM molecules . The spatial profile of the C-terminal lobe or full Ca2+ saturation of CaM may then be less sensitive to the location of Ca2+ channels . On the other hand , if the N-terminal lobe Ca2+ saturation is fast as compared to the Ca2+ diffusion , its Ca2+ saturation may be more closely localized to the Ca2+ channels . Thus , three biophysical factors become important in understanding the spatial domain of Ca2+-CaM interactions . The first is how fast each lobe of CaM becomes Ca2+ saturated with a given concentration of Ca2+ . The second is how fast Ca2+ ions escape from the spine . The third is how steep or flat the gradient of Ca2+ ion distribution will be in the spine head with a given Ca2+ injection rate through Ca2+ channels . In this section , we analyze the first biophysical factor , which we call the ( mean ) first passage time: the ( average ) length of transition time required for each lobe of CaM molecule to reach the Ca2+ saturated state from a basal ( apo- ) state . In fact , a mathematical formula is already available to calculate this mean first passage time ( Equations 5 , 29 in [29] ) . In their single molecule biophysical analysis , Shaevitz et al . [29] used an algebraic recursive method to derive the Laplace transform of the first passage time distribution . Fig . 1B and Eq . 1∼2 explain their formalism applied to Ca2+-CaM interactions . Here we define State “0” as a Ca2+ free ( apo ) form , State “1” as one Ca2+ ion bound form , and State “2” as a two Ca2+ ion bound form of a given lobe . The symbols kXij in Fig . 1B denotes the rate constant between State i and State j ( i , j = 0 , 1 , 2 ) of lobe X ( = N or C ) . Thus , each lobe has three states and the whole CaM molecule has nine states ( Fig . 1C ) . The resultant Laplace transform of the distribution of first passage time is: ( 1 ) where [Ca] is the given concentration of Ca2+ ( Note , in order to apply Eq . 29 in [29] , we needed to multiply the association rate constant by the concentration of Ca2+ ) . Here we assume the system is well-stirred and the concentration of Ca2+ is constant ( time-invariant ) . Then , the mean first passage time ( <t> ) can easily be found through differentiation ( see Eq . 5 in [29] ) : ( 2 ) Note that the dissociation rate ( ) of the second Ca2+ is not included in the formula . The latter rate determines the lifetime of fully Ca2+ saturated state of each lobe but it does not influence the first passage time . Therefore , three kinetic rates ( , , ) and Ca2+ concentration determine the lobe specific first passage time . Note that both lobes have similar association rates for the first Ca2+ ions ( ) ( Fig . 1A ) . The difference in the second Ca2+ binding rates ( ) is large as compared to the dissociation of the first Ca2+ ion ( ) ( Fig . 1A ) . Thus , in Eq . 2 , the second Ca2+ binding rates ( , ) determine the difference of the first passage time between the N- and C-lobes . Fig . 2A is a numerical display of this formula showing that the first passage time sharply increases as we decrease the Ca2+ concentration ( the unit of time , y-axis , is in seconds ) . As predicted , the mean first passage time for the C-terminal lobe ( magenta ) is much longer than the N-lobe ( blue ) . For comparison , we show the first passage time for full Ca2+ saturation of CaM; the mean first passage time to reach the state N2C2 in Fig . 1C . As one can see from the diagram in Fig . 1C , this first passage time depends on all Ca2+ association and dissociation pathways for both lobes and is influenced by the lifetime of the Ca2+ saturated states of each lobe . The corresponding mathematical formula will be much more complicated than Eq . 1 and 2 and therefore , we calculated this quantity numerically using an extended version of the Gillespie type stochastic algorithm ( see [8] , [30] for more details ) . The results presented in Fig . 2A suggest that the N-terminal lobe may respond to a short Ca2+ transient but the C-terminal lobe may not if the transient is shorter than the first passage time of C-lobe Ca2+ saturation . For example , NMDA receptor type Ca2+ transients ( ∼1 µM peak with duration of ∼80–200 ms ) may not result in significant CaM saturation in the spine . In fact , at a ∼1 µM Ca2+ concentration , the mean first passage time for the C-terminal lobe ( or full Ca2+ saturation of CaM ) is much longer than the duration of the Ca2+ transient ( Fig . 2A upper right inset ) . Such straightforward interpretation of the first passage time analysis , however , could be misleading . Note that we have only discussed the mean but not the entire distribution ( or standard deviation ) of the first passage time . In addition , we ignored the fact that the number of Ca2+ ions may be limited in the dendritic spines and that their concentration is not constant as postulated in Eq . 1∼2: the N-terminal lobe and the C-terminal lobes on the same or different CaM molecules will compete for the limited number of Ca2+ ions . As for the stochastic fluctuation , we can derive the standard deviation of the first passage time using the same analytic method described above: ( 3 ) The resultant standard deviation is very close to the mean first passage time for all Ca2+ concentrations ( i . e . , the coefficient of variation is >0 . 9 for all [Ca2+]<10 µM ) . The second term in the right-hand side of Eq . 3 ( ) is small because the first Ca2+ binding rate ( ) for both lobes are high and therefore the ratio of the right-hand sides of Eq . 3 and Eq . 2 approaches 1 . Fig . 2B and C show the histograms of the first passage time distribution for the N-terminal lobe ( blue ) and the C-terminal lobe ( magenta ) Ca2+ saturation , respectively , taken from a single stochastic simulation ( the same bin size , 5ms , for both lobes and the total number of CaM molecules is 400 ) . Fig . 2B clearly shows that the Ca2+ saturation of the C-terminal lobe is possible even if the mean first passage time is shorter than that of Ca2+ transient . However , the inset of Fig . 2B and 2C , i . e . , the histogram up to 80 ms , predict that the N-terminal lobe Ca2+ saturation predominates and precedes that of the C-terminal lobe during the short Ca2+ transient . Knowing that two lobes of CaM compete for the limited amount of available Ca2+ ions in the dendritic spines , we predict that the N-terminal dominance for the short Ca2+ transient is more prominent in neurons . This type of analysis , however , is further complicated when taking into account the non-homogeneous spatial distribution of molecules . When Ca2+ ions enter the spine head through a Ca2+ channel , a steep spatial gradient of Ca2+may be formed around the channel mouth ( depending on the Ca2+ injection rate ) . At a single molecule level , it is the transient local ( microscopic ) “concentration” of Ca2+ ( i . e . , the number of Ca2+ collision events ) felt by a CaM molecule that determines the probability of Ca2+ saturation of a given lobe of each CaM molecule . A CaM molecule can experience much higher ( local ) Ca2+ “concentration” than indicated by the bulk Ca2+ transient depending on its location with respect to the Ca2+ source . The present work aims to describe a detailed analysis of this spatial stochastic phenomenon . However , before going into the detailed simulations , it is necessary to dissect each of the biophysical factors that we discussed at the beginning of this section . The last two of these factors determine the space- and time- dependent Ca2+ profile in the spines . Without such a systematic dissection , the interpretation of simulation results when trying to determine the spatial/temporal profile of CaM activation would not be possible . We next explored how fast Ca2+ ions escape from the spine . The second factor that will determine the spatial profile of CaM activation is the escape rate of Ca2+ from the spine . Ca2+ ions that enter the spine through ion channels will eventually diffuse into the dendrites or be extruded by the Ca2+ pumps [23] . Here we focus on the impact of spine geometry and Ca2+ pumps on the escape rate of Ca2+ from the spines . We carry out this analysis in a stepwise manner . We first analyze the escape of Ca2+ via pure diffusion without Ca2+ pumps ( or buffers ) and establish the impact of spine morphology on the Ca2+ escape rate ( Fig . 3A and B ) . Then we add Ca2+ pumps to examine their impact ( Fig . 3C ) . This way we can isolate and understand the contribution of each of these factors in the regulation of the Ca2+ escape rate . In neurons , Ca2+ buffers such as CaM also influence this escape rate but in a highly complicated manner . We will study the effect of Ca2+ binding proteins ( CaM ) in the later sections when we combine all known biophysical factors in the detailed simulations . Fig . 3A shows the time courses of Ca2+ decay for three different spine neck geometries . Here , we randomly placed a fixed number of Ca2+ ions ( = 400 that corresponds to ∼10 µM ) in the head of a spherical spine and let them diffuse out of the spine to the dendrite . The diffusion coefficient ( ) of Ca2+ was set to 200∼225 µm2/s ( nm2/µs ) [31] . Each curve in Fig . 3A represents the average of 100 simulation runs . Clearly , the longer and the narrower the neck , the slower the Ca2+ decay process . This is a so-called narrow escape problem and has been extensively investigated [32] , [33] . As predicted by these theoretical studies , the simulated Ca2+ decay transient is well approximated by a single exponential decay term . These decay time constants fit well ( the relative error <5% ) with one of the pre-existing mathematical formula ( the left-hand side of Eq . 4 below ) : ( 4 ) where , , and are the volume of the spine head , the length and the radius of spine neck , and the volume of neck , respectively [32] . Fig . 3B summaries our simulation results for different spine geometries . We plot the narrow escape time ( ) against the ratio of spine head and neck volume ( ) ( x-axis ) . As shown all data points are aligned on straight lines , indicating that the narrow escape time is a linear function of the volume ratio ( ) ( see the right-hand side of Eq . 4 ) . Note that Eq . 4 was previously tested against experimental data of molecular diffusion ( using photo-bleaching recovery of fluorescein-dextran and enhanced green fluorescent protein ) across spine-dendrite junctions in CA1 neurons [21] , [24] . In other words , Eq . 4 is consistent with escape of diffusing molecules from real spines on CA1 neurons . Additional simulations confirm that Eq . 4 fits well with real spines when morphologies from 3D EM reconstructions are used ( http://synapses . clm . utexas . edu/ ) ( data not shown ) . Another biophysical factor that regulates the Ca2+ decay from spines is Ca2+ pumps [20] , [23] . The main Ca2+ extrusion mechanisms in CA1 spines are Na+/Ca2+ exchangers ( NCX , NCKX ) and plasma membrane Ca2+ ATPase ( PMCA ) [20] , [34] . We have modeled both of them using the kinetic scheme used in [35] ( see Methods for more details ) . Fig . 3C shows a Ca2+ clearance process with standard spine morphology ( 500 nm spine head diameter , 500 nm spine neck length and 150 nm spine neck diameter ) with ( dashed black line ) and without pumps ( solid black line ) . The fast decay time constant of Ca2+ in the presence of pump is ∼45% of the narrow escape time without pumps ( ∼5–6 ms ) . In this analysis , we have included NCX/NCKX and PMCA at the concentration close to the highest level known in the literature to examine the maximal impact that Ca2+ pumps would have on Ca2+ clearance . The Ca2+ transients with reduced number of pumps lie between the dashed and solid lines ( data not shown ) . Overall , the analyses in Fig . 3 show that the narrow escape time of Ca2+ without buffers in a standard spine in the presence of pumps is ∼5 ms or shorter . In the subsequent section , we will show that a major Ca2+ buffer in CA1 pyramidal neurons ( i . e . , CaM ) slows down the Ca2+ decay to ∼10∼20 ms ( the latter is close to that observed in the Ca2+ imaging analyses [20] . It is this brief time window that each lobe of CaM becomes Ca2+ saturated or not during each Ca2+ spike . The first passage time becomes a critical factor to understand the spatial profile of Ca2+-CaM interactions . Having established the impact of spine geometry on the Ca2+ extrusion process , we now analyze the third biophysical factor that influences the spatial gradient of spine Ca2+: the Ca2+ injection rate of channels . Since the kinetics of the voltage-gated Ca2+ channels and NMDA receptors are highly complicated , we used a “model stochastic Ca2+ channel” in this section . A single stochastic Ca2+ channel was placed on the top of the head of a standard spine ( black circle in Fig . 4A; see Fig . 3C for the standard morphology of CA1 dendritic spine ) . This channel injects Ca2+ at a given ( average ) rate and we examine the relation between the Ca2+ injection rate and the spatio-temporal profile of Ca2+ transients in the spine . To realize the impact of Ca2+ injection rate in isolation on the spatiotemporal Ca2+ profile , there are no pumps or Ca2+ binding buffers in this model spine . Once injected , Ca2+ ions travel via simple diffusion until they are absorbed from the compartment at the spine-dendrite boundary ( see the vertical arrow in Fig . 4A ) . We varied the rate , but the total number of injected Ca2+ ions was set to 700 so that the peak Ca2+ concentration would be in a physiological range ( ∼6–16 µM , i . e . , ∼250–650 Ca2+ ions; see panel C of Fig . 4 ) . Note these numbers are taken from the lowest estimated Ca2+ injection rate of NMDA receptors and the higher Ca2+ injection rates of voltage gated Ca2+ channels ( [36] , [37] , [38] ) . Fig . 4 Panel A and B show the location of Ca2+ ions ( not to scale ) at designated time points after the start of Ca2+ injection . The mean Ca2+ injection rates in Panel A and B are 1 . 4 and 0 . 07 Ca2+ ions per microsecond , respectively . At the higher injection rate ( 1 . 4 ions/µs ) , there is a build-up of Ca2+ ions near the channel ( Panel A ) while such a build up is not evident in Panel B . Note that the time points chosen for Panel A and B are 20-fold different so that the total number of Ca2+ injected by the indicated time points in Panel A ( 10 , 20 , 100 , 200 µs ) and B ( 200 , 400 , 2000 , 4000 µs ) are identical . Ca2+ ions can travel ∼140 nm from the channel via diffusion before the next Ca2+ ion exits the channel at injection rate of 0 . 07 ions/µs . At a higher Ca2+ injection rate , Ca2+ ions will accumulate near the channel pore before they diffuse away ( red in Fig . 4C ) . As anticipated , the lower Ca2+ injection rate ( black ) leads to a much lower peak Ca2+ number ( concentration ) than the higher Ca2+ injection rates . Ca2+ ion can travel more than 1 µm away from the channel during 1 ms . During a 10 ms Ca2+ injection period , a significant fraction of Ca2+ ions has already left the spine . Thus , we have lower Ca2+ peak than at the higher Ca2+ injection rate . After the peak , the Ca2+ level decreases with a time constant of ∼7–8 ms for all Ca2+ injection rates . This decay process is controlled by the diffusion and is consistent with the narrow escape rate we calculated in Fig . 3 . Fig . 4 clearly shows the impact of Ca2+ injection rates on the spatial and temporal dynamics of Ca2+ transients in dendritic spines . The relative lack of a Ca2+ gradient in Fig . 4B and the long first passage time of the C-terminal lobe of CaM in Fig . 2 suggest that a spatial gradient of the Ca2+-saturated C-terminal lobe may not form . However , as mentioned at the beginning of this section , we need to include CaM and examine the combined effect of all of these biophysical factors on the spatial profile of Ca2+-CaM interactions . The second half of Results provides this analysis . In the previous sections , we studied the impact of three biophysical factors: the first passage time ( Fig . 2 ) , the narrow escape time ( Fig . 3 ) , and the impact of Ca2+ injection rate on the Ca2+ micro-domain ( Fig . 4 ) . In this section , we wish to study the combined effects of these factors on the spatial-temporal pattern of Ca2+-CaM interaction . As a first step , we placed a single “model Ca2+ channel” as in Fig . 4 but add CaM to assess the impact of Ca2+ injection rates on the Ca2+-CaM interaction . Besides the “artificial” model channel , we included CaM and Ca2+ pumps . We distributed 1600 molecules of CaM ( i . e . , 40 µM ) uniformly within the spine volume ( the estimated concentration of CaM in CA1 dendritic spines is 10∼100 µM , [8] ) . Before injecting Ca2+ ions the entire system is equilibrated at basal Ca2+ conditions , i . e . , ∼40–46 Ca2+ bound CaM molecules with ∼2 free Ca2+ ions ( the latter correspond to 50 nM of basal free Ca2+ concentration ) . At this basal condition , majority of CaM molecules are Ca2+ free or in a single Ca2+ bound form and none of their lobes are Ca2+ saturated . The diffusion coefficient of CaM varies between 2∼20 µm2/s ( nm2/µs ) [30] , [39] . In this section , we set it to 20 µm2/s ( nm2/µs ) ( but see our comments below ) . The results in Fig . 5 show the dynamics of Ca2+/CaM with a channel of high Ca2+ injection rate ( 1 . 4 Ca2+ ions/µs and a total of 700 Ca2+ ions are injected as in Fig . 4 ) . Fig . 5A shows the number of Ca2+ saturated N- and C-lobes ( blue and magenta , respectively ) and fully Ca2+-saturated CaM . The number of free Ca2+ ions in the spine is shown in Fig . 5B . Both Fig . 5A and 5B are taken from the same single simulation run . The result of stochastic simulation varies from one simulation run to the other; however , the overall qualitative dynamics in Fig . 5A and 5B are similar among different simulation runs . The N-terminal lobe of CaM binds Ca2+ much faster than the C-terminal lobe ( Fig . 2 ) . As a consequence , the number of Ca2+ saturated N-terminal lobes increases rapidly as Ca2+ is injected ( blue line in Fig . 5A ) . After the termination of Ca2+ injection ( at 500 µs ) , the N-terminal lobes quickly release Ca2+ and the C-terminal lobes slowly bind the available Ca2+ ( Fig . 5A ) . Once bound , Ca2+ remains associated with the C-lobe for a relatively long time ( the decay time constant is ∼120 ms ) and the C-lobes therefore trap Ca2+ in the spine ( Fig . 5A ) . The free Ca2+ level eventually returns to the basal level after a few hundred ms ( data not shown ) . Another important point to note is that even at this high Ca2+ injection rate , the total number of fully Ca2+-saturated CaM molecule is less than ∼7 . This number varies from simulation to simulation , but with a single Ca2+ channel , the number remains below 10 ( over 100 simulation runs ) , a remarkably low number . Panels C , E , and G of Fig . 5 show the spatial dynamics of each lobe of CaM taken from 15 simulation runs . During the early rising phase of their Ca2+ saturation , each lobe of CaM exhibits a nano-domain near the channel pore . For example , in Fig . 5E , we record the location ( red circle ) of each CaM molecule when its N-lobe becomes first Ca2+ saturated . We plot these accumulated locations of “first Ca2+ saturation event” up to the different designated time point in the figure ( note each lobe may undergo multiple cycles of Ca2+ saturation , but only the first one is recorded in Panel C , E , and G in Fig . 5 and in subsequent figures ) . The formation of a Ca2+/CaM nano-domain is clear . A similar but less obvious nano-domain is observed for the C-terminal lobe ( Panel C ) and for the fully Ca2+-saturated CaM . To further confirm these observations , we performed spatial point pattern analysis ( see Methods and [28] , [40] , [41] ) . In this statistical analysis , we counted the number of the Ca2+ saturation events ( e . g . , as shown in Fig . 5E for the N-terminal lobe ) and then randomly distributed the same number of points within the spine volume . We calculated a so-called ( Besag's ) L-function ( see Methods for details ) for this random point pattern . We repeated this process 1000 times and calculated the mean and the maximum and minimum envelope of the L-function ( the black dotted lines in Fig . 5F ) for the set of 1000 randomly generated spatial patterns . We then calculated the L-function for the original data point pattern of Ca2+ saturation and compared this ( the red line in Fig . 5F ) with that of complete spatial randomness ( the black lines in Fig . 5F ) . The L-function of data ( red ) is outside of the maximum and minimum envelopes ( black dotted lines ) indicating that the given point pattern is not random . In this case , L-function is larger than the maximum envelope and it is typical of spatial clustering . We performed a similar analysis for the C-terminal lobe ( Fig . 5D ) and fully Ca2+ saturated CaM ( Fig . 5H ) and obtained the same conclusion ( non-randomness ) . For all cases , we also performed ( two-sample ) Kolmogorov-Smirnov ( goodness-of-fit hypothesis ) test ( significance level = 0 . 05 ) [28] to verify the conclusion of envelope test . In summary , the high Ca2+ injection rate results in a transient Ca2+-CaM nano-domain ( for both lobes of CaM ) . The N-terminal lobe responds to and senses the Ca2+ gradient much faster than the C-lobe ( blue Fig . 5A ) . The C-lobe's response is resistant to the Ca2+ gradient because of its longer first passage time ( i . e . , slow binding kinetics of Ca2+ ) . Note we recorded and analyzed only the first Ca2+ saturation events for each lobe of each CaM molecules . The relatively widespread C-terminal lobe Ca2+ saturation in Panel C , therefore , is not because the high affinity C-terminal lobe carries Ca2+ ions while diffusing away from the channel . What if we reduce the Ca2+ injection rate ? Fig . 4 indicates that the spatial gradient of Ca2+ is less prominent with a reduced Ca2+ injection rate . One possible scenario is that , under such a condition , only N-terminal lobe with higher Ca2+ binding kinetics ( Fig . 2 ) can detect and sense the spatial gradient . The Ca2+ saturation of C-terminal lobe and/or full Ca2+ saturation of CaM may show relatively homogeneous spatial patterns under this condition . Fig . 6 shows results to test this prediction . The simulation conditions are the same as in Fig . 5 except the Ca2+ injection rate is reduced to 0 . 07 Ca2+ per microsecond . This is close to the lowest Ca2+ injection rate observed for a single NMDA receptor Ca2+ current [36] , [37] , [38] . Panel A and B in Fig . 6 show the population dynamics of Ca2+ saturated N- and C-terminal lobe , fully Ca2+ saturated CaM ( A ) , and free Ca2+ ions ( B ) . The difference in the rising phase of Ca2+ saturated N- and C- terminal lobes observed in Fig . 5A becomes less obvious at these lower rates of Ca2+ influx . The Ca2+ saturated N- and C-terminal lobes increase at a similar rate but the N-terminal lobe exhibits a larger fluctuation due to its fast Ca2+ dissociation rate . Again , the number of fully Ca2+ saturated molecules is small ( less than 5∼10 ) over the course of a 25 ms simulation experiment . In addition , the location of Ca2+ saturation for each lobe becomes less localized around the channel ( Fig . 6C and 6E ) . It still looks like the N-lobe exhibits a nano-domain but it is unclear by a simple inspection of the data as to whether a nano-domain exists for the C-terminal lobe . Up to the time points 2 ms and 4 ms , the Ca2+ saturation of the C-terminal lobe takes place throughout the entire spine head . The distribution of these points appears to be random . To confirm whether this pattern is random or not , we carried out the same statistical analysis as that used in Fig . 5 ( panel D , F , H ) . Clearly , the data point patterns in Panel D and F ( red line ) are closer to the maximum envelope ( black dotted line ) of complete spatial randomness but the N-terminal lobe data pattern shows a deviation from the complete spatial randomness . This result was again confirmed by Kolmogorov-Smirnov test . The spatial pattern of the C-terminal lobe and full Ca2+-CaM saturation lie within the maximum/minimum envelope and did not suggest significant deviations from the spatial randomness . In conclusion , the N-terminal lobe exhibits a transient Ca2+-activated nano-domain at both lower and higher Ca2+ injection rates . This indicates that the kinetic property of the N-terminal lobe ( Fig . 1 and 2 ) is the major determinant of the spatial pattern formation by the N-terminal lobe . In fact , we repeated simulations used to produce Figs . 5 and 6 with different spine morphologies ( with shorter and longer spine neck as shown in Fig . 3A ) and obtained similar results as to the N-terminal lobe specific nano-domain ( Fig . S1 and Fig . S2 ) . We also set the diffusion coefficient of CaM to 2 µm2/s ( nm2/µs ) and repeated simulations in Fig . 5 and 6 ( Fig . S1 and Fig . S2 ) . As long as CaM molecules are randomly distributed within the spine volume ( at time 0 ) , neither the diffusion coefficient nor the concentration of CaM ( even when reduced to 10 µM ) affected the high sensitivity of the N-terminal lobe to the Ca2+ influx . It appears that the Ca2+ binding kinetics of CaM ( first passage time ) is the major determinant of the lobe specific spatial pattern formation during Ca2+ influx . In addition , the spatial pattern of fully Ca2+ saturated CaM was also influenced by the Ca2+ injection rate ( Fig . 5A , 6A , 5H , and 6H ) . Recall that Ca2+ dissociation from the C-terminal lobe is slower than from the N-terminal lobe ( Fig . 1A ) . The C-terminal lobe remains fully Ca2+ saturated for extended time ( >100 ms ) during which CaM ( or any Brownian particle of the same diffusion coefficient ) can travel a distance equal to or larger than the entire spine head volume . CaM can reach its fully Ca2+ saturated state when additional Ca2+ binds to the N-terminal lobe ( note again , the first Ca2+ saturation event of the C-terminal lobe is less sensitive to the location of the Ca2+ source as compared to the N-terminal lobe ) . Alternatively , if Ca2+ injection rate is high and the transient Ca2+ concentration is adequate , CaM can reach the fully Ca2+ saturated state via N-terminal lobe Ca2+ saturation before Ca2+ saturates the C-terminal lobe because the first passage time for the N-terminal lobe is shorter than the C-terminal lobe ( Fig . 2 ) . The latter pathway may be responsible for the nano-domain of fully Ca2+ saturated CaM observed in Fig . 5G and Fig . 5H . If these two modes of Ca2+ saturation exist , they would have different physiological impacts of CaM signaling system as the two lobes of CaM have distinctive binding affinities for different targets . A detailed inspection of Fig . 5 and Fig . 6 simulation results in the next section reveals and confirms these two Ca2+ saturation pathways of CaM and their dependence on the Ca2+ injection rates . Fig . 7 presents results from studies on the Ca2+ saturation pathway of CaM at the single molecule level . In Fig . 7A and 7B , we randomly selected a CaM molecule from the simulation presented in Fig . 5 , and analyzed its spatial location and Ca2+ binding state . We plot the trajectory of this molecule in the spine with different colors representing the different Ca2+ occupied states . The red is for the fully Ca2+ saturated state ( State N2C2 in Fig . 7A or Fig . 7E ) , magenta for State N1C2 and N2C1 ( three Ca2+ bound state ) , yellow for State N1C1 , N0C2 and N2C0 ( two Ca2+ bound state ) , green for State N0C1 and N1C0 ( one Ca2+ bound state ) , and blue for State N0C0 ( apo CaM ) ( see Fig . 7A and Fig . 7E for the notation ) . Note the direct state change between the states of the same color will never occur ( see Fig . 7E ) . The choice of color for different states seems complicated but by using this strategy , we can explicitly show the state changes of a CaM molecule with a minimum number of colors . The CaM molecule we selected for Fig . 7A and B was located relatively close to the channel at time 0 ( in blue , but not clearly visible behind other colors in Fig . 7B ) . It went through N0C1 ( green ) and N1C1 ( yellow ) states , reached the N-terminal Ca2+ saturated state ( N2C1 , magenta ) , and then fully Ca2+ saturated ( N2C2 , red ) near the channel ( use Fig . 7A and 7E to follow these state changes ) . In other words , this CaM molecule follows the sequence of N-terminal lobe Ca2+ saturation before becoming fully Ca2+ saturated ( indicated by the arrow in Fig . 7A ) . There is no C-terminal lobe Ca2+ saturation before the N-terminal lobe . After becoming fully Ca2+ saturated , the molecule started to move away from the channel but its C-terminal lobe remained Ca2+ saturated and stays in the N2C2 ( red ) , N1C2 ( magenta ) , and N0C2 ( yellow ) states as it explores the space close to the channel ( Fig . 7B ) . Fig . 7C and 7D show the single molecule analysis for the low injection rate ( 0 . 07 Ca2+ ions/µs ) . We randomly selected a CaM molecule from the simulation presented in Fig . 6 and kept track of its state change ( Fig . 7C ) and spatial location ( Fig . 7D ) . This CaM molecule was located in the middle of the spine head at the beginning of the simulation and explored a large area in the spine head in N0C0 ( blue ) state before reaching the N0C1 ( green ) state . It briefly went into the N1C1 ( yellow ) state and returned to the N0C1 ( green ) state and then it reached the N0C2 ( yellow ) state , the Ca2+ saturated state of the C-terminal lobe ( indicated by the arrow in Fig . 7C; also follow these state changes in Fig . 7E ) . After the C-terminal lobe saturation , it undergoes a rapid Ca2+ binding to the N-terminal lobe ( at time ∼6 . 5 ms ) via states N1C2 ( magenta ) to reach the fully Ca2+ saturated state ( N2C2 , red ) ( Fig . 7C ) . After Ca2+ is released from the fully Ca2+ saturated C-terminal lobe , this CaM molecule undergoes multiple state changes between N0C0 ( blue ) , N0C1 ( green ) , and N1C1 ( yellow ) states ( see Fig . 7C and E ) . These analyses ( Fig . 7A and 7C ) revealed two distinctive Ca2+ saturation pathways: N-terminal first pathway and C-terminal first pathway ( see Fig . 7E ) . Fig . 7F and 7G present results that address the generality of the single examples shown in 7A and 7C . In these figures , we use the data from Fig . 5/6 and plot the number of CaM molecules that have reached the Ca2+ saturated state ( for the first time ) up to each time point ( cumulative sum ) . We plot the number of CaM molecules who have reached saturation via N-terminal lobe saturation first ( blue ) and via C-terminal lobe first ( magenta ) . At the lower Ca2+ injection rate , the C-terminal lobe first is the dominant pathway ( Fig . 7F ) . At the higher Ca2+ injection rate , the probability of CaM reaching the fully Ca2+ saturated state via the N-terminal lobe first pathway is significantly increased , especially during the first 5 ms ( Fig . 7G ) . Note it is this first ∼5 ms time period that the number of Ca2+ saturated N-terminal lobes exceed that of the Ca2+ saturated C-terminal lobe ( Fig . 5A ) . Overall , the C-terminal lobe first pathway exists for both low and high Ca2+ injection rates . The Ca2+ saturation of CaM via the N-terminal lobe dominant pathway only becomes prominent at higher Ca2+ injection rates . So far we have analyzed the lobe-specific Ca2+-CaM spatial domains using a “model” channel . The purpose of this arrangement was to systematically analyze the impact of Ca2+ injection rates that may underlie possible lobe-specific Ca2+-CaM nano-domains . We now explore the same issue under a more realistic situation . Instead of a single “model” Ca2+ channel , we place multiple NMDA receptors on the spine head and analyze the impact of their spatial distribution on the lobe-specific Ca2+-CaM nano-domain . As stated earlier , NMDA receptors are the major Ca2+ source in CA1 spines [25] . The estimated number of NMDA receptors lie between 5∼20 [42] , [43] . The number and distribution of NMDA receptor may vary from one spine to the other . To gauge the impact of the spatial localization of NMDA receptors , we decided to create two extreme cases . In Fig . 8 , we placed 20 NMDA receptors in a 200 nm diameter area of the spine membrane to mimic NMDA receptors embedded in the post-synaptic density . In Fig . 9 , we uniformly distributed the same number of NMDA receptors over the entire spine head . In both cases , we populated the spine volume with the same number of CaM molecules and Ca2+ pumps as in Fig . 5 and 6 ( see Methods for more details of simulation ) . In panel A and B of Fig . 8 and 9 , we show the Ca2+ binding kinetics and free Ca2+ transients of single simulation runs of each case . The stochastic fluctuation ( opening and closing ) of NMDA receptors dictates the Ca2+ transient as predicted by previous work [43] . Interestingly , we could not find any significant differences between the two different distribution patterns of NMDA receptors ( Fig . 8 and Fig . 9 ) in terms of overall Ca2+ ( or Ca2+ binding to CaM ) transients . To show the spatial patterns of Ca2+ saturation , we compiled the results of 20 simulation runs ( of 20∼25 ms , for each NMDA receptor distribution pattern ) and plot the locations of the Ca2+ saturated N- and C-lobe and fully Ca2+ saturated CaM as before ( Fig . 8 C∼H and Fig . 9 C∼H ) . For both distribution patterns of NMDA receptors , the N-terminal lobe Ca2+ saturation exhibits deviations from spatial randomness ( Fig . 8F and Fig . 9F ) . In the case of NMDA receptor clusters ( Fig . 8 ) , a transient nano-domain of Ca2+ saturated N-terminal lobe is formed close to the receptor cluster and visible in the 2D projection of the data . In contrast , there is no detectable focus of clustering of Ca2+ saturated N-terminal lobe for homogenous NMDA receptor distributions ( compare Fig . 8E and 9E at 4 ms ) . However , our methodology ( Ripley's K-function/Besag's L-function ) still detected a slight deviation from complete spatial randomness ( Fig . 9F ) . This may suggest that the N-terminal lobe is still sensitive to the location of NMDA receptors but their spatial pattern of Ca2+ saturation was not clearly visible in the 2D projection of the data . The C-terminal lobe exhibits a minor and weak deviation from the spatial randomness for both cases . Overall , the N-terminal lobe shows a nano-domain regardless of the spatial distribution pattern of NMDA receptors .
We have analyzed the lobe specific spatial and temporal pattern of Ca2+-CaM interactions at the single molecule level in synaptic spine compartments . Ca2+ metabolism in neuronal spines is a dauntingly complicated process that involves nonlinear interactions between channels , pumps , CaM , and other potential Ca2+ binding proteins . We focused on three primary biophysical factors , Ca2+ binding kinetics of CaM , Ca2+ clearance from the spine compartment , and Ca2+ injection rate , and dissected the spatial pattern of Ca2+-CaM interactions in a stepwise manner . Our results indicate that the N-terminal lobe and the C-terminal lobe of CaM have different functions in decoding Ca2+ signals in space and time . The N-terminal lobe is more sensitive to the Ca2+ transients while the C-terminal lobe is relatively resistant to the spatial gradient of Ca2+ . Our systematic dissection ( Fig . 2∼9 ) strongly indicated that the Ca2+ binding kinetics to each lobe of CaM is the key regulatory mechanism of the spatial pattern of the Ca2+-CaM system . Our simulation study also identified two Ca2+ saturation pathways and their Ca2+ injection-rate dependencies: the C-terminal lobe first vs . the N-terminal lobe first pathways . The simulation results showed that the former is especially prominent with the low Ca2+ injection rate . What are the implications of the lobe specific functionalities of CaM , especially for the CaM-and NMDA receptor-dependent synaptic plasticity that involves CaMKII and calcineurin ? In order to understand this issue , one must pay close attention to the details of Ca2+-CaM-target interactions . Each lobe of CaM ( as well as the entire CaM molecule ) undergoes a series of conformational changes upon Ca2+ and/or target binding . In fact , the Ca2+ binding and target association are thermodynamically coupled ( see [8] ) . Target binding increases or decreases the affinity of Ca2+ of CaM while Ca2+ binding in turn changes the binding kinetics of CaM towards its targets ( see below for more discussion ) . The changes in the Ca2+ binding kinetics upon target binding ( i . e . , due to the different conformational states of CaM ) is a critical factor that may affect the spatial profile of Ca2+-CaM-target activation . Another important issue to consider is that a fraction of CaM molecules may already exist in a complex with its target even at basal Ca2+ concentrations . Interestingly , recent experimental and modeling work suggested that the N-terminal lobe of CaM preferentially interacts with CaMKII before the C-terminal lobe [19] , [44] . In fact , these kinetic studies suggest that CaM remained bound to CaMKII for extended periods at basal Ca2+ concentrations via the N-terminal lobe . This mode of CaM-CaMKII interaction is different from the so-called CaM-trapping by auto-phosphorylated CaMKII ( see [19] for full discussion of this issue ) . Once bound to CaMKII via the N-terminal lobe , the C-terminal lobe of the same CaM molecule interacts with CaMKII . When bound to CaMKII , the Ca2+ binding kinetics of the C-terminal lobe are accelerated by the law of detailed balance [19] . As shown in Fig . S3 , CaMKII bound C- and N-terminal lobes both have faster Ca2+ binding kinetics ( Panel A ) and shorter first passage time for Ca2+ saturation ( Panel B ) . The present work ( Fig . 2 , 5∼9 ) predicts that CaMKII-bound CaM may exhibit a nano-domain as observed in the target-free N-terminal lobe as long as the distribution of CaMKII is homogeneous within the spines . The latter assumption ( homogenous distribution of CaMKII ) may not be the case . However , recent experimental results indicated the presence of a nano-domain of CaMKII activation in CA1 spines [45] . Since CaMKII plays a key role in LTP ( long-term potentiation ) induction , further investigation of this CaMKII nano-domain is critical . What if the C-terminal lobe preferentially interacts with calcineurin which underlies LTD ( long-term depression ) induction ? Then , each of the two lobes of CaM differentially regulates these two opposing processes of synaptic plasticity . This may seem like an attractive hypothesis and in fact , our preliminary modeling study indicated that the C-terminal lobe of CaM has a higher affinity toward calcineurin than the N-terminal lobe . However , the affinity of calcineurin for CaM is extremely high [17] and as a consequence , most of the calcineurin molecules may already be bound to Ca2+-CaM even at the basal free Ca2+ concentrations in CA1 spines . On the other hand , for full activation , additional Ca2+ must bind the regulatory subunit ( subunit B , CnB ) of calcineurin [17] . If the Ca2+ binding kinetics of CnB is similar to that of the C-terminal lobe of CaM , one would expect a spatial and temporal pattern of calcineurin activation to be similar to the C-lobe specific Ca2+-CaM activation domain . Detailed experimental characterization of the Ca2+ binding kinetics of the “CaM-like” subunit of calcineurin ( CnB ) is necessary . In CA1 pyramidal neurons , another critical factor , RC3 ( neurogranin , Ng ) , regulates the induction of NMDA-receptor and CaM-dependent synaptic plasticity . RC3 is highly enriched in CA1 spines and is known to regulate the transition between the induction of LTP vs . LTD [46] , [47] . The biochemical analysis of RC3-CaM interactions suggested that it may have an additional impact on the spatial nano-domain of Ca2+-CaM . RC3 binds CaM ( even in the absence of Ca2+ ) and accelerates the Ca2+ dissociation from the C-terminal lobe thereby decreasing its affinity toward Ca2+ [30] , [48] . The thermodynamic reciprocal interaction between Ca2+ binding and target interaction that we mentioned earlier may play an important role in determining the spatial dynamics of Ca2+-CaM-RC3 interactions . The released Ca2+ ion can bind the N-terminal lobe of the same or another CaM molecule . We predict that RC3 has a positive impact on the N-terminal specific Ca2+-CaM nano-domain and on the nano-domain of CaMKII bound CaM . In addition , RC3 is known to interact with membrane phosphatidic acid [49] . The spatial distribution of RC3 and the mobility of CaM-RC3 may have an additional significant impact of the spatial dynamics of Ca2+-CaM activation . Overall , genetic studies clearly suggest a critical role of RC3 in the regulation of Ca2+ dynamics in spines [46] , [47] . Together with CaMKII , RC3 is another molecular target for future study using the particle-based Monte Carlo simulation . The persistent existence of N-terminal lobe specific Ca2+-CaM nano-domain ( Fig . 5∼Fig . 9 ) may at first seem reminiscent of the traditional view on Ca2+ micro-domains . However , we must point out that “Ca2+ domains” and “Ca2+-CaM domains” are , strictly speaking , different concepts . A “Ca2+ nano-domain” is defined by the mean distance traveled by Ca2+ ions before being captured by buffer ( Ca2+ binding protein ) or being extruded . Only under certain conditions , for example , when the Ca2+ binding rate is faster than the diffusion of Ca2+ , are “Ca2+ domain” and “Ca2+-buffer” domain closely related in space . Clearly , the C- and N- terminal lobe specific Ca2+-CaM domains respond differently for the same Ca2+ input ( Fig . 5 and 6 ) and the spatial profile ( and size ) of the C-terminal lobe domain is different from the “ ( free ) Ca2+-domain” . Fig . S4 illustrates this point and shows the distributions of Ca2+ ions , Ca2+ saturated N-terminal and fully Ca2+ saturated CaM from a single simulation run in Fig . 5 and 6 . Clearly , the size and spatial profile of these domains are not identical . The spatial profile of the “Ca2+” signal ( [Ca]i below ) , in the presence of excess unsaturable mobile buffers , is given by the following equation [50]: ( 5 ) where , is the single channel Ca2+ current , is the diffusion coefficient of Ca2+ ( defined earlier ) , the distance from the channel , [Ca]0 is the bulk Ca2+ concentration , and denotes the mean path length of a Ca2+ ion travels before being captured by buffer , B is the buffer concentration is the Ca2+ binding rate , and F is the Faraday constant . This and many other mathematical formulas have been developed ( see reviews in [16] ) but they are not very useful to study the spatial profile of Ca2+-CaM or for any other protein or buffer with multiple Ca2+ binding sites of different binding kinetics . Furthermore , in a small sub-cellular compartment , like CA1 spines , the number but not the concentration of molecules is important . As an illustration , when the equation for the steady-state Ca2+ concentration profile is applied to an L-type Ca2+ channel , it predicts a sharp Ca2+ gradient which results in 100 µM Ca2+ concentration at a distance of ∼4 nm from the channel ( see Fig . 1C in [51] ) . 100 µM of Ca2+ within 4 nm distance of a channel is more than sufficient to saturate the C-terminal lobe of CaM , but it corresponds to less than 1 molecule of Ca2+ ion , leading to a contradiction . In order to understand the spatial information flow of the Ca2+ signaling system in dendritic spines , one must explicitly calculate the first passage time distribution of Ca2+ saturation of CaM and their spatial profile using an accurate particle-based Monte Carlo algorithm and appropriate data analysis method ( e . g . , statistical point pattern analysis ) as we did in this study . In addition , it is important to note that the two lobes of CaM , with almost opposite impacts on Ca2+-CaM nano-domains , reside in the same molecule and are competing for a limited amount of Ca2+ as we discussed in the Results ( Fig . 2 ) . This again implies that the N- and C- terminal lobes decode Ca2+ signals in a different manner , and potentially serve distinct cellular functions . The current work is the first step to understand this unique functionality of CaM at the single molecule level . The Ca2+ transient in dendritic spines is regulated by highly nonlinear interactions between voltage-gated Ca2+ channels , K+ channels , and glutamate receptors . This important issue was recently reviewed in [25] . Clearly , Ca2+-activated K+ channels ( SK channels ) in hippocampal neurons shape the Ca2+ transients in spines and a direct coupling between voltage-gated Ca2+ channels and SK channels via “Ca2+ nano-domains” is a critical regulatory factor of spine Ca2+ metabolism . In addition , CaM itself regulates the activities of Ca2+ channels and Ca2+ pumps ( PMCA ) [52] . Without the detailed knowledge of these issues , we are not able to quantitatively address their impacts on spine Ca2+ dynamics . It is also difficult to make correct interpretations of pre-existing Ca2+ imaging experimental data ( e . g . , roles of pump in the diffusional coupling between dendrites and spines ) . For these reasons , in this study we focused on the initial rising phase of Ca2+ transients and therefore only studied the impacts of Ca2+ injection rate that are relevant for any Ca2+ channels . With these limitations in mind , we repeated all simulations in Fig . 5∼9 without Ca2+ pumps and discovered that the resultant spatial profile of lobe specific Ca2+-CaM domains were similar to the results with Ca2+ pumps ( data not shown ) . As long as Ca2+ pumps are uniformly distributed , the Ca2+ binding kinetics of CaM dictates the spatial and temporal pattern of the Ca2+-CaM interaction . We have not , however , tested spatially non-uniform distribution of Ca2+ pumps such as clusters of PMCA/NCX/NCXK tightly coupled to Ca2+ channels . This is an open area of future research . Finally , the smooth endoplasmic reticulum ( SER ) is another source of Ca2+ that potentially influences Ca2+ transients in the spine . Although our simulator is fully capable of implementing SER structures and Ca2+ release from this source , only a small subset of dendritic spines on CA1 pyramidal neurons contain SER [53] . Furthermore , a recent study suggested a strong link between the SER containing spines and metabotropic glutamate receptor dependent synaptic depression [54] which is an interesting but different topic than the focus of the present work .
CaM is a bi-lobed molecule that has two Ca2+-binding sites within each lobe . Fig . 1A shows how this kinetic mechanism is modeled . Each lobe of CaM has three different states dependent on the number of bound Ca2+ ions: ( apo ) -CaM , ( Ca2+ ) -CaM and ( Ca2+ ) 2-CaM ( the horizontal arrows in Fig 1A ) . The resultant CaM model has nine Ca2+ binding states ( Fig . 1C ) . We assume that Ca2+ binding to the C-lobe and N-lobe are independent and that inter-lobular cooperativity is not considered . The rate constants of Ca2+ binding to each lobe are taken from our previous work [8] , [30] . This model is a simplification of our more elaborate model of CaM [19] . In the latter modeling scheme , Ca2+ association and dissociation at each Ca2+ binding site of CaM were explicitly modeled . Further refinement of the latter detailed model is also possible by taking into account of open ( relaxed ) and inactive closed ( tense ) states of each EF-hand of CaM as proposed by Stefan et al . [55] . We repeated the first passage time analysis in Fig . 2 using the former detailed model and confirmed that there is no qualitative difference between the detailed and simplified models . Future efforts will be made to incorporate the idea of relaxed and tense states in our simulations to specifically examine their consequences on Ca2+/CaM/target interactions . The Ca2+ transient in the spine ( head ) is regulated by a highly complicated set of nested feedback loops [25] . This includes ionotropic glutamate receptors ( AMPA receptors and NMDA receptors ) , CaV2 . 3 voltage-sensitive Ca2+ channels , small conductance Ca2+-activated K+ channel ( SK channels ) , and voltage-gated Na+ channels . The role of voltage-gated CaV2 . 3 channels and Na channels have been largely unknown until recently [25] , [56] . On the other hand , the nature of ionotropic glutamate receptors such as NMDA receptors , the major source of Ca2+ influx into the spine compartment , has been extensively studied in the past and we used a recently published model for our simulation ( Fig . 8 and 9 ) [43] . The functional roles [34] , [57] , [58] , [59] and molecular expression [60] , [61] , [62] of Ca2+ pumps have been studied; however , very limited quantitative information is available regarding the number , ( intra-spine ) distribution , and detailed kinetics properties of these Ca2+ pumps . The membrane densities of the plasma membrane Ca2+-ATPase ( PMCA ) and the Na+-Ca2+ exchanger ( NCX ) are 150∼300/µm2 of membrane and 32∼60/µm2 membrane , respectively [35] , [63] . Since we do not have reliable data for the intra-spine distribution of these pumps , we decided to use the maximum estimated membrane densities for each pump to evaluate their impacts on Ca2+ dynamics ( Fig . 3C ) . The PMCA kinetic constants are 0 . 2 µM Km for Ca2+ and a turnover rate of ∼100 s−1 and NCX has a Km of 3 µM and a turnover rate of ∼1000 s−1 [35] . For initial investigations we fixed the resting extrusion at 25 ions per second and 48 ions per second for PMCA and NCX , respectively [35] . The reaction scheme for the Ca2+ pump is similar to the one in [35]: ( 6 ) where , , and are Ca2+ inside the spine , extruded Ca2+ , pump , and Ca2+-pump complex . PMCA hydrolyzes one ATP molecule per Ca2+ ion transported , i . e . , exchanges one Ca2+ for one H+ ( see recent reviews by Di Leva et al . [52] ) . NCX exchanges three Na+ for one Ca2+ and NCKX imports four Na+ while exporting one Ca2+ and one K+ ( reviewed in [64] ) . Provided that we do not model the diffusions of Na+ or K+ or ATP hydrolysis , Eq . 6 captures the essential characteristics of these Ca2+ pumps ( see Discussion for Ca2+-CaM dependent regulation of PMCA ) . Finally , we randomly incorporated Ca2+ leak channels so that the net flux of Ca2+ is 0 at rest ( 50 nM Ca2+ ) . The NMDA receptor kinetics was taken from previous modeling work [43] . Although our CDS simulator is fully capable of simulating glutamate release and diffusion in the synaptic cleft , this issue was not a focus of the present study . Instead , we assumed that each NMDA receptor was exposed to a constant level of glutamate as in previous modeling work [43] , i . e . , we stimulated the NMDA receptors for 0 . 1 ms with 1 mM of glutamate application and observed the subsequent Ca2+/CaM activation in the spine . The stochastic fluctuation of Ca2+ influx is then due to the stochastic kinetics of NMDA receptors . All other numerical analyses including spatial point pattern analysis and first passage time calculation were carried out under the Matlab environment ( The MathWorks , Inc . , Natick , MA , USA ) . The algorithmic principle of the event-driven particle-based Monte Carlo simulator ( CDS ) is described in [65] and the software is downloadable from our website ( http://nba . uth . tmc . edu/cds ) . The CDS algorithm uses the discretized Brownian motion and relies on the first passage theory and event-driven simulation scheme . The overview of pre-existing particle-based Monte Carlo simulations ( Smoldyn [66] , GFRD [67] , the coarse-grained molecular simulator described by Ridgway et al . [68] , and MCell [27] ) and differences between these simulator and CDS are also discussed in [65] . Under the CDS algorithm scheme , we calculate the first passage time ( and probability ) of molecular collisions and chemical reactions for each molecule in the simulation and create a list of all possible future events and their timing . We execute all of these molecular collisions and chemical reactions exactly as they happen one-by-one while moving all molecules simultaneously in the space . Every time we execute an event , we update the event list based on the new location or chemical status of the molecules . The time interval between two consecutive events varies from one simulation step to the other . Therefore , unlike time-driven Monte Carlo algorithms ( e . g . , MCell and Smoldyn ) , there is no fixed time step in CDS . This event-driven scheme is the only accurate way to handle molecular collisions in a crowded cellular environment . In some cases , the interval between two successive events ( collision or chemical reaction ) becomes long and may result in the non-Brownian motion of molecules . To avoid this situation , we add “change of direction of move” to the event list so that the direction of molecular motion is constantly randomized at least once every10 ns ( the jump length of Ca2+ ion during this time period is smaller than the size of CaM molecule ) . In the CDS simulations , the radius of gyration of CaM ( 2 . 2 nm ) was used to set the size of CaM molecules . The radius of Ca2+ ion was set to 0 . 2∼0 . 25 nm ( larger than its atomic radius ) taking into account its hydration shell [69] , i . e . , we modeled Ca2+ as a solvated ion while simulating its diffusion and interactions with proteins . The diffusion coefficient ( ) of Ca2+ in non-buffered cytoplasm is 200∼225 µm2/s ( nm2/µs ) [31] . The idea behind the Ripley's K-function , or its derivative Besag's L-function , is that if the distribution of the points is random , the number of points within a distance is proportional to if there is no spatial boundary in the system . Suppose we have a 3D spatial distribution of points ( ∼ ) and denotes the number of all points within a distance of the particular point . The Ripley's K-function is defined by ( 7 ) where is the density of particles , the average number of particles in a unit ball [28] , [41] . The expected value for a random Poisson distribution in infinite space is . The Besag's L-function is a derivative of K-function and is defined by ( 8 ) so that its expected value for a random Poisson process in infinite space is ( linear ) . A deviation of L-function from the spatial randomness indicates a clustering or repulsion of the point distribution . We can calculate K-functions with respect to a specific point in space such as a Ca2+ channel ( instead of 's ) , but in this work , we calculated ( Besag's ) L-function for all points in space . The latter type of L-function is important and very useful as the clustering of points ( the location of Ca2+ saturation ) can happen in the middle of the spine head when multiple channels exist or when multiple cycles of Ca2+ binding and unbinding to the same CaM molecule take place ( Fig . 8 and 9 ) . Our data represent an analysis of inter-point ( inter-Ca2+-saturation point ) distance distribution at all distance scales and over the entire spine compartment . The important point to note is that in a confined and complicated geometry such as dendritic spines , a simple mathematical formula of Besag's L-function for complete spatial randomness is unavailable . To overcome this constraint , we created 1000 sets of randomly distributed points in the spine of the same number of data points and then calculated the L-function for the data and for the simulated random point patterns . If the resultant L-function of the data deviates from the simulated point pattern , we can conclude that the data points are not randomly distributed .
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Calmodulin is a versatile Ca2+ signal mediator and a buffer in a wide variety of body organs including the heart and brain . In the brain , calmodulin regulates intracellular molecular processes that change the strength of connectivity between neurons , thus contributing to various brain functions including memory formation . The exact molecular mechanism as to how calmodulin regulates these processes is not yet known . Interestingly , in other excitable tissues , including the heart , each of two lobes of calmodulin responds differentially toward Ca2+ influx and toward its target molecules ( e . g . , ion channels ) . This way , calmodulin precisely controls the Ca2+ dynamics of the cell . We wish to test if a similar mechanism may be operational in neurons so that two lobes of calmodulin interact differentially with Ca2+ ions to activate different downstream molecules that control the strength of connections between neurons . We constructed a detailed simulation of calmodulin that allows us to keep track of its interactions with Ca2+ ions and target proteins at the single molecule level . The simulation predicts that two lobes of calmodulin respond differentially to Ca2+ influx both in space and in time . This work opens a door to future experimental testing of the lobe-specific control of neural function by calmodulin .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"neuroscience/theoretical",
"neuroscience",
"cell",
"biology/cell",
"signaling",
"biophysics/theory",
"and",
"simulation",
"computational",
"biology/computational",
"neuroscience",
"neuroscience/neuronal",
"signaling",
"mechanisms",
"biochemistry/cell",
"signaling",
"and",
"trafficking",
"structures",
"biochemistry/theory",
"and",
"simulation",
"computational",
"biology/systems",
"biology",
"chemical",
"biology/chemical",
"biology",
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] |
2010
|
Lobe Specific Ca2+-Calmodulin Nano-Domain in Neuronal Spines: A Single Molecule Level Analysis
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Estimates of leptospirosis morbidity identified Oceania as the region with highest burden . Besides Australia and New Zealand , Oceania is home of Pacific Island Countries and Territories , most of which are developing countries facing a number of challenges . Their archipelago geography notably affects health infrastructure and access to healthcare . Although human leptospirosis was formerly identified in Vanuatu , there is a lack of knowledge of this disease in the country . We aimed to identify leptospirosis in outpatients visiting the hospital . We conducted a clinical study to investigate leptospirosis as a cause of non-malarial acute febrile illness in Vanuatu . A total 161 outpatients visiting the outpatient clinics at Port Vila Central Hospital for internal medicine were recruited over 20 month . We showed that leptospirosis significantly affects humans in Vanuatu: 12 cases were confirmed by real-time PCR on acute blood samples ( n = 5 ) or by high serology titers evidencing a recent infection ( MAT titer ≥800 or ELISA≥18 Units , n = 7 ) . A high rate of positive serology was also evidenced , by MAT ( 100<titer<800 , 9 patients ) or ELISA IgM ( ELISA≥12 Units , 20 patients , including 6 also positive in MAT ) , showing frequent exposure to pathogenic leptospires , notably from serogroup Australis . The high numbers of both seropositive patients and acute leptospirosis cases observed in outpatients visiting Port Vila Central Hospital suggest a high exposure to pathogenic Leptospira in the population studied . The MAT serology pointing to serogroup Australis as well as exposure history suggest that livestock animals largely contribute to the burden of human leptospirosis in Vanuatu . The analysis of residential and travel data suggests that the risk might even be higher in other islands of the Vanuatu archipelago . Altogether , our study emphasizes the need to increase awareness and build laboratory capacity to improve the medical care of leptospirosis in Vanuatu .
Leptospirosis is among the most widespread zoonosis worldwide . Pathogenic leptospires colonize the renal tubules of asymptomatic chronically infected reservoir mammals , including rodents and livestock . The bacteria are then shed through the urine in the environment , where they can survive for weeks to months in favorable hot and humid hydro-telluric environments . This epidemiological trait is regarded as the main cause of leptospirosis seasonality , with highest incidence being observed during hot and rainy periods globally . Most human infections occur in freshwater or mud , during occupational ( notably agriculture and farming ) or recreational ( e . g . freshwater bathing ) activities [1] . Its global morbidity and mortality were recently estimated to 1 . 03 million cases and 58 , 900 deaths annually , mostly in resource-poor countries [2] . The burden imposed to populations globally is in the same range as the burden of Leishmaniosis , Schistosomiasis or Lymphatic filariasis [3] . The death toll of leptospirosis is therefore five times the total number of fatalities of the 2013–2016 Ebola outbreak every year [4] . Interestingly , both estimates of the burden [2 , 3] place Oceania as the region of highest incidence and highest burden by far . Yet , very little is known on human leptospirosis in many Pacific Island Countries and Territories ( PICTs ) , earning it the well-deserved name of neglected tropical disease . Many of these countries including Vanuatu experience climatic and environmental conditions prone to favor leptospirosis . Furthermore , some population groups experience precarious living and sanitation conditions . It is likely that human leptospirosis occurs at high rates in these settings , a risk that might also increase because of climate change . Vanuatu is an island nation located in the South Pacific . This archipelago of volcanic origin is made of more than 80 islands ( with 65 being inhabited ) spread over ca . 1 , 300 km from North to South ( Fig 1 ) . Vanuatu has a ca . 272 , 500 population , predominantly rural . The rural lifestyle involves frequent exposure to natural freshwater bodies , subsistence agriculture and farming , including free-ranging pigs , which are also used in customs ceremonies . Vanuatu was also given the highest risk of exposure to natural disaster worldwide , notably being highly exposed to earthquakes and tropical cyclones [5] . In Vanuatu , leptospirosis was first identified in cattle through a seroprevalence study in the 1980s [6] . The first reported cases of human leptospirosis were identified in patients in Port Vila by the New Caledonian Institut Pasteur in the early 1990s [7] . An additional eight cases were reported from Santo Island in the early 2000s [8] . A published case of an Australian tourist contracting ( ultimately fatal ) leptospirosis during a holiday in Vanuatu in the early 2000’s highlights the leptospirosis risk associated with freshwater bathing [9] . Lastly , a regional study in 2003–2005 included 10 patients from Vanuatu , showing positive serology in one case [10] . Taken together , published information on human leptospirosis in Vanuatu is scant . When considering domestic mammals ( as both possible susceptible hosts and reservoirs for human infections ) , farmers proved to have a limited knowledge of the disease , although leptospirosis was ranked first in a regional priority list for farm animal biosecurity established by a panel of regional experts from the animal health and production sectors [11] . In this study , we aimed at quantifying the number of leptospirosis cases and the seroprevalence of anti-Leptospira antibodies in patients visiting the outpatient clinic of the internal medicine ward at Vila Central Hospital , the national reference hospital in Vanuatu .
Written informed consent was obtained from all included patients or their legal representative for minors . The study was endorsed by Institut Pasteur under number RBM 2012 . 33 after approval by Comité de Protection des Personnes Ile de France and was approved by the Comité Consultatif sur le Traitement de l’Information en matière de Recherche dans le domaine de la Santé ( CCTIRS ) with number 12 . 770 after validation by Vanuatu Ministry of Health . The anonymous database was registered with the French Commission Nationale de l’Informatique et des Libertés ( CNIL ) . The Strobe checklist is presented as S2 Supporting information file . Port Vila Central Hospital ( VCH ) is the 150-bed central hospital of Vanuatu . It is located in Port Vila , the capital of Vanuatu on the island of Efate . The Efate population is ca . 41 , 600 inhabitants , including ca . 25 , 000 in Port Vila . Inter-island migration in Vanuatu , however , is intense and patients from other islands and provinces sometimes refer to VCH . The outpatient clinics receives an average of 1 , 400 patients per week . The aim of the study was to identify leptospirosis cases by investigating patients presenting at the outpatient clinic at VCH in Vanuatu . We therefore used the leptospirosis clinical description by the World Health Organization [12] . The following inclusion criteria were selected: acute febrile illness with headache and myalgia and any of the following: prostration , conjunctival injection , meningeal syndrome , anuria or oliguria and/or proteinuria , icterus , hemorrhages , Cardiac arrhythmia or deficiency , skin rash ( S1 Supporting Information ) . However , these inclusion criteria were not used in routine practice and no rigorous inclusion criteria were actually used . A sample of patients aged 15 year old or older visiting the outpatient clinic at Vila Central Hospital ( VCH ) , Port Vila , Vanuatu were included in the study . After medical evaluation addressing the patient complaints , the medical practitioner decided whether the clinical presentation was compatible with leptospirosis . If so , the consent of the patient or his/her legal representative was sought after information on the disease and on the goals of the study . After having an informed consent form signed , the patients were referred to the laboratory where they were screened for malaria . Only malaria-negative patients were included in this study . The medical staff in the laboratory was trained about leptospirosis , about the process of the study and the administration of a standardized questionnaire about patient exposure risk factors ( occupation and contact with various water sources and with animals in the three weeks before onset of symptoms ) . This questionnaire was administered in local pidgin language ( bislama ) and written in either English or French ( S1 Supporting information ) . Demographic data ( sex , age ) , clinical presentation and the data about possible exposure were transferred to Institut Pasteur in New Caledonia ( IPNC ) after de-identification ( S1 Supporting information ) . In VCH laboratory , patients were first screened for malaria by the classical thick blood smear technique from capillary blood . Blood was then collected in a plain tube and serum was used for a malaria rapid diagnostic test ( CareStart Malaria HRP2/pLDH ( pf/PAN ) Combo ) . When negative , a Leptospira serology was made using Leptospira IgM ELISA ( Panbio ) . In some patients , urine was also collected and immediately frozen . The remaining serum and the urine when available were sent together with the case documentation form to IPNC . At IPNC , DNA was extracted from 200 μL serum or urine using the MagnaPure LightCycler ( Roche Diagnostics ) . Leptospira DNA was detected by real-time PCR targeting the lipL32 gene , specific of pathogenic leptospires [13] , on a LightCycler 480 . The microscopic agglutination test ( MAT ) —the reference technique for Leptospira serology—was also used ( when sufficient volume was available ) using a regional 24-strain panel described before [10] and as recommended by WHO . Positive serological cutoff were set at a titer of 100 for MAT or 12 for ELISA [14] . Probable acute leptospirosis cases were defined as patients with a high serological titer ( MAT ≥ 800 or ELISA ≥ 18 ) , suggestive of a recent infection . The serogroup with the highest titer was considered as the putative infecting serogroup . Confirmed acute leptospirosis cases were defined as patients with a positive qPCR . Data were entered in an Excel spreadsheet ( Microsoft Corporation , Redmond , WA , USA ) and analyzed using Stata 13 ( College Station , TX , USA ) . Variables were analyzed descriptively before bivariate analysis explored the association between outcome ( leptospiral disease and then positive serology ) with exposure variables ( sociodemographic , clinical , environmental exposures ) using a 5% statistical threshold . Categorical variables were dichotomized and successively tested using bilateral Fisher’s tests . Variables associated with a significance level of 0 . 2 were integrated into a logistic regression model to compute odds-ratios .
From January 2013 to August 2014 , 161 patients ( 84 females and 77 males , M:F = 0 . 92 ) were included . Patients’ age ranged from 15 to 75 years ( mean 34; median 30; IQR 23–40 ) . Most patients included in our series visited the outpatient clinic during the first quarter of 2013 ( n = 45 ) or 2014 ( n = 43 ) and 128 ( 79 . 5% ) patients visited during the first half of both years taken together . The quarterly distribution of the samples are summarized in Table 1 . The chief complaint was “feeling unwell” , with headache ( 79% ) and myalgia/arthralgia ( 75% ) being most frequently reported ( Table 2 ) . All patients but two ( unknown signs and symptoms ) reported or presented at least one of the following: Headache ( n = 127; 79 . 9% ) ; fever or chills ( n = 69; 43 . 4% ) ; myalgia or arthralgia ( n = 120; 75 . 5% ) ; prostration ( n = 27; 17% ) ; respiratory discomfort or cough ( n = 17; 10 . 7% ) ; jaundice ( n = 11; 6 . 9% ) ; oliguria or anuria ( n = 9; 5 . 7% ) . However , only 23 patients ( 14 . 3% ) fulfilled the inclusion criteria initially defined for the study . All blood samples were tested for leptospiraemia using qPCR . In addition , 53 urine samples from the same patients were also tested . No urine sample was positive , but five blood samples were qPCR-positive . For serology and following logistical issues , 31 samples were tested by IgM ELISA only , 45 by MAT only and 85 with both techniques . These results are summarized in Table 3 . Twelve ( 7 . 45% ) cases of acute leptospirosis were identified as confirmed ( n = 5 ) or probable ( n = 7 ) . Only two of these cases were reported to fulfill the inclusion criteria . The seasonal distribution of samples was biased towards the rainy season during the first quarter of each year ( Table 1 ) and was too irregular to document a possible seasonal pattern . Exposures significantly associated with clinical leptospirosis in bivariate analysis were male gender , the use of water from a well or a natural source at home , fishing in freshwater or contact with pigs . Residing at least part-time on islands other than Efate also was associated with clinical leptospirosis . Detailed statistics and the corresponding Odds Ratio are presented in Table 4 . A multilevel logistic regression found that only living at least part time on another island than Efate ( OR 8 . 64; CI95% 1 . 22–61 . 41; p = 0 . 03 ) remained significantly associated with acute leptospirosis after adjustment for other factors . From a clinical standpoint , cough or hemoptysis were significantly associated with acute leptospirosis ( OR = 9 . 9 CI95% [1 . 4994; 75 . 4296]; p = 0 . 008 ) . A positive serology was identified in 29 ( 18% ) patients ( including nine of the 12 cases ) . Positive serology was more frequent in males than females , but this difference did not reach statistical significance ( p = 0 . 22 ) . Patients residing at least part time on another island than Efate had a slightly and statistically borderline risk of having a positive serology compared to patients residing on Efate ( 27 . 7% vs . 14 . 3% , p = 0 . 07 ) . Contact with freshwater ( in rivers , lakes or irrigated culture ) was a risk factor ( p = 0 . 004 ) , as was using water from a well or natural source ( p = 0 . 008 ) . Lastly , contact with pigs ( p<0 . 01 ) or with cattle ( p<0 . 01 ) were statistically significantly associated with seropositivity in bivariate analysis . A logistic regression model integrating these various factors found no significant risk factor , only contact with pigs retained borderline significance after adjustment for all other factors . Detailed statistics and Odds Ratios are presented in Table 4 . Using MAT , 130 samples were tested , yielding 15 ( 11 . 5% ) positive results . In our series , a higher titer was found for serogroups Pyrogenes ( n = 1 ) , Icterohaemorrhagiae ( n = 1 ) , Louisiana ( n = 1 ) and Panama ( n = 1 ) , three had co-agglutinations with no serogroup giving a higher titer , and serogroup Australis was the most prevalent , with eight positive sera .
This study was aimed to identify leptospirosis cases in patients visiting the outpatient clinic at Port Vila Central Hospital in Vanuatu . The patient selection used has a number of biases . The exclusion of malaria cases had a very low impact on the patient selection , since malaria is considered eradicated in the Shefa province and very few malaria cases had to be excluded . Most patients were also recruited during the hot and rainy season . Because a strong seasonality is frequently reported in leptospirosis incidence , including in the neighboring archipelagoes of New Caledonia and Wallis & Futuna [14 , 15] , this bias is possibly leading to an over-estimation of the incidence of the disease annually . The patient recruitment initially aimed at including patients with a clinical presentation recognized as a standard presentation in leptospirosis by WHO [12] . However , the inclusion criteria were not strictly respected and only 14 . 3% of patients fulfilled this suspicion definition and less than half of the patients were febrile or reported previous fever or chills . However , even if patients were not selected using a rigorous leptospirosis clinical suspicion , they were selected for a leptospirosis suspicion by medical doctors . This suggests that more leptospirosis cases would have been identified if the selection had focused strictly on acute febrile patients , but also that the number of leptospirosis is higher than in the total outpatient population . Taken together , the patient selection used in this study prevents from using the results of our study to evaluate the burden of leptospirosis in the country . Still , we evidenced both a significant number of incident leptospirosis cases and a high seroprevalence for Leptospira among outpatients . Yet , there is currently no specific health policy and very poor awareness on this disease in the country . Our results highlight the need to strengthen awareness of the medical community and reinforce laboratory diagnostic capabilities . Recent evaluations of the leptospirosis burden globally have identified Oceania as the region of highest morbidity [2] and with the highest burden in terms of Disability Adjusted Life Years [3] . Leptospirosis is also considered the infectious disease posing the greatest risk for livestock health and productivity [11] . In Vanuatu , however , only two studies have investigated leptospirosis in humans [7 , 8] . Despite evidence of leptospirosis in returning tourists after exposure to freshwater [9] , leptospirosis is most frequently not considered for routine patient diagnostics in Vanuatu . Here , we used a sample of patients who visited the outpatient clinic of Port Vila Central Hospital . Although only a minority of patients had evidence of fever or reported fever or chills ( 43% ) , all clinically documented patients had at least one clinical sign compatible with leptospirosis . Our patient study likely does not reflect VCH outpatients , but patients were not strictly selected for suspected leptospirosis . Using post-hoc analysis with rigorous diagnosis criteria and case definitions , we were able to identify 12 ( 7 . 45% ) leptospirosis cases . Because the patient sample of this study is season-biased with a majority ( 80% ) of inclusions in the first half of the year ( encompassing the hot rainy season ) , this incidence cannot be extrapolated to the entire year . It can nevertheless be assumed that this number reflects a high incidence of leptospirosis among outpatients visiting Port Vila Central Hospital , at least during the hot rainy season . Combining MAT and ELISA results , we evidenced 18 . 0% serological positivity . The seroprevalence observed was 11 . 5% using the reference technique MAT , a technique known to be poorly sensitive at the early stage of the disease , possibly leading to false negative results in early samples [16] . In contrast , it was 18 . 1% using ELISA , a technique allowing earlier detection of antibodies but with lower specificity compared to MAT , therefore possibly leading to some level of false positivity [17] . The risk factors associated with a positive serology point to well-known risks , such as freshwater exposure and contact with farm animals , notably pigs and cattle , but not goats , horses or sheep . Exposure to freshwater during fishing activities was also a risk for leptospirosis . Interestingly , MAT results point to Australis as the dominant serogroup , which is frequently involved in human cases in various Pacific Islands and most frequently pointing to a swine reservoir [10 , 14 , 18–22] . This finding together with the fact that contact with pigs was a significant risk factor for overt disease and borderline for positive serology points to pigs as a potential contributor to human leptospirosis in Vanuatu , as observed in other PICTs . Another finding was that patients living at least part time in another island in the archipelago also had a higher risk of presenting clinical leptospirosis . Though this might be caused by a recruitment bias of patients forwarded by peripheral health centers , this also suggests that leptospirosis incidence could be yet higher in other islands of Vanuatu , where medical care is frequently harder to access . This may be explained by a different , more rural lifestyle , in which farming , agriculture and freshwater exposure are part of everyday activity for most of the population . Because severe leptospirosis fatality is high in the absence of intensive care and renal replacement therapies , this finding suggests that the hidden morbidity of leptospirosis is possibly associated with a high mortality on these islands . One major cause for insufficient recognition of this disease in the region relates to the diagnostic challenges . Because the clinical presentation of leptospirosis is non-specific and polymorphic , reliable diagnosis involves detecting leptospires in biological fluids ( mostly blood or urine ) or by seroconversion ( requiring two successive samples ) as detected by the micro-agglutination test ( MAT ) [23] . These diagnostic techniques should be introduced in Vanuatu with scientific and technical support from reference centers with relevant experience . Reliable early diagnosis paves the way for effective case management , greatly improves patient prognosis and provides information for public health policymaking . For public health managers , the acknowledgement that this zoonotic and environmental disease is present and an assessment of its burden can help guide decisions affecting local or regional health priorities , implementation of veterinary public health policy , physical planning ( access to clean water sources or waste management ) or the control of wild or stray animals . Further research is needed to gain a precise knowledge of the burden of leptospirosis in this archipelago , including in peripheral health centers . This could be achieved by the routine implementation of real-time PCR and IgM ELISA together with the support of a reference center for MAT [23] . Because this study suggests a significant role of non-rodent reservoir animals as a source of human leptospirosis , the role of livestock should also be investigated . To conclude , our exploratory study shows that a significant number of patients visiting the outpatient clinic at Port Vila Central Hospital have leptospirosis , highlighting the need for increased awareness in the medical community as well as in the general population , especially during the hot and rainy season from January to April . Because the diagnosis of leptospirosis requires biological confirmation , medical laboratories should also be trained and supported for this purpose .
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Leptospirosis is thought to impose its highest burden to tropical island populations , especially in the Pacific region of Oceania . Yet , very few information has ever been reported from some of the Pacific Island Countries and Territories , including Vanuatu . In this study , we aimed at evidencing leptospirosis in a convenience sample of outpatients visiting Port Vila Central Hospital , the major hospital of this archipelago country in the South Pacific . Using reference laboratory techniques , our study confirms a significant number of acute leptospirosis cases , as well as a high level of previous exposure in outpatients visiting this hospital . Risk factor analysis point to livestock and lifestyle as important factors exposing to leptospirosis in this developing country . Furthermore , there is evidence that the disease probably has a higher incidence in remote islands outside of the capital of Efate in this very wide archipelago . Taken together , our results point to a need of increased awareness , notably in peripheral health centers and of better access to laboratory confirmation for surveillance .
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2018
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High incidence of leptospirosis in an observational study of hospital outpatients in Vanuatu highlights the need for improved awareness and diagnostic capacities
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Blood flow and mechanical forces in the ventricle are implicated in cardiac development and trabeculation . However , the mechanisms of mechanotransduction remain elusive . This is due in part to the challenges associated with accurately quantifying mechanical forces in the developing heart . We present a novel computational framework to simulate cardiac hemodynamics in developing zebrafish embryos by coupling 4-D light sheet imaging with a stabilized finite element flow solver , and extract time-dependent mechanical stimuli data . We employ deformable image registration methods to segment the motion of the ventricle from high resolution 4-D light sheet image data . This results in a robust and efficient workflow , as segmentation need only be performed at one cardiac phase , while wall position in the other cardiac phases is found by image registration . Ventricular hemodynamics are then quantified by numerically solving the Navier-Stokes equations in the moving wall domain with our validated flow solver . We demonstrate the applicability of the workflow in wild type zebrafish and three treated fish types that disrupt trabeculation: ( a ) chemical treatment using AG1478 , an ErbB2 signaling inhibitor that inhibits proliferation and differentiation of cardiac trabeculation; ( b ) injection of gata1a morpholino oligomer ( gata1aMO ) suppressing hematopoiesis and resulting in attenuated trabeculation; ( c ) weak-atriumm58 mutant ( wea ) with inhibited atrial contraction leading to a highly undeveloped ventricle and poor cardiac function . Our simulations reveal elevated wall shear stress ( WSS ) in wild type and AG1478 compared to gata1aMO and wea . High oscillatory shear index ( OSI ) in the grooves between trabeculae , compared to lower values on the ridges , in the wild type suggest oscillatory forces as a possible regulatory mechanism of cardiac trabeculation development . The framework has broad applicability for future cardiac developmental studies focused on quantitatively investigating the role of hemodynamic forces and mechanotransduction during morphogenesis .
Ventricular trabeculation is tightly regulated by both genetic programming and biomechanical forces such as hemodynamic pressure and shear stress . [1–8] Trabeculae formation leads to a complex network of endocardial protrusions ( trabeculae ) into the ventricle that form ridges and grooves . [9] During cardiac morphogenesis , the ventricular myocardium ( heart tissue ) differentiates into two layers , an outer compact zone and an inner trabeculated zone . Disruptions in any of the normal developmental processes can lead to either excess trabeculation , a congenital condition known as non-compaction cardiomyopathy , [7 , 10–13] or a significant reduction in trabeculation that is usually associated with ventricular compact zone deficiencies such as hypoplastic left heart syndrome ( HLHS ) . [1 , 14] Both these conditions can lead to heart failure and high mortality during embryonic development . While the genetic mechanisms underlying cardiac morphogenesis have been extensively studied , the impact of biomechanical forces such as hemodynamic shear remains elusive , due in part to the significant challenges associated with quantifying hemodynamic forces in developing hearts . [3 , 4 , 7 , 15] Several studies have examined mechanotransduction during ventricular trabeculation using in vitro techniques such as particle image velocimetry ( PIV ) . [4 , 16 , 17] Although non-invasive , these 2-D image-based techniques are limited by interpolation errors that arise when extracting the three-component ( 3C ) velocity vector field as well as the lack of resolution of the near-wall velocity profile . [4 , 17] These challenges compromise the accuracy of endocardial wall shear stress ( WSS ) measurements , which are of central importance to understanding shear-regulated mechanotransduction . Moreover , extracting hemodynamic pressure data , which is linked to cardiac valvulogenesis , [5 , 18] from PIV measurements is non-trivial . Computational fluid dynamics ( CFD ) provides an attractive alternative for quantifying space-time resolved velocity and pressure fields in subject-specific geometries . CFD has been widely applied to simulate blood flow , to facilitate clinical decision-making , and to study the progression of cardiovascular disease . [19–26] CFD has also been applied to study developmental dynamics in chick embryos such as aortic arch morphogenesis , [27 , 28] aortic valve and outflow tract morphogenesis , [29–31] and the onset of congenital heart disease such as HLHS . [14] We have previously demonstrated , using moving domain CFD coupled with in vivo imaging of zebrafish embryos , a method to computationally quantify the spatio-temporal variation of endocardial WSS and pressure gradients across the atrio-ventricular canal in two dimensions . [32] We subsequently developed 4-D imaging ( 3-D in space + time ) using light sheets with selective plane illumination microscopy ( SPIM ) coupled with a non-gated synchronization algorithm to elucidate hemodynamic regulation mechanisms of Notch signaling pathways during cardiac trabeculation in genetically manipulated zebrafish embryos . [33] While numerous image-based CFD modeling techniques have been developed for human hearts based on magnetic resonance imaging ( MRI ) or computed tomographic ( CT ) data , [34–36] there remains a need for efficient frameworks applicable to cardiac developmental studies using high resolution embryonic heart images . We present a computational framework to quantify biomechanical forces , including endocardial WSS and oscillatory shear index ( OSI ) in zebrafish embryos with and without cardiac trabeculation . We also compare kinetic energy density and rate of viscous energy dissipation due to changes in ventricular trabeculation . Our framework employs robust and efficient image processing techniques based on the open-source SimVascular [37] software framework to build the anatomic model , and employs validated stabilized finite element methods for blood flow simulation in moving domains . [37–41] We apply this computational framework to quantify in detail the shear regulation of cardiac trabeculation during morphogenesis in zebrafish embryos in response to genetic and chemical treatments . In the following sections , we present the computational pipeline which proceeds from 4-D image data to computing ventricular hemodynamics in zebrafish embryos . We provide details on the genetic and chemical treatments of the wild type zebrafish to investigate the role of shear on cardiac trabeculation . Finally , we quantify differences in hemodynamic conditions between the wild type and treated variants over the course of cardiac development . In the present study , we limit our attention to velocity-derived quantities such as WSS , OSI , kinetic energy and dissipation although other mechanobiology regulators such as pressure gradients and wall strains are also likely factors affecting cardiac morphogenesis .
We first present the computational workflow , which proceeds from 4-D light sheet images of zebrafish embryos to ventricular blood flow modeling using moving domain CFD ( Fig 1 ) .
We observe a substantial change in cardiac contractility due to chemical treatment with AG1478 compared with the gata1aMO injected fish and the wea mutant ( Fig 5 ) . In response to chemical treatment AG1478 , SV increases by 48% whereas EF marginally increases by ∼7% compared to the wild type ( Fig 5a ) . Genetically treated gata1aMO and wea have reduced cardiac contractility with respect to the wild type . For gata1aMO , SV is smaller by 45% and EF is smaller by 23% compared to the wild type ( Fig 5a ) . On the other hand , wea mutant has a significantly smaller SV and EF ( Fig 5a ) . We note similar trends in the ventricular volume variation during the cardiac cycle for all the fish types ( Fig 5b ) . Additionally , we note a prolonged diastole ( ventricular filling ) in response to AG1478 treatment , such that the ventricle reaches a maximum volume much later in the cardiac cycle ( Fig 5b ) . On the other hand , the ventricular volume variation for gata1aMO and wea remains nearly in phase with the wild type ( Fig 5b ) . Localized zones of high and low WSS occur in the wild type zebrafish at early diastole ( top frame of Fig 6a ) , which correspond to the sites of the trabecular ridges and grooves , respectively . On the other hand , AG1478 and gata1aMO have more uniformly low WSS on the ventricular surface , except for the sites of inflow jet impingement . The regions of higher WSS occurring in the artificial inflow and outflow annuli extensions are not included in our analysis . Further into the cardiac cycle ( middle row , Fig 6 ) , WSS is higher over most of the ventricular endocardium for the wild type zebrafish , whereas the WSS is lower for both AG1478 and gata1aMO . During systole ( bottom row , Fig 6 ) , mild variation in WSS is noted around the sites of trabeculations in the wild type model , which are not present in AG1478 and gata1aMO treated fish . In the case of wea mutant ( Fig 6d ) , WSS is uniformly low throughout the cardiac cycle , which is attributed to its poor cardiac contractility and function ( Fig 5 ) . In Fig 7a , we note that the diastolic and systolic behavior of AAWSS appear to be reversed between the wild type and genetically manipulated fish types ( gata1aMO , wea ) , and the chemically treated AG1478 . First , we observe a diastolic peak of AAWSS for the former group , whereas the latter has a systolic peak . Second , during diastole , AAWSS rises and falls more sharply for the wild type and the genetic variants ( gata1aMO , wea ) , whereas it plateaus for the chemically treated AG1478 . However , during systole the trend is reversed between the two groups . The time-averaged shear stress ( AWSS ) differs slightly between the wild type and AG1478 ( Fig 7b ) . On the other hand , gata1aMO exhibits a higher time-averaged value compared to the wild type . This is due to a higher systolic shear that is spread over a wider range of the cardiac cycle for gata1aMO compared to the wild type ( Fig 7a ) . Nevertheless , with a reduced viscosity ( gata1aMOμ1/4 ) , the AWSS of gata1aMO is significantly lower compared to both the wild type and AG1478 ( Fig 7b ) . The shear profile for wea mutant is consistently low over the cardiac cycle ( Fig 7a ) , as is the time average ( Fig 7b ) . In Fig 8a for the wild type , OSI is higher in the trabecular grooves , but lower in the trabecular ridges and on the rest of the smooth endocardium . On the other hand , we observe fewer sites with high OSI for the treated fish types ( AG1478 , gata1aMO and wea ) . Both KE ¯ and Φ ¯ exhibit a two-peak profile during the cardiac cycle ( Fig 9 ) . While the first peak occurs during early diastole and varies sharply with a narrow spread , the second peak occurs between mid-diastole and mid-systole with a wider spread and a lower peak value . We also note that the shapes of the time variation of the kinetic energy density KE ¯ and the rate of viscous dissipation Φ ¯ curves are similar for all fish types but only differ in magnitude ( Fig 9 ) . We note that AG1478 has higher peak value of kinetic energy KE ¯ ( Fig 9a ) but substantially reduced dissipation Φ ¯ ( Fig 9b ) compared to the wild type . The genetically treated gata1aMO has a lower peak KE ¯ and Φ ¯ compared to the wild type . However , this difference in energy budget between gata1aMO and the wild type is reduced during late diastole to early systole , and by late systole , the gata1aMO has marginally higher kinetic energy and dissipation compared to the wild type ( Fig 9 ) . Lowering the blood viscosity by 4 ( gata1aMO1/4 ) results in reduced energy dissipation compared to either wild type or AG1478 ( Fig 9b ) . Both KE ¯ and Φ ¯ are an order of magnitude lower for wea mutant fish and are therefore negligible ( Fig 9 ) .
We have developed a novel computational framework to quantify time-dependent hemodynamic forces in developing embryos based on 4-D light sheet imaging data . Our efficient and streamlined workflow employs deformable image registration methods for extracting the endocardial motion , and is coupled with a stabilized variational multiscale finite element flow solver that is validated and optimized for modeling cardiac hemodynamics . We have demonstrated the workflow in wild type zebrafish and in three treated fish types disrupting normal cardiac trabeculation . These variants include: ( a ) chemical treatment using AG1478 that inhibits ErbB2 signaling; ( b ) injection of gata1a morpholino oligomer ( gata1aMO ) suppressing hematopoiesis and thereby , reducing blood viscosity and shear; and ( c ) weak-atriumm58 mutant ( wea ) with attenuated atrial contraction . Our simulations revealed high oscillatory shear index ( OSI ) in the grooves between trabeculae compared to the ridges in wild type zebrafish , suggesting oscillatory forces to be implicated in cardiac trabeculation . Our analysis also indicates that the presence of endocardial trabeculations significantly enhances viscous losses in the wild type zebrafish compared to the treated variants , although , the magnitude of this increase is small compared to the total cardiac work . This framework is broadly applicable in other cardiac developmental studies focused on quantifying mechanobiologically relevant forces during morphogenesis .
|
We present a novel computational workflow for quantifying hemodynamic forces in developing zebrafish embryos by coupling high resolution 4-D light sheet imaging with a moving domain blood flow solver . Our framework employs deformable image registration to extract the motion of the ventricle from high resolution image data . This produces a robust and efficient workflow , as segmentation is performed at only one cardiac phase , while the wall position in other cardiac phases is found from the displacement field obtained during image registration . This approach avoids a laborious process of manual segmentation in all cardiac phases , and minimizes spurious errors arising from manual processing . Our validated flow solver is optimized for cardiac hemodynamics with backflow stabilization , efficient data management and dynamic remeshing algorithms for moving domains . We demonstrate the utility of the framework in wild type zebrafish and three treated variants in which the formation of cardiac trabeculations is disrupted . In this study , we then quantify the relationship between oscillatory shear forces and the presence or absence of ventricular trabeculation during cardiac development . Our framework has broad applicability in cardiac developmental studies focused on quantitatively investigating the mechanobiology during morphogenesis .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2017
|
A method to quantify mechanobiologic forces during zebrafish cardiac development using 4-D light sheet imaging and computational modeling
|
The determinants of parasite persistence or elimination after treatment and clinical resolution of cutaneous leishmaniasis ( CL ) are unknown . We investigated clinical and parasitological parameters associated with the presence and viability of Leishmania after treatment and resolution of CL caused by L . Viannia . Seventy patients who were treated with meglumine antimoniate ( n = 38 ) or miltefosine ( n = 32 ) and cured , were included in this study . Leishmania persistence and viability were determined by detection of kDNA and 7SLRNA transcripts , respectively , before , at the end of treatment ( EoT ) , and 13 weeks after initiation of treatment in lesions and swabs of nasal and tonsillar mucosa . Sixty percent of patients ( 42/70 ) had evidence of Leishmania persistence at EoT and 30% ( 9/30 ) 13 weeks after treatment initiation . A previous episode of CL was found to be a protective factor for detectable Leishmania persistence ( OR: 0 . 16 , 95%CI: 0 . 03–0 . 92 ) . kDNA genotyping could not discern differences between parasite populations that persisted and those isolated at diagnosis . Leishmania persist in skin and mucosal tissues in a high proportion of patients who achieved therapeutic cure of CL . This finding prompts assessment of the contribution of persistent infection in transmission and endemicity of CL , and in disease reactivation and protective immunity .
Over 95% of clinical manifestations of human infections caused by Leishmania species of the Viannia subgenus consist of cutaneous lesions . Although L . Viannia species are typically considered dermotropic , infection is systemic [1 , 2] . The presence and viability of parasites in lesion scars and in otherwise healthy tissues , including blood , skin , nasal and conjunctival mucosa , have been documented in patients with active cutaneous disease [3–6] and in individuals with asymptomatic infection residing in endemic areas of L . Viannia transmission [7] . The ability of L . Viannia parasites to colonize host tissues without causing signs or symptoms of disease reflects a host-pathogen relationship that is permissive for microbial persistence . Loss of susceptibility for meglumine antimoniate and miltefosine has been reported after a single treatment course [8 , 9] . Thus , subclinical persistence of Leishmania after chemotherapeutic interventions could favor the development of acquired drug resistance and selection of non-susceptible parasite populations , risking the usefulness of available and potentially new drugs . The systemic detection of subclinical Leishmania infection after clinical resolution of disease whether therapeutically achieved or after self-resolution , together with persistent asymptomatic infection , could reveal a previously unrecognized magnitude of the human population harboring viable parasites . The clinical and epidemiological impact of subclinical infection , and the factors that underlie parasite persistence or elimination , are unknown . In this study we explored clinical and parasitological factors associated with Leishmania persistence after standard-of-care treatment of cutaneous leishmaniasis ( CL ) caused by L . Viannia .
This study was approved and monitored by the Institutional Review Board for Ethical Conduct of Research Involving Human Subjects of the Centro Internacional de Entrenamiento e Investigaciones Médicas ( CIDEIM ) with approval code CIEIH 1221 , in accordance with national and international guidelines . All individuals voluntarily participated in the study . Written informed consent was obtained from each participant . This study was designed to explore the associations of clinical and parasitological factors with the persistence of Leishmania after supervised treatment and follow-up , and documented clinical resolution of CL . We included a cohort of CL patients who consulted CIDEIM outpatient clinics in Cali ( Valle , Colombia ) and Tumaco ( Nariño , Colombia ) between years 2011 and 2014 , who participated in clinical studies that included prospective treatment follow-up . Demographic and clinical information from study participants was obtained at diagnosis and follow up visits . The primary outcome of analysis was post-treatment parasite persistence . We qualitatively determined this by molecular detection of Leishmania nucleic acids ( kDNA ) from cutaneous lesions and nasal and tonsillar mucosa samples obtained from patients before and after treatment ( end of treatment and at 13 weeks after initiation of treatment ) . Parasite viability was assessed by amplification and quantification of Leishmania 7SLRNA gene transcripts in all available lesion samples and for kDNA-positive mucosal samples [10] . Independent variables included sex , age , self-reported ethnicity , weight , self-reported time of lesion evolution , number of lesions , previous episode of leishmaniasis , and parasite loads in lesion aspirates , as well as drug-related variables including prescribed drug and adherence to treatment . Clinical histories and samples from 70 adult patients with parasitological diagnosis of CL were included in this study . All participants were ≥18 years of age , received supervised standard-of-care treatment with meglumine antimoniate or miltefosine according to the national guidelines [11] , and had clinical follow-up at the end of treatment and 13 weeks after initiation of treatment , the time at which therapeutic outcome was determined . Cure was defined as the re-epithelialization of all cutaneous lesions without inflammatory signs . Samples of active lesions and lesion scars were taken by needle aspirate of the border of the lesion or at the periphery of the scar . Swab samples of cutaneous lesions , nasal mucosa , and palatine tonsil mucosa were obtained by gently rubbing sterile swabs ( BuccalAmp kit; Epicenter Biotechnologies ) over the mucosal surface [10] . Lesion and mucosal samples were collected from all patients before treatment , as were lesion samples at the end of treatment ( Fig 1 ) . Mucosal swabs were obtained from 41 participants at the end of treatment . Samples were also obtained from 30 patients at 13 weeks of follow-up . All samples were preserved in TRIzol Reagent ( Life Technologies ) and stored at -80°C until processed . DNA and RNA were extracted using the AllPrep DNA/RNA Minikit ( Qiagen ) according to manufacturer´s recommendations . Leishmania was detected by PCR amplification and southern blot of minicircle kDNA as previously described [4] , which has a limit of detection of 0 . 3 fg of parasite DNA ( ~10−2 parasites per reaction ) [12] . A positive control of L . V . panamensis ( MHOM/CO/86/1166 ) DNA and a negative water control were included in each PCR run . PCR amplification of the human GAPDH gene was used as quality control of the extracted DNA [7] . DNAse treated RNA was used for quantitative reverse transcriptase PCR ( qRT-PCR ) of the Leishmania 7SLRNA transcript to evaluate parasite viability and quantify parasite burden . The limit of detection of this method is 102 parasites per reaction [10] . Parasite loads were calculated by extrapolation to a standard curve and normalized to the number of human nucleated cells using TATA box binding protein amplification [7] . Detection of amplification products was performed using SYBR Green Master Mix ( Applied Biosystems ) on a BioRad CFX-96 platform . For those kDNA positive samples that were below the limit of detection of the 7SLRNA qRT-PCR , a maximum likelihood estimate of 0 . 0001 parasites per reaction was calculated as previously described [7] . A nested PCR reaction to amplify and sequence the conserved region of Leishmania minicircle kDNA was performed using external primers LVp1-Fw and LVp1-Rv and internal primers LVp1-Fw and LVp5-Rv [7] . Sequences were analyzed using BioEdit v7 . 2 . 5 . Genetic distances and trees were calculated and constructed using MEGA 7 . 0 . Population structure was explored with the STRUCTURE 2 . 3 . 4 software [13] . Runs were performed under the following parameters: burn-in period of 20 , 000 iterations , 200 , 000 Markov Chain Monte Carlo iterations and admixture model . A series of three runs was performed for each K value between 1 and 10 . STRUCTURE outputs were visualized using STRUCTURE HARVESTER and used for selection of the number of genetic groups that best fitted the data [14] . Estimated fixation index ( FST ) values were retrieved from STRUCTURE . Fourteen microsatellite loci distributed in 13 Leishmania chromosomes were amplified by PCR from log-phase promastigote DNA [15] . The size of the microsatellites was determined by mobility of the PCR products in 4 . 5% agarose gels . Genetic distances were estimated using MSA4 . 05 and Populations-2 . 1 software . UPGMA trees were constructed using MEGA7 . 0 and compared to those generated by kDNA genotyping . Descriptive statistics were used to summarize the demographic , clinical and parasitological characteristics of the sample . Differences in frequencies were explored using McNemar’s test for paired nominal data , and X2 and Fisher's exact tests for unpaired data . Logistic regression analysis was used to identify independent predictors of post-treatment parasite persistence at the lesion site [16] . All variables associated with parasite persistence at the p<0 . 2 level in unadjusted analyses were entered into a multivariable model . The strength of association was determined by calculating the odds ratio ( OR ) and 95% confidence interval ( CI ) using the Wolf method [16 , 17] . For the multivariable model , model fit using the goodness-of-fit test and model discrimination using the c-statistic , were analyzed [17] . All statistical analyses were performed using STATA 12 ( StataCorp , College Station , TX , USA ) .
Seventy patients with active CL , who received standard-of-care treatment with parenteral meglumine antimoniate ( n = 38 ) or oral miltefosine ( n = 32 ) , were included in this study . Characteristics of the sample are summarized in Table 1 . Patients were mostly young adult males of Afrocolombian or mestizo ethnicity . For the majority of participants ( 86% ) , the diagnosis was the first episode of CL . Clinical manifestations were predominantly characterized by single lesions with a median evolution time of 2 . 5 months . L . V . panamensis was the most frequently isolated species ( in 93% of patients ) . Adherence to greater than 90% of the treatment regimen was achieved in 91% of study participants . None of the patients had symptoms or clinical signs of mucosal involvement . Evidence of Leishmania persistence ( defined by amplification of minicircle kDNA or 7SLRNA transcripts in at least one lesion or mucosal sample ) was found in 60% of patients ( 42/70 ) at the end of treatment and in 30% ( 9/30 ) at 13 weeks follow-up . Both of these proportions were significantly lower than the baseline parasite detection rate of 88% ( 62/70 ) before treatment ( Fig 2A , left panel ) . Molecular detection of Leishmania in lesions was achieved in 49% of patients at the end of treatment and in 27% at 13 weeks , compared with 85% before treatment ( Fig 2A , center panel ) . Leishmania was found in swab samples from mucosa in 45% of patients before treatment ( Fig 2A , right panel ) . In contrast to the decline in the proportion of detection of parasites at the lesion site , no significant decrease in the frequency of patients with Leishmania-positive mucosal samples was found at the end of treatment compared with pre-treatment samples . However , only one of 30 patients had a positive mucosal sample at 13 weeks follow-up . Parasite viability was evidenced by detection of 7SLRNA transcripts in 83% of patients ( 58/70 ) before treatment , indicating underestimation of the proportion of patients in which viable parasites were detected . This proportion decreased to 51% at the end of treatment and to 13% at 13 week of follow-up ( Fig 2B , left panel ) . At the end of treatment , viable Leishmania were detected in a similar proportion of patients’ lesion and mucosal samples ( 46% and 55% , respectively; Fig 2B , center and right panels ) . At week 13 , 7SLRNA transcripts were found in samples from lesions of only four patients . Parasite loads at the lesion site were overall lower at the end of treatment compared with parasite loads quantified before treatment ( median parasite loads 14 . 5 and 40 parasites/1000 mammalian cells , respectively ) . Among the clinical and parasitological factors analyzed ( Table 1 ) , individuals with a prior history of CL were less likely to have detectable Leishmania after treatment and clinical cure than those without a previous episode of CL ( 20% vs . 55% , respectively ) . Adjusting for sex , the received drug and the municipality of infection ( which were significant at p<0 . 2 ) , a previous symptomatic episode of leishmaniasis was significantly associated with decreased risk of parasite persistence , with an adjusted odds ratio of 0 . 16 ( 95% CI: 0 . 03–0 . 92 ) . The model fit well and discrimination was good ( Table 2 , c-statistic = 0 . 73 ) . We have reported the use of genotyping of the conserved region of minicircle kDNA to explore the genetic diversity of Leishmania strains causing subclinical and asymptomatic infections [7] . To examine the relatedness of Leishmania populations during active disease and those that persist after treatment , we conducted an initial comparative analysis of multilocus microsatellite typing ( MLMT ) and kDNA genotyping of strains isolated before treatment and at treatment failure ( S1 Table ) from six CL patients . MLMT showed that parasites isolated before treatment grouped within the same branch as those isolated at treatment failure for each individual patient ( Fig 3A , S2 Table ) . For five out of the six analyzed pairs of strains , groups defined by kDNA genotyping ( Fig 3B , S3 Table ) were concordant with those obtained by MLMT . Strains that were concordantly grouped by kDNA genotyping and MLMT shared ≥96% sequence homology in the sequenced region of the conserved block of minicircle kDNA ( Fig 3C ) . We performed kDNA genotyping on paired samples obtained pre-treatment and at the end of treatment from mucosal tissues ( nasal and tonsillar ) and lesions of 21 patients . Good quality sequences were obtained from 11 of the 21 patients: 10 sample pairs from lesion aspirates , two from tonsillar swabs , and one from nasal swabs ( S3 Table ) . Genetic distances were calculated and population structure analyses were conducted alongside a panel of 46 L . V . panamensis clinical isolates obtained from CL patients across Colombia ( Nariño [n = 31] , Valle del Cauca [n = 5] , Risaralda [n = 3] , Choco [n = 6] and Putumayo [n = 1] ) , one L . V . panamensis reference strain , and one L . V . panamensis , 5 L . infantum and 4 L . mexicana kDNA sequences retrieved from NCBI ( S1 Table ) . The selection of L . V . panamensis strains included in this panel and their proportions were reflective of the geographic distribution ( municipality of infection ) of study participants . Within the L . V . panamensis cluster , three subpopulations could be discerned responding primarily to geographic distribution: LP1 and LP2 corresponding to strains predominantly isolated from Nariño and LP3 to strains from Valle del Cauca and Risaralda . All minicircle kDNA sequences obtained from study participants pre- and post-treatment grouped within the LP1 cluster and fixation indices >0 . 3 supported the identified clusters ( Fig 4 ) . These data indicate that persistent Leishmania subpopulations within this group of CL patients were not genetically distinct from those isolated at diagnosis .
The outcome of antileishmanial chemotherapy is determined by clinical parameters of healing and non-healing responses . Although the parasitological response in spleen or bone marrow aspirates is a secondary indicator of therapeutic responsiveness during treatment of visceral leishmaniasis , microbiological clearance is not considered a reliable measure of healing of dermal disease [18 , 19] . Our results revealed a high frequency of Leishmania persistence in mucosal tissues and at the lesion site in patients who were systematically followed 13 weeks after end of treatment , had a median overall treatment adherence of 100% and achieved therapeutic clinical cure of CL . Detection of kDNA molecules does not demonstrate parasite viability despite being rapidly degraded after parasite death [20] . However , due to the short half-life and lability of RNA molecules , detection of Leishmania 7SLRNA gene transcripts substantiates the persistence of viable parasites after clinical cure [10] . Although our current and previous findings [21] and that of others [22] show that clinical resolution of CL is accompanied by a reduction in parasite burden at the lesion site , Leishmania persistence is the norm rather than the exception , suggesting that other factors beyond parasite elimination contribute to the efficacy of antileishmanial therapy . In contrast to the significant reduction in the frequency of Leishmania-positive lesion samples , no decrease in the frequency of parasite detection at mucosal sites ( either tonsillar or nasal mucosal samples ) was found at the end of treatment . This observation suggests that mucosal sites could be a privileged niche for parasite persistence after drug treatment , either by pharmacokinetic differences in drug distribution and accumulation and/or immunological divergence between mucosal vs . skin tissues . Indeed , the detection of Leishmania in mucosal tissues in asymptomatic individuals and in CL patients without signs or symptoms of mucosal involvement [2 , 7] , and the development of mucosal disease years after an episode of CL , support the silent persistence of Leishmania in these anatomical sites . Whether antileishmanial therapy should be aimed at complete parasite elimination ( sterile cure ) is a matter of debate . Persistent infection without signs of disease in animal models has provided evidence that such infection may promote immunity to subsequent infections [23–25] . In contrast , well documented clinical cases of disease reactivation in the context of immune suppression [26–28] or local trauma [29 , 30] and mucosal involvement years after an episode of CL [31–33] support parasite persistence as a risk factor for reactivation of disease . Notably , the presence of a scar typical of CL and/or a positive Montenegro skin test reaction , both indicative of prior infection , have been shown to significantly increase the risk of re-activation of infection and development of CL in a prospective investigation of incidence of infection and disease in endemically exposed communities [33] . We have previously shown that amastigotes were less frequently observable in biopsies of active lesions of patients having scars suggestive of prior leishmaniasis [34] . Concordantly , our present results demonstrate a significant association between history of a previous episode of CL and a lower frequency of detectable Leishmania persistence at the end of treatment in CL patients who received supervised standard-of-care treatment with meglumine antimoniate or miltefosine . Interestingly , a negative skin test at diagnosis has been identified as a risk factor for relapse after treatment with pentavalent antimonials [35] . Therefore , it is plausible that acquired protective immune responses could contribute to enhanced parasite control during therapeutic intervention , thereby reducing the parasite burden , detectable parasite persistence and treatment failure . Although not statistically significant , Leishmania persistence was more frequently detected in men , during treatment with meglumine antimoniate and among individuals from municipalities other than Nariño . In the case of parasitic infectious diseases , sexually mature men are often more susceptible to infection due to hormonal factors [36–38] . Higher susceptibility of male hamsters to L . V . panamensis infection has been associated with a more permissive immune environment for parasite survival [39] . However , the extent and causality of the relationship between sex and persistent infection after drug exposure in humans remains unknown . Pharmacokinetic and pharmacodynamic differences between drugs could also influence the post treatment persistence and burden of infection . Meglumine antimoniate has a short elimination half-life ( ~20h , [40] ) , in contrast to miltefosine which has a first elimination half-life of 7 . 05 days and a terminal elimination half-life of 30 . 9 days [41] . The short half-life of antimonials could result in reduced time of drug exposure of intracellular parasites promoting the higher frequency of parasite persistence . In addition , based on a retrospective cohort study of 230 CL patients , we have recently found that treatment with meglumine antimoniate ( vs . miltefosine ) is a risk factor for therapeutic failure [42] . The potential contribution of other demographic variables to Leishmania persistence such as genetic background or race remains to be determined . However , that individuals from municipalities other than Nariño presented higher frequency of parasite persistence could be indicative of parasite subpopulations or host determinants related to geographic origin , specifically contributing to Leishmania persistence . Selection of parasite subpopulations within the human host is considered to contribute to emergence of drug resistance . Characterization of parasites causing subclinical infections is restricted by the inability to isolate , culture , and propagate the parasite . Based on kDNA sequence homology and population structure analyses , strains and patient samples could be clustered based on geographical origin . However , we found no evidence of selection of parasite subpopulation after treatment and clinical cure . Phenotypic analyses such as drug susceptibility testing , and sequence analyses of kDNA and ITS rDNA have revealed differences in parasites isolated at diagnosis and at treatment failure in individuals with cutaneous and visceral leishmaniasis [8 , 9 , 43 , 44] . It is plausible that implementation of sequencing methods at the genome level could contribute to discriminate and characterize subclinically persistent Leishmania subpopulations . Association analyses of clinical , parasitological and drug-related factors with parasite persistence at later time points ( eg . week 13 and 26 after end of treatment ) were precluded in this study due to sample size limitations . However , our results and those of others demonstrate that currently available treatments do not eliminate Leishmania . This contributes to the proportion of the persistently infected human population , which includes patients with therapeutically achieved cure , those with self-resolution of active disease and individuals with asymptomatic infection . Despite compelling circumstantial evidence , anthroponotic transmission of Leishmania ( Viannia ) remains uncertain . Nevertheless , the high proportion of individuals harboring infection in endemic areas , coupled with the rising importance of domestic transmission of CL , and the demonstrated risk of disease re-activation [45–47] , highlight the importance of this subclinically infected human population in control strategies for CL . Prospective studies designed to interrogate the dynamics of parasite clearance and reactivation of disease are needed to establish the clinical and epidemiological impact of Leishmania persistence .
|
Control of cutaneous leishmaniasis ( CL ) in the Americas is dependent upon active case detection and treatment . The efficacy and effectiveness of therapeutic interventions is based on clinical resolution of disease , not on parasitological clearance . The detection of dermotropic Leishmania in tissues such as nasal and conjunctival mucosa , blood and healthy skin in the absence of signs and symptoms of disease , suggests that despite clinical resolution , parasites persist subclinically . We examined clinical and parasitological factors associated with Leishmania persistence after standard-of-care treatment of CL caused by L . Viannia . We found that a high proportion of CL patients with therapeutically achieved clinical resolution of CL harbor viable Leishmania . A previous episode of CL was found to be a protective factor for parasite persistence . Treated , clinically cured CL patients constitute an important proportion of a persistently infected human population whose clinical and epidemiological significance remains to be determined .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2017
|
Clinical and parasitological factors in parasite persistence after treatment and clinical cure of cutaneous leishmaniasis
|
The evolution of substitutions conferring drug resistance to HIV-1 is both episodic , occurring when patients are on antiretroviral therapy , and strongly directional , with site-specific resistant residues increasing in frequency over time . While methods exist to detect episodic diversifying selection and continuous directional selection , no evolutionary model combining these two properties has been proposed . We present two models of episodic directional selection ( MEDS and EDEPS ) which allow the a priori specification of lineages expected to have undergone directional selection . The models infer the sites and target residues that were likely subject to directional selection , using either codon or protein sequences . Compared to its null model of episodic diversifying selection , MEDS provides a superior fit to most sites known to be involved in drug resistance , and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives . This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance .
Among positively selected evolutionary changes , a distinction can be made between diversifying selection , where any nucleotide substitutions that change the amino acid are favored , and directional selection , where only substitutions towards a small number of target amino acids are selected for . Detection of genes or sites evolving under positive selection [1]–[6] has been dominated by methods which explicitly or implicitly assume diversifying positive selection . This assumption allows evolution to be modeled as a continuous-time Markov process without assuming that any particular residue is the preferred target of substitutions at any sites . For most models of diversifying selection , apart from a single rate governing amino acid change , the process is no different from one site to the next . By contrast , models have been proposed in which specific residues do have special status at specific sites . In models of toggling selection [7] , substitutions away from a site-specific “wild type” amino acid are likely to be followed by reversions to that amino acid . Models of directional selection [8] , [9] allow substitution rates towards a site-specific “target” amino acid to be accelerated . By making a distinction among all possible targets of a substitution , such models allow the detection of positive selection favoring mutations towards one amino acid , even at sites where the overall rate of amino acid change is decreased by purifying selection . For a review of codon models of selection , see [10] . A second distinction is that between selective pressure that is constant over time , and selective pressure that changes over time , possibly instantaneously – we shall refer to the latter as episodic selection . Several authors have studied models that allow evolutionary rates to change over time , including models in which the selective pressure is different on different branches [11]–[14] as well as various models [15]–[17] in which the rate of evolution at any site may change at any point in time . We are specifically interested in the former type of model , under which rate changes occur simultaneously at a particular set of sites - as would be expected under an external change in selective pressure , i . e . episodic selection . This type of selection is applicable to countless real world scenarios that have been studied extensively: examples include the evolution of lysozyme in response to diet changes [18] , the adaptation of HIV to different host populations [14] , the evolution of the rhodopsin pigment following changes in habitat [19] , and the adaptation of HIV-1 [20] , [21] and Influenza A Virus ( IAV ) [22] genes following zoonosis events . For a review on the evidence for episodic selection in large numbers of protein sequences , see [23] . Here , we consider the evolution of drug resistance in HIV-1 following the treatment of a subset of the host population . We expect that selective pressure will be both episodic , with drug-induced adaptive amino acid changes occurring only in patients receiving therapy , and directional , with site-specific target residues increasing in frequency over time in the treated subset . HIV-1 experiences a variety of other selective pressures , most prominently due to host immune response ( e . g . [14] , [24] ) , but because such response is nearly unique in each host , we expect that the majority of concerted selective changes in subjects on treatment will be drug-induced . Previous approaches to detect positive selection driving treatment resistance have had variable success . Crandall et al . [25] showed that normalized ratios of non-synonymous to synonymous substitution counts ( ) obtained by the counting method of Nei and Gojobori [1] failed to show consistent evidence of selection , despite obvious resistance associated substitutions occurring in parallel in many patients . Chen et al . [26] used a contingency-table counting method to characterize positive selection towards specific amino acids in a sample of approximately sequences . However , their approach ignored the phylogenetic relationships between samples which can cause selection to be conflated with founder effects [22] , [27] . Lemey et al . [28] used the branch-site model of Yang and Nielsen [12] – a model of episodic diversifying selection – to analyze the evolution of drug resistance over a transmission chain . A number of sites were inferred to be under positive selection , of which some were associated with drug resistance . Seoighe et al . [8] modeled the evolution of reverse transcriptase between pre- and post-treatment samples from patients . They successfully detected some of the major drug resistance mutations with few false positives . In this paper we aim to demonstrate that explicitly modeling the directional and episodic character of the evolution of drug resistance increases the power and accuracy to detect drug resistance sites . We introduce a codon-based Model of Episodic Directional Selection ( MEDS ) and a model of protein evolution called Episodic Directional Evolution of Protein Sequences ( EDEPS ) , and show that both models outperform models that lack either the episodic or directional components .
Our codon model of episodic directional selection assumes that branches on the phylogenetic tree can be partitioned into foreground ( F ) and background ( B ) subsets a priori . Evolution along background branches is described by a standard codon model ( , see below ) . In the model for foreground branches ( ) , directional selection is incorporated via an elevated rate of substitutions towards a target amino acid . MEDS extends two previously proposed models of coding sequence evolution: 1 ) the episodic component of MEDS is structurally identical to the Internal Fixed Effects Likelihood ( IFEL ) model proposed in [14] , although IFEL is used to detect diversifying selection along internal branches only , and , 2 ) the directional component is introduced in a manner similar to that in the model of directional selection proposed by Seoighe et al . [8] . We used [29] as our baseline codon model: it combines a general time-reversible ( GTR ) model of nucleotide substitution with separate synonymous ( ) and non-synonymous ( ) rates . To facilitate reading , table 1 summarizes the properties of each model . Following Seoighe et al . [8] we add a directional selection parameter to modulate the rate of substitutions to the target residue in the model assigned to foreground branches . If represents the amino-acid encoded by codon , then the instantaneous rates of change between codons and ( ) are given by: ( 1 ) for the foreground and ( 2 ) for the background branches . We assume that does not change significantly between foreground and background branches . Indeed , available evidence ( e . g . [30]–[32] ) suggests that synonymous rate variation among sites is due to biological processes which change slowly , e . g . RNA secondary structure , transcriptional or translational efficiency , relative to the nearly instant change in the selective environment due to the presence of ARV . In principle , the model can readily handle such variation . and can be inferred independently . is the GTR-based rate of the underlying nucleotide substitution from codon to , shared between and . Equilibrium frequency parameters are derived with the corrected estimator [33] . While the same values are used for background and foreground models , when the equilibrium frequencies of will depart from those dictated by , although we do not need to calculate these new equilibrium frequencies explicitly . This feature is essential because directional evolution changes the character frequencies at a site . We also experimented with a version of the model where the factor in the last line of Equation 1 was omitted – this had essentially no impact on the results . To ensure that defines a valid Markov process generator , along the diagonal of we set: ( 3 ) Model fitting proceeds in two stages: ( a ) estimating the parameters shared across sites , and ( b ) site-wise analysis [6] , [34] . The branch lengths and and , without the directional component ( i . e . ) , are first optimized over the entire alignment to obtain gene-wide parameter estimates in the presence of potentially ubiquitous purifying or diversifying selection . The nucleotide rate parameters ( ) and relative branch lengths are then fixed for subsequent analyses . From then , the analysis proceeds site by site . We define the null model by setting , a special case of the alternative directional model ( is free to vary ) , and equivalent to IFEL [14] . The null model has 3 free parameters per site: ( synonymous substitution rate ) , ( non-synonymous substitution rate along foreground lineages ) and ( non-synonymous substitution rate along backfround lineages ) . The alternative model has a single additional parameter , , biasing substitutions towards . To test for selection towards amino acid at a specific site , we obtain maximum likelihood scores for the null and alternative models and perform a likelihood-ratio test ( LRT ) with one degree of freedom based on the asymptotic distribution of the likelihood-ratio statistic . The above test treats nucleotide substitution rates and branch length parameters at a single site as known , even though these are estimated across sites under a simpler model . It is possible that this could affect inference if these estimates were substantially biased . Our simulations suggest that the test performs well in spite of this computational shortcut , and using different models to infer these parameters does not substantially affect the test results on the empirical data we analyze here . Additionally , the asymptotic approximation implicit in MEDS relies on the intuition that when the number of sequences increases , the number of branches in the tree will increase , so that substitutions on those branches will constitute different ( although dependent ) realizations of the process . We note that the asymptotic approximation for our test requires not only many branches but also many foreground branches . While theoretical results justifying our use of the approximation are currently lacking , our simulations ( see below ) suggest that the use of the appears to lead to a conservative test for the conditions we have examined . Scanning a site for selection towards any possible amino acid ( ) involves testing 20 hypotheses , and we employ Bonferroni correction [35] to control the site-wise Type I error rate . For computational efficiency , we skip invariant sites and restrict potential values of to those observed at a given site . Because these reductions are informed by the data , we still employ the -test Bonferroni correction at each site . To assess the importance of the directional component of MEDS , we adapt IFEL to test for episodic diversifying selection along foreground branches and use it as a benchmark for MEDS . As the branches of interest are mostly terminal , the name , IFEL , is no longer appropriate , and we rename the model FEEDS , for ‘Fixed Effects Episodic Diversifying Selection’ . The alternative model for FEEDS is identical to the null model for MEDS , allowing , and to vary for each site . To test for non-neutral selection along foreground branches , we set up a null model with , and use an LRT ( one degree of freedom ) to determine whether the alternative model fits better than the null model . If results in a significant likelihood improvement , we have evidence for diversifying selection along foreground branches . This test for episodic diversifying selection has three features that distinguish it from the popular branch-site model of Yang and Nielsen [12] and Zhang , Nielsen and Yang [36]: 1 ) it uses a sitewise likelihood-ratio test [5] , otherwise known as a fixed effects likelihood [6] approach , 2 ) it allows site-to-site synonymous rate variation , which has been shown to be ubiquitous and can cause spurious detection of diversifying selection if ignored [29] and 3 ) it allows diversifying selection on the background branches in both the null and alternative models . MEDS shares these properties , allowing us to attribute any performance differences specifically to the directional component of MEDS . Throughout the analyses we also compare our results against DEPS ( full results in tables S1 to S3 ) , a method for detecting non-episodic directional selection proposed by Kosakovsky Pond et al . [9] . DEPS identifies sites with increased substitution rates towards specific amino acids , but it differs from MEDS in three ways: 1 ) DEPS models directional selection at the amino acid level rather than the codon level , 2 ) DEPS uses a Random Effects Likelihood ( REL ) framework to bias selection towards target amino acids across all sites , relying on an empirical Bayes analysis to identify sites of interest and 3 ) in DEPS , directional selection affects all branches of the phylogeny . It is a straightforward exercise to modify DEPS to incorporate the episodic nature of MEDS – namely , we restrict accelerated substitutions towards a target residue ( and retard substitutions away from it ) to foreground branches , while background branches always evolve according to the baseline protein substitution model specific to HIV-1 [37] . The entire testing framework of DEPS , as described in Kosakovsky Pond et al . [9] , applies without change . It is well known that amino acid substitution rates depend on the residues involved ( e . g . see [38] ) , and specifying a baseline model which includes unequal substitution rates provides a qualitative advance over MEDS . Conversely , because DEPS works with protein sequences , the natural proxy of approximately neutral evolution ( the rate of synonymous substitutions ) is not available . All models and their accompanying LRTs are implemented in a HyPhy Batch Language script [39] , and all code and test datasets are available on the MEDS section of the HyPhy wiki ( www . hyphy . org ) and included in the latest HyPhy distribution ( version 2 . 0020101225 or later ) . We analyzed three HIV-1 datasets obtained from the South African mirror of the Stanford HIV Drug Resistance Database ( HIVdb ) [40] , [41] . Synthetic datasets were generated by simulation to investigate the power and false positive rate of MEDS . The primary goal of this paper is to show that MEDS and EDEPS perform well on medium-sized datasets constructed under a variety of conditions . Every empirical dataset includes sequences sampled from both treated and untreated patients , but we varied the inclusion criteria from one dataset to the next . An ideal dataset for detecting drug resistance would include pre- and post-treatment samples from the same patients ( as in our reverse transcriptase dataset ) , but often such data are not available , e . g . when sequences are obtained from patients experiencing regimen failure . To evaluate the performance of MEDS and EDEPS when pre- and post-treatment sequence pairs were not available ( our protease and integrate datasets ) , we selected pre-treatment sequences using heuristic measures of proximity to the post-treatment samples , as one would be forced to do under such circumstances . Exactly which factors are responsible for performance variation is left as a topic for future research . The objective of each analysis was to detect sites ( and corresponding amino acids ) that are involved in drug resistance . For validation , we used the curated list of drug resistance associated mutations ( DRAMs ) which is available from the Stanford HIVdb ( http://hivdb . stanford . edu ) . This list is produced every year and approved by the International AIDS Society ( http://www . iasusa . org/resistance_mutations/ ) . These mutations have been rigorously validated with genotype-phenotype and genotype-clinical data and are known to confer varying levels of resistance to one or more antiretroviral agents – they can therefore be used as a ground truth for evaluating the performance of our methods . We screened each sequence for evidence of recombination ( known to have a biasing effect on selection detection , e . g . [42] ) using SCUEAL [43] and excluded any sequences showing support for either inter- or intra-subtype recombination , and using the Rega HIV-1 Subtyping tool Version 2 . 0 [44] , excluding any sequences with clear inter-subtype recombination .
MEDS detected twenty substitutions at seventeen sites under significant directional selection at , after correcting for multiple tests ( see tables 2 and S4 ) . Of these , five are known NRTI drug resistance associated mutations ( DRAMs ) ( 41L , 116Y , 151M , 184V and 215F ) and six are known NNRTI DRAMs ( 100I , 103N , 181I , 188L , 190S and 230L ) . Additionally , 228R is listed as an accessory NRTI mutation . The eight detected substitutions that have not been experimentally or clinically associated with drug resistance are 64K , 98S , 104Y , 151Q , 165L , 188Y , 215T and 286A . Interestingly , at three of these sites ( 151 , 188 and 215 ) selection was detected both towards the wildtype and towards resistant residues . EDEPS agreed with MEDS on eleven sites , detected additional DRAMs 62V , 77L and 115F , missed two MEDS-reported DRAMs ( 41L and 116Y ) , and found episodic selection at 162S and 174R which are not known to confer drug resistance . Remarkably , FEEDS detected only six sites under diversifying selection ( table S5 ) , two of which are known resistance mutations , strongly supporting the inclusion of a directional component in the model . A continuous directional selection model ( DEPS ) detected 46 sites under directional selection with Bayes factors ( see table S1 ) , only ten of which are on the HIVdb list . This indicates that focusing our attention on branches where the evolutionary environment shifts is advantageous for finding evidence of adaptive response to such shifts . MEDS detected nine substitutions under directional selection at ( tables 3 and S6 ) . Of these , two are major DRAMs ( 90M and 84V ) . Three are accessory polymorphic mutations ( 13V , 60E and 93L ) under selective pressure from the drugs . 74S is a non-polymorphic accessory mutation . EDEPS agreed with MEDS on three ( 13V , 84V and 90M ) , detected one more major mutation , 82A , and an accessory mutation at 71V . Interestingly , 60E and 61E found by MEDS involve substitutions ( and ) which , in HIV , are much more frequent than the mean substitution rate [37] . Because MEDS sets the background rate of non-synonymous substitutions to the same value for all pairs of residues , it could use to compensate for the overall underestimation of rates that are much greater than the mean rate . FEEDS identified six sites involved in diversifying selection ( table S7 ) , with all six listed on HIVdb . In addition to two sites already detected by MEDS ( 74 and 90 ) , sites 10 and 71 are listed as accessory mutations , while 54 and 82 are major resistance mutations . DEPS appeared to be much more conservative on this dataset , detecting four sites under directional selection , two of which are listed on HIVdb ( see table S2 ) . MEDS detected six substitutions under significant directional selection at the 1% level ( see tables 4 and S8 ) . Four ( 140S , 143R , 148H and 155H ) appear on the HIVdb list of mutations associated with a fold decrease in Raltegravir susceptibility . Two are listed as mutations selected by Raltegravir ( 72I and 97A ) . EDEPS confirmed five DRAMs ( 97A , 140S , 143R , 148H and 155H ) , together with a 163R accessory substitution and a 221Q mutation which is not a known DRAM . FEEDS found seven sites under diversifying selection ( table S9 ) , six of which are known resistance mutations . 230 is the only correctly identified resistance site in the integrase dataset that is detected as being under diversifying selection by FEEDS , but not directional selection by MEDS . 230 R and N are listed as selected by Raltegravir . DEPS detected 39 substitutions under directional selection ( see table S3 ) , nine of which appear on the HIVdb list . Comparing the fit of FEEDS and MEDS on known resistance sites in all three datasets , LRTs reject a null model of FEEDS in favor of MEDS on 24 sites , with FEEDS being favored on five ( four from protease and one from integrase ) . Note that FEEDS might still be useful for detecting these sites , but the LRT demonstrates that MEDS is a better model of the process . This suggests that episodic directional selection is , in most cases , a better characterization of the evolution of drug resistance . Overall , FEEDS detects fourteen true positives , while MEDS and EDEPS detect 24 each ( although not the same 24 ) . Where FEEDS appears to have a reasonably low rate of false positives but misses a large number of true positives , DEPS detects a large number of true positives but with a very high false positive rate . This is expected as DEPS will detect substitutions under selection along background branches that are not related to drug resistance . The power of MEDS , like that of other codon methods , strongly depends on the information content of the sequences , specifically on the number of times that substitutions toward the target occur along the foreground lineages . For example , even when is 1000 , no substitutions towards occur on half the sites simulated on the phylogeny with sixteen foreground branches . The primary reason for this is that affects only the instantaneous substitution rate from a codon to its direct neighbors; if none of the direct neighbors of are visited along a foreground branch , a change in will not affect the process . Hence , we tabulate MEDS results only for sites with at least one substitution towards the target on any foreground branch . Table 5 shows that the power is positively correlated with . MEDS appears to be quite powerful , even when the number of foreground branches is small , achieving , for example , power with with only eight foreground branches . Table 6 displays the power of MEDS conditioned on the number of substitutions towards the target on foreground branches . With only one substitution there is almost no power , but moderate power ( ) occurs with two substitutions towards , and with five or more substitutions towards , the power is almost . For data simulated with two target residues , each on eight foreground branches , the occurrence of at least one substitution towards both targets is infrequent . From sites simulated with values of 2 , 5 and 10 , this occurs only 58 times , and is never detected . From sites simulated with for both targets , substitutions to both targets occur 174 times . MEDS detects substitutions to at least one target in of such sites , but only detects substitutions to both targets in of such sites . With , we see 306 of 1600 sites with substitutions to both targets , and MEDS detects substitutions to at least one target in of these sites , and to both targets in . Table 7 shows how the power increases with the number of substitutions towards both targets on the foreground branches . Since there too many possible combinations and too few observations , we display power in a cumulative manner ( i . e . substitutions towards both targets ) . MEDS behaves conservatively . With data simulated under the null model , far fewer sites are identified as under episodic directional selection than would be expected from the nominal p-value thresholds . Across all four foreground configurations , only one false positive detection ( , with Bonferroni correction ) occurs on the 32 foreground branch phylogeny , and none on the others . With , with 4 , 8 , 16 and 32 branches , we have false positive rates of 0 , 0 . 0025 , 0 . 0075 and 0 . 01; with , we have 0 . 005 , 0 . 005 , 0 . 0125 and 0 . 02 , respectively . This is most likely due to FEL methods being generally conservative [6] as well as the conservative nature of Bonferroni correction . The effect of the correction is compounded because increasing the frequency of one amino acid reduces the frequency of the others , and thus the twenty tests are not independent . Table 8 shows the false positive rate for alignments simulated under site specific equilibrium frequencies . MEDS is still conservative under this scenario , and the false positive rates do not appear to be influenced by the concentration parameter .
We have proposed a codon ( MEDS ) and a protein ( EDEPS ) model of episodic directional selection , and demonstrated their performance on three HIV-1 datasets , where drug-induced directional episodic selection is expected to operate . We have also proposed a model of episodic diversifying selection ( FEEDS ) , to rigorously evaluate the importance of modeling the directional component of natural selection . As expected , on all datasets , our episodic directional tests strongly outperform a test for continuous directional selection ( DEPS ) for detecting drug resistance sites . The assumptions of DEPS are inappropriate for the analysis of episodic selection , where selection is limited to specific regions of the phylogeny , because DEPS assumes uniform selection over the whole phylogeny . This serves as a caution against using suboptimal models , rather than a criticism of DEPS . We tested MEDS with extensive simulations . MEDS is a conservative test , even when strong constraints on the amino acid state space are introduced in the form of site-specific equilibrium frequencies . Under the alternative model , good power is achieved even when relatively few substitutions towards target amino acids take place along foreground branches . When we deviate from the alternative model and elevate the substitution rate towards several target residues , the power to detect both targets is lower than it would be assuming independence . This reduction in power is expected: as the number of targets along foreground branches increases , the directional nature of the process is lost . Hughes [48] argues that diversifying selection is only appropriate for modeling pathogen-host co-evolution , and that the constantly shifting environment is required for the standard diversifying selection model to be appropriate . Our results highlight that models of diversifying selection also serve as reasonable approximations in instances where selective constraints allow escape to many different residues , such as codon 54 in protease , which has V , T , A , L and M as major drug resistant residues . However , at most sites conferring drug resistance , directional models better approximate reality – positive selection acts only on one or a few specific mutations , while the rest are suppressed by purifying selection . The simulations presented in Table 7 illustrate how much power MEDS can be expected to have in cases such as site 54 in protease . This example also suggests a future extension of MEDS , where instead of considering one target residue at a time , substitution rates could be elevated towards classes of target residues . Another interesting property of directional models is exemplified by a substitution in the protease dataset . 93L is a polymorphic mutation selected for by protease inhibitors . Despite L already being the most common residue in subtype C , the model detects selective pressure towards it – the proportion of L residues is indeed lower in nave sequences . At the population level this appears as purifying selection: the most common amino acid increases in frequency . This is nevertheless detected by our test . Far from being problematic , such information could be useful for directing treatment , if it turns out that patients with I at position 93 are more susceptible to PI therapy . Such observations should , of course , be directly verified with clinical data . There are clear differences in organism-wide amino acid exchangeabilities in HIV-1 [37] , yet the null model of MEDS ( and the vast majority of other codon-models ) posit that the non-synonymous substitution rate does not depend on the residues . We evaluated the effect of this assumption by comparing MEDS with an episodic version of DEPS – a test that specifically incorporates a heterogeneous exchangeability matrix in the evolutionary model . With a few exceptions , MEDS and EDEPS return overlapping sets of directionally evolving residues and identify the same targets . There are several sites in protease and integrase , where MEDS may be misclassifying non-uniform exchangeabilities as directional selection , hence another extension of MEDS would be to incorporate multiple non-synonymous substitution rates [38] . MEDS and EDEPS were designed with HIV-1 drug resistance in mind , but should be applicable wherever episodic directional selection occurs along multiple lineages . To use the models , two specific conditions must be met: 1 ) Lineages expected to be under directional selection must be known a priori , at least approximately . This is necessary to partition the phylogeny into foreground and background regions . 2 ) A rich collection of background sequences are needed . With HIV-1 , this translates to requiring treatment naive sequences . Variety in these sequences is also important . If all the background sequences were so closely related that the foreground and background regions were separated by a single branch , if would be difficult to separate directional selection from founder effects , which would result in a loss of power . If the background sequences are spread about the phylogeny , however , founder effects are rendered unlikely and the test for directional selection should be well powered . With HIV-1 drug resistance datasets , the foreground labeling strategy might prove important . On the RT dataset , branch-labeling was straightforward , as we had access to pre-treatment sequences for each patient . This is not the case for most real-world datasets , and other approximate labeling schemes , as well as the robustness of the results to these differences , should be investigated . Another consideration is the rooting of the tree . With directional models , the expected amino acid frequencies change across the phylogeny , and the position of the root becomes important [9] . With MEDS and EDEPS , the directional component only affects foreground branches . Consequently , the tree can be rooted on any background branch and the likelihood will be unaffected [49] . Amidst growing concerns about an epidemic of circulating drug resistant HIV-1 , the WHO and SATuRN are recommending a scale-up of drug resistance surveillance [41] , [50] . This is to ensure the long-term success of the world's largest antiretroviral treatment programs , located in Africa . We see improved models of the sequence evolution playing a role in characterizing local differences in treatment resistance patterns , perhaps driven by different treatment regimens , adherence and transmission dynamics , and possibly identifying new resistance mutations .
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When exposed to treatment , HIV-1 and other rapidly evolving viruses have the capacity to acquire drug resistance mutations ( DRAMs ) , which limit the efficacy of antivirals . There are a number of experimentally well characterized HIV-1 DRAMs , but many mutations whose roles are not fully understood have also been reported . In this manuscript we construct evolutionary models that identify the locations and targets of mutations conferring resistance to antiretrovirals from viral sequences sampled from treated and untreated individuals . While the evolution of drug resistance is a classic example of natural selection , existing analyses fail to detect the majority of DRAMs . We show that , in order to identify resistance mutations from sequence data , it is necessary to recognize that in this case natural selection is both episodic ( it only operates when the virus is exposed to the drugs ) and directional ( only mutations to a particular amino-acid confer resistance while allowing the virus to continue replicating ) . The new class of models that allow for the episodic and directional nature of adaptive evolution performs very well at recovering known DRAMs , can be useful at identifying unknown resistance-associated mutations , and is generally applicable to a variety of biological scenarios where similar selective forces are at play .
|
[
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"hiv",
"evolutionary",
"modeling",
"biology",
"computational",
"biology",
"viral",
"diseases"
] |
2012
|
Modeling HIV-1 Drug Resistance as Episodic Directional Selection
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The Huntington’s disease ( HD ) protein , huntingtin ( HTT ) , is a large protein consisting of 3144 amino acids and has conserved N-terminal sequences that are followed by a polyglutamine ( polyQ ) repeat . Loss of Htt is known to cause embryonic lethality in mice , whereas polyQ expansion leads to adult neuronal degeneration . Whether N-terminal HTT is essential for neuronal development or contributes only to late-onset neurodegeneration remains unknown . We established HTT knock-in mice ( N160Q-KI ) expressing the first 208 amino acids of HTT with 160Q , and they show age-dependent HTT aggregates in the brain and neurological phenotypes . Importantly , the N-terminal mutant HTT also preferentially accumulates in the striatum , the brain region most affected in HD , indicating the importance of N-terminal HTT in selective neuropathology . That said , homozygous N160Q-KI mice are also embryonic lethal , suggesting that N-terminal HTT alone is unable to support embryonic development . Using Htt knockout neurons , we found that loss of Htt selectively affects the survival of developing neuronal cells , but not astrocytes , in culture . This neuronal degeneration could be rescued by a truncated HTT lacking the first 237 amino acids , but not by N-terminal HTT ( 1–208 amino acids ) . Also , the rescue effect depends on the region in HTT known to be involved in intracellular trafficking . Thus , the N-terminal HTT region may not be essential for the survival of developing neurons , but when carrying a large polyQ repeat , can cause selective neuropathology . These findings imply a possible therapeutic benefit of removing the N-terminal region of HTT containing the polyQ repeat to treat the neurodegeneration in HD .
Huntington’s disease ( HD ) is caused by a polyglutamine expansion in the N-terminal region of huntingtin ( HTT ) . Despite the protein’s ubiquitous expression in the brain and body , mutant HTT causes selective neuronal degeneration in the brain , which is characterized by the preferential loss of neuronal cells in the striatum in the early disease stage and extensive neurodegeneration in a variety of brain regions in later disease stages [1] . The progressive neurodegeneration is consistent with late-onset neurological symptoms in HD , which become increasingly severe with age and lead to death 10–15 years after the onset of symptoms . Thus , age-dependent mutant HTT toxicity is a characteristic of HD , and understanding the mechanism behind this toxicity is key to developing effective treatments for HD . Normal HTT consists of 3144 amino acids and is considered to be a scaffold protein that associates with a number of other proteins and participates in a wide range of cellular functions , including intracellular trafficking of a variety of proteins [2 , 3] . In support of HTT’s important function , knocking out the Htt gene leads to the early death of mouse embryos at embryonic day 8 . 5 [4–6] . Interestingly , mutant HTT with expanded polyglutamine can rescue this embryonic lethality phenotype [7 , 8] , indicating that the expansion of polyglutamine does not affect the early development of the animal , but causes late-onset neurodegeneration and neurological symptoms . Also , the polyQ domain appears to be non-essential for low species . For example , the polyQ stretch is absent in the N-terminal of HTT in Drosophila melanogaster and Ciona intestinalis ( sea squirt ) and contains four glutamines in fish , birds , and amphibians [9] . The proline-rich domain ( polyP ) , which follows the polyQ stretch , is found only in mammals . Although polyP may contribute to the solubility of HTT [10] , deletion of polyQ or the proline-rich domain does not affect mouse development [11 , 12] . N-terminal HTT fragments can be generated by proteolytic processing and other mechanisms . For example , exon 1 HTT is produced by aberrant splicing of the HTT gene [13] . It is important to investigate whether N-terminal HTT is required for the early development or survival of mammalian animals . Although evidence from a variety of HD mouse models indicates a toxic gain of function from polyglutamine expansion , important issues remain to be addressed . First , we know that HD mice expressing full-length HTT show preferential accumulation of N-terminal HTT aggregates in the mouse striatum [14–17] , although whether this striatal accumulation requires the context of full-length HTT remains unclear . Another important issue is whether N-terminal HTT is essential for early embryonic or animal development . Addressing this issue is especially critical for understanding the function of HTT , since extensive studies have focused on the N-terminal 17 amino acids of HTT that are very conserved in various species [18–23] . Given the important issues above , we established a new HD mouse knock-in model that expresses N-terminal mutant HTT ( 208 amino acids ) with 160Q under the control of the endogenous mouse promoter . These heterozygous mice show a preferential accumulation of mutant HTT in striatal neurons and age-dependent neurological phenotypes . Moreover , expression of N-terminal HTT cannot rescue the embryonic lethal phenotypes in the absence of full-length mouse Htt . Rather , we found that HTT lacking the N-terminal region ( 1–237 amino acids ) was able to rescue the degeneration of cultured neurons due to the loss of Htt . Our studies suggest that the N-terminal region of HTT is nonessential for developing neurons or neuronal survival , and therefore can be removed to eliminate polyQ toxicity in treating HD .
To investigate the function and toxicity of N-terminal mutant HTT , we replaced exon1 of the mouse Htt gene with the cDNA encoding the first 208 amino acids of human HTT containing 160Q ( N160Q ) and a stop codon via gene targeting in mouse embryonic stem ( ES ) cells ( Fig 1A ) . The targeting vector containing two loxP sites that flank the human HTT DNAs and neomycin resistant gene was transfected into mouse ES cells . Positive ES cells containing the targeted HD gene were then injected into C57B1/6J blastocysts to generate heterozygous N160Q KI mice . PCR and DNA sequencing analyses of genomic DNA verified the presence of the large CAG repeat encoding 160Q ( Fig 1B ) . Comparing heterozygous N160Q KI with heterozygous full-length HTT ( F140Q ) KI mice via RT-PCR analysis of HTT mRNA expression indicated that N160Q is expressed at a lower level than full-length mutant HTT in F140Q KI mice ( Fig 1C ) . Quantitative analysis of mRNA expression suggests that N160Q is expressed at 51–75% of full-length mutant HTT in different brain regions ( Fig 1D ) . The insertion of the Neo selection DNA in the intron after exon1 may interfere with splicing or RNA stability , resulting in a lower level of mutant HTT mRNAs than endogenous normal HTT mRNAs . The expression of N160Q at the protein level in the mouse brain is confirmed by western blotting with 1C2 antibody that specifically reacts with the expanded polyQ repeats in N-terminal mutant HTT ( Fig 1E ) . We used 1C2 immunostaining to examine the distribution of N160Q in KI mice and found that N160Q KI mice at 1 year of age show obvious HTT staining in the striatum , which is more abundant than in other brain regions , such as the cortex and cerebellum ( Fig 2A ) . This preferential accumulation of N160Q in the striatum is more abundant in mice at the age of 16 months and is similar to F140Q KI mice that express full-length mutant HTT at 12 months ( Fig 2B ) . Like F140Q and other KI mice in which mutant HTT accumulates in the striatum in an age-dependent manner [14–17] , N160Q-KI mice show a progressive accumulation of mutant HTT in the striatum ( Fig 2C ) . Thus , the first 208 amino acids of HTT appear to be capable of mediating the preferential distribution of HTT in the striatum . We know that HD KI mice show reactive astrocytes , an early neuronal injury event [24–27] . Examining N160Q KI mice also revealed reactive astrocytes , which are more abundant in the striatum in older mice ( 12 months ) than young mice ( 6 months ) ( Fig 3A ) . Considering that N160Q preferentially accumulates in the striatum , we wondered whether the extent of reactive astrocytes is associated with the expression of N160Q . We used immunocytochemistry to examine the reactive astrocytes and quantified the relative extent of reactive astrocytes . By focusing on the striatum , cortex , and cerebellum in N160Q mice at 12 months of age , we found that the striatum shows the most abundant reactive astrocytes ( Fig 3B ) . Using the same method as the one we reported recently to quantify reactive astrocytes [28] , we verified that reactive astrocytes are the most abundant in the striatum and more abundant in the cortex than the cerebellum ( Fig 3C ) . All these findings indicate that the accumulation of N160Q can cause age-dependent reactive astrocytes or early neuronal damage . Slow and progressive development of neurological symptoms is the characteristic of HD KI mice [14–16] . Similarly , N160Q-KI mice also developed age-dependent neurological phenotypes . They grew normally as wild type mice from birth till 16 months , and some N160Q-KI mice then died earlier than wild type mice ( Fig 4A ) ; however , only the 24-month-old N160Q-KI mice experienced body weight loss ( Fig 4B ) . Rotarod and balance beam tests are commonly used to assess the motor function of HD mice . We found that N160Q KI mice started to show impaired performance on the rotarod at 8 months compared with age-matched WT mice ( Fig 4C ) . The balance beam test also revealed that N160Q-KI mice at 12 months of age took a longer time to cross the beam and slipped more frequently , which reflects a motor coordination defect ( Fig 4D ) . Thus , like full-length HTT KI mice , N160Q-KI mice develop age-dependent neurological phenotypes , though these phenotypes are milder than those in HD mice that overexpress transgenic mutant HTT . Establishment of N160Q-KI mice allowed us to explore whether expression of the N-terminal HTT only could support early embryo development . Thus , we crossed heterozygous N160Q-KI mice and then genotyped all live newborn mice; however , we were unable to identify any live pups with the homozygous N160Q-KI genotype . This fact suggests that homozygous N160Q-KI embryos may not be able to develop to term . To confirm this , we collected embryos at day E8 . 5 and E9 . 5 . Analysis of the embryos revealed that all healthy embryos were either the wild type or heterozygous N160Q-KI genotype . Some abnormal or absorbed embryos were identified as homozygous N160Q KI genotype at a 25% rate ( Fig 5 ) . Thus , unlike a large polyQ repeat ( 175Q ) that does not affect the viability of newborn mice [29 , 30] and transgenic full-length mutant HTT with expanded polyQ repeats that can rescue the Htt loss-mediated embryonic lethal phenotype [7 , 8 , 15] , N-terminal HTT ( 1–208 amino acids ) with an additional expanded polyQ repeat cannot support early embryonic development of mice when full-length mouse Htt is absent . We know that HTT is important for brain development and the survival of developing neuronal cells [31–35] , so the failure of N160Q-KI to rescue Htt loss-mediated embryonic lethality led us to investigate whether an HTT region other than the N-terminal region is important for neuronal development and survival . To this end , we generated a truncated HTT ( tHTT ) that lacks the first 237 amino acids , including the polyQ domain ( Fig 6A ) . This truncated htt was expressed in HEK293 cells via transfection , and its expression was examined by western blotting with the antibody 2166 , which was generated with HTT peptides containing 181–810 amino acids and reacts with the mouse Htt epitope at 421–434 amino acids [36] ( Fig 6B ) . We also expressed normal full-length HTT containing 23Q as a control for comparison ( Fig 6C ) . Immunocytochemical staining shows that tHTT , like full-length HTT ( fHTT ) , is distributed predominantly in the cytoplasm ( Fig 6C ) . Transfection of tHTT in cultured primate neurons from the mouse cortex also verified that tHTT is distributed in the cytoplasm ( Fig 6D ) . Since normal HTT is predominantly present in the cytoplasm and an expanded polyQ stretch can lead to the nuclear accumulation of HTT and other cytoplasmic proteins [37] , the cytoplasmic distribution of tHTT also suggests that this truncated HTT can perform the normal function of full-length HTT in the cytoplasm . Next , we examined whether tHTT is protective for neuronal cells that have depleted the expression of endogenous Htt . We first used PC12 cells because they can be efficiently transfected with shRNA to suppress Htt expression , and their neurite extension can be used to assess the effect of Htt deficiency on the neuronal differentiation property . We used shRNA , which is reported to efficiently inhibit HTT expression [38] , to suppress Htt expression in the presence or absence of tHTT in PC12 cells ( Fig 7A ) . This shRNA vector also expressed GFP , allowing us to identify cells in which Htt expression is inhibited , and tHTT expression could be identified by anti-HTT labeling . Double immunofluorescent staining showed that shRNA expression alone inhibited neurite extension of PC12 cells in response to NGF , while tHTT could prevent this neuritic extension defect ( Fig 7B ) . However , expression of nHTT was unable to prevent the neuritic defect caused by the loss of Htt ( Fig 7C ) . Quantification of the number of PC12 cells with neurites longer than two cell bodies confirmed that tHTT , but not nHTT , could reverse the neuritic extension defect caused by shRNA inhibition of Htt expression ( Fig 7D ) . Neurite extension is largely dependent on intracellular trafficking , and htt is known to be involved in intracellular trafficking [1 , 3] . Further , a previous study showed that HTT binds to dynein and acts in a complex along with dynactin [39–42] and Hap1 to facilitate vesicular transport , while HTT residues 600–698 are both necessary and sufficient for binding to dynein [39 , 40] . Thus , we generated a mutant HTT ( dHTT ) with deletion of the dynein-binding region ( 600–698 aa ) and expressed it in PC12 cells , as well ( Fig 7E ) . In this experiment , the transfected HTT is linked to RFP by a self-cleaving 2A peptide ( P2A ) , whose self-cleavage in cells can separate RFP and transfected HTT . Thus , red fluorescent cells would also express transfected HTT and could be identified for examination . Compared with tHTT , dHTT was unable to prevent the neurite extension defect caused by HTT-shRNA ( Fig 7F ) . This result supports the idea that the region in HTT important for binding to dynactin and intracellular trafficking is required for neuronal differentiation and development . To further test the protective effect of tHTT in primary neuronal cells , we cultured neurons from conditional Htt knockout mice in which the mouse Htt gene is flanked by loxP and can be deleted upon Cre expression [43] . After cultured striatal cells were infected with adenoviral GFP-Cre , cells showing GFP signals should have the Htt gene depleted . We found that neuronal cells displayed fragmented neurites after the Htt gene is depleted via Cre expression . However , there was no morphological change in cultured astrocytes that also express GFP-Cre ( Fig 8A ) . We then counted the percentage of neurons and astrocytes with GFP-Cre relative to cells that were infected by adenoviral GFP without Cre and found there was a significant reduction in the surviving neurons after the Htt gene is deleted by Cre ( Fig 8B ) . Western blotting was also performed to validate the depletion of Htt expression in primary cell cultures from the brain of the homozygous floxed Htt mice ( Fig 8C ) . Next , we expressed nHTT , tHTT , and fHTT in conditional Htt knockout neurons that express GFP-Cre . N-terminal HTT ( nHTT ) was unable to rescue the neuritic degeneration of neurons after deletion of the Htt gene , while fHTT and tHTT were capable of rescuing this neuritic degeneration , which was demonstrated by both double immunofluorescent staining ( Fig 8D ) and quantitative analysis of the numbers of neurons with long neurites ( Fig 8E ) . Although we used adenoviral GFP as a control to compare with adenoviral GFP-Cre to identify the specific effect of Cre-mediated Htt depletion , tamoxifen-induced Cre expression can also lead to gene depletion without overexpressing viral Cre . Thus , we crossed conditional Htt KO mice to transgenic mice that express ER-Cre , which can be activated by tamoxifen to enter the nucleus and delete the floxed genes by loxP sites ( Fig 9A ) . The primary cortical neurons were isolated from the crossed mice and co-transfected with RFP-tHTT or RFP-dHTT and GFP , as GFP immunofluorescent staining could clearly reveal the processes of transfected neurons . The transfected cells were then treated with tamoxifen to deplete endogenous mouse Htt . We found that RFP-tHTT , but not RFP-dHTT that lacks the dynein-binding region , could rescue the defective processes of neuronal cells caused by the depletion of endogenous mouse Htt ( Fig 9B ) . Western blotting confirmed that mouse Htt is depleted by tamoxifen treatment ( Fig 9C ) , and counting the numbers of transfected cells also showed that expression of tHTT , but not dHTT , was able to rescue neuritic degeneration caused by the loss of mouse Htt ( Fig 9D ) . Taken together , loss of Htt appears to affect the differentiation and neurites of developing neurons , and this adverse effect can be alleviated by expression of tHTT without the N-terminal region .
The function of the N-terminal region containing the polyQ domain is an important focus of investigation . This is because the first 17 amino acids of HTT are well conserved in different species , and modifying them can alter HTT toxicity [19 , 20 , 22] and deletion of N17 can facilitate the accumulation of mutant Htt in the nucleus of BACHD transgenic mice [20] . However , although polyQ expansion in N-terminal HTT can cause age-dependent neurodegeneration , this expansion does not affect early development in most HD patients . Consistently , mice expressing mutant HTT with large polyQ repeats show no abnormalities in embryonic and early development . Also , transgenic full-length human HTT with expanded polyQ repeats can rescue the embryonic lethal phenotype in mice [8 , 44] . Deletion of the N-terminal 17 amino acids , polyQ , or the proline-rich domain does not affect mouse development [11 , 12] . Moreover , the N-terminal region of HTT ( <500 amino acids ) interacts with a large number of partners [45 , 46] . All these findings underscore the importance of understanding the role of the N-terminal region of HTT in addition to the polyQ domain . We know that HTT is important for brain development and neuronal survival [29] . Conditional knockout of Htt in the postnatal period by expression of Cre under the CamKII promoter is reported to also lead to the degeneration of a limited number of neurons and testis cells in mice [43] . By studying N160Q KI mice , our findings provide evidence that N-terminal HTT is unable to support embryonic development but can cause age-dependent neurological symptoms . In our new KI mouse model , the mouse Htt exon 1 is replaced with human N-terminal HTT ( 1–208 aa ) . Because long polyQ repeats can cause juvenile HD cases , it may be that a large polyQ repeat can affect the function of HTT in early development . Our studies cannot exclude the possibility that 160Q affects the function of N-terminal HTT during early embryo development so that no homozygous N160Q KI mice were born . Thus , a rigorous control would be to establish knock-in mice that express N-terminal HTT with a normal polyQ repeat to examine whether N-terminal HTT is required for embryonic development . The milder phenotypes of N160Q KI mice can be explained by multiple possibilities . We found that N160Q expression is lower than the full-length mutant HTT in heterozygous KI mice that also express one allele of the mutant HTT gene . Insertion of the Neo selection cassette in intron 1 that follows exon 1 may affect the splicing and/or stability of mRNA encoded by the exon 1 DNAs , thus resulting in the low level of N160Q transcripts in the mouse brain . Exon 1 mutant HTT can be produced by aberrant splicing and contributes to the pathology of HD KI mice [13] . However , our N160Q KI mice express a cDNA that is unable to produce exon 1 via aberrant splicing , such that the lower level of exon 1 mutant HTT in N160Q KI mice may also account for the less severe phenotypes . Furthermore , the size of N-terminal HTT fragments may determine the severity and nature of HD neuropathology , as smaller N-terminal mutant HTT fragments are more prone to misfolding and aggregation . Our N160Q KI mice express a longer N-terminal HTT fragment ( 1–208 aa ) than R6/2 mice , which express the smaller exon 1 mutant HTT ( 1–67 aa ) at a level similar to endogenous HTT and show much more severe and progressive neurological phenotypes [47] . Nevertheless , N160Q-KI mice show typical age-dependent HTT aggregation in the brain and late-onset neurological symptoms . The neurological symptoms in N160Q-KI mice are milder than in those HD mice that overexpress mutant HTT . N160Q-KI mice also show preferential accumulation of mutant HTT in striatal neurons , in the same manner as full-length HTT KI mice [14–16] . This preferential accumulation mirrors the vulnerability of striatal neurons in HD patient brains . The interesting finding in our study is that the first 208 amino acids of HTT are sufficient to mediate the selective accumulation of mutant HTT in striatal neurons , suggesting that proteolytic N-terminal fragments of mutant HTT can also selectively accumulate in striatal neurons . This finding will spur further studies to explore the critical region in the N-terminal HTT sequences and mechanisms that underlie the selective striatal neuronal degeneration in HD . Heterozygous Htt KO mice with the expression of Htt at 50% of wild type Htt are known to develop and grow normally . The failure of N160Q to rescue embryonic lethality could be due to its low expression level . However , since early lethality can be bypassed in hypomorphic Htt mutant mice in which Htt expression is about one third of wild type levels [35] and since the N160Q level in heterozygous mice is at 51–75% of full-length mutant HTT in heterozygous F140Q KI mice , the death of homozygous N160Q embryos is more likely due to the loss of functional HTT . The transfection data that overexpressed N-terminal HTT cannot prevent the HTT loss-mediated degeneration of cultured neurons also suggest that N-terminal HTT ( 1–208 aa ) may lack the functional domain ( s ) that are essential for neuronal survival . In support of this idea , our findings suggest that a truncated HTT lacking the N-terminal region is able to prevent neuronal degeneration caused by the loss of HTT in developing neurons . We have used three different approaches to reduce Htt expression via shRNA , GFP-Cre expression , and tamoxifen-induced depletion . All these methods can efficiently reduce Htt expression in cultured neuronal cells , allowing us to show that truncated HTT without the N-terminal region is protective against HTT loss-mediated degeneration of developing neurons . In addition , the lack of a dynein-binding domain in tHTT diminished the protective effect , also supporting the role of HTT in intracellular trafficking . It should be pointed out that HTT is a multifaceted protein that can functionally regulate many cellular functions . For example , tHTT also contains the domain that may be involved in autophagic function [48 , 49] . Multiple functional domains in HTT could play broad or cell type-specific roles in different types of cellular functions . Neuritic extension is critically dependent on intracellular trafficking , and depleting the region critical for intracellular trafficking may have a more dramatic impact on neurite elongation . Whether and how other functional domains in tHTT are important for early development and neuronal survival remains to be investigated . Our findings also uncover important therapeutic implications for HD . The modification of N17 amino acids is found to alter HD pathology in transgenic mice [19 , 20 , 22] . The modification relies on the phosphorylation and ubiquitination of HTT and reveals some druggable targets . However , specific drugs that can selectively inhibit HTT toxicity without other side effects remain to be developed . Anti-sense and shRNA have become powerful approaches to suppress HTT expression [50–52] . Because HTT is essential for embryonic development , considerable efforts have gone in to developing specific inhibitors that may only affect mutant but not wild type HTT [48] . Our recent study shows that depletion of Htt in adult neurons is non-deleterious and transgenic HTT without N-terminal region is functionable [53] . Although whether loss of HTT can affect neuronal function in adult brain remains to be investigated , it is likely that removal of N-terminal region of mutant HTT is therapeutically beneficial . Recently developed new technology , such as CRISPR-Cas9 , would allow achievement of this deletion . Because CRISPR-Cas9 can selectively delete the targeted gene in postmitotic neuronal cells [54–57] and its targeting can permanently delete the gene , removal of N-terminal HTT could be a more efficient therapeutic approach than those that require continuous administration of drugs or chemicals for disease treatment .
All procedures were performed in accordance with the NIH and U . S . Public Health Service’s Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at Emory University with an approved IACUC protocol ( 2002557 ) , which is accredited by the American Association for Accreditation of Laboratory Care ( AAALC ) . All of the care for the animals is consistent with standard operating procedures , and all efforts were made to minimize suffering . Full-length HTT in pEBVHis vector and N-terminal HTT in PRK vectors were established in our previous studies [58 , 59] . To generate truncated HTT ( tHTT ) with the deletion of N-terminal HTT ( 1–237 aa ) , the truncated HTT was released from full-length HTT plasmids by XhoI and NotI digestion , and inserted into pEBVHis vector . The DNAs for RFP , P2A-Flag , and HTT ( 238–600 aa ) were generated by PCR . Primers used were as follows: RFP ( forward , 5’- CC ATC GAT GGC ACC ATG GCC TCC TCC GAG AAC GTC ATC-3’; reverse , 5’- CC ATC GAT CTC GAG CAG GAA CAG GTG GTG GCG G-3’ ) ; P2A-Flag ( forward , 5’- TCC GCT CGA GGG AGG AAG CGG AGC TAC TAA CTT C-3’; reverse , 5’- CCG GAA TTC TTT GTC ATC GTC ATC CTT GTA GTC TTT G-3’ ) ; HTT ( 238–600 aa ) ( forward , 5’- CCG GAA TTC ATG GCT TCT TTT GGC AAT TTT GC-3’; reverse , 5’- TCC CCC GGG GGG CTG TCC AAT CTG CAG-3’ ) . The digested DNA products were cloned to pRK5 vector to generate dHTT with deletion of the dynein-binding region ( 601–698 aa ) . The anti-huntingtin antibodies ( rabbit EM48 and mouse mEM48 ) were produced previously in our laboratory [60] . The mouse antibody 1C2 was purchased from Millipore ( Temecula , CA ) . The mouse anti-γ-tubulin antibody was purchased from Sigma-Aldrich ( St . Louis , MO ) and used at a 1:50 , 000 dilution . Additional antibodies used were those against GAPDH ( Ambion ) , HTT ( 2166 , Millipore ) , β-tubulin III ( Abcam ) , and Flag ( Abcam ) . Adenoviral GFP-Cre was obtained from Vector Biolabs . The shRNA plasmids used to knock down HTT ( siHTT13 ) and GFP ( siGFP ) were obtained from Dr . Nicole Déglon’s lab [38] . All animal procedures were approved by the Institutional Animal Care and Use Committee of Emory University . Full-length mutant HTT knock-in ( CAG140 ) mice were provided by Dr . Michael Levine at UCLA [61] and were maintained in the animal facility at Emory University in accordance with institutional guidelines . N208-160Q knock-in mice ( N160Q KI ) were generated by targeting a human HTT cDNA encoding the first 208 amino acids with 160CAG repeats to exon1 of the mouse Htt gene . A transcriptional stop codon was added to the 3’ end of HTT cDNA in the targeting vector to ensure the expression of truncated HTT . This targeting vector contains the PGK-neomycin resistance gene that is flanked by two loxP sites and was transfected via electroporation into SV/129-derived embryonic stem ( ES ) cells . Positive ES cells containing the targeted vector were identified by genomic DNA PCR and southern blotting , and two clones of these cells were then injected by the Emory Transgenic Facility into C57B1/6J blastocysts to generate chimeric mice . Heterozygous N160Q KI mice were then produced by mating male chimeric mice with female wild-type C57B1/6J mice . PCR genotyping of N208-143Q mice used the following primers ( forward 5'-GGC CTT CGA GTC CCT CAA GTC CTT CCA G-3' and reverse 5'-TGC TGC TGA GGC TGA GGA AGC TGA G-3' ) . Conditional Htt KO mice in which the mouse Htt gene is flanked by loxP were provided by Scott Zeitlin at University of Virginia [43] . To generate inducible Htt knockout mice , we crossed conditional Htt KO mice with B6 . Cg-Tg ( CAG-cre/Esr1 ) 5Amc/J; The Jackson Laboratory ) transgenic mice that express tamoxifen-inducible Cre throughout the body to generate homozygous Htt-floxed mice that also express Cre . HEK293 cells were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% FBS , 100 μ g/mL penicillin , 100 units/mL streptomycin , and 250 μ g/μL fungizone amphotericin B . Cells were incubated at 37°C in 5% CO2 . At a confluency of 70% , the cells were transfected with 1–2 μ g/well ( 6-well plate ) or 0 . 5–1 μ g/well ( 12-well plate ) of DNA and lipofectamine ( Invitrogen ) for 48 h . Brains of postnatal ( day 1–3 ) murine pups were used for culturing cortical astrocytes . Following dissection , cortex was subjected to 0 . 3 mg/ml papain digestion . Cell suspension flew through 70- μ m nylon cell strainers ( Fisher ) . Cells were plated onto Petri dishes , and culture medium was replaced 24 h later and once every 3 days thereafter . Microglia and oligodendrocytes were removed from cultures by shaking at DIV14 . Remaining cells were detached with 0 . 25% trypsin and plated for the following experiments . For cortical neuron cultures , cortical neurons were prepared from postnatal day 0 murine pups . Cortex was digested with 0 . 3 mg/ml papain . Cell suspension was filtered through 40- μ m nylon cell strainers ( Fisher ) to remove debris . Neurons were plated at 1×106 on poly-D-lysine-coated 6-well plates , and cultured in Neurobasal-A medium supplemented with B27 and glutamine ( Invitrogen ) . Half the culture medium was changed with fresh medium every 3 days . PC12 cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% horse serum , 5% fetal bovine serum , 100 U/ml penicillin , 100 μ g/ml streptomycin , and 0 . 25 μ g/ml amphotericin B . To evaluate neurite outgrowth , PC12 cells were treated with NGF ( 100 ng/ml ) . For transfection , cells were transfected with plasmid DNA using Lipofectamine 2000 ( 2mg/mL from Invitrogen ) for 6 h in serum-free medium . Mice were anesthetized and perfused intracardially with phosphate-buffered saline ( PBS , pH 7 . 2 ) for 30 s , followed by 4% paraformaldehyde in 0 . 1 M phosphate buffer ( PB ) at pH 7 . 2 . Brains were removed , cryoprotected in 30% sucrose at 4°C , and sectioned at 40 μm using a freezing microtome . Free-floating sections were in 4% paraformaldehyde in 0 . 1 M phosphate buffer for 10 min and preblocked in 4% normal goat serum in PBS , 0 . 1% Triton X , and then incubated with mEM48 antibody at 4°C for 48 h . The immunoreactive product was visualized with the Avidin–Biotin Complex kit ( Vector ABC Elite , Burlingame , CA , USA ) . Cultured cells were fixed with 4% paraformaldehyde for 15 min , and then blocked for 1 h with 3% BSA and 0 . 2% triton X-100 in 1X PBS . Cells were incubated overnight with primary antibodies in 3% BSA in 1X PBS . The nucleus was visualized using Hoechst staining ( Molecular Probes ) at a dilution of 1:5000 . Fluorescent images were obtained using a Zeiss microscope ( Axiovert 200 MOT ) with a digital camera ( Hamamatsu Orca-100 ) and OpenLAB software ( Improvision Inc ) . We used a quantitative method described in our previous study [28] to quantify the degenerated cultured neurons or PC12 cells with elongated processes; images were collected from at least 5 different randomly selected areas per brain section or plate . To measure GFAP immunostaining intensity , ImageJ software was used . Colored images obtained with a 40X objective , NA 0 . 95 , were first converted to 8-bit black-and-white images . The “Threshold” function was used to adjust the background to highlight GFAP-specific staining . The same threshold was applied to all images analyzed . Finally , the “Measure” function was used to quantify GFAP staining intensity in each image . Each group with 5 to 8 images were examined . Transfected PC12 cells were also imaged randomly to quantify cells with normally elongated neurites that were longer than two cell bodies , and at least 500 transfected cells per group were used for statistical analysis . Mouse body weight was measured twice every month , and survival was monitored regularly . The motor function of the mice was assessed with the rotarod test ( Rotamex , Columbus Instruments ) . Mice were trained on the rotarod at 5 RPM for 5 min for 3 consecutive days . After training , the mice were tested for 3 consecutive days , 3 trials per day . The rotarod gradually accelerated to 40 RPM over a 5-min period . Latency to fall was recorded for each trial . The balance beam test was run using a 0 . 6 cm thick meter stick suspended from a platform on both sides by metal grips . The total running distance was roughly 0 . 8 m . There was a bright light at the starting point and a dark box at the endpoint . Prior to data collection , each mouse was trained for 3 consecutive days with 3 runs per day . Mouse brain tissues or harvested cells were lysed in ice-cold RIPA buffer ( 50 mM Tris pH 8 . 0 , 150 mM NaCl , 1 mM EDTA pH 8 . 0 , 1 mM EGTA pH 8 . 0 , 0 . 1% SDS , 0 . 5% DOC , and 1% Triton X-100 ) containing Halt protease inhibitor cocktail ( Thermo Scientific ) and phosphatase inhibitors . The lysates were incubated on ice for 30 min , sonicated , and centrifuged at top speed for 10 min . The supernatants were subjected to SDS-PAGE . The proteins on the gel were transferred to a nitrocellulose membrane , which was then blocked with 5% milk/PBS for 1 h at room temperature . The blot was incubated with primary antibodies in 3% BSA/PBS overnight at 4°C . After 3 washes in PBS , the blot was incubated with HRP-conjugated secondary antibodies in 5% milk/PBS for 1 h at room temperature . After 3 washes in PBS , ECL Prime ( GE Healthcare ) was then used to detect immunoreactive bands on the blot . For RT-PCR , total RNA was isolated from the mouse brain tissues using the RNeasy Lipid Tissue Mini Kit ( Qiagen ) . Reverse transcription reactions were performed with 1 . 5 μ g of total RNA using the Superscript III First-Strand Synthesis System ( Invitrogen , 18080–051 ) . cDNA ( 100 ng ) was combined with 10 μ l SYBR Select Master Mix ( Applied Biosystems , 4472908 ) and 1 μ l of each primer in a 20- μ l reaction . The reaction was performed in the Eppendorf , Realplex Mastercycler thermocycler using primers ( primers specific for human HTT were 459S 5’-GCCGCCTCCTCAGCTTCCTCAG-3’ and 565A 5’-GTCGGTGCAGCGGCTCCTC-3’ [62] . The relative values of PCR results were normalized by GAPDH levels prior to calculations . All data are expressed as mean±SEM . The statistical significance was determined by two-tailed Student’s t-tests or two-way ANOVA , followed when appropriate by post hoc t-tests using GraphPad Prism 5 . 0 software . A value of p<0 . 05 was considered statistically significant .
|
The 17 amino acids in the N-terminal region of huntingtin ( HTT ) are conserved in a wide range of species and are followed by a polyglutamine repeat whose expansion causes selective neurodegeneration in Huntington’s disease ( HD ) . Loss of Htt can affect developing neurons and early embryonic development in mice . Whether N-terminal HTT is important for the survival of developing neurons or contributes mainly to a gain of toxic function in HD remains unknown . In the current study , we generated N-terminal mutant HTT knock-in mice and found that N-terminal HTT with an expanded polyQ repeat is unable to support the early development of mice , but can cause age-dependent neurological phenotypes . Further , we show that a truncated HTT without the N-terminal region can rescue the Htt loss-mediated degeneration of developing neurons . Our studies suggest that removal of the N-terminal region of mutant HTT could be a strategy to abolish the neuronal toxicity of mutant HTT .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"astrocytes",
"neurodegenerative",
"diseases",
"genetic",
"diseases",
"brain",
"neurites",
"neuroscience",
"macroglial",
"cells",
"animal",
"models",
"developmental",
"biology",
"model",
"organisms",
"embryos",
"autosomal",
"dominant",
"diseases",
"neuronal",
"dendrites",
"huntington",
"disease",
"research",
"and",
"analysis",
"methods",
"embryology",
"animal",
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"neostriatum",
"glial",
"cells",
"clinical",
"genetics",
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"anatomy",
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"neurology",
"neurons",
"genetics",
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"cellular",
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] |
2016
|
N-terminal Huntingtin Knock-In Mice: Implications of Removing the N-terminal Region of Huntingtin for Therapy
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Gametogenesis is a sexually dimorphic process requiring profound differences in germ cell differentiation between the sexes . In mammals , the presence of heteromorphic sex chromosomes in males creates additional sex-specific challenges , including incomplete X and Y pairing during meiotic prophase . This triggers formation of a heterochromatin domain , the XY body . The XY body disassembles after prophase , but specialized sex chromatin persists , with further modification , through meiosis . Here , we investigate the function of DMRT7 , a mammal-specific protein related to the invertebrate sexual regulators Doublesex and MAB-3 . We find that DMRT7 preferentially localizes to the XY body in the pachytene stage of meiotic prophase and is required for male meiosis . In Dmrt7 mutants , meiotic pairing and recombination appear normal , and a transcriptionally silenced XY body with appropriate chromatin marks is formed , but most germ cells undergo apoptosis during pachynema . A minority of mutant cells can progress to diplonema , but many of these escaping cells have abnormal sex chromatin lacking histone H3K9 di- and trimethylation and heterochromatin protein 1β accumulation , modifications that normally occur between pachynema and diplonema . Based on the localization of DMRT7 to the XY body and the sex chromatin defects observed in Dmrt7 mutants , we conclude that DMRT7 plays a role in the sex chromatin transformation that occurs between pachynema and diplonema . We suggest that DMRT7 may help control the transition from meiotic sex chromosome inactivation to postmeiotic sex chromatin in males . In addition , because it is found in all branches of mammals , but not in other vertebrates , Dmrt7 may shed light on evolution of meiosis and of sex chromatin .
Sexual differentiation generates anatomical , physiological , and behavioral dimorphisms that are essential for sexual reproduction . Many of these dimorphisms affect somatic cells , but the sexual dimorphisms that most directly mediate sexual reproduction are those of the gametes themselves . Gametes differ between the sexes in size and morphology , sometimes dramatically so , reflecting their very different roles in zygote formation . Indeed , the morphology of the gametes is what defines sex: females are the sex that produces the larger gametes and males produce the smaller ones . Mammalian meiosis is regulated sex-specifically starting in embryogenesis and continuing through much of adult life ( reviewed in [1] ) . For example , the timing and synchrony of meiosis are very different in the two sexes . In females , germ cells synchronously initiate meiosis in the embryo and arrest during meiotic prophase I . After puberty , oocytes are selectively recruited for ovulation , when they proceed to metaphase II and then complete meiosis after fertilization occurs [2] . In contrast , male meiosis occurs entirely postnatally , without the arrest periods found in females . In females , each meiosis can produce a single haploid oocyte ( and two extruded polar bodies ) , whereas each male meiosis can produce four haploid spermatocytes . Other meiotic processes , such as recombination and chromosome pairing ( synapsis ) , occur in both sexes but operate somewhat differently . For example , there is a higher failure rate for meiosis in females , with human oocyte aneuploidy rates up to 25% versus about 2% in human sperm [3] , and this may indicate that the checkpoints controlling and monitoring the events of meiotic progression in males are more stringent . Consistent with this idea , genetic analysis of a number of meiotic regulatory genes in the mouse has demonstrated a much stronger requirement in males than in females [1 , 4] . The existence of heteromorphic sex chromosomes , such as the XX/XY system of mammals , creates sex-specific challenges . One is the need for mechanisms to balance expression of sex-linked genes between the sexes , which in mammals is accomplished by X chromosome inactivation in females [5 , 6] . In male germ cells there is another sex-specific consideration during meiosis . In prophase I , when the homologous chromosomes synapse and homologous recombination occurs , X and Y chromosome pairing is limited to a region termed the pseudoautosomal region , leaving large portions of each chromosome unpaired . In eutherian and marsupial mammals , these unpaired chromosome regions are associated with a specialized chromatin domain termed the XY body or sex body . The function of the XY body is uncertain [7–11] , but there is evidence that it is essential for male meiotic progression [12] . Several proteins are reported to localize to the XY body , including BRCA1 , ATR , the histone variant H3 . 3 , and modified histones such as ubiquitinated H2A ( Ub-H2A ) and phosphorylated H2AX ( γH2AX ) [12–15] . In the XY body , the sex chromosomes are transcriptionally silenced in a process termed meiotic sex chromosome inactivation ( MSCI ) . The XY body disappears after pachynema; however , many sex-linked genes remain transcriptionally silent into spermiogenesis [16] . This maintenance of silencing is associated with a distinct set of chromatin marks that define a sex chromatin domain termed postmeiotic sex chromatin ( PMSC ) [16 , 17] . Regulators of sexual differentiation have been identified in a number of organisms , but in contrast to many other developmental processes , such as axial patterning or development of many body parts , the molecular mechanisms that regulate sexual differentiation are highly variable between phyla . A notable exception involves genes related to doublesex ( dsx ) of Drosophila , which share a Doublesex/MAB-3 DNA-binding motif called the DM domain [18 , 19] . DM domain–encoding genes have been shown to regulate various aspects of sexual differentiation in insects , nematodes , and mammals [20] . The mab-3 gene of Caenorhabditis elegans has been shown to function analogously to DSX in several respects and can be functionally replaced by the male isoform of DSX , suggesting that the similarity in the sequence of these genes may stem from conservation of an ancestral DM domain sexual regulator [18 , 21 , 22] . Vertebrates also have DM domain genes , and analysis to date , although limited , has shown that these genes also control sexual differentiation . Mammals have seven DM domain genes ( Dmrt genes ) , several of which exhibit sexually dimorphic mRNA expression [23 , 24] . The best studied of these genes , Dmrt1 , is expressed in the differentiating male genital ridges and adult testis of mammals , birds , fish , and reptiles , and a recently duplicated Dmrt1 gene functions as the Y-linked testis-determining gene in the Medaka fish [25–29] . Human DMRT1 maps to an autosomal locus , which , when hemizygous , is associated with defective testicular development and consequent XY feminization [30] . Similarly , mice homozygous for a null mutation in Dmrt1 have severe defects in testis differentiation involving both germ cells and Sertoli cells [31] . Female mice mutant in Dmrt4 have polyovular follicles , indicating that this gene also plays a role in gonadal development [32] . It appears from these studies that the involvement of DM domain genes in sexual differentiation is ancient and conserved . However , vertebrate Dmrt gene function is not limited to sexual differentiation: Dmrt2 is required in both sexes for segmentation in mice and fish [33–35] . Here , we have investigated the expression and function of the Dmrt7 gene in the mouse . Dmrt7 is expressed only in the gonad , and , unlike the other Dmrt genes , appears to be present exclusively in mammals and not in nonmammalian vertebrates [23 , 36] . We find that DMRT7 protein is expressed only in germ cells and is selectively localized to the XY body of male pachytene germ cells . To test its function , we generated a conditional null mutation of Dmrt7 in the mouse . We find that Dmrt7 is required in males for progression beyond the pachytene stage of meiotic prophase but is not required in females . In rare mutant cells that survive to diplonema , we observed sex chromatin abnormalities . Based on these observations , we suggest that Dmrt7 plays a critical role in a male-specific chromatin transition between pachynema and diplonema during meiotic prophase .
Our previous mRNA expression analysis suggested a possible meiotic function for Dmrt7 , based on the expression of Dmrt7 mRNA in the fetal gonads of the two sexes [23] . In the fetal ovary , Dmrt7 mRNA was detected primarily from E13 . 5 to E15 . 5 , the time during which meiosis progresses from pre-meiotic replication to the pachytene stage [4] , whereas Dmrt7 expression in the non-meiotic fetal testis was very low . Because this earlier work did not examine adult Dmrt7 expression , we first performed reverse transcriptase ( RT ) -PCR on mRNA from ten adult organs and detected strong Dmrt7 mRNA expression in the testis and a trace of expression in heart , but not in any other tissue tested ( Figure 1A ) . We examined the timing of Dmrt7 mRNA expression during postnatal testis development and detected strong expression beginning at 2 wk , which roughly coincides with the onset of the pachytene stage during the first synchronous wave of spermatogenesis ( Figure 1B ) [37] . To investigate DMRT7 protein expression , we generated an antibody against the C-terminal portion of the protein . The antibody was raised against a unique region lacking the DM domain in order to avoid cross-reaction with other DM domain proteins . Immunofluorescent staining with purified DMRT7 antisera showed that DMRT7 protein is expressed predominantly in mid- to late-pachytene spermatocytes ( Figure 1C ) , as well as in sperm , and is not detectable in other germ cell types including spermatogonia and round spermatids . We did not detect DMRT7 protein in somatic cells such as Sertoli cells , peritubular myoid cells , or Leydig cells . To more precisely determine the pachytene stages of DMRT7 expression , we double-stained with an antibody to GATA1 , which is expressed in Sertoli cells from stages VII to IX [38] . This confirmed that DMRT7 is expressed in mid- to late-pachytene spermatocytes , starting slightly earlier than stage VII and extending through stage IX ( unpublished data ) . Within pachytene spermatocytes , DMRT7 is concentrated in the XY body , or sex body , a densely staining chromatin domain that harbors the sex chromosomes . These undergo transcriptional inactivation and heterochromatinization as a result of their incomplete pairing during prophase of mammalian male meiosis [17] . To verify DMRT7 protein expression in the XY body , we double-stained mouse testis sections for DMRT7 and small ubiquitin-related modifier 1 ( SUMO-1 ) , which is concentrated in the XY body during pachynema [39 , 40] . DMRT7 and SUMO-1 were colocalized , confirming that DMRT7 protein is preferentially localized to the XY body ( Figure 1D ) . We also confirmed XY body localization of DMRT7 by double staining for other markers including Ub-H2A and γH2AX ( unpublished data ) . DMRT7 is not preferentially localized to the XY body at all stages but instead is dynamic . Based on epithelial staging , it appears that DMRT7 localizes to the XY body from mid- to late-pachynema , becomes diffusely distributed in late-pachynema , and disappears in diplonema ( unpublished data ) . This localization was confirmed by staining of meiotic spreads ( Figure S1 ) . DMRT7 also is specifically localized in sperm , with antibody staining mainly in the perinuclear ring of the sperm head manchette . This staining coincided with the epithelial stages in which DMRT7 localizes to the XY body in spermatocytes ( Figure 1C and 1D ) . To establish the functional requirement for Dmrt7 , we generated Dmrt7−/− mice by targeted disruption in embryonic stem ( ES ) cells using a strategy diagrammed in Figure S2A . The Dmrt7 gene has nine exons with the DM domain encoded by the second and third exons . Because the DM domain is essential for function of other genes , including mab-3 , mab-23 , and dsx [18 , 19 , 41] , we generated a conditionally targeted “floxed” allele in which the DM domain–containing exons of Dmrt7 are flanked by recognition sites for the Cre recombinase ( loxP sites ) . The targeting vector also contained a neomycin resistance cassette ( neo ) flanked by Flpe recognition sites . The removal of these sequences by Cre-mediated recombination eliminates the DM domain and the translational start site , thus generating a putative null allele . We identified three homologously targeted ES cell clones by Southern blotting ( Figure S2B ) and injected cells from two clones into C57BL/6 blastocysts . Chimeric animals from both cell lines transmitted the targeted allele through the germ line . Targeted animals were bred to β-actin Cre mice to delete the DM domain–encoding exons , generating the Dmrt7− allele , or to Flpe transgenic mice to delete the neo cassette , generating the Dmrt7flox allele . Dmrt7+/− mice were interbred to generate Dmrt7−/− mice . To confirm the lack of functional DMRT7 protein in Dmrt7−/−testes , we stained meiotic spreads from Dmrt7 mutants ( Figure S1 ) and sections from mutant testes ( Figure S2C ) and carried out western blot analysis ( unpublished data ) . In each case , we detected no DMRT7 protein in the mutant testes . Breeding of Dmrt7 heterozygotes produced homozygous mutant progeny of both sexes at the expected frequency ( 63/264; 23% ) . Male and female homozygous mutants were viable , grew to adulthood normally , and exhibited normal sexual behavior . Female homozygotes were fertile , produced litters of normal size , and had no obvious ovarian abnormalities as judged by histological analysis ( unpublished data ) . In contrast , Dmrt7 homozygous mutant males were completely infertile and had testes about one-third the weight of those of heterozygous or wild-type adult littermates ( Figure 2 ) . To determine when defective testis development begins in Dmrt7 mutants , we compared the testes of wild-type and mutant littermates during the first wave of spermatogenesis . Prior to postnatal day 14 ( P14 ) , mutant testes appeared histologically normal and the testis weights were similar to those of heterozygous and wild-type littermates , indicating that spermatogonia and early meiotic germ cells form normally ( Figure 2B; unpublished data ) . Thereafter , the testes of the Dmrt7 mutant mice ceased to grow and the weight difference was significant . Microscopic examination of P21 and P42 Dmrt7 mutant testes revealed that germ cells arrest in pachynema , and later stages of germ cells are largely missing ( Figure 2C and 2D ) . Dmrt7 mutant mice are deficient in postmeiotic spermatids and lack epididymal spermatozoa , although a few cells develop to the round spermatid stage . These meiotic defects are in agreement with a recent preliminary analysis of another Dmrt7 mutation [42] . While some Dmrt7 mutant tubules are highly vacuolated and contain primarily Sertoli cells and spermatogonia , others have abundant primary spermatocytes . In addition , some tubules contain multinucleated cells and cells with darkly stained nuclei that are typical of apoptotic cells ( Figure 2D ) . Since Dmrt7 mutant testes lack most post-pachytene cells , we used TUNEL analysis to test whether the missing cells are eliminated by apoptosis . At 3 wk , Dmrt7 mutant testes contain significantly more apoptotic cells than those of wild-type controls . The percentage of tubule sections with five or more apoptotic nuclei was about three times higher in Dmrt7 mutants compared with wild-type ( 20% versus 7%; Figure 2E ) . A similar elevation of apoptosis was apparent in mutant testes at 7 wk ( Figure 2F ) . In mutants , many apoptotic cells were in the middle of the tubules , whereas the apoptotic cells in wild-type occur mainly near the seminiferous tubule periphery . The numbers of Sertoli cells were not significantly different between wild-type and mutant testes , and we observed no difference in somatic cell apoptosis in mutants ( unpublished data ) . From these results , we conclude that loss of Dmrt7 causes a block in meiotic progression , mainly in pachynema , leading to the elimination , by apoptosis , of the arrested cells . To better define the spermatogenic stage at which Dmrt7−/− male germ cells arrest and die , we used antibodies against several stage-specific germ cell markers . TRA98 is expressed in PGCs and spermatogonia [43] . In the wild-type adult testis , strongly staining TRA98-positive cells form a layer one cell deep; however , in the mutant TRA98 , strongly positive cells were abnormally organized , and some tubules had a layer several cells deep ( Figure 3A ) . The BC7 antibody recognizes spermatocytes in the leptotene to early-pachytene stages [44] . Dmrt7 mutant testes had BC7-positive cells in approximately normal numbers , but again abnormally organized , with many positive cells in the center rather than the periphery of the tubules ( Figure 3B ) . The TRA369 antibody recognizes a calmegin protein expressed in pachytene spermatocytes through elongated spermatids [45] . In contrast to the earlier stages , far fewer TRA369-positive cells were present in mutant testes relative to wild-type ( Figure 3C ) . We also quantitated the number of cells at each meiotic stage using spermatocyte spreads , assaying chromosome-pairing status by staining for SYCP3 , a component of the synaptonemal complex ( Figure 4 ) . We found that Dmrt7 mutants accumulate pachytene cells but have greatly reduced numbers of cells in late-pachynema and beyond . Together , these results confirm that the meiotic arrest in Dmrt7 mutants occurs primarily during pachynema and results in efficient elimination of arrested cells . Defects in chromosome pairing , synapsis , or recombination can trigger pachytene arrest and apoptosis [46] . We therefore examined these events in Dmrt7 mutant testes . To assess homolog synapsis , we used antibodies to SYCP1 , a synaptonemal complex transverse element component , and SYCP3 , a component of the axial element , which remains on the desynapsed axes during diplonema [47 , 48] . Formation of synaptonemal complexes in the mutant was indistinguishable from that in wild-type , as indicated by the proper accumulation of SYCP1 ( unpublished data ) and SYCP3 ( Figure 5A ) . Likewise , the Dmrt7 mutant zygotene spermatocytes showed normal accumulation of the early recombination repair marker RAD51 , suggesting that early meiotic recombination is not significantly affected ( Figure 5B ) . Dmrt7 mutant spermatocytes exhibited the expected decline in the presence of RAD51 foci associated with the autosomal synaptonemal complexes ( Figure 5B; unpublished data ) [49] . The few surviving cells that progressed beyond pachynema also underwent apparently normal desynapsis during diplonema ( Figure 5A ) . From these results , we conclude that chromosomal pairing , synapsis , recombination , and desynapsis during prophase I in Dmrt7 mutant males are grossly normal . Sertoli cells interact with germ cells during spermatogenesis and the interaction is critical for germ cell maturation [50] . Although we did not detect DMRT7 expression in Sertoli cells by antibody staining , we nevertheless considered the possibility that Sertoli cell defects might contribute to the male-specific germ line failure of Dmrt7 mutants . To characterize Sertoli cell differentiation , we examined expression of the Sertoli cell markers GATA4 ( a marker of immature postnatal Sertoli cells ) and GATA1 ( a mature Sertoli cell marker ) . The levels of these proteins appeared normal relative to wild-type at P14 and P42 ( Figure 6A–6C ) , as did the androgen receptor ( Figure S3; unpublished data ) . However , the organization of Sertoli cells in Dmrt7 mutant testes was abnormal: in some tubules GATA1-positive Sertoli cell nuclei were displaced from their usual close apposition with the basement membrane ( Figure 6C ) . In such tubules , nuclei of pre-meiotic germ cells and spermatocytes were packed close to the basal membrane and few germ cells were found in the adlumenal region . The aberrant Sertoli cell organization in Dmrt7 mutant testes raised the possibility that the germ cell phenotype might indirectly result from defects in Sertoli cell function . To test this possibility , we deleted Dmrt7 just in the Sertoli cell lineage by crossing mice carrying the floxed Dmrt7 allele with Dhh-Cre transgenic mice [51] . The Desert hedgehog ( Dhh ) promoter is active starting at about E12 . 5 in pre-Sertoli cells but not in germ cells , allowing deletion of Dmrt7 in Sertoli cells well before any likely requirement for its function [52] . Testicular size in Sertoli-targeted ( SC-Dmrt7KO ) animals was slightly reduced from that of wild-type , but histological analysis revealed no obvious difference between wild-type and SC-Dmrt7KO testes ( Figure 6D ) . Spermatogenesis appeared normal , mature sperm were present , and SC-Dmrt7KO mice were fertile . In addition , GATA1 staining showed that Sertoli cell nuclei were located adjacent to the basement membrane as in wild-type ( Figure 6E ) . These results suggest the germ cell defects of Dmrt7 mutants are not caused by lack of Dmrt7 in Sertoli cells . Rather , the abnormal organization of Sertoli cells appears to result from lack of Dmrt7 in the germ line . The data presented so far indicate that Dmrt7 mutant germ cells undergo apparently normal early meiosis and then arrest during pachynema due to a strict requirement for Dmrt7 in the germ line . To better understand the basis of the meiotic arrest , we more closely examined meiotic germ cells in the mutant . We focused on the XY body , which is thought to be essential for meiotic progression and is the site of preferential DMRT7 localization . Condensation of the X and Y chromosomes begins in late-zygotene cells , and , by mid-pachynema ( when homologous chromosome pairs are fully aligned ) the sex chromatin forms a microscopically visible spherical structure near the nuclear periphery [53] . We first asked whether DMRT7 is required for XY body formation by evaluating several characteristic XY body chromatin features . First , we tested γH2AX expression by immunofluorescent staining . H2AX is a variant of H2A that is crucial for XY body formation and MSCI [12] . γH2AX localized normally to the XY body of DMRT7 mutant cells in meiotic spreads ( Figure 7A ) , and many γH2AX-positive puncta were present in germ cells of Dmrt7 mutant testes ( Figure 7B ) . Next , we examined SUMO-1 localization in the mutant testis . SUMO-1 expression normally increases in the XY body of early- to mid-pachytene spermatocytes at the time of sex chromosome condensation . Prior to the completion of the first meiotic division , SUMO-1 disappears from the XY body as the X and Y chromosomes desynapse [40] . Punctate SUMO-1 localization was present in Dmrt7 mutant germ cells , again consistent with formation of a correctly marked XY body ( Figure 7C ) . However , some tubules in mutants had multiple layers of cells with SUMO-1-condensed spots ( Figure 7C ) , rather than the normal single layer of cells . This accumulation of XY body–containing cells also was apparent with γH2AX staining and is consistent with a developmental arrest of mutant cells in mid- to late-pachytene . We also examined Ub-H2A localization in Dmrt7 mutant testes . In early-pachytene , Ub-H2A is concentrated in the XY body; by mid-pachytene Ub-H2A is observed throughout the entire nucleus , but it again becomes limited to the XY body in late-pachytene spermatocytes [13] . Analysis of nuclear spreads revealed that Ub-H2A localizes normally to the XY body in Dmrt7 mutants ( Figure S4 ) . Collectively , these results indicate that Dmrt7 mutant germ cells can establish an XY body with at least some of the normal chromatin marks . Although the XY body can form during pachynema in Dmrt7 mutants , we considered the possibility that transcriptional silencing might not be properly established . This would be consistent with the Dmrt7 phenotype: pachytene cells that escape from MSCI normally are eliminated prior to late-pachytene [17] . Recently , MSCI has been shown to continue into meiosis II and spermiogenesis , apparently mediated by a distinct chromatin compartment termed postmeiotic sex chromatin ( PMSC ) that is established starting in diplonema [16] . We therefore asked whether the pachytene germ cell death in Dmrt7 mutants is associated with a failure either to initiate or to maintain sex chromosome inactivation . First , we examined the mid-pachytene XY body . To examine XY transcriptional status , we carried out Cot-1 RNA fluorescence in situ hybridization ( FISH ) to detect nascent RNA polymerase II transcription , combined with DAPI staining to locate the XY body on spreads of seminiferous tubules ( Figure 8A and 8B ) . In Dmrt7 mutants , the XY body was formed and excluded Cot-1 hybridization ( Figure 8B ) , indicating that transcriptional silencing is established normally in mutant pachytene cells . We also examined expression of the Y-linked gene Rbmy , which normally is inactivated during pachytene and reactivated after secondary meiosis begins [54 , 55] . Rbmy was inactivated normally in pachytene cells of Dmrt7 mutants , based on immunofluorescent staining with an anti-RBMY antibody ( Figure S5 ) . We also examined heterochromatin protein 1 beta ( HP1β ) , which normally localizes to the X centromere at mid-pachynema and then spreads through the XY body as it internalizes during diplonema [56] . We found that HP1β localization is normal in DMRT7 mutant cells in mid-pachynema ( Figure 8C and 8D ) . These results suggest that XY body formation and initiation of MSCI both occur normally in Dmrt7 mutant germ cells . We next considered the possibility that sex chromatin is established normally but is not properly modified as cells exit pachynema and begin to form PMSC . Although most Dmrt7 mutant cells are eliminated by apoptosis prior to diplonema , we were able to examine epigenetic markers of PMSC in rare Dmrt7 mutant spermatocytes that escaped pachytene arrest and progressed into diplonema . First , we examined nascent transcription by Cot-1 hybridization . Although heterochromatic regions generally showed lower Cot-1 signal than euchromatic regions ( Figure 8E and 8F ) , in some mutant cells the sex chromatin appeared to be incompletely silenced relative to wild-type ( Figure 8F ) . We also examined three epigenetic signatures of PMSC: histone H3 dimethylated or trimethylated at lysine-9 ( H3-2meK9 , H3-3meK9 ) and spreading of HP1β through the XY body [16 , 57 , 58] ( S . H . Namekawa , unpublished data ) . We observed defects in sex chromatin localization of all three markers in Dmrt7 diplotene cells . Although HP1β localization to the X chromosome centromere initially appeared normal at mid-pachynema , we observed Dmrt7 mutant diplotene cells that failed to show spreading of HP1β to the entire XY body ( Figure 8G and 8H ) . Similarly , we found Dmrt7 mutant diplotene cells lacking accumulation of H3-2meK9 and H3-3meK9 marks onto the sex chromatin ( Figure 8I–8L ) . Not all Dmrt7 mutant diplotene cells showed abnormal localization of HP1β to the sex chromatin ( Figure 8M ) . In one experiment , 11/27 mutant cells in diplonema lacked HP1β on the XY body , as compared with 2/22 wild-type cells . We hypothesize that the mutant cells with normal HP1β may be those that can complete meiosis ( Figures 4 and 5 ) . Some of the mutant diplotene cells showing abnormal sex chromatin also had abnormal autosomal γH2AX staining ( Figure 8L ) . γH2AX localizes to double-strand DNA breaks , so this staining may indicate that some diplotene mutant cells are approaching or entering apoptosis [59] . We did not observe sex chromatin defects prior to diplonema , but we cannot exclude the possibility that earlier defects exist and the affected cells are rapidly eliminated . In the preceding experiments , we staged cells based on XY body internalization . Because this process could be abnormal in the mutant cells , we also staged mutant cells by chromosome morphology using an antibody to SYCP3 ( Figure 9 ) . This independently identified Dmrt7 mutant diplotene cells lacking HP1β accumulation in the XY body , such as the example in Figure 9B . From these results , we conclude that Dmrt7 mutant cells establish a normal XY body in mid-pachynema , but then have multiple epigenetic defects in the sex chromatin transition from pachynema to diplonema .
In this study , we find that the DM domain protein DMRT7 is required for male germ cells to complete meiotic prophase but is dispensable in the female germ line . In males , DMRT7 expression is highest in pachytene spermatocytes , and the protein preferentially localizes to the XY body . Consistent with this expression , we found that most mutant male germ cells arrest in pachynema and undergo apoptosis , although a small proportion can progress to diplonema and sometimes beyond . Examination of chromatin and nascent transcription in mutant cells that progressed to diplonema revealed sex chromatin abnormalities , as discussed below . The pachytene stage of prophase involves tremendous chromosomal changes as the homologs align , synapse , and recombine . During this period , at least one pachytene surveillance system exists to monitor key events of meiotic progression . Cells in which any of these events is anomalous are efficiently eliminated by apoptosis [46] . Another key event of pachynema in male mammals is the packaging of the sex chromosomes into the XY body and the establishment of MSCI . Examination of male meiosis in XYY mice and mice carrying a sex-chromosome-to-autosome translocation showed that cells in which a sex chromosome escapes MSCI are eliminated prior to late-pachynema [17] . This indicates that the establishment of MSCI also is subject to surveillance . Since the arrest and apoptosis of Dmrt7 mutant spermatocytes could result from perturbation of any of the critical pachytene events mentioned above , we tested whether they occur abnormally in the mutant cells . We found that chromosomal synapsis and recombination appear normal in Dmrt7 mutant cells . We therefore focused on the XY body , the most prominent site of DMRT7 accumulation . First , we tested whether the XY body forms and MSCI is established in Dmrt7 mutant cells . Surprisingly , we found that these cells form an XY body with normal morphology and proper accumulation of all the chromatin marks we examined . Moreover , Cot-1 hybridization and analysis of RBMY expression demonstrated that MSCI initiates normally in the XY body of mid-pachytene Dmrt7 mutant cells . We did , however , observe three specific defects in the sex chromatin of Dmrt7 mutant germ cells that avoided arrest in pachynema and were able to enter diplonema . Normally cells accumulate H3-2meK9 and H3-3meK9 marks and HP1β protein on the sex chromatin as they progress to diplonema , but we observed mutant diplotene cells lacking these features . Thus , although a minority of Dmrt7 mutant germ cells can progress from pachynema to diplonema , there are defects in sex chromatin modification during the transition . A function in male sex chromatin can reconcile the findings that DMRT7 is required for meiosis , but only in males , and is present only in mammals . A proportion of mutant diplotene cells have apparently normal sex chromatin ( for example , Figure 8M ) ; these are likely to be the cells that can progress beyond diplonema . Because most Dmrt7 mutant germ cells are eliminated by apoptosis around the time at which we observed sex chromatin defects , a simple model is that the apoptosis is a consequence of the sex chromatin defects . The reciprocal situation ( sex chromatin defects caused by apoptosis ) is possible , but seems unlikely , because we observed mutant cells with sex chromatin defects but no indications of apoptosis . Alternatively , apoptosis and abnormal sex chromatin may be two independent consequences of Dmrt7 loss . This question cannot be answered definitively until we know the detailed molecular mechanism of DMRT7 . A number of other proteins have been identified that interact with the XY body , including histone variants and modified histones , a testis-specific histone methyl transferase , chromobox proteins , an orphan receptor germ-cell nuclear factor , and recombination-related proteins [60] . A common feature of these proteins is involvement with heterochromatin and/or transcriptional repression . DMRT7 is unusual among XY body proteins in being related to highly site-specific transcriptional regulators . An attractive speculation is that DMRT7 may provide sequence specificity in recruiting other proteins , such as chromatin modifiers , to the XY body as part of the transition to PMSC . Chromatin regulation may be a common mechanism for DM domain proteins , as we find that other DM domain proteins associate with chromatin modifying complexes ( M . W . Murphy , D . Zarkower , and V . J . Bardwell , unpublished data ) . The finding that Dmrt7 is essential for mammalian meiosis expands the known functions of this gene family . Invertebrate DM domain genes so far have only been found to function in somatic cells . Two other DM domain genes , Dmrt1 and Dmrt4 , do affect germ cell development in the mouse . Dmrt1 is required in pre-meiotic male germ cells for differentiation of gonocytes into spermatogonia , as well as in Sertoli cells , but it is not expressed in meiotic cells [31] ( S . Kim and D . Zarkower , unpublished data ) . The requirement for DMRT1 in pre-meiotic germ cells and DMRT7 in meiotic germ cells demonstrates that DM domain proteins act at multiple critical points of male germ cell development . Ovaries of Dmrt4 mutant females have polyovular follicles ( follicles containing multiple oocytes ) , but it is unknown whether this reflects a defect in the germ line or the soma . It is notable that at least three mammalian DM domain genes affect gonadal development only in one sex , given the similar roles of related proteins in directing sex-specific somatic development in other phyla . Strikingly , Dmrt7 is present , not only in placental mammals , but also in marsupials and a monotreme ( egg-laying mammal ) , the platypus , which has a clear Dmrt7 ortholog [36] . However , no close Dmrt7 ortholog has been reported in nonmammalian vertebrates , and our database searches did not reveal one . Thus , Dmrt7 likely arose , presumably by duplication and divergence of another Dmrt gene , shortly before or coincident with the mammalian radiation . Monotremes have five X and five Y chromosomes , which form an extended pairing chain during meiosis and appear unrelated to the sex chromosomes of the other mammals [61] . The presence of Dmrt7 in both lineages may support a common origin for either the sex chromosomes or the sex chromatin of monotremes and other mammals . A plausible model is that Dmrt7 evolved during the establishment of mammalian sex determination to help cope with ancestral differences in gene dosage , chromosome pairing , recombination , or other meiotic issues arising from sex chromosome heteromorphy . In this regard , we speculate that the recruitment of Dmrt7 during mammalian evolution may be analogous to the recruitment of chromatin regulatory complexes to achieve somatic dosage compensation during evolution of heteromorphic sex chromosomes in several phyla ( reviewed in [62] ) . It will be of interest to determine whether DMRT7 localizes to sex chromosomes during monotreme meiosis . In summary , we have found that the mammal-specific DM domain protein DMRT7 is essential for meiotic prophase progression in males . DMRT7 localizes to the sex chromosomes after they are assembled into specialized heterochromatin , and many Dmrt7 mutant cells have epigenetic defects in the modification of the sex chromatin between pachytene and diplotene . Although Dmrt7 belongs to an ancient and conserved gene family , it is found only in mammals , and to our knowledge DMRT7 is the only example of a mammal-specific protein that is essential for meiosis . It will be important to determine the precise mechanism by which DMRT7 affects sex chromatin regulation during male meiosis .
A mouse Dmrt7 cDNA fragment containing sequences from exon 8 was used to screen a mouse BAC library from the 129/SvJ strain ( Stratagene , http://www . stratagene . com ) , and clones containing promoter sequences were isolated and sequenced to obtain Dmrt7 genomic sequence . The targeting vector pDZ169 ( diagrammed in Figure S2 ) was constructed by the following scheme: The vector pDZ157 was used as a backbone vector [31] . 3′ to Pgk-neo and the loxP site , we inserted , as a XmaI/XmaI DNA fragment , the third intron of Dmrt7 ( from 366 bp to 2 , 773 bp downstream of exon 3 ) generated by PCR . 5′ to Pgk-neo , we inserted an EcoRI/NotI PCR fragment extending from 4 , 107 bp to 336 bp 5′ of the Dmrt7 translational start . Finally , we inserted a loxP site and NotI site 336 bp 5′ of the Dmrt7 translational start . In the resulting vector , the second and third exons of Dmrt7 are flanked by loxP sites ( floxed ) . The Dmrt7-containing portions of pDZ169 were completely sequenced . pDZ169 was linearized with PmeI and electroporated into CJ7 ES cells ( originally derived from the 129S1 strain ) . Three homologous recombinants were identified from 296 G418-resistant colonies by Southern hybridization by use of a DNA probe from the sequences upstream of exon 1 to screen genomic DNA digested with EcoRI . Homologous recombination was confirmed on both ends of the targeted region by Southern hybridization . Probes for Southern hybridization were made by PCR using primers DM5S10/DM5S11 ( 5′ probe ) and DM5PR1/DM5PR2 ( 3′ probe ) , listed below . Two targeted ES cell clones containing the floxed allele Dmrt7neo were injected into C57Bl/6 blastocysts to generate chimeras . Chimeric males were bred with C57Bl/6 females to generate heterozygotes carrying Dmrt7neo . Dmrt7+/Dmrt7neo females were bred with male β-actin-Cre transgenic mice to delete the floxed sequences and generate heterozygous Dmrt7−/+ deletion mutants , which were interbred to generate homozygous Dmrt7−/− mutants . For genotyping , tail-clip DNA was amplified for 35 cycles . Chromosomal sex was determined by PCR with primers to the Y chromosome gene Zfy ( below ) . The wild-type Dmrt7 allele Dmrt7+ was detected by PCR with DM5S4/DM5S5 , with an annealing temperature of 60 °C . The Dmrt7flox allele was detected by PCR with DM5S5F/DM7KO7R with an annealing temperature of 62 °C . The deleted Dmrt7 allele Dmrt7− was detected with DM5S3/DM7KO7R with an annealing temperature of 62 °C . RT-PCR for Dmrt7 expression analysis was as described [23] using primers SK111/SK112 with an annealing temperature of 62 °C . Dissected testes were fixed in Bouin's fixative or phosphate-buffered formalin overnight at 4 °C , progressively dehydrated in a graded ethanol series , and embedded in paraffin wax . Sections ( 6 μm ) were deparaffinized , rehydrated , and stained with hematoxylin and eosin . For TUNEL analyses , deparaffinized sections were treated with proteinase K for 15 min and quenched in 3 . 0% hydrogen peroxide in PBS for 5 min at room temperature . Subsequently , nuclear staining in apoptotic cells was detected using ApopTag kit ( Chemicon , http://www . chemicon . com ) according to the manufacturer's instructions . Slides with paraffin sections were washed in PBT ( 0 . 1% Tween 20 in PBS ) and autoclaved in 10 mM citric acid ( pH 6 . 0 ) to retrieve antigenicity . Slides were blocked in 5% serum ( matched to the species of the secondary antibody ) in PBS for 1 h at room temperature and incubated with primary antibodies overnight at 4 °C prior to detection with secondary antibodies . Rabbit polyclonal antibodies to DMRT7 were raised against a purified fusion protein containing glutathione-S-transferase ( GST ) fused to the C-terminal 279 amino acids of DMRT7 . Antibodies to GST were removed by GST-affigel 10 chromatography and the antiserum was then purified by GST-DMRT7-affigel 15 chromatography . DMRT7 antibody was used at 1:200 dilution with a goat anti-rabbit secondary antibody ( Molecular Probes , http://www . invitrogen . com ) at 1:200 dilution . Other primary antibodies used for immunofluorescence were rat anti-GATA1 ( 1:200 , Santa Cruz Biotechnology , http://www . scbt . com , sc-265 ) , goat anti-GATA4 ( 1:200 , Santa Cruz Biotechnology , sc-1237 ) , rat anti-TRA98 ( 1:200 , gift of H . Tanaka and Y . Nishimune ) , rat anti-BC7 ( 1:50 , gift of H . Tanaka and Y . Nishimune ) , rat anti-TRA369 ( 1:200 , gift of H . Tanaka and Y . Nishimune ) , rabbit anti-RAD51 ( 1:600 Calbiochem , http://www . calbiochem . com , PC130 ) , mouse anti-GMP-1/SUMO-1 ( 1:200 , Zymed , http://invitrogen . com , 33–2400 ) , rabbit anti-phospho-H2AX ( Ser139 ) ( 1:200 , Upstate , http://www . millipore . com , 01–164 ) , mouse anti-phospho-H2AX ( 1:200 , Upstate , 05–636 ) , mouse anti-SYCP3 ( 1:200 , Abcam , http://www . abcam . com , ab12452 ) , rabbit anti-HP1β ( 1:100 , Abcam , ab10478 ) , rabbit anti-H3-2meK9 ( 1:100 , Upstate , 07–441 ) , rabbit anti-H3-3meK9 ( 1:200 , Upstate , 07–442 ) , rabbit anti-AR ( N-20 ) ( 1:200 , Santa Cruz Biotechnology , sc-816 ) , and mouse anti-αSMA clone 1A4 ( 1:800 , Sigma , http://www . sigmaaldrich . com , A2547 ) . Secondary antibodies used were goat anti-rabbit Alexa 488 , goat anti-rabbit Alexa 594 , goat anti-rat Alexa 594 , and goat anti-mouse Alexa 488 ( Molecular Probes ) used at 1:250 . Donkey anti-goat FITC ( Jackson ImmunoResearch Laboratories , http://www . jacksonimmuno . com ) and donkey anti-rabbit Texas Red ( Jackson ) were used at 1:50 according to the manufacturer's instructions . Meiotic chromosome spread preparations were made from 3-wk-old mice , prepared as described by Reinholdt et al . [63] . For analysis of PMSC and Cot-1 RNA FISH , meiotic slides were prepared as previously described [16] . Slides containing chromosome spreads or meiotic spermatocytes were subjected to immunofluorescent staining or RNA FISH , as previously described [16 , 63] . For combined RNA FISH/immunostaining , we carried out RNA FISH first , followed by immunofluorescence . DNA FISH was performed using chromosome painting ( Cambio , http://www . cambio . co . uk ) . Z-sections were captured by Zeiss Axioplan microscope ( Zeiss , http://www . zeiss . com ) and processed by Openlab ( Improvision , http://www . improvision . com ) .
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Genes related to the sexual regulator Doublesex of Drosophila have been found to control sexual development in a wide variety of animals , ranging from roundworms to mammals . In this paper , we investigate the function of the Dmrt7 gene , one of seven related genes in the mouse . Female mammals are XX and males are XY , a chromosomal difference that presents specific challenges during the meiotic phase of male germ cell development . Some of these are thought to be overcome by incorporating the X and Y chromosomes into a specialized structure called the XY body . We find that DMRT7 protein is present in germ cells , localizes to the male XY body during meiosis , and is essential for male but not female fertility . The XY body normally is altered by recruitment of additional proteins and by specific modifications to histone proteins between the pachytene and diplotene stages of meiosis , but modification of the “sex chromatin” in Dmrt7 mutant cells is abnormal during this period . Because Dmrt7 is found in all branches of mammals , but not in other vertebrates , these results may indicate some commonality in regulation of sex chromatin among the mammals .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"meiosis",
"developmental",
"biology",
"gametogenesis",
"dm",
"domain",
"dmrt7",
"xy",
"body"
] |
2007
|
A Mammal-Specific Doublesex Homolog Associates with Male Sex Chromatin and Is Required for Male Meiosis
|
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